From 5d546ebb80629931cba13cdd7fc44e40b10895ef Mon Sep 17 00:00:00 2001 From: elisno Date: Tue, 2 Jul 2024 15:20:32 +0000 Subject: [PATCH] deploy: cleanlab/cleanlab@e67c4aeedd6310b5ad112e4c90674400bc877e0e --- master/.buildinfo | 2 +- .../cleanlab/benchmarking/index.doctree | Bin 3248 -> 3248 bytes .../benchmarking/noise_generation.doctree | Bin 81345 -> 81345 bytes .../.doctrees/cleanlab/classification.doctree | Bin 290603 -> 290603 bytes master/.doctrees/cleanlab/count.doctree | Bin 283717 -> 283717 bytes .../.doctrees/cleanlab/data_valuation.doctree | Bin 26578 -> 26578 bytes .../cleanlab/datalab/datalab.doctree | Bin 174487 -> 174487 bytes .../guide/_templates/issue_types_tip.doctree | Bin 4354 -> 4354 bytes .../guide/custom_issue_manager.doctree | Bin 31452 -> 31452 bytes .../guide/generating_cluster_ids.doctree | Bin 6318 -> 6318 bytes .../cleanlab/datalab/guide/index.doctree | Bin 12087 -> 12087 bytes .../guide/issue_type_description.doctree | Bin 250944 -> 250944 bytes .../cleanlab/datalab/guide/table.doctree | Bin 63584 -> 63584 bytes .../.doctrees/cleanlab/datalab/index.doctree | Bin 5445 -> 5445 bytes .../cleanlab/datalab/internal/data.doctree | Bin 105136 -> 105136 bytes .../datalab/internal/data_issues.doctree | Bin 77301 -> 77301 bytes .../cleanlab/datalab/internal/factory.doctree | Bin 64553 -> 64553 bytes .../cleanlab/datalab/internal/index.doctree | Bin 4573 -> 4573 bytes .../datalab/internal/issue_finder.doctree | Bin 46989 -> 46989 bytes .../_notices/not_registered.doctree | Bin 3440 -> 3440 bytes .../issue_manager/data_valuation.doctree | Bin 79832 -> 79832 bytes .../internal/issue_manager/duplicate.doctree | Bin 75245 -> 75245 bytes .../internal/issue_manager/imbalance.doctree | Bin 68346 -> 68346 bytes .../internal/issue_manager/index.doctree | Bin 5282 -> 5282 bytes .../issue_manager/issue_manager.doctree | Bin 80662 -> 80662 bytes .../internal/issue_manager/label.doctree | Bin 88614 -> 88614 bytes .../issue_manager/multilabel/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/multilabel/label.doctree | Bin 79258 -> 79258 bytes .../internal/issue_manager/noniid.doctree | Bin 90556 -> 90556 bytes .../internal/issue_manager/null.doctree | Bin 68181 -> 68181 bytes .../internal/issue_manager/outlier.doctree | Bin 78825 -> 78825 bytes .../issue_manager/regression/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/regression/label.doctree | Bin 108542 -> 108542 bytes .../underperforming_group.doctree | Bin 114895 -> 114895 bytes .../datalab/internal/model_outputs.doctree | Bin 78458 -> 78458 bytes .../cleanlab/datalab/internal/report.doctree | Bin 34190 -> 34190 bytes .../cleanlab/datalab/internal/task.doctree | Bin 57819 -> 57819 bytes .../datalab/optional_dependencies.doctree | Bin 3451 -> 3451 bytes master/.doctrees/cleanlab/dataset.doctree | Bin 100920 -> 100920 bytes .../cleanlab/experimental/cifar_cnn.doctree | Bin 407995 -> 407995 bytes .../cleanlab/experimental/coteaching.doctree | Bin 48525 -> 48525 bytes .../cleanlab/experimental/index.doctree | Bin 5365 -> 5365 bytes .../experimental/label_issues_batched.doctree | Bin 158466 -> 158466 bytes .../experimental/mnist_pytorch.doctree | Bin 555175 -> 555175 bytes .../experimental/span_classification.doctree | Bin 34890 -> 34890 bytes master/.doctrees/cleanlab/filter.doctree | Bin 94218 -> 94218 bytes .../.doctrees/cleanlab/internal/index.doctree | Bin 4532 -> 4532 bytes .../internal/label_quality_utils.doctree | Bin 19410 -> 19410 bytes .../cleanlab/internal/latent_algebra.doctree | Bin 85348 -> 85348 bytes .../internal/multiannotator_utils.doctree | Bin 46750 -> 46750 bytes .../internal/multilabel_scorer.doctree | Bin 183513 -> 183513 bytes .../internal/multilabel_utils.doctree | Bin 34042 -> 34042 bytes .../cleanlab/internal/neighbor/index.doctree | Bin 6725 -> 6725 bytes .../internal/neighbor/knn_graph.doctree | Bin 111899 -> 111899 bytes .../cleanlab/internal/neighbor/metric.doctree | Bin 38404 -> 38404 bytes .../cleanlab/internal/neighbor/search.doctree | Bin 32456 -> 32456 bytes .../cleanlab/internal/outlier.doctree | Bin 29778 -> 29778 bytes .../token_classification_utils.doctree | Bin 69171 -> 69171 bytes .../.doctrees/cleanlab/internal/util.doctree | Bin 212686 -> 212686 bytes .../cleanlab/internal/validation.doctree | Bin 41565 -> 41565 bytes .../cleanlab/models/fasttext.doctree | Bin 2465 -> 2465 bytes .../.doctrees/cleanlab/models/index.doctree | Bin 5009 -> 5009 bytes .../.doctrees/cleanlab/models/keras.doctree | Bin 106237 -> 106237 bytes .../.doctrees/cleanlab/multiannotator.doctree | Bin 165197 -> 165197 bytes .../multilabel_classification/dataset.doctree | Bin 67275 -> 67275 bytes .../multilabel_classification/filter.doctree | Bin 86794 -> 86794 bytes .../multilabel_classification/index.doctree | Bin 4916 -> 4916 bytes .../multilabel_classification/rank.doctree | Bin 47085 -> 47085 bytes .../cleanlab/object_detection/filter.doctree | Bin 38032 -> 38032 bytes .../cleanlab/object_detection/index.doctree | Bin 3852 -> 3852 bytes .../cleanlab/object_detection/rank.doctree | Bin 149811 -> 149811 bytes .../cleanlab/object_detection/summary.doctree | Bin 166920 -> 166920 bytes master/.doctrees/cleanlab/outlier.doctree | Bin 98688 -> 98688 bytes master/.doctrees/cleanlab/rank.doctree | Bin 113711 -> 113711 bytes .../cleanlab/regression/index.doctree | Bin 3738 -> 3738 bytes .../cleanlab/regression/learn.doctree | Bin 222189 -> 222189 bytes .../cleanlab/regression/rank.doctree | Bin 19815 -> 19815 bytes .../cleanlab/segmentation/filter.doctree | Bin 28604 -> 28604 bytes .../cleanlab/segmentation/index.doctree | Bin 3788 -> 3788 bytes .../cleanlab/segmentation/rank.doctree | Bin 51266 -> 51266 bytes .../cleanlab/segmentation/summary.doctree | Bin 69021 -> 69021 bytes .../token_classification/filter.doctree | Bin 27210 -> 27210 bytes .../token_classification/index.doctree | Bin 3934 -> 3934 bytes .../token_classification/rank.doctree | Bin 60167 -> 60167 bytes .../token_classification/summary.doctree | Bin 79564 -> 79564 bytes master/.doctrees/environment.pickle | Bin 16378937 -> 16378654 bytes master/.doctrees/index.doctree | Bin 42636 -> 42636 bytes master/.doctrees/migrating/migrate_v2.doctree | Bin 28116 -> 28116 bytes .../tutorials/clean_learning/tabular.ipynb | 130 +- .../tutorials/clean_learning/text.ipynb | 1762 +++---- .../nbsphinx/tutorials/datalab/audio.ipynb | 1188 ++--- .../tutorials/datalab/datalab_advanced.ipynb | 320 +- .../datalab/datalab_quickstart.ipynb | 138 +- .../nbsphinx/tutorials/datalab/image.ipynb | 4078 ++++++++--------- .../nbsphinx/tutorials/datalab/tabular.ipynb | 138 +- .../nbsphinx/tutorials/datalab/text.ipynb | 172 +- .../tutorials/datalab/workflows.ipynb | 1124 ++--- .../nbsphinx/tutorials/dataset_health.ipynb | 34 +- master/.doctrees/nbsphinx/tutorials/faq.ipynb | 608 +-- .../nbsphinx/tutorials/indepth_overview.ipynb | 210 +- .../nbsphinx/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 98 +- .../nbsphinx/tutorials/object_detection.ipynb | 186 +- .../nbsphinx/tutorials/outliers.ipynb | 926 +--- .../nbsphinx/tutorials/regression.ipynb | 202 +- .../nbsphinx/tutorials/segmentation.ipynb | 950 ++-- .../tutorials/token_classification.ipynb | 142 +- .../tutorials/clean_learning/index.doctree | Bin 3019 -> 3019 bytes .../tutorials/clean_learning/tabular.doctree | Bin 60765 -> 60765 bytes .../tutorials/clean_learning/text.doctree | Bin 230169 -> 230169 bytes .../.doctrees/tutorials/datalab/audio.doctree | Bin 333645 -> 333645 bytes .../datalab/datalab_advanced.doctree | Bin 203507 -> 203507 bytes .../datalab/datalab_quickstart.doctree | Bin 142186 -> 142186 bytes .../.doctrees/tutorials/datalab/image.doctree | Bin 514366 -> 514366 bytes .../.doctrees/tutorials/datalab/index.doctree | Bin 3367 -> 3367 bytes .../tutorials/datalab/tabular.doctree | Bin 120657 -> 120657 bytes .../.doctrees/tutorials/datalab/text.doctree | Bin 149929 -> 149929 bytes .../tutorials/datalab/workflows.doctree | Bin 405645 -> 405645 bytes .../tutorials/dataset_health.doctree | Bin 325916 -> 325916 bytes master/.doctrees/tutorials/faq.doctree | Bin 199353 -> 199353 bytes .../tutorials/indepth_overview.doctree | Bin 220325 -> 220325 bytes master/.doctrees/tutorials/index.doctree | Bin 3139 -> 3139 bytes .../tutorials/multiannotator.doctree | Bin 137334 -> 137334 bytes .../multilabel_classification.doctree | Bin 64488 -> 64488 bytes .../tutorials/object_detection.doctree | Bin 140181 -> 140181 bytes master/.doctrees/tutorials/outliers.doctree | Bin 104189 -> 104195 bytes .../tutorials/pred_probs_cross_val.doctree | Bin 17310 -> 17310 bytes master/.doctrees/tutorials/regression.doctree | Bin 106940 -> 106940 bytes .../.doctrees/tutorials/segmentation.doctree | Bin 1994473 -> 1994473 bytes .../tutorials/token_classification.doctree | Bin 176655 -> 176667 bytes master/_modules/cleanlab/internal/util.html | 21 +- .../tutorials/clean_learning/tabular.ipynb | 2 +- .../tutorials/clean_learning/text.ipynb | 2 +- master/_sources/tutorials/datalab/audio.ipynb | 2 +- .../tutorials/datalab/datalab_advanced.ipynb | 2 +- .../datalab/datalab_quickstart.ipynb | 2 +- .../_sources/tutorials/datalab/tabular.ipynb | 2 +- master/_sources/tutorials/datalab/text.ipynb | 2 +- .../_sources/tutorials/dataset_health.ipynb | 2 +- .../_sources/tutorials/indepth_overview.ipynb | 2 +- .../_sources/tutorials/multiannotator.ipynb | 2 +- .../tutorials/multilabel_classification.ipynb | 2 +- .../_sources/tutorials/object_detection.ipynb | 2 +- master/_sources/tutorials/outliers.ipynb | 2 +- master/_sources/tutorials/regression.ipynb | 2 +- master/_sources/tutorials/segmentation.ipynb | 2 +- .../tutorials/token_classification.ipynb | 2 +- master/searchindex.js | 2 +- master/tutorials/clean_learning/tabular.ipynb | 130 +- master/tutorials/clean_learning/text.html | 18 +- master/tutorials/clean_learning/text.ipynb | 1762 +++---- master/tutorials/datalab/audio.html | 2 +- master/tutorials/datalab/audio.ipynb | 1188 ++--- .../tutorials/datalab/datalab_advanced.html | 4 +- .../tutorials/datalab/datalab_advanced.ipynb | 320 +- .../datalab/datalab_quickstart.ipynb | 138 +- master/tutorials/datalab/image.html | 58 +- master/tutorials/datalab/image.ipynb | 4078 ++++++++--------- master/tutorials/datalab/tabular.ipynb | 138 +- master/tutorials/datalab/text.html | 2 +- master/tutorials/datalab/text.ipynb | 172 +- master/tutorials/datalab/workflows.html | 422 +- master/tutorials/datalab/workflows.ipynb | 1124 ++--- master/tutorials/dataset_health.ipynb | 34 +- master/tutorials/faq.html | 6 +- master/tutorials/faq.ipynb | 608 +-- master/tutorials/indepth_overview.ipynb | 210 +- master/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 98 +- master/tutorials/object_detection.ipynb | 186 +- master/tutorials/outliers.html | 6 +- master/tutorials/outliers.ipynb | 926 +--- master/tutorials/regression.ipynb | 202 +- master/tutorials/segmentation.html | 10 +- master/tutorials/segmentation.ipynb | 950 ++-- master/tutorials/token_classification.html | 20 +- master/tutorials/token_classification.ipynb | 142 +- versioning.js | 2 +- 178 files changed, 12371 insertions(+), 13340 deletions(-) diff --git a/master/.buildinfo b/master/.buildinfo index 2ab302ff0..90f554846 100644 --- a/master/.buildinfo +++ b/master/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 44ecf819919d54495a9d115c9a756342 +config: 9a84ce39768e5004457710fb533df182 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index 6faf3f534508b735323b977a4db44c631867ff72..c30a94616428769f11ed97736d8a922cc21688d6 100644 GIT binary patch delta 117 zcmdlWxj}M6IHO^HvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Yvazw5fw_U%<_^XHPBOG{axZ5D0PCV4^8f$< delta 117 zcmdlWxj}M6IHRGNQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF YfkASziFuOg<_^XHPBOG{axZ5D06vr=nE(I) diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree index b69e207af8c7409829cef332dd156b9017af37d9..525e8b8576a8df844fe9a5149479df3c496c89da 100644 GIT binary patch delta 1464 zcmX^3o8{ndmJRWYhWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24OjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= O>;#%AUw zDFy}x$;l?>Nv4}8FtU=NZL*_z+-4PKKNiw$1ZkbFYs1JtS%9OEEbZ-^EjjOjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= OmMsd(X%H-h!l6Kr#6{<(RNHyx4|ty6`@Hu(?>ToWmNga2 znrURto}jnwW?6@;q%`PmE%UUyg6-~*$J^p`x3oFjp~H@LkH^vCbQ}qmmbx5nhszjY z9{`!Y`_pkTUaxApSKDT+C}aM_(f>SH;5sC)HdX_11(WKlfoy$Z$zt?3Dk+df-HX(oHq^li0R305Yr`6dHwK1#FC?y&b26K_O8E0x z$OzB(rDEDnrMc5=upAI?-Ns#%d7oVx0u1DUou{7}yr~2e*XX_RRC<4u`+>)gIcb{)&no$= z?RW(?F;mYiwC3Z*;5BWQVI%2s3VD^7Iq23E{yh!MK#Ny--%d>I7c;qB6J5(Zhc+H^ z%1+9rJI>SS>=QnE60@lh0~VG=;bndeorq0`o;V-MKx>#^=QeTo7mtDlBn_I=0rA#> z%o8!Yl1*<_WmHYpMMxJrZKNw5)+DS*7IX~RjQ=Mprnx+&8u=>HrAkciQ#27uS2HP6 zsEi?xMAL?(%{gRQm`TLKezlUu-O6|)*i6%*iekE=+`t^_NVec0ke+0K=Sc+@GPp*C a1?oi`R=bmA`ZI?T7n?unB!Bcr?Y{x9ox?u> delta 4197 zcmbuC-%C?r7{__vbLOwM&P<#sLKI5BBsaBBhz7~7FidG8bt^O zM|iss5{7hPfpwCni*CG%F1siw*AOg)!cibd;zjRCsc!e(AMpKtp6_#>_j%tl9nYSQ zXU{Y)MD0;*G(Pp7Y6T<($kWuM++O z7B(XDed(CC)3~+xkfYkErD9+%&6+!n@HZdK^{vEU?VBG~xaRIv1LnS7y98@z*GG}V zVD@=7#-hkUFtt>mu={AyRF%doF!^&j3&G@A7P||RH>~UtOfI#vk1#p9gH6KZcrL4i z$uIL*1R#?fk|!`!FJw^w`e%F4yIt%HO!nq}7-o0!JCJ!87E#MlcrzR)+ znvd=IdYU`MhAIK^)@|HH_IvE&Fkm1D>>T~f;w`0^xK8g$q|^H&+z&i{%t_lccvj6{ zZO1FHiJ3-jp*0^b0k3JZ3>!(8lgO*Y%t1Fh`1cGj11(`~uT-tcR zDJLbH?gY=EGmrV$3CyNW3|d$=MV5IlIuV-=R}*|V3$0;(o!i8nUpxjHkTht`1jJhh zGGD|UN)EkMl`%C{7a?67w2`S?wx(c3vY?~LX8b=<3C-mzwa8bIE;ZumK1CDZOw~@& zB4r$TB$_rPZO$Ug!b~C-_N&!2;ZY`4c{!F*1<%ktqPjlEM#iktQ$M42)VYd zv0g!L2;Xt|!c2PPY-V)*Lat+LJ$94p*aN<8WH=U_e$NHYA=lPVVdu%U)iZhvxmsJ} zzmS`f7?T>vjU3KYTXMCYOHU!!ue-8ekrz4kn?(w>cu7ycz$9V6d0pMSAhNXTPxe!n zoE{s+D7yL9#oL_Zo3UB%j+H&>+NTFTX0(`YAjQbO{f0E-EixQ{WP*|!;}K)h-2ebn8myXJ%@`$MV)j5 KwgbCfElL0gTo82t delta 3571 zcmX@QLh$Gc!3~~_hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4~t74c{!F*1<%ktqPjlEM#iktQ$M42)VYd zv0g!L2;Xt|!c2PPY-V)*Lat+LJ$94p*aN<8WH=U_e$NHYA=lPVVdu%U)iZhvxmsJ} zzmS`f7?T>vjU3KYTXMCYOHU!!ue-8ekrz4kn?(w>cu7ycz$9V6d0pMSAhNXTPxe!n zoE{s+D7yL9#oL_Zo3UB%j+H&>+NTFTX0(`YAjQbO{f0E-EixQ{WP*|!;}K)h-2ebn8myXJ%@`$MV)j5 KwgbCfElL3Jqd#x} diff --git a/master/.doctrees/cleanlab/data_valuation.doctree b/master/.doctrees/cleanlab/data_valuation.doctree index 927ec9acf24e8103a86dcfc64eb0a1c5c76da578..c1ea3660a6c75ee1c906fe7f2a914f0ac8b35e76 100644 GIT binary patch delta 477 zcmca~p7GLo#tqSohWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<6c7;liFt5MKl^HruDOyp^u{ExMPES-Uy*j-u3(>aM>;#%AUw zDFy}x$;l?>Nv4x~7;liFt5MKl^HruDOyp^u{ExMPES-Uy*j-u3(>aM8!1bxWe^v+!=|xVseAxa4AvA6+0@$Wm!@j(y>Tgol|m2PIi<005DYv zHL(T5DbxCmI;t)hY--k$1n5G#ovBvKCI}5P$tD{CR0*}P<()^$A@m%}+CLx-E1`C_ za;Vt_S*HO?3|DO(s$f$}7t(Jb9cJV4He{c4^j=qtLQRL+?d}Jt^-)hZYMn^Rs5Nob zhg#1Lcv0)&R0^=NLi#Ui3=g^iqpGii^VVGhunr^_WvxR?$jwA@m)X0KpGa06Jvn+E z$*QA)u{dfCWYVa$Gdqh~pJ|U#>&im|iVlzWpy=piGn%z`>OE?mpGJO^RedGc%v;oI zo&AcU{Vy&7hmMilAZo076+}_f>(fB9V!D3z<&7U(2S1yf+i!*LB@b)NcVJeJ+L$kA z2R_duj~9~ju-<}#ntKZs>|*hE8JKyo^wEN$>SD>p?8_yN;abacV&%h=#J2@2z(s+e z9b{_?1ZQw&m7E9VeD^Ps--da$ zy$7k8$0?ma9o`VVzA;)nnM delta 4228 zcmbuC-Aj{E9LG6lZMiLN*^7A*Swe3&&X@Mg2x(GCZKA7<3=(~wy)+fa(5bAX7bFSE zkUfkCg5feuVimDNoFMF`s77WkuOz$bMsKA1B05k01^fC3e14zb_xJqH*_pO%(UvVQ zTS={>E*|TN3=h;aHr7hPhF~ZZQ0g6Za=;h1qRRBY?`?ZES7tiBbr?$WqlGkcQ>% zPPTrmMS`re040X2wvUyu899RVTSzD@9qvH(Nk{KRLKJFRVYmAppw>tIeW-Oh=0vT% z1Ffj_!jK2G9v>M2tgMjuiyDYECqR$nrKTCb(DsP&ol7`3iHG@$5sx*tU+Gc9P==*)Z6x-g6UD69HPu(`LW z)tdc^qVX43fkVegZWJ|Eyz--{>Ge6FSutHN`|`$%t%H|k<_}w;d&$k3@?DtKtv2OL z*^$o+$m50N+$>s?E&($yt$ws%sJgUjW7TV`9K*G?=fuj#Go*C~R)C8F z{%Vk|Dv-sR_yxI$@M#Y&H)>TbTEb(eY0Va9VC168 z;BKf554ZVg9=q8+nqUv`!46uE9bHfIX$7wg(G(W`Ki=F+H_XLH=W?7*0gq2ja6UeK igJx~TOEp(gz)$YeUSQw2fQOJ>+x#QHlRM{V7ySoek{n3@ diff --git a/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree b/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree index 9dbaf7511bdd9216aa9faff421c05ae60e550a07..fe92a1a9874012a8ee7be61bf8f648ca58d8a436 100644 GIT binary patch delta 62 zcmZotYEs(Z#$uSCY?)GSnr^0VoNS(CYLRA^oMxVCVVP)Xo|s}_o@!)}W?^BFXlP)P RY;0_1U~XWxxtyh&2LMES5wZXP delta 62 zcmZotYEs(Z#$sq@R8o+cQC6gHX=!MlVv>@Ynw(^8W|Ww0mY8UkYG7_|kZf#bZjxeP RV33?_VxDBWxtyh&2LM+>5`_Q& diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree index 9d91a41468728fdcf9f81f30a09f07e6d5d303d6..e389b61733b67c8446288cee8b70937baec23cd4 100644 GIT binary patch delta 64 zcmccfmGRD3#ti|ChWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri T1}4eI#%2cQ24>;#%AUw TDFy}x$;l?>Nv4|{8KWx!?79^! diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index 7b6c01d613f0830c3e0156e8c64d472b284522e5..ad6540e85db82dcabdf5cfbc2853e3c2336ef5b8 100644 GIT binary patch delta 62 zcmZ2yxXy4xHltyFvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Rvazw5fw_U%<~fW);s9r~5&i%G delta 62 zcmZ2yxXy4xHlv}LQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg<~fW);sAPk643wv diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index 4b7ab93a121adf27ddd832c699f6d002a6e8ba5e..fc8266930ca377dc9175054e4fbc10b1787b135d 100644 GIT binary patch delta 62 zcmdlUw>@q{G^1gDvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Rvazw5fw_U%<{rk?x&VHT68QiC delta 62 zcmdlUw>@q{G^3%JQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg<{rk?x&VDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri j1}4eI#%2cQ24?z`V+E9(cQLl_VgzBP?YkJ6+b#hBF7O$Y delta 81 zcmX@Gg8#q@{tc;&MrKAO1(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw jDFy}x$;l?>Nv8UfV+E9(cQLl_VgzBP?YkJ6+b#hBPr4ds diff --git a/master/.doctrees/cleanlab/datalab/guide/table.doctree b/master/.doctrees/cleanlab/datalab/guide/table.doctree index 156a285958aa272919dce4b8ca3c2c130bf26a71..40d805d2a76dd2e319fde70d5b91012ad62c0d55 100644 GIT binary patch delta 64 zcmaFxf%(A)<_)J=4fB&NQ_4-#&Ge0v&67+m(#(?6%u_8a6AjH1Qw+>gjSSK(EDRD2 U4NQ`ajm-?q4a_$GVNLl20Qoo-+W-In delta 64 zcmaFxf%(A)<_)J=4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW UQVa|Xl9Nr$lT0`NVNLl201OTmt^fc4 diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree index b94bcd53f07f20b66541fcedec019543c71b7c98..9f7146d41b4a0b25bcdbc380bc99b71a0c3cfad4 100644 GIT binary patch delta 117 zcmX@AbyRDEFQZ|8vSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Yvazw5fw_U%<~qjhf@Ek*6TZ&~03rM$Hvj+t delta 117 zcmX@AbyRDEFQcKEQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF YfkASziFuOg<~qjhf@Ek*6TZ&~0Ce9Y-2eap diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index b24fc8317fe911d188583325b82734a5b3085f45..c59a3cacc0e05a26ac07f22d9cf8794243c84335 100644 GIT binary patch delta 5702 zcmbtYUu;ul6wevlZqV+o>#}T(NC)W{I<+lxZ3iP8ZVMw!)@-3647!!FI(2LUZVp%( zM!{&1O+L+M0OKBbF+rUH4xQWJ?$5+H#DLzpv~CB{F*@AeY2=ig24>+jDw z=XdTsxpSYp=Jd0bBBPn6{jW4W7Y#LJV*487*=Qyk8;m!lBeC@UaBOfxI2(_L(~fj~>A49p_I1omj2o{K48isOoHR zFRFU6t{G?(m)Bpw@|@!4@G2KH)08+9FX4r#7i*IR{}mKYG`Y{dyiPQ4{0t`$=aSnv zziBtvH__Ts%vW0{utBFpqQfV?OVx@8ZC_(GaH8XT1A_Jn?@o`noE>6+*Hs{GqP)I9 z!Otgr*z9e%yXRG`dlq~FF1?!1Cv(`;En!oHb{qU@-z4t6RGdhg;@i}HxCWb{zhgVk z4xB<$aLec6%h`+*_(#JM= zhRLU=FM+e*EAMuNF!Ox+n=kwrRM^IoAliG+Bfo5;HT>hvzt99dJ7^pGi>rFJoNB^x}0S(mV zs46Dxwkg2XTO~ARI*`}+3Ms3o(*Z~K8VbmK8~vhQ^iv;}OuHGHDX2zQQ4$;I5|WYC zw8w!HrJX#2?S?&xAz0*On_pQ2%WA#4($aq|O{`X0*A^>wHnbSTYTB5l)U8&nHdJk_ zW7DzWZ1vmthTB*ld>Oc{a?tN#jtTQg7-TRR+h7}PFcE9Vq#*kv`);m`KL0M<*WaIW z&hOlNxO3NBbNbmvb&>jGhYt*A#zIX^b;Vv)Za@XbMFua1h-Mc~aTD^j| z6z?v8)hqcqZ$GYH$!mQtgX-)q7t0{yN4V9`7&OC|o<4}xo8eCbomj0I{&4MHRCPAE z4^_Qf(+sqUOKU&C@|@u2@G2KH(}Xy`v4|HUUaU+o z!3SbKZ1y(X-SaBeJqx}7mtM*5$FtbfEn!oH_89zG-z4t6Se#Cp;&$RbT!YQf-?4*d z2hO4?_{Hbp>q99g@Q+6d&;(fOn@68@pf08}E5zfYKY(4-2J#H=#dq+H@kTTen&#q3 z7r${Liw43n5}2}H2hWQ~Q+~0!yW9S0#*r$aRC#EHgxRkn_@sEJ(lN~JfY z#Q|Qvw3QaYglc&UUL?PLk$N3)%x$ND(kbsjV__N5Btezih6mhgrY4z2v;Dt``7R=d zm3ZXeL0Ye_#3+ge?1>$ZI^ILi6gikqRTr7^e28jPvX3@8v6E*ySw~yd*gopSLu;s( zG_^axOO0PCeFy0exG#pyabc9Oo1HrY`zX0s@BKNsmXg0xOHP=X_z|&4K&}naR_>dA=|+ z3t3vzCp*XpZ}ydlCeP;d%?>gb$gp{{qhb;}Sq{$G?4apMUexAnR@3fNAkWsxYTA60 z<9$5H(hUye$-WZ2o8x_tvyyFu#O7ez*4z!NE|ncciQiRnWy#EBz{r!>{ITE!CAMc4|0mP$o63vGw^(Ge zTxAR;NnWdd85yCrnX!2{8QLZ@HaBlBXjdYmL<4Fq*lgTW&rL@1W^B%wyr7MH@`+pO zn`>v7krUcbBQ}T6nnX_Jwee8l=I*7Z$?-Qx>yK3(;-vd~^TtCP$VnO-57liJILRYO zx{Z?s{}oJrf0JkO$y@))*S)#)4wC>`+G{8Cec;(__|}=6fEN4*(XRJt6B&tYv*5qy zoMbo}n6ReH@Gy#QU&g_Bmz)v=Xux)$0c2M9vD58^7)2*X%o5&iBEq>;#%AUw zDFy}x$;l?>Nv50I7@5e>HrY`zX0s@BKNsmXg0xOHP=X_z|&4K&}naR_>dA=|+ z3t3vzCp*XpZ}ydlCeP;d%?>gb$gp{{qhb;}Sq{$G?4apMUexAnR@3fNAkWsxYTA60 z<9$5H(hUye$-WZ2o8x_tvyyFu#O7ez*4z!NE|ncciQiRnWy#EBz{r!>{ITE!CAMc4|0mP$o63vGw^(Ge zTxAR;NnWdd85yCrnX!2{8QLZ@HaBlBXjdYmL<4Fq*lgTW&rL@1W^B%wyr7MH@`+pO zn`>v7krUcbBQ}T6nnX_Jwee8l=I*7Z$?-Qx>yK3(;-vd~^TtCP$VnO-57liJILRYO zx{Z?s{}oJrf0JkO$y@))*S)#)4wC>`+G{8Cec;(__|}=6fEN4*(XRJt6B&tYv*5qy zoMbo}n6ReH@Gy#QU&g_Bmz)v=Xux)$0c2M9vD58^7)2*X%o5&iBEq>;#%AUw zDFy}x$;l?>Nv50Y7+aV~*Eac~VZr1D;sTpDv-k*+t-YB{omR5Ko3Be8VkS@fW-D1+ z7P7SVY&KCcBv0$a&FboIWW*0JpeJtD*Stwy98KJON+*z9Tkq;!Cd1av4-L1llNGv^ zo9(T7GRV?exH)&R3mbV_C+9Ba-JG!MlQJ3F1s{}c{&`M`oFoC%I{D{0$<4Pfv+|H` i^Jc*Ze_2V_Hd*jN-{!N=428+i3d!;Oo7erSW&{8}!)fCH diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree index 25f831459676f95a49e28d743dec32fed39b2095..63d258cf665c1711ebb24826e589a54088bc7d52 100644 GIT binary patch delta 122 zcmcbsd{=pcKciuOvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF fvazw5fw_U%<_5;O%tj<@)1Ul+U21bCYY7hk*xVzI delta 122 zcmcbsd{=pcKck_UQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF ffkASziFuOg<_5;O%tj<@)1Ul+U21bCYY7hkGz=v) diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree index 6041f725af56a08ef2ef634b783b05c4d70523bf..26478b0b09c008cb16af6379bec2c4b9d08a3d0e 100644 GIT binary patch delta 1125 zcmeBu&(!;#X+t=pVSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8}}t>r3+h delta 1125 zcmeBu&(!;#X+t=pp_x%hL1so-k-nv+p?Qi)N@{9ylChalVzOCcqFJhexw%2Iv6;C^ zih+Saa`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8~0^{94HX diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree index a11216833a4acef7b90997d2292e01f09902f689..5b587082174b323e2c266b2c5958a777c0f12fa2 100644 GIT binary patch delta 62 zcmew$^+9TbE0bYHib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg<}#*VTmXm{6Sx2X diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree index 32d73bfa5c44bb0b2357837776cdf2e4581d7141..eca7a379fd7dac498ceeec488479f1b7c1b57a28 100644 GIT binary patch delta 2628 zcmccdp5?}SmJQL2hWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<6c81IsyOVOxqvjlSk=EeSN$Vg3-Ax21Swh8Sd$ImeBo5INoZJ_qe_0e^d z*!(K?6&WE8_xHg>JxXl9ll+Jh+gGPAB_q-{H|CtgL%khskx0fV046iwPC{q&sKx!}-B%q-z64{LIb&mX&dk zsnu=+6B&NpT)0b=oC>CJ*Q(8lhtfF7aIB(H!Q_TF(vu%t5ZpZF%mi|Z7pM`NA6(c@ zUfm3Gf!gM{`;9#0d0=zH8zoILv;yOJ`Z^Itw#mvZ!rNQL7+;VX+mjcVv2M3fU`!=5 Mp>5ArXUr7<06E+jJ^%m! delta 2628 zcmccdp5?}SmJQL2hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4x~81IsyOVOxqvjlSk=EeSN$Vg3-Ax21Swh8Sd$ImeBo5INoZJ_qe_0e^d z*!(K?6&WE8_xHg>JxXl9ll+Jh+gGPAB_q-{H|CtgL%khskx0fV046iwPC{q&sKx!}-B%q-z64{LIb&mX&dk zsnu=+6B&NpT)0b=oC>CJ*Q(8lhtfF7aIB(H!Q_TF(vu%t5ZpZF%mi|Z7pM`NA6(c@ zUfm3Gf!gM{`;9#0d0=zH8zoILv;yOJ`Z^Itw#mvZ!rNQL7+;VX+mjcVv2M3fU`!=5 Mp>5ArXUr7<0BQ9)uK)l5 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index ca099eaff6a536bcf65e15a474b8a51f7ee28dc9..da44232255cf922ba960693633e8777b0814c2be 100644 GIT binary patch delta 2632 zcmaERn&s_jmJNZ7w)x4HDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24?yx`N_rl#rdU0$*Gek+8b{^$T*kLkYrlB_WVQ+7B zUB4u^0EEejzP`gh!hj(*WA1fOL+BK%8kZbz~FW$|O^QV$)J4pNPWoG2s?w`gs z`NKM%$tJNu)2E9ws&2O0c!<0NkUcs6y!2!pf1b_z_e>z8r~#%SxDk{0?_u36esB$W z^>6m(`16Z+$cjyn3)D6rzVk_gEbaZ%<9Qfaw^y+-zU3g#0AMa+-L50R7)EA#nBFVJ V$g$l^oKcFr04UiGY`Z;S1OP7}8CU=S delta 2632 zcmaERn&s_jmJNZ7wq`~p1(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv8TK`N_rl#rdU0$*Gek+8b{^$T*kLkYrlB_WVQ+7B zUB4u^0EEejzP`gh!hj(*WA1fOL+BK%8kZbz~FW$|O^QV$)J4pNPWoG2s?w`gs z`NKM%$tJNu)2E9ws&2O0c!<0NkUcs6y!2!pf1b_z_e>z8r~#%SxDk{0?_u36esB$W z^>6m(`16Z+$cjyn3)D6rzVk_gEbaZ%<9Qfaw^y+-zU3g#0AMa+-L50R7)EA#nBFVJ V$g$l^oKcFr04UiGY`Z;S1OV5*J0k!9 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index b2d3007a8415f5db27e3a126c605f30faf4eb8e3..092051f73a0bf8bdcc7e3ec3846e6170237899b8 100644 GIT binary patch delta 2563 zcmex0mF3q|mJNZ7w)x4HDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24?yx`N_rl#rdU0$*GeYcNuR!$T*kLkYrHY?fMl{eoO@Zlm~`{cE<9FyNm|0Cao&1+?SDRY4E*O+9CFjcgK+oFf4nov4FiyNPrtY1`X8iSDkz;2 z+fzgCk!kzp2jQiZ*lZbfjZB*-gZ%wDb{@Hr)}UvwIWD<|oy;WDprM@8+3xF64&+#E8|+N}S|+qT6WmoZf6U3bbFFVoA2`dMcYQ z%p)@h+a@nqBe*$wMIU*^M%(5EYc}wb?(fZwy9CJ425fdr+#G!3iU#R6LRu`qpnCC9 lh}?z)D7`0q>;#%AUw zDFy}x$;l?>Nv8TK`N_rl#rdU0$*GeYcNuR!$T*kLkYrHY?fMl{eoO@Zlm~`{cE<9FyNm|0Cao&1+?SDRY4E*O+9CFjcgK+oFf4nov4FiyNPrtY1`X8iSDkz;2 z+fzgCk!kzp2jQiZ*lZbfjZB*-gZ%wDb{@Hr)}UvwIWD<|oy;WDprM@8+3xF64&+#E8|+N}S|+qT6WmoZf6U3bbFFVoA2`dMcYQ z%p)@h+a@nqBe*$wMIU*^M%(5EYc}wb?(fZwy9CJ425fdr+#G!3iU#R6LRu`qpnCC9 lh}?z)D7`0qHib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg<}OAFVE}Bw5{Cc) diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree index aece9c41be88effaf77422328aa03b26a5e069c7..128c530e91564b15d39380e86a21d554a8e0daca 100644 GIT binary patch delta 2506 zcmbRCjb++5mJN}NhWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24)yo*bO zl{_OhcL;1?CsXSu2_5o6taI}XDK<6oY@IyAF`7J`o7Xy>qb#6VH~V@tkm+S$RD73y=}Yn?+L!$P0(A%?@cF$@4$NX8x=cGHnJsJTT_~CAPoM*Q3Pt--SCUv3+7` z4Y`q~;KsfAa@8qLvXV{rWIbQ5&B?7CJVvJW&Fc<)B(Hkwn!N6Sz~-#u+T{7W zYjeOwQSxfXuFVQJZOOECvR*FFW|ha1b=>{x}+?!**Ns_65J5c|2 SJ+kZuwWRpBFE(Z@W(NRgh4zsE delta 2506 zcmbRCjb++5mJN}NhGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4~-7+;W~ZL;Hvn#uAk4WwHL)Y&)yo*bO zl{_OhcL;1?CsXSu2_5o6taI}XDK<6oY@IyAF`7J`o7Xy>qb#6VH~V@tkm+S$RD73y=}Yn?+L!$P0(A%?@cF$@4$NX8x=cGHnJsJTT_~CAPoM*Q3Pt--SCUv3+7` z4Y`q~;KsfAa@8qLvXV{rWIbQ5&B?7CJVvJW&Fc<)B(Hkwn!N6Sz~-#u+T{7W zYjeOwQSxfXuFVQJZOOECvR*FFW|ha1b=>{x}+?!**Ns_65J5c|2 SJ+kZuwWRpBFE(Z@W(NQpSr?c9 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree index 2d723214d97cca727da56ed592c62580ee182cf8..c986ffaae9404480eeedcb1cdfabc8173b204522 100644 GIT binary patch delta 3034 zcmZ3sg>~5$)(xJFhWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24h$65c%L(kb#v4xr+eBFp_b+kvg?cZ>kp@s{cU delta 3034 zcmZ3sg>~5$)(xJFhGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4~t7%z~aZL(oY&UA|iM*hj)mh$65c%L(kb#v4xr+eBFp_b+kvg?cZ>i#Prpq7 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree index dc42b20316534fb28feef6c5461654de475152fa..630ab0e574feca70c57d59575857dc51dc7e6b7a 100644 GIT binary patch delta 62 zcmaDV^HgR-Fr#69vSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Rvazw5fw_U%<`%|>JOFd;64n3! delta 62 zcmaDV^HgR-Fr%TFQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg<`%|>JOGBY6R7|I diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree index 7c6d2b966c972367b23e2afbdeb4be7931e9e59a..9db6baddb918ccf59709e7ac3f484b400a3a976f 100644 GIT binary patch delta 2706 zcmbRBnq}5&mJObahWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24bfARxdV)IhX z26DYEU?j1bUFRYb*^Zakyi31_OxrgL81XVwVDo>|6=d2BOy&}sJ1yQ*;_oP%4oYl4 zVz-tO+k>4dDG9oDZuOLC=l8xtp5u3HzT&r=ytKY|b8Sc{c_F@c^MSDE#$9ZwdUHqX0<+f-Y?o* zw|y&lQC&2-ZoAHAracRJ$#OhMyY%Gs*SR<6oNDDE--ylYuQO?qrG3`+8b-z+>;#%AUw zDFy}x$;l?>Nv4~t7(bGsZSukRrs)Ta8TlvcvJ{eTD^UB)$pS{go2yw5vXHG^e6uXK z7%O?&H~$sr;vi4!bfARxdV)IhX z26DYEU?j1bUFRYb*^Zakyi31_OxrgL81XVwVDo>|6=d2BOy&}sJ1yQ*;_oP%4oYl4 zVz-tO+k>4dDG9oDZuOLC=l8xtp5u3HzT&r=ytKY|b8Sc{c_F@c^MSDE#$9ZwdUHqX0<+f-Y?o* zw|y&lQC&2-ZoAHAracRJ$#OhMyY%Gs*SR<6oNDDE--ylYuQO?qrG3`+8b-z+3aH-A6LxWvSD`dbR;cIN9C65ngRH`- zX|h9`;O2zWo5|Jw;1ciV%U8CN>v)iMkGrqPwcTMk&*qQMn#s#9K)(aE8^7h@Bs1s~ z>e(hw_{zPR`FkOG9>|#P$HJ(w)s(S;ykaI}J5c{xUb37Iwp)7pdLhPq9tsSY4m5y! z`(1g)Z5m{m(lq_QJ0sh6A5X^H>i~u)Am?{7O delta 3021 zcmdmUn03!#)(zf_hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4}?7_XC|ZL;9Jyy-XM8TltOvJ{eTD@gm~1zf_LRaoz`kf(j~03aH-A6LxWvSD`dbR;cIN9C65ngRH`- zX|h9`;O2zWo5|Jw;1ciV%U8CN>v)iMkGrqPwcTMk&*qQMn#s#9K)(aE8^7h@Bs1s~ z>e(hw_{zPR`FkOG9>|#P$HJ(w)s(S;ykaI}J5c{xUb37Iwp)7pdLhPq9tsSY4m5y! z`(1g)Z5m{m(lq_QJ0sh6A5X^H>i~vQYzq$Yb diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree index 487859ad69fddda19fd7e803b0b08fe20fe050ad..4f34d87f0509d8df9e6117460c811660c81129c9 100644 GIT binary patch delta 2688 zcmbuA-77<39LF0wbGB>~$pvu=&5d^E*g2cp5XDMygCs9ACR|XPsEJ9+g{FDP1$nta zxv=?3TuHfc;|fKjU2`K`$=k_a;JZKI)93qre$VfDu9vjeOWIhSrfk^f>>acB%T~#! z^w=GK+2>dK98RxI@%D;JUzzB4I7F{aEcZ#0EGnXGW!0(WY}3Ngfu@bK{hMcrib1xb z&!pqDS`CbcAu>WYhIB22GTJuhVRb{SO5iAv?cpJ_it{v*Uxb}jh^mBdJa`rp8Y`L0 zqKC3ah%3234}7sSj8$fIV_i*w0yDBp^Qd*!eu`Ro=PqESL`6d?*z?6b2N-|1myxS_ z4DC5yTZ0Cks`sGie8W8w_$)rb+|BtUHFY9)_!lR4a|Ai%KXW0iwVvR?3#qu>n~c4s z1KJ|B_j>n2GUm^ocrv{w{T6UwY<<8;T|;}wbEghX2pZ{p^vD1#rHVhw8Kg{G%^+@G zPN#zCGxFN2b`?Kgc}Kn}72n)2QE+PpwX68S&S4tVeU|cKLEy&GS^OdwGET5qK*u6i x2CBP!LgpQPSfuycA>d7;=!4D(h8A8p@;t@S8F#<(wEvc)1MRUdo9M>a73}%qo&$`(+smk; zdJOG3UR#3(o~rkt=zPN+68J1W!Q9RHWN+$3?(i>8?&b(`%75lUT5CPQgBMb9yEhqo zO9!+?YVY;#g=Ea1zIZadCwna5z}R}9k-7%kwL+&S=6rL2RnypQ1>vIV?p4?(OLW=4>C@$*szX8 zt_)On`Gm|ndcR2THv_<%M$vnn5ezN7aO8Q4qciS)pF)snYcgb<^!mX{76B6Y%zX@GWMlm%R zqFs4&Npv|mAq~@hB=#AZj^CV+IEzf1H*ZKL*H&PVDR0hC*CW$ru=cXd-DLV1teq>j zmD~suC==KmTI9)2Mj{18*aSzm$pr>1o8zkdILI`@-%Mb0QL{8Bnc5q=MK?$EvU8EA zeY5C{Cu9cQ=Jj*+$f;yN!6vXqVDsX|+sO>|%>ru@$Ox{@3%3@N7fI7L-`*!ehK=AN zVS*$7<}D|x$+21SW6NaAE4-UOUaTgkD1>R(yEcoLjO3yCv43;NlMEixwQg4Yn5{{= vHgIt9d$MkKmu4h4RRVJ)>-J{~jIYT|)sy7|*thqoF&-i#Lv05(k29D6VZM8W delta 2967 zcmaF)oaN>;#%AUw zDFy}x$;l?>Nv50Y7_X6`ZF-|2WBp_nmO|2PoGi#SeX}j=ViwZ1ZWiQv!%Dig$&5;! zo3{%{3X-mMGo#WZGJ*$aW6$IOm0hG;2-V3w`9g@j)mipF)snYcgb<^!mX{76B6Y%zX@GWMlm%R zqFs4&Npv|mAq~@hB=#AZj^CV+IEzf1H*ZKL*H&PVDR0hC*CW$ru=cXd-DLV1teq>j zmD~suC==KmTI9)2Mj{18*aSzm$pr>1o8zkdILI`@-%Mb0QL{8Bnc5q=MK?$EvU8EA zeY5C{Cu9cQ=Jj*+$f;yN!6vXqVDsX|+sO>|%>ru@$Ox{@3%3@N7fI7L-`*!ehK=AN zVS*$7<}D|x$+21SW6NaAE4-UOUaTgkD1>R(yEcoLjO3yCv43;NlMEixwQg4Yn5{{= vHgIt9d$MkKmu4h4RRVJ)>-J{~jIYT|)sy7|*thqoF&-i#Lv05(k29D6LMx-^ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree index 187b792900f09f61c28314b08dfc959d64369e9b..c1798c93a596d34249e04ab9cf43aaf9dad5c426 100644 GIT binary patch delta 62 zcmaDV^HgR-Fr#69vSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Rvazw5fw_U%<`%|>JOFd;64n3! delta 62 zcmaDV^HgR-Fr%TFQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg<`%|>JOGBY6R7|I diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree index 7581e44e356b3db829566445612e5dd8ab7eb883..0c798c3f5f04e5f5574be29d33fecc16090830de 100644 GIT binary patch delta 3483 zcmbuC(MwZd7{>W(XIjTiY^4)RkRT!|zqve{7ll^?g>smPB~k0lxfmrbTu@;{7f7jv zhp~%ltX3fuZ1v(kh{%hgE6LqNH;YElMK`e#)I~8m$D6#}vp?YXKJW9q@B1B2W!+p^ zH)s2pV?yomU+ErKI-RQPe78HGr~%iQyT|8n`TX29*2M#EH}^UC5!LBbxQi=vm6ZWZ zV|+$yH$|%#G{_~{Mt|5VK#n@jETgK~vlY~9T%AKzO@Y^dDwTuB&?|4gw1}pAJ6r&o z)i*z>^`2VuFJq@)*E~q#n@e;~|E|r9omyu`Yv_UoVpe1ln@xD< z4%g6uJ`p=j-uh8%4ugvsaG;des1A4%jSTW3GmYF&*Ms=Ew2eMLsh~LbSV&~T+dy># zmtHN%Ain2@MJugIbou>5^jXp?7;ZMmnqgRRxYv#7=q9XJjT{{)(~$^ bcw?yf4PH#%WK%7Wd*j!rQ7k`T!`uG>@{<%( delta 3483 zcmbuC-%C?r7{_^QXIgU;Tj|6SB#4O0*SXH{qVQ^vQV5+=WF1m@0pe~BhIo{;$zWW0{&-eL$pXYfGr?O_Q zteLZYtV?xXxilV(ggbkBy4-%3KM>Hyx>cu7Q++-)z};@Hb*pZdpL4EhF8A0Wy2{D` zCK#UDQ(=^_3&Q?82j6X+wGX7MA`Yd$EsWexwsxO9t9|uT%w3O<%At@QNiF>1n zT?73)SVYJAc6bg|RZp0(3&>H&sU=i3d%A*pjcc>0s_E=&K$XhDBj}YkUsyoXy&Wz9 z&FY(<6urk9#6ZlhKT$T)A}OT#|79HX>zW5id}E2uso%AkaZu~@XboM^K+J|rVxtM~ z?7ELYLn^L_P~fe0m8gtq!3E^z=pVM=S05B(Vs?6fM5P_CGA5#r~Polgw886;$hF4@Fo)F5jKS5a>q&bVH?ILzD%-qxqO~Qz!0Qf;4!woGlqC; b!W%=)uk&)^CYx-5+#A0}jdJM$8{Ya4`T0V7 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree index 03c345e772fe331f30be4a797430694913a4e91e..2ca4cd5ec4b4c68a6b1ff9b5909743d7cbadc03e 100644 GIT binary patch delta 3812 zcmbuB>q}E{7{~dkyEJvpjnphFT3AAtb6V~kBS#mEWG2y-sHk2B}6)lhaqvAz8Y5>AasIQrGlZ)}jx& zVNI6_Zr}(+rfHa;#zM=`B{7VRoS|Qv^CeKj8T#UhqE`R5m8GDyzjYL~*6rE}SSjum z(1=nU=K!M|_hx9-z7y!2R}L&itAD`59_9H!jBp zmdV4sl}fsf84-K2gp+)|*Px{5@b4G50+Gq_Iecy3AZVx9KtPZAZ=?rL-brT$x9hPd zZkz_^*3R8Ybx}@_y*<{g$DSUl(PRI3o7ZEnxVun~y?*2kbXkiFTTauiucAy0;@$dMPJf2dp$S6j;1EBpNQDqq98f$dZ@ZbH4YoZZ4^9#>wjXI9n0 zvt-5myDoeSW(y=e>;kx;+T}7Tw%yF4E^1>8D&h^PslDt2^tNcM=MXbkk?Fl5Me{CE z+9hUItwHt>%J9D`mwVXaMx+<*B=Yx}Q4Ni-4s3x7&zwOWd&~}ky!vU3{jUB8>V=ez delta 3812 zcmbuB>q}E{7{~dkyEJvpjnphFT3AAtb9C)!-5TU5dNUz{l2H(3P>}>d??z|SH@)n7dvnkl;?->YeYi;EPufyx}iQUc4CXeX!c$_{?5IC{fDLA|w$BCjt=-w{fV?O{Y z?v6@9ot2K1iimUp4@2TKebsN&LFhEGN*P0+Ne4L{4cZ=5LFNK(eBNNf!Ya@<$<<&Y z-KJgx+`vhQOw$-YhlQ4*OD$n+H~~Y+?}BfhtHyOUOBoNwMI`|N3Et)vuI4SXPy8JQ&PN825wQU|1;EVZc2&` zuaU-hGnMq7)+6>}2|Ia$*Px^q@$Un>fXJlyBEETW1hi9Z*r&z(H`0$MZ>RGkd$iaS zH+sOi)pNH~U6j*e?~Dbs*wdpmTI?Th^IGf`cb99i*N?w}E^BdN%PIS49{MD!_)J?l zO+1}9VwZJvuZ6C@7+;3bQhi4?)utOkoYuV^!un3p&3Eqsl;XK|6F6(-oRwaG+ld}? z0sr%Jiv_YLq?vFP+43#WKUA)WD{W=$m38TQg|A@&U^``%n^x|uWw)@Ir{tGgnOU*% zELpMit_$CSg#t+zy9h3*db#wn@j1inv2c>Ja+?y)7#1I?i-vWO{c_6z1C0xz$!JZDAH6f@QgD*6x%tf!V20`U{`2MIH z!KmL`RTGT*`)GGC>TzrH&<7S7Cxu~K=kbI|bY_*=K&Fivb|(&j>ZCH~W4`Aju9sd1 zv+!G~Z!!3JHl^hPSIwA}k%xo?1O`inJ$`w?_AF@)hOjb}&o|d+`d_Bok#7-jusM|JP$wvG6Uzglq2smIYeB3dr3H;?DC$P?~Lnr&mp6`yl42u{qsQP z*yXhVFmicoX}EJo>SMepT;5}3>M;B%i=S=HNa7V5Bj>hDNNLD$Hg(J*%Dw$g%DkWy zMrZdmoWjU4PoQ_C;=stOpCUa=GMuf?&A^p&Z~r6!lNFECO~0D<`$vH*SwQpDzyah+ z0`S6T;Urn(De+Z1Xh3H046PQjc;xjaq$Ok*%%d8lCHxLM<;>}trbD@Gd=^>30z=#M z2y*m)^wo5ErUTgDUj$nE@g)N%;fdJ|$iXjopov)n<)nwQ|69`kpn3cq{P3`Q9!3c9 zkE}v9Xd{3(`I=P%R3Y*@6oM;Z0{F#Y_sI^@kvdoqUrF(GyL~e{AqpgsuR`y6U=TjS zahiu~sjo4^*AOYJz;8=`H9}B(a-xM{+)IFh3R*IKu`$9rq+^tf8+=o-!mt+bq5S!z L_Z<+0Wg))-l4kcJ delta 4920 zcmb_gTS!v@80O^6xz$!pZJ`z+f@QhmrJa;61c9NHiuBM_Nar+5W43i6qqQ!SCeHk& z*^PSZrCjw>1JN`oLhNM*VcLaCC{0AnAf(%wM}i*v*25mZ%m1J6|IT4EBdVDZ)ty~J zsyWSRuiA6yfGIOGRVvRYx7n;^>E^UjtGTq)Y!f9(w5FS-jB-&Ft=0^wY#lcUY5+!e zx9sMGu)Sgbt{8;U@`w`8#ke)$CeyV;-g|n_kM#4KG0DS$Ijz zl~B}gEjt;C`ukWnm-kCiPn3XH2mOtcXVVSILT4I%?5@Lt%v zf!-e7oYiG=*4Tr;e|}X`z;TxJl8&@r>v&0R6OdUBIJ%I$1{{r?d*l_MD4TYYy)lx0 zx#ULM^nxEAqY!BLmn#o_c8UulN1Z$uf&+OFBbS{qs=yrB8#TPDp$vtKo=>2#=vlyh z&Bf^9=)7ugUaXMP;%x^F$P8T6R*k&R#EfRWo*Ps5#?v%2x3aQhCCsl)K6EPmc=MG~*j7`fnqgp`I1XH&;CqWq}eMLB1b z!svW>38ye}d@uBlR2&#N?lID{B*WSA)C^oXKk6R`V6x(2x*AYZN#GrDC39$=7^p(7 zBnaRAB!VPM5~aL+02+`Pyn}H%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*Je%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*Je`8I{4vEHaLap()1kY)8T{2E~Su2+~DH z5IvV7jEE!*mOMne(VHQ7V|XQqV4D{TLhQ;rh0ZJa2b^zzz~}uuKfdq#?pD0J74P>x zkMhDwWb)k;>W#Rijd$y|YS~&=Pne2kW`vfF30v2NsfaEs9#@4XR4#aad);GXLWLh{Ntt{Fp|u|$gM}HHQ5VWkM8DC>uItJ z?RE3sQ`EZAe;u`ccnDkH4Lm?XSWCa6Vl2RK2O|yc6UDW39q5%k!+XedYw2m8A6)^A zB+-MhQPld!YzI(QGVRz>rnx>5NBi7-)(aGSHlp(L`$!Cw+_=wudK_blstG?)F2q`a zkdMYWf*;R(1B#Y*o}HV*rktYLxfY&zb2S9^eLERsbzuy(OZ?hm4*Tx}RhO>tLqmrjFH43cvPo;HLm*aMnnve1hEX^GfP# wP292iY6u*-SCSi9d#r2+~DH z5IvU>jEE!*mOO;Jv6~Tiqj)8RU|Sakf$Yk=0?#Y?2b`~ez~}uuKfdq#?w6hWW#^Y( zM@Us>rpCwSrfEtSW}78itw`nkLM!szowF2-A#c=?>02emd=Aqz4CR zufBkheC+b$QZIdQPXb~iI?A7#-XmfoI>Em$)zt%5JvhiSzRng19j4B}p?#9|;Ex9T zn6=bVdZpibqcj4r9B)DSg^t&+d6D=U(b5H|*a`l6(+wC&W?{IiAGOANVC$dT^QiS- zJc9PRedjT1-Rf&ct*`IH)|UhKkPudrvs6$#{NiDt&VHh}nydl6vSa7~nQk??z$>YB zz(^8397&E~3zK1fvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Rvazw5fw_U%=B-S}xB!VM6GZ?3 delta 62 zcmew@^;>E~3zMOlQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg=B-S}xB#2*6c_*i diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index 76dd51e1cf5710f51c3786b3e469b6f0420ac4c7..a3827a6629a21ea891ca2c35f93bb59179e57ee8 100644 GIT binary patch delta 1253 zcmdlng>Ag*W3#8}h6-0u~hf+c$SGwlV?$JL+V~ delta 1253 zcmdlng>A>;#%AUw zDFy}x$;l?>Nv4zQ7!Q-KYkGhzqtWIiOb^+})I0eFX9Ia!jVFH)FxdQuYXU2o`Zs?N z2xcNv@8k_)rkn2w7m%xe^9HfqWJD4uxQ)Sfvq;vHYd2WGjLa^I?9Z2TrO19?#pf*K zhW!Tt+09$k1cfQl?{0O_icI~}*K0FMZf-5L7oo@i$1cZu@(c*t&dS9oMTYmc1Cvji rBzcw^Z?`gMJWhtC;50iOs9AV>g*W3#8}h6-0u~hf+c$SGwlV?$v2t}q diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree index 7cff3119e529848432b4fa56e6b624da9a57923d..6087e396efb029e5d4ade6a46ab4121ee65758d7 100644 GIT binary patch delta 13101 zcmbuG`E!kD6vyYhOSnmHZdUu!hAqfHr2)P#t1K`hmVs4&sz-rE`ZHQ)OOe7~P_&U2pgyzlegTh`E_ ztf50m0u9bi8<@T%Y({u+Xj;Vdu*l5tw9JT%$br)aL`+LJM`VPUGb1C-(*~GdOA8GR zH%FMmgVVFq76mWL&RLk2VaeX8v+gv9c){{?Mj&E`*qBLgfZ}AX znnh8tc+Xr?+qV9u+!l5s2Q8-8f#XQzZB)mIq~+u%p660EK(`sNG$?Wb#UeYOOu|^B zWpdp|%k46{vu{bEQGN5flsaV`|%?fjU2=BD4osdIiDVcddyIp_^Aov4MSF&_%coxju>; zXB(CEa01d{BqFwTu`s2QJ5T7SxTCGizjaqG!3`Yduj~T8vO30pMTBB%(L%*x${sin z=~WMxU|^K80a*e2E*kNs5{rt(#hHpGx{j7D)a`907j9#l4NIF(kZEWiCep!I)b`T< zKMz)=D;LmcSbANDPegk~;+HBO9G#=ALu0TPmZi;7KXky^YZ_Om2&ZBY(!qllTa+_B z(Lhe0R17V)Nt;q7674sZUcF)Q%yY^Cv>3ekykhnMOTaN#lqmF^WVKddGe{PN-m0s( zm8b@YEH8C0=)%$^8u%HE;~`;8PqiaD7ljmia#MhMs0$do&A~n5TEl3~(rQvS%pt`Qf+$o=TW`Q} z<`Z0n?k6+@yYE-80rwG(li%hbq=PrFI;hs5V?o$$R+Om*@wilNZhaYE2E)=NtCuql zqBc-#D?X@KHC|Dt-a@C9Bd3>MO?TDpVa~?oCu$2cPmb}|e1KmMwy2VfYg<33qKQt? z+KX=cRZsD-Kuh5@NtzjbZLpV$rQx$+XG8K3(VJvP2K#HiEaTekR7gUGD0CqelJkg&bIZIs< zUw)=H!xvipJY76E)zu{qU2&EUoq)d@*gq(U_zZa?SJlNO-?jDKYWs%@%fFdi_xcak CNCCS5 delta 13101 zcmbuG`E$&77{}-N9xIzuuu;HVI z7F7l{Qrq?sw_#EpZOUk(#ue#7T-C-=VWOYi?Tq}I_x=Iz_v?9{&+~ac-|y$Ud!(-A zk-CExX^oheX`MLHnr(@Wwq!b8I(&YNxbIC~3O1->pJWBngBo3t>+!%{ei$8sWQiHc-q129_n^Eb{+ZUqJ+dHSAxjxvH zk5XxG5Gu8Q1yjopz|{8-!aHZ^w{>WRZ*YIKOotD{v#IpxR+M_@#ClZP>hu{@T2Kz3 zbkokRLaDZMm(g`KR=_>n_QDmET5@p%nrnI0bCf#%@)ndTuHHqd9d1lPsUbCkQL4}H zdr@l2E%>C1tlf-KbMD2Uxh6h1hElivJshPL*PTPDPoKcgh=2TBg;G;rz}*zr@D!wS zCDBN*#9Xe@KPWe)8Tq4H&68H5+Ab#Qj%pv7={~Am96(mI&dEVkfpU+wq;%x=NWQdl zSvy*Ra<9KeYtVd+9jOrIhIXb+sCHZ!twt{D9JN~BokpVEc@{c^YJZHRU1*)nR*D0- zT-ck=pv;=SG!z`;Q*YAOu+nsKVj~BBwt!?&?5}!? zTPbRY$njHmf-WpwVu7E*xE>OQby8cQb5TgKFV}~t``d!CI~)w6N4#3=jsbs`pdRvd z%i&sz>g!G!3_?11^5W5Ib#r$D5g+HNCh>$RM6}3N@4I8g(rQe5%puJcf+$wYnr^^y z<`Z0n4#zYjd+%1S0{0QFli%SWq=P>%-K$hG$Xquh-vNK~)Zd^tW?3jlsS*r-Y}u1($DiY7Kq zYa!b2R(-|8)mj=aPt`2wYlE{?EDfKByBm^^h+QW;GCWudWEt0Xw?Yy!B$pva*tBV3 zU~nwE`!;&qI%*Tj2To}}v;pzruNqklc|9@_Ee1=6C+Ne5YjJaHwZ-m~z$m1H4==l~ zHNbBvjv>P)(!0Wkk8?~(hd{1U^bL{~5c{U<6`+>uXXtAOp<=JY zdI4HAA;l&RIH{jQAOCp51-%HK0#fYBGb{Ct=m6jqxAic%@gwf*4}cxd$xroXNX}B1 z$`_yO4e*6lcVB}Kr+XR(qbttRp*8SV1Lp??k(4cO70JHNXD= Dpn0y* diff --git a/master/.doctrees/cleanlab/experimental/coteaching.doctree b/master/.doctrees/cleanlab/experimental/coteaching.doctree index 08d42f0b16304c56ad6b84f7527f6e8f08d0fefc..05147e6ba2e4e498f7a8884cc9288c08a35e8955 100644 GIT binary patch delta 1676 zcmeDE&D8swX+tohZGN(4O1WvenZ9wdd6KC`nptw1d8&nFqM>{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$sZ)tH%}AZ$V7qmPhwSM+P?XN#A!-wPLpvW z(`K;0gXPvyV*47!UP^3#q8v_%?a$O4DY5;MMlvO~KhU;hAv4KL5ai#y#$Y7}1=?R& zGE0)F-62$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm909K>~IsgCw delta 1676 zcmeDE&D8swX+toht(j3tL1so-k-nv+p?Qi)N@{9ylChalVzOCcqFJhexw%2Iv6;C^ zih+Saa{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$sZ)tH%}AZ$V7qmPhwSM+P?XN#A!-wPLpvW z(`K;0gXPvyV*47!UP^3#q8v_%?a$O4DY5;MMlvO~KhU;hAv4KL5ai#y#$Y7}1=?R& zGE0)F-62$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm90Obi9djJ3c diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree index a81693fa396cb650d6834f1cf8ad58b739947b92..59bc979a09ebae32bfdbf3b6ea597e7fa37ba0e0 100644 GIT binary patch delta 117 zcmeyW`Big6IHO^HvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Yvazw5fw_U%<_<Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF YfkASziFuOg<_<9YcP3cG6L_;z4Ga|wEAl+o!-KI%+|Ypaok{JHm@#+WHX`M5UMFwFg1)q>!lK!AWz-dEfm5p6B=5nR#b6zuJ*s z?RaKEB%Lslmj*4ZUrp%agE3P}n0hK^#3On=SMdNp1BLTBV zDkdLp+0o@ZaUHG7g%%oW&xZnbZ&>Ehz-TL35#*5|&>IB#+SUPLI-d_M(2^}X-52G( z;ip8Lr~9sXNJ~~v-pLpGSsM-KYg*V#MDOqaOiRu2?T57wdvI3;+6g~LbwidZU%|)bYT##&$}o<{S3n`Q$)a$5Bs{&1 zA`?F#fG@v+m$;Hy6o$!8+CFAk?hknJaSaYq;~R$3KRD4%>yTmRw@4v&s|vE?yI#?%q%E#V%h3s4B25DuPTO}i@l K(0@ky)%726blnR8 delta 3290 zcmbuB%WD%+6o)y}rt}df(NIi%MkLrSq?3m;j8JG%1kpu{ih`gdlWA0x1|L}jq-h|6 zAkjMLRSSv)E!cwED;xyV)<;kfm2QgLE(F1qLZX5T?=*|7=gc4QeZSw_d(XX7Snen+ zcRWi&M2%j|oIaI3->>TtEfr6hrjd-P(S)HU5~`_anqtIMEuK;o#W3Ppau>e?YYCV& zR2}7mEnB*rC$6G3x!6KOt=Uk(>J7_08W?URD}p={1bTxYU)dNZrt{g*94$F*jrB!& zZ}=$@=VN`>JftPFlehA@e%40A`HC9$64Cp6KGRb3eDeXdhm@M)olBy_^+oWD#Y z^@Ue7l20%0rcLn#x8CS9v|x?Dn{?in4^{V50|(yUbsi`#oC3f4;d;tB$|j3&3Fmjh zu#XDA7=Yh|p=%_M>;-v{n9MvyIPRQ9@yW4v=bT;mp|ue5;8Y2+gd3_U_@*!CPMgduMV?s51lxjBVr z5YoiCE8&d_d?GwLcKs3)um`tQp`Gw^WE*l!`3gQZQwKkLRDmQOSptRFCWpfHk?{03 zie!F30AGFsFL5PvC=BDBw0+F6{2%b*<2vl4#y1S*e{i&$)*;KzZID9jRt5aHv`(rK zw;_u{b7+n84)}A2be6Jyf-B*sm{blr4;2q)nk|k~qeZGc&b4VYNMBN77hCVODf@i8VKJ;^ZvbQfpLBWLA!CSyXgpge^1MVp}%Jk`onW z$&9c}$(lSl(qgkj+PAQNKvf-_>hPB>229H9mU;QxFKI=X++O7$;OO+q1?fUQ#L?~h z6Qo+sysP}rNO0gG4mqGec3?|GnBXC*`G)sBH#_so#A)UjuAGSb+wd8LlZ%o3!jozj!p6Hux>XDvW=#O9qt ziH_ya=5^+IY~=wUSi4iI7H6ZA+>TU~I&u!~z*u8gV16X}Dt?W68Qduv2)!qK) z4=8m;UksY-)BVjTb^ky*NToxJeGOtMQeo(rNRu>n6XpKd$k2|eJvoRWuQOuCq+Ao5 zhn6|V%nT?uD43biQhgr6cB9&m5iAVVc80P6R9i8MMWS^s9K$+LZr?bz1bfXXCFLnr ziEmc8>;;_idCWgJbfyQN4eH#*{7(sE{@dzb*2r^vGXYNVLVF( zn9j?O`V$%Q0HtfkyvX*V+~Op*0L`}~nORZp{1ldga_6VA=TU7!I@^nC`67mVE+8>e z^`gwvS!@-s_ju2Gmd_6svH-f0%WR%FgiynBmVi2th~ej`*qJ~G9e#y9;hDpR61E9+ zD5RP-Y?$KyGdPJASOa|B;9R+^_qVXc!0p7VM@jSe;_WPmKdfL^-49wfAYpJ;MaBC$ z<4CRb(1qBaT6VKS9}v%;Ce{WfLBinGKtSwlT>hs5v4X+ZSQ6Yo-Cb-ba9X;0Ux^!{% zKk8*Sf#e@+jr=%gW0Uh9pL7Y$LQPP7q7I6McpV4_JngKlHm5h%6$ zx}P|SBHjlhUQA=cr8GpWw{3f|=$p`qk`->-R;zRa7=~OMenyn^I`CDitLLx0Ao
bs)ONhgqQYn>|W=gw}M%BgT%cM%+s3RfXi0n(D zdzlK#q)W*Crbm2!t8@@emkY5U4c;#OD#-oK$$Jh+Ih;5%@wJ&!3YFJLKLr9IVPhQ& z;GOM4zi*b_Mmk0BH6iwHTnd?qEP%v{GI`_Mgc^kz-94+osA#M}TC0J6-nS>lVnJpaBVkVIaY)H3)4kTijuYqD`IYX%~){yd)2M$K6C^R%6_x$4pF>>J$O#Qzwluq;{5T}hV zH=Q(i(Xf4nCXYkP9>1X2J*$Oq@u7Z$(gS}M(2tQdA3sxUvqI(p_WMo70pND4rVPly}jwi|)==M!@|b#DnSGB&7rH z%d~#Uhs^U8{6^6o4yP)W6WrgLJCCXY0)!kRIvL^&UNwVWEDE) zb#d!FwPyuL=0Y66^FCJNF@sY3z$`z?KchAxOD4iEqklB0HsprW#Z8Us3S`pP#W(+| z#=+#a3o7!JFG(*{-(0VBjmgDDB|YT&AHmMLRbkE-lCuPU_2q;I zR)vsb)3t1V!=haUt`4V`GA3#N1}1Xx_dzQ^lC6dDJI`w2#GcW>|EaoMNVx0(^8Y~? z0lXmaf39T t9OAW;axQ4`zysj-VPss_Y!M!WK_ZL~J&NGO(_T4|Pz0$Y{<{60`M>Hei;(~T delta 15985 zcmbuG`&U%g701`PckaC~$iv|wzDV&=r8RLxhLJW(5MuyMiXst2RAfLw5g}1qjH1hk ziCUI%mYx(%Si}lS>vL*$QmxczYY13ONEAbZSgA_UDq3QsM0(~oybmXfm^OmkI9v>4k!D@@L<>X{9o){gOl^vax6`f{TJ_>;bD3$K;_gSiUZK#PB&-&Mvw6M=jW5 z_Ec#ddRo;usfSgwS2`+d#@L zrI(!&|KNdIosj~mS2_A;+&4ke635jd1Dcp>)qGk zfFE!94N9H5Z5EpAlwZG$Qe$^OgORg`<9DW^)akn(p}GEx&qS%$Dkh@Ty1gq=>X(&q zDD|&Zvr+2V-+ql!Z8fV=>gHO1R2qAz9F_j^XdasD_Tz7$)VdSFsI>G0n0ovqOf5bQ z@0`3d56}v0=aSJfN$26SNv%JEQWrIrq0+aSE}>FWGknvTzSxUW(_5~f>-yD~a1RHx zwW8GB?KNnwx32#erIz0~j8boQb^%l#zKE@LtiN4?V)xyh31Icnw4(bIN@YFpRd?s> z-=oxNy|HMn&-68+)C2t)Ae9a?_7#YwXoaC;B1LNK7RvpLk)a(`b7}xXUT4INNlOCQ z9JI{2CT2jn!6D3qmg=)mwg=UQ4rAe{wj+%7quTNjEE=tI;V9OQa(l=c$uf0?M^K$38)|wehSPs55PRo?Sqhj}llK zz;s@I)R)AN2Pj=T>Ls=hhD0gledjZuZX0UyzmM>t)=K>Nl zRS(KMlg(BFdyjW-VEOz|Aq%9dOPJLYhfu0t#u8Bn5;6P&6+06Mr6aGfr#y4mxQ1;8 z9SW&>EgPb^{|ruI1=a#zH#k==`~9tKA#gkKsu9v0zHkQ%=8wwRHTQ$o4M-TAQ&I5& z&NxzQJ#-=Vr{+DZ&bd^4wHuI@O+G^MI~#Er77u z3@`t;7nqU1)5u!EBuE&n4g$o^#^rx55GxpRoh8E!)YZub1E-~{_l4M(cHd@|$j;MS z@Q*$07LfcCt&yML%*3Z`QBvvchwT0U)N6xL@}fbC)PXhv35Vz57EB6|&Z66xb_Pjp zzV0VZqKFT`h!@hR2q_&A>uuXsEczyFykvpfw#6dd1co8khMzV`dL8(x)z$M?UzGf5 z{tW3cj28~yz=_XKmEK0`uJ0HjHqxdH>1D)WR*95Gi?gIXNTcfFvK*-bIO<42e|Vqk%i5Uj@6rIeGU%DVGyx0eoGSluBjQ($9cENZ43L z19)e<(C?b0w~$WJdrgSF>60&{NvH#DX_e}LoLzgo;i@#n4^6!M-%=ucr3rBmt$ZkD zn?XEJ8!KM{we)_3JbNG@E?ObaBm2+g!N{?ui>;I8W5Yl)zmp;R@b&ZL4$y#v!SMi0 zY@?_Gc>s_%iM&@J)2m*u{4#ko0Oh)P$QyD>7?7^MHJ2WceR+R1RBZiV5C-+gp{CQ1 z#u&V*?zDU#9y@i1*pK9Pbc1u<;n1hDFR}pt&w>jFZ@w%`GEsK3Z2d919wM57WbT!X z-X1wzlnogc(1An@b2U)xEN2+i#u-wd_Q1hN<%NcN)=;>=+RaA$<6LgNYY>Zg`QZ zhbZOfNkAWtQY`TD2yq~lMJVfmDcv=8y*WiWiQ=g~UU?54yXf9bWfHI=kLagxHTv5vm{XR!6fe>IUTLGDU?> zd0pHxN9|q?lDQBE^1P4K1k9ks-ao^S^3SRb$dZZhOX;8Wsuj86ba7*Yx*VDGb@8pg zsqrwm^`eUWa@o1wrF6Ynt%J$4FRAn4oEKbClVEaMtGW{=SG1|kFnRbj75SNltJ+IC zROGvDmw4t4bpxF9Yn^H_aAUa0@7__b!v&jgPep$F;36M>pxy__Bzk)+obJI#>VDv= z1mK- zq*jTL<1#cGziHO40au4pOPP_{e}IWx{C&{EkJ_|we)l;og4nYf_&-&b3kjEj}n zBak;HYlB^17ni)GeE=-R5B*5^O*6V*SrV1?2BtnIokUUBjd7p(B=SW1`*tmQDsGU@ uQ$oFVQSL=80eArXK8(y8nstH)VUP&pLysaj`HWX?G!#J^iN9`tYx*C$k6{@A diff --git a/master/.doctrees/cleanlab/experimental/span_classification.doctree b/master/.doctrees/cleanlab/experimental/span_classification.doctree index dbceb0a410c8176aeb8ae5b3bbde3c28f53e3968..d925561ac89bfe4cc95b52dba363d77cbfea666c 100644 GIT binary patch delta 976 zcmX>#f$7u)rVXi#hWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24#f$7u)rVXi#hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4~pG4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb75JE5A diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree index 11fc8952db51178f93bd8d959744501eb02abf08..4c11b8257c6d36924349a0fbe53ff9713f714b8c 100644 GIT binary patch delta 1139 zcmeBrz}oeIb%QsfVSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>LXJt*yfkAB4h>`SbLY^H!^~4^G5XroMidec(R>=_~tW) z^U2e0IQ>F6qu}Oy){K&5Sr6486q>S)eEqDG8zh9c8}KlCkeANEjsP1n9cT#a_U|H$ r2c*e&j^On7HjK*KeGM7)xhXJZJJ1w0L$VAonB2+Dzr8h&v6T@3jaN|B delta 1139 zcmeBrz}oeIb%Qsfp_x%hL1so-k-nv+p?Qi)N@{9ylChalVzOCcqFJhexw%2Iv6;C^ zih+SaaLXJt*yfkAB4h>`SbLY^H!^~4^G5XroMidec(R>=_~tW) z^U2e0IQ>F6qu}Oy){K&5Sr6486q>S)eEqDG8zh9c8}KlCkeANEjsP1n9cT#a_U|H$ r2c*e&j^On7HjK*KeGM7)xhXJZJJ1w0L$VAonB2+Dzr8h&v6T@3OOIcx diff --git a/master/.doctrees/cleanlab/internal/index.doctree b/master/.doctrees/cleanlab/internal/index.doctree index ad1475cf53a95eae6c2a057d6ab44e309c2be504..8ebe260a1eed4b88569d6551e17a83ce47dfb000 100644 GIT binary patch delta 117 zcmdm@yhV9~KciuOvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Yvazw5fw_U%<_5-YW-_$duvYK@0RJE%Jpcdz delta 117 zcmdm@yhV9~Kck_UQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF YfkASziFuOg<_5-YW-_$duvYK@08$bn;{X5v diff --git a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree index 2507b66d52c7c48accf396ed1dce2961c2dd827e..6513a315db91ae3f007c6102553f900ca3a92195 100644 GIT binary patch delta 480 zcmcaKo$=Ci#to^AhWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?>;#%AUw zDFy}x$;l?>Nv4~pF*1>%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi83gZNqi delta 1690 zcmaDdi}lGY)(z2&hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4x~7(bJt>mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi85dk=j8 diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index c4662a7d57355a42c94f7f48d853691411ba9830..99da55a29114db878a1c42d45197d0fdc5c01461 100644 GIT binary patch delta 1932 zcmbRDmTBHwrVZ(ghWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$>;#%AUw zDFy}x$;l?>Nv4};FtU=NZSqH!#LfE56Uozhk92oVUZ^`|^At8A4zje)*qqOAL5^D& z>dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$+$Fe`~|F`sF4MlaF`q>ElaOEcSTcpS%YtkguU#$FzG zvv*=R(Ze7~5nc2GRtG#r#p-sGMlG^LM2RT15S!ISb+PQaoo5WC?&drF0iW;p`}>{W zdEq<@Tb_k2H&n8!!B<$*mcPTD>+*T3^9yR-zFLpJpip&s)EdR(U#Zj<6ey}w$@95f zZpEXxb88xW&AH7DEqi=^7V>7Z`{bcBY86RV^0c@Q1Vi<%A!=inOidWh&nH|rVJI81 zr}5WGEnx5nK54C>m^&Y_PvFiDUO(>)=q!Ufcvsqw@lfz~rj*-w$HJpn*EN1;@m{QM zjlWu2iPfy}cgxnJt`k4>fv%Uf-4w?#$XBJjq6H&))#r1Z` zwT{_}O+39c9jiBjvz*OVHd@w$2apOC5Z%s|@?+R8)Oet(1FKr&-9P;f%oY!MEdVC3 z2aRv6oKfjw1Fl>R zt^qJ+g!LbUE+GT$)gGXAuKsl&8aQ~=g}OE#Z$@1&pIQfObDkCYB?&lK)L?)YcP|1_ zmg!%}Q?EHKm@_-jXJHe)A2FIA=vzDoLs{cvGhZ{ zj8;awzA?+*}@I9*D# zgcw!K;%X&<#zyvHI!lV^uOd5bF=p8o121~&$x7rgGF^rgn9JVVWm_A_3*%@u+#0FfQ5m@hjK+IzFIynm*7}E+VBD06Qazx+JX$4@&4K|?vHY!(Q z&9pi(dQO*Is~Gaq^hMFb#yKl?9j2*3wGlSCgJ#W--q#5IGh6M> Z7GpXs1k7gy9))QM_7bWT@^`q1eg*>^;B^21 delta 5860 zcmb`L-%pcQ9L94F6f1?+;(#I~h=VM1QQksr!^OaukX`IY0uzR~*}N@HMypa_ptI+j)zDYsv>8?=yZdG*$6pu&oSGql} z7DZ9~ewW9$t-Php-&x+-*45z;u&B3;T_cmuq*Wx@#Pidh5e(G_Ca8^_GqqzlAI!LD z!caDC&*u-ay1?KGe978KF?T*~U&NgqJoLtC&{+m|@PYhy(xKozOlh$3-lF4J*EK%6 z=^$3O#urMPu$ndg*&z{<4TieJKa+|zT zDiqdEayo@5g}M}2j?#R9i~~DFzUvZeQz*uIh{=k#)uMa5h*|HOCAs3jNs@t_Os}#m zONk9patH{dU*1Yv#nvB)4Y)cJupYlo%wpyu`5rhhRupnX@eo;XB;U~$1z^YxHmLtP zBUfVjj+_knb{4~!sLHthrU>T(+lfiv0EB+A#*y29jR z2sy4d!3LZZ)lORfCSW?glln4~=XA++iwQ3+*qA(QoU>xzQJM!-n_#nhX-RJKzDDTJ c*mifBnAK?&V7@BwdyLj%FQFzOe}}8-zge*@{r~^~ diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree index 3a17929475963e2c315e43fa32f036fe65eec1c2..1fe52a61ea654e1e6f6f4ad2e71b7cbb36a23db4 100644 GIT binary patch delta 1199 zcmey>$@Hs}X+u1tVSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGm4&s;bF delta 1199 zcmey>$@Hs}X+u1tp_x%hL1so-k-nv+p?Qi)N@{9ylChalVzOCcqFJhexw%2Iv6;C^ zih+Saa1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGoA=WMh9 diff --git a/master/.doctrees/cleanlab/internal/neighbor/index.doctree b/master/.doctrees/cleanlab/internal/neighbor/index.doctree index 0e2c27f08dac1f38ef2c100453b6c893d2e3a990..cc2a21d71672619c3af1a6f8e0b58d5a335ee6d9 100644 GIT binary patch delta 122 zcmX?Va@1slKciuOvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF fvazw5fw_U%<_5;oJVqpI)1Ry$Aia48-&$?}+VUfD delta 122 zcmX?Va@1slKck_UQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF ffkASziFuOg<_5;oJVqpI)1Ry$Aia48-&$?}HXWXiQvnh3jok}kSuGpNQeu+{S+}B*rOkr#o>%F1W6=w{dQ4kKm#4WKo-XxNh zY>#kn_P6TdBujhl=5iNLGW@^!qpPGY={8Px!8Ao_hi9FZa2{_Q~h xKiNsw2eNnZc0Msi^*Hi0*KB`O!Wc?sh}Ud?QpU()L%Pk-dXIm5(`Lq*i~x+SJ?H=c delta 1928 zcmbRJif#5Qwhe)dwq`~p1(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv8TK`N_rl#rdU0$*Ge+x~gs7!dS~_NU||Ntx=QrFgK8=bMtYQBo^|t zPCm{ey!kk%D|yWXiQvnh3jok}kSuGpNQeu+{S+}B*rOkr#o>%F1W6=w{dQ4kKm#4WKo-XxNh zY>#kn_P6TdBujhl=5iNLGW@^!qpPGY={8Px!8Ao_hi9FZa2{_Q~h xKiNsw2eNnZc0Msi^*Hi0*KB`O!Wc?sh}Ud?QpU()L%Pk-dXIm5(`Lq*i~uU7R-ym^ diff --git a/master/.doctrees/cleanlab/internal/neighbor/metric.doctree b/master/.doctrees/cleanlab/internal/neighbor/metric.doctree index 54cb263b005b5f14153d4c12a7043dcad3632f05..dd27034ad569f452120286acecec95f7401c27be 100644 GIT binary patch delta 1023 zcmZo!!_=~dX@fVTVSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>j^z3rV*WqC*KEsC~12 Q?lv_tYzF0`swrKJ00{gq-~a#s delta 1023 zcmZo!!_=~dX@fVTp_x%hL1so-k-nv+p?Qi)N@{9ylChalVzOCcqFJhexw%2Iv6;C^ zih+Saaj^z3rV*WqC*KEsC~12 Q?lv_tYzF0`swrKJ0HKOK;{X5v diff --git a/master/.doctrees/cleanlab/internal/neighbor/search.doctree b/master/.doctrees/cleanlab/internal/neighbor/search.doctree index af8f7b0b5d958e448c070dc9362879552ed9dbc0..9ae25bbdc95e03fd91f22ef990f2c5dd5f093b50 100644 GIT binary patch delta 527 zcmX@{m+{13#tq(#hWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ240u kYW*hWCr*~u)%*mT?RlQ>d0ASCUtpET3 delta 527 zcmX@{m+{13#tq(#hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4}?7&nliZE`I071Ax6Y|B}?`8ta!3+Y-n+j2^?lCBMCW76i=d=>0u kYW*hWCr*~u)%*mT?RlQ>d0Oie~7ytkO diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index 79e0f4f741bf3f58bcb503196640da1acabc7bc3..641b566cd83d4973248bff0526bd6a750d25a1d7 100644 GIT binary patch delta 731 zcmccgg7MM|#tpuVhWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<7%81IpxD_+!P^F^jPg=ATrG>;#%AUw zDFy}x$;l?>Nv4zQ81IpxD_+!P^F^jPg=ATrGw)x4HDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24?yx`N_rl#rdU0$*GeArSvA>W4vofvME5FS)0R{7ci2ib^25uM*hhQ zSPRM0-nID*n;sikTKhKdr*V^+tfWxB*2Ua*aC^Oh|NWZJ&DVA~S|@@(Gx;kh}Pwod-=TyXMiH=)hXUWzD@ NWqa0kU=cr$5dafU^zQ%w delta 1705 zcmdlyhh_5|mJP*>wq`~p1(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv8TK`N_rl#rdU0$*GeArSvA>W4vofvME5FS)0R{7ci2ib^25uM*hhQ zSPRM0-nID*n;sikTKhKdr*V^+tfWxB*2Ua*aC^Oh|NWZJ&DVA~S|@@(Gx;kh}Pwod-=TyXMiH=)hXUWzD@ NWqa0kU=cr$5dg~y3=#kU diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree index 869d1ee95d20c2dddfa0c8b68bc755db0ba14d68..0e9b74e785ff7cc152e95c4d681bad572abf3172 100644 GIT binary patch delta 7878 zcmbuE{ZHFf6vlIJ*A{7|E${DJaH|PHS16;fOj?&Il4fj3MkWrhYk`4KWLuP>WKNW9 z$wCHS@n$b|%QhCW7dCfU&M+8_lVvV}pbkxNDqv##L4?FPOCrQ?%iJIE@CQ84=Q-!z zbG|q2*rIW4(Ri(hWjA{Zd~a>2%Uf^vI(Bc!ug&w;I%@I@JlPJ9&*rGfvDN11+dSE} zT(8}pXLH!{lvXwYsHUxja+vElCKbUvjY)B7TOfJ8bnA*NkhDqK61O5Je zxVz`K??tIM%AjvWXiiV=KyFbu@HacR0kz7~iYc_t@ycp|7!EsD?LnzyySq{9l5ZDE zU8w6ssp$?lgC>EjOv$Dq7&_~_T&Oe z4FoDt>hUu-QR@8J29&zccM+v-?uV)FOZ_Od=Sl%uYs0m_Q0c$21(g~JzQeAeDuh~E z(JTvxPeedNS1zkZ-qT}JrFujwn?~bfA-Pj|Z7N!7XX>$Su`HkeZNd!FZEFnD71TX} z%N18pOSc)TjK@60X*V;{=|uJ`c6QFNflv*!q{(1ktX@eB=VlgIQ4G}}nfAI_oDPg} z>^5Jeq}$D8c$wv3`@$*yHxYX#c`QwoF|!GZ;}V~HSRB3XWog(q^pQbSJ$nbcp)Rql zm8DWb8ymzXnt;&vk^)WG7-Ci>eWu4 zV+L&TJ45xPindv16+b>?|KK&HQdN+ZuONl)MkZgol794N@+ZZeyDV0y{m|1NjK_dm zkeVa;I`FOvRhvL5W_}JdP_EXP=(%`a0xsXMJ~EBBqQm;ee!Xb6b1g09@_e+2$0pY| z^5aq1wNgSRpOtU}jTZ9*;NA+EkZO_=35PIH*(>}g&Nom+74Jdy;|HS2@8uETXec3l z(8rG?gY{<}Ocl@e@i@wOmrtQZs3z3)0WUOQA!t)KKY|`$*ZXFA;S~3w%OI(bH-K7k zSaOr-jO1pD9^g7~m`HWG2x^tW6A3gl#2o8=d*_;a4e2$gQ3uYc#C01+Nrk!sZlW>EqI z>c_E|$J6XXeii++s3w%O#MhvY&`gUaS==&dz;_0z`j5fvVlhpVv^})gW*ZCH3!A$vXBdpe$ugHfP=_Ws6)-XWAVT7tB@yDcW$q7n_yeBj^PF?< zIp3Rhe91VzWV}|$a_qS+`~0;Bnm6R<=QwcZJNl)Bh|5v6V(fT`|F11PoUNA$iGl^O}Y!>-{Yl*m ziYut4+l*BtU>@R(n;Gd$68jZ9J7?HHs0LcnWU?>Tt|o?aGwWMb4Amfon%yj32gW#l zo3BwaY$h_i%5t%N;S~Ryh}tP0N0X(@6b;33iO)SOp5FAbbnF}Y$RNtk-o|dIOKfjt zX_VN;hOh~FWU%WH%f@~Za*Fg1SuE+h*##_tlT?GKMCbsV%2=U^E?r?wSpQe_rzIOe zr*r;qrW5l&uow|F&rEdxF6%R5rHGIfsaE> zPeftYN(q^KR?H1FR>Tj2dn;r@s!3`j9Kt}Qukm9z-$3P+ya&}!9E_oWmq&o3p@j57 zA3vG`)}MVSO+44n<0RqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_V>;#%AUw zDFy}x$;l?>Nv8TK`N_rl#rdU0$*GeKQ)MTwW!z^-vMFGl3BtOYpE0#DlCOR8{ssJ# zzp)gOZ9>RqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_VHib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg=6XgY4gg07MQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF YfkASziFuOg<{HMuY-DH);Be#y068!smjD0& diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree index 209d866751f54e76520491424a8cc70e551791b1..4260a48e930ffee621660a1a8bb50cc9ac14915d 100644 GIT binary patch delta 4131 zcmbuC(M!`&6vsK`bZ*PcO<7brsgaoGZ?0{>#e_X*ZJ;w)L=T0yxoMe*v{l$3#e5Je zgd1E7(Gf^b+2DfrNyeH!|sloj!tKtJ?siO)TlEYbwyNnyUo?!p|~RTN>o*qcAIi4Y_~fVm*Qmoa7&ZB zjLiEytgT?98YiW#k6}MqsE?uK0g~_<*`obX9$Ic@f7F4!67rIU?|X1^mhhUixl7r+ z?`L7|GI3EsrUpxKwFcRT3$urDrxJZEgi;1bYFtm0ek0SxC$Lv6ESS|`*gpa@ zb0uQPYt9y7W-WKl%2GH7{jwC5caO?atPN9Hii(j5S&GY}QCW(}gAQ4W?y-<8MK5W_ z4du@3ETXL*u#z771~+0XHGVoDCnc-NBI2V~vh>+Vo=>H4E^IYfg9KnBPt&-_3b0?# zdQ3QAqRbc5x-PiEuD&V#mFoxL%0|TkjeEz za$@Z(5?-j;!*4SMD7l}WD+e7(Z)w=#k0o6D|9fcJ@!w6j+1vGC>8-{?a&>6wOEa_~ zJyKOrMRf54{Nq8G#ytc-6(+U3yaKGaU6?i4u)q**6J`xe#{ij3S%Xz8TtpIZ(Ssqh-g72l#(V|MQ=C4TO>rrV?Ei)-DhAK3nD7f)4CvN`D8N~aEY2(YI-hQuHnMb zPm66(LI?UlPc~m&=hI!#irvvWPFy&3<3civGBpSqE~1iRw_^rFxD4aMGV?+V^vHcE cB(-Z+x;zRm@Xy4+h0%fmW#HlBuRW9Z7a)-2`2YX_ delta 4131 zcmbuC-%Ha`7{+D-o?PFYkssnIaa?|!(snBaw$1Kol}bWw==VKbAmHifKA( zLhH}M-OI#9`B{U%3|Fh4eK6>t#lnIq@i1Z{M92OS z7??8=MP75R2m@=sb4HfJG3b${sJeSpmLfe&Whp90CS)ltjdsaWgdc=tDSE~NvJ}@y z8*V7KUuO~Zxxqww=^NaLvE=xve4Lb|ri(}`HIe1drR4ck66eBF(=|u{Hu5xsi!3ku z^{iKq118EsF|CV0KfCg}39-MkYejf4*Sm8XB!Y2uA5A3SB8z>gK`usSTphy2h?h*R z6_OL_uSj^IW)Hv37NF!FdbR>IB(tevOFx!z?f>tgX2*ZG;AU^fgJm|G56RV`rY{Z9 zf%Hh4!Avx<1N`Gbn87^+4;3c0yrL3JxLuetSU18DZWHDV^v3|1OgV#;2`(TBxadI_ zvV1;P4WE$b?(|-B5|C-=;YJ0XNFCm717ry1+8(WkJ}H8G4PcQN!gAW~h759m#4;)8 z?;BwQmm{JDRZvD+sF~hwhPOzFtjF5)W$ro+D_9UwiI&#+LCq)IK!;1z>{8Qm;c^uh zjviWUff72<2U@c6>L#CwfDgN)x1G3f>cNF%4rOW(R9r+Q#cszehHx3ig=O}I=;)FA dP)KUiCb}{TFYwPq$A!_N4rSox;;%iM_ZRgP8OQ(t diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree index 59ee569e6e9fc15038ce732b3ea245726c793d9c..b1e6fedafceaaf9168a43e380a1a4cb198e8d94c 100644 GIT binary patch delta 1709 zcmX@x#dWreYeO`nVSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>n(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$xjHnj3&CexoPb`Qw)=Q@Y!WH=Ta zr|+HbkZWtH+ahvpJ?QzEoJ8oToCr$#tedTa=82IJ%uxO8n~O4k>?BYBWCsJT>D_M` zMYc<+Fy0U(-;nKnW{gfrr0d_#IFpf^i*$X{f%+1*&t1$I;Ypt6=*jW6T-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y)#=@*)5L delta 1709 zcmX@x#dWreYeO`np_x%hL1so-k-nv+p?Qi)N@{9ylChalVzOCcqFJhexw%2Iv6;C^ zih+Saan(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$xjHnj3&CexoPb`Qw)=Q@Y!WH=Ta zr|+HbkZWtH+ahvpJ?QzEoJ8oToCr$#tedTa=82IJ%uxO8n~O4k>?BYBWCsJT>D_M` zMYc<+Fy0U(-;nKnW{gfrr0d_#IFpf^i*$X{f%+1*&t1$I;Ypt6=*jW6T-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y*8R2}J+^ diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree index dc5e97f5e3e18a9d76e7b1a9e49b177f28957b39..0d28685f5d8673309bd4014d1306baafc901dec8 100644 GIT binary patch delta 1200 zcmX@z%W}GxWrHuHVSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>WbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgS4bUK9WT delta 1200 zcmX@z%W}GxWrHuHp_x%hL1so-k-nv+p?Qi)N@{9ylChalVzOCcqFJhexw%2Iv6;C^ zih+SaaWbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgYsnZjb-~ diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree index 3499f3ef63762c51374578dd007ef0599def39f1..3ee618000edc6b63da1ae67cfedaf6f49b03bfe7 100644 GIT binary patch delta 751 zcmeBL#@e-vb%QsfVSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>vSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF uvazw5fw_U1eoB6Fv3_xWX;E^j{^UXqjm_5?MOckUHwUO!d9wri7j6KO@hL9= delta 139 zcmdm@wnc42A)}p{QAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF ufkASziFuN#eoB6Fv3_xWX;E^j{^UXqjm_5?MOckUHwUO!d9wri7j6Lj6)M#L diff --git a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree index a32fcb1c61e3792a2be5698ebcbd862518fb2a44..bec4f5e2cb84b4b293214a069ba77274720db730 100644 GIT binary patch delta 760 zcmaF+p6Ts-rVZ|lhWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~vmYdy1 delta 760 zcmaF+p6Ts-rVZ|lhGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4}C7&*w%wvp)MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~vYJuk} diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree index 9c904d1e541fced3423206c5af1464092a744bcd..5e76907d91ccd3aaa557401b896c3a32f2113eb0 100644 GIT binary patch delta 474 zcmbQRl4-(9rVZYVhWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<6M7&nrk%Yi3o@&=|w>;#%AUw zDFy}x$;l?>Nv4x)7&nrk%Yi3o@&=|wyg_K&uEyRY?)GSnr^0VoNS(CYLRA^oMxVCVVP)Xo|s}_o@!)}W?^BFXlP)P XY;0_1U~XWxc>-fEIokY~_i+ON;o%`z delta 117 zcmeB?>yg_K&uD07R8o+cQC6gHX=!MlVv>@Ynw(^8W|Ww0mY8UkYG7_|kZf#bZjxeP XV33?_VxDBWc>-fEIokY~_i+ONG}9vf diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index 2dabc732ff653202c539309061f8becd77c4995b..b347984bf087e98b7b01eed2beccace996221809 100644 GIT binary patch delta 1704 zcmdlyiF5NL&JFI2hWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<5h7~hhiYoVau<|j;cOyp^uKGmIxfAR*_Lb9}1Z9c>Hk)159^_z3~ znaObU=7oZP$l?61IWQWvMb58^@)NKxmvd>&m~tY zqk0OtT1B+hk*l>(cRefViEwkEu@Nun+9n4Y=WJGRH1H%{>*mI;iR9###;&x@DA z8E2B|i0K6iOyb*tfl)0$o&z8T)U9On$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^0LUcp&Hw-a delta 1704 zcmdlyiF5NL&JFI2hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4x47~hhiYoVau<|j;cOyp^uKGmIxfAR*_Lb9}1Z9c>Hk)159^_z3~ znaObU=7oZP$l?61IWQWvMb58^@)NKxmvd>&m~tY zqk0OtT1B+hk*l>(cRefViEwkEu@Nun+9n4Y=WJGRH1H%{>*mI;iR9###;&x@DA z8E2B|i0K6iOyb*tfl)0$o&z8T)U9On$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^0H34>@c;k- diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree index b3de610b3ad0f33a7dfabeaf35d66305974f6c25..fcb7e8ed31e8cfa80016c6ed3d1299cbdcaa6a68 100644 GIT binary patch delta 2429 zcmbW&%_~Gv7zXgp^)&_&vk=$hD~gFb#@u_E5Qa%&fvc=Eb7zL4tTY>pG7BZ7tFE%J z7@3t)FHXr$N_MsurV&f!V_~IS`3vUx1Afo*p7(S*n+NvI1N+$+b1&&#;h1+$ZujU) z$g4(WJ*q_1uAo~9h9xD^Aw^YH3c96E-Q$rZMUss*Mu5urri^rcxp61#VVa(DRs@V% z#p{raQ>pE{5TYA&RlbX?H{;u&AIB|r)C9#U-?7#NiPa}yYcl2pCAsP@QEE0mqepFP zzDHY6TUOE5wCfmbO{x+o>8&Gz(gL{vr8nvuKq=$%=RnUbL63dcK%r4$*bVkAXxvK4tpX?LTVSHGl`7*}h=pxf2)9Clj_fUe zh_h5h_Rh4N&WrciawvOjJx|SWbvK!5CghpJJ=X3AzS#%3`@bY^CGmc25-$3MFL3Rq MWo!Axm@wJ<3p5Ja1poj5 delta 2429 zcmbW&%_~Gv7zXgp^*xA~g}5ePQA}Jj<6dJz7$%8@ag~*3?o5qXNSKYUG7F_4U3Ha( z#mKCbdT~m2QnItPFpXF$9}6qx%3m|vUovQ`9) zn#CKCT&I%!?*fQ!(pA|mvfi|3lYXqX*imB?t$4>;VD$nxy9(Q_Zn!_)1DZO42ZYYs7jG!q8OukBw0`A7ydSkQ~dR%5TknhwVJG-J1rPD#zZD^uZ>M( zSEc?33^uT-3UGoWAFDHbHde|fyV(u)EvVc~$*u$^=$&PvzMU-PYLJEUu@G*C1gx1` z{;X${7o&o|=NIP(`uzX^ diff --git a/master/.doctrees/cleanlab/outlier.doctree b/master/.doctrees/cleanlab/outlier.doctree index a4c3cf789e0eb770dd6135f4902abc9b919a6515..dc847d1b66c87b01e39558d029605c00f6117a1c 100644 GIT binary patch delta 1486 zcmZo@W@~6>+u+M+n4fH!Qf``Vrf-~Vo@8o~W|o|0o@!y4XlS08Vql(XWRPZIVUTEO zV3KTXY-V6?U^cmq@h};>KByUOUc$7Bg*>g(SJX1{Pd>n2NS1cj&C5Ah$+OvY@^TKL z%_h9OtYq2jGTA|jb@Of^0e14VZ+4K1ASaGKsDW(e*eoiuii>ocH-At&L7uH{oAot! z_mghpWT=BDr|%3VQ}=?CvYUN&wUHOiE|V9WRN9=m|0a3fhG@TeFqw&L$182Vd?b|; z+s_}1A=CEF3r>o#k?HUWd$~3nUpPQUA_GS`NPE%kc0=-PpC-o0y1kf}(UeT8_WP-4Zmyv delta 1486 zcmZo@W@~6>+u+M+Xl7JWkeN|dq;F|yXr5w{lA4;FWNc=Xm~57qXqIYVZf=llY-Vnf zVqjp9oNQvAWIDNy@h};>KByUOUc$7Bg*>g(SJX1{Pd>n2NS1cj&C5Ah$+OvY@^TKL z%_h9OtYq2jGTA|jb@Of^0e14VZ+4K1ASaGKsDW(e*eoiuii>ocH-At&L7uH{oAot! z_mghpWT=BDr|%3VQ}=?CvYUN&wUHOiE|V9WRN9=m|0a3fhG@TeFqw&L$182Vd?b|; z+s_}1A=CEF3r>o#k?HUWd$~3nUpPQUA_GS`NPE%kc0=-PpC-o0y1kf}(UeT8_WP7%+oXg diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree index 1f3e7029527f5cec251130c7922ed9eccddb6620..13da1dc6c85bb31d9468c71fe75af3fd22d3d255 100644 GIT binary patch delta 2066 zcmZ4ggKhl}whiu#hWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<5h7P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY DA|Fev delta 2066 zcmZ4ggKhl}whiu#hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4x47P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY DUrA;R diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree index c86ef9e9d7ab79b915a490d2c51e1b1251eaaf95..bbd14521ba0cc2f29a8786c34320497f0db50584 100644 GIT binary patch delta 121 zcmbOwJ4<#$Fr#69vSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF cvazw5fw_U%<`%|wGPF&ez$m&oklB+P0Bg=78vpHib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF cfkASziFuOg<`%|wGPF&ez$m&oklB+P0L4io!2kdN diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree index bf0b281a96a0cf6119105b391555e17765a58b3b..835a7c884cc237d6136cf0760e859b6faeca9143 100644 GIT binary patch delta 4106 zcmbuC+e=hY6vjFGj80AJI8zaae5fo8tUQxBXQX^e11+e=TNl!qM#oAVYt%0Epol$~ zf!2|BF)#YmO<4VQQ0+ELW?=coYXhgL2k1!CWbL}WP zIT*xFDk3J&hf&w*VM5Urm&Q=EE7l62!WI7sV6u@S;_h_^iq_uv0H7jx8+LTuc?~$G z^E`ELAKFFH`$tgM(~r&ouEO&;3!A19F-z|N$8?Emp4>w7yga@cb?tZ-MP2(|l%cMH ziDA^$oJydsw&_mPb=j*5)b+;1GhfN&aXVW=v6$j_&PU~d zliBI)h|;|uQmHoen>N17$y|IOCWA`WGcSF%vm|gfGPSfENx3yya5x$tC}T^hAdkJp zgV5+oKJ#l}5JyKeu5V=-G*re^>`qmiQiP<)HQ-{AY}75l1WC1LfM)7h6zFY|Z{6=U z>lI7Ak-NwqX05=Pr{G3D-o-Rp-of0!z)dTVRA_You|LmIx6#!eHVUk8o`SiAo%Fts zHDm+!f4&!M>Ei{~tItEBkn9tzzskA-$t-%o>}%Ko(tgx*)J96iG;8Ub-1fZ$%irSJ0k;F0beF5BPoGT5GSp*4|5VnM-q- z4;mDgyRfILt+g*wP*URZgo;B=O~J+@cVQst4g}myy2qmji`<^#kgn^&V6mrhrx;Vd z0hr;4mGP>2)o!fXY!exl!+GYd|7m>5?}2cyNNoG1f~emWPXp0OkJAC7l@+a6xxL24 zey1^7VG{@UY3b(O`3XPU&z}~kxx(gMx!t^gJg#I6EfCoz_<&~xgoZ`O@B{-xqlpWJ^knm;3`~?Gq7nI7K`)_a7>k`=E*HI&&$)hP}lBf5!7|)MJej) zpBYD8jk!4LYMJjrUDv%TM_qp`z+<~So{jD1TCw`ql1QiP<)HQ-{AY}DPr1WC51pBCy^1n6y&Z{6<} z^A$_Jk$cJ7%-Vr7Pr?m+x|eCRzMDCLftykwsnBi%Vt<~c?xm{(Y!X=EJPC6N+v)uf ztIq`N|9mgD(#H#IP+x{ZK3QkjaHV+#l35Ib+570FQw<@L_;2D|CT^!s8&yAAKa$_G RoE_#9m*c98w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7>;#%AUw zDFy}x$;l?>Nv4zg7;lrI>w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7>;#%AUw zDFy}x$;l?>Nv4x)7*`sRtV@4#Ah*oq9%h5dk!<{vH!<~)X~gCpW`8C!^+K($C11Pl qjdq#FcIHO^HvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF fvazw5fw_U%<_^YMMkA87=}(@(BDUF=xt<#Uy^12^ delta 122 zcmX>jdq#FcIHRGNQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF ffkASziFuOg<_^YMMkA87=}(@(BDUF=xt<#U7`h~h diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree index 0fac2b57d4f286e2766bd10a05696456a02845b8..a046f0cd0ca918847ef0433e31c0b1f024bd0fbe 100644 GIT binary patch delta 707 zcmX>!f%(t`<_+$QhWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<5h7$1_MOP9@lPt%^Z~iMalaEZTYFh2VWNCGuT;D0a*{1F^7kSz@*LPmCAw#P? LYv|_N$I=-A0w>Sj delta 707 zcmX>!f%(t`<_+$QhGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4x47$1_MOP9@lPt%^Z~iMalaEZTYFh2VWNCGuT;D0a*{1F^7kSz@*LPmCAw#P? LYv|_N$I=-Aj=0&v diff --git a/master/.doctrees/cleanlab/segmentation/summary.doctree b/master/.doctrees/cleanlab/segmentation/summary.doctree index 7be7e94f96fad8921b0402ff96c8d2b407b3a1b9..860a48f2c2688e1571ab3550f90d4382468139f7 100644 GIT binary patch delta 1026 zcmbO`n`Q27mJPm)hWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<7%7+;aDYx+fQM*qz(m>ikN(;Gc`D{CQnT74$_bMa69ASk@~D4QZH zWoC$P?&R4-MxcSb4|9k3=6if?WCjD+jLjbe=TqngiOt3$6_mIE7@iWF{l%ZMkQ<)$ zQi7Wg$?WE*L_fFgPcL%y+X;$po>mdeONsvKwmNTe_2-KUY;L>c!A^<(=v%Iar0d@f IOp9k20ZZg94*&oF delta 1026 zcmbO`n`Q27mJPm)hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4zQ7+;aDYx+fQM*qz(m>ikN(;Gc`D{CQnT74$_bMa69ASk@~D4QZH zWoC$P?&R4-MxcSb4|9k3=6if?WCjD+jLjbe=TqngiOt3$6_mIE7@iWF{l%ZMkQ<)$ zQi7Wg$?WE*L_fFgPcL%y+X;$po>mdeONsvKwmNTe_2-KUY;L>c!A^<(=v%Iar0d@f IOp9k20gqQYKL7v# diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree index c15c4f123facaad1ddaccdb6f089ef5ff69689c8..50eb6a3616959a1a21e20d9f5baf7ba6dc2d39ed 100644 GIT binary patch delta 483 zcmX?gh4IuC#tq(#hWW{sDdnc=X8Oj-=1Ha&X=cf3=BXBziH7EhDF)`LMh0mX76yri z1}4eI#%2cQ24<6M82^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dcOckTC!N delta 483 zcmX?gh4IuC#tq(#hGs@31(_LTMf#SOhUO_IDXFQ+NycVIiOFV(iDsz==H>>;#%AUw zDFy}x$;l?>Nv4x)82^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dd&PmdOAB diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index 4bec0e213c746ee1590c017ba95a39e37659318e..8f28ac885f50663e8c197d00656d6826f952e30e 100644 GIT binary patch delta 122 zcmca7cTa9ZI-_BJvSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF fvazw5fw_U%<{6CZ8I4HRrayTfi`?ev%*VL_>Ov$V delta 122 zcmca7cTa9ZI-{YPQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF ffkASziFuOg<{6CZ8I4HRrayTfi`?ev%*VL_MRFy{ diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index 70d7b364b6d5e795ca6f926fd29f46c1b95d9b3c..af3ec093e2b0e97b93c4167d2810333f859f84f6 100644 GIT binary patch delta 699 zcmZp_#@v35d4oHnVSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>5JwS%vfu4Z5LVWXV{<-9Z1I!4C&2NNSDR9MRUeVc>;#%AUw zDFy}x$;l?>Nv50Y7@5e?wvlNK6X~{1-pCX`-QSdvXYx5JwS%vfu4Z5LVWXV{<-9Z1I!4C&2NNSDR9MRUeVcvd>Yq>6E%6Bi@wR^dQ@;}9wC>P%?rd&eFnC|7v$HbS2Dc!YHsj@NUV#?0A+)d%D zw51kz#-{kGTld7_y-M`kVsQ)*H7`>k=BElJ%Q%b0pntQ{%T!Y;e zwdv}veG}sQ_l@ro-7P*DbugLF=u{lI2fIsyjN(P)TxC!_G^#S+xl36-CqpWpm97V= znGf{{#FgYO3i9_pzy}S$b+Vg*zg{emf6~JYmYRCIMd==p#WnNr1G?GyS3NCUQQ$h5 zP@K=|W#-E8ucwqj)j~njYmJNWgSzQK%)w+i@9FBDS=7#_MO(NqRGACoX7QUY>cFe{ zHa*|0b6H+oyeOv!XVW`l0;##fo#5=V6cc~8r-Luu!Gr2s`PA+TzFAn5e^Bz^qmM(wQQq=f8~kd2SwWW8O0;{+o5h=6k41g*uDneEYii508N)_O7O1k9EA737@w0+ zntv1b6RKzgNxdF~kRC+vSHc3o@zvGMJQru>++fq`hi=sWaI6v!IJ4oQlt0%cj9=E# z!T%Yl<1->Li8N=sD89P42-u(Gj^V5IaPny#!Z|6gj;z54M_7VX1*YqoIJkc@U#)X6 z|G1->-x6u!4|FVoRH(TW8^%}d5y&gURQlxjE`1Ybr7H_+4UF&Khkq6B^#Bbw$CIt?kw$AvpN52$V$WhYFxoQRGo7NRXsZ*=0|O51)G8Y(^H&p*(nm#D7DFq+O;9VTH>0DS?R*5 zH+qC|W}xZ6-u#~cDFODkaFx)`O8@Sb(F7e-ev)F=r2og3G(>qFDGfd|H?VM+LnTre%aXoCjIL^$uz}d?ETtxe~ z-BsKmEqAzscXtitHz$&jQ3Sg{1wOSygfKva%=+IWm~V{YPecUrU&DCdM?$NQ4x&}5?x)ol*XgrmcZWS z#3@%K|MNeS<(F$4&9$wv-mi%C@REs0hl$jsvzJH*h!pfDf=CaDWI0!qNJohD(qcqdx_LKnMyfCD$$`4DO*V-zx-Gty&#hN4waS@X~WlMq->Mqe^OBM>a3PT z{74E*c5FkWr$l;rza5e85UJS*D*a8QXVR{uYz~pSmFiBUOd|Df*^@{IiKMOEi%1KI z)bv~*A}vHx%4GSB+f;l_3eUCfM+#RE>G|#9M0!M|s>+c>T1}*@#i(?QNP|m_A!X}{ zG;Q2WBIOckXfK{fe-Y{awAn;jgryYu@vT&xO$z@fWDY5OPo&m4fJpg7YOI+@qHv{u z5h-R?${`}|AYyhdl~xg{f$kV7yGtZ_HkD2i=}5=pq%50AQKP7Iib%7&o*-q9iS%B4 zib&UpWE?}KHAGT@Qz@kI1Q9<>K24;TMEbKrK9P12X?Hf2E)%KWb1Ho$lHx0s{vguW z=7TJFD9a*Jov7+oEZrbdR3!BfB7GpzjFTmzaoHsz)v0xsNHd94ZEWq5xa>5MLN-w8B9XeC99RmM z?I)6?xUw>qo)KxYJiJP6D_?FI;bSSdz*qcB)j>li`4wHK^{3C{in|g=lc%v z@&^_cU(HqYi7-j#$;{27Njrr-r%mk_Wu*L-l?^Z_h50)3fH3Bkv*ZyMz#u+%*yC}gpbUq$G0Eh;?*Pk_`LC6o=b7@*G3rmw`(*r zGcMO3V(ljIR9*Q4b%rb zAz`RWeAX{z`Nb3De9R$04-|2C!)pO6v_UNv+D@`onZ=9{NgKVyDF-jNl+x0s|OUP3jDXHpDUowoCPsE*&B zQIo$lsSJN|K@?wlbxGcyT?4g%CRx(LKRPxur6Qk{-j;8<0H1)TT}+|r#2UPJRTFfy zCRY=!^1?x^EW#>EM+@ueQFER=kOn3VM^?l0GCuvEc+1cttOGwFy(!;udIVBL4P>!O z+~554kp@0`Mlez6mkci{8In;GHHznK9|{ht^)st;aipvBXLKZ1LgbJ-@obK}`CV`S zuv{YlGdcxN@%WSo#M22nFD>E6htG=S7W~8XTxINs0SWyF_lqK(cuj&Qf0h*~*}|>h z>(8FWJ>qZ9p3SYs)le)e`FV3xay++Vn!eb_@jiBhhQvCxH=gft>&0hovw~_Xs;l|N zbGwo;;w6aZw+(K^mz~#@uf4^!6UYIOgD>J=Ep!4)MOy$K4QBIuHk9W_FSKy) zd3ACG8n7a{e3GlWs4ahceHc;{UN;b`eA-YC$cS#{$3I$BgMYWe#Pvq1Z^UN|Q}fpr ztN4_m(R`c5p{SAH2*mr9k*GReTF~kPHoN#GO9KBjB;kO@%MIKK&A1MH?m9IZu53bz zhOH?N<$RT4_4$lU>C8+U|ND?e$g3O4@uCFPnYpwLpP3aeoNh%;pW)jtUCf;)K7@u; z&}mO&c@VW8UY%ej?f9m1Yx9Gb8TeLH$+!!atZrxKH|9$DVQZ@+jID(Jcb&cEvo;zE z2573sKY8_v;vCOcUJ)V3OBCFJvnwi-p}oqYQWy!@2QOD}N$2dQ1u=#P{rr_-{Igm3 z47`HDXWX1L6e$a@U@*nf{Ow5*{Dy6TXv8VwhgUNAXz}kBa4zxic>Ko!J7gmnKM@#S zsLUO<&ZrH)wb>@V2!M?l^H++gc59xO>ZaTXrqZSHX-< ze8~69!Y67#M%jM;eEhnWI12xD%2~m8Onl;c8_zBCB7Z3&7|4o;91$8T@F?`t`XK(| zJZ#tR)6lGcsT7UySiQh`q;UxUX2Y+Dzz%dmj2~W7;hx76pd!Omd|r+pUt_bEuRg3N zpPAgJU@G0XDT=n)1)bVbGccjYz=T1AdL{O!w!e$|Q<`(}&KYKqA3j{pzg=nJk8aZQ zN$aiui`+@R@|NygHqp?FElU3A78Q4j5JnQnFE-o{-PpOlB@EGg%tviK7Km4Am}X>D zR5WPvAR7&HBZ5h^Cv3B-@Tv`$Mx)ZGC~!7%lmnSo#ht*$_*Ym<_&e*v_$S+p$QAH% z4%aG!&FJ&cGJMCacznYvF#VBKjcz) zd4>S_l)Pd5z`bL^;&xdwEP!3E$A0|ieV_Q(d(yzGR#|@h*S#Y(F;PkVd!U%cYc@kLNQob|1ROMRet4Ni>Hscodjd#xZ&QO& z!Kjt&qeDUQ5S%$S6r9GHwTFiCuH&fzB};ZHL0U{7{f|n|5_0`S>7xpc82O?{KJc!i zsl4l08t*wgi4)F_*^{dQmDc7e`0V3D!NR+_n9WQ+^2893T5h|7UvM&sUv^?B=pVP; zkJp`g2-HT@xaVXd_=;KJOu!?ED_!Kroc@K|&vR#n^Eb|o;zyhr4NiT!q~LF!AH)yL zAH|=(FbZ_oc-fD?bYT!k{Ogh*{~><}NHie>w7)Qfe|&a0xb~bLo1V?D#heS?`mcAUPn&&$X~x0C&SlT zz4Q3$m#T1AwxnJf#Qpf~WH6vs!w|m1eI>g8DBaTVN_!4Lt^Q-G{KujA63+} zHPkeM+f6w^dvR`+4?L1vD`kUB234zb3K&(IYpwucv7_~HP9<)Jz_ffdj?t-;A%bm7 zm}+5mO|HH`TKY4WO)U)b8gTn0)W0%fxr?+(IJ5=#2MtTur9F2~IH7fCZVf{RVTtbC zbqVPf7}|r=@tJ>_VQe36mW)AJ2x}BLQ@*JTf`MF&!13}hE=0(F8-rDd2Nc+K0(Vhh zUO$~{AIR2PUvOp@XMr8(aeFM(G|}5QF(dW95P?BK$zUHBBUs_`A+E7NwcIhTiI4@S z3bG&07Gzsr>o`~BoTH`R}3C%)t!2uD*sa3zIm2flDSgrn(V z(QktLc9V);3v{{%i1r9aODje10@yUy3v{-i&5WdQkpez5iGuy93;gU6Q5UG;6%7&> z1{xyh7D7jUj~1zf?!7G~sxM@llox#y`qZh4Xqn(6y4s=}!U>}KqTd)?8V>`VKz<`p zI5^lQ$P5?6it5qs!B6c(=U94GfCzu>B#ISWi&H8-vcrk`*B0n-T3<9_i{2DrAd; zp!%z*iy86ZyrN(@Aw)bMX&Jap;*1~fTR|f#Sk)}v!?ITN(Nw`nR&hOSsPhi-VirTi zAC;E?`P{HdbIvB$0ac19OM;1B@mq#o{S&DKSQ4#}u|E(4{s|TXixd%`z@2IYqXcof6s3Rk z7Wc&UYW5cg;q#g&iO*xNFEUX445z`#;tx0t93pOy>-QWX9*WPuHCp@`AHSL+zJb%7 zY2ti*e9AcS42)x&D4vJ$;Z!Qu%Mjnj<;7-+vssK&U}k{KtihoaH+!cZ9^*@h*(< zX)g;8i24GtgSXu;-i1^BL2)kb*2yE{wXBqkN5w^1<~yR-yT`>-aDNg{iL+S=>^v<_ zXBlh>frt6xewaYZ^Wx>K1m<552Qq7*&LukM+_}PTf~c$F)yxEj-ViUr8Zg`zvs@_> zEq1>nKEP~(WB0^^F@dTN*-bF^k@yfZfyYn8t1y8X&&6w4ecr=aM7>R|~ueCtlz+!`sSWcVIYS!B5?#ayG1Z1lgSm61MKRLt zMbhepK~lnv%cYAk*T$EA93(vMlmt%*Tu}k_2AO9s!Dq?K0DactT zQG?hP!{s2~P)rA7ev|ew`i9G#o6;7HlhAiyB?x{hb-_b-q%|;E`9tYE9Ew?wrTefP z|3Z2l4@6b}lC};&DNx8VaFm4WWq)J2h)XuY!ZwRG0r~Hw8nCy)NPpn`O{xYNzZ;u^ zMU`d0`8qhfifjX;<^=F62dXLI;o7njxKA_c%AVq3V|0C4Z)~VXO=QIwc82g2)&wL~ zdJqU=TgfC~`+%XdLHU-lbXGNBD2SknA#&jCpvHQzx}{7@hlV|^Wf%PzfXTG-YV^># zaB3IXMwa08A=HL3#%@d-5t&990;a9Jnfc~TQ3#DxF^(LJ^0d-3s6b>ypY8d^`jAEyuN%a7uTJ*O!xPi#)hjV)>UAFbr~ zaQVKDw0uk_T3#fcmOt(yzl_Td^rGcyeQ0^nezg2qe|ZirUo@DOcNj{`rNe3YZzJTp zarxLZT3+*4TK;S-E#EL+o`=g5r_%DM>9qV(1}fL#M>x~{1}1dxme?=8SN~bPR}Thh z&E<7KwFYu0csy0E0~IF7TLR@Yxede`6?&RWJ&azmBFFM-4vP?uZJqHmoH9#(5j$L$ zd9*dx0@~W~g_KSDV)+5grpXFgu3AOQ;c8kwJX@ZR%j<8V<$hae`R1*(JULhX9M33S z_R0;6j@&BukUWOLk0Ek7atk^2gghI2M6t8-5;*O6QNDnY3%OHS3nCXTrY)5B4t`0! z_x7LEd#}EgE3o(83sls^9K*E=;=K=b3gW#Pdc}V1z0C}Y9$23{?TVU=x0mT{aX7Vz zLTls>&<7nJR#hx!=nsDM0-mg<_{efSH(-n+3Z&*J9l#0wio(7%6-PLbx1xy#)~u~i zOK7%RJw=3&UED}Y;`xB19*jtgpqP#JR zjxe;3!pJgzAR-;%iGGSq5#=x{NimU?!^{DS-YoGzI7}X@7$R`cj8L!)=>*0)QsHHo zjv=c2UTA(;nqoYw3eta3bYwQe&~XJS_{iOkxg?IU49$^he=1UVSyqLuAiqEBSdwj1&SL2Y_TPZd5o~IS*9Xd z=-6Pc&z%i5J9`xPGrjl>*QOkjnn zeMC``<>CyvbycvZ!AJ{8I$l)@hS~g-;CX|QN-$jz&9K@j#o|D)pjfOPzPX|ZXBg6O z@Kgnr4^&gZdDj&$X?Nkg+Y0o68JQj9?<&qxUBa3V6tTjTxcrGiBb%@zPEHgBcKqz813B%{!ER0bsuT(qAXxbvoxco5wDem+=Vy4c zliwpX?ZV|GKc5;i2m1Nc_{(5Fhm4-Ed8A)%0L>1c;MYKbvs!p@y5FBhHgAgtowq9d zz^m{wGFWbnUm1Vu9BEtpw2aR2Pec*)^Ae64SZ-fI*S_TybnW&*KfT}~@}qu71;4p< z-0z8S^y(SE)q+uHo%j1DfGxk~*Hy5(@qu3hIn_w=bH5`hRyAaNRv58)%<$w_zn(!< z4fO*3%QHq|H1HwHb9Mg5gg*Qc;vXqEvC-=9v-K^Tzt7eOoc=zAJo5Vc6tcgF|9YWX zN~Hf=747fXO8z4Sjj5~p#|!TCOFjS3f{RRT>HmkIv0-ukhlEIL)Y(5z$lAO3uNSh{ zy834c*=Y&>i!d}jZ%G_91Uy{r zk6wwuL3wVoe@T}6IA|QBt_o2DlLhT64*0(oh93WX|4Kqww!Y=ROYkwzqXIpCeC&T% z(Aepx{u_ks)aU+Y!FRX6_V37YFM=@3`zs(2JnwUnfLw;FCwMy|V5*>ol&FB)g2xW191t2r zop)8k0H5=gY7%f&@D@j#0H2$_Z5!ZI@A38lKJ`xOSdgvTDIi=hTUxgOKgJ6#;sgF5 zu-|<*3`q)@5kez>_xONj%tJkLTLE!?dVqoDK~kS{5*R0}I4|I5A<}0q2=Gmry%z-} z38UcCl>z$&2i&td;Ip8XqZ9BjvjB6xalz)AsrTwXv+!J1D`2W%Ev z+Hj%3niVbuBn!c^??ym*A**;0FkA444Nn4=3fcIV1?DdMy1?9lZvx5)xXnHWY${9$ zMQ+Oo>|mnv_=9f&!aN>j4lK@WTogu;voiwaATvEc23Cazy5I<1Kqv=n?m!#J`Guuwg9quIT{hC+;Od=$7q&};h_foBReRf5yD$v}dX!8Ld=~@^L;+Jo7&nJRD1EWgqNK7Au@YTD>5G*I6_vhNxm;hl zOQ5~HsnU0$qHeA9#Y)>ar7u>#by4~Tz|B3BzF3*iOX-W1j(wCNf;z?~DOD_?fOJp? z{C%TZ065hqNDXeCXefn6hbb4z==64VjMAUwqT2_nfC*{J(n5!`Cn$X@*@2UkzLji? zbfs@4J0e5rTTe9SmA<>A#pf!=3C1}vU%5;e$c8LaYK6YfTdkbP(swj@MnQDvc4ip& zfZjjeRn%Sq%(|o;!mf>LP_cnZ1@2u|wkr9< zAGM1{&q<{i=Jd7OLDdE-f4KIFGFDt@7|{2+a(>}a^4C0&g1sHk_UXhM%By0e$ZmI( z>;DgN0m2QLvt6u!o9-&b(*HYT6^MDNl)y@ll}X(HAu2?u*-E4b^gNXwzWS&ftKuHO z!E)6)DN5Z+)g=z4pH-?_{wS?#Qa#3Ughe$`fzr($6?%aB0URHy$`GN{5Uy&1&+AcA z^$yqXSYCx5V0i$yS621Y6`nQn0SDODR5gt|0yed6TNm8A99j&(W~#yjfC6O1KL~&m znydU6h7P#Dr7DJHO9<7ApL1Ll2U_%0p(nd}_9web_y^UWb5|7toXVQPa9Jl+x)|vv zrmJct!>}NL**aDW*Rk<`2@-?kms|5*=&vt5F5jeO2j7L=iv2 z2Uhh}=|JblDl@z>T!kKxI|8huRHGP~aN2$Bs#X^u=dWGs~07{*kRD)zFods3rG=-q=Ku*E3M^@kkAF=wUp6U(THP* zr5>w1g|f=ar>bh0NX!cr%hptZdvtlJYQ-4xJs^r0e>o!2{wu3%B*F!Msd_VNB5~V_ zAoO_m5nvMsF$QZF*gz7*^j0C#U?O}W3mS+C#QOy?1Sv!qS*(4B1C>FuaoY-Y5aWE6 z2pemHCNmQl797+Q6Sy4`#PT{6QJ~!zbeb_B6JfeJXagot(H6w=f(0Qk$sTlpnZRGp zpf#AlDo@aQw$UIFzV!yF@UlZ39+b+8;fV-Buhks^Z;J;p4hD%ZGAigAvn#AA5!3=J z;_p)I1j>~O%4WF{gUr|zw5SnY8iEQz3m9$L6s+jZq3`<2Bt_w;Nepx~2dm1}mrN)%xYXrCQ5yTR`aH{p2eI#xChB`C zAN{#;YS!gM1AvfJKy}b5gQj@32NSKFpstEn!lV1BPvQ>M@25u3upWUs2C7%$?%?ZYf@dlVWa!W&y1PmMFv4GUK>ano4K_e>Q-@&YT%Iq2i=F+4(p-oZKo|17FGg3}$vHFa^gi%Mw5GA`L0g1mwEC7|EH`yn7z zZI^&=^)=l=ud*5)NUEbzfpsENQ}E5CDGox*Ya#_VqvhmI!>sa}KN&mxznb1Cw<%ym zMNKkVIKah~H9z6>a#h;rrPVd)^|d3gXl+f!!t~Vyrfcmc`o2ru`ly@j+yASZ;)a^y zzHX9o(#-)4HPeZaV>OIxGSXYpxD48wYF6OB9c)hfHnk<~o4&2)5#xfV0jSbL6G-3d zoExV}LvBGwgs85XS*%k5M40E>MiYmenX)S~ zR>OL&)ex>4uSv(FQt26*cm52cDZXzAcP-Rppf3}RH``&a#hOSli1}*PfK^!^lF-q&aYGuLX4Fc8I0spAcXBJjvMO^}593@_$rhKNwwXp82M zKT5TGH5v;_y;n4)@$t4-H6?I5<+`RTKL6!y%{3)TPrlTkmtsD{E^jnqDN4J4)F|+I ziJvuMCA6v94YfIiYASZLHUM0kILZY3H`31Y z=axW48*Q=#rK3A&KRUUkAnjn|x?t~U?T$jVbE_}v(xWljbs=-cwA8>Av$Z9~B==~p zb{5S|U8s!`kz*$pYm3pGB1>D9=GrXN#?##K71|t*oHu2a7SdzQv$a_iMY=&-fgX#^ z(e|ggGn;)iGPY{x(Yh6KwWn#RX@~YCt^0nrwi`XxXP>qM&0XHFok(*74rzPQ+|wgI zymd!?43bZ1Gbu{w86ShBvpxnd&--fR{O)7W?vnNl#jEg#HlNnD+|c%>$L8G9qPL%y z(ihxL-qDVvhuhrOzNM#je5{>Dk9~creL!<>UT8Pa?rnah-AZ#q|MWHJdaGSbkLAD9 zE~2?HAGEbNUo)3JYU@&3*lDF1F}`|8*)n zYt>m41n@7fPH>=V;X1*A?ibSu4&;sUp>>Y&k11jpOpmQO5&#*h~RFSGW!PTzR&Qt*vWEOQ+Y-3CsDfz7vRj;Pp{D5moxD zu|B1jn&?ydab`-aNzC@Uoe{Zt7?INMD@e>*nHlK2^pp@^y2;5??oEOMNwd zTjo>c%9TD<{+jJm<;QitHhORL)hM>f*T$PIK2F)_g|+HRMU!DsaZ zrB>bGEB+)G)jU`T(cqTB?jUlkPPgE0bR^i_BRIm39DAP>ypiJN4h-H#bEA@jTT=bq z8R6^4vQfePXs4C%20yMkjWp65_-3f1$L{NRzaA60MpYH)Xg@6#F|?gr1K$nzfs z_oEDq&w{5>2Jow|#_+#`yU@~Soc?(L;rB$Tzd<#5MXldV@fK?Jqv){+z5Wh8W;N)i z(l&Nl^c*ccY17}OrFjm$)j;r`m(^dRR`{d3eyCuhTKZS?yskg%uhCp=UHx%-Ua{u- z)>PwsTfLw^|Bm`(>UGn*>0>BLes}#^TvrRH_SFAD`!TGqel*pUY_R?w?a`N1{U=&O zI#wS}^;acBFIaF7uis1UmcB@TopKqmL_b7B*mIkFIykgh|COSg-KH0&syR@8C?C3o2oB@dBjmm?&i4y>tRgD^G%6&Kc5ZlD z$RO(Ee`SQ!p?ED9gy`rLJv=kyIc<07hLCC$`6vvDrh|^+M94PE{`BdPx3u*9<&ev? zZ@X`YY^cR;#A{L;P&Y_3g9eS!&n2A#Hp>ap$Sg=RW%I2>CNf}bc1Fi%&ckn4a+@$HlUBEZG`6f2DCG} z5hgS=jKS%>#`L@uv4%DH{3gu|H}LVOmWFHi{D8Iwbk)5PrnWcq#p&~ohPgN$-`Ox6 zrMX>c_5IywGn0B4a&WZ@eP}b|`q5@2N%XwK186gegA6w@Op_soTloB#5rz?1UOUPV zjZ(VkdpX815+Cf7W{_YVB#kpH#&Xa^!+V^{rWnwh#2ew^bi*N>PM$`=OJ^81VEOJ0 z!+M;~;0-NMNdgYEqPz%rTtC;M`_IC!C_?Tr4i%yv=|FNg$6Hf6dkw2tJ@8q4J{7Fh;;%Ta7*I7d|41z!M0M=bLDB?n_(};uP8DS-Tomp65Z+)M&g`Z{f#|ksQjeb z*b9@zx&hI8qa3!^8sl-zJ;BB#oJJXp{d{M@okpWbfeW2R90R#tBME^OMT}MOx%VQB zBm|trX)8%lMiK&dVvHmNmX$P;5a?6JNJ7B&laYi#&x%H(jq{c1d9$h-iR%xlVI-~} z`?Ha_{^7b5uUrFq-p7U%FRzJ_`1*k66mNG+idUo!J?~LlBhkjH4pbXIcc$7%=|Xv) z>1G@&2Q{0ilEH6%jm(c%r5+0P2OB3pKwl@0oEHEy`x)c>xOMQ`5M#CorP;%cO&EqC ze&I+P@S8Hq0AkLgM~4?KMEAl5r5NvV=RnrDo^?THs*zBjdgcSlL( zH`9!xMTo$F8Ag^z2@pmb7R8aa%g#1V#%=EhEN#=y|25axoWUhw+G68)ta!yTBg^NZ zQT1e4ZMjjz(s^8&JaYPSB_5&9fzE4KoCg%(WMvy4vv@!OK)LnCR7_<1MkC8kegV$k zImSQ42KG6pKn2tuGBW+{ z%6A0o5u=0AN^zia^8El%^|7%ekRLTZXBa974~zn*9W$O_RW5KkZ^+<_Ge#rJ?S8_c zs{4i-yn1hx!tC?LNM?ojVETZa7tj~Lk6ku8pzfma9m}>G;?*3)48E^|^RF9wV0R(D z6LO3C4u6N$cPJ{zzHbaf&zBo}F!oj;hhRQ4jyKJL^N}$SynJE|0u9urvS9dQ;{!G? zqEHY)U1Rhjfz0*6_vN6s(PV-nUmHb?{v=@CdT-Q&qE^(gHt&od3)L|M#q_Qpbt1JX z5ZsiR&=%u2V=$gzuqVyFY|?>@5VeuO?o9$q6}+P` zHAiD5EE{MV!fFAC2$d@g7Yq3NiLpEEt2Omt_9(lXb!T&WvD zbc8}46;+s?eaGqpyV^}PS@turU%{ebrr~nIo4}+{pF7=;G}XlJ6cJ-$c?6$8N0c!2 zVeaE6U~+)yGbS~xQ_AFIwH|z0-V}#|1=g=@dd#7;bX61jfd4rdUEOp7r(bHAHsf?d zEz?Gv#{FzMi_@68rrkJwUXPY%HlXFPjZ8=JaZ?l1Sr1B|4m4fBa$d4&J?jVxUk^2* zFK8liqfFrjly>AzMwX3qGEgAJ&1W5^q86Y*W|7M*yg|}vo~asM?@U{0T2@#{JG{Bb zv;?E2WSN#09_`Fh)6dK^a#Z;nND!ySF&EoqA^aX=g zaW73n=<8DL?wLkl_x$~#X*nCv7#RN8beBaVh{YJV^QnnkvlV+`TJhrqAi}uIqBvBq zGl+>amxgQJniey%CkQflG@^n)YkKue>UWxV|tz6wq-nftSJ z`Cl-F;h}*s_e_;J_{?TT_a)DPIH!5dk9#5T@zDSTM_+&4hjjua05L?8ogeY__ z6^0Fk!4W8g&Y8;QD~zGj7M84Qt|#-wN=9QED^;4Z#!6ezvxQj&lbSJy2|-|tkdXyb zgpLE1Tbt$ZS}QZUEq@NwY0DZWzryA1m;>WiSh1rS?M0mfxt&--0V3b|lf~~{JnLErrW0k=slgug+==_sb z1N%=g50TL9yXj^l%c5C;MHgfb%yFQ>QgbjU2FxsD14aTzXD39!C%`;aOlX0L3(PFP zqDQcLu29(F!G-1(ESHD~VKfcxqRY&K1vWV=%`CU75!N{PZI!ttqk?+?^#iofs8H9e z19|r>z2Sh3=5-Pdkktk#w*ehaZErEdPFv0BXKS%#!OmPW%VWNXDQQdvPeSuDmPqm; zI6&iz=9-|ITU-(@*lF%cH3)Nbdn& zjkd_Z{b-Ac4(^SMS?URqC$jvvv9nU$9Np{-79H1i&l(A`08Wg^f0o5 z<$^GNYO7dw2->_>)#3~LWz{WZSe9&~Hg z$y2g99MPmd_{~tvO5dTY)y5Kx^+5K<`n9)oXAB4*@Exxjj*GKwBI?w_x?Ld%gQ~~*OX_k|PA&oVF?x{*F|$IWte?2XA7Ew^D`|0aLhtWUB>Y50SaKi-40GOK<}u^!MfgV1!%I_ zq5wO!cJ+Uc729g53$;ru=zCFu$v7cV4xu6le{&X{pV);Oz)f``!qK zZ5~+A?*me9VBURS*S9>jY+`gu0a*6T(pIon(re2MA*i(PE!%{ky8OYiS;#Kfx;;2&bEios(1?UZm3 z8)K~tC&{eqMBHvz*3X)T)9nG)3>ix6X|3pf+HQC-*xD4QdV@7jjM9r{Yj$B81gJp= zsAjh^e81{D>kbacqBZGm^vQ~x{{(K!uge%KgFEd100e~oG9dJF+R)>=+SSMma+RE1L z?6yjTGpkt9l4v(vQQgY&tBpQlP2s4z*5?ic)Vrs(9I_VrV!L$=PF(}64RP9kuyrC% zKMc2C#p(Gm)>Swi`KvVxr?!dKNdYKb#alaISu)r93r;62r1dXkT4!Rp#|mo)eBPlP zYilf9x6$*K?4aj8+e6PwIbh9WSY!A)R1ai8jRHPCZe1uvY9=eC`T5p|g)p7~S?s(u z1QV}u2@NtRZF@ra)WJ+qrl)Yw!+-M)WTY2 zZBrPoQ~3QOEYMQXmcVhhVOk}dNzU!0wC>cjt^4sY1^DJv1IWvI;1A!|vh5UuTPGW7 z;QqR{k|LUwG_aW&Mq=RFVvB?88`%z09FP`ko58>l!vF-Mf*YIK(j=5q^;Wjl0=+eD zY~zKa)jHakZkYI*aDZW*Z4C1X0?sw)*kL)ZnkZR5bP7y+j=o9`XGXt8AzNA z9^Ge~!Pu!W0DaX<22}@b=#rQWKGjdymif`_y9>4x9L=7+Xp;-B^7yu`x11jB_83ij zG~4W{t*Mk|x4yC+lhN$754I!12??KUONFfQiw)g0BCXy0YTGKbwp?a^FZkWs0K1)G zh{n;^2aHa;VFF7yn+)ve-69HZR@=iERw4h!3II2z{iFuYMqPTqAf0`bh$=o)Z_g3T z7;mx{SAYQ%M%m$7mwhWsi@piL0b*ql5%7h_{+{FJ!~3E3X%cQ8oompC)LDG)J9N^D z2U?KuBC-?+DQ-_;2&LPx_A&Sy=PFQSokImXN7{d9>FmE@=ET@peispe%muH8*oEf- z2b8d*LF`rkR1H{K#(s~ja|F;1`<1gZ{f;rBlnGAfIdpJUReLEVvKv_ozien%h$%Hi ztexR<5h7rwZ;zpRD`7wj`$v|699ITco7y$tX$L#YD*&+0x1STZd|OyJE?XAc zcd^VN1zh5@?D+zh8q4jw3Zs`-E9~tVu37=&vK%_s+m}%u@WMv>PL7N%aLFcn6_#hX z5#&7JOurET_APdqfz$3pZ(i=Q2MaAE>?vp=W}m$X%dP}zA!!^Hr|nf)TbOeR%^xX;qNMQj75gKB{cqRo=!bXkTn!iAu%o94NVeOpf)=Ci z6de6<*Dhvl9H?jBKtIs=!rq=`{v!&i7WiBV{=VcjfxkL9C&D!!?AP&njLho`zS>_g zj40S$<^dnDj4s0aiyW=#8UbFEI%?CE47v~Gn8a{>Kw5w)YDcD6;0opjIds4o=rF_h zU`K+`(j|jK!Wet=0D=z&d1Z^p;69V%q+r>FR>v8^oTKcH{w!)o(@+w~nI1h1iiI*6i*;o9`rAlsz5jI*Md3^m2HF6BGj+ z=m);Z(TRf{>xJykBOG=*g~7ooj>|%~<9>BCWpyr)x%h?@j+x}xBpAM0y2BU8ifIMR z&P;bKVQvDAep29U<+g(UOC0^+kvR^J(Aej>j)}~$H`=?=&jWUjM4u~M;>ZvXgO)mW z25{Hub(Us>V0-)4;Tg4h2LPL>?t#fyPWdl%IB>Px@X5{%0KwdojR= zs)EzcIoh&}T|NW{JaWO|R~Q7A8(hRT_-Y7jIPTsLV#A{b0adK)FIeLb2l`a!b@1_; z;{>azGH*BzF?MJjtbW_E6%%-W*Kz#E2|(1bAuP*VRBs)u^~iA!w{3Xp$p7)Su~V#r zhn_i@E;dNzi!U9gFoETNI?k{Xh<)oQ#WW~|ICJRc?Xb^|?GnVI#5X1%C=ue^zU{vA zBZ~{*y6}kDc@Jx0t;~6nK??+j}nLynD=UGg^tYRl{CdkQlJ%KeaNb5X;2~^NK zkNmg>Aku&z%aRAxn-9?S!*n>y?hHl*;2Nj%IET_?x3dh>GqFL!{IDI%hJIb&hfdgOo8I9h44oqEC2HvT*MZ z=Ts!Xh9jK8jKMb@BJ2lPyYdw0O|0FAsm@}I&O9AtzIFtGnB&N!lO|F37&nEwL+n)N z7#24`<8&W-S#W2rvpLIU0YSi*1yHuwnSk}xH_Len4Y;tvN+(_?!6MntQH*PyKH&R> zz=}=I6hQSA&=*8pfaNKXs0Sc6UIOF^vN9oH;PV@oq>u|>vC;F9w>oE1I zvlKqQ^^P+IpI`c+a}Q4ceCmust_)qTosV$G{NFiSGdo>eU5^Z;OmZxR$G$kxV;I-r z<-cg)WqzYHdT_2xW_aW(|6Jy`RJc62jp6<-_Bbh6(w0 zK6-Uf(&j4M)j?h^x;kiJcQIWZxEi~XL4J3)1K3z) z1Jc4=SATrI16+64dRQUcb%!Nr5RvKNRwb7L@0e7A&KOs;0#uxs5�cB7g8G#)W<~ z{F}=RXOwqc6j7eTD!Lf9PXKNTqNaMW430jYDrl(c`im+IhSqeU{YtDmSiG*QH_LET zfC~_=2AeYu)LrNb2GdmZBI7BB*-=$q#-YLe?Wfs!oU1q-7j(#2I%T`ibL=jk(DN|5?T z(Y~k!gA^&*!N<}G$P7#_hSV>ie=bwf*b5%4949=v*4v@48S>l)9pkR+6< zY(+iG%qfm`R`6pTBIueZ`ab!W@0^8ZQ0^&)uh7(}fwS!&w*8y?opPL@hj|UI&dAUFZ%zA^q`^%fYZ*q9%*E zjwUs-voyVAXb;%pmP^5C{=OimmCXz~PYmw}lb*R+F;2GMVS)jAkx2%@YZuxGAw5;T zbD@0!lD+!Lh4uwV_UK=(M8??Z3#x5$CcvMh?i`jY0b~?{5DqtlxX}ypgiaZY8*K@Y z>?)5NZ3&RL zwcKb+fMj>pb=PNh)1<36WB_dmn8A`K;mu*WCT5@c1qb?tsV^Q4XmZRQ2jrvO z=(7OF+$^Ip!kYxX<-3KaxSFN9SBg=;KmF>y&oKA-(CjdGocj!m?+}ETW9WfM@*G#Y zsqRbI4&2k~z7Df=97X11H<3uV4WR4^F0(^yswJ_Ph4Q?6SL$INqo zjAGRn>MhGwhELb@VO@y!GcHJwN`5`#KETKXB0spptPNDz4tJb)FU5xGaM8`O^7r-0 z4&PmJGhKpwM*x4gcVQ~cue*;hDg+0H9lpNdK3X`f%((5&!&I8xbN^O2D)-$?H+qm( zCWBX>+%CG&k@n2J9Q#VxYgS(&Xc6$_pYFdHRU;s*f9F1gRWtCT`yiWYCPUOQruQ^_ zT0xKdes!Ze+c{vxH)a-Cy%De>=V99Y_)g`i*n{=~b3mrd!*YrC9jLm(Q;~55o(ytc z-U!539>SAPXM;RwTO<8M4P!>naY_} zJ<}NO9RboDzRwF^*7M|Gy<|3|h&>y7(4FQS7#-_j`s9UAKTewM{wPE56eCeM2JbaewQg8w8fJH zYfSaDWOjOTJ+^VS2W=V&^s?uAnC@v4dm=dDx@_tK589o{fo~Rj(C$nQ+`r6&c4ubwrq0X%}X?pR$lR-Et?$p$1M-qvdMw%?t9RdO%60Z@t`f699ZY2 z2W{Epz?e6lag4U13v`GU=%W?fN_q{E^0j|B=qT|P>Y@QS&pz-2_yYiNnbf<2W#-M& zd;5YtFw_B3nt9_uxXh~qubO#TE*%im7;x)ZXasES=Upxa87njz5UBJXXPCzcR_b4& zcDPLCJ;mT<5Fz-fkhV#n?(=xRi725nVP2Lk3j{YF#NG{+!P4R0pBObw5kUWDUJc0X z8EORTA)aFNi)cATy+2cxL3xz-s)Ue*Z%TThlxBC8_oBDAuukF33SRW~7RlDG4cd8`wobk~-?a|jg#w#NoxB?g#YPhEWx9g-&SpVZ?@obD z_wL@Ug<|uthnHnL1$<}oxVIO5+l>VI#(v%{g<=z(=w-U3`p)Li0Ph%qP4i^d2p>cJ z=ICJWL1Fk>INY0EsGjLYdXEZN&&GJy77D9ds+Z-B8Np^X!vaBPShOg7IL`Z;jt=1Q zK7xS3v-kL3LaFoX9peRBCfFiq!p(H0Ql z6*147!)(HWj&7ds{lYkdAP+5o8NbMzBk-z|`G3JHKg-K>vHBl z-fST|`KfomknQl?i@v8tPH?>RuBHA0ufF!8PuG#7nvVqt&p#C)to~AfFygNQgv#Fv z5I%9C4FwLZ`PKT+0nB_;D~G8-P)sp1d}RsUFY&QF;tWL_ltiJi-cY6uNgCZ4KDuHt zCA5cyqL+&Z;+ODHv^7bxSBr+eWOYN(d3IO`M7xr1mJLZn4}Dk=zn-+eN@zKjy-17z z(Q}CN1w~b?6N)w;Nw@qOhN8_!l6~JKv>{^;d;H^B#A>ZWC$j875|Zdy#3ceVb-z%w z`AC@kGB^}%K9cN+(V=Mbkz{M7g!--w0@6a!<|8>;bz&&md?eX@{|{^58Q#>-0SdA`b`4ck zllC32FA5-?1{QXw{w}l^JYEs-icCbk4nHO<_8mX14yZx&|D6Ftw z0eF7AT%a$kyBcuKR${mC2B{r|q|FOzxq|<#fM%qb9G8zHunAA_%fWub0>54hxOykR zOVZz_2LbCz5&75nq==lW4=6pSok zQOm$jGQl-%0x|W3AULXBAilCLirF2k5Ax|`y?di`;48vzD%kW3ykyThy=~$R&j$s@ zQW_m0R!NB=T!sZMmijI18X1V;tROsnw6)l3Y#>Iq;%?yhK#Xif@%F^Pb8@vq(*p04 zrn3csi`Z^Q)oiFZI}j6(2;A5oteSG17ub^6Bt4}FYUGN*d{P8e5ZIH6H@S+71Mv)} zU{+&oAcj$*_;N!ahEbw;ZF67`;(V^>S7B8B-GNI;VH9U7Ugc0 zf$}^UPTMK6iETcYbq`gofNA9vKDKOF)y5vMxxB*CQ3_oZR%Y&sBH|+!ewEfi@1wx9 z4uXHjN{V4b#2=WupF&FOAe{$Ds>We=k8kP;{c9*TlVNX$D#|nWzyK$3Qz~}ZNxk|` zh@!fzes7J!(rP!fR^2SGt%xPg(|=%=N4mm6y#m)hgl>L}R^Zx)D2|L(%qBQihK_Xv zVAxgJhV@eUr87;cVi94MV~HyLS@&!A%SpSzOVSj-P~@H7O3{@)w^91Dc;}#|tV5=P z5(#n?2*^@|+cW=Nm~N;|p5huoFhK@kq$IGP&5E8Bt3j0Mj5Sus2U?rdR~)pHXw`0{ zSWKfe9T4yc!Hog3Y3{%-XZv}BV+TbPCALF|IpEYq(N)qejP0S=VIw4hZ+lsbHTx)5 z$#-w{Rg8C#elTdT^#>V4twou=8wM?WR~Ry$U4MbZ@bO;qS9-gZw?&~Wy#va&L3 z&{8oN=Fd}1^$^0d$hiv4*8N=J3td+$&e}>1Qm{@zSyqvrQpe8~wysx@!f`7CbNyA} z!Th@@z7EHmZB+D=74cxR;+3qPM?1Nr;r0!`?pExUi=*}`aBE)Njohzj;V2bCCkyaYLo-0TJz7?SVD+PYN zOmH#1QEVjmD=XBYw+d2tZv|-fPBFrkg-`PLWWRq<{8Sn)o&Q!GlKRxFzVsa(0ZD3Mr`s8BJB)XX}qiwgv&z0h&TF^X$X}hRSzPqCRn*#t{Jpm<}yE! zYI;)h1Ox@?36lWsfH{COPcc)O;eK)fj0q3wPaGCTu&MVIA}wWS2#OABPY3}48I7iF zzHv#|=Y~OEti&g{7&e=O^kt+0UYUnC#Q?8v5LAU~#DLhJ)0~k1)G!)#yjw2Pr7-2sPgo zFn+{uoV{aYo5*8!MbSV#HjGMC9PRPUl^i&=q=m)Xpz_MC)4Q;1^MfZFlq zK%eiF`(>ESCQ3@sCWPA2R7r}BhVN7kW}ka0t}MBD|%PP-Bp? zSY~r$NGa9QYMAmGWkMq43Exo5YttCzT!{=6k5l4iCT}k2H$i!V@-~)$oi0t+#D{&h~Ja@`U z>vO;R*?PBnv2s0;!c-oRc}Y3)!$5h|UX1GCvR#=(@U4JC4Jsp1bvdXc?PLkP3_Bkp z=gSN|u54^8ky>z4iQnH6dI&h9Bt>8ra6J8bR*Auv06TF(iNTjBuDYZwAY0+u2TJ^$ z_PT10u=%?35xHwwU{YYtEhT>7K}@rpZd;~VpYAI?WffO{u6!jcq~>eoW_jlO^baM* zS3;cMJ}B{2uPCvV4c_<8A{NQQq>)tz-a5$uoel&S_1WOB|*0gY)01%9A<^vS5aRDkoUbLiMKyQW0;J{@hDtV~2kf_f=7j zx^dY!sw*h^t6CA&I|>jXh+3IHUbIb9HfOMk64VMIuz{5D{SZ}z18U>&2o>cRqG)Ms zCDIu-!9q0g+cB!8w#ed#@v6P0vX$_ODpC~mh05{Cs=b`b)M={2rJ|ymp(1S}eW9YA zr8>^3d}gYnrJ^!#j_L^6*hB+G%~S2*RQ_0?qAYxTk!ji@6)8shf)M>UtDbX=ZfjMP!=PWt*sNF4BDIsM<;9+Rq&>%XPk~2qQFJSR?#NV)!LfM31%%1ZE9vOmN zD6>1X)`_f88_*Ym(t}S?HrG(29i&jFY~5f3CDaiJBGmEuE*P`zJb@vNgE8CA6Ub^2 zjM;XcKvbLHwnQ`P@YTWD1)YPZQTEgXL2+>Q&mqB>Smz0}9}$d+b)LZG3Bj0H=Lwvd z5sZm-p1{2CgE2$Z6KGWwjEQxg0Gl6-iFKZU&yrwFtn&oUFAv7VI#0m9I2aS_Jb`Cx zf{hgW!e$%8rfm*(;NL>Pk4*C%r}Z`ke;{DN@a@6xh{_$!7VQldCtW+jlKsIU1b@kG zx;tQ)6m4U3TY4Ap<;h^mR*QApY-VDpJGhnve`k+~_?Dp#zpH|GR-FrOX>arCtM?6u z{T5uhAW{A4eg)`uIheG^pZ_v=1WULUOv!gC>NdF>EFbLLcs=-o9V>}z=fQU04t`AV z_Z$!qa3sNk7*^gOZSMW{IC!|dM5NP;V8V$lK&(R)$By;3gTtHPx3<#bo&O9bY`}0) zw!_cwf=v`>ij@~IeD5-unU{yQgSIvysV=OnQ#)^V-X_G0nP1@>>d#$6@C{7SR98Jh zD0VJAg};H>!YhOnI$IIg{PrOpEc;E!*GE1T6+>lyip>Rw-Z>#RWDc#(A&03PDiwr~B4NSdWZjUJGKaDCLv~O(Xub=1CcEG8 zCLxpvRB<W zuB2Y`1zNnVxvqQ2S;|7nf4~gs6>>#xy$*dt4iH-JAAtT4)Ia3G|A3d%ppeTluiuB1 zo|hl28x}%|rrAHl^Mf`cLN3X?az~TQ^gjR{$A+|_IClZV-_~UKwq_;i5a_Q{LLO13 zj{&a(KJyUn-$uK5p2b>3dgm_i<9DISWElPiCt&8B zDioiD`X33?I)r9RUI1Qo4}C|BX+|={sRes_hn|#`yr_R@Z@Co$hJ@aeiJGlq1$9HuzGsvEN?1U^JSsF>`m8fUkF|q`oK|g zT8cHHyU5z)h_GRO=y_S2KWqveBC9ZHTPPmjm&|)_DO6}a6k3^O9tefex}d_=+B~Sl+B^^4LO_}RK6DbB_BzxbKD-G1 zjl#o%Kv^iky8EQ13GJKETK4R20KSvk?p^5ne=i9jR#l2Sz9MQ--Th`FTvq`qp(B&o&&sU z9CloGdna3jQI>iHk(sT+9A%iF+l5iiQ3#ms9l{nIMP)ar@7KQCB9h;dyhCQ_mILpJ1m#U>&uM8VXiMoWz>|PVLfU?fd zD;Et9my%RV>q#57hE<>}6NqlzWq;V$nW}Y%!UmGDZkob;VCmVgtz?8nSz&(A@MhRc z%EFeAXG-rd{DRe{>XjkyX_$9e%M5hD+pykbXCpA1+xol0y?0^wX<6=Z;OghFEtC}i z!E&m7xEG9Z44+|ZAsAXV{0A~Y0k^q)conJJ!#%ff%vdI9f`_#@-YXn)#fZCU-r;{x z&dLb#^NOE)gSuw;Y%BR|0pW+K&XaLChH#FsKo#yRORzQ6N_$ecwb)c+m7q#Qcp7EF zNJwyM&%FxpZF0DeqpW_GniP)jLADC>h1HqiY2=0wzz=f5@$=R~&wI_`xaK5^lM2E& z$i@7+;R7hEn}Ykbp4Eb&q;+^^8H?$JcMgw{CM+I+%Z2KS2;!T=f&Z3h`zqxSI2Vle+oZ9v|0fZ4$roR<1(k< z)OA-lE^~_F%|qe1%qfbOPFYQ0$!V(zbT}K1%bel|^Dl?vGN&k3y%CPfoT7N;PB<=e ziekA(;ke8xioZV%?@e}ciyDW0{bAzGzlW2yP=pokev9uyh*#gc{uSPt*b=LXY}9g+ z@DMw7+lnlrNy~J0xssalN}y%Q$OCTrs+}ma5}SB+a~M!XZA-CTUYV7zuBNO*VBHZ? zbkU`T`XRn$lhw)-4+yF$pS?${3GiEx`Y}-h6JThtdWkJtbho7k(}bxhSp)=!vEk~L z6u&JhFV(7xB@VU+uCdb%YH3-AIJG<5v8+mEwp*u8AWRem@D{zA^ld&-c|^2TGrBm6 zW&mLdDZ5cvuQNN9r*>k>&);~lj!a6*!=2h%G?~GMo25L7aFR| z+DSJ3vWa>gO+gbNt(ls1zCpz%zf39B{uN9zxoZ;gkabZ^-ZD= z?98yT8#ouLZEd9vR%3*^I#n#VIaYn0%mgdDvKuOm19Y9B{$L}~88S&7N?9z|Y*qJW zix#09nG&xV4wq)Bm&hJu>KrvLJ_{QdI8Qx?U;`FxOxHSJO{`q?vE$cq?7q5PapQu`KA**J*Tco>_jrc zjtgpoM27`kRC}>$lhnSDa7o>fGSwHvnqN~puyJEG1K{`#wU_MSU*1w5rXb>Q(Sl9$ z(|AL(N9u5U;T2i^#}=X`&(y1AyPoorf3)mGoCZDgCM=oXsD7OmBhZM5pPQBmp^ z8g=bf5fOTIq*~9uV0N?7$cOpS-{HEs7zLhvQ2$J@<$s|1LF2#G8zsP5H8jghg9pfR zJ25bmOoerOuP$3u-Xg1*gt4wpnlj+xq!}lh%kpxXQi_ca*;HQhq;$WX7fKw4tvsRb z#H!WMT$JWX?XF>IYJ!Z{1*}IOctD(#QITj#8k|X4m zi#nq@`)!uzw}3uylFaC1Q?hgxQ2Zisa}6mB{hFE|*tF72uw{w$a1m^N8`7xpPdvAF z8q)kwMsbG8T{OojZiEA{yxyqtEB>0=EVj3XGCj1Sz|}q$=fTqZYUD{-vwj*<3LXJl ztu2zn*rD%6l>jRLxBt(%U3v&KpCgfU=( zraN%~O)Ss)L&^+|$;M_Mo1V}-ot4bgP}V!FYt5|r67<0*zt{X^$HK4o_keM;HJEEi z6u1ANaU=M@nR^CS=CF}0cfN*jqT?U+9bwG^O?qir__H~SHH0)moC=^Q4)#JLtnYFS zA;_}`RIg~Wof8~dp|O*7edTA(146g7;3Y!tI*q#r+w@0&Zx*#4&Gjb3-r(+N>j8`Q zYEq;>jW3Kx!7hjCCc7bd>k6g9$W#eI9 z+UJCo+AkJU@<~%3s(zylCCvQ(iNbswwew)8uNLz?@~#c_t7v!1h8cGls2`*KNDzwcnWD{Q zolIJPc5bY1J(gF!qbGA6@r?&-eng8OI*Zpv|9d+Vpb(Ux-A`yNDDqOaUUnhD-exs|24*pfEdZZyRT zSXcP5tu~3+-U7iN#<$m+C}%G$qLJH29EE3nv_AGy`|KK^Eu!h72mCouyO5}c9uPHz zyDr{+*s5XL8B`9p3bl;P!FQyU!_?7q4*SNGg2UhAv}0vGd_Pf3iNr-;IW+r@N9;53*qtH-}Hd=S=wGQhvSUS!ETPW7GX5y9O^LhMr{C;EYOaU z`$d}{wbN+I5LltGe5ux!Fdkdkgu-_#v;$%9~R%?68Y<^g) zomnc~TwkZ{BV)DRplwgY0&Fu+d6?<7xs=VdVT*P*(SYR!57@44C<`@hr*cR6Z3cP#cDsqw(J6BZ!r<05 z?a$Pv0feb;C(Qs-?r4h~gsX$6548uSjt={tX)(Kon8xjSrR_>N1TAz{?X&h9IQF;p zG?^+Q)>JKmwEFyo5@MBS${hvXEd4B6=-nC-Zz%g# zUjT!DD~M)yO*h@yxgl!&l2*KbOK?#}RHQZ`Hv)HtsLrrZ6_HG|r8;a;c!V4DRY#m5 z5|I(EXe0b(({_%EpseVKv8-s4)}Gx+jBtku2@xl#lsF0?ib`hWn;L;1d0U?`*B_ea zN5oKuNwHY?vbLwwO!q;)?05rL7jUl|G0~B|v8j^568HJrGuO#&6wIwr1U24d2#>Fl zH;O3v_ah6OZX98YXHI)m@qoL{BlMUlofm^!MW`v#B3cw{+M$gTJKiROu>Qk_507Xo z9fq}Q7tt9LDj*wH*g0a#zZZ>Tb%j-3B7h=^sBG6GVveK{wyIaecq)hUy(7HH9LDyG z_)+E%J1}A*l|!3B5u_I>gdVbnM&PVWw8iPdh++Sp10c*Hij&qXj%dKP2Ksx!-Ek4A z71-#KD&An2AF+q9gaHV4s)A(T9-AVz_HD58T z5?kO<8$Sy7x~Ug?5t-}A`ah1Sz_QLYb}1U!$C2f=jHnKC9!GSlD9KUDrnVDhaIiiO z@NoeeiTgQyZT=AjF44TG+Dm1L@>6RsE-&4+t5dsWIjPvUNp0pR75!S*E|#lZX;b@e z`3DcX*LISt*$k=Ox~yDnOzjnNwXn&x6;9IKwzFza@uVuPS}mO~i|x?1GE81s8_)Xk z_GOi}*Y+w}9pMX~x7VIUR*-<+GN7$1RNGs7y0=7j@5S2qDR7~sh-@IeRe2RNW?HQ8?M0~h9;u6sZLFs{{^p;v}(DWxHJWj5Qa zqpWy|y5H2($w!~ANZftK(zf7V?`?Y>_r`28RpyE!e?qDU02TMQ&Sx! zp0+{)LM9P)n-4eI>OOK7JcTq3eD5mc_E)3YjlKFbU>gS%9I4_^#skYx}Ln*mTuHh zUR)3u4>sv2%Ytxvv+f~(_SS8>CA6N}6NeSuWumq8gu%OX1)RXCJ-Yc+0&e?s2Z{F6 z6Z#+0HRA*>9ieN#@-f{xa`VqPscXXtyepxY9`6J_A?A#Zl!;SRUVBc`fcr&?2H@63 zU0t&9moDqNaN$$0(S;v(UALWMq}>6EWF?tb2jfmG1=A;tF{scYgV|2nHW*GID!(|z}nnbt7S}FRrWK#%D z*AL+Ye#z8R;%Onon=E~MGJ(&z`uUu|{Cv6w?iJ|AkqNlh(=X-();G{o&dUo8eEd$| zpG@GJCi>o-z~pB1A>mAOJt<+RV4vGc{}U(hu8p3O$wD&Zc6$1#ytRXV5hu{7Gu<65 z>!LqS9+N6`*W=d)m$5TF^{c33QjOkv(k`V?-_pMNL!3b80eZ^U=mo~^fqMGn*f>OA z!U=d5>VKtbp#AVt7~$QK`n#OKkuiEg9uZhKR$qtQ16NGY-{a4oJXyb$D*WRq`UhkJ zy=UmBasu|V=p7)pNI#fk9Ggua51-A^*Tl6W<}qJSd96^GVbcZredLa~f05J=CjF=< zENeoKrFzn)jUayI7pW0Ct)>r!8`kJ6ldYiVdi_}5f*&@}tsr@m{x-)@Y|)RRnsV+| z{ReIZ89Vh$sTk9C>1T2twf5>M$A?6N)Y~VygxG`hfwAzAK7!mJ^^WT6av^>>t|u

Source code for cleanlab.internal.util

 from cleanlab.typing import DatasetLike, LabelLike
 
 
-
[docs]def remove_noise_from_class(noise_matrix, class_without_noise) -> np.ndarray: +
[docs]def remove_noise_from_class(noise_matrix: np.ndarray, class_without_noise: int) -> np.ndarray: """A helper function in the setting of PU learning. Sets all P(label=class_without_noise|true_label=any_other_class) = 0 in noise_matrix for pulearning setting, where we have @@ -668,17 +668,16 @@

Source code for cleanlab.internal.util

     x = np.copy(noise_matrix)
 
     # Set P( labels = cwn | y != cwn) = 0 (no noise)
-    x[cwn, [i for i in range(K) if i != cwn]] = 0.0
+    class_arange = np.arange(K)
+    x[cwn, class_arange[class_arange != cwn]] = 0.0
 
     # Normalize columns by increasing diagonal terms
     # Ensures noise_matrix is a valid probability matrix
-    for i in range(K):
-        x[i][i] = 1 - float(np.sum(x[:, i]) - x[i][i])
-
+    np.fill_diagonal(x, 1 - (np.sum(x, axis=0) - np.diag(x)))
     return x
-
[docs]def clip_noise_rates(noise_matrix) -> np.ndarray: +
[docs]def clip_noise_rates(noise_matrix: np.ndarray) -> np.ndarray: """Clip all noise rates to proper range [0,1), but do not modify the diagonal terms because they are not noise rates. @@ -693,19 +692,11 @@

Source code for cleanlab.internal.util

         Diagonal terms are not noise rates, but are consistency P(label=k|true_label=k)
         Assumes columns of noise_matrix sum to 1"""
 
-    def clip_noise_rate_range(noise_rate) -> float:
-        """Clip noise rate P(label=k'|true_label=k) or P(true_label=k|label=k')
-        into proper range [0,1)"""
-        return min(max(noise_rate, 0.0), 0.9999)
-
-    # Vectorize clip_noise_rate_range for efficiency with np.ndarrays.
-    vectorized_clip = np.vectorize(clip_noise_rate_range)
-
     # Preserve because diagonal entries are not noise rates.
     diagonal = np.diagonal(noise_matrix)
 
     # Clip all noise rates (efficiently).
-    noise_matrix = vectorized_clip(noise_matrix)
+    noise_matrix = np.clip(noise_matrix, 0, 0.9999)
 
     # Put unmodified diagonal back.
     np.fill_diagonal(noise_matrix, diagonal)
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 9699fd59f..46b37d704 100644
--- a/master/_sources/tutorials/clean_learning/tabular.ipynb
+++ b/master/_sources/tutorials/clean_learning/tabular.ipynb
@@ -120,7 +120,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb
index af77ff1a5..ede05464f 100644
--- a/master/_sources/tutorials/clean_learning/text.ipynb
+++ b/master/_sources/tutorials/clean_learning/text.ipynb
@@ -129,7 +129,7 @@
     "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # disable parallelism to avoid deadlocks with huggingface\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb
index b012c3b83..8111dc9de 100644
--- a/master/_sources/tutorials/datalab/audio.ipynb
+++ b/master/_sources/tutorials/datalab/audio.ipynb
@@ -91,7 +91,7 @@
     "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
index 40b596a7c..59d67f1f0 100644
--- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
@@ -87,7 +87,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]  # TODO: make sure this list is updated\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
index 6d03ae333..b77f310a5 100644
--- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
@@ -85,7 +85,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]  # TODO: make sure this list is updated\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index ac39104cf..9b208f9fe 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -80,7 +80,7 @@
     "dependencies = [\"cleanlab\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb
index 3e5552460..469ab488a 100644
--- a/master/_sources/tutorials/datalab/text.ipynb
+++ b/master/_sources/tutorials/datalab/text.ipynb
@@ -90,7 +90,7 @@
     "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # disable parallelism to avoid deadlocks with huggingface\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index 7af5e7f6e..c59730731 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -79,7 +79,7 @@
     "dependencies = [\"cleanlab\", \"requests\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb
index e036f973f..6ca13ceb0 100644
--- a/master/_sources/tutorials/indepth_overview.ipynb
+++ b/master/_sources/tutorials/indepth_overview.ipynb
@@ -62,7 +62,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb
index 56543bad0..8f5165de5 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -95,7 +95,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb
index 348a544a8..5d2685b6c 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index 2d80f1068..1f90e9ca9 100644
--- a/master/_sources/tutorials/object_detection.ipynb
+++ b/master/_sources/tutorials/object_detection.ipynb
@@ -77,7 +77,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb
index b6dbc6271..97e4513a6 100644
--- a/master/_sources/tutorials/outliers.ipynb
+++ b/master/_sources/tutorials/outliers.ipynb
@@ -119,7 +119,7 @@
     "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb
index fe223cf83..bb22c545c 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -110,7 +110,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb
index f5ced067f..d2b0bbb42 100644
--- a/master/_sources/tutorials/segmentation.ipynb
+++ b/master/_sources/tutorials/segmentation.ipynb
@@ -91,7 +91,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb
index f02e0094c..d8a611d23 100644
--- a/master/_sources/tutorials/token_classification.ipynb
+++ b/master/_sources/tutorials/token_classification.ipynb
@@ -95,7 +95,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/searchindex.js b/master/searchindex.js
index 47f457c5d..ed2af2ad8 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 85, 90, 91, 99, 101, 102], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 90, 91, 99, 101, 102], "generate_noise_matrix_from_trac": [0, 1, 90, 91, 99, 101, 102], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 17, 41, 46, 48, 49, 50, 51, 55, 56, 57, 69, 92, 96, 97, 108], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 27, 30, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 84, 85, 90, 97, 105], "benchmark": [1, 38, 84, 85, 90, 91, 99, 101, 102], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105], "": [1, 2, 3, 4, 10, 19, 33, 37, 38, 42, 46, 49, 52, 54, 55, 57, 62, 63, 67, 69, 70, 71, 72, 74, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "core": [1, 41, 44, 76, 78], "algorithm": [1, 2, 8, 10, 32, 39, 43, 54, 55, 57, 62, 71, 80, 82, 84, 96, 98, 99, 101, 108], "These": [1, 2, 3, 4, 5, 8, 10, 22, 38, 40, 42, 43, 44, 45, 52, 60, 62, 63, 66, 70, 71, 75, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "introduc": [1, 89, 96, 98, 99], "synthet": [1, 101, 102, 107], "nois": [1, 2, 3, 37, 44, 47, 57, 63, 90, 91, 96, 97, 101, 106], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 16, 17, 21, 22, 23, 25, 30, 32, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 90, 96, 100, 104, 105], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 33, 35, 37, 41, 43, 44, 47, 49, 50, 57, 62, 63, 64, 65, 66, 71, 72, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 96, 100, 101, 104, 105, 106, 107], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 26, 27, 28, 29, 31, 32, 40, 41, 42, 43, 44, 47, 49, 53, 57, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 90, 94, 100, 101, 105], "specif": [1, 3, 5, 9, 15, 16, 17, 28, 34, 35, 40, 52, 53, 54, 60, 64, 67, 70, 79, 83, 92, 94, 95, 99, 103, 108], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "modul": [1, 3, 14, 15, 16, 17, 22, 25, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 51, 52, 54, 55, 57, 60, 62, 67, 70, 71, 72, 84, 92, 98, 102], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 17, 19, 24, 31, 35, 37, 38, 39, 41, 42, 44, 47, 51, 52, 54, 55, 57, 61, 62, 63, 64, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108], "gener": [1, 2, 3, 7, 10, 19, 24, 26, 34, 37, 49, 52, 54, 57, 58, 71, 72, 74, 79, 88, 89, 90, 91, 92, 95, 97, 98, 99, 101, 102, 104, 105, 107, 108], "valid": [1, 2, 3, 5, 10, 13, 33, 35, 37, 44, 45, 47, 48, 49, 52, 54, 55, 57, 62, 64, 67, 70, 72, 74, 75, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "matric": [1, 3, 47, 98], "which": [1, 2, 3, 5, 7, 10, 13, 14, 15, 17, 19, 23, 27, 33, 34, 35, 37, 38, 42, 43, 44, 47, 49, 53, 54, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "learn": [1, 2, 3, 4, 5, 9, 10, 15, 17, 23, 31, 34, 39, 40, 41, 42, 44, 46, 48, 53, 54, 57, 60, 62, 64, 71, 73, 75, 78, 82, 84, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 106], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106, 107, 108], "possibl": [1, 2, 3, 7, 10, 37, 38, 42, 44, 46, 47, 49, 64, 65, 66, 67, 69, 70, 71, 72, 74, 80, 82, 83, 91, 96, 98, 99, 101, 102, 103, 106, 107, 108], "noisi": [1, 2, 3, 10, 37, 39, 42, 44, 47, 57, 63, 64, 66, 72, 74, 75, 76, 78, 79, 85, 90, 91, 94, 95, 96, 98, 100, 101], "given": [1, 2, 3, 5, 10, 15, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "matrix": [1, 2, 3, 5, 10, 17, 19, 32, 37, 44, 46, 47, 50, 52, 57, 58, 64, 67, 69, 70, 71, 72, 94, 96, 103, 104], "trace": [1, 90, 91, 99, 101, 102], "valu": [1, 2, 3, 4, 5, 10, 13, 14, 17, 19, 23, 27, 28, 33, 35, 37, 38, 39, 41, 42, 44, 46, 47, 49, 52, 53, 54, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 83, 88, 89, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "more": [1, 2, 3, 4, 5, 7, 9, 10, 14, 15, 17, 19, 27, 37, 38, 41, 42, 43, 46, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 107, 108], "function": [1, 2, 3, 4, 5, 7, 10, 14, 15, 17, 24, 27, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 96, 97, 98, 99, 101, 102, 103, 107, 108], "noise_matrix": [1, 2, 3, 10, 47, 57, 90, 91, 99, 101, 102], "py": [1, 3, 34, 38, 39, 44, 47, 49, 84, 90, 91, 99, 101, 102], "verbos": [1, 2, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 41, 44, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 90, 99, 101], "fals": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 48, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 103, 104, 106, 107], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83], "prior": [1, 2, 3, 37, 44, 47, 49], "repres": [1, 2, 3, 7, 10, 13, 17, 19, 27, 33, 35, 37, 41, 44, 47, 50, 52, 53, 55, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 108], "p": [1, 2, 3, 5, 10, 37, 44, 46, 47, 55, 57, 62, 70, 71, 72, 76, 94, 95, 96, 99, 101, 108], "true_label": [1, 2, 3, 37, 47, 57, 99, 101], "k": [1, 2, 3, 4, 5, 8, 10, 13, 17, 19, 20, 24, 27, 29, 32, 37, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 65, 66, 67, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 87, 89, 90, 91, 96, 98, 99, 101, 102, 103, 104, 107, 108], "check": [1, 2, 5, 6, 9, 10, 13, 17, 28, 35, 38, 41, 42, 48, 58, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 98, 99, 101, 102, 106], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 14, 23, 27, 39, 42, 47, 49, 55, 69, 74, 88, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106], "achiev": [1, 2, 38, 39, 42, 74, 98, 101, 108], "better": [1, 5, 10, 44, 53, 62, 64, 72, 74, 75, 84, 88, 89, 91, 94, 95, 96, 98, 99, 102, 103, 104, 108], "than": [1, 2, 3, 4, 7, 9, 10, 27, 29, 32, 37, 44, 53, 57, 61, 62, 67, 69, 71, 72, 74, 78, 82, 87, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "random": [1, 2, 3, 7, 10, 19, 32, 41, 49, 52, 62, 72, 74, 87, 89, 90, 91, 92, 94, 96, 98, 99, 101, 102, 104], "perform": [1, 2, 4, 7, 10, 27, 29, 32, 38, 42, 49, 51, 52, 53, 70, 74, 84, 87, 88, 90, 98, 99, 101, 102, 105, 106], "averag": [1, 3, 5, 10, 23, 29, 37, 38, 42, 49, 55, 62, 63, 70, 71, 72, 98, 101, 104], "amount": [1, 3, 92], "paramet": [1, 2, 3, 4, 5, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 95, 96], "np": [1, 2, 3, 4, 5, 7, 17, 19, 32, 37, 39, 41, 43, 44, 46, 47, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "ndarrai": [1, 2, 3, 4, 5, 17, 24, 26, 27, 31, 32, 33, 37, 39, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 96, 108], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 54, 55, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 84, 87, 88, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 17, 19, 27, 33, 37, 39, 41, 42, 43, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "shape": [1, 2, 3, 4, 5, 17, 19, 37, 39, 41, 43, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 89, 96, 97, 98, 99, 102, 103, 104, 107, 108], "condit": [1, 2, 3, 47, 53, 56, 57, 72, 92, 99, 108], "probabl": [1, 2, 3, 5, 8, 10, 17, 24, 26, 29, 33, 37, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 107, 108], "k_": [1, 2, 3, 47, 57], "k_y": [1, 2, 3, 47, 57], "contain": [1, 2, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 46, 47, 51, 52, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107], "fraction": [1, 2, 3, 10, 21, 39, 47, 57, 62, 74, 94, 98], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 55, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 101, 102, 103, 105, 106, 107, 108], "everi": [1, 2, 3, 4, 5, 10, 17, 38, 42, 44, 47, 56, 57, 64, 72, 74, 75, 87, 89, 90, 91, 92, 94, 95, 98, 101, 103, 105, 107, 108], "class": [1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 54, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 105, 106, 108], "other": [1, 2, 3, 5, 10, 17, 23, 28, 37, 38, 40, 41, 42, 44, 47, 50, 52, 57, 58, 60, 62, 63, 66, 70, 71, 72, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 104, 107, 108], "assum": [1, 2, 3, 13, 44, 47, 52, 56, 57, 72, 76, 79, 98, 102, 104, 106, 107, 108], "column": [1, 2, 3, 5, 10, 11, 13, 14, 31, 37, 41, 44, 47, 49, 50, 53, 56, 57, 62, 63, 64, 66, 67, 70, 71, 72, 74, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "sum": [1, 2, 3, 27, 32, 33, 37, 47, 49, 57, 63, 64, 66, 69, 74, 90, 91, 92, 98, 99, 101, 102, 107, 108], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 97, 98, 105], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 21, 23, 24, 26, 27, 32, 33, 34, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "true": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 49, 52, 56, 57, 58, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "return": [1, 2, 3, 4, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 62, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 62, 63, 64, 66, 67, 83, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 62, 64, 66, 69, 70, 71, 72, 74, 75, 80, 82, 83, 84, 89, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 74, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 65, 71, 90, 91, 92, 94, 95, 99], "yield": [1, 38, 42], "between": [1, 5, 10, 16, 17, 22, 23, 25, 27, 30, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 52, 53, 54, 55, 60, 62, 63, 66, 69, 71, 72, 74, 75, 78, 82, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "below": [1, 3, 4, 5, 10, 37, 38, 41, 42, 44, 46, 49, 55, 62, 63, 64, 69, 70, 78, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "we": [1, 2, 3, 5, 7, 10, 14, 23, 38, 41, 42, 44, 49, 57, 58, 61, 62, 69, 70, 72, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "loop": [1, 3, 47, 57, 92, 103], "implement": [1, 2, 3, 4, 9, 15, 23, 38, 39, 41, 42, 47, 51, 53, 54, 57, 71, 74, 84, 87, 89, 90, 94, 104, 105], "what": [1, 5, 9, 10, 17, 34, 37, 39, 41, 44, 62, 63, 67, 69, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "doe": [1, 2, 3, 7, 10, 41, 42, 44, 49, 52, 55, 58, 69, 70, 74, 76, 78, 82, 88, 89, 90, 91, 92, 94, 95, 97, 102, 106, 107], "do": [1, 2, 5, 9, 10, 37, 41, 42, 57, 58, 71, 72, 76, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "fast": 1, "explain": [1, 10, 96], "python": [1, 2, 42, 61, 74, 90, 91, 96, 97, 104], "pseudocod": [1, 105], "happen": [1, 10, 44, 64, 95, 101, 107], "n": [1, 2, 3, 5, 7, 37, 38, 41, 42, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 87, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "without": [1, 2, 5, 9, 10, 13, 15, 21, 38, 42, 54, 66, 74, 84, 88, 89, 95, 96, 98, 99, 103, 104], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 46, 48, 55, 56, 57, 61, 62, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107], "distinct": [1, 19, 57, 108], "natur": [1, 10, 101, 104], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 83, 85, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 107, 108], "0": [1, 2, 3, 4, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "count_joint": 1, "len": [1, 2, 3, 7, 37, 41, 47, 56, 57, 58, 71, 72, 74, 87, 88, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "y": [1, 2, 3, 5, 8, 19, 31, 32, 42, 47, 49, 57, 58, 61, 70, 74, 75, 88, 89, 90, 91, 94, 96, 98, 99, 101, 102, 104, 106], "round": [1, 41, 44, 57, 74, 96, 98, 106], "astyp": [1, 101], "int": [1, 2, 3, 4, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 39, 41, 42, 44, 49, 50, 52, 53, 54, 55, 56, 57, 58, 63, 64, 66, 70, 71, 72, 74, 76, 78, 79, 80, 83, 89, 90, 92, 96, 103, 104], "rang": [1, 3, 5, 7, 13, 47, 49, 55, 57, 70, 74, 75, 92, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 14, 17, 23, 37, 41, 44, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "pragma": 1, "cover": [1, 3, 85, 96, 97, 98], "choic": [1, 8, 44, 53, 55, 92, 98, 102, 104], "replac": [1, 56, 61, 72, 87, 88, 90, 91, 92, 95, 96, 97, 98, 101, 104], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 52, 72, 89, 90, 91], "05": [1, 10, 27, 31, 56, 70, 74, 80, 82, 94, 97, 98, 99, 103], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 90, 91, 99, 101, 102], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 90, 91, 92, 96, 98, 99, 101, 102, 107], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 27, 40, 42, 49, 74, 87, 89, 90, 91, 94, 96, 97, 99, 101, 102], "max_it": [1, 88, 89, 95, 104], "10000": [1, 41, 97, 98], "x": [1, 2, 3, 5, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 38, 39, 42, 44, 46, 47, 49, 52, 54, 56, 57, 58, 61, 62, 64, 70, 71, 72, 74, 76, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 104, 106], "diagon": [1, 3, 5, 44, 47, 57], "equal": [1, 3, 10, 13, 52, 64, 69, 79, 105], "creat": [1, 2, 9, 17, 19, 38, 41, 42, 44, 57, 74, 84, 88, 89, 92, 94, 95, 98, 107, 108], "impli": [1, 10, 37, 63, 70], "float": [1, 2, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 40, 41, 42, 44, 46, 48, 49, 55, 56, 57, 62, 63, 64, 66, 69, 70, 74, 78, 82, 89, 90, 91, 99, 101, 102], "entri": [1, 3, 5, 10, 37, 38, 42, 44, 46, 50, 52, 55, 57, 62, 63, 64, 67, 87, 88, 94, 95, 99, 102, 103, 106], "maximum": [1, 10, 71, 79, 83, 107], "minimum": [1, 8, 10, 21, 44, 46, 64, 69, 82], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 17, 27, 38, 42, 44, 52, 69, 74, 90, 98, 99, 101, 103, 104], "default": [1, 2, 3, 4, 5, 7, 10, 11, 15, 17, 29, 31, 34, 37, 38, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 90, 92, 96, 98, 106, 107], "If": [1, 2, 3, 4, 5, 10, 13, 14, 17, 27, 29, 35, 37, 38, 41, 42, 44, 46, 47, 49, 52, 53, 56, 57, 61, 62, 63, 64, 67, 69, 70, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "have": [1, 2, 3, 4, 5, 7, 9, 10, 17, 22, 25, 27, 30, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "all": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 23, 34, 37, 38, 41, 42, 43, 44, 47, 49, 50, 52, 56, 57, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 90, 96], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 61, 62, 63, 65, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 62, 66, 70, 74, 79, 83, 84, 87, 88, 89, 91, 95, 98, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 60, 61, 64, 71, 72, 74, 76, 84, 85, 89, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 61, 62, 63, 64, 66, 69, 70, 71, 72, 74, 78, 79, 80, 82, 88, 91, 92, 95, 96, 97, 98, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 61, 64, 66, 67, 69, 71, 72, 74, 75, 87, 88, 90, 91, 92, 94, 95, 96, 97, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 89, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 61, 62, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 90, 91, 94, 95, 96, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 61, 62, 67, 69, 70, 71, 72, 74, 75, 79, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 60, 61, 62, 64, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 69, 72, 74, 87, 88, 90, 91, 92, 96, 97, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 62, 63, 67, 69, 70, 71, 72, 74, 76, 79, 83, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 61, 62, 71, 74, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 62, 84, 90, 91, 95, 96, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 90, 92, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 90, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 61, 62, 64, 66, 70, 71, 72, 74, 75, 78, 80, 82, 85, 87, 88, 89, 92, 94, 95, 96, 98, 99, 102, 103, 104, 106, 107, 108], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 17, 24, 27, 31, 37, 38, 41, 42, 43, 44, 46, 47, 52, 53, 55, 56, 57, 58, 61, 62, 71, 72, 74, 76, 78, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 101, 102, 103, 104, 105, 106, 107, 108], "guarante": [1, 3, 5, 16, 22, 25, 30, 38, 40, 42, 45, 47, 60, 85], "produc": [1, 2, 5, 9, 10, 17, 49, 62, 72, 74, 76, 78, 84, 87, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108], "higher": [1, 5, 10, 37, 44, 46, 47, 49, 55, 61, 62, 63, 74, 91, 95, 96, 98, 103], "opposit": [1, 108], "occur": [1, 3, 10, 37, 56, 69, 90, 91, 92, 98, 104], "small": [1, 3, 10, 37, 41, 49, 52, 55, 57, 63, 70, 88, 92, 95, 97, 102, 104], "numpi": [1, 3, 4, 5, 7, 10, 13, 19, 32, 33, 41, 42, 43, 49, 52, 55, 56, 58, 61, 66, 69, 74, 75, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "max": [1, 44, 71, 72, 91, 92, 96, 104], "tri": [1, 38, 42, 105], "befor": [1, 2, 3, 38, 42, 55, 57, 71, 74, 79, 87, 88, 95, 96, 98, 99, 101, 104, 106], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 17, 24, 29, 31, 37, 38, 41, 42, 44, 47, 49, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 89, 90, 91, 92, 94, 98, 99, 102, 106, 107], "left": [1, 2, 44, 46, 55, 57, 64, 67, 70, 90, 91, 102, 103, 104, 107], "stochast": 1, "exceed": 1, "m": [1, 5, 38, 42, 48, 49, 52, 53, 62, 67, 69, 70, 71, 90, 91, 97, 101, 102, 103, 108], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 38, 42, 61, 98, 99, 107], "length": [1, 5, 13, 27, 28, 37, 39, 44, 57, 64, 67, 71, 72, 74, 76, 79, 83, 87, 89, 102, 104, 107, 108], "must": [1, 2, 3, 4, 5, 7, 17, 37, 38, 39, 40, 42, 44, 47, 49, 50, 55, 57, 60, 61, 62, 63, 64, 71, 72, 74, 76, 78, 79, 80, 82, 83, 89, 96, 101, 105, 107, 108], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 13, 37, 41, 44, 50, 57, 58, 62, 64, 70, 76, 78, 79, 80, 82, 83, 87, 88, 89, 98, 101, 102, 103, 107, 108], "ball": [1, 97], "bin": [1, 3, 64, 90, 91, 104], "ensur": [1, 2, 10, 38, 42, 52, 54, 55, 57, 58, 61, 69, 72, 74, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 104, 105, 106], "most": [1, 3, 5, 7, 10, 17, 37, 41, 44, 49, 61, 62, 63, 64, 67, 69, 70, 71, 72, 75, 78, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107], "least": [1, 4, 10, 19, 32, 37, 41, 62, 63, 69, 72, 82, 92, 98, 101, 104, 107], "int_arrai": [1, 57], "can": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 102, 103, 104, 105, 106, 107, 108], "model": [2, 3, 4, 5, 9, 10, 11, 17, 19, 31, 33, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 54, 56, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105, 107, 108], "For": [2, 3, 5, 7, 9, 10, 12, 17, 23, 36, 37, 38, 41, 42, 44, 47, 49, 52, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 80, 82, 83, 84, 87, 88, 89, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108], "regular": [2, 3, 41, 61], "multi": [2, 3, 4, 10, 33, 37, 38, 41, 42, 44, 48, 49, 50, 57, 58, 63, 64, 65, 66, 71, 72, 84, 96, 98, 99, 100], "task": [2, 5, 7, 10, 11, 12, 13, 15, 16, 17, 26, 31, 34, 37, 41, 47, 49, 50, 55, 57, 62, 64, 72, 74, 84, 88, 89, 95, 96, 97, 98, 99, 102, 104, 106, 107, 108], "cleanlearn": [2, 3, 10, 24, 31, 38, 57, 61, 73, 74, 75, 84, 85, 87, 88, 106], "wrap": [2, 38, 42, 51, 61, 71, 74, 84, 87, 88, 90, 91, 94, 95, 99, 106], "instanc": [2, 3, 5, 6, 7, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 61, 70, 71, 74, 79, 87, 89, 90, 91, 92, 94, 95, 98, 99, 103], "sklearn": [2, 3, 4, 5, 8, 10, 19, 32, 37, 42, 49, 53, 54, 57, 61, 71, 74, 75, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106], "classifi": [2, 3, 42, 49, 57, 62, 65, 71, 72, 84, 85, 87, 88, 89, 94, 95, 98, 101, 102, 104, 105, 107, 108], "adher": [2, 42, 74], "estim": [2, 3, 4, 5, 9, 14, 23, 37, 41, 42, 44, 47, 57, 62, 63, 64, 69, 71, 74, 76, 78, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 98, 100, 103, 104, 105, 106, 107, 108], "api": [2, 3, 15, 61, 67, 70, 71, 74, 85, 96, 98, 106], "defin": [2, 3, 5, 7, 10, 15, 23, 37, 38, 39, 41, 42, 44, 72, 74, 76, 90, 91, 94, 97, 98, 101, 104, 108], "four": [2, 10, 97, 99, 108], "clf": [2, 3, 5, 49, 74, 84, 87, 94, 96, 98, 99, 102], "fit": [2, 3, 5, 8, 10, 19, 40, 42, 52, 54, 60, 61, 71, 73, 74, 84, 87, 88, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 108], "sample_weight": [2, 42, 74, 99], "predict_proba": [2, 5, 37, 40, 42, 49, 60, 61, 87, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 104], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 17, 23, 24, 26, 29, 31, 33, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 88, 97, 98, 99, 100, 104, 106, 107, 108], "score": [2, 3, 4, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 43, 44, 46, 49, 55, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 104, 106], "data": [2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 49, 50, 52, 53, 54, 57, 60, 61, 62, 63, 64, 65, 69, 71, 72, 73, 74, 79, 80, 81, 82, 83, 85, 88, 92, 93, 100, 105], "e": [2, 3, 5, 10, 13, 23, 33, 37, 38, 41, 42, 44, 47, 49, 50, 52, 57, 58, 62, 63, 64, 65, 67, 70, 71, 72, 74, 76, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106], "featur": [2, 3, 4, 5, 8, 10, 11, 17, 19, 20, 24, 27, 28, 29, 31, 32, 49, 52, 53, 54, 57, 71, 74, 84, 87, 90, 91, 94, 95, 96, 98, 99, 101, 102, 106], "element": [2, 3, 5, 37, 43, 44, 46, 57, 62, 64, 72, 79, 80, 82, 88, 89, 95, 96, 98, 108], "first": [2, 5, 10, 18, 27, 28, 37, 41, 49, 52, 57, 62, 63, 67, 70, 72, 74, 87, 88, 89, 90, 92, 94, 96, 98, 101, 102, 103, 104, 106, 107, 108], "index": [2, 10, 27, 37, 44, 51, 52, 54, 56, 57, 58, 63, 72, 74, 79, 82, 83, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "should": [2, 3, 5, 7, 10, 15, 23, 27, 32, 33, 37, 38, 41, 42, 44, 46, 47, 49, 52, 54, 55, 56, 57, 61, 62, 63, 66, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 107, 108], "correspond": [2, 3, 5, 10, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 37, 38, 41, 42, 43, 44, 46, 47, 49, 52, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "differ": [2, 5, 7, 10, 14, 16, 22, 25, 27, 28, 30, 37, 38, 40, 41, 42, 44, 45, 49, 52, 55, 57, 58, 60, 62, 67, 69, 71, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 104, 105, 106], "sampl": [2, 3, 5, 8, 10, 17, 21, 44, 46, 49, 52, 53, 54, 64, 67, 70, 72, 74, 75, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "size": [2, 10, 32, 38, 41, 42, 44, 49, 52, 53, 64, 69, 70, 74, 76, 78, 88, 92, 94, 98, 99, 101, 102, 103, 105, 107], "here": [2, 5, 7, 10, 15, 41, 44, 47, 61, 62, 63, 64, 66, 67, 70, 71, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "re": [2, 5, 38, 42, 54, 56, 62, 74, 84, 87, 88, 89, 90, 94, 95, 98, 106, 107, 108], "weight": [2, 10, 38, 39, 42, 49, 52, 62, 69, 72, 74, 88, 89, 90, 91, 95], "loss": [2, 39, 61, 72, 74, 92], "while": [2, 3, 10, 38, 41, 42, 48, 49, 57, 74, 84, 92, 96, 98, 99, 101, 102, 106], "train": [2, 3, 4, 5, 9, 10, 17, 19, 33, 38, 39, 40, 42, 49, 57, 61, 62, 67, 70, 71, 74, 75, 85, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 105, 107, 108], "support": [2, 3, 4, 5, 13, 15, 34, 35, 41, 43, 49, 57, 58, 61, 71, 72, 82, 84, 85, 89, 90, 91, 92, 96, 98], "your": [2, 3, 5, 9, 10, 17, 37, 38, 40, 41, 42, 44, 49, 54, 57, 60, 61, 62, 63, 64, 66, 71, 72, 74, 75, 76, 78, 79, 85, 87, 88, 89, 92, 94, 97, 101, 102, 103, 104, 105, 106, 107, 108], "recommend": [2, 5, 7, 10, 14, 17, 41, 44, 62, 90, 91, 92, 96, 98, 105, 106], "furthermor": 2, "correctli": [2, 3, 10, 37, 38, 42, 44, 47, 52, 58, 63, 64, 69, 70, 74, 76, 88, 95, 96, 98, 102, 103, 106, 107], "clonabl": [2, 74], "via": [2, 5, 7, 10, 11, 14, 17, 19, 23, 37, 39, 41, 42, 49, 53, 57, 62, 67, 70, 71, 72, 74, 75, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 102, 103, 104, 105, 106, 107, 108], "base": [2, 3, 4, 5, 7, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 47, 48, 49, 52, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 69, 71, 72, 74, 75, 78, 80, 82, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "clone": [2, 74, 102], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 66, 70, 74, 80, 85, 90, 96, 98, 99, 101, 102, 103, 104, 106, 108], "multipl": [2, 3, 5, 10, 13, 14, 35, 37, 44, 55, 56, 62, 63, 64, 66, 69, 70, 74, 84, 90, 91, 92, 94, 98, 100, 102, 103, 106], "g": [2, 3, 5, 10, 13, 23, 33, 37, 38, 42, 44, 50, 52, 57, 64, 65, 67, 70, 71, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106], "manual": [2, 74, 87, 88, 89, 96, 98, 104, 105, 106, 108], "pytorch": [2, 38, 39, 42, 74, 84, 89, 92, 98, 100, 102, 107], "call": [2, 3, 5, 6, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 49, 57, 61, 71, 74, 88, 89, 90, 91, 95, 98, 99, 102, 104, 105, 106, 107, 108], "__init__": [2, 39, 74, 92], "independ": [2, 3, 10, 63, 74, 95, 96, 105, 106, 108], "compat": [2, 38, 41, 42, 54, 61, 74, 75, 78, 82, 84, 87, 88, 96, 98, 105, 106], "neural": [2, 39, 61, 71, 74, 89, 92, 98, 102, 104, 106], "network": [2, 38, 39, 42, 61, 71, 74, 88, 89, 92, 95, 98, 102, 104, 106], "typic": [2, 10, 38, 42, 54, 71, 74, 87, 88, 89, 91, 92, 94, 95, 104, 105], "initi": [2, 3, 14, 19, 38, 42, 52, 62, 74, 87, 95, 98], "insid": [2, 42, 74, 98, 99], "There": [2, 3, 7, 52, 84, 99, 101], "two": [2, 3, 10, 19, 27, 37, 38, 41, 42, 50, 52, 53, 54, 57, 67, 69, 70, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106, 107, 108], "new": [2, 7, 9, 10, 15, 23, 38, 41, 42, 48, 52, 56, 57, 62, 74, 88, 89, 90, 95, 97, 98, 104, 105, 108], "notion": 2, "confid": [2, 3, 10, 23, 37, 41, 44, 47, 49, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 78, 82, 84, 87, 92, 94, 95, 99, 101, 102, 103, 105, 107, 108], "packag": [2, 5, 7, 9, 10, 12, 16, 36, 40, 44, 45, 57, 60, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "prune": [2, 3, 44, 64, 74, 85, 103], "everyth": [2, 70, 99], "els": [2, 70, 90, 92, 96, 97, 98, 101, 102, 103], "mathemat": [2, 3, 10, 47, 102], "keep": [2, 14, 15, 57, 84, 90, 96, 97, 98, 107], "belong": [2, 3, 10, 37, 44, 46, 47, 52, 63, 64, 65, 66, 71, 72, 76, 80, 82, 83, 91, 92, 99, 102, 104, 107, 108], "2": [2, 3, 4, 5, 7, 10, 11, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 75, 79, 80, 82, 83, 97, 98, 105], "error": [2, 3, 5, 10, 38, 42, 43, 44, 46, 47, 57, 63, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 82, 85, 87, 89, 90, 91, 94, 95, 96, 97, 100], "erron": [2, 3, 37, 44, 47, 57, 63, 64, 72, 74, 75, 76, 104, 106], "import": [2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 41, 43, 49, 52, 55, 56, 62, 66, 69, 74, 75, 80, 82, 83, 84, 87, 88, 94, 95, 96, 98, 102, 103, 104, 106, 107, 108], "linear_model": [2, 5, 37, 57, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logisticregress": [2, 3, 5, 37, 57, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logreg": 2, "cl": [2, 15, 31, 74, 84, 87, 88, 98, 99, 106], "pass": [2, 3, 5, 8, 10, 11, 13, 14, 15, 17, 24, 31, 34, 38, 41, 42, 44, 48, 49, 52, 54, 57, 61, 62, 64, 70, 71, 72, 74, 79, 80, 84, 88, 89, 90, 91, 95, 97, 98, 99, 101, 103, 104, 106], "x_train": [2, 87, 90, 91, 99, 101, 102, 106], "labels_maybe_with_error": 2, "had": [2, 3, 74, 103], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 52, 60, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 88, 93, 100, 101, 105, 106], "pred": [2, 44, 57, 87, 88, 105, 106], "x_test": [2, 87, 90, 91, 99, 102, 106], "might": [2, 5, 10, 52, 62, 74, 79, 87, 88, 90, 91, 92, 96, 98, 103], "case": [2, 3, 10, 14, 37, 49, 52, 62, 74, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 104, 106, 108], "standard": [2, 3, 5, 31, 37, 44, 61, 63, 64, 66, 72, 74, 84, 87, 90, 91, 94, 97, 99, 103], "adapt": [2, 38, 40, 57, 60, 74, 104], "skorch": [2, 74, 84, 98], "kera": [2, 60, 67, 70, 74, 84, 98, 103], "scikera": [2, 61, 74, 98], "open": [2, 41, 96, 97, 103, 108], "doesn": [2, 10, 74, 84], "t": [2, 3, 4, 7, 10, 18, 28, 29, 38, 39, 41, 42, 43, 44, 49, 55, 56, 66, 71, 72, 74, 80, 82, 83, 84, 90, 91, 92, 94, 95, 96, 97, 99, 102, 103, 106, 108], "alreadi": [2, 5, 10, 17, 38, 41, 42, 47, 52, 61, 62, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 103, 104, 106], "exist": [2, 5, 10, 13, 19, 38, 41, 42, 54, 56, 61, 67, 69, 71, 74, 84, 85, 87, 88, 90, 91, 95, 101, 108], "made": [2, 5, 17, 38, 42, 53, 74, 87, 88, 92, 95, 96, 98, 101, 103, 105, 106], "easi": [2, 12, 47, 74, 90, 91, 97, 98, 99, 102], "inherit": [2, 7, 39, 74], "baseestim": [2, 42, 74], "yourmodel": [2, 74], "def": [2, 7, 15, 38, 42, 61, 74, 88, 89, 90, 91, 92, 96, 97, 98, 99, 101, 102, 104, 106, 108], "self": [2, 3, 5, 7, 10, 13, 14, 15, 17, 32, 38, 39, 41, 42, 44, 49, 71, 72, 74, 87, 90, 92, 96, 97, 102, 107, 108], "refer": [2, 10, 17, 38, 42, 43, 63, 64, 66, 67, 69, 70, 71, 74, 78, 79, 90, 91, 92, 94, 95, 96, 98, 99, 102, 105, 106], "origin": [2, 5, 10, 42, 43, 44, 56, 57, 61, 63, 64, 67, 70, 71, 74, 75, 78, 80, 82, 87, 88, 90, 92, 94, 95, 98, 99, 103, 104, 106, 108], "total": [2, 3, 4, 37, 41, 57, 63, 83, 92, 98, 107], "state": [2, 3, 5, 38, 39, 42, 48, 74, 99, 102, 103, 108], "art": [2, 39, 99, 102], "northcutt": [2, 3, 37, 71, 72], "et": [2, 3, 37, 39, 71, 72], "al": [2, 3, 37, 39, 71, 72], "2021": [2, 3, 37, 71, 72], "weak": [2, 70], "supervis": [2, 10, 90, 91, 98, 101], "find": [2, 5, 9, 10, 14, 15, 17, 20, 21, 23, 24, 26, 27, 28, 29, 32, 33, 37, 38, 40, 41, 42, 43, 44, 48, 54, 56, 57, 60, 67, 70, 71, 72, 74, 76, 80, 82, 85, 90, 100, 105], "uncertainti": [2, 10, 46, 71, 74, 98, 104, 106], "It": [2, 3, 5, 7, 10, 13, 14, 17, 23, 28, 31, 33, 34, 35, 38, 42, 44, 47, 49, 52, 53, 55, 62, 69, 70, 74, 84, 90, 91, 92, 98, 99, 102, 105], "work": [2, 3, 7, 10, 13, 31, 37, 38, 41, 42, 44, 47, 56, 57, 58, 61, 62, 72, 74, 84, 85, 88, 90, 91, 96, 97, 104, 106], "includ": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 38, 40, 41, 42, 52, 56, 57, 60, 62, 63, 66, 67, 71, 72, 74, 78, 79, 80, 82, 84, 85, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 108], "deep": [2, 40, 42, 60, 61, 74, 95], "see": [2, 3, 5, 7, 10, 14, 15, 34, 37, 38, 41, 42, 43, 44, 49, 54, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "subfield": 2, "theori": [2, 99], "machin": [2, 4, 5, 9, 10, 15, 17, 34, 40, 55, 60, 74, 87, 88, 90, 91, 96, 97, 101], "across": [2, 3, 5, 7, 10, 14, 23, 37, 41, 49, 63, 70, 71, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 106], "varieti": [2, 87, 88, 98], "like": [2, 3, 5, 6, 7, 10, 15, 33, 37, 38, 41, 42, 44, 47, 57, 61, 62, 63, 66, 67, 69, 72, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "pu": [2, 57], "input": [2, 3, 5, 9, 17, 27, 37, 38, 41, 42, 47, 49, 52, 53, 56, 57, 58, 61, 70, 74, 84, 85, 88, 91, 92, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "discret": [2, 35, 44, 47, 57, 71, 72, 76, 78, 79], "vector": [2, 3, 4, 5, 10, 17, 44, 47, 49, 50, 52, 57, 71, 72, 84, 88, 89, 90, 91, 92, 94, 95, 99, 102, 103, 104, 107, 108], "would": [2, 3, 5, 10, 38, 41, 42, 44, 53, 57, 64, 74, 84, 88, 90, 92, 98, 99, 104, 106, 108], "obtain": [2, 5, 8, 10, 17, 44, 62, 64, 67, 70, 72, 75, 89, 91, 95, 98, 101, 103, 105, 107, 108], "been": [2, 4, 37, 44, 47, 52, 56, 57, 62, 63, 67, 69, 71, 72, 74, 89, 90, 94, 98, 99, 101, 102, 103, 104, 107, 108], "dure": [2, 10, 17, 52, 54, 71, 74, 87, 88, 89, 94, 95, 96, 98, 99, 102, 105, 106, 108], "denot": [2, 3, 47, 49, 57, 64, 71, 72, 82], "tild": 2, "paper": [2, 4, 10, 62, 71, 80, 82, 97, 99, 101, 104, 106, 108], "cv_n_fold": [2, 3, 74, 88], "5": [2, 3, 4, 5, 8, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 42, 44, 46, 48, 49, 57, 62, 63, 66, 67, 70, 74, 75, 82, 88, 90, 95, 97, 98, 102, 103, 104, 105, 107, 108], "converge_latent_estim": [2, 3], "pulearn": [2, 57], "find_label_issues_kwarg": [2, 10, 74, 85, 98, 99], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 64, 80, 98], "clean": [2, 69, 72, 74, 75, 84, 87, 88, 90, 91, 97, 106], "even": [2, 3, 7, 9, 10, 37, 41, 46, 47, 57, 74, 89, 96, 98, 99, 101, 102, 103], "messi": [2, 74, 99], "ridden": [2, 74], "autom": [2, 9, 10, 74, 84, 91, 97, 98], "robust": [2, 47, 52, 74, 91, 96, 98], "prone": [2, 74], "out": [2, 3, 5, 10, 17, 29, 38, 42, 44, 49, 52, 61, 64, 65, 67, 70, 71, 72, 74, 75, 83, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108], "current": [2, 3, 5, 7, 10, 11, 14, 15, 23, 38, 42, 43, 44, 49, 62, 69, 74, 90, 91, 98, 101, 103], "intend": [2, 14, 15, 16, 17, 33, 34, 35, 45, 52, 62, 78, 82, 89, 90, 91, 95, 99], "A": [2, 3, 4, 5, 7, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 66, 69, 70, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 103, 105, 108], "follow": [2, 3, 10, 15, 31, 35, 37, 38, 41, 42, 49, 51, 55, 62, 63, 67, 69, 70, 71, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "experiment": [2, 38, 39, 41, 42, 43, 64, 85, 98], "wrapper": [2, 61, 87, 88, 89, 106], "around": [2, 69, 90, 91, 103, 104, 108], "fasttext": [2, 60], "store": [2, 4, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 71, 74, 87, 88, 94, 95, 96, 97, 98, 107, 108], "along": [2, 49, 64, 82, 90, 91, 92, 96, 98, 104], "dimens": [2, 57, 76, 79, 92, 98, 104, 107], "select": [2, 9, 10, 27, 51, 62, 72, 92, 96, 101, 104], "split": [2, 3, 5, 10, 13, 41, 49, 56, 57, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 102, 105, 108], "cross": [2, 3, 10, 37, 44, 47, 48, 49, 64, 67, 70, 72, 74, 75, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "fold": [2, 3, 37, 44, 47, 74, 87, 89, 94, 97, 98, 103, 107], "By": [2, 37, 63, 64, 74, 90, 96, 107], "need": [2, 3, 10, 11, 37, 38, 41, 42, 44, 52, 54, 63, 64, 66, 71, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 107], "holdout": [2, 3, 74], "comput": [2, 3, 4, 5, 7, 8, 10, 20, 21, 23, 24, 27, 28, 29, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 52, 53, 54, 57, 62, 63, 64, 66, 69, 70, 71, 72, 74, 75, 76, 78, 84, 85, 88, 90, 91, 97, 99, 100, 103, 104, 106, 107], "them": [2, 3, 5, 7, 9, 10, 12, 13, 28, 33, 36, 38, 40, 41, 42, 44, 54, 60, 62, 71, 74, 85, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 107, 108], "numer": [2, 3, 4, 5, 10, 14, 23, 31, 35, 49, 52, 53, 69, 71, 74, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 99, 101, 102, 104, 106], "consist": [2, 3, 38, 42, 51, 57, 62, 96, 107, 108], "latent": [2, 3, 47], "thei": [2, 3, 5, 16, 22, 25, 27, 30, 38, 39, 40, 42, 44, 45, 52, 55, 57, 61, 64, 69, 72, 74, 75, 78, 82, 84, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106, 108], "relat": [2, 3, 10, 14, 20, 21, 27, 28, 29, 32, 47, 57, 63, 74, 91, 95], "close": [2, 3, 10, 41, 47, 71, 89, 90, 91, 92, 94, 95, 96, 98, 99, 103], "form": [2, 3, 10, 38, 39, 42, 47, 56, 57, 72, 74, 98], "equival": [2, 3, 38, 42, 47, 71, 104, 106], "iter": [2, 3, 37, 38, 42, 44, 57, 63, 64, 74, 98, 101, 107], "enforc": [2, 38, 42, 57], "perfectli": [2, 37, 63, 99], "certain": [2, 3, 5, 38, 42, 61, 70, 74, 90, 91, 96, 97, 103, 104], "dict": [2, 3, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 48, 49, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 82, 90, 91, 92, 98, 108], "keyword": [2, 3, 5, 10, 11, 17, 24, 28, 31, 38, 41, 42, 44, 46, 49, 52, 54, 56, 61, 62, 64, 70, 71, 72, 74, 79, 80, 82, 90], "filter": [2, 3, 10, 41, 43, 56, 63, 65, 66, 68, 70, 77, 78, 79, 81, 82, 83, 84, 85, 87, 88, 89, 92, 95, 96, 97, 98, 102, 103, 106, 107, 108], "find_label_issu": [2, 3, 10, 31, 40, 41, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 98, 102, 103, 106, 107, 108], "particularli": [2, 84, 101, 104], "filter_bi": [2, 3, 41, 44, 64, 85, 98], "frac_nois": [2, 44, 64, 80, 98], "min_examples_per_class": [2, 44, 64, 98, 99], "impact": [2, 4, 10, 90, 91, 92, 96], "ml": [2, 4, 5, 9, 10, 16, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 101, 102, 106], "accuraci": [2, 39, 72, 87, 88, 89, 92, 98, 99, 101, 104, 106, 107], "n_job": [2, 41, 44, 64, 76, 78, 80, 98, 104, 107], "disabl": [2, 38, 42, 44, 104], "process": [2, 3, 7, 14, 17, 33, 38, 41, 42, 44, 52, 56, 62, 64, 70, 76, 78, 80, 88, 89, 90, 96, 98, 101, 105], "caus": [2, 44, 49, 90, 91, 96, 98], "rank": [2, 3, 10, 37, 41, 43, 44, 49, 63, 64, 65, 67, 68, 70, 71, 73, 77, 79, 80, 81, 83, 84, 85, 87, 88, 90, 91, 97, 98, 102, 103, 104, 107, 108], "get_label_quality_scor": [2, 40, 41, 43, 44, 45, 49, 62, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77, 78, 80, 81, 82, 85, 98, 99, 102, 103, 107, 108], "adjust_pred_prob": [2, 10, 66, 71, 72, 99], "control": [2, 5, 9, 10, 17, 41, 44, 62, 70, 71, 74, 80, 82, 90, 91, 96, 97, 98], "how": [2, 3, 5, 10, 13, 14, 15, 17, 23, 37, 38, 39, 41, 42, 47, 57, 62, 63, 66, 67, 69, 71, 72, 74, 78, 82, 84, 87, 88, 90, 91, 92, 94, 95, 96, 97, 103, 104, 105, 106, 107], "much": [2, 10, 37, 41, 44, 74, 96, 97, 98, 99, 101, 104], "output": [2, 3, 5, 10, 17, 33, 38, 39, 42, 47, 57, 61, 62, 63, 67, 69, 70, 71, 74, 78, 79, 82, 83, 84, 85, 88, 89, 90, 92, 95, 97, 98, 103, 104, 105, 106], "print": [2, 5, 7, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 57, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "suppress": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79, 107, 108], "statement": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79], "big": [2, 41, 64, 70, 74, 99], "limit": [2, 5, 17, 41, 52, 64, 96, 103, 107, 108], "memori": [2, 38, 41, 42, 64, 70, 76, 78, 90, 107], "label_issues_batch": [2, 40, 64, 98], "find_label_issues_batch": [2, 40, 41, 64, 98], "pred_prob": [2, 3, 5, 8, 10, 11, 17, 24, 26, 27, 29, 32, 33, 37, 41, 43, 44, 46, 47, 48, 49, 50, 57, 58, 62, 63, 64, 66, 67, 70, 71, 72, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106], "threshold": [2, 3, 4, 7, 10, 19, 20, 21, 23, 29, 31, 32, 41, 55, 69, 70, 71, 72, 78, 82, 90, 96, 103, 104, 107, 108], "inverse_noise_matrix": [2, 3, 10, 47, 57, 85, 99], "label_issu": [2, 41, 44, 64, 67, 74, 76, 85, 87, 88, 89, 92, 95, 98, 99, 102, 106], "clf_kwarg": [2, 3, 10, 74], "clf_final_kwarg": [2, 74], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 37, 41, 44, 46, 52, 62, 63, 64, 66, 67, 69, 70, 72, 74, 75, 78, 82, 84, 89, 92, 94, 95, 99, 101, 103, 105, 106], "result": [2, 3, 9, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 41, 42, 44, 46, 55, 57, 64, 66, 67, 70, 72, 74, 75, 76, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 106, 107, 108], "identifi": [2, 3, 5, 7, 9, 10, 13, 17, 28, 34, 37, 41, 43, 44, 52, 64, 67, 70, 72, 74, 75, 76, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 102, 104, 106, 107, 108], "final": [2, 10, 74, 87, 94, 96, 103, 105, 106], "remain": [2, 74, 85, 87, 88, 92, 96, 102, 106, 108], "datasetlik": [2, 57, 74], "beyond": [2, 5, 7, 9, 10, 12, 36, 84, 87, 88, 106, 107], "pd": [2, 3, 5, 7, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 48, 61, 62, 63, 74, 82, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 106, 108], "datafram": [2, 3, 5, 7, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 48, 57, 58, 61, 62, 63, 74, 79, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 106, 107, 108], "scipi": [2, 4, 5, 14, 53, 57, 71, 96], "spars": [2, 4, 5, 10, 14, 17, 19, 32, 52, 57, 58, 94, 96], "csr_matrix": [2, 4, 5, 14, 17, 19, 32, 52, 96], "torch": [2, 38, 39, 42, 88, 89, 92, 95, 97, 104], "util": [2, 5, 10, 17, 34, 38, 39, 42, 45, 52, 61, 62, 67, 70, 74, 84, 85, 89, 90, 91, 92, 98, 99, 104], "tensorflow": [2, 57, 61, 84, 89, 98], "object": [2, 5, 10, 13, 14, 17, 33, 34, 38, 39, 41, 42, 49, 52, 54, 57, 58, 61, 64, 67, 68, 69, 70, 71, 74, 82, 84, 88, 89, 91, 92, 94, 98, 99, 100, 102, 106], "list": [2, 3, 5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 43, 44, 50, 52, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 85, 88, 89, 90, 91, 92, 96, 97, 98, 99, 102, 103, 106, 108], "index_list": 2, "subset": [2, 3, 5, 17, 37, 41, 44, 57, 72, 79, 83, 87, 88, 89, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 108], "wa": [2, 3, 13, 15, 41, 55, 57, 62, 63, 69, 71, 83, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 105, 107, 108], "abl": [2, 3, 10, 74, 89, 98, 99, 101, 102], "format": [2, 3, 5, 10, 13, 33, 38, 41, 42, 44, 47, 48, 49, 50, 52, 57, 58, 61, 62, 63, 64, 67, 70, 71, 72, 74, 76, 78, 79, 82, 83, 87, 90, 91, 92, 94, 96, 97, 101, 106, 107, 108], "make": [2, 3, 5, 19, 38, 41, 42, 49, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "sure": [2, 5, 41, 44, 49, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106], "shuffl": [2, 10, 57, 89, 92, 95, 96, 102, 104], "ha": [2, 3, 5, 6, 10, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 43, 47, 49, 52, 56, 57, 62, 67, 69, 74, 80, 82, 83, 84, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 108], "batch": [2, 41, 57, 61, 62, 76, 78, 92, 98, 104], "order": [2, 5, 10, 35, 37, 38, 42, 43, 44, 47, 48, 49, 55, 57, 62, 63, 64, 67, 70, 71, 72, 76, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 106, 107, 108], "destroi": [2, 57], "oper": [2, 38, 41, 42, 52, 57, 61, 72, 84, 87, 88, 95, 98, 104], "eg": [2, 5, 10, 57, 67, 70, 90, 91, 98], "repeat": [2, 57, 62, 101, 104], "appli": [2, 35, 38, 40, 42, 44, 49, 50, 52, 56, 57, 66, 71, 80, 87, 88, 89, 90, 91, 92, 94, 96, 98, 101, 102, 104, 105, 106, 107], "array_lik": [2, 3, 37, 44, 57, 64, 71, 75], "some": [2, 3, 5, 10, 15, 23, 37, 38, 40, 42, 44, 47, 52, 56, 57, 60, 62, 63, 64, 66, 67, 70, 71, 72, 74, 76, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "seri": [2, 3, 41, 57, 58, 74, 82, 98], "row": [2, 3, 5, 10, 14, 28, 33, 37, 41, 44, 46, 47, 52, 53, 57, 62, 63, 64, 66, 71, 72, 74, 79, 80, 82, 83, 87, 89, 92, 94, 95, 96, 97, 98, 101, 102, 104, 108], "rather": [2, 3, 5, 10, 27, 37, 57, 61, 62, 69, 78, 82, 88, 97, 101, 105, 106, 107, 108], "leav": [2, 44], "per": [2, 3, 5, 7, 10, 14, 37, 41, 44, 49, 56, 62, 63, 64, 66, 69, 70, 72, 75, 76, 78, 82, 91, 98, 103, 108], "determin": [2, 3, 10, 13, 17, 23, 27, 31, 37, 41, 44, 49, 52, 57, 62, 64, 67, 69, 72, 78, 82, 90, 96, 98, 101, 103, 104, 106], "cutoff": [2, 3, 53, 104], "consid": [2, 3, 4, 5, 10, 14, 17, 24, 27, 29, 32, 37, 38, 42, 44, 52, 54, 57, 62, 69, 71, 72, 75, 78, 82, 87, 88, 89, 92, 94, 95, 96, 98, 99, 103, 104, 105, 106, 107], "section": [2, 3, 7, 10, 85, 92, 94, 96, 98, 103], "3": [2, 3, 4, 5, 7, 10, 11, 35, 37, 38, 42, 44, 47, 48, 49, 50, 53, 55, 56, 57, 61, 64, 71, 72, 74, 75, 80, 82, 97, 98, 105], "equat": [2, 3, 47], "advanc": [2, 3, 5, 9, 10, 17, 69, 71, 82, 85, 91, 93, 96, 98, 99], "user": [2, 3, 5, 9, 10, 15, 17, 28, 33, 34, 35, 38, 42, 44, 52, 61, 69, 71, 72, 74, 78, 82, 99], "specifi": [2, 3, 4, 5, 8, 10, 14, 15, 17, 19, 32, 34, 38, 41, 42, 44, 49, 52, 54, 56, 61, 62, 63, 64, 67, 69, 71, 72, 74, 75, 83, 85, 88, 89, 91, 92, 95, 101, 103, 106], "automat": [2, 3, 5, 27, 37, 84, 87, 88, 92, 94, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "greater": [2, 3, 4, 5, 7, 9, 10, 29, 41, 53, 57, 69, 91, 97, 98, 108], "count": [2, 23, 27, 37, 41, 44, 47, 57, 63, 64, 70, 85, 92, 96, 98, 103], "observ": [2, 3, 47, 54, 89, 90, 91, 96, 101, 104, 106], "mislabel": [2, 10, 37, 41, 43, 44, 47, 62, 63, 64, 67, 69, 72, 78, 80, 82, 83, 84, 87, 88, 89, 92, 94, 95, 98, 99, 103, 106], "one": [2, 3, 5, 7, 10, 27, 37, 38, 41, 42, 43, 44, 49, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 104, 105, 106, 108], "get_label_issu": [2, 40, 41, 73, 74, 87, 88, 99, 106], "either": [2, 3, 4, 7, 10, 38, 41, 42, 44, 53, 62, 64, 69, 71, 72, 76, 78, 91, 96, 98, 102, 103], "boolean": [2, 7, 10, 23, 41, 44, 54, 56, 62, 64, 67, 72, 74, 76, 78, 79, 84, 88, 89, 91, 92, 95, 98, 103, 106, 107], "label_issues_mask": [2, 44, 72, 74, 85], "indic": [2, 3, 4, 5, 7, 10, 14, 23, 37, 41, 42, 43, 44, 46, 49, 52, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "its": [2, 5, 7, 9, 10, 17, 38, 41, 42, 44, 52, 54, 55, 56, 64, 67, 70, 71, 72, 74, 76, 80, 82, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108], "return_indices_ranked_bi": [2, 41, 44, 64, 80, 85, 87, 88, 98, 99], "significantli": [2, 10, 92, 96, 99, 101, 105], "reduc": [2, 41, 44, 57, 89, 96, 98], "time": [2, 10, 38, 41, 42, 57, 62, 83, 85, 87, 88, 90, 92, 94, 97, 98, 99, 103, 104, 106, 107, 108], "take": [2, 5, 10, 37, 38, 42, 48, 49, 52, 54, 57, 61, 72, 87, 92, 94, 101, 102, 103, 108], "run": [2, 5, 6, 7, 9, 10, 11, 12, 15, 17, 27, 28, 33, 36, 38, 41, 42, 54, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 108], "skip": [2, 10, 38, 42, 74, 89, 96, 98, 102, 108], "slow": [2, 3], "step": [2, 7, 27, 49, 70, 92, 96, 99, 101, 105], "caution": [2, 5, 98], "previous": [2, 5, 14, 57, 71, 74, 85, 87, 89, 90, 94, 95, 101, 105], "assign": [2, 7, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 38, 42, 48, 49, 57, 74, 87, 90, 92, 94, 96, 98, 106, 107, 108], "individu": [2, 4, 7, 10, 14, 27, 38, 42, 43, 62, 66, 69, 72, 74, 80, 82, 85, 87, 91, 94, 96, 97, 98, 101, 102, 103, 108], "still": [2, 41, 42, 57, 71, 87, 92, 98, 104], "extra": [2, 38, 42, 57, 61, 62, 63, 74, 92, 95, 98, 101, 104], "receiv": [2, 10, 38, 42, 43, 63, 66, 67, 74, 76, 80, 91, 103], "overwritten": [2, 74], "callabl": [2, 3, 4, 10, 27, 38, 42, 49, 52, 53, 54, 56, 61, 66, 98], "x_val": 2, "y_val": 2, "map": [2, 3, 13, 41, 42, 45, 48, 56, 57, 70, 72, 74, 79, 89, 90, 91, 92, 96, 98, 99, 102, 108], "appropri": [2, 10, 17, 35, 53, 64, 72, 90, 94, 102, 103], "earli": [2, 92], "stop": [2, 92], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 23, 37, 57, 71, 87, 90, 92, 94, 96, 102, 106, 108], "f": [2, 7, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106], "ignor": [2, 38, 42, 56, 61, 74, 79, 83, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "allow": [2, 37, 38, 41, 42, 46, 54, 57, 62, 70, 71, 74, 76, 78, 88, 89, 92, 96, 98, 105, 107], "access": [2, 10, 14, 38, 42, 74, 91, 92, 97, 102], "hyperparamet": [2, 66, 71, 92], "purpos": [2, 52, 90, 91, 96, 98, 102, 106], "want": [2, 5, 10, 37, 41, 52, 58, 62, 64, 74, 88, 90, 92, 95, 97, 101, 103, 104, 105, 107, 108], "explicitli": [2, 8, 10, 42, 52, 74], "yourself": [2, 5, 41, 91, 96], "altern": [2, 7, 10, 49, 54, 57, 61, 62, 72, 85, 88, 89, 92, 94, 95, 97, 98, 99, 101, 102, 104, 106], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 27, 31, 38, 41, 42, 44, 52, 57, 61, 62, 64, 71, 72, 74, 78, 79, 82, 83, 84, 87, 88, 90, 91, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 107], "effect": [2, 10, 28, 38, 42, 62, 71, 74, 92, 94, 95, 96, 98, 104], "offer": [2, 5, 9, 10, 88, 89, 90, 91, 95, 98, 99, 102], "after": [2, 3, 5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 62, 74, 88, 90, 92, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107], "attribut": [2, 5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 49, 54, 71, 74, 87, 90, 96], "label_issues_df": [2, 74, 92], "similar": [2, 10, 37, 38, 42, 54, 57, 62, 66, 67, 69, 71, 74, 78, 82, 90, 91, 92, 94, 95, 96, 98, 99, 103, 104, 107], "document": [2, 3, 5, 15, 17, 37, 38, 41, 42, 43, 44, 49, 56, 61, 63, 64, 66, 69, 70, 71, 74, 78, 79, 80, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "descript": [2, 5, 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 37, 43, 57, 67, 74, 90, 91], "were": [2, 3, 5, 10, 37, 42, 52, 63, 69, 82, 87, 89, 94, 98, 99, 101, 103, 105, 107], "present": [2, 3, 5, 10, 13, 14, 21, 37, 57, 71, 79, 84, 92, 96, 98, 104], "actual": [2, 3, 5, 10, 37, 52, 62, 63, 72, 91, 98, 99, 108], "num_class": [2, 37, 41, 57, 61, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 104], "uniqu": [2, 32, 57, 79, 90, 96, 98, 102, 104], "given_label": [2, 5, 11, 26, 31, 37, 47, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107, 108], "normal": [2, 3, 19, 27, 32, 44, 46, 49, 55, 56, 57, 72, 96, 98, 99, 104], "trick": [2, 98], "distribut": [2, 3, 5, 10, 27, 29, 37, 42, 44, 48, 55, 62, 70, 71, 72, 84, 90, 91, 92, 94, 95, 96, 103, 104], "account": [2, 37, 62, 66, 71, 72, 88, 95, 98, 99, 101, 102, 104, 106], "word": [2, 3, 56, 82, 83, 98], "remov": [2, 10, 32, 37, 38, 42, 44, 74, 84, 87, 88, 92, 95, 96, 97, 98, 102, 104, 106], "so": [2, 3, 5, 6, 7, 10, 15, 27, 35, 37, 38, 41, 42, 44, 52, 57, 62, 63, 69, 72, 74, 78, 82, 89, 90, 91, 92, 95, 96, 99, 102, 104, 107], "proportion": [2, 10, 44], "just": [2, 3, 5, 10, 14, 33, 37, 39, 41, 57, 61, 72, 74, 76, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106, 107], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 14, 32, 38, 39, 42, 44, 49, 55, 56, 57, 62, 64, 66, 71, 72, 74, 75, 76, 84, 87, 88, 89, 92, 95, 96, 97, 98, 99, 104, 105, 106], "detect": [2, 5, 7, 9, 14, 15, 17, 19, 23, 29, 43, 52, 55, 65, 67, 68, 69, 70, 71, 72, 73, 74, 77, 81, 84, 87, 88, 90, 93, 97, 100, 102, 106, 107, 108], "arg": [2, 13, 23, 28, 32, 38, 39, 42, 49, 57, 72, 74], "kwarg": [2, 7, 10, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 43, 49, 52, 61, 70, 74, 76, 78, 79, 80, 98], "test": [2, 5, 10, 27, 42, 49, 52, 61, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 105, 106, 108], "expect": [2, 3, 10, 38, 42, 44, 49, 52, 62, 71, 72, 74, 87, 88, 96, 98, 99, 101, 102, 103, 106, 108], "class_predict": 2, "evalu": [2, 10, 38, 39, 40, 41, 42, 70, 74, 87, 88, 89, 90, 91, 92, 98, 99, 101, 105, 106, 107], "simpli": [2, 10, 37, 72, 88, 90, 91, 94, 95, 98, 99, 102, 106, 107, 108], "quantifi": [2, 4, 5, 7, 10, 14, 44, 66, 71, 74, 84, 91, 92, 94, 95, 96, 99, 103], "save_spac": [2, 10, 73, 74], "potenti": [2, 10, 37, 44, 56, 64, 67, 70, 72, 74, 76, 78, 83, 85, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "cach": [2, 88, 95], "panda": [2, 5, 7, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 57, 58, 61, 62, 63, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 106, 107], "unlik": [2, 10, 44, 46, 49, 61, 63, 64, 66, 82, 90, 101, 102, 104, 106], "both": [2, 5, 10, 17, 27, 37, 38, 42, 44, 52, 57, 62, 64, 72, 76, 78, 83, 84, 90, 92, 98, 99, 101, 108], "mask": [2, 41, 44, 56, 57, 64, 67, 72, 74, 76, 78, 79, 84, 97, 98, 101, 103, 107, 108], "prefer": [2, 72, 80, 102], "plan": 2, "subsequ": [2, 3, 38, 42, 54, 88, 95, 98, 99, 103], "invok": [2, 38, 42, 99, 105], "scratch": [2, 52, 74], "To": [2, 5, 7, 9, 10, 12, 14, 17, 27, 36, 38, 41, 42, 43, 44, 61, 62, 64, 66, 70, 71, 72, 74, 75, 76, 78, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "share": [2, 10, 72, 74], "mostli": [2, 57, 69, 74, 102, 106], "longer": [2, 35, 48, 49, 56, 74, 85, 88, 95, 98, 103], "info": [2, 5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 74, 82, 91, 96, 97, 108], "about": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 39, 41, 46, 62, 63, 66, 70, 74, 79, 82, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 104], "docstr": [2, 37, 38, 42, 57, 74, 97, 99], "unless": [2, 38, 42, 52, 74, 98], "our": [2, 3, 10, 61, 62, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "is_label_issu": [2, 11, 31, 74, 88, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "entir": [2, 10, 27, 41, 44, 47, 63, 64, 69, 72, 74, 76, 78, 79, 84, 90, 91, 96, 98, 103, 104, 105, 107, 108], "accur": [2, 3, 5, 9, 10, 17, 37, 41, 44, 53, 62, 63, 64, 67, 70, 72, 74, 75, 76, 78, 79, 85, 91, 92, 94, 95, 96, 98, 101, 106], "label_qu": [2, 62, 74, 88, 99, 101, 106], "measur": [2, 5, 37, 62, 63, 74, 84, 87, 96, 97, 98, 99, 101, 102, 106, 107, 108], "qualiti": [2, 3, 5, 7, 9, 10, 14, 31, 32, 37, 41, 43, 44, 46, 49, 62, 63, 64, 66, 67, 69, 72, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 106], "lower": [2, 4, 5, 7, 10, 14, 29, 41, 49, 55, 62, 63, 66, 69, 70, 72, 74, 75, 78, 82, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "eas": 2, "comparison": [2, 38, 42, 70, 99, 101], "against": [2, 38, 42, 90, 94, 96, 98, 101, 102], "predicted_label": [2, 5, 11, 26, 31, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107], "ad": [2, 38, 42, 91, 101, 106], "precis": [2, 53, 55, 64, 67, 70, 96, 97, 98, 99, 107, 108], "definit": [2, 7, 35, 49, 74, 87, 94], "accessor": [2, 74], "describ": [2, 10, 19, 62, 71, 72, 74, 80, 82, 99, 101, 102, 103, 105, 108], "precomput": [2, 4, 5, 47, 52, 74, 97], "clear": [2, 38, 42, 54, 74, 88, 95, 106], "save": [2, 5, 17, 38, 41, 42, 70, 74, 96, 98, 103, 107, 108], "space": [2, 5, 10, 71, 74, 92, 94, 96, 97], "place": [2, 38, 42, 52, 57, 74, 87, 101], "larg": [2, 9, 10, 41, 52, 74, 92, 94, 95, 98, 103, 104, 107, 108], "deploi": [2, 9, 10, 74, 92, 94, 95, 98], "care": [2, 10, 38, 42, 52, 74, 95, 96, 98, 99], "avail": [2, 4, 5, 7, 10, 13, 15, 34, 42, 54, 74, 98, 99, 101, 103, 106], "cannot": [2, 5, 13, 15, 57, 105, 108], "anymor": 2, "classmethod": [2, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 42, 49, 74], "__init_subclass__": [2, 40, 42, 73, 74], "set_": [2, 42, 74], "_request": [2, 42, 74], "pep": [2, 42, 74], "487": [2, 42, 74], "look": [2, 5, 7, 10, 17, 38, 42, 57, 74, 79, 87, 90, 91, 94, 95, 98, 99, 101, 102, 103, 104, 107, 108], "inform": [2, 5, 7, 10, 14, 17, 34, 38, 42, 54, 57, 62, 63, 67, 70, 74, 79, 82, 83, 84, 89, 90, 94, 95, 96, 97, 99, 101, 104, 107, 108], "__metadata_request__": [2, 42, 74], "infer": [2, 42, 57, 74, 79, 83, 87, 88, 92, 101, 102], "signatur": [2, 38, 42, 74], "accept": [2, 38, 42, 54, 55, 72, 74, 90, 91, 98], "metadata": [2, 10, 42, 74, 92, 94, 95, 108], "through": [2, 5, 7, 42, 74, 88, 89, 91, 95, 96, 97, 98, 101, 103, 104], "develop": [2, 9, 42, 54, 74, 98, 99, 108], "request": [2, 42, 74, 87, 88, 91, 95, 96, 97, 102, 108], "those": [2, 3, 4, 10, 41, 42, 44, 51, 61, 62, 64, 70, 74, 78, 82, 83, 84, 89, 92, 96, 98, 103, 107], "http": [2, 4, 5, 7, 9, 10, 12, 19, 36, 38, 39, 41, 42, 46, 54, 57, 67, 70, 71, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "www": [2, 42, 74, 96, 104], "org": [2, 4, 19, 38, 39, 42, 54, 57, 71, 74, 98, 99, 108], "dev": [2, 42, 74], "0487": [2, 42, 74], "get_metadata_rout": [2, 40, 42, 73, 74], "rout": [2, 42, 74], "pleas": [2, 38, 42, 61, 74, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 104, 106, 108], "guid": [2, 7, 10, 42, 74, 85, 89, 90, 91, 92, 93, 94, 95, 96, 99], "mechan": [2, 38, 42, 74], "metadatarequest": [2, 42, 74], "encapsul": [2, 17, 42, 69, 74], "get_param": [2, 40, 42, 60, 61, 73, 74], "subobject": [2, 42, 74], "param": [2, 10, 38, 42, 61, 71, 74, 98], "name": [2, 5, 6, 7, 10, 11, 13, 14, 33, 35, 37, 38, 42, 48, 49, 53, 57, 61, 62, 63, 70, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 102, 106, 107, 108], "set_fit_request": [2, 40, 42, 73, 74], "str": [2, 3, 4, 5, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 47, 49, 52, 53, 54, 55, 56, 57, 61, 62, 63, 67, 69, 70, 72, 74, 79, 83, 89, 90, 98, 101, 102, 103, 108], "unchang": [2, 38, 42, 74, 108], "relev": [2, 17, 27, 42, 74, 92, 94, 96], "enable_metadata_rout": [2, 42, 74], "set_config": [2, 42, 74], "meta": [2, 42, 74], "rais": [2, 4, 5, 13, 14, 35, 38, 42, 46, 49, 52, 55, 74, 98], "alia": [2, 38, 42, 74], "metadata_rout": [2, 42, 74], "retain": [2, 42, 57, 74], "chang": [2, 33, 35, 38, 41, 42, 46, 74, 82, 87, 88, 89, 90, 95, 98, 103, 104, 108], "version": [2, 4, 5, 7, 9, 10, 12, 16, 22, 25, 30, 36, 38, 40, 42, 45, 46, 57, 60, 61, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 108], "sub": [2, 42, 69, 74], "pipelin": [2, 42, 74, 106], "otherwis": [2, 4, 7, 10, 35, 37, 38, 41, 42, 44, 50, 53, 55, 56, 57, 64, 74, 76, 78, 79, 83, 88, 95, 98], "updat": [2, 14, 38, 41, 42, 52, 61, 74, 85, 90, 92], "set_param": [2, 40, 42, 60, 61, 73, 74], "simpl": [2, 38, 42, 44, 62, 72, 74, 87, 88, 90, 91, 92, 94, 95, 101, 104, 106], "well": [2, 3, 9, 10, 38, 42, 46, 47, 62, 64, 70, 72, 74, 79, 82, 83, 85, 90, 91, 92, 94, 95, 98, 99, 101, 103, 104], "nest": [2, 38, 42, 43, 58, 74, 80, 82, 83, 108], "latter": [2, 38, 42, 74, 104], "compon": [2, 42, 74], "__": [2, 42, 74], "set_score_request": [2, 73, 74], "structur": [3, 71, 94, 96, 98], "unobserv": 3, "less": [3, 4, 5, 10, 32, 41, 49, 62, 71, 72, 76, 78, 82, 92, 94, 96, 97, 98, 99, 103, 108], "channel": [3, 89, 99], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 37, 47, 57, 63, 88, 91, 97], "inv": 3, "confident_joint": [3, 23, 37, 44, 57, 63, 64, 85, 98, 99], "un": 3, "under": [3, 10, 38, 42, 63, 70, 71, 91, 96, 104], "joint": [3, 37, 44, 47, 57, 63, 64, 97], "num_label_issu": [3, 41, 44, 64, 79, 83, 85], "estimation_method": [3, 41], "off_diagon": 3, "multi_label": [3, 37, 44, 57, 58, 64, 102], "don": [3, 84, 91, 92, 94, 95, 99, 103, 106], "statis": 3, "compute_confident_joint": [3, 37, 44, 57, 64, 99], "off": [3, 44, 57, 69, 92, 96, 99, 103, 104], "j": [3, 5, 37, 38, 42, 43, 44, 64, 67, 70, 71, 80, 82, 83, 90, 91, 99, 107, 108], "confident_learn": [3, 44, 64, 99], "off_diagonal_calibr": 3, "calibr": [3, 4, 44, 57, 62, 101], "cj": [3, 47, 57], "axi": [3, 32, 47, 49, 55, 76, 79, 89, 90, 91, 92, 96, 98, 99, 101, 102, 104, 106, 107], "bincount": [3, 90, 91, 99, 101, 102], "alwai": [3, 10, 38, 42, 57, 87, 88, 89, 99, 106], "estimate_issu": 3, "over": [3, 5, 10, 38, 41, 42, 69, 70, 76, 78, 87, 91, 92, 94, 96, 97, 98, 99, 104, 106], "As": [3, 7, 84, 90, 91, 95, 99, 106, 108], "add": [3, 5, 7, 13, 14, 38, 42, 61, 70, 88, 89, 90, 91, 92, 95, 96, 98, 99, 102], "approach": [3, 37, 41, 44, 61, 87, 94, 96, 99, 102, 104, 106], "custom": [3, 7, 10, 12, 31, 38, 41, 42, 49, 56, 72, 88, 91, 95, 96, 99, 106], "know": [3, 10, 90, 91, 92, 94, 95, 98, 99, 101, 106], "cut": [3, 69, 84, 99], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 33, 103, 104, 108], "underestim": 3, "few": [3, 9, 10, 70, 84, 96, 98, 101, 102, 103, 104, 108], "4": [3, 4, 5, 10, 11, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 48, 49, 56, 66, 67, 69, 70, 72, 75, 82, 97, 98, 102, 107, 108], "detail": [3, 4, 5, 10, 15, 17, 34, 37, 38, 42, 43, 49, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 78, 79, 80, 84, 85, 89, 98, 102, 104, 108], "num_issu": [3, 7, 41, 89, 90, 91, 92, 94, 95, 96, 99], "calibrate_confident_joint": 3, "up": [3, 7, 10, 18, 27, 28, 31, 44, 49, 51, 61, 62, 88, 97, 98, 103, 106, 108], "p_": [3, 37, 44], "pair": [3, 5, 10, 37, 44, 99], "v": [3, 10, 41, 63, 64, 66, 72, 90, 91, 102, 103, 104, 105], "rest": [3, 5, 7, 9, 10, 12, 36, 63, 64, 66, 74, 87, 88, 90, 91, 92, 94, 95, 98, 99, 101, 106], "fashion": [3, 5, 76, 87], "2x2": 3, "incorrectli": [3, 37, 63, 64, 67, 94, 108], "calibrated_cj": 3, "c": [3, 10, 55, 56, 64, 72, 84, 87, 89, 90, 91, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106], "whose": [3, 4, 5, 10, 29, 38, 42, 47, 52, 56, 62, 66, 69, 75, 78, 82, 83, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 104, 107, 108], "truli": [3, 104, 107], "estimate_joint": [3, 37, 99], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 64, 70, 99, 103, 105, 107, 108], "return_indices_of_off_diagon": 3, "frequenc": [3, 27, 62, 63, 70, 79, 103, 104], "done": [3, 10, 61, 74, 90, 98, 99, 102, 104, 105], "overfit": [3, 10, 67, 70, 87, 89, 90, 91, 92, 94, 95, 105], "classifict": 3, "singl": [3, 5, 9, 10, 13, 27, 37, 38, 42, 43, 49, 50, 57, 62, 63, 69, 70, 71, 72, 82, 87, 89, 90, 96, 98, 99, 102, 103], "baselin": [3, 38, 44, 88, 104, 106], "proxi": 3, "union": [3, 5, 13, 27, 49, 52, 53, 54, 57, 58, 64, 70, 74, 82, 98], "tupl": [3, 32, 38, 42, 43, 47, 48, 50, 52, 56, 57, 62, 64, 70, 78, 80, 82, 83, 89, 108], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 41, 47, 52, 53, 62, 71, 76, 78, 84, 88, 92, 96, 98, 107], "practic": [3, 87, 88, 91, 92, 99, 104, 106], "complet": [3, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "gist": 3, "cj_ish": 3, "guess": [3, 47, 99, 101], "8": [3, 5, 7, 8, 48, 49, 50, 56, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "parallel": [3, 44, 70, 80, 97], "again": [3, 61, 87, 98, 104], "simplifi": [3, 15, 98], "understand": [3, 9, 10, 37, 63, 70, 91, 96, 99, 100, 106, 107, 108], "100": [3, 4, 38, 42, 52, 53, 55, 71, 72, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 102, 103, 104, 108], "optim": [3, 38, 39, 42, 61, 92, 96, 101], "speed": [3, 44, 88, 97, 98, 106], "dtype": [3, 24, 26, 27, 32, 38, 42, 56, 57, 66, 82, 89, 96, 103], "enumer": [3, 38, 42, 89, 90, 91, 92, 96, 108], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 42, 49, 57, 82, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "num_confident_bin": 3, "argmax": [3, 44, 72, 76, 79, 89, 96, 98, 99, 103, 104, 107], "elif": 3, "estimate_lat": 3, "py_method": [3, 47], "cnt": [3, 47], "1d": [3, 5, 13, 17, 33, 41, 44, 49, 50, 52, 57, 58, 66, 75, 87, 89, 96], "eqn": [3, 47], "margin": [3, 44, 47, 49, 72], "marginal_p": [3, 47], "shorthand": [3, 14], "proport": [3, 10, 37, 63, 99, 105], "poorli": [3, 47, 87, 96], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 99], "variabl": [3, 7, 15, 28, 57, 74, 75, 89, 90, 94, 99, 102, 106], "exact": [3, 10, 47, 52, 87, 90, 91, 92, 94, 96], "within": [3, 4, 5, 10, 16, 33, 38, 39, 42, 43, 45, 64, 69, 78, 80, 82, 90, 91, 92, 98, 103, 107], "percent": 3, "often": [3, 37, 47, 63, 98, 99, 105, 107], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 57, 58, 70, 87, 88, 89, 90, 92, 94, 95, 98, 102, 103, 104, 106], "wai": [3, 5, 10, 52, 61, 84, 85, 87, 88, 89, 90, 91, 94, 95, 98, 99, 101, 102, 103, 105], "pro": 3, "con": 3, "pred_proba": [3, 105], "combin": [3, 37, 90, 92, 96, 97, 98, 99, 105, 106], "becaus": [3, 47, 53, 57, 69, 95, 96, 98, 99, 101, 103], "littl": [3, 41, 96, 97, 103, 108], "uniform": [3, 72, 97, 98, 99], "20": [3, 7, 43, 83, 89, 92, 95, 96, 97, 98, 99, 103, 106, 107, 108], "Such": [3, 92, 104], "bound": [3, 24, 26, 38, 42, 56, 66, 67, 69, 70, 103], "reason": [3, 23, 38, 42, 53, 71], "comment": [3, 56, 96, 108], "end": [3, 5, 38, 42, 54, 70], "file": [3, 5, 13, 40, 41, 60, 70, 87, 89, 90, 94, 95, 97, 98, 103, 104, 107, 108], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 99], "handl": [3, 5, 7, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 52, 53, 54, 85, 87, 88, 90, 91, 92, 94, 95, 96, 99, 107, 108], "five": [3, 67, 70, 99, 103], "estimate_cv_predicted_prob": [3, 99], "estimate_noise_matric": 3, "get_confident_threshold": [3, 40, 41], "amongst": [3, 10, 103], "confident_threshold": [3, 10, 23, 24, 41, 71], "point": [4, 5, 7, 9, 10, 19, 27, 38, 42, 52, 54, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101], "valuat": [4, 9, 19], "help": [4, 37, 38, 42, 70, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 108], "u": [4, 87, 88, 89, 90, 92, 94, 96, 98, 99, 101, 102, 105, 106, 107, 108], "assess": [4, 10, 96, 103], "contribut": [4, 10, 19, 96, 103], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 17, 19, 20, 27, 29, 32, 45, 51, 94, 96], "metric": [4, 5, 10, 19, 20, 22, 27, 29, 32, 45, 51, 52, 54, 55, 57, 61, 70, 71, 87, 88, 89, 92, 94, 95, 96, 99, 106], "10": [4, 10, 19, 20, 24, 27, 29, 32, 38, 39, 52, 70, 71, 72, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "shaplei": [4, 10, 19], "nearest": [4, 5, 10, 17, 24, 27, 29, 51, 52, 53, 54, 55, 71, 91, 95, 96, 104], "neighbor": [4, 5, 10, 17, 19, 24, 27, 29, 45, 52, 53, 54, 55, 71, 90, 91, 92, 94, 95, 96, 98, 104], "knn": [4, 10, 14, 19, 27, 29, 32, 51, 52, 53, 54, 55, 71, 94, 104], "graph": [4, 5, 10, 14, 17, 19, 27, 32, 51, 52], "calcul": [4, 10, 19, 27, 41, 49, 51, 52, 55, 62, 66, 67, 69, 70, 71, 74, 78, 92, 96, 97], "directli": [4, 5, 10, 15, 17, 34, 35, 41, 54, 61, 62, 88, 91, 95, 96, 98, 102, 103, 106], "lowest": [4, 10, 62, 70, 91, 92, 94, 96, 98, 101, 102, 103, 107], "fall": [4, 10, 69, 78, 82, 99, 104], "flag": [4, 10, 23, 27, 44, 49, 63, 64, 67, 74, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 104, 106, 107], "approxim": [4, 10, 19, 41, 54, 71, 96, 101], "top": [4, 5, 10, 37, 41, 43, 44, 57, 64, 67, 70, 72, 79, 83, 84, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 106, 108], "found": [4, 5, 7, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 102, 104, 106, 108], "arxiv": [4, 19, 99], "ab": [4, 19, 99, 103], "1908": 4, "08619": 4, "1911": [4, 19], "07128": [4, 19], "embed": [4, 5, 10, 17, 71, 84, 88, 89, 90, 91, 94, 95, 96, 99, 102, 106], "represent": [4, 5, 10, 17, 35, 38, 42, 50, 52, 64, 84, 88, 89, 90, 91, 92, 95, 98, 99, 104], "suppli": [4, 102, 103, 106], "2d": [4, 5, 17, 33, 41, 49, 50, 52, 56, 57, 62, 87, 89, 96, 102], "num_exampl": [4, 5, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 63, 89, 90, 91, 92, 94, 95, 99], "num_featur": [4, 5, 17, 38, 42, 61], "distanc": [4, 5, 10, 17, 19, 27, 29, 32, 51, 52, 53, 54, 55, 69, 71, 94, 96, 104], "construct": [4, 5, 7, 10, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 51, 52, 54, 61, 96], "nearestneighbor": [4, 5, 10, 19, 52, 54, 71, 94, 104], "cosin": [4, 10, 52, 53, 55, 71, 96, 104], "dim": [4, 71, 92, 107], "euclidean": [4, 5, 10, 52, 53, 55, 69, 71, 94], "dimension": [4, 27, 53, 57, 89, 99, 104], "scikit": [4, 42, 53, 54, 57, 71, 84, 87, 88, 89, 90, 91, 94, 95, 96, 98, 106], "fewer": [4, 10, 44, 57, 71, 96, 103], "stabl": [4, 16, 22, 25, 30, 40, 45, 54, 57, 60, 71, 85, 89, 90, 91, 92, 94, 95, 99], "exce": [4, 52, 92, 96], "transform": [4, 10, 33, 49, 52, 55, 57, 71, 72, 87, 88, 91, 92, 95, 104, 108], "rel": [4, 10, 37, 52, 62, 63, 71, 90, 91, 92, 94, 95, 99, 104], "adjust": [4, 39, 44, 52, 66, 71, 72, 84, 96, 99], "closer": [4, 10, 69, 103], "highli": [4, 91, 92], "influenti": 4, "posit": [4, 5, 10, 38, 42, 55, 57, 70, 96, 97, 104], "convers": 4, "neg": [4, 10, 69, 70, 90, 91, 96, 97], "valueerror": [4, 5, 13, 14, 35, 46, 49, 52, 55, 98], "neither": [4, 5, 10, 15, 53, 103], "nor": [4, 5, 10, 15], "larger": [4, 19, 53, 74, 76, 78, 92, 95, 97, 98], "55": [4, 56, 96, 97, 103, 106], "525": 4, "unifi": 5, "audit": [5, 9, 13, 14, 17, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 106], "kind": [5, 6, 7, 10, 96, 97], "addit": [5, 7, 9, 12, 14, 34, 36, 38, 42, 49, 52, 54, 58, 62, 70, 79, 80, 87, 88, 89, 90, 94, 95, 96, 99, 101, 104, 105], "depend": [5, 7, 9, 12, 13, 14, 36, 40, 44, 46, 57, 60, 64, 71, 74, 75, 84, 96], "instal": [5, 7, 9, 12, 36, 38, 40, 41, 42, 44, 60, 61, 76, 78], "pip": [5, 7, 9, 12, 36, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "development": [5, 7, 9, 12, 36], "git": [5, 7, 9, 12, 36, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "github": [5, 7, 9, 12, 36, 38, 39, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "com": [5, 7, 9, 12, 36, 38, 39, 41, 46, 57, 71, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "egg": [5, 7, 9, 12, 36, 84, 97], "label_nam": [5, 7, 8, 10, 11, 13, 19, 32, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "image_kei": [5, 10, 92, 96], "interfac": [5, 9, 10, 54, 84, 98, 99], "librari": [5, 10, 42, 54, 67, 70, 71, 84, 88, 90, 95, 96, 97, 98], "goal": [5, 106], "track": [5, 7, 14, 15, 84, 90, 97, 98, 99], "intermedi": [5, 9, 91], "statist": [5, 10, 14, 23, 27, 37, 62, 63, 70, 91, 94, 95, 96, 99], "convert": [5, 10, 13, 35, 38, 42, 50, 55, 58, 62, 69, 78, 82, 85, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103], "hug": [5, 10, 13, 92], "face": [5, 10, 13, 17, 92, 97, 102], "kei": [5, 7, 10, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 49, 62, 63, 69, 71, 90, 91, 92, 95, 98, 99, 101, 103], "string": [5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 42, 53, 57, 62, 63, 75, 79, 82, 83, 88, 94, 95, 96, 98, 101, 102, 108], "dictionari": [5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 48, 57, 62, 63, 66, 67, 69, 70, 90, 91, 94, 95, 96, 99, 101, 102, 103], "path": [5, 13, 38, 41, 42, 70, 89, 90, 98, 103], "local": [5, 7, 10, 13, 38, 39, 42, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "text": [5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 43, 49, 71, 80, 82, 83, 84, 86, 90, 91, 93, 97, 98, 99, 100, 101, 104], "txt": [5, 13, 108], "csv": [5, 13, 87, 88, 94, 95, 106], "json": [5, 13], "hub": [5, 13], "multiclass": [5, 13, 16, 49, 57, 62, 102], "regress": [5, 7, 10, 11, 13, 15, 17, 22, 31, 33, 35, 88, 90, 91, 95, 100, 101, 104], "multilabel": [5, 10, 11, 13, 15, 16, 22, 26, 33, 35, 50, 102], "imag": [5, 9, 37, 42, 67, 69, 70, 71, 76, 78, 79, 84, 90, 91, 93, 97, 98, 100, 101, 102, 103, 105, 107], "field": [5, 10, 38, 42], "themselv": [5, 87, 88, 96, 106], "pil": [5, 92, 96], "cleanvis": [5, 10, 96], "level": [5, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 52, 56, 80, 82, 91, 92, 98, 100, 102, 107], "load_dataset": [5, 13, 92], "glue": 5, "sst2": 5, "properti": [5, 13, 14, 35, 38, 42], "has_label": [5, 13], "class_nam": [5, 13, 21, 37, 43, 63, 70, 79, 83, 84, 97, 99, 103, 107, 108], "empti": [5, 13, 47, 62, 91, 96, 98, 102], "find_issu": [5, 6, 7, 8, 10, 11, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_typ": [5, 6, 7, 8, 10, 11, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "sort": [5, 17, 41, 44, 49, 62, 64, 67, 69, 70, 72, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 103, 106, 107, 108], "common": [5, 10, 14, 17, 91, 93, 96, 97, 98, 99, 102, 103, 107], "real": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "world": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "interact": [5, 17, 95, 98], "thereof": [5, 17], "insight": [5, 17, 70, 101], "best": [5, 9, 10, 17, 48, 62, 72, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 108], "properli": [5, 10, 41, 48, 52, 57, 58, 76, 89, 90, 91, 92, 94, 95, 98, 99, 102, 104, 106, 107], "respect": [5, 38, 42, 67, 70, 89, 90, 91, 92, 94, 95, 99, 102, 103], "lexicograph": [5, 48, 57, 89, 90, 91, 92, 94, 95, 99, 102], "squar": [5, 57, 74, 97, 106], "csr": [5, 52, 96], "evenli": 5, "omit": [5, 69, 70, 92, 96, 103], "itself": [5, 33, 38, 42, 52, 96, 103], "three": [5, 10, 37, 62, 63, 74, 79, 87, 89, 90, 91, 94, 97, 99, 101, 105, 106, 107, 108], "indptr": [5, 96], "wise": 5, "start": [5, 7, 10, 35, 38, 39, 42, 49, 84, 102, 108], "th": [5, 10, 43, 48, 56, 57, 62, 64, 67, 69, 70, 71, 80, 82, 83, 95, 102, 103, 108], "ascend": [5, 37, 63, 92, 99], "segment": [5, 76, 78, 79, 100], "reflect": [5, 10, 52, 87, 88, 94, 95, 101, 103, 104, 106], "maintain": [5, 61], "kneighbors_graph": [5, 19, 54, 94], "illustr": [5, 96], "todens": 5, "second": [5, 49, 57, 70, 72, 90, 94, 98, 99, 108], "duplic": [5, 9, 22, 23, 38, 42, 52, 84, 90, 96, 99, 106], "explicit": 5, "precend": 5, "collect": [5, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 62, 96, 98, 101, 108], "unspecifi": [5, 17, 44, 64], "interest": [5, 17, 23, 79, 83, 87, 88, 95, 96, 99, 106, 107, 108], "constructor": [5, 10, 11, 17, 24, 31, 52, 54], "issuemanag": [5, 9, 14, 15, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 34], "respons": [5, 17, 23, 54, 74, 75, 97, 106, 108], "random_st": [5, 87, 89, 90, 91, 92, 96, 99, 102, 104], "lab": [5, 6, 8, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 41, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106], "comprehens": [5, 84, 92, 102, 106], "nbr": 5, "n_neighbor": [5, 10, 19, 52, 54, 71, 96], "mode": [5, 12, 19, 38, 41, 42, 104], "4x4": 5, "float64": [5, 27, 38, 42, 82], "compress": [5, 10, 52, 57, 76, 78, 96], "toarrai": [5, 52, 96], "NOT": [5, 41, 95], "23606798": 5, "41421356": [5, 52], "configur": [5, 17, 49, 91], "suppos": [5, 10, 67, 87, 88, 104, 106], "who": [5, 69, 87, 94, 96, 99, 108], "manag": [5, 8, 9, 10, 14, 15, 16, 17, 18, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 61, 90, 98], "clean_learning_kwarg": [5, 10, 11, 24, 31, 98, 106], "labelissuemanag": [5, 10, 15, 22, 24], "prune_method": [5, 85], "prune_by_noise_r": [5, 44, 64, 99], "report": [5, 7, 12, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 83, 84, 89, 90, 91, 94, 95, 98, 99, 102, 106, 108], "include_descript": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34], "show_summary_scor": [5, 34], "show_all_issu": [5, 34], "summari": [5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 43, 60, 61, 63, 68, 77, 78, 80, 81, 82, 85, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 106, 107, 108], "show": [5, 7, 27, 38, 42, 48, 57, 70, 79, 83, 87, 91, 92, 94, 95, 96, 97, 98, 99, 101, 104, 106, 107, 108], "suffer": [5, 10, 14, 23, 64, 72, 83, 96, 108], "onc": [5, 23, 37, 38, 42, 87, 90, 98, 99, 102, 103], "familiar": [5, 96], "overal": [5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 43, 49, 62, 63, 66, 69, 70, 74, 78, 79, 80, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 103, 108], "sever": [5, 7, 10, 13, 14, 23, 38, 41, 42, 44, 66, 69, 71, 72, 78, 82, 84, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 108], "compar": [5, 62, 71, 82, 90, 91, 94, 96, 99, 103], "issue_summari": [5, 7, 10, 14, 96], "With": [5, 9, 10, 41, 88, 95, 98, 99, 101, 106, 107, 108], "usag": [5, 41, 61], "usual": [5, 13, 33, 34, 92, 101, 106], "ti": [5, 62], "exhibit": [5, 7, 10, 14, 79, 89, 90, 91, 92, 94, 95, 99, 103], "ie": [5, 74], "likelihood": [5, 10, 41, 43, 44, 64, 69, 71, 72, 76, 80, 96], "wherea": [5, 10, 57, 64, 87, 88, 105], "outlier": [5, 9, 11, 15, 22, 23, 32, 45, 52, 72, 84, 90, 91, 96, 99, 100, 106], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 99, 106], "global": [5, 7, 10, 23, 38, 42, 97], "non_iid": [5, 10, 11, 15, 27, 91, 92, 94, 95, 96, 99], "hypothesi": [5, 96], "iid": [5, 7, 9, 27, 94, 99], "never": [5, 89, 99, 102, 104, 105], "someth": [5, 7, 10, 38, 42, 72, 103], "123": [5, 90, 91], "456": [5, 87, 88, 89], "nearest_neighbor": 5, "7": [5, 10, 49, 50, 61, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "9": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 43, 49, 50, 66, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "distance_to_nearest_neighbor": [5, 11, 90, 91, 92, 94, 95, 99], "789": 5, "get_issu": [5, 10, 14, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_nam": [5, 6, 7, 10, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91, 92, 94, 95, 99], "focu": [5, 10, 14, 95, 96, 107, 108], "full": [5, 10, 14, 41, 61, 70, 92, 108], "summar": [5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 63, 79, 83, 84, 107], "specific_issu": [5, 14], "lie": [5, 10, 71, 72, 88, 89, 90, 91, 92, 94, 95, 96, 99], "get_issue_summari": [5, 10, 14, 91, 96], "get_info": [5, 14, 91, 95, 96, 97], "yet": [5, 18, 28, 61, 97, 101], "list_possible_issue_typ": [5, 15, 16], "regist": [5, 7, 15, 16, 18, 28, 38, 42, 90], "rtype": [5, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42], "registri": [5, 15, 16], "list_default_issue_typ": [5, 15, 16], "folder": [5, 89, 90, 92], "load": [5, 13, 41, 70, 92, 97, 98, 99, 103, 104, 107, 108], "futur": [5, 10, 23, 38, 42, 62, 84, 90, 95], "overwrit": [5, 90], "separ": [5, 37, 49, 66, 90, 91, 92, 96, 98, 103, 105], "static": 5, "rememb": [5, 95, 98, 99], "part": [5, 10, 38, 42, 44, 67, 69, 70, 89, 90, 96, 97, 107, 108], "ident": [5, 10, 23, 57, 95, 96], "datalab": [6, 8, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 84, 87, 88, 97, 101, 106], "walk": 7, "alongsid": [7, 38, 42, 90, 98], "pre": [7, 8, 10, 38, 42, 90, 91, 106], "runtim": [7, 38, 41, 42, 74, 76, 78, 89, 92, 98], "issue_manager_factori": [7, 15, 90], "myissuemanag": [7, 15], "myissuemanagerforregress": 7, "decor": [7, 15], "ll": [7, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "thing": [7, 42, 88, 96, 99, 106], "next": [7, 62, 84, 87, 88, 89, 94, 95, 96, 98, 101, 103, 106, 108], "dummi": 7, "randint": [7, 32, 49, 90, 91, 96], "mark": [7, 10, 85, 103, 104, 106], "regard": [7, 91, 99], "rand": [7, 49, 52, 90, 91, 96], "is_": [7, 10, 90], "_issu": [7, 10, 90], "issue_score_kei": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "whole": [7, 10, 27, 38, 42, 91, 96], "make_summari": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "popul": [7, 95], "verbosity_level": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "std": [7, 103], "raw_scor": 7, "bit": 7, "involv": [7, 41, 79, 83, 96, 98, 102], "intermediate_arg": 7, "min": [7, 49, 69, 82, 90, 98, 104], "sin_filt": 7, "sin": 7, "arang": [7, 96], "kernel": [7, 96], "affect": [7, 10, 38, 42, 53, 76, 82, 95, 96, 98], "easili": [7, 47, 85, 87, 88, 89, 91, 94, 95, 99, 101, 102, 104, 105, 106, 107], "hard": [7, 42, 97, 104], "sai": [7, 10, 38, 42, 96, 102, 107], "anoth": [7, 10, 23, 37, 41, 53, 56, 69, 72, 88, 94, 95, 96, 98, 99, 101, 104], "try": [7, 9, 10, 41, 44, 61, 62, 76, 78, 84, 91, 92, 94, 95, 98, 99, 107], "won": [7, 38, 42, 90, 91, 98, 102], "issue_manag": [7, 10, 12, 14, 16, 19, 20, 21, 24, 26, 27, 28, 29, 31, 32, 90], "instanti": [7, 17, 41, 61, 71, 88, 89, 91, 94], "477762": 7, "286455": 7, "term": [7, 10, 47, 57, 70, 89, 90, 91, 92, 94, 95, 99], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 20, 29, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 103, 104, 106, 107, 108], "003042": 7, "058117": 7, "11": [7, 10, 61, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "121908": 7, "15": [7, 55, 61, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "169312": 7, "17": [7, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 90, 91, 96, 97, 99], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 32], "group": [8, 9, 27, 32, 97, 103, 108], "dbscan": [8, 10, 32], "hdbscan": 8, "etc": [8, 10, 23, 33, 38, 42, 47, 61, 62, 80, 84, 90, 91, 94, 95, 98, 99, 102, 106], "sensit": [8, 10, 55, 96], "ep": [8, 32, 70], "radiu": 8, "min_sampl": [8, 32], "kmean": [8, 96], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 32, 96], "cluster_id": [8, 10, 11, 32, 96], "labels_": 8, "underperforming_group": [8, 10, 11, 15, 22, 91, 92, 94, 95, 96, 99], "search": [9, 10, 21, 27, 28, 45, 51, 52, 53, 56, 74, 96, 98, 105], "nondefault": 9, "Near": [9, 98], "imbal": [9, 22, 66, 71, 72, 91], "null": [9, 11, 15, 22, 91, 92, 95, 99], "togeth": [9, 10, 47, 88, 90, 91, 92, 94, 95, 99, 106, 108], "built": [9, 49], "own": [9, 38, 40, 42, 54, 60, 66, 67, 70, 76, 80, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 106, 107, 108], "prerequisit": 9, "basic": [9, 42, 61, 94, 95, 96, 104], "fulli": [9, 10, 38, 42, 61, 98], "platform": [9, 10, 84, 92, 94, 95, 98], "write": [9, 10], "code": [9, 10, 38, 42, 47, 57, 61, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "being": [9, 10, 14, 37, 38, 42, 44, 49, 56, 57, 72, 87, 94, 98, 99, 106, 107], "100x": [9, 10], "faster": [9, 10, 41, 71, 74, 76, 78, 98, 99], "intellig": [9, 10], "quickli": [9, 10, 39, 87, 89, 92, 94, 95, 98, 102, 104, 107, 108], "fix": [9, 10, 62, 88, 95, 96, 99, 106], "scientist": [9, 10], "million": [9, 10, 108], "thank": [9, 10], "ai": [9, 10, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 101, 102, 104, 106, 108], "suggest": [9, 10, 37, 62, 63, 69, 88, 92, 95, 96, 98, 106], "power": [9, 10, 92, 94, 95, 97, 99, 108], "automl": [9, 10, 84, 98], "system": [9, 10, 89, 92, 94, 95, 107], "foundat": [9, 10, 84, 96], "improv": [9, 10, 62, 87, 88, 91, 92, 97, 98, 99, 106, 107], "click": [9, 10, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "tune": [9, 10, 88, 89, 95, 97, 104], "serv": [9, 10, 14, 17, 101], "auto": [9, 10, 87, 88, 91, 97, 98, 106], "free": [9, 10, 84, 89, 91, 92, 94, 95, 98, 99], "page": [10, 91, 98, 99], "variou": [10, 14, 31, 40, 58, 60, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "why": [10, 95], "matter": [10, 37, 63], "didn": [10, 96], "plu": [10, 106], "ye": [10, 11], "near_dupl": [10, 11, 15, 20, 90, 91, 92, 94, 95, 96, 98, 99], "class_imbal": [10, 11, 15, 21, 91, 92, 94, 95, 96, 99], "data_valu": [10, 11, 15, 22, 96], "No": [10, 11, 87, 88, 95, 96, 98], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 69, 87, 88, 108], "issue_scor": 10, "atyp": [10, 71, 90, 91, 92, 94, 95, 99, 104], "datapoint": [10, 32, 44, 49, 57, 72, 75, 84, 87, 88, 89, 90, 91, 94, 95, 98, 105, 106], "is_issu": [10, 23], "primarili": 10, "former": [10, 38, 42], "investig": [10, 89], "expertis": 10, "interpret": [10, 97, 98, 99, 102, 106], "annot": [10, 37, 48, 62, 63, 64, 66, 67, 69, 70, 79, 82, 83, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 103, 107], "dissimilar": [10, 94, 95], "preced": 10, "incorrect": [10, 69, 72, 75, 87, 89, 90, 91, 92, 94, 95, 96, 99, 103, 106], "due": [10, 41, 44, 72, 76, 78, 89, 90, 91, 92, 94, 95, 99, 106], "appear": [10, 37, 48, 63, 64, 67, 75, 91, 92, 94, 95, 96, 106, 107], "now": [10, 41, 85, 87, 88, 89, 91, 96, 98, 101, 103, 104, 106, 108], "token": [10, 43, 56, 78, 79, 80, 81, 82, 83, 98, 99, 100], "hamper": [10, 92, 97], "analyt": [10, 84, 96, 98, 101], "lead": [10, 69, 72, 92, 96, 103], "draw": [10, 90, 91], "conclus": [10, 95], "let": [10, 38, 42, 71, 72, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "sort_valu": [10, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 106], "head": [10, 87, 88, 89, 91, 92, 94, 95, 96, 97, 99, 101, 106], "97": [10, 87, 97, 98, 99, 103, 106, 108], "064045": 10, "58": [10, 87, 91, 96, 97, 99, 103], "680894": 10, "41": [10, 96, 97, 103, 106], "746043": 10, "794894": 10, "98": [10, 97, 98, 106], "802911": 10, "give": [10, 49, 72, 99, 101, 107], "li": [10, 71], "especi": [10, 87, 88, 92, 96, 98, 106], "veri": [10, 37, 63, 67, 69, 88, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106], "rare": [10, 44, 70, 90, 91, 92, 94, 95, 98, 99], "anomal": [10, 72, 90, 91, 92, 94, 95, 99], "articl": [10, 41, 98], "blog": 10, "unexpect": [10, 38, 42, 95], "consequ": 10, "inspect": [10, 88, 89, 91, 92, 99, 103, 106], "011562": 10, "62": [10, 96, 99, 103, 106], "019657": 10, "22": [10, 89, 90, 92, 96, 97, 99, 102, 103, 108], "035243": 10, "040907": 10, "42": [10, 49, 95, 96, 97, 103, 108], "056865": 10, "smaller": [10, 71, 96, 102, 103], "extrem": [10, 90, 91, 92, 94, 95, 96, 98, 99], "record": [10, 38, 42, 89, 94, 106], "abbrevi": 10, "misspel": 10, "typo": [10, 83], "resolut": 10, "video": [10, 97], "audio": [10, 90, 91, 93, 98], "minor": [10, 56], "variat": 10, "translat": 10, "d": [10, 55, 87, 94, 95, 96, 98, 99, 102, 106, 108], "constant": [10, 32, 74], "median": [10, 31, 55], "question": [10, 23, 84, 99], "nearli": [10, 23, 91, 92, 94, 95], "awar": [10, 85, 99], "presenc": [10, 52, 54, 99], "36": [10, 96, 97, 108], "066009": 10, "80": [10, 39, 87, 94, 102, 106], "003906": 10, "093245": 10, "005599": 10, "27": [10, 94, 96, 97, 99, 103, 108], "156720": 10, "009751": 10, "72": [10, 96, 97, 99, 102, 106], "signific": [10, 94, 95, 99], "violat": [10, 94, 95, 96, 99], "assumpt": [10, 94, 95, 96, 99], "changepoint": [10, 94, 95, 99], "shift": [10, 52, 54, 94, 95, 99], "drift": [10, 91, 94, 96, 99], "autocorrel": [10, 94, 95, 99], "almost": [10, 94, 95, 99], "adjac": [10, 52, 94, 95, 99], "tend": [10, 37, 47, 94, 95, 99, 107, 108], "sequenti": [10, 38, 42, 61, 92], "pai": [10, 95], "attent": [10, 96], "realli": [10, 88, 95, 101, 107], "mere": 10, "highlight": [10, 79, 83, 90, 91, 94, 96, 107], "necessarili": [10, 62, 70, 95, 99], "wrong": [10, 62, 67, 69, 85, 88, 90, 91, 95, 98, 99, 103], "gap": 10, "b": [10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 56, 57, 82, 87, 94, 95, 96, 97, 98, 99, 105, 108], "x1": [10, 67, 70, 103], "x2": [10, 67, 70, 103], "10th": 10, "100th": 10, "90": [10, 82, 87, 94, 99, 105, 106], "similarli": [10, 38, 42, 90, 92, 94, 98, 103], "associ": [10, 13, 17, 33, 35, 38, 42, 70, 101], "blogpost": 10, "proper": [10, 57, 62, 67, 70, 87, 92, 95, 98, 101, 103], "scenario": [10, 52, 54, 72, 90, 91], "underli": [10, 43, 54, 71, 80, 82, 108], "stem": [10, 71, 104], "evolv": 10, "influenc": 10, "act": [10, 69, 90], "accordingli": [10, 33, 52], "emploi": [10, 102, 104], "partit": [10, 105], "ahead": 10, "good": [10, 38, 42, 55, 61, 63, 69, 72, 76, 78, 79, 84, 92, 94, 95], "problem": [10, 33, 41, 49, 79, 84, 90, 91, 92, 95, 98], "deploy": [10, 87, 88, 99, 106], "overlook": [10, 69, 103], "fact": 10, "thu": [10, 37, 42, 63, 87, 89, 94, 95, 99, 105, 108], "diagnos": [10, 91, 98], "24": [10, 89, 96, 97, 99, 101, 103, 106], "681458": 10, "37": [10, 90, 96, 97], "804582": 10, "64": [10, 42, 87, 92, 94, 96, 99, 103], "810646": 10, "815691": 10, "78": [10, 87, 94, 97, 99, 103, 106], "834293": 10, "Be": [10, 42], "cautiou": 10, "behavior": [10, 17, 37, 38, 42, 70, 98], "rarest": [10, 91], "q": [10, 103], "subpar": 10, "special": [10, 52, 56], "techniqu": [10, 103], "smote": 10, "asymmetr": [10, 37], "28": [10, 92, 95, 96, 97, 99, 101, 108], "75": [10, 49, 90, 91, 96, 97, 101, 102, 103, 106, 108], "33": [10, 38, 42, 96, 97, 103], "68": [10, 87, 97, 99, 103], "excess": [10, 92], "dark": [10, 107], "bright": [10, 96, 108], "blurri": [10, 92, 96], "lack": [10, 61, 96], "unusu": [10, 103, 104], "cluster": [10, 19, 32], "slice": 10, "poor": [10, 96], "subpopul": 10, "faq": [10, 84, 91, 92, 94, 95, 100], "get_self_confidence_for_each_label": [10, 49, 72], "r": [10, 41, 74, 90, 91, 96, 106, 107], "tabular": [10, 84, 86, 90, 91, 93, 96, 98, 101], "categor": [10, 71, 86, 87, 90, 91, 93, 98, 106], "encod": [10, 50, 70, 76, 79, 87, 88, 94, 95, 98, 106, 107], "71": [10, 96, 97, 99, 103, 106], "70": [10, 82, 94, 96], "69": [10, 99, 106], "subgroup": [10, 96], "wors": [10, 96, 101], "ratio": [10, 96], "miss": [10, 28, 38, 42, 57, 67, 69, 98, 103, 106], "pattern": [10, 96], "isn": [10, 18, 28], "scalabl": 10, "sacrific": 10, "One": [10, 57, 71, 98], "quantif": 10, "39": [10, 88, 89, 90, 92, 95, 96, 97, 98, 103, 106, 107, 108], "32": [10, 89, 90, 96, 97, 101, 103], "valuabl": [10, 19, 96], "exert": [10, 91], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 22, 24, 31], "health_summari": [10, 24, 37, 84, 97], "health_summary_kwarg": 10, "tandem": [10, 97], "view": [10, 38, 42, 43, 44, 78, 80, 82, 84, 87, 88, 89, 90, 91, 94, 95, 97, 99, 101, 102, 103, 104, 105, 106, 108], "ood_kwarg": 10, "outofdistribut": [10, 29, 71, 104], "outsid": [10, 98, 102], "outlierissuemanag": [10, 15, 22, 29], "nearduplicateissuemanag": [10, 15, 20, 22], "noniidissuemanag": [10, 15, 22, 27], "num_permut": [10, 27], "permut": [10, 27], "significance_threshold": [10, 27], "signic": 10, "noniid": [10, 22], "classimbalanceissuemanag": [10, 15, 21, 22], "underperforminggroupissuemanag": [10, 15, 22, 32], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 32], "filter_cluster_id": [10, 22, 32], "clustering_kwarg": [10, 32], "nullissuemanag": [10, 15, 22, 28], "datavaluationissuemanag": [10, 15, 19, 22], "codeblock": 10, "demonstr": [10, 41, 52, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107], "howev": [10, 38, 42, 52, 57, 87, 88, 89, 92, 94, 95, 96, 101, 105, 107], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 63], "versa": [10, 63], "light": [10, 92, 96, 97, 103, 107], "29": [10, 92, 96, 97, 101, 102, 103, 107, 108], "low_inform": [10, 92, 96], "odd_aspect_ratio": [10, 92, 96], "35": [10, 90, 96, 97, 101, 102, 103], "odd_siz": [10, 92, 96], "doc": [10, 38, 42, 71, 84, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 104, 106, 108], "label_scor": [11, 24, 26, 31, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "is_outlier_issu": [11, 90, 91, 92, 94, 95, 96, 99], "outlier_scor": [11, 29, 90, 91, 92, 94, 95, 96, 99, 104], "is_near_duplicate_issu": [11, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_scor": [11, 20, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_set": [11, 20, 22, 90, 91, 92, 94, 95, 98, 99], "is_non_iid_issu": [11, 91, 94, 95, 96, 99], "non_iid_scor": [11, 27, 91, 94, 95, 96, 99], "is_class_imbalance_issu": [11, 91, 96], "class_imbalance_scor": [11, 21, 91, 96], "is_underperforming_group_issu": [11, 91, 96], "underperforming_group_scor": [11, 32, 91, 96], "is_null_issu": [11, 91, 96], "null_scor": [11, 28, 91, 96], "is_data_valuation_issu": [11, 96], "data_valuation_scor": [11, 19, 96], "studio": [12, 84, 91, 92, 94, 95, 98], "data_issu": [12, 16, 17, 34], "issue_find": [12, 16], "factori": [12, 16, 17], "model_output": [12, 16], "except": [13, 38, 42, 61, 72, 90, 91, 92, 101], "dataformaterror": [13, 16], "add_not": 13, "with_traceback": 13, "tb": 13, "__traceback__": 13, "datasetdicterror": [13, 16], "datasetdict": 13, "datasetloaderror": [13, 16], "dataset_typ": 13, "fail": 13, "hold": 13, "sublist": 13, "map_to_int": 13, "abc": [13, 23, 33], "is_avail": [13, 92], "dataissu": [14, 16, 17, 34], "central": [14, 108], "repositori": 14, "strategi": [14, 49, 96, 98], "_infostrategi": 14, "basi": 14, "collect_statist": 14, "reus": [14, 23], "avoid": [14, 38, 41, 42, 44, 52, 57, 64, 67, 70, 74, 76, 78, 90, 91, 98], "recomput": [14, 88], "weighted_knn_graph": 14, "issue_manager_that_computes_knn_graph": 14, "collect_issues_from_issue_manag": 14, "collect_issues_from_imagelab": 14, "imagelab": 14, "set_health_scor": 14, "health": [14, 24, 37, 63, 84], "get_data_statist": [14, 16], "concret": 15, "subclass": [15, 38, 42, 71, 90], "regressionlabelissuemanag": [15, 22, 30, 31], "multilabelissuemanag": [15, 22, 25, 26], "from_str": [15, 35, 45, 49], "my_issu": 15, "logic": [15, 35, 41, 44, 76, 78], "issuefind": [16, 17, 34], "modeloutput": [16, 33], "multiclasspredprob": [16, 33], "regressionpredict": [16, 33], "multilabelpredprob": [16, 33], "instati": 17, "public": [17, 96, 99, 103, 107, 108], "creation": [17, 42, 96], "execut": [17, 38, 42, 90, 98, 103], "coordin": [17, 67, 69, 70, 103, 108], "At": [17, 70, 98], "get_available_issue_typ": 17, "direct": [18, 28, 38, 42, 54, 61], "vstack": [19, 57, 92, 97, 98, 99, 101, 102], "25": [19, 27, 38, 49, 55, 91, 92, 96, 97, 99, 101, 102, 103, 108], "classvar": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "short": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 56, 57], "item": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 90, 91, 92, 98, 99, 101, 102], "some_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "additional_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "default_threshold": [19, 22, 29], "collect_info": [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "info_to_omit": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "compos": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 38, 42, 88, 95, 104], "is_x_issu": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "x_score": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_a": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b1": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b2": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "report_str": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34], "_": [20, 21, 23, 24, 26, 27, 28, 31, 32, 49, 56, 57, 84, 87, 89, 90, 92, 96, 97, 99, 102], "occurr": [20, 21, 23, 27, 28, 29, 32, 56], "median_nn_dist": 20, "bleed": [22, 25, 30, 40], "edg": [22, 25, 30, 40, 69, 84, 99, 108], "sharp": [22, 25, 30, 40], "get_health_summari": [22, 24], "ood": [22, 29, 71, 72, 104], "simplified_kolmogorov_smirnov_test": [22, 27], "outlier_cluster_label": [22, 32], "no_underperforming_cluster_id": [22, 32], "perform_clust": [22, 32], "get_worst_clust": [22, 32], "find_issues_with_predict": [22, 30, 31], "find_issues_with_featur": [22, 30, 31], "believ": [23, 107], "priori": [23, 99], "abstract": [23, 33], "applic": [24, 62, 98, 99, 101, 108], "typevar": [24, 26, 38, 42, 56, 66, 69, 70], "scalartyp": [24, 26], "covari": [24, 26, 74, 106], "summary_dict": 24, "neighbor_histogram": 27, "non_neighbor_histogram": 27, "kolmogorov": 27, "smirnov": 27, "largest": [27, 41, 49, 52, 72, 76, 78, 103, 107], "empir": [27, 48, 62], "cumul": 27, "ecdf": 27, "histogram": [27, 94, 96, 106], "absolut": [27, 31], "trial": 27, "null_track": 28, "extend": [28, 50, 61, 92, 96, 103, 104, 108], "superclass": 28, "arbitrari": [28, 37, 78, 82, 90, 104, 106], "prompt": 28, "address": [28, 88, 90, 91, 95, 98], "enabl": [28, 42, 54], "scaling_factor": [29, 55], "37037": 29, "q3_avg_dist": 29, "iqr_avg_dist": 29, "median_outlier_scor": 29, "issue_threshold": 29, "multipli": [31, 55], "deleg": 31, "confus": [32, 33, 37, 38, 42, 44, 57, 70, 88, 108], "50": [32, 42, 96, 98, 99, 101, 103, 104, 106], "keepdim": [32, 98], "signifi": 32, "absenc": 32, "int64": [32, 89, 101], "npt": 32, "int_": 32, "id": [32, 62, 90, 92, 96, 98, 101], "unique_cluster_id": 32, "_description_": 32, "performed_clust": 32, "worst_cluster_id": 32, "convent": [33, 35], "subject": [33, 35], "meant": [33, 35], "Not": [33, 54], "mainli": [33, 104, 108], "content": [33, 71, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "fetch": [33, 41, 89, 91, 98], "datset": 34, "exclud": [34, 43, 79, 83, 90, 108], "get_report": 34, "enum": [35, 49], "qualnam": [35, 49], "boundari": [35, 49, 90, 91], "continu": [35, 61, 87, 88, 92, 95, 96, 98, 101, 103, 106, 108], "binari": [35, 49, 57, 64, 66, 99, 108], "simultan": [35, 106], "task_str": 35, "is_classif": 35, "__contains__": [35, 45, 49], "member": [35, 38, 42, 49, 90], "typeerror": [35, 49], "12": [35, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "__getitem__": [35, 45, 49], "match": [35, 37, 38, 42, 44, 49, 62, 63, 72, 90, 91, 92, 97, 103, 105, 107], "__iter__": [35, 45, 49], "__len__": [35, 45, 49], "alias": [35, 49], "is_regress": 35, "is_multilabel": 35, "overview": [37, 52, 87, 88, 89, 91, 92, 94, 95, 101, 103, 104, 106, 108], "modifi": [37, 38, 41, 42, 52, 54, 57, 96, 98, 99], "rank_classes_by_label_qu": [37, 91], "merg": [37, 52, 56, 84, 97, 98, 108], "find_overlapping_class": [37, 98, 99], "problemat": [37, 63, 79, 83, 89, 103, 108], "unnorm": [37, 63, 99], "abov": [37, 38, 41, 42, 54, 57, 62, 69, 70, 72, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 105, 106, 107, 108], "model_select": [37, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 106], "cross_val_predict": [37, 42, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 105, 106], "get_data_labels_from_dataset": 37, "yourfavoritemodel": [37, 99], "cv": [37, 49, 87, 89, 90, 91, 94, 96, 99, 101], "df": [37, 57, 83, 89, 96, 98], "overall_label_qu": [37, 63], "col": 37, "prob": [37, 56, 99, 105], "divid": [37, 63, 72], "label_nois": [37, 63], "human": [37, 97, 107, 108], "clearli": [37, 72, 92, 103, 107], "num": [37, 63, 97, 99], "overlap": [37, 84, 97, 98, 99], "ontolog": 37, "publish": [37, 108], "therefor": [37, 72, 96], "vehicl": [37, 97], "truck": [37, 97, 104, 107], "intuit": [37, 63], "car": [37, 97, 103, 107], "frequent": [37, 62, 96, 98, 106], "characterist": [37, 96], "l": [37, 38, 42, 67, 69, 70], "class1": 37, "class2": 37, "relationship": 37, "dog": [37, 57, 63, 65, 79, 97, 98, 104, 105, 108], "cat": [37, 57, 63, 65, 97, 98, 104, 105], "captur": [37, 89, 103, 104, 107], "co": [37, 38, 39], "noisy_label": [37, 90, 91, 102], "overlapping_class": 37, "descend": [37, 38, 42, 49, 63, 70], "overall_label_health_scor": [37, 63, 99], "half": [37, 38, 40, 42, 63, 97, 108], "health_scor": [37, 63], "classes_by_label_qu": [37, 91], "cnn": [38, 40, 42, 92], "cifar": [38, 39, 96, 97, 104], "teach": [38, 39], "bhanml": 38, "blob": [38, 96], "master": [38, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106], "call_bn": [38, 40], "bn": 38, "input_channel": 38, "n_output": 38, "dropout_r": 38, "top_bn": 38, "architectur": [38, 42], "shown": [38, 70, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 105, 107, 108], "forward": [38, 39, 40, 42, 92, 101], "overridden": [38, 42], "although": [38, 42, 71, 87, 94], "recip": [38, 42], "afterward": [38, 42], "sinc": [38, 42, 46, 58, 63, 70, 78, 82, 98, 101, 102, 103, 105, 108], "hook": [38, 42, 97], "silent": [38, 41, 42], "t_destin": [38, 40, 42], "__call__": [38, 40, 42, 45, 49], "add_modul": [38, 40, 42], "child": [38, 42], "fn": [38, 42, 70], "recurs": [38, 42, 49], "submodul": [38, 42, 51], "children": [38, 40, 42, 108], "nn": [38, 39, 42, 52, 92], "init": [38, 42, 99], "no_grad": [38, 42, 92, 104], "init_weight": [38, 42], "linear": [38, 42, 88, 92, 95], "fill_": [38, 42], "net": [38, 42, 89, 92, 97], "in_featur": [38, 42], "out_featur": [38, 42], "bia": [38, 42, 92, 96], "tensor": [38, 39, 42, 89, 92, 104], "requires_grad": [38, 42], "bfloat16": [38, 40, 42], "cast": [38, 42, 89], "buffer": [38, 40, 42, 96], "datatyp": [38, 42], "xdoctest": [38, 42], "undefin": [38, 42], "var": [38, 42], "buf": [38, 42], "20l": [38, 42], "1l": [38, 42], "5l": [38, 42], "call_super_init": [38, 40, 42], "immedi": [38, 42, 104], "compil": [38, 40, 42, 61], "cpu": [38, 40, 42, 44, 89, 92], "move": [38, 42, 49, 85, 97], "cuda": [38, 40, 42, 89, 92], "devic": [38, 42, 89, 92], "gpu": [38, 42, 88, 89, 95], "live": [38, 42], "copi": [38, 42, 74, 87, 89, 90, 91, 94, 96, 98, 102, 105, 106], "doubl": [38, 40, 42], "dump_patch": [38, 40, 42], "eval": [38, 40, 42, 92, 102, 104], "dropout": [38, 42], "batchnorm": [38, 42], "grad": [38, 42], "extra_repr": [38, 40, 42], "line": [38, 42, 84, 90, 96, 97, 101, 104, 108], "get_buff": [38, 40, 42], "target": [38, 39, 42, 74, 75, 96, 104, 106], "throw": [38, 42], "get_submodul": [38, 40, 42], "explan": [38, 42], "qualifi": [38, 42], "referenc": [38, 42], "attributeerror": [38, 42], "invalid": [38, 42, 95], "resolv": [38, 42, 108], "get_extra_st": [38, 40, 42], "state_dict": [38, 40, 42], "set_extra_st": [38, 40, 42], "build": [38, 42, 52, 92, 96, 107], "picklabl": [38, 42], "serial": [38, 42], "backward": [38, 42, 92], "break": [38, 42, 92, 96, 103], "pickl": [38, 42, 103], "get_paramet": [38, 40, 42], "net_b": [38, 42], "net_c": [38, 42], "conv": [38, 42], "conv2d": [38, 42, 92], "16": [38, 42, 49, 52, 61, 78, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 107, 108], "kernel_s": [38, 42], "stride": [38, 42], "200": [38, 42, 72, 97, 103, 108], "diagram": [38, 42, 105], "degre": [38, 42], "queri": [38, 42, 52, 54, 91, 92, 96, 98, 102], "named_modul": [38, 40, 42], "o": [38, 42, 55, 56, 89, 90, 91, 97, 98, 99, 102, 103, 108], "transit": [38, 42], "ipu": [38, 40, 42], "load_state_dict": [38, 40, 42], "strict": [38, 42, 49], "persist": [38, 42], "strictli": [38, 42], "inplac": [38, 42, 96, 101], "preserv": [38, 42, 57], "namedtupl": [38, 42], "missing_kei": [38, 42], "unexpected_kei": [38, 42], "runtimeerror": [38, 42], "idx": [38, 42, 57, 58, 70, 90, 92, 96, 98, 99, 101, 103, 104], "named_buff": [38, 40, 42], "prefix": [38, 42, 89, 108], "remove_dupl": [38, 42], "prepend": [38, 42], "running_var": [38, 42], "named_children": [38, 40, 42], "conv4": [38, 42], "conv5": [38, 42], "memo": [38, 42], "named_paramet": [38, 40, 42], "register_backward_hook": [38, 40, 42], "deprec": [38, 42, 46], "favor": [38, 42], "register_full_backward_hook": [38, 40, 42], "removablehandl": [38, 42], "register_buff": [38, 40, 42], "running_mean": [38, 42], "register_forward_hook": [38, 40, 42], "with_kwarg": [38, 42], "always_cal": [38, 42], "possibli": [38, 42, 87, 94], "fire": [38, 42, 97], "register_module_forward_hook": [38, 42], "regardless": [38, 42, 90, 91], "register_forward_pre_hook": [38, 40, 42], "And": [38, 42], "forward_pr": [38, 42], "register_module_forward_pre_hook": [38, 42], "gradient": [38, 42, 92, 94, 106], "grad_input": [38, 42], "grad_output": [38, 42], "technic": [38, 42], "caller": [38, 42], "register_module_full_backward_hook": [38, 42], "register_full_backward_pre_hook": [38, 40, 42], "backward_pr": [38, 42], "register_module_full_backward_pre_hook": [38, 42], "register_load_state_dict_post_hook": [38, 40, 42], "post": [38, 42, 52], "incompatible_kei": [38, 42], "modif": [38, 42, 52], "thrown": [38, 42], "register_modul": [38, 40, 42], "register_paramet": [38, 40, 42], "register_state_dict_pre_hook": [38, 40, 42], "keep_var": [38, 42], "requires_grad_": [38, 40, 42], "autograd": [38, 42], "freez": [38, 42, 88, 89, 95], "finetun": [38, 42], "gan": [38, 42], "share_memori": [38, 40, 42], "share_memory_": [38, 42], "destin": [38, 42], "shallow": [38, 42], "releas": [38, 42, 61, 85, 98], "design": [38, 42, 52], "ordereddict": [38, 42], "detach": [38, 42, 92], "non_block": [38, 42], "memory_format": [38, 42], "channels_last": [38, 42], "Its": [38, 42, 49, 63, 69], "complex": [38, 42], "integr": [38, 42, 54, 84, 98], "asynchron": [38, 42], "host": [38, 42], "pin": [38, 42, 88, 95, 97], "desir": [38, 42, 52, 56, 70], "4d": [38, 42], "ignore_w": [38, 42], "determinist": [38, 42, 89], "1913": [38, 42], "3420": [38, 42], "5113": [38, 42], "2325": [38, 42], "env": [38, 42], "torch_doctest_cuda1": [38, 42], "gpu1": [38, 42], "1914": [38, 42], "5112": [38, 42], "2324": [38, 42], "float16": [38, 42], "cdoubl": [38, 42], "3741": [38, 42], "2382": [38, 42], "5593": [38, 42], "4443": [38, 42], "complex128": [38, 42], "6122": [38, 42], "1150": [38, 42], "to_empti": [38, 40, 42], "storag": [38, 42], "dst_type": [38, 42], "xpu": [38, 40, 42], "zero_grad": [38, 40, 42, 92], "set_to_non": [38, 42], "reset": [38, 42], "context": [38, 42, 103], "noisili": [39, 99], "han": 39, "2018": 39, "cifar_cnn": [39, 40], "loss_coteach": [39, 40], "y_1": 39, "y_2": 39, "forget_r": 39, "class_weight": 39, "logit": [39, 61, 92], "decim": [39, 57], "forget": [39, 49, 108], "rate_schedul": 39, "epoch": [39, 40, 42, 92, 98], "initialize_lr_schedul": [39, 40], "lr": [39, 40, 42], "001": [39, 72, 96, 98], "250": [39, 90, 91, 99, 103], "epoch_decay_start": 39, "schedul": 39, "beta": 39, "adam": 39, "adjust_learning_r": [39, 40], "alpha_plan": 39, "beta1_plan": 39, "forget_rate_schedul": [39, 40], "num_gradu": 39, "expon": 39, "tell": [39, 88, 92, 95, 99], "train_load": [39, 42], "model1": [39, 99], "optimizer1": 39, "model2": [39, 99], "optimizer2": 39, "dataload": [39, 92, 104], "parser": 39, "parse_arg": 39, "num_iter_per_epoch": 39, "print_freq": 39, "topk": 39, "top1": 39, "top5": 39, "test_load": 39, "offici": [40, 60, 96, 108], "wish": [40, 60, 104, 107, 108], "adj_confident_thresholds_shar": [40, 41], "labels_shar": [40, 41], "pred_probs_shar": [40, 41], "labelinspector": [40, 41, 98], "get_num_issu": [40, 41], "get_quality_scor": [40, 41], "update_confident_threshold": [40, 41], "score_label_qu": [40, 41], "split_arr": [40, 41], "span_classif": 40, "display_issu": [40, 43, 77, 78, 79, 80, 81, 82, 83, 107, 108], "mnist_pytorch": 40, "get_mnist_dataset": [40, 42], "get_sklearn_digits_dataset": [40, 42], "simplenet": [40, 42], "batch_siz": [40, 41, 42, 76, 78, 92, 98, 104, 107], "log_interv": [40, 42], "momentum": [40, 42], "no_cuda": [40, 42], "test_batch_s": [40, 42, 92], "loader": [40, 42, 92], "set_predict_proba_request": [40, 42], "set_predict_request": [40, 42], "coteach": [40, 85], "mini": [41, 76, 78, 98], "low_self_confid": [41, 44, 64], "self_confid": [41, 44, 45, 49, 64, 66, 72, 80, 82, 87, 88, 98, 99], "conveni": [41, 54, 87, 88, 89, 95], "script": 41, "labels_fil": [41, 98], "pred_probs_fil": [41, 98], "quality_score_kwarg": 41, "num_issue_kwarg": 41, "return_mask": 41, "variant": [41, 62, 107], "read": [41, 46, 91, 98, 99, 104, 108], "zarr": [41, 98], "memmap": [41, 107], "pythonspe": 41, "mmap": [41, 98], "hdf5": 41, "further": [41, 43, 63, 64, 66, 69, 70, 78, 79, 89, 98], "yourfil": 41, "npy": [41, 97, 98, 107], "mmap_mod": [41, 107], "tip": [41, 44, 61, 98], "save_arrai": 41, "your_arrai": 41, "disk": [41, 97, 98], "npz": [41, 108], "maxim": [41, 62, 76, 78, 107], "multiprocess": [41, 44, 64, 76, 78, 92, 98], "linux": [41, 76, 78], "physic": [41, 44, 76, 78, 103], "psutil": [41, 44, 76, 78], "labels_arrai": [41, 58], "predprob": 41, "pred_probs_arrai": 41, "back": [41, 52, 70, 90, 98, 103, 104], "store_result": 41, "becom": [41, 96, 104], "verifi": [41, 54, 98, 101, 104], "long": [41, 62, 71, 101], "enough": [41, 57, 96, 98], "chunk": [41, 105], "ram": [41, 97], "end_index": 41, "labels_batch": 41, "pred_probs_batch": 41, "batch_result": 41, "indices_of_examples_with_issu": [41, 98], "shortcut": 41, "encount": [41, 44, 76], "1000": [41, 89, 95, 98, 104], "aggreg": [41, 45, 49, 62, 66, 69, 72, 82, 98, 99, 101], "seen": [41, 98, 104, 108], "far": [41, 62], "label_quality_scor": [41, 66, 69, 72, 75, 99, 103], "method1": 41, "method2": 41, "normalized_margin": [41, 44, 45, 49, 64, 66, 72, 80, 82], "low_normalized_margin": [41, 44, 64], "issue_indic": [41, 69, 92], "update_num_issu": 41, "arr": [41, 98], "chunksiz": 41, "convnet": 42, "bespok": [42, 61], "download": [42, 89, 96, 98, 104], "mnist": [42, 84, 89, 97], "handwritten": 42, "digit": [42, 89, 97], "last": [42, 49, 67, 70, 90, 91, 98, 101, 103, 108], "sklearn_digits_test_s": 42, "01": [42, 72, 74, 89, 96, 99, 102, 103], "templat": 42, "flexibli": 42, "among": [42, 62, 99], "test_set": 42, "overrid": 42, "train_idx": [42, 57, 104], "train_label": [42, 88, 104], "span": 43, "sentenc": [43, 56, 80, 82, 83, 88, 95], "token_classif": [43, 56, 80, 82, 83, 98], "encourag": [44, 64, 72, 75], "multilabel_classif": [44, 63, 64, 66, 72, 98, 102], "pred_probs_by_class": 44, "prune_count_matrix_col": 44, "rank_by_kwarg": [44, 64, 72, 99], "num_to_remove_per_class": [44, 64], "bad": [44, 52, 64, 69, 72, 95, 98], "seem": [44, 99, 102], "aren": 44, "confidence_weighted_entropi": [44, 45, 49, 64, 66, 72, 80, 82], "label_issues_idx": [44, 72], "entropi": [44, 46, 48, 49, 71, 72], "prune_by_class": [44, 64, 99], "predicted_neq_given": [44, 64, 99], "prune_counts_matrix": 44, "smallest": [44, 72], "unus": 44, "number_of_mislabeled_examples_in_class_k": 44, "delet": [44, 84, 88, 98], "too": [44, 49, 52, 71, 92, 98, 103], "thread": [44, 64], "window": [44, 97], "shorter": [44, 67], "find_predicted_neq_given": 44, "find_label_issues_using_argmax_confusion_matrix": 44, "remove_noise_from_class": [45, 57], "clip_noise_r": [45, 57], "clip_valu": [45, 57], "value_count": [45, 57, 98], "value_counts_fill_missing_class": [45, 57], "get_missing_class": [45, 57], "round_preserving_sum": [45, 57], "round_preserving_row_tot": [45, 57], "estimate_pu_f1": [45, 57], "confusion_matrix": [45, 57], "print_square_matrix": [45, 57], "print_noise_matrix": [45, 57, 99], "print_inverse_noise_matrix": [45, 57], "print_joint_matrix": [45, 57, 99], "compress_int_arrai": [45, 57], "train_val_split": [45, 57], "subset_x_i": [45, 57], "subset_label": [45, 57], "subset_data": [45, 57], "extract_indices_tf": [45, 57], "unshuffle_tensorflow_dataset": [45, 57], "is_torch_dataset": [45, 57], "is_tensorflow_dataset": [45, 57], "csr_vstack": [45, 57], "append_extra_datapoint": [45, 57], "get_num_class": [45, 57], "num_unique_class": [45, 57], "get_unique_class": [45, 57], "format_label": [45, 57], "smart_display_datafram": [45, 57], "force_two_dimens": [45, 57], "latent_algebra": [45, 85], "compute_ps_py_inv_noise_matrix": [45, 47], "compute_py_inv_noise_matrix": [45, 47], "compute_inv_noise_matrix": [45, 47], "compute_noise_matrix_from_invers": [45, 47], "compute_pi": [45, 47], "compute_pyx": [45, 47], "label_quality_util": 45, "get_normalized_entropi": [45, 46], "multilabel_util": [45, 102], "stack_compl": [45, 50], "get_onehot_num_class": [45, 50], "int2onehot": [45, 50, 102], "onehot2int": [45, 50, 102], "multilabel_scor": [45, 66], "classlabelscor": [45, 49], "exponential_moving_averag": [45, 49, 66], "softmin": [45, 49, 66, 69, 78, 82], "possible_method": [45, 49], "multilabelscor": [45, 49], "get_class_label_quality_scor": [45, 49], "multilabel_pi": [45, 49], "get_cross_validated_multilabel_pred_prob": [45, 49], "default_k": [45, 51, 52], "features_to_knn": [45, 51, 52], "construct_knn_graph_from_index": [45, 51, 52, 54], "create_knn_graph_and_index": [45, 51, 52], "correct_knn_graph": [45, 51, 52, 96], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [45, 51, 52], "correct_knn_distances_and_indic": [45, 51, 52], "high_dimension_cutoff": [45, 51, 53], "row_count_cutoff": [45, 51, 53], "decide_euclidean_metr": [45, 51, 53], "decide_default_metr": [45, 51, 53], "construct_knn": [45, 51, 54], "transform_distances_to_scor": [45, 55], "correct_precision_error": [45, 55], "token_classification_util": [45, 108], "get_sent": [45, 56, 108], "filter_sent": [45, 56, 108], "process_token": [45, 56], "merge_prob": [45, 56], "color_sent": [45, 56], "assert_valid_input": [45, 58], "assert_valid_class_label": [45, 58], "assert_nonempty_input": [45, 58], "assert_indexing_work": [45, 58], "labels_to_arrai": [45, 58], "labels_to_list_multilabel": [45, 58], "min_allowed_prob": 46, "wikipedia": 46, "activ": [46, 48, 61, 62, 84, 101], "towardsdatasci": 46, "cheatsheet": 46, "ec57bc067c0b": 46, "clip": [46, 57, 89, 96], "behav": 46, "unnecessari": [46, 98], "slightli": [46, 87, 88], "interv": [46, 49, 104], "herein": 47, "inexact": 47, "cours": 47, "propag": 47, "throughout": [47, 57, 74, 83, 89, 101, 107, 108], "increas": [47, 55, 69, 71, 72, 89, 90, 96, 98, 101, 102, 108], "dot": [47, 82, 98], "true_labels_class_count": 47, "pyx": 47, "multiannot": 48, "assert_valid_inputs_multiannot": 48, "labels_multiannot": [48, 62], "ensembl": [48, 49, 62, 72, 87, 94, 98, 102, 104, 106], "allow_single_label": 48, "annotator_id": 48, "assert_valid_pred_prob": 48, "pred_probs_unlabel": [48, 62], "format_multiannotator_label": [48, 62, 101], "formatted_label": [48, 57], "old": [48, 57, 85, 97], "check_consensus_label_class": 48, "consensus_label": [48, 62, 101], "consensus_method": [48, 62], "consensu": [48, 62, 84, 100, 108], "establish": [48, 61, 88, 106], "compute_soft_cross_entropi": 48, "soft": [48, 97], "find_best_temp_scal": 48, "coarse_search_rang": [48, 74, 98], "fine_search_s": [48, 74, 98], "temperatur": [48, 49, 69, 78, 82], "scale": [48, 55, 87, 96, 97, 98, 104, 107], "factor": [48, 49, 55, 76, 78], "minim": [48, 69, 104], "temp_scale_pred_prob": 48, "temp": 48, "sharpen": [48, 97], "smoothen": 48, "get_normalized_margin_for_each_label": [49, 72], "get_confidence_weighted_entropy_for_each_label": [49, 72], "scorer": 49, "alpha": [49, 66, 69, 90, 91, 96, 99, 102, 106], "exponenti": 49, "ema": 49, "s_1": 49, "s_k": 49, "ema_k": 49, "accord": [49, 64, 94, 95, 99, 108], "formula": [49, 55], "_t": 49, "cdot": 49, "s_t": 49, "qquad": 49, "leq": 49, "_1": 49, "recent": [49, 108], "success": 49, "previou": [49, 52, 92, 94, 98, 103], "discount": 49, "s_ema": 49, "175": [49, 92, 99, 103], "underflow": 49, "nan": [49, 62, 87, 94, 96, 101, 106], "aggregated_scor": 49, "base_scor": 49, "base_scorer_kwarg": 49, "aggregator_kwarg": [49, 66], "n_sampl": [49, 96], "n_label": 49, "worst": [49, 101], "class_label_quality_scor": 49, "452": 49, "new_scor": 49, "575": 49, "get_label_quality_scores_per_class": [49, 65, 66], "ml_scorer": 49, "binar": [49, 50], "reformat": [49, 89], "wider": 49, "splitter": 49, "kfold": [49, 92], "onevsrestclassifi": [49, 102], "randomforestclassifi": [49, 99, 102], "n_split": [49, 92, 102], "pred_prob_slic": 50, "onehot": 50, "hot": [50, 64, 70, 76, 79, 87, 94, 97, 98, 106, 107], "onehot_matrix": 50, "pairwis": [51, 53, 71], "reli": [52, 71, 88, 89, 90, 91, 95, 103, 104, 106], "sklearn_knn_kwarg": 52, "correction_featur": 52, "discourag": 52, "flexibl": [52, 98], "manner": [52, 66, 87, 88, 96, 101, 106], "701": 52, "900": [52, 87, 94, 106], "436": 52, "000": [52, 88, 92, 95, 96, 97, 108], "idea": [52, 72, 103], "dens": [52, 61, 96], "33140006": 52, "76210367": 52, "correct_exact_dupl": 52, "mutual": [52, 63, 102], "vari": [52, 69, 91], "exact_duplicate_set": 52, "main": [52, 62], "front": [52, 97], "consider": 52, "capabl": [52, 84], "come": [52, 57, 90, 91, 98, 107], "misidentif": 52, "corrected_dist": 52, "corrected_indic": 52, "sqrt": 52, "distant": 52, "suitabl": [53, 62, 87, 94, 96], "slower": 53, "decid": [53, 62, 88, 95, 97, 101, 106, 108], "predefin": 53, "met": [53, 108], "euclidean_dist": [53, 71], "spatial": [53, 71], "decis": [53, 87, 90, 91], "That": [53, 99, 102], "cosine_dist": 53, "knn_kwarg": 54, "html": [54, 57, 67, 70, 71, 89, 90, 91, 92, 94, 95, 98, 99], "kneighbor": 54, "metric_param": 54, "n_features_in_": 54, "effective_metric_params_": 54, "effective_metric_": 54, "n_samples_fit_": 54, "__sklearn_is_fitted__": 54, "conduct": 54, "is_fit": 54, "trail": 54, "underscor": 54, "avg_dist": 55, "exp": [55, 71, 72, 90], "dt": 55, "right": [55, 67, 70, 88, 95, 102, 103, 104], "strength": [55, 70, 96], "pronounc": 55, "differenti": 55, "ly": 55, "rule": [55, 56, 97], "thumb": 55, "ood_features_scor": [55, 71, 104], "88988177": 55, "80519832": 55, "toler": 55, "minkowski": 55, "noth": 55, "epsilon": 55, "sensibl": 55, "fixed_scor": 55, "readabl": 56, "lambda": [56, 89, 90, 98, 101], "long_sent": 56, "headlin": 56, "charact": [56, 57], "s1": 56, "s2": 56, "processed_token": 56, "alecnlcb": 56, "entiti": [56, 84, 98, 108], "mapped_ent": 56, "unique_ident": 56, "loc": [56, 90, 91, 92, 94, 96, 108], "nbitbas": [56, 66], "probs_merg": 56, "0125": [56, 82], "0375": 56, "075": 56, "025": 56, "color": [56, 79, 90, 91, 94, 96, 99, 102, 104, 106, 107], "red": [56, 70, 90, 91, 96, 97, 99, 102, 103, 104, 107], "colored_sent": 56, "termcolor": 56, "31msentenc": 56, "0m": 56, "ancillari": 57, "class_without_nois": 57, "any_other_class": 57, "choos": [57, 72, 87, 94, 98, 99, 106], "tradition": 57, "new_sum": 57, "fill": 57, "major": [57, 62, 85, 92, 104], "versu": [57, 99], "obviou": 57, "cgdeboer": 57, "iteround": 57, "reach": 57, "prob_s_eq_1": 57, "claesen": 57, "f1": [57, 70, 95, 99], "BE": 57, "left_nam": 57, "top_nam": 57, "titl": [57, 90, 91, 96, 99, 102, 104], "short_titl": 57, "round_plac": 57, "pretti": [57, 99], "joint_matrix": 57, "num_possible_valu": 57, "holdout_idx": 57, "extract": [57, 71, 88, 89, 94, 95, 96, 101, 104, 107], "allow_shuffl": 57, "turn": [57, 84, 103], "shuffledataset": 57, "histori": 57, "pre_x": 57, "buffer_s": 57, "csr_matric": 57, "append": [57, 89, 92, 96, 97, 98, 99, 101, 102, 103, 104, 108], "bottom": [57, 67, 70, 96, 103], "to_data": 57, "from_data": 57, "taken": 57, "label_matrix": 57, "canon": 57, "displai": [57, 70, 79, 83, 88, 89, 94, 95, 96, 99, 108], "jupyt": [57, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "notebook": [57, 62, 89, 91, 97, 98, 99, 101, 102, 103, 107, 108], "consol": 57, "allow_missing_class": 58, "allow_one_class": 58, "length_x": 58, "labellik": 58, "labels_list": [58, 64], "keraswrappermodel": [60, 61, 84], "keraswrappersequenti": [60, 61], "tf": [61, 89], "legaci": 61, "newer": 61, "interim": 61, "advis": [61, 102], "stabil": [61, 71], "until": 61, "accommod": 61, "keraswrapp": 61, "huggingface_keras_imdb": 61, "unit": [61, 108], "model_kwarg": [61, 74], "compile_kwarg": 61, "sparsecategoricalcrossentropi": 61, "layer": [61, 88, 89, 95, 104], "my_keras_model": 61, "from_logit": 61, "declar": 61, "apply_softmax": 61, "analysi": [62, 96], "analyz": [62, 84, 96, 99, 101, 102], "get_label_quality_multiannot": [62, 101], "vote": 62, "crowdsourc": [62, 84, 101], "dawid": [62, 101], "skene": [62, 101], "analog": [62, 97, 101], "chosen": [62, 72, 96, 98, 101], "crowdlab": [62, 101], "unlabel": [62, 92, 94, 95, 101, 104, 107], "get_active_learning_scor": [62, 101], "activelab": [62, 101], "priorit": [62, 69, 103, 107, 108], "showcas": 62, "best_qual": 62, "quality_method": 62, "calibrate_prob": 62, "return_detailed_qu": 62, "return_annotator_stat": 62, "return_weight": 62, "label_quality_score_kwarg": 62, "did": [62, 63, 87, 88, 89, 94, 99, 101, 106], "majority_vot": 62, "broken": [62, 70, 97, 106], "highest": [62, 70, 90, 92, 105], "0th": 62, "consensus_quality_scor": [62, 101], "annotator_agr": [62, 101], "reman": 62, "1st": 62, "2nd": [62, 76], "3rd": 62, "consensus_label_suffix": 62, "consensus_quality_score_suffix": 62, "suffix": 62, "emsembl": 62, "weigh": [62, 97], "agreement": [62, 101], "agre": 62, "prevent": [62, 98], "overconfid": [62, 105], "detailed_label_qu": [62, 101], "annotator_stat": [62, 101], "model_weight": 62, "annotator_weight": 62, "warn": 62, "labels_info": 62, "num_annot": [62, 101], "deriv": [62, 101], "quality_annotator_1": 62, "quality_annotator_2": 62, "quality_annotator_m": 62, "annotator_qu": [62, 101], "num_examples_label": [62, 101], "agreement_with_consensu": [62, 101], "worst_class": [62, 101], "trustworthi": [62, 101, 106], "get_label_quality_multiannotator_ensembl": 62, "weigtht": 62, "budget": 62, "retrain": [62, 88, 106], "active_learning_scor": 62, "active_learning_scores_unlabel": 62, "get_active_learning_scores_ensembl": 62, "henc": [62, 89, 90, 101], "get_majority_vote_label": [62, 101], "event": 62, "lastli": [62, 94], "convert_long_to_wide_dataset": 62, "labels_multiannotator_long": 62, "wide": [62, 87, 88, 89], "labels_multiannotator_wid": 62, "common_multilabel_issu": [63, 65], "exclus": [63, 102], "rank_classes_by_multilabel_qu": [63, 65], "overall_multilabel_health_scor": [63, 65], "multilabel_health_summari": [63, 65], "classes_by_multilabel_qu": 63, "inner": [64, 78, 96], "find_multilabel_issues_per_class": [64, 65], "per_class_label_issu": 64, "label_issues_list": 64, "pred_probs_list": [64, 72, 92, 99], "anim": [65, 104], "rat": 65, "predat": 65, "pet": 65, "reptil": 65, "box": [67, 69, 70, 97, 103], "object_detect": [67, 69, 70, 103], "return_indices_ranked_by_scor": [67, 103], "overlapping_label_check": [67, 69], "suboptim": [67, 69], "locat": [67, 69, 96, 103, 107, 108], "bbox": [67, 70, 103], "image_nam": [67, 70], "y1": [67, 70, 103], "y2": [67, 70, 103], "later": [67, 70, 71, 88, 108], "corner": [67, 70, 103], "xyxi": [67, 70, 103], "io": [67, 70, 89, 96, 97], "keras_cv": [67, 70], "bounding_box": [67, 70, 103], "detectron": [67, 70, 103], "detectron2": [67, 70, 103], "readthedoc": [67, 70], "en": [67, 70], "latest": [67, 70], "visual": [67, 68, 70, 87, 90, 91, 92, 106, 108], "draw_box": [67, 70], "mmdetect": [67, 70, 103], "swap": [67, 69, 79, 83], "penal": [67, 69], "concern": [67, 69, 84, 91], "issues_from_scor": [68, 69, 77, 78, 79, 81, 82, 83, 103, 107, 108], "compute_overlooked_box_scor": [68, 69], "compute_badloc_box_scor": [68, 69], "compute_swap_box_scor": [68, 69], "pool_box_scores_per_imag": [68, 69], "object_counts_per_imag": [68, 70, 103], "bounding_box_size_distribut": [68, 70, 103], "class_label_distribut": [68, 70, 103], "get_sorted_bbox_count_idx": [68, 70], "plot_class_size_distribut": [68, 70], "plot_class_distribut": [68, 70], "get_average_per_class_confusion_matrix": [68, 70], "calculate_per_class_metr": [68, 70], "aggregation_weight": 69, "imperfect": [69, 98], "chose": [69, 101, 103], "imperfectli": [69, 103], "dirti": [69, 72, 75, 106], "subtyp": 69, "badloc": 69, "nonneg": 69, "high_probability_threshold": 69, "auxiliary_input": [69, 70], "iou": [69, 70], "heavili": 69, "auxiliarytypesdict": 69, "pred_label": [69, 88], "pred_label_prob": 69, "pred_bbox": 69, "lab_label": 69, "lab_bbox": 69, "similarity_matrix": 69, "min_possible_similar": 69, "scores_overlook": 69, "low_probability_threshold": 69, "scores_badloc": 69, "accident": [69, 88, 94, 95, 98], "scores_swap": 69, "box_scor": 69, "image_scor": [69, 78, 107], "discov": [70, 91, 96, 108], "abnorm": [70, 92, 103], "auxiliari": [70, 104, 107], "_get_valid_inputs_for_compute_scor": 70, "object_count": 70, "down": 70, "bbox_siz": 70, "class_distribut": 70, "plot": [70, 90, 91, 96, 99, 102, 104, 106, 107], "sorted_idx": [70, 104], "class_to_show": 70, "hidden": [70, 104], "max_class_to_show": 70, "plt": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "matplotlib": [70, 79, 90, 91, 92, 96, 99, 102, 103, 104, 106], "pyplot": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "prediction_threshold": 70, "overlai": [70, 103], "figsiz": [70, 90, 91, 92, 96, 99, 102, 104], "save_path": [70, 103], "blue": [70, 97, 99, 103], "overlaid": 70, "side": [70, 97, 103], "figur": [70, 96, 99, 102, 104, 106], "extens": [70, 99, 101], "png": [70, 96, 103], "pdf": [70, 71], "svg": 70, "num_proc": [70, 92], "intersect": [70, 98], "tp": 70, "fp": 70, "ground": [70, 97, 99, 101, 106], "truth": [70, 99, 101, 106], "bias": [70, 96], "avg_metr": 70, "distionari": 70, "95": [70, 80, 82, 94, 97, 99, 106], "per_class_metr": 70, "Of": 71, "find_top_issu": [71, 72, 104], "behind": [71, 99], "dist_metr": 71, "subtract": [71, 72], "renorm": [71, 72, 98], "least_confid": 71, "sum_": 71, "log": [71, 72, 85], "softmax": [71, 78, 82, 92], "literatur": 71, "gen": 71, "liu": 71, "lochman": 71, "zach": 71, "openaccess": 71, "thecvf": 71, "cvpr2023": 71, "liu_gen_pushing_the_limits_of_softmax": 71, "based_out": 71, "distribution_detection_cvpr_2023_pap": 71, "fit_scor": [71, 104], "ood_predictions_scor": 71, "pretrain": [71, 88, 89, 95, 104], "adjust_confident_threshold": 71, "probabilist": [71, 87, 89, 90, 91, 94, 95, 104, 105], "order_label_issu": [72, 85], "whichev": [72, 105], "argsort": [72, 88, 92, 95, 99, 103, 104, 106], "max_": 72, "get_label_quality_ensemble_scor": [72, 98, 99], "weight_ensemble_members_bi": 72, "custom_weight": 72, "log_loss_search_t_valu": 72, "0001": [72, 97], "scheme": 72, "log_loss_search": 72, "log_loss": [72, 95], "1e0": 72, "1e1": 72, "1e2": 72, "2e2": 72, "quality_scor": [72, 104], "forth": 72, "top_issue_indic": 72, "rank_bi": [72, 85], "weird": [72, 83], "minu": 72, "prob_label": 72, "max_prob_not_label": 72, "AND": [72, 95], "get_epistemic_uncertainti": [73, 74], "get_aleatoric_uncertainti": [73, 74], "corrupt": [74, 106], "linearregress": [74, 98, 106], "y_with_nois": 74, "n_boot": [74, 98], "include_aleatoric_uncertainti": [74, 98], "sole": [74, 87, 90, 101, 104], "bootstrap": [74, 98, 106], "resampl": [74, 89, 98], "epistem": [74, 98, 104, 106], "aleator": [74, 98, 106], "model_final_kwarg": 74, "coars": 74, "thorough": [74, 98], "fine": [74, 88, 89, 95, 104], "grain": 74, "grid": [74, 96], "varianc": [74, 99], "epistemic_uncertainti": 74, "residu": [74, 75, 98], "deviat": [74, 103, 106], "aleatoric_uncertainti": 74, "outr": 75, "contin": 75, "raw": [75, 84, 85, 91, 92, 97, 98, 101, 103, 104, 106], "aka": [75, 89, 99, 103, 106, 108], "00323821": 75, "33692597": 75, "00191686": 75, "semant": [76, 78, 79, 100], "pixel": [76, 78, 79, 92, 104, 107], "h": [76, 78, 79, 107], "height": [76, 78, 79, 107], "w": [76, 78, 79, 107], "width": [76, 78, 79, 107], "labels_one_hot": [76, 79, 107], "stream": [76, 104, 108], "downsampl": [76, 78, 107], "shrink": [76, 78], "divis": [76, 78, 90], "common_label_issu": [77, 79, 81, 83, 107, 108], "filter_by_class": [77, 79, 107], "segmant": [78, 79], "num_pixel_issu": [78, 107], "product": [78, 92, 96, 98], "pixel_scor": [78, 107], "enter": 79, "legend": [79, 90, 91, 96, 102, 103, 106, 107], "colormap": 79, "background": [79, 96], "person": [79, 98, 103, 107, 108], "ambigu": [79, 83, 88, 89, 95, 97, 99, 108], "systemat": [79, 83, 101], "misunderstood": [79, 83], "issues_df": [79, 92], "class_index": 79, "issues_subset": [79, 83], "filter_by_token": [81, 83, 108], "token_score_method": 82, "sentence_score_method": 82, "sentence_score_kwarg": 82, "compris": [82, 83], "token_scor": [82, 108], "converg": 82, "toward": [82, 96], "_softmin_sentence_scor": 82, "sentence_scor": [82, 108], "token_info": 82, "02": [82, 90, 91, 96, 99, 103, 108], "03": [82, 94, 96, 97, 99, 103, 108], "04": [82, 94, 96, 103], "08": [82, 96, 99, 103, 104, 106, 108], "commonli": [83, 85, 90, 91, 102, 108], "But": [83, 95, 99, 106, 108], "restrict": [83, 98], "reliabl": [84, 87, 89, 96, 98, 101, 107], "thousand": 84, "imagenet": [84, 97], "popular": [84, 101, 103], "centric": [84, 92, 94, 95, 100], "minut": [84, 87, 88, 89, 94, 95, 97, 101, 102, 103, 106, 107, 108], "conda": 84, "feature_embed": [84, 104], "Then": [84, 87, 88, 92, 98], "your_dataset": [84, 89, 90, 91, 92, 94, 95, 98], "column_name_of_label": [84, 89, 90, 91, 92, 94, 95], "plagu": [84, 91], "untrain": 84, "\u30c4": 84, "label_issues_info": [84, 91], "sklearn_compatible_model": 84, "framework": [84, 102, 103], "complianc": 84, "tag": [84, 102, 108], "sequenc": 84, "recognit": [84, 89, 98, 108], "train_data": [84, 87, 88, 104, 106], "gotten": 84, "test_data": [84, 87, 88, 99, 102, 104, 106], "deal": [84, 91, 96], "tutori": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "feel": [84, 89, 91, 98], "ask": [84, 98], "slack": [84, 98], "project": [84, 106], "welcom": 84, "commun": [84, 98], "guidelin": [84, 103], "piec": 84, "smart": [84, 92, 94, 95, 98], "edit": [84, 98], "easier": [84, 96, 99], "unreli": [84, 87, 89, 94, 95, 96], "link": [84, 89, 97, 103], "older": 85, "outlin": 85, "substitut": 85, "v2": [85, 87, 94], "get_noise_indic": 85, "psx": 85, "sorted_index_method": 85, "order_label_error": 85, "label_errors_bool": 85, "latent_estim": 85, "num_label_error": 85, "learningwithnoisylabel": 85, "neatli": 85, "organ": [85, 87, 94, 97, 108], "reorgan": 85, "baseline_method": 85, "incorpor": [85, 99], "research": [85, 99], "polyplex": 85, "terminologi": 85, "label_error": 85, "quickstart": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 101, 102, 103, 104, 106, 107, 108], "sql": [87, 94], "databas": [87, 94], "excel": [87, 94], "parquet": [87, 94], "student": [87, 94, 106, 108], "grade": [87, 94, 106], "exam": [87, 94, 106], "letter": [87, 94, 108], "hundr": [87, 94], "mistak": [87, 88, 92, 94, 95], "extratreesclassifi": 87, "extratre": 87, "ranked_label_issu": [87, 88], "branch": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "preprocess": [87, 88, 91, 94, 96, 104, 106], "standardscal": [87, 94, 104], "labelencod": [87, 88], "train_test_split": [87, 88, 90, 91, 104], "accuracy_scor": [87, 88, 89, 95, 99], "grades_data": [87, 94], "read_csv": [87, 88, 94, 95, 96, 106], "demo": [87, 91, 94, 102], "stud_id": [87, 94], "exam_1": [87, 94, 106], "exam_2": [87, 94, 106], "exam_3": [87, 94, 106], "letter_grad": [87, 94], "f48f73": [87, 94], "53": [87, 90, 91, 94, 96, 97, 102, 103], "00": [87, 90, 91, 94, 96, 97, 104], "77": [87, 90, 91, 94, 103, 108], "0bd4e7": [87, 94], "81": [87, 94, 95, 103, 106, 108], "great": [87, 94, 97], "particip": [87, 94], "cb9d7a": [87, 94], "61": [87, 94, 96, 99, 103, 106], "94": [87, 94, 97, 99, 103, 106], "9acca4": [87, 94], "48": [87, 94, 96, 97, 99, 103], "x_raw": [87, 94], "labels_raw": 87, "interg": [87, 88], "categorical_featur": [87, 106], "x_encod": [87, 94], "get_dummi": [87, 94, 106], "drop_first": [87, 94], "numeric_featur": [87, 94], "scaler": [87, 94, 104], "x_process": [87, 94], "fit_transform": [87, 94, 96], "bring": [87, 88, 92, 94, 95, 101, 106], "byod": [87, 88, 92, 94, 95, 101, 106], "tress": 87, "held": [87, 89, 94, 95, 97, 103, 104, 105], "straightforward": [87, 89, 94], "benefit": [87, 89, 105, 107], "num_crossval_fold": [87, 89, 94, 101], "tabl": [87, 94, 97, 101], "212": [87, 99], "review": [87, 88, 91, 94, 95, 97, 98, 99, 103, 106, 107, 108], "iloc": [87, 88, 89, 94, 95, 96, 106], "92": [87, 90, 99, 103], "93": [87, 97, 103, 106, 108], "827": 87, "99": [87, 96, 97, 99], "86": [87, 91, 92, 94, 99, 103, 106], "74": [87, 96, 103, 106], "637": [87, 94], "79": [87, 97, 103], "65": [87, 90, 96, 103], "cheat": 87, "0pt": 87, "120": [87, 90, 91], "233": 87, "83": [87, 99, 103, 106, 108], "76": [87, 99, 102, 103, 106], "suspici": [87, 94], "carefulli": [87, 92, 94, 95], "examin": [87, 90, 91, 94, 96, 103], "labels_train": 87, "labels_test": 87, "test_siz": [87, 88, 90, 91], "acc_og": [87, 88], "783068783068783": 87, "robustli": [87, 88, 106], "14": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "acc_cl": [87, 88], "8095238095238095": 87, "blindli": [87, 88, 89, 98, 106], "trust": [87, 88, 89, 98, 99, 101, 105, 106], "effort": [87, 88, 106], "intent": [88, 95], "servic": [88, 95, 98], "onlin": [88, 95], "bank": [88, 95, 97], "banking77": [88, 95], "oo": [88, 95], "categori": [88, 92, 95, 96], "shortlist": [88, 95, 106], "scope": [88, 95], "logist": [88, 90, 91, 95, 101, 104], "probabilit": [88, 89], "drop": [88, 94, 96, 98, 101, 106], "earlier": [88, 108], "sentence_transform": [88, 95], "sentencetransform": [88, 95], "payment": [88, 95], "cancel_transf": [88, 95], "transfer": [88, 95], "fund": [88, 95], "cancel": [88, 95], "transact": [88, 95], "my": [88, 95], "revert": [88, 95], "morn": [88, 95], "realis": [88, 95], "yesterdai": [88, 95], "rent": [88, 95], "tomorrow": [88, 95], "raw_text": [88, 95], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "card_about_to_expir": [88, 95], "lost_or_stolen_phon": [88, 95], "getting_spare_card": [88, 95], "change_pin": [88, 95], "card_payment_fee_charg": [88, 95], "supported_cards_and_curr": [88, 95], "beneficiary_not_allow": [88, 95], "visa_or_mastercard": [88, 95], "apple_pay_or_google_pai": [88, 95], "card": [88, 95, 97], "utter": [88, 95], "encond": 88, "test_label": [88, 99, 102, 104], "suit": [88, 95, 96, 97, 98], "electra": [88, 95], "discrimin": [88, 95], "googl": [88, 95], "train_text": 88, "test_text": 88, "home": [88, 95, 97], "runner": [88, 95], "google_electra": [88, 95], "pool": [88, 95, 98, 104], "leverag": [88, 89, 95, 98, 99, 101], "computation": [88, 89, 95], "intens": [88, 89, 95], "400": [88, 95], "858371": 88, "547274": 88, "826228": 88, "966008": 88, "792449": 88, "identified_issu": [88, 106], "lowest_quality_label": [88, 89, 95, 99, 106], "to_numpi": [88, 95, 96, 106], "44": [88, 96, 97, 102, 103], "646": 88, "390": 88, "628": 88, "121": [88, 99], "702": 88, "863": 88, "135": 88, "337": [88, 103], "735": 88, "print_as_df": 88, "inverse_transform": 88, "charg": [88, 95], "cash": [88, 95], "holidai": [88, 95], "sent": [88, 95, 108], "mine": [88, 95], "expir": [88, 95], "fight": 88, "hors": [88, 97, 104], "duck": [88, 97], "me": [88, 95, 96], "whoever": [88, 95], "consum": [88, 106], "18": [88, 89, 95, 96, 97, 98, 99, 103, 104, 106, 107], "baseline_model": [88, 106], "87": [88, 91, 92, 103, 106], "acceler": [88, 106], "19": [88, 89, 92, 95, 96, 97, 98, 99, 103, 104, 106, 107], "89": [88, 90, 94, 103, 106], "spoken": 89, "500": [89, 96, 104, 108], "english": [89, 97], "pronunci": 89, "wav": 89, "huggingfac": [89, 90, 91, 92, 98], "voxceleb": 89, "speech": [89, 108], "your_pred_prob": [89, 90, 91, 94, 95], "tensorflow_io": 89, "huggingface_hub": 89, "reproduc": [89, 94, 96, 99, 101], "command": 89, "wget": [89, 103, 107, 108], "navig": 89, "browser": 89, "jakobovski": 89, "archiv": [89, 108], "v1": 89, "tar": [89, 96, 104], "gz": [89, 96, 104], "mkdir": [89, 108], "spoken_digit": 89, "xf": 89, "6_nicolas_32": 89, "data_path": 89, "listdir": 89, "nondeterminist": 89, "file_nam": 89, "endswith": 89, "file_path": 89, "join": [89, 92, 96, 98], "7_george_26": 89, "0_nicolas_24": 89, "0_nicolas_6": 89, "listen": 89, "display_exampl": 89, "expand": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "pulldown": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "colab": [89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "tfio": 89, "pathlib": 89, "ipython": [89, 96], "load_wav_16k_mono": 89, "filenam": 89, "khz": 89, "file_cont": 89, "read_fil": 89, "sample_r": 89, "decode_wav": 89, "desired_channel": 89, "squeez": 89, "rate_in": 89, "rate_out": 89, "16000": 89, "wav_file_nam": 89, "audio_r": 89, "wav_file_exampl": 89, "plai": [89, 97, 98], "button": 89, "wav_file_name_exampl": 89, "7_jackson_43": 89, "hear": 89, "extractor": 89, "encoderclassifi": 89, "spkrec": 89, "xvect": 89, "feature_extractor": 89, "from_hparam": 89, "run_opt": 89, "uncom": [89, 96], "ffmpeg": 89, "backend": 89, "wav_audio_file_path": 89, "torchaudio": 89, "extract_audio_embed": 89, "emb": [89, 92], "signal": 89, "encode_batch": 89, "embeddings_list": [89, 92], "embeddings_arrai": 89, "512": [89, 92], "196311": 89, "319459": 89, "478975": 89, "2890875": 89, "8170238": 89, "89265": 89, "898056": 89, "256195": 89, "559641": 89, "559721": 89, "62067": 89, "285245": 89, "21": [89, 90, 96, 97, 99, 103, 106, 108], "709627": 89, "5033693": 89, "913803": 89, "819831": 89, "1831515": 89, "208763": 89, "084257": 89, "3210397": 89, "005453": 89, "216152": 89, "478235": 89, "6821785": 89, "053807": 89, "242471": 89, "091424": 89, "78334856": 89, "03954": 89, "23": [89, 92, 96, 97, 99, 103, 106], "569176": 89, "761097": 89, "1258295": 89, "753237": 89, "3508866": 89, "598274": 89, "23712": 89, "2500": 89, "tol": 89, "decreas": [89, 96, 98], "cv_accuraci": 89, "9708": 89, "issue_type_descript": [89, 90, 91, 92, 94, 95, 99], "lt": [89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 104], "gt": [89, 90, 91, 92, 94, 95, 96, 99, 101, 108], "9976": 89, "986": 89, "002161": 89, "176": [89, 97, 99, 102], "002483": 89, "2318": 89, "004411": 89, "1005": 89, "004857": 89, "1871": 89, "007494": 89, "040587": 89, "999207": 89, "999377": 89, "975220": 89, "999367": 89, "identified_label_issu": [89, 95], "516": 89, "1946": 89, "469": 89, "2132": 89, "worth": [89, 99], "6_yweweler_25": 89, "7_nicolas_43": 89, "6_theo_27": 89, "6_yweweler_36": 89, "6_yweweler_14": 89, "6_yweweler_35": 89, "6_nicolas_8": 89, "sound": 89, "quit": [89, 104], "underneath": 90, "hood": [90, 96, 98], "alert": 90, "introduct": 90, "mayb": [90, 91, 95], "your_feature_matrix": [90, 91], "toi": [90, 91, 92, 96, 97, 99, 101], "inf": [90, 91], "mid": [90, 91], "bins_map": [90, 91], "create_data": [90, 91], "y_bin": [90, 91], "y_i": [90, 91], "y_bin_idx": [90, 91], "y_train": [90, 91, 99, 106], "y_test": [90, 91, 99, 106], "y_train_idx": [90, 91], "y_test_idx": [90, 91], "slide": [90, 91, 97], "frame": [90, 91], "x_out": [90, 91], "tini": [90, 91], "concaten": [90, 91, 105], "y_out": [90, 91], "y_out_bin": [90, 91], "y_out_bin_idx": [90, 91], "exact_duplicate_idx": [90, 91], "x_duplic": [90, 91], "y_duplic": [90, 91], "y_duplicate_idx": [90, 91], "noisy_labels_idx": [90, 91, 102], "scatter": [90, 91, 96, 99, 102, 106], "black": [90, 91, 97, 106], "cyan": [90, 91], "plot_data": [90, 91, 96, 99, 102, 106], "fig": [90, 91, 92, 96, 97, 104, 106], "ax": [90, 91, 92, 96, 104, 106], "subplot": [90, 91, 92, 96, 104], "set_titl": [90, 91, 92, 96, 104], "set_xlabel": [90, 91], "x_1": [90, 91], "fontsiz": [90, 91, 92, 96, 99, 102], "set_ylabel": [90, 91], "x_2": [90, 91], "set_xlim": [90, 91], "set_ylim": [90, 91], "linestyl": [90, 91, 96], "circl": [90, 91, 99, 102], "misclassifi": [90, 91], "zip": [90, 91, 92, 96, 103, 108], "label_err": [90, 91], "180": [90, 91, 103], "marker": [90, 91], "facecolor": [90, 91, 96], "edgecolor": [90, 91, 96], "linewidth": [90, 91, 96, 104], "dup": [90, 91], "first_legend": [90, 91], "align": [90, 91], "title_fontproperti": [90, 91], "semibold": [90, 91], "second_legend": [90, 91], "45": [90, 91, 96, 97, 99, 103, 108], "gca": [90, 91], "add_artist": [90, 91], "tight_layout": [90, 91, 96], "ideal": [90, 91], "remaind": 90, "modal": [90, 91, 98, 101], "132": [90, 91, 99, 103], "9318": 90, "006940": 90, "007830": 90, "40": [90, 91, 95, 96, 97], "014828": 90, "107": [90, 91, 99, 102], "021241": 90, "026407": 90, "notic": [90, 99, 101, 103], "3558": [90, 91], "126": [90, 91, 99, 103], "006636": [90, 91], "130": [90, 91], "012571": [90, 91], "129": [90, 91], "127": [90, 91], "014909": [90, 91], "128": [90, 91, 92], "017443": [90, 91], "6160": [90, 91], "131": [90, 91, 107], "000000e": [90, 91], "000002": [90, 91], "463180e": [90, 91], "07": [90, 91, 92, 94, 96, 99, 103, 106, 108], "51": [90, 91, 94, 96, 97, 99, 103], "161148": [90, 91], "859087e": [90, 91], "30": [90, 91, 92, 96, 97, 98, 102, 107, 108], "3453": 90, "029542": 90, "031182": 90, "057961": 90, "058244": 90, "54": [90, 96, 97, 99, 103, 108], "039122": 90, "044598": 90, "105": [90, 103], "105196": 90, "133654": 90, "43": [90, 96, 97, 99, 103], "168033": 90, "125": 90, "101107": 90, "183382": 90, "109": [90, 97, 103, 108], "209259": 90, "211042": 90, "221316": 90, "average_ood_scor": 90, "34530442089193386": 90, "52": [90, 96, 97, 103], "169820": 90, "087324e": 90, "259024": 90, "583757e": 90, "91": [90, 103], "346458": 90, "341292e": 90, "specfi": 90, "new_lab": 90, "scoring_funct": 90, "div": 90, "rem": 90, "inv_scal": 90, "49": [90, 96, 97, 99, 103], "superstitionissuemanag": 90, "unlucki": 90, "superstit": 90, "to_seri": 90, "issues_mask": 90, "summary_scor": 90, "9242": 90, "is_superstition_issu": 90, "superstition_scor": 90, "26": [90, 92, 96, 97, 99, 101, 103], "047581": 90, "090635": 90, "129591": 90, "164840": 90, "lurk": [91, 92, 99], "thoroughli": 91, "8561": 91, "001908": 91, "003564": 91, "007331": 91, "008963": 91, "009664": 91, "0227": 91, "022727": 91, "conceptu": 91, "856061": 91, "355772": 91, "616034": 91, "821750": 91, "901562": 91, "betweeen": 91, "859131": 91, "417707": 91, "664083": 91, "970324": 91, "816953": 91, "375317": 91, "641516": 91, "890575": 91, "531021": 91, "460593": 91, "601188": 91, "826147": 91, "752808": 91, "321635": 91, "562539": 91, "948362": 91, "090243": 91, "472909": 91, "746763": 91, "878267": 91, "examples_w_issu": [91, 98], "013445": 91, "025184": 91, "026376": 91, "inde": [91, 95], "miscellan": [91, 93, 108], "428571": 91, "111111": 91, "571429": 91, "407407": 91, "592593": 91, "337838": 91, "092593": 91, "662162": 91, "333333": [91, 97], "952381": 91, "666667": [91, 96], "portion": 91, "huge": [91, 99], "worri": [91, 95], "critic": 91, "60": [92, 96, 99, 106], "torchvis": [92, 96, 104], "tensordataset": 92, "stratifiedkfold": [92, 102], "tqdm": 92, "autonotebook": 92, "math": 92, "fashion_mnist": 92, "num_row": [92, 96], "60000": 92, "transformed_dataset": [92, 96], "with_format": 92, "255": [92, 97], "cpu_count": 92, "torch_dataset": 92, "quick": [92, 102, 104], "super": [92, 94, 95], "relu": 92, "batchnorm2d": 92, "maxpool2d": 92, "lazylinear": 92, "flatten": [92, 96], "get_test_accuraci": 92, "testload": [92, 104], "energi": 92, "trainload": [92, 104], "n_epoch": 92, "patienc": 92, "criterion": 92, "crossentropyloss": 92, "adamw": 92, "best_test_accuraci": 92, "start_epoch": 92, "running_loss": 92, "best_epoch": 92, "end_epoch": 92, "3f": [92, 106], "acc": [92, 99], "time_taken": 92, "compute_embed": 92, "compute_pred_prob": 92, "train_batch_s": 92, "num_work": 92, "worker": [92, 108], "train_id_list": 92, "test_id_list": 92, "train_id": 92, "test_id": 92, "embeddings_model": 92, "ntrain": 92, "trainset": 92, "testset": 92, "pin_memori": 92, "fold_embed": 92, "fold_pred_prob": 92, "finish": 92, "482": 92, "720": 92, "801": 92, "329": [92, 94, 103], "88": [92, 97, 99, 102, 103, 106], "195": 92, "468": 92, "493": 92, "060": 92, "793": 92, "330": [92, 103], "505": 92, "570": 92, "476": 92, "340": 92, "822": 92, "328": [92, 103], "310": 92, "reorder": 92, "hstack": [92, 98, 99, 101], "vision": 92, "grayscal": [92, 96], "max_preval": [92, 96], "7714": 92, "3772": 92, "3585": 92, "166": 92, "3651": 92, "27080": 92, "873833e": 92, "40378": 92, "915575e": 92, "25316": 92, "390277e": 92, "06": [92, 99, 103, 108], "2090": 92, "751164e": 92, "14999": 92, "881301e": 92, "9569": 92, "11262": 92, "000003": 92, "coat": [92, 97], "shirt": [92, 97], "19228": 92, "000010": 92, "dress": 92, "32657": 92, "000013": 92, "bag": [92, 97, 104, 105], "21282": 92, "000016": 92, "53564": 92, "000018": 92, "pullov": 92, "6321": 92, "30968": 92, "001267": 92, "30659": 92, "000022": [92, 108], "47824": 92, "001454": 92, "3370": 92, "000026": 92, "54565": 92, "001854": 92, "9762": 92, "258": 92, "47139": 92, "000033": 92, "166980": 92, "986195": 92, "997205": 92, "sandal": [92, 97], "948781": 92, "999358": 92, "54078": 92, "17371": 92, "000025": 92, "plot_label_issue_exampl": 92, "ncol": [92, 104], "nrow": [92, 104], "ceil": 92, "axes_list": 92, "label_issue_indic": 92, "gl": 92, "sl": 92, "fontdict": 92, "imshow": [92, 96, 104], "cmap": [92, 96, 106], "grai": 92, "subplots_adjust": 92, "hspace": 92, "outsiz": 92, "outlier_issu": [92, 95], "outlier_issues_df": 92, "depict": [92, 102, 103, 104, 105, 107], "plot_outlier_issues_exampl": 92, "n_comparison_imag": 92, "sample_from_class": 92, "number_of_sampl": 92, "non_outlier_indic": 92, "isnul": [92, 96], "non_outlier_indices_excluding_curr": 92, "sampled_indic": 92, "label_scores_of_sampl": 92, "top_score_indic": 92, "top_label_indic": 92, "sampled_imag": 92, "get_image_given_label_and_sampl": 92, "image_from_dataset": 92, "corresponding_label": 92, "comparison_imag": 92, "images_to_plot": 92, "idlist": 92, "iterrow": 92, "near_duplicate_issu": [92, 98], "closest": 92, "counterpart": 92, "near_duplicate_issues_df": 92, "plot_near_duplicate_issue_exampl": 92, "seen_id_pair": 92, "get_image_and_given_label_and_predicted_label": 92, "duplicate_imag": 92, "nd_set": 92, "challeng": 92, "dark_issu": 92, "reveal": [92, 103, 107], "dark_scor": [92, 96], "dark_issues_df": 92, "is_dark_issu": 92, "34848": 92, "203922": 92, "50270": 92, "204588": 92, "3936": 92, "213098": 92, "733": 92, "217686": 92, "8094": 92, "230118": 92, "plot_image_issue_exampl": 92, "difficult": 92, "disproportion": [92, 96], "lowinfo_issu": 92, "low_information_scor": [92, 96], "lowinfo_issues_df": 92, "is_low_information_issu": 92, "53050": 92, "067975": 92, "40875": 92, "089929": 92, "9594": 92, "092601": 92, "34825": 92, "107744": 92, "37530": 92, "108516": 92, "lot": 92, "workflow": [93, 98, 100, 106], "histgradientboostingclassifi": 94, "cat_featur": 94, "boost": [94, 98, 101, 106], "xgboost": [94, 98, 106], "think": [94, 95, 98, 102, 107, 108], "nonzero": 94, "358": 94, "941": 94, "294": [94, 103], "46": [94, 96, 97, 99, 103, 108], "7109": 94, "000005": [94, 95], "886": 94, "000059": 94, "709": 94, "000104": 94, "723": 94, "000169": 94, "689": 94, "000181": 94, "3590": 94, "051882e": 94, "683133e": 94, "536582e": 94, "406589e": 94, "324246e": 94, "6165": 94, "582": 94, "185": [94, 96, 97, 103, 108], "187": [94, 97], "898": 94, "0000": [94, 95, 97, 99], "865": 94, "515002": 94, "837": 94, "556480": 94, "622": 94, "593068": 94, "593207": 94, "920": 94, "618041": 94, "4386345844794593e": 94, "issue_result": 94, "000842": 94, "555944": 94, "004374": 94, "sorted_issu": 94, "73": [94, 96, 97, 102, 103, 106], "deserv": 94, "outlier_result": 94, "sorted_outli": 94, "56": [94, 96, 97, 106], "96": [94, 96, 97, 99, 102, 103, 106], "style": [94, 96, 107], "font": 94, "18px": 94, "ff00ff": 94, "bac": 94, "unintend": [94, 95, 96], "duplicate_result": 94, "lowest_scoring_dupl": 94, "idxmin": [94, 98], "indices_to_displai": 94, "tolist": [94, 98, 102], "perhap": [94, 99, 101], "second_lowest_scoring_dupl": 94, "next_indices_to_displai": 94, "wari": [94, 95, 98], "dive": [95, 96], "your_featur": 95, "text_embed": 95, "data_dict": [95, 99, 101], "85": [95, 103], "38": [95, 96, 97, 103], "9710": 95, "981": 95, "974": 95, "000146": 95, "982": [95, 97], "000224": 95, "971": 95, "000507": 95, "980": [95, 97], "000960": 95, "3584": 95, "994": 95, "009642": 95, "999": 95, "013067": 95, "013841": 95, "433": 95, "014722": 95, "989": 95, "018224": 95, "6070": 95, "160": [95, 106], "095724": 95, "148": 95, "006237": 95, "546": 95, "099341": 95, "514": 95, "006485": 95, "481": 95, "123418": 95, "008165": 95, "313": [95, 103], "564102": 95, "572258": 95, "574915": 95, "31": [95, 96, 97, 99, 101, 103], "575507": 95, "575874": 95, "792090": 95, "257611": 95, "698710": 95, "182121": 95, "771619": 95, "data_with_suggested_label": 95, "suggested_label": 95, "withdraw": 95, "monei": 95, "lowest_quality_outli": 95, "OR": 95, "636c65616e6c616220697320617765736f6d6521": 95, "phone": [95, 97], "gone": 95, "samp": 95, "br": 95, "press": [95, 108], "nonsens": 95, "sens": 95, "detriment": 95, "duplicate_issu": 95, "fee": 95, "go": [95, 96, 97, 99], "strongli": [95, 96], "p_valu": 95, "benign": 95, "curat": 95, "bigger": 96, "make_classif": 96, "5000": [96, 104], "n_featur": 96, "n_inform": 96, "n_redund": 96, "n_repeat": 96, "n_class": 96, "n_clusters_per_class": 96, "flip_i": 96, "class_sep": 96, "faiss": 96, "x_faiss": 96, "float32": [96, 103], "normalize_l2": 96, "index_factori": 96, "hnsw32": 96, "flat": [96, 97], "metric_inner_product": 96, "a_min": 96, "a_max": 96, "create_knn_graph": 96, "assert": 96, "indices_1d": 96, "ravel": 96, "distances_1d": 96, "sort_graph_by_row_valu": 96, "warn_when_not_sort": 96, "50000": 96, "524": 96, "991400": 96, "356924": 96, "363": 96, "619581": 96, "108": [96, 103], "500000": 96, "651929": 96, "999827": 96, "031217": 96, "933716": 96, "627345": 96, "998540": 96, "530909": 96, "296974": 96, "646765": 96, "942721": 96, "332824": 96, "803246": 96, "625202": 96, "999816": 96, "474031": 96, "706253": 96, "655108": 96, "997703": 96, "131466": 96, "912389": 96, "639200": 96, "4995": 96, "998646": 96, "504755": 96, "746777": 96, "680033": 96, "4996": 96, "894230": 96, "340986": 96, "816472": 96, "640711": 96, "4997": 96, "999100": 96, "428545": 96, "592421": 96, "658949": 96, "4998": 96, "986792": 96, "273710": 96, "618033": 96, "4999": 96, "986776": 96, "273524": 96, "618084": 96, "instabl": 96, "proxim": 96, "analys": 96, "comfort": 96, "explor": [96, 103, 104], "third": 96, "parti": [96, 108], "newsgroup": 96, "alt": [96, 97], "atheism": [96, 97], "sci": [96, 97], "fetch_20newsgroup": 96, "newsgroups_train": 96, "header": 96, "footer": 96, "quot": 96, "df_text": 96, "target_nam": 96, "enlighten": 96, "omnipot": 96, "19apr199320262420": 96, "kelvin": 96, "jpl": 96, "nasa": 96, "gov": 96, "baa": 96, "nhenri": 96, "he": 96, "nno": 96, "ge": 96, "nlucki": 96, "babi": [96, 97], "tfidfvector": 96, "feature_extract": 96, "x_vector": 96, "data_valuation_issu": 96, "147": [96, 99, 103], "500047": 96, "500093": 96, "499953": 96, "1068": 96, "1069": 96, "1070": 96, "1071": 96, "1072": 96, "1073": 96, "concentr": 96, "seaborn": 96, "sn": 96, "distinguish": 96, "strip": 96, "stripplot": 96, "hue": [96, 106], "dodg": 96, "jitter": 96, "axvlin": [96, 104], "xlabel": 96, "ourselv": 96, "make_blob": 96, "center": [96, 97], "cluster_std": 96, "n_noisy_label": 96, "meaning": [96, 98, 104], "silhouette_scor": 96, "gridsearchcv": 96, "silhouett": 96, "cluster_label": 96, "fit_predict": 96, "param_grid": 96, "grid_search": 96, "best_kmean": 96, "best_estimator_": 96, "underperforming_group_issu": 96, "328308": 96, "tab10": 96, "domain": 96, "knowledg": [96, 99], "dataset_tsv": 96, "ag": [96, 106], "gender": 96, "educ": 96, "experi": 96, "highsalari": 96, "indiana": 96, "phd": 96, "male": 96, "bachelor": 96, "femal": 96, "kansa": 96, "school": [96, 97], "ohio": 96, "57": [96, 97, 99], "california": 96, "59": [96, 97, 103], "34": [96, 97, 99, 101, 103, 108], "63": [96, 99, 103, 106], "47": [96, 97, 103], "stringio": 96, "sep": [96, 108], "simplic": [96, 102], "ordinalencod": 96, "columns_to_encod": 96, "encoded_df": 96, "salari": 96, "573681": 96, "underpin": 96, "caught": 96, "whenev": 96, "generate_data_depend": 96, "num_sampl": 96, "a1": 96, "a2": 96, "a3": 96, "375": 96, "975": 96, "non_iid_issu": 96, "796474": 96, "842432": 96, "922562": 96, "820759": 96, "873136": 96, "887373": 96, "825101": 96, "855875": 96, "751795": 96, "835796": 96, "ylabel": [96, 104], "coolwarm": 96, "colorbar": [96, 106], "strong": 96, "evid": 96, "inter": 96, "mitig": 96, "risk": 96, "deeper": 96, "tsv": 96, "tab": 96, "pars": 96, "annual_spend": 96, "number_of_transact": 96, "last_purchase_d": 96, "rural": 96, "4099": 96, "2024": [96, 108], "6421": 96, "nat": 96, "suburban": 96, "5436": 96, "4046": 96, "66": [96, 97], "3467": 96, "67": [96, 97, 103, 106], "4757": 96, "4199": 96, "4991": 96, "4655": 96, "82": [96, 97, 99, 103, 106], "5584": 96, "urban": 96, "3102": 96, "6637": 96, "9167": 96, "6790": 96, "5327": 96, "parse_d": 96, "lose": 96, "intact": 96, "encode_categorical_column": 96, "placehold": 96, "dropna": [96, 101], "category_to_numb": 96, "_encod": 96, "gender_encod": 96, "location_encod": 96, "focus": [96, 99, 101, 102, 106], "null_issu": 96, "833333": 96, "sorted_indic": [96, 103], "sorted_df": 96, "nice": 96, "styler": 96, "combined_df": 96, "concat": [96, 106], "highlight_null_valu": 96, "val": [96, 99], "yellow": [96, 97], "highlight_datalab_column": 96, "lightblu": 96, "highlight_is_null_issu": 96, "orang": [96, 97], "styled_df": 96, "nbsp": [96, 98, 99], "160000": 96, "820000": 96, "460000": 96, "470000": 96, "960000": 96, "620000": 96, "550000": 96, "660000": 96, "670000": [96, 97], "370000": 96, "530000": 96, "710000": 96, "020000": 96, "320000": 96, "990000": 96, "rarer": 96, "fairer": 96, "randomli": [96, 99], "class_imbalance_issu": 96, "countplot": 96, "xtick": 96, "rotat": 96, "ytick": 96, "filtered_df": 96, "xy": 96, "va": 96, "textual": 96, "get_ytick": 96, "nbar": 96, "nimbal": 96, "get_legend_handles_label": 96, "title_fonts": 96, "aspect": 96, "anomali": [96, 103], "enhanc": [96, 99, 101, 103], "artifici": 96, "alter": [96, 98], "darken": 96, "blurry_scor": 96, "odd_aspect_ratio_scor": 96, "setup": 96, "cifar10": 96, "markdown": 96, "root": [96, 104], "selected_class": 96, "convert_to_png_imag": 96, "bytesio": [96, 97], "seek": 96, "max_num_imag": 96, "list_imag": 96, "list_label": 96, "num_imag": 96, "img": [96, 104, 106], "toronto": [96, 104], "edu": [96, 104], "kriz": [96, 104], "170498071": [96, 104], "69520911": 96, "78it": 96, "dataset_dict": 96, "from_dict": [96, 98], "apply_dark": 96, "transformed_list_imag": 96, "transformed_dataset_dict": 96, "plot_imag": [96, 104], "num_images_to_plot": 96, "num_col": 96, "hide": 96, "get_property_scor": 96, "_spurious_correl": 96, "get_specific_property_scor": 96, "property_scores_df": 96, "property_nam": 96, "standard_property_scor": 96, "transformed_property_scor": 96, "295": [96, 103], "light_scor": 96, "415": 96, "325": 96, "odd_size_scor": 96, "grayscale_scor": 96, "015": 96, "refin": 97, "instruct": [97, 98], "studi": [97, 103], "mnist_test_set": 97, "imagenet_val_set": 97, "tench": 97, "goldfish": 97, "white": [97, 108], "shark": 97, "tiger": 97, "hammerhead": 97, "electr": 97, "rai": 97, "stingrai": 97, "cock": 97, "hen": 97, "ostrich": 97, "brambl": 97, "goldfinch": 97, "hous": 97, "finch": 97, "junco": 97, "indigo": 97, "bunt": 97, "american": [97, 108], "robin": 97, "bulbul": 97, "jai": 97, "magpi": 97, "chickade": 97, "dipper": 97, "kite": 97, "bald": 97, "eagl": 97, "vultur": 97, "grei": 97, "owl": 97, "salamand": 97, "smooth": 97, "newt": 97, "spot": [97, 98, 103], "axolotl": 97, "bullfrog": 97, "tree": 97, "frog": [97, 104], "tail": 97, "loggerhead": 97, "sea": 97, "turtl": 97, "leatherback": 97, "mud": 97, "terrapin": 97, "band": 97, "gecko": 97, "green": [97, 108], "iguana": 97, "carolina": 97, "anol": 97, "desert": 97, "grassland": 97, "whiptail": 97, "lizard": 97, "agama": 97, "frill": 97, "neck": 97, "allig": 97, "gila": 97, "monster": 97, "european": 97, "chameleon": 97, "komodo": 97, "dragon": 97, "nile": 97, "crocodil": 97, "triceratop": 97, "worm": 97, "snake": 97, "ring": 97, "eastern": 97, "hog": 97, "nose": 97, "kingsnak": 97, "garter": 97, "water": 97, "vine": 97, "night": 97, "boa": 97, "constrictor": 97, "african": 97, "rock": 97, "indian": 97, "cobra": 97, "mamba": 97, "saharan": 97, "horn": 97, "viper": 97, "diamondback": 97, "rattlesnak": 97, "sidewind": 97, "trilobit": 97, "harvestman": 97, "scorpion": 97, "garden": 97, "spider": 97, "barn": 97, "southern": 97, "widow": 97, "tarantula": 97, "wolf": 97, "tick": 97, "centiped": 97, "grous": 97, "ptarmigan": 97, "ruf": 97, "prairi": 97, "peacock": 97, "quail": 97, "partridg": 97, "parrot": 97, "macaw": 97, "sulphur": 97, "crest": 97, "cockatoo": 97, "lorikeet": 97, "coucal": 97, "bee": 97, "eater": 97, "hornbil": 97, "hummingbird": 97, "jacamar": 97, "toucan": 97, "breast": 97, "mergans": 97, "goos": 97, "swan": 97, "tusker": 97, "echidna": 97, "platypu": 97, "wallabi": 97, "koala": 97, "wombat": 97, "jellyfish": 97, "anemon": 97, "brain": 97, "coral": 97, "flatworm": 97, "nematod": 97, "conch": 97, "snail": 97, "slug": 97, "chiton": 97, "chamber": 97, "nautilu": 97, "dung": 97, "crab": 97, "fiddler": 97, "king": 97, "lobster": 97, "spini": 97, "crayfish": 97, "hermit": 97, "isopod": 97, "stork": 97, "spoonbil": 97, "flamingo": 97, "heron": 97, "egret": 97, "bittern": 97, "crane": 97, "bird": [97, 104], "limpkin": 97, "gallinul": 97, "coot": 97, "bustard": 97, "ruddi": 97, "turnston": 97, "dunlin": 97, "redshank": 97, "dowitch": 97, "oystercatch": 97, "pelican": 97, "penguin": 97, "albatross": 97, "whale": 97, "killer": 97, "dugong": 97, "lion": 97, "chihuahua": 97, "japanes": 97, "chin": 97, "maltes": 97, "pekinges": 97, "shih": 97, "tzu": 97, "charl": 97, "spaniel": 97, "papillon": 97, "terrier": 97, "rhodesian": 97, "ridgeback": 97, "afghan": [97, 108], "hound": 97, "basset": 97, "beagl": 97, "bloodhound": 97, "bluetick": 97, "coonhound": 97, "tan": 97, "walker": 97, "foxhound": 97, "redbon": 97, "borzoi": 97, "irish": 97, "wolfhound": 97, "italian": 97, "greyhound": 97, "whippet": 97, "ibizan": 97, "norwegian": 97, "elkhound": 97, "otterhound": 97, "saluki": 97, "scottish": 97, "deerhound": 97, "weimaran": 97, "staffordshir": 97, "bull": 97, "bedlington": 97, "border": 97, "kerri": 97, "norfolk": 97, "norwich": 97, "yorkshir": 97, "wire": 97, "fox": 97, "lakeland": 97, "sealyham": 97, "airedal": 97, "cairn": 97, "australian": 97, "dandi": 97, "dinmont": 97, "boston": 97, "miniatur": 97, "schnauzer": 97, "giant": 97, "tibetan": 97, "silki": 97, "wheaten": 97, "west": 97, "highland": 97, "lhasa": 97, "apso": 97, "retriev": 97, "curli": 97, "golden": 97, "labrador": 97, "chesapeak": 97, "bai": 97, "german": [97, 108], "shorthair": 97, "pointer": 97, "vizsla": 97, "setter": 97, "gordon": 97, "brittani": 97, "clumber": 97, "springer": 97, "welsh": 97, "cocker": 97, "sussex": 97, "kuvasz": 97, "schipperk": 97, "groenendael": 97, "malinoi": 97, "briard": 97, "kelpi": 97, "komondor": 97, "sheepdog": 97, "shetland": 97, "colli": 97, "bouvier": 97, "de": 97, "flandr": 97, "rottweil": 97, "shepherd": 97, "dobermann": 97, "pinscher": 97, "swiss": [97, 108], "mountain": 97, "bernes": 97, "appenzel": 97, "sennenhund": 97, "entlebuch": 97, "boxer": 97, "bullmastiff": 97, "mastiff": 97, "french": 97, "bulldog": 97, "dane": 97, "st": 97, "bernard": 97, "huski": 97, "alaskan": 97, "malamut": 97, "siberian": 97, "dalmatian": 97, "affenpinsch": 97, "basenji": 97, "pug": 97, "leonberg": 97, "newfoundland": 97, "pyrenean": 97, "samoi": 97, "pomeranian": 97, "chow": 97, "keeshond": 97, "griffon": 97, "bruxelloi": 97, "pembrok": 97, "corgi": 97, "cardigan": 97, "poodl": 97, "mexican": 97, "hairless": 97, "tundra": 97, "coyot": 97, "dingo": 97, "dhole": 97, "wild": 97, "hyena": 97, "kit": 97, "arctic": 97, "tabbi": 97, "persian": 97, "siames": 97, "egyptian": 97, "mau": 97, "cougar": 97, "lynx": 97, "leopard": 97, "snow": 97, "jaguar": 97, "cheetah": 97, "brown": [97, 107], "bear": 97, "polar": 97, "sloth": 97, "mongoos": 97, "meerkat": 97, "beetl": 97, "ladybug": 97, "longhorn": 97, "leaf": 97, "rhinocero": 97, "weevil": 97, "fly": 97, "ant": 97, "grasshopp": 97, "cricket": 97, "stick": 97, "insect": 97, "cockroach": 97, "manti": 97, "cicada": 97, "leafhopp": 97, "lacew": 97, "dragonfli": 97, "damselfli": 97, "admir": 97, "ringlet": 97, "monarch": 97, "butterfli": 97, "gossam": 97, "wing": 97, "starfish": 97, "urchin": 97, "cucumb": 97, "cottontail": 97, "rabbit": 97, "hare": 97, "angora": 97, "hamster": 97, "porcupin": 97, "squirrel": 97, "marmot": 97, "beaver": 97, "guinea": 97, "pig": 97, "sorrel": 97, "zebra": 97, "boar": 97, "warthog": 97, "hippopotamu": 97, "ox": 97, "buffalo": 97, "bison": 97, "bighorn": 97, "sheep": 97, "alpin": 97, "ibex": 97, "hartebeest": 97, "impala": 97, "gazel": 97, "dromedari": 97, "llama": 97, "weasel": 97, "mink": 97, "polecat": 97, "foot": 97, "ferret": 97, "otter": 97, "skunk": 97, "badger": 97, "armadillo": 97, "toed": 97, "orangutan": 97, "gorilla": 97, "chimpanze": 97, "gibbon": 97, "siamang": 97, "guenon": 97, "pata": 97, "monkei": 97, "baboon": 97, "macaqu": 97, "langur": 97, "colobu": 97, "probosci": 97, "marmoset": 97, "capuchin": 97, "howler": 97, "titi": 97, "geoffroi": 97, "lemur": 97, "indri": 97, "asian": 97, "eleph": 97, "bush": 97, "snoek": 97, "eel": 97, "coho": 97, "salmon": 97, "beauti": 97, "clownfish": 97, "sturgeon": 97, "garfish": 97, "lionfish": 97, "pufferfish": 97, "abacu": 97, "abaya": 97, "academ": 97, "gown": 97, "accordion": 97, "acoust": 97, "guitar": 97, "aircraft": 97, "carrier": 97, "airlin": 97, "airship": 97, "altar": 97, "ambul": 97, "amphibi": 97, "clock": [97, 108], "apiari": 97, "apron": 97, "wast": 97, "assault": 97, "rifl": 97, "backpack": 97, "bakeri": 97, "balanc": 97, "beam": 97, "balloon": 97, "ballpoint": 97, "pen": 97, "aid": 97, "banjo": 97, "balust": 97, "barbel": 97, "barber": 97, "chair": [97, 103], "barbershop": 97, "baromet": 97, "barrel": 97, "wheelbarrow": 97, "basebal": 97, "basketbal": 97, "bassinet": 97, "bassoon": 97, "swim": 97, "cap": 97, "bath": 97, "towel": 97, "bathtub": 97, "station": 97, "wagon": 97, "lighthous": 97, "beaker": 97, "militari": 97, "beer": 97, "bottl": 97, "glass": 97, "bell": 97, "cot": 97, "bib": 97, "bicycl": [97, 107], "bikini": 97, "binder": 97, "binocular": 97, "birdhous": 97, "boathous": 97, "bobsleigh": 97, "bolo": 97, "tie": 97, "poke": 97, "bonnet": 97, "bookcas": 97, "bookstor": 97, "bow": 97, "brass": 97, "bra": 97, "breakwat": 97, "breastplat": 97, "broom": 97, "bucket": 97, "buckl": 97, "bulletproof": 97, "vest": 97, "butcher": 97, "shop": 97, "taxicab": 97, "cauldron": 97, "candl": 97, "cannon": 97, "cano": 97, "mirror": [97, 103], "carousel": 97, "tool": [97, 99, 101], "carton": 97, "wheel": 97, "teller": 97, "cassett": 97, "player": 97, "castl": 97, "catamaran": 97, "cd": 97, "cello": 97, "mobil": [97, 108], "chain": 97, "fenc": [97, 107], "mail": 97, "chainsaw": 97, "chest": 97, "chiffoni": 97, "chime": 97, "china": 97, "cabinet": 97, "christma": 97, "stock": 97, "church": 97, "movi": 97, "theater": 97, "cleaver": 97, "cliff": 97, "dwell": 97, "cloak": 97, "clog": 97, "cocktail": 97, "shaker": 97, "coffe": 97, "mug": 97, "coffeemak": 97, "coil": 97, "lock": 97, "keyboard": 97, "confectioneri": 97, "ship": [97, 104], "corkscrew": 97, "cornet": 97, "cowboi": 97, "boot": 97, "hat": 97, "cradl": 97, "crash": 97, "helmet": 97, "crate": 97, "infant": 97, "bed": 97, "crock": 97, "pot": 97, "croquet": 97, "crutch": 97, "cuirass": 97, "dam": 97, "desk": 97, "desktop": 97, "rotari": 97, "dial": 97, "telephon": 97, "diaper": 97, "watch": 97, "dine": 97, "dishcloth": 97, "dishwash": 97, "disc": 97, "brake": 97, "dock": 97, "sled": 97, "dome": 97, "doormat": 97, "drill": 97, "rig": 97, "drum": 97, "drumstick": 97, "dumbbel": 97, "dutch": 97, "oven": 97, "fan": 97, "locomot": 97, "entertain": 97, "envelop": 97, "espresso": 97, "powder": 97, "feather": 97, "fireboat": 97, "engin": [97, 107], "screen": 97, "sheet": 97, "flagpol": 97, "flute": 97, "footbal": 97, "forklift": 97, "fountain": 97, "poster": 97, "freight": 97, "fry": 97, "pan": 97, "fur": 97, "garbag": 97, "ga": 97, "pump": 97, "goblet": 97, "kart": 97, "golf": 97, "cart": 97, "gondola": 97, "gong": 97, "grand": 97, "piano": 97, "greenhous": 97, "grill": 97, "groceri": 97, "guillotin": 97, "barrett": 97, "hair": 97, "sprai": 97, "hammer": 97, "dryer": 97, "hand": [97, 99], "handkerchief": 97, "drive": 97, "harmonica": 97, "harp": 97, "harvest": 97, "hatchet": 97, "holster": 97, "honeycomb": 97, "hoop": 97, "skirt": 97, "horizont": 97, "bar": 97, "drawn": 97, "hourglass": 97, "ipod": 97, "cloth": 97, "iron": 97, "jack": 97, "lantern": 97, "jean": 97, "jeep": 97, "jigsaw": 97, "puzzl": 97, "pull": 97, "rickshaw": 97, "joystick": 97, "kimono": 97, "knee": 97, "pad": 97, "knot": 97, "ladl": 97, "lampshad": 97, "laptop": 97, "lawn": 97, "mower": 97, "knife": 97, "lifeboat": 97, "lighter": 97, "limousin": 97, "ocean": 97, "liner": 97, "lipstick": 97, "slip": 97, "shoe": 97, "lotion": 97, "speaker": 97, "loup": 97, "sawmil": 97, "magnet": 97, "compass": 97, "mailbox": 97, "tight": 97, "tank": 97, "manhol": 97, "maraca": 97, "marimba": 97, "maypol": 97, "maze": 97, "cup": [97, 103], "medicin": 97, "megalith": 97, "microphon": 97, "microwav": 97, "milk": 97, "minibu": 97, "miniskirt": 97, "minivan": 97, "missil": 97, "mitten": [97, 98], "mix": 97, "bowl": 97, "modem": 97, "monasteri": 97, "monitor": 97, "mope": 97, "mortar": 97, "mosqu": 97, "mosquito": 97, "scooter": 97, "bike": 97, "tent": 97, "mous": [97, 98], "mousetrap": 97, "van": 97, "muzzl": 97, "nail": 97, "brace": 97, "necklac": 97, "nippl": 97, "obelisk": 97, "obo": 97, "ocarina": 97, "odomet": 97, "oil": 97, "oscilloscop": 97, "overskirt": 97, "bullock": 97, "oxygen": 97, "packet": 97, "paddl": 97, "padlock": 97, "paintbrush": 97, "pajama": 97, "palac": [97, 108], "parachut": 97, "park": 97, "bench": 97, "meter": 97, "passeng": 97, "patio": 97, "payphon": 97, "pedest": 97, "pencil": 97, "perfum": 97, "petri": 97, "dish": 97, "photocopi": 97, "plectrum": 97, "pickelhaub": 97, "picket": 97, "pickup": 97, "pier": 97, "piggi": 97, "pill": 97, "pillow": 97, "ping": 97, "pong": 97, "pinwheel": 97, "pirat": 97, "pitcher": 97, "plane": 97, "planetarium": 97, "plastic": 97, "plate": 97, "rack": 97, "plow": 97, "plunger": 97, "polaroid": 97, "camera": 97, "pole": [97, 107], "polic": 97, "poncho": 97, "billiard": 97, "soda": 97, "potter": 97, "prayer": 97, "rug": 97, "printer": 97, "prison": 97, "projectil": 97, "projector": 97, "hockei": 97, "puck": 97, "punch": 97, "purs": 97, "quill": 97, "quilt": 97, "race": 97, "racket": 97, "radiat": 97, "radio": 97, "telescop": 97, "rain": 97, "recreat": 97, "reel": 97, "reflex": 97, "refriger": 97, "remot": 97, "restaur": 97, "revolv": 97, "rotisseri": 97, "eras": 97, "rugbi": 97, "ruler": 97, "safe": 97, "safeti": 97, "salt": 97, "sarong": 97, "saxophon": 97, "scabbard": 97, "bu": [97, 107], "schooner": 97, "scoreboard": 97, "crt": 97, "screw": 97, "screwdriv": 97, "seat": 97, "belt": 97, "sew": 97, "shield": 97, "shoji": 97, "basket": 97, "shovel": 97, "shower": 97, "curtain": 97, "ski": 97, "sleep": 97, "door": 97, "slot": 97, "snorkel": 97, "snowmobil": 97, "snowplow": 97, "soap": 97, "dispens": 97, "soccer": [97, 108], "sock": [97, 98], "solar": 97, "thermal": 97, "collector": 97, "sombrero": 97, "soup": 97, "heater": 97, "shuttl": 97, "spatula": 97, "motorboat": 97, "web": 97, "spindl": 97, "sport": [97, 108], "spotlight": 97, "stage": 97, "steam": 97, "arch": 97, "bridg": 97, "steel": 97, "stethoscop": 97, "scarf": 97, "stone": 97, "wall": [97, 107], "stopwatch": 97, "stove": 97, "strainer": 97, "tram": 97, "stretcher": 97, "couch": 97, "stupa": 97, "submarin": 97, "sundial": 97, "sunglass": 97, "sunscreen": 97, "suspens": 97, "mop": 97, "sweatshirt": 97, "swimsuit": 97, "swing": 97, "switch": 97, "syring": 97, "lamp": 97, "tape": 97, "teapot": 97, "teddi": 97, "televis": [97, 108], "tenni": 97, "thatch": 97, "roof": 97, "thimbl": 97, "thresh": 97, "throne": 97, "tile": 97, "toaster": 97, "tobacco": 97, "toilet": 97, "totem": 97, "tow": 97, "tractor": 97, "semi": 97, "trailer": 97, "trai": 97, "trench": 97, "tricycl": 97, "trimaran": 97, "tripod": 97, "triumphal": 97, "trolleybu": 97, "trombon": 97, "tub": 97, "turnstil": 97, "typewrit": 97, "umbrella": 97, "unicycl": 97, "upright": 97, "vacuum": 97, "cleaner": 97, "vase": 97, "vault": 97, "velvet": 97, "vend": 97, "vestment": 97, "viaduct": 97, "violin": 97, "volleybal": 97, "waffl": 97, "wallet": 97, "wardrob": 97, "sink": 97, "wash": 97, "jug": 97, "tower": 97, "whiskei": 97, "whistl": 97, "wig": 97, "shade": [97, 107], "windsor": 97, "wine": 97, "wok": 97, "wooden": 97, "spoon": 97, "wool": 97, "rail": 97, "shipwreck": 97, "yawl": 97, "yurt": 97, "websit": 97, "comic": 97, "book": 97, "crossword": 97, "traffic": [97, 103, 107], "sign": [97, 107, 108], "dust": 97, "jacket": [97, 103], "menu": 97, "guacamol": 97, "consomm": 97, "trifl": 97, "ic": 97, "cream": 97, "pop": 97, "baguett": 97, "bagel": 97, "pretzel": 97, "cheeseburg": 97, "mash": 97, "potato": 97, "cabbag": 97, "broccoli": 97, "cauliflow": 97, "zucchini": 97, "spaghetti": 97, "squash": 97, "acorn": 97, "butternut": 97, "artichok": 97, "pepper": [97, 98], "cardoon": 97, "mushroom": 97, "granni": 97, "smith": 97, "strawberri": 97, "lemon": 97, "pineappl": 97, "banana": 97, "jackfruit": 97, "custard": 97, "appl": 97, "pomegran": 97, "hai": 97, "carbonara": 97, "chocol": 97, "syrup": 97, "dough": 97, "meatloaf": 97, "pizza": 97, "pie": 97, "burrito": 97, "eggnog": 97, "alp": 97, "bubbl": 97, "reef": 97, "geyser": 97, "lakeshor": 97, "promontori": 97, "shoal": 97, "seashor": 97, "vallei": 97, "volcano": 97, "bridegroom": 97, "scuba": 97, "diver": 97, "rapese": 97, "daisi": 97, "ladi": 97, "slipper": 97, "corn": 97, "rose": 97, "hip": 97, "chestnut": 97, "fungu": 97, "agar": 97, "gyromitra": 97, "stinkhorn": 97, "earth": 97, "star": 97, "wood": 97, "bolet": 97, "ear": 97, "cifar10_test_set": 97, "airplan": [97, 104], "automobil": [97, 104], "deer": [97, 104], "cifar100_test_set": 97, "aquarium_fish": 97, "boi": 97, "camel": 97, "caterpillar": 97, "cattl": [97, 108], "cloud": 97, "dinosaur": 97, "dolphin": 97, "flatfish": 97, "forest": 97, "girl": 97, "kangaroo": 97, "lawn_mow": 97, "man": 97, "maple_tre": 97, "motorcycl": [97, 107], "oak_tre": 97, "orchid": 97, "palm_tre": 97, "pear": 97, "pickup_truck": 97, "pine_tre": 97, "plain": 97, "poppi": 97, "possum": 97, "raccoon": 97, "road": [97, 107], "rocket": 97, "seal": 97, "shrew": 97, "skyscrap": 97, "streetcar": 97, "sunflow": 97, "sweet_pepp": 97, "trout": 97, "tulip": 97, "willow_tre": 97, "woman": [97, 103], "caltech256": 97, "ak47": 97, "bat": 97, "glove": 97, "birdbath": 97, "blimp": 97, "bonsai": 97, "boom": 97, "breadmak": 97, "buddha": 97, "bulldoz": 97, "cactu": 97, "cake": 97, "tire": 97, "cartman": 97, "cereal": 97, "chandeli": 97, "chess": 97, "board": 97, "chimp": 97, "chopstick": 97, "coffin": 97, "coin": 97, "comet": 97, "cormor": 97, "globe": 97, "diamond": 97, "dice": 97, "doorknob": 97, "drink": 97, "straw": 97, "dumb": 97, "eiffel": 97, "elk": 97, "ewer": 97, "eyeglass": 97, "fern": 97, "fighter": 97, "jet": [97, 106], "extinguish": 97, "hydrant": 97, "firework": 97, "flashlight": 97, "floppi": 97, "fri": 97, "frisbe": 97, "galaxi": 97, "giraff": 97, "goat": 97, "gate": 97, "grape": 97, "pick": [97, 98], "hamburg": 97, "hammock": 97, "harpsichord": 97, "hawksbil": 97, "helicopt": 97, "hibiscu": 97, "homer": 97, "simpson": 97, "horsesho": 97, "air": 97, "skeleton": 97, "ibi": 97, "cone": 97, "iri": 97, "jesu": 97, "christ": 97, "joi": 97, "kayak": 97, "ketch": 97, "ladder": 97, "lath": 97, "licens": 97, "lightbulb": 97, "lightn": 97, "mandolin": 97, "mar": 97, "mattress": 97, "megaphon": 97, "menorah": 97, "microscop": 97, "minaret": 97, "minotaur": 97, "motorbik": 97, "mussel": 97, "neckti": 97, "octopu": 97, "palm": 97, "pilot": 97, "paperclip": 97, "shredder": 97, "pci": 97, "peopl": [97, 103], "pez": 97, "picnic": 97, "pram": 97, "prai": 97, "pyramid": 97, "rainbow": 97, "roulett": 97, "saddl": 97, "saturn": 97, "segwai": 97, "propel": 97, "sextant": 97, "music": 97, "skateboard": 97, "smokestack": 97, "sneaker": 97, "boat": 97, "stain": 97, "steer": 97, "stirrup": 97, "superman": 97, "sushi": 97, "armi": [97, 108], "sword": 97, "tambourin": 97, "teepe": 97, "court": 97, "theodolit": 97, "tomato": 97, "tombston": 97, "tour": 97, "pisa": 97, "treadmil": 97, "fork": 97, "tweezer": 97, "unicorn": 97, "vcr": 97, "waterfal": 97, "watermelon": 97, "weld": 97, "windmil": 97, "xylophon": 97, "yarmulk": 97, "yo": 97, "toad": 97, "twenty_news_test_set": 97, "comp": 97, "graphic": [97, 107], "misc": [97, 108], "sy": 97, "ibm": 97, "pc": 97, "hardwar": 97, "mac": 97, "forsal": 97, "rec": 97, "crypt": 97, "electron": 97, "med": 97, "soc": 97, "religion": 97, "christian": [97, 108], "talk": [97, 108], "polit": 97, "gun": 97, "mideast": 97, "amazon": 97, "neutral": 97, "imdb_test_set": 97, "all_class": 97, "20news_test_set": 97, "_load_classes_predprobs_label": 97, "dataset_nam": 97, "labelerror": 97, "url_bas": 97, "5392f6c71473055060be3044becdde1cbc18284d": 97, "url_label": 97, "original_test_label": 97, "_original_label": 97, "url_prob": 97, "cross_validated_predicted_prob": 97, "_pyx": 97, "num_part": 97, "datatset": 97, "allow_pickl": 97, "pred_probs_part": 97, "url": 97, "_of_": 97, "nload": 97, "imdb": 97, "ve": [97, 98, 99, 101, 103], "capit": 97, "29780": 97, "256": [97, 98, 103], "780": 97, "medic": [97, 108], "doctor": 97, "254": [97, 103], "359223": 97, "640777": 97, "184": [97, 99], "258427": 97, "341176": 97, "263158": 97, "658824": 97, "337349": 97, "246575": 97, "662651": 97, "248": 97, "330000": 97, "355769": 97, "251": [97, 103], "167": [97, 99, 103], "252": 97, "112": 97, "253": [97, 103], "022989": 97, "049505": 97, "190": [97, 99, 103], "002216": 97, "000974": 97, "000873": 97, "000739": 97, "32635": 97, "32636": 97, "32637": 97, "32638": 97, "32639": 97, "32640": 97, "051": 97, "002242": 97, "997758": 97, "002088": 97, "001045": 97, "997912": 97, "002053": 97, "997947": 97, "001980": 97, "000991": 97, "998020": 97, "001946": 97, "002915": 97, "998054": 97, "001938": 97, "002904": 97, "998062": 97, "001020": 97, "998980": 97, "001018": 97, "002035": 97, "998982": 97, "999009": 97, "0003": 97, "0002": 97, "071": 97, "067269": 97, "929": 97, "046": 97, "058243": 97, "954": 97, "035": 97, "032096": 97, "965": 97, "031": 97, "012232": 97, "969": 97, "022": 97, "025896": 97, "978": 97, "020": [97, 99], "013092": 97, "018": 97, "013065": 97, "016": 97, "030542": 97, "984": 97, "013": 97, "020833": 97, "987": 97, "012": 97, "010020": 97, "988": 97, "0073": 97, "0020": 97, "0016": 97, "0015": 97, "0014": 97, "0013": 97, "0012": 97, "0010": 97, "0008": 97, "0007": 97, "0006": 97, "0005": 97, "0004": 97, "244": [97, 103], "452381": 97, "459770": 97, "523364": 97, "460784": 97, "446602": 97, "103774": 97, "030612": 97, "110092": 97, "049020": 97, "0034": 97, "0032": 97, "0026": 97, "0025": 97, "4945": 97, "4946": 97, "4947": 97, "4948": 97, "4949": 97, "4950": 97, "846": 97, "7532": 97, "532": 97, "034483": 97, "009646": 97, "965517": 97, "030457": 97, "020513": 97, "969543": 97, "028061": 97, "035443": 97, "971939": 97, "025316": 97, "005168": 97, "974684": 97, "049751": 97, "979487": 97, "019920": 97, "042802": 97, "980080": 97, "017677": 97, "005115": 97, "982323": 97, "012987": 97, "005236": 97, "987013": 97, "012723": 97, "025126": 97, "987277": 97, "010989": 97, "008264": 97, "989011": 97, "010283": 97, "027778": 97, "989717": 97, "009677": 97, "990323": 97, "007614": 97, "010127": 97, "992386": 97, "005051": 97, "994949": 97, "005025": 97, "994975": 97, "005013": 97, "994987": 97, "001859": 97, "001328": 97, "000929": 97, "000664": 97, "186": [97, 99], "188": [97, 99, 102], "189": [97, 99], "snippet": 98, "nlp": [98, 108], "mind": [98, 99], "alphanumer": 98, "facilit": 98, "seamless": 98, "classlabel": 98, "guidanc": 98, "labels_str": 98, "datalab_str": 98, "labels_int": 98, "remap": 98, "datalab_int": 98, "my_dict": 98, "pet_nam": 98, "rover": 98, "rocki": 98, "speci": 98, "datalab_dataset": 98, "number_of_class": 98, "total_number_of_data_point": 98, "feed": 98, "alphabet": 98, "labels_proper_format": 98, "your_classifi": 98, "issues_datafram": 98, "class_predicted_for_flagged_exampl": 98, "class_predicted_for_all_exampl": 98, "grant": 98, "On": [98, 99, 103], "merged_dataset": 98, "label_column_nam": 98, "datataset": 98, "fair": [98, 99], "game": 98, "speedup": [98, 104], "tempfil": 98, "mkdtemp": 98, "sped": 98, "anywai": 98, "pred_probs_merg": 98, "merge_rare_class": 98, "count_threshold": 98, "class_mapping_orig2new": 98, "heath_summari": 98, "num_examples_per_class": 98, "rare_class": 98, "num_classes_merg": 98, "other_class": 98, "labels_merg": 98, "new_c": 98, "merged_prob": 98, "new_class": 98, "original_class": 98, "num_check": 98, "ones_array_ref": 98, "isclos": 98, "though": [98, 99, 108], "successfulli": 98, "virtuou": [98, 101], "cycl": [98, 101], "jointli": 98, "junk": 98, "clutter": 98, "unknown": 98, "caltech": 98, "combined_boolean_mask": 98, "mask1": 98, "mask2": 98, "gradientboostingclassifi": [98, 99], "true_error": [98, 99, 102], "101": [98, 103], "102": [98, 102, 103], "104": [98, 99, 103], "model_to_find_error": 98, "model_to_return": 98, "cl0": 98, "randomizedsearchcv": 98, "expens": 98, "param_distribut": 98, "learning_r": [98, 99], "max_depth": [98, 99], "magnitud": 98, "coeffici": [98, 106], "optin": 98, "environ": [98, 99], "rerun": [98, 99], "cell": [98, 99], "unabl": [98, 99], "render": [98, 99], "nbviewer": [98, 99], "cleanlearninginot": [98, 99], "fittedcleanlearn": [98, 99], "linearregressionlinearregress": 98, "unexpectedli": 98, "emphas": 98, "crucial": 98, "merge_duplicate_set": 98, "merge_kei": 98, "construct_group_kei": 98, "merged_set": 98, "consolidate_set": 98, "issubset": 98, "frozenset": 98, "sets_list": 98, "mutabl": 98, "new_set": 98, "current_set": 98, "intersecting_set": 98, "lowest_score_strategi": 98, "sub_df": 98, "filter_near_dupl": 98, "strategy_fn": 98, "strategy_kwarg": 98, "duplicate_row": 98, "group_kei": 98, "to_keep_indic": 98, "groupbi": 98, "explod": 98, "to_remov": 98, "isin": [98, 104], "kept": 98, "ids_to_remove_seri": 98, "assist": 98, "streamlin": 98, "ux": 98, "agpl": 98, "compani": 98, "commerci": 98, "email": 98, "team": 98, "discuss": 98, "anywher": 98, "profession": 98, "expert": 98, "depth": 99, "survei": [99, 108], "scienc": 99, "multivariate_norm": [99, 101, 102], "make_data": [99, 101], "cov": [99, 101, 102], "avg_trac": [99, 102], "py_tru": 99, "noise_matrix_tru": 99, "noise_marix": 99, "s_test": 99, "noisy_test_label": 99, "purpl": 99, "namespac": 99, "exec": 99, "markerfacecolor": [99, 102], "markeredgecolor": [99, 102, 106], "markers": [99, 102, 106], "markeredgewidth": [99, 102, 106], "realist": 99, "7560": 99, "637318e": 99, "896262e": 99, "548391e": 99, "923417e": 99, "375075e": 99, "3454": 99, "014051": 99, "020451": 99, "249": [99, 103, 108], "042594": 99, "043859": 99, "045954": 99, "6120": 99, "023714": 99, "007136": 99, "119": [99, 103], "107266": 99, "103": [99, 103], "033738": 99, "238": [99, 103], "119505": 99, "236": [99, 103, 108], "037843": 99, "222": 99, "614915": 99, "122": [99, 103], "624422": 99, "625965": 99, "626079": 99, "118": 99, "627675": 99, "695223": 99, "323529": 99, "523015": 99, "013720": 99, "675727": 99, "646521": 99, "anyth": 99, "magic": 99, "liter": 99, "identif": 99, "x27": 99, "logisticregressionlogisticregress": 99, "ever": 99, "092": 99, "040": 99, "024": 99, "004": 99, "surpris": 99, "1705": 99, "01936": 99, "ton": 99, "yourfavoritemodel1": 99, "merged_label": 99, "merged_test_label": 99, "newli": [99, 101], "yourfavoritemodel2": 99, "yourfavoritemodel3": 99, "cl3": 99, "takeawai": 99, "my_test_pred_prob": 99, "my_test_pr": 99, "issues_test": 99, "corrected_test_label": 99, "pretend": 99, "cl_test_pr": 99, "fairli": 99, "label_acc": 99, "percentag": 99, "offset": 99, "nquestion": 99, "overestim": 99, "answer": 99, "experienc": 99, "prioiri": 99, "known": 99, "versatil": 99, "label_issues_indic": 99, "213": [99, 103], "218": [99, 103], "152": 99, "197": [99, 103], "196": [99, 103], "170": 99, "214": 99, "164": [99, 102], "198": [99, 103], "191": [99, 103], "117": [99, 106], "206": [99, 103], "115": [99, 103, 108], "193": 99, "194": 99, "201": [99, 103], "174": 99, "163": 99, "150": [99, 101, 103, 108], "169": 99, "151": [99, 103], "168": 99, "precision_scor": 99, "recall_scor": 99, "f1_score": 99, "true_label_issu": 99, "filter_by_list": 99, "718750": [99, 101], "807018": 99, "912": 99, "733333": 99, "800000": 99, "721311": 99, "792793": 99, "908": 99, "676923": 99, "765217": 99, "892": 99, "567901": 99, "702290": 99, "844": 99, "gaug": 99, "label_issues_count": 99, "155": [99, 103], "156": 99, "172": [99, 102], "157": 99, "easiest": 99, "modular": 99, "penalti": 99, "l2": 99, "model3": 99, "n_estim": 99, "cv_pred_probs_1": 99, "cv_pred_probs_2": 99, "cv_pred_probs_3": 99, "label_quality_scores_best": 99, "cv_pred_probs_ensembl": 99, "label_quality_scores_bett": 99, "superior": [99, 105], "timm": 100, "glad": 101, "multiannotator_label": 101, "300": [101, 108], "noisier": 101, "111": [101, 106], "local_data": [101, 102], "true_labels_train": [101, 102], "noise_matrix_bett": 101, "noise_matrix_wors": 101, "transpos": [101, 104], "zfill": 101, "row_na_check": 101, "notna": 101, "reset_index": 101, "a0001": 101, "a0002": 101, "a0003": 101, "a0004": 101, "a0005": 101, "a0006": 101, "a0007": 101, "a0008": 101, "a0009": 101, "a0010": 101, "a0041": 101, "a0042": 101, "a0043": 101, "a0044": 101, "a0045": 101, "a0046": 101, "a0047": 101, "a0048": 101, "a0049": 101, "a0050": 101, "na": 101, "60856743": 101, "41693214": 101, "40908785": 101, "87147629": 101, "64941785": 101, "10774851": 101, "0524466": 101, "71853246": 101, "37169848": 101, "66031048": 101, "multiannotator_util": 101, "crude": 101, "straight": 101, "majority_vote_label": 101, "736118": 101, "757751": 101, "782232": 101, "715565": 101, "824256": 101, "quality_annotator_a0001": 101, "quality_annotator_a0002": 101, "quality_annotator_a0003": 101, "quality_annotator_a0004": 101, "quality_annotator_a0005": 101, "quality_annotator_a0006": 101, "quality_annotator_a0007": 101, "quality_annotator_a0008": 101, "quality_annotator_a0009": 101, "quality_annotator_a0010": 101, "quality_annotator_a0041": 101, "quality_annotator_a0042": 101, "quality_annotator_a0043": 101, "quality_annotator_a0044": 101, "quality_annotator_a0045": 101, "quality_annotator_a0046": 101, "quality_annotator_a0047": 101, "quality_annotator_a0048": 101, "quality_annotator_a0049": 101, "quality_annotator_a0050": 101, "070564": 101, "216078": 101, "119188": 101, "alongisd": 101, "244981": 101, "208333": 101, "295979": 101, "294118": 101, "324197": 101, "310345": 101, "355316": 101, "346154": 101, "439732": 101, "480000": 101, "a0031": 101, "523205": 101, "580645": 101, "a0034": 101, "535313": 101, "607143": 101, "a0021": 101, "606999": 101, "a0015": 101, "609526": 101, "678571": 101, "a0011": 101, "621103": 101, "692308": 101, "improved_consensus_label": 101, "majority_vote_accuraci": 101, "cleanlab_label_accuraci": 101, "8581081081081081": 101, "9797297297297297": 101, "besid": 101, "sorted_consensus_quality_scor": 101, "worst_qual": 101, "better_qu": 101, "worst_quality_accuraci": 101, "better_quality_accuraci": 101, "9893238434163701": 101, "improved_pred_prob": 101, "treat": [101, 102, 106, 108], "analzi": 101, "copyright": 102, "advertis": 102, "violenc": 102, "nsfw": 102, "celeba": 102, "make_multilabel_data": 102, "boxes_coordin": 102, "box_multilabel": 102, "make_multi": 102, "bx1": 102, "by1": 102, "bx2": 102, "by2": 102, "label_list": 102, "ur": 102, "upper": 102, "inidx": 102, "logical_and": 102, "inv_d": 102, "labels_idx": 102, "true_labels_test": 102, "dict_unique_label": 102, "get_color_arrai": 102, "dcolor": 102, "aa4400": 102, "55227f": 102, "55a100": 102, "00ff00": 102, "007f7f": 102, "386b55": 102, "0000ff": 102, "y_onehot": 102, "single_class_label": 102, "stratifi": [102, 105], "kf": 102, "train_index": 102, "test_index": 102, "clf_cv": 102, "x_train_cv": 102, "x_test_cv": 102, "y_train_cv": 102, "y_test_cv": 102, "y_pred_cv": 102, "saw": 102, "num_to_displai": 102, "09": [102, 103, 106, 108], "275": 102, "267": 102, "225": 102, "171": 102, "234": 102, "165": 102, "227": [102, 103], "262": [102, 103], "263": [102, 103], "266": [102, 103], "139": 102, "143": [102, 103], "216": [102, 103], "265": 102, "159": [102, 103], "despit": [102, 108], "suspect": 102, "888": 102, "8224": 102, "9632": 102, "968": 102, "6512": 102, "0444": 102, "774": 102, "labels_binary_format": 102, "labels_list_format": 102, "surround": 103, "scene": 103, "coco": 103, "everydai": 103, "has_label_issu": 103, "nc": [103, 107, 108], "s3": [103, 107, 108], "amazonaw": [103, 107, 108], "objectdetectionbenchmark": 103, "tutorial_obj": 103, "pkl": 103, "example_imag": 103, "unzip": [103, 108], "_separate_label": 103, "_separate_predict": 103, "begin": 103, "image_path": 103, "rb": 103, "image_to_visu": 103, "seg_map": 103, "334": 103, "bboxes_ignor": 103, "290": 103, "286": 103, "285": 103, "224": 103, "231": [103, 108], "293": 103, "235": 103, "289": 103, "282": 103, "281": 103, "271": 103, "280": 103, "277": 103, "279": 103, "287": 103, "299": 103, "276": 103, "307": 103, "321": 103, "326": 103, "333": 103, "261": 103, "319": 103, "257": 103, "283": 103, "243": 103, "303": 103, "316": 103, "247": 103, "323": 103, "327": 103, "226": 103, "228": 103, "232": 103, "219": 103, "239": 103, "240": 103, "209": 103, "242": 103, "202": 103, "230": 103, "215": 103, "220": 103, "229": 103, "217": 103, "237": 103, "207": 103, "204": 103, "84": [103, 106], "205": 103, "223": 103, "153": 103, "149": 103, "140": 103, "124": 103, "246": 103, "268": 103, "273": 103, "284": 103, "110": 103, "136": 103, "145": 103, "173": 103, "297": 103, "317": 103, "192": 103, "332": 103, "324": 103, "203": 103, "320": 103, "314": 103, "199": 103, "291": 103, "000000481413": 103, "jpg": 103, "42398": 103, "44503": 103, "29968": 103, "336": 103, "21005": 103, "9978472": 103, "forgot": 103, "drew": 103, "label_issue_idx": 103, "num_examples_to_show": 103, "138": 103, "candid": 103, "97489622": 103, "70610878": 103, "98764951": 103, "88899237": 103, "99085805": 103, "issue_idx": 103, "95569726e": 103, "03354841e": 103, "57510169e": 103, "58447666e": 103, "39755858e": 103, "issue_to_visu": 103, "000000009483": 103, "95569726168054e": 103, "addition": [103, 107], "visibl": 103, "missmatch": 103, "likelei": 103, "agnost": 103, "vaidat": 103, "inconsist": 103, "000000395701": 103, "033548411774308e": 103, "armchair": 103, "tv": 103, "000000154004": 103, "38300759625496356": 103, "foreground": 103, "000000448410": 103, "0008575101690203273": 103, "crowd": 103, "alon": 103, "resembl": [103, 104], "000000499768": 103, "9748962231208227": 103, "000000521141": 103, "8889923658893665": 103, "000000143931": 103, "9876495074395956": 103, "bonu": 103, "uncov": 103, "irregular": 103, "object_detection_util": 103, "calculate_bounding_box_area": 103, "num_imgs_to_show": 103, "lab_object_count": 103, "pred_object_count": 103, "000000430073": 103, "000000183709": 103, "000000189475": 103, "label_norm": 103, "pred_norm": 103, "area": [103, 107], "lab_area": 103, "pred_area": 103, "lab_area_mean": 103, "lab_area_std": 103, "max_deviation_valu": 103, "max_deviation_class": 103, "deviation_valu": 103, "deviation_class": 103, "mean_area": 103, "std_area": 103, "class_area": 103, "deviations_awai": 103, "max_deviation_index": 103, "num_imgs_to_show_per_class": 103, "class_num": 103, "000000422886": 103, "000000341828": 103, "000000461009": 103, "train_feature_embed": 104, "ood_train_feature_scor": 104, "test_feature_embed": 104, "ood_test_feature_scor": 104, "ood_train_predictions_scor": 104, "train_pred_prob": 104, "ood_test_predictions_scor": 104, "test_pred_prob": 104, "pylab": 104, "rcparam": 104, "baggingclassifi": 104, "therebi": 104, "rescal": 104, "transform_norm": 104, "totensor": 104, "animal_class": 104, "non_animal_class": 104, "animal_idx": 104, "test_idx": 104, "19884004": 104, "38it": 104, "visualize_outli": 104, "txt_class": 104, "npimg": 104, "show_label": 104, "data_subset": 104, "resnet50": 104, "corpu": 104, "2048": 104, "embed_imag": 104, "create_model": 104, "strang": 104, "odd": 104, "train_ood_features_scor": 104, "top_train_ood_features_idx": 104, "fun": 104, "negat": 104, "homogen": 104, "bottom_train_ood_features_idx": 104, "test_ood_features_scor": 104, "top_ood_features_idx": 104, "inevit": 104, "trade": 104, "5th": 104, "percentil": 104, "fifth_percentil": 104, "plt_rang": 104, "hist": 104, "train_outlier_scor": 104, "test_outlier_scor": 104, "ood_features_indic": 104, "revisit": 104, "return_invers": 104, "train_feature_embeddings_sc": 104, "test_feature_embeddings_sc": 104, "train_pred_label": 104, "9702": 104, "train_ood_predictions_scor": 104, "test_ood_predictions_scor": 104, "lost": 104, "unsuit": 105, "ok": [105, 108], "convention": 105, "aforement": 105, "hypothet": 105, "contrast": 105, "tradit": 105, "disjoint": 105, "out_of_sample_pred_probs_for_a": 105, "out_of_sample_pred_probs_for_b": 105, "out_of_sample_pred_probs_for_c": 105, "out_of_sample_pred_prob": 105, "price": 106, "incom": 106, "sensor": 106, "histgradientboostingregressor": 106, "r2_score": 106, "student_grades_r": 106, "final_scor": 106, "true_final_scor": 106, "homework": 106, "3d": 106, "mpl_toolkit": 106, "mplot3d": 106, "axes3d": 106, "errors_idx": 106, "add_subplot": 106, "z": 106, "errors_mask": 106, "feature_column": 106, "predicted_column": 106, "x_train_raw": 106, "x_test_raw": 106, "randomforestregressor": 106, "385101": 106, "499503": 106, "698255": 106, "776647": 106, "109373": 106, "170547": 106, "481096": 106, "984759": 106, "645270": 106, "795928": 106, "141": 106, "659": 106, "367": 106, "318": 106, "305": 106, "560": 106, "657": 106, "688": 106, "view_datapoint": 106, "preds_og": 106, "r2_og": 106, "838": 106, "found_label_issu": 106, "preds_cl": 106, "r2_cl": 106, "926": 106, "favorit": 106, "968627e": 106, "228799": 106, "646674e": 106, "402962": 106, "323818e": 106, "952758": 106, "422144e": 106, "456908": 106, "465815e": 106, "753968": 106, "791186e": 106, "110719": 106, "485156e": 106, "670640": 106, "225300e": 106, "749976": 106, "499679e": 106, "947007": 106, "067882e": 106, "648396": 106, "synthia": 107, "imagesegment": 107, "given_mask": 107, "predicted_mask": 107, "set_printopt": [107, 108], "sky": 107, "sidewalk": 107, "veget": 107, "terrain": 107, "rider": 107, "pred_probs_filepath": 107, "1088": 107, "1920": 107, "label_filepath": 107, "synthia_class": 107, "maunal": 107, "100000": 107, "244800": 107, "leftmost": 107, "middl": [107, 108], "infact": 107, "rightmost": 107, "discrep": 107, "3263230": 107, "783381": 107, "275110": 107, "255917": 107, "78225": 107, "55990": 107, "54315": 107, "33591": 107, "24645": 107, "21054": 107, "15045": 107, "14171": 107, "13832": 107, "13498": 107, "11490": 107, "9164": 107, "8769": 107, "6999": 107, "6031": 107, "5011": 107, "mistakenli": 107, "class_issu": 107, "aim": [107, 108], "domin": 107, "bunch": 108, "conll": 108, "2003": 108, "love": 108, "n_i": 108, "optional_list_of_ordered_class_nam": 108, "deepai": 108, "conll2003": 108, "rm": 108, "tokenclassif": 108, "2400": 108, "52e0": 108, "1a00": 108, "871": 108, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "kb": 108, "mb": 108, "directori": 108, "inflat": 108, "182": 108, "113": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "bert": 108, "read_npz": 108, "filepath": 108, "corrsespond": 108, "iob2": 108, "given_ent": 108, "entity_map": 108, "readfil": 108, "startswith": 108, "docstart": 108, "isalpha": 108, "isupp": 108, "indices_to_preview": 108, "nsentenc": 108, "eu": 108, "reject": 108, "boycott": 108, "british": 108, "lamb": 108, "00030412": 108, "00023826": 108, "99936208": 108, "00007009": 108, "00002545": 108, "99998795": 108, "00000401": 108, "00000218": 108, "00000455": 108, "00000131": 108, "00000749": 108, "99996115": 108, "00001371": 108, "0000087": 108, "00000895": 108, "99998936": 108, "00000382": 108, "00000178": 108, "00000366": 108, "00000137": 108, "99999101": 108, "00000266": 108, "00000174": 108, "0000035": 108, "00000109": 108, "99998768": 108, "00000482": 108, "00000202": 108, "00000438": 108, "0000011": 108, "00000465": 108, "99996392": 108, "00001105": 108, "0000116": 108, "00000878": 108, "99998671": 108, "00000364": 108, "00000213": 108, "00000472": 108, "00000281": 108, "99999073": 108, "00000211": 108, "00000159": 108, "00000442": 108, "00000115": 108, "peter": 108, "blackburn": 108, "00000358": 108, "00000529": 108, "99995623": 108, "0000129": 108, "0000024": 108, "00001812": 108, "99994141": 108, "00001645": 108, "00002162": 108, "brussel": 108, "1996": 108, "00001172": 108, "00000821": 108, "00004661": 108, "0000618": 108, "99987167": 108, "99999061": 108, "00000201": 108, "00000195": 108, "00000408": 108, "00000135": 108, "2254": 108, "2907": 108, "19392": 108, "9962": 108, "8904": 108, "19303": 108, "12918": 108, "9256": 108, "11855": 108, "18392": 108, "20426": 108, "19402": 108, "14744": 108, "19371": 108, "4645": 108, "10331": 108, "9430": 108, "6143": 108, "18367": 108, "12914": 108, "todai": 108, "weather": 108, "march": 108, "scalfaro": 108, "northern": 108, "himself": 108, "said": 108, "germani": 108, "nastja": 108, "rysich": 108, "north": 108, "spla": 108, "fought": 108, "khartoum": 108, "govern": 108, "south": 108, "1983": 108, "autonomi": 108, "animist": 108, "region": 108, "moslem": 108, "arabis": 108, "mayor": 108, "antonio": 108, "gonzalez": 108, "garcia": 108, "revolutionari": 108, "wednesdai": 108, "troop": 108, "raid": 108, "farm": 108, "stole": 108, "rape": 108, "women": 108, "spring": 108, "chg": 108, "hrw": 108, "12pct": 108, "princ": 108, "photo": 108, "moment": 108, "spokeswoman": 108, "rainier": 108, "told": 108, "reuter": 108, "danila": 108, "carib": 108, "w224": 108, "equip": 108, "radiomet": 108, "earn": 108, "19996": 108, "london": 108, "denom": 108, "sale": 108, "uk": 108, "jp": 108, "fr": 108, "maccabi": 108, "hapoel": 108, "haifa": 108, "tel": 108, "aviv": 108, "hospit": 108, "rever": 108, "roman": 108, "cathol": 108, "nun": 108, "admit": 108, "calcutta": 108, "week": 108, "ago": 108, "fever": 108, "vomit": 108, "allianc": 108, "embattl": 108, "kabul": 108, "salang": 108, "highwai": 108, "mondai": 108, "tuesdai": 108, "suprem": 108, "council": 108, "led": 108, "jumbish": 108, "milli": 108, "movement": 108, "warlord": 108, "abdul": 108, "rashid": 108, "dostum": 108, "dollar": 108, "exchang": 108, "3570": 108, "12049": 108, "born": 108, "1937": 108, "provinc": 108, "anhui": 108, "dai": 108, "came": 108, "shanghai": 108, "citi": 108, "prolif": 108, "author": 108, "teacher": 108, "chines": 108, "16764": 108, "1990": 108, "historian": 108, "alan": 108, "john": 108, "percival": 108, "taylor": 108, "di": 108, "20446": 108, "pace": 108, "bowler": 108, "ian": 108, "harvei": 108, "claim": 108, "victoria": 108, "15514": 108, "cotti": 108, "osc": 108, "foreign": 108, "minist": 108, "7525": 108, "sultan": 108, "specter": 108, "crown": 108, "abdullah": 108, "defenc": 108, "aviat": 108, "jeddah": 108, "saudi": 108, "agenc": 108, "2288": 108, "hi": 108, "customari": 108, "outfit": 108, "champion": 108, "damp": 108, "scalp": 108, "canada": 108, "reign": 108, "olymp": 108, "donovan": 108, "bailei": 108, "1992": 108, "linford": 108, "christi": 108, "britain": 108, "1984": 108, "1988": 108, "carl": 108, "lewi": 108, "ambigi": 108, "punctuat": 108, "chicago": 108, "digest": 108, "philadelphia": 108, "usda": 108, "york": 108, "token_issu": 108, "471": 108, "kean": 108, "year": 108, "contract": 108, "manchest": 108, "19072": 108, "societi": 108, "bite": 108, "deliv": 108, "19910": 108, "father": 108, "clarenc": 108, "woolmer": 108, "renam": 108, "uttar": 108, "pradesh": 108, "india": 108, "ranji": 108, "trophi": 108, "nation": 108, "championship": 108, "captain": 108, "1949": 108, "15658": 108, "19879": 108, "iii": 108, "brian": 108, "shimer": 108, "randi": 108, "jone": 108, "19104": 108}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [37, 0, 0, "-", "dataset"], [40, 0, 0, "-", "experimental"], [44, 0, 0, "-", "filter"], [45, 0, 0, "-", "internal"], [60, 0, 0, "-", "models"], [62, 0, 0, "-", "multiannotator"], [65, 0, 0, "-", "multilabel_classification"], [68, 0, 0, "-", "object_detection"], [71, 0, 0, "-", "outlier"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "regression"], [77, 0, 0, "-", "segmentation"], [81, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [16, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[13, 0, 0, "-", "data"], [14, 0, 0, "-", "data_issues"], [17, 0, 0, "-", "issue_finder"], [15, 0, 0, "-", "issue_manager_factory"], [33, 0, 0, "-", "model_outputs"], [34, 0, 0, "-", "report"], [35, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[13, 2, 1, "", "Data"], [13, 5, 1, "", "DataFormatError"], [13, 5, 1, "", "DatasetDictError"], [13, 5, 1, "", "DatasetLoadError"], [13, 2, 1, "", "Label"], [13, 2, 1, "", "MultiClass"], [13, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[14, 2, 1, "", "DataIssues"], [14, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[14, 3, 1, "", "collect_issues_from_imagelab"], [14, 3, 1, "", "collect_issues_from_issue_manager"], [14, 3, 1, "", "collect_statistics"], [14, 3, 1, "", "get_info"], [14, 3, 1, "", "get_issue_summary"], [14, 3, 1, "", "get_issues"], [14, 6, 1, "", "info"], [14, 6, 1, "", "issue_summary"], [14, 6, 1, "", "issues"], [14, 3, 1, "", "set_health_score"], [14, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[17, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[17, 3, 1, "", "find_issues"], [17, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[19, 0, 0, "-", "data_valuation"], [20, 0, 0, "-", "duplicate"], [21, 0, 0, "-", "imbalance"], [23, 0, 0, "-", "issue_manager"], [24, 0, 0, "-", "label"], [27, 0, 0, "-", "noniid"], [28, 0, 0, "-", "null"], [29, 0, 0, "-", "outlier"], [32, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[19, 6, 1, "", "DEFAULT_THRESHOLD"], [19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 6, 1, "", "near_duplicate_sets"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[24, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 3, 1, "", "get_health_summary"], [24, 6, 1, "", "health_summary_parameters"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, 2, 1, "", "NonIIDIssueManager"], [27, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "find_issues"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[28, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[29, 6, 1, "", "DEFAULT_THRESHOLDS"], [29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 6, 1, "", "metric"], [29, 6, 1, "", "ood"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[31, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, 2, 1, "", "RegressionLabelIssueManager"], [31, 1, 1, "", "find_issues_with_features"], [31, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[32, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [32, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [32, 3, 1, "", "collect_info"], [32, 6, 1, "", "description"], [32, 3, 1, "", "filter_cluster_ids"], [32, 3, 1, "", "find_issues"], [32, 3, 1, "", "get_worst_cluster"], [32, 6, 1, "", "info"], [32, 6, 1, "", "issue_name"], [32, 6, 1, "", "issue_score_key"], [32, 6, 1, "", "issues"], [32, 3, 1, "", "make_summary"], [32, 3, 1, "", "perform_clustering"], [32, 3, 1, "", "report"], [32, 6, 1, "", "summary"], [32, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, 7, 1, "", "REGISTRY"], [15, 1, 1, "", "list_default_issue_types"], [15, 1, 1, "", "list_possible_issue_types"], [15, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[33, 2, 1, "", "ModelOutput"], [33, 2, 1, "", "MultiClassPredProbs"], [33, 2, 1, "", "MultiLabelPredProbs"], [33, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[34, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[34, 3, 1, "", "get_report"], [34, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[35, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[35, 6, 1, "", "CLASSIFICATION"], [35, 6, 1, "", "MULTILABEL"], [35, 6, 1, "", "REGRESSION"], [35, 3, 1, "", "__contains__"], [35, 3, 1, "", "__getitem__"], [35, 3, 1, "", "__iter__"], [35, 3, 1, "", "__len__"], [35, 3, 1, "", "from_str"], [35, 4, 1, "", "is_classification"], [35, 4, 1, "", "is_multilabel"], [35, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[37, 1, 1, "", "find_overlapping_classes"], [37, 1, 1, "", "health_summary"], [37, 1, 1, "", "overall_label_health_score"], [37, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[38, 0, 0, "-", "cifar_cnn"], [39, 0, 0, "-", "coteaching"], [41, 0, 0, "-", "label_issues_batched"], [42, 0, 0, "-", "mnist_pytorch"], [43, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[38, 2, 1, "", "CNN"], [38, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[38, 6, 1, "", "T_destination"], [38, 3, 1, "", "__call__"], [38, 3, 1, "", "add_module"], [38, 3, 1, "", "apply"], [38, 3, 1, "", "bfloat16"], [38, 3, 1, "", "buffers"], [38, 6, 1, "", "call_super_init"], [38, 3, 1, "", "children"], [38, 3, 1, "", "compile"], [38, 3, 1, "", "cpu"], [38, 3, 1, "", "cuda"], [38, 3, 1, "", "double"], [38, 6, 1, "", "dump_patches"], [38, 3, 1, "", "eval"], [38, 3, 1, "", "extra_repr"], [38, 3, 1, "", "float"], [38, 3, 1, "id0", "forward"], [38, 3, 1, "", "get_buffer"], [38, 3, 1, "", "get_extra_state"], [38, 3, 1, "", "get_parameter"], [38, 3, 1, "", "get_submodule"], [38, 3, 1, "", "half"], [38, 3, 1, "", "ipu"], [38, 3, 1, "", "load_state_dict"], [38, 3, 1, "", "modules"], [38, 3, 1, "", "named_buffers"], [38, 3, 1, "", "named_children"], [38, 3, 1, "", "named_modules"], [38, 3, 1, "", "named_parameters"], [38, 3, 1, "", "parameters"], [38, 3, 1, "", "register_backward_hook"], [38, 3, 1, "", "register_buffer"], [38, 3, 1, "", "register_forward_hook"], [38, 3, 1, "", "register_forward_pre_hook"], [38, 3, 1, "", "register_full_backward_hook"], [38, 3, 1, "", "register_full_backward_pre_hook"], [38, 3, 1, "", "register_load_state_dict_post_hook"], [38, 3, 1, "", "register_module"], [38, 3, 1, "", "register_parameter"], [38, 3, 1, "", "register_state_dict_pre_hook"], [38, 3, 1, "", "requires_grad_"], [38, 3, 1, "", "set_extra_state"], [38, 3, 1, "", "share_memory"], [38, 3, 1, "", "state_dict"], [38, 3, 1, "", "to"], [38, 3, 1, "", "to_empty"], [38, 3, 1, "", "train"], [38, 6, 1, "", "training"], [38, 3, 1, "", "type"], [38, 3, 1, "", "xpu"], [38, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[39, 1, 1, "", "adjust_learning_rate"], [39, 1, 1, "", "evaluate"], [39, 1, 1, "", "forget_rate_scheduler"], [39, 1, 1, "", "initialize_lr_scheduler"], [39, 1, 1, "", "loss_coteaching"], [39, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[41, 2, 1, "", "LabelInspector"], [41, 7, 1, "", "adj_confident_thresholds_shared"], [41, 1, 1, "", "find_label_issues_batched"], [41, 7, 1, "", "labels_shared"], [41, 7, 1, "", "pred_probs_shared"], [41, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[41, 3, 1, "", "get_confident_thresholds"], [41, 3, 1, "", "get_label_issues"], [41, 3, 1, "", "get_num_issues"], [41, 3, 1, "", "get_quality_scores"], [41, 3, 1, "", "score_label_quality"], [41, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[42, 2, 1, "", "CNN"], [42, 2, 1, "", "SimpleNet"], [42, 1, 1, "", "get_mnist_dataset"], [42, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[42, 3, 1, "", "__init_subclass__"], [42, 6, 1, "", "batch_size"], [42, 6, 1, "", "dataset"], [42, 6, 1, "", "epochs"], [42, 3, 1, "id0", "fit"], [42, 3, 1, "", "get_metadata_routing"], [42, 3, 1, "", "get_params"], [42, 6, 1, "", "loader"], [42, 6, 1, "", "log_interval"], [42, 6, 1, "", "lr"], [42, 6, 1, "", "momentum"], [42, 6, 1, "", "no_cuda"], [42, 3, 1, "id1", "predict"], [42, 3, 1, "id4", "predict_proba"], [42, 6, 1, "", "seed"], [42, 3, 1, "", "set_fit_request"], [42, 3, 1, "", "set_params"], [42, 3, 1, "", "set_predict_proba_request"], [42, 3, 1, "", "set_predict_request"], [42, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[42, 6, 1, "", "T_destination"], [42, 3, 1, "", "__call__"], [42, 3, 1, "", "add_module"], [42, 3, 1, "", "apply"], [42, 3, 1, "", "bfloat16"], [42, 3, 1, "", "buffers"], [42, 6, 1, "", "call_super_init"], [42, 3, 1, "", "children"], [42, 3, 1, "", "compile"], [42, 3, 1, "", "cpu"], [42, 3, 1, "", "cuda"], [42, 3, 1, "", "double"], [42, 6, 1, "", "dump_patches"], [42, 3, 1, "", "eval"], [42, 3, 1, "", "extra_repr"], [42, 3, 1, "", "float"], [42, 3, 1, "", "forward"], [42, 3, 1, "", "get_buffer"], [42, 3, 1, "", "get_extra_state"], [42, 3, 1, "", "get_parameter"], [42, 3, 1, "", "get_submodule"], [42, 3, 1, "", "half"], [42, 3, 1, "", "ipu"], [42, 3, 1, "", "load_state_dict"], [42, 3, 1, "", "modules"], [42, 3, 1, "", "named_buffers"], [42, 3, 1, "", "named_children"], [42, 3, 1, "", "named_modules"], [42, 3, 1, "", "named_parameters"], [42, 3, 1, "", "parameters"], [42, 3, 1, "", "register_backward_hook"], [42, 3, 1, "", "register_buffer"], [42, 3, 1, "", "register_forward_hook"], [42, 3, 1, "", "register_forward_pre_hook"], [42, 3, 1, "", "register_full_backward_hook"], [42, 3, 1, "", "register_full_backward_pre_hook"], [42, 3, 1, "", "register_load_state_dict_post_hook"], [42, 3, 1, "", "register_module"], [42, 3, 1, "", "register_parameter"], [42, 3, 1, "", "register_state_dict_pre_hook"], [42, 3, 1, "", "requires_grad_"], [42, 3, 1, "", "set_extra_state"], [42, 3, 1, "", "share_memory"], [42, 3, 1, "", "state_dict"], [42, 3, 1, "", "to"], [42, 3, 1, "", "to_empty"], [42, 3, 1, "", "train"], [42, 6, 1, "", "training"], [42, 3, 1, "", "type"], [42, 3, 1, "", "xpu"], [42, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[43, 1, 1, "", "display_issues"], [43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[44, 1, 1, "", "find_label_issues"], [44, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [44, 1, 1, "", "find_predicted_neq_given"], [44, 7, 1, "", "pred_probs_by_class"], [44, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[46, 0, 0, "-", "label_quality_utils"], [47, 0, 0, "-", "latent_algebra"], [48, 0, 0, "-", "multiannotator_utils"], [49, 0, 0, "-", "multilabel_scorer"], [50, 0, 0, "-", "multilabel_utils"], [51, 0, 0, "-", "neighbor"], [55, 0, 0, "-", "outlier"], [56, 0, 0, "-", "token_classification_utils"], [57, 0, 0, "-", "util"], [58, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[46, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, 1, 1, "", "compute_inv_noise_matrix"], [47, 1, 1, "", "compute_noise_matrix_from_inverse"], [47, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [47, 1, 1, "", "compute_py"], [47, 1, 1, "", "compute_py_inv_noise_matrix"], [47, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[48, 1, 1, "", "assert_valid_inputs_multiannotator"], [48, 1, 1, "", "assert_valid_pred_probs"], [48, 1, 1, "", "check_consensus_label_classes"], [48, 1, 1, "", "compute_soft_cross_entropy"], [48, 1, 1, "", "find_best_temp_scaler"], [48, 1, 1, "", "format_multiannotator_labels"], [48, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[49, 2, 1, "", "Aggregator"], [49, 2, 1, "", "ClassLabelScorer"], [49, 2, 1, "", "MultilabelScorer"], [49, 1, 1, "", "exponential_moving_average"], [49, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "multilabel_py"], [49, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[49, 3, 1, "", "__call__"], [49, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[49, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [49, 6, 1, "", "NORMALIZED_MARGIN"], [49, 6, 1, "", "SELF_CONFIDENCE"], [49, 3, 1, "", "__call__"], [49, 3, 1, "", "__contains__"], [49, 3, 1, "", "__getitem__"], [49, 3, 1, "", "__iter__"], [49, 3, 1, "", "__len__"], [49, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[49, 3, 1, "", "__call__"], [49, 3, 1, "", "aggregate"], [49, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[50, 1, 1, "", "get_onehot_num_classes"], [50, 1, 1, "", "int2onehot"], [50, 1, 1, "", "onehot2int"], [50, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[52, 0, 0, "-", "knn_graph"], [53, 0, 0, "-", "metric"], [54, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[52, 7, 1, "", "DEFAULT_K"], [52, 1, 1, "", "construct_knn_graph_from_index"], [52, 1, 1, "", "correct_knn_distances_and_indices"], [52, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [52, 1, 1, "", "correct_knn_graph"], [52, 1, 1, "", "create_knn_graph_and_index"], [52, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[53, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [53, 7, 1, "", "ROW_COUNT_CUTOFF"], [53, 1, 1, "", "decide_default_metric"], [53, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[55, 1, 1, "", "correct_precision_errors"], [55, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, 1, 1, "", "color_sentence"], [56, 1, 1, "", "filter_sentence"], [56, 1, 1, "", "get_sentence"], [56, 1, 1, "", "mapping"], [56, 1, 1, "", "merge_probs"], [56, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[57, 1, 1, "", "append_extra_datapoint"], [57, 1, 1, "", "clip_noise_rates"], [57, 1, 1, "", "clip_values"], [57, 1, 1, "", "compress_int_array"], [57, 1, 1, "", "confusion_matrix"], [57, 1, 1, "", "csr_vstack"], [57, 1, 1, "", "estimate_pu_f1"], [57, 1, 1, "", "extract_indices_tf"], [57, 1, 1, "", "force_two_dimensions"], [57, 1, 1, "", "format_labels"], [57, 1, 1, "", "get_missing_classes"], [57, 1, 1, "", "get_num_classes"], [57, 1, 1, "", "get_unique_classes"], [57, 1, 1, "", "is_tensorflow_dataset"], [57, 1, 1, "", "is_torch_dataset"], [57, 1, 1, "", "num_unique_classes"], [57, 1, 1, "", "print_inverse_noise_matrix"], [57, 1, 1, "", "print_joint_matrix"], [57, 1, 1, "", "print_noise_matrix"], [57, 1, 1, "", "print_square_matrix"], [57, 1, 1, "", "remove_noise_from_class"], [57, 1, 1, "", "round_preserving_row_totals"], [57, 1, 1, "", "round_preserving_sum"], [57, 1, 1, "", "smart_display_dataframe"], [57, 1, 1, "", "subset_X_y"], [57, 1, 1, "", "subset_data"], [57, 1, 1, "", "subset_labels"], [57, 1, 1, "", "train_val_split"], [57, 1, 1, "", "unshuffle_tensorflow_dataset"], [57, 1, 1, "", "value_counts"], [57, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[58, 1, 1, "", "assert_indexing_works"], [58, 1, 1, "", "assert_nonempty_input"], [58, 1, 1, "", "assert_valid_class_labels"], [58, 1, 1, "", "assert_valid_inputs"], [58, 1, 1, "", "labels_to_array"], [58, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[61, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[61, 2, 1, "", "KerasWrapperModel"], [61, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[62, 1, 1, "", "convert_long_to_wide_dataset"], [62, 1, 1, "", "get_active_learning_scores"], [62, 1, 1, "", "get_active_learning_scores_ensemble"], [62, 1, 1, "", "get_label_quality_multiannotator"], [62, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [62, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[63, 0, 0, "-", "dataset"], [64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[63, 1, 1, "", "common_multilabel_issues"], [63, 1, 1, "", "multilabel_health_summary"], [63, 1, 1, "", "overall_multilabel_health_score"], [63, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, 1, 1, "", "find_label_issues"], [64, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[69, 1, 1, "", "compute_badloc_box_scores"], [69, 1, 1, "", "compute_overlooked_box_scores"], [69, 1, 1, "", "compute_swap_box_scores"], [69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"], [69, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[70, 1, 1, "", "bounding_box_size_distribution"], [70, 1, 1, "", "calculate_per_class_metrics"], [70, 1, 1, "", "class_label_distribution"], [70, 1, 1, "", "get_average_per_class_confusion_matrix"], [70, 1, 1, "", "get_sorted_bbox_count_idxs"], [70, 1, 1, "", "object_counts_per_image"], [70, 1, 1, "", "plot_class_distribution"], [70, 1, 1, "", "plot_class_size_distributions"], [70, 1, 1, "", "visualize"]], "cleanlab.outlier": [[71, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[71, 3, 1, "", "fit"], [71, 3, 1, "", "fit_score"], [71, 3, 1, "", "score"]], "cleanlab.rank": [[72, 1, 1, "", "find_top_issues"], [72, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [72, 1, 1, "", "get_label_quality_ensemble_scores"], [72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "get_normalized_margin_for_each_label"], [72, 1, 1, "", "get_self_confidence_for_each_label"], [72, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[74, 0, 0, "-", "learn"], [75, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[74, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[74, 3, 1, "", "__init_subclass__"], [74, 3, 1, "", "find_label_issues"], [74, 3, 1, "", "fit"], [74, 3, 1, "", "get_aleatoric_uncertainty"], [74, 3, 1, "", "get_epistemic_uncertainty"], [74, 3, 1, "", "get_label_issues"], [74, 3, 1, "", "get_metadata_routing"], [74, 3, 1, "", "get_params"], [74, 3, 1, "", "predict"], [74, 3, 1, "", "save_space"], [74, 3, 1, "", "score"], [74, 3, 1, "", "set_fit_request"], [74, 3, 1, "", "set_params"], [74, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[75, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[76, 0, 0, "-", "filter"], [78, 0, 0, "-", "rank"], [79, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[76, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[78, 1, 1, "", "get_label_quality_scores"], [78, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[79, 1, 1, "", "common_label_issues"], [79, 1, 1, "", "display_issues"], [79, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[80, 0, 0, "-", "filter"], [82, 0, 0, "-", "rank"], [83, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[80, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[82, 1, 1, "", "get_label_quality_scores"], [82, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[83, 1, 1, "", "common_label_issues"], [83, 1, 1, "", "display_issues"], [83, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 87, 88, 92, 94, 95, 98, 99, 102, 108], "count": [3, 99], "data_valu": [4, 19], "datalab": [5, 7, 9, 10, 12, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102], "creat": [7, 90, 91, 96, 99, 101], "your": [7, 84, 90, 91, 95, 96, 98, 99], "own": 7, "issu": [7, 9, 10, 22, 31, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "manag": [7, 22], "prerequisit": 7, "implement": 7, "issuemanag": [7, 90], "basic": 7, "check": [7, 96], "intermedi": 7, "advanc": [7, 90], "us": [7, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "gener": [8, 96], "cluster": [8, 96, 98], "id": 8, "guid": [9, 12], "type": [9, 10, 99], "custom": [9, 90], "cleanlab": [9, 10, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "studio": [9, 10], "easi": [9, 10, 84, 92, 94, 95], "mode": [9, 10, 84, 92, 94, 95], "can": [10, 91, 97, 98, 99, 101], "detect": [10, 89, 91, 92, 94, 95, 96, 98, 99, 103, 104], "estim": [10, 99, 101, 102], "each": 10, "input": 10, "label": [10, 24, 26, 31, 84, 87, 88, 89, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 29, 55, 71, 92, 94, 95, 102, 104], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 91, 92, 94, 95], "duplic": [10, 20, 91, 92, 94, 95, 98, 102], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 95, 96], "iid": [10, 95, 96], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 85, 96, 99, 107], "imbal": [10, 21, 96], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 92, 96, 104], "specif": [10, 22, 96, 107], "underperform": [10, 96, 98], "group": [10, 96, 98], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 28, 96], "is_null_issu": 10, "null_scor": 10, "data": [10, 13, 84, 87, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "valuat": [10, 96], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 96], "paramet": [10, 99], "get": [12, 90, 91, 101, 102, 103, 107, 108], "start": [12, 97], "api": 12, "refer": 12, "data_issu": 14, "factori": 15, "intern": [16, 45], "issue_find": 17, "issue_manag": [22, 23], "regist": 22, "ml": [22, 98, 99], "task": [22, 35], "multilabel": 25, "noniid": 27, "regress": [30, 73, 74, 75, 98, 106], "prioriti": 31, "order": 31, "find": [31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "underperforming_group": 32, "model_output": 33, "report": [34, 92], "dataset": [37, 63, 84, 88, 89, 91, 92, 95, 96, 97, 98, 99, 102, 103, 104, 106, 107, 108], "cifar_cnn": 38, "coteach": 39, "experiment": 40, "label_issues_batch": 41, "mnist_pytorch": 42, "span_classif": 43, "filter": [44, 64, 67, 76, 80, 99], "label_quality_util": 46, "latent_algebra": 47, "multiannotator_util": 48, "multilabel_scor": 49, "multilabel_util": 50, "neighbor": 51, "knn_graph": 52, "metric": 53, "search": [54, 90], "token_classification_util": 56, "util": 57, "valid": [58, 92, 105], "fasttext": 59, "model": [60, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106], "kera": 61, "multiannot": [62, 101], "multilabel_classif": 65, "rank": [66, 69, 72, 75, 78, 82, 99], "object_detect": 68, "summari": [70, 79, 83], "learn": [74, 91, 98, 99], "segment": [77, 107], "token_classif": [81, 108], "open": [84, 98], "sourc": [84, 98], "document": 84, "quickstart": 84, "1": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "instal": [84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "2": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "common": [84, 85, 108], "3": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "handl": [84, 98], "error": [84, 88, 92, 98, 99, 101, 102, 103, 106, 107, 108], "train": [84, 87, 88, 89, 96, 98, 104, 106], "robust": [84, 87, 88, 99, 106], "noisi": [84, 87, 88, 99, 106], "4": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 103, 104, 106], "curat": 84, "fix": [84, 98], "level": [84, 97, 99, 108], "5": [84, 87, 89, 91, 92, 94, 96, 99, 101, 106], "improv": [84, 101], "via": [84, 99, 101], "mani": [84, 99], "other": [84, 101, 103, 106], "techniqu": 84, "contribut": 84, "how": [85, 98, 99, 101, 102, 108], "migrat": 85, "version": 85, "0": 85, "from": [85, 87, 88, 90, 91, 99, 106], "pre": [85, 89, 96, 98, 104], "function": [85, 90], "name": 85, "chang": 85, "modul": [85, 99], "new": 85, "remov": 85, "argument": [85, 90], "variabl": 85, "cleanlearn": [86, 98, 99], "tutori": [86, 93, 97, 100], "structur": 87, "tabular": [87, 94], "requir": [87, 88, 90, 91, 92, 94, 95, 101, 102, 103, 104, 106, 107, 108], "depend": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "load": [87, 88, 89, 90, 91, 94, 95, 96, 106], "process": [87, 94, 104, 106], "select": [87, 94], "comput": [87, 89, 92, 94, 95, 96, 98, 101, 105], "out": [87, 89, 90, 91, 92, 94, 95, 101, 105], "sampl": [87, 89, 90, 91, 92, 94, 95, 101, 105], "predict": [87, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 105], "probabl": [87, 89, 90, 91, 92, 94, 95, 96, 101, 105], "more": [87, 88, 91, 99, 106], "text": [88, 95, 96, 108], "format": [88, 95, 98, 102, 103], "defin": [88, 92, 95, 96, 106], "potenti": [88, 101, 106], "an": [89, 92, 98], "audio": 89, "import": [89, 90, 91, 92, 97, 99, 101], "them": [89, 97, 99], "speechbrain": 89, "featur": [89, 92, 104], "fit": 89, "linear": 89, "workflow": [90, 96, 99], "audit": [90, 91], "classifi": [90, 91, 96], "instanti": 90, "object": [90, 96, 103], "increment": 90, "specifi": [90, 98], "nondefault": 90, "save": 90, "ad": 90, "A": 91, "unifi": 91, "all": [91, 99], "kind": [91, 103], "skip": [91, 97, 99, 101], "detail": [91, 97, 99, 101], "about": 91, "addit": 91, "inform": [91, 92], "fetch": [92, 97], "normal": 92, "fashion": 92, "mnist": 92, "prepar": [92, 96], "k": [92, 94, 105], "fold": [92, 105], "cross": [92, 105], "embed": [92, 104], "7": [92, 99], "view": 92, "most": [92, 108], "like": 92, "exampl": [92, 98, 99, 104], "sever": 92, "set": [92, 99], "dark": [92, 96], "top": [92, 107], "low": 92, "numer": 94, "categor": [94, 96], "column": 94, "construct": 94, "nearest": 94, "neighbour": 94, "graph": [94, 96], "drift": [95, 102], "miscellan": 96, "acceler": 96, "knn": 96, "obtain": 96, "identifi": [96, 98, 103], "explan": 96, "vector": 96, "perform": 96, "visual": [96, 99, 103, 104, 107], "score": [96, 99, 101, 102, 103, 107, 108], "synthet": 96, "result": 96, "predefin": 96, "slice": [96, 98], "i": [96, 98, 99, 105], "catch": 96, "valu": 96, "encod": 96, "initi": [96, 101], "sort": 96, "6": [96, 99], "spuriou": 96, "correl": 96, "pass": 96, "relat": 96, "transform": 96, "imageenh": 96, "induc": 96, "properti": 96, "origin": 96, "understand": 97, "evalu": 97, "health": [97, 99], "8": [97, 99], "popular": 97, "faq": 98, "what": [98, 99, 105], "do": [98, 99], "infer": 98, "correct": 98, "ha": 98, "flag": 98, "should": 98, "v": 98, "test": [98, 99, 104], "big": 98, "limit": 98, "memori": 98, "why": 98, "isn": 98, "t": 98, "work": [98, 99, 101, 108], "me": 98, "differ": [98, 103], "clean": [98, 99], "final": 98, "hyperparamet": 98, "tune": 98, "onli": 98, "one": [98, 99, 102, 107], "doe": [98, 101, 108], "take": 98, "so": 98, "long": 98, "when": [98, 99], "run": 98, "licens": 98, "under": 98, "answer": 98, "question": 98, "The": 99, "centric": 99, "ai": 99, "machin": 99, "find_label_issu": 99, "line": 99, "code": 99, "twenti": 99, "lowest": 99, "qualiti": [99, 101, 102, 103, 107, 108], "see": 99, "now": 99, "let": 99, "": 99, "happen": 99, "we": 99, "merg": 99, "seafoam": 99, "green": 99, "yellow": 99, "too": 99, "you": 99, "re": 99, "One": 99, "rule": 99, "overal": [99, 107], "accur": 99, "thi": 99, "directli": 99, "fulli": 99, "character": 99, "nois": 99, "matrix": [99, 102], "joint": 99, "prior": 99, "true": 99, "distribut": 99, "flip": 99, "rate": 99, "ani": 99, "again": 99, "support": 99, "lot": 99, "method": 99, "filter_bi": 99, "automat": 99, "everi": 99, "uniqu": 99, "num_label_issu": 99, "threshold": 99, "found": 99, "Not": 99, "sure": 99, "ensembl": 99, "multipl": [99, 101], "predictor": 99, "consensu": 101, "annot": 101, "major": 101, "vote": 101, "better": 101, "statist": 101, "compar": 101, "inspect": 101, "retrain": 101, "further": 101, "multi": 102, "beyond": 102, "mislabel": [102, 107, 108], "given": 102, "hot": 102, "binari": 102, "without": 102, "applic": 102, "real": 102, "download": [103, 107, 108], "objectlab": 103, "exploratori": 103, "analysi": 103, "pytorch": 104, "timm": 104, "cifar10": 104, "some": 104, "pred_prob": [104, 107, 108], "wai": 106, "semant": 107, "which": 107, "ar": 107, "commonli": 107, "focus": 107, "token": 108, "word": 108, "sentenc": 108, "contain": 108, "particular": 108}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [19, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"], [96, "id8"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[96, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[96, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[96, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.data"], [14, "module-cleanlab.datalab.internal.data_issues"], [15, "module-cleanlab.datalab.internal.issue_manager_factory"], [16, "module-cleanlab.datalab.internal"], [17, "module-cleanlab.datalab.internal.issue_finder"], [19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [20, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [21, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [27, "module-cleanlab.datalab.internal.issue_manager.noniid"], [28, "module-cleanlab.datalab.internal.issue_manager.null"], [29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [33, "module-cleanlab.datalab.internal.model_outputs"], [34, "module-cleanlab.datalab.internal.report"], [35, "module-cleanlab.datalab.internal.task"], [37, "module-cleanlab.dataset"], [38, "module-cleanlab.experimental.cifar_cnn"], [39, "module-cleanlab.experimental.coteaching"], [40, "module-cleanlab.experimental"], [41, "module-cleanlab.experimental.label_issues_batched"], [42, "module-cleanlab.experimental.mnist_pytorch"], [43, "module-cleanlab.experimental.span_classification"], [44, "module-cleanlab.filter"], [45, "module-cleanlab.internal"], [46, "module-cleanlab.internal.label_quality_utils"], [47, "module-cleanlab.internal.latent_algebra"], [48, "module-cleanlab.internal.multiannotator_utils"], [49, "module-cleanlab.internal.multilabel_scorer"], [50, "module-cleanlab.internal.multilabel_utils"], [51, "module-cleanlab.internal.neighbor"], [52, "module-cleanlab.internal.neighbor.knn_graph"], [53, "module-cleanlab.internal.neighbor.metric"], [54, "module-cleanlab.internal.neighbor.search"], [55, "module-cleanlab.internal.outlier"], [56, "module-cleanlab.internal.token_classification_utils"], [57, "module-cleanlab.internal.util"], [58, "module-cleanlab.internal.validation"], [60, "module-cleanlab.models"], [61, "module-cleanlab.models.keras"], [62, "module-cleanlab.multiannotator"], [63, "module-cleanlab.multilabel_classification.dataset"], [64, "module-cleanlab.multilabel_classification.filter"], [65, "module-cleanlab.multilabel_classification"], [66, "module-cleanlab.multilabel_classification.rank"], [67, "module-cleanlab.object_detection.filter"], [68, "module-cleanlab.object_detection"], [69, "module-cleanlab.object_detection.rank"], [70, "module-cleanlab.object_detection.summary"], [71, "module-cleanlab.outlier"], [72, "module-cleanlab.rank"], [73, "module-cleanlab.regression"], [74, "module-cleanlab.regression.learn"], [75, "module-cleanlab.regression.rank"], [76, "module-cleanlab.segmentation.filter"], [77, "module-cleanlab.segmentation"], [78, "module-cleanlab.segmentation.rank"], [79, "module-cleanlab.segmentation.summary"], [80, "module-cleanlab.token_classification.filter"], [81, "module-cleanlab.token_classification"], [82, "module-cleanlab.token_classification.rank"], [83, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[13, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[13, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[13, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[13, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[13, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[16, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[17, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[28, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[34, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[34, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[35, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[35, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[37, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.forward"], [38, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[40, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [42, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [42, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [42, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[44, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[44, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[44, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[45, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[46, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
+Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 85, 90, 91, 99, 101, 102], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 90, 91, 99, 101, 102], "generate_noise_matrix_from_trac": [0, 1, 90, 91, 99, 101, 102], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 17, 41, 46, 48, 49, 50, 51, 55, 56, 57, 69, 92, 96, 97, 108], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 27, 30, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 84, 85, 90, 97, 105], "benchmark": [1, 38, 84, 85, 90, 91, 99, 101, 102], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105], "": [1, 2, 3, 4, 10, 19, 33, 37, 38, 42, 46, 49, 52, 54, 55, 57, 62, 63, 67, 69, 70, 71, 72, 74, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "core": [1, 41, 44, 76, 78], "algorithm": [1, 2, 8, 10, 32, 39, 43, 54, 55, 57, 62, 71, 80, 82, 84, 96, 98, 99, 101, 108], "These": [1, 2, 3, 4, 5, 8, 10, 22, 38, 40, 42, 43, 44, 45, 52, 60, 62, 63, 66, 70, 71, 75, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "introduc": [1, 89, 96, 98, 99], "synthet": [1, 101, 102, 107], "nois": [1, 2, 3, 37, 44, 47, 57, 63, 90, 91, 96, 97, 101, 106], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 16, 17, 21, 22, 23, 25, 30, 32, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 90, 96, 100, 104, 105], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 33, 35, 37, 41, 43, 44, 47, 49, 50, 57, 62, 63, 64, 65, 66, 71, 72, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 96, 100, 101, 104, 105, 106, 107], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 26, 27, 28, 29, 31, 32, 40, 41, 42, 43, 44, 47, 49, 53, 57, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 90, 94, 100, 101, 105], "specif": [1, 3, 5, 9, 15, 16, 17, 28, 34, 35, 40, 52, 53, 54, 60, 64, 67, 70, 79, 83, 92, 94, 95, 99, 103, 108], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "modul": [1, 3, 14, 15, 16, 17, 22, 25, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 51, 52, 54, 55, 57, 60, 62, 67, 70, 71, 72, 84, 92, 98, 102], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 17, 19, 24, 31, 35, 37, 38, 39, 41, 42, 44, 47, 51, 52, 54, 55, 57, 61, 62, 63, 64, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108], "gener": [1, 2, 3, 7, 10, 19, 24, 26, 34, 37, 49, 52, 54, 57, 58, 71, 72, 74, 79, 88, 89, 90, 91, 92, 95, 97, 98, 99, 101, 102, 104, 105, 107, 108], "valid": [1, 2, 3, 5, 10, 13, 33, 35, 37, 44, 45, 47, 48, 49, 52, 54, 55, 57, 62, 64, 67, 70, 72, 74, 75, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "matric": [1, 3, 47, 98], "which": [1, 2, 3, 5, 7, 10, 13, 14, 15, 17, 19, 23, 27, 33, 34, 35, 37, 38, 42, 43, 44, 47, 49, 53, 54, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "learn": [1, 2, 3, 4, 5, 9, 10, 15, 17, 23, 31, 34, 39, 40, 41, 42, 44, 46, 48, 53, 54, 57, 60, 62, 64, 71, 73, 75, 78, 82, 84, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 106], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106, 107, 108], "possibl": [1, 2, 3, 7, 10, 37, 38, 42, 44, 46, 47, 49, 64, 65, 66, 67, 69, 70, 71, 72, 74, 80, 82, 83, 91, 96, 98, 99, 101, 102, 103, 106, 107, 108], "noisi": [1, 2, 3, 10, 37, 39, 42, 44, 47, 57, 63, 64, 66, 72, 74, 75, 76, 78, 79, 85, 90, 91, 94, 95, 96, 98, 100, 101], "given": [1, 2, 3, 5, 10, 15, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "matrix": [1, 2, 3, 5, 10, 17, 19, 32, 37, 44, 46, 47, 50, 52, 57, 58, 64, 67, 69, 70, 71, 72, 94, 96, 103, 104], "trace": [1, 90, 91, 99, 101, 102], "valu": [1, 2, 3, 4, 5, 10, 13, 14, 17, 19, 23, 27, 28, 33, 35, 37, 38, 39, 41, 42, 44, 46, 47, 49, 52, 53, 54, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 83, 88, 89, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "more": [1, 2, 3, 4, 5, 7, 9, 10, 14, 15, 17, 19, 27, 37, 38, 41, 42, 43, 46, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 107, 108], "function": [1, 2, 3, 4, 5, 7, 10, 14, 15, 17, 24, 27, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 96, 97, 98, 99, 101, 102, 103, 107, 108], "noise_matrix": [1, 2, 3, 10, 47, 57, 90, 91, 99, 101, 102], "py": [1, 3, 34, 38, 39, 44, 47, 49, 84, 90, 91, 99, 101, 102], "verbos": [1, 2, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 41, 44, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 90, 99, 101], "fals": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 48, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 103, 104, 106, 107], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83], "prior": [1, 2, 3, 37, 44, 47, 49], "repres": [1, 2, 3, 7, 10, 13, 17, 19, 27, 33, 35, 37, 41, 44, 47, 50, 52, 53, 55, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 108], "p": [1, 2, 3, 5, 10, 37, 44, 46, 47, 55, 57, 62, 70, 71, 72, 76, 94, 95, 96, 99, 101, 108], "true_label": [1, 2, 3, 37, 47, 57, 99, 101], "k": [1, 2, 3, 4, 5, 8, 10, 13, 17, 19, 20, 24, 27, 29, 32, 37, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 65, 66, 67, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 87, 89, 90, 91, 96, 98, 99, 101, 102, 103, 104, 107, 108], "check": [1, 2, 5, 6, 9, 10, 13, 17, 28, 35, 38, 41, 42, 48, 58, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 98, 99, 101, 102, 106], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 14, 23, 27, 39, 42, 47, 49, 55, 69, 74, 88, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106], "achiev": [1, 2, 38, 39, 42, 74, 98, 101, 108], "better": [1, 5, 10, 44, 53, 62, 64, 72, 74, 75, 84, 88, 89, 91, 94, 95, 96, 98, 99, 102, 103, 104, 108], "than": [1, 2, 3, 4, 7, 9, 10, 27, 29, 32, 37, 44, 53, 57, 61, 62, 67, 69, 71, 72, 74, 78, 82, 87, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "random": [1, 2, 3, 7, 10, 19, 32, 41, 49, 52, 62, 72, 74, 87, 89, 90, 91, 92, 94, 96, 98, 99, 101, 102, 104], "perform": [1, 2, 4, 7, 10, 27, 29, 32, 38, 42, 49, 51, 52, 53, 70, 74, 84, 87, 88, 90, 98, 99, 101, 102, 105, 106], "averag": [1, 3, 5, 10, 23, 29, 37, 38, 42, 49, 55, 62, 63, 70, 71, 72, 98, 101, 104], "amount": [1, 3, 92], "paramet": [1, 2, 3, 4, 5, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 95, 96], "np": [1, 2, 3, 4, 5, 7, 17, 19, 32, 37, 39, 41, 43, 44, 46, 47, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "ndarrai": [1, 2, 3, 4, 5, 17, 24, 26, 27, 31, 32, 33, 37, 39, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 96, 108], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 54, 55, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 84, 87, 88, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 17, 19, 27, 33, 37, 39, 41, 42, 43, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "shape": [1, 2, 3, 4, 5, 17, 19, 37, 39, 41, 43, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 89, 96, 97, 98, 99, 102, 103, 104, 107, 108], "condit": [1, 2, 3, 47, 53, 56, 57, 72, 92, 99, 108], "probabl": [1, 2, 3, 5, 8, 10, 17, 24, 26, 29, 33, 37, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 107, 108], "k_": [1, 2, 3, 47, 57], "k_y": [1, 2, 3, 47, 57], "contain": [1, 2, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 46, 47, 51, 52, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107], "fraction": [1, 2, 3, 10, 21, 39, 47, 57, 62, 74, 94, 98], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 55, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 101, 102, 103, 105, 106, 107, 108], "everi": [1, 2, 3, 4, 5, 10, 17, 38, 42, 44, 47, 56, 57, 64, 72, 74, 75, 87, 89, 90, 91, 92, 94, 95, 98, 101, 103, 105, 107, 108], "class": [1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 54, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 105, 106, 108], "other": [1, 2, 3, 5, 10, 17, 23, 28, 37, 38, 40, 41, 42, 44, 47, 50, 52, 57, 58, 60, 62, 63, 66, 70, 71, 72, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 104, 107, 108], "assum": [1, 2, 3, 13, 44, 47, 52, 56, 57, 72, 76, 79, 98, 102, 104, 106, 107, 108], "column": [1, 2, 3, 5, 10, 11, 13, 14, 31, 37, 41, 44, 47, 49, 50, 53, 56, 57, 62, 63, 64, 66, 67, 70, 71, 72, 74, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "sum": [1, 2, 3, 27, 32, 33, 37, 47, 49, 57, 63, 64, 66, 69, 74, 90, 91, 92, 98, 99, 101, 102, 107, 108], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 97, 98, 105], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 21, 23, 24, 26, 27, 32, 33, 34, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "true": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 49, 52, 56, 57, 58, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "return": [1, 2, 3, 4, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 62, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 62, 63, 64, 66, 67, 83, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 62, 64, 66, 69, 70, 71, 72, 74, 75, 80, 82, 83, 84, 89, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 74, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 65, 71, 90, 91, 92, 94, 95, 99], "yield": [1, 38, 42], "between": [1, 5, 10, 16, 17, 22, 23, 25, 27, 30, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 52, 53, 54, 55, 60, 62, 63, 66, 69, 71, 72, 74, 75, 78, 82, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "below": [1, 3, 4, 5, 10, 37, 38, 41, 42, 44, 46, 49, 55, 62, 63, 64, 69, 70, 78, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "we": [1, 2, 3, 5, 7, 10, 14, 23, 38, 41, 42, 44, 49, 57, 58, 61, 62, 69, 70, 72, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "loop": [1, 3, 47, 57, 92, 103], "implement": [1, 2, 3, 4, 9, 15, 23, 38, 39, 41, 42, 47, 51, 53, 54, 57, 71, 74, 84, 87, 89, 90, 94, 104, 105], "what": [1, 5, 9, 10, 17, 34, 37, 39, 41, 44, 62, 63, 67, 69, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "doe": [1, 2, 3, 7, 10, 41, 42, 44, 49, 52, 55, 58, 69, 70, 74, 76, 78, 82, 88, 89, 90, 91, 92, 94, 95, 97, 102, 106, 107], "do": [1, 2, 5, 9, 10, 37, 41, 42, 57, 58, 71, 72, 76, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "fast": 1, "explain": [1, 10, 96], "python": [1, 2, 42, 61, 74, 90, 91, 96, 97, 104], "pseudocod": [1, 105], "happen": [1, 10, 44, 64, 95, 101, 107], "n": [1, 2, 3, 5, 7, 37, 38, 41, 42, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 87, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "without": [1, 2, 5, 9, 10, 13, 15, 21, 38, 42, 54, 66, 74, 84, 88, 89, 95, 96, 98, 99, 103, 104], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 46, 48, 55, 56, 57, 61, 62, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107], "distinct": [1, 19, 57, 108], "natur": [1, 10, 101, 104], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 83, 85, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 107, 108], "0": [1, 2, 3, 4, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "count_joint": 1, "len": [1, 2, 3, 7, 37, 41, 47, 56, 57, 58, 71, 72, 74, 87, 88, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "y": [1, 2, 3, 5, 8, 19, 31, 32, 42, 47, 49, 57, 58, 61, 70, 74, 75, 88, 89, 90, 91, 94, 96, 98, 99, 101, 102, 104, 106], "round": [1, 41, 44, 57, 74, 96, 98, 106], "astyp": [1, 101], "int": [1, 2, 3, 4, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 39, 41, 42, 44, 49, 50, 52, 53, 54, 55, 56, 57, 58, 63, 64, 66, 70, 71, 72, 74, 76, 78, 79, 80, 83, 89, 90, 92, 96, 103, 104], "rang": [1, 3, 5, 7, 13, 47, 49, 55, 57, 70, 74, 75, 92, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 14, 17, 23, 37, 41, 44, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "pragma": 1, "cover": [1, 3, 85, 96, 97, 98], "choic": [1, 8, 44, 53, 55, 92, 98, 102, 104], "replac": [1, 56, 61, 72, 87, 88, 90, 91, 92, 95, 96, 97, 98, 101, 104], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 52, 72, 89, 90, 91], "05": [1, 10, 27, 31, 56, 70, 74, 80, 82, 94, 97, 98, 99, 103], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 90, 91, 99, 101, 102], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 90, 91, 92, 96, 98, 99, 101, 102, 107], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 27, 40, 42, 49, 74, 87, 89, 90, 91, 94, 96, 97, 99, 101, 102], "max_it": [1, 88, 89, 95, 104], "10000": [1, 41, 97, 98], "x": [1, 2, 3, 5, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 38, 39, 42, 44, 46, 47, 49, 52, 54, 56, 57, 58, 61, 62, 64, 70, 71, 72, 74, 76, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 104, 106], "diagon": [1, 3, 5, 44, 47, 57], "equal": [1, 3, 10, 13, 52, 64, 69, 79, 105], "creat": [1, 2, 9, 17, 19, 38, 41, 42, 44, 57, 74, 84, 88, 89, 92, 94, 95, 98, 107, 108], "impli": [1, 10, 37, 63, 70], "float": [1, 2, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 40, 41, 42, 44, 46, 48, 49, 55, 56, 57, 62, 63, 64, 66, 69, 70, 74, 78, 82, 89, 90, 91, 99, 101, 102], "entri": [1, 3, 5, 10, 37, 38, 42, 44, 46, 50, 52, 55, 57, 62, 63, 64, 67, 87, 88, 94, 95, 99, 102, 103, 106], "maximum": [1, 10, 71, 79, 83, 107], "minimum": [1, 8, 10, 21, 44, 46, 64, 69, 82], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 17, 27, 38, 42, 44, 52, 69, 74, 90, 98, 99, 101, 103, 104], "default": [1, 2, 3, 4, 5, 7, 10, 11, 15, 17, 29, 31, 34, 37, 38, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 90, 92, 96, 98, 106, 107], "If": [1, 2, 3, 4, 5, 10, 13, 14, 17, 27, 29, 35, 37, 38, 41, 42, 44, 46, 47, 49, 52, 53, 56, 57, 61, 62, 63, 64, 67, 69, 70, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "have": [1, 2, 3, 4, 5, 7, 9, 10, 17, 22, 25, 27, 30, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "all": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 23, 34, 37, 38, 41, 42, 43, 44, 47, 49, 50, 52, 56, 57, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 90, 96], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 61, 62, 63, 65, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 62, 66, 70, 74, 79, 83, 84, 87, 88, 89, 91, 95, 98, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 60, 61, 64, 71, 72, 74, 76, 84, 85, 89, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 61, 62, 63, 64, 66, 69, 70, 71, 72, 74, 78, 79, 80, 82, 88, 91, 92, 95, 96, 97, 98, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 61, 64, 66, 67, 69, 71, 72, 74, 75, 87, 88, 90, 91, 92, 94, 95, 96, 97, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 89, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 61, 62, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 90, 91, 94, 95, 96, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 61, 62, 67, 69, 70, 71, 72, 74, 75, 79, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 60, 61, 62, 64, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 69, 72, 74, 87, 88, 90, 91, 92, 96, 97, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 62, 63, 67, 69, 70, 71, 72, 74, 76, 79, 83, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 61, 62, 71, 74, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 62, 84, 90, 91, 95, 96, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 90, 92, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 90, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 61, 62, 64, 66, 70, 71, 72, 74, 75, 78, 80, 82, 85, 87, 88, 89, 92, 94, 95, 96, 98, 99, 102, 103, 104, 106, 107, 108], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 17, 24, 27, 31, 37, 38, 41, 42, 43, 44, 46, 47, 52, 53, 55, 56, 57, 58, 61, 62, 71, 72, 74, 76, 78, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 101, 102, 103, 104, 105, 106, 107, 108], "guarante": [1, 3, 5, 16, 22, 25, 30, 38, 40, 42, 45, 47, 60, 85], "produc": [1, 2, 5, 9, 10, 17, 49, 62, 72, 74, 76, 78, 84, 87, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108], "higher": [1, 5, 10, 37, 44, 46, 47, 49, 55, 61, 62, 63, 74, 91, 95, 96, 98, 103], "opposit": [1, 108], "occur": [1, 3, 10, 37, 56, 69, 90, 91, 92, 98, 104], "small": [1, 3, 10, 37, 41, 49, 52, 55, 57, 63, 70, 88, 92, 95, 97, 102, 104], "numpi": [1, 3, 4, 5, 7, 10, 13, 19, 32, 33, 41, 42, 43, 49, 52, 55, 56, 58, 61, 66, 69, 74, 75, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "max": [1, 44, 71, 72, 91, 92, 96, 104], "tri": [1, 38, 42, 105], "befor": [1, 2, 3, 38, 42, 55, 57, 71, 74, 79, 87, 88, 95, 96, 98, 99, 101, 104, 106], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 17, 24, 29, 31, 37, 38, 41, 42, 44, 47, 49, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 89, 90, 91, 92, 94, 98, 99, 102, 106, 107], "left": [1, 2, 44, 46, 55, 57, 64, 67, 70, 90, 91, 102, 103, 104, 107], "stochast": 1, "exceed": 1, "m": [1, 5, 38, 42, 48, 49, 52, 53, 62, 67, 69, 70, 71, 90, 91, 97, 101, 102, 103, 108], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 38, 42, 61, 98, 99, 107], "length": [1, 5, 13, 27, 28, 37, 39, 44, 57, 64, 67, 71, 72, 74, 76, 79, 83, 87, 89, 102, 104, 107, 108], "must": [1, 2, 3, 4, 5, 7, 17, 37, 38, 39, 40, 42, 44, 47, 49, 50, 55, 57, 60, 61, 62, 63, 64, 71, 72, 74, 76, 78, 79, 80, 82, 83, 89, 96, 101, 105, 107, 108], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 13, 37, 41, 44, 50, 57, 58, 62, 64, 70, 76, 78, 79, 80, 82, 83, 87, 88, 89, 98, 101, 102, 103, 107, 108], "ball": [1, 97], "bin": [1, 3, 64, 90, 91, 104], "ensur": [1, 2, 10, 38, 42, 52, 54, 55, 57, 58, 61, 69, 72, 74, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 104, 105, 106], "most": [1, 3, 5, 7, 10, 17, 37, 41, 44, 49, 61, 62, 63, 64, 67, 69, 70, 71, 72, 75, 78, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107], "least": [1, 4, 10, 19, 32, 37, 41, 62, 63, 69, 72, 82, 92, 98, 101, 104, 107], "int_arrai": [1, 57], "can": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 102, 103, 104, 105, 106, 107, 108], "model": [2, 3, 4, 5, 9, 10, 11, 17, 19, 31, 33, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 54, 56, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105, 107, 108], "For": [2, 3, 5, 7, 9, 10, 12, 17, 23, 36, 37, 38, 41, 42, 44, 47, 49, 52, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 80, 82, 83, 84, 87, 88, 89, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108], "regular": [2, 3, 41, 61], "multi": [2, 3, 4, 10, 33, 37, 38, 41, 42, 44, 48, 49, 50, 57, 58, 63, 64, 65, 66, 71, 72, 84, 96, 98, 99, 100], "task": [2, 5, 7, 10, 11, 12, 13, 15, 16, 17, 26, 31, 34, 37, 41, 47, 49, 50, 55, 57, 62, 64, 72, 74, 84, 88, 89, 95, 96, 97, 98, 99, 102, 104, 106, 107, 108], "cleanlearn": [2, 3, 10, 24, 31, 38, 57, 61, 73, 74, 75, 84, 85, 87, 88, 106], "wrap": [2, 38, 42, 51, 61, 71, 74, 84, 87, 88, 90, 91, 94, 95, 99, 106], "instanc": [2, 3, 5, 6, 7, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 61, 70, 71, 74, 79, 87, 89, 90, 91, 92, 94, 95, 98, 99, 103], "sklearn": [2, 3, 4, 5, 8, 10, 19, 32, 37, 42, 49, 53, 54, 57, 61, 71, 74, 75, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106], "classifi": [2, 3, 42, 49, 57, 62, 65, 71, 72, 84, 85, 87, 88, 89, 94, 95, 98, 101, 102, 104, 105, 107, 108], "adher": [2, 42, 74], "estim": [2, 3, 4, 5, 9, 14, 23, 37, 41, 42, 44, 47, 57, 62, 63, 64, 69, 71, 74, 76, 78, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 98, 100, 103, 104, 105, 106, 107, 108], "api": [2, 3, 15, 61, 67, 70, 71, 74, 85, 96, 98, 106], "defin": [2, 3, 5, 7, 10, 15, 23, 37, 38, 39, 41, 42, 44, 72, 74, 76, 90, 91, 94, 97, 98, 101, 104, 108], "four": [2, 10, 97, 99, 108], "clf": [2, 3, 5, 49, 74, 84, 87, 94, 96, 98, 99, 102], "fit": [2, 3, 5, 8, 10, 19, 40, 42, 52, 54, 60, 61, 71, 73, 74, 84, 87, 88, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 108], "sample_weight": [2, 42, 74, 99], "predict_proba": [2, 5, 37, 40, 42, 49, 60, 61, 87, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 104], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 17, 23, 24, 26, 29, 31, 33, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 88, 97, 98, 99, 100, 104, 106, 107, 108], "score": [2, 3, 4, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 43, 44, 46, 49, 55, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 104, 106], "data": [2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 49, 50, 52, 53, 54, 57, 60, 61, 62, 63, 64, 65, 69, 71, 72, 73, 74, 79, 80, 81, 82, 83, 85, 88, 92, 93, 100, 105], "e": [2, 3, 5, 10, 13, 23, 33, 37, 38, 41, 42, 44, 47, 49, 50, 52, 57, 58, 62, 63, 64, 65, 67, 70, 71, 72, 74, 76, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106], "featur": [2, 3, 4, 5, 8, 10, 11, 17, 19, 20, 24, 27, 28, 29, 31, 32, 49, 52, 53, 54, 57, 71, 74, 84, 87, 90, 91, 94, 95, 96, 98, 99, 101, 102, 106], "element": [2, 3, 5, 37, 43, 44, 46, 57, 62, 64, 72, 79, 80, 82, 88, 89, 95, 96, 98, 108], "first": [2, 5, 10, 18, 27, 28, 37, 41, 49, 52, 57, 62, 63, 67, 70, 72, 74, 87, 88, 89, 90, 92, 94, 96, 98, 101, 102, 103, 104, 106, 107, 108], "index": [2, 10, 27, 37, 44, 51, 52, 54, 56, 57, 58, 63, 72, 74, 79, 82, 83, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "should": [2, 3, 5, 7, 10, 15, 23, 27, 32, 33, 37, 38, 41, 42, 44, 46, 47, 49, 52, 54, 55, 56, 57, 61, 62, 63, 66, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 107, 108], "correspond": [2, 3, 5, 10, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 37, 38, 41, 42, 43, 44, 46, 47, 49, 52, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "differ": [2, 5, 7, 10, 14, 16, 22, 25, 27, 28, 30, 37, 38, 40, 41, 42, 44, 45, 49, 52, 55, 57, 58, 60, 62, 67, 69, 71, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 104, 105, 106], "sampl": [2, 3, 5, 8, 10, 17, 21, 44, 46, 49, 52, 53, 54, 64, 67, 70, 72, 74, 75, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "size": [2, 10, 32, 38, 41, 42, 44, 49, 52, 53, 64, 69, 70, 74, 76, 78, 88, 92, 94, 98, 99, 101, 102, 103, 105, 107], "here": [2, 5, 7, 10, 15, 41, 44, 47, 61, 62, 63, 64, 66, 67, 70, 71, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "re": [2, 5, 38, 42, 54, 56, 62, 74, 84, 87, 88, 89, 90, 94, 95, 98, 106, 107, 108], "weight": [2, 10, 38, 39, 42, 49, 52, 62, 69, 72, 74, 88, 89, 90, 91, 95], "loss": [2, 39, 61, 72, 74, 92], "while": [2, 3, 10, 38, 41, 42, 48, 49, 57, 74, 84, 92, 96, 98, 99, 101, 102, 106], "train": [2, 3, 4, 5, 9, 10, 17, 19, 33, 38, 39, 40, 42, 49, 57, 61, 62, 67, 70, 71, 74, 75, 85, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 105, 107, 108], "support": [2, 3, 4, 5, 13, 15, 34, 35, 41, 43, 49, 57, 58, 61, 71, 72, 82, 84, 85, 89, 90, 91, 92, 96, 98], "your": [2, 3, 5, 9, 10, 17, 37, 38, 40, 41, 42, 44, 49, 54, 57, 60, 61, 62, 63, 64, 66, 71, 72, 74, 75, 76, 78, 79, 85, 87, 88, 89, 92, 94, 97, 101, 102, 103, 104, 105, 106, 107, 108], "recommend": [2, 5, 7, 10, 14, 17, 41, 44, 62, 90, 91, 92, 96, 98, 105, 106], "furthermor": 2, "correctli": [2, 3, 10, 37, 38, 42, 44, 47, 52, 58, 63, 64, 69, 70, 74, 76, 88, 95, 96, 98, 102, 103, 106, 107], "clonabl": [2, 74], "via": [2, 5, 7, 10, 11, 14, 17, 19, 23, 37, 39, 41, 42, 49, 53, 57, 62, 67, 70, 71, 72, 74, 75, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 102, 103, 104, 105, 106, 107, 108], "base": [2, 3, 4, 5, 7, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 47, 48, 49, 52, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 69, 71, 72, 74, 75, 78, 80, 82, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "clone": [2, 74, 102], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 66, 70, 74, 80, 85, 90, 96, 98, 99, 101, 102, 103, 104, 106, 108], "multipl": [2, 3, 5, 10, 13, 14, 35, 37, 44, 55, 56, 62, 63, 64, 66, 69, 70, 74, 84, 90, 91, 92, 94, 98, 100, 102, 103, 106], "g": [2, 3, 5, 10, 13, 23, 33, 37, 38, 42, 44, 50, 52, 57, 64, 65, 67, 70, 71, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106], "manual": [2, 74, 87, 88, 89, 96, 98, 104, 105, 106, 108], "pytorch": [2, 38, 39, 42, 74, 84, 89, 92, 98, 100, 102, 107], "call": [2, 3, 5, 6, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 49, 57, 61, 71, 74, 88, 89, 90, 91, 95, 98, 99, 102, 104, 105, 106, 107, 108], "__init__": [2, 39, 74, 92], "independ": [2, 3, 10, 63, 74, 95, 96, 105, 106, 108], "compat": [2, 38, 41, 42, 54, 61, 74, 75, 78, 82, 84, 87, 88, 96, 98, 105, 106], "neural": [2, 39, 61, 71, 74, 89, 92, 98, 102, 104, 106], "network": [2, 38, 39, 42, 61, 71, 74, 88, 89, 92, 95, 98, 102, 104, 106], "typic": [2, 10, 38, 42, 54, 71, 74, 87, 88, 89, 91, 92, 94, 95, 104, 105], "initi": [2, 3, 14, 19, 38, 42, 52, 62, 74, 87, 95, 98], "insid": [2, 42, 74, 98, 99], "There": [2, 3, 7, 52, 84, 99, 101], "two": [2, 3, 10, 19, 27, 37, 38, 41, 42, 50, 52, 53, 54, 57, 67, 69, 70, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106, 107, 108], "new": [2, 7, 9, 10, 15, 23, 38, 41, 42, 48, 52, 56, 57, 62, 74, 88, 89, 90, 95, 97, 98, 104, 105, 108], "notion": 2, "confid": [2, 3, 10, 23, 37, 41, 44, 47, 49, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 78, 82, 84, 87, 92, 94, 95, 99, 101, 102, 103, 105, 107, 108], "packag": [2, 5, 7, 9, 10, 12, 16, 36, 40, 44, 45, 57, 60, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "prune": [2, 3, 44, 64, 74, 85, 103], "everyth": [2, 70, 99], "els": [2, 70, 90, 92, 96, 97, 98, 101, 102, 103], "mathemat": [2, 3, 10, 47, 102], "keep": [2, 14, 15, 57, 84, 90, 96, 97, 98, 107], "belong": [2, 3, 10, 37, 44, 46, 47, 52, 63, 64, 65, 66, 71, 72, 76, 80, 82, 83, 91, 92, 99, 102, 104, 107, 108], "2": [2, 3, 4, 5, 7, 10, 11, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 75, 79, 80, 82, 83, 97, 98, 105], "error": [2, 3, 5, 10, 38, 42, 43, 44, 46, 47, 57, 63, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 82, 85, 87, 89, 90, 91, 94, 95, 96, 97, 100], "erron": [2, 3, 37, 44, 47, 57, 63, 64, 72, 74, 75, 76, 104, 106], "import": [2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 41, 43, 49, 52, 55, 56, 62, 66, 69, 74, 75, 80, 82, 83, 84, 87, 88, 94, 95, 96, 98, 102, 103, 104, 106, 107, 108], "linear_model": [2, 5, 37, 57, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logisticregress": [2, 3, 5, 37, 57, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logreg": 2, "cl": [2, 15, 31, 74, 84, 87, 88, 98, 99, 106], "pass": [2, 3, 5, 8, 10, 11, 13, 14, 15, 17, 24, 31, 34, 38, 41, 42, 44, 48, 49, 52, 54, 57, 61, 62, 64, 70, 71, 72, 74, 79, 80, 84, 88, 89, 90, 91, 95, 97, 98, 99, 101, 103, 104, 106], "x_train": [2, 87, 90, 91, 99, 101, 102, 106], "labels_maybe_with_error": 2, "had": [2, 3, 74, 103], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 52, 60, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 88, 93, 100, 101, 105, 106], "pred": [2, 44, 57, 87, 88, 105, 106], "x_test": [2, 87, 90, 91, 99, 102, 106], "might": [2, 5, 10, 52, 62, 74, 79, 87, 88, 90, 91, 92, 96, 98, 103], "case": [2, 3, 10, 14, 37, 49, 52, 62, 74, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 104, 106, 108], "standard": [2, 3, 5, 31, 37, 44, 61, 63, 64, 66, 72, 74, 84, 87, 90, 91, 94, 97, 99, 103], "adapt": [2, 38, 40, 57, 60, 74, 104], "skorch": [2, 74, 84, 98], "kera": [2, 60, 67, 70, 74, 84, 98, 103], "scikera": [2, 61, 74, 98], "open": [2, 41, 96, 97, 103, 108], "doesn": [2, 10, 74, 84], "t": [2, 3, 4, 7, 10, 18, 28, 29, 38, 39, 41, 42, 43, 44, 49, 55, 56, 66, 71, 72, 74, 80, 82, 83, 84, 90, 91, 92, 94, 95, 96, 97, 99, 102, 103, 106, 108], "alreadi": [2, 5, 10, 17, 38, 41, 42, 47, 52, 61, 62, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 103, 104, 106], "exist": [2, 5, 10, 13, 19, 38, 41, 42, 54, 56, 61, 67, 69, 71, 74, 84, 85, 87, 88, 90, 91, 95, 101, 108], "made": [2, 5, 17, 38, 42, 53, 74, 87, 88, 92, 95, 96, 98, 101, 103, 105, 106], "easi": [2, 12, 47, 74, 90, 91, 97, 98, 99, 102], "inherit": [2, 7, 39, 74], "baseestim": [2, 42, 74], "yourmodel": [2, 74], "def": [2, 7, 15, 38, 42, 61, 74, 88, 89, 90, 91, 92, 96, 97, 98, 99, 101, 102, 104, 106, 108], "self": [2, 3, 5, 7, 10, 13, 14, 15, 17, 32, 38, 39, 41, 42, 44, 49, 71, 72, 74, 87, 90, 92, 96, 97, 102, 107, 108], "refer": [2, 10, 17, 38, 42, 43, 63, 64, 66, 67, 69, 70, 71, 74, 78, 79, 90, 91, 92, 94, 95, 96, 98, 99, 102, 105, 106], "origin": [2, 5, 10, 42, 43, 44, 56, 57, 61, 63, 64, 67, 70, 71, 74, 75, 78, 80, 82, 87, 88, 90, 92, 94, 95, 98, 99, 103, 104, 106, 108], "total": [2, 3, 4, 37, 41, 57, 63, 83, 92, 98, 107], "state": [2, 3, 5, 38, 39, 42, 48, 74, 99, 102, 103, 108], "art": [2, 39, 99, 102], "northcutt": [2, 3, 37, 71, 72], "et": [2, 3, 37, 39, 71, 72], "al": [2, 3, 37, 39, 71, 72], "2021": [2, 3, 37, 71, 72], "weak": [2, 70], "supervis": [2, 10, 90, 91, 98, 101], "find": [2, 5, 9, 10, 14, 15, 17, 20, 21, 23, 24, 26, 27, 28, 29, 32, 33, 37, 38, 40, 41, 42, 43, 44, 48, 54, 56, 57, 60, 67, 70, 71, 72, 74, 76, 80, 82, 85, 90, 100, 105], "uncertainti": [2, 10, 46, 71, 74, 98, 104, 106], "It": [2, 3, 5, 7, 10, 13, 14, 17, 23, 28, 31, 33, 34, 35, 38, 42, 44, 47, 49, 52, 53, 55, 62, 69, 70, 74, 84, 90, 91, 92, 98, 99, 102, 105], "work": [2, 3, 7, 10, 13, 31, 37, 38, 41, 42, 44, 47, 56, 57, 58, 61, 62, 72, 74, 84, 85, 88, 90, 91, 96, 97, 104, 106], "includ": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 38, 40, 41, 42, 52, 56, 57, 60, 62, 63, 66, 67, 71, 72, 74, 78, 79, 80, 82, 84, 85, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 108], "deep": [2, 40, 42, 60, 61, 74, 95], "see": [2, 3, 5, 7, 10, 14, 15, 34, 37, 38, 41, 42, 43, 44, 49, 54, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "subfield": 2, "theori": [2, 99], "machin": [2, 4, 5, 9, 10, 15, 17, 34, 40, 55, 60, 74, 87, 88, 90, 91, 96, 97, 101], "across": [2, 3, 5, 7, 10, 14, 23, 37, 41, 49, 63, 70, 71, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 106], "varieti": [2, 87, 88, 98], "like": [2, 3, 5, 6, 7, 10, 15, 33, 37, 38, 41, 42, 44, 47, 57, 61, 62, 63, 66, 67, 69, 72, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "pu": [2, 57], "input": [2, 3, 5, 9, 17, 27, 37, 38, 41, 42, 47, 49, 52, 53, 56, 57, 58, 61, 70, 74, 84, 85, 88, 91, 92, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "discret": [2, 35, 44, 47, 57, 71, 72, 76, 78, 79], "vector": [2, 3, 4, 5, 10, 17, 44, 47, 49, 50, 52, 57, 71, 72, 84, 88, 89, 90, 91, 92, 94, 95, 99, 102, 103, 104, 107, 108], "would": [2, 3, 5, 10, 38, 41, 42, 44, 53, 57, 64, 74, 84, 88, 90, 92, 98, 99, 104, 106, 108], "obtain": [2, 5, 8, 10, 17, 44, 62, 64, 67, 70, 72, 75, 89, 91, 95, 98, 101, 103, 105, 107, 108], "been": [2, 4, 37, 44, 47, 52, 56, 57, 62, 63, 67, 69, 71, 72, 74, 89, 90, 94, 98, 99, 101, 102, 103, 104, 107, 108], "dure": [2, 10, 17, 52, 54, 71, 74, 87, 88, 89, 94, 95, 96, 98, 99, 102, 105, 106, 108], "denot": [2, 3, 47, 49, 57, 64, 71, 72, 82], "tild": 2, "paper": [2, 4, 10, 62, 71, 80, 82, 97, 99, 101, 104, 106, 108], "cv_n_fold": [2, 3, 74, 88], "5": [2, 3, 4, 5, 8, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 42, 44, 46, 48, 49, 57, 62, 63, 66, 67, 70, 74, 75, 82, 88, 90, 95, 97, 98, 102, 103, 104, 105, 107, 108], "converge_latent_estim": [2, 3], "pulearn": [2, 57], "find_label_issues_kwarg": [2, 10, 74, 85, 98, 99], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 64, 80, 98], "clean": [2, 69, 72, 74, 75, 84, 87, 88, 90, 91, 97, 106], "even": [2, 3, 7, 9, 10, 37, 41, 46, 47, 57, 74, 89, 96, 98, 99, 101, 102, 103], "messi": [2, 74, 99], "ridden": [2, 74], "autom": [2, 9, 10, 74, 84, 91, 97, 98], "robust": [2, 47, 52, 74, 91, 96, 98], "prone": [2, 74], "out": [2, 3, 5, 10, 17, 29, 38, 42, 44, 49, 52, 61, 64, 65, 67, 70, 71, 72, 74, 75, 83, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108], "current": [2, 3, 5, 7, 10, 11, 14, 15, 23, 38, 42, 43, 44, 49, 62, 69, 74, 90, 91, 98, 101, 103], "intend": [2, 14, 15, 16, 17, 33, 34, 35, 45, 52, 62, 78, 82, 89, 90, 91, 95, 99], "A": [2, 3, 4, 5, 7, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 66, 69, 70, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 103, 105, 108], "follow": [2, 3, 10, 15, 31, 35, 37, 38, 41, 42, 49, 51, 55, 62, 63, 67, 69, 70, 71, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "experiment": [2, 38, 39, 41, 42, 43, 64, 85, 98], "wrapper": [2, 61, 87, 88, 89, 106], "around": [2, 69, 90, 91, 103, 104, 108], "fasttext": [2, 60], "store": [2, 4, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 71, 74, 87, 88, 94, 95, 96, 97, 98, 107, 108], "along": [2, 49, 64, 82, 90, 91, 92, 96, 98, 104], "dimens": [2, 57, 76, 79, 92, 98, 104, 107], "select": [2, 9, 10, 27, 51, 62, 72, 92, 96, 101, 104], "split": [2, 3, 5, 10, 13, 41, 49, 56, 57, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 102, 105, 108], "cross": [2, 3, 10, 37, 44, 47, 48, 49, 64, 67, 70, 72, 74, 75, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "fold": [2, 3, 37, 44, 47, 74, 87, 89, 94, 97, 98, 103, 107], "By": [2, 37, 63, 64, 74, 90, 96, 107], "need": [2, 3, 10, 11, 37, 38, 41, 42, 44, 52, 54, 63, 64, 66, 71, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 107], "holdout": [2, 3, 74], "comput": [2, 3, 4, 5, 7, 8, 10, 20, 21, 23, 24, 27, 28, 29, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 52, 53, 54, 57, 62, 63, 64, 66, 69, 70, 71, 72, 74, 75, 76, 78, 84, 85, 88, 90, 91, 97, 99, 100, 103, 104, 106, 107], "them": [2, 3, 5, 7, 9, 10, 12, 13, 28, 33, 36, 38, 40, 41, 42, 44, 54, 60, 62, 71, 74, 85, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 107, 108], "numer": [2, 3, 4, 5, 10, 14, 23, 31, 35, 49, 52, 53, 69, 71, 74, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 99, 101, 102, 104, 106], "consist": [2, 3, 38, 42, 51, 57, 62, 96, 107, 108], "latent": [2, 3, 47], "thei": [2, 3, 5, 16, 22, 25, 27, 30, 38, 39, 40, 42, 44, 45, 52, 55, 57, 61, 64, 69, 72, 74, 75, 78, 82, 84, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106, 108], "relat": [2, 3, 10, 14, 20, 21, 27, 28, 29, 32, 47, 57, 63, 74, 91, 95], "close": [2, 3, 10, 41, 47, 71, 89, 90, 91, 92, 94, 95, 96, 98, 99, 103], "form": [2, 3, 10, 38, 39, 42, 47, 56, 57, 72, 74, 98], "equival": [2, 3, 38, 42, 47, 71, 104, 106], "iter": [2, 3, 37, 38, 42, 44, 57, 63, 64, 74, 98, 101, 107], "enforc": [2, 38, 42, 57], "perfectli": [2, 37, 63, 99], "certain": [2, 3, 5, 38, 42, 61, 70, 74, 90, 91, 96, 97, 103, 104], "dict": [2, 3, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 48, 49, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 82, 90, 91, 92, 98, 108], "keyword": [2, 3, 5, 10, 11, 17, 24, 28, 31, 38, 41, 42, 44, 46, 49, 52, 54, 56, 61, 62, 64, 70, 71, 72, 74, 79, 80, 82, 90], "filter": [2, 3, 10, 41, 43, 56, 63, 65, 66, 68, 70, 77, 78, 79, 81, 82, 83, 84, 85, 87, 88, 89, 92, 95, 96, 97, 98, 102, 103, 106, 107, 108], "find_label_issu": [2, 3, 10, 31, 40, 41, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 98, 102, 103, 106, 107, 108], "particularli": [2, 84, 101, 104], "filter_bi": [2, 3, 41, 44, 64, 85, 98], "frac_nois": [2, 44, 64, 80, 98], "min_examples_per_class": [2, 44, 64, 98, 99], "impact": [2, 4, 10, 90, 91, 92, 96], "ml": [2, 4, 5, 9, 10, 16, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 101, 102, 106], "accuraci": [2, 39, 72, 87, 88, 89, 92, 98, 99, 101, 104, 106, 107], "n_job": [2, 41, 44, 64, 76, 78, 80, 98, 104, 107], "disabl": [2, 38, 42, 44, 104], "process": [2, 3, 7, 14, 17, 33, 38, 41, 42, 44, 52, 56, 62, 64, 70, 76, 78, 80, 88, 89, 90, 96, 98, 101, 105], "caus": [2, 44, 49, 90, 91, 96, 98], "rank": [2, 3, 10, 37, 41, 43, 44, 49, 63, 64, 65, 67, 68, 70, 71, 73, 77, 79, 80, 81, 83, 84, 85, 87, 88, 90, 91, 97, 98, 102, 103, 104, 107, 108], "get_label_quality_scor": [2, 40, 41, 43, 44, 45, 49, 62, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77, 78, 80, 81, 82, 85, 98, 99, 102, 103, 107, 108], "adjust_pred_prob": [2, 10, 66, 71, 72, 99], "control": [2, 5, 9, 10, 17, 41, 44, 62, 70, 71, 74, 80, 82, 90, 91, 96, 97, 98], "how": [2, 3, 5, 10, 13, 14, 15, 17, 23, 37, 38, 39, 41, 42, 47, 57, 62, 63, 66, 67, 69, 71, 72, 74, 78, 82, 84, 87, 88, 90, 91, 92, 94, 95, 96, 97, 103, 104, 105, 106, 107], "much": [2, 10, 37, 41, 44, 74, 96, 97, 98, 99, 101, 104], "output": [2, 3, 5, 10, 17, 33, 38, 39, 42, 47, 57, 61, 62, 63, 67, 69, 70, 71, 74, 78, 79, 82, 83, 84, 85, 88, 89, 90, 92, 95, 97, 98, 103, 104, 105, 106], "print": [2, 5, 7, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 57, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "suppress": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79, 107, 108], "statement": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79], "big": [2, 41, 64, 70, 74, 99], "limit": [2, 5, 17, 41, 52, 64, 96, 103, 107, 108], "memori": [2, 38, 41, 42, 64, 70, 76, 78, 90, 107], "label_issues_batch": [2, 40, 64, 98], "find_label_issues_batch": [2, 40, 41, 64, 98], "pred_prob": [2, 3, 5, 8, 10, 11, 17, 24, 26, 27, 29, 32, 33, 37, 41, 43, 44, 46, 47, 48, 49, 50, 57, 58, 62, 63, 64, 66, 67, 70, 71, 72, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106], "threshold": [2, 3, 4, 7, 10, 19, 20, 21, 23, 29, 31, 32, 41, 55, 69, 70, 71, 72, 78, 82, 90, 96, 103, 104, 107, 108], "inverse_noise_matrix": [2, 3, 10, 47, 57, 85, 99], "label_issu": [2, 41, 44, 64, 67, 74, 76, 85, 87, 88, 89, 92, 95, 98, 99, 102, 106], "clf_kwarg": [2, 3, 10, 74], "clf_final_kwarg": [2, 74], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 37, 41, 44, 46, 52, 62, 63, 64, 66, 67, 69, 70, 72, 74, 75, 78, 82, 84, 89, 92, 94, 95, 99, 101, 103, 105, 106], "result": [2, 3, 9, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 41, 42, 44, 46, 55, 57, 64, 66, 67, 70, 72, 74, 75, 76, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 106, 107, 108], "identifi": [2, 3, 5, 7, 9, 10, 13, 17, 28, 34, 37, 41, 43, 44, 52, 64, 67, 70, 72, 74, 75, 76, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 102, 104, 106, 107, 108], "final": [2, 10, 74, 87, 94, 96, 103, 105, 106], "remain": [2, 74, 85, 87, 88, 92, 96, 102, 106, 108], "datasetlik": [2, 57, 74], "beyond": [2, 5, 7, 9, 10, 12, 36, 84, 87, 88, 106, 107], "pd": [2, 3, 5, 7, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 48, 61, 62, 63, 74, 82, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 106, 108], "datafram": [2, 3, 5, 7, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 48, 57, 58, 61, 62, 63, 74, 79, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 106, 107, 108], "scipi": [2, 4, 5, 14, 53, 57, 71, 96], "spars": [2, 4, 5, 10, 14, 17, 19, 32, 52, 57, 58, 94, 96], "csr_matrix": [2, 4, 5, 14, 17, 19, 32, 52, 96], "torch": [2, 38, 39, 42, 88, 89, 92, 95, 97, 104], "util": [2, 5, 10, 17, 34, 38, 39, 42, 45, 52, 61, 62, 67, 70, 74, 84, 85, 89, 90, 91, 92, 98, 99, 104], "tensorflow": [2, 57, 61, 84, 89, 98], "object": [2, 5, 10, 13, 14, 17, 33, 34, 38, 39, 41, 42, 49, 52, 54, 57, 58, 61, 64, 67, 68, 69, 70, 71, 74, 82, 84, 88, 89, 91, 92, 94, 98, 99, 100, 102, 106], "list": [2, 3, 5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 43, 44, 50, 52, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 85, 88, 89, 90, 91, 92, 96, 97, 98, 99, 102, 103, 106, 108], "index_list": 2, "subset": [2, 3, 5, 17, 37, 41, 44, 57, 72, 79, 83, 87, 88, 89, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 108], "wa": [2, 3, 13, 15, 41, 55, 57, 62, 63, 69, 71, 83, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 105, 107, 108], "abl": [2, 3, 10, 74, 89, 98, 99, 101, 102], "format": [2, 3, 5, 10, 13, 33, 38, 41, 42, 44, 47, 48, 49, 50, 52, 57, 58, 61, 62, 63, 64, 67, 70, 71, 72, 74, 76, 78, 79, 82, 83, 87, 90, 91, 92, 94, 96, 97, 101, 106, 107, 108], "make": [2, 3, 5, 19, 38, 41, 42, 49, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "sure": [2, 5, 41, 44, 49, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106], "shuffl": [2, 10, 57, 89, 92, 95, 96, 102, 104], "ha": [2, 3, 5, 6, 10, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 43, 47, 49, 52, 56, 57, 62, 67, 69, 74, 80, 82, 83, 84, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 108], "batch": [2, 41, 57, 61, 62, 76, 78, 92, 98, 104], "order": [2, 5, 10, 35, 37, 38, 42, 43, 44, 47, 48, 49, 55, 57, 62, 63, 64, 67, 70, 71, 72, 76, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 106, 107, 108], "destroi": [2, 57], "oper": [2, 38, 41, 42, 52, 57, 61, 72, 84, 87, 88, 95, 98, 104], "eg": [2, 5, 10, 57, 67, 70, 90, 91, 98], "repeat": [2, 57, 62, 101, 104], "appli": [2, 35, 38, 40, 42, 44, 49, 50, 52, 56, 57, 66, 71, 80, 87, 88, 89, 90, 91, 92, 94, 96, 98, 101, 102, 104, 105, 106, 107], "array_lik": [2, 3, 37, 44, 57, 64, 71, 75], "some": [2, 3, 5, 10, 15, 23, 37, 38, 40, 42, 44, 47, 52, 56, 57, 60, 62, 63, 64, 66, 67, 70, 71, 72, 74, 76, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "seri": [2, 3, 41, 57, 58, 74, 82, 98], "row": [2, 3, 5, 10, 14, 28, 33, 37, 41, 44, 46, 47, 52, 53, 57, 62, 63, 64, 66, 71, 72, 74, 79, 80, 82, 83, 87, 89, 92, 94, 95, 96, 97, 98, 101, 102, 104, 108], "rather": [2, 3, 5, 10, 27, 37, 57, 61, 62, 69, 78, 82, 88, 97, 101, 105, 106, 107, 108], "leav": [2, 44], "per": [2, 3, 5, 7, 10, 14, 37, 41, 44, 49, 56, 62, 63, 64, 66, 69, 70, 72, 75, 76, 78, 82, 91, 98, 103, 108], "determin": [2, 3, 10, 13, 17, 23, 27, 31, 37, 41, 44, 49, 52, 57, 62, 64, 67, 69, 72, 78, 82, 90, 96, 98, 101, 103, 104, 106], "cutoff": [2, 3, 53, 104], "consid": [2, 3, 4, 5, 10, 14, 17, 24, 27, 29, 32, 37, 38, 42, 44, 52, 54, 57, 62, 69, 71, 72, 75, 78, 82, 87, 88, 89, 92, 94, 95, 96, 98, 99, 103, 104, 105, 106, 107], "section": [2, 3, 7, 10, 85, 92, 94, 96, 98, 103], "3": [2, 3, 4, 5, 7, 10, 11, 35, 37, 38, 42, 44, 47, 48, 49, 50, 53, 55, 56, 57, 61, 64, 71, 72, 74, 75, 80, 82, 97, 98, 105], "equat": [2, 3, 47], "advanc": [2, 3, 5, 9, 10, 17, 69, 71, 82, 85, 91, 93, 96, 98, 99], "user": [2, 3, 5, 9, 10, 15, 17, 28, 33, 34, 35, 38, 42, 44, 52, 61, 69, 71, 72, 74, 78, 82, 99], "specifi": [2, 3, 4, 5, 8, 10, 14, 15, 17, 19, 32, 34, 38, 41, 42, 44, 49, 52, 54, 56, 61, 62, 63, 64, 67, 69, 71, 72, 74, 75, 83, 85, 88, 89, 91, 92, 95, 101, 103, 106], "automat": [2, 3, 5, 27, 37, 84, 87, 88, 92, 94, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "greater": [2, 3, 4, 5, 7, 9, 10, 29, 41, 53, 57, 69, 91, 97, 98, 108], "count": [2, 23, 27, 37, 41, 44, 47, 57, 63, 64, 70, 85, 92, 96, 98, 103], "observ": [2, 3, 47, 54, 89, 90, 91, 96, 101, 104, 106], "mislabel": [2, 10, 37, 41, 43, 44, 47, 62, 63, 64, 67, 69, 72, 78, 80, 82, 83, 84, 87, 88, 89, 92, 94, 95, 98, 99, 103, 106], "one": [2, 3, 5, 7, 10, 27, 37, 38, 41, 42, 43, 44, 49, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 104, 105, 106, 108], "get_label_issu": [2, 40, 41, 73, 74, 87, 88, 99, 106], "either": [2, 3, 4, 7, 10, 38, 41, 42, 44, 53, 62, 64, 69, 71, 72, 76, 78, 91, 96, 98, 102, 103], "boolean": [2, 7, 10, 23, 41, 44, 54, 56, 62, 64, 67, 72, 74, 76, 78, 79, 84, 88, 89, 91, 92, 95, 98, 103, 106, 107], "label_issues_mask": [2, 44, 72, 74, 85], "indic": [2, 3, 4, 5, 7, 10, 14, 23, 37, 41, 42, 43, 44, 46, 49, 52, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "its": [2, 5, 7, 9, 10, 17, 38, 41, 42, 44, 52, 54, 55, 56, 64, 67, 70, 71, 72, 74, 76, 80, 82, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108], "return_indices_ranked_bi": [2, 41, 44, 64, 80, 85, 87, 88, 98, 99], "significantli": [2, 10, 92, 96, 99, 101, 105], "reduc": [2, 41, 44, 57, 89, 96, 98], "time": [2, 10, 38, 41, 42, 57, 62, 83, 85, 87, 88, 90, 92, 94, 97, 98, 99, 103, 104, 106, 107, 108], "take": [2, 5, 10, 37, 38, 42, 48, 49, 52, 54, 57, 61, 72, 87, 92, 94, 101, 102, 103, 108], "run": [2, 5, 6, 7, 9, 10, 11, 12, 15, 17, 27, 28, 33, 36, 38, 41, 42, 54, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 108], "skip": [2, 10, 38, 42, 74, 89, 96, 98, 102, 108], "slow": [2, 3], "step": [2, 7, 27, 49, 70, 92, 96, 99, 101, 105], "caution": [2, 5, 98], "previous": [2, 5, 14, 57, 71, 74, 85, 87, 89, 90, 94, 95, 101, 105], "assign": [2, 7, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 38, 42, 48, 49, 57, 74, 87, 90, 92, 94, 96, 98, 106, 107, 108], "individu": [2, 4, 7, 10, 14, 27, 38, 42, 43, 62, 66, 69, 72, 74, 80, 82, 85, 87, 91, 94, 96, 97, 98, 101, 102, 103, 108], "still": [2, 41, 42, 57, 71, 87, 92, 98, 104], "extra": [2, 38, 42, 57, 61, 62, 63, 74, 92, 95, 98, 101, 104], "receiv": [2, 10, 38, 42, 43, 63, 66, 67, 74, 76, 80, 91, 103], "overwritten": [2, 74], "callabl": [2, 3, 4, 10, 27, 38, 42, 49, 52, 53, 54, 56, 61, 66, 98], "x_val": 2, "y_val": 2, "map": [2, 3, 13, 41, 42, 45, 48, 56, 57, 70, 72, 74, 79, 89, 90, 91, 92, 96, 98, 99, 102, 108], "appropri": [2, 10, 17, 35, 53, 64, 72, 90, 94, 102, 103], "earli": [2, 92], "stop": [2, 92], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 23, 37, 57, 71, 87, 90, 92, 94, 96, 102, 106, 108], "f": [2, 7, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106], "ignor": [2, 38, 42, 56, 61, 74, 79, 83, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "allow": [2, 37, 38, 41, 42, 46, 54, 57, 62, 70, 71, 74, 76, 78, 88, 89, 92, 96, 98, 105, 107], "access": [2, 10, 14, 38, 42, 74, 91, 92, 97, 102], "hyperparamet": [2, 66, 71, 92], "purpos": [2, 52, 90, 91, 96, 98, 102, 106], "want": [2, 5, 10, 37, 41, 52, 58, 62, 64, 74, 88, 90, 92, 95, 97, 101, 103, 104, 105, 107, 108], "explicitli": [2, 8, 10, 42, 52, 74], "yourself": [2, 5, 41, 91, 96], "altern": [2, 7, 10, 49, 54, 57, 61, 62, 72, 85, 88, 89, 92, 94, 95, 97, 98, 99, 101, 102, 104, 106], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 27, 31, 38, 41, 42, 44, 52, 57, 61, 62, 64, 71, 72, 74, 78, 79, 82, 83, 84, 87, 88, 90, 91, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 107], "effect": [2, 10, 28, 38, 42, 62, 71, 74, 92, 94, 95, 96, 98, 104], "offer": [2, 5, 9, 10, 88, 89, 90, 91, 95, 98, 99, 102], "after": [2, 3, 5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 62, 74, 88, 90, 92, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107], "attribut": [2, 5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 49, 54, 71, 74, 87, 90, 96], "label_issues_df": [2, 74, 92], "similar": [2, 10, 37, 38, 42, 54, 57, 62, 66, 67, 69, 71, 74, 78, 82, 90, 91, 92, 94, 95, 96, 98, 99, 103, 104, 107], "document": [2, 3, 5, 15, 17, 37, 38, 41, 42, 43, 44, 49, 56, 61, 63, 64, 66, 69, 70, 71, 74, 78, 79, 80, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "descript": [2, 5, 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 37, 43, 57, 67, 74, 90, 91], "were": [2, 3, 5, 10, 37, 42, 52, 63, 69, 82, 87, 89, 94, 98, 99, 101, 103, 105, 107], "present": [2, 3, 5, 10, 13, 14, 21, 37, 57, 71, 79, 84, 92, 96, 98, 104], "actual": [2, 3, 5, 10, 37, 52, 62, 63, 72, 91, 98, 99, 108], "num_class": [2, 37, 41, 57, 61, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 104], "uniqu": [2, 32, 57, 79, 90, 96, 98, 102, 104], "given_label": [2, 5, 11, 26, 31, 37, 47, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107, 108], "normal": [2, 3, 19, 27, 32, 44, 46, 49, 55, 56, 57, 72, 96, 98, 99, 104], "trick": [2, 98], "distribut": [2, 3, 5, 10, 27, 29, 37, 42, 44, 48, 55, 62, 70, 71, 72, 84, 90, 91, 92, 94, 95, 96, 103, 104], "account": [2, 37, 62, 66, 71, 72, 88, 95, 98, 99, 101, 102, 104, 106], "word": [2, 3, 56, 82, 83, 98], "remov": [2, 10, 32, 37, 38, 42, 44, 74, 84, 87, 88, 92, 95, 96, 97, 98, 102, 104, 106], "so": [2, 3, 5, 6, 7, 10, 15, 27, 35, 37, 38, 41, 42, 44, 52, 57, 62, 63, 69, 72, 74, 78, 82, 89, 90, 91, 92, 95, 96, 99, 102, 104, 107], "proportion": [2, 10, 44], "just": [2, 3, 5, 10, 14, 33, 37, 39, 41, 57, 61, 72, 74, 76, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106, 107], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 14, 32, 38, 39, 42, 44, 49, 55, 56, 57, 62, 64, 66, 71, 72, 74, 75, 76, 84, 87, 88, 89, 92, 95, 96, 97, 98, 99, 104, 105, 106], "detect": [2, 5, 7, 9, 14, 15, 17, 19, 23, 29, 43, 52, 55, 65, 67, 68, 69, 70, 71, 72, 73, 74, 77, 81, 84, 87, 88, 90, 93, 97, 100, 102, 106, 107, 108], "arg": [2, 13, 23, 28, 32, 38, 39, 42, 49, 57, 72, 74], "kwarg": [2, 7, 10, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 43, 49, 52, 61, 70, 74, 76, 78, 79, 80, 98], "test": [2, 5, 10, 27, 42, 49, 52, 61, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 105, 106, 108], "expect": [2, 3, 10, 38, 42, 44, 49, 52, 62, 71, 72, 74, 87, 88, 96, 98, 99, 101, 102, 103, 106, 108], "class_predict": 2, "evalu": [2, 10, 38, 39, 40, 41, 42, 70, 74, 87, 88, 89, 90, 91, 92, 98, 99, 101, 105, 106, 107], "simpli": [2, 10, 37, 72, 88, 90, 91, 94, 95, 98, 99, 102, 106, 107, 108], "quantifi": [2, 4, 5, 7, 10, 14, 44, 66, 71, 74, 84, 91, 92, 94, 95, 96, 99, 103], "save_spac": [2, 10, 73, 74], "potenti": [2, 10, 37, 44, 56, 64, 67, 70, 72, 74, 76, 78, 83, 85, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "cach": [2, 88, 95], "panda": [2, 5, 7, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 57, 58, 61, 62, 63, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 106, 107], "unlik": [2, 10, 44, 46, 49, 61, 63, 64, 66, 82, 90, 101, 102, 104, 106], "both": [2, 5, 10, 17, 27, 37, 38, 42, 44, 52, 57, 62, 64, 72, 76, 78, 83, 84, 90, 92, 98, 99, 101, 108], "mask": [2, 41, 44, 56, 57, 64, 67, 72, 74, 76, 78, 79, 84, 97, 98, 101, 103, 107, 108], "prefer": [2, 72, 80, 102], "plan": 2, "subsequ": [2, 3, 38, 42, 54, 88, 95, 98, 99, 103], "invok": [2, 38, 42, 99, 105], "scratch": [2, 52, 74], "To": [2, 5, 7, 9, 10, 12, 14, 17, 27, 36, 38, 41, 42, 43, 44, 61, 62, 64, 66, 70, 71, 72, 74, 75, 76, 78, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "share": [2, 10, 72, 74], "mostli": [2, 57, 69, 74, 102, 106], "longer": [2, 35, 48, 49, 56, 74, 85, 88, 95, 98, 103], "info": [2, 5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 74, 82, 91, 96, 97, 108], "about": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 39, 41, 46, 62, 63, 66, 70, 74, 79, 82, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 104], "docstr": [2, 37, 38, 42, 57, 74, 97, 99], "unless": [2, 38, 42, 52, 74, 98], "our": [2, 3, 10, 61, 62, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "is_label_issu": [2, 11, 31, 74, 88, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "entir": [2, 10, 27, 41, 44, 47, 63, 64, 69, 72, 74, 76, 78, 79, 84, 90, 91, 96, 98, 103, 104, 105, 107, 108], "accur": [2, 3, 5, 9, 10, 17, 37, 41, 44, 53, 62, 63, 64, 67, 70, 72, 74, 75, 76, 78, 79, 85, 91, 92, 94, 95, 96, 98, 101, 106], "label_qu": [2, 62, 74, 88, 99, 101, 106], "measur": [2, 5, 37, 62, 63, 74, 84, 87, 96, 97, 98, 99, 101, 102, 106, 107, 108], "qualiti": [2, 3, 5, 7, 9, 10, 14, 31, 32, 37, 41, 43, 44, 46, 49, 62, 63, 64, 66, 67, 69, 72, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 106], "lower": [2, 4, 5, 7, 10, 14, 29, 41, 49, 55, 62, 63, 66, 69, 70, 72, 74, 75, 78, 82, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "eas": 2, "comparison": [2, 38, 42, 70, 99, 101], "against": [2, 38, 42, 90, 94, 96, 98, 101, 102], "predicted_label": [2, 5, 11, 26, 31, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107], "ad": [2, 38, 42, 91, 101, 106], "precis": [2, 53, 55, 64, 67, 70, 96, 97, 98, 99, 107, 108], "definit": [2, 7, 35, 49, 74, 87, 94], "accessor": [2, 74], "describ": [2, 10, 19, 62, 71, 72, 74, 80, 82, 99, 101, 102, 103, 105, 108], "precomput": [2, 4, 5, 47, 52, 74, 97], "clear": [2, 38, 42, 54, 74, 88, 95, 106], "save": [2, 5, 17, 38, 41, 42, 70, 74, 96, 98, 103, 107, 108], "space": [2, 5, 10, 71, 74, 92, 94, 96, 97], "place": [2, 38, 42, 52, 57, 74, 87, 101], "larg": [2, 9, 10, 41, 52, 74, 92, 94, 95, 98, 103, 104, 107, 108], "deploi": [2, 9, 10, 74, 92, 94, 95, 98], "care": [2, 10, 38, 42, 52, 74, 95, 96, 98, 99], "avail": [2, 4, 5, 7, 10, 13, 15, 34, 42, 54, 74, 98, 99, 101, 103, 106], "cannot": [2, 5, 13, 15, 57, 105, 108], "anymor": 2, "classmethod": [2, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 42, 49, 74], "__init_subclass__": [2, 40, 42, 73, 74], "set_": [2, 42, 74], "_request": [2, 42, 74], "pep": [2, 42, 74], "487": [2, 42, 74], "look": [2, 5, 7, 10, 17, 38, 42, 57, 74, 79, 87, 90, 91, 94, 95, 98, 99, 101, 102, 103, 104, 107, 108], "inform": [2, 5, 7, 10, 14, 17, 34, 38, 42, 54, 57, 62, 63, 67, 70, 74, 79, 82, 83, 84, 89, 90, 94, 95, 96, 97, 99, 101, 104, 107, 108], "__metadata_request__": [2, 42, 74], "infer": [2, 42, 57, 74, 79, 83, 87, 88, 92, 101, 102], "signatur": [2, 38, 42, 74], "accept": [2, 38, 42, 54, 55, 72, 74, 90, 91, 98], "metadata": [2, 10, 42, 74, 92, 94, 95, 108], "through": [2, 5, 7, 42, 74, 88, 89, 91, 95, 96, 97, 98, 101, 103, 104], "develop": [2, 9, 42, 54, 74, 98, 99, 108], "request": [2, 42, 74, 87, 88, 91, 95, 96, 97, 102, 108], "those": [2, 3, 4, 10, 41, 42, 44, 51, 61, 62, 64, 70, 74, 78, 82, 83, 84, 89, 92, 96, 98, 103, 107], "http": [2, 4, 5, 7, 9, 10, 12, 19, 36, 38, 39, 41, 42, 46, 54, 57, 67, 70, 71, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "www": [2, 42, 74, 96, 104], "org": [2, 4, 19, 38, 39, 42, 54, 57, 71, 74, 98, 99, 108], "dev": [2, 42, 74], "0487": [2, 42, 74], "get_metadata_rout": [2, 40, 42, 73, 74], "rout": [2, 42, 74], "pleas": [2, 38, 42, 61, 74, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 104, 106, 108], "guid": [2, 7, 10, 42, 74, 85, 89, 90, 91, 92, 93, 94, 95, 96, 99], "mechan": [2, 38, 42, 74], "metadatarequest": [2, 42, 74], "encapsul": [2, 17, 42, 69, 74], "get_param": [2, 40, 42, 60, 61, 73, 74], "subobject": [2, 42, 74], "param": [2, 10, 38, 42, 61, 71, 74, 98], "name": [2, 5, 6, 7, 10, 11, 13, 14, 33, 35, 37, 38, 42, 48, 49, 53, 57, 61, 62, 63, 70, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 102, 106, 107, 108], "set_fit_request": [2, 40, 42, 73, 74], "str": [2, 3, 4, 5, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 47, 49, 52, 53, 54, 55, 56, 57, 61, 62, 63, 67, 69, 70, 72, 74, 79, 83, 89, 90, 98, 101, 102, 103, 108], "unchang": [2, 38, 42, 74, 108], "relev": [2, 17, 27, 42, 74, 92, 94, 96], "enable_metadata_rout": [2, 42, 74], "set_config": [2, 42, 74], "meta": [2, 42, 74], "rais": [2, 4, 5, 13, 14, 35, 38, 42, 46, 49, 52, 55, 74, 98], "alia": [2, 38, 42, 74], "metadata_rout": [2, 42, 74], "retain": [2, 42, 57, 74], "chang": [2, 33, 35, 38, 41, 42, 46, 74, 82, 87, 88, 89, 90, 95, 98, 103, 104, 108], "version": [2, 4, 5, 7, 9, 10, 12, 16, 22, 25, 30, 36, 38, 40, 42, 45, 46, 57, 60, 61, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 108], "sub": [2, 42, 69, 74], "pipelin": [2, 42, 74, 106], "otherwis": [2, 4, 7, 10, 35, 37, 38, 41, 42, 44, 50, 53, 55, 56, 57, 64, 74, 76, 78, 79, 83, 88, 95, 98], "updat": [2, 14, 38, 41, 42, 52, 61, 74, 85, 90, 92], "set_param": [2, 40, 42, 60, 61, 73, 74], "simpl": [2, 38, 42, 44, 62, 72, 74, 87, 88, 90, 91, 92, 94, 95, 101, 104, 106], "well": [2, 3, 9, 10, 38, 42, 46, 47, 62, 64, 70, 72, 74, 79, 82, 83, 85, 90, 91, 92, 94, 95, 98, 99, 101, 103, 104], "nest": [2, 38, 42, 43, 58, 74, 80, 82, 83, 108], "latter": [2, 38, 42, 74, 104], "compon": [2, 42, 74], "__": [2, 42, 74], "set_score_request": [2, 73, 74], "structur": [3, 71, 94, 96, 98], "unobserv": 3, "less": [3, 4, 5, 10, 32, 41, 49, 62, 71, 72, 76, 78, 82, 92, 94, 96, 97, 98, 99, 103, 108], "channel": [3, 89, 99], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 37, 47, 57, 63, 88, 91, 97], "inv": 3, "confident_joint": [3, 23, 37, 44, 57, 63, 64, 85, 98, 99], "un": 3, "under": [3, 10, 38, 42, 63, 70, 71, 91, 96, 104], "joint": [3, 37, 44, 47, 57, 63, 64, 97], "num_label_issu": [3, 41, 44, 64, 79, 83, 85], "estimation_method": [3, 41], "off_diagon": 3, "multi_label": [3, 37, 44, 57, 58, 64, 102], "don": [3, 84, 91, 92, 94, 95, 99, 103, 106], "statis": 3, "compute_confident_joint": [3, 37, 44, 57, 64, 99], "off": [3, 44, 57, 69, 92, 96, 99, 103, 104], "j": [3, 5, 37, 38, 42, 43, 44, 64, 67, 70, 71, 80, 82, 83, 90, 91, 99, 107, 108], "confident_learn": [3, 44, 64, 99], "off_diagonal_calibr": 3, "calibr": [3, 4, 44, 57, 62, 101], "cj": [3, 47, 57], "axi": [3, 32, 47, 49, 55, 76, 79, 89, 90, 91, 92, 96, 98, 99, 101, 102, 104, 106, 107], "bincount": [3, 90, 91, 99, 101, 102], "alwai": [3, 10, 38, 42, 57, 87, 88, 89, 99, 106], "estimate_issu": 3, "over": [3, 5, 10, 38, 41, 42, 69, 70, 76, 78, 87, 91, 92, 94, 96, 97, 98, 99, 104, 106], "As": [3, 7, 84, 90, 91, 95, 99, 106, 108], "add": [3, 5, 7, 13, 14, 38, 42, 61, 70, 88, 89, 90, 91, 92, 95, 96, 98, 99, 102], "approach": [3, 37, 41, 44, 61, 87, 94, 96, 99, 102, 104, 106], "custom": [3, 7, 10, 12, 31, 38, 41, 42, 49, 56, 72, 88, 91, 95, 96, 99, 106], "know": [3, 10, 90, 91, 92, 94, 95, 98, 99, 101, 106], "cut": [3, 69, 84, 99], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 33, 103, 104, 108], "underestim": 3, "few": [3, 9, 10, 70, 84, 96, 98, 101, 102, 103, 104, 108], "4": [3, 4, 5, 10, 11, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 48, 49, 56, 66, 67, 69, 70, 72, 75, 82, 97, 98, 102, 107, 108], "detail": [3, 4, 5, 10, 15, 17, 34, 37, 38, 42, 43, 49, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 78, 79, 80, 84, 85, 89, 98, 102, 104, 108], "num_issu": [3, 7, 41, 89, 90, 91, 92, 94, 95, 96, 99], "calibrate_confident_joint": 3, "up": [3, 7, 10, 18, 27, 28, 31, 44, 49, 51, 61, 62, 88, 97, 98, 103, 106, 108], "p_": [3, 37, 44], "pair": [3, 5, 10, 37, 44, 99], "v": [3, 10, 41, 63, 64, 66, 72, 90, 91, 102, 103, 104, 105], "rest": [3, 5, 7, 9, 10, 12, 36, 63, 64, 66, 74, 87, 88, 90, 91, 92, 94, 95, 98, 99, 101, 106], "fashion": [3, 5, 76, 87], "2x2": 3, "incorrectli": [3, 37, 63, 64, 67, 94, 108], "calibrated_cj": 3, "c": [3, 10, 55, 56, 64, 72, 84, 87, 89, 90, 91, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106], "whose": [3, 4, 5, 10, 29, 38, 42, 47, 52, 56, 62, 66, 69, 75, 78, 82, 83, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 104, 107, 108], "truli": [3, 104, 107], "estimate_joint": [3, 37, 99], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 64, 70, 99, 103, 105, 107, 108], "return_indices_of_off_diagon": 3, "frequenc": [3, 27, 62, 63, 70, 79, 103, 104], "done": [3, 10, 61, 74, 90, 98, 99, 102, 104, 105], "overfit": [3, 10, 67, 70, 87, 89, 90, 91, 92, 94, 95, 105], "classifict": 3, "singl": [3, 5, 9, 10, 13, 27, 37, 38, 42, 43, 49, 50, 57, 62, 63, 69, 70, 71, 72, 82, 87, 89, 90, 96, 98, 99, 102, 103], "baselin": [3, 38, 44, 88, 104, 106], "proxi": 3, "union": [3, 5, 13, 27, 49, 52, 53, 54, 57, 58, 64, 70, 74, 82, 98], "tupl": [3, 32, 38, 42, 43, 47, 48, 50, 52, 56, 57, 62, 64, 70, 78, 80, 82, 83, 89, 108], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 41, 47, 52, 53, 62, 71, 76, 78, 84, 88, 92, 96, 98, 107], "practic": [3, 87, 88, 91, 92, 99, 104, 106], "complet": [3, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "gist": 3, "cj_ish": 3, "guess": [3, 47, 99, 101], "8": [3, 5, 7, 8, 48, 49, 50, 56, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "parallel": [3, 44, 70, 80, 97], "again": [3, 61, 87, 98, 104], "simplifi": [3, 15, 98], "understand": [3, 9, 10, 37, 63, 70, 91, 96, 99, 100, 106, 107, 108], "100": [3, 4, 38, 42, 52, 53, 55, 71, 72, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 102, 103, 104, 108], "optim": [3, 38, 39, 42, 61, 92, 96, 101], "speed": [3, 44, 88, 97, 98, 106], "dtype": [3, 24, 26, 27, 32, 38, 42, 56, 57, 66, 82, 89, 96, 103], "enumer": [3, 38, 42, 89, 90, 91, 92, 96, 108], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 42, 49, 57, 82, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "num_confident_bin": 3, "argmax": [3, 44, 72, 76, 79, 89, 96, 98, 99, 103, 104, 107], "elif": 3, "estimate_lat": 3, "py_method": [3, 47], "cnt": [3, 47], "1d": [3, 5, 13, 17, 33, 41, 44, 49, 50, 52, 57, 58, 66, 75, 87, 89, 96], "eqn": [3, 47], "margin": [3, 44, 47, 49, 72], "marginal_p": [3, 47], "shorthand": [3, 14], "proport": [3, 10, 37, 63, 99, 105], "poorli": [3, 47, 87, 96], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 99], "variabl": [3, 7, 15, 28, 57, 74, 75, 89, 90, 94, 99, 102, 106], "exact": [3, 10, 47, 52, 87, 90, 91, 92, 94, 96], "within": [3, 4, 5, 10, 16, 33, 38, 39, 42, 43, 45, 64, 69, 78, 80, 82, 90, 91, 92, 98, 103, 107], "percent": 3, "often": [3, 37, 47, 63, 98, 99, 105, 107], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 57, 58, 70, 87, 88, 89, 90, 92, 94, 95, 98, 102, 103, 104, 106], "wai": [3, 5, 10, 52, 61, 84, 85, 87, 88, 89, 90, 91, 94, 95, 98, 99, 101, 102, 103, 105], "pro": 3, "con": 3, "pred_proba": [3, 105], "combin": [3, 37, 90, 92, 96, 97, 98, 99, 105, 106], "becaus": [3, 47, 53, 57, 69, 95, 96, 98, 99, 101, 103], "littl": [3, 41, 96, 97, 103, 108], "uniform": [3, 72, 97, 98, 99], "20": [3, 7, 43, 83, 89, 92, 95, 96, 97, 98, 99, 103, 106, 107, 108], "Such": [3, 92, 104], "bound": [3, 24, 26, 38, 42, 56, 66, 67, 69, 70, 103], "reason": [3, 23, 38, 42, 53, 71], "comment": [3, 56, 96, 108], "end": [3, 5, 38, 42, 54, 70], "file": [3, 5, 13, 40, 41, 60, 70, 87, 89, 90, 94, 95, 97, 98, 103, 104, 107, 108], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 99], "handl": [3, 5, 7, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 52, 53, 54, 85, 87, 88, 90, 91, 92, 94, 95, 96, 99, 107, 108], "five": [3, 67, 70, 99, 103], "estimate_cv_predicted_prob": [3, 99], "estimate_noise_matric": 3, "get_confident_threshold": [3, 40, 41], "amongst": [3, 10, 103], "confident_threshold": [3, 10, 23, 24, 41, 71], "point": [4, 5, 7, 9, 10, 19, 27, 38, 42, 52, 54, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101], "valuat": [4, 9, 19], "help": [4, 37, 38, 42, 70, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 108], "u": [4, 87, 88, 89, 90, 92, 94, 96, 98, 99, 101, 102, 105, 106, 107, 108], "assess": [4, 10, 96, 103], "contribut": [4, 10, 19, 96, 103], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 17, 19, 20, 27, 29, 32, 45, 51, 94, 96], "metric": [4, 5, 10, 19, 20, 22, 27, 29, 32, 45, 51, 52, 54, 55, 57, 61, 70, 71, 87, 88, 89, 92, 94, 95, 96, 99, 106], "10": [4, 10, 19, 20, 24, 27, 29, 32, 38, 39, 52, 70, 71, 72, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "shaplei": [4, 10, 19], "nearest": [4, 5, 10, 17, 24, 27, 29, 51, 52, 53, 54, 55, 71, 91, 95, 96, 104], "neighbor": [4, 5, 10, 17, 19, 24, 27, 29, 45, 52, 53, 54, 55, 71, 90, 91, 92, 94, 95, 96, 98, 104], "knn": [4, 10, 14, 19, 27, 29, 32, 51, 52, 53, 54, 55, 71, 94, 104], "graph": [4, 5, 10, 14, 17, 19, 27, 32, 51, 52], "calcul": [4, 10, 19, 27, 41, 49, 51, 52, 55, 62, 66, 67, 69, 70, 71, 74, 78, 92, 96, 97], "directli": [4, 5, 10, 15, 17, 34, 35, 41, 54, 61, 62, 88, 91, 95, 96, 98, 102, 103, 106], "lowest": [4, 10, 62, 70, 91, 92, 94, 96, 98, 101, 102, 103, 107], "fall": [4, 10, 69, 78, 82, 99, 104], "flag": [4, 10, 23, 27, 44, 49, 63, 64, 67, 74, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 104, 106, 107], "approxim": [4, 10, 19, 41, 54, 71, 96, 101], "top": [4, 5, 10, 37, 41, 43, 44, 57, 64, 67, 70, 72, 79, 83, 84, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 106, 108], "found": [4, 5, 7, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 102, 104, 106, 108], "arxiv": [4, 19, 99], "ab": [4, 19, 99, 103], "1908": 4, "08619": 4, "1911": [4, 19], "07128": [4, 19], "embed": [4, 5, 10, 17, 71, 84, 88, 89, 90, 91, 94, 95, 96, 99, 102, 106], "represent": [4, 5, 10, 17, 35, 38, 42, 50, 52, 64, 84, 88, 89, 90, 91, 92, 95, 98, 99, 104], "suppli": [4, 102, 103, 106], "2d": [4, 5, 17, 33, 41, 49, 50, 52, 56, 57, 62, 87, 89, 96, 102], "num_exampl": [4, 5, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 63, 89, 90, 91, 92, 94, 95, 99], "num_featur": [4, 5, 17, 38, 42, 61], "distanc": [4, 5, 10, 17, 19, 27, 29, 32, 51, 52, 53, 54, 55, 69, 71, 94, 96, 104], "construct": [4, 5, 7, 10, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 51, 52, 54, 61, 96], "nearestneighbor": [4, 5, 10, 19, 52, 54, 71, 94, 104], "cosin": [4, 10, 52, 53, 55, 71, 96, 104], "dim": [4, 71, 92, 107], "euclidean": [4, 5, 10, 52, 53, 55, 69, 71, 94], "dimension": [4, 27, 53, 57, 89, 99, 104], "scikit": [4, 42, 53, 54, 57, 71, 84, 87, 88, 89, 90, 91, 94, 95, 96, 98, 106], "fewer": [4, 10, 44, 57, 71, 96, 103], "stabl": [4, 16, 22, 25, 30, 40, 45, 54, 57, 60, 71, 85, 89, 90, 91, 92, 94, 95, 99], "exce": [4, 52, 92, 96], "transform": [4, 10, 33, 49, 52, 55, 57, 71, 72, 87, 88, 91, 92, 95, 104, 108], "rel": [4, 10, 37, 52, 62, 63, 71, 90, 91, 92, 94, 95, 99, 104], "adjust": [4, 39, 44, 52, 66, 71, 72, 84, 96, 99], "closer": [4, 10, 69, 103], "highli": [4, 91, 92], "influenti": 4, "posit": [4, 5, 10, 38, 42, 55, 57, 70, 96, 97, 104], "convers": 4, "neg": [4, 10, 69, 70, 90, 91, 96, 97], "valueerror": [4, 5, 13, 14, 35, 46, 49, 52, 55, 98], "neither": [4, 5, 10, 15, 53, 103], "nor": [4, 5, 10, 15], "larger": [4, 19, 53, 74, 76, 78, 92, 95, 97, 98], "55": [4, 56, 96, 97, 103, 106], "525": 4, "unifi": 5, "audit": [5, 9, 13, 14, 17, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 106], "kind": [5, 6, 7, 10, 96, 97], "addit": [5, 7, 9, 12, 14, 34, 36, 38, 42, 49, 52, 54, 58, 62, 70, 79, 80, 87, 88, 89, 90, 94, 95, 96, 99, 101, 104, 105], "depend": [5, 7, 9, 12, 13, 14, 36, 40, 44, 46, 57, 60, 64, 71, 74, 75, 84, 96], "instal": [5, 7, 9, 12, 36, 38, 40, 41, 42, 44, 60, 61, 76, 78], "pip": [5, 7, 9, 12, 36, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "development": [5, 7, 9, 12, 36], "git": [5, 7, 9, 12, 36, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "github": [5, 7, 9, 12, 36, 38, 39, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "com": [5, 7, 9, 12, 36, 38, 39, 41, 46, 57, 71, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "egg": [5, 7, 9, 12, 36, 84, 97], "label_nam": [5, 7, 8, 10, 11, 13, 19, 32, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "image_kei": [5, 10, 92, 96], "interfac": [5, 9, 10, 54, 84, 98, 99], "librari": [5, 10, 42, 54, 67, 70, 71, 84, 88, 90, 95, 96, 97, 98], "goal": [5, 106], "track": [5, 7, 14, 15, 84, 90, 97, 98, 99], "intermedi": [5, 9, 91], "statist": [5, 10, 14, 23, 27, 37, 62, 63, 70, 91, 94, 95, 96, 99], "convert": [5, 10, 13, 35, 38, 42, 50, 55, 58, 62, 69, 78, 82, 85, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103], "hug": [5, 10, 13, 92], "face": [5, 10, 13, 17, 92, 97, 102], "kei": [5, 7, 10, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 49, 62, 63, 69, 71, 90, 91, 92, 95, 98, 99, 101, 103], "string": [5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 42, 53, 57, 62, 63, 75, 79, 82, 83, 88, 94, 95, 96, 98, 101, 102, 108], "dictionari": [5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 48, 57, 62, 63, 66, 67, 69, 70, 90, 91, 94, 95, 96, 99, 101, 102, 103], "path": [5, 13, 38, 41, 42, 70, 89, 90, 98, 103], "local": [5, 7, 10, 13, 38, 39, 42, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "text": [5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 43, 49, 71, 80, 82, 83, 84, 86, 90, 91, 93, 97, 98, 99, 100, 101, 104], "txt": [5, 13, 108], "csv": [5, 13, 87, 88, 94, 95, 106], "json": [5, 13], "hub": [5, 13], "multiclass": [5, 13, 16, 49, 57, 62, 102], "regress": [5, 7, 10, 11, 13, 15, 17, 22, 31, 33, 35, 88, 90, 91, 95, 100, 101, 104], "multilabel": [5, 10, 11, 13, 15, 16, 22, 26, 33, 35, 50, 102], "imag": [5, 9, 37, 42, 67, 69, 70, 71, 76, 78, 79, 84, 90, 91, 93, 97, 98, 100, 101, 102, 103, 105, 107], "field": [5, 10, 38, 42], "themselv": [5, 87, 88, 96, 106], "pil": [5, 92, 96], "cleanvis": [5, 10, 96], "level": [5, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 52, 56, 80, 82, 91, 92, 98, 100, 102, 107], "load_dataset": [5, 13, 92], "glue": 5, "sst2": 5, "properti": [5, 13, 14, 35, 38, 42], "has_label": [5, 13], "class_nam": [5, 13, 21, 37, 43, 63, 70, 79, 83, 84, 97, 99, 103, 107, 108], "empti": [5, 13, 47, 62, 91, 96, 98, 102], "find_issu": [5, 6, 7, 8, 10, 11, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_typ": [5, 6, 7, 8, 10, 11, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "sort": [5, 17, 41, 44, 49, 62, 64, 67, 69, 70, 72, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 103, 106, 107, 108], "common": [5, 10, 14, 17, 91, 93, 96, 97, 98, 99, 102, 103, 107], "real": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "world": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "interact": [5, 17, 95, 98], "thereof": [5, 17], "insight": [5, 17, 70, 101], "best": [5, 9, 10, 17, 48, 62, 72, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 108], "properli": [5, 10, 41, 48, 52, 57, 58, 76, 89, 90, 91, 92, 94, 95, 98, 99, 102, 104, 106, 107], "respect": [5, 38, 42, 67, 70, 89, 90, 91, 92, 94, 95, 99, 102, 103], "lexicograph": [5, 48, 57, 89, 90, 91, 92, 94, 95, 99, 102], "squar": [5, 57, 74, 97, 106], "csr": [5, 52, 96], "evenli": 5, "omit": [5, 69, 70, 92, 96, 103], "itself": [5, 33, 38, 42, 52, 96, 103], "three": [5, 10, 37, 62, 63, 74, 79, 87, 89, 90, 91, 94, 97, 99, 101, 105, 106, 107, 108], "indptr": [5, 96], "wise": 5, "start": [5, 7, 10, 35, 38, 39, 42, 49, 84, 102, 108], "th": [5, 10, 43, 48, 56, 57, 62, 64, 67, 69, 70, 71, 80, 82, 83, 95, 102, 103, 108], "ascend": [5, 37, 63, 92, 99], "segment": [5, 76, 78, 79, 100], "reflect": [5, 10, 52, 87, 88, 94, 95, 101, 103, 104, 106], "maintain": [5, 61], "kneighbors_graph": [5, 19, 54, 94], "illustr": [5, 96], "todens": 5, "second": [5, 49, 57, 70, 72, 90, 94, 98, 99, 108], "duplic": [5, 9, 22, 23, 38, 42, 52, 84, 90, 96, 99, 106], "explicit": 5, "precend": 5, "collect": [5, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 62, 96, 98, 101, 108], "unspecifi": [5, 17, 44, 64], "interest": [5, 17, 23, 79, 83, 87, 88, 95, 96, 99, 106, 107, 108], "constructor": [5, 10, 11, 17, 24, 31, 52, 54], "issuemanag": [5, 9, 14, 15, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 34], "respons": [5, 17, 23, 54, 74, 75, 97, 106, 108], "random_st": [5, 87, 89, 90, 91, 92, 96, 99, 102, 104], "lab": [5, 6, 8, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 41, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106], "comprehens": [5, 84, 92, 102, 106], "nbr": 5, "n_neighbor": [5, 10, 19, 52, 54, 71, 96], "mode": [5, 12, 19, 38, 41, 42, 104], "4x4": 5, "float64": [5, 27, 38, 42, 82], "compress": [5, 10, 52, 57, 76, 78, 96], "toarrai": [5, 52, 96], "NOT": [5, 41, 95], "23606798": 5, "41421356": [5, 52], "configur": [5, 17, 49, 91], "suppos": [5, 10, 67, 87, 88, 104, 106], "who": [5, 69, 87, 94, 96, 99, 108], "manag": [5, 8, 9, 10, 14, 15, 16, 17, 18, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 61, 90, 98], "clean_learning_kwarg": [5, 10, 11, 24, 31, 98, 106], "labelissuemanag": [5, 10, 15, 22, 24], "prune_method": [5, 85], "prune_by_noise_r": [5, 44, 64, 99], "report": [5, 7, 12, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 83, 84, 89, 90, 91, 94, 95, 98, 99, 102, 106, 108], "include_descript": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34], "show_summary_scor": [5, 34], "show_all_issu": [5, 34], "summari": [5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 43, 60, 61, 63, 68, 77, 78, 80, 81, 82, 85, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 106, 107, 108], "show": [5, 7, 27, 38, 42, 48, 57, 70, 79, 83, 87, 91, 92, 94, 95, 96, 97, 98, 99, 101, 104, 106, 107, 108], "suffer": [5, 10, 14, 23, 64, 72, 83, 96, 108], "onc": [5, 23, 37, 38, 42, 87, 90, 98, 99, 102, 103], "familiar": [5, 96], "overal": [5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 43, 49, 62, 63, 66, 69, 70, 74, 78, 79, 80, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 103, 108], "sever": [5, 7, 10, 13, 14, 23, 38, 41, 42, 44, 66, 69, 71, 72, 78, 82, 84, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 108], "compar": [5, 62, 71, 82, 90, 91, 94, 96, 99, 103], "issue_summari": [5, 7, 10, 14, 96], "With": [5, 9, 10, 41, 88, 95, 98, 99, 101, 106, 107, 108], "usag": [5, 41, 61], "usual": [5, 13, 33, 34, 92, 101, 106], "ti": [5, 62], "exhibit": [5, 7, 10, 14, 79, 89, 90, 91, 92, 94, 95, 99, 103], "ie": [5, 74], "likelihood": [5, 10, 41, 43, 44, 64, 69, 71, 72, 76, 80, 96], "wherea": [5, 10, 57, 64, 87, 88, 105], "outlier": [5, 9, 11, 15, 22, 23, 32, 45, 52, 72, 84, 90, 91, 96, 99, 100, 106], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 99, 106], "global": [5, 7, 10, 23, 38, 42, 97], "non_iid": [5, 10, 11, 15, 27, 91, 92, 94, 95, 96, 99], "hypothesi": [5, 96], "iid": [5, 7, 9, 27, 94, 99], "never": [5, 89, 99, 102, 104, 105], "someth": [5, 7, 10, 38, 42, 72, 103], "123": [5, 90, 91], "456": [5, 87, 88, 89], "nearest_neighbor": 5, "7": [5, 10, 49, 50, 61, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "9": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 43, 49, 50, 66, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "distance_to_nearest_neighbor": [5, 11, 90, 91, 92, 94, 95, 99], "789": 5, "get_issu": [5, 10, 14, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_nam": [5, 6, 7, 10, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91, 92, 94, 95, 99], "focu": [5, 10, 14, 95, 96, 107, 108], "full": [5, 10, 14, 41, 61, 70, 92, 108], "summar": [5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 63, 79, 83, 84, 107], "specific_issu": [5, 14], "lie": [5, 10, 71, 72, 88, 89, 90, 91, 92, 94, 95, 96, 99], "get_issue_summari": [5, 10, 14, 91, 96], "get_info": [5, 14, 91, 95, 96, 97], "yet": [5, 18, 28, 61, 97, 101], "list_possible_issue_typ": [5, 15, 16], "regist": [5, 7, 15, 16, 18, 28, 38, 42, 90], "rtype": [5, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42], "registri": [5, 15, 16], "list_default_issue_typ": [5, 15, 16], "folder": [5, 89, 90, 92], "load": [5, 13, 41, 70, 92, 97, 98, 99, 103, 104, 107, 108], "futur": [5, 10, 23, 38, 42, 62, 84, 90, 95], "overwrit": [5, 90], "separ": [5, 37, 49, 66, 90, 91, 92, 96, 98, 103, 105], "static": 5, "rememb": [5, 95, 98, 99], "part": [5, 10, 38, 42, 44, 67, 69, 70, 89, 90, 96, 97, 107, 108], "ident": [5, 10, 23, 57, 95, 96], "datalab": [6, 8, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 84, 87, 88, 97, 101, 106], "walk": 7, "alongsid": [7, 38, 42, 90, 98], "pre": [7, 8, 10, 38, 42, 90, 91, 106], "runtim": [7, 38, 41, 42, 74, 76, 78, 89, 92, 98], "issue_manager_factori": [7, 15, 90], "myissuemanag": [7, 15], "myissuemanagerforregress": 7, "decor": [7, 15], "ll": [7, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "thing": [7, 42, 88, 96, 99, 106], "next": [7, 62, 84, 87, 88, 89, 94, 95, 96, 98, 101, 103, 106, 108], "dummi": 7, "randint": [7, 32, 49, 90, 91, 96], "mark": [7, 10, 85, 103, 104, 106], "regard": [7, 91, 99], "rand": [7, 49, 52, 90, 91, 96], "is_": [7, 10, 90], "_issu": [7, 10, 90], "issue_score_kei": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "whole": [7, 10, 27, 38, 42, 91, 96], "make_summari": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "popul": [7, 95], "verbosity_level": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "std": [7, 103], "raw_scor": 7, "bit": 7, "involv": [7, 41, 79, 83, 96, 98, 102], "intermediate_arg": 7, "min": [7, 49, 69, 82, 90, 98, 104], "sin_filt": 7, "sin": 7, "arang": [7, 96], "kernel": [7, 96], "affect": [7, 10, 38, 42, 53, 76, 82, 95, 96, 98], "easili": [7, 47, 85, 87, 88, 89, 91, 94, 95, 99, 101, 102, 104, 105, 106, 107], "hard": [7, 42, 97, 104], "sai": [7, 10, 38, 42, 96, 102, 107], "anoth": [7, 10, 23, 37, 41, 53, 56, 69, 72, 88, 94, 95, 96, 98, 99, 101, 104], "try": [7, 9, 10, 41, 44, 61, 62, 76, 78, 84, 91, 92, 94, 95, 98, 99, 107], "won": [7, 38, 42, 90, 91, 98, 102], "issue_manag": [7, 10, 12, 14, 16, 19, 20, 21, 24, 26, 27, 28, 29, 31, 32, 90], "instanti": [7, 17, 41, 61, 71, 88, 89, 91, 94], "477762": 7, "286455": 7, "term": [7, 10, 47, 57, 70, 89, 90, 91, 92, 94, 95, 99], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 20, 29, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 103, 104, 106, 107, 108], "003042": 7, "058117": 7, "11": [7, 10, 61, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "121908": 7, "15": [7, 55, 61, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "169312": 7, "17": [7, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 90, 91, 96, 97, 99], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 32], "group": [8, 9, 27, 32, 97, 103, 108], "dbscan": [8, 10, 32], "hdbscan": 8, "etc": [8, 10, 23, 33, 38, 42, 47, 61, 62, 80, 84, 90, 91, 94, 95, 98, 99, 102, 106], "sensit": [8, 10, 55, 96], "ep": [8, 32, 70], "radiu": 8, "min_sampl": [8, 32], "kmean": [8, 96], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 32, 96], "cluster_id": [8, 10, 11, 32, 96], "labels_": 8, "underperforming_group": [8, 10, 11, 15, 22, 91, 92, 94, 95, 96, 99], "search": [9, 10, 21, 27, 28, 45, 51, 52, 53, 56, 74, 96, 98, 105], "nondefault": 9, "Near": [9, 98], "imbal": [9, 22, 66, 71, 72, 91], "null": [9, 11, 15, 22, 91, 92, 95, 99], "togeth": [9, 10, 47, 88, 90, 91, 92, 94, 95, 99, 106, 108], "built": [9, 49], "own": [9, 38, 40, 42, 54, 60, 66, 67, 70, 76, 80, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 106, 107, 108], "prerequisit": 9, "basic": [9, 42, 61, 94, 95, 96, 104], "fulli": [9, 10, 38, 42, 61, 98], "platform": [9, 10, 84, 92, 94, 95, 98], "write": [9, 10], "code": [9, 10, 38, 42, 47, 57, 61, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "being": [9, 10, 14, 37, 38, 42, 44, 49, 56, 57, 72, 87, 94, 98, 99, 106, 107], "100x": [9, 10], "faster": [9, 10, 41, 71, 74, 76, 78, 98, 99], "intellig": [9, 10], "quickli": [9, 10, 39, 87, 89, 92, 94, 95, 98, 102, 104, 107, 108], "fix": [9, 10, 62, 88, 95, 96, 99, 106], "scientist": [9, 10], "million": [9, 10, 108], "thank": [9, 10], "ai": [9, 10, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 101, 102, 104, 106, 108], "suggest": [9, 10, 37, 62, 63, 69, 88, 92, 95, 96, 98, 106], "power": [9, 10, 92, 94, 95, 97, 99, 108], "automl": [9, 10, 84, 98], "system": [9, 10, 89, 92, 94, 95, 107], "foundat": [9, 10, 84, 96], "improv": [9, 10, 62, 87, 88, 91, 92, 97, 98, 99, 106, 107], "click": [9, 10, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "tune": [9, 10, 88, 89, 95, 97, 104], "serv": [9, 10, 14, 17, 101], "auto": [9, 10, 87, 88, 91, 97, 98, 106], "free": [9, 10, 84, 89, 91, 92, 94, 95, 98, 99], "page": [10, 91, 98, 99], "variou": [10, 14, 31, 40, 58, 60, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "why": [10, 95], "matter": [10, 37, 63], "didn": [10, 96], "plu": [10, 106], "ye": [10, 11], "near_dupl": [10, 11, 15, 20, 90, 91, 92, 94, 95, 96, 98, 99], "class_imbal": [10, 11, 15, 21, 91, 92, 94, 95, 96, 99], "data_valu": [10, 11, 15, 22, 96], "No": [10, 11, 87, 88, 95, 96, 98], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 69, 87, 88, 108], "issue_scor": 10, "atyp": [10, 71, 90, 91, 92, 94, 95, 99, 104], "datapoint": [10, 32, 44, 49, 57, 72, 75, 84, 87, 88, 89, 90, 91, 94, 95, 98, 105, 106], "is_issu": [10, 23], "primarili": 10, "former": [10, 38, 42], "investig": [10, 89], "expertis": 10, "interpret": [10, 97, 98, 99, 102, 106], "annot": [10, 37, 48, 62, 63, 64, 66, 67, 69, 70, 79, 82, 83, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 103, 107], "dissimilar": [10, 94, 95], "preced": 10, "incorrect": [10, 69, 72, 75, 87, 89, 90, 91, 92, 94, 95, 96, 99, 103, 106], "due": [10, 41, 44, 72, 76, 78, 89, 90, 91, 92, 94, 95, 99, 106], "appear": [10, 37, 48, 63, 64, 67, 75, 91, 92, 94, 95, 96, 106, 107], "now": [10, 41, 85, 87, 88, 89, 91, 96, 98, 101, 103, 104, 106, 108], "token": [10, 43, 56, 78, 79, 80, 81, 82, 83, 98, 99, 100], "hamper": [10, 92, 97], "analyt": [10, 84, 96, 98, 101], "lead": [10, 69, 72, 92, 96, 103], "draw": [10, 90, 91], "conclus": [10, 95], "let": [10, 38, 42, 71, 72, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "sort_valu": [10, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 106], "head": [10, 87, 88, 89, 91, 92, 94, 95, 96, 97, 99, 101, 106], "97": [10, 87, 97, 98, 99, 103, 106, 108], "064045": 10, "58": [10, 87, 91, 96, 97, 99, 103], "680894": 10, "41": [10, 96, 97, 103, 106], "746043": 10, "794894": 10, "98": [10, 97, 98, 106], "802911": 10, "give": [10, 49, 72, 99, 101, 107], "li": [10, 71], "especi": [10, 87, 88, 92, 96, 98, 106], "veri": [10, 37, 63, 67, 69, 88, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106], "rare": [10, 44, 70, 90, 91, 92, 94, 95, 98, 99], "anomal": [10, 72, 90, 91, 92, 94, 95, 99], "articl": [10, 41, 98], "blog": 10, "unexpect": [10, 38, 42, 95], "consequ": 10, "inspect": [10, 88, 89, 91, 92, 99, 103, 106], "011562": 10, "62": [10, 96, 99, 103, 106], "019657": 10, "22": [10, 89, 90, 92, 96, 97, 99, 102, 103, 108], "035243": 10, "040907": 10, "42": [10, 49, 95, 96, 97, 103, 108], "056865": 10, "smaller": [10, 71, 96, 102, 103], "extrem": [10, 90, 91, 92, 94, 95, 96, 98, 99], "record": [10, 38, 42, 89, 94, 106], "abbrevi": 10, "misspel": 10, "typo": [10, 83], "resolut": 10, "video": [10, 97], "audio": [10, 90, 91, 93, 98], "minor": [10, 56], "variat": 10, "translat": 10, "d": [10, 55, 87, 94, 95, 96, 98, 99, 102, 106, 108], "constant": [10, 32, 74], "median": [10, 31, 55], "question": [10, 23, 84, 99], "nearli": [10, 23, 91, 92, 94, 95], "awar": [10, 85, 99], "presenc": [10, 52, 54, 99], "36": [10, 96, 97, 108], "066009": 10, "80": [10, 39, 87, 94, 102, 106], "003906": 10, "093245": 10, "005599": 10, "27": [10, 94, 96, 97, 99, 103, 108], "156720": 10, "009751": 10, "72": [10, 96, 97, 99, 102, 106], "signific": [10, 94, 95, 99], "violat": [10, 94, 95, 96, 99], "assumpt": [10, 94, 95, 96, 99], "changepoint": [10, 94, 95, 99], "shift": [10, 52, 54, 94, 95, 99], "drift": [10, 91, 94, 96, 99], "autocorrel": [10, 94, 95, 99], "almost": [10, 94, 95, 99], "adjac": [10, 52, 94, 95, 99], "tend": [10, 37, 47, 94, 95, 99, 107, 108], "sequenti": [10, 38, 42, 61, 92], "pai": [10, 95], "attent": [10, 96], "realli": [10, 88, 95, 101, 107], "mere": 10, "highlight": [10, 79, 83, 90, 91, 94, 96, 107], "necessarili": [10, 62, 70, 95, 99], "wrong": [10, 62, 67, 69, 85, 88, 90, 91, 95, 98, 99, 103], "gap": 10, "b": [10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 56, 57, 82, 87, 94, 95, 96, 97, 98, 99, 105, 108], "x1": [10, 67, 70, 103], "x2": [10, 67, 70, 103], "10th": 10, "100th": 10, "90": [10, 82, 87, 94, 99, 105, 106, 108], "similarli": [10, 38, 42, 90, 92, 94, 98, 103], "associ": [10, 13, 17, 33, 35, 38, 42, 70, 101], "blogpost": 10, "proper": [10, 57, 62, 67, 70, 87, 92, 95, 98, 101, 103], "scenario": [10, 52, 54, 72, 90, 91], "underli": [10, 43, 54, 71, 80, 82, 108], "stem": [10, 71, 104], "evolv": 10, "influenc": 10, "act": [10, 69, 90], "accordingli": [10, 33, 52], "emploi": [10, 102, 104], "partit": [10, 105], "ahead": 10, "good": [10, 38, 42, 55, 61, 63, 69, 72, 76, 78, 79, 84, 92, 94, 95], "problem": [10, 33, 41, 49, 79, 84, 90, 91, 92, 95, 98], "deploy": [10, 87, 88, 99, 106], "overlook": [10, 69, 103], "fact": 10, "thu": [10, 37, 42, 63, 87, 89, 94, 95, 99, 105, 108], "diagnos": [10, 91, 98], "24": [10, 89, 96, 97, 99, 101, 103, 106, 108], "681458": 10, "37": [10, 90, 96, 97], "804582": 10, "64": [10, 42, 87, 92, 94, 96, 99, 103], "810646": 10, "815691": 10, "78": [10, 87, 94, 97, 99, 103, 106], "834293": 10, "Be": [10, 42], "cautiou": 10, "behavior": [10, 17, 37, 38, 42, 70, 98], "rarest": [10, 91], "q": [10, 103], "subpar": 10, "special": [10, 52, 56], "techniqu": [10, 103], "smote": 10, "asymmetr": [10, 37], "28": [10, 92, 95, 96, 97, 99, 101, 108], "75": [10, 49, 90, 91, 96, 97, 101, 102, 103, 106, 108], "33": [10, 38, 42, 96, 97, 103], "68": [10, 87, 97, 99, 103], "excess": [10, 92], "dark": [10, 107], "bright": [10, 96, 108], "blurri": [10, 92, 96], "lack": [10, 61, 96], "unusu": [10, 103, 104], "cluster": [10, 19, 32], "slice": 10, "poor": [10, 96], "subpopul": 10, "faq": [10, 84, 91, 92, 94, 95, 100], "get_self_confidence_for_each_label": [10, 49, 72], "r": [10, 41, 74, 90, 91, 96, 106, 107], "tabular": [10, 84, 86, 90, 91, 93, 96, 98, 101], "categor": [10, 71, 86, 87, 90, 91, 93, 98, 106], "encod": [10, 50, 70, 76, 79, 87, 88, 94, 95, 98, 106, 107], "71": [10, 96, 97, 99, 103, 106], "70": [10, 82, 94, 96], "69": [10, 99, 106], "subgroup": [10, 96], "wors": [10, 96, 101], "ratio": [10, 96], "miss": [10, 28, 38, 42, 57, 67, 69, 98, 103, 106], "pattern": [10, 96], "isn": [10, 18, 28], "scalabl": 10, "sacrific": 10, "One": [10, 57, 71, 98], "quantif": 10, "39": [10, 88, 89, 90, 92, 95, 96, 97, 98, 103, 106, 107, 108], "32": [10, 89, 90, 96, 97, 101, 103], "valuabl": [10, 19, 96], "exert": [10, 91], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 22, 24, 31], "health_summari": [10, 24, 37, 84, 97], "health_summary_kwarg": 10, "tandem": [10, 97], "view": [10, 38, 42, 43, 44, 78, 80, 82, 84, 87, 88, 89, 90, 91, 94, 95, 97, 99, 101, 102, 103, 104, 105, 106, 108], "ood_kwarg": 10, "outofdistribut": [10, 29, 71, 104], "outsid": [10, 98, 102], "outlierissuemanag": [10, 15, 22, 29], "nearduplicateissuemanag": [10, 15, 20, 22], "noniidissuemanag": [10, 15, 22, 27], "num_permut": [10, 27], "permut": [10, 27], "significance_threshold": [10, 27], "signic": 10, "noniid": [10, 22], "classimbalanceissuemanag": [10, 15, 21, 22], "underperforminggroupissuemanag": [10, 15, 22, 32], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 32], "filter_cluster_id": [10, 22, 32], "clustering_kwarg": [10, 32], "nullissuemanag": [10, 15, 22, 28], "datavaluationissuemanag": [10, 15, 19, 22], "codeblock": 10, "demonstr": [10, 41, 52, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107], "howev": [10, 38, 42, 52, 57, 87, 88, 89, 92, 94, 95, 96, 101, 105, 107], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 63], "versa": [10, 63], "light": [10, 92, 96, 97, 103, 107], "29": [10, 92, 96, 97, 101, 102, 103, 107, 108], "low_inform": [10, 92, 96], "odd_aspect_ratio": [10, 92, 96], "35": [10, 90, 96, 97, 101, 102, 103], "odd_siz": [10, 92, 96], "doc": [10, 38, 42, 71, 84, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 104, 106, 108], "label_scor": [11, 24, 26, 31, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "is_outlier_issu": [11, 90, 91, 92, 94, 95, 96, 99], "outlier_scor": [11, 29, 90, 91, 92, 94, 95, 96, 99, 104], "is_near_duplicate_issu": [11, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_scor": [11, 20, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_set": [11, 20, 22, 90, 91, 92, 94, 95, 98, 99], "is_non_iid_issu": [11, 91, 94, 95, 96, 99], "non_iid_scor": [11, 27, 91, 94, 95, 96, 99], "is_class_imbalance_issu": [11, 91, 96], "class_imbalance_scor": [11, 21, 91, 96], "is_underperforming_group_issu": [11, 91, 96], "underperforming_group_scor": [11, 32, 91, 96], "is_null_issu": [11, 91, 96], "null_scor": [11, 28, 91, 96], "is_data_valuation_issu": [11, 96], "data_valuation_scor": [11, 19, 96], "studio": [12, 84, 91, 92, 94, 95, 98], "data_issu": [12, 16, 17, 34], "issue_find": [12, 16], "factori": [12, 16, 17], "model_output": [12, 16], "except": [13, 38, 42, 61, 72, 90, 91, 92, 101], "dataformaterror": [13, 16], "add_not": 13, "with_traceback": 13, "tb": 13, "__traceback__": 13, "datasetdicterror": [13, 16], "datasetdict": 13, "datasetloaderror": [13, 16], "dataset_typ": 13, "fail": 13, "hold": 13, "sublist": 13, "map_to_int": 13, "abc": [13, 23, 33], "is_avail": [13, 92], "dataissu": [14, 16, 17, 34], "central": [14, 108], "repositori": 14, "strategi": [14, 49, 96, 98], "_infostrategi": 14, "basi": 14, "collect_statist": 14, "reus": [14, 23], "avoid": [14, 38, 41, 42, 44, 52, 57, 64, 67, 70, 74, 76, 78, 90, 91, 98], "recomput": [14, 88], "weighted_knn_graph": 14, "issue_manager_that_computes_knn_graph": 14, "collect_issues_from_issue_manag": 14, "collect_issues_from_imagelab": 14, "imagelab": 14, "set_health_scor": 14, "health": [14, 24, 37, 63, 84], "get_data_statist": [14, 16], "concret": 15, "subclass": [15, 38, 42, 71, 90], "regressionlabelissuemanag": [15, 22, 30, 31], "multilabelissuemanag": [15, 22, 25, 26], "from_str": [15, 35, 45, 49], "my_issu": 15, "logic": [15, 35, 41, 44, 76, 78], "issuefind": [16, 17, 34], "modeloutput": [16, 33], "multiclasspredprob": [16, 33], "regressionpredict": [16, 33], "multilabelpredprob": [16, 33], "instati": 17, "public": [17, 96, 99, 103, 107, 108], "creation": [17, 42, 96], "execut": [17, 38, 42, 90, 98, 103], "coordin": [17, 67, 69, 70, 103, 108], "At": [17, 70, 98], "get_available_issue_typ": 17, "direct": [18, 28, 38, 42, 54, 61], "vstack": [19, 57, 92, 97, 98, 99, 101, 102], "25": [19, 27, 38, 49, 55, 91, 92, 96, 97, 99, 101, 102, 103, 108], "classvar": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "short": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 56, 57], "item": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 90, 91, 92, 98, 99, 101, 102], "some_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "additional_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "default_threshold": [19, 22, 29], "collect_info": [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "info_to_omit": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "compos": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 38, 42, 88, 95, 104], "is_x_issu": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "x_score": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_a": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b1": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b2": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "report_str": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34], "_": [20, 21, 23, 24, 26, 27, 28, 31, 32, 49, 56, 57, 84, 87, 89, 90, 92, 96, 97, 99, 102], "occurr": [20, 21, 23, 27, 28, 29, 32, 56], "median_nn_dist": 20, "bleed": [22, 25, 30, 40], "edg": [22, 25, 30, 40, 69, 84, 99, 108], "sharp": [22, 25, 30, 40], "get_health_summari": [22, 24], "ood": [22, 29, 71, 72, 104], "simplified_kolmogorov_smirnov_test": [22, 27], "outlier_cluster_label": [22, 32], "no_underperforming_cluster_id": [22, 32], "perform_clust": [22, 32], "get_worst_clust": [22, 32], "find_issues_with_predict": [22, 30, 31], "find_issues_with_featur": [22, 30, 31], "believ": [23, 107], "priori": [23, 99], "abstract": [23, 33], "applic": [24, 62, 98, 99, 101, 108], "typevar": [24, 26, 38, 42, 56, 66, 69, 70], "scalartyp": [24, 26], "covari": [24, 26, 74, 106], "summary_dict": 24, "neighbor_histogram": 27, "non_neighbor_histogram": 27, "kolmogorov": 27, "smirnov": 27, "largest": [27, 41, 49, 52, 72, 76, 78, 103, 107], "empir": [27, 48, 62], "cumul": 27, "ecdf": 27, "histogram": [27, 94, 96, 106], "absolut": [27, 31], "trial": 27, "null_track": 28, "extend": [28, 50, 61, 92, 96, 103, 104, 108], "superclass": 28, "arbitrari": [28, 37, 78, 82, 90, 104, 106], "prompt": 28, "address": [28, 88, 90, 91, 95, 98], "enabl": [28, 42, 54], "scaling_factor": [29, 55], "37037": 29, "q3_avg_dist": 29, "iqr_avg_dist": 29, "median_outlier_scor": 29, "issue_threshold": 29, "multipli": [31, 55], "deleg": 31, "confus": [32, 33, 37, 38, 42, 44, 57, 70, 88, 108], "50": [32, 42, 96, 98, 99, 101, 103, 104, 106], "keepdim": [32, 98], "signifi": 32, "absenc": 32, "int64": [32, 89, 101], "npt": 32, "int_": 32, "id": [32, 62, 90, 92, 96, 98, 101], "unique_cluster_id": 32, "_description_": 32, "performed_clust": 32, "worst_cluster_id": 32, "convent": [33, 35], "subject": [33, 35], "meant": [33, 35], "Not": [33, 54], "mainli": [33, 104, 108], "content": [33, 71, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "fetch": [33, 41, 89, 91, 98], "datset": 34, "exclud": [34, 43, 79, 83, 90, 108], "get_report": 34, "enum": [35, 49], "qualnam": [35, 49], "boundari": [35, 49, 90, 91], "continu": [35, 61, 87, 88, 92, 95, 96, 98, 101, 103, 106, 108], "binari": [35, 49, 57, 64, 66, 99, 108], "simultan": [35, 106], "task_str": 35, "is_classif": 35, "__contains__": [35, 45, 49], "member": [35, 38, 42, 49, 90], "typeerror": [35, 49], "12": [35, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "__getitem__": [35, 45, 49], "match": [35, 37, 38, 42, 44, 49, 62, 63, 72, 90, 91, 92, 97, 103, 105, 107], "__iter__": [35, 45, 49], "__len__": [35, 45, 49], "alias": [35, 49], "is_regress": 35, "is_multilabel": 35, "overview": [37, 52, 87, 88, 89, 91, 92, 94, 95, 101, 103, 104, 106, 108], "modifi": [37, 38, 41, 42, 52, 54, 57, 96, 98, 99], "rank_classes_by_label_qu": [37, 91], "merg": [37, 52, 56, 84, 97, 98, 108], "find_overlapping_class": [37, 98, 99], "problemat": [37, 63, 79, 83, 89, 103, 108], "unnorm": [37, 63, 99], "abov": [37, 38, 41, 42, 54, 57, 62, 69, 70, 72, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 105, 106, 107, 108], "model_select": [37, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 106], "cross_val_predict": [37, 42, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 105, 106], "get_data_labels_from_dataset": 37, "yourfavoritemodel": [37, 99], "cv": [37, 49, 87, 89, 90, 91, 94, 96, 99, 101], "df": [37, 57, 83, 89, 96, 98], "overall_label_qu": [37, 63], "col": 37, "prob": [37, 56, 99, 105], "divid": [37, 63, 72], "label_nois": [37, 63], "human": [37, 97, 107, 108], "clearli": [37, 72, 92, 103, 107], "num": [37, 63, 97, 99], "overlap": [37, 84, 97, 98, 99], "ontolog": 37, "publish": [37, 108], "therefor": [37, 72, 96], "vehicl": [37, 97], "truck": [37, 97, 104, 107], "intuit": [37, 63], "car": [37, 97, 103, 107], "frequent": [37, 62, 96, 98, 106], "characterist": [37, 96], "l": [37, 38, 42, 67, 69, 70], "class1": 37, "class2": 37, "relationship": 37, "dog": [37, 57, 63, 65, 79, 97, 98, 104, 105, 108], "cat": [37, 57, 63, 65, 97, 98, 104, 105], "captur": [37, 89, 103, 104, 107], "co": [37, 38, 39], "noisy_label": [37, 90, 91, 102], "overlapping_class": 37, "descend": [37, 38, 42, 49, 63, 70], "overall_label_health_scor": [37, 63, 99], "half": [37, 38, 40, 42, 63, 97, 108], "health_scor": [37, 63], "classes_by_label_qu": [37, 91], "cnn": [38, 40, 42, 92], "cifar": [38, 39, 96, 97, 104], "teach": [38, 39], "bhanml": 38, "blob": [38, 96], "master": [38, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106], "call_bn": [38, 40], "bn": 38, "input_channel": 38, "n_output": 38, "dropout_r": 38, "top_bn": 38, "architectur": [38, 42], "shown": [38, 70, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 105, 107, 108], "forward": [38, 39, 40, 42, 92, 101], "overridden": [38, 42], "although": [38, 42, 71, 87, 94], "recip": [38, 42], "afterward": [38, 42], "sinc": [38, 42, 46, 58, 63, 70, 78, 82, 98, 101, 102, 103, 105, 108], "hook": [38, 42, 97], "silent": [38, 41, 42], "t_destin": [38, 40, 42], "__call__": [38, 40, 42, 45, 49], "add_modul": [38, 40, 42], "child": [38, 42], "fn": [38, 42, 70], "recurs": [38, 42, 49], "submodul": [38, 42, 51], "children": [38, 40, 42, 108], "nn": [38, 39, 42, 52, 92], "init": [38, 42, 99], "no_grad": [38, 42, 92, 104], "init_weight": [38, 42], "linear": [38, 42, 88, 92, 95], "fill_": [38, 42], "net": [38, 42, 89, 92, 97], "in_featur": [38, 42], "out_featur": [38, 42], "bia": [38, 42, 92, 96], "tensor": [38, 39, 42, 89, 92, 104], "requires_grad": [38, 42], "bfloat16": [38, 40, 42], "cast": [38, 42, 89], "buffer": [38, 40, 42, 96], "datatyp": [38, 42], "xdoctest": [38, 42], "undefin": [38, 42], "var": [38, 42], "buf": [38, 42], "20l": [38, 42], "1l": [38, 42], "5l": [38, 42], "call_super_init": [38, 40, 42], "immedi": [38, 42, 104], "compil": [38, 40, 42, 61], "cpu": [38, 40, 42, 44, 89, 92], "move": [38, 42, 49, 85, 97], "cuda": [38, 40, 42, 89, 92], "devic": [38, 42, 89, 92], "gpu": [38, 42, 88, 89, 95], "live": [38, 42], "copi": [38, 42, 74, 87, 89, 90, 91, 94, 96, 98, 102, 105, 106], "doubl": [38, 40, 42], "dump_patch": [38, 40, 42], "eval": [38, 40, 42, 92, 102, 104], "dropout": [38, 42], "batchnorm": [38, 42], "grad": [38, 42], "extra_repr": [38, 40, 42], "line": [38, 42, 84, 90, 96, 97, 101, 104, 108], "get_buff": [38, 40, 42], "target": [38, 39, 42, 74, 75, 96, 104, 106], "throw": [38, 42], "get_submodul": [38, 40, 42], "explan": [38, 42], "qualifi": [38, 42], "referenc": [38, 42], "attributeerror": [38, 42], "invalid": [38, 42, 95], "resolv": [38, 42, 108], "get_extra_st": [38, 40, 42], "state_dict": [38, 40, 42], "set_extra_st": [38, 40, 42], "build": [38, 42, 52, 92, 96, 107], "picklabl": [38, 42], "serial": [38, 42], "backward": [38, 42, 92], "break": [38, 42, 92, 96, 103], "pickl": [38, 42, 103], "get_paramet": [38, 40, 42], "net_b": [38, 42], "net_c": [38, 42], "conv": [38, 42], "conv2d": [38, 42, 92], "16": [38, 42, 49, 52, 61, 78, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 107, 108], "kernel_s": [38, 42], "stride": [38, 42], "200": [38, 42, 72, 97, 103, 108], "diagram": [38, 42, 105], "degre": [38, 42], "queri": [38, 42, 52, 54, 91, 92, 96, 98, 102], "named_modul": [38, 40, 42], "o": [38, 42, 55, 56, 89, 90, 91, 97, 98, 99, 102, 103, 108], "transit": [38, 42], "ipu": [38, 40, 42], "load_state_dict": [38, 40, 42], "strict": [38, 42, 49], "persist": [38, 42], "strictli": [38, 42], "inplac": [38, 42, 96, 101], "preserv": [38, 42, 57], "namedtupl": [38, 42], "missing_kei": [38, 42], "unexpected_kei": [38, 42], "runtimeerror": [38, 42], "idx": [38, 42, 57, 58, 70, 90, 92, 96, 98, 99, 101, 103, 104], "named_buff": [38, 40, 42], "prefix": [38, 42, 89, 108], "remove_dupl": [38, 42], "prepend": [38, 42], "running_var": [38, 42], "named_children": [38, 40, 42], "conv4": [38, 42], "conv5": [38, 42], "memo": [38, 42], "named_paramet": [38, 40, 42], "register_backward_hook": [38, 40, 42], "deprec": [38, 42, 46], "favor": [38, 42], "register_full_backward_hook": [38, 40, 42], "removablehandl": [38, 42], "register_buff": [38, 40, 42], "running_mean": [38, 42], "register_forward_hook": [38, 40, 42], "with_kwarg": [38, 42], "always_cal": [38, 42], "possibli": [38, 42, 87, 94], "fire": [38, 42, 97], "register_module_forward_hook": [38, 42], "regardless": [38, 42, 90, 91], "register_forward_pre_hook": [38, 40, 42], "And": [38, 42], "forward_pr": [38, 42], "register_module_forward_pre_hook": [38, 42], "gradient": [38, 42, 92, 94, 106], "grad_input": [38, 42], "grad_output": [38, 42], "technic": [38, 42], "caller": [38, 42], "register_module_full_backward_hook": [38, 42], "register_full_backward_pre_hook": [38, 40, 42], "backward_pr": [38, 42], "register_module_full_backward_pre_hook": [38, 42], "register_load_state_dict_post_hook": [38, 40, 42], "post": [38, 42, 52], "incompatible_kei": [38, 42], "modif": [38, 42, 52], "thrown": [38, 42], "register_modul": [38, 40, 42], "register_paramet": [38, 40, 42], "register_state_dict_pre_hook": [38, 40, 42], "keep_var": [38, 42], "requires_grad_": [38, 40, 42], "autograd": [38, 42], "freez": [38, 42, 88, 89, 95], "finetun": [38, 42], "gan": [38, 42], "share_memori": [38, 40, 42], "share_memory_": [38, 42], "destin": [38, 42], "shallow": [38, 42], "releas": [38, 42, 61, 85, 98], "design": [38, 42, 52], "ordereddict": [38, 42], "detach": [38, 42, 92], "non_block": [38, 42], "memory_format": [38, 42], "channels_last": [38, 42], "Its": [38, 42, 49, 63, 69], "complex": [38, 42], "integr": [38, 42, 54, 84, 98], "asynchron": [38, 42], "host": [38, 42], "pin": [38, 42, 88, 95, 97], "desir": [38, 42, 52, 56, 70], "4d": [38, 42], "ignore_w": [38, 42], "determinist": [38, 42, 89], "1913": [38, 42], "3420": [38, 42], "5113": [38, 42], "2325": [38, 42], "env": [38, 42], "torch_doctest_cuda1": [38, 42], "gpu1": [38, 42], "1914": [38, 42], "5112": [38, 42], "2324": [38, 42], "float16": [38, 42], "cdoubl": [38, 42], "3741": [38, 42], "2382": [38, 42], "5593": [38, 42], "4443": [38, 42], "complex128": [38, 42], "6122": [38, 42], "1150": [38, 42], "to_empti": [38, 40, 42], "storag": [38, 42], "dst_type": [38, 42], "xpu": [38, 40, 42], "zero_grad": [38, 40, 42, 92], "set_to_non": [38, 42], "reset": [38, 42], "context": [38, 42, 103], "noisili": [39, 99], "han": 39, "2018": 39, "cifar_cnn": [39, 40], "loss_coteach": [39, 40], "y_1": 39, "y_2": 39, "forget_r": 39, "class_weight": 39, "logit": [39, 61, 92], "decim": [39, 57], "forget": [39, 49, 108], "rate_schedul": 39, "epoch": [39, 40, 42, 92, 98], "initialize_lr_schedul": [39, 40], "lr": [39, 40, 42], "001": [39, 72, 96, 98], "250": [39, 90, 91, 99, 103], "epoch_decay_start": 39, "schedul": 39, "beta": 39, "adam": 39, "adjust_learning_r": [39, 40], "alpha_plan": 39, "beta1_plan": 39, "forget_rate_schedul": [39, 40], "num_gradu": 39, "expon": 39, "tell": [39, 88, 92, 95, 99], "train_load": [39, 42], "model1": [39, 99], "optimizer1": 39, "model2": [39, 99], "optimizer2": 39, "dataload": [39, 92, 104], "parser": 39, "parse_arg": 39, "num_iter_per_epoch": 39, "print_freq": 39, "topk": 39, "top1": 39, "top5": 39, "test_load": 39, "offici": [40, 60, 96, 108], "wish": [40, 60, 104, 107, 108], "adj_confident_thresholds_shar": [40, 41], "labels_shar": [40, 41], "pred_probs_shar": [40, 41], "labelinspector": [40, 41, 98], "get_num_issu": [40, 41], "get_quality_scor": [40, 41], "update_confident_threshold": [40, 41], "score_label_qu": [40, 41], "split_arr": [40, 41], "span_classif": 40, "display_issu": [40, 43, 77, 78, 79, 80, 81, 82, 83, 107, 108], "mnist_pytorch": 40, "get_mnist_dataset": [40, 42], "get_sklearn_digits_dataset": [40, 42], "simplenet": [40, 42], "batch_siz": [40, 41, 42, 76, 78, 92, 98, 104, 107], "log_interv": [40, 42], "momentum": [40, 42], "no_cuda": [40, 42], "test_batch_s": [40, 42, 92], "loader": [40, 42, 92], "set_predict_proba_request": [40, 42], "set_predict_request": [40, 42], "coteach": [40, 85], "mini": [41, 76, 78, 98], "low_self_confid": [41, 44, 64], "self_confid": [41, 44, 45, 49, 64, 66, 72, 80, 82, 87, 88, 98, 99], "conveni": [41, 54, 87, 88, 89, 95], "script": 41, "labels_fil": [41, 98], "pred_probs_fil": [41, 98], "quality_score_kwarg": 41, "num_issue_kwarg": 41, "return_mask": 41, "variant": [41, 62, 107], "read": [41, 46, 91, 98, 99, 104, 108], "zarr": [41, 98], "memmap": [41, 107], "pythonspe": 41, "mmap": [41, 98], "hdf5": 41, "further": [41, 43, 63, 64, 66, 69, 70, 78, 79, 89, 98], "yourfil": 41, "npy": [41, 97, 98, 107], "mmap_mod": [41, 107], "tip": [41, 44, 61, 98], "save_arrai": 41, "your_arrai": 41, "disk": [41, 97, 98], "npz": [41, 108], "maxim": [41, 62, 76, 78, 107], "multiprocess": [41, 44, 64, 76, 78, 92, 98], "linux": [41, 76, 78], "physic": [41, 44, 76, 78, 103], "psutil": [41, 44, 76, 78], "labels_arrai": [41, 58], "predprob": 41, "pred_probs_arrai": 41, "back": [41, 52, 70, 90, 98, 103, 104], "store_result": 41, "becom": [41, 96, 104], "verifi": [41, 54, 98, 101, 104], "long": [41, 62, 71, 101], "enough": [41, 57, 96, 98], "chunk": [41, 105], "ram": [41, 97], "end_index": 41, "labels_batch": 41, "pred_probs_batch": 41, "batch_result": 41, "indices_of_examples_with_issu": [41, 98], "shortcut": 41, "encount": [41, 44, 76], "1000": [41, 89, 95, 98, 104], "aggreg": [41, 45, 49, 62, 66, 69, 72, 82, 98, 99, 101], "seen": [41, 98, 104, 108], "far": [41, 62], "label_quality_scor": [41, 66, 69, 72, 75, 99, 103], "method1": 41, "method2": 41, "normalized_margin": [41, 44, 45, 49, 64, 66, 72, 80, 82], "low_normalized_margin": [41, 44, 64], "issue_indic": [41, 69, 92], "update_num_issu": 41, "arr": [41, 98], "chunksiz": 41, "convnet": 42, "bespok": [42, 61], "download": [42, 89, 96, 98, 104], "mnist": [42, 84, 89, 97], "handwritten": 42, "digit": [42, 89, 97], "last": [42, 49, 67, 70, 90, 91, 98, 101, 103, 108], "sklearn_digits_test_s": 42, "01": [42, 72, 74, 89, 96, 99, 102, 103, 104], "templat": 42, "flexibli": 42, "among": [42, 62, 99], "test_set": 42, "overrid": 42, "train_idx": [42, 57, 104], "train_label": [42, 88, 104], "span": 43, "sentenc": [43, 56, 80, 82, 83, 88, 95], "token_classif": [43, 56, 80, 82, 83, 98], "encourag": [44, 64, 72, 75], "multilabel_classif": [44, 63, 64, 66, 72, 98, 102], "pred_probs_by_class": 44, "prune_count_matrix_col": 44, "rank_by_kwarg": [44, 64, 72, 99], "num_to_remove_per_class": [44, 64], "bad": [44, 52, 64, 69, 72, 95, 98], "seem": [44, 99, 102], "aren": 44, "confidence_weighted_entropi": [44, 45, 49, 64, 66, 72, 80, 82], "label_issues_idx": [44, 72], "entropi": [44, 46, 48, 49, 71, 72], "prune_by_class": [44, 64, 99], "predicted_neq_given": [44, 64, 99], "prune_counts_matrix": 44, "smallest": [44, 72], "unus": 44, "number_of_mislabeled_examples_in_class_k": 44, "delet": [44, 84, 88, 98], "too": [44, 49, 52, 71, 92, 98, 103], "thread": [44, 64], "window": [44, 97], "shorter": [44, 67], "find_predicted_neq_given": 44, "find_label_issues_using_argmax_confusion_matrix": 44, "remove_noise_from_class": [45, 57], "clip_noise_r": [45, 57], "clip_valu": [45, 57], "value_count": [45, 57, 98], "value_counts_fill_missing_class": [45, 57], "get_missing_class": [45, 57], "round_preserving_sum": [45, 57], "round_preserving_row_tot": [45, 57], "estimate_pu_f1": [45, 57], "confusion_matrix": [45, 57], "print_square_matrix": [45, 57], "print_noise_matrix": [45, 57, 99], "print_inverse_noise_matrix": [45, 57], "print_joint_matrix": [45, 57, 99], "compress_int_arrai": [45, 57], "train_val_split": [45, 57], "subset_x_i": [45, 57], "subset_label": [45, 57], "subset_data": [45, 57], "extract_indices_tf": [45, 57], "unshuffle_tensorflow_dataset": [45, 57], "is_torch_dataset": [45, 57], "is_tensorflow_dataset": [45, 57], "csr_vstack": [45, 57], "append_extra_datapoint": [45, 57], "get_num_class": [45, 57], "num_unique_class": [45, 57], "get_unique_class": [45, 57], "format_label": [45, 57], "smart_display_datafram": [45, 57], "force_two_dimens": [45, 57], "latent_algebra": [45, 85], "compute_ps_py_inv_noise_matrix": [45, 47], "compute_py_inv_noise_matrix": [45, 47], "compute_inv_noise_matrix": [45, 47], "compute_noise_matrix_from_invers": [45, 47], "compute_pi": [45, 47], "compute_pyx": [45, 47], "label_quality_util": 45, "get_normalized_entropi": [45, 46], "multilabel_util": [45, 102], "stack_compl": [45, 50], "get_onehot_num_class": [45, 50], "int2onehot": [45, 50, 102], "onehot2int": [45, 50, 102], "multilabel_scor": [45, 66], "classlabelscor": [45, 49], "exponential_moving_averag": [45, 49, 66], "softmin": [45, 49, 66, 69, 78, 82], "possible_method": [45, 49], "multilabelscor": [45, 49], "get_class_label_quality_scor": [45, 49], "multilabel_pi": [45, 49], "get_cross_validated_multilabel_pred_prob": [45, 49], "default_k": [45, 51, 52], "features_to_knn": [45, 51, 52], "construct_knn_graph_from_index": [45, 51, 52, 54], "create_knn_graph_and_index": [45, 51, 52], "correct_knn_graph": [45, 51, 52, 96], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [45, 51, 52], "correct_knn_distances_and_indic": [45, 51, 52], "high_dimension_cutoff": [45, 51, 53], "row_count_cutoff": [45, 51, 53], "decide_euclidean_metr": [45, 51, 53], "decide_default_metr": [45, 51, 53], "construct_knn": [45, 51, 54], "transform_distances_to_scor": [45, 55], "correct_precision_error": [45, 55], "token_classification_util": [45, 108], "get_sent": [45, 56, 108], "filter_sent": [45, 56, 108], "process_token": [45, 56], "merge_prob": [45, 56], "color_sent": [45, 56], "assert_valid_input": [45, 58], "assert_valid_class_label": [45, 58], "assert_nonempty_input": [45, 58], "assert_indexing_work": [45, 58], "labels_to_arrai": [45, 58], "labels_to_list_multilabel": [45, 58], "min_allowed_prob": 46, "wikipedia": 46, "activ": [46, 48, 61, 62, 84, 101], "towardsdatasci": 46, "cheatsheet": 46, "ec57bc067c0b": 46, "clip": [46, 57, 89, 96], "behav": 46, "unnecessari": [46, 98], "slightli": [46, 87, 88], "interv": [46, 49, 104], "herein": 47, "inexact": 47, "cours": 47, "propag": 47, "throughout": [47, 57, 74, 83, 89, 101, 107, 108], "increas": [47, 55, 69, 71, 72, 89, 90, 96, 98, 101, 102, 108], "dot": [47, 82, 98], "true_labels_class_count": 47, "pyx": 47, "multiannot": 48, "assert_valid_inputs_multiannot": 48, "labels_multiannot": [48, 62], "ensembl": [48, 49, 62, 72, 87, 94, 98, 102, 104, 106], "allow_single_label": 48, "annotator_id": 48, "assert_valid_pred_prob": 48, "pred_probs_unlabel": [48, 62], "format_multiannotator_label": [48, 62, 101], "formatted_label": [48, 57], "old": [48, 57, 85, 97], "check_consensus_label_class": 48, "consensus_label": [48, 62, 101], "consensus_method": [48, 62], "consensu": [48, 62, 84, 100, 108], "establish": [48, 61, 88, 106], "compute_soft_cross_entropi": 48, "soft": [48, 97], "find_best_temp_scal": 48, "coarse_search_rang": [48, 74, 98], "fine_search_s": [48, 74, 98], "temperatur": [48, 49, 69, 78, 82], "scale": [48, 55, 87, 96, 97, 98, 104, 107], "factor": [48, 49, 55, 76, 78], "minim": [48, 69, 104], "temp_scale_pred_prob": 48, "temp": 48, "sharpen": [48, 97], "smoothen": 48, "get_normalized_margin_for_each_label": [49, 72], "get_confidence_weighted_entropy_for_each_label": [49, 72], "scorer": 49, "alpha": [49, 66, 69, 90, 91, 96, 99, 102, 106], "exponenti": 49, "ema": 49, "s_1": 49, "s_k": 49, "ema_k": 49, "accord": [49, 64, 94, 95, 99, 108], "formula": [49, 55], "_t": 49, "cdot": 49, "s_t": 49, "qquad": 49, "leq": 49, "_1": 49, "recent": [49, 108], "success": 49, "previou": [49, 52, 92, 94, 98, 103], "discount": 49, "s_ema": 49, "175": [49, 92, 99, 103], "underflow": 49, "nan": [49, 62, 87, 94, 96, 101, 106], "aggregated_scor": 49, "base_scor": 49, "base_scorer_kwarg": 49, "aggregator_kwarg": [49, 66], "n_sampl": [49, 96], "n_label": 49, "worst": [49, 101], "class_label_quality_scor": 49, "452": 49, "new_scor": 49, "575": 49, "get_label_quality_scores_per_class": [49, 65, 66], "ml_scorer": 49, "binar": [49, 50], "reformat": [49, 89], "wider": 49, "splitter": 49, "kfold": [49, 92], "onevsrestclassifi": [49, 102], "randomforestclassifi": [49, 99, 102], "n_split": [49, 92, 102], "pred_prob_slic": 50, "onehot": 50, "hot": [50, 64, 70, 76, 79, 87, 94, 97, 98, 106, 107], "onehot_matrix": 50, "pairwis": [51, 53, 71], "reli": [52, 71, 88, 89, 90, 91, 95, 103, 104, 106], "sklearn_knn_kwarg": 52, "correction_featur": 52, "discourag": 52, "flexibl": [52, 98], "manner": [52, 66, 87, 88, 96, 101, 106], "701": 52, "900": [52, 87, 94, 106], "436": 52, "000": [52, 88, 92, 95, 96, 97, 108], "idea": [52, 72, 103], "dens": [52, 61, 96], "33140006": 52, "76210367": 52, "correct_exact_dupl": 52, "mutual": [52, 63, 102], "vari": [52, 69, 91], "exact_duplicate_set": 52, "main": [52, 62], "front": [52, 97], "consider": 52, "capabl": [52, 84], "come": [52, 57, 90, 91, 98, 107], "misidentif": 52, "corrected_dist": 52, "corrected_indic": 52, "sqrt": 52, "distant": 52, "suitabl": [53, 62, 87, 94, 96], "slower": 53, "decid": [53, 62, 88, 95, 97, 101, 106, 108], "predefin": 53, "met": [53, 108], "euclidean_dist": [53, 71], "spatial": [53, 71], "decis": [53, 87, 90, 91], "That": [53, 99, 102], "cosine_dist": 53, "knn_kwarg": 54, "html": [54, 57, 67, 70, 71, 89, 90, 91, 92, 94, 95, 98, 99], "kneighbor": 54, "metric_param": 54, "n_features_in_": 54, "effective_metric_params_": 54, "effective_metric_": 54, "n_samples_fit_": 54, "__sklearn_is_fitted__": 54, "conduct": 54, "is_fit": 54, "trail": 54, "underscor": 54, "avg_dist": 55, "exp": [55, 71, 72, 90], "dt": 55, "right": [55, 67, 70, 88, 95, 102, 103, 104], "strength": [55, 70, 96], "pronounc": 55, "differenti": 55, "ly": 55, "rule": [55, 56, 97], "thumb": 55, "ood_features_scor": [55, 71, 104], "88988177": 55, "80519832": 55, "toler": 55, "minkowski": 55, "noth": 55, "epsilon": 55, "sensibl": 55, "fixed_scor": 55, "readabl": 56, "lambda": [56, 89, 90, 98, 101], "long_sent": 56, "headlin": 56, "charact": [56, 57], "s1": 56, "s2": 56, "processed_token": 56, "alecnlcb": 56, "entiti": [56, 84, 98, 108], "mapped_ent": 56, "unique_ident": 56, "loc": [56, 90, 91, 92, 94, 96, 108], "nbitbas": [56, 66], "probs_merg": 56, "0125": [56, 82], "0375": 56, "075": 56, "025": 56, "color": [56, 79, 90, 91, 94, 96, 99, 102, 104, 106, 107], "red": [56, 70, 90, 91, 96, 97, 99, 102, 103, 104, 107], "colored_sent": 56, "termcolor": 56, "31msentenc": 56, "0m": 56, "ancillari": 57, "class_without_nois": 57, "any_other_class": 57, "choos": [57, 72, 87, 94, 98, 99, 106], "tradition": 57, "new_sum": 57, "fill": 57, "major": [57, 62, 85, 92, 104], "versu": [57, 99], "obviou": 57, "cgdeboer": 57, "iteround": 57, "reach": 57, "prob_s_eq_1": 57, "claesen": 57, "f1": [57, 70, 95, 99], "BE": 57, "left_nam": 57, "top_nam": 57, "titl": [57, 90, 91, 96, 99, 102, 104], "short_titl": 57, "round_plac": 57, "pretti": [57, 99], "joint_matrix": 57, "num_possible_valu": 57, "holdout_idx": 57, "extract": [57, 71, 88, 89, 94, 95, 96, 101, 104, 107], "allow_shuffl": 57, "turn": [57, 84, 103], "shuffledataset": 57, "histori": 57, "pre_x": 57, "buffer_s": 57, "csr_matric": 57, "append": [57, 89, 92, 96, 97, 98, 99, 101, 102, 103, 104, 108], "bottom": [57, 67, 70, 96, 103], "to_data": 57, "from_data": 57, "taken": 57, "label_matrix": 57, "canon": 57, "displai": [57, 70, 79, 83, 88, 89, 94, 95, 96, 99, 108], "jupyt": [57, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "notebook": [57, 62, 89, 91, 97, 98, 99, 101, 102, 103, 107, 108], "consol": 57, "allow_missing_class": 58, "allow_one_class": 58, "length_x": 58, "labellik": 58, "labels_list": [58, 64], "keraswrappermodel": [60, 61, 84], "keraswrappersequenti": [60, 61], "tf": [61, 89], "legaci": 61, "newer": 61, "interim": 61, "advis": [61, 102], "stabil": [61, 71], "until": 61, "accommod": 61, "keraswrapp": 61, "huggingface_keras_imdb": 61, "unit": [61, 108], "model_kwarg": [61, 74], "compile_kwarg": 61, "sparsecategoricalcrossentropi": 61, "layer": [61, 88, 89, 95, 104], "my_keras_model": 61, "from_logit": 61, "declar": 61, "apply_softmax": 61, "analysi": [62, 96], "analyz": [62, 84, 96, 99, 101, 102], "get_label_quality_multiannot": [62, 101], "vote": 62, "crowdsourc": [62, 84, 101], "dawid": [62, 101], "skene": [62, 101], "analog": [62, 97, 101], "chosen": [62, 72, 96, 98, 101], "crowdlab": [62, 101], "unlabel": [62, 92, 94, 95, 101, 104, 107], "get_active_learning_scor": [62, 101], "activelab": [62, 101], "priorit": [62, 69, 103, 107, 108], "showcas": 62, "best_qual": 62, "quality_method": 62, "calibrate_prob": 62, "return_detailed_qu": 62, "return_annotator_stat": 62, "return_weight": 62, "label_quality_score_kwarg": 62, "did": [62, 63, 87, 88, 89, 94, 99, 101, 106], "majority_vot": 62, "broken": [62, 70, 97, 106], "highest": [62, 70, 90, 92, 105], "0th": 62, "consensus_quality_scor": [62, 101], "annotator_agr": [62, 101], "reman": 62, "1st": 62, "2nd": [62, 76], "3rd": 62, "consensus_label_suffix": 62, "consensus_quality_score_suffix": 62, "suffix": 62, "emsembl": 62, "weigh": [62, 97], "agreement": [62, 101], "agre": 62, "prevent": [62, 98], "overconfid": [62, 105], "detailed_label_qu": [62, 101], "annotator_stat": [62, 101], "model_weight": 62, "annotator_weight": 62, "warn": 62, "labels_info": 62, "num_annot": [62, 101], "deriv": [62, 101], "quality_annotator_1": 62, "quality_annotator_2": 62, "quality_annotator_m": 62, "annotator_qu": [62, 101], "num_examples_label": [62, 101], "agreement_with_consensu": [62, 101], "worst_class": [62, 101], "trustworthi": [62, 101, 106], "get_label_quality_multiannotator_ensembl": 62, "weigtht": 62, "budget": 62, "retrain": [62, 88, 106], "active_learning_scor": 62, "active_learning_scores_unlabel": 62, "get_active_learning_scores_ensembl": 62, "henc": [62, 89, 90, 101], "get_majority_vote_label": [62, 101], "event": 62, "lastli": [62, 94], "convert_long_to_wide_dataset": 62, "labels_multiannotator_long": 62, "wide": [62, 87, 88, 89], "labels_multiannotator_wid": 62, "common_multilabel_issu": [63, 65], "exclus": [63, 102], "rank_classes_by_multilabel_qu": [63, 65], "overall_multilabel_health_scor": [63, 65], "multilabel_health_summari": [63, 65], "classes_by_multilabel_qu": 63, "inner": [64, 78, 96], "find_multilabel_issues_per_class": [64, 65], "per_class_label_issu": 64, "label_issues_list": 64, "pred_probs_list": [64, 72, 92, 99], "anim": [65, 104], "rat": 65, "predat": 65, "pet": 65, "reptil": 65, "box": [67, 69, 70, 97, 103], "object_detect": [67, 69, 70, 103], "return_indices_ranked_by_scor": [67, 103], "overlapping_label_check": [67, 69], "suboptim": [67, 69], "locat": [67, 69, 96, 103, 107, 108], "bbox": [67, 70, 103], "image_nam": [67, 70], "y1": [67, 70, 103], "y2": [67, 70, 103], "later": [67, 70, 71, 88, 108], "corner": [67, 70, 103], "xyxi": [67, 70, 103], "io": [67, 70, 89, 96, 97], "keras_cv": [67, 70], "bounding_box": [67, 70, 103], "detectron": [67, 70, 103], "detectron2": [67, 70, 103], "readthedoc": [67, 70], "en": [67, 70], "latest": [67, 70], "visual": [67, 68, 70, 87, 90, 91, 92, 106, 108], "draw_box": [67, 70], "mmdetect": [67, 70, 103], "swap": [67, 69, 79, 83], "penal": [67, 69], "concern": [67, 69, 84, 91], "issues_from_scor": [68, 69, 77, 78, 79, 81, 82, 83, 103, 107, 108], "compute_overlooked_box_scor": [68, 69], "compute_badloc_box_scor": [68, 69], "compute_swap_box_scor": [68, 69], "pool_box_scores_per_imag": [68, 69], "object_counts_per_imag": [68, 70, 103], "bounding_box_size_distribut": [68, 70, 103], "class_label_distribut": [68, 70, 103], "get_sorted_bbox_count_idx": [68, 70], "plot_class_size_distribut": [68, 70], "plot_class_distribut": [68, 70], "get_average_per_class_confusion_matrix": [68, 70], "calculate_per_class_metr": [68, 70], "aggregation_weight": 69, "imperfect": [69, 98], "chose": [69, 101, 103], "imperfectli": [69, 103], "dirti": [69, 72, 75, 106], "subtyp": 69, "badloc": 69, "nonneg": 69, "high_probability_threshold": 69, "auxiliary_input": [69, 70], "iou": [69, 70], "heavili": 69, "auxiliarytypesdict": 69, "pred_label": [69, 88], "pred_label_prob": 69, "pred_bbox": 69, "lab_label": 69, "lab_bbox": 69, "similarity_matrix": 69, "min_possible_similar": 69, "scores_overlook": 69, "low_probability_threshold": 69, "scores_badloc": 69, "accident": [69, 88, 94, 95, 98], "scores_swap": 69, "box_scor": 69, "image_scor": [69, 78, 107], "discov": [70, 91, 96, 108], "abnorm": [70, 92, 103], "auxiliari": [70, 104, 107], "_get_valid_inputs_for_compute_scor": 70, "object_count": 70, "down": 70, "bbox_siz": 70, "class_distribut": 70, "plot": [70, 90, 91, 96, 99, 102, 104, 106, 107], "sorted_idx": [70, 104], "class_to_show": 70, "hidden": [70, 104], "max_class_to_show": 70, "plt": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "matplotlib": [70, 79, 90, 91, 92, 96, 99, 102, 103, 104, 106], "pyplot": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "prediction_threshold": 70, "overlai": [70, 103], "figsiz": [70, 90, 91, 92, 96, 99, 102, 104], "save_path": [70, 103], "blue": [70, 97, 99, 103], "overlaid": 70, "side": [70, 97, 103], "figur": [70, 96, 99, 102, 104, 106], "extens": [70, 99, 101], "png": [70, 96, 103], "pdf": [70, 71], "svg": 70, "num_proc": [70, 92], "intersect": [70, 98], "tp": 70, "fp": 70, "ground": [70, 97, 99, 101, 106], "truth": [70, 99, 101, 106], "bias": [70, 96], "avg_metr": 70, "distionari": 70, "95": [70, 80, 82, 94, 97, 99, 106, 108], "per_class_metr": 70, "Of": 71, "find_top_issu": [71, 72, 104], "behind": [71, 99], "dist_metr": 71, "subtract": [71, 72], "renorm": [71, 72, 98], "least_confid": 71, "sum_": 71, "log": [71, 72, 85], "softmax": [71, 78, 82, 92], "literatur": 71, "gen": 71, "liu": 71, "lochman": 71, "zach": 71, "openaccess": 71, "thecvf": 71, "cvpr2023": 71, "liu_gen_pushing_the_limits_of_softmax": 71, "based_out": 71, "distribution_detection_cvpr_2023_pap": 71, "fit_scor": [71, 104], "ood_predictions_scor": 71, "pretrain": [71, 88, 89, 95, 104], "adjust_confident_threshold": 71, "probabilist": [71, 87, 89, 90, 91, 94, 95, 104, 105], "order_label_issu": [72, 85], "whichev": [72, 105], "argsort": [72, 88, 92, 95, 99, 103, 104, 106], "max_": 72, "get_label_quality_ensemble_scor": [72, 98, 99], "weight_ensemble_members_bi": 72, "custom_weight": 72, "log_loss_search_t_valu": 72, "0001": [72, 97], "scheme": 72, "log_loss_search": 72, "log_loss": [72, 95], "1e0": 72, "1e1": 72, "1e2": 72, "2e2": 72, "quality_scor": [72, 104], "forth": 72, "top_issue_indic": 72, "rank_bi": [72, 85], "weird": [72, 83], "minu": 72, "prob_label": 72, "max_prob_not_label": 72, "AND": [72, 95], "get_epistemic_uncertainti": [73, 74], "get_aleatoric_uncertainti": [73, 74], "corrupt": [74, 106], "linearregress": [74, 98, 106], "y_with_nois": 74, "n_boot": [74, 98], "include_aleatoric_uncertainti": [74, 98], "sole": [74, 87, 90, 101, 104], "bootstrap": [74, 98, 106], "resampl": [74, 89, 98], "epistem": [74, 98, 104, 106], "aleator": [74, 98, 106], "model_final_kwarg": 74, "coars": 74, "thorough": [74, 98], "fine": [74, 88, 89, 95, 104], "grain": 74, "grid": [74, 96], "varianc": [74, 99], "epistemic_uncertainti": 74, "residu": [74, 75, 98], "deviat": [74, 103, 106], "aleatoric_uncertainti": 74, "outr": 75, "contin": 75, "raw": [75, 84, 85, 91, 92, 97, 98, 101, 103, 104, 106], "aka": [75, 89, 99, 103, 106, 108], "00323821": 75, "33692597": 75, "00191686": 75, "semant": [76, 78, 79, 100], "pixel": [76, 78, 79, 92, 104, 107], "h": [76, 78, 79, 107], "height": [76, 78, 79, 107], "w": [76, 78, 79, 107], "width": [76, 78, 79, 107], "labels_one_hot": [76, 79, 107], "stream": [76, 104, 108], "downsampl": [76, 78, 107], "shrink": [76, 78], "divis": [76, 78, 90], "common_label_issu": [77, 79, 81, 83, 107, 108], "filter_by_class": [77, 79, 107], "segmant": [78, 79], "num_pixel_issu": [78, 107], "product": [78, 92, 96, 98], "pixel_scor": [78, 107], "enter": 79, "legend": [79, 90, 91, 96, 102, 103, 106, 107], "colormap": 79, "background": [79, 96], "person": [79, 98, 103, 107, 108], "ambigu": [79, 83, 88, 89, 95, 97, 99, 108], "systemat": [79, 83, 101], "misunderstood": [79, 83], "issues_df": [79, 92], "class_index": 79, "issues_subset": [79, 83], "filter_by_token": [81, 83, 108], "token_score_method": 82, "sentence_score_method": 82, "sentence_score_kwarg": 82, "compris": [82, 83], "token_scor": [82, 108], "converg": 82, "toward": [82, 96], "_softmin_sentence_scor": 82, "sentence_scor": [82, 108], "token_info": 82, "02": [82, 90, 91, 96, 99, 103, 108], "03": [82, 94, 96, 97, 99, 103, 108], "04": [82, 94, 96, 103], "08": [82, 96, 99, 103, 106, 108], "commonli": [83, 85, 90, 91, 102, 108], "But": [83, 95, 99, 106, 108], "restrict": [83, 98], "reliabl": [84, 87, 89, 96, 98, 101, 107], "thousand": 84, "imagenet": [84, 97], "popular": [84, 101, 103], "centric": [84, 92, 94, 95, 100], "minut": [84, 87, 88, 89, 94, 95, 97, 101, 102, 103, 106, 107, 108], "conda": 84, "feature_embed": [84, 104], "Then": [84, 87, 88, 92, 98], "your_dataset": [84, 89, 90, 91, 92, 94, 95, 98], "column_name_of_label": [84, 89, 90, 91, 92, 94, 95], "plagu": [84, 91], "untrain": 84, "\u30c4": 84, "label_issues_info": [84, 91], "sklearn_compatible_model": 84, "framework": [84, 102, 103], "complianc": 84, "tag": [84, 102, 108], "sequenc": 84, "recognit": [84, 89, 98, 108], "train_data": [84, 87, 88, 104, 106], "gotten": 84, "test_data": [84, 87, 88, 99, 102, 104, 106], "deal": [84, 91, 96], "tutori": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "feel": [84, 89, 91, 98], "ask": [84, 98], "slack": [84, 98], "project": [84, 106], "welcom": 84, "commun": [84, 98], "guidelin": [84, 103], "piec": 84, "smart": [84, 92, 94, 95, 98], "edit": [84, 98], "easier": [84, 96, 99], "unreli": [84, 87, 89, 94, 95, 96], "link": [84, 89, 97, 103], "older": 85, "outlin": 85, "substitut": 85, "v2": [85, 87, 94], "get_noise_indic": 85, "psx": 85, "sorted_index_method": 85, "order_label_error": 85, "label_errors_bool": 85, "latent_estim": 85, "num_label_error": 85, "learningwithnoisylabel": 85, "neatli": 85, "organ": [85, 87, 94, 97, 108], "reorgan": 85, "baseline_method": 85, "incorpor": [85, 99], "research": [85, 99], "polyplex": 85, "terminologi": 85, "label_error": 85, "quickstart": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 101, 102, 103, 104, 106, 107, 108], "sql": [87, 94], "databas": [87, 94], "excel": [87, 94], "parquet": [87, 94], "student": [87, 94, 106, 108], "grade": [87, 94, 106], "exam": [87, 94, 106], "letter": [87, 94, 108], "hundr": [87, 94], "mistak": [87, 88, 92, 94, 95], "extratreesclassifi": 87, "extratre": 87, "ranked_label_issu": [87, 88], "branch": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "preprocess": [87, 88, 91, 94, 96, 104, 106], "standardscal": [87, 94, 104], "labelencod": [87, 88], "train_test_split": [87, 88, 90, 91, 104], "accuracy_scor": [87, 88, 89, 95, 99], "grades_data": [87, 94], "read_csv": [87, 88, 94, 95, 96, 106], "demo": [87, 91, 94, 102], "stud_id": [87, 94], "exam_1": [87, 94, 106], "exam_2": [87, 94, 106], "exam_3": [87, 94, 106], "letter_grad": [87, 94], "f48f73": [87, 94], "53": [87, 90, 91, 94, 96, 97, 102, 103], "00": [87, 90, 91, 94, 96, 97, 104], "77": [87, 90, 91, 94, 103], "0bd4e7": [87, 94], "81": [87, 94, 95, 103, 106, 108], "great": [87, 94, 97], "particip": [87, 94], "cb9d7a": [87, 94], "61": [87, 94, 96, 99, 103, 106], "94": [87, 94, 97, 99, 103, 106], "9acca4": [87, 94], "48": [87, 94, 96, 97, 99, 103], "x_raw": [87, 94], "labels_raw": 87, "interg": [87, 88], "categorical_featur": [87, 106], "x_encod": [87, 94], "get_dummi": [87, 94, 106], "drop_first": [87, 94], "numeric_featur": [87, 94], "scaler": [87, 94, 104], "x_process": [87, 94], "fit_transform": [87, 94, 96], "bring": [87, 88, 92, 94, 95, 101, 106], "byod": [87, 88, 92, 94, 95, 101, 106], "tress": 87, "held": [87, 89, 94, 95, 97, 103, 104, 105], "straightforward": [87, 89, 94], "benefit": [87, 89, 105, 107], "num_crossval_fold": [87, 89, 94, 101], "tabl": [87, 94, 97, 101], "212": [87, 99], "review": [87, 88, 91, 94, 95, 97, 98, 99, 103, 106, 107, 108], "iloc": [87, 88, 89, 94, 95, 96, 106], "92": [87, 90, 99, 103, 108], "93": [87, 97, 103, 106, 108], "827": 87, "99": [87, 96, 97, 99], "86": [87, 91, 92, 94, 99, 103, 106], "74": [87, 96, 103, 106], "637": [87, 94], "79": [87, 97, 103], "65": [87, 90, 96, 103], "cheat": 87, "0pt": 87, "120": [87, 90, 91], "233": 87, "83": [87, 99, 103, 106, 108], "76": [87, 99, 102, 103, 106], "suspici": [87, 94], "carefulli": [87, 92, 94, 95], "examin": [87, 90, 91, 94, 96, 103], "labels_train": 87, "labels_test": 87, "test_siz": [87, 88, 90, 91], "acc_og": [87, 88], "783068783068783": 87, "robustli": [87, 88, 106], "14": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "acc_cl": [87, 88], "8095238095238095": 87, "blindli": [87, 88, 89, 98, 106], "trust": [87, 88, 89, 98, 99, 101, 105, 106], "effort": [87, 88, 106], "intent": [88, 95], "servic": [88, 95, 98], "onlin": [88, 95], "bank": [88, 95, 97], "banking77": [88, 95], "oo": [88, 95], "categori": [88, 92, 95, 96], "shortlist": [88, 95, 106], "scope": [88, 95], "logist": [88, 90, 91, 95, 101, 104], "probabilit": [88, 89], "drop": [88, 94, 96, 98, 101, 106], "earlier": [88, 108], "sentence_transform": [88, 95], "sentencetransform": [88, 95], "payment": [88, 95], "cancel_transf": [88, 95], "transfer": [88, 95], "fund": [88, 95], "cancel": [88, 95], "transact": [88, 95], "my": [88, 95], "revert": [88, 95], "morn": [88, 95], "realis": [88, 95], "yesterdai": [88, 95], "rent": [88, 95], "tomorrow": [88, 95], "raw_text": [88, 95], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "apple_pay_or_google_pai": [88, 95], "getting_spare_card": [88, 95], "card_payment_fee_charg": [88, 95], "beneficiary_not_allow": [88, 95], "card_about_to_expir": [88, 95], "lost_or_stolen_phon": [88, 95], "visa_or_mastercard": [88, 95], "supported_cards_and_curr": [88, 95], "change_pin": [88, 95], "card": [88, 95, 97], "utter": [88, 95], "encond": 88, "test_label": [88, 99, 102, 104], "suit": [88, 95, 96, 97, 98], "electra": [88, 95], "discrimin": [88, 95], "googl": [88, 95], "train_text": 88, "test_text": 88, "home": [88, 95, 97], "runner": [88, 95], "google_electra": [88, 95], "pool": [88, 95, 98, 104], "leverag": [88, 89, 95, 98, 99, 101], "computation": [88, 89, 95], "intens": [88, 89, 95], "400": [88, 95], "858371": 88, "547274": 88, "826228": 88, "966008": 88, "792449": 88, "identified_issu": [88, 106], "lowest_quality_label": [88, 89, 95, 99, 106], "to_numpi": [88, 95, 96, 106], "44": [88, 96, 97, 102, 103], "646": 88, "390": 88, "628": 88, "121": [88, 99], "702": 88, "863": 88, "135": 88, "337": [88, 103], "735": 88, "print_as_df": 88, "inverse_transform": 88, "charg": [88, 95], "cash": [88, 95], "holidai": [88, 95], "sent": [88, 95, 108], "mine": [88, 95], "expir": [88, 95], "fight": 88, "hors": [88, 97, 104], "duck": [88, 97], "me": [88, 95, 96], "whoever": [88, 95], "consum": [88, 106], "18": [88, 89, 95, 96, 97, 98, 99, 103, 104, 106, 107], "baseline_model": [88, 106], "87": [88, 91, 92, 103, 106], "acceler": [88, 106], "19": [88, 89, 92, 95, 96, 97, 98, 99, 103, 104, 106, 107, 108], "89": [88, 90, 94, 103, 106], "spoken": 89, "500": [89, 96, 104, 108], "english": [89, 97], "pronunci": 89, "wav": 89, "huggingfac": [89, 90, 91, 92, 98], "voxceleb": 89, "speech": [89, 108], "your_pred_prob": [89, 90, 91, 94, 95], "tensorflow_io": 89, "huggingface_hub": 89, "reproduc": [89, 94, 96, 99, 101], "command": 89, "wget": [89, 103, 107, 108], "navig": 89, "browser": 89, "jakobovski": 89, "archiv": [89, 108], "v1": 89, "tar": [89, 96, 104], "gz": [89, 96, 104], "mkdir": [89, 108], "spoken_digit": 89, "xf": 89, "6_nicolas_32": 89, "data_path": 89, "listdir": 89, "nondeterminist": 89, "file_nam": 89, "endswith": 89, "file_path": 89, "join": [89, 92, 96, 98], "7_george_26": 89, "0_nicolas_24": 89, "0_nicolas_6": 89, "listen": 89, "display_exampl": 89, "expand": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "pulldown": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "colab": [89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "tfio": 89, "pathlib": 89, "ipython": [89, 96], "load_wav_16k_mono": 89, "filenam": 89, "khz": 89, "file_cont": 89, "read_fil": 89, "sample_r": 89, "decode_wav": 89, "desired_channel": 89, "squeez": 89, "rate_in": 89, "rate_out": 89, "16000": 89, "wav_file_nam": 89, "audio_r": 89, "wav_file_exampl": 89, "plai": [89, 97, 98], "button": 89, "wav_file_name_exampl": 89, "7_jackson_43": 89, "hear": 89, "extractor": 89, "encoderclassifi": 89, "spkrec": 89, "xvect": 89, "feature_extractor": 89, "from_hparam": 89, "run_opt": 89, "uncom": [89, 96], "ffmpeg": 89, "backend": 89, "wav_audio_file_path": 89, "torchaudio": 89, "extract_audio_embed": 89, "emb": [89, 92], "signal": 89, "encode_batch": 89, "embeddings_list": [89, 92], "embeddings_arrai": 89, "512": [89, 92], "196311": 89, "319459": 89, "478975": 89, "2890875": 89, "8170238": 89, "89265": 89, "898056": 89, "256195": 89, "559641": 89, "559721": 89, "62067": 89, "285245": 89, "21": [89, 90, 96, 97, 99, 103, 106, 108], "709627": 89, "5033693": 89, "913803": 89, "819831": 89, "1831515": 89, "208763": 89, "084257": 89, "3210397": 89, "005453": 89, "216152": 89, "478235": 89, "6821785": 89, "053807": 89, "242471": 89, "091424": 89, "78334856": 89, "03954": 89, "23": [89, 92, 96, 97, 99, 103, 106, 108], "569176": 89, "761097": 89, "1258295": 89, "753237": 89, "3508866": 89, "598274": 89, "23712": 89, "2500": 89, "tol": 89, "decreas": [89, 96, 98], "cv_accuraci": 89, "9708": 89, "issue_type_descript": [89, 90, 91, 92, 94, 95, 99], "lt": [89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 104], "gt": [89, 90, 91, 92, 94, 95, 96, 99, 101, 108], "9976": 89, "986": 89, "002161": 89, "176": [89, 97, 99, 102], "002483": 89, "2318": 89, "004411": 89, "1005": 89, "004857": 89, "1871": 89, "007494": 89, "040587": 89, "999207": 89, "999377": 89, "975220": 89, "999367": 89, "identified_label_issu": [89, 95], "516": 89, "1946": 89, "469": 89, "2132": 89, "worth": [89, 99], "6_yweweler_25": 89, "7_nicolas_43": 89, "6_theo_27": 89, "6_yweweler_36": 89, "6_yweweler_14": 89, "6_yweweler_35": 89, "6_nicolas_8": 89, "sound": 89, "quit": [89, 104], "underneath": 90, "hood": [90, 96, 98], "alert": 90, "introduct": 90, "mayb": [90, 91, 95], "your_feature_matrix": [90, 91], "toi": [90, 91, 92, 96, 97, 99, 101], "inf": [90, 91], "mid": [90, 91], "bins_map": [90, 91], "create_data": [90, 91], "y_bin": [90, 91], "y_i": [90, 91], "y_bin_idx": [90, 91], "y_train": [90, 91, 99, 106], "y_test": [90, 91, 99, 106], "y_train_idx": [90, 91], "y_test_idx": [90, 91], "slide": [90, 91, 97], "frame": [90, 91], "x_out": [90, 91], "tini": [90, 91], "concaten": [90, 91, 105], "y_out": [90, 91], "y_out_bin": [90, 91], "y_out_bin_idx": [90, 91], "exact_duplicate_idx": [90, 91], "x_duplic": [90, 91], "y_duplic": [90, 91], "y_duplicate_idx": [90, 91], "noisy_labels_idx": [90, 91, 102], "scatter": [90, 91, 96, 99, 102, 106], "black": [90, 91, 97, 106], "cyan": [90, 91], "plot_data": [90, 91, 96, 99, 102, 106], "fig": [90, 91, 92, 96, 97, 104, 106], "ax": [90, 91, 92, 96, 104, 106], "subplot": [90, 91, 92, 96, 104], "set_titl": [90, 91, 92, 96, 104], "set_xlabel": [90, 91], "x_1": [90, 91], "fontsiz": [90, 91, 92, 96, 99, 102], "set_ylabel": [90, 91], "x_2": [90, 91], "set_xlim": [90, 91], "set_ylim": [90, 91], "linestyl": [90, 91, 96], "circl": [90, 91, 99, 102], "misclassifi": [90, 91], "zip": [90, 91, 92, 96, 103, 108], "label_err": [90, 91], "180": [90, 91, 103], "marker": [90, 91], "facecolor": [90, 91, 96], "edgecolor": [90, 91, 96], "linewidth": [90, 91, 96, 104], "dup": [90, 91], "first_legend": [90, 91], "align": [90, 91], "title_fontproperti": [90, 91], "semibold": [90, 91], "second_legend": [90, 91], "45": [90, 91, 96, 97, 99, 103], "gca": [90, 91], "add_artist": [90, 91], "tight_layout": [90, 91, 96], "ideal": [90, 91], "remaind": 90, "modal": [90, 91, 98, 101], "132": [90, 91, 99, 103], "9318": 90, "006940": 90, "007830": 90, "40": [90, 91, 95, 96, 97], "014828": 90, "107": [90, 91, 99, 102], "021241": 90, "026407": 90, "notic": [90, 99, 101, 103], "3558": [90, 91], "126": [90, 91, 99, 103], "006636": [90, 91], "130": [90, 91], "012571": [90, 91], "129": [90, 91], "127": [90, 91], "014909": [90, 91], "128": [90, 91, 92], "017443": [90, 91], "6160": [90, 91], "131": [90, 91, 107, 108], "000000e": [90, 91], "000002": [90, 91], "463180e": [90, 91], "07": [90, 91, 92, 94, 96, 99, 103, 106, 108], "51": [90, 91, 94, 96, 97, 99, 103], "161148": [90, 91], "859087e": [90, 91], "30": [90, 91, 92, 96, 97, 98, 102, 107, 108], "3453": 90, "029542": 90, "031182": 90, "057961": 90, "058244": 90, "54": [90, 96, 97, 99, 103], "039122": 90, "044598": 90, "105": [90, 103], "105196": 90, "133654": 90, "43": [90, 96, 97, 99, 103], "168033": 90, "125": 90, "101107": 90, "183382": 90, "109": [90, 97, 103], "209259": 90, "211042": 90, "221316": 90, "average_ood_scor": 90, "34530442089193386": 90, "52": [90, 96, 97, 103, 108], "169820": 90, "087324e": 90, "259024": 90, "583757e": 90, "91": [90, 103], "346458": 90, "341292e": 90, "specfi": 90, "new_lab": 90, "scoring_funct": 90, "div": 90, "rem": 90, "inv_scal": 90, "49": [90, 96, 97, 99, 103], "superstitionissuemanag": 90, "unlucki": 90, "superstit": 90, "to_seri": 90, "issues_mask": 90, "summary_scor": 90, "9242": 90, "is_superstition_issu": 90, "superstition_scor": 90, "26": [90, 92, 96, 97, 99, 101, 103], "047581": 90, "090635": 90, "129591": 90, "164840": 90, "lurk": [91, 92, 99], "thoroughli": 91, "8561": 91, "001908": 91, "003564": 91, "007331": 91, "008963": 91, "009664": 91, "0227": 91, "022727": 91, "conceptu": 91, "856061": 91, "355772": 91, "616034": 91, "821750": 91, "901562": 91, "betweeen": 91, "859131": 91, "417707": 91, "664083": 91, "970324": 91, "816953": 91, "375317": 91, "641516": 91, "890575": 91, "531021": 91, "460593": 91, "601188": 91, "826147": 91, "752808": 91, "321635": 91, "562539": 91, "948362": 91, "090243": 91, "472909": 91, "746763": 91, "878267": 91, "examples_w_issu": [91, 98], "013445": 91, "025184": 91, "026376": 91, "inde": [91, 95], "miscellan": [91, 93, 108], "428571": 91, "111111": 91, "571429": 91, "407407": 91, "592593": 91, "337838": 91, "092593": 91, "662162": 91, "333333": [91, 97], "952381": 91, "666667": [91, 96], "portion": 91, "huge": [91, 99], "worri": [91, 95], "critic": 91, "60": [92, 96, 99, 106], "torchvis": [92, 96, 104], "tensordataset": 92, "stratifiedkfold": [92, 102], "tqdm": 92, "autonotebook": 92, "math": 92, "fashion_mnist": 92, "num_row": [92, 96], "60000": 92, "transformed_dataset": [92, 96], "with_format": 92, "255": [92, 97], "cpu_count": 92, "torch_dataset": 92, "quick": [92, 102, 104], "super": [92, 94, 95], "relu": 92, "batchnorm2d": 92, "maxpool2d": 92, "lazylinear": 92, "flatten": [92, 96], "get_test_accuraci": 92, "testload": [92, 104], "energi": 92, "trainload": [92, 104], "n_epoch": 92, "patienc": 92, "criterion": 92, "crossentropyloss": 92, "adamw": 92, "best_test_accuraci": 92, "start_epoch": 92, "running_loss": 92, "best_epoch": 92, "end_epoch": 92, "3f": [92, 106], "acc": [92, 99], "time_taken": 92, "compute_embed": 92, "compute_pred_prob": 92, "train_batch_s": 92, "num_work": 92, "worker": [92, 108], "train_id_list": 92, "test_id_list": 92, "train_id": 92, "test_id": 92, "embeddings_model": 92, "ntrain": 92, "trainset": 92, "testset": 92, "pin_memori": 92, "fold_embed": 92, "fold_pred_prob": 92, "finish": 92, "482": 92, "720": 92, "690": 92, "329": [92, 94, 103], "88": [92, 97, 99, 102, 103, 106], "195": 92, "414": 92, "493": 92, "060": 92, "642": 92, "330": [92, 103], "505": 92, "471": [92, 108], "476": 92, "340": 92, "668": 92, "328": [92, 103], "310": 92, "531": 92, "reorder": 92, "hstack": [92, 98, 99, 101], "vision": 92, "grayscal": [92, 96], "max_preval": [92, 96], "7714": 92, "3772": 92, "3585": 92, "166": 92, "3651": 92, "27080": 92, "873833e": 92, "40378": 92, "915575e": 92, "25316": 92, "390277e": 92, "06": [92, 99, 103, 108], "2090": 92, "751164e": 92, "14999": 92, "881301e": 92, "9569": 92, "11262": 92, "000003": 92, "coat": [92, 97], "shirt": [92, 97], "19228": 92, "000010": 92, "dress": 92, "32657": 92, "000013": 92, "bag": [92, 97, 104, 105], "21282": 92, "000016": 92, "53564": 92, "000018": 92, "pullov": 92, "6321": 92, "30968": 92, "001267": 92, "30659": 92, "000022": [92, 108], "47824": 92, "001454": 92, "3370": 92, "000026": 92, "54565": 92, "001854": 92, "9762": 92, "258": 92, "47139": 92, "000033": 92, "166980": 92, "986195": 92, "997205": 92, "sandal": [92, 97], "948781": 92, "999358": 92, "54078": 92, "17371": 92, "000025": 92, "plot_label_issue_exampl": 92, "ncol": [92, 104], "nrow": [92, 104], "ceil": 92, "axes_list": 92, "label_issue_indic": 92, "gl": 92, "sl": 92, "fontdict": 92, "imshow": [92, 96, 104], "cmap": [92, 96, 106], "grai": 92, "subplots_adjust": 92, "hspace": 92, "outsiz": 92, "outlier_issu": [92, 95], "outlier_issues_df": 92, "depict": [92, 102, 103, 104, 105, 107], "plot_outlier_issues_exampl": 92, "n_comparison_imag": 92, "sample_from_class": 92, "number_of_sampl": 92, "non_outlier_indic": 92, "isnul": [92, 96], "non_outlier_indices_excluding_curr": 92, "sampled_indic": 92, "label_scores_of_sampl": 92, "top_score_indic": 92, "top_label_indic": 92, "sampled_imag": 92, "get_image_given_label_and_sampl": 92, "image_from_dataset": 92, "corresponding_label": 92, "comparison_imag": 92, "images_to_plot": 92, "idlist": 92, "iterrow": 92, "near_duplicate_issu": [92, 98], "closest": 92, "counterpart": 92, "near_duplicate_issues_df": 92, "plot_near_duplicate_issue_exampl": 92, "seen_id_pair": 92, "get_image_and_given_label_and_predicted_label": 92, "duplicate_imag": 92, "nd_set": 92, "challeng": 92, "dark_issu": 92, "reveal": [92, 103, 107], "dark_scor": [92, 96], "dark_issues_df": 92, "is_dark_issu": 92, "34848": 92, "203922": 92, "50270": 92, "204588": 92, "3936": 92, "213098": 92, "733": 92, "217686": 92, "8094": 92, "230118": 92, "plot_image_issue_exampl": 92, "difficult": 92, "disproportion": [92, 96], "lowinfo_issu": 92, "low_information_scor": [92, 96], "lowinfo_issues_df": 92, "is_low_information_issu": 92, "53050": 92, "067975": 92, "40875": 92, "089929": 92, "9594": 92, "092601": 92, "34825": 92, "107744": 92, "37530": 92, "108516": 92, "lot": 92, "workflow": [93, 98, 100, 106], "histgradientboostingclassifi": 94, "cat_featur": 94, "boost": [94, 98, 101, 106], "xgboost": [94, 98, 106], "think": [94, 95, 98, 102, 107, 108], "nonzero": 94, "358": 94, "941": 94, "294": [94, 103], "46": [94, 96, 97, 99, 103], "7109": 94, "000005": [94, 95], "886": 94, "000059": 94, "709": 94, "000104": 94, "723": 94, "000169": 94, "689": 94, "000181": 94, "3590": 94, "051882e": 94, "683133e": 94, "536582e": 94, "406589e": 94, "324246e": 94, "6165": 94, "582": 94, "185": [94, 96, 97, 103, 108], "187": [94, 97], "898": 94, "0000": [94, 95, 97, 99], "865": 94, "515002": 94, "837": 94, "556480": 94, "622": 94, "593068": 94, "593207": 94, "920": 94, "618041": 94, "4386345844794593e": 94, "issue_result": 94, "000842": 94, "555944": 94, "004374": 94, "sorted_issu": 94, "73": [94, 96, 97, 102, 103, 106], "deserv": 94, "outlier_result": 94, "sorted_outli": 94, "56": [94, 96, 97, 106], "96": [94, 96, 97, 99, 102, 103, 106], "style": [94, 96, 107], "font": 94, "18px": 94, "ff00ff": 94, "bac": 94, "unintend": [94, 95, 96], "duplicate_result": 94, "lowest_scoring_dupl": 94, "idxmin": [94, 98], "indices_to_displai": 94, "tolist": [94, 98, 102], "perhap": [94, 99, 101], "second_lowest_scoring_dupl": 94, "next_indices_to_displai": 94, "wari": [94, 95, 98], "dive": [95, 96], "your_featur": 95, "text_embed": 95, "data_dict": [95, 99, 101], "85": [95, 103], "38": [95, 96, 97, 103], "9710": 95, "981": 95, "974": 95, "000146": 95, "982": [95, 97], "000224": 95, "971": 95, "000507": 95, "980": [95, 97], "000960": 95, "3584": 95, "994": 95, "009642": 95, "999": 95, "013067": 95, "013841": 95, "433": 95, "014722": 95, "989": 95, "018224": 95, "6070": 95, "160": [95, 106], "095724": 95, "148": 95, "006237": 95, "546": 95, "099341": 95, "514": 95, "006485": 95, "481": 95, "123418": 95, "008165": 95, "313": [95, 103], "564102": 95, "572258": 95, "574915": 95, "31": [95, 96, 97, 99, 101, 103], "575507": 95, "575874": 95, "792090": 95, "257611": 95, "698710": 95, "182121": 95, "771619": 95, "data_with_suggested_label": 95, "suggested_label": 95, "withdraw": 95, "monei": 95, "lowest_quality_outli": 95, "OR": 95, "636c65616e6c616220697320617765736f6d6521": 95, "phone": [95, 97], "gone": 95, "samp": 95, "br": 95, "press": [95, 108], "nonsens": 95, "sens": 95, "detriment": 95, "duplicate_issu": 95, "fee": 95, "go": [95, 96, 97, 99], "strongli": [95, 96], "p_valu": 95, "benign": 95, "curat": 95, "bigger": 96, "make_classif": 96, "5000": [96, 104], "n_featur": 96, "n_inform": 96, "n_redund": 96, "n_repeat": 96, "n_class": 96, "n_clusters_per_class": 96, "flip_i": 96, "class_sep": 96, "faiss": 96, "x_faiss": 96, "float32": [96, 103], "normalize_l2": 96, "index_factori": 96, "hnsw32": 96, "flat": [96, 97], "metric_inner_product": 96, "a_min": 96, "a_max": 96, "create_knn_graph": 96, "assert": 96, "indices_1d": 96, "ravel": 96, "distances_1d": 96, "sort_graph_by_row_valu": 96, "warn_when_not_sort": 96, "50000": 96, "523": 96, "991400": 96, "356958": 96, "362": 96, "619565": 96, "108": [96, 103], "500000": 96, "651929": 96, "999827": 96, "031217": 96, "933716": 96, "627345": 96, "998540": 96, "530909": 96, "296974": 96, "646765": 96, "942721": 96, "332824": 96, "803246": 96, "625202": 96, "999816": 96, "474031": 96, "706253": 96, "655108": 96, "997703": 96, "131466": 96, "912389": 96, "639200": 96, "4995": 96, "998646": 96, "504755": 96, "746777": 96, "680033": 96, "4996": 96, "894230": 96, "340986": 96, "816472": 96, "640711": 96, "4997": 96, "999100": 96, "428545": 96, "592421": 96, "658949": 96, "4998": 96, "986792": 96, "273710": 96, "618033": 96, "4999": 96, "986776": 96, "273524": 96, "618084": 96, "instabl": 96, "proxim": 96, "analys": 96, "comfort": 96, "explor": [96, 103, 104], "third": 96, "parti": [96, 108], "newsgroup": 96, "alt": [96, 97], "atheism": [96, 97], "sci": [96, 97], "fetch_20newsgroup": 96, "newsgroups_train": 96, "header": 96, "footer": 96, "quot": 96, "df_text": 96, "target_nam": 96, "enlighten": 96, "omnipot": 96, "19apr199320262420": 96, "kelvin": 96, "jpl": 96, "nasa": 96, "gov": 96, "baa": 96, "nhenri": 96, "he": 96, "nno": 96, "ge": 96, "nlucki": 96, "babi": [96, 97], "tfidfvector": 96, "feature_extract": 96, "x_vector": 96, "data_valuation_issu": 96, "147": [96, 99, 103], "500047": 96, "500093": 96, "499953": 96, "1068": 96, "1069": 96, "1070": 96, "1071": 96, "1072": 96, "1073": 96, "concentr": 96, "seaborn": 96, "sn": 96, "distinguish": 96, "strip": 96, "stripplot": 96, "hue": [96, 106], "dodg": 96, "jitter": 96, "axvlin": [96, 104], "xlabel": 96, "ourselv": 96, "make_blob": 96, "center": [96, 97], "cluster_std": 96, "n_noisy_label": 96, "meaning": [96, 98, 104], "silhouette_scor": 96, "gridsearchcv": 96, "silhouett": 96, "cluster_label": 96, "fit_predict": 96, "param_grid": 96, "grid_search": 96, "best_kmean": 96, "best_estimator_": 96, "underperforming_group_issu": 96, "328308": 96, "tab10": 96, "domain": 96, "knowledg": [96, 99], "dataset_tsv": 96, "ag": [96, 106], "gender": 96, "educ": 96, "experi": 96, "highsalari": 96, "indiana": 96, "phd": 96, "male": 96, "bachelor": 96, "femal": 96, "kansa": 96, "school": [96, 97], "ohio": 96, "57": [96, 97, 99], "california": 96, "59": [96, 97, 103], "34": [96, 97, 99, 101, 103, 108], "63": [96, 99, 103, 106], "47": [96, 97, 103], "stringio": 96, "sep": [96, 108], "simplic": [96, 102], "ordinalencod": 96, "columns_to_encod": 96, "encoded_df": 96, "salari": 96, "573681": 96, "underpin": 96, "caught": 96, "whenev": 96, "generate_data_depend": 96, "num_sampl": 96, "a1": 96, "a2": 96, "a3": 96, "375": 96, "975": 96, "non_iid_issu": 96, "796474": 96, "842432": 96, "922562": 96, "820759": 96, "873136": 96, "887373": 96, "825101": 96, "855875": 96, "751795": 96, "835796": 96, "ylabel": [96, 104], "coolwarm": 96, "colorbar": [96, 106], "strong": 96, "evid": 96, "inter": 96, "mitig": 96, "risk": 96, "deeper": 96, "tsv": 96, "tab": 96, "pars": 96, "annual_spend": 96, "number_of_transact": 96, "last_purchase_d": 96, "rural": 96, "4099": 96, "2024": [96, 108], "6421": 96, "nat": 96, "suburban": 96, "5436": 96, "4046": 96, "66": [96, 97], "3467": 96, "67": [96, 97, 103, 106], "4757": 96, "4199": 96, "4991": 96, "4655": 96, "82": [96, 97, 99, 103, 106], "5584": 96, "urban": 96, "3102": 96, "6637": 96, "9167": 96, "6790": 96, "5327": 96, "parse_d": 96, "lose": 96, "intact": 96, "encode_categorical_column": 96, "placehold": 96, "dropna": [96, 101], "category_to_numb": 96, "_encod": 96, "gender_encod": 96, "location_encod": 96, "focus": [96, 99, 101, 102, 106], "null_issu": 96, "833333": 96, "sorted_indic": [96, 103], "sorted_df": 96, "nice": 96, "styler": 96, "combined_df": 96, "concat": [96, 106], "highlight_null_valu": 96, "val": [96, 99], "yellow": [96, 97], "highlight_datalab_column": 96, "lightblu": 96, "highlight_is_null_issu": 96, "orang": [96, 97], "styled_df": 96, "nbsp": [96, 98, 99], "160000": 96, "820000": 96, "460000": 96, "470000": 96, "960000": 96, "620000": 96, "550000": 96, "660000": 96, "670000": [96, 97], "370000": 96, "530000": 96, "710000": 96, "020000": 96, "320000": 96, "990000": 96, "rarer": 96, "fairer": 96, "randomli": [96, 99], "class_imbalance_issu": 96, "countplot": 96, "xtick": 96, "rotat": 96, "ytick": 96, "filtered_df": 96, "xy": 96, "va": 96, "textual": 96, "get_ytick": 96, "nbar": 96, "nimbal": 96, "get_legend_handles_label": 96, "title_fonts": 96, "aspect": 96, "anomali": [96, 103], "enhanc": [96, 99, 101, 103], "artifici": 96, "alter": [96, 98], "darken": 96, "blurry_scor": 96, "odd_aspect_ratio_scor": 96, "setup": 96, "cifar10": 96, "markdown": 96, "root": [96, 104], "selected_class": 96, "convert_to_png_imag": 96, "bytesio": [96, 97], "seek": 96, "max_num_imag": 96, "list_imag": 96, "list_label": 96, "num_imag": 96, "img": [96, 104, 106], "toronto": [96, 104], "edu": [96, 104], "kriz": [96, 104], "170498071": [96, 104], "56242831": 96, "52it": 96, "dataset_dict": 96, "from_dict": [96, 98], "apply_dark": 96, "transformed_list_imag": 96, "transformed_dataset_dict": 96, "plot_imag": [96, 104], "num_images_to_plot": 96, "num_col": 96, "hide": 96, "get_property_scor": 96, "_spurious_correl": 96, "get_specific_property_scor": 96, "property_scores_df": 96, "property_nam": 96, "standard_property_scor": 96, "transformed_property_scor": 96, "295": [96, 103], "light_scor": 96, "415": 96, "325": 96, "odd_size_scor": 96, "grayscale_scor": 96, "015": 96, "refin": 97, "instruct": [97, 98], "studi": [97, 103], "mnist_test_set": 97, "imagenet_val_set": 97, "tench": 97, "goldfish": 97, "white": [97, 108], "shark": 97, "tiger": 97, "hammerhead": 97, "electr": 97, "rai": 97, "stingrai": 97, "cock": 97, "hen": 97, "ostrich": 97, "brambl": 97, "goldfinch": 97, "hous": 97, "finch": 97, "junco": 97, "indigo": 97, "bunt": 97, "american": [97, 108], "robin": 97, "bulbul": 97, "jai": 97, "magpi": 97, "chickade": 97, "dipper": 97, "kite": 97, "bald": 97, "eagl": 97, "vultur": 97, "grei": 97, "owl": 97, "salamand": 97, "smooth": 97, "newt": 97, "spot": [97, 98, 103], "axolotl": 97, "bullfrog": 97, "tree": 97, "frog": [97, 104], "tail": 97, "loggerhead": 97, "sea": 97, "turtl": 97, "leatherback": 97, "mud": 97, "terrapin": 97, "band": 97, "gecko": 97, "green": [97, 108], "iguana": 97, "carolina": 97, "anol": 97, "desert": 97, "grassland": 97, "whiptail": 97, "lizard": 97, "agama": 97, "frill": 97, "neck": 97, "allig": 97, "gila": 97, "monster": 97, "european": 97, "chameleon": 97, "komodo": 97, "dragon": 97, "nile": 97, "crocodil": 97, "triceratop": 97, "worm": 97, "snake": 97, "ring": 97, "eastern": 97, "hog": 97, "nose": 97, "kingsnak": 97, "garter": 97, "water": 97, "vine": 97, "night": 97, "boa": 97, "constrictor": 97, "african": 97, "rock": 97, "indian": 97, "cobra": 97, "mamba": 97, "saharan": 97, "horn": 97, "viper": 97, "diamondback": 97, "rattlesnak": 97, "sidewind": 97, "trilobit": 97, "harvestman": 97, "scorpion": 97, "garden": 97, "spider": 97, "barn": 97, "southern": 97, "widow": 97, "tarantula": 97, "wolf": 97, "tick": 97, "centiped": 97, "grous": 97, "ptarmigan": 97, "ruf": 97, "prairi": 97, "peacock": 97, "quail": 97, "partridg": 97, "parrot": 97, "macaw": 97, "sulphur": 97, "crest": 97, "cockatoo": 97, "lorikeet": 97, "coucal": 97, "bee": 97, "eater": 97, "hornbil": 97, "hummingbird": 97, "jacamar": 97, "toucan": 97, "breast": 97, "mergans": 97, "goos": 97, "swan": 97, "tusker": 97, "echidna": 97, "platypu": 97, "wallabi": 97, "koala": 97, "wombat": 97, "jellyfish": 97, "anemon": 97, "brain": 97, "coral": 97, "flatworm": 97, "nematod": 97, "conch": 97, "snail": 97, "slug": 97, "chiton": 97, "chamber": 97, "nautilu": 97, "dung": 97, "crab": 97, "fiddler": 97, "king": 97, "lobster": 97, "spini": 97, "crayfish": 97, "hermit": 97, "isopod": 97, "stork": 97, "spoonbil": 97, "flamingo": 97, "heron": 97, "egret": 97, "bittern": 97, "crane": 97, "bird": [97, 104], "limpkin": 97, "gallinul": 97, "coot": 97, "bustard": 97, "ruddi": 97, "turnston": 97, "dunlin": 97, "redshank": 97, "dowitch": 97, "oystercatch": 97, "pelican": 97, "penguin": 97, "albatross": 97, "whale": 97, "killer": 97, "dugong": 97, "lion": 97, "chihuahua": 97, "japanes": 97, "chin": 97, "maltes": 97, "pekinges": 97, "shih": 97, "tzu": 97, "charl": 97, "spaniel": 97, "papillon": 97, "terrier": 97, "rhodesian": 97, "ridgeback": 97, "afghan": [97, 108], "hound": 97, "basset": 97, "beagl": 97, "bloodhound": 97, "bluetick": 97, "coonhound": 97, "tan": 97, "walker": 97, "foxhound": 97, "redbon": 97, "borzoi": 97, "irish": 97, "wolfhound": 97, "italian": 97, "greyhound": 97, "whippet": 97, "ibizan": 97, "norwegian": 97, "elkhound": 97, "otterhound": 97, "saluki": 97, "scottish": 97, "deerhound": 97, "weimaran": 97, "staffordshir": 97, "bull": 97, "bedlington": 97, "border": 97, "kerri": 97, "norfolk": 97, "norwich": 97, "yorkshir": 97, "wire": 97, "fox": 97, "lakeland": 97, "sealyham": 97, "airedal": 97, "cairn": 97, "australian": 97, "dandi": 97, "dinmont": 97, "boston": 97, "miniatur": 97, "schnauzer": 97, "giant": 97, "tibetan": 97, "silki": 97, "wheaten": 97, "west": 97, "highland": 97, "lhasa": 97, "apso": 97, "retriev": 97, "curli": 97, "golden": 97, "labrador": 97, "chesapeak": 97, "bai": 97, "german": [97, 108], "shorthair": 97, "pointer": 97, "vizsla": 97, "setter": 97, "gordon": 97, "brittani": 97, "clumber": 97, "springer": 97, "welsh": 97, "cocker": 97, "sussex": 97, "kuvasz": 97, "schipperk": 97, "groenendael": 97, "malinoi": 97, "briard": 97, "kelpi": 97, "komondor": 97, "sheepdog": 97, "shetland": 97, "colli": 97, "bouvier": 97, "de": 97, "flandr": 97, "rottweil": 97, "shepherd": 97, "dobermann": 97, "pinscher": 97, "swiss": [97, 108], "mountain": 97, "bernes": 97, "appenzel": 97, "sennenhund": 97, "entlebuch": 97, "boxer": 97, "bullmastiff": 97, "mastiff": 97, "french": 97, "bulldog": 97, "dane": 97, "st": 97, "bernard": 97, "huski": 97, "alaskan": 97, "malamut": 97, "siberian": 97, "dalmatian": 97, "affenpinsch": 97, "basenji": 97, "pug": 97, "leonberg": 97, "newfoundland": 97, "pyrenean": 97, "samoi": 97, "pomeranian": 97, "chow": 97, "keeshond": 97, "griffon": 97, "bruxelloi": 97, "pembrok": 97, "corgi": 97, "cardigan": 97, "poodl": 97, "mexican": 97, "hairless": 97, "tundra": 97, "coyot": 97, "dingo": 97, "dhole": 97, "wild": 97, "hyena": 97, "kit": 97, "arctic": 97, "tabbi": 97, "persian": 97, "siames": 97, "egyptian": 97, "mau": 97, "cougar": 97, "lynx": 97, "leopard": 97, "snow": 97, "jaguar": 97, "cheetah": 97, "brown": [97, 107], "bear": 97, "polar": 97, "sloth": 97, "mongoos": 97, "meerkat": 97, "beetl": 97, "ladybug": 97, "longhorn": 97, "leaf": 97, "rhinocero": 97, "weevil": 97, "fly": 97, "ant": 97, "grasshopp": 97, "cricket": 97, "stick": 97, "insect": 97, "cockroach": 97, "manti": 97, "cicada": 97, "leafhopp": 97, "lacew": 97, "dragonfli": 97, "damselfli": 97, "admir": 97, "ringlet": 97, "monarch": 97, "butterfli": 97, "gossam": 97, "wing": 97, "starfish": 97, "urchin": 97, "cucumb": 97, "cottontail": 97, "rabbit": 97, "hare": 97, "angora": 97, "hamster": 97, "porcupin": 97, "squirrel": 97, "marmot": 97, "beaver": 97, "guinea": 97, "pig": 97, "sorrel": 97, "zebra": 97, "boar": 97, "warthog": 97, "hippopotamu": 97, "ox": 97, "buffalo": 97, "bison": 97, "bighorn": 97, "sheep": 97, "alpin": 97, "ibex": 97, "hartebeest": 97, "impala": 97, "gazel": 97, "dromedari": 97, "llama": 97, "weasel": 97, "mink": 97, "polecat": 97, "foot": 97, "ferret": 97, "otter": 97, "skunk": 97, "badger": 97, "armadillo": 97, "toed": 97, "orangutan": 97, "gorilla": 97, "chimpanze": 97, "gibbon": 97, "siamang": 97, "guenon": 97, "pata": 97, "monkei": 97, "baboon": 97, "macaqu": 97, "langur": 97, "colobu": 97, "probosci": 97, "marmoset": 97, "capuchin": 97, "howler": 97, "titi": 97, "geoffroi": 97, "lemur": 97, "indri": 97, "asian": 97, "eleph": 97, "bush": 97, "snoek": 97, "eel": 97, "coho": 97, "salmon": 97, "beauti": 97, "clownfish": 97, "sturgeon": 97, "garfish": 97, "lionfish": 97, "pufferfish": 97, "abacu": 97, "abaya": 97, "academ": 97, "gown": 97, "accordion": 97, "acoust": 97, "guitar": 97, "aircraft": 97, "carrier": 97, "airlin": 97, "airship": 97, "altar": 97, "ambul": 97, "amphibi": 97, "clock": [97, 108], "apiari": 97, "apron": 97, "wast": 97, "assault": 97, "rifl": 97, "backpack": 97, "bakeri": 97, "balanc": 97, "beam": 97, "balloon": 97, "ballpoint": 97, "pen": 97, "aid": 97, "banjo": 97, "balust": 97, "barbel": 97, "barber": 97, "chair": [97, 103], "barbershop": 97, "baromet": 97, "barrel": 97, "wheelbarrow": 97, "basebal": 97, "basketbal": 97, "bassinet": 97, "bassoon": 97, "swim": 97, "cap": 97, "bath": 97, "towel": 97, "bathtub": 97, "station": 97, "wagon": 97, "lighthous": 97, "beaker": 97, "militari": 97, "beer": 97, "bottl": 97, "glass": 97, "bell": 97, "cot": 97, "bib": 97, "bicycl": [97, 107], "bikini": 97, "binder": 97, "binocular": 97, "birdhous": 97, "boathous": 97, "bobsleigh": 97, "bolo": 97, "tie": 97, "poke": 97, "bonnet": 97, "bookcas": 97, "bookstor": 97, "bow": 97, "brass": 97, "bra": 97, "breakwat": 97, "breastplat": 97, "broom": 97, "bucket": 97, "buckl": 97, "bulletproof": 97, "vest": 97, "butcher": 97, "shop": 97, "taxicab": 97, "cauldron": 97, "candl": 97, "cannon": 97, "cano": 97, "mirror": [97, 103], "carousel": 97, "tool": [97, 99, 101], "carton": 97, "wheel": 97, "teller": 97, "cassett": 97, "player": 97, "castl": 97, "catamaran": 97, "cd": 97, "cello": 97, "mobil": [97, 108], "chain": 97, "fenc": [97, 107], "mail": 97, "chainsaw": 97, "chest": 97, "chiffoni": 97, "chime": 97, "china": 97, "cabinet": 97, "christma": 97, "stock": 97, "church": 97, "movi": 97, "theater": 97, "cleaver": 97, "cliff": 97, "dwell": 97, "cloak": 97, "clog": 97, "cocktail": 97, "shaker": 97, "coffe": 97, "mug": 97, "coffeemak": 97, "coil": 97, "lock": 97, "keyboard": 97, "confectioneri": 97, "ship": [97, 104], "corkscrew": 97, "cornet": 97, "cowboi": 97, "boot": 97, "hat": 97, "cradl": 97, "crash": 97, "helmet": 97, "crate": 97, "infant": 97, "bed": 97, "crock": 97, "pot": 97, "croquet": 97, "crutch": 97, "cuirass": 97, "dam": 97, "desk": 97, "desktop": 97, "rotari": 97, "dial": 97, "telephon": 97, "diaper": 97, "watch": 97, "dine": 97, "dishcloth": 97, "dishwash": 97, "disc": 97, "brake": 97, "dock": 97, "sled": 97, "dome": 97, "doormat": 97, "drill": 97, "rig": 97, "drum": 97, "drumstick": 97, "dumbbel": 97, "dutch": 97, "oven": 97, "fan": 97, "locomot": 97, "entertain": 97, "envelop": 97, "espresso": 97, "powder": 97, "feather": 97, "fireboat": 97, "engin": [97, 107], "screen": 97, "sheet": 97, "flagpol": 97, "flute": 97, "footbal": 97, "forklift": 97, "fountain": 97, "poster": 97, "freight": 97, "fry": 97, "pan": 97, "fur": 97, "garbag": 97, "ga": 97, "pump": 97, "goblet": 97, "kart": 97, "golf": 97, "cart": 97, "gondola": 97, "gong": 97, "grand": 97, "piano": 97, "greenhous": 97, "grill": 97, "groceri": 97, "guillotin": 97, "barrett": 97, "hair": 97, "sprai": 97, "hammer": 97, "dryer": 97, "hand": [97, 99], "handkerchief": 97, "drive": 97, "harmonica": 97, "harp": 97, "harvest": 97, "hatchet": 97, "holster": 97, "honeycomb": 97, "hoop": 97, "skirt": 97, "horizont": 97, "bar": 97, "drawn": 97, "hourglass": 97, "ipod": 97, "cloth": 97, "iron": 97, "jack": 97, "lantern": 97, "jean": 97, "jeep": 97, "jigsaw": 97, "puzzl": 97, "pull": 97, "rickshaw": 97, "joystick": 97, "kimono": 97, "knee": 97, "pad": 97, "knot": 97, "ladl": 97, "lampshad": 97, "laptop": 97, "lawn": 97, "mower": 97, "knife": 97, "lifeboat": 97, "lighter": 97, "limousin": 97, "ocean": 97, "liner": 97, "lipstick": 97, "slip": 97, "shoe": 97, "lotion": 97, "speaker": 97, "loup": 97, "sawmil": 97, "magnet": 97, "compass": 97, "mailbox": 97, "tight": 97, "tank": 97, "manhol": 97, "maraca": 97, "marimba": 97, "maypol": 97, "maze": 97, "cup": [97, 103], "medicin": 97, "megalith": 97, "microphon": 97, "microwav": 97, "milk": 97, "minibu": 97, "miniskirt": 97, "minivan": 97, "missil": 97, "mitten": [97, 98], "mix": 97, "bowl": 97, "modem": 97, "monasteri": 97, "monitor": 97, "mope": 97, "mortar": 97, "mosqu": 97, "mosquito": 97, "scooter": 97, "bike": 97, "tent": 97, "mous": [97, 98], "mousetrap": 97, "van": 97, "muzzl": 97, "nail": 97, "brace": 97, "necklac": 97, "nippl": 97, "obelisk": 97, "obo": 97, "ocarina": 97, "odomet": 97, "oil": 97, "oscilloscop": 97, "overskirt": 97, "bullock": 97, "oxygen": 97, "packet": 97, "paddl": 97, "padlock": 97, "paintbrush": 97, "pajama": 97, "palac": [97, 108], "parachut": 97, "park": 97, "bench": 97, "meter": 97, "passeng": 97, "patio": 97, "payphon": 97, "pedest": 97, "pencil": 97, "perfum": 97, "petri": 97, "dish": 97, "photocopi": 97, "plectrum": 97, "pickelhaub": 97, "picket": 97, "pickup": 97, "pier": 97, "piggi": 97, "pill": 97, "pillow": 97, "ping": 97, "pong": 97, "pinwheel": 97, "pirat": 97, "pitcher": 97, "plane": 97, "planetarium": 97, "plastic": 97, "plate": 97, "rack": 97, "plow": 97, "plunger": 97, "polaroid": 97, "camera": 97, "pole": [97, 107], "polic": 97, "poncho": 97, "billiard": 97, "soda": 97, "potter": 97, "prayer": 97, "rug": 97, "printer": 97, "prison": 97, "projectil": 97, "projector": 97, "hockei": 97, "puck": 97, "punch": 97, "purs": 97, "quill": 97, "quilt": 97, "race": 97, "racket": 97, "radiat": 97, "radio": 97, "telescop": 97, "rain": 97, "recreat": 97, "reel": 97, "reflex": 97, "refriger": 97, "remot": 97, "restaur": 97, "revolv": 97, "rotisseri": 97, "eras": 97, "rugbi": 97, "ruler": 97, "safe": 97, "safeti": 97, "salt": 97, "sarong": 97, "saxophon": 97, "scabbard": 97, "bu": [97, 107], "schooner": 97, "scoreboard": 97, "crt": 97, "screw": 97, "screwdriv": 97, "seat": 97, "belt": 97, "sew": 97, "shield": 97, "shoji": 97, "basket": 97, "shovel": 97, "shower": 97, "curtain": 97, "ski": 97, "sleep": 97, "door": 97, "slot": 97, "snorkel": 97, "snowmobil": 97, "snowplow": 97, "soap": 97, "dispens": 97, "soccer": [97, 108], "sock": [97, 98], "solar": 97, "thermal": 97, "collector": 97, "sombrero": 97, "soup": 97, "heater": 97, "shuttl": 97, "spatula": 97, "motorboat": 97, "web": 97, "spindl": 97, "sport": [97, 108], "spotlight": 97, "stage": 97, "steam": 97, "arch": 97, "bridg": 97, "steel": 97, "stethoscop": 97, "scarf": 97, "stone": 97, "wall": [97, 107], "stopwatch": 97, "stove": 97, "strainer": 97, "tram": 97, "stretcher": 97, "couch": 97, "stupa": 97, "submarin": 97, "sundial": 97, "sunglass": 97, "sunscreen": 97, "suspens": 97, "mop": 97, "sweatshirt": 97, "swimsuit": 97, "swing": 97, "switch": 97, "syring": 97, "lamp": 97, "tape": 97, "teapot": 97, "teddi": 97, "televis": [97, 108], "tenni": 97, "thatch": 97, "roof": 97, "thimbl": 97, "thresh": 97, "throne": 97, "tile": 97, "toaster": 97, "tobacco": 97, "toilet": 97, "totem": 97, "tow": 97, "tractor": 97, "semi": 97, "trailer": 97, "trai": 97, "trench": 97, "tricycl": 97, "trimaran": 97, "tripod": 97, "triumphal": 97, "trolleybu": 97, "trombon": 97, "tub": 97, "turnstil": 97, "typewrit": 97, "umbrella": 97, "unicycl": 97, "upright": 97, "vacuum": 97, "cleaner": 97, "vase": 97, "vault": 97, "velvet": 97, "vend": 97, "vestment": 97, "viaduct": 97, "violin": 97, "volleybal": 97, "waffl": 97, "wallet": 97, "wardrob": 97, "sink": 97, "wash": 97, "jug": 97, "tower": 97, "whiskei": 97, "whistl": 97, "wig": 97, "shade": [97, 107], "windsor": 97, "wine": 97, "wok": 97, "wooden": 97, "spoon": 97, "wool": 97, "rail": 97, "shipwreck": 97, "yawl": 97, "yurt": 97, "websit": 97, "comic": 97, "book": 97, "crossword": 97, "traffic": [97, 103, 107], "sign": [97, 107, 108], "dust": 97, "jacket": [97, 103], "menu": 97, "guacamol": 97, "consomm": 97, "trifl": 97, "ic": 97, "cream": 97, "pop": 97, "baguett": 97, "bagel": 97, "pretzel": 97, "cheeseburg": 97, "mash": 97, "potato": 97, "cabbag": 97, "broccoli": 97, "cauliflow": 97, "zucchini": 97, "spaghetti": 97, "squash": 97, "acorn": 97, "butternut": 97, "artichok": 97, "pepper": [97, 98], "cardoon": 97, "mushroom": 97, "granni": 97, "smith": 97, "strawberri": 97, "lemon": 97, "pineappl": 97, "banana": 97, "jackfruit": 97, "custard": 97, "appl": 97, "pomegran": 97, "hai": 97, "carbonara": 97, "chocol": 97, "syrup": 97, "dough": 97, "meatloaf": 97, "pizza": 97, "pie": 97, "burrito": 97, "eggnog": 97, "alp": 97, "bubbl": 97, "reef": 97, "geyser": 97, "lakeshor": 97, "promontori": 97, "shoal": 97, "seashor": 97, "vallei": 97, "volcano": 97, "bridegroom": 97, "scuba": 97, "diver": 97, "rapese": 97, "daisi": 97, "ladi": 97, "slipper": 97, "corn": 97, "rose": 97, "hip": 97, "chestnut": 97, "fungu": 97, "agar": 97, "gyromitra": 97, "stinkhorn": 97, "earth": 97, "star": 97, "wood": 97, "bolet": 97, "ear": 97, "cifar10_test_set": 97, "airplan": [97, 104], "automobil": [97, 104], "deer": [97, 104], "cifar100_test_set": 97, "aquarium_fish": 97, "boi": 97, "camel": 97, "caterpillar": 97, "cattl": [97, 108], "cloud": 97, "dinosaur": 97, "dolphin": 97, "flatfish": 97, "forest": 97, "girl": 97, "kangaroo": 97, "lawn_mow": 97, "man": 97, "maple_tre": 97, "motorcycl": [97, 107], "oak_tre": 97, "orchid": 97, "palm_tre": 97, "pear": 97, "pickup_truck": 97, "pine_tre": 97, "plain": 97, "poppi": 97, "possum": 97, "raccoon": 97, "road": [97, 107], "rocket": 97, "seal": 97, "shrew": 97, "skyscrap": 97, "streetcar": 97, "sunflow": 97, "sweet_pepp": 97, "trout": 97, "tulip": 97, "willow_tre": 97, "woman": [97, 103], "caltech256": 97, "ak47": 97, "bat": 97, "glove": 97, "birdbath": 97, "blimp": 97, "bonsai": 97, "boom": 97, "breadmak": 97, "buddha": 97, "bulldoz": 97, "cactu": 97, "cake": 97, "tire": 97, "cartman": 97, "cereal": 97, "chandeli": 97, "chess": 97, "board": 97, "chimp": 97, "chopstick": 97, "coffin": 97, "coin": 97, "comet": 97, "cormor": 97, "globe": 97, "diamond": 97, "dice": 97, "doorknob": 97, "drink": 97, "straw": 97, "dumb": 97, "eiffel": 97, "elk": 97, "ewer": 97, "eyeglass": 97, "fern": 97, "fighter": 97, "jet": [97, 106], "extinguish": 97, "hydrant": 97, "firework": 97, "flashlight": 97, "floppi": 97, "fri": 97, "frisbe": 97, "galaxi": 97, "giraff": 97, "goat": 97, "gate": 97, "grape": 97, "pick": [97, 98], "hamburg": 97, "hammock": 97, "harpsichord": 97, "hawksbil": 97, "helicopt": 97, "hibiscu": 97, "homer": 97, "simpson": 97, "horsesho": 97, "air": 97, "skeleton": 97, "ibi": 97, "cone": 97, "iri": 97, "jesu": 97, "christ": 97, "joi": 97, "kayak": 97, "ketch": 97, "ladder": 97, "lath": 97, "licens": 97, "lightbulb": 97, "lightn": 97, "mandolin": 97, "mar": 97, "mattress": 97, "megaphon": 97, "menorah": 97, "microscop": 97, "minaret": 97, "minotaur": 97, "motorbik": 97, "mussel": 97, "neckti": 97, "octopu": 97, "palm": 97, "pilot": 97, "paperclip": 97, "shredder": 97, "pci": 97, "peopl": [97, 103], "pez": 97, "picnic": 97, "pram": 97, "prai": 97, "pyramid": 97, "rainbow": 97, "roulett": 97, "saddl": 97, "saturn": 97, "segwai": 97, "propel": 97, "sextant": 97, "music": 97, "skateboard": 97, "smokestack": 97, "sneaker": 97, "boat": 97, "stain": 97, "steer": 97, "stirrup": 97, "superman": 97, "sushi": 97, "armi": [97, 108], "sword": 97, "tambourin": 97, "teepe": 97, "court": 97, "theodolit": 97, "tomato": 97, "tombston": 97, "tour": 97, "pisa": 97, "treadmil": 97, "fork": 97, "tweezer": 97, "unicorn": 97, "vcr": 97, "waterfal": 97, "watermelon": 97, "weld": 97, "windmil": 97, "xylophon": 97, "yarmulk": 97, "yo": 97, "toad": 97, "twenty_news_test_set": 97, "comp": 97, "graphic": [97, 107], "misc": [97, 108], "sy": 97, "ibm": 97, "pc": 97, "hardwar": 97, "mac": 97, "forsal": 97, "rec": 97, "crypt": 97, "electron": 97, "med": 97, "soc": 97, "religion": 97, "christian": [97, 108], "talk": [97, 108], "polit": 97, "gun": 97, "mideast": 97, "amazon": 97, "neutral": 97, "imdb_test_set": 97, "all_class": 97, "20news_test_set": 97, "_load_classes_predprobs_label": 97, "dataset_nam": 97, "labelerror": 97, "url_bas": 97, "5392f6c71473055060be3044becdde1cbc18284d": 97, "url_label": 97, "original_test_label": 97, "_original_label": 97, "url_prob": 97, "cross_validated_predicted_prob": 97, "_pyx": 97, "num_part": 97, "datatset": 97, "allow_pickl": 97, "pred_probs_part": 97, "url": 97, "_of_": 97, "nload": 97, "imdb": 97, "ve": [97, 98, 99, 101, 103], "capit": 97, "29780": 97, "256": [97, 98, 103], "780": 97, "medic": [97, 108], "doctor": 97, "254": [97, 103], "359223": 97, "640777": 97, "184": [97, 99], "258427": 97, "341176": 97, "263158": 97, "658824": 97, "337349": 97, "246575": 97, "662651": 97, "248": 97, "330000": 97, "355769": 97, "251": [97, 103], "167": [97, 99, 103], "252": 97, "112": 97, "253": [97, 103], "022989": 97, "049505": 97, "190": [97, 99, 103], "002216": 97, "000974": 97, "000873": 97, "000739": 97, "32635": 97, "32636": 97, "32637": 97, "32638": 97, "32639": 97, "32640": 97, "051": 97, "002242": 97, "997758": 97, "002088": 97, "001045": 97, "997912": 97, "002053": 97, "997947": 97, "001980": 97, "000991": 97, "998020": 97, "001946": 97, "002915": 97, "998054": 97, "001938": 97, "002904": 97, "998062": 97, "001020": 97, "998980": 97, "001018": 97, "002035": 97, "998982": 97, "999009": 97, "0003": 97, "0002": 97, "071": 97, "067269": 97, "929": 97, "046": 97, "058243": 97, "954": 97, "035": 97, "032096": 97, "965": 97, "031": 97, "012232": 97, "969": 97, "022": 97, "025896": 97, "978": 97, "020": [97, 99], "013092": 97, "018": 97, "013065": 97, "016": 97, "030542": 97, "984": 97, "013": 97, "020833": 97, "987": 97, "012": 97, "010020": 97, "988": 97, "0073": 97, "0020": 97, "0016": 97, "0015": 97, "0014": 97, "0013": 97, "0012": 97, "0010": 97, "0008": 97, "0007": 97, "0006": 97, "0005": 97, "0004": 97, "244": [97, 103, 108], "452381": 97, "459770": 97, "523364": 97, "460784": 97, "446602": 97, "103774": 97, "030612": 97, "110092": 97, "049020": 97, "0034": 97, "0032": 97, "0026": 97, "0025": 97, "4945": 97, "4946": 97, "4947": 97, "4948": 97, "4949": 97, "4950": 97, "846": 97, "7532": 97, "532": 97, "034483": 97, "009646": 97, "965517": 97, "030457": 97, "020513": 97, "969543": 97, "028061": 97, "035443": 97, "971939": 97, "025316": 97, "005168": 97, "974684": 97, "049751": 97, "979487": 97, "019920": 97, "042802": 97, "980080": 97, "017677": 97, "005115": 97, "982323": 97, "012987": 97, "005236": 97, "987013": 97, "012723": 97, "025126": 97, "987277": 97, "010989": 97, "008264": 97, "989011": 97, "010283": 97, "027778": 97, "989717": 97, "009677": 97, "990323": 97, "007614": 97, "010127": 97, "992386": 97, "005051": 97, "994949": 97, "005025": 97, "994975": 97, "005013": 97, "994987": 97, "001859": 97, "001328": 97, "000929": 97, "000664": 97, "186": [97, 99], "188": [97, 99, 102], "189": [97, 99], "snippet": 98, "nlp": [98, 108], "mind": [98, 99], "alphanumer": 98, "facilit": 98, "seamless": 98, "classlabel": 98, "guidanc": 98, "labels_str": 98, "datalab_str": 98, "labels_int": 98, "remap": 98, "datalab_int": 98, "my_dict": 98, "pet_nam": 98, "rover": 98, "rocki": 98, "speci": 98, "datalab_dataset": 98, "number_of_class": 98, "total_number_of_data_point": 98, "feed": 98, "alphabet": 98, "labels_proper_format": 98, "your_classifi": 98, "issues_datafram": 98, "class_predicted_for_flagged_exampl": 98, "class_predicted_for_all_exampl": 98, "grant": 98, "On": [98, 99, 103], "merged_dataset": 98, "label_column_nam": 98, "datataset": 98, "fair": [98, 99], "game": 98, "speedup": [98, 104], "tempfil": 98, "mkdtemp": 98, "sped": 98, "anywai": 98, "pred_probs_merg": 98, "merge_rare_class": 98, "count_threshold": 98, "class_mapping_orig2new": 98, "heath_summari": 98, "num_examples_per_class": 98, "rare_class": 98, "num_classes_merg": 98, "other_class": 98, "labels_merg": 98, "new_c": 98, "merged_prob": 98, "new_class": 98, "original_class": 98, "num_check": 98, "ones_array_ref": 98, "isclos": 98, "though": [98, 99, 108], "successfulli": 98, "virtuou": [98, 101], "cycl": [98, 101], "jointli": 98, "junk": 98, "clutter": 98, "unknown": 98, "caltech": 98, "combined_boolean_mask": 98, "mask1": 98, "mask2": 98, "gradientboostingclassifi": [98, 99], "true_error": [98, 99, 102], "101": [98, 103], "102": [98, 102, 103], "104": [98, 99, 103], "model_to_find_error": 98, "model_to_return": 98, "cl0": 98, "randomizedsearchcv": 98, "expens": 98, "param_distribut": 98, "learning_r": [98, 99], "max_depth": [98, 99], "magnitud": 98, "coeffici": [98, 106], "optin": 98, "environ": [98, 99], "rerun": [98, 99], "cell": [98, 99], "unabl": [98, 99], "render": [98, 99], "nbviewer": [98, 99], "cleanlearninginot": [98, 99], "fittedcleanlearn": [98, 99], "linearregressionlinearregress": 98, "unexpectedli": 98, "emphas": 98, "crucial": 98, "merge_duplicate_set": 98, "merge_kei": 98, "construct_group_kei": 98, "merged_set": 98, "consolidate_set": 98, "issubset": 98, "frozenset": 98, "sets_list": 98, "mutabl": 98, "new_set": 98, "current_set": 98, "intersecting_set": 98, "lowest_score_strategi": 98, "sub_df": 98, "filter_near_dupl": 98, "strategy_fn": 98, "strategy_kwarg": 98, "duplicate_row": 98, "group_kei": 98, "to_keep_indic": 98, "groupbi": 98, "explod": 98, "to_remov": 98, "isin": [98, 104], "kept": 98, "ids_to_remove_seri": 98, "assist": 98, "streamlin": 98, "ux": 98, "agpl": 98, "compani": 98, "commerci": 98, "email": 98, "team": 98, "discuss": 98, "anywher": 98, "profession": 98, "expert": 98, "depth": 99, "survei": [99, 108], "scienc": 99, "multivariate_norm": [99, 101, 102], "make_data": [99, 101], "cov": [99, 101, 102], "avg_trac": [99, 102], "py_tru": 99, "noise_matrix_tru": 99, "noise_marix": 99, "s_test": 99, "noisy_test_label": 99, "purpl": 99, "namespac": 99, "exec": 99, "markerfacecolor": [99, 102], "markeredgecolor": [99, 102, 106], "markers": [99, 102, 106], "markeredgewidth": [99, 102, 106], "realist": 99, "7560": 99, "637318e": 99, "896262e": 99, "548391e": 99, "923417e": 99, "375075e": 99, "3454": 99, "014051": 99, "020451": 99, "249": [99, 103], "042594": 99, "043859": 99, "045954": 99, "6120": 99, "023714": 99, "007136": 99, "119": [99, 103], "107266": 99, "103": [99, 103], "033738": 99, "238": [99, 103], "119505": 99, "236": [99, 103, 108], "037843": 99, "222": 99, "614915": 99, "122": [99, 103], "624422": 99, "625965": 99, "626079": 99, "118": 99, "627675": 99, "695223": 99, "323529": 99, "523015": 99, "013720": 99, "675727": 99, "646521": 99, "anyth": 99, "magic": 99, "liter": 99, "identif": 99, "x27": 99, "logisticregressionlogisticregress": 99, "ever": 99, "092": 99, "040": 99, "024": 99, "004": 99, "surpris": 99, "1705": 99, "01936": 99, "ton": 99, "yourfavoritemodel1": 99, "merged_label": 99, "merged_test_label": 99, "newli": [99, 101], "yourfavoritemodel2": 99, "yourfavoritemodel3": 99, "cl3": 99, "takeawai": 99, "my_test_pred_prob": 99, "my_test_pr": 99, "issues_test": 99, "corrected_test_label": 99, "pretend": 99, "cl_test_pr": 99, "fairli": 99, "label_acc": 99, "percentag": 99, "offset": 99, "nquestion": 99, "overestim": 99, "answer": 99, "experienc": 99, "prioiri": 99, "known": 99, "versatil": 99, "label_issues_indic": 99, "213": [99, 103], "218": [99, 103], "152": 99, "197": [99, 103], "196": [99, 103], "170": 99, "214": 99, "164": [99, 102], "198": [99, 103], "191": [99, 103], "117": [99, 106], "206": [99, 103], "115": [99, 103], "193": 99, "194": 99, "201": [99, 103], "174": 99, "163": 99, "150": [99, 101, 103], "169": 99, "151": [99, 103], "168": 99, "precision_scor": 99, "recall_scor": 99, "f1_score": 99, "true_label_issu": 99, "filter_by_list": 99, "718750": [99, 101], "807018": 99, "912": 99, "733333": 99, "800000": 99, "721311": 99, "792793": 99, "908": 99, "676923": 99, "765217": 99, "892": 99, "567901": 99, "702290": 99, "844": 99, "gaug": 99, "label_issues_count": 99, "155": [99, 103], "156": 99, "172": [99, 102], "157": 99, "easiest": 99, "modular": 99, "penalti": 99, "l2": 99, "model3": 99, "n_estim": 99, "cv_pred_probs_1": 99, "cv_pred_probs_2": 99, "cv_pred_probs_3": 99, "label_quality_scores_best": 99, "cv_pred_probs_ensembl": 99, "label_quality_scores_bett": 99, "superior": [99, 105], "timm": 100, "glad": 101, "multiannotator_label": 101, "300": [101, 108], "noisier": 101, "111": [101, 106], "local_data": [101, 102], "true_labels_train": [101, 102], "noise_matrix_bett": 101, "noise_matrix_wors": 101, "transpos": [101, 104], "zfill": 101, "row_na_check": 101, "notna": 101, "reset_index": 101, "a0001": 101, "a0002": 101, "a0003": 101, "a0004": 101, "a0005": 101, "a0006": 101, "a0007": 101, "a0008": 101, "a0009": 101, "a0010": 101, "a0041": 101, "a0042": 101, "a0043": 101, "a0044": 101, "a0045": 101, "a0046": 101, "a0047": 101, "a0048": 101, "a0049": 101, "a0050": 101, "na": 101, "60856743": 101, "41693214": 101, "40908785": 101, "87147629": 101, "64941785": 101, "10774851": 101, "0524466": 101, "71853246": 101, "37169848": 101, "66031048": 101, "multiannotator_util": 101, "crude": 101, "straight": 101, "majority_vote_label": 101, "736118": 101, "757751": 101, "782232": 101, "715565": 101, "824256": 101, "quality_annotator_a0001": 101, "quality_annotator_a0002": 101, "quality_annotator_a0003": 101, "quality_annotator_a0004": 101, "quality_annotator_a0005": 101, "quality_annotator_a0006": 101, "quality_annotator_a0007": 101, "quality_annotator_a0008": 101, "quality_annotator_a0009": 101, "quality_annotator_a0010": 101, "quality_annotator_a0041": 101, "quality_annotator_a0042": 101, "quality_annotator_a0043": 101, "quality_annotator_a0044": 101, "quality_annotator_a0045": 101, "quality_annotator_a0046": 101, "quality_annotator_a0047": 101, "quality_annotator_a0048": 101, "quality_annotator_a0049": 101, "quality_annotator_a0050": 101, "070564": 101, "216078": 101, "119188": 101, "alongisd": 101, "244981": 101, "208333": 101, "295979": 101, "294118": 101, "324197": 101, "310345": 101, "355316": 101, "346154": 101, "439732": 101, "480000": 101, "a0031": 101, "523205": 101, "580645": 101, "a0034": 101, "535313": 101, "607143": 101, "a0021": 101, "606999": 101, "a0015": 101, "609526": 101, "678571": 101, "a0011": 101, "621103": 101, "692308": 101, "improved_consensus_label": 101, "majority_vote_accuraci": 101, "cleanlab_label_accuraci": 101, "8581081081081081": 101, "9797297297297297": 101, "besid": 101, "sorted_consensus_quality_scor": 101, "worst_qual": 101, "better_qu": 101, "worst_quality_accuraci": 101, "better_quality_accuraci": 101, "9893238434163701": 101, "improved_pred_prob": 101, "treat": [101, 102, 106, 108], "analzi": 101, "copyright": 102, "advertis": 102, "violenc": 102, "nsfw": 102, "celeba": 102, "make_multilabel_data": 102, "boxes_coordin": 102, "box_multilabel": 102, "make_multi": 102, "bx1": 102, "by1": 102, "bx2": 102, "by2": 102, "label_list": 102, "ur": 102, "upper": 102, "inidx": 102, "logical_and": 102, "inv_d": 102, "labels_idx": 102, "true_labels_test": 102, "dict_unique_label": 102, "get_color_arrai": 102, "dcolor": 102, "aa4400": 102, "55227f": 102, "55a100": 102, "00ff00": 102, "007f7f": 102, "386b55": 102, "0000ff": 102, "y_onehot": 102, "single_class_label": 102, "stratifi": [102, 105], "kf": 102, "train_index": 102, "test_index": 102, "clf_cv": 102, "x_train_cv": 102, "x_test_cv": 102, "y_train_cv": 102, "y_test_cv": 102, "y_pred_cv": 102, "saw": 102, "num_to_displai": 102, "09": [102, 103, 106], "275": 102, "267": 102, "225": 102, "171": 102, "234": 102, "165": 102, "227": [102, 103], "262": [102, 103], "263": [102, 103], "266": [102, 103], "139": 102, "143": [102, 103], "216": [102, 103, 108], "265": 102, "159": [102, 103], "despit": [102, 108], "suspect": 102, "888": 102, "8224": 102, "9632": 102, "968": 102, "6512": 102, "0444": 102, "774": 102, "labels_binary_format": 102, "labels_list_format": 102, "surround": 103, "scene": 103, "coco": 103, "everydai": 103, "has_label_issu": 103, "nc": [103, 107, 108], "s3": [103, 107, 108], "amazonaw": [103, 107, 108], "objectdetectionbenchmark": 103, "tutorial_obj": 103, "pkl": 103, "example_imag": 103, "unzip": [103, 108], "_separate_label": 103, "_separate_predict": 103, "begin": 103, "image_path": 103, "rb": 103, "image_to_visu": 103, "seg_map": 103, "334": 103, "bboxes_ignor": 103, "290": 103, "286": 103, "285": 103, "224": 103, "231": 103, "293": 103, "235": 103, "289": 103, "282": 103, "281": 103, "271": 103, "280": 103, "277": 103, "279": 103, "287": 103, "299": 103, "276": 103, "307": 103, "321": 103, "326": 103, "333": 103, "261": 103, "319": 103, "257": 103, "283": 103, "243": 103, "303": 103, "316": 103, "247": 103, "323": 103, "327": 103, "226": 103, "228": 103, "232": 103, "219": 103, "239": 103, "240": 103, "209": 103, "242": 103, "202": 103, "230": 103, "215": 103, "220": 103, "229": 103, "217": [103, 108], "237": 103, "207": 103, "204": 103, "84": [103, 106], "205": 103, "223": 103, "153": 103, "149": 103, "140": 103, "124": 103, "246": 103, "268": 103, "273": 103, "284": 103, "110": 103, "136": 103, "145": 103, "173": 103, "297": 103, "317": 103, "192": 103, "332": 103, "324": 103, "203": 103, "320": 103, "314": 103, "199": 103, "291": 103, "000000481413": 103, "jpg": 103, "42398": 103, "44503": 103, "29968": 103, "336": 103, "21005": 103, "9978472": 103, "forgot": 103, "drew": 103, "label_issue_idx": 103, "num_examples_to_show": 103, "138": 103, "candid": 103, "97489622": 103, "70610878": 103, "98764951": 103, "88899237": 103, "99085805": 103, "issue_idx": 103, "95569726e": 103, "03354841e": 103, "57510169e": 103, "58447666e": 103, "39755858e": 103, "issue_to_visu": 103, "000000009483": 103, "95569726168054e": 103, "addition": [103, 107], "visibl": 103, "missmatch": 103, "likelei": 103, "agnost": 103, "vaidat": 103, "inconsist": 103, "000000395701": 103, "033548411774308e": 103, "armchair": 103, "tv": 103, "000000154004": 103, "38300759625496356": 103, "foreground": 103, "000000448410": 103, "0008575101690203273": 103, "crowd": 103, "alon": 103, "resembl": [103, 104], "000000499768": 103, "9748962231208227": 103, "000000521141": 103, "8889923658893665": 103, "000000143931": 103, "9876495074395956": 103, "bonu": 103, "uncov": 103, "irregular": 103, "object_detection_util": 103, "calculate_bounding_box_area": 103, "num_imgs_to_show": 103, "lab_object_count": 103, "pred_object_count": 103, "000000430073": 103, "000000183709": 103, "000000189475": 103, "label_norm": 103, "pred_norm": 103, "area": [103, 107], "lab_area": 103, "pred_area": 103, "lab_area_mean": 103, "lab_area_std": 103, "max_deviation_valu": 103, "max_deviation_class": 103, "deviation_valu": 103, "deviation_class": 103, "mean_area": 103, "std_area": 103, "class_area": 103, "deviations_awai": 103, "max_deviation_index": 103, "num_imgs_to_show_per_class": 103, "class_num": 103, "000000422886": 103, "000000341828": 103, "000000461009": 103, "train_feature_embed": 104, "ood_train_feature_scor": 104, "test_feature_embed": 104, "ood_test_feature_scor": 104, "ood_train_predictions_scor": 104, "train_pred_prob": 104, "ood_test_predictions_scor": 104, "test_pred_prob": 104, "pylab": 104, "rcparam": 104, "baggingclassifi": 104, "therebi": 104, "rescal": 104, "transform_norm": 104, "totensor": 104, "animal_class": 104, "non_animal_class": 104, "animal_idx": 104, "test_idx": 104, "106209257": 104, "98it": 104, "visualize_outli": 104, "txt_class": 104, "npimg": 104, "show_label": 104, "data_subset": 104, "resnet50": 104, "corpu": 104, "2048": 104, "embed_imag": 104, "create_model": 104, "strang": 104, "odd": 104, "train_ood_features_scor": 104, "top_train_ood_features_idx": 104, "fun": 104, "negat": 104, "homogen": 104, "bottom_train_ood_features_idx": 104, "test_ood_features_scor": 104, "top_ood_features_idx": 104, "inevit": 104, "trade": 104, "5th": 104, "percentil": 104, "fifth_percentil": 104, "plt_rang": 104, "hist": 104, "train_outlier_scor": 104, "test_outlier_scor": 104, "ood_features_indic": 104, "revisit": 104, "return_invers": 104, "train_feature_embeddings_sc": 104, "test_feature_embeddings_sc": 104, "train_pred_label": 104, "9702": 104, "train_ood_predictions_scor": 104, "test_ood_predictions_scor": 104, "lost": 104, "unsuit": 105, "ok": [105, 108], "convention": 105, "aforement": 105, "hypothet": 105, "contrast": 105, "tradit": 105, "disjoint": 105, "out_of_sample_pred_probs_for_a": 105, "out_of_sample_pred_probs_for_b": 105, "out_of_sample_pred_probs_for_c": 105, "out_of_sample_pred_prob": 105, "price": 106, "incom": 106, "sensor": 106, "histgradientboostingregressor": 106, "r2_score": 106, "student_grades_r": 106, "final_scor": 106, "true_final_scor": 106, "homework": 106, "3d": 106, "mpl_toolkit": 106, "mplot3d": 106, "axes3d": 106, "errors_idx": 106, "add_subplot": 106, "z": 106, "errors_mask": 106, "feature_column": 106, "predicted_column": 106, "x_train_raw": 106, "x_test_raw": 106, "randomforestregressor": 106, "385101": 106, "499503": 106, "698255": 106, "776647": 106, "109373": 106, "170547": 106, "481096": 106, "984759": 106, "645270": 106, "795928": 106, "141": 106, "659": 106, "367": 106, "318": 106, "305": 106, "560": 106, "657": 106, "688": 106, "view_datapoint": 106, "preds_og": 106, "r2_og": 106, "838": 106, "found_label_issu": 106, "preds_cl": 106, "r2_cl": 106, "926": 106, "favorit": 106, "968627e": 106, "228799": 106, "646674e": 106, "402962": 106, "323818e": 106, "952758": 106, "422144e": 106, "456908": 106, "465815e": 106, "753968": 106, "791186e": 106, "110719": 106, "485156e": 106, "670640": 106, "225300e": 106, "749976": 106, "499679e": 106, "947007": 106, "067882e": 106, "648396": 106, "synthia": 107, "imagesegment": 107, "given_mask": 107, "predicted_mask": 107, "set_printopt": [107, 108], "sky": 107, "sidewalk": 107, "veget": 107, "terrain": 107, "rider": 107, "pred_probs_filepath": 107, "1088": 107, "1920": 107, "label_filepath": 107, "synthia_class": 107, "maunal": 107, "100000": 107, "244800": 107, "leftmost": 107, "middl": [107, 108], "infact": 107, "rightmost": 107, "discrep": 107, "3263230": 107, "783381": 107, "275110": 107, "255917": 107, "78225": 107, "55990": 107, "54315": 107, "33591": 107, "24645": 107, "21054": 107, "15045": 107, "14171": 107, "13832": 107, "13498": 107, "11490": 107, "9164": 107, "8769": 107, "6999": 107, "6031": 107, "5011": 107, "mistakenli": 107, "class_issu": 107, "aim": [107, 108], "domin": 107, "bunch": 108, "conll": 108, "2003": 108, "love": 108, "n_i": 108, "optional_list_of_ordered_class_nam": 108, "deepai": 108, "conll2003": 108, "rm": 108, "tokenclassif": 108, "2400": 108, "52e0": 108, "1a00": 108, "871": 108, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "95mb": 108, "mb": 108, "directori": 108, "inflat": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "7mb": 108, "bert": 108, "read_npz": 108, "filepath": 108, "corrsespond": 108, "iob2": 108, "given_ent": 108, "entity_map": 108, "readfil": 108, "startswith": 108, "docstart": 108, "isalpha": 108, "isupp": 108, "indices_to_preview": 108, "nsentenc": 108, "eu": 108, "reject": 108, "boycott": 108, "british": 108, "lamb": 108, "00030412": 108, "00023826": 108, "99936208": 108, "00007009": 108, "00002545": 108, "99998795": 108, "00000401": 108, "00000218": 108, "00000455": 108, "00000131": 108, "00000749": 108, "99996115": 108, "00001371": 108, "0000087": 108, "00000895": 108, "99998936": 108, "00000382": 108, "00000178": 108, "00000366": 108, "00000137": 108, "99999101": 108, "00000266": 108, "00000174": 108, "0000035": 108, "00000109": 108, "99998768": 108, "00000482": 108, "00000202": 108, "00000438": 108, "0000011": 108, "00000465": 108, "99996392": 108, "00001105": 108, "0000116": 108, "00000878": 108, "99998671": 108, "00000364": 108, "00000213": 108, "00000472": 108, "00000281": 108, "99999073": 108, "00000211": 108, "00000159": 108, "00000442": 108, "00000115": 108, "peter": 108, "blackburn": 108, "00000358": 108, "00000529": 108, "99995623": 108, "0000129": 108, "0000024": 108, "00001812": 108, "99994141": 108, "00001645": 108, "00002162": 108, "brussel": 108, "1996": 108, "00001172": 108, "00000821": 108, "00004661": 108, "0000618": 108, "99987167": 108, "99999061": 108, "00000201": 108, "00000195": 108, "00000408": 108, "00000135": 108, "2254": 108, "2907": 108, "19392": 108, "9962": 108, "8904": 108, "19303": 108, "12918": 108, "9256": 108, "11855": 108, "18392": 108, "20426": 108, "19402": 108, "14744": 108, "19371": 108, "4645": 108, "10331": 108, "9430": 108, "6143": 108, "18367": 108, "12914": 108, "todai": 108, "weather": 108, "march": 108, "scalfaro": 108, "northern": 108, "himself": 108, "said": 108, "germani": 108, "nastja": 108, "rysich": 108, "north": 108, "spla": 108, "fought": 108, "khartoum": 108, "govern": 108, "south": 108, "1983": 108, "autonomi": 108, "animist": 108, "region": 108, "moslem": 108, "arabis": 108, "mayor": 108, "antonio": 108, "gonzalez": 108, "garcia": 108, "revolutionari": 108, "wednesdai": 108, "troop": 108, "raid": 108, "farm": 108, "stole": 108, "rape": 108, "women": 108, "spring": 108, "chg": 108, "hrw": 108, "12pct": 108, "princ": 108, "photo": 108, "moment": 108, "spokeswoman": 108, "rainier": 108, "told": 108, "reuter": 108, "danila": 108, "carib": 108, "w224": 108, "equip": 108, "radiomet": 108, "earn": 108, "19996": 108, "london": 108, "denom": 108, "sale": 108, "uk": 108, "jp": 108, "fr": 108, "maccabi": 108, "hapoel": 108, "haifa": 108, "tel": 108, "aviv": 108, "hospit": 108, "rever": 108, "roman": 108, "cathol": 108, "nun": 108, "admit": 108, "calcutta": 108, "week": 108, "ago": 108, "fever": 108, "vomit": 108, "allianc": 108, "embattl": 108, "kabul": 108, "salang": 108, "highwai": 108, "mondai": 108, "tuesdai": 108, "suprem": 108, "council": 108, "led": 108, "jumbish": 108, "milli": 108, "movement": 108, "warlord": 108, "abdul": 108, "rashid": 108, "dostum": 108, "dollar": 108, "exchang": 108, "3570": 108, "12049": 108, "born": 108, "1937": 108, "provinc": 108, "anhui": 108, "dai": 108, "came": 108, "shanghai": 108, "citi": 108, "prolif": 108, "author": 108, "teacher": 108, "chines": 108, "16764": 108, "1990": 108, "historian": 108, "alan": 108, "john": 108, "percival": 108, "taylor": 108, "di": 108, "20446": 108, "pace": 108, "bowler": 108, "ian": 108, "harvei": 108, "claim": 108, "victoria": 108, "15514": 108, "cotti": 108, "osc": 108, "foreign": 108, "minist": 108, "7525": 108, "sultan": 108, "specter": 108, "crown": 108, "abdullah": 108, "defenc": 108, "aviat": 108, "jeddah": 108, "saudi": 108, "agenc": 108, "2288": 108, "hi": 108, "customari": 108, "outfit": 108, "champion": 108, "damp": 108, "scalp": 108, "canada": 108, "reign": 108, "olymp": 108, "donovan": 108, "bailei": 108, "1992": 108, "linford": 108, "christi": 108, "britain": 108, "1984": 108, "1988": 108, "carl": 108, "lewi": 108, "ambigi": 108, "punctuat": 108, "chicago": 108, "digest": 108, "philadelphia": 108, "usda": 108, "york": 108, "token_issu": 108, "kean": 108, "year": 108, "contract": 108, "manchest": 108, "19072": 108, "societi": 108, "bite": 108, "deliv": 108, "19910": 108, "father": 108, "clarenc": 108, "woolmer": 108, "renam": 108, "uttar": 108, "pradesh": 108, "india": 108, "ranji": 108, "trophi": 108, "nation": 108, "championship": 108, "captain": 108, "1949": 108, "15658": 108, "19879": 108, "iii": 108, "brian": 108, "shimer": 108, "randi": 108, "jone": 108, "19104": 108}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [37, 0, 0, "-", "dataset"], [40, 0, 0, "-", "experimental"], [44, 0, 0, "-", "filter"], [45, 0, 0, "-", "internal"], [60, 0, 0, "-", "models"], [62, 0, 0, "-", "multiannotator"], [65, 0, 0, "-", "multilabel_classification"], [68, 0, 0, "-", "object_detection"], [71, 0, 0, "-", "outlier"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "regression"], [77, 0, 0, "-", "segmentation"], [81, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [16, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[13, 0, 0, "-", "data"], [14, 0, 0, "-", "data_issues"], [17, 0, 0, "-", "issue_finder"], [15, 0, 0, "-", "issue_manager_factory"], [33, 0, 0, "-", "model_outputs"], [34, 0, 0, "-", "report"], [35, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[13, 2, 1, "", "Data"], [13, 5, 1, "", "DataFormatError"], [13, 5, 1, "", "DatasetDictError"], [13, 5, 1, "", "DatasetLoadError"], [13, 2, 1, "", "Label"], [13, 2, 1, "", "MultiClass"], [13, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[14, 2, 1, "", "DataIssues"], [14, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[14, 3, 1, "", "collect_issues_from_imagelab"], [14, 3, 1, "", "collect_issues_from_issue_manager"], [14, 3, 1, "", "collect_statistics"], [14, 3, 1, "", "get_info"], [14, 3, 1, "", "get_issue_summary"], [14, 3, 1, "", "get_issues"], [14, 6, 1, "", "info"], [14, 6, 1, "", "issue_summary"], [14, 6, 1, "", "issues"], [14, 3, 1, "", "set_health_score"], [14, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[17, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[17, 3, 1, "", "find_issues"], [17, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[19, 0, 0, "-", "data_valuation"], [20, 0, 0, "-", "duplicate"], [21, 0, 0, "-", "imbalance"], [23, 0, 0, "-", "issue_manager"], [24, 0, 0, "-", "label"], [27, 0, 0, "-", "noniid"], [28, 0, 0, "-", "null"], [29, 0, 0, "-", "outlier"], [32, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[19, 6, 1, "", "DEFAULT_THRESHOLD"], [19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 6, 1, "", "near_duplicate_sets"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[24, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 3, 1, "", "get_health_summary"], [24, 6, 1, "", "health_summary_parameters"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, 2, 1, "", "NonIIDIssueManager"], [27, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "find_issues"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[28, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[29, 6, 1, "", "DEFAULT_THRESHOLDS"], [29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 6, 1, "", "metric"], [29, 6, 1, "", "ood"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[31, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, 2, 1, "", "RegressionLabelIssueManager"], [31, 1, 1, "", "find_issues_with_features"], [31, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[32, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [32, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [32, 3, 1, "", "collect_info"], [32, 6, 1, "", "description"], [32, 3, 1, "", "filter_cluster_ids"], [32, 3, 1, "", "find_issues"], [32, 3, 1, "", "get_worst_cluster"], [32, 6, 1, "", "info"], [32, 6, 1, "", "issue_name"], [32, 6, 1, "", "issue_score_key"], [32, 6, 1, "", "issues"], [32, 3, 1, "", "make_summary"], [32, 3, 1, "", "perform_clustering"], [32, 3, 1, "", "report"], [32, 6, 1, "", "summary"], [32, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, 7, 1, "", "REGISTRY"], [15, 1, 1, "", "list_default_issue_types"], [15, 1, 1, "", "list_possible_issue_types"], [15, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[33, 2, 1, "", "ModelOutput"], [33, 2, 1, "", "MultiClassPredProbs"], [33, 2, 1, "", "MultiLabelPredProbs"], [33, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[34, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[34, 3, 1, "", "get_report"], [34, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[35, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[35, 6, 1, "", "CLASSIFICATION"], [35, 6, 1, "", "MULTILABEL"], [35, 6, 1, "", "REGRESSION"], [35, 3, 1, "", "__contains__"], [35, 3, 1, "", "__getitem__"], [35, 3, 1, "", "__iter__"], [35, 3, 1, "", "__len__"], [35, 3, 1, "", "from_str"], [35, 4, 1, "", "is_classification"], [35, 4, 1, "", "is_multilabel"], [35, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[37, 1, 1, "", "find_overlapping_classes"], [37, 1, 1, "", "health_summary"], [37, 1, 1, "", "overall_label_health_score"], [37, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[38, 0, 0, "-", "cifar_cnn"], [39, 0, 0, "-", "coteaching"], [41, 0, 0, "-", "label_issues_batched"], [42, 0, 0, "-", "mnist_pytorch"], [43, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[38, 2, 1, "", "CNN"], [38, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[38, 6, 1, "", "T_destination"], [38, 3, 1, "", "__call__"], [38, 3, 1, "", "add_module"], [38, 3, 1, "", "apply"], [38, 3, 1, "", "bfloat16"], [38, 3, 1, "", "buffers"], [38, 6, 1, "", "call_super_init"], [38, 3, 1, "", "children"], [38, 3, 1, "", "compile"], [38, 3, 1, "", "cpu"], [38, 3, 1, "", "cuda"], [38, 3, 1, "", "double"], [38, 6, 1, "", "dump_patches"], [38, 3, 1, "", "eval"], [38, 3, 1, "", "extra_repr"], [38, 3, 1, "", "float"], [38, 3, 1, "id0", "forward"], [38, 3, 1, "", "get_buffer"], [38, 3, 1, "", "get_extra_state"], [38, 3, 1, "", "get_parameter"], [38, 3, 1, "", "get_submodule"], [38, 3, 1, "", "half"], [38, 3, 1, "", "ipu"], [38, 3, 1, "", "load_state_dict"], [38, 3, 1, "", "modules"], [38, 3, 1, "", "named_buffers"], [38, 3, 1, "", "named_children"], [38, 3, 1, "", "named_modules"], [38, 3, 1, "", "named_parameters"], [38, 3, 1, "", "parameters"], [38, 3, 1, "", "register_backward_hook"], [38, 3, 1, "", "register_buffer"], [38, 3, 1, "", "register_forward_hook"], [38, 3, 1, "", "register_forward_pre_hook"], [38, 3, 1, "", "register_full_backward_hook"], [38, 3, 1, "", "register_full_backward_pre_hook"], [38, 3, 1, "", "register_load_state_dict_post_hook"], [38, 3, 1, "", "register_module"], [38, 3, 1, "", "register_parameter"], [38, 3, 1, "", "register_state_dict_pre_hook"], [38, 3, 1, "", "requires_grad_"], [38, 3, 1, "", "set_extra_state"], [38, 3, 1, "", "share_memory"], [38, 3, 1, "", "state_dict"], [38, 3, 1, "", "to"], [38, 3, 1, "", "to_empty"], [38, 3, 1, "", "train"], [38, 6, 1, "", "training"], [38, 3, 1, "", "type"], [38, 3, 1, "", "xpu"], [38, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[39, 1, 1, "", "adjust_learning_rate"], [39, 1, 1, "", "evaluate"], [39, 1, 1, "", "forget_rate_scheduler"], [39, 1, 1, "", "initialize_lr_scheduler"], [39, 1, 1, "", "loss_coteaching"], [39, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[41, 2, 1, "", "LabelInspector"], [41, 7, 1, "", "adj_confident_thresholds_shared"], [41, 1, 1, "", "find_label_issues_batched"], [41, 7, 1, "", "labels_shared"], [41, 7, 1, "", "pred_probs_shared"], [41, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[41, 3, 1, "", "get_confident_thresholds"], [41, 3, 1, "", "get_label_issues"], [41, 3, 1, "", "get_num_issues"], [41, 3, 1, "", "get_quality_scores"], [41, 3, 1, "", "score_label_quality"], [41, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[42, 2, 1, "", "CNN"], [42, 2, 1, "", "SimpleNet"], [42, 1, 1, "", "get_mnist_dataset"], [42, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[42, 3, 1, "", "__init_subclass__"], [42, 6, 1, "", "batch_size"], [42, 6, 1, "", "dataset"], [42, 6, 1, "", "epochs"], [42, 3, 1, "id0", "fit"], [42, 3, 1, "", "get_metadata_routing"], [42, 3, 1, "", "get_params"], [42, 6, 1, "", "loader"], [42, 6, 1, "", "log_interval"], [42, 6, 1, "", "lr"], [42, 6, 1, "", "momentum"], [42, 6, 1, "", "no_cuda"], [42, 3, 1, "id1", "predict"], [42, 3, 1, "id4", "predict_proba"], [42, 6, 1, "", "seed"], [42, 3, 1, "", "set_fit_request"], [42, 3, 1, "", "set_params"], [42, 3, 1, "", "set_predict_proba_request"], [42, 3, 1, "", "set_predict_request"], [42, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[42, 6, 1, "", "T_destination"], [42, 3, 1, "", "__call__"], [42, 3, 1, "", "add_module"], [42, 3, 1, "", "apply"], [42, 3, 1, "", "bfloat16"], [42, 3, 1, "", "buffers"], [42, 6, 1, "", "call_super_init"], [42, 3, 1, "", "children"], [42, 3, 1, "", "compile"], [42, 3, 1, "", "cpu"], [42, 3, 1, "", "cuda"], [42, 3, 1, "", "double"], [42, 6, 1, "", "dump_patches"], [42, 3, 1, "", "eval"], [42, 3, 1, "", "extra_repr"], [42, 3, 1, "", "float"], [42, 3, 1, "", "forward"], [42, 3, 1, "", "get_buffer"], [42, 3, 1, "", "get_extra_state"], [42, 3, 1, "", "get_parameter"], [42, 3, 1, "", "get_submodule"], [42, 3, 1, "", "half"], [42, 3, 1, "", "ipu"], [42, 3, 1, "", "load_state_dict"], [42, 3, 1, "", "modules"], [42, 3, 1, "", "named_buffers"], [42, 3, 1, "", "named_children"], [42, 3, 1, "", "named_modules"], [42, 3, 1, "", "named_parameters"], [42, 3, 1, "", "parameters"], [42, 3, 1, "", "register_backward_hook"], [42, 3, 1, "", "register_buffer"], [42, 3, 1, "", "register_forward_hook"], [42, 3, 1, "", "register_forward_pre_hook"], [42, 3, 1, "", "register_full_backward_hook"], [42, 3, 1, "", "register_full_backward_pre_hook"], [42, 3, 1, "", "register_load_state_dict_post_hook"], [42, 3, 1, "", "register_module"], [42, 3, 1, "", "register_parameter"], [42, 3, 1, "", "register_state_dict_pre_hook"], [42, 3, 1, "", "requires_grad_"], [42, 3, 1, "", "set_extra_state"], [42, 3, 1, "", "share_memory"], [42, 3, 1, "", "state_dict"], [42, 3, 1, "", "to"], [42, 3, 1, "", "to_empty"], [42, 3, 1, "", "train"], [42, 6, 1, "", "training"], [42, 3, 1, "", "type"], [42, 3, 1, "", "xpu"], [42, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[43, 1, 1, "", "display_issues"], [43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[44, 1, 1, "", "find_label_issues"], [44, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [44, 1, 1, "", "find_predicted_neq_given"], [44, 7, 1, "", "pred_probs_by_class"], [44, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[46, 0, 0, "-", "label_quality_utils"], [47, 0, 0, "-", "latent_algebra"], [48, 0, 0, "-", "multiannotator_utils"], [49, 0, 0, "-", "multilabel_scorer"], [50, 0, 0, "-", "multilabel_utils"], [51, 0, 0, "-", "neighbor"], [55, 0, 0, "-", "outlier"], [56, 0, 0, "-", "token_classification_utils"], [57, 0, 0, "-", "util"], [58, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[46, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, 1, 1, "", "compute_inv_noise_matrix"], [47, 1, 1, "", "compute_noise_matrix_from_inverse"], [47, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [47, 1, 1, "", "compute_py"], [47, 1, 1, "", "compute_py_inv_noise_matrix"], [47, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[48, 1, 1, "", "assert_valid_inputs_multiannotator"], [48, 1, 1, "", "assert_valid_pred_probs"], [48, 1, 1, "", "check_consensus_label_classes"], [48, 1, 1, "", "compute_soft_cross_entropy"], [48, 1, 1, "", "find_best_temp_scaler"], [48, 1, 1, "", "format_multiannotator_labels"], [48, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[49, 2, 1, "", "Aggregator"], [49, 2, 1, "", "ClassLabelScorer"], [49, 2, 1, "", "MultilabelScorer"], [49, 1, 1, "", "exponential_moving_average"], [49, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "multilabel_py"], [49, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[49, 3, 1, "", "__call__"], [49, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[49, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [49, 6, 1, "", "NORMALIZED_MARGIN"], [49, 6, 1, "", "SELF_CONFIDENCE"], [49, 3, 1, "", "__call__"], [49, 3, 1, "", "__contains__"], [49, 3, 1, "", "__getitem__"], [49, 3, 1, "", "__iter__"], [49, 3, 1, "", "__len__"], [49, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[49, 3, 1, "", "__call__"], [49, 3, 1, "", "aggregate"], [49, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[50, 1, 1, "", "get_onehot_num_classes"], [50, 1, 1, "", "int2onehot"], [50, 1, 1, "", "onehot2int"], [50, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[52, 0, 0, "-", "knn_graph"], [53, 0, 0, "-", "metric"], [54, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[52, 7, 1, "", "DEFAULT_K"], [52, 1, 1, "", "construct_knn_graph_from_index"], [52, 1, 1, "", "correct_knn_distances_and_indices"], [52, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [52, 1, 1, "", "correct_knn_graph"], [52, 1, 1, "", "create_knn_graph_and_index"], [52, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[53, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [53, 7, 1, "", "ROW_COUNT_CUTOFF"], [53, 1, 1, "", "decide_default_metric"], [53, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[55, 1, 1, "", "correct_precision_errors"], [55, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, 1, 1, "", "color_sentence"], [56, 1, 1, "", "filter_sentence"], [56, 1, 1, "", "get_sentence"], [56, 1, 1, "", "mapping"], [56, 1, 1, "", "merge_probs"], [56, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[57, 1, 1, "", "append_extra_datapoint"], [57, 1, 1, "", "clip_noise_rates"], [57, 1, 1, "", "clip_values"], [57, 1, 1, "", "compress_int_array"], [57, 1, 1, "", "confusion_matrix"], [57, 1, 1, "", "csr_vstack"], [57, 1, 1, "", "estimate_pu_f1"], [57, 1, 1, "", "extract_indices_tf"], [57, 1, 1, "", "force_two_dimensions"], [57, 1, 1, "", "format_labels"], [57, 1, 1, "", "get_missing_classes"], [57, 1, 1, "", "get_num_classes"], [57, 1, 1, "", "get_unique_classes"], [57, 1, 1, "", "is_tensorflow_dataset"], [57, 1, 1, "", "is_torch_dataset"], [57, 1, 1, "", "num_unique_classes"], [57, 1, 1, "", "print_inverse_noise_matrix"], [57, 1, 1, "", "print_joint_matrix"], [57, 1, 1, "", "print_noise_matrix"], [57, 1, 1, "", "print_square_matrix"], [57, 1, 1, "", "remove_noise_from_class"], [57, 1, 1, "", "round_preserving_row_totals"], [57, 1, 1, "", "round_preserving_sum"], [57, 1, 1, "", "smart_display_dataframe"], [57, 1, 1, "", "subset_X_y"], [57, 1, 1, "", "subset_data"], [57, 1, 1, "", "subset_labels"], [57, 1, 1, "", "train_val_split"], [57, 1, 1, "", "unshuffle_tensorflow_dataset"], [57, 1, 1, "", "value_counts"], [57, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[58, 1, 1, "", "assert_indexing_works"], [58, 1, 1, "", "assert_nonempty_input"], [58, 1, 1, "", "assert_valid_class_labels"], [58, 1, 1, "", "assert_valid_inputs"], [58, 1, 1, "", "labels_to_array"], [58, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[61, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[61, 2, 1, "", "KerasWrapperModel"], [61, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[62, 1, 1, "", "convert_long_to_wide_dataset"], [62, 1, 1, "", "get_active_learning_scores"], [62, 1, 1, "", "get_active_learning_scores_ensemble"], [62, 1, 1, "", "get_label_quality_multiannotator"], [62, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [62, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[63, 0, 0, "-", "dataset"], [64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[63, 1, 1, "", "common_multilabel_issues"], [63, 1, 1, "", "multilabel_health_summary"], [63, 1, 1, "", "overall_multilabel_health_score"], [63, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, 1, 1, "", "find_label_issues"], [64, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[69, 1, 1, "", "compute_badloc_box_scores"], [69, 1, 1, "", "compute_overlooked_box_scores"], [69, 1, 1, "", "compute_swap_box_scores"], [69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"], [69, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[70, 1, 1, "", "bounding_box_size_distribution"], [70, 1, 1, "", "calculate_per_class_metrics"], [70, 1, 1, "", "class_label_distribution"], [70, 1, 1, "", "get_average_per_class_confusion_matrix"], [70, 1, 1, "", "get_sorted_bbox_count_idxs"], [70, 1, 1, "", "object_counts_per_image"], [70, 1, 1, "", "plot_class_distribution"], [70, 1, 1, "", "plot_class_size_distributions"], [70, 1, 1, "", "visualize"]], "cleanlab.outlier": [[71, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[71, 3, 1, "", "fit"], [71, 3, 1, "", "fit_score"], [71, 3, 1, "", "score"]], "cleanlab.rank": [[72, 1, 1, "", "find_top_issues"], [72, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [72, 1, 1, "", "get_label_quality_ensemble_scores"], [72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "get_normalized_margin_for_each_label"], [72, 1, 1, "", "get_self_confidence_for_each_label"], [72, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[74, 0, 0, "-", "learn"], [75, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[74, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[74, 3, 1, "", "__init_subclass__"], [74, 3, 1, "", "find_label_issues"], [74, 3, 1, "", "fit"], [74, 3, 1, "", "get_aleatoric_uncertainty"], [74, 3, 1, "", "get_epistemic_uncertainty"], [74, 3, 1, "", "get_label_issues"], [74, 3, 1, "", "get_metadata_routing"], [74, 3, 1, "", "get_params"], [74, 3, 1, "", "predict"], [74, 3, 1, "", "save_space"], [74, 3, 1, "", "score"], [74, 3, 1, "", "set_fit_request"], [74, 3, 1, "", "set_params"], [74, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[75, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[76, 0, 0, "-", "filter"], [78, 0, 0, "-", "rank"], [79, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[76, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[78, 1, 1, "", "get_label_quality_scores"], [78, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[79, 1, 1, "", "common_label_issues"], [79, 1, 1, "", "display_issues"], [79, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[80, 0, 0, "-", "filter"], [82, 0, 0, "-", "rank"], [83, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[80, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[82, 1, 1, "", "get_label_quality_scores"], [82, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[83, 1, 1, "", "common_label_issues"], [83, 1, 1, "", "display_issues"], [83, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 87, 88, 92, 94, 95, 98, 99, 102, 108], "count": [3, 99], "data_valu": [4, 19], "datalab": [5, 7, 9, 10, 12, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102], "creat": [7, 90, 91, 96, 99, 101], "your": [7, 84, 90, 91, 95, 96, 98, 99], "own": 7, "issu": [7, 9, 10, 22, 31, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "manag": [7, 22], "prerequisit": 7, "implement": 7, "issuemanag": [7, 90], "basic": 7, "check": [7, 96], "intermedi": 7, "advanc": [7, 90], "us": [7, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "gener": [8, 96], "cluster": [8, 96, 98], "id": 8, "guid": [9, 12], "type": [9, 10, 99], "custom": [9, 90], "cleanlab": [9, 10, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "studio": [9, 10], "easi": [9, 10, 84, 92, 94, 95], "mode": [9, 10, 84, 92, 94, 95], "can": [10, 91, 97, 98, 99, 101], "detect": [10, 89, 91, 92, 94, 95, 96, 98, 99, 103, 104], "estim": [10, 99, 101, 102], "each": 10, "input": 10, "label": [10, 24, 26, 31, 84, 87, 88, 89, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 29, 55, 71, 92, 94, 95, 102, 104], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 91, 92, 94, 95], "duplic": [10, 20, 91, 92, 94, 95, 98, 102], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 95, 96], "iid": [10, 95, 96], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 85, 96, 99, 107], "imbal": [10, 21, 96], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 92, 96, 104], "specif": [10, 22, 96, 107], "underperform": [10, 96, 98], "group": [10, 96, 98], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 28, 96], "is_null_issu": 10, "null_scor": 10, "data": [10, 13, 84, 87, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "valuat": [10, 96], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 96], "paramet": [10, 99], "get": [12, 90, 91, 101, 102, 103, 107, 108], "start": [12, 97], "api": 12, "refer": 12, "data_issu": 14, "factori": 15, "intern": [16, 45], "issue_find": 17, "issue_manag": [22, 23], "regist": 22, "ml": [22, 98, 99], "task": [22, 35], "multilabel": 25, "noniid": 27, "regress": [30, 73, 74, 75, 98, 106], "prioriti": 31, "order": 31, "find": [31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "underperforming_group": 32, "model_output": 33, "report": [34, 92], "dataset": [37, 63, 84, 88, 89, 91, 92, 95, 96, 97, 98, 99, 102, 103, 104, 106, 107, 108], "cifar_cnn": 38, "coteach": 39, "experiment": 40, "label_issues_batch": 41, "mnist_pytorch": 42, "span_classif": 43, "filter": [44, 64, 67, 76, 80, 99], "label_quality_util": 46, "latent_algebra": 47, "multiannotator_util": 48, "multilabel_scor": 49, "multilabel_util": 50, "neighbor": 51, "knn_graph": 52, "metric": 53, "search": [54, 90], "token_classification_util": 56, "util": 57, "valid": [58, 92, 105], "fasttext": 59, "model": [60, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106], "kera": 61, "multiannot": [62, 101], "multilabel_classif": 65, "rank": [66, 69, 72, 75, 78, 82, 99], "object_detect": 68, "summari": [70, 79, 83], "learn": [74, 91, 98, 99], "segment": [77, 107], "token_classif": [81, 108], "open": [84, 98], "sourc": [84, 98], "document": 84, "quickstart": 84, "1": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "instal": [84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "2": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "common": [84, 85, 108], "3": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "handl": [84, 98], "error": [84, 88, 92, 98, 99, 101, 102, 103, 106, 107, 108], "train": [84, 87, 88, 89, 96, 98, 104, 106], "robust": [84, 87, 88, 99, 106], "noisi": [84, 87, 88, 99, 106], "4": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 103, 104, 106], "curat": 84, "fix": [84, 98], "level": [84, 97, 99, 108], "5": [84, 87, 89, 91, 92, 94, 96, 99, 101, 106], "improv": [84, 101], "via": [84, 99, 101], "mani": [84, 99], "other": [84, 101, 103, 106], "techniqu": 84, "contribut": 84, "how": [85, 98, 99, 101, 102, 108], "migrat": 85, "version": 85, "0": 85, "from": [85, 87, 88, 90, 91, 99, 106], "pre": [85, 89, 96, 98, 104], "function": [85, 90], "name": 85, "chang": 85, "modul": [85, 99], "new": 85, "remov": 85, "argument": [85, 90], "variabl": 85, "cleanlearn": [86, 98, 99], "tutori": [86, 93, 97, 100], "structur": 87, "tabular": [87, 94], "requir": [87, 88, 90, 91, 92, 94, 95, 101, 102, 103, 104, 106, 107, 108], "depend": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "load": [87, 88, 89, 90, 91, 94, 95, 96, 106], "process": [87, 94, 104, 106], "select": [87, 94], "comput": [87, 89, 92, 94, 95, 96, 98, 101, 105], "out": [87, 89, 90, 91, 92, 94, 95, 101, 105], "sampl": [87, 89, 90, 91, 92, 94, 95, 101, 105], "predict": [87, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 105], "probabl": [87, 89, 90, 91, 92, 94, 95, 96, 101, 105], "more": [87, 88, 91, 99, 106], "text": [88, 95, 96, 108], "format": [88, 95, 98, 102, 103], "defin": [88, 92, 95, 96, 106], "potenti": [88, 101, 106], "an": [89, 92, 98], "audio": 89, "import": [89, 90, 91, 92, 97, 99, 101], "them": [89, 97, 99], "speechbrain": 89, "featur": [89, 92, 104], "fit": 89, "linear": 89, "workflow": [90, 96, 99], "audit": [90, 91], "classifi": [90, 91, 96], "instanti": 90, "object": [90, 96, 103], "increment": 90, "specifi": [90, 98], "nondefault": 90, "save": 90, "ad": 90, "A": 91, "unifi": 91, "all": [91, 99], "kind": [91, 103], "skip": [91, 97, 99, 101], "detail": [91, 97, 99, 101], "about": 91, "addit": 91, "inform": [91, 92], "fetch": [92, 97], "normal": 92, "fashion": 92, "mnist": 92, "prepar": [92, 96], "k": [92, 94, 105], "fold": [92, 105], "cross": [92, 105], "embed": [92, 104], "7": [92, 99], "view": 92, "most": [92, 108], "like": 92, "exampl": [92, 98, 99, 104], "sever": 92, "set": [92, 99], "dark": [92, 96], "top": [92, 107], "low": 92, "numer": 94, "categor": [94, 96], "column": 94, "construct": 94, "nearest": 94, "neighbour": 94, "graph": [94, 96], "drift": [95, 102], "miscellan": 96, "acceler": 96, "knn": 96, "obtain": 96, "identifi": [96, 98, 103], "explan": 96, "vector": 96, "perform": 96, "visual": [96, 99, 103, 104, 107], "score": [96, 99, 101, 102, 103, 107, 108], "synthet": 96, "result": 96, "predefin": 96, "slice": [96, 98], "i": [96, 98, 99, 105], "catch": 96, "valu": 96, "encod": 96, "initi": [96, 101], "sort": 96, "6": [96, 99], "spuriou": 96, "correl": 96, "pass": 96, "relat": 96, "transform": 96, "imageenh": 96, "induc": 96, "properti": 96, "origin": 96, "understand": 97, "evalu": 97, "health": [97, 99], "8": [97, 99], "popular": 97, "faq": 98, "what": [98, 99, 105], "do": [98, 99], "infer": 98, "correct": 98, "ha": 98, "flag": 98, "should": 98, "v": 98, "test": [98, 99, 104], "big": 98, "limit": 98, "memori": 98, "why": 98, "isn": 98, "t": 98, "work": [98, 99, 101, 108], "me": 98, "differ": [98, 103], "clean": [98, 99], "final": 98, "hyperparamet": 98, "tune": 98, "onli": 98, "one": [98, 99, 102, 107], "doe": [98, 101, 108], "take": 98, "so": 98, "long": 98, "when": [98, 99], "run": 98, "licens": 98, "under": 98, "answer": 98, "question": 98, "The": 99, "centric": 99, "ai": 99, "machin": 99, "find_label_issu": 99, "line": 99, "code": 99, "twenti": 99, "lowest": 99, "qualiti": [99, 101, 102, 103, 107, 108], "see": 99, "now": 99, "let": 99, "": 99, "happen": 99, "we": 99, "merg": 99, "seafoam": 99, "green": 99, "yellow": 99, "too": 99, "you": 99, "re": 99, "One": 99, "rule": 99, "overal": [99, 107], "accur": 99, "thi": 99, "directli": 99, "fulli": 99, "character": 99, "nois": 99, "matrix": [99, 102], "joint": 99, "prior": 99, "true": 99, "distribut": 99, "flip": 99, "rate": 99, "ani": 99, "again": 99, "support": 99, "lot": 99, "method": 99, "filter_bi": 99, "automat": 99, "everi": 99, "uniqu": 99, "num_label_issu": 99, "threshold": 99, "found": 99, "Not": 99, "sure": 99, "ensembl": 99, "multipl": [99, 101], "predictor": 99, "consensu": 101, "annot": 101, "major": 101, "vote": 101, "better": 101, "statist": 101, "compar": 101, "inspect": 101, "retrain": 101, "further": 101, "multi": 102, "beyond": 102, "mislabel": [102, 107, 108], "given": 102, "hot": 102, "binari": 102, "without": 102, "applic": 102, "real": 102, "download": [103, 107, 108], "objectlab": 103, "exploratori": 103, "analysi": 103, "pytorch": 104, "timm": 104, "cifar10": 104, "some": 104, "pred_prob": [104, 107, 108], "wai": 106, "semant": 107, "which": 107, "ar": 107, "commonli": 107, "focus": 107, "token": 108, "word": 108, "sentenc": 108, "contain": 108, "particular": 108}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [19, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"], [96, "id8"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[96, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[96, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[96, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.data"], [14, "module-cleanlab.datalab.internal.data_issues"], [15, "module-cleanlab.datalab.internal.issue_manager_factory"], [16, "module-cleanlab.datalab.internal"], [17, "module-cleanlab.datalab.internal.issue_finder"], [19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [20, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [21, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [27, "module-cleanlab.datalab.internal.issue_manager.noniid"], [28, "module-cleanlab.datalab.internal.issue_manager.null"], [29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [33, "module-cleanlab.datalab.internal.model_outputs"], [34, "module-cleanlab.datalab.internal.report"], [35, "module-cleanlab.datalab.internal.task"], [37, "module-cleanlab.dataset"], [38, "module-cleanlab.experimental.cifar_cnn"], [39, "module-cleanlab.experimental.coteaching"], [40, "module-cleanlab.experimental"], [41, "module-cleanlab.experimental.label_issues_batched"], [42, "module-cleanlab.experimental.mnist_pytorch"], [43, "module-cleanlab.experimental.span_classification"], [44, "module-cleanlab.filter"], [45, "module-cleanlab.internal"], [46, "module-cleanlab.internal.label_quality_utils"], [47, "module-cleanlab.internal.latent_algebra"], [48, "module-cleanlab.internal.multiannotator_utils"], [49, "module-cleanlab.internal.multilabel_scorer"], [50, "module-cleanlab.internal.multilabel_utils"], [51, "module-cleanlab.internal.neighbor"], [52, "module-cleanlab.internal.neighbor.knn_graph"], [53, "module-cleanlab.internal.neighbor.metric"], [54, "module-cleanlab.internal.neighbor.search"], [55, "module-cleanlab.internal.outlier"], [56, "module-cleanlab.internal.token_classification_utils"], [57, "module-cleanlab.internal.util"], [58, "module-cleanlab.internal.validation"], [60, "module-cleanlab.models"], [61, "module-cleanlab.models.keras"], [62, "module-cleanlab.multiannotator"], [63, "module-cleanlab.multilabel_classification.dataset"], [64, "module-cleanlab.multilabel_classification.filter"], [65, "module-cleanlab.multilabel_classification"], [66, "module-cleanlab.multilabel_classification.rank"], [67, "module-cleanlab.object_detection.filter"], [68, "module-cleanlab.object_detection"], [69, "module-cleanlab.object_detection.rank"], [70, "module-cleanlab.object_detection.summary"], [71, "module-cleanlab.outlier"], [72, "module-cleanlab.rank"], [73, "module-cleanlab.regression"], [74, "module-cleanlab.regression.learn"], [75, "module-cleanlab.regression.rank"], [76, "module-cleanlab.segmentation.filter"], [77, "module-cleanlab.segmentation"], [78, "module-cleanlab.segmentation.rank"], [79, "module-cleanlab.segmentation.summary"], [80, "module-cleanlab.token_classification.filter"], [81, "module-cleanlab.token_classification"], [82, "module-cleanlab.token_classification.rank"], [83, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[13, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[13, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[13, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[13, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[13, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[16, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[17, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[28, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[34, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[34, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[35, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[35, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[37, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.forward"], [38, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[40, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [42, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [42, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [42, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[44, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[44, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[44, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[45, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[46, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index 0c56c3881..aa00ed6e9 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:24.117516Z",
-     "iopub.status.busy": "2024-07-02T12:00:24.117048Z",
-     "iopub.status.idle": "2024-07-02T12:00:25.333194Z",
-     "shell.execute_reply": "2024-07-02T12:00:25.332647Z"
+     "iopub.execute_input": "2024-07-02T15:09:49.406100Z",
+     "iopub.status.busy": "2024-07-02T15:09:49.405638Z",
+     "iopub.status.idle": "2024-07-02T15:09:50.626225Z",
+     "shell.execute_reply": "2024-07-02T15:09:50.625679Z"
     },
     "nbsphinx": "hidden"
    },
@@ -126,7 +126,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -151,10 +151,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:25.335570Z",
-     "iopub.status.busy": "2024-07-02T12:00:25.335300Z",
-     "iopub.status.idle": "2024-07-02T12:00:25.352966Z",
-     "shell.execute_reply": "2024-07-02T12:00:25.352544Z"
+     "iopub.execute_input": "2024-07-02T15:09:50.628776Z",
+     "iopub.status.busy": "2024-07-02T15:09:50.628382Z",
+     "iopub.status.idle": "2024-07-02T15:09:50.646656Z",
+     "shell.execute_reply": "2024-07-02T15:09:50.646174Z"
     }
    },
    "outputs": [],
@@ -195,10 +195,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:25.355177Z",
-     "iopub.status.busy": "2024-07-02T12:00:25.354929Z",
-     "iopub.status.idle": "2024-07-02T12:00:25.498882Z",
-     "shell.execute_reply": "2024-07-02T12:00:25.498315Z"
+     "iopub.execute_input": "2024-07-02T15:09:50.649040Z",
+     "iopub.status.busy": "2024-07-02T15:09:50.648771Z",
+     "iopub.status.idle": "2024-07-02T15:09:50.799686Z",
+     "shell.execute_reply": "2024-07-02T15:09:50.799107Z"
     }
    },
    "outputs": [
@@ -305,10 +305,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:25.528732Z",
-     "iopub.status.busy": "2024-07-02T12:00:25.528329Z",
-     "iopub.status.idle": "2024-07-02T12:00:25.532259Z",
-     "shell.execute_reply": "2024-07-02T12:00:25.531790Z"
+     "iopub.execute_input": "2024-07-02T15:09:50.830515Z",
+     "iopub.status.busy": "2024-07-02T15:09:50.830286Z",
+     "iopub.status.idle": "2024-07-02T15:09:50.833956Z",
+     "shell.execute_reply": "2024-07-02T15:09:50.833391Z"
     }
    },
    "outputs": [],
@@ -329,10 +329,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:25.534236Z",
-     "iopub.status.busy": "2024-07-02T12:00:25.534064Z",
-     "iopub.status.idle": "2024-07-02T12:00:25.542721Z",
-     "shell.execute_reply": "2024-07-02T12:00:25.542178Z"
+     "iopub.execute_input": "2024-07-02T15:09:50.836142Z",
+     "iopub.status.busy": "2024-07-02T15:09:50.835713Z",
+     "iopub.status.idle": "2024-07-02T15:09:50.843960Z",
+     "shell.execute_reply": "2024-07-02T15:09:50.843409Z"
     }
    },
    "outputs": [],
@@ -384,10 +384,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:25.544841Z",
-     "iopub.status.busy": "2024-07-02T12:00:25.544667Z",
-     "iopub.status.idle": "2024-07-02T12:00:25.547142Z",
-     "shell.execute_reply": "2024-07-02T12:00:25.546723Z"
+     "iopub.execute_input": "2024-07-02T15:09:50.846292Z",
+     "iopub.status.busy": "2024-07-02T15:09:50.845872Z",
+     "iopub.status.idle": "2024-07-02T15:09:50.848589Z",
+     "shell.execute_reply": "2024-07-02T15:09:50.848046Z"
     }
    },
    "outputs": [],
@@ -409,10 +409,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:25.549121Z",
-     "iopub.status.busy": "2024-07-02T12:00:25.548952Z",
-     "iopub.status.idle": "2024-07-02T12:00:26.069775Z",
-     "shell.execute_reply": "2024-07-02T12:00:26.069166Z"
+     "iopub.execute_input": "2024-07-02T15:09:50.850511Z",
+     "iopub.status.busy": "2024-07-02T15:09:50.850252Z",
+     "iopub.status.idle": "2024-07-02T15:09:51.372873Z",
+     "shell.execute_reply": "2024-07-02T15:09:51.372266Z"
     }
    },
    "outputs": [],
@@ -446,10 +446,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:26.072294Z",
-     "iopub.status.busy": "2024-07-02T12:00:26.072111Z",
-     "iopub.status.idle": "2024-07-02T12:00:27.964122Z",
-     "shell.execute_reply": "2024-07-02T12:00:27.963476Z"
+     "iopub.execute_input": "2024-07-02T15:09:51.375361Z",
+     "iopub.status.busy": "2024-07-02T15:09:51.375157Z",
+     "iopub.status.idle": "2024-07-02T15:09:53.243284Z",
+     "shell.execute_reply": "2024-07-02T15:09:53.242604Z"
     }
    },
    "outputs": [
@@ -481,10 +481,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:27.966793Z",
-     "iopub.status.busy": "2024-07-02T12:00:27.966128Z",
-     "iopub.status.idle": "2024-07-02T12:00:27.975803Z",
-     "shell.execute_reply": "2024-07-02T12:00:27.975266Z"
+     "iopub.execute_input": "2024-07-02T15:09:53.246075Z",
+     "iopub.status.busy": "2024-07-02T15:09:53.245483Z",
+     "iopub.status.idle": "2024-07-02T15:09:53.255700Z",
+     "shell.execute_reply": "2024-07-02T15:09:53.255167Z"
     }
    },
    "outputs": [
@@ -605,10 +605,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:27.977956Z",
-     "iopub.status.busy": "2024-07-02T12:00:27.977648Z",
-     "iopub.status.idle": "2024-07-02T12:00:27.981829Z",
-     "shell.execute_reply": "2024-07-02T12:00:27.981303Z"
+     "iopub.execute_input": "2024-07-02T15:09:53.257868Z",
+     "iopub.status.busy": "2024-07-02T15:09:53.257460Z",
+     "iopub.status.idle": "2024-07-02T15:09:53.261706Z",
+     "shell.execute_reply": "2024-07-02T15:09:53.261166Z"
     }
    },
    "outputs": [],
@@ -633,10 +633,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:27.984025Z",
-     "iopub.status.busy": "2024-07-02T12:00:27.983701Z",
-     "iopub.status.idle": "2024-07-02T12:00:27.990825Z",
-     "shell.execute_reply": "2024-07-02T12:00:27.990380Z"
+     "iopub.execute_input": "2024-07-02T15:09:53.263822Z",
+     "iopub.status.busy": "2024-07-02T15:09:53.263391Z",
+     "iopub.status.idle": "2024-07-02T15:09:53.270955Z",
+     "shell.execute_reply": "2024-07-02T15:09:53.270531Z"
     }
    },
    "outputs": [],
@@ -658,10 +658,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:27.992803Z",
-     "iopub.status.busy": "2024-07-02T12:00:27.992505Z",
-     "iopub.status.idle": "2024-07-02T12:00:28.104238Z",
-     "shell.execute_reply": "2024-07-02T12:00:28.103750Z"
+     "iopub.execute_input": "2024-07-02T15:09:53.273195Z",
+     "iopub.status.busy": "2024-07-02T15:09:53.272768Z",
+     "iopub.status.idle": "2024-07-02T15:09:53.386175Z",
+     "shell.execute_reply": "2024-07-02T15:09:53.385548Z"
     }
    },
    "outputs": [
@@ -691,10 +691,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:28.106465Z",
-     "iopub.status.busy": "2024-07-02T12:00:28.106127Z",
-     "iopub.status.idle": "2024-07-02T12:00:28.108811Z",
-     "shell.execute_reply": "2024-07-02T12:00:28.108400Z"
+     "iopub.execute_input": "2024-07-02T15:09:53.388505Z",
+     "iopub.status.busy": "2024-07-02T15:09:53.388085Z",
+     "iopub.status.idle": "2024-07-02T15:09:53.390961Z",
+     "shell.execute_reply": "2024-07-02T15:09:53.390511Z"
     }
    },
    "outputs": [],
@@ -715,10 +715,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:28.110759Z",
-     "iopub.status.busy": "2024-07-02T12:00:28.110457Z",
-     "iopub.status.idle": "2024-07-02T12:00:30.104044Z",
-     "shell.execute_reply": "2024-07-02T12:00:30.103432Z"
+     "iopub.execute_input": "2024-07-02T15:09:53.392859Z",
+     "iopub.status.busy": "2024-07-02T15:09:53.392685Z",
+     "iopub.status.idle": "2024-07-02T15:09:55.359879Z",
+     "shell.execute_reply": "2024-07-02T15:09:55.359148Z"
     }
    },
    "outputs": [],
@@ -738,10 +738,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:30.106906Z",
-     "iopub.status.busy": "2024-07-02T12:00:30.106328Z",
-     "iopub.status.idle": "2024-07-02T12:00:30.117548Z",
-     "shell.execute_reply": "2024-07-02T12:00:30.117099Z"
+     "iopub.execute_input": "2024-07-02T15:09:55.362970Z",
+     "iopub.status.busy": "2024-07-02T15:09:55.362388Z",
+     "iopub.status.idle": "2024-07-02T15:09:55.374161Z",
+     "shell.execute_reply": "2024-07-02T15:09:55.373705Z"
     }
    },
    "outputs": [
@@ -771,10 +771,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-02T12:00:30.119573Z",
-     "iopub.status.busy": "2024-07-02T12:00:30.119249Z",
-     "iopub.status.idle": "2024-07-02T12:00:30.150922Z",
-     "shell.execute_reply": "2024-07-02T12:00:30.150454Z"
+     "iopub.execute_input": "2024-07-02T15:09:55.376352Z",
+     "iopub.status.busy": "2024-07-02T15:09:55.375903Z",
+     "iopub.status.idle": "2024-07-02T15:09:55.432383Z",
+     "shell.execute_reply": "2024-07-02T15:09:55.431845Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index c0155c6ba..9c18d0c40 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -817,7 +817,7 @@ 

2. Load and format the text dataset
 This dataset has 10 classes.
-Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'getting_spare_card', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay'}
+Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'change_pin'}
 

Let’s print the first example in the train set.

@@ -880,43 +880,43 @@

2. Load and format the text dataset

-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1213,7 +1213,7 @@

4. Train a more robust model from noisy labels -{"state": {"5d425fc517de40599859741dfdf6bb2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7a181aaf8967410dae1ce2d0e0d9856a": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "6f739705eccc46afb2020460a828b56b": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5d425fc517de40599859741dfdf6bb2e", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_7a181aaf8967410dae1ce2d0e0d9856a", "tabbable": null, "tooltip": null, "value": 391.0}}, "796c15ee275d4dff935fe8e20583896d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "33adffdfde344f24ab11355d2abf0744": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ebe70157b15a43cebe4c33d29744783b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_796c15ee275d4dff935fe8e20583896d", "placeholder": "\u200b", "style": "IPY_MODEL_33adffdfde344f24ab11355d2abf0744", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "465f57a032274e4dadbee2eb87856ef1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "465f0d2ce3444dfd9bd85fd2529dc52c": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d015bcf5ce69476ab54fd8e945eaa689": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_465f57a032274e4dadbee2eb87856ef1", "placeholder": "\u200b", "style": "IPY_MODEL_465f0d2ce3444dfd9bd85fd2529dc52c", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200766.4kB/s]"}}, "e9a141532bb34d7697db7a780d7a2002": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e89a8a43528e42c38eca656e48b7da7e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ebe70157b15a43cebe4c33d29744783b", "IPY_MODEL_6f739705eccc46afb2020460a828b56b", "IPY_MODEL_d015bcf5ce69476ab54fd8e945eaa689"], "layout": "IPY_MODEL_e9a141532bb34d7697db7a780d7a2002", "tabbable": null, "tooltip": null}}, "078724370bc24c649597fb04791e1a0e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5376601de0794106a7b8777224bcafd4": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "b83ba78e112b427d90a55129eecf514c": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_078724370bc24c649597fb04791e1a0e", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5376601de0794106a7b8777224bcafd4", "tabbable": null, "tooltip": null, "value": 2211.0}}, "1d21e4a5af7c4ae0aadfd84ea1555716": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "038607d420fe468e857b145df291678c": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "52a83385affa4985b26bec259ee14740": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1d21e4a5af7c4ae0aadfd84ea1555716", "placeholder": "\u200b", "style": "IPY_MODEL_038607d420fe468e857b145df291678c", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "982a96cc3c0b4c00938e722c374cd707": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9e5f0df62415449eb138994f79e6d9e0": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f7e3eba4cb63469d997967f982f4a1df": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_982a96cc3c0b4c00938e722c374cd707", "placeholder": "\u200b", "style": "IPY_MODEL_9e5f0df62415449eb138994f79e6d9e0", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007389kB/s]"}}, "8e1a3b5aa6b14adc8ebdc1566696e505": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ca42a9ff17da48fab63132c9d67266dd": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_52a83385affa4985b26bec259ee14740", "IPY_MODEL_b83ba78e112b427d90a55129eecf514c", "IPY_MODEL_f7e3eba4cb63469d997967f982f4a1df"], "layout": "IPY_MODEL_8e1a3b5aa6b14adc8ebdc1566696e505", "tabbable": null, "tooltip": null}}, "b03695b8bbe44d13a5612d5120ea2a28": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "25890da51b1a4e519cdfb13a8c6a9b74": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "036fc6d42c084bba8e6ff1d651c36d55": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b03695b8bbe44d13a5612d5120ea2a28", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_25890da51b1a4e519cdfb13a8c6a9b74", "tabbable": null, "tooltip": null, "value": 665.0}}, "e7e2566da91d4dde83d80ed19a91e263": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "20b3e8ae925a497db651bb3e420ccedd": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d66a39fee74f43488e2d84a8afff525a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e7e2566da91d4dde83d80ed19a91e263", "placeholder": "\u200b", "style": "IPY_MODEL_20b3e8ae925a497db651bb3e420ccedd", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "285337fc5df34cbfa6c9ed52458ebf3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "80145043305a4ffabe28cb2a16f379de": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "1372d9e4b7724cdead58971eebb0f969": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_285337fc5df34cbfa6c9ed52458ebf3d", "placeholder": "\u200b", "style": "IPY_MODEL_80145043305a4ffabe28cb2a16f379de", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u2007118kB/s]"}}, "330425c288064ecea245f9a589f86dce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "283fc6563d5645c9a2d53edd642983d4": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_d66a39fee74f43488e2d84a8afff525a", "IPY_MODEL_036fc6d42c084bba8e6ff1d651c36d55", "IPY_MODEL_1372d9e4b7724cdead58971eebb0f969"], "layout": "IPY_MODEL_330425c288064ecea245f9a589f86dce", "tabbable": null, "tooltip": null}}, "2adec3ea4ab147fca4247ce82a707f41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "03cd490c005341539cee9b6bd0c64509": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "eccbc90dc8114497bb7438d438edc7f2": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_2adec3ea4ab147fca4247ce82a707f41", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_03cd490c005341539cee9b6bd0c64509", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "3c023990927b4f04a4351901432e7d8f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9814c1a1993c40a683e790580ecf178a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d6ea8d2f7af14ef69353a0ae60cf677e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3c023990927b4f04a4351901432e7d8f", "placeholder": "\u200b", "style": "IPY_MODEL_9814c1a1993c40a683e790580ecf178a", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "0298beb970f94688915a8e32a774126c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "eb0935dc83294cc7a1acad3dd963f608": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "060119cb42d74e19bb4e92690f697a5e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0298beb970f94688915a8e32a774126c", "placeholder": "\u200b", "style": "IPY_MODEL_eb0935dc83294cc7a1acad3dd963f608", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:00<00:00,\u2007200MB/s]"}}, "a6d4217338164b029ad977bd11fb8d9e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e90e40189b0e460d90a444df7fe6d1a9": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_d6ea8d2f7af14ef69353a0ae60cf677e", "IPY_MODEL_eccbc90dc8114497bb7438d438edc7f2", "IPY_MODEL_060119cb42d74e19bb4e92690f697a5e"], "layout": "IPY_MODEL_a6d4217338164b029ad977bd11fb8d9e", "tabbable": null, "tooltip": null}}, "da42c63dc73b49f8b62f507344120b1a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "60b3d9bfd8e147ef947e8e81fd9fb70e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c08801bd218a4933bad779d4baa0b544": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_da42c63dc73b49f8b62f507344120b1a", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_60b3d9bfd8e147ef947e8e81fd9fb70e", "tabbable": null, "tooltip": null, "value": 466062.0}}, "757f11a9bf36405e89b2715bc5278a25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e7529ab3626947b3ac4bc76edafa711a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "365ffce5f68c44638088fdbae83f6f7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_757f11a9bf36405e89b2715bc5278a25", "placeholder": "\u200b", "style": "IPY_MODEL_e7529ab3626947b3ac4bc76edafa711a", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "581a302d09fd45e49c7f87d39dd3c921": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0607d3fa923e46caa07bbdf1220db2b6": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "0f51dc70dda342b1b6b17e56b63d5f14": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_581a302d09fd45e49c7f87d39dd3c921", "placeholder": "\u200b", "style": "IPY_MODEL_0607d3fa923e46caa07bbdf1220db2b6", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u200715.8MB/s]"}}, "e14305181a4f4a1aba92bf0159aa7bf3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "018045ff81b24bf7b8b7b92eeb3e59db": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_365ffce5f68c44638088fdbae83f6f7b", "IPY_MODEL_c08801bd218a4933bad779d4baa0b544", "IPY_MODEL_0f51dc70dda342b1b6b17e56b63d5f14"], "layout": "IPY_MODEL_e14305181a4f4a1aba92bf0159aa7bf3", "tabbable": null, "tooltip": null}}, "0bade9b66cc6401491c957e37747b252": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "290e38f7423c457a9127d9cd3394ab4e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "654d58802fd94f6ead893ddbb0e3d131": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0bade9b66cc6401491c957e37747b252", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_290e38f7423c457a9127d9cd3394ab4e", "tabbable": null, "tooltip": null, "value": 48.0}}, "1820812db0184a3ba2bd25871cc78e2f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "57bc4c06dbfc46c89ae707951f55ed3a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d25c524b9c4444829470ac057e0fae42": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1820812db0184a3ba2bd25871cc78e2f", "placeholder": "\u200b", "style": "IPY_MODEL_57bc4c06dbfc46c89ae707951f55ed3a", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "fad0806770b14f9086fd1b3b755413fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "814ed56db234446a92fe939efe5a477b": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "66ee22f8cf5b4a78ab33aee929a5fbd0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fad0806770b14f9086fd1b3b755413fb", "placeholder": "\u200b", "style": "IPY_MODEL_814ed56db234446a92fe939efe5a477b", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20078.21kB/s]"}}, "98b6d5dd0c4546948933a4d8faa4bbc9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "eb1f3f2a9964471a8a7688badac98c84": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_d25c524b9c4444829470ac057e0fae42", "IPY_MODEL_654d58802fd94f6ead893ddbb0e3d131", "IPY_MODEL_66ee22f8cf5b4a78ab33aee929a5fbd0"], "layout": "IPY_MODEL_98b6d5dd0c4546948933a4d8faa4bbc9", "tabbable": null, "tooltip": null}}, "de5b529ba5b7466f857e19bdfdcddd30": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c187a19bc3e941088eef67c273bf61ed": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "dbc2947dd29d4e92a20d79a1f5606f98": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_de5b529ba5b7466f857e19bdfdcddd30", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c187a19bc3e941088eef67c273bf61ed", "tabbable": null, "tooltip": null, "value": 231508.0}}, "5ec8a2f5f2034c158f6d2c361a60ea25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "853f7130b22d49bfaf39b5d7bf7af4ce": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "c9ff9f5312794e5381359492c09edaaf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5ec8a2f5f2034c158f6d2c361a60ea25", "placeholder": "\u200b", "style": "IPY_MODEL_853f7130b22d49bfaf39b5d7bf7af4ce", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "4ed3c0ca07f04a87b53a6c5d68d36cf6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5efd9793a22541dd9dd8f09fcfbec967": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "cc4d2d6857724ea0a2f3ec85a6f395ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4ed3c0ca07f04a87b53a6c5d68d36cf6", "placeholder": "\u200b", "style": "IPY_MODEL_5efd9793a22541dd9dd8f09fcfbec967", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u200731.3MB/s]"}}, "f3decebc5af44971b456d5da642e43b2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "edb46e1892c744119cd3f4a130dfb3e3": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_c9ff9f5312794e5381359492c09edaaf", "IPY_MODEL_dbc2947dd29d4e92a20d79a1f5606f98", "IPY_MODEL_cc4d2d6857724ea0a2f3ec85a6f395ea"], "layout": "IPY_MODEL_f3decebc5af44971b456d5da642e43b2", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"3edc8ef534274f15b48e90d83188b494": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d76fd6c68456446baaae1b57ccf46065": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "914b3f22f6b446608a80b2bd86dde07b": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3edc8ef534274f15b48e90d83188b494", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_d76fd6c68456446baaae1b57ccf46065", "tabbable": null, "tooltip": null, "value": 391.0}}, "038891c782ab46f4ba836914abbfc5ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bc071c694fb7476a8213ad06a4cef625": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "fcaf1661c4314a27a12b7c20cbce3cc8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_038891c782ab46f4ba836914abbfc5ce", "placeholder": "\u200b", "style": "IPY_MODEL_bc071c694fb7476a8213ad06a4cef625", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "80258d9522ac4e858c828ed1eba37fb6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2df9eaceb2e14e8fa3a19b25c7d76f35": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6e6cda23dd024255af57333cc9d9bfea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_80258d9522ac4e858c828ed1eba37fb6", "placeholder": "\u200b", "style": "IPY_MODEL_2df9eaceb2e14e8fa3a19b25c7d76f35", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200767.9kB/s]"}}, "de51505fe22c4b5fa21abe797f7a7d7a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c943f13df8c04e77aae4c7ca2cbbd613": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_fcaf1661c4314a27a12b7c20cbce3cc8", "IPY_MODEL_914b3f22f6b446608a80b2bd86dde07b", "IPY_MODEL_6e6cda23dd024255af57333cc9d9bfea"], "layout": "IPY_MODEL_de51505fe22c4b5fa21abe797f7a7d7a", "tabbable": null, "tooltip": null}}, "b01dff0aaef24c71a79429be65e9ce07": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3d25676d34544705a2bcbe334c412efc": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "43953f2b89bb41408d6583c47201913d": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b01dff0aaef24c71a79429be65e9ce07", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3d25676d34544705a2bcbe334c412efc", "tabbable": null, "tooltip": null, "value": 2211.0}}, "4cbe66fa6f75404e9a5e6814de5f2203": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1740b9557dc0484c95a45ed25a63585f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "5316135ae1ac4592a7b4239c553c02c6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4cbe66fa6f75404e9a5e6814de5f2203", "placeholder": "\u200b", "style": "IPY_MODEL_1740b9557dc0484c95a45ed25a63585f", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "9f0bab6dcc71427ba6d39cfcb15d877f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7416ceea465444209f091f4e54c9aebe": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e0adbaa3ffa64032933136aebb9a1f7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9f0bab6dcc71427ba6d39cfcb15d877f", "placeholder": "\u200b", "style": "IPY_MODEL_7416ceea465444209f091f4e54c9aebe", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007401kB/s]"}}, "cbb206b9c0d64fe0b479fb7cc9df570b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "06405b534d7c49db89f3d29b52da1f80": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_5316135ae1ac4592a7b4239c553c02c6", "IPY_MODEL_43953f2b89bb41408d6583c47201913d", "IPY_MODEL_e0adbaa3ffa64032933136aebb9a1f7b"], "layout": "IPY_MODEL_cbb206b9c0d64fe0b479fb7cc9df570b", "tabbable": null, "tooltip": null}}, "fabb2363238a4c1a91de78e13b8a0a3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6680b7e73b4d4bb589857c254d0632ef": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c18be04c279b46b48b79ee15e32435e6": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fabb2363238a4c1a91de78e13b8a0a3e", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6680b7e73b4d4bb589857c254d0632ef", "tabbable": null, "tooltip": null, "value": 665.0}}, "6ef7ce225dc34f17b94e5dbe3a820e35": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a99583c6f0e24d69872cbda693c983eb": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "59eec5b7926b48ae8d162ecab0db1ec2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6ef7ce225dc34f17b94e5dbe3a820e35", "placeholder": "\u200b", "style": "IPY_MODEL_a99583c6f0e24d69872cbda693c983eb", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "c33f4e7c33ca4ebd8cb646737881a850": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3bd0776bd0b840e8b2a1d15afd1e27f8": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e32cd0a87f0f4ad18f72cc198abad4fd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c33f4e7c33ca4ebd8cb646737881a850", "placeholder": "\u200b", "style": "IPY_MODEL_3bd0776bd0b840e8b2a1d15afd1e27f8", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u2007118kB/s]"}}, "505538aed9b54bebb4c3f0107daadf44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1146fbcfe9cf41da81392df94520265c": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_59eec5b7926b48ae8d162ecab0db1ec2", "IPY_MODEL_c18be04c279b46b48b79ee15e32435e6", "IPY_MODEL_e32cd0a87f0f4ad18f72cc198abad4fd"], "layout": "IPY_MODEL_505538aed9b54bebb4c3f0107daadf44", "tabbable": null, "tooltip": null}}, "ea436305a9d143d09b28e8d32eb3020a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "360120e496a841119a2a238bfb8c7631": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "38b1bd4b5ec44d41981a433be46d45f0": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ea436305a9d143d09b28e8d32eb3020a", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_360120e496a841119a2a238bfb8c7631", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "7b0e3596ec8d47089daa31da5c13b681": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "502164ca582c4a0bbb3b20dc28ae0b2c": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b65073e7ff2e476bb34d75a0faf77c6f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7b0e3596ec8d47089daa31da5c13b681", "placeholder": "\u200b", "style": "IPY_MODEL_502164ca582c4a0bbb3b20dc28ae0b2c", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "6ed70f51e8b34c1094db85d132e5ce9c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "acbecbc8b02440768730f4625e2f605e": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "9a092b3142994e27b931efc3d8141660": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6ed70f51e8b34c1094db85d132e5ce9c", "placeholder": "\u200b", "style": "IPY_MODEL_acbecbc8b02440768730f4625e2f605e", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:00<00:00,\u2007239MB/s]"}}, "7f092e8ede4e4d63a5fde7a51f92e32c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bd1aa12a83f148a6a04b7394dd645fb3": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b65073e7ff2e476bb34d75a0faf77c6f", "IPY_MODEL_38b1bd4b5ec44d41981a433be46d45f0", "IPY_MODEL_9a092b3142994e27b931efc3d8141660"], "layout": "IPY_MODEL_7f092e8ede4e4d63a5fde7a51f92e32c", "tabbable": null, "tooltip": null}}, "7042493643924b098a59e80ef3a6125e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7a60c769a9a94e748e35f240733664b3": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "d9d22d6398f34538ac40fff96fa8abaf": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7042493643924b098a59e80ef3a6125e", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_7a60c769a9a94e748e35f240733664b3", "tabbable": null, "tooltip": null, "value": 466062.0}}, "6833c949ea36409b943f5aa9a751fe85": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0d5dda5aa9e840a7969a3facb0c17f1f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ef479a0478b644bbbd209856e893f0ed": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6833c949ea36409b943f5aa9a751fe85", "placeholder": "\u200b", "style": "IPY_MODEL_0d5dda5aa9e840a7969a3facb0c17f1f", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "6c0bd2a44f6e49e7bb1931ece0eab850": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b6e52d34f81d421d9eba7c8cc9b7847a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "8b024eb1e7d34d94b81d05d691a3874d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6c0bd2a44f6e49e7bb1931ece0eab850", "placeholder": "\u200b", "style": "IPY_MODEL_b6e52d34f81d421d9eba7c8cc9b7847a", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u200716.0MB/s]"}}, "cefce6f533124c4e90d7c277319c65e9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3840f91804d14d5aa30e594b1e1d7fa3": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ef479a0478b644bbbd209856e893f0ed", "IPY_MODEL_d9d22d6398f34538ac40fff96fa8abaf", "IPY_MODEL_8b024eb1e7d34d94b81d05d691a3874d"], "layout": "IPY_MODEL_cefce6f533124c4e90d7c277319c65e9", "tabbable": null, "tooltip": null}}, "769b1ec27342400b9cb6e41638eaa9d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d5eda741ae08481f90152525ac295029": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "3ac7761e49b1406fb28de06a9a09ffc6": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_769b1ec27342400b9cb6e41638eaa9d4", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_d5eda741ae08481f90152525ac295029", "tabbable": null, "tooltip": null, "value": 48.0}}, "f25b6a55df5d45bc80be73c91794168b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e970db685471489481e8d686d97b3cc2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2694565fc4d4493db595fec20b7b65bf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f25b6a55df5d45bc80be73c91794168b", "placeholder": "\u200b", "style": "IPY_MODEL_e970db685471489481e8d686d97b3cc2", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "0da07d715efe402083873e0661e556ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ae1e6cca03b34513818a3bd133ae6f5a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "a588c0dcd0974f8da33c7bdf3d4a35e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0da07d715efe402083873e0661e556ea", "placeholder": "\u200b", "style": "IPY_MODEL_ae1e6cca03b34513818a3bd133ae6f5a", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20079.25kB/s]"}}, "ce7bece14db441b3b9a7a133c7e71a07": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4522cd2764fc425b83a55e8426ac45e2": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2694565fc4d4493db595fec20b7b65bf", "IPY_MODEL_3ac7761e49b1406fb28de06a9a09ffc6", "IPY_MODEL_a588c0dcd0974f8da33c7bdf3d4a35e8"], "layout": "IPY_MODEL_ce7bece14db441b3b9a7a133c7e71a07", "tabbable": null, "tooltip": null}}, "07d79636c68343278e1f779c27aef268": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fba1e85bf6444f50b1917a3eb9c35bcc": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c5eaef5240de4848ba66275755fe00f6": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_07d79636c68343278e1f779c27aef268", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fba1e85bf6444f50b1917a3eb9c35bcc", "tabbable": null, "tooltip": null, "value": 231508.0}}, "3a43140a399d4c6b8942f19c238c99d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "131d5d7c7d344158b759f1061ef8d42d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "54ed34f093ee42d68b280d3bd01e5599": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3a43140a399d4c6b8942f19c238c99d0", "placeholder": "\u200b", "style": "IPY_MODEL_131d5d7c7d344158b759f1061ef8d42d", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "c176725128c3447ebe665fa5ac3e316f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "59de812fe1e841889d9b5bfc883f3b40": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "7fd953032b654b6080e9188c21e62fc9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c176725128c3447ebe665fa5ac3e316f", "placeholder": "\u200b", "style": "IPY_MODEL_59de812fe1e841889d9b5bfc883f3b40", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u200738.4MB/s]"}}, "44b0dfbf62fd4d088800332e97908228": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9a72b5f7de474f75bb371e057f0f1914": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_54ed34f093ee42d68b280d3bd01e5599", "IPY_MODEL_c5eaef5240de4848ba66275755fe00f6", "IPY_MODEL_7fd953032b654b6080e9188c21e62fc9"], "layout": "IPY_MODEL_44b0dfbf62fd4d088800332e97908228", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb index d42308ae9..cac09ab25 100644 --- a/master/tutorials/clean_learning/text.ipynb +++ b/master/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:34.059784Z", - "iopub.status.busy": "2024-07-02T12:00:34.059279Z", - "iopub.status.idle": "2024-07-02T12:00:36.809187Z", - "shell.execute_reply": "2024-07-02T12:00:36.808623Z" + "iopub.execute_input": "2024-07-02T15:09:59.845378Z", + "iopub.status.busy": "2024-07-02T15:09:59.845205Z", + "iopub.status.idle": "2024-07-02T15:10:02.560189Z", + "shell.execute_reply": "2024-07-02T15:10:02.559618Z" }, "nbsphinx": "hidden" }, @@ -135,7 +135,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -160,10 +160,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.811854Z", - "iopub.status.busy": "2024-07-02T12:00:36.811437Z", - "iopub.status.idle": "2024-07-02T12:00:36.814737Z", - "shell.execute_reply": "2024-07-02T12:00:36.814309Z" + "iopub.execute_input": "2024-07-02T15:10:02.562794Z", + "iopub.status.busy": "2024-07-02T15:10:02.562496Z", + "iopub.status.idle": "2024-07-02T15:10:02.565788Z", + "shell.execute_reply": "2024-07-02T15:10:02.565349Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.816857Z", - "iopub.status.busy": "2024-07-02T12:00:36.816534Z", - "iopub.status.idle": "2024-07-02T12:00:36.819520Z", - "shell.execute_reply": "2024-07-02T12:00:36.819089Z" + "iopub.execute_input": "2024-07-02T15:10:02.567948Z", + "iopub.status.busy": "2024-07-02T15:10:02.567553Z", + "iopub.status.idle": "2024-07-02T15:10:02.570524Z", + "shell.execute_reply": "2024-07-02T15:10:02.570092Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.821601Z", - "iopub.status.busy": "2024-07-02T12:00:36.821264Z", - "iopub.status.idle": "2024-07-02T12:00:36.862716Z", - "shell.execute_reply": "2024-07-02T12:00:36.862142Z" + "iopub.execute_input": "2024-07-02T15:10:02.572562Z", + "iopub.status.busy": "2024-07-02T15:10:02.572231Z", + "iopub.status.idle": "2024-07-02T15:10:02.699550Z", + "shell.execute_reply": "2024-07-02T15:10:02.699010Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.864907Z", - "iopub.status.busy": "2024-07-02T12:00:36.864568Z", - "iopub.status.idle": "2024-07-02T12:00:36.868079Z", - "shell.execute_reply": "2024-07-02T12:00:36.867616Z" + "iopub.execute_input": "2024-07-02T15:10:02.702025Z", + "iopub.status.busy": "2024-07-02T15:10:02.701663Z", + "iopub.status.idle": "2024-07-02T15:10:02.705030Z", + "shell.execute_reply": "2024-07-02T15:10:02.704599Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.870408Z", - "iopub.status.busy": "2024-07-02T12:00:36.870073Z", - "iopub.status.idle": "2024-07-02T12:00:36.873573Z", - "shell.execute_reply": "2024-07-02T12:00:36.873016Z" + "iopub.execute_input": "2024-07-02T15:10:02.707115Z", + "iopub.status.busy": "2024-07-02T15:10:02.706775Z", + "iopub.status.idle": "2024-07-02T15:10:02.709922Z", + "shell.execute_reply": "2024-07-02T15:10:02.709360Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'getting_spare_card', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay'}\n" + "Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'change_pin'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.875763Z", - "iopub.status.busy": "2024-07-02T12:00:36.875423Z", - "iopub.status.idle": "2024-07-02T12:00:36.878670Z", - "shell.execute_reply": "2024-07-02T12:00:36.878216Z" + "iopub.execute_input": "2024-07-02T15:10:02.711932Z", + "iopub.status.busy": "2024-07-02T15:10:02.711538Z", + "iopub.status.idle": "2024-07-02T15:10:02.714467Z", + "shell.execute_reply": "2024-07-02T15:10:02.713938Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.880795Z", - "iopub.status.busy": "2024-07-02T12:00:36.880374Z", - "iopub.status.idle": "2024-07-02T12:00:36.883787Z", - "shell.execute_reply": "2024-07-02T12:00:36.883314Z" + "iopub.execute_input": "2024-07-02T15:10:02.716605Z", + "iopub.status.busy": "2024-07-02T15:10:02.716210Z", + "iopub.status.idle": "2024-07-02T15:10:02.719587Z", + "shell.execute_reply": "2024-07-02T15:10:02.719150Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.885847Z", - "iopub.status.busy": "2024-07-02T12:00:36.885533Z", - "iopub.status.idle": "2024-07-02T12:00:41.284528Z", - "shell.execute_reply": "2024-07-02T12:00:41.283984Z" + "iopub.execute_input": "2024-07-02T15:10:02.721398Z", + "iopub.status.busy": "2024-07-02T15:10:02.721231Z", + "iopub.status.idle": "2024-07-02T15:10:07.115741Z", + "shell.execute_reply": "2024-07-02T15:10:07.115100Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e89a8a43528e42c38eca656e48b7da7e", + "model_id": "c943f13df8c04e77aae4c7ca2cbbd613", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca42a9ff17da48fab63132c9d67266dd", + "model_id": "06405b534d7c49db89f3d29b52da1f80", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "283fc6563d5645c9a2d53edd642983d4", + "model_id": "1146fbcfe9cf41da81392df94520265c", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e90e40189b0e460d90a444df7fe6d1a9", + "model_id": "bd1aa12a83f148a6a04b7394dd645fb3", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "018045ff81b24bf7b8b7b92eeb3e59db", + "model_id": "3840f91804d14d5aa30e594b1e1d7fa3", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb1f3f2a9964471a8a7688badac98c84", + "model_id": "4522cd2764fc425b83a55e8426ac45e2", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "edb46e1892c744119cd3f4a130dfb3e3", + "model_id": "9a72b5f7de474f75bb371e057f0f1914", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:41.287341Z", - "iopub.status.busy": "2024-07-02T12:00:41.286878Z", - "iopub.status.idle": "2024-07-02T12:00:41.289761Z", - "shell.execute_reply": "2024-07-02T12:00:41.289214Z" + "iopub.execute_input": "2024-07-02T15:10:07.118564Z", + "iopub.status.busy": "2024-07-02T15:10:07.118178Z", + "iopub.status.idle": "2024-07-02T15:10:07.121171Z", + "shell.execute_reply": "2024-07-02T15:10:07.120694Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:41.291735Z", - "iopub.status.busy": "2024-07-02T12:00:41.291455Z", - "iopub.status.idle": "2024-07-02T12:00:41.294547Z", - "shell.execute_reply": "2024-07-02T12:00:41.294136Z" + "iopub.execute_input": "2024-07-02T15:10:07.123119Z", + "iopub.status.busy": "2024-07-02T15:10:07.122798Z", + "iopub.status.idle": "2024-07-02T15:10:07.125865Z", + "shell.execute_reply": "2024-07-02T15:10:07.125458Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:41.296484Z", - "iopub.status.busy": "2024-07-02T12:00:41.296165Z", - "iopub.status.idle": "2024-07-02T12:00:44.031023Z", - "shell.execute_reply": "2024-07-02T12:00:44.030422Z" + "iopub.execute_input": "2024-07-02T15:10:07.127724Z", + "iopub.status.busy": "2024-07-02T15:10:07.127405Z", + "iopub.status.idle": "2024-07-02T15:10:09.752308Z", + "shell.execute_reply": "2024-07-02T15:10:09.751709Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.034106Z", - "iopub.status.busy": "2024-07-02T12:00:44.033286Z", - "iopub.status.idle": "2024-07-02T12:00:44.041018Z", - "shell.execute_reply": "2024-07-02T12:00:44.040563Z" + "iopub.execute_input": "2024-07-02T15:10:09.755206Z", + "iopub.status.busy": "2024-07-02T15:10:09.754521Z", + "iopub.status.idle": "2024-07-02T15:10:09.762087Z", + "shell.execute_reply": "2024-07-02T15:10:09.761397Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.043124Z", - "iopub.status.busy": "2024-07-02T12:00:44.042704Z", - "iopub.status.idle": "2024-07-02T12:00:44.046699Z", - "shell.execute_reply": "2024-07-02T12:00:44.046142Z" + "iopub.execute_input": "2024-07-02T15:10:09.764089Z", + "iopub.status.busy": "2024-07-02T15:10:09.763785Z", + "iopub.status.idle": "2024-07-02T15:10:09.767525Z", + "shell.execute_reply": "2024-07-02T15:10:09.767095Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.048913Z", - "iopub.status.busy": "2024-07-02T12:00:44.048523Z", - "iopub.status.idle": "2024-07-02T12:00:44.051593Z", - "shell.execute_reply": "2024-07-02T12:00:44.051082Z" + "iopub.execute_input": "2024-07-02T15:10:09.769470Z", + "iopub.status.busy": "2024-07-02T15:10:09.769149Z", + "iopub.status.idle": "2024-07-02T15:10:09.772223Z", + "shell.execute_reply": "2024-07-02T15:10:09.771699Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.053726Z", - "iopub.status.busy": "2024-07-02T12:00:44.053345Z", - "iopub.status.idle": "2024-07-02T12:00:44.056186Z", - "shell.execute_reply": "2024-07-02T12:00:44.055762Z" + "iopub.execute_input": "2024-07-02T15:10:09.774298Z", + "iopub.status.busy": "2024-07-02T15:10:09.773989Z", + "iopub.status.idle": "2024-07-02T15:10:09.776793Z", + "shell.execute_reply": "2024-07-02T15:10:09.776376Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.058086Z", - "iopub.status.busy": "2024-07-02T12:00:44.057784Z", - "iopub.status.idle": "2024-07-02T12:00:44.064436Z", - "shell.execute_reply": "2024-07-02T12:00:44.063922Z" + "iopub.execute_input": "2024-07-02T15:10:09.778772Z", + "iopub.status.busy": "2024-07-02T15:10:09.778454Z", + "iopub.status.idle": "2024-07-02T15:10:09.785054Z", + "shell.execute_reply": "2024-07-02T15:10:09.784624Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.066501Z", - "iopub.status.busy": "2024-07-02T12:00:44.066197Z", - "iopub.status.idle": "2024-07-02T12:00:44.289398Z", - "shell.execute_reply": "2024-07-02T12:00:44.288882Z" + "iopub.execute_input": "2024-07-02T15:10:09.787107Z", + "iopub.status.busy": "2024-07-02T15:10:09.786788Z", + "iopub.status.idle": "2024-07-02T15:10:10.037396Z", + "shell.execute_reply": "2024-07-02T15:10:10.036833Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.292040Z", - "iopub.status.busy": "2024-07-02T12:00:44.291643Z", - "iopub.status.idle": "2024-07-02T12:00:44.466523Z", - "shell.execute_reply": "2024-07-02T12:00:44.466004Z" + "iopub.execute_input": "2024-07-02T15:10:10.039906Z", + "iopub.status.busy": "2024-07-02T15:10:10.039540Z", + "iopub.status.idle": "2024-07-02T15:10:10.215217Z", + "shell.execute_reply": "2024-07-02T15:10:10.214642Z" }, "scrolled": true }, @@ -1058,10 +1058,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.469939Z", - "iopub.status.busy": "2024-07-02T12:00:44.468998Z", - "iopub.status.idle": "2024-07-02T12:00:44.473947Z", - "shell.execute_reply": "2024-07-02T12:00:44.473442Z" + "iopub.execute_input": "2024-07-02T15:10:10.217686Z", + "iopub.status.busy": "2024-07-02T15:10:10.217327Z", + "iopub.status.idle": "2024-07-02T15:10:10.221199Z", + "shell.execute_reply": "2024-07-02T15:10:10.220704Z" }, "nbsphinx": "hidden" }, @@ -1105,31 +1105,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "018045ff81b24bf7b8b7b92eeb3e59db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_365ffce5f68c44638088fdbae83f6f7b", - "IPY_MODEL_c08801bd218a4933bad779d4baa0b544", - "IPY_MODEL_0f51dc70dda342b1b6b17e56b63d5f14" - ], - "layout": "IPY_MODEL_e14305181a4f4a1aba92bf0159aa7bf3", - "tabbable": null, - "tooltip": null - } - }, - "0298beb970f94688915a8e32a774126c": { + "038891c782ab46f4ba836914abbfc5ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1182,108 +1158,31 @@ "width": null } }, - "036fc6d42c084bba8e6ff1d651c36d55": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b03695b8bbe44d13a5612d5120ea2a28", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_25890da51b1a4e519cdfb13a8c6a9b74", - "tabbable": null, - "tooltip": null, - "value": 665.0 - } - }, - "038607d420fe468e857b145df291678c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "03cd490c005341539cee9b6bd0c64509": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "060119cb42d74e19bb4e92690f697a5e": { + "06405b534d7c49db89f3d29b52da1f80": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0298beb970f94688915a8e32a774126c", - "placeholder": "​", - "style": "IPY_MODEL_eb0935dc83294cc7a1acad3dd963f608", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_5316135ae1ac4592a7b4239c553c02c6", + "IPY_MODEL_43953f2b89bb41408d6583c47201913d", + "IPY_MODEL_e0adbaa3ffa64032933136aebb9a1f7b" + ], + "layout": "IPY_MODEL_cbb206b9c0d64fe0b479fb7cc9df570b", "tabbable": null, - "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 200MB/s]" - } - }, - "0607d3fa923e46caa07bbdf1220db2b6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null } }, - "078724370bc24c649597fb04791e1a0e": { + "07d79636c68343278e1f779c27aef268": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1336,7 +1235,25 @@ "width": null } }, - "0bade9b66cc6401491c957e37747b252": { + "0d5dda5aa9e840a7969a3facb0c17f1f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0da07d715efe402083873e0661e556ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1389,7 +1306,67 @@ "width": null } }, - "0f51dc70dda342b1b6b17e56b63d5f14": { + "1146fbcfe9cf41da81392df94520265c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_59eec5b7926b48ae8d162ecab0db1ec2", + "IPY_MODEL_c18be04c279b46b48b79ee15e32435e6", + "IPY_MODEL_e32cd0a87f0f4ad18f72cc198abad4fd" + ], + "layout": "IPY_MODEL_505538aed9b54bebb4c3f0107daadf44", + "tabbable": null, + "tooltip": null + } + }, + "131d5d7c7d344158b759f1061ef8d42d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1740b9557dc0484c95a45ed25a63585f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "2694565fc4d4493db595fec20b7b65bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1404,38 +1381,99 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_581a302d09fd45e49c7f87d39dd3c921", + "layout": "IPY_MODEL_f25b6a55df5d45bc80be73c91794168b", "placeholder": "​", - "style": "IPY_MODEL_0607d3fa923e46caa07bbdf1220db2b6", + "style": "IPY_MODEL_e970db685471489481e8d686d97b3cc2", "tabbable": null, "tooltip": null, - "value": " 466k/466k [00:00<00:00, 15.8MB/s]" + "value": "tokenizer_config.json: 100%" } }, - "1372d9e4b7724cdead58971eebb0f969": { + "2df9eaceb2e14e8fa3a19b25c7d76f35": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "360120e496a841119a2a238bfb8c7631": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3840f91804d14d5aa30e594b1e1d7fa3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ef479a0478b644bbbd209856e893f0ed", + "IPY_MODEL_d9d22d6398f34538ac40fff96fa8abaf", + "IPY_MODEL_8b024eb1e7d34d94b81d05d691a3874d" + ], + "layout": "IPY_MODEL_cefce6f533124c4e90d7c277319c65e9", + "tabbable": null, + "tooltip": null + } + }, + "38b1bd4b5ec44d41981a433be46d45f0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_285337fc5df34cbfa6c9ed52458ebf3d", - "placeholder": "​", - "style": "IPY_MODEL_80145043305a4ffabe28cb2a16f379de", + "layout": "IPY_MODEL_ea436305a9d143d09b28e8d32eb3020a", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_360120e496a841119a2a238bfb8c7631", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 118kB/s]" + "value": 54245363.0 } }, - "1820812db0184a3ba2bd25871cc78e2f": { + "3a43140a399d4c6b8942f19c238c99d0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1488,60 +1526,33 @@ "width": null } }, - "1d21e4a5af7c4ae0aadfd84ea1555716": { - "model_module": "@jupyter-widgets/base", + "3ac7761e49b1406fb28de06a9a09ffc6": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_769b1ec27342400b9cb6e41638eaa9d4", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d5eda741ae08481f90152525ac295029", + "tabbable": null, + "tooltip": null, + "value": 48.0 } }, - "20b3e8ae925a497db651bb3e420ccedd": { + "3bd0776bd0b840e8b2a1d15afd1e27f8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1559,7 +1570,7 @@ "text_color": null } }, - "25890da51b1a4e519cdfb13a8c6a9b74": { + "3d25676d34544705a2bcbe334c412efc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1575,31 +1586,7 @@ "description_width": "" } }, - "283fc6563d5645c9a2d53edd642983d4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d66a39fee74f43488e2d84a8afff525a", - "IPY_MODEL_036fc6d42c084bba8e6ff1d651c36d55", - "IPY_MODEL_1372d9e4b7724cdead58971eebb0f969" - ], - "layout": "IPY_MODEL_330425c288064ecea245f9a589f86dce", - "tabbable": null, - "tooltip": null - } - }, - "285337fc5df34cbfa6c9ed52458ebf3d": { + "3edc8ef534274f15b48e90d83188b494": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1652,23 +1639,33 @@ "width": null } }, - "290e38f7423c457a9127d9cd3394ab4e": { + "43953f2b89bb41408d6583c47201913d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b01dff0aaef24c71a79429be65e9ce07", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3d25676d34544705a2bcbe334c412efc", + "tabbable": null, + "tooltip": null, + "value": 2211.0 } }, - "2adec3ea4ab147fca4247ce82a707f41": { + "44b0dfbf62fd4d088800332e97908228": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1721,7 +1718,31 @@ "width": null } }, - "330425c288064ecea245f9a589f86dce": { + "4522cd2764fc425b83a55e8426ac45e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2694565fc4d4493db595fec20b7b65bf", + "IPY_MODEL_3ac7761e49b1406fb28de06a9a09ffc6", + "IPY_MODEL_a588c0dcd0974f8da33c7bdf3d4a35e8" + ], + "layout": "IPY_MODEL_ce7bece14db441b3b9a7a133c7e71a07", + "tabbable": null, + "tooltip": null + } + }, + "4cbe66fa6f75404e9a5e6814de5f2203": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1774,7 +1795,7 @@ "width": null } }, - "33adffdfde344f24ab11355d2abf0744": { + "502164ca582c4a0bbb3b20dc28ae0b2c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1792,30 +1813,7 @@ "text_color": null } }, - "365ffce5f68c44638088fdbae83f6f7b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_757f11a9bf36405e89b2715bc5278a25", - "placeholder": "​", - "style": "IPY_MODEL_e7529ab3626947b3ac4bc76edafa711a", - "tabbable": null, - "tooltip": null, - "value": "tokenizer.json: 100%" - } - }, - "3c023990927b4f04a4351901432e7d8f": { + "505538aed9b54bebb4c3f0107daadf44": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1868,7 +1866,53 @@ "width": null } }, - "465f0d2ce3444dfd9bd85fd2529dc52c": { + "5316135ae1ac4592a7b4239c553c02c6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4cbe66fa6f75404e9a5e6814de5f2203", + "placeholder": "​", + "style": "IPY_MODEL_1740b9557dc0484c95a45ed25a63585f", + "tabbable": null, + "tooltip": null, + "value": "README.md: 100%" + } + }, + "54ed34f093ee42d68b280d3bd01e5599": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3a43140a399d4c6b8942f19c238c99d0", + "placeholder": "​", + "style": "IPY_MODEL_131d5d7c7d344158b759f1061ef8d42d", + "tabbable": null, + "tooltip": null, + "value": "vocab.txt: 100%" + } + }, + "59de812fe1e841889d9b5bfc883f3b40": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1886,7 +1930,46 @@ "text_color": null } }, - "465f57a032274e4dadbee2eb87856ef1": { + "59eec5b7926b48ae8d162ecab0db1ec2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6ef7ce225dc34f17b94e5dbe3a820e35", + "placeholder": "​", + "style": "IPY_MODEL_a99583c6f0e24d69872cbda693c983eb", + "tabbable": null, + "tooltip": null, + "value": "config.json: 100%" + } + }, + "6680b7e73b4d4bb589857c254d0632ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "6833c949ea36409b943f5aa9a751fe85": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1939,7 +2022,7 @@ "width": null } }, - "4ed3c0ca07f04a87b53a6c5d68d36cf6": { + "6c0bd2a44f6e49e7bb1931ece0eab850": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1992,7 +2075,7 @@ "width": null } }, - "52a83385affa4985b26bec259ee14740": { + "6e6cda23dd024255af57333cc9d9bfea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2007,49 +2090,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1d21e4a5af7c4ae0aadfd84ea1555716", + "layout": "IPY_MODEL_80258d9522ac4e858c828ed1eba37fb6", "placeholder": "​", - "style": "IPY_MODEL_038607d420fe468e857b145df291678c", + "style": "IPY_MODEL_2df9eaceb2e14e8fa3a19b25c7d76f35", "tabbable": null, "tooltip": null, - "value": "README.md: 100%" - } - }, - "5376601de0794106a7b8777224bcafd4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "57bc4c06dbfc46c89ae707951f55ed3a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": " 391/391 [00:00<00:00, 67.9kB/s]" } }, - "581a302d09fd45e49c7f87d39dd3c921": { + "6ed70f51e8b34c1094db85d132e5ce9c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2102,7 +2151,7 @@ "width": null } }, - "5d425fc517de40599859741dfdf6bb2e": { + "6ef7ce225dc34f17b94e5dbe3a820e35": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2155,7 +2204,7 @@ "width": null } }, - "5ec8a2f5f2034c158f6d2c361a60ea25": { + "7042493643924b098a59e80ef3a6125e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2208,7 +2257,7 @@ "width": null } }, - "5efd9793a22541dd9dd8f09fcfbec967": { + "7416ceea465444209f091f4e54c9aebe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2226,105 +2275,14 @@ "text_color": null } }, - "60b3d9bfd8e147ef947e8e81fd9fb70e": { - "model_module": "@jupyter-widgets/controls", + "769b1ec27342400b9cb6e41638eaa9d4": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "654d58802fd94f6ead893ddbb0e3d131": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0bade9b66cc6401491c957e37747b252", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_290e38f7423c457a9127d9cd3394ab4e", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } - }, - "66ee22f8cf5b4a78ab33aee929a5fbd0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fad0806770b14f9086fd1b3b755413fb", - "placeholder": "​", - "style": "IPY_MODEL_814ed56db234446a92fe939efe5a477b", - "tabbable": null, - "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 8.21kB/s]" - } - }, - "6f739705eccc46afb2020460a828b56b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5d425fc517de40599859741dfdf6bb2e", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_7a181aaf8967410dae1ce2d0e0d9856a", - "tabbable": null, - "tooltip": null, - "value": 391.0 - } - }, - "757f11a9bf36405e89b2715bc5278a25": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -2370,7 +2328,23 @@ "width": null } }, - "796c15ee275d4dff935fe8e20583896d": { + "7a60c769a9a94e748e35f240733664b3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "7b0e3596ec8d47089daa31da5c13b681": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2423,77 +2397,7 @@ "width": null } }, - "7a181aaf8967410dae1ce2d0e0d9856a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "80145043305a4ffabe28cb2a16f379de": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "814ed56db234446a92fe939efe5a477b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "853f7130b22d49bfaf39b5d7bf7af4ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "8e1a3b5aa6b14adc8ebdc1566696e505": { + "7f092e8ede4e4d63a5fde7a51f92e32c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2546,25 +2450,30 @@ "width": null } }, - "9814c1a1993c40a683e790580ecf178a": { + "7fd953032b654b6080e9188c21e62fc9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c176725128c3447ebe665fa5ac3e316f", + "placeholder": "​", + "style": "IPY_MODEL_59de812fe1e841889d9b5bfc883f3b40", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 38.4MB/s]" } }, - "982a96cc3c0b4c00938e722c374cd707": { + "80258d9522ac4e858c828ed1eba37fb6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2617,7 +2526,103 @@ "width": null } }, - "98b6d5dd0c4546948933a4d8faa4bbc9": { + "8b024eb1e7d34d94b81d05d691a3874d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6c0bd2a44f6e49e7bb1931ece0eab850", + "placeholder": "​", + "style": "IPY_MODEL_b6e52d34f81d421d9eba7c8cc9b7847a", + "tabbable": null, + "tooltip": null, + "value": " 466k/466k [00:00<00:00, 16.0MB/s]" + } + }, + "914b3f22f6b446608a80b2bd86dde07b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3edc8ef534274f15b48e90d83188b494", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d76fd6c68456446baaae1b57ccf46065", + "tabbable": null, + "tooltip": null, + "value": 391.0 + } + }, + "9a092b3142994e27b931efc3d8141660": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6ed70f51e8b34c1094db85d132e5ce9c", + "placeholder": "​", + "style": "IPY_MODEL_acbecbc8b02440768730f4625e2f605e", + "tabbable": null, + "tooltip": null, + "value": " 54.2M/54.2M [00:00<00:00, 239MB/s]" + } + }, + "9a72b5f7de474f75bb371e057f0f1914": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_54ed34f093ee42d68b280d3bd01e5599", + "IPY_MODEL_c5eaef5240de4848ba66275755fe00f6", + "IPY_MODEL_7fd953032b654b6080e9188c21e62fc9" + ], + "layout": "IPY_MODEL_44b0dfbf62fd4d088800332e97908228", + "tabbable": null, + "tooltip": null + } + }, + "9f0bab6dcc71427ba6d39cfcb15d877f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2670,7 +2675,30 @@ "width": null } }, - "9e5f0df62415449eb138994f79e6d9e0": { + "a588c0dcd0974f8da33c7bdf3d4a35e8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0da07d715efe402083873e0661e556ea", + "placeholder": "​", + "style": "IPY_MODEL_ae1e6cca03b34513818a3bd133ae6f5a", + "tabbable": null, + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 9.25kB/s]" + } + }, + "a99583c6f0e24d69872cbda693c983eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2688,60 +2716,43 @@ "text_color": null } }, - "a6d4217338164b029ad977bd11fb8d9e": { - "model_module": "@jupyter-widgets/base", + "acbecbc8b02440768730f4625e2f605e": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ae1e6cca03b34513818a3bd133ae6f5a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b03695b8bbe44d13a5612d5120ea2a28": { + "b01dff0aaef24c71a79429be65e9ce07": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2794,98 +2805,66 @@ "width": null } }, - "b83ba78e112b427d90a55129eecf514c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_078724370bc24c649597fb04791e1a0e", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5376601de0794106a7b8777224bcafd4", - "tabbable": null, - "tooltip": null, - "value": 2211.0 - } - }, - "c08801bd218a4933bad779d4baa0b544": { + "b65073e7ff2e476bb34d75a0faf77c6f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_da42c63dc73b49f8b62f507344120b1a", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_60b3d9bfd8e147ef947e8e81fd9fb70e", + "layout": "IPY_MODEL_7b0e3596ec8d47089daa31da5c13b681", + "placeholder": "​", + "style": "IPY_MODEL_502164ca582c4a0bbb3b20dc28ae0b2c", "tabbable": null, "tooltip": null, - "value": 466062.0 + "value": "pytorch_model.bin: 100%" } }, - "c187a19bc3e941088eef67c273bf61ed": { + "b6e52d34f81d421d9eba7c8cc9b7847a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c9ff9f5312794e5381359492c09edaaf": { + "bc071c694fb7476a8213ad06a4cef625": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5ec8a2f5f2034c158f6d2c361a60ea25", - "placeholder": "​", - "style": "IPY_MODEL_853f7130b22d49bfaf39b5d7bf7af4ce", - "tabbable": null, - "tooltip": null, - "value": "vocab.txt: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ca42a9ff17da48fab63132c9d67266dd": { + "bd1aa12a83f148a6a04b7394dd645fb3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2900,131 +2879,95 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_52a83385affa4985b26bec259ee14740", - "IPY_MODEL_b83ba78e112b427d90a55129eecf514c", - "IPY_MODEL_f7e3eba4cb63469d997967f982f4a1df" + "IPY_MODEL_b65073e7ff2e476bb34d75a0faf77c6f", + "IPY_MODEL_38b1bd4b5ec44d41981a433be46d45f0", + "IPY_MODEL_9a092b3142994e27b931efc3d8141660" ], - "layout": "IPY_MODEL_8e1a3b5aa6b14adc8ebdc1566696e505", + "layout": "IPY_MODEL_7f092e8ede4e4d63a5fde7a51f92e32c", "tabbable": null, "tooltip": null } }, - "cc4d2d6857724ea0a2f3ec85a6f395ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4ed3c0ca07f04a87b53a6c5d68d36cf6", - "placeholder": "​", - "style": "IPY_MODEL_5efd9793a22541dd9dd8f09fcfbec967", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 31.3MB/s]" - } - }, - "d015bcf5ce69476ab54fd8e945eaa689": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_465f57a032274e4dadbee2eb87856ef1", - "placeholder": "​", - "style": "IPY_MODEL_465f0d2ce3444dfd9bd85fd2529dc52c", - "tabbable": null, - "tooltip": null, - "value": " 391/391 [00:00<00:00, 66.4kB/s]" - } - }, - "d25c524b9c4444829470ac057e0fae42": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1820812db0184a3ba2bd25871cc78e2f", - "placeholder": "​", - "style": "IPY_MODEL_57bc4c06dbfc46c89ae707951f55ed3a", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" - } - }, - "d66a39fee74f43488e2d84a8afff525a": { - "model_module": "@jupyter-widgets/controls", + "c176725128c3447ebe665fa5ac3e316f": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e7e2566da91d4dde83d80ed19a91e263", - "placeholder": "​", - "style": "IPY_MODEL_20b3e8ae925a497db651bb3e420ccedd", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d6ea8d2f7af14ef69353a0ae60cf677e": { + "c18be04c279b46b48b79ee15e32435e6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3c023990927b4f04a4351901432e7d8f", - "placeholder": "​", - "style": "IPY_MODEL_9814c1a1993c40a683e790580ecf178a", + "layout": "IPY_MODEL_fabb2363238a4c1a91de78e13b8a0a3e", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6680b7e73b4d4bb589857c254d0632ef", "tabbable": null, "tooltip": null, - "value": "pytorch_model.bin: 100%" + "value": 665.0 } }, - "da42c63dc73b49f8b62f507344120b1a": { + "c33f4e7c33ca4ebd8cb646737881a850": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3077,7 +3020,7 @@ "width": null } }, - "dbc2947dd29d4e92a20d79a1f5606f98": { + "c5eaef5240de4848ba66275755fe00f6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3093,17 +3036,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_de5b529ba5b7466f857e19bdfdcddd30", + "layout": "IPY_MODEL_07d79636c68343278e1f779c27aef268", "max": 231508.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_c187a19bc3e941088eef67c273bf61ed", + "style": "IPY_MODEL_fba1e85bf6444f50b1917a3eb9c35bcc", "tabbable": null, "tooltip": null, "value": 231508.0 } }, - "de5b529ba5b7466f857e19bdfdcddd30": { + "c943f13df8c04e77aae4c7ca2cbbd613": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fcaf1661c4314a27a12b7c20cbce3cc8", + "IPY_MODEL_914b3f22f6b446608a80b2bd86dde07b", + "IPY_MODEL_6e6cda23dd024255af57333cc9d9bfea" + ], + "layout": "IPY_MODEL_de51505fe22c4b5fa21abe797f7a7d7a", + "tabbable": null, + "tooltip": null + } + }, + "cbb206b9c0d64fe0b479fb7cc9df570b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3156,7 +3123,7 @@ "width": null } }, - "e14305181a4f4a1aba92bf0159aa7bf3": { + "ce7bece14db441b3b9a7a133c7e71a07": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3209,25 +3176,7 @@ "width": null } }, - "e7529ab3626947b3ac4bc76edafa711a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "e7e2566da91d4dde83d80ed19a91e263": { + "cefce6f533124c4e90d7c277319c65e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3280,55 +3229,65 @@ "width": null } }, - "e89a8a43528e42c38eca656e48b7da7e": { + "d5eda741ae08481f90152525ac295029": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ebe70157b15a43cebe4c33d29744783b", - "IPY_MODEL_6f739705eccc46afb2020460a828b56b", - "IPY_MODEL_d015bcf5ce69476ab54fd8e945eaa689" - ], - "layout": "IPY_MODEL_e9a141532bb34d7697db7a780d7a2002", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "e90e40189b0e460d90a444df7fe6d1a9": { + "d76fd6c68456446baaae1b57ccf46065": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d9d22d6398f34538ac40fff96fa8abaf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d6ea8d2f7af14ef69353a0ae60cf677e", - "IPY_MODEL_eccbc90dc8114497bb7438d438edc7f2", - "IPY_MODEL_060119cb42d74e19bb4e92690f697a5e" - ], - "layout": "IPY_MODEL_a6d4217338164b029ad977bd11fb8d9e", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7042493643924b098a59e80ef3a6125e", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7a60c769a9a94e748e35f240733664b3", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 466062.0 } }, - "e9a141532bb34d7697db7a780d7a2002": { + "de51505fe22c4b5fa21abe797f7a7d7a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3381,49 +3340,7 @@ "width": null } }, - "eb0935dc83294cc7a1acad3dd963f608": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "eb1f3f2a9964471a8a7688badac98c84": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d25c524b9c4444829470ac057e0fae42", - "IPY_MODEL_654d58802fd94f6ead893ddbb0e3d131", - "IPY_MODEL_66ee22f8cf5b4a78ab33aee929a5fbd0" - ], - "layout": "IPY_MODEL_98b6d5dd0c4546948933a4d8faa4bbc9", - "tabbable": null, - "tooltip": null - } - }, - "ebe70157b15a43cebe4c33d29744783b": { + "e0adbaa3ffa64032933136aebb9a1f7b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3438,65 +3355,56 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_796c15ee275d4dff935fe8e20583896d", + "layout": "IPY_MODEL_9f0bab6dcc71427ba6d39cfcb15d877f", "placeholder": "​", - "style": "IPY_MODEL_33adffdfde344f24ab11355d2abf0744", + "style": "IPY_MODEL_7416ceea465444209f091f4e54c9aebe", "tabbable": null, "tooltip": null, - "value": ".gitattributes: 100%" + "value": " 2.21k/2.21k [00:00<00:00, 401kB/s]" } }, - "eccbc90dc8114497bb7438d438edc7f2": { + "e32cd0a87f0f4ad18f72cc198abad4fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2adec3ea4ab147fca4247ce82a707f41", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_03cd490c005341539cee9b6bd0c64509", + "layout": "IPY_MODEL_c33f4e7c33ca4ebd8cb646737881a850", + "placeholder": "​", + "style": "IPY_MODEL_3bd0776bd0b840e8b2a1d15afd1e27f8", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": " 665/665 [00:00<00:00, 118kB/s]" } }, - "edb46e1892c744119cd3f4a130dfb3e3": { + "e970db685471489481e8d686d97b3cc2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c9ff9f5312794e5381359492c09edaaf", - "IPY_MODEL_dbc2947dd29d4e92a20d79a1f5606f98", - "IPY_MODEL_cc4d2d6857724ea0a2f3ec85a6f395ea" - ], - "layout": "IPY_MODEL_f3decebc5af44971b456d5da642e43b2", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f3decebc5af44971b456d5da642e43b2": { + "ea436305a9d143d09b28e8d32eb3020a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3549,7 +3457,7 @@ "width": null } }, - "f7e3eba4cb63469d997967f982f4a1df": { + "ef479a0478b644bbbd209856e893f0ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3564,15 +3472,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_982a96cc3c0b4c00938e722c374cd707", + "layout": "IPY_MODEL_6833c949ea36409b943f5aa9a751fe85", "placeholder": "​", - "style": "IPY_MODEL_9e5f0df62415449eb138994f79e6d9e0", + "style": "IPY_MODEL_0d5dda5aa9e840a7969a3facb0c17f1f", "tabbable": null, "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 389kB/s]" + "value": "tokenizer.json: 100%" + } + }, + "f25b6a55df5d45bc80be73c91794168b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "fad0806770b14f9086fd1b3b755413fb": { + "fabb2363238a4c1a91de78e13b8a0a3e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3624,6 +3585,45 @@ "visibility": null, "width": null } + }, + "fba1e85bf6444f50b1917a3eb9c35bcc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fcaf1661c4314a27a12b7c20cbce3cc8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_038891c782ab46f4ba836914abbfc5ce", + "placeholder": "​", + "style": "IPY_MODEL_bc071c694fb7476a8213ad06a4cef625", + "tabbable": null, + "tooltip": null, + "value": ".gitattributes: 100%" + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/audio.html b/master/tutorials/datalab/audio.html index 5756d470b..4aeb3965a 100644 --- a/master/tutorials/datalab/audio.html +++ b/master/tutorials/datalab/audio.html @@ -1347,7 +1347,7 @@

5. Use cleanlab to find label issues -{"state": {"f1379d86855941e4a6388b556616e327": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "40a775a4588f452f8bf5d7fbc03bc9ba": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "280097d7d8744e47bd5924ed20469bd6": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f1379d86855941e4a6388b556616e327", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_40a775a4588f452f8bf5d7fbc03bc9ba", "tabbable": null, "tooltip": null, "value": 2041.0}}, "513013579fcb4448832ca2cf5f6e0fc2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "edbc3e1e1e514aeea602d52281c3dfa3": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "73fd95beddfa4d59833b8b729dc2ef1b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_513013579fcb4448832ca2cf5f6e0fc2", "placeholder": "\u200b", "style": "IPY_MODEL_edbc3e1e1e514aeea602d52281c3dfa3", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "d3b969344b34427e9067d2e51d96dd59": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9ad076b10aa949c583d59ebf787d18d9": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "38e4ea17dfd74406801cb8ef4fa01bfb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d3b969344b34427e9067d2e51d96dd59", "placeholder": "\u200b", "style": "IPY_MODEL_9ad076b10aa949c583d59ebf787d18d9", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007501kB/s]"}}, "96e34a60ae404938aef646ab844636ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fbe4b54842f84d388c29da52e5a714cb": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_73fd95beddfa4d59833b8b729dc2ef1b", "IPY_MODEL_280097d7d8744e47bd5924ed20469bd6", "IPY_MODEL_38e4ea17dfd74406801cb8ef4fa01bfb"], "layout": "IPY_MODEL_96e34a60ae404938aef646ab844636ca", "tabbable": null, "tooltip": null}}, "a93eeb28cf894cdba2553c527a3bb1a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6b1e51bd7dfd4e7e9b96650627dd4347": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "b4c75c905856457c980a958861d26460": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a93eeb28cf894cdba2553c527a3bb1a7", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6b1e51bd7dfd4e7e9b96650627dd4347", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "5740512f403c4e52a3ce092bc9b6022f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9fbff358e60544609b6d2a561cf6f85c": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "3e0efe779a52444991f5fa51e7b53a87": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5740512f403c4e52a3ce092bc9b6022f", "placeholder": "\u200b", "style": "IPY_MODEL_9fbff358e60544609b6d2a561cf6f85c", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "1df8c0f2d29d45779e226e08c41aefd8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7a9eaff0d94f402eb6726bbbe282d200": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "4d17c45bac644d16890f974d9bf14252": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1df8c0f2d29d45779e226e08c41aefd8", "placeholder": "\u200b", "style": "IPY_MODEL_7a9eaff0d94f402eb6726bbbe282d200", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u2007121MB/s]"}}, "af604b66416942c3beafdb0f24f5fb27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cb4111006dc844c69976ab2a2fd47bf5": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_3e0efe779a52444991f5fa51e7b53a87", "IPY_MODEL_b4c75c905856457c980a958861d26460", "IPY_MODEL_4d17c45bac644d16890f974d9bf14252"], "layout": "IPY_MODEL_af604b66416942c3beafdb0f24f5fb27", "tabbable": null, "tooltip": null}}, "5fc9fb12e7584385b6dbcc71ea004933": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cbf0488c8e70416aabb7cb8cffe59c43": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "cf0f8cce4150428582199659b7ecc31f": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5fc9fb12e7584385b6dbcc71ea004933", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_cbf0488c8e70416aabb7cb8cffe59c43", "tabbable": null, "tooltip": null, "value": 3201.0}}, "c5e86d52ed71402dbadb1a831d3a99dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3b3e7e9c2d3a481a8b66e18c87d96abe": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d5743decd00649e19fbee18925104825": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c5e86d52ed71402dbadb1a831d3a99dd", "placeholder": "\u200b", "style": "IPY_MODEL_3b3e7e9c2d3a481a8b66e18c87d96abe", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "249f3be21f17476cb286f35a4beae4ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "28c2d54c60d54e18885566cb5f99ba0a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f5b3c5a8ab94472a8c12d10627c4a3b2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_249f3be21f17476cb286f35a4beae4ea", "placeholder": "\u200b", "style": "IPY_MODEL_28c2d54c60d54e18885566cb5f99ba0a", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007820kB/s]"}}, "b7930e24a3604c3783c6342017146161": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fc94797f46734484ae1adb8c6aac5095": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_d5743decd00649e19fbee18925104825", "IPY_MODEL_cf0f8cce4150428582199659b7ecc31f", "IPY_MODEL_f5b3c5a8ab94472a8c12d10627c4a3b2"], "layout": "IPY_MODEL_b7930e24a3604c3783c6342017146161", "tabbable": null, "tooltip": null}}, "f9d1becbce8d4a0e92d1291261488c36": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7fb74483ba564e44bf8528608dd0c00d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "2073d888478c4148aa6af8f01d1a55c0": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f9d1becbce8d4a0e92d1291261488c36", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_7fb74483ba564e44bf8528608dd0c00d", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "1e578c121b074fe689ec2d0de3438add": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "65678011a834483f81123fce6a847a0f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "1879dcf933dc47ef80752de84b1be173": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1e578c121b074fe689ec2d0de3438add", "placeholder": "\u200b", "style": "IPY_MODEL_65678011a834483f81123fce6a847a0f", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "39378c4365b84e2ba8a40f5da8c49a6b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7e28797dcb31400985bcdae5b8c5fb36": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b182da74164e4c6da9b9218000f3b471": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_39378c4365b84e2ba8a40f5da8c49a6b", "placeholder": "\u200b", "style": "IPY_MODEL_7e28797dcb31400985bcdae5b8c5fb36", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u2007133MB/s]"}}, "7b71cae594754cd4b51bdf0cc3937dc8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7ac998d738294e6c82fb7330a80ef819": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_1879dcf933dc47ef80752de84b1be173", "IPY_MODEL_2073d888478c4148aa6af8f01d1a55c0", "IPY_MODEL_b182da74164e4c6da9b9218000f3b471"], "layout": "IPY_MODEL_7b71cae594754cd4b51bdf0cc3937dc8", "tabbable": null, "tooltip": null}}, "0b4a72514d5e48d9a5de944d58a5949b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "54ae4e63d9c84d078262621e2d8e350f": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "4209fa1ed28448c4a745350a1042e490": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0b4a72514d5e48d9a5de944d58a5949b", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_54ae4e63d9c84d078262621e2d8e350f", "tabbable": null, "tooltip": null, "value": 128619.0}}, "6fe26799ef5342b7ae1161bc8ef4ec8a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ba779947520844b1b4e9e788604b7ed8": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "68b7a074f4a5491984f03ffaed29102c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6fe26799ef5342b7ae1161bc8ef4ec8a", "placeholder": "\u200b", "style": "IPY_MODEL_ba779947520844b1b4e9e788604b7ed8", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "b95daff336854c7aa1ca5dbdf574af6e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "65c231ce763c440980afbfa1c4eaab86": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "743444b6670b4fa99cc0396e4b32f5e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b95daff336854c7aa1ca5dbdf574af6e", "placeholder": "\u200b", "style": "IPY_MODEL_65c231ce763c440980afbfa1c4eaab86", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u200710.6MB/s]"}}, "f70f441fdd5147c685302ccf8dbb2370": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9946d2c65d6146e5a82c395bc8875caf": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_68b7a074f4a5491984f03ffaed29102c", "IPY_MODEL_4209fa1ed28448c4a745350a1042e490", "IPY_MODEL_743444b6670b4fa99cc0396e4b32f5e4"], "layout": "IPY_MODEL_f70f441fdd5147c685302ccf8dbb2370", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"4a111d06ad644ff3b7dfcd8f9a93b27f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "60b559ea05d04ab2aefd425629200a9b": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "6816c11c8cd945968d5b0ce5adfb87f5": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4a111d06ad644ff3b7dfcd8f9a93b27f", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_60b559ea05d04ab2aefd425629200a9b", "tabbable": null, "tooltip": null, "value": 2041.0}}, "d116ac8503dc4fa19dbf254269e57e81": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "865a2e4e16fe4c8fab1e00a4c6cd135d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "85e7b3e36e7c47129e29727d2f646f2f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d116ac8503dc4fa19dbf254269e57e81", "placeholder": "\u200b", "style": "IPY_MODEL_865a2e4e16fe4c8fab1e00a4c6cd135d", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "d16b4b3c59ba49d9a539a98b5d9ace25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8712797b65e1448f8a93f64e6e581af5": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "0dfc7f988177492e852e1a1e2e2e3c55": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d16b4b3c59ba49d9a539a98b5d9ace25", "placeholder": "\u200b", "style": "IPY_MODEL_8712797b65e1448f8a93f64e6e581af5", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007502kB/s]"}}, "a3dbbfce424340438202c2a4f54314d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8531becaffca44989779b1d37c65260e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_85e7b3e36e7c47129e29727d2f646f2f", "IPY_MODEL_6816c11c8cd945968d5b0ce5adfb87f5", "IPY_MODEL_0dfc7f988177492e852e1a1e2e2e3c55"], "layout": "IPY_MODEL_a3dbbfce424340438202c2a4f54314d7", "tabbable": null, "tooltip": null}}, "5d471f2ed23e49dbb159b32a3c2d406e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "511baacd1c3e4816bbe0b13284bf3fdd": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "23ef0aabeddc46538e90abc8e42e36c0": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5d471f2ed23e49dbb159b32a3c2d406e", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_511baacd1c3e4816bbe0b13284bf3fdd", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "86ca63a102954afa94795c3e61c20880": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e9b3119fe459439e8146cddd6880a9f1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "0c9f4529a42246ee88b9b31ac1212ba1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_86ca63a102954afa94795c3e61c20880", "placeholder": "\u200b", "style": "IPY_MODEL_e9b3119fe459439e8146cddd6880a9f1", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "299896a97d41456cac583393c5611a30": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6b7cf1b25d364dbab4d57e7ee83bbf50": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "a408ccf89ec14eceb847510634138186": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_299896a97d41456cac583393c5611a30", "placeholder": "\u200b", "style": "IPY_MODEL_6b7cf1b25d364dbab4d57e7ee83bbf50", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u2007190MB/s]"}}, "3c2e1352b6dd4da68e969419d9ecdaea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ad707c12b59d4885be4f947c794ca0ea": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_0c9f4529a42246ee88b9b31ac1212ba1", "IPY_MODEL_23ef0aabeddc46538e90abc8e42e36c0", "IPY_MODEL_a408ccf89ec14eceb847510634138186"], "layout": "IPY_MODEL_3c2e1352b6dd4da68e969419d9ecdaea", "tabbable": null, "tooltip": null}}, "fd00c0575ed7437ca5aabc5c08229d32": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "44dd265825e9454e868ab092b351a312": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "7f009f8c42734f8e9fa9ea99cf520312": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fd00c0575ed7437ca5aabc5c08229d32", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_44dd265825e9454e868ab092b351a312", "tabbable": null, "tooltip": null, "value": 3201.0}}, "5940aac0c62e430f99781eb883247639": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "84fa5f8c2433478c9dc01b8a41eb48d5": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "df22226a67914b8a9d0693b25dffdc60": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5940aac0c62e430f99781eb883247639", "placeholder": "\u200b", "style": "IPY_MODEL_84fa5f8c2433478c9dc01b8a41eb48d5", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "7b05aea7bfdd48ddbe010dce6c119c9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d1a86a88d50a4bb2aee04ac90b0d069a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "618905a858344a0488c8d600292e4cc3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7b05aea7bfdd48ddbe010dce6c119c9a", "placeholder": "\u200b", "style": "IPY_MODEL_d1a86a88d50a4bb2aee04ac90b0d069a", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007790kB/s]"}}, "bf8a89f9ccd04b1a857340ebae05002e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "678fc99edb84497dbac731cf9a1fa79f": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_df22226a67914b8a9d0693b25dffdc60", "IPY_MODEL_7f009f8c42734f8e9fa9ea99cf520312", "IPY_MODEL_618905a858344a0488c8d600292e4cc3"], "layout": "IPY_MODEL_bf8a89f9ccd04b1a857340ebae05002e", "tabbable": null, "tooltip": null}}, "6a601518732a456897e40425c7447332": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8063b86fd1644501bd7a35a8f8c91e8d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "4526bd738aca4e52beef9dbf333987c6": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6a601518732a456897e40425c7447332", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_8063b86fd1644501bd7a35a8f8c91e8d", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "0fca732af2064a0da3c0e7502ad49282": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fb83ceda077c4e68aad24838206b09cc": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "026a8d3d502347a290f99b02f8529d28": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0fca732af2064a0da3c0e7502ad49282", "placeholder": "\u200b", "style": "IPY_MODEL_fb83ceda077c4e68aad24838206b09cc", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "1b8db158701349408c5bdef740765f91": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c0c1b2f629d145fa8b43d2ac3c1494a6": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "72f97078b8fc4a6d9b507a102418aea1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1b8db158701349408c5bdef740765f91", "placeholder": "\u200b", "style": "IPY_MODEL_c0c1b2f629d145fa8b43d2ac3c1494a6", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u2007176MB/s]"}}, "cdbef2f290304f54be4b5de93eb10cc1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "562674bc865d4b9c866de03e113283d6": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_026a8d3d502347a290f99b02f8529d28", "IPY_MODEL_4526bd738aca4e52beef9dbf333987c6", "IPY_MODEL_72f97078b8fc4a6d9b507a102418aea1"], "layout": "IPY_MODEL_cdbef2f290304f54be4b5de93eb10cc1", "tabbable": null, "tooltip": null}}, "a5ba789c57d244a6afdd80d9221598a2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ba0d6ceeadc14ee192ae1dc31b4e4186": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "5fd920ec2856445d98a6f7e8f0229c4d": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a5ba789c57d244a6afdd80d9221598a2", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_ba0d6ceeadc14ee192ae1dc31b4e4186", "tabbable": null, "tooltip": null, "value": 128619.0}}, "0fb76291c4d34ebdba9ffa24ed513e0a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "97d74ea76f524faea7cd1c349d25ed2d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "bc115d8e973a43f98d22e93fcdeb0d0a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0fb76291c4d34ebdba9ffa24ed513e0a", "placeholder": "\u200b", "style": "IPY_MODEL_97d74ea76f524faea7cd1c349d25ed2d", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "795a99fe1a5a4fe28962d2835b6d0806": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ef52679868914ac0a1bac86a98893d61": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "60d05f62061f46e7b7f871ccf49bce6e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_795a99fe1a5a4fe28962d2835b6d0806", "placeholder": "\u200b", "style": "IPY_MODEL_ef52679868914ac0a1bac86a98893d61", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u200711.1MB/s]"}}, "7ab9a8d4e3e64a0f90d15eb2e77ce38b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c0a1f533272a4f6cb65d54cbace4208e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_bc115d8e973a43f98d22e93fcdeb0d0a", "IPY_MODEL_5fd920ec2856445d98a6f7e8f0229c4d", "IPY_MODEL_60d05f62061f46e7b7f871ccf49bce6e"], "layout": "IPY_MODEL_7ab9a8d4e3e64a0f90d15eb2e77ce38b", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/audio.ipynb b/master/tutorials/datalab/audio.ipynb index 9db139a3f..a4fd4545f 100644 --- a/master/tutorials/datalab/audio.ipynb +++ b/master/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:48.153712Z", - "iopub.status.busy": "2024-07-02T12:00:48.153535Z", - "iopub.status.idle": "2024-07-02T12:00:53.266339Z", - "shell.execute_reply": "2024-07-02T12:00:53.265786Z" + "iopub.execute_input": "2024-07-02T15:10:13.381463Z", + "iopub.status.busy": "2024-07-02T15:10:13.381288Z", + "iopub.status.idle": "2024-07-02T15:10:18.674436Z", + "shell.execute_reply": "2024-07-02T15:10:18.673907Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:53.268847Z", - "iopub.status.busy": "2024-07-02T12:00:53.268512Z", - "iopub.status.idle": "2024-07-02T12:00:53.271688Z", - "shell.execute_reply": "2024-07-02T12:00:53.271237Z" + "iopub.execute_input": "2024-07-02T15:10:18.676864Z", + "iopub.status.busy": "2024-07-02T15:10:18.676521Z", + "iopub.status.idle": "2024-07-02T15:10:18.679999Z", + "shell.execute_reply": "2024-07-02T15:10:18.679435Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:53.273790Z", - "iopub.status.busy": "2024-07-02T12:00:53.273468Z", - "iopub.status.idle": "2024-07-02T12:00:53.277843Z", - "shell.execute_reply": "2024-07-02T12:00:53.277413Z" + "iopub.execute_input": "2024-07-02T15:10:18.681962Z", + "iopub.status.busy": "2024-07-02T15:10:18.681787Z", + "iopub.status.idle": "2024-07-02T15:10:18.686141Z", + "shell.execute_reply": "2024-07-02T15:10:18.685703Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-02T12:00:53.279840Z", - "iopub.status.busy": "2024-07-02T12:00:53.279499Z", - "iopub.status.idle": "2024-07-02T12:00:54.884749Z", - "shell.execute_reply": "2024-07-02T12:00:54.884125Z" + "iopub.execute_input": "2024-07-02T15:10:18.688033Z", + "iopub.status.busy": "2024-07-02T15:10:18.687785Z", + "iopub.status.idle": "2024-07-02T15:10:20.393053Z", + "shell.execute_reply": "2024-07-02T15:10:20.392456Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-02T12:00:54.887464Z", - "iopub.status.busy": "2024-07-02T12:00:54.887081Z", - "iopub.status.idle": "2024-07-02T12:00:54.897463Z", - "shell.execute_reply": "2024-07-02T12:00:54.897041Z" + "iopub.execute_input": "2024-07-02T15:10:20.395802Z", + "iopub.status.busy": "2024-07-02T15:10:20.395334Z", + "iopub.status.idle": "2024-07-02T15:10:20.407068Z", + "shell.execute_reply": "2024-07-02T15:10:20.406544Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:54.899593Z", - "iopub.status.busy": "2024-07-02T12:00:54.899256Z", - "iopub.status.idle": "2024-07-02T12:00:54.904661Z", - "shell.execute_reply": "2024-07-02T12:00:54.904214Z" + "iopub.execute_input": "2024-07-02T15:10:20.409159Z", + "iopub.status.busy": "2024-07-02T15:10:20.408835Z", + "iopub.status.idle": "2024-07-02T15:10:20.414421Z", + "shell.execute_reply": "2024-07-02T15:10:20.413846Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-02T12:00:54.906699Z", - "iopub.status.busy": "2024-07-02T12:00:54.906445Z", - "iopub.status.idle": "2024-07-02T12:00:55.370547Z", - "shell.execute_reply": "2024-07-02T12:00:55.370054Z" + "iopub.execute_input": "2024-07-02T15:10:20.416521Z", + "iopub.status.busy": "2024-07-02T15:10:20.416054Z", + "iopub.status.idle": "2024-07-02T15:10:20.875781Z", + "shell.execute_reply": "2024-07-02T15:10:20.875260Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:55.372729Z", - "iopub.status.busy": "2024-07-02T12:00:55.372455Z", - "iopub.status.idle": "2024-07-02T12:00:56.373788Z", - "shell.execute_reply": "2024-07-02T12:00:56.373190Z" + "iopub.execute_input": "2024-07-02T15:10:20.877916Z", + "iopub.status.busy": "2024-07-02T15:10:20.877560Z", + "iopub.status.idle": "2024-07-02T15:10:21.631226Z", + "shell.execute_reply": "2024-07-02T15:10:21.630744Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-07-02T12:00:56.376073Z", - "iopub.status.busy": "2024-07-02T12:00:56.375890Z", - "iopub.status.idle": "2024-07-02T12:00:56.393884Z", - "shell.execute_reply": "2024-07-02T12:00:56.393321Z" + "iopub.execute_input": "2024-07-02T15:10:21.633680Z", + "iopub.status.busy": "2024-07-02T15:10:21.633336Z", + "iopub.status.idle": "2024-07-02T15:10:21.651564Z", + "shell.execute_reply": "2024-07-02T15:10:21.651138Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:56.396057Z", - "iopub.status.busy": "2024-07-02T12:00:56.395720Z", - "iopub.status.idle": "2024-07-02T12:00:56.398930Z", - "shell.execute_reply": "2024-07-02T12:00:56.398478Z" + "iopub.execute_input": "2024-07-02T15:10:21.653547Z", + "iopub.status.busy": "2024-07-02T15:10:21.653247Z", + "iopub.status.idle": "2024-07-02T15:10:21.656414Z", + "shell.execute_reply": "2024-07-02T15:10:21.655863Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:56.400749Z", - "iopub.status.busy": "2024-07-02T12:00:56.400581Z", - "iopub.status.idle": "2024-07-02T12:01:10.956584Z", - "shell.execute_reply": "2024-07-02T12:01:10.955969Z" + "iopub.execute_input": "2024-07-02T15:10:21.658634Z", + "iopub.status.busy": "2024-07-02T15:10:21.658142Z", + "iopub.status.idle": "2024-07-02T15:10:35.825662Z", + "shell.execute_reply": "2024-07-02T15:10:35.825086Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:10.959440Z", - "iopub.status.busy": "2024-07-02T12:01:10.959028Z", - "iopub.status.idle": "2024-07-02T12:01:10.962902Z", - "shell.execute_reply": "2024-07-02T12:01:10.962374Z" + "iopub.execute_input": "2024-07-02T15:10:35.828473Z", + "iopub.status.busy": "2024-07-02T15:10:35.828094Z", + "iopub.status.idle": "2024-07-02T15:10:35.831789Z", + "shell.execute_reply": "2024-07-02T15:10:35.831277Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:10.964878Z", - "iopub.status.busy": "2024-07-02T12:01:10.964705Z", - "iopub.status.idle": "2024-07-02T12:01:11.664747Z", - "shell.execute_reply": "2024-07-02T12:01:11.664181Z" + "iopub.execute_input": "2024-07-02T15:10:35.833874Z", + "iopub.status.busy": "2024-07-02T15:10:35.833468Z", + "iopub.status.idle": "2024-07-02T15:10:36.552465Z", + "shell.execute_reply": "2024-07-02T15:10:36.551895Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.667592Z", - "iopub.status.busy": "2024-07-02T12:01:11.667207Z", - "iopub.status.idle": "2024-07-02T12:01:11.671960Z", - "shell.execute_reply": "2024-07-02T12:01:11.671464Z" + "iopub.execute_input": "2024-07-02T15:10:36.556106Z", + "iopub.status.busy": "2024-07-02T15:10:36.555160Z", + "iopub.status.idle": "2024-07-02T15:10:36.561881Z", + "shell.execute_reply": "2024-07-02T15:10:36.561370Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.674352Z", - "iopub.status.busy": "2024-07-02T12:01:11.673986Z", - "iopub.status.idle": "2024-07-02T12:01:11.769978Z", - "shell.execute_reply": "2024-07-02T12:01:11.769317Z" + "iopub.execute_input": "2024-07-02T15:10:36.565373Z", + "iopub.status.busy": "2024-07-02T15:10:36.564458Z", + "iopub.status.idle": "2024-07-02T15:10:36.658752Z", + "shell.execute_reply": "2024-07-02T15:10:36.658223Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.772290Z", - "iopub.status.busy": "2024-07-02T12:01:11.771936Z", - "iopub.status.idle": "2024-07-02T12:01:11.785262Z", - "shell.execute_reply": "2024-07-02T12:01:11.784787Z" + "iopub.execute_input": "2024-07-02T15:10:36.661210Z", + "iopub.status.busy": "2024-07-02T15:10:36.660924Z", + "iopub.status.idle": "2024-07-02T15:10:36.673696Z", + "shell.execute_reply": "2024-07-02T15:10:36.673268Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.787484Z", - "iopub.status.busy": "2024-07-02T12:01:11.787145Z", - "iopub.status.idle": "2024-07-02T12:01:11.795270Z", - "shell.execute_reply": "2024-07-02T12:01:11.794713Z" + "iopub.execute_input": "2024-07-02T15:10:36.675623Z", + "iopub.status.busy": "2024-07-02T15:10:36.675445Z", + "iopub.status.idle": "2024-07-02T15:10:36.683122Z", + "shell.execute_reply": "2024-07-02T15:10:36.682702Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.797390Z", - "iopub.status.busy": "2024-07-02T12:01:11.797080Z", - "iopub.status.idle": "2024-07-02T12:01:11.801551Z", - "shell.execute_reply": "2024-07-02T12:01:11.800973Z" + "iopub.execute_input": "2024-07-02T15:10:36.685019Z", + "iopub.status.busy": "2024-07-02T15:10:36.684848Z", + "iopub.status.idle": "2024-07-02T15:10:36.688952Z", + "shell.execute_reply": "2024-07-02T15:10:36.688536Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.803467Z", - "iopub.status.busy": "2024-07-02T12:01:11.803275Z", - "iopub.status.idle": "2024-07-02T12:01:11.809289Z", - "shell.execute_reply": "2024-07-02T12:01:11.808826Z" + "iopub.execute_input": "2024-07-02T15:10:36.690791Z", + "iopub.status.busy": "2024-07-02T15:10:36.690602Z", + "iopub.status.idle": "2024-07-02T15:10:36.696393Z", + "shell.execute_reply": "2024-07-02T15:10:36.695933Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.811355Z", - "iopub.status.busy": "2024-07-02T12:01:11.811010Z", - "iopub.status.idle": "2024-07-02T12:01:11.924674Z", - "shell.execute_reply": "2024-07-02T12:01:11.924087Z" + "iopub.execute_input": "2024-07-02T15:10:36.698276Z", + "iopub.status.busy": "2024-07-02T15:10:36.698106Z", + "iopub.status.idle": "2024-07-02T15:10:36.808722Z", + "shell.execute_reply": "2024-07-02T15:10:36.808237Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.927078Z", - "iopub.status.busy": "2024-07-02T12:01:11.926676Z", - "iopub.status.idle": "2024-07-02T12:01:12.029810Z", - "shell.execute_reply": "2024-07-02T12:01:12.029255Z" + "iopub.execute_input": "2024-07-02T15:10:36.810751Z", + "iopub.status.busy": "2024-07-02T15:10:36.810575Z", + "iopub.status.idle": "2024-07-02T15:10:36.915062Z", + "shell.execute_reply": "2024-07-02T15:10:36.914621Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:12.031968Z", - "iopub.status.busy": "2024-07-02T12:01:12.031613Z", - "iopub.status.idle": "2024-07-02T12:01:12.132022Z", - "shell.execute_reply": "2024-07-02T12:01:12.131402Z" + "iopub.execute_input": "2024-07-02T15:10:36.917156Z", + "iopub.status.busy": "2024-07-02T15:10:36.916831Z", + "iopub.status.idle": "2024-07-02T15:10:37.019921Z", + "shell.execute_reply": "2024-07-02T15:10:37.019441Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:12.134167Z", - "iopub.status.busy": "2024-07-02T12:01:12.133985Z", - "iopub.status.idle": "2024-07-02T12:01:12.235981Z", - "shell.execute_reply": "2024-07-02T12:01:12.235470Z" + "iopub.execute_input": "2024-07-02T15:10:37.022021Z", + "iopub.status.busy": "2024-07-02T15:10:37.021843Z", + "iopub.status.idle": "2024-07-02T15:10:37.126427Z", + "shell.execute_reply": "2024-07-02T15:10:37.125880Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:12.237957Z", - "iopub.status.busy": "2024-07-02T12:01:12.237775Z", - "iopub.status.idle": "2024-07-02T12:01:12.241026Z", - "shell.execute_reply": "2024-07-02T12:01:12.240474Z" + "iopub.execute_input": "2024-07-02T15:10:37.128724Z", + "iopub.status.busy": "2024-07-02T15:10:37.128301Z", + "iopub.status.idle": "2024-07-02T15:10:37.131483Z", + "shell.execute_reply": "2024-07-02T15:10:37.131015Z" }, "nbsphinx": "hidden" }, @@ -1392,7 +1392,76 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0b4a72514d5e48d9a5de944d58a5949b": { + "026a8d3d502347a290f99b02f8529d28": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0fca732af2064a0da3c0e7502ad49282", + "placeholder": "​", + "style": "IPY_MODEL_fb83ceda077c4e68aad24838206b09cc", + "tabbable": null, + "tooltip": null, + "value": "classifier.ckpt: 100%" + } + }, + "0c9f4529a42246ee88b9b31ac1212ba1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_86ca63a102954afa94795c3e61c20880", + "placeholder": "​", + "style": "IPY_MODEL_e9b3119fe459439e8146cddd6880a9f1", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "0dfc7f988177492e852e1a1e2e2e3c55": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d16b4b3c59ba49d9a539a98b5d9ace25", + "placeholder": "​", + "style": "IPY_MODEL_8712797b65e1448f8a93f64e6e581af5", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 502kB/s]" + } + }, + "0fb76291c4d34ebdba9ffa24ed513e0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1445,30 +1514,7 @@ "width": null } }, - "1879dcf933dc47ef80752de84b1be173": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1e578c121b074fe689ec2d0de3438add", - "placeholder": "​", - "style": "IPY_MODEL_65678011a834483f81123fce6a847a0f", - "tabbable": null, - "tooltip": null, - "value": "classifier.ckpt: 100%" - } - }, - "1df8c0f2d29d45779e226e08c41aefd8": { + "0fca732af2064a0da3c0e7502ad49282": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1521,7 +1567,7 @@ "width": null } }, - "1e578c121b074fe689ec2d0de3438add": { + "1b8db158701349408c5bdef740765f91": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1574,7 +1620,7 @@ "width": null } }, - "2073d888478c4148aa6af8f01d1a55c0": { + "23ef0aabeddc46538e90abc8e42e36c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1590,17 +1636,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f9d1becbce8d4a0e92d1291261488c36", - "max": 15856877.0, + "layout": "IPY_MODEL_5d471f2ed23e49dbb159b32a3c2d406e", + "max": 16887676.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_7fb74483ba564e44bf8528608dd0c00d", + "style": "IPY_MODEL_511baacd1c3e4816bbe0b13284bf3fdd", "tabbable": null, "tooltip": null, - "value": 15856877.0 + "value": 16887676.0 } }, - "249f3be21f17476cb286f35a4beae4ea": { + "299896a97d41456cac583393c5611a30": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1653,74 +1699,7 @@ "width": null } }, - "280097d7d8744e47bd5924ed20469bd6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f1379d86855941e4a6388b556616e327", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_40a775a4588f452f8bf5d7fbc03bc9ba", - "tabbable": null, - "tooltip": null, - "value": 2041.0 - } - }, - "28c2d54c60d54e18885566cb5f99ba0a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "38e4ea17dfd74406801cb8ef4fa01bfb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d3b969344b34427e9067d2e51d96dd59", - "placeholder": "​", - "style": "IPY_MODEL_9ad076b10aa949c583d59ebf787d18d9", - "tabbable": null, - "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 501kB/s]" - } - }, - "39378c4365b84e2ba8a40f5da8c49a6b": { + "3c2e1352b6dd4da68e969419d9ecdaea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1773,48 +1752,7 @@ "width": null } }, - "3b3e7e9c2d3a481a8b66e18c87d96abe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "3e0efe779a52444991f5fa51e7b53a87": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5740512f403c4e52a3ce092bc9b6022f", - "placeholder": "​", - "style": "IPY_MODEL_9fbff358e60544609b6d2a561cf6f85c", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" - } - }, - "40a775a4588f452f8bf5d7fbc03bc9ba": { + "44dd265825e9454e868ab092b351a312": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1830,7 +1768,7 @@ "description_width": "" } }, - "4209fa1ed28448c4a745350a1042e490": { + "4526bd738aca4e52beef9dbf333987c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1846,40 +1784,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0b4a72514d5e48d9a5de944d58a5949b", - "max": 128619.0, + "layout": "IPY_MODEL_6a601518732a456897e40425c7447332", + "max": 15856877.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_54ae4e63d9c84d078262621e2d8e350f", - "tabbable": null, - "tooltip": null, - "value": 128619.0 - } - }, - "4d17c45bac644d16890f974d9bf14252": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1df8c0f2d29d45779e226e08c41aefd8", - "placeholder": "​", - "style": "IPY_MODEL_7a9eaff0d94f402eb6726bbbe282d200", + "style": "IPY_MODEL_8063b86fd1644501bd7a35a8f8c91e8d", "tabbable": null, "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 121MB/s]" + "value": 15856877.0 } }, - "513013579fcb4448832ca2cf5f6e0fc2": { + "4a111d06ad644ff3b7dfcd8f9a93b27f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1932,7 +1847,7 @@ "width": null } }, - "54ae4e63d9c84d078262621e2d8e350f": { + "511baacd1c3e4816bbe0b13284bf3fdd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1948,7 +1863,31 @@ "description_width": "" } }, - "5740512f403c4e52a3ce092bc9b6022f": { + "562674bc865d4b9c866de03e113283d6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_026a8d3d502347a290f99b02f8529d28", + "IPY_MODEL_4526bd738aca4e52beef9dbf333987c6", + "IPY_MODEL_72f97078b8fc4a6d9b507a102418aea1" + ], + "layout": "IPY_MODEL_cdbef2f290304f54be4b5de93eb10cc1", + "tabbable": null, + "tooltip": null + } + }, + "5940aac0c62e430f99781eb883247639": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2001,7 +1940,7 @@ "width": null } }, - "5fc9fb12e7584385b6dbcc71ea004933": { + "5d471f2ed23e49dbb159b32a3c2d406e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2054,43 +1993,49 @@ "width": null } }, - "65678011a834483f81123fce6a847a0f": { + "5fd920ec2856445d98a6f7e8f0229c4d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a5ba789c57d244a6afdd80d9221598a2", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ba0d6ceeadc14ee192ae1dc31b4e4186", + "tabbable": null, + "tooltip": null, + "value": 128619.0 } }, - "65c231ce763c440980afbfa1c4eaab86": { + "60b559ea05d04ab2aefd425629200a9b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "68b7a074f4a5491984f03ffaed29102c": { + "60d05f62061f46e7b7f871ccf49bce6e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2105,87 +2050,18 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6fe26799ef5342b7ae1161bc8ef4ec8a", + "layout": "IPY_MODEL_795a99fe1a5a4fe28962d2835b6d0806", "placeholder": "​", - "style": "IPY_MODEL_ba779947520844b1b4e9e788604b7ed8", + "style": "IPY_MODEL_ef52679868914ac0a1bac86a98893d61", "tabbable": null, "tooltip": null, - "value": "label_encoder.txt: 100%" + "value": " 129k/129k [00:00<00:00, 11.1MB/s]" } }, - "6b1e51bd7dfd4e7e9b96650627dd4347": { + "618905a858344a0488c8d600292e4cc3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "6fe26799ef5342b7ae1161bc8ef4ec8a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "73fd95beddfa4d59833b8b729dc2ef1b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", @@ -2197,80 +2073,65 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_513013579fcb4448832ca2cf5f6e0fc2", + "layout": "IPY_MODEL_7b05aea7bfdd48ddbe010dce6c119c9a", "placeholder": "​", - "style": "IPY_MODEL_edbc3e1e1e514aeea602d52281c3dfa3", + "style": "IPY_MODEL_d1a86a88d50a4bb2aee04ac90b0d069a", "tabbable": null, "tooltip": null, - "value": "hyperparams.yaml: 100%" + "value": " 3.20k/3.20k [00:00<00:00, 790kB/s]" } }, - "743444b6670b4fa99cc0396e4b32f5e4": { + "678fc99edb84497dbac731cf9a1fa79f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b95daff336854c7aa1ca5dbdf574af6e", - "placeholder": "​", - "style": "IPY_MODEL_65c231ce763c440980afbfa1c4eaab86", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_df22226a67914b8a9d0693b25dffdc60", + "IPY_MODEL_7f009f8c42734f8e9fa9ea99cf520312", + "IPY_MODEL_618905a858344a0488c8d600292e4cc3" + ], + "layout": "IPY_MODEL_bf8a89f9ccd04b1a857340ebae05002e", "tabbable": null, - "tooltip": null, - "value": " 129k/129k [00:00<00:00, 10.6MB/s]" - } - }, - "7a9eaff0d94f402eb6726bbbe282d200": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null } }, - "7ac998d738294e6c82fb7330a80ef819": { + "6816c11c8cd945968d5b0ce5adfb87f5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1879dcf933dc47ef80752de84b1be173", - "IPY_MODEL_2073d888478c4148aa6af8f01d1a55c0", - "IPY_MODEL_b182da74164e4c6da9b9218000f3b471" - ], - "layout": "IPY_MODEL_7b71cae594754cd4b51bdf0cc3937dc8", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4a111d06ad644ff3b7dfcd8f9a93b27f", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_60b559ea05d04ab2aefd425629200a9b", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 2041.0 } }, - "7b71cae594754cd4b51bdf0cc3937dc8": { + "6a601518732a456897e40425c7447332": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2323,7 +2184,7 @@ "width": null } }, - "7e28797dcb31400985bcdae5b8c5fb36": { + "6b7cf1b25d364dbab4d57e7ee83bbf50": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2341,23 +2202,30 @@ "text_color": null } }, - "7fb74483ba564e44bf8528608dd0c00d": { + "72f97078b8fc4a6d9b507a102418aea1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1b8db158701349408c5bdef740765f91", + "placeholder": "​", + "style": "IPY_MODEL_c0c1b2f629d145fa8b43d2ac3c1494a6", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 176MB/s]" } }, - "96e34a60ae404938aef646ab844636ca": { + "795a99fe1a5a4fe28962d2835b6d0806": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2410,67 +2278,7 @@ "width": null } }, - "9946d2c65d6146e5a82c395bc8875caf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_68b7a074f4a5491984f03ffaed29102c", - "IPY_MODEL_4209fa1ed28448c4a745350a1042e490", - "IPY_MODEL_743444b6670b4fa99cc0396e4b32f5e4" - ], - "layout": "IPY_MODEL_f70f441fdd5147c685302ccf8dbb2370", - "tabbable": null, - "tooltip": null - } - }, - "9ad076b10aa949c583d59ebf787d18d9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "9fbff358e60544609b6d2a561cf6f85c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a93eeb28cf894cdba2553c527a3bb1a7": { + "7ab9a8d4e3e64a0f90d15eb2e77ce38b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2523,7 +2331,7 @@ "width": null } }, - "af604b66416942c3beafdb0f24f5fb27": { + "7b05aea7bfdd48ddbe010dce6c119c9a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2576,7 +2384,91 @@ "width": null } }, - "b182da74164e4c6da9b9218000f3b471": { + "7f009f8c42734f8e9fa9ea99cf520312": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fd00c0575ed7437ca5aabc5c08229d32", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_44dd265825e9454e868ab092b351a312", + "tabbable": null, + "tooltip": null, + "value": 3201.0 + } + }, + "8063b86fd1644501bd7a35a8f8c91e8d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "84fa5f8c2433478c9dc01b8a41eb48d5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8531becaffca44989779b1d37c65260e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_85e7b3e36e7c47129e29727d2f646f2f", + "IPY_MODEL_6816c11c8cd945968d5b0ce5adfb87f5", + "IPY_MODEL_0dfc7f988177492e852e1a1e2e2e3c55" + ], + "layout": "IPY_MODEL_a3dbbfce424340438202c2a4f54314d7", + "tabbable": null, + "tooltip": null + } + }, + "85e7b3e36e7c47129e29727d2f646f2f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2591,41 +2483,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_39378c4365b84e2ba8a40f5da8c49a6b", + "layout": "IPY_MODEL_d116ac8503dc4fa19dbf254269e57e81", "placeholder": "​", - "style": "IPY_MODEL_7e28797dcb31400985bcdae5b8c5fb36", + "style": "IPY_MODEL_865a2e4e16fe4c8fab1e00a4c6cd135d", "tabbable": null, "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 133MB/s]" + "value": "hyperparams.yaml: 100%" } }, - "b4c75c905856457c980a958861d26460": { + "865a2e4e16fe4c8fab1e00a4c6cd135d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a93eeb28cf894cdba2553c527a3bb1a7", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6b1e51bd7dfd4e7e9b96650627dd4347", - "tabbable": null, - "tooltip": null, - "value": 16887676.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b7930e24a3604c3783c6342017146161": { + "86ca63a102954afa94795c3e61c20880": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2678,7 +2562,43 @@ "width": null } }, - "b95daff336854c7aa1ca5dbdf574af6e": { + "8712797b65e1448f8a93f64e6e581af5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "97d74ea76f524faea7cd1c349d25ed2d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a3dbbfce424340438202c2a4f54314d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2731,25 +2651,30 @@ "width": null } }, - "ba779947520844b1b4e9e788604b7ed8": { + "a408ccf89ec14eceb847510634138186": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_299896a97d41456cac583393c5611a30", + "placeholder": "​", + "style": "IPY_MODEL_6b7cf1b25d364dbab4d57e7ee83bbf50", + "tabbable": null, + "tooltip": null, + "value": " 16.9M/16.9M [00:00<00:00, 190MB/s]" } }, - "c5e86d52ed71402dbadb1a831d3a99dd": { + "a5ba789c57d244a6afdd80d9221598a2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2802,7 +2727,7 @@ "width": null } }, - "cb4111006dc844c69976ab2a2fd47bf5": { + "ad707c12b59d4885be4f947c794ca0ea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2817,16 +2742,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3e0efe779a52444991f5fa51e7b53a87", - "IPY_MODEL_b4c75c905856457c980a958861d26460", - "IPY_MODEL_4d17c45bac644d16890f974d9bf14252" + "IPY_MODEL_0c9f4529a42246ee88b9b31ac1212ba1", + "IPY_MODEL_23ef0aabeddc46538e90abc8e42e36c0", + "IPY_MODEL_a408ccf89ec14eceb847510634138186" ], - "layout": "IPY_MODEL_af604b66416942c3beafdb0f24f5fb27", + "layout": "IPY_MODEL_3c2e1352b6dd4da68e969419d9ecdaea", "tabbable": null, "tooltip": null } }, - "cbf0488c8e70416aabb7cb8cffe59c43": { + "ba0d6ceeadc14ee192ae1dc31b4e4186": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2842,33 +2767,30 @@ "description_width": "" } }, - "cf0f8cce4150428582199659b7ecc31f": { + "bc115d8e973a43f98d22e93fcdeb0d0a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5fc9fb12e7584385b6dbcc71ea004933", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cbf0488c8e70416aabb7cb8cffe59c43", + "layout": "IPY_MODEL_0fb76291c4d34ebdba9ffa24ed513e0a", + "placeholder": "​", + "style": "IPY_MODEL_97d74ea76f524faea7cd1c349d25ed2d", "tabbable": null, "tooltip": null, - "value": 3201.0 + "value": "label_encoder.txt: 100%" } }, - "d3b969344b34427e9067d2e51d96dd59": { + "bf8a89f9ccd04b1a857340ebae05002e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2921,30 +2843,31 @@ "width": null } }, - "d5743decd00649e19fbee18925104825": { + "c0a1f533272a4f6cb65d54cbace4208e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c5e86d52ed71402dbadb1a831d3a99dd", - "placeholder": "​", - "style": "IPY_MODEL_3b3e7e9c2d3a481a8b66e18c87d96abe", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_bc115d8e973a43f98d22e93fcdeb0d0a", + "IPY_MODEL_5fd920ec2856445d98a6f7e8f0229c4d", + "IPY_MODEL_60d05f62061f46e7b7f871ccf49bce6e" + ], + "layout": "IPY_MODEL_7ab9a8d4e3e64a0f90d15eb2e77ce38b", "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" + "tooltip": null } }, - "edbc3e1e1e514aeea602d52281c3dfa3": { + "c0c1b2f629d145fa8b43d2ac3c1494a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2962,7 +2885,7 @@ "text_color": null } }, - "f1379d86855941e4a6388b556616e327": { + "cdbef2f290304f54be4b5de93eb10cc1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3015,30 +2938,7 @@ "width": null } }, - "f5b3c5a8ab94472a8c12d10627c4a3b2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_249f3be21f17476cb286f35a4beae4ea", - "placeholder": "​", - "style": "IPY_MODEL_28c2d54c60d54e18885566cb5f99ba0a", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 820kB/s]" - } - }, - "f70f441fdd5147c685302ccf8dbb2370": { + "d116ac8503dc4fa19dbf254269e57e81": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3091,7 +2991,7 @@ "width": null } }, - "f9d1becbce8d4a0e92d1291261488c36": { + "d16b4b3c59ba49d9a539a98b5d9ace25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3144,52 +3044,152 @@ "width": null } }, - "fbe4b54842f84d388c29da52e5a714cb": { + "d1a86a88d50a4bb2aee04ac90b0d069a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_73fd95beddfa4d59833b8b729dc2ef1b", - "IPY_MODEL_280097d7d8744e47bd5924ed20469bd6", - "IPY_MODEL_38e4ea17dfd74406801cb8ef4fa01bfb" - ], - "layout": "IPY_MODEL_96e34a60ae404938aef646ab844636ca", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "fc94797f46734484ae1adb8c6aac5095": { + "df22226a67914b8a9d0693b25dffdc60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d5743decd00649e19fbee18925104825", - "IPY_MODEL_cf0f8cce4150428582199659b7ecc31f", - "IPY_MODEL_f5b3c5a8ab94472a8c12d10627c4a3b2" - ], - "layout": "IPY_MODEL_b7930e24a3604c3783c6342017146161", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5940aac0c62e430f99781eb883247639", + "placeholder": "​", + "style": "IPY_MODEL_84fa5f8c2433478c9dc01b8a41eb48d5", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "e9b3119fe459439e8146cddd6880a9f1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ef52679868914ac0a1bac86a98893d61": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fb83ceda077c4e68aad24838206b09cc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fd00c0575ed7437ca5aabc5c08229d32": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } } }, diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html index 508844a95..a88328502 100644 --- a/master/tutorials/datalab/datalab_advanced.html +++ b/master/tutorials/datalab/datalab_advanced.html @@ -1291,7 +1291,7 @@

Functionality 3: Save and load Datalab objects

-
+
@@ -1566,7 +1566,7 @@

Functionality 4: Adding a custom IssueManager -{"state": {"92d343740ab348028d512cbabde596de": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "160374201c2049b98c39d1da42e6f09d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "4d30844fcfff423583118cba2ebebe1b": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_92d343740ab348028d512cbabde596de", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_160374201c2049b98c39d1da42e6f09d", "tabbable": null, "tooltip": null, "value": 132.0}}, "23609831ef654449b59fb8c4f8a2bb30": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ada4493def764ffa859a5d6ba4d315fb": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2c61a80b080b4e158a20edb5c4a1ac84": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_23609831ef654449b59fb8c4f8a2bb30", "placeholder": "\u200b", "style": "IPY_MODEL_ada4493def764ffa859a5d6ba4d315fb", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "8addd7af612b43d395a8dfcfeb6287ef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bd9b705b24884f74a14e8bfdd7ee8634": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "430e528b6e30444ea44c9f7dacbfcc30": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8addd7af612b43d395a8dfcfeb6287ef", "placeholder": "\u200b", "style": "IPY_MODEL_bd9b705b24884f74a14e8bfdd7ee8634", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200713162.98\u2007examples/s]"}}, "5e818fd01e87406a87c87fc7bc810095": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "59b4478dd8e7455d94d80c6cac5956e7": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2c61a80b080b4e158a20edb5c4a1ac84", "IPY_MODEL_4d30844fcfff423583118cba2ebebe1b", "IPY_MODEL_430e528b6e30444ea44c9f7dacbfcc30"], "layout": "IPY_MODEL_5e818fd01e87406a87c87fc7bc810095", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"e5651455523845919804bfd3f20d32fd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "da3ba2f2d038490c8a65361852a477f2": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "07b42c7871184a77913db05041f70f6c": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e5651455523845919804bfd3f20d32fd", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_da3ba2f2d038490c8a65361852a477f2", "tabbable": null, "tooltip": null, "value": 132.0}}, "eae1af9f890445fab406fb6b04a570ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b7fb3ecf4b354ed5aa31f23e9c2d7f20": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b58f2ef4defc4008a4fb84b628c0df08": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_eae1af9f890445fab406fb6b04a570ff", "placeholder": "\u200b", "style": "IPY_MODEL_b7fb3ecf4b354ed5aa31f23e9c2d7f20", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "81406c4c29884619bacbf6314e1bb90e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a43777fd323b46498d1b65ddfdcb03d7": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ef016c3dc0df4a9a878a4f9644a436dd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_81406c4c29884619bacbf6314e1bb90e", "placeholder": "\u200b", "style": "IPY_MODEL_a43777fd323b46498d1b65ddfdcb03d7", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200713503.28\u2007examples/s]"}}, "f8cacbb114a946fb8b37956128a62704": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "850cf9c5dd514c5c8c5878e12d30f5ca": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b58f2ef4defc4008a4fb84b628c0df08", "IPY_MODEL_07b42c7871184a77913db05041f70f6c", "IPY_MODEL_ef016c3dc0df4a9a878a4f9644a436dd"], "layout": "IPY_MODEL_f8cacbb114a946fb8b37956128a62704", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index 58bbdaa8a..0a658abc0 100644 --- a/master/tutorials/datalab/datalab_advanced.ipynb +++ b/master/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:15.541042Z", - "iopub.status.busy": "2024-07-02T12:01:15.540869Z", - "iopub.status.idle": "2024-07-02T12:01:16.706079Z", - "shell.execute_reply": "2024-07-02T12:01:16.705546Z" + "iopub.execute_input": "2024-07-02T15:10:41.435250Z", + "iopub.status.busy": "2024-07-02T15:10:41.434904Z", + "iopub.status.idle": "2024-07-02T15:10:42.616974Z", + "shell.execute_reply": "2024-07-02T15:10:42.616367Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.708528Z", - "iopub.status.busy": "2024-07-02T12:01:16.708127Z", - "iopub.status.idle": "2024-07-02T12:01:16.711112Z", - "shell.execute_reply": "2024-07-02T12:01:16.710676Z" + "iopub.execute_input": "2024-07-02T15:10:42.619570Z", + "iopub.status.busy": "2024-07-02T15:10:42.619310Z", + "iopub.status.idle": "2024-07-02T15:10:42.622452Z", + "shell.execute_reply": "2024-07-02T15:10:42.621992Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.713182Z", - "iopub.status.busy": "2024-07-02T12:01:16.712867Z", - "iopub.status.idle": "2024-07-02T12:01:16.721179Z", - "shell.execute_reply": "2024-07-02T12:01:16.720739Z" + "iopub.execute_input": "2024-07-02T15:10:42.624524Z", + "iopub.status.busy": "2024-07-02T15:10:42.624220Z", + "iopub.status.idle": "2024-07-02T15:10:42.632638Z", + "shell.execute_reply": "2024-07-02T15:10:42.632176Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.723125Z", - "iopub.status.busy": "2024-07-02T12:01:16.722823Z", - "iopub.status.idle": "2024-07-02T12:01:16.727946Z", - "shell.execute_reply": "2024-07-02T12:01:16.727497Z" + "iopub.execute_input": "2024-07-02T15:10:42.634681Z", + "iopub.status.busy": "2024-07-02T15:10:42.634369Z", + "iopub.status.idle": "2024-07-02T15:10:42.638869Z", + "shell.execute_reply": "2024-07-02T15:10:42.638430Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.730061Z", - "iopub.status.busy": "2024-07-02T12:01:16.729738Z", - "iopub.status.idle": "2024-07-02T12:01:16.910261Z", - "shell.execute_reply": "2024-07-02T12:01:16.909774Z" + "iopub.execute_input": "2024-07-02T15:10:42.640929Z", + "iopub.status.busy": "2024-07-02T15:10:42.640599Z", + "iopub.status.idle": "2024-07-02T15:10:42.823237Z", + "shell.execute_reply": "2024-07-02T15:10:42.822755Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.912657Z", - "iopub.status.busy": "2024-07-02T12:01:16.912383Z", - "iopub.status.idle": "2024-07-02T12:01:17.280864Z", - "shell.execute_reply": "2024-07-02T12:01:17.280305Z" + "iopub.execute_input": "2024-07-02T15:10:42.825617Z", + "iopub.status.busy": "2024-07-02T15:10:42.825349Z", + "iopub.status.idle": "2024-07-02T15:10:43.193502Z", + "shell.execute_reply": "2024-07-02T15:10:43.192923Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:17.283183Z", - "iopub.status.busy": "2024-07-02T12:01:17.282742Z", - "iopub.status.idle": "2024-07-02T12:01:17.305912Z", - "shell.execute_reply": "2024-07-02T12:01:17.305342Z" + "iopub.execute_input": "2024-07-02T15:10:43.195821Z", + "iopub.status.busy": "2024-07-02T15:10:43.195490Z", + "iopub.status.idle": "2024-07-02T15:10:43.218270Z", + "shell.execute_reply": "2024-07-02T15:10:43.217850Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:17.308190Z", - "iopub.status.busy": "2024-07-02T12:01:17.307876Z", - "iopub.status.idle": "2024-07-02T12:01:17.318887Z", - "shell.execute_reply": "2024-07-02T12:01:17.318342Z" + "iopub.execute_input": "2024-07-02T15:10:43.220248Z", + "iopub.status.busy": "2024-07-02T15:10:43.219922Z", + "iopub.status.idle": "2024-07-02T15:10:43.230680Z", + "shell.execute_reply": "2024-07-02T15:10:43.230226Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:17.321139Z", - "iopub.status.busy": "2024-07-02T12:01:17.320805Z", - "iopub.status.idle": "2024-07-02T12:01:19.303196Z", - "shell.execute_reply": "2024-07-02T12:01:19.302567Z" + "iopub.execute_input": "2024-07-02T15:10:43.232767Z", + "iopub.status.busy": "2024-07-02T15:10:43.232457Z", + "iopub.status.idle": "2024-07-02T15:10:45.202083Z", + "shell.execute_reply": "2024-07-02T15:10:45.201442Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.305724Z", - "iopub.status.busy": "2024-07-02T12:01:19.305235Z", - "iopub.status.idle": "2024-07-02T12:01:19.326596Z", - "shell.execute_reply": "2024-07-02T12:01:19.326111Z" + "iopub.execute_input": "2024-07-02T15:10:45.204518Z", + "iopub.status.busy": "2024-07-02T15:10:45.204239Z", + "iopub.status.idle": "2024-07-02T15:10:45.224676Z", + "shell.execute_reply": "2024-07-02T15:10:45.224228Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.328751Z", - "iopub.status.busy": "2024-07-02T12:01:19.328411Z", - "iopub.status.idle": "2024-07-02T12:01:19.346909Z", - "shell.execute_reply": "2024-07-02T12:01:19.346408Z" + "iopub.execute_input": "2024-07-02T15:10:45.226664Z", + "iopub.status.busy": "2024-07-02T15:10:45.226491Z", + "iopub.status.idle": "2024-07-02T15:10:45.243690Z", + "shell.execute_reply": "2024-07-02T15:10:45.243258Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.349172Z", - "iopub.status.busy": "2024-07-02T12:01:19.348833Z", - "iopub.status.idle": "2024-07-02T12:01:19.364109Z", - "shell.execute_reply": "2024-07-02T12:01:19.363523Z" + "iopub.execute_input": "2024-07-02T15:10:45.245491Z", + "iopub.status.busy": "2024-07-02T15:10:45.245321Z", + "iopub.status.idle": "2024-07-02T15:10:45.259517Z", + "shell.execute_reply": "2024-07-02T15:10:45.259087Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.366447Z", - "iopub.status.busy": "2024-07-02T12:01:19.366041Z", - "iopub.status.idle": "2024-07-02T12:01:19.385525Z", - "shell.execute_reply": "2024-07-02T12:01:19.384972Z" + "iopub.execute_input": "2024-07-02T15:10:45.261615Z", + "iopub.status.busy": "2024-07-02T15:10:45.261244Z", + "iopub.status.idle": "2024-07-02T15:10:45.279892Z", + "shell.execute_reply": "2024-07-02T15:10:45.279369Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "59b4478dd8e7455d94d80c6cac5956e7", + "model_id": "850cf9c5dd514c5c8c5878e12d30f5ca", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.387568Z", - "iopub.status.busy": "2024-07-02T12:01:19.387355Z", - "iopub.status.idle": "2024-07-02T12:01:19.403995Z", - "shell.execute_reply": "2024-07-02T12:01:19.403416Z" + "iopub.execute_input": "2024-07-02T15:10:45.282051Z", + "iopub.status.busy": "2024-07-02T15:10:45.281611Z", + "iopub.status.idle": "2024-07-02T15:10:45.296424Z", + "shell.execute_reply": "2024-07-02T15:10:45.295992Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.406166Z", - "iopub.status.busy": "2024-07-02T12:01:19.405840Z", - "iopub.status.idle": "2024-07-02T12:01:19.411828Z", - "shell.execute_reply": "2024-07-02T12:01:19.411266Z" + "iopub.execute_input": "2024-07-02T15:10:45.298329Z", + "iopub.status.busy": "2024-07-02T15:10:45.298157Z", + "iopub.status.idle": "2024-07-02T15:10:45.304307Z", + "shell.execute_reply": "2024-07-02T15:10:45.303885Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.414062Z", - "iopub.status.busy": "2024-07-02T12:01:19.413631Z", - "iopub.status.idle": "2024-07-02T12:01:19.432239Z", - "shell.execute_reply": "2024-07-02T12:01:19.431665Z" + "iopub.execute_input": "2024-07-02T15:10:45.306200Z", + "iopub.status.busy": "2024-07-02T15:10:45.306029Z", + "iopub.status.idle": "2024-07-02T15:10:45.323469Z", + "shell.execute_reply": "2024-07-02T15:10:45.323042Z" } }, "outputs": [ @@ -1447,23 +1447,33 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "160374201c2049b98c39d1da42e6f09d": { + "07b42c7871184a77913db05041f70f6c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e5651455523845919804bfd3f20d32fd", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_da3ba2f2d038490c8a65361852a477f2", + "tabbable": null, + "tooltip": null, + "value": 132.0 } }, - "23609831ef654449b59fb8c4f8a2bb30": { + "81406c4c29884619bacbf6314e1bb90e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1516,30 +1526,49 @@ "width": null } }, - "2c61a80b080b4e158a20edb5c4a1ac84": { + "850cf9c5dd514c5c8c5878e12d30f5ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_23609831ef654449b59fb8c4f8a2bb30", - "placeholder": "​", - "style": "IPY_MODEL_ada4493def764ffa859a5d6ba4d315fb", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b58f2ef4defc4008a4fb84b628c0df08", + "IPY_MODEL_07b42c7871184a77913db05041f70f6c", + "IPY_MODEL_ef016c3dc0df4a9a878a4f9644a436dd" + ], + "layout": "IPY_MODEL_f8cacbb114a946fb8b37956128a62704", "tabbable": null, - "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" + "tooltip": null + } + }, + "a43777fd323b46498d1b65ddfdcb03d7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "430e528b6e30444ea44c9f7dacbfcc30": { + "b58f2ef4defc4008a4fb84b628c0df08": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1554,65 +1583,49 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8addd7af612b43d395a8dfcfeb6287ef", + "layout": "IPY_MODEL_eae1af9f890445fab406fb6b04a570ff", "placeholder": "​", - "style": "IPY_MODEL_bd9b705b24884f74a14e8bfdd7ee8634", + "style": "IPY_MODEL_b7fb3ecf4b354ed5aa31f23e9c2d7f20", "tabbable": null, "tooltip": null, - "value": " 132/132 [00:00<00:00, 13162.98 examples/s]" + "value": "Saving the dataset (1/1 shards): 100%" } }, - "4d30844fcfff423583118cba2ebebe1b": { + "b7fb3ecf4b354ed5aa31f23e9c2d7f20": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_92d343740ab348028d512cbabde596de", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_160374201c2049b98c39d1da42e6f09d", - "tabbable": null, - "tooltip": null, - "value": 132.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "59b4478dd8e7455d94d80c6cac5956e7": { + "da3ba2f2d038490c8a65361852a477f2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2c61a80b080b4e158a20edb5c4a1ac84", - "IPY_MODEL_4d30844fcfff423583118cba2ebebe1b", - "IPY_MODEL_430e528b6e30444ea44c9f7dacbfcc30" - ], - "layout": "IPY_MODEL_5e818fd01e87406a87c87fc7bc810095", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "5e818fd01e87406a87c87fc7bc810095": { + "e5651455523845919804bfd3f20d32fd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1665,7 +1678,7 @@ "width": null } }, - "8addd7af612b43d395a8dfcfeb6287ef": { + "eae1af9f890445fab406fb6b04a570ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1718,7 +1731,30 @@ "width": null } }, - "92d343740ab348028d512cbabde596de": { + "ef016c3dc0df4a9a878a4f9644a436dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_81406c4c29884619bacbf6314e1bb90e", + "placeholder": "​", + "style": "IPY_MODEL_a43777fd323b46498d1b65ddfdcb03d7", + "tabbable": null, + "tooltip": null, + "value": " 132/132 [00:00<00:00, 13503.28 examples/s]" + } + }, + "f8cacbb114a946fb8b37956128a62704": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1770,42 +1806,6 @@ "visibility": null, "width": null } - }, - "ada4493def764ffa859a5d6ba4d315fb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "bd9b705b24884f74a14e8bfdd7ee8634": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 61c4891f1..cf7301700 100644 --- a/master/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:22.152510Z", - "iopub.status.busy": "2024-07-02T12:01:22.152333Z", - "iopub.status.idle": "2024-07-02T12:01:23.345486Z", - "shell.execute_reply": "2024-07-02T12:01:23.344925Z" + "iopub.execute_input": "2024-07-02T15:10:48.203913Z", + "iopub.status.busy": "2024-07-02T15:10:48.203743Z", + "iopub.status.idle": "2024-07-02T15:10:49.370874Z", + "shell.execute_reply": "2024-07-02T15:10:49.370326Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.348223Z", - "iopub.status.busy": "2024-07-02T12:01:23.347674Z", - "iopub.status.idle": "2024-07-02T12:01:23.350818Z", - "shell.execute_reply": "2024-07-02T12:01:23.350357Z" + "iopub.execute_input": "2024-07-02T15:10:49.373236Z", + "iopub.status.busy": "2024-07-02T15:10:49.372955Z", + "iopub.status.idle": "2024-07-02T15:10:49.375887Z", + "shell.execute_reply": "2024-07-02T15:10:49.375403Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.352826Z", - "iopub.status.busy": "2024-07-02T12:01:23.352642Z", - "iopub.status.idle": "2024-07-02T12:01:23.361928Z", - "shell.execute_reply": "2024-07-02T12:01:23.361407Z" + "iopub.execute_input": "2024-07-02T15:10:49.377883Z", + "iopub.status.busy": "2024-07-02T15:10:49.377688Z", + "iopub.status.idle": "2024-07-02T15:10:49.386512Z", + "shell.execute_reply": "2024-07-02T15:10:49.386078Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.363999Z", - "iopub.status.busy": "2024-07-02T12:01:23.363568Z", - "iopub.status.idle": "2024-07-02T12:01:23.368394Z", - "shell.execute_reply": "2024-07-02T12:01:23.367822Z" + "iopub.execute_input": "2024-07-02T15:10:49.388331Z", + "iopub.status.busy": "2024-07-02T15:10:49.388162Z", + "iopub.status.idle": "2024-07-02T15:10:49.392743Z", + "shell.execute_reply": "2024-07-02T15:10:49.392198Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.370691Z", - "iopub.status.busy": "2024-07-02T12:01:23.370280Z", - "iopub.status.idle": "2024-07-02T12:01:23.560449Z", - "shell.execute_reply": "2024-07-02T12:01:23.559925Z" + "iopub.execute_input": "2024-07-02T15:10:49.394895Z", + "iopub.status.busy": "2024-07-02T15:10:49.394722Z", + "iopub.status.idle": "2024-07-02T15:10:49.580391Z", + "shell.execute_reply": "2024-07-02T15:10:49.579904Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.563109Z", - "iopub.status.busy": "2024-07-02T12:01:23.562666Z", - "iopub.status.idle": "2024-07-02T12:01:23.933479Z", - "shell.execute_reply": "2024-07-02T12:01:23.932844Z" + "iopub.execute_input": "2024-07-02T15:10:49.582895Z", + "iopub.status.busy": "2024-07-02T15:10:49.582500Z", + "iopub.status.idle": "2024-07-02T15:10:49.951559Z", + "shell.execute_reply": "2024-07-02T15:10:49.951015Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.935860Z", - "iopub.status.busy": "2024-07-02T12:01:23.935411Z", - "iopub.status.idle": "2024-07-02T12:01:23.938217Z", - "shell.execute_reply": "2024-07-02T12:01:23.937776Z" + "iopub.execute_input": "2024-07-02T15:10:49.953780Z", + "iopub.status.busy": "2024-07-02T15:10:49.953420Z", + "iopub.status.idle": "2024-07-02T15:10:49.956065Z", + "shell.execute_reply": "2024-07-02T15:10:49.955645Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.940195Z", - "iopub.status.busy": "2024-07-02T12:01:23.940017Z", - "iopub.status.idle": "2024-07-02T12:01:23.974114Z", - "shell.execute_reply": "2024-07-02T12:01:23.973647Z" + "iopub.execute_input": "2024-07-02T15:10:49.958088Z", + "iopub.status.busy": "2024-07-02T15:10:49.957749Z", + "iopub.status.idle": "2024-07-02T15:10:49.991460Z", + "shell.execute_reply": "2024-07-02T15:10:49.991052Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.976287Z", - "iopub.status.busy": "2024-07-02T12:01:23.976112Z", - "iopub.status.idle": "2024-07-02T12:01:26.051828Z", - "shell.execute_reply": "2024-07-02T12:01:26.051244Z" + "iopub.execute_input": "2024-07-02T15:10:49.993592Z", + "iopub.status.busy": "2024-07-02T15:10:49.993200Z", + "iopub.status.idle": "2024-07-02T15:10:52.000228Z", + "shell.execute_reply": "2024-07-02T15:10:51.999641Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.054329Z", - "iopub.status.busy": "2024-07-02T12:01:26.053806Z", - "iopub.status.idle": "2024-07-02T12:01:26.073654Z", - "shell.execute_reply": "2024-07-02T12:01:26.073152Z" + "iopub.execute_input": "2024-07-02T15:10:52.002802Z", + "iopub.status.busy": "2024-07-02T15:10:52.002295Z", + "iopub.status.idle": "2024-07-02T15:10:52.021391Z", + "shell.execute_reply": "2024-07-02T15:10:52.020959Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.075978Z", - "iopub.status.busy": "2024-07-02T12:01:26.075603Z", - "iopub.status.idle": "2024-07-02T12:01:26.082158Z", - "shell.execute_reply": "2024-07-02T12:01:26.081661Z" + "iopub.execute_input": "2024-07-02T15:10:52.023564Z", + "iopub.status.busy": "2024-07-02T15:10:52.023238Z", + "iopub.status.idle": "2024-07-02T15:10:52.029818Z", + "shell.execute_reply": "2024-07-02T15:10:52.029240Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.084369Z", - "iopub.status.busy": "2024-07-02T12:01:26.084032Z", - "iopub.status.idle": "2024-07-02T12:01:26.090027Z", - "shell.execute_reply": "2024-07-02T12:01:26.089524Z" + "iopub.execute_input": "2024-07-02T15:10:52.031965Z", + "iopub.status.busy": "2024-07-02T15:10:52.031647Z", + "iopub.status.idle": "2024-07-02T15:10:52.037297Z", + "shell.execute_reply": "2024-07-02T15:10:52.036772Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.092307Z", - "iopub.status.busy": "2024-07-02T12:01:26.091888Z", - "iopub.status.idle": "2024-07-02T12:01:26.102686Z", - "shell.execute_reply": "2024-07-02T12:01:26.102114Z" + "iopub.execute_input": "2024-07-02T15:10:52.039441Z", + "iopub.status.busy": "2024-07-02T15:10:52.039151Z", + "iopub.status.idle": "2024-07-02T15:10:52.049413Z", + "shell.execute_reply": "2024-07-02T15:10:52.048911Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.104843Z", - "iopub.status.busy": "2024-07-02T12:01:26.104499Z", - "iopub.status.idle": "2024-07-02T12:01:26.113923Z", - "shell.execute_reply": "2024-07-02T12:01:26.113353Z" + "iopub.execute_input": "2024-07-02T15:10:52.051475Z", + "iopub.status.busy": "2024-07-02T15:10:52.051095Z", + "iopub.status.idle": "2024-07-02T15:10:52.060097Z", + "shell.execute_reply": "2024-07-02T15:10:52.059640Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.116196Z", - "iopub.status.busy": "2024-07-02T12:01:26.115857Z", - "iopub.status.idle": "2024-07-02T12:01:26.122959Z", - "shell.execute_reply": "2024-07-02T12:01:26.122462Z" + "iopub.execute_input": "2024-07-02T15:10:52.062179Z", + "iopub.status.busy": "2024-07-02T15:10:52.061837Z", + "iopub.status.idle": "2024-07-02T15:10:52.068765Z", + "shell.execute_reply": "2024-07-02T15:10:52.068314Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.125128Z", - "iopub.status.busy": "2024-07-02T12:01:26.124796Z", - "iopub.status.idle": "2024-07-02T12:01:26.134864Z", - "shell.execute_reply": "2024-07-02T12:01:26.134300Z" + "iopub.execute_input": "2024-07-02T15:10:52.070862Z", + "iopub.status.busy": "2024-07-02T15:10:52.070545Z", + "iopub.status.idle": "2024-07-02T15:10:52.079842Z", + "shell.execute_reply": "2024-07-02T15:10:52.079380Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.137332Z", - "iopub.status.busy": "2024-07-02T12:01:26.136913Z", - "iopub.status.idle": "2024-07-02T12:01:26.152852Z", - "shell.execute_reply": "2024-07-02T12:01:26.152376Z" + "iopub.execute_input": "2024-07-02T15:10:52.081933Z", + "iopub.status.busy": "2024-07-02T15:10:52.081594Z", + "iopub.status.idle": "2024-07-02T15:10:52.097277Z", + "shell.execute_reply": "2024-07-02T15:10:52.096807Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 7f856f6ea..25690c004 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -727,49 +727,49 @@

2. Fetch and normalize the Fashion-MNIST dataset

-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+

Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.

@@ -1082,7 +1082,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1114,7 +1114,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1146,7 +1146,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1937,35 +1937,35 @@

Dark images - is_dark_issue dark_score + is_dark_issue 34848 - True 0.203922 + True 50270 - True 0.204588 + True 3936 - True 0.213098 + True 733 - True 0.217686 + True 8094 - True 0.230118 + True @@ -2115,7 +2115,7 @@

Easy ModeCleanlab Studio which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!

diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb index 3baceeb0b..2852ac72e 100644 --- a/master/tutorials/datalab/image.ipynb +++ b/master/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:28.896200Z", - "iopub.status.busy": "2024-07-02T12:01:28.896023Z", - "iopub.status.idle": "2024-07-02T12:01:31.827318Z", - "shell.execute_reply": "2024-07-02T12:01:31.826688Z" + "iopub.execute_input": "2024-07-02T15:10:54.880751Z", + "iopub.status.busy": "2024-07-02T15:10:54.880594Z", + "iopub.status.idle": "2024-07-02T15:10:57.696869Z", + "shell.execute_reply": "2024-07-02T15:10:57.696388Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:31.829957Z", - "iopub.status.busy": "2024-07-02T12:01:31.829648Z", - "iopub.status.idle": "2024-07-02T12:01:31.833462Z", - "shell.execute_reply": "2024-07-02T12:01:31.833002Z" + "iopub.execute_input": "2024-07-02T15:10:57.699412Z", + "iopub.status.busy": "2024-07-02T15:10:57.698969Z", + "iopub.status.idle": "2024-07-02T15:10:57.702504Z", + "shell.execute_reply": "2024-07-02T15:10:57.702065Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:31.835341Z", - "iopub.status.busy": "2024-07-02T12:01:31.835170Z", - "iopub.status.idle": "2024-07-02T12:01:42.989836Z", - "shell.execute_reply": "2024-07-02T12:01:42.989362Z" + "iopub.execute_input": "2024-07-02T15:10:57.704607Z", + "iopub.status.busy": "2024-07-02T15:10:57.704218Z", + "iopub.status.idle": "2024-07-02T15:11:08.972759Z", + "shell.execute_reply": "2024-07-02T15:11:08.972290Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d4c59b0bfa86424a8c95a71f890f5454", + "model_id": "76447603597c41e58c504ba366dedf8b", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ffbe85316974d029eab626642378580", + "model_id": "74d7207adb634a9a9648063cd4ebf05d", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a9f98ff0f0446e7b89c4fe4fffc3418", + "model_id": "24554a44a66045a29398e71c18b39f2f", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "39838b65ab134d2a9a445437586fec98", + "model_id": "52a2b90360f7460f9d5e8e206e5b7b47", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4d801b30b791427d9103f41505cf1a3e", + "model_id": "1eca5328aef44e1ca18c8c422f647377", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d1f1b12cc3545b0b78b6f64afe61ba8", + "model_id": "c8ad57476e81431f9ef31378a786d5e9", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "495daf880acd479da7fa63fedf1e1368", + "model_id": "4761c3ddf1a643e8bda01b752e44ad8b", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96b3b9a948504544be06e5692d10926d", + "model_id": "8d04c2d222424f08b06b6508223878ed", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:42.992144Z", - "iopub.status.busy": "2024-07-02T12:01:42.991695Z", - "iopub.status.idle": "2024-07-02T12:01:42.995507Z", - "shell.execute_reply": "2024-07-02T12:01:42.995062Z" + "iopub.execute_input": "2024-07-02T15:11:08.975154Z", + "iopub.status.busy": "2024-07-02T15:11:08.974702Z", + "iopub.status.idle": "2024-07-02T15:11:08.978606Z", + "shell.execute_reply": "2024-07-02T15:11:08.978061Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:42.997511Z", - "iopub.status.busy": "2024-07-02T12:01:42.997189Z", - "iopub.status.idle": "2024-07-02T12:01:54.313084Z", - "shell.execute_reply": "2024-07-02T12:01:54.312563Z" + "iopub.execute_input": "2024-07-02T15:11:08.980647Z", + "iopub.status.busy": "2024-07-02T15:11:08.980365Z", + "iopub.status.idle": "2024-07-02T15:11:20.198567Z", + "shell.execute_reply": "2024-07-02T15:11:20.197917Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5191d0744a454151b8fae157e5a21ef4", + "model_id": "ea88c13811944930a76ece93362f7e4c", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:54.315561Z", - "iopub.status.busy": "2024-07-02T12:01:54.315315Z", - "iopub.status.idle": "2024-07-02T12:02:13.013990Z", - "shell.execute_reply": "2024-07-02T12:02:13.013360Z" + "iopub.execute_input": "2024-07-02T15:11:20.201174Z", + "iopub.status.busy": "2024-07-02T15:11:20.200947Z", + "iopub.status.idle": "2024-07-02T15:11:38.612541Z", + "shell.execute_reply": "2024-07-02T15:11:38.611926Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.016850Z", - "iopub.status.busy": "2024-07-02T12:02:13.016410Z", - "iopub.status.idle": "2024-07-02T12:02:13.021208Z", - "shell.execute_reply": "2024-07-02T12:02:13.020777Z" + "iopub.execute_input": "2024-07-02T15:11:38.615766Z", + "iopub.status.busy": "2024-07-02T15:11:38.615417Z", + "iopub.status.idle": "2024-07-02T15:11:38.621062Z", + "shell.execute_reply": "2024-07-02T15:11:38.620540Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.023194Z", - "iopub.status.busy": "2024-07-02T12:02:13.022869Z", - "iopub.status.idle": "2024-07-02T12:02:13.027182Z", - "shell.execute_reply": "2024-07-02T12:02:13.026649Z" + "iopub.execute_input": "2024-07-02T15:11:38.623170Z", + "iopub.status.busy": "2024-07-02T15:11:38.622849Z", + "iopub.status.idle": "2024-07-02T15:11:38.627084Z", + "shell.execute_reply": "2024-07-02T15:11:38.626551Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.029208Z", - "iopub.status.busy": "2024-07-02T12:02:13.028904Z", - "iopub.status.idle": "2024-07-02T12:02:13.037801Z", - "shell.execute_reply": "2024-07-02T12:02:13.037284Z" + "iopub.execute_input": "2024-07-02T15:11:38.628931Z", + "iopub.status.busy": "2024-07-02T15:11:38.628726Z", + "iopub.status.idle": "2024-07-02T15:11:38.637629Z", + "shell.execute_reply": "2024-07-02T15:11:38.637111Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.039783Z", - "iopub.status.busy": "2024-07-02T12:02:13.039463Z", - "iopub.status.idle": "2024-07-02T12:02:13.066102Z", - "shell.execute_reply": "2024-07-02T12:02:13.065500Z" + "iopub.execute_input": "2024-07-02T15:11:38.639743Z", + "iopub.status.busy": "2024-07-02T15:11:38.639336Z", + "iopub.status.idle": "2024-07-02T15:11:38.665352Z", + "shell.execute_reply": "2024-07-02T15:11:38.664931Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.068543Z", - "iopub.status.busy": "2024-07-02T12:02:13.068350Z", - "iopub.status.idle": "2024-07-02T12:02:45.178356Z", - "shell.execute_reply": "2024-07-02T12:02:45.177789Z" + "iopub.execute_input": "2024-07-02T15:11:38.667332Z", + "iopub.status.busy": "2024-07-02T15:11:38.667160Z", + "iopub.status.idle": "2024-07-02T15:12:10.330212Z", + "shell.execute_reply": "2024-07-02T15:12:10.329611Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.801\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.690\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.468\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.414\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec86bd0afa46422aa85bf2778e427f2a", + "model_id": "860c6216e3754afa972fdf5b5a0980a0", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0b406e9eaf143599fd4e302b57381b4", + "model_id": "bf64e375efe14d25b7e951f059b16c23", "version_major": 2, "version_minor": 0 }, @@ -840,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.793\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.642\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.570\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.471\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bfd46491d1764708be24b2103e5e6cb5", + "model_id": "8259ba9a3539477db64cbdd68592e635", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1696a28972cf4c1c95e3e3bf755c8d21", + "model_id": "da2c01112d1f4e749b0ca2c79b09927f", "version_major": 2, "version_minor": 0 }, @@ -898,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.822\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.668\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.476\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.531\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "32f22fc4e23745929d001d9647682786", + "model_id": "26d250d79c2447489401eb9ab9ace7df", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "846e19cb26a94bdba7b363dce398b69c", + "model_id": "a3115a3594ce4aa497f8a610abb0af9e", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:45.181036Z", - "iopub.status.busy": "2024-07-02T12:02:45.180584Z", - "iopub.status.idle": "2024-07-02T12:02:45.194402Z", - "shell.execute_reply": "2024-07-02T12:02:45.193957Z" + "iopub.execute_input": "2024-07-02T15:12:10.332761Z", + "iopub.status.busy": "2024-07-02T15:12:10.332362Z", + "iopub.status.idle": "2024-07-02T15:12:10.346556Z", + "shell.execute_reply": "2024-07-02T15:12:10.346082Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:45.196378Z", - "iopub.status.busy": "2024-07-02T12:02:45.196060Z", - "iopub.status.idle": "2024-07-02T12:02:45.659461Z", - "shell.execute_reply": "2024-07-02T12:02:45.658926Z" + "iopub.execute_input": "2024-07-02T15:12:10.348951Z", + "iopub.status.busy": "2024-07-02T15:12:10.348618Z", + "iopub.status.idle": "2024-07-02T15:12:10.823258Z", + "shell.execute_reply": "2024-07-02T15:12:10.822713Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:45.661921Z", - "iopub.status.busy": "2024-07-02T12:02:45.661522Z", - "iopub.status.idle": "2024-07-02T12:04:21.084670Z", - "shell.execute_reply": "2024-07-02T12:04:21.084011Z" + "iopub.execute_input": "2024-07-02T15:12:10.825656Z", + "iopub.status.busy": "2024-07-02T15:12:10.825310Z", + "iopub.status.idle": "2024-07-02T15:13:46.428675Z", + "shell.execute_reply": "2024-07-02T15:13:46.428018Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "683ea97790a64507b71e617e6bb1960f", + "model_id": "b66bf1f268f64f16b0ab04fbfef16cb7", "version_major": 2, "version_minor": 0 }, @@ -1144,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.087384Z", - "iopub.status.busy": "2024-07-02T12:04:21.086898Z", - "iopub.status.idle": "2024-07-02T12:04:21.530187Z", - "shell.execute_reply": "2024-07-02T12:04:21.529650Z" + "iopub.execute_input": "2024-07-02T15:13:46.431322Z", + "iopub.status.busy": "2024-07-02T15:13:46.430773Z", + "iopub.status.idle": "2024-07-02T15:13:46.883257Z", + "shell.execute_reply": "2024-07-02T15:13:46.882712Z" } }, "outputs": [ @@ -1293,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.532970Z", - "iopub.status.busy": "2024-07-02T12:04:21.532489Z", - "iopub.status.idle": "2024-07-02T12:04:21.594306Z", - "shell.execute_reply": "2024-07-02T12:04:21.593726Z" + "iopub.execute_input": "2024-07-02T15:13:46.885977Z", + "iopub.status.busy": "2024-07-02T15:13:46.885501Z", + "iopub.status.idle": "2024-07-02T15:13:46.948513Z", + "shell.execute_reply": "2024-07-02T15:13:46.947996Z" } }, "outputs": [ @@ -1400,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.597613Z", - "iopub.status.busy": "2024-07-02T12:04:21.597278Z", - "iopub.status.idle": "2024-07-02T12:04:21.605873Z", - "shell.execute_reply": "2024-07-02T12:04:21.605434Z" + "iopub.execute_input": "2024-07-02T15:13:46.950792Z", + "iopub.status.busy": "2024-07-02T15:13:46.950469Z", + "iopub.status.idle": "2024-07-02T15:13:46.958869Z", + "shell.execute_reply": "2024-07-02T15:13:46.958422Z" } }, "outputs": [ @@ -1533,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.607881Z", - "iopub.status.busy": "2024-07-02T12:04:21.607595Z", - "iopub.status.idle": "2024-07-02T12:04:21.612387Z", - "shell.execute_reply": "2024-07-02T12:04:21.611934Z" + "iopub.execute_input": "2024-07-02T15:13:46.960882Z", + "iopub.status.busy": "2024-07-02T15:13:46.960564Z", + "iopub.status.idle": "2024-07-02T15:13:46.965390Z", + "shell.execute_reply": "2024-07-02T15:13:46.964852Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.614443Z", - "iopub.status.busy": "2024-07-02T12:04:21.614030Z", - "iopub.status.idle": "2024-07-02T12:04:22.120240Z", - "shell.execute_reply": "2024-07-02T12:04:22.119680Z" + "iopub.execute_input": "2024-07-02T15:13:46.967456Z", + "iopub.status.busy": "2024-07-02T15:13:46.967155Z", + "iopub.status.idle": "2024-07-02T15:13:47.465450Z", + "shell.execute_reply": "2024-07-02T15:13:47.464898Z" } }, "outputs": [ @@ -1620,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.122526Z", - "iopub.status.busy": "2024-07-02T12:04:22.122160Z", - "iopub.status.idle": "2024-07-02T12:04:22.130544Z", - "shell.execute_reply": "2024-07-02T12:04:22.130091Z" + "iopub.execute_input": "2024-07-02T15:13:47.467701Z", + "iopub.status.busy": "2024-07-02T15:13:47.467369Z", + "iopub.status.idle": "2024-07-02T15:13:47.475692Z", + "shell.execute_reply": "2024-07-02T15:13:47.475239Z" } }, "outputs": [ @@ -1790,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.132648Z", - "iopub.status.busy": "2024-07-02T12:04:22.132322Z", - "iopub.status.idle": "2024-07-02T12:04:22.139582Z", - "shell.execute_reply": "2024-07-02T12:04:22.139132Z" + "iopub.execute_input": "2024-07-02T15:13:47.477736Z", + "iopub.status.busy": "2024-07-02T15:13:47.477444Z", + "iopub.status.idle": "2024-07-02T15:13:47.484538Z", + "shell.execute_reply": "2024-07-02T15:13:47.483995Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.141499Z", - "iopub.status.busy": "2024-07-02T12:04:22.141182Z", - "iopub.status.idle": "2024-07-02T12:04:22.871798Z", - "shell.execute_reply": "2024-07-02T12:04:22.871228Z" + "iopub.execute_input": "2024-07-02T15:13:47.486504Z", + "iopub.status.busy": "2024-07-02T15:13:47.486124Z", + "iopub.status.idle": "2024-07-02T15:13:48.236887Z", + "shell.execute_reply": "2024-07-02T15:13:48.236330Z" } }, "outputs": [ @@ -1909,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.874107Z", - "iopub.status.busy": "2024-07-02T12:04:22.873751Z", - "iopub.status.idle": "2024-07-02T12:04:22.889160Z", - "shell.execute_reply": "2024-07-02T12:04:22.888693Z" + "iopub.execute_input": "2024-07-02T15:13:48.238951Z", + "iopub.status.busy": "2024-07-02T15:13:48.238743Z", + "iopub.status.idle": "2024-07-02T15:13:48.254003Z", + "shell.execute_reply": "2024-07-02T15:13:48.253445Z" } }, "outputs": [ @@ -2069,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.891280Z", - "iopub.status.busy": "2024-07-02T12:04:22.890945Z", - "iopub.status.idle": "2024-07-02T12:04:22.896314Z", - "shell.execute_reply": "2024-07-02T12:04:22.895869Z" + "iopub.execute_input": "2024-07-02T15:13:48.256077Z", + "iopub.status.busy": "2024-07-02T15:13:48.255753Z", + "iopub.status.idle": "2024-07-02T15:13:48.261132Z", + "shell.execute_reply": "2024-07-02T15:13:48.260713Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.898366Z", - "iopub.status.busy": "2024-07-02T12:04:22.898042Z", - "iopub.status.idle": "2024-07-02T12:04:23.354782Z", - "shell.execute_reply": "2024-07-02T12:04:23.354256Z" + "iopub.execute_input": "2024-07-02T15:13:48.263200Z", + "iopub.status.busy": "2024-07-02T15:13:48.262806Z", + "iopub.status.idle": "2024-07-02T15:13:48.721823Z", + "shell.execute_reply": "2024-07-02T15:13:48.721244Z" } }, "outputs": [ @@ -2202,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.357430Z", - "iopub.status.busy": "2024-07-02T12:04:23.357055Z", - "iopub.status.idle": "2024-07-02T12:04:23.366373Z", - "shell.execute_reply": "2024-07-02T12:04:23.365890Z" + "iopub.execute_input": "2024-07-02T15:13:48.724484Z", + "iopub.status.busy": "2024-07-02T15:13:48.724285Z", + "iopub.status.idle": "2024-07-02T15:13:48.733522Z", + "shell.execute_reply": "2024-07-02T15:13:48.732818Z" } }, "outputs": [ @@ -2230,47 +2230,47 @@ " \n", " \n", " \n", - " is_dark_issue\n", " dark_score\n", + " is_dark_issue\n", " \n", " \n", " \n", " \n", " 34848\n", - " True\n", " 0.203922\n", + " True\n", " \n", " \n", " 50270\n", - " True\n", " 0.204588\n", + " True\n", " \n", " \n", " 3936\n", - " True\n", " 0.213098\n", + " True\n", " \n", " \n", " 733\n", - " True\n", " 0.217686\n", + " True\n", " \n", " \n", " 8094\n", - " True\n", " 0.230118\n", + " True\n", " \n", " \n", "\n", "

" ], "text/plain": [ - " is_dark_issue dark_score\n", - "34848 True 0.203922\n", - "50270 True 0.204588\n", - "3936 True 0.213098\n", - "733 True 0.217686\n", - "8094 True 0.230118" + " dark_score is_dark_issue\n", + "34848 0.203922 True\n", + "50270 0.204588 True\n", + "3936 0.213098 True\n", + "733 0.217686 True\n", + "8094 0.230118 True" ] }, "execution_count": 26, @@ -2333,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.368851Z", - "iopub.status.busy": "2024-07-02T12:04:23.368495Z", - "iopub.status.idle": "2024-07-02T12:04:23.374119Z", - "shell.execute_reply": "2024-07-02T12:04:23.373635Z" + "iopub.execute_input": "2024-07-02T15:13:48.735985Z", + "iopub.status.busy": "2024-07-02T15:13:48.735796Z", + "iopub.status.idle": "2024-07-02T15:13:48.741485Z", + "shell.execute_reply": "2024-07-02T15:13:48.740930Z" }, "nbsphinx": "hidden" }, @@ -2373,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.376452Z", - "iopub.status.busy": "2024-07-02T12:04:23.376105Z", - "iopub.status.idle": "2024-07-02T12:04:23.576168Z", - "shell.execute_reply": "2024-07-02T12:04:23.575585Z" + "iopub.execute_input": "2024-07-02T15:13:48.743854Z", + "iopub.status.busy": "2024-07-02T15:13:48.743665Z", + "iopub.status.idle": "2024-07-02T15:13:48.944292Z", + "shell.execute_reply": "2024-07-02T15:13:48.943813Z" } }, "outputs": [ @@ -2418,10 +2418,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.578422Z", - "iopub.status.busy": "2024-07-02T12:04:23.578237Z", - "iopub.status.idle": "2024-07-02T12:04:23.586182Z", - "shell.execute_reply": "2024-07-02T12:04:23.585742Z" + "iopub.execute_input": "2024-07-02T15:13:48.946415Z", + "iopub.status.busy": "2024-07-02T15:13:48.946254Z", + "iopub.status.idle": "2024-07-02T15:13:48.953697Z", + "shell.execute_reply": "2024-07-02T15:13:48.953257Z" } }, "outputs": [ @@ -2507,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.588296Z", - "iopub.status.busy": "2024-07-02T12:04:23.587874Z", - "iopub.status.idle": "2024-07-02T12:04:23.783615Z", - "shell.execute_reply": "2024-07-02T12:04:23.783030Z" + "iopub.execute_input": "2024-07-02T15:13:48.955509Z", + "iopub.status.busy": "2024-07-02T15:13:48.955356Z", + "iopub.status.idle": "2024-07-02T15:13:49.147359Z", + "shell.execute_reply": "2024-07-02T15:13:49.146829Z" } }, "outputs": [ @@ -2550,10 +2550,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.785886Z", - "iopub.status.busy": "2024-07-02T12:04:23.785554Z", - "iopub.status.idle": "2024-07-02T12:04:23.789936Z", - "shell.execute_reply": "2024-07-02T12:04:23.789389Z" + "iopub.execute_input": "2024-07-02T15:13:49.149499Z", + "iopub.status.busy": "2024-07-02T15:13:49.149335Z", + "iopub.status.idle": "2024-07-02T15:13:49.153453Z", + "shell.execute_reply": "2024-07-02T15:13:49.153029Z" }, "nbsphinx": "hidden" }, @@ -2590,7 +2590,71 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "007a3563b0514e35b0a7409f1a0e8668": { + "017871e5d3284cbbbede907271767d8c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_837e8c16ffdd41a4ae0f4631055cca57", + "placeholder": "​", + "style": "IPY_MODEL_9c4688aea9f9425883500665dfa60bf9", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "01c7b5f2e46d450e9f0ee68d2e6e7184": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "039cadb1d8a94b7d86d6d048e6ff9a52": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dce15d5cb91b42a780b136a398da089e", + "placeholder": "​", + "style": "IPY_MODEL_05aeaba6e4c6492d92f32a92995787a1", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:01<00:00, 8784.47 examples/s]" + } + }, + "04cb19cb27ec48d8bf07ddb3dbafdbf7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2643,7 +2707,41 @@ "width": null } }, - "0205ebdad1d64de8a1bd7d1c741d5fcb": { + "05aeaba6e4c6492d92f32a92995787a1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0b15bb47dbed43cbaed90ce690a3f879": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0ce0d8843432485ea62b457bcb0faf43": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2696,7 +2794,181 @@ "width": null } }, - "052177a0e94b458eb71c811c3229f857": { + "0e4e51c9e4794c289497fc9034858879": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_459779a55e494dea92f7ce2871528083", + "placeholder": "​", + "style": "IPY_MODEL_72292d3faee34bf1949667097765550e", + "tabbable": null, + "tooltip": null, + "value": "Downloading readme: 100%" + } + }, + "0fab8f6f053241e4a76c15c455304f10": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "135c72d3fed34ffc90902f7faf2f27b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1361429788e54a748edb03027a9cab6a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "13b268a2d27e4d6c855f5088b35b9247": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6a1591c936ba4e709d74e0ec278cefd6", + "IPY_MODEL_24566f0819db4eecbcd0b9831228c09f", + "IPY_MODEL_78981540b49a4ac4b4674a46e0b71bd0" + ], + "layout": "IPY_MODEL_d9e695fa0c8741669e44cf4502f36d47", + "tabbable": null, + "tooltip": null + } + }, + "1711a1f1944742ea8f38809143bc3cc8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2b8b9a59fe67431c926345fe531d94ed", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_964944a145304bd58d1fd62856054c5a", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "17a61e4c38a64575bc341e3504130922": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_75137f463796461d87f332d1e82c826b", + "placeholder": "​", + "style": "IPY_MODEL_3a2f200c9979405ea1799e8b257d2962", + "tabbable": null, + "tooltip": null, + "value": " 8.85k/8.85k [00:00<00:00, 1.51MB/s]" + } + }, + "195a744ab07644a29d3e3e02108d0aa2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b31e5df3571f4ac28345bf70cd4e947c", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0b15bb47dbed43cbaed90ce690a3f879", + "tabbable": null, + "tooltip": null, + "value": 4422102.0 + } + }, + "1a4cf1e028c44677995965a00cb4aa35": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2749,61 +3021,8 @@ "width": null } }, - "0826f288ec1f413988e02f4ef8849c28": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "088b60ea8401405ea27804efbb34b231": { - "model_module": "@jupyter-widgets/controls", + "1a67cc49a0714635846f10e32202bddc": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { @@ -2818,40 +3037,43 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_40e0d768badf43758c95df2555d1a977", - "max": 40.0, + "layout": "IPY_MODEL_ffc2140e49b04844ba200898835e603c", + "max": 8845.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_29258ac24b084216b09e806906959044", + "style": "IPY_MODEL_97bd081fbc2c473fb8852e3e2ef1391d", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 8845.0 } }, - "0a39ed2c512b4f33865071d90e585e06": { + "1bf57cd82d684206a95f2c1e179d96ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ac0dd2e3b9574dfba4e088b07dc9917a", - "placeholder": "​", - "style": "IPY_MODEL_7d2cf7fe1d884127b2ea048061916752", + "layout": "IPY_MODEL_c639afb167db44978154a2d4054f1d40", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ccea9ba4e771468a95236d2db5bf0264", "tabbable": null, "tooltip": null, - "value": " 5.15k/5.15k [00:00<00:00, 778kB/s]" + "value": 40.0 } }, - "0b2e2b62fbba4e8e919185c698964e99": { + "1c413b52ad994cc6818c637e38897e16": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2904,7 +3126,7 @@ "width": null } }, - "0cae058fc562457bbb502b466cfdfcab": { + "1d882b744195442f85a86d91cabda0fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2957,7 +3179,25 @@ "width": null } }, - "0d1f1b12cc3545b0b78b6f64afe61ba8": { + "1e4c7bfe71e44d6dbf26a8dff0f90509": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1eca5328aef44e1ca18c8c422f647377": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2972,42 +3212,39 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_b1d5272979684bff96c71500a455d400", - "IPY_MODEL_6b095fb924ef41358579ec879bf0f9fe", - "IPY_MODEL_0a39ed2c512b4f33865071d90e585e06" + "IPY_MODEL_017871e5d3284cbbbede907271767d8c", + "IPY_MODEL_195a744ab07644a29d3e3e02108d0aa2", + "IPY_MODEL_5b344538fc3b43a98f559b15bee9a5fa" ], - "layout": "IPY_MODEL_72961bb987d24298bfdb11eb59546963", + "layout": "IPY_MODEL_ddee9f52a482431d83538c9f941bfbe9", "tabbable": null, "tooltip": null } }, - "0e97775e042e4049bb15d621385de0b0": { + "1f029c9a5f7842a19a17b9c9f75ecc48": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d84b7a5330814c309bc2e3a29fd936ef", - "max": 26421880.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f4bfad9cd0da4d31a8d5c783407a73c8", + "layout": "IPY_MODEL_f039d6ee9460446c9cbfff2777f2bb15", + "placeholder": "​", + "style": "IPY_MODEL_5795395d630f42f09192fdb4e89f8465", "tabbable": null, "tooltip": null, - "value": 26421880.0 + "value": " 40/40 [00:00<00:00, 66.57it/s]" } }, - "0ec179f1c53a4547a89ad81af9b56fa0": { + "1fe8a37c7c4b481eb559efba20e116ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3023,35 +3260,70 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d90986d0f1bb4e8ebf632742ca0c49a3", - "max": 8845.0, + "layout": "IPY_MODEL_cfdfd821fb03474ba31a8ca207281b52", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_7004a3b5592a40ee8328f00432adfc79", + "style": "IPY_MODEL_2130bdaf86db48658ae28f45cec5400b", "tabbable": null, "tooltip": null, - "value": 8845.0 + "value": 40.0 } }, - "0f8071081d82450c9dd7fd9a927b6b1f": { - "model_module": "@jupyter-widgets/controls", + "1ffe24ac54fe4cb49579cb75ed7db9b5": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0f83d37dd1394bc7a4f3ffc18b97b21c": { + "20fc3b9b9a4b48d881bc11004e753c5e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3104,96 +3376,23 @@ "width": null } }, - "1262e1266fb7438b9a905b26b4129cf0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_eaa35cd4c5ae462181de3ad1ab98c2d1", - "placeholder": "​", - "style": "IPY_MODEL_9bc90fd3c0264b26b5bf1c99f4b9caad", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "159251baf5e8425b8c9f8d7acb9abc55": { + "2130bdaf86db48658ae28f45cec5400b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "1696a28972cf4c1c95e3e3bf755c8d21": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_bd7060172fb747a6ae92a503a3922356", - "IPY_MODEL_5779a50d69944438b953e80eb37bbcac", - "IPY_MODEL_359bb8b698c94cf8b32d650da4752723" - ], - "layout": "IPY_MODEL_be6b57b7d12a497fbc96e8e89b08f15a", - "tabbable": null, - "tooltip": null - } - }, - "1a9f98ff0f0446e7b89c4fe4fffc3418": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1262e1266fb7438b9a905b26b4129cf0", - "IPY_MODEL_0e97775e042e4049bb15d621385de0b0", - "IPY_MODEL_56000f81200d41e0bfa6f6b08d883916" - ], - "layout": "IPY_MODEL_75221b9dde234f55bcd73f8bba5f3fa5", - "tabbable": null, - "tooltip": null + "bar_color": null, + "description_width": "" } }, - "1e5f76275f894a96a2d651d0a386fdf9": { + "23fe73f9bf064ef5bd44fb6b0e295456": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3246,30 +3445,7 @@ "width": null } }, - "1ef3f8ae36734efb86b54939cb9711d4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7b58054c0d6a418194fc7e1f039c639b", - "placeholder": "​", - "style": "IPY_MODEL_864d773ce416475ba6a1e506b36063dc", - "tabbable": null, - "tooltip": null, - "value": " 4/4 [00:00<00:00, 1198.12it/s]" - } - }, - "1f5d840cd56a4162b5ad826e1f2902c2": { + "244159f1aa444aedbd2e3e9382b4326b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3322,39 +3498,31 @@ "width": null } }, - "1fb6203f0a3f47309b3fefc7cf4a8522": { + "24554a44a66045a29398e71c18b39f2f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7e502dd5790a480887b970c2862e5170", + "IPY_MODEL_be2ba8934caf4f6eb77db4a243219f26", + "IPY_MODEL_b36cf6fb19144ae68ceeda6d14a0c88e" + ], + "layout": "IPY_MODEL_5d925a37f7f24e27bf2be497abcd3368", + "tabbable": null, + "tooltip": null } }, - "1fdf0f2ba1e440ccaf02e74fc4b28520": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "21a5ba5f675d47938f0e977783a004ba": { + "24566f0819db4eecbcd0b9831228c09f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3370,130 +3538,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2d5192bd86654b04b8a872e4841a73ee", - "max": 60000.0, + "layout": "IPY_MODEL_34591a8e01774b23835f7bec29c46ffb", + "max": 4.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_1fb6203f0a3f47309b3fefc7cf4a8522", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "21a93c7e2dfc4569b325ed80637cf469": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ff08f8bfa09d42838688c6f725adb306", - "placeholder": "​", - "style": "IPY_MODEL_aea843e930ac410596a0f4cb4f6520e0", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 65.61it/s]" - } - }, - "240d9d285e4c42e9a24006d9d2988868": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1e5f76275f894a96a2d651d0a386fdf9", - "placeholder": "​", - "style": "IPY_MODEL_416fa493cc9c4ff6ad13e8b6e6aedbaa", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" - } - }, - "25f2773590254b58b3ac4b1c0a886c35": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f1c238f4a14549229bdf80d577253ccf", - "placeholder": "​", - "style": "IPY_MODEL_f0b50dd1b20c48b6911a694433d48e05", + "style": "IPY_MODEL_618936f2cae44a09978936d5100bc0a5", "tabbable": null, "tooltip": null, - "value": " 4.83k/4.83k [00:00<00:00, 623kB/s]" - } - }, - "27b9b136f3bb4b1a80402df468b5c136": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": 4.0 } }, - "2865f56223344611aa17c9ec66a2d090": { + "26d250d79c2447489401eb9ab9ace7df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f9978a29787547e3bc5e59bde742651c", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cd6bbb8ca872405fa17b2571965191b3", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e452cfce19364c6fb6b50db4144baec6", + "IPY_MODEL_cb9288f8e40c422ea8a86e41ea2ba6df", + "IPY_MODEL_76f7038f04f14f28ba39042b9a0b29ca" + ], + "layout": "IPY_MODEL_e09e931dcf844151835a19cfddd0f459", "tabbable": null, - "tooltip": null, - "value": 60000.0 + "tooltip": null } }, - "29258ac24b084216b09e806906959044": { + "270f6bf2c3e643ea8dd1a756817909fe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3509,25 +3588,7 @@ "description_width": "" } }, - "2c4aaf8a7a84451d93bb9c185069cfe6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "2d5192bd86654b04b8a872e4841a73ee": { + "2b356c8f0be148beb637a84faef5510a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3580,31 +3641,7 @@ "width": null } }, - "2ffbe85316974d029eab626642378580": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_62e4f2d9c4534ebbab778993d057a978", - "IPY_MODEL_0ec179f1c53a4547a89ad81af9b56fa0", - "IPY_MODEL_5c88bd64b2f44f8ab2aa0198c89462d3" - ], - "layout": "IPY_MODEL_44a3e2129c4243e4862df806c2d8c5af", - "tabbable": null, - "tooltip": null - } - }, - "306584df97934511bd6de92ca49b025b": { + "2b8b9a59fe67431c926345fe531d94ed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3657,7 +3694,30 @@ "width": null } }, - "30c3b868ba4b46ea9bcdb05e1c6d5613": { + "2e0923cec6894e0bb926973c7f50f3d3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c867ebcf347146abb7ec6115797953c1", + "placeholder": "​", + "style": "IPY_MODEL_5b97df5a1e0d485f8420059c34c61136", + "tabbable": null, + "tooltip": null, + "value": "Map (num_proc=4): 100%" + } + }, + "2f6cf6dc5a4845588c6a21a1c21879bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3710,7 +3770,7 @@ "width": null } }, - "3256f32acb0e4cf5b5de459cdd30f479": { + "2ffdee00ac1f47dcaad4ccd7cac60390": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3725,15 +3785,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5e25f06738614802b41458873966a7a2", + "layout": "IPY_MODEL_f7d5ee1b172841528a12c8d5a15409f3", "placeholder": "​", - "style": "IPY_MODEL_3b900abb40be48198b5e1814d396c692", + "style": "IPY_MODEL_b40cdc4a6ff640758611c963c4a3014d", "tabbable": null, "tooltip": null, - "value": " 10000/10000 [00:01<00:00, 8596.66 examples/s]" + "value": "100%" + } + }, + "3145d8f1a50346caa657e7b6ade92792": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "32b658a174274a4997618547ef7ef447": { + "34591a8e01774b23835f7bec29c46ffb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3786,57 +3864,25 @@ "width": null } }, - "32f22fc4e23745929d001d9647682786": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7684491703b342af8b42512fb30334e4", - "IPY_MODEL_fe1973b9b1fa4957b9894f465a0fe87c", - "IPY_MODEL_c9693739925b43cd83cb4e68fc01ecc9" - ], - "layout": "IPY_MODEL_dd1415dc221544d78c38cecb125e95de", - "tabbable": null, - "tooltip": null - } - }, - "33c9e2d67e4e498a9badfc73dd036c12": { + "34eceefcb9cd4c0390182a3da1170a7b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a2e082221e6d4efe981d8286fcaa40bd", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_422391611be34f58bd16d29a7a790f7f", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "33e89871cb224a0bb17051bdb6a4736f": { + "3557a3c3c9e84bdd905c60c08626cdc2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3889,7 +3935,25 @@ "width": null } }, - "3598bd0162e44744bf0e88509c1fcc05": { + "3a2f200c9979405ea1799e8b257d2962": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3bbe5464e48448959dd872a56942a7f5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3942,39 +4006,16 @@ "width": null } }, - "359bb8b698c94cf8b32d650da4752723": { - "model_module": "@jupyter-widgets/controls", + "3c7b41e10bf74f948457cd90e200afe1": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d44de894f1eb4db0ad9f986867905216", - "placeholder": "​", - "style": "IPY_MODEL_91eaa66c2eb641e689fc8028aee35c80", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.56it/s]" - } - }, - "372ab13ae5c8452ead0d7884763b3fc8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, @@ -4018,30 +4059,7 @@ "width": null } }, - "3746c22618f54648a82eac82edf13ec6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0826f288ec1f413988e02f4ef8849c28", - "placeholder": "​", - "style": "IPY_MODEL_7fd76734cd4b4cec9bba7cdd76aaea68", - "tabbable": null, - "tooltip": null, - "value": " 29.5k/29.5k [00:00<00:00, 4.49MB/s]" - } - }, - "3851a48a716b467ba9b981bd35b1822c": { + "41acfac3e7634989b2ccc6a4e9503ea6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4094,31 +4112,30 @@ "width": null } }, - "39838b65ab134d2a9a445437586fec98": { + "4398abd231514ecb8426aaa36111102f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_bf04eda28d94482ebdbf589d87951c61", - "IPY_MODEL_92fe75b0bd1341e9878165f8c906fc19", - "IPY_MODEL_3746c22618f54648a82eac82edf13ec6" - ], - "layout": "IPY_MODEL_5d3e10744606438787cdfd6315052b40", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e1a517549d364c30bc66ec975d022bdc", + "placeholder": "​", + "style": "IPY_MODEL_9975419ead304f1bb35f1f16ba689746", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "3b900abb40be48198b5e1814d396c692": { + "43ad8b951bf64a8b94108e8d800b7f93": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4136,7 +4153,7 @@ "text_color": null } }, - "3ed339ad73774a5cae0f763c178cbf4b": { + "459779a55e494dea92f7ce2871528083": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4189,25 +4206,80 @@ "width": null } }, - "409b5a2567834d99aba6129faa130451": { + "4761c3ddf1a643e8bda01b752e44ad8b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a685a21bed8147549eee85cbac9f5358", + "IPY_MODEL_53e6c8e4d9e0441a8b68c5ea09e71ff1", + "IPY_MODEL_726586d1a8d9496886b7c74c9ae9216a" + ], + "layout": "IPY_MODEL_86c15161ec474c7ea837b47a73b55834", + "tabbable": null, + "tooltip": null + } + }, + "47b85a2880604d3dac3aeaeda306130f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c61855779cd74bc097076c41da0eed54", + "placeholder": "​", + "style": "IPY_MODEL_5b1660aaad334a86aba0c255fa391c8d", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "483cd57673244db39d3524fef0835d46": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_492f8e571e174d8f92e7b18b8e13bc62", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_94e97c6d2fc14e229a4c96a99d543e33", + "tabbable": null, + "tooltip": null, + "value": 60000.0 } }, - "40e0d768badf43758c95df2555d1a977": { + "492f8e571e174d8f92e7b18b8e13bc62": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4260,41 +4332,7 @@ "width": null } }, - "416fa493cc9c4ff6ad13e8b6e6aedbaa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "422391611be34f58bd16d29a7a790f7f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "44a3e2129c4243e4862df806c2d8c5af": { + "4a3fd3d2d678476d980c97d8064b895c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4347,7 +4385,7 @@ "width": null } }, - "46552aea691e492084a7278f7a059830": { + "4cb21215cfbb4594b1f5a243ec53d04e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4365,7 +4403,7 @@ "text_color": null } }, - "488f0ba8d1c24d58adaefa23ebad9e9f": { + "4f03fb7101fe41f5becc6ecfabbba17a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4381,57 +4419,80 @@ "description_width": "" } }, - "4957f5fe7164427d8b712200dde5c3ab": { + "52a2b90360f7460f9d5e8e206e5b7b47": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e6fa033915fa4a5987c2c8fa374502e4", + "IPY_MODEL_ec5400e36f274359b296a5d6e0e33a39", + "IPY_MODEL_7c31f8bf76884e7683d9da37b70c4cc1" + ], + "layout": "IPY_MODEL_ddbe3a04ddff46c5bdfab6de8a35f7cc", + "tabbable": null, + "tooltip": null + } + }, + "53049123e6a9407b8314d7f6b14f99af": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ddb1bbe42c9e442091cc9c4122b5de26", - "max": 4422102.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e85e1e91a24f4199a9a4b3e9abe8696f", + "layout": "IPY_MODEL_ad18fb7ca0984499bf68129daeec626a", + "placeholder": "​", + "style": "IPY_MODEL_b42a1a7b436f4dfcab7ea91187e0b743", "tabbable": null, "tooltip": null, - "value": 4422102.0 + "value": " 40/40 [00:00<00:00, 66.30it/s]" } }, - "495daf880acd479da7fa63fedf1e1368": { + "53e6c8e4d9e0441a8b68c5ea09e71ff1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_240d9d285e4c42e9a24006d9d2988868", - "IPY_MODEL_21a5ba5f675d47938f0e977783a004ba", - "IPY_MODEL_513c96503e024d098d8203aa3b604a38" - ], - "layout": "IPY_MODEL_306584df97934511bd6de92ca49b025b", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9a4fcd9b64a048d28bbf5ab053b0a6d7", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0fab8f6f053241e4a76c15c455304f10", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 60000.0 } }, - "4a57dceefdc148afb5c7afa8adec5114": { + "5795395d630f42f09192fdb4e89f8465": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4449,47 +4510,66 @@ "text_color": null } }, - "4aadbbb3e859454a93556ff943f76e5b": { + "5b1660aaad334a86aba0c255fa391c8d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "4d801b30b791427d9103f41505cf1a3e": { + "5b344538fc3b43a98f559b15bee9a5fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8dbd4d3399124f6d8275c1d0fdfe9983", - "IPY_MODEL_4957f5fe7164427d8b712200dde5c3ab", - "IPY_MODEL_cab386a86c594ee2885f6d1679103b3b" - ], - "layout": "IPY_MODEL_625866dbaec44035a15f3927c4b770e5", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3557a3c3c9e84bdd905c60c08626cdc2", + "placeholder": "​", + "style": "IPY_MODEL_a121eb4e9f9e48609d3a4b697bab56c8", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 4.42M/4.42M [00:00<00:00, 79.2MB/s]" + } + }, + "5b97df5a1e0d485f8420059c34c61136": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "4dda8d782c00439b8a9eefdfe211c961": { + "5bfde149dd324016b5d45c6bddf7fad2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4542,25 +4622,7 @@ "width": null } }, - "4ec1fcd52f8c4d51bb3475d2f3c24732": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4ff41db514e041798fc3d0bf13325104": { + "5d925a37f7f24e27bf2be497abcd3368": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4613,12 +4675,81 @@ "width": null } }, - "513c96503e024d098d8203aa3b604a38": { - "model_module": "@jupyter-widgets/controls", + "603bc1c8b13349f78643f74115dd3fc5": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "618936f2cae44a09978936d5100bc0a5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "6719b46ec6814ef5869cf0982143c632": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", @@ -4628,39 +4759,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9009c4b403d146b493913cc05ca55a44", + "layout": "IPY_MODEL_04cb19cb27ec48d8bf07ddb3dbafdbf7", "placeholder": "​", - "style": "IPY_MODEL_ac82938d47c543a89ca5def5e546d7da", + "style": "IPY_MODEL_951638e8145c4431963a7a3c2f7e4b09", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:07<00:00, 8644.15 examples/s]" + "value": "100%" } }, - "5191d0744a454151b8fae157e5a21ef4": { - "model_module": "@jupyter-widgets/controls", + "67307a5269b545deb5492d878a0da28f": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_fffb62594db04599b3628dceafda46f1", - "IPY_MODEL_2865f56223344611aa17c9ec66a2d090", - "IPY_MODEL_d4be07fa12674628ae93c0119edbf6e1" - ], - "layout": "IPY_MODEL_052177a0e94b458eb71c811c3229f857", - "tabbable": null, - "tooltip": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "51bb6dd9acdc4544b4d17f4f020b1764": { + "691ecad63a504dffa766ac43d3b78fb4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4713,7 +4873,7 @@ "width": null } }, - "55923d8f76544ee5b5e53cb28dcbbcc5": { + "6a1591c936ba4e709d74e0ec278cefd6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4728,64 +4888,51 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0f83d37dd1394bc7a4f3ffc18b97b21c", + "layout": "IPY_MODEL_b30172a2a47d40f0a77a14540ecd18d6", "placeholder": "​", - "style": "IPY_MODEL_8be082c69bad41aa815fa99c34e5a9ea", + "style": "IPY_MODEL_4cb21215cfbb4594b1f5a243ec53d04e", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 65.15it/s]" + "value": "Computing checksums: 100%" } }, - "56000f81200d41e0bfa6f6b08d883916": { + "6d8a49f522924aaaabb198187245001e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9eace039e9d343e1a0113042a3582776", - "placeholder": "​", - "style": "IPY_MODEL_409b5a2567834d99aba6129faa130451", - "tabbable": null, - "tooltip": null, - "value": " 26.4M/26.4M [00:00<00:00, 120MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5779a50d69944438b953e80eb37bbcac": { + "72292d3faee34bf1949667097765550e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1f5d840cd56a4162b5ad826e1f2902c2", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_488f0ba8d1c24d58adaefa23ebad9e9f", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5c88bd64b2f44f8ab2aa0198c89462d3": { + "726586d1a8d9496886b7c74c9ae9216a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4800,15 +4947,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ed5bd36e898a43dc9c5cb8e283abbead", + "layout": "IPY_MODEL_c3c495d6cade4b65834b43700b7d4655", "placeholder": "​", - "style": "IPY_MODEL_890e03a51fce4dd185e5a42dc5da23ed", + "style": "IPY_MODEL_f5501a6381964ea3b5684ee5ac2a2990", "tabbable": null, "tooltip": null, - "value": " 8.85k/8.85k [00:00<00:00, 1.48MB/s]" + "value": " 60000/60000 [00:06<00:00, 8666.45 examples/s]" } }, - "5c9cad60b37f4313a6899ca1a71bbee0": { + "727d6b8ed0214669ae4a1c260b9eb2ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4824,7 +4971,7 @@ "description_width": "" } }, - "5d3e10744606438787cdfd6315052b40": { + "73b0c283a1d549d786b83bf092b5a247": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4877,7 +5024,31 @@ "width": null } }, - "5e25f06738614802b41458873966a7a2": { + "74d7207adb634a9a9648063cd4ebf05d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0e4e51c9e4794c289497fc9034858879", + "IPY_MODEL_1a67cc49a0714635846f10e32202bddc", + "IPY_MODEL_17a61e4c38a64575bc341e3504130922" + ], + "layout": "IPY_MODEL_9cc09cfd956141a89cc11fdb073b27f5", + "tabbable": null, + "tooltip": null + } + }, + "75137f463796461d87f332d1e82c826b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4930,7 +5101,77 @@ "width": null } }, - "6095c69a4f934899a783495c289c15a3": { + "76447603597c41e58c504ba366dedf8b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_efabf4edc4f14504b06bad7e71659998", + "IPY_MODEL_bb9b88f5052e43e4861dcbd84c5aa196", + "IPY_MODEL_e5a63f7446074676b31de8da1ee38a2b" + ], + "layout": "IPY_MODEL_4a3fd3d2d678476d980c97d8064b895c", + "tabbable": null, + "tooltip": null + } + }, + "76f7038f04f14f28ba39042b9a0b29ca": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0ce0d8843432485ea62b457bcb0faf43", + "placeholder": "​", + "style": "IPY_MODEL_923f0259202b48ac847034f1d10cd3e9", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 63.59it/s]" + } + }, + "78981540b49a4ac4b4674a46e0b71bd0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d4d042ad47854553b9660b36249c7347", + "placeholder": "​", + "style": "IPY_MODEL_01c7b5f2e46d450e9f0ee68d2e6e7184", + "tabbable": null, + "tooltip": null, + "value": " 4/4 [00:00<00:00, 1262.77it/s]" + } + }, + "79afef11e9d74259a71e933ac999caf5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4983,7 +5224,7 @@ "width": null } }, - "6102ff846ce741a4aa1c83f94cf213b4": { + "7c31f8bf76884e7683d9da37b70c4cc1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4998,15 +5239,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0b2e2b62fbba4e8e919185c698964e99", + "layout": "IPY_MODEL_1c413b52ad994cc6818c637e38897e16", "placeholder": "​", - "style": "IPY_MODEL_a2c46edab30a43d3ad1274496e23dc19", + "style": "IPY_MODEL_954cd8e3b16c4419af3905829990415a", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 68.42it/s]" + "value": " 29.5k/29.5k [00:00<00:00, 4.33MB/s]" } }, - "625866dbaec44035a15f3927c4b770e5": { + "7cb2c22fa5f843e79a58ef3c50f8eab3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5059,7 +5300,7 @@ "width": null } }, - "62e4f2d9c4534ebbab778993d057a978": { + "7e502dd5790a480887b970c2862e5170": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5074,15 +5315,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4dda8d782c00439b8a9eefdfe211c961", + "layout": "IPY_MODEL_691ecad63a504dffa766ac43d3b78fb4", "placeholder": "​", - "style": "IPY_MODEL_d89ee00461d14abe96f3f0cdcfa3da61", + "style": "IPY_MODEL_135c72d3fed34ffc90902f7faf2f27b0", "tabbable": null, "tooltip": null, - "value": "Downloading readme: 100%" + "value": "Downloading data: 100%" } }, - "640d5d2692d64656a67cd09cee644495": { + "80bc3ab0f2e34cba9a1c31d7411ecbc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5098,108 +5339,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_007a3563b0514e35b0a7409f1a0e8668", - "max": 4.0, + "layout": "IPY_MODEL_c932cb59d82141628dd6ec40fc18f737", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_ded2f3b115fb46e48b5699012c011fa5", + "style": "IPY_MODEL_b4c1e3125e1f43ae99fc53aa3a9c0505", "tabbable": null, "tooltip": null, - "value": 4.0 + "value": 60000.0 } }, - "645dbaa1cd59415da2cc2b69430972fa": { + "8259ba9a3539477db64cbdd68592e635": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_33e89871cb224a0bb17051bdb6a4736f", - "placeholder": "​", - "style": "IPY_MODEL_159251baf5e8425b8c9f8d7acb9abc55", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "647ec368e3354d458cfed043dad850ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "683ea97790a64507b71e617e6bb1960f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e1aad335fcff4496842cc4f52b05fb6a", - "IPY_MODEL_be4ee2ccce154754b1f1e8d49134ee62", - "IPY_MODEL_edb3baaca40743c08801b6e9bff25752" + "IPY_MODEL_47b85a2880604d3dac3aeaeda306130f", + "IPY_MODEL_1711a1f1944742ea8f38809143bc3cc8", + "IPY_MODEL_df9aa39a8a3a44d8bb0fd0ea66fb7093" ], - "layout": "IPY_MODEL_51bb6dd9acdc4544b4d17f4f020b1764", + "layout": "IPY_MODEL_3bbe5464e48448959dd872a56942a7f5", "tabbable": null, "tooltip": null } }, - "6b095fb924ef41358579ec879bf0f9fe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3ed339ad73774a5cae0f763c178cbf4b", - "max": 5148.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1fdf0f2ba1e440ccaf02e74fc4b28520", - "tabbable": null, - "tooltip": null, - "value": 5148.0 - } - }, - "6ca7ade3edba440ba7226afd89340130": { + "837e8c16ffdd41a4ae0f4631055cca57": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5252,60 +5426,49 @@ "width": null } }, - "6ccb235983834c00ac32be1422c16641": { - "model_module": "@jupyter-widgets/base", + "8555aae383d74d6c851e5296a2f76824": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "860c6216e3754afa972fdf5b5a0980a0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2ffdee00ac1f47dcaad4ccd7cac60390", + "IPY_MODEL_1fe8a37c7c4b481eb559efba20e116ac", + "IPY_MODEL_a972ac7ecf4c4746bb430a3161226786" + ], + "layout": "IPY_MODEL_73b0c283a1d549d786b83bf092b5a247", + "tabbable": null, + "tooltip": null } }, - "6f3f698e19f14817b2b2fa6c67e55a47": { + "86c15161ec474c7ea837b47a73b55834": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5358,23 +5521,72 @@ "width": null } }, - "7004a3b5592a40ee8328f00432adfc79": { + "8d04c2d222424f08b06b6508223878ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ef2ba57d7cd44e3cb5973e6264f86666", + "IPY_MODEL_bfc4b95406a34e3ca4e93013d10e4852", + "IPY_MODEL_039cadb1d8a94b7d86d6d048e6ff9a52" + ], + "layout": "IPY_MODEL_ae9e474ca22b45648e40de05673fbd79", + "tabbable": null, + "tooltip": null + } + }, + "8f9cbba88a5d4fe29d7de8c5c017526e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ca783c0a89cb4d7aabe9391975acb8ff", + "placeholder": "​", + "style": "IPY_MODEL_43ad8b951bf64a8b94108e8d800b7f93", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "923f0259202b48ac847034f1d10cd3e9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "72961bb987d24298bfdb11eb59546963": { + "940ecb97d50847cda5f82f56b828ce1e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5427,7 +5639,7 @@ "width": null } }, - "736ef5bce23e47429bfcb196fb8b85b3": { + "944cd1ea980e485caca5409c07bca11c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5480,30 +5692,41 @@ "width": null } }, - "743f0d74186d4b7093498a547ab9ac95": { + "94e97c6d2fc14e229a4c96a99d543e33": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7b5cc6d910b546ce964b6b2bbec05343", - "placeholder": "​", - "style": "IPY_MODEL_b3495f2ab71b4f848590703a190ddebc", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 68.82it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "951638e8145c4431963a7a3c2f7e4b09": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "751475cab3c24730bab0fbab4d5284f2": { + "954cd8e3b16c4419af3905829990415a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5521,7 +5744,7 @@ "text_color": null } }, - "75221b9dde234f55bcd73f8bba5f3fa5": { + "95c819d6d1bb48a4a1718e13d4b708d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5574,86 +5797,62 @@ "width": null } }, - "759a332c02e64ce79a364cd518bc163e": { + "964944a145304bd58d1fd62856054c5a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "96a4611a123247198b0f710c5bf75bd7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6095c69a4f934899a783495c289c15a3", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5c9cad60b37f4313a6899ca1a71bbee0", + "layout": "IPY_MODEL_41acfac3e7634989b2ccc6a4e9503ea6", + "placeholder": "​", + "style": "IPY_MODEL_1e4c7bfe71e44d6dbf26a8dff0f90509", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "Downloading data: 100%" } }, - "75af0524012d41858477feaee9949557": { - "model_module": "@jupyter-widgets/base", + "97bd081fbc2c473fb8852e3e2ef1391d": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "7684491703b342af8b42512fb30334e4": { + "9882b87501b84979bb5ca2b3e35d53d1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5668,15 +5867,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a61e85ca959b4dafbe01836e2add2005", + "layout": "IPY_MODEL_1ffe24ac54fe4cb49579cb75ed7db9b5", "placeholder": "​", - "style": "IPY_MODEL_4ec1fcd52f8c4d51bb3475d2f3c24732", + "style": "IPY_MODEL_cbfecf1072ed4e2caaa31da07e0da756", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 60000/60000 [00:36<00:00, 1731.74it/s]" + } + }, + "9975419ead304f1bb35f1f16ba689746": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "7b58054c0d6a418194fc7e1f039c639b": { + "9a4fcd9b64a048d28bbf5ab053b0a6d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5729,7 +5946,23 @@ "width": null } }, - "7b5cc6d910b546ce964b6b2bbec05343": { + "9aee6db9bd984fa5a023e92a81ba822d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "9b19aa98c0d64d849bf08b178b9841e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5782,7 +6015,7 @@ "width": null } }, - "7d2cf7fe1d884127b2ea048061916752": { + "9c4688aea9f9425883500665dfa60bf9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5800,7 +6033,7 @@ "text_color": null } }, - "7df97d24399f4f8a980e937ed234ad3c": { + "9cc09cfd956141a89cc11fdb073b27f5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5853,7 +6086,7 @@ "width": null } }, - "7fd76734cd4b4cec9bba7cdd76aaea68": { + "9dfddac443ca480cac3f02058d42e8a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5871,7 +6104,7 @@ "text_color": null } }, - "823bddadbc6644c283f25f9cc6a18fc8": { + "a121eb4e9f9e48609d3a4b697bab56c8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5889,30 +6122,7 @@ "text_color": null } }, - "831b82774fb644faa4c010b37968f99b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_32b658a174274a4997618547ef7ef447", - "placeholder": "​", - "style": "IPY_MODEL_0f8071081d82450c9dd7fd9a927b6b1f", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "846e19cb26a94bdba7b363dce398b69c": { + "a3115a3594ce4aa497f8a610abb0af9e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5927,50 +6137,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_99c1a284b0e04c9a85ab4ee83de08fc0", - "IPY_MODEL_759a332c02e64ce79a364cd518bc163e", - "IPY_MODEL_743f0d74186d4b7093498a547ab9ac95" + "IPY_MODEL_6719b46ec6814ef5869cf0982143c632", + "IPY_MODEL_f7496bea7da04557b5e943e71814b3ad", + "IPY_MODEL_1f029c9a5f7842a19a17b9c9f75ecc48" ], - "layout": "IPY_MODEL_c5594aabc34c40e2bd69e47ea0624f4a", + "layout": "IPY_MODEL_f34228361c9a43dc9eb3dc33677084e0", "tabbable": null, "tooltip": null } }, - "864d773ce416475ba6a1e506b36063dc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "877c0b06e48d4616b74e70c7b9e6abff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "890e03a51fce4dd185e5a42dc5da23ed": { + "a64c2baabe354ca8a0f8f64ae6bc4ede": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5988,7 +6164,7 @@ "text_color": null } }, - "89956ffd6bd842798b8752c8f8fbcef6": { + "a685a21bed8147549eee85cbac9f5358": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6003,15 +6179,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bd6988dd8c6745d9a63c01e21b21baa3", + "layout": "IPY_MODEL_cc7be77366654c01991ea476f2293f63", "placeholder": "​", - "style": "IPY_MODEL_647ec368e3354d458cfed043dad850ce", + "style": "IPY_MODEL_6d8a49f522924aaaabb198187245001e", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Generating train split: 100%" } }, - "8a3f12d334d645c8a42ca8a0292075a9": { + "a8e1731ca82b4390af97a6c9c6efcc51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6064,25 +6240,7 @@ "width": null } }, - "8be082c69bad41aa815fa99c34e5a9ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "8dbd4d3399124f6d8275c1d0fdfe9983": { + "a972ac7ecf4c4746bb430a3161226786": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6097,15 +6255,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6ca7ade3edba440ba7226afd89340130", + "layout": "IPY_MODEL_9b19aa98c0d64d849bf08b178b9841e7", "placeholder": "​", - "style": "IPY_MODEL_bdd6eef2f72d4192953e456956c77bd9", + "style": "IPY_MODEL_3145d8f1a50346caa657e7b6ade92792", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": " 40/40 [00:00<00:00, 59.36it/s]" + } + }, + "ad18fb7ca0984499bf68129daeec626a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "9009c4b403d146b493913cc05ca55a44": { + "ae9e474ca22b45648e40de05673fbd79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6158,7 +6369,7 @@ "width": null } }, - "91eaa66c2eb641e689fc8028aee35c80": { + "af859482bc704e13a4993c698c9ba181": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6176,106 +6387,154 @@ "text_color": null } }, - "92fe75b0bd1341e9878165f8c906fc19": { - "model_module": "@jupyter-widgets/controls", + "b30172a2a47d40f0a77a14540ecd18d6": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3851a48a716b467ba9b981bd35b1822c", - "max": 29515.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4aadbbb3e859454a93556ff943f76e5b", - "tabbable": null, - "tooltip": null, - "value": 29515.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "94beed83d4f34e37885835c7ee53b3e7": { - "model_module": "@jupyter-widgets/controls", + "b31e5df3571f4ac28345bf70cd4e947c": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c9e6261cabb7413784e072a38690acc3", - "max": 4833.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c9722775eec64862b3c787b4a9da67b5", - "tabbable": null, - "tooltip": null, - "value": 4833.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "96b3b9a948504544be06e5692d10926d": { + "b36cf6fb19144ae68ceeda6d14a0c88e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e451b86b3b73462bb7d10f31b67e7f35", - "IPY_MODEL_ff2c7db5dbda44b8b24ca18490cb3473", - "IPY_MODEL_3256f32acb0e4cf5b5de459cdd30f479" - ], - "layout": "IPY_MODEL_ed769dcb47ca423fb840b23690485ebe", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a8e1731ca82b4390af97a6c9c6efcc51", + "placeholder": "​", + "style": "IPY_MODEL_d2b86bd7871548f5b1e0d2d312d2ef24", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 26.4M/26.4M [00:00<00:00, 109MB/s]" } }, - "99c1a284b0e04c9a85ab4ee83de08fc0": { + "b40cdc4a6ff640758611c963c4a3014d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_da9f7efe26b243e7bed64d6bd9746699", - "placeholder": "​", - "style": "IPY_MODEL_bff32efaaebb4a2789332d03ffc174f6", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "9bc90fd3c0264b26b5bf1c99f4b9caad": { + "b42a1a7b436f4dfcab7ea91187e0b743": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6293,7 +6552,47 @@ "text_color": null } }, - "9eace039e9d343e1a0113042a3582776": { + "b4c1e3125e1f43ae99fc53aa3a9c0505": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "b66bf1f268f64f16b0ab04fbfef16cb7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8f9cbba88a5d4fe29d7de8c5c017526e", + "IPY_MODEL_80bc3ab0f2e34cba9a1c31d7411ecbc5", + "IPY_MODEL_9882b87501b84979bb5ca2b3e35d53d1" + ], + "layout": "IPY_MODEL_79afef11e9d74259a71e933ac999caf5", + "tabbable": null, + "tooltip": null + } + }, + "b76a6f2e44334c838c6350a5d3bbb530": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6346,7 +6645,59 @@ "width": null } }, - "a0b406e9eaf143599fd4e302b57381b4": { + "bb9b88f5052e43e4861dcbd84c5aa196": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c0e6653485214e6295a6c309d77804a3", + "max": 4833.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4f03fb7101fe41f5becc6ecfabbba17a", + "tabbable": null, + "tooltip": null, + "value": 4833.0 + } + }, + "be2ba8934caf4f6eb77db4a243219f26": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b76a6f2e44334c838c6350a5d3bbb530", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cd71b5032d334106871d93c7901358eb", + "tabbable": null, + "tooltip": null, + "value": 26421880.0 + } + }, + "bf64e375efe14d25b7e951f059b16c23": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6361,16 +6712,42 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_89956ffd6bd842798b8752c8f8fbcef6", - "IPY_MODEL_088b60ea8401405ea27804efbb34b231", - "IPY_MODEL_6102ff846ce741a4aa1c83f94cf213b4" + "IPY_MODEL_4398abd231514ecb8426aaa36111102f", + "IPY_MODEL_ef6365260ca348ed903c1353490fc655", + "IPY_MODEL_cfbabccb48c14370b2a05af8b6c6a2ce" ], - "layout": "IPY_MODEL_7df97d24399f4f8a980e937ed234ad3c", + "layout": "IPY_MODEL_20fc3b9b9a4b48d881bc11004e753c5e", "tabbable": null, "tooltip": null } }, - "a201453cadfa4bccaf26c6f446b4e7ee": { + "bfc4b95406a34e3ca4e93013d10e4852": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_23fe73f9bf064ef5bd44fb6b0e295456", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9aee6db9bd984fa5a023e92a81ba822d", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "c0e6653485214e6295a6c309d77804a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6423,25 +6800,7 @@ "width": null } }, - "a2c46edab30a43d3ad1274496e23dc19": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a2e082221e6d4efe981d8286fcaa40bd": { + "c3c495d6cade4b65834b43700b7d4655": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6494,7 +6853,7 @@ "width": null } }, - "a3af79b4c771458595c44a209a768d66": { + "c61855779cd74bc097076c41da0eed54": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6547,7 +6906,7 @@ "width": null } }, - "a61e85ca959b4dafbe01836e2add2005": { + "c639afb167db44978154a2d4054f1d40": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6600,23 +6959,7 @@ "width": null } }, - "a955b675afa4453385243c0af21d7bb7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "ac0dd2e3b9574dfba4e088b07dc9917a": { + "c867ebcf347146abb7ec6115797953c1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6669,100 +7012,84 @@ "width": null } }, - "ac82938d47c543a89ca5def5e546d7da": { + "c8ad57476e81431f9ef31378a786d5e9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_96a4611a123247198b0f710c5bf75bd7", + "IPY_MODEL_cf26e95faaa94e23b57c2179d5e9c64e", + "IPY_MODEL_d6650de0045c4384af30511ccab8a0f7" + ], + "layout": "IPY_MODEL_d0b1f8cfd74c4375af09960b50139328", + "tabbable": null, + "tooltip": null } }, - "aea843e930ac410596a0f4cb4f6520e0": { - "model_module": "@jupyter-widgets/controls", + "c932cb59d82141628dd6ec40fc18f737": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b1d5272979684bff96c71500a455d400": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_372ab13ae5c8452ead0d7884763b3fc8", - "placeholder": "​", - "style": "IPY_MODEL_27b9b136f3bb4b1a80402df468b5c136", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "b3495f2ab71b4f848590703a190ddebc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b3f80c6a23394b5dbcb460b9a19b34ff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "bd6988dd8c6745d9a63c01e21b21baa3": { + "ca783c0a89cb4d7aabe9391975acb8ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6815,30 +7142,7 @@ "width": null } }, - "bd7060172fb747a6ae92a503a3922356": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a201453cadfa4bccaf26c6f446b4e7ee", - "placeholder": "​", - "style": "IPY_MODEL_2c4aaf8a7a84451d93bb9c185069cfe6", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "bdd1580a8e924b29bc29315b007f1f26": { + "cabee28dffcd4272a0c22c2d5c770596": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6891,51 +7195,7 @@ "width": null } }, - "bdd6eef2f72d4192953e456956c77bd9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "be4ee2ccce154754b1f1e8d49134ee62": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_75af0524012d41858477feaee9949557", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_877c0b06e48d4616b74e70c7b9e6abff", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "be6b57b7d12a497fbc96e8e89b08f15a": { + "cac4ab6d168f46baa1c90ed6d428997f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6988,54 +7248,33 @@ "width": null } }, - "bf04eda28d94482ebdbf589d87951c61": { + "cb9288f8e40c422ea8a86e41ea2ba6df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0205ebdad1d64de8a1bd7d1c741d5fcb", - "placeholder": "​", - "style": "IPY_MODEL_f0fcd694fc07412a83769122e48dd5c1", + "layout": "IPY_MODEL_1d882b744195442f85a86d91cabda0fb", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_eab0669b062645d4ac17d3d2f88de120", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "bfd46491d1764708be24b2103e5e6cb5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_645dbaa1cd59415da2cc2b69430972fa", - "IPY_MODEL_33c9e2d67e4e498a9badfc73dd036c12", - "IPY_MODEL_21a93c7e2dfc4569b325ed80637cf469" - ], - "layout": "IPY_MODEL_4ff41db514e041798fc3d0bf13325104", - "tabbable": null, - "tooltip": null + "value": 40.0 } }, - "bff32efaaebb4a2789332d03ffc174f6": { + "cbfecf1072ed4e2caaa31da07e0da756": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7053,7 +7292,7 @@ "text_color": null } }, - "c34dc272c24c40c697da60df89a38f25": { + "cc12a064eb004d6390ea2006fbb725ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7071,7 +7310,7 @@ "text_color": null } }, - "c4fcca6ce699447399e56dfc004afddb": { + "cc7be77366654c01991ea476f2293f63": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7124,7 +7363,7 @@ "width": null } }, - "c5594aabc34c40e2bd69e47ea0624f4a": { + "cca5b38ac94f4b60ab4f617db8198ff8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7177,30 +7416,23 @@ "width": null } }, - "c9693739925b43cd83cb4e68fc01ecc9": { + "ccea9ba4e771468a95236d2db5bf0264": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_bdd1580a8e924b29bc29315b007f1f26", - "placeholder": "​", - "style": "IPY_MODEL_751475cab3c24730bab0fbab4d5284f2", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 68.93it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "c9722775eec64862b3c787b4a9da67b5": { + "cd71b5032d334106871d93c7901358eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -7216,7 +7448,56 @@ "description_width": "" } }, - "c9e6261cabb7413784e072a38690acc3": { + "cf26e95faaa94e23b57c2179d5e9c64e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_940ecb97d50847cda5f82f56b828ce1e", + "max": 5148.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_727d6b8ed0214669ae4a1c260b9eb2ba", + "tabbable": null, + "tooltip": null, + "value": 5148.0 + } + }, + "cfbabccb48c14370b2a05af8b6c6a2ce": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_95c819d6d1bb48a4a1718e13d4b708d7", + "placeholder": "​", + "style": "IPY_MODEL_ec1afef96b264d888ab90ae59248acd5", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 65.10it/s]" + } + }, + "cfdfd821fb03474ba31a8ca207281b52": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7269,80 +7550,60 @@ "width": null } }, - "cab386a86c594ee2885f6d1679103b3b": { - "model_module": "@jupyter-widgets/controls", + "d0b1f8cfd74c4375af09960b50139328": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6f3f698e19f14817b2b2fa6c67e55a47", - "placeholder": "​", - "style": "IPY_MODEL_c34dc272c24c40c697da60df89a38f25", - "tabbable": null, - "tooltip": null, - "value": " 4.42M/4.42M [00:00<00:00, 53.3MB/s]" - } - }, - "cd6bbb8ca872405fa17b2571965191b3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "cf4a395a6d57451092b05d86340035d6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d15f06ef2c454254bee0f59957e49d4b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d2775a5c6f9b4c439174c1b5f34446b5": { + "d2b86bd7871548f5b1e0d2d312d2ef24": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7360,7 +7621,7 @@ "text_color": null } }, - "d44de894f1eb4db0ad9f986867905216": { + "d4d042ad47854553b9660b36249c7347": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7413,7 +7674,7 @@ "width": null } }, - "d4be07fa12674628ae93c0119edbf6e1": { + "d6650de0045c4384af30511ccab8a0f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7428,57 +7689,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d82285fcd9b84504a28fb9c9b1ad268f", + "layout": "IPY_MODEL_cabee28dffcd4272a0c22c2d5c770596", "placeholder": "​", - "style": "IPY_MODEL_edb2e6f6c0214dae8109a703bb56a3ba", + "style": "IPY_MODEL_34eceefcb9cd4c0390182a3da1170a7b", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 5257.56 examples/s]" + "value": " 5.15k/5.15k [00:00<00:00, 871kB/s]" } }, - "d4c59b0bfa86424a8c95a71f890f5454": { + "d73ad391537b4ea7a7841fe7944270c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f2b0f79e655c4494923cc58f73494551", - "IPY_MODEL_94beed83d4f34e37885835c7ee53b3e7", - "IPY_MODEL_25f2773590254b58b3ac4b1c0a886c35" - ], - "layout": "IPY_MODEL_d75e706d71cc4c7d8ec28bc9b0e5e02a", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1a4cf1e028c44677995965a00cb4aa35", + "placeholder": "​", + "style": "IPY_MODEL_a64c2baabe354ca8a0f8f64ae6bc4ede", "tabbable": null, - "tooltip": null - } - }, - "d72826ff906b4e3e81e8eb6291d1cc9a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 7362.49 examples/s]" } }, - "d75e706d71cc4c7d8ec28bc9b0e5e02a": { + "d9e695fa0c8741669e44cf4502f36d47": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7531,7 +7773,49 @@ "width": null } }, - "d82285fcd9b84504a28fb9c9b1ad268f": { + "da2c01112d1f4e749b0ca2c79b09927f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fc1d8341e6624186a80fabb78e1c9a60", + "IPY_MODEL_1bf57cd82d684206a95f2c1e179d96ce", + "IPY_MODEL_53049123e6a9407b8314d7f6b14f99af" + ], + "layout": "IPY_MODEL_2b356c8f0be148beb637a84faef5510a", + "tabbable": null, + "tooltip": null + } + }, + "dc0ef01ff0b14b05bd75d62108c813b6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "dce15d5cb91b42a780b136a398da089e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7584,7 +7868,25 @@ "width": null } }, - "d84b7a5330814c309bc2e3a29fd936ef": { + "dd1800dbb9064a41a02af5ec0f1f5b67": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ddbe3a04ddff46c5bdfab6de8a35f7cc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7637,25 +7939,7 @@ "width": null } }, - "d89ee00461d14abe96f3f0cdcfa3da61": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d90986d0f1bb4e8ebf632742ca0c49a3": { + "ddee9f52a482431d83538c9f941bfbe9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7708,7 +7992,7 @@ "width": null } }, - "d9f443785177406fb3840783c441fddc": { + "df9aa39a8a3a44d8bb0fd0ea66fb7093": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7723,68 +8007,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e186a328526e44f2abaeb0fbc2e6a273", + "layout": "IPY_MODEL_2f6cf6dc5a4845588c6a21a1c21879bc", "placeholder": "​", - "style": "IPY_MODEL_d2775a5c6f9b4c439174c1b5f34446b5", + "style": "IPY_MODEL_dd1800dbb9064a41a02af5ec0f1f5b67", "tabbable": null, "tooltip": null, - "value": "Computing checksums: 100%" + "value": " 40/40 [00:00<00:00, 63.93it/s]" } }, - "da9f7efe26b243e7bed64d6bd9746699": { - "model_module": "@jupyter-widgets/base", + "e03718c7114049e881b5ec8c92d14511": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "dd1415dc221544d78c38cecb125e95de": { + "e09e931dcf844151835a19cfddd0f459": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7837,7 +8084,7 @@ "width": null } }, - "ddb1bbe42c9e442091cc9c4122b5de26": { + "e1a517549d364c30bc66ec975d022bdc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7890,76 +8137,30 @@ "width": null } }, - "ded2f3b115fb46e48b5699012c011fa5": { + "e452cfce19364c6fb6b50db4144baec6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "e186a328526e44f2abaeb0fbc2e6a273": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cca5b38ac94f4b60ab4f617db8198ff8", + "placeholder": "​", + "style": "IPY_MODEL_8555aae383d74d6c851e5296a2f76824", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "e1aad335fcff4496842cc4f52b05fb6a": { + "e5a63f7446074676b31de8da1ee38a2b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7974,15 +8175,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a3af79b4c771458595c44a209a768d66", + "layout": "IPY_MODEL_cac4ab6d168f46baa1c90ed6d428997f", "placeholder": "​", - "style": "IPY_MODEL_823bddadbc6644c283f25f9cc6a18fc8", + "style": "IPY_MODEL_af859482bc704e13a4993c698c9ba181", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 4.83k/4.83k [00:00<00:00, 612kB/s]" } }, - "e451b86b3b73462bb7d10f31b67e7f35": { + "e6d9862771cc40a5bc3f7cb24e629eba": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e6fa033915fa4a5987c2c8fa374502e4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7997,68 +8214,73 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8a3f12d334d645c8a42ca8a0292075a9", + "layout": "IPY_MODEL_3c7b41e10bf74f948457cd90e200afe1", "placeholder": "​", - "style": "IPY_MODEL_4a57dceefdc148afb5c7afa8adec5114", + "style": "IPY_MODEL_9dfddac443ca480cac3f02058d42e8a5", "tabbable": null, "tooltip": null, - "value": "Generating test split: 100%" + "value": "Downloading data: 100%" } }, - "e57ec2490fdc4028b01834557f09baa2": { - "model_module": "@jupyter-widgets/base", + "ea88c13811944930a76ece93362f7e4c": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2e0923cec6894e0bb926973c7f50f3d3", + "IPY_MODEL_483cd57673244db39d3524fef0835d46", + "IPY_MODEL_d73ad391537b4ea7a7841fe7944270c0" + ], + "layout": "IPY_MODEL_7cb2c22fa5f843e79a58ef3c50f8eab3", + "tabbable": null, + "tooltip": null + } + }, + "eab0669b062645d4ac17d3d2f88de120": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "ec1afef96b264d888ab90ae59248acd5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e6e9051f4c2a49228c32a25f39a57f4c": { + "ec5400e36f274359b296a5d6e0e33a39": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -8074,110 +8296,89 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e57ec2490fdc4028b01834557f09baa2", - "max": 40.0, + "layout": "IPY_MODEL_944cd1ea980e485caca5409c07bca11c", + "max": 29515.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_d15f06ef2c454254bee0f59957e49d4b", + "style": "IPY_MODEL_270f6bf2c3e643ea8dd1a756817909fe", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 29515.0 } }, - "e85e1e91a24f4199a9a4b3e9abe8696f": { + "ef2ba57d7cd44e3cb5973e6264f86666": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_244159f1aa444aedbd2e3e9382b4326b", + "placeholder": "​", + "style": "IPY_MODEL_cc12a064eb004d6390ea2006fbb725ed", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" } }, - "eaa35cd4c5ae462181de3ad1ab98c2d1": { - "model_module": "@jupyter-widgets/base", + "ef6365260ca348ed903c1353490fc655": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f6079e4e10e545668732bdada9c65e44", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e03718c7114049e881b5ec8c92d14511", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "ec86bd0afa46422aa85bf2778e427f2a": { + "efabf4edc4f14504b06bad7e71659998": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_831b82774fb644faa4c010b37968f99b", - "IPY_MODEL_e6e9051f4c2a49228c32a25f39a57f4c", - "IPY_MODEL_55923d8f76544ee5b5e53cb28dcbbcc5" - ], - "layout": "IPY_MODEL_0cae058fc562457bbb502b466cfdfcab", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5bfde149dd324016b5d45c6bddf7fad2", + "placeholder": "​", + "style": "IPY_MODEL_dc0ef01ff0b14b05bd75d62108c813b6", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Downloading builder script: 100%" } }, - "ed5bd36e898a43dc9c5cb8e283abbead": { + "f039d6ee9460446c9cbfff2777f2bb15": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8230,7 +8431,7 @@ "width": null } }, - "ed769dcb47ca423fb840b23690485ebe": { + "f34228361c9a43dc9eb3dc33677084e0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8283,7 +8484,7 @@ "width": null } }, - "edb2e6f6c0214dae8109a703bb56a3ba": { + "f5501a6381964ea3b5684ee5ac2a2990": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -8301,90 +8502,7 @@ "text_color": null } }, - "edb3baaca40743c08801b6e9bff25752": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3598bd0162e44744bf0e88509c1fcc05", - "placeholder": "​", - "style": "IPY_MODEL_cf4a395a6d57451092b05d86340035d6", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:36<00:00, 1626.06it/s]" - } - }, - "ee5d45a366aa46a4a7c3de67f844b7ba": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d9f443785177406fb3840783c441fddc", - "IPY_MODEL_640d5d2692d64656a67cd09cee644495", - "IPY_MODEL_1ef3f8ae36734efb86b54939cb9711d4" - ], - "layout": "IPY_MODEL_c4fcca6ce699447399e56dfc004afddb", - "tabbable": null, - "tooltip": null - } - }, - "f0b50dd1b20c48b6911a694433d48e05": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f0fcd694fc07412a83769122e48dd5c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f1c238f4a14549229bdf80d577253ccf": { + "f6079e4e10e545668732bdada9c65e44": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8437,99 +8555,33 @@ "width": null } }, - "f2b0f79e655c4494923cc58f73494551": { + "f7496bea7da04557b5e943e71814b3ad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f8aafa4a992b42cf95a5ace2356676d6", - "placeholder": "​", - "style": "IPY_MODEL_d72826ff906b4e3e81e8eb6291d1cc9a", + "layout": "IPY_MODEL_67307a5269b545deb5492d878a0da28f", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e6d9862771cc40a5bc3f7cb24e629eba", "tabbable": null, "tooltip": null, - "value": "Downloading builder script: 100%" - } - }, - "f4bfad9cd0da4d31a8d5c783407a73c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "f8aafa4a992b42cf95a5ace2356676d6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "value": 40.0 } }, - "f9978a29787547e3bc5e59bde742651c": { + "f7d5ee1b172841528a12c8d5a15409f3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8582,33 +8634,30 @@ "width": null } }, - "fe1973b9b1fa4957b9894f465a0fe87c": { + "fc1d8341e6624186a80fabb78e1c9a60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_736ef5bce23e47429bfcb196fb8b85b3", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b3f80c6a23394b5dbcb460b9a19b34ff", + "layout": "IPY_MODEL_603bc1c8b13349f78643f74115dd3fc5", + "placeholder": "​", + "style": "IPY_MODEL_1361429788e54a748edb03027a9cab6a", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "100%" } }, - "ff08f8bfa09d42838688c6f725adb306": { + "ffc2140e49b04844ba200898835e603c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8660,55 +8709,6 @@ "visibility": null, "width": null } - }, - "ff2c7db5dbda44b8b24ca18490cb3473": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6ccb235983834c00ac32be1422c16641", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a955b675afa4453385243c0af21d7bb7", - "tabbable": null, - "tooltip": null, - "value": 10000.0 - } - }, - "fffb62594db04599b3628dceafda46f1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_30c3b868ba4b46ea9bcdb05e1c6d5613", - "placeholder": "​", - "style": "IPY_MODEL_46552aea691e492084a7278f7a059830", - "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 32831e810..079f8c422 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:27.356934Z", - "iopub.status.busy": "2024-07-02T12:04:27.356523Z", - "iopub.status.idle": "2024-07-02T12:04:28.474290Z", - "shell.execute_reply": "2024-07-02T12:04:28.473753Z" + "iopub.execute_input": "2024-07-02T15:13:52.731591Z", + "iopub.status.busy": "2024-07-02T15:13:52.731198Z", + "iopub.status.idle": "2024-07-02T15:13:53.826850Z", + "shell.execute_reply": "2024-07-02T15:13:53.826290Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.476781Z", - "iopub.status.busy": "2024-07-02T12:04:28.476419Z", - "iopub.status.idle": "2024-07-02T12:04:28.493512Z", - "shell.execute_reply": "2024-07-02T12:04:28.493079Z" + "iopub.execute_input": "2024-07-02T15:13:53.829437Z", + "iopub.status.busy": "2024-07-02T15:13:53.829016Z", + "iopub.status.idle": "2024-07-02T15:13:53.846142Z", + "shell.execute_reply": "2024-07-02T15:13:53.845712Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.495747Z", - "iopub.status.busy": "2024-07-02T12:04:28.495323Z", - "iopub.status.idle": "2024-07-02T12:04:28.552204Z", - "shell.execute_reply": "2024-07-02T12:04:28.551635Z" + "iopub.execute_input": "2024-07-02T15:13:53.848204Z", + "iopub.status.busy": "2024-07-02T15:13:53.847818Z", + "iopub.status.idle": "2024-07-02T15:13:53.884392Z", + "shell.execute_reply": "2024-07-02T15:13:53.883872Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.554311Z", - "iopub.status.busy": "2024-07-02T12:04:28.553993Z", - "iopub.status.idle": "2024-07-02T12:04:28.557548Z", - "shell.execute_reply": "2024-07-02T12:04:28.557017Z" + "iopub.execute_input": "2024-07-02T15:13:53.887171Z", + "iopub.status.busy": "2024-07-02T15:13:53.886837Z", + "iopub.status.idle": "2024-07-02T15:13:53.890668Z", + "shell.execute_reply": "2024-07-02T15:13:53.890246Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.559563Z", - "iopub.status.busy": "2024-07-02T12:04:28.559241Z", - "iopub.status.idle": "2024-07-02T12:04:28.566506Z", - "shell.execute_reply": "2024-07-02T12:04:28.566080Z" + "iopub.execute_input": "2024-07-02T15:13:53.892601Z", + "iopub.status.busy": "2024-07-02T15:13:53.892297Z", + "iopub.status.idle": "2024-07-02T15:13:53.899797Z", + "shell.execute_reply": "2024-07-02T15:13:53.899259Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.568485Z", - "iopub.status.busy": "2024-07-02T12:04:28.568190Z", - "iopub.status.idle": "2024-07-02T12:04:28.570814Z", - "shell.execute_reply": "2024-07-02T12:04:28.570270Z" + "iopub.execute_input": "2024-07-02T15:13:53.901915Z", + "iopub.status.busy": "2024-07-02T15:13:53.901601Z", + "iopub.status.idle": "2024-07-02T15:13:53.904220Z", + "shell.execute_reply": "2024-07-02T15:13:53.903685Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.572815Z", - "iopub.status.busy": "2024-07-02T12:04:28.572491Z", - "iopub.status.idle": "2024-07-02T12:04:31.525677Z", - "shell.execute_reply": "2024-07-02T12:04:31.525153Z" + "iopub.execute_input": "2024-07-02T15:13:53.906153Z", + "iopub.status.busy": "2024-07-02T15:13:53.905838Z", + "iopub.status.idle": "2024-07-02T15:13:56.829546Z", + "shell.execute_reply": "2024-07-02T15:13:56.829019Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:31.528465Z", - "iopub.status.busy": "2024-07-02T12:04:31.528045Z", - "iopub.status.idle": "2024-07-02T12:04:31.537314Z", - "shell.execute_reply": "2024-07-02T12:04:31.536783Z" + "iopub.execute_input": "2024-07-02T15:13:56.832266Z", + "iopub.status.busy": "2024-07-02T15:13:56.832063Z", + "iopub.status.idle": "2024-07-02T15:13:56.841280Z", + "shell.execute_reply": "2024-07-02T15:13:56.840813Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:31.539264Z", - "iopub.status.busy": "2024-07-02T12:04:31.539089Z", - "iopub.status.idle": "2024-07-02T12:04:33.395993Z", - "shell.execute_reply": "2024-07-02T12:04:33.395329Z" + "iopub.execute_input": "2024-07-02T15:13:56.843320Z", + "iopub.status.busy": "2024-07-02T15:13:56.843129Z", + "iopub.status.idle": "2024-07-02T15:13:58.717626Z", + "shell.execute_reply": "2024-07-02T15:13:58.717017Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.398417Z", - "iopub.status.busy": "2024-07-02T12:04:33.397878Z", - "iopub.status.idle": "2024-07-02T12:04:33.416211Z", - "shell.execute_reply": "2024-07-02T12:04:33.415751Z" + "iopub.execute_input": "2024-07-02T15:13:58.720164Z", + "iopub.status.busy": "2024-07-02T15:13:58.719607Z", + "iopub.status.idle": "2024-07-02T15:13:58.738219Z", + "shell.execute_reply": "2024-07-02T15:13:58.737654Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.418164Z", - "iopub.status.busy": "2024-07-02T12:04:33.417840Z", - "iopub.status.idle": "2024-07-02T12:04:33.425514Z", - "shell.execute_reply": "2024-07-02T12:04:33.425080Z" + "iopub.execute_input": "2024-07-02T15:13:58.740165Z", + "iopub.status.busy": "2024-07-02T15:13:58.739856Z", + "iopub.status.idle": "2024-07-02T15:13:58.747692Z", + "shell.execute_reply": "2024-07-02T15:13:58.747149Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.427421Z", - "iopub.status.busy": "2024-07-02T12:04:33.427245Z", - "iopub.status.idle": "2024-07-02T12:04:33.435924Z", - "shell.execute_reply": "2024-07-02T12:04:33.435472Z" + "iopub.execute_input": "2024-07-02T15:13:58.749890Z", + "iopub.status.busy": "2024-07-02T15:13:58.749354Z", + "iopub.status.idle": "2024-07-02T15:13:58.758107Z", + "shell.execute_reply": "2024-07-02T15:13:58.757568Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.437900Z", - "iopub.status.busy": "2024-07-02T12:04:33.437577Z", - "iopub.status.idle": "2024-07-02T12:04:33.445125Z", - "shell.execute_reply": "2024-07-02T12:04:33.444685Z" + "iopub.execute_input": "2024-07-02T15:13:58.760206Z", + "iopub.status.busy": "2024-07-02T15:13:58.759870Z", + "iopub.status.idle": "2024-07-02T15:13:58.767460Z", + "shell.execute_reply": "2024-07-02T15:13:58.767003Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.447029Z", - "iopub.status.busy": "2024-07-02T12:04:33.446852Z", - "iopub.status.idle": "2024-07-02T12:04:33.455323Z", - "shell.execute_reply": "2024-07-02T12:04:33.454897Z" + "iopub.execute_input": "2024-07-02T15:13:58.769386Z", + "iopub.status.busy": "2024-07-02T15:13:58.769213Z", + "iopub.status.idle": "2024-07-02T15:13:58.777797Z", + "shell.execute_reply": "2024-07-02T15:13:58.777350Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.457305Z", - "iopub.status.busy": "2024-07-02T12:04:33.457003Z", - "iopub.status.idle": "2024-07-02T12:04:33.464266Z", - "shell.execute_reply": "2024-07-02T12:04:33.463800Z" + "iopub.execute_input": "2024-07-02T15:13:58.779615Z", + "iopub.status.busy": "2024-07-02T15:13:58.779445Z", + "iopub.status.idle": "2024-07-02T15:13:58.786751Z", + "shell.execute_reply": "2024-07-02T15:13:58.786316Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.466390Z", - "iopub.status.busy": "2024-07-02T12:04:33.465996Z", - "iopub.status.idle": "2024-07-02T12:04:33.473134Z", - "shell.execute_reply": "2024-07-02T12:04:33.472705Z" + "iopub.execute_input": "2024-07-02T15:13:58.788616Z", + "iopub.status.busy": "2024-07-02T15:13:58.788445Z", + "iopub.status.idle": "2024-07-02T15:13:58.796817Z", + "shell.execute_reply": "2024-07-02T15:13:58.796328Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.475300Z", - "iopub.status.busy": "2024-07-02T12:04:33.474982Z", - "iopub.status.idle": "2024-07-02T12:04:33.482977Z", - "shell.execute_reply": "2024-07-02T12:04:33.482536Z" + "iopub.execute_input": "2024-07-02T15:13:58.799200Z", + "iopub.status.busy": "2024-07-02T15:13:58.798774Z", + "iopub.status.idle": "2024-07-02T15:13:58.807454Z", + "shell.execute_reply": "2024-07-02T15:13:58.806894Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 2bf6c15a0..1d126d098 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -791,7 +791,7 @@

2. Load and format the text dataset
 This dataset has 10 classes.
-Classes: {'visa_or_mastercard', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged'}
+Classes: {'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'card_payment_fee_charged', 'cancel_transfer', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card'}
 

Let’s view the i-th example in the dataset:

diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 8395c410d..5204560ef 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:36.240740Z", - "iopub.status.busy": "2024-07-02T12:04:36.240404Z", - "iopub.status.idle": "2024-07-02T12:04:38.828958Z", - "shell.execute_reply": "2024-07-02T12:04:38.828416Z" + "iopub.execute_input": "2024-07-02T15:14:01.500489Z", + "iopub.status.busy": "2024-07-02T15:14:01.500322Z", + "iopub.status.idle": "2024-07-02T15:14:04.113035Z", + "shell.execute_reply": "2024-07-02T15:14:04.112481Z" }, "nbsphinx": "hidden" }, @@ -96,7 +96,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.831414Z", - "iopub.status.busy": "2024-07-02T12:04:38.831139Z", - "iopub.status.idle": "2024-07-02T12:04:38.834207Z", - "shell.execute_reply": "2024-07-02T12:04:38.833787Z" + "iopub.execute_input": "2024-07-02T15:14:04.115579Z", + "iopub.status.busy": "2024-07-02T15:14:04.115125Z", + "iopub.status.idle": "2024-07-02T15:14:04.118367Z", + "shell.execute_reply": "2024-07-02T15:14:04.117915Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.836176Z", - "iopub.status.busy": "2024-07-02T12:04:38.835855Z", - "iopub.status.idle": "2024-07-02T12:04:38.838727Z", - "shell.execute_reply": "2024-07-02T12:04:38.838306Z" + "iopub.execute_input": "2024-07-02T15:14:04.120314Z", + "iopub.status.busy": "2024-07-02T15:14:04.119999Z", + "iopub.status.idle": "2024-07-02T15:14:04.123081Z", + "shell.execute_reply": "2024-07-02T15:14:04.122619Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.840549Z", - "iopub.status.busy": "2024-07-02T12:04:38.840377Z", - "iopub.status.idle": "2024-07-02T12:04:38.923955Z", - "shell.execute_reply": "2024-07-02T12:04:38.923459Z" + "iopub.execute_input": "2024-07-02T15:14:04.125041Z", + "iopub.status.busy": "2024-07-02T15:14:04.124728Z", + "iopub.status.idle": "2024-07-02T15:14:04.163294Z", + "shell.execute_reply": "2024-07-02T15:14:04.162806Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.926011Z", - "iopub.status.busy": "2024-07-02T12:04:38.925614Z", - "iopub.status.idle": "2024-07-02T12:04:38.929422Z", - "shell.execute_reply": "2024-07-02T12:04:38.928857Z" + "iopub.execute_input": "2024-07-02T15:14:04.165499Z", + "iopub.status.busy": "2024-07-02T15:14:04.165073Z", + "iopub.status.idle": "2024-07-02T15:14:04.168687Z", + "shell.execute_reply": "2024-07-02T15:14:04.168240Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged'}\n" + "Classes: {'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'card_payment_fee_charged', 'cancel_transfer', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.931544Z", - "iopub.status.busy": "2024-07-02T12:04:38.931095Z", - "iopub.status.idle": "2024-07-02T12:04:38.934251Z", - "shell.execute_reply": "2024-07-02T12:04:38.933726Z" + "iopub.execute_input": "2024-07-02T15:14:04.170669Z", + "iopub.status.busy": "2024-07-02T15:14:04.170357Z", + "iopub.status.idle": "2024-07-02T15:14:04.173526Z", + "shell.execute_reply": "2024-07-02T15:14:04.172982Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.936534Z", - "iopub.status.busy": "2024-07-02T12:04:38.936327Z", - "iopub.status.idle": "2024-07-02T12:04:42.537806Z", - "shell.execute_reply": "2024-07-02T12:04:42.537162Z" + "iopub.execute_input": "2024-07-02T15:14:04.175608Z", + "iopub.status.busy": "2024-07-02T15:14:04.175312Z", + "iopub.status.idle": "2024-07-02T15:14:07.867281Z", + "shell.execute_reply": "2024-07-02T15:14:07.866722Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:42.540458Z", - "iopub.status.busy": "2024-07-02T12:04:42.540268Z", - "iopub.status.idle": "2024-07-02T12:04:43.423626Z", - "shell.execute_reply": "2024-07-02T12:04:43.423064Z" + "iopub.execute_input": "2024-07-02T15:14:07.870054Z", + "iopub.status.busy": "2024-07-02T15:14:07.869647Z", + "iopub.status.idle": "2024-07-02T15:14:08.750932Z", + "shell.execute_reply": "2024-07-02T15:14:08.750350Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:43.426912Z", - "iopub.status.busy": "2024-07-02T12:04:43.426508Z", - "iopub.status.idle": "2024-07-02T12:04:43.429416Z", - "shell.execute_reply": "2024-07-02T12:04:43.428926Z" + "iopub.execute_input": "2024-07-02T15:14:08.753892Z", + "iopub.status.busy": "2024-07-02T15:14:08.753472Z", + "iopub.status.idle": "2024-07-02T15:14:08.756403Z", + "shell.execute_reply": "2024-07-02T15:14:08.755906Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:43.431781Z", - "iopub.status.busy": "2024-07-02T12:04:43.431407Z", - "iopub.status.idle": "2024-07-02T12:04:45.304891Z", - "shell.execute_reply": "2024-07-02T12:04:45.304275Z" + "iopub.execute_input": "2024-07-02T15:14:08.759587Z", + "iopub.status.busy": "2024-07-02T15:14:08.758650Z", + "iopub.status.idle": "2024-07-02T15:14:10.695173Z", + "shell.execute_reply": "2024-07-02T15:14:10.694552Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.309001Z", - "iopub.status.busy": "2024-07-02T12:04:45.307874Z", - "iopub.status.idle": "2024-07-02T12:04:45.333199Z", - "shell.execute_reply": "2024-07-02T12:04:45.332708Z" + "iopub.execute_input": "2024-07-02T15:14:10.699111Z", + "iopub.status.busy": "2024-07-02T15:14:10.697727Z", + "iopub.status.idle": "2024-07-02T15:14:10.723548Z", + "shell.execute_reply": "2024-07-02T15:14:10.723039Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.336771Z", - "iopub.status.busy": "2024-07-02T12:04:45.335844Z", - "iopub.status.idle": "2024-07-02T12:04:45.346004Z", - "shell.execute_reply": "2024-07-02T12:04:45.345452Z" + "iopub.execute_input": "2024-07-02T15:14:10.727082Z", + "iopub.status.busy": "2024-07-02T15:14:10.726140Z", + "iopub.status.idle": "2024-07-02T15:14:10.737117Z", + "shell.execute_reply": "2024-07-02T15:14:10.736707Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.348315Z", - "iopub.status.busy": "2024-07-02T12:04:45.347931Z", - "iopub.status.idle": "2024-07-02T12:04:45.352195Z", - "shell.execute_reply": "2024-07-02T12:04:45.351669Z" + "iopub.execute_input": "2024-07-02T15:14:10.739972Z", + "iopub.status.busy": "2024-07-02T15:14:10.739233Z", + "iopub.status.idle": "2024-07-02T15:14:10.744512Z", + "shell.execute_reply": "2024-07-02T15:14:10.744100Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.354318Z", - "iopub.status.busy": "2024-07-02T12:04:45.354009Z", - "iopub.status.idle": "2024-07-02T12:04:45.360212Z", - "shell.execute_reply": "2024-07-02T12:04:45.359737Z" + "iopub.execute_input": "2024-07-02T15:14:10.746541Z", + "iopub.status.busy": "2024-07-02T15:14:10.746363Z", + "iopub.status.idle": "2024-07-02T15:14:10.752732Z", + "shell.execute_reply": "2024-07-02T15:14:10.752214Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.362212Z", - "iopub.status.busy": "2024-07-02T12:04:45.361899Z", - "iopub.status.idle": "2024-07-02T12:04:45.368332Z", - "shell.execute_reply": "2024-07-02T12:04:45.367912Z" + "iopub.execute_input": "2024-07-02T15:14:10.754855Z", + "iopub.status.busy": "2024-07-02T15:14:10.754542Z", + "iopub.status.idle": "2024-07-02T15:14:10.760876Z", + "shell.execute_reply": "2024-07-02T15:14:10.760354Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.370347Z", - "iopub.status.busy": "2024-07-02T12:04:45.370035Z", - "iopub.status.idle": "2024-07-02T12:04:45.375916Z", - "shell.execute_reply": "2024-07-02T12:04:45.375352Z" + "iopub.execute_input": "2024-07-02T15:14:10.762917Z", + "iopub.status.busy": "2024-07-02T15:14:10.762536Z", + "iopub.status.idle": "2024-07-02T15:14:10.768287Z", + "shell.execute_reply": "2024-07-02T15:14:10.767766Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.377933Z", - "iopub.status.busy": "2024-07-02T12:04:45.377533Z", - "iopub.status.idle": "2024-07-02T12:04:45.386285Z", - "shell.execute_reply": "2024-07-02T12:04:45.385744Z" + "iopub.execute_input": "2024-07-02T15:14:10.770234Z", + "iopub.status.busy": "2024-07-02T15:14:10.769934Z", + "iopub.status.idle": "2024-07-02T15:14:10.778237Z", + "shell.execute_reply": "2024-07-02T15:14:10.777705Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.388235Z", - "iopub.status.busy": "2024-07-02T12:04:45.387909Z", - "iopub.status.idle": "2024-07-02T12:04:45.393341Z", - "shell.execute_reply": "2024-07-02T12:04:45.392791Z" + "iopub.execute_input": "2024-07-02T15:14:10.780199Z", + "iopub.status.busy": "2024-07-02T15:14:10.779892Z", + "iopub.status.idle": "2024-07-02T15:14:10.785104Z", + "shell.execute_reply": "2024-07-02T15:14:10.784582Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.395404Z", - "iopub.status.busy": "2024-07-02T12:04:45.395057Z", - "iopub.status.idle": "2024-07-02T12:04:45.400341Z", - "shell.execute_reply": "2024-07-02T12:04:45.399863Z" + "iopub.execute_input": "2024-07-02T15:14:10.787024Z", + "iopub.status.busy": "2024-07-02T15:14:10.786715Z", + "iopub.status.idle": "2024-07-02T15:14:10.791931Z", + "shell.execute_reply": "2024-07-02T15:14:10.791409Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.402359Z", - "iopub.status.busy": "2024-07-02T12:04:45.402038Z", - "iopub.status.idle": "2024-07-02T12:04:45.405437Z", - "shell.execute_reply": "2024-07-02T12:04:45.405020Z" + "iopub.execute_input": "2024-07-02T15:14:10.793948Z", + "iopub.status.busy": "2024-07-02T15:14:10.793644Z", + "iopub.status.idle": "2024-07-02T15:14:10.797169Z", + "shell.execute_reply": "2024-07-02T15:14:10.796651Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.407623Z", - "iopub.status.busy": "2024-07-02T12:04:45.407307Z", - "iopub.status.idle": "2024-07-02T12:04:45.412091Z", - "shell.execute_reply": "2024-07-02T12:04:45.411668Z" + "iopub.execute_input": "2024-07-02T15:14:10.799179Z", + "iopub.status.busy": "2024-07-02T15:14:10.798916Z", + "iopub.status.idle": "2024-07-02T15:14:10.804228Z", + "shell.execute_reply": "2024-07-02T15:14:10.803755Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index cef347d5d..dee8c6eb4 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -833,7 +833,7 @@

4. Identify Data Issues Using Datalab @@ -879,13 +879,13 @@

4. Identify Data Issues Using Datalab - +
- - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
@@ -3564,7 +3564,7 @@

1. Load the Dataset
-100%|██████████| 170498071/170498071 [00:02<00:00, 69520911.78it/s]
+100%|██████████| 170498071/170498071 [00:03<00:00, 56242831.52it/s]
 
-
+
@@ -3896,7 +3896,7 @@

Image-specific property scores in the transformed dataset -{"state": {"302d670260304f5d973a1863227c2b38": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2ce33b586399430db7231ec582a8ad1c": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "ccd3930d3b25423fb8d520dc87205752": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_302d670260304f5d973a1863227c2b38", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_2ce33b586399430db7231ec582a8ad1c", "tabbable": null, "tooltip": null, "value": 200.0}}, "57d53163a3e24cfb8adf32a3c2859334": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d6c64d036d3c464bba338c11b7d7e118": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "37657cc47549425e81123fbc00061dcd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_57d53163a3e24cfb8adf32a3c2859334", "placeholder": "\u200b", "style": "IPY_MODEL_d6c64d036d3c464bba338c11b7d7e118", "tabbable": null, "tooltip": null, "value": "100%"}}, "d6941ea7ad6a41efb80f48dde9923682": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a55c5a0d7aca4c16a982994a5595ca08": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6bdd7248294f4094a2da7c7af2e67e50": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d6941ea7ad6a41efb80f48dde9923682", "placeholder": "\u200b", "style": "IPY_MODEL_a55c5a0d7aca4c16a982994a5595ca08", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007811.85it/s]"}}, "440b53038a3d4c4c964a83e8b710361f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ab730d681373436cbffc495350a9abe1": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_37657cc47549425e81123fbc00061dcd", "IPY_MODEL_ccd3930d3b25423fb8d520dc87205752", "IPY_MODEL_6bdd7248294f4094a2da7c7af2e67e50"], "layout": "IPY_MODEL_440b53038a3d4c4c964a83e8b710361f", "tabbable": null, "tooltip": null}}, "797a5104afa24ca5b172ddc308a704ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "34fad403248e49fb9d7ed5541db4875e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "e621caf6c19d4d638ba32cd7caed9a15": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_797a5104afa24ca5b172ddc308a704ec", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_34fad403248e49fb9d7ed5541db4875e", "tabbable": null, "tooltip": null, "value": 200.0}}, "1245fefd15c748ca9a6c437e90990634": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4c9fcf59ee52451aad0a525849ecf86b": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "3f75258f70194866856b4da554e4dbeb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1245fefd15c748ca9a6c437e90990634", "placeholder": "\u200b", "style": "IPY_MODEL_4c9fcf59ee52451aad0a525849ecf86b", "tabbable": null, "tooltip": null, "value": "100%"}}, "5fccbfa0a7a94b55a6825fc52ecdeee3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9d67c6a8b80b4718975da970d5ba6be1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "06e95a0f1df9408095248eef0924c604": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5fccbfa0a7a94b55a6825fc52ecdeee3", "placeholder": "\u200b", "style": "IPY_MODEL_9d67c6a8b80b4718975da970d5ba6be1", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007725.51it/s]"}}, "22612fb7095f4323876a32fa6832ebee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e44decacc70f4d08b59475e297136aab": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_3f75258f70194866856b4da554e4dbeb", "IPY_MODEL_e621caf6c19d4d638ba32cd7caed9a15", "IPY_MODEL_06e95a0f1df9408095248eef0924c604"], "layout": "IPY_MODEL_22612fb7095f4323876a32fa6832ebee", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"7f367a2cdd5445f58aecb1320024dca9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5f4143d1143347bf8d67acbd62e4c7a9": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "ba0f29fa569646e89dd03db3974a4a00": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7f367a2cdd5445f58aecb1320024dca9", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5f4143d1143347bf8d67acbd62e4c7a9", "tabbable": null, "tooltip": null, "value": 200.0}}, "246224e7d6e14ee996208e5a901506a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "669c9d816b274632945703bb33ea88b1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "22dce5e6cbbd456899db36ca71231b83": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_246224e7d6e14ee996208e5a901506a3", "placeholder": "\u200b", "style": "IPY_MODEL_669c9d816b274632945703bb33ea88b1", "tabbable": null, "tooltip": null, "value": "100%"}}, "d1fa249a3b3741948f9b90a3eba494cd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "df530a10186c40c8b9ba0ace062c0018": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b7a191fc264f425c94ccbd4b2e6ff5bf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d1fa249a3b3741948f9b90a3eba494cd", "placeholder": "\u200b", "style": "IPY_MODEL_df530a10186c40c8b9ba0ace062c0018", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007806.22it/s]"}}, "a6d4bb6587dc4b0ab299cde66d887195": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "edeb0eb92f8e493694db63fbedcce068": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_22dce5e6cbbd456899db36ca71231b83", "IPY_MODEL_ba0f29fa569646e89dd03db3974a4a00", "IPY_MODEL_b7a191fc264f425c94ccbd4b2e6ff5bf"], "layout": "IPY_MODEL_a6d4bb6587dc4b0ab299cde66d887195", "tabbable": null, "tooltip": null}}, "49bd46daef6e4afaae2104d6fddc5eff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "13f79e5c34544a20b6e43544e002e0d6": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "d03e8f0da10e418392f2df6f61dea5ed": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_49bd46daef6e4afaae2104d6fddc5eff", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_13f79e5c34544a20b6e43544e002e0d6", "tabbable": null, "tooltip": null, "value": 200.0}}, "945bafb17f6c4017b07c073239844118": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0ba1b4f02e98442dbf83a9d402d61603": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ae7e7acb2667481d93c3d5d070d947f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_945bafb17f6c4017b07c073239844118", "placeholder": "\u200b", "style": "IPY_MODEL_0ba1b4f02e98442dbf83a9d402d61603", "tabbable": null, "tooltip": null, "value": "100%"}}, "21919d64401941e69bdd1e7fdeb39391": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b2c4bbe171d649419f7ff67ac4f27ab6": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "752199f5b1064d09a833e6e140acf999": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_21919d64401941e69bdd1e7fdeb39391", "placeholder": "\u200b", "style": "IPY_MODEL_b2c4bbe171d649419f7ff67ac4f27ab6", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007781.88it/s]"}}, "5fe4a241c042442299e152f3905b32e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a53db32e879b421b9c5a2cb90345b461": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ae7e7acb2667481d93c3d5d070d947f1", "IPY_MODEL_d03e8f0da10e418392f2df6f61dea5ed", "IPY_MODEL_752199f5b1064d09a833e6e140acf999"], "layout": "IPY_MODEL_5fe4a241c042442299e152f3905b32e6", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index 62a8a980c..9f74b4e12 100644 --- a/master/tutorials/datalab/workflows.ipynb +++ b/master/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:48.475916Z", - "iopub.status.busy": "2024-07-02T12:04:48.475349Z", - "iopub.status.idle": "2024-07-02T12:04:48.903298Z", - "shell.execute_reply": "2024-07-02T12:04:48.902818Z" + "iopub.execute_input": "2024-07-02T15:14:14.103983Z", + "iopub.status.busy": "2024-07-02T15:14:14.103826Z", + "iopub.status.idle": "2024-07-02T15:14:14.532907Z", + "shell.execute_reply": "2024-07-02T15:14:14.532306Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:48.905906Z", - "iopub.status.busy": "2024-07-02T12:04:48.905515Z", - "iopub.status.idle": "2024-07-02T12:04:49.030978Z", - "shell.execute_reply": "2024-07-02T12:04:49.030445Z" + "iopub.execute_input": "2024-07-02T15:14:14.535883Z", + "iopub.status.busy": "2024-07-02T15:14:14.535387Z", + "iopub.status.idle": "2024-07-02T15:14:14.663925Z", + "shell.execute_reply": "2024-07-02T15:14:14.663366Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:49.033125Z", - "iopub.status.busy": "2024-07-02T12:04:49.032895Z", - "iopub.status.idle": "2024-07-02T12:04:49.055963Z", - "shell.execute_reply": "2024-07-02T12:04:49.055416Z" + "iopub.execute_input": "2024-07-02T15:14:14.666105Z", + "iopub.status.busy": "2024-07-02T15:14:14.665873Z", + "iopub.status.idle": "2024-07-02T15:14:14.688697Z", + "shell.execute_reply": "2024-07-02T15:14:14.688145Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:49.058382Z", - "iopub.status.busy": "2024-07-02T12:04:49.057963Z", - "iopub.status.idle": "2024-07-02T12:04:51.680557Z", - "shell.execute_reply": "2024-07-02T12:04:51.680002Z" + "iopub.execute_input": "2024-07-02T15:14:14.691372Z", + "iopub.status.busy": "2024-07-02T15:14:14.691132Z", + "iopub.status.idle": "2024-07-02T15:14:17.410594Z", + "shell.execute_reply": "2024-07-02T15:14:17.410094Z" } }, "outputs": [ @@ -235,7 +235,7 @@ "Finding class_imbalance issues ...\n", "Finding underperforming_group issues ...\n", "\n", - "Audit complete. 524 issues found in the dataset.\n" + "Audit complete. 523 issues found in the dataset.\n" ] }, { @@ -280,13 +280,13 @@ " \n", " 2\n", " outlier\n", - " 0.356924\n", - " 363\n", + " 0.356958\n", + " 362\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619581\n", + " 0.619565\n", " 108\n", " \n", " \n", @@ -315,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356924 363\n", - "3 near_duplicate 0.619581 108\n", + "2 outlier 0.356958 362\n", + "3 near_duplicate 0.619565 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651929 0" @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:51.683932Z", - "iopub.status.busy": "2024-07-02T12:04:51.683392Z", - "iopub.status.idle": "2024-07-02T12:04:59.515985Z", - "shell.execute_reply": "2024-07-02T12:04:59.515371Z" + "iopub.execute_input": "2024-07-02T15:14:17.413158Z", + "iopub.status.busy": "2024-07-02T15:14:17.412638Z", + "iopub.status.idle": "2024-07-02T15:14:25.265742Z", + "shell.execute_reply": "2024-07-02T15:14:25.265250Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:59.518078Z", - "iopub.status.busy": "2024-07-02T12:04:59.517894Z", - "iopub.status.idle": "2024-07-02T12:04:59.659289Z", - "shell.execute_reply": "2024-07-02T12:04:59.658739Z" + "iopub.execute_input": "2024-07-02T15:14:25.267894Z", + "iopub.status.busy": "2024-07-02T15:14:25.267556Z", + "iopub.status.idle": "2024-07-02T15:14:25.428084Z", + "shell.execute_reply": "2024-07-02T15:14:25.427532Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:59.661683Z", - "iopub.status.busy": "2024-07-02T12:04:59.661350Z", - "iopub.status.idle": "2024-07-02T12:05:00.957856Z", - "shell.execute_reply": "2024-07-02T12:05:00.957311Z" + "iopub.execute_input": "2024-07-02T15:14:25.430688Z", + "iopub.status.busy": "2024-07-02T15:14:25.430400Z", + "iopub.status.idle": "2024-07-02T15:14:26.733556Z", + "shell.execute_reply": "2024-07-02T15:14:26.733008Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:00.960128Z", - "iopub.status.busy": "2024-07-02T12:05:00.959785Z", - "iopub.status.idle": "2024-07-02T12:05:01.381421Z", - "shell.execute_reply": "2024-07-02T12:05:01.380807Z" + "iopub.execute_input": "2024-07-02T15:14:26.735854Z", + "iopub.status.busy": "2024-07-02T15:14:26.735515Z", + "iopub.status.idle": "2024-07-02T15:14:27.149306Z", + "shell.execute_reply": "2024-07-02T15:14:27.148705Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.383745Z", - "iopub.status.busy": "2024-07-02T12:05:01.383267Z", - "iopub.status.idle": "2024-07-02T12:05:01.392315Z", - "shell.execute_reply": "2024-07-02T12:05:01.391863Z" + "iopub.execute_input": "2024-07-02T15:14:27.151755Z", + "iopub.status.busy": "2024-07-02T15:14:27.151216Z", + "iopub.status.idle": "2024-07-02T15:14:27.160230Z", + "shell.execute_reply": "2024-07-02T15:14:27.159782Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.394282Z", - "iopub.status.busy": "2024-07-02T12:05:01.393956Z", - "iopub.status.idle": "2024-07-02T12:05:01.411562Z", - "shell.execute_reply": "2024-07-02T12:05:01.411139Z" + "iopub.execute_input": "2024-07-02T15:14:27.162273Z", + "iopub.status.busy": "2024-07-02T15:14:27.161949Z", + "iopub.status.idle": "2024-07-02T15:14:27.180092Z", + "shell.execute_reply": "2024-07-02T15:14:27.179529Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.413543Z", - "iopub.status.busy": "2024-07-02T12:05:01.413221Z", - "iopub.status.idle": "2024-07-02T12:05:01.630162Z", - "shell.execute_reply": "2024-07-02T12:05:01.629562Z" + "iopub.execute_input": "2024-07-02T15:14:27.183621Z", + "iopub.status.busy": "2024-07-02T15:14:27.183436Z", + "iopub.status.idle": "2024-07-02T15:14:27.404912Z", + "shell.execute_reply": "2024-07-02T15:14:27.404293Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.632639Z", - "iopub.status.busy": "2024-07-02T12:05:01.632236Z", - "iopub.status.idle": "2024-07-02T12:05:01.650528Z", - "shell.execute_reply": "2024-07-02T12:05:01.649988Z" + "iopub.execute_input": "2024-07-02T15:14:27.407504Z", + "iopub.status.busy": "2024-07-02T15:14:27.407113Z", + "iopub.status.idle": "2024-07-02T15:14:27.426425Z", + "shell.execute_reply": "2024-07-02T15:14:27.425957Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.652709Z", - "iopub.status.busy": "2024-07-02T12:05:01.652303Z", - "iopub.status.idle": "2024-07-02T12:05:01.816760Z", - "shell.execute_reply": "2024-07-02T12:05:01.816173Z" + "iopub.execute_input": "2024-07-02T15:14:27.428485Z", + "iopub.status.busy": "2024-07-02T15:14:27.428302Z", + "iopub.status.idle": "2024-07-02T15:14:27.595938Z", + "shell.execute_reply": "2024-07-02T15:14:27.595360Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.818813Z", - "iopub.status.busy": "2024-07-02T12:05:01.818633Z", - "iopub.status.idle": "2024-07-02T12:05:01.828263Z", - "shell.execute_reply": "2024-07-02T12:05:01.827827Z" + "iopub.execute_input": "2024-07-02T15:14:27.598162Z", + "iopub.status.busy": "2024-07-02T15:14:27.597979Z", + "iopub.status.idle": "2024-07-02T15:14:27.607922Z", + "shell.execute_reply": "2024-07-02T15:14:27.607375Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.830285Z", - "iopub.status.busy": "2024-07-02T12:05:01.830099Z", - "iopub.status.idle": "2024-07-02T12:05:01.839416Z", - "shell.execute_reply": "2024-07-02T12:05:01.838852Z" + "iopub.execute_input": "2024-07-02T15:14:27.610002Z", + "iopub.status.busy": "2024-07-02T15:14:27.609825Z", + "iopub.status.idle": "2024-07-02T15:14:27.619372Z", + "shell.execute_reply": "2024-07-02T15:14:27.618837Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.841444Z", - "iopub.status.busy": "2024-07-02T12:05:01.841118Z", - "iopub.status.idle": "2024-07-02T12:05:01.878960Z", - "shell.execute_reply": "2024-07-02T12:05:01.878541Z" + "iopub.execute_input": "2024-07-02T15:14:27.621551Z", + "iopub.status.busy": "2024-07-02T15:14:27.621164Z", + "iopub.status.idle": "2024-07-02T15:14:27.651909Z", + "shell.execute_reply": "2024-07-02T15:14:27.651479Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.881007Z", - "iopub.status.busy": "2024-07-02T12:05:01.880679Z", - "iopub.status.idle": "2024-07-02T12:05:01.883255Z", - "shell.execute_reply": "2024-07-02T12:05:01.882829Z" + "iopub.execute_input": "2024-07-02T15:14:27.653825Z", + "iopub.status.busy": "2024-07-02T15:14:27.653548Z", + "iopub.status.idle": "2024-07-02T15:14:27.656169Z", + "shell.execute_reply": "2024-07-02T15:14:27.655741Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.885223Z", - "iopub.status.busy": "2024-07-02T12:05:01.884900Z", - "iopub.status.idle": "2024-07-02T12:05:01.903469Z", - "shell.execute_reply": "2024-07-02T12:05:01.903010Z" + "iopub.execute_input": "2024-07-02T15:14:27.658186Z", + "iopub.status.busy": "2024-07-02T15:14:27.657882Z", + "iopub.status.idle": "2024-07-02T15:14:27.676913Z", + "shell.execute_reply": "2024-07-02T15:14:27.676456Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.905390Z", - "iopub.status.busy": "2024-07-02T12:05:01.905216Z", - "iopub.status.idle": "2024-07-02T12:05:01.909303Z", - "shell.execute_reply": "2024-07-02T12:05:01.908869Z" + "iopub.execute_input": "2024-07-02T15:14:27.679075Z", + "iopub.status.busy": "2024-07-02T15:14:27.678723Z", + "iopub.status.idle": "2024-07-02T15:14:27.683007Z", + "shell.execute_reply": "2024-07-02T15:14:27.682466Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.911113Z", - "iopub.status.busy": "2024-07-02T12:05:01.910943Z", - "iopub.status.idle": "2024-07-02T12:05:01.938117Z", - "shell.execute_reply": "2024-07-02T12:05:01.937659Z" + "iopub.execute_input": "2024-07-02T15:14:27.684997Z", + "iopub.status.busy": "2024-07-02T15:14:27.684696Z", + "iopub.status.idle": "2024-07-02T15:14:27.717340Z", + "shell.execute_reply": "2024-07-02T15:14:27.716802Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.940161Z", - "iopub.status.busy": "2024-07-02T12:05:01.939837Z", - "iopub.status.idle": "2024-07-02T12:05:02.252666Z", - "shell.execute_reply": "2024-07-02T12:05:02.252098Z" + "iopub.execute_input": "2024-07-02T15:14:27.719447Z", + "iopub.status.busy": "2024-07-02T15:14:27.719135Z", + "iopub.status.idle": "2024-07-02T15:14:28.089427Z", + "shell.execute_reply": "2024-07-02T15:14:28.088856Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.254862Z", - "iopub.status.busy": "2024-07-02T12:05:02.254429Z", - "iopub.status.idle": "2024-07-02T12:05:02.257607Z", - "shell.execute_reply": "2024-07-02T12:05:02.257069Z" + "iopub.execute_input": "2024-07-02T15:14:28.091789Z", + "iopub.status.busy": "2024-07-02T15:14:28.091461Z", + "iopub.status.idle": "2024-07-02T15:14:28.094696Z", + "shell.execute_reply": "2024-07-02T15:14:28.094164Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.259719Z", - "iopub.status.busy": "2024-07-02T12:05:02.259383Z", - "iopub.status.idle": "2024-07-02T12:05:02.272004Z", - "shell.execute_reply": "2024-07-02T12:05:02.271534Z" + "iopub.execute_input": "2024-07-02T15:14:28.096729Z", + "iopub.status.busy": "2024-07-02T15:14:28.096462Z", + "iopub.status.idle": "2024-07-02T15:14:28.109432Z", + "shell.execute_reply": "2024-07-02T15:14:28.109008Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.273862Z", - "iopub.status.busy": "2024-07-02T12:05:02.273687Z", - "iopub.status.idle": "2024-07-02T12:05:02.287267Z", - "shell.execute_reply": "2024-07-02T12:05:02.286829Z" + "iopub.execute_input": "2024-07-02T15:14:28.111301Z", + "iopub.status.busy": "2024-07-02T15:14:28.111131Z", + "iopub.status.idle": "2024-07-02T15:14:28.124546Z", + "shell.execute_reply": "2024-07-02T15:14:28.124107Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.289083Z", - "iopub.status.busy": "2024-07-02T12:05:02.288916Z", - "iopub.status.idle": "2024-07-02T12:05:02.298453Z", - "shell.execute_reply": "2024-07-02T12:05:02.298027Z" + "iopub.execute_input": "2024-07-02T15:14:28.126667Z", + "iopub.status.busy": "2024-07-02T15:14:28.126240Z", + "iopub.status.idle": "2024-07-02T15:14:28.136518Z", + "shell.execute_reply": "2024-07-02T15:14:28.135974Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.300283Z", - "iopub.status.busy": "2024-07-02T12:05:02.300116Z", - "iopub.status.idle": "2024-07-02T12:05:02.309664Z", - "shell.execute_reply": "2024-07-02T12:05:02.309126Z" + "iopub.execute_input": "2024-07-02T15:14:28.138549Z", + "iopub.status.busy": "2024-07-02T15:14:28.138251Z", + "iopub.status.idle": "2024-07-02T15:14:28.147091Z", + "shell.execute_reply": "2024-07-02T15:14:28.146561Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.311452Z", - "iopub.status.busy": "2024-07-02T12:05:02.311286Z", - "iopub.status.idle": "2024-07-02T12:05:02.314989Z", - "shell.execute_reply": "2024-07-02T12:05:02.314531Z" + "iopub.execute_input": "2024-07-02T15:14:28.149207Z", + "iopub.status.busy": "2024-07-02T15:14:28.148904Z", + "iopub.status.idle": "2024-07-02T15:14:28.152652Z", + "shell.execute_reply": "2024-07-02T15:14:28.152121Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.316924Z", - "iopub.status.busy": "2024-07-02T12:05:02.316631Z", - "iopub.status.idle": "2024-07-02T12:05:02.366687Z", - "shell.execute_reply": "2024-07-02T12:05:02.366234Z" + "iopub.execute_input": "2024-07-02T15:14:28.154967Z", + "iopub.status.busy": "2024-07-02T15:14:28.154533Z", + "iopub.status.idle": "2024-07-02T15:14:28.210134Z", + "shell.execute_reply": "2024-07-02T15:14:28.209520Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.368916Z", - "iopub.status.busy": "2024-07-02T12:05:02.368532Z", - "iopub.status.idle": "2024-07-02T12:05:02.374010Z", - "shell.execute_reply": "2024-07-02T12:05:02.373493Z" + "iopub.execute_input": "2024-07-02T15:14:28.212907Z", + "iopub.status.busy": "2024-07-02T15:14:28.212352Z", + "iopub.status.idle": "2024-07-02T15:14:28.219047Z", + "shell.execute_reply": "2024-07-02T15:14:28.218594Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.375995Z", - "iopub.status.busy": "2024-07-02T12:05:02.375691Z", - "iopub.status.idle": "2024-07-02T12:05:02.386423Z", - "shell.execute_reply": "2024-07-02T12:05:02.385887Z" + "iopub.execute_input": "2024-07-02T15:14:28.221050Z", + "iopub.status.busy": "2024-07-02T15:14:28.220676Z", + "iopub.status.idle": "2024-07-02T15:14:28.232201Z", + "shell.execute_reply": "2024-07-02T15:14:28.231649Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.388574Z", - "iopub.status.busy": "2024-07-02T12:05:02.388272Z", - "iopub.status.idle": "2024-07-02T12:05:02.563243Z", - "shell.execute_reply": "2024-07-02T12:05:02.562691Z" + "iopub.execute_input": "2024-07-02T15:14:28.234255Z", + "iopub.status.busy": "2024-07-02T15:14:28.233933Z", + "iopub.status.idle": "2024-07-02T15:14:28.446890Z", + "shell.execute_reply": "2024-07-02T15:14:28.446307Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.565412Z", - "iopub.status.busy": "2024-07-02T12:05:02.565240Z", - "iopub.status.idle": "2024-07-02T12:05:02.572732Z", - "shell.execute_reply": "2024-07-02T12:05:02.572280Z" + "iopub.execute_input": "2024-07-02T15:14:28.449143Z", + "iopub.status.busy": "2024-07-02T15:14:28.448684Z", + "iopub.status.idle": "2024-07-02T15:14:28.456578Z", + "shell.execute_reply": "2024-07-02T15:14:28.456036Z" }, "nbsphinx": "hidden" }, @@ -3760,10 +3760,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.574589Z", - "iopub.status.busy": "2024-07-02T12:05:02.574422Z", - "iopub.status.idle": "2024-07-02T12:05:08.693945Z", - "shell.execute_reply": "2024-07-02T12:05:08.693406Z" + "iopub.execute_input": "2024-07-02T15:14:28.458595Z", + "iopub.status.busy": "2024-07-02T15:14:28.458296Z", + "iopub.status.idle": "2024-07-02T15:14:35.258258Z", + "shell.execute_reply": "2024-07-02T15:14:35.257674Z" } }, "outputs": [ @@ -3787,7 +3787,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 917504/170498071 [00:00<00:20, 8347158.96it/s]" + " 0%| | 458752/170498071 [00:00<00:37, 4495236.08it/s]" ] }, { @@ -3795,7 +3795,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 9601024/170498071 [00:00<00:03, 52614403.72it/s]" + " 2%|▏ | 4227072/170498071 [00:00<00:07, 23242348.53it/s]" ] }, { @@ -3803,7 +3803,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 18481152/170498071 [00:00<00:02, 68746962.66it/s]" + " 5%|▌ | 9306112/170498071 [00:00<00:04, 35527365.93it/s]" ] }, { @@ -3811,7 +3811,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 25493504/170498071 [00:00<00:02, 68028252.66it/s]" + " 8%|▊ | 13926400/170498071 [00:00<00:03, 39660501.21it/s]" ] }, { @@ -3819,7 +3819,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32571392/170498071 [00:00<00:02, 68946396.69it/s]" + " 11%|█ | 18644992/170498071 [00:00<00:03, 42142752.96it/s]" ] }, { @@ -3827,7 +3827,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 39845888/170498071 [00:00<00:01, 70065798.28it/s]" + " 14%|█▎ | 23166976/170498071 [00:00<00:03, 43171320.39it/s]" ] }, { @@ -3835,7 +3835,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 46891008/170498071 [00:00<00:01, 68706053.96it/s]" + " 16%|█▌ | 27688960/170498071 [00:00<00:03, 43778497.63it/s]" ] }, { @@ -3843,7 +3843,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 54394880/170498071 [00:00<00:01, 70657768.03it/s]" + " 19%|█▉ | 32276480/170498071 [00:00<00:03, 44429066.54it/s]" ] }, { @@ -3851,7 +3851,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 61505536/170498071 [00:00<00:01, 69454102.48it/s]" + " 22%|██▏ | 36732928/170498071 [00:00<00:03, 44053184.08it/s]" ] }, { @@ -3859,7 +3859,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 69074944/170498071 [00:01<00:01, 71043124.94it/s]" + " 24%|██▍ | 41156608/170498071 [00:01<00:03, 43037571.10it/s]" ] }, { @@ -3867,7 +3867,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 76218368/170498071 [00:01<00:01, 69909000.72it/s]" + " 27%|██▋ | 45481984/170498071 [00:01<00:02, 41764924.25it/s]" ] }, { @@ -3875,7 +3875,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 83230720/170498071 [00:01<00:01, 69743647.72it/s]" + " 29%|██▉ | 49741824/170498071 [00:01<00:02, 41917563.82it/s]" ] }, { @@ -3883,7 +3883,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 92930048/170498071 [00:01<00:00, 77765718.10it/s]" + " 32%|███▏ | 54296576/170498071 [00:01<00:02, 42876516.22it/s]" ] }, { @@ -3891,7 +3891,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 100794368/170498071 [00:01<00:00, 77921748.40it/s]" + " 35%|███▍ | 58884096/170498071 [00:01<00:02, 43617372.00it/s]" ] }, { @@ -3899,7 +3899,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▎ | 108625920/170498071 [00:01<00:00, 74716954.24it/s]" + " 37%|███▋ | 63504384/170498071 [00:01<00:02, 44261340.15it/s]" ] }, { @@ -3907,7 +3907,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 116162560/170498071 [00:01<00:00, 72297265.09it/s]" + " 40%|███▉ | 68124672/170498071 [00:01<00:02, 44684329.80it/s]" ] }, { @@ -3915,7 +3915,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 123437056/170498071 [00:01<00:00, 71923065.09it/s]" + " 43%|████▎ | 72810496/170498071 [00:01<00:02, 45260580.12it/s]" ] }, { @@ -3923,7 +3923,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 130678784/170498071 [00:01<00:00, 70005310.34it/s]" + " 45%|████▌ | 77430784/170498071 [00:01<00:02, 45420696.51it/s]" ] }, { @@ -3931,7 +3931,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 138280960/170498071 [00:01<00:00, 71572260.33it/s]" + " 48%|████▊ | 82018304/170498071 [00:01<00:01, 45522327.94it/s]" ] }, { @@ -3939,7 +3939,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 145489920/170498071 [00:02<00:00, 69364483.36it/s]" + " 51%|█████ | 87097344/170498071 [00:02<00:01, 47025132.26it/s]" ] }, { @@ -3947,7 +3947,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 152961024/170498071 [00:02<00:00, 70793844.72it/s]" + " 56%|█████▌ | 95223808/170498071 [00:02<00:01, 57144332.93it/s]" ] }, { @@ -3955,7 +3955,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 160071680/170498071 [00:02<00:00, 69055168.81it/s]" + " 61%|██████ | 103972864/170498071 [00:02<00:01, 66190969.00it/s]" ] }, { @@ -3963,7 +3963,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 167608320/170498071 [00:02<00:00, 70630354.07it/s]" + " 66%|██████▌ | 112721920/170498071 [00:02<00:00, 72211785.85it/s]" ] }, { @@ -3971,7 +3971,55 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:02<00:00, 69520911.78it/s]" + " 72%|███████▏ | 122159104/170498071 [00:02<00:00, 78777574.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 77%|███████▋ | 130809856/170498071 [00:02<00:00, 81070781.57it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 82%|████████▏ | 139427840/170498071 [00:02<00:00, 82534573.38it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|████████▋ | 147685376/170498071 [00:02<00:00, 81824762.39it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 91%|█████████▏| 155877376/170498071 [00:02<00:00, 80442403.29it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 96%|█████████▋| 164397056/170498071 [00:02<00:00, 81837467.80it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:03<00:00, 56242831.52it/s]" ] }, { @@ -4045,10 +4093,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:08.696598Z", - "iopub.status.busy": "2024-07-02T12:05:08.696059Z", - "iopub.status.idle": "2024-07-02T12:05:08.763426Z", - "shell.execute_reply": "2024-07-02T12:05:08.762929Z" + "iopub.execute_input": "2024-07-02T15:14:35.261268Z", + "iopub.status.busy": "2024-07-02T15:14:35.260525Z", + "iopub.status.idle": "2024-07-02T15:14:35.329313Z", + "shell.execute_reply": "2024-07-02T15:14:35.328751Z" } }, "outputs": [], @@ -4070,10 +4118,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:08.765613Z", - "iopub.status.busy": "2024-07-02T12:05:08.765285Z", - "iopub.status.idle": "2024-07-02T12:05:08.805806Z", - "shell.execute_reply": "2024-07-02T12:05:08.805281Z" + "iopub.execute_input": "2024-07-02T15:14:35.331778Z", + "iopub.status.busy": "2024-07-02T15:14:35.331348Z", + "iopub.status.idle": "2024-07-02T15:14:35.379236Z", + "shell.execute_reply": "2024-07-02T15:14:35.378772Z" } }, "outputs": [], @@ -4107,10 +4155,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:08.807933Z", - "iopub.status.busy": "2024-07-02T12:05:08.807600Z", - "iopub.status.idle": "2024-07-02T12:05:10.199005Z", - "shell.execute_reply": "2024-07-02T12:05:10.198447Z" + "iopub.execute_input": "2024-07-02T15:14:35.381457Z", + "iopub.status.busy": "2024-07-02T15:14:35.381124Z", + "iopub.status.idle": "2024-07-02T15:14:36.847060Z", + "shell.execute_reply": "2024-07-02T15:14:36.846506Z" } }, "outputs": [ @@ -4184,10 +4232,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:10.201199Z", - "iopub.status.busy": "2024-07-02T12:05:10.200858Z", - "iopub.status.idle": "2024-07-02T12:05:10.987916Z", - "shell.execute_reply": "2024-07-02T12:05:10.987295Z" + "iopub.execute_input": "2024-07-02T15:14:36.849295Z", + "iopub.status.busy": "2024-07-02T15:14:36.848928Z", + "iopub.status.idle": "2024-07-02T15:14:37.628043Z", + "shell.execute_reply": "2024-07-02T15:14:37.627421Z" } }, "outputs": [ @@ -4202,7 +4250,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab730d681373436cbffc495350a9abe1", + "model_id": "edeb0eb92f8e493694db63fbedcce068", "version_major": 2, "version_minor": 0 }, @@ -4226,7 +4274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e44decacc70f4d08b59475e297136aab", + "model_id": "a53db32e879b421b9c5a2cb90345b461", "version_major": 2, "version_minor": 0 }, @@ -4476,30 +4524,41 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "06e95a0f1df9408095248eef0924c604": { + "0ba1b4f02e98442dbf83a9d402d61603": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5fccbfa0a7a94b55a6825fc52ecdeee3", - "placeholder": "​", - "style": "IPY_MODEL_9d67c6a8b80b4718975da970d5ba6be1", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 725.51it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1245fefd15c748ca9a6c437e90990634": { + "13f79e5c34544a20b6e43544e002e0d6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "21919d64401941e69bdd1e7fdeb39391": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4552,7 +4611,30 @@ "width": null } }, - "22612fb7095f4323876a32fa6832ebee": { + "22dce5e6cbbd456899db36ca71231b83": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_246224e7d6e14ee996208e5a901506a3", + "placeholder": "​", + "style": "IPY_MODEL_669c9d816b274632945703bb33ea88b1", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "246224e7d6e14ee996208e5a901506a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4605,23 +4687,7 @@ "width": null } }, - "2ce33b586399430db7231ec582a8ad1c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "302d670260304f5d973a1863227c2b38": { + "49bd46daef6e4afaae2104d6fddc5eff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4674,7 +4740,7 @@ "width": null } }, - "34fad403248e49fb9d7ed5541db4875e": { + "5f4143d1143347bf8d67acbd62e4c7a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4690,53 +4756,7 @@ "description_width": "" } }, - "37657cc47549425e81123fbc00061dcd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_57d53163a3e24cfb8adf32a3c2859334", - "placeholder": "​", - "style": "IPY_MODEL_d6c64d036d3c464bba338c11b7d7e118", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "3f75258f70194866856b4da554e4dbeb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1245fefd15c748ca9a6c437e90990634", - "placeholder": "​", - "style": "IPY_MODEL_4c9fcf59ee52451aad0a525849ecf86b", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "440b53038a3d4c4c964a83e8b710361f": { + "5fe4a241c042442299e152f3905b32e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4789,7 +4809,7 @@ "width": null } }, - "4c9fcf59ee52451aad0a525849ecf86b": { + "669c9d816b274632945703bb33ea88b1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4807,7 +4827,30 @@ "text_color": null } }, - "57d53163a3e24cfb8adf32a3c2859334": { + "752199f5b1064d09a833e6e140acf999": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_21919d64401941e69bdd1e7fdeb39391", + "placeholder": "​", + "style": "IPY_MODEL_b2c4bbe171d649419f7ff67ac4f27ab6", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 781.88it/s]" + } + }, + "7f367a2cdd5445f58aecb1320024dca9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4860,7 +4903,7 @@ "width": null } }, - "5fccbfa0a7a94b55a6825fc52ecdeee3": { + "945bafb17f6c4017b07c073239844118": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4913,30 +4956,31 @@ "width": null } }, - "6bdd7248294f4094a2da7c7af2e67e50": { + "a53db32e879b421b9c5a2cb90345b461": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d6941ea7ad6a41efb80f48dde9923682", - "placeholder": "​", - "style": "IPY_MODEL_a55c5a0d7aca4c16a982994a5595ca08", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ae7e7acb2667481d93c3d5d070d947f1", + "IPY_MODEL_d03e8f0da10e418392f2df6f61dea5ed", + "IPY_MODEL_752199f5b1064d09a833e6e140acf999" + ], + "layout": "IPY_MODEL_5fe4a241c042442299e152f3905b32e6", "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 811.85it/s]" + "tooltip": null } }, - "797a5104afa24ca5b172ddc308a704ec": { + "a6d4bb6587dc4b0ab299cde66d887195": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4989,25 +5033,30 @@ "width": null } }, - "9d67c6a8b80b4718975da970d5ba6be1": { + "ae7e7acb2667481d93c3d5d070d947f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_945bafb17f6c4017b07c073239844118", + "placeholder": "​", + "style": "IPY_MODEL_0ba1b4f02e98442dbf83a9d402d61603", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "a55c5a0d7aca4c16a982994a5595ca08": { + "b2c4bbe171d649419f7ff67ac4f27ab6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5025,31 +5074,30 @@ "text_color": null } }, - "ab730d681373436cbffc495350a9abe1": { + "b7a191fc264f425c94ccbd4b2e6ff5bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_37657cc47549425e81123fbc00061dcd", - "IPY_MODEL_ccd3930d3b25423fb8d520dc87205752", - "IPY_MODEL_6bdd7248294f4094a2da7c7af2e67e50" - ], - "layout": "IPY_MODEL_440b53038a3d4c4c964a83e8b710361f", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d1fa249a3b3741948f9b90a3eba494cd", + "placeholder": "​", + "style": "IPY_MODEL_df530a10186c40c8b9ba0ace062c0018", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 200/200 [00:00<00:00, 806.22it/s]" } }, - "ccd3930d3b25423fb8d520dc87205752": { + "ba0f29fa569646e89dd03db3974a4a00": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5065,17 +5113,43 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_302d670260304f5d973a1863227c2b38", + "layout": "IPY_MODEL_7f367a2cdd5445f58aecb1320024dca9", "max": 200.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2ce33b586399430db7231ec582a8ad1c", + "style": "IPY_MODEL_5f4143d1143347bf8d67acbd62e4c7a9", "tabbable": null, "tooltip": null, "value": 200.0 } }, - "d6941ea7ad6a41efb80f48dde9923682": { + "d03e8f0da10e418392f2df6f61dea5ed": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_49bd46daef6e4afaae2104d6fddc5eff", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_13f79e5c34544a20b6e43544e002e0d6", + "tabbable": null, + "tooltip": null, + "value": 200.0 + } + }, + "d1fa249a3b3741948f9b90a3eba494cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5128,7 +5202,7 @@ "width": null } }, - "d6c64d036d3c464bba338c11b7d7e118": { + "df530a10186c40c8b9ba0ace062c0018": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5146,7 +5220,7 @@ "text_color": null } }, - "e44decacc70f4d08b59475e297136aab": { + "edeb0eb92f8e493694db63fbedcce068": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5161,40 +5235,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3f75258f70194866856b4da554e4dbeb", - "IPY_MODEL_e621caf6c19d4d638ba32cd7caed9a15", - "IPY_MODEL_06e95a0f1df9408095248eef0924c604" + "IPY_MODEL_22dce5e6cbbd456899db36ca71231b83", + "IPY_MODEL_ba0f29fa569646e89dd03db3974a4a00", + "IPY_MODEL_b7a191fc264f425c94ccbd4b2e6ff5bf" ], - "layout": "IPY_MODEL_22612fb7095f4323876a32fa6832ebee", + "layout": "IPY_MODEL_a6d4bb6587dc4b0ab299cde66d887195", "tabbable": null, "tooltip": null } - }, - "e621caf6c19d4d638ba32cd7caed9a15": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_797a5104afa24ca5b172ddc308a704ec", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_34fad403248e49fb9d7ed5541db4875e", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } } }, "version_major": 2, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 05afc2f2e..46444aea9 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:14.883207Z", - "iopub.status.busy": "2024-07-02T12:05:14.882732Z", - "iopub.status.idle": "2024-07-02T12:05:15.976658Z", - "shell.execute_reply": "2024-07-02T12:05:15.976156Z" + "iopub.execute_input": "2024-07-02T15:14:41.637741Z", + "iopub.status.busy": "2024-07-02T15:14:41.637272Z", + "iopub.status.idle": "2024-07-02T15:14:42.748122Z", + "shell.execute_reply": "2024-07-02T15:14:42.747575Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:15.979210Z", - "iopub.status.busy": "2024-07-02T12:05:15.978822Z", - "iopub.status.idle": "2024-07-02T12:05:15.981689Z", - "shell.execute_reply": "2024-07-02T12:05:15.981162Z" + "iopub.execute_input": "2024-07-02T15:14:42.750701Z", + "iopub.status.busy": "2024-07-02T15:14:42.750299Z", + "iopub.status.idle": "2024-07-02T15:14:42.753136Z", + "shell.execute_reply": "2024-07-02T15:14:42.752592Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:15.983805Z", - "iopub.status.busy": "2024-07-02T12:05:15.983602Z", - "iopub.status.idle": "2024-07-02T12:05:15.994757Z", - "shell.execute_reply": "2024-07-02T12:05:15.994295Z" + "iopub.execute_input": "2024-07-02T15:14:42.755371Z", + "iopub.status.busy": "2024-07-02T15:14:42.755052Z", + "iopub.status.idle": "2024-07-02T15:14:42.766462Z", + "shell.execute_reply": "2024-07-02T15:14:42.766035Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:15.996851Z", - "iopub.status.busy": "2024-07-02T12:05:15.996526Z", - "iopub.status.idle": "2024-07-02T12:05:19.883673Z", - "shell.execute_reply": "2024-07-02T12:05:19.883072Z" + "iopub.execute_input": "2024-07-02T15:14:42.768527Z", + "iopub.status.busy": "2024-07-02T15:14:42.768199Z", + "iopub.status.idle": "2024-07-02T15:14:48.317038Z", + "shell.execute_reply": "2024-07-02T15:14:48.316439Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index 5a32f21d3..b910d04a5 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -831,13 +831,13 @@

How can I find label issues in big datasets with limited memory?

-
+
-
+
@@ -1702,7 +1702,7 @@

Can’t find an answer to your question?new Github issue. Our developers may also provide personalized assistance in our Slack Community.

Professional support and services are also available from our ML experts, learn more by emailing: team@cleanlab.ai

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 964629f99..d639cd18b 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:21.944164Z", - "iopub.status.busy": "2024-07-02T12:05:21.943684Z", - "iopub.status.idle": "2024-07-02T12:05:23.029911Z", - "shell.execute_reply": "2024-07-02T12:05:23.029367Z" + "iopub.execute_input": "2024-07-02T15:14:50.408143Z", + "iopub.status.busy": "2024-07-02T15:14:50.407965Z", + "iopub.status.idle": "2024-07-02T15:14:51.502266Z", + "shell.execute_reply": "2024-07-02T15:14:51.501686Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:23.032775Z", - "iopub.status.busy": "2024-07-02T12:05:23.032157Z", - "iopub.status.idle": "2024-07-02T12:05:23.035645Z", - "shell.execute_reply": "2024-07-02T12:05:23.035092Z" + "iopub.execute_input": "2024-07-02T15:14:51.504987Z", + "iopub.status.busy": "2024-07-02T15:14:51.504521Z", + "iopub.status.idle": "2024-07-02T15:14:51.507736Z", + "shell.execute_reply": "2024-07-02T15:14:51.507307Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:23.037598Z", - "iopub.status.busy": "2024-07-02T12:05:23.037330Z", - "iopub.status.idle": "2024-07-02T12:05:26.140141Z", - "shell.execute_reply": "2024-07-02T12:05:26.139387Z" + "iopub.execute_input": "2024-07-02T15:14:51.509847Z", + "iopub.status.busy": "2024-07-02T15:14:51.509517Z", + "iopub.status.idle": "2024-07-02T15:14:54.665499Z", + "shell.execute_reply": "2024-07-02T15:14:54.664870Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.143157Z", - "iopub.status.busy": "2024-07-02T12:05:26.142519Z", - "iopub.status.idle": "2024-07-02T12:05:26.174588Z", - "shell.execute_reply": "2024-07-02T12:05:26.174022Z" + "iopub.execute_input": "2024-07-02T15:14:54.668720Z", + "iopub.status.busy": "2024-07-02T15:14:54.667931Z", + "iopub.status.idle": "2024-07-02T15:14:54.700443Z", + "shell.execute_reply": "2024-07-02T15:14:54.699878Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.177140Z", - "iopub.status.busy": "2024-07-02T12:05:26.176847Z", - "iopub.status.idle": "2024-07-02T12:05:26.205277Z", - "shell.execute_reply": "2024-07-02T12:05:26.204606Z" + "iopub.execute_input": "2024-07-02T15:14:54.702890Z", + "iopub.status.busy": "2024-07-02T15:14:54.702662Z", + "iopub.status.idle": "2024-07-02T15:14:54.732809Z", + "shell.execute_reply": "2024-07-02T15:14:54.732249Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.208173Z", - "iopub.status.busy": "2024-07-02T12:05:26.207802Z", - "iopub.status.idle": "2024-07-02T12:05:26.210662Z", - "shell.execute_reply": "2024-07-02T12:05:26.210230Z" + "iopub.execute_input": "2024-07-02T15:14:54.735364Z", + "iopub.status.busy": "2024-07-02T15:14:54.735130Z", + "iopub.status.idle": "2024-07-02T15:14:54.738210Z", + "shell.execute_reply": "2024-07-02T15:14:54.737649Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.212655Z", - "iopub.status.busy": "2024-07-02T12:05:26.212352Z", - "iopub.status.idle": "2024-07-02T12:05:26.214801Z", - "shell.execute_reply": "2024-07-02T12:05:26.214383Z" + "iopub.execute_input": "2024-07-02T15:14:54.740326Z", + "iopub.status.busy": "2024-07-02T15:14:54.739950Z", + "iopub.status.idle": "2024-07-02T15:14:54.742624Z", + "shell.execute_reply": "2024-07-02T15:14:54.742090Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.216825Z", - "iopub.status.busy": "2024-07-02T12:05:26.216567Z", - "iopub.status.idle": "2024-07-02T12:05:26.239503Z", - "shell.execute_reply": "2024-07-02T12:05:26.238989Z" + "iopub.execute_input": "2024-07-02T15:14:54.744773Z", + "iopub.status.busy": "2024-07-02T15:14:54.744389Z", + "iopub.status.idle": "2024-07-02T15:14:54.767963Z", + "shell.execute_reply": "2024-07-02T15:14:54.767405Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3fbed235b41419c8dcc7c6dc31f69a4", + "model_id": "0af6d2097bac4b69850c70d9d5904db8", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55f5d02e58414e189c4d35720f6593e4", + "model_id": "5e3107780da94917a2e6e00a57affa5f", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.245285Z", - "iopub.status.busy": "2024-07-02T12:05:26.244763Z", - "iopub.status.idle": "2024-07-02T12:05:26.251470Z", - "shell.execute_reply": "2024-07-02T12:05:26.251055Z" + "iopub.execute_input": "2024-07-02T15:14:54.773314Z", + "iopub.status.busy": "2024-07-02T15:14:54.772881Z", + "iopub.status.idle": "2024-07-02T15:14:54.779377Z", + "shell.execute_reply": "2024-07-02T15:14:54.778951Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.253486Z", - "iopub.status.busy": "2024-07-02T12:05:26.253192Z", - "iopub.status.idle": "2024-07-02T12:05:26.256606Z", - "shell.execute_reply": "2024-07-02T12:05:26.256082Z" + "iopub.execute_input": "2024-07-02T15:14:54.781444Z", + "iopub.status.busy": "2024-07-02T15:14:54.781005Z", + "iopub.status.idle": "2024-07-02T15:14:54.784548Z", + "shell.execute_reply": "2024-07-02T15:14:54.784032Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.258538Z", - "iopub.status.busy": "2024-07-02T12:05:26.258279Z", - "iopub.status.idle": "2024-07-02T12:05:26.264446Z", - "shell.execute_reply": "2024-07-02T12:05:26.264008Z" + "iopub.execute_input": "2024-07-02T15:14:54.786545Z", + "iopub.status.busy": "2024-07-02T15:14:54.786237Z", + "iopub.status.idle": "2024-07-02T15:14:54.792562Z", + "shell.execute_reply": "2024-07-02T15:14:54.792035Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.266379Z", - "iopub.status.busy": "2024-07-02T12:05:26.266007Z", - "iopub.status.idle": "2024-07-02T12:05:26.301431Z", - "shell.execute_reply": "2024-07-02T12:05:26.300735Z" + "iopub.execute_input": "2024-07-02T15:14:54.794631Z", + "iopub.status.busy": "2024-07-02T15:14:54.794246Z", + "iopub.status.idle": "2024-07-02T15:14:54.828207Z", + "shell.execute_reply": "2024-07-02T15:14:54.827507Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.304129Z", - "iopub.status.busy": "2024-07-02T12:05:26.303754Z", - "iopub.status.idle": "2024-07-02T12:05:26.336384Z", - "shell.execute_reply": "2024-07-02T12:05:26.335710Z" + "iopub.execute_input": "2024-07-02T15:14:54.830484Z", + "iopub.status.busy": "2024-07-02T15:14:54.830261Z", + "iopub.status.idle": "2024-07-02T15:14:54.860257Z", + "shell.execute_reply": "2024-07-02T15:14:54.859690Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.339079Z", - "iopub.status.busy": "2024-07-02T12:05:26.338735Z", - "iopub.status.idle": "2024-07-02T12:05:26.455537Z", - "shell.execute_reply": "2024-07-02T12:05:26.454922Z" + "iopub.execute_input": "2024-07-02T15:14:54.863007Z", + "iopub.status.busy": "2024-07-02T15:14:54.862546Z", + "iopub.status.idle": "2024-07-02T15:14:54.984115Z", + "shell.execute_reply": "2024-07-02T15:14:54.983484Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.458378Z", - "iopub.status.busy": "2024-07-02T12:05:26.457687Z", - "iopub.status.idle": "2024-07-02T12:05:29.464168Z", - "shell.execute_reply": "2024-07-02T12:05:29.463628Z" + "iopub.execute_input": "2024-07-02T15:14:54.986796Z", + "iopub.status.busy": "2024-07-02T15:14:54.986226Z", + "iopub.status.idle": "2024-07-02T15:14:58.051483Z", + "shell.execute_reply": "2024-07-02T15:14:58.050808Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.466470Z", - "iopub.status.busy": "2024-07-02T12:05:29.466106Z", - "iopub.status.idle": "2024-07-02T12:05:29.522164Z", - "shell.execute_reply": "2024-07-02T12:05:29.521722Z" + "iopub.execute_input": "2024-07-02T15:14:58.053810Z", + "iopub.status.busy": "2024-07-02T15:14:58.053595Z", + "iopub.status.idle": "2024-07-02T15:14:58.112425Z", + "shell.execute_reply": "2024-07-02T15:14:58.111852Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.524149Z", - "iopub.status.busy": "2024-07-02T12:05:29.523825Z", - "iopub.status.idle": "2024-07-02T12:05:29.563088Z", - "shell.execute_reply": "2024-07-02T12:05:29.562637Z" + "iopub.execute_input": "2024-07-02T15:14:58.114567Z", + "iopub.status.busy": "2024-07-02T15:14:58.114179Z", + "iopub.status.idle": "2024-07-02T15:14:58.154327Z", + "shell.execute_reply": "2024-07-02T15:14:58.153741Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "c8a16553", + "id": "50482bad", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "fae60230", + "id": "07405bb8", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "9569bf2b", + "id": "f375f11d", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "570b1222", + "id": "ada84c58", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.565181Z", - "iopub.status.busy": "2024-07-02T12:05:29.564854Z", - "iopub.status.idle": "2024-07-02T12:05:29.572447Z", - "shell.execute_reply": "2024-07-02T12:05:29.571983Z" + "iopub.execute_input": "2024-07-02T15:14:58.156555Z", + "iopub.status.busy": "2024-07-02T15:14:58.156257Z", + "iopub.status.idle": "2024-07-02T15:14:58.163817Z", + "shell.execute_reply": "2024-07-02T15:14:58.163319Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "a87b6fe0", + "id": "13fb70ab", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "26953078", + "id": "692524aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.574436Z", - "iopub.status.busy": "2024-07-02T12:05:29.574108Z", - "iopub.status.idle": "2024-07-02T12:05:29.592051Z", - "shell.execute_reply": "2024-07-02T12:05:29.591598Z" + "iopub.execute_input": "2024-07-02T15:14:58.165738Z", + "iopub.status.busy": "2024-07-02T15:14:58.165567Z", + "iopub.status.idle": "2024-07-02T15:14:58.184376Z", + "shell.execute_reply": "2024-07-02T15:14:58.183834Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "948c6a32", + "id": "c63f4c73", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.594121Z", - "iopub.status.busy": "2024-07-02T12:05:29.593804Z", - "iopub.status.idle": "2024-07-02T12:05:29.596796Z", - "shell.execute_reply": "2024-07-02T12:05:29.596261Z" + "iopub.execute_input": "2024-07-02T15:14:58.186438Z", + "iopub.status.busy": "2024-07-02T15:14:58.186138Z", + "iopub.status.idle": "2024-07-02T15:14:58.189362Z", + "shell.execute_reply": "2024-07-02T15:14:58.188854Z" } }, "outputs": [ @@ -1622,25 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1e8b9b429c6a4df5b632d8335fdb02e7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "31b4169790de40918177589ab5b35e53": { + "03bdac28b0744e63957425aef141438c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1693,39 +1675,31 @@ "width": null } }, - "3e0c64c5666d42f5a0006507f8bef3cf": { + "0af6d2097bac4b69850c70d9d5904db8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "42ef207d69534acdbaf463021cfc93cf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b05e5e24c03d4b8393b6fbc900c09846", + "IPY_MODEL_1a648d3a4fa24399916eb3b310f94eb1", + "IPY_MODEL_1d20270beb494280ba9716e526d53f5b" + ], + "layout": "IPY_MODEL_162ace65984b48448d278f7f058df9b9", + "tabbable": null, + "tooltip": null } }, - "47199e38a1de47d2b40f863611c9c287": { + "162ace65984b48448d278f7f058df9b9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1778,25 +1752,56 @@ "width": null } }, - "507bd342f43644e28c3e257c443121b3": { + "1a648d3a4fa24399916eb3b310f94eb1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2fc72a84067d46678a572aa49ebcbfee", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d390dd0db893478398a63bc195614ea3", + "tabbable": null, + "tooltip": null, + "value": 50.0 } }, - "546f976ecd3443c7ae6b00cfbd3063d7": { + "1d20270beb494280ba9716e526d53f5b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_03bdac28b0744e63957425aef141438c", + "placeholder": "​", + "style": "IPY_MODEL_d92d13f5147b4721964e817fcd0e6a7b", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1078393.58it/s]" + } + }, + "1edeca25122644478494bfcfeda4647f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1849,31 +1854,23 @@ "width": null } }, - "55f5d02e58414e189c4d35720f6593e4": { + "28fb5636d77344d0af4d56b62998af86": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f01dfba04fcd45ceb75c23f67e8886c7", - "IPY_MODEL_617a9f7f01f040228329a5ec756d97f6", - "IPY_MODEL_ed7c570506e6416fb02ab5e72e3ceb03" - ], - "layout": "IPY_MODEL_829e711fb4284e36a06bf2a1d8c1d975", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "60269b97e2604128baf4ce6cdc816ee4": { + "2fc72a84067d46678a572aa49ebcbfee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1926,59 +1923,72 @@ "width": null } }, - "6135cc292d2a4431bf055b0d0936e234": { + "46803bc4f974489e87287e18bb02597e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_60269b97e2604128baf4ce6cdc816ee4", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3e0c64c5666d42f5a0006507f8bef3cf", + "layout": "IPY_MODEL_ee2eaee4046a40398bf58b1b4a9e3ebc", + "placeholder": "​", + "style": "IPY_MODEL_65acdb25c5c948c9848681db00fdc46b", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": "number of examples processed for checking labels: " } }, - "617a9f7f01f040228329a5ec756d97f6": { + "5e3107780da94917a2e6e00a57affa5f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ba0bb27ac92840eeb82c447bf3772478", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_42ef207d69534acdbaf463021cfc93cf", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_46803bc4f974489e87287e18bb02597e", + "IPY_MODEL_d31a1289a39a46899da8b556759347e9", + "IPY_MODEL_6e15813423a14f6c9174690b8907c81c" + ], + "layout": "IPY_MODEL_1edeca25122644478494bfcfeda4647f", "tabbable": null, - "tooltip": null, - "value": 50.0 + "tooltip": null + } + }, + "65acdb25c5c948c9848681db00fdc46b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "73aeda6759084147870440cb627c1d38": { + "6e15813423a14f6c9174690b8907c81c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1993,15 +2003,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b60d3a9c115940b998532778042a9156", + "layout": "IPY_MODEL_ac3767a4657d48b4a0218f3167e710d9", "placeholder": "​", - "style": "IPY_MODEL_1e8b9b429c6a4df5b632d8335fdb02e7", + "style": "IPY_MODEL_f0f4c38d78db4623a4f8710c6f263222", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1101879.42it/s]" + "value": " 10000/? [00:00<00:00, 1603327.22it/s]" } }, - "829e711fb4284e36a06bf2a1d8c1d975": { + "a9d39a3ea28d44c682f45f076bc8bd67": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2054,25 +2064,60 @@ "width": null } }, - "894752d3c3ad46abbf5f852be62b9157": { - "model_module": "@jupyter-widgets/controls", + "ac3767a4657d48b4a0218f3167e710d9": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "94d1e49e872241989a3aaf081a4914f3": { + "b05e5e24c03d4b8393b6fbc900c09846": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2087,57 +2132,57 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_546f976ecd3443c7ae6b00cfbd3063d7", + "layout": "IPY_MODEL_d697f27b641d4fa4a8cece8123222bff", "placeholder": "​", - "style": "IPY_MODEL_894752d3c3ad46abbf5f852be62b9157", + "style": "IPY_MODEL_eddf7a5ddfe14a858eb2826e37c0b231", "tabbable": null, "tooltip": null, "value": "number of examples processed for estimating thresholds: " } }, - "b3fbed235b41419c8dcc7c6dc31f69a4": { + "d31a1289a39a46899da8b556759347e9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_94d1e49e872241989a3aaf081a4914f3", - "IPY_MODEL_6135cc292d2a4431bf055b0d0936e234", - "IPY_MODEL_73aeda6759084147870440cb627c1d38" - ], - "layout": "IPY_MODEL_47199e38a1de47d2b40f863611c9c287", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a9d39a3ea28d44c682f45f076bc8bd67", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_28fb5636d77344d0af4d56b62998af86", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 50.0 } }, - "b60c569797aa438d8c67c0007154831b": { + "d390dd0db893478398a63bc195614ea3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "b60d3a9c115940b998532778042a9156": { + "d697f27b641d4fa4a8cece8123222bff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2190,60 +2235,43 @@ "width": null } }, - "ba0bb27ac92840eeb82c447bf3772478": { - "model_module": "@jupyter-widgets/base", + "d92d13f5147b4721964e817fcd0e6a7b": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "d554d48cbc1b4a5caca9da8c04018917": { + "eddf7a5ddfe14a858eb2826e37c0b231": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ee2eaee4046a40398bf58b1b4a9e3ebc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2296,50 +2324,22 @@ "width": null } }, - "ed7c570506e6416fb02ab5e72e3ceb03": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_31b4169790de40918177589ab5b35e53", - "placeholder": "​", - "style": "IPY_MODEL_b60c569797aa438d8c67c0007154831b", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1638080.06it/s]" - } - }, - "f01dfba04fcd45ceb75c23f67e8886c7": { + "f0f4c38d78db4623a4f8710c6f263222": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d554d48cbc1b4a5caca9da8c04018917", - "placeholder": "​", - "style": "IPY_MODEL_507bd342f43644e28c3e257c443121b3", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index 31db58268..6e9b55b48 100644 --- a/master/tutorials/indepth_overview.ipynb +++ b/master/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:32.646814Z", - "iopub.status.busy": "2024-07-02T12:05:32.646634Z", - "iopub.status.idle": "2024-07-02T12:05:33.799016Z", - "shell.execute_reply": "2024-07-02T12:05:33.798421Z" + "iopub.execute_input": "2024-07-02T15:15:01.547795Z", + "iopub.status.busy": "2024-07-02T15:15:01.547635Z", + "iopub.status.idle": "2024-07-02T15:15:02.724422Z", + "shell.execute_reply": "2024-07-02T15:15:02.723868Z" }, "nbsphinx": "hidden" }, @@ -68,7 +68,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:33.801518Z", - "iopub.status.busy": "2024-07-02T12:05:33.801117Z", - "iopub.status.idle": "2024-07-02T12:05:33.979293Z", - "shell.execute_reply": "2024-07-02T12:05:33.978808Z" + "iopub.execute_input": "2024-07-02T15:15:02.727054Z", + "iopub.status.busy": "2024-07-02T15:15:02.726599Z", + "iopub.status.idle": "2024-07-02T15:15:02.907470Z", + "shell.execute_reply": "2024-07-02T15:15:02.906926Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:33.981747Z", - "iopub.status.busy": "2024-07-02T12:05:33.981411Z", - "iopub.status.idle": "2024-07-02T12:05:33.992581Z", - "shell.execute_reply": "2024-07-02T12:05:33.992150Z" + "iopub.execute_input": "2024-07-02T15:15:02.909852Z", + "iopub.status.busy": "2024-07-02T15:15:02.909658Z", + "iopub.status.idle": "2024-07-02T15:15:02.920956Z", + "shell.execute_reply": "2024-07-02T15:15:02.920549Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:33.994624Z", - "iopub.status.busy": "2024-07-02T12:05:33.994295Z", - "iopub.status.idle": "2024-07-02T12:05:34.203292Z", - "shell.execute_reply": "2024-07-02T12:05:34.202749Z" + "iopub.execute_input": "2024-07-02T15:15:02.923032Z", + "iopub.status.busy": "2024-07-02T15:15:02.922709Z", + "iopub.status.idle": "2024-07-02T15:15:03.157261Z", + "shell.execute_reply": "2024-07-02T15:15:03.156698Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:34.205578Z", - "iopub.status.busy": "2024-07-02T12:05:34.205242Z", - "iopub.status.idle": "2024-07-02T12:05:34.231392Z", - "shell.execute_reply": "2024-07-02T12:05:34.230966Z" + "iopub.execute_input": "2024-07-02T15:15:03.159542Z", + "iopub.status.busy": "2024-07-02T15:15:03.159306Z", + "iopub.status.idle": "2024-07-02T15:15:03.185836Z", + "shell.execute_reply": "2024-07-02T15:15:03.185396Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:34.233560Z", - "iopub.status.busy": "2024-07-02T12:05:34.233135Z", - "iopub.status.idle": "2024-07-02T12:05:36.181908Z", - "shell.execute_reply": "2024-07-02T12:05:36.181255Z" + "iopub.execute_input": "2024-07-02T15:15:03.188049Z", + "iopub.status.busy": "2024-07-02T15:15:03.187618Z", + "iopub.status.idle": "2024-07-02T15:15:05.211831Z", + "shell.execute_reply": "2024-07-02T15:15:05.211148Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:36.184389Z", - "iopub.status.busy": "2024-07-02T12:05:36.183843Z", - "iopub.status.idle": "2024-07-02T12:05:36.201856Z", - "shell.execute_reply": "2024-07-02T12:05:36.201294Z" + "iopub.execute_input": "2024-07-02T15:15:05.214216Z", + "iopub.status.busy": "2024-07-02T15:15:05.213865Z", + "iopub.status.idle": "2024-07-02T15:15:05.231692Z", + "shell.execute_reply": "2024-07-02T15:15:05.231165Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:36.204241Z", - "iopub.status.busy": "2024-07-02T12:05:36.203963Z", - "iopub.status.idle": "2024-07-02T12:05:37.598285Z", - "shell.execute_reply": "2024-07-02T12:05:37.597675Z" + "iopub.execute_input": "2024-07-02T15:15:05.233970Z", + "iopub.status.busy": "2024-07-02T15:15:05.233542Z", + "iopub.status.idle": "2024-07-02T15:15:06.669686Z", + "shell.execute_reply": "2024-07-02T15:15:06.669077Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.600758Z", - "iopub.status.busy": "2024-07-02T12:05:37.600219Z", - "iopub.status.idle": "2024-07-02T12:05:37.613480Z", - "shell.execute_reply": "2024-07-02T12:05:37.612921Z" + "iopub.execute_input": "2024-07-02T15:15:06.672583Z", + "iopub.status.busy": "2024-07-02T15:15:06.671803Z", + "iopub.status.idle": "2024-07-02T15:15:06.685525Z", + "shell.execute_reply": "2024-07-02T15:15:06.685058Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.615558Z", - "iopub.status.busy": "2024-07-02T12:05:37.615275Z", - "iopub.status.idle": "2024-07-02T12:05:37.682573Z", - "shell.execute_reply": "2024-07-02T12:05:37.681981Z" + "iopub.execute_input": "2024-07-02T15:15:06.687638Z", + "iopub.status.busy": "2024-07-02T15:15:06.687306Z", + "iopub.status.idle": "2024-07-02T15:15:06.760352Z", + "shell.execute_reply": "2024-07-02T15:15:06.759817Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.685019Z", - "iopub.status.busy": "2024-07-02T12:05:37.684694Z", - "iopub.status.idle": "2024-07-02T12:05:37.893897Z", - "shell.execute_reply": "2024-07-02T12:05:37.893417Z" + "iopub.execute_input": "2024-07-02T15:15:06.762567Z", + "iopub.status.busy": "2024-07-02T15:15:06.762336Z", + "iopub.status.idle": "2024-07-02T15:15:06.973074Z", + "shell.execute_reply": "2024-07-02T15:15:06.972522Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.896031Z", - "iopub.status.busy": "2024-07-02T12:05:37.895697Z", - "iopub.status.idle": "2024-07-02T12:05:37.912159Z", - "shell.execute_reply": "2024-07-02T12:05:37.911619Z" + "iopub.execute_input": "2024-07-02T15:15:06.975381Z", + "iopub.status.busy": "2024-07-02T15:15:06.975014Z", + "iopub.status.idle": "2024-07-02T15:15:06.992441Z", + "shell.execute_reply": "2024-07-02T15:15:06.991996Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.914291Z", - "iopub.status.busy": "2024-07-02T12:05:37.913990Z", - "iopub.status.idle": "2024-07-02T12:05:37.923838Z", - "shell.execute_reply": "2024-07-02T12:05:37.923277Z" + "iopub.execute_input": "2024-07-02T15:15:06.994338Z", + "iopub.status.busy": "2024-07-02T15:15:06.994162Z", + "iopub.status.idle": "2024-07-02T15:15:07.004135Z", + "shell.execute_reply": "2024-07-02T15:15:07.003687Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.925873Z", - "iopub.status.busy": "2024-07-02T12:05:37.925449Z", - "iopub.status.idle": "2024-07-02T12:05:38.005405Z", - "shell.execute_reply": "2024-07-02T12:05:38.004805Z" + "iopub.execute_input": "2024-07-02T15:15:07.005979Z", + "iopub.status.busy": "2024-07-02T15:15:07.005810Z", + "iopub.status.idle": "2024-07-02T15:15:07.089012Z", + "shell.execute_reply": "2024-07-02T15:15:07.088395Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.007885Z", - "iopub.status.busy": "2024-07-02T12:05:38.007370Z", - "iopub.status.idle": "2024-07-02T12:05:38.126166Z", - "shell.execute_reply": "2024-07-02T12:05:38.125636Z" + "iopub.execute_input": "2024-07-02T15:15:07.091284Z", + "iopub.status.busy": "2024-07-02T15:15:07.091062Z", + "iopub.status.idle": "2024-07-02T15:15:07.217284Z", + "shell.execute_reply": "2024-07-02T15:15:07.216745Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.128463Z", - "iopub.status.busy": "2024-07-02T12:05:38.128096Z", - "iopub.status.idle": "2024-07-02T12:05:38.132029Z", - "shell.execute_reply": "2024-07-02T12:05:38.131380Z" + "iopub.execute_input": "2024-07-02T15:15:07.219493Z", + "iopub.status.busy": "2024-07-02T15:15:07.219260Z", + "iopub.status.idle": "2024-07-02T15:15:07.223285Z", + "shell.execute_reply": "2024-07-02T15:15:07.222834Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.134113Z", - "iopub.status.busy": "2024-07-02T12:05:38.133792Z", - "iopub.status.idle": "2024-07-02T12:05:38.137656Z", - "shell.execute_reply": "2024-07-02T12:05:38.137186Z" + "iopub.execute_input": "2024-07-02T15:15:07.225147Z", + "iopub.status.busy": "2024-07-02T15:15:07.224971Z", + "iopub.status.idle": "2024-07-02T15:15:07.228887Z", + "shell.execute_reply": "2024-07-02T15:15:07.228428Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.139628Z", - "iopub.status.busy": "2024-07-02T12:05:38.139306Z", - "iopub.status.idle": "2024-07-02T12:05:38.175873Z", - "shell.execute_reply": "2024-07-02T12:05:38.175335Z" + "iopub.execute_input": "2024-07-02T15:15:07.231022Z", + "iopub.status.busy": "2024-07-02T15:15:07.230634Z", + "iopub.status.idle": "2024-07-02T15:15:07.267559Z", + "shell.execute_reply": "2024-07-02T15:15:07.267094Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.177802Z", - "iopub.status.busy": "2024-07-02T12:05:38.177621Z", - "iopub.status.idle": "2024-07-02T12:05:38.222062Z", - "shell.execute_reply": "2024-07-02T12:05:38.221459Z" + "iopub.execute_input": "2024-07-02T15:15:07.269540Z", + "iopub.status.busy": "2024-07-02T15:15:07.269232Z", + "iopub.status.idle": "2024-07-02T15:15:07.311391Z", + "shell.execute_reply": "2024-07-02T15:15:07.310918Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.225715Z", - "iopub.status.busy": "2024-07-02T12:05:38.225497Z", - "iopub.status.idle": "2024-07-02T12:05:38.315625Z", - "shell.execute_reply": "2024-07-02T12:05:38.315082Z" + "iopub.execute_input": "2024-07-02T15:15:07.313490Z", + "iopub.status.busy": "2024-07-02T15:15:07.313161Z", + "iopub.status.idle": "2024-07-02T15:15:07.408862Z", + "shell.execute_reply": "2024-07-02T15:15:07.408302Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.318154Z", - "iopub.status.busy": "2024-07-02T12:05:38.317969Z", - "iopub.status.idle": "2024-07-02T12:05:38.405501Z", - "shell.execute_reply": "2024-07-02T12:05:38.404891Z" + "iopub.execute_input": "2024-07-02T15:15:07.411502Z", + "iopub.status.busy": "2024-07-02T15:15:07.411209Z", + "iopub.status.idle": "2024-07-02T15:15:07.496801Z", + "shell.execute_reply": "2024-07-02T15:15:07.496253Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.407826Z", - "iopub.status.busy": "2024-07-02T12:05:38.407489Z", - "iopub.status.idle": "2024-07-02T12:05:38.614829Z", - "shell.execute_reply": "2024-07-02T12:05:38.614370Z" + "iopub.execute_input": "2024-07-02T15:15:07.499171Z", + "iopub.status.busy": "2024-07-02T15:15:07.498817Z", + "iopub.status.idle": "2024-07-02T15:15:07.704826Z", + "shell.execute_reply": "2024-07-02T15:15:07.704295Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.617073Z", - "iopub.status.busy": "2024-07-02T12:05:38.616735Z", - "iopub.status.idle": "2024-07-02T12:05:38.796547Z", - "shell.execute_reply": "2024-07-02T12:05:38.796035Z" + "iopub.execute_input": "2024-07-02T15:15:07.706982Z", + "iopub.status.busy": "2024-07-02T15:15:07.706641Z", + "iopub.status.idle": "2024-07-02T15:15:07.893000Z", + "shell.execute_reply": "2024-07-02T15:15:07.892303Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.798843Z", - "iopub.status.busy": "2024-07-02T12:05:38.798472Z", - "iopub.status.idle": "2024-07-02T12:05:38.804480Z", - "shell.execute_reply": "2024-07-02T12:05:38.804052Z" + "iopub.execute_input": "2024-07-02T15:15:07.895600Z", + "iopub.status.busy": "2024-07-02T15:15:07.895219Z", + "iopub.status.idle": "2024-07-02T15:15:07.901308Z", + "shell.execute_reply": "2024-07-02T15:15:07.900873Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.806348Z", - "iopub.status.busy": "2024-07-02T12:05:38.806175Z", - "iopub.status.idle": "2024-07-02T12:05:39.020330Z", - "shell.execute_reply": "2024-07-02T12:05:39.019866Z" + "iopub.execute_input": "2024-07-02T15:15:07.903351Z", + "iopub.status.busy": "2024-07-02T15:15:07.903038Z", + "iopub.status.idle": "2024-07-02T15:15:08.118284Z", + "shell.execute_reply": "2024-07-02T15:15:08.117695Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:39.022452Z", - "iopub.status.busy": "2024-07-02T12:05:39.022256Z", - "iopub.status.idle": "2024-07-02T12:05:40.077777Z", - "shell.execute_reply": "2024-07-02T12:05:40.077247Z" + "iopub.execute_input": "2024-07-02T15:15:08.120578Z", + "iopub.status.busy": "2024-07-02T15:15:08.120236Z", + "iopub.status.idle": "2024-07-02T15:15:09.203021Z", + "shell.execute_reply": "2024-07-02T15:15:09.202483Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index dfb026440..e3e8817fd 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:43.484936Z", - "iopub.status.busy": "2024-07-02T12:05:43.484760Z", - "iopub.status.idle": "2024-07-02T12:05:44.574684Z", - "shell.execute_reply": "2024-07-02T12:05:44.574061Z" + "iopub.execute_input": "2024-07-02T15:15:12.510036Z", + "iopub.status.busy": "2024-07-02T15:15:12.509861Z", + "iopub.status.idle": "2024-07-02T15:15:13.631469Z", + "shell.execute_reply": "2024-07-02T15:15:13.630838Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.577417Z", - "iopub.status.busy": "2024-07-02T12:05:44.576983Z", - "iopub.status.idle": "2024-07-02T12:05:44.579868Z", - "shell.execute_reply": "2024-07-02T12:05:44.579405Z" + "iopub.execute_input": "2024-07-02T15:15:13.634301Z", + "iopub.status.busy": "2024-07-02T15:15:13.633841Z", + "iopub.status.idle": "2024-07-02T15:15:13.636840Z", + "shell.execute_reply": "2024-07-02T15:15:13.636388Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.581906Z", - "iopub.status.busy": "2024-07-02T12:05:44.581588Z", - "iopub.status.idle": "2024-07-02T12:05:44.588930Z", - "shell.execute_reply": "2024-07-02T12:05:44.588511Z" + "iopub.execute_input": "2024-07-02T15:15:13.639070Z", + "iopub.status.busy": "2024-07-02T15:15:13.638755Z", + "iopub.status.idle": "2024-07-02T15:15:13.646413Z", + "shell.execute_reply": "2024-07-02T15:15:13.645954Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.591022Z", - "iopub.status.busy": "2024-07-02T12:05:44.590587Z", - "iopub.status.idle": "2024-07-02T12:05:44.643404Z", - "shell.execute_reply": "2024-07-02T12:05:44.642882Z" + "iopub.execute_input": "2024-07-02T15:15:13.648424Z", + "iopub.status.busy": "2024-07-02T15:15:13.648104Z", + "iopub.status.idle": "2024-07-02T15:15:13.695570Z", + "shell.execute_reply": "2024-07-02T15:15:13.695113Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.645347Z", - "iopub.status.busy": "2024-07-02T12:05:44.645170Z", - "iopub.status.idle": "2024-07-02T12:05:44.661922Z", - "shell.execute_reply": "2024-07-02T12:05:44.661404Z" + "iopub.execute_input": "2024-07-02T15:15:13.697840Z", + "iopub.status.busy": "2024-07-02T15:15:13.697478Z", + "iopub.status.idle": "2024-07-02T15:15:13.714358Z", + "shell.execute_reply": "2024-07-02T15:15:13.713787Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.663786Z", - "iopub.status.busy": "2024-07-02T12:05:44.663593Z", - "iopub.status.idle": "2024-07-02T12:05:44.667360Z", - "shell.execute_reply": "2024-07-02T12:05:44.666837Z" + "iopub.execute_input": "2024-07-02T15:15:13.716418Z", + "iopub.status.busy": "2024-07-02T15:15:13.716235Z", + "iopub.status.idle": "2024-07-02T15:15:13.720328Z", + "shell.execute_reply": "2024-07-02T15:15:13.719874Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.669486Z", - "iopub.status.busy": "2024-07-02T12:05:44.669101Z", - "iopub.status.idle": "2024-07-02T12:05:44.685613Z", - "shell.execute_reply": "2024-07-02T12:05:44.685195Z" + "iopub.execute_input": "2024-07-02T15:15:13.722265Z", + "iopub.status.busy": "2024-07-02T15:15:13.722093Z", + "iopub.status.idle": "2024-07-02T15:15:13.738589Z", + "shell.execute_reply": "2024-07-02T15:15:13.738172Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.687438Z", - "iopub.status.busy": "2024-07-02T12:05:44.687261Z", - "iopub.status.idle": "2024-07-02T12:05:44.713068Z", - "shell.execute_reply": "2024-07-02T12:05:44.712511Z" + "iopub.execute_input": "2024-07-02T15:15:13.740448Z", + "iopub.status.busy": "2024-07-02T15:15:13.740273Z", + "iopub.status.idle": "2024-07-02T15:15:13.766807Z", + "shell.execute_reply": "2024-07-02T15:15:13.766364Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.714998Z", - "iopub.status.busy": "2024-07-02T12:05:44.714828Z", - "iopub.status.idle": "2024-07-02T12:05:46.561058Z", - "shell.execute_reply": "2024-07-02T12:05:46.560413Z" + "iopub.execute_input": "2024-07-02T15:15:13.768717Z", + "iopub.status.busy": "2024-07-02T15:15:13.768540Z", + "iopub.status.idle": "2024-07-02T15:15:15.660293Z", + "shell.execute_reply": "2024-07-02T15:15:15.659737Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.563695Z", - "iopub.status.busy": "2024-07-02T12:05:46.563390Z", - "iopub.status.idle": "2024-07-02T12:05:46.570695Z", - "shell.execute_reply": "2024-07-02T12:05:46.570276Z" + "iopub.execute_input": "2024-07-02T15:15:15.663110Z", + "iopub.status.busy": "2024-07-02T15:15:15.662673Z", + "iopub.status.idle": "2024-07-02T15:15:15.669361Z", + "shell.execute_reply": "2024-07-02T15:15:15.668883Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.572666Z", - "iopub.status.busy": "2024-07-02T12:05:46.572452Z", - "iopub.status.idle": "2024-07-02T12:05:46.585257Z", - "shell.execute_reply": "2024-07-02T12:05:46.584820Z" + "iopub.execute_input": "2024-07-02T15:15:15.671496Z", + "iopub.status.busy": "2024-07-02T15:15:15.671115Z", + "iopub.status.idle": "2024-07-02T15:15:15.683951Z", + "shell.execute_reply": "2024-07-02T15:15:15.683520Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.587355Z", - "iopub.status.busy": "2024-07-02T12:05:46.586953Z", - "iopub.status.idle": "2024-07-02T12:05:46.593328Z", - "shell.execute_reply": "2024-07-02T12:05:46.592850Z" + "iopub.execute_input": "2024-07-02T15:15:15.685932Z", + "iopub.status.busy": "2024-07-02T15:15:15.685735Z", + "iopub.status.idle": "2024-07-02T15:15:15.691990Z", + "shell.execute_reply": "2024-07-02T15:15:15.691571Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.595350Z", - "iopub.status.busy": "2024-07-02T12:05:46.595021Z", - "iopub.status.idle": "2024-07-02T12:05:46.597564Z", - "shell.execute_reply": "2024-07-02T12:05:46.597149Z" + "iopub.execute_input": "2024-07-02T15:15:15.693946Z", + "iopub.status.busy": "2024-07-02T15:15:15.693759Z", + "iopub.status.idle": "2024-07-02T15:15:15.696269Z", + "shell.execute_reply": "2024-07-02T15:15:15.695843Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.599508Z", - "iopub.status.busy": "2024-07-02T12:05:46.599184Z", - "iopub.status.idle": "2024-07-02T12:05:46.602546Z", - "shell.execute_reply": "2024-07-02T12:05:46.602058Z" + "iopub.execute_input": "2024-07-02T15:15:15.698086Z", + "iopub.status.busy": "2024-07-02T15:15:15.697916Z", + "iopub.status.idle": "2024-07-02T15:15:15.701287Z", + "shell.execute_reply": "2024-07-02T15:15:15.700768Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.604583Z", - "iopub.status.busy": "2024-07-02T12:05:46.604261Z", - "iopub.status.idle": "2024-07-02T12:05:46.606854Z", - "shell.execute_reply": "2024-07-02T12:05:46.606416Z" + "iopub.execute_input": "2024-07-02T15:15:15.703245Z", + "iopub.status.busy": "2024-07-02T15:15:15.702979Z", + "iopub.status.idle": "2024-07-02T15:15:15.705625Z", + "shell.execute_reply": "2024-07-02T15:15:15.705105Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.608809Z", - "iopub.status.busy": "2024-07-02T12:05:46.608533Z", - "iopub.status.idle": "2024-07-02T12:05:46.612540Z", - "shell.execute_reply": "2024-07-02T12:05:46.612106Z" + "iopub.execute_input": "2024-07-02T15:15:15.707758Z", + "iopub.status.busy": "2024-07-02T15:15:15.707334Z", + "iopub.status.idle": "2024-07-02T15:15:15.711674Z", + "shell.execute_reply": "2024-07-02T15:15:15.711211Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.614617Z", - "iopub.status.busy": "2024-07-02T12:05:46.614295Z", - "iopub.status.idle": "2024-07-02T12:05:46.642333Z", - "shell.execute_reply": "2024-07-02T12:05:46.641923Z" + "iopub.execute_input": "2024-07-02T15:15:15.713628Z", + "iopub.status.busy": "2024-07-02T15:15:15.713453Z", + "iopub.status.idle": "2024-07-02T15:15:15.742599Z", + "shell.execute_reply": "2024-07-02T15:15:15.742060Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.644398Z", - "iopub.status.busy": "2024-07-02T12:05:46.644076Z", - "iopub.status.idle": "2024-07-02T12:05:46.648349Z", - "shell.execute_reply": "2024-07-02T12:05:46.647909Z" + "iopub.execute_input": "2024-07-02T15:15:15.744764Z", + "iopub.status.busy": "2024-07-02T15:15:15.744458Z", + "iopub.status.idle": "2024-07-02T15:15:15.749091Z", + "shell.execute_reply": "2024-07-02T15:15:15.748548Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 02d580b54..cd94e39a4 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:49.390201Z", - "iopub.status.busy": "2024-07-02T12:05:49.390029Z", - "iopub.status.idle": "2024-07-02T12:05:50.506272Z", - "shell.execute_reply": "2024-07-02T12:05:50.505689Z" + "iopub.execute_input": "2024-07-02T15:15:18.624231Z", + "iopub.status.busy": "2024-07-02T15:15:18.623753Z", + "iopub.status.idle": "2024-07-02T15:15:19.807437Z", + "shell.execute_reply": "2024-07-02T15:15:19.806877Z" }, "nbsphinx": "hidden" }, @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -105,10 +105,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:50.508865Z", - "iopub.status.busy": "2024-07-02T12:05:50.508468Z", - "iopub.status.idle": "2024-07-02T12:05:50.696756Z", - "shell.execute_reply": "2024-07-02T12:05:50.696292Z" + "iopub.execute_input": "2024-07-02T15:15:19.810010Z", + "iopub.status.busy": "2024-07-02T15:15:19.809534Z", + "iopub.status.idle": "2024-07-02T15:15:20.005847Z", + "shell.execute_reply": "2024-07-02T15:15:20.005329Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:50.698941Z", - "iopub.status.busy": "2024-07-02T12:05:50.698699Z", - "iopub.status.idle": "2024-07-02T12:05:50.711704Z", - "shell.execute_reply": "2024-07-02T12:05:50.711226Z" + "iopub.execute_input": "2024-07-02T15:15:20.008548Z", + "iopub.status.busy": "2024-07-02T15:15:20.008063Z", + "iopub.status.idle": "2024-07-02T15:15:20.021462Z", + "shell.execute_reply": "2024-07-02T15:15:20.021022Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:50.713503Z", - "iopub.status.busy": "2024-07-02T12:05:50.713332Z", - "iopub.status.idle": "2024-07-02T12:05:53.318405Z", - "shell.execute_reply": "2024-07-02T12:05:53.317873Z" + "iopub.execute_input": "2024-07-02T15:15:20.023553Z", + "iopub.status.busy": "2024-07-02T15:15:20.023228Z", + "iopub.status.idle": "2024-07-02T15:15:22.667041Z", + "shell.execute_reply": "2024-07-02T15:15:22.666472Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:53.320633Z", - "iopub.status.busy": "2024-07-02T12:05:53.320318Z", - "iopub.status.idle": "2024-07-02T12:05:54.676476Z", - "shell.execute_reply": "2024-07-02T12:05:54.675931Z" + "iopub.execute_input": "2024-07-02T15:15:22.669429Z", + "iopub.status.busy": "2024-07-02T15:15:22.669046Z", + "iopub.status.idle": "2024-07-02T15:15:24.080473Z", + "shell.execute_reply": "2024-07-02T15:15:24.079910Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:54.678848Z", - "iopub.status.busy": "2024-07-02T12:05:54.678408Z", - "iopub.status.idle": "2024-07-02T12:05:54.682336Z", - "shell.execute_reply": "2024-07-02T12:05:54.681800Z" + "iopub.execute_input": "2024-07-02T15:15:24.082867Z", + "iopub.status.busy": "2024-07-02T15:15:24.082524Z", + "iopub.status.idle": "2024-07-02T15:15:24.086566Z", + "shell.execute_reply": "2024-07-02T15:15:24.086070Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:54.684325Z", - "iopub.status.busy": "2024-07-02T12:05:54.683937Z", - "iopub.status.idle": "2024-07-02T12:05:56.558099Z", - "shell.execute_reply": "2024-07-02T12:05:56.557479Z" + "iopub.execute_input": "2024-07-02T15:15:24.088468Z", + "iopub.status.busy": "2024-07-02T15:15:24.088287Z", + "iopub.status.idle": "2024-07-02T15:15:26.051644Z", + "shell.execute_reply": "2024-07-02T15:15:26.051027Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:56.560538Z", - "iopub.status.busy": "2024-07-02T12:05:56.560208Z", - "iopub.status.idle": "2024-07-02T12:05:56.567803Z", - "shell.execute_reply": "2024-07-02T12:05:56.567265Z" + "iopub.execute_input": "2024-07-02T15:15:26.054487Z", + "iopub.status.busy": "2024-07-02T15:15:26.053807Z", + "iopub.status.idle": "2024-07-02T15:15:26.061647Z", + "shell.execute_reply": "2024-07-02T15:15:26.061203Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:56.569739Z", - "iopub.status.busy": "2024-07-02T12:05:56.569446Z", - "iopub.status.idle": "2024-07-02T12:05:59.160999Z", - "shell.execute_reply": "2024-07-02T12:05:59.160450Z" + "iopub.execute_input": "2024-07-02T15:15:26.063701Z", + "iopub.status.busy": "2024-07-02T15:15:26.063447Z", + "iopub.status.idle": "2024-07-02T15:15:28.644430Z", + "shell.execute_reply": "2024-07-02T15:15:28.643824Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:59.163107Z", - "iopub.status.busy": "2024-07-02T12:05:59.162773Z", - "iopub.status.idle": "2024-07-02T12:05:59.166191Z", - "shell.execute_reply": "2024-07-02T12:05:59.165684Z" + "iopub.execute_input": "2024-07-02T15:15:28.646593Z", + "iopub.status.busy": "2024-07-02T15:15:28.646407Z", + "iopub.status.idle": "2024-07-02T15:15:28.649931Z", + "shell.execute_reply": "2024-07-02T15:15:28.649426Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:59.168252Z", - "iopub.status.busy": "2024-07-02T12:05:59.167849Z", - "iopub.status.idle": "2024-07-02T12:05:59.171322Z", - "shell.execute_reply": "2024-07-02T12:05:59.170794Z" + "iopub.execute_input": "2024-07-02T15:15:28.651842Z", + "iopub.status.busy": "2024-07-02T15:15:28.651670Z", + "iopub.status.idle": "2024-07-02T15:15:28.654914Z", + "shell.execute_reply": "2024-07-02T15:15:28.654497Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:59.173235Z", - "iopub.status.busy": "2024-07-02T12:05:59.172937Z", - "iopub.status.idle": "2024-07-02T12:05:59.176035Z", - "shell.execute_reply": "2024-07-02T12:05:59.175500Z" + "iopub.execute_input": "2024-07-02T15:15:28.656734Z", + "iopub.status.busy": "2024-07-02T15:15:28.656564Z", + "iopub.status.idle": "2024-07-02T15:15:28.659904Z", + "shell.execute_reply": "2024-07-02T15:15:28.659358Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index 7ce8a7f2b..a35b0cd70 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:01.378322Z", - "iopub.status.busy": "2024-07-02T12:06:01.377923Z", - "iopub.status.idle": "2024-07-02T12:06:02.503419Z", - "shell.execute_reply": "2024-07-02T12:06:02.502819Z" + "iopub.execute_input": "2024-07-02T15:15:30.956908Z", + "iopub.status.busy": "2024-07-02T15:15:30.956487Z", + "iopub.status.idle": "2024-07-02T15:15:32.095214Z", + "shell.execute_reply": "2024-07-02T15:15:32.094654Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -109,10 +109,10 @@ "id": "c90449c8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:02.505878Z", - "iopub.status.busy": "2024-07-02T12:06:02.505606Z", - "iopub.status.idle": "2024-07-02T12:06:03.484637Z", - "shell.execute_reply": "2024-07-02T12:06:03.483911Z" + "iopub.execute_input": "2024-07-02T15:15:32.097678Z", + "iopub.status.busy": "2024-07-02T15:15:32.097267Z", + "iopub.status.idle": "2024-07-02T15:15:33.338055Z", + "shell.execute_reply": "2024-07-02T15:15:33.337365Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.487478Z", - "iopub.status.busy": "2024-07-02T12:06:03.486983Z", - "iopub.status.idle": "2024-07-02T12:06:03.490372Z", - "shell.execute_reply": "2024-07-02T12:06:03.489937Z" + "iopub.execute_input": "2024-07-02T15:15:33.340749Z", + "iopub.status.busy": "2024-07-02T15:15:33.340321Z", + "iopub.status.idle": "2024-07-02T15:15:33.343719Z", + "shell.execute_reply": "2024-07-02T15:15:33.343229Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.492668Z", - "iopub.status.busy": "2024-07-02T12:06:03.492302Z", - "iopub.status.idle": "2024-07-02T12:06:03.499701Z", - "shell.execute_reply": "2024-07-02T12:06:03.499223Z" + "iopub.execute_input": "2024-07-02T15:15:33.345667Z", + "iopub.status.busy": "2024-07-02T15:15:33.345338Z", + "iopub.status.idle": "2024-07-02T15:15:33.351615Z", + "shell.execute_reply": "2024-07-02T15:15:33.351194Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.501657Z", - "iopub.status.busy": "2024-07-02T12:06:03.501478Z", - "iopub.status.idle": "2024-07-02T12:06:03.984496Z", - "shell.execute_reply": "2024-07-02T12:06:03.983911Z" + "iopub.execute_input": "2024-07-02T15:15:33.353788Z", + "iopub.status.busy": "2024-07-02T15:15:33.353318Z", + "iopub.status.idle": "2024-07-02T15:15:33.838412Z", + "shell.execute_reply": "2024-07-02T15:15:33.837799Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.987155Z", - "iopub.status.busy": "2024-07-02T12:06:03.986711Z", - "iopub.status.idle": "2024-07-02T12:06:03.992050Z", - "shell.execute_reply": "2024-07-02T12:06:03.991587Z" + "iopub.execute_input": "2024-07-02T15:15:33.840873Z", + "iopub.status.busy": "2024-07-02T15:15:33.840457Z", + "iopub.status.idle": "2024-07-02T15:15:33.845948Z", + "shell.execute_reply": "2024-07-02T15:15:33.845370Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.993958Z", - "iopub.status.busy": "2024-07-02T12:06:03.993639Z", - "iopub.status.idle": "2024-07-02T12:06:03.997330Z", - "shell.execute_reply": "2024-07-02T12:06:03.996906Z" + "iopub.execute_input": "2024-07-02T15:15:33.848087Z", + "iopub.status.busy": "2024-07-02T15:15:33.847762Z", + "iopub.status.idle": "2024-07-02T15:15:33.851505Z", + "shell.execute_reply": "2024-07-02T15:15:33.851083Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.999294Z", - "iopub.status.busy": "2024-07-02T12:06:03.998989Z", - "iopub.status.idle": "2024-07-02T12:06:04.886721Z", - "shell.execute_reply": "2024-07-02T12:06:04.886183Z" + "iopub.execute_input": "2024-07-02T15:15:33.853551Z", + "iopub.status.busy": "2024-07-02T15:15:33.853155Z", + "iopub.status.idle": "2024-07-02T15:15:34.718833Z", + "shell.execute_reply": "2024-07-02T15:15:34.718192Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:04.889094Z", - "iopub.status.busy": "2024-07-02T12:06:04.888730Z", - "iopub.status.idle": "2024-07-02T12:06:05.104977Z", - "shell.execute_reply": "2024-07-02T12:06:05.104560Z" + "iopub.execute_input": "2024-07-02T15:15:34.721211Z", + "iopub.status.busy": "2024-07-02T15:15:34.720852Z", + "iopub.status.idle": "2024-07-02T15:15:34.944154Z", + "shell.execute_reply": "2024-07-02T15:15:34.943692Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:05.107009Z", - "iopub.status.busy": "2024-07-02T12:06:05.106744Z", - "iopub.status.idle": "2024-07-02T12:06:05.111011Z", - "shell.execute_reply": "2024-07-02T12:06:05.110475Z" + "iopub.execute_input": "2024-07-02T15:15:34.946483Z", + "iopub.status.busy": "2024-07-02T15:15:34.946141Z", + "iopub.status.idle": "2024-07-02T15:15:34.950453Z", + "shell.execute_reply": "2024-07-02T15:15:34.950017Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:05.112841Z", - "iopub.status.busy": "2024-07-02T12:06:05.112667Z", - "iopub.status.idle": "2024-07-02T12:06:05.549544Z", - "shell.execute_reply": "2024-07-02T12:06:05.548895Z" + "iopub.execute_input": "2024-07-02T15:15:34.952518Z", + "iopub.status.busy": "2024-07-02T15:15:34.952202Z", + "iopub.status.idle": "2024-07-02T15:15:35.406704Z", + "shell.execute_reply": "2024-07-02T15:15:35.406148Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:05.552420Z", - "iopub.status.busy": "2024-07-02T12:06:05.552234Z", - "iopub.status.idle": "2024-07-02T12:06:05.880895Z", - "shell.execute_reply": "2024-07-02T12:06:05.880300Z" + "iopub.execute_input": "2024-07-02T15:15:35.409869Z", + "iopub.status.busy": "2024-07-02T15:15:35.409486Z", + "iopub.status.idle": "2024-07-02T15:15:35.740831Z", + "shell.execute_reply": "2024-07-02T15:15:35.740278Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:05.883106Z", - "iopub.status.busy": "2024-07-02T12:06:05.882705Z", - "iopub.status.idle": "2024-07-02T12:06:06.240971Z", - "shell.execute_reply": "2024-07-02T12:06:06.240404Z" + "iopub.execute_input": "2024-07-02T15:15:35.743697Z", + "iopub.status.busy": "2024-07-02T15:15:35.743347Z", + "iopub.status.idle": "2024-07-02T15:15:36.106871Z", + "shell.execute_reply": "2024-07-02T15:15:36.106275Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:06.243379Z", - "iopub.status.busy": "2024-07-02T12:06:06.243189Z", - "iopub.status.idle": "2024-07-02T12:06:06.680772Z", - "shell.execute_reply": "2024-07-02T12:06:06.680290Z" + "iopub.execute_input": "2024-07-02T15:15:36.110205Z", + "iopub.status.busy": "2024-07-02T15:15:36.109829Z", + "iopub.status.idle": "2024-07-02T15:15:36.549166Z", + "shell.execute_reply": "2024-07-02T15:15:36.548631Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:06.682984Z", - "iopub.status.busy": "2024-07-02T12:06:06.682675Z", - "iopub.status.idle": "2024-07-02T12:06:07.129389Z", - "shell.execute_reply": "2024-07-02T12:06:07.128744Z" + "iopub.execute_input": "2024-07-02T15:15:36.553350Z", + "iopub.status.busy": "2024-07-02T15:15:36.553003Z", + "iopub.status.idle": "2024-07-02T15:15:36.974053Z", + "shell.execute_reply": "2024-07-02T15:15:36.973378Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:07.132269Z", - "iopub.status.busy": "2024-07-02T12:06:07.132092Z", - "iopub.status.idle": "2024-07-02T12:06:07.345651Z", - "shell.execute_reply": "2024-07-02T12:06:07.345066Z" + "iopub.execute_input": "2024-07-02T15:15:36.976911Z", + "iopub.status.busy": "2024-07-02T15:15:36.976726Z", + "iopub.status.idle": "2024-07-02T15:15:37.190142Z", + "shell.execute_reply": "2024-07-02T15:15:37.189597Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:07.347943Z", - "iopub.status.busy": "2024-07-02T12:06:07.347569Z", - "iopub.status.idle": "2024-07-02T12:06:07.545897Z", - "shell.execute_reply": "2024-07-02T12:06:07.545303Z" + "iopub.execute_input": "2024-07-02T15:15:37.192342Z", + "iopub.status.busy": "2024-07-02T15:15:37.191989Z", + "iopub.status.idle": "2024-07-02T15:15:37.390057Z", + "shell.execute_reply": "2024-07-02T15:15:37.389444Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:07.548054Z", - "iopub.status.busy": "2024-07-02T12:06:07.547721Z", - "iopub.status.idle": "2024-07-02T12:06:07.550610Z", - "shell.execute_reply": "2024-07-02T12:06:07.550172Z" + "iopub.execute_input": "2024-07-02T15:15:37.392297Z", + "iopub.status.busy": "2024-07-02T15:15:37.391973Z", + "iopub.status.idle": "2024-07-02T15:15:37.394998Z", + "shell.execute_reply": "2024-07-02T15:15:37.394453Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:07.552606Z", - "iopub.status.busy": "2024-07-02T12:06:07.552209Z", - "iopub.status.idle": "2024-07-02T12:06:08.545283Z", - "shell.execute_reply": "2024-07-02T12:06:08.544691Z" + "iopub.execute_input": "2024-07-02T15:15:37.397009Z", + "iopub.status.busy": "2024-07-02T15:15:37.396673Z", + "iopub.status.idle": "2024-07-02T15:15:38.375549Z", + "shell.execute_reply": "2024-07-02T15:15:38.375024Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:08.550100Z", - "iopub.status.busy": "2024-07-02T12:06:08.549675Z", - "iopub.status.idle": "2024-07-02T12:06:08.692703Z", - "shell.execute_reply": "2024-07-02T12:06:08.692222Z" + "iopub.execute_input": "2024-07-02T15:15:38.378310Z", + "iopub.status.busy": "2024-07-02T15:15:38.377935Z", + "iopub.status.idle": "2024-07-02T15:15:38.576337Z", + "shell.execute_reply": "2024-07-02T15:15:38.575768Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:08.694865Z", - "iopub.status.busy": "2024-07-02T12:06:08.694525Z", - "iopub.status.idle": "2024-07-02T12:06:08.829794Z", - "shell.execute_reply": "2024-07-02T12:06:08.829310Z" + "iopub.execute_input": "2024-07-02T15:15:38.578422Z", + "iopub.status.busy": "2024-07-02T15:15:38.578242Z", + "iopub.status.idle": "2024-07-02T15:15:38.716353Z", + "shell.execute_reply": "2024-07-02T15:15:38.715888Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:08.832030Z", - "iopub.status.busy": "2024-07-02T12:06:08.831714Z", - "iopub.status.idle": "2024-07-02T12:06:09.569943Z", - "shell.execute_reply": "2024-07-02T12:06:09.569367Z" + "iopub.execute_input": "2024-07-02T15:15:38.718767Z", + "iopub.status.busy": "2024-07-02T15:15:38.718383Z", + "iopub.status.idle": "2024-07-02T15:15:39.383126Z", + "shell.execute_reply": "2024-07-02T15:15:39.382541Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:09.572191Z", - "iopub.status.busy": "2024-07-02T12:06:09.571856Z", - "iopub.status.idle": "2024-07-02T12:06:09.575442Z", - "shell.execute_reply": "2024-07-02T12:06:09.575034Z" + "iopub.execute_input": "2024-07-02T15:15:39.385201Z", + "iopub.status.busy": "2024-07-02T15:15:39.385018Z", + "iopub.status.idle": "2024-07-02T15:15:39.388752Z", + "shell.execute_reply": "2024-07-02T15:15:39.388195Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index e308a0576..78064e277 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -780,7 +780,7 @@

2. Pre-process the Cifar10 dataset
-100%|██████████| 170498071/170498071 [00:08<00:00, 19884004.38it/s]
+100%|██████████| 170498071/170498071 [00:01<00:00, 106209257.98it/s]
 

-
+
@@ -1124,7 +1124,7 @@

4. Use cleanlab and here.

diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index 12c6da264..e7ee45271 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:11.678697Z", - "iopub.status.busy": "2024-07-02T12:06:11.678521Z", - "iopub.status.idle": "2024-07-02T12:06:14.408240Z", - "shell.execute_reply": "2024-07-02T12:06:14.407674Z" + "iopub.execute_input": "2024-07-02T15:15:41.499853Z", + "iopub.status.busy": "2024-07-02T15:15:41.499683Z", + "iopub.status.idle": "2024-07-02T15:15:44.231209Z", + "shell.execute_reply": "2024-07-02T15:15:44.230660Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "id": "4396f544", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:14.410934Z", - "iopub.status.busy": "2024-07-02T12:06:14.410443Z", - "iopub.status.idle": "2024-07-02T12:06:14.735244Z", - "shell.execute_reply": "2024-07-02T12:06:14.734679Z" + "iopub.execute_input": "2024-07-02T15:15:44.233719Z", + "iopub.status.busy": "2024-07-02T15:15:44.233290Z", + "iopub.status.idle": "2024-07-02T15:15:44.547799Z", + "shell.execute_reply": "2024-07-02T15:15:44.547256Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:14.737835Z", - "iopub.status.busy": "2024-07-02T12:06:14.737360Z", - "iopub.status.idle": "2024-07-02T12:06:14.741543Z", - "shell.execute_reply": "2024-07-02T12:06:14.741013Z" + "iopub.execute_input": "2024-07-02T15:15:44.550457Z", + "iopub.status.busy": "2024-07-02T15:15:44.550003Z", + "iopub.status.idle": "2024-07-02T15:15:44.553889Z", + "shell.execute_reply": "2024-07-02T15:15:44.553463Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:14.743746Z", - "iopub.status.busy": "2024-07-02T12:06:14.743385Z", - "iopub.status.idle": "2024-07-02T12:06:25.921071Z", - "shell.execute_reply": "2024-07-02T12:06:25.920486Z" + "iopub.execute_input": "2024-07-02T15:15:44.555964Z", + "iopub.status.busy": "2024-07-02T15:15:44.555530Z", + "iopub.status.idle": "2024-07-02T15:15:48.811407Z", + "shell.execute_reply": "2024-07-02T15:15:48.810907Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 458752/170498071 [00:00<00:37, 4550205.38it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8200886.72it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 2686976/170498071 [00:00<00:11, 14867624.00it/s]" + " 6%|▋ | 10780672/170498071 [00:00<00:02, 58894029.31it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 4915200/170498071 [00:00<00:09, 18176569.25it/s]" + " 13%|█▎ | 22380544/170498071 [00:00<00:01, 84273722.65it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 7110656/170498071 [00:00<00:08, 19525356.25it/s]" + " 20%|█▉ | 33783808/170498071 [00:00<00:01, 95827715.47it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 9273344/170498071 [00:00<00:08, 20138060.31it/s]" + " 27%|██▋ | 45383680/170498071 [00:00<00:01, 102972274.05it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 11468800/170498071 [00:00<00:07, 20583296.62it/s]" + " 33%|███▎ | 56721408/170498071 [00:00<00:01, 106415655.53it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 13565952/170498071 [00:00<00:07, 20618122.34it/s]" + " 40%|████ | 68288512/170498071 [00:00<00:00, 109377801.86it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 15695872/170498071 [00:00<00:07, 20684064.34it/s]" + " 47%|████▋ | 79790080/170498071 [00:00<00:00, 111060852.43it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 17793024/170498071 [00:00<00:07, 20210099.70it/s]" + " 54%|█████▎ | 91291648/170498071 [00:00<00:00, 112242317.21it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 19857408/170498071 [00:01<00:07, 20157298.26it/s]" + " 60%|██████ | 102727680/170498071 [00:01<00:00, 112875530.06it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 21889024/170498071 [00:01<00:07, 19580366.36it/s]" + " 67%|██████▋ | 114262016/170498071 [00:01<00:00, 113610104.30it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 23887872/170498071 [00:01<00:07, 19689752.59it/s]" + " 74%|███████▎ | 125665280/170498071 [00:01<00:00, 112903553.22it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 26148864/170498071 [00:01<00:07, 20522936.05it/s]" + " 81%|████████ | 137396224/170498071 [00:01<00:00, 114077850.23it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 28901376/170498071 [00:01<00:06, 22488420.49it/s]" + " 87%|████████▋ | 148897792/170498071 [00:01<00:00, 114231113.69it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 31260672/170498071 [00:01<00:06, 22666713.87it/s]" + " 94%|█████████▍| 160399360/170498071 [00:01<00:00, 114421071.55it/s]" ] }, { @@ -372,535 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 33783808/170498071 [00:01<00:05, 23420171.66it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 21%|██ | 36143104/170498071 [00:01<00:05, 23367837.44it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 23%|██▎ | 38567936/170498071 [00:01<00:05, 23628433.32it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 24%|██▍ | 40992768/170498071 [00:01<00:05, 23729287.62it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 25%|██▌ | 43384832/170498071 [00:02<00:05, 23481823.27it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 27%|██▋ | 45809664/170498071 [00:02<00:05, 23565140.27it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 28%|██▊ | 48168960/170498071 [00:02<00:05, 22124551.71it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 30%|██▉ | 50429952/170498071 [00:02<00:05, 21597165.25it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 31%|███ | 52625408/170498071 [00:02<00:05, 21122055.47it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 32%|███▏ | 54755328/170498071 [00:02<00:05, 20674704.10it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 33%|███▎ | 56852480/170498071 [00:02<00:05, 20193072.76it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 35%|███▍ | 59015168/170498071 [00:02<00:05, 20474965.70it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 36%|███▌ | 61440000/170498071 [00:02<00:05, 21428625.80it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 37%|███▋ | 63602688/170498071 [00:03<00:05, 20984454.80it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 39%|███▊ | 65732608/170498071 [00:03<00:05, 20040214.69it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 40%|███▉ | 67764224/170498071 [00:03<00:05, 19617119.66it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 41%|████ | 69763072/170498071 [00:03<00:05, 19368566.16it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 42%|████▏ | 71729152/170498071 [00:03<00:05, 18942200.76it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 43%|████▎ | 73760768/170498071 [00:03<00:05, 19136506.47it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 44%|████▍ | 75694080/170498071 [00:03<00:05, 18546539.77it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 46%|████▌ | 77856768/170498071 [00:03<00:04, 19310897.00it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 47%|████▋ | 79855616/170498071 [00:03<00:04, 19370411.60it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 48%|████▊ | 81821696/170498071 [00:03<00:04, 18841681.57it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 49%|████▉ | 83722240/170498071 [00:04<00:04, 18578900.08it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 50%|█████ | 85590016/170498071 [00:04<00:04, 18310455.55it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 51%|█████▏ | 87425024/170498071 [00:04<00:04, 17994534.24it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 52%|█████▏ | 89227264/170498071 [00:04<00:04, 17969991.24it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 53%|█████▎ | 91029504/170498071 [00:04<00:04, 17885343.83it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 54%|█████▍ | 92864512/170498071 [00:04<00:04, 17966202.49it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 56%|█████▌ | 95223808/170498071 [00:04<00:03, 19459620.88it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 57%|█████▋ | 97583104/170498071 [00:04<00:03, 20637975.50it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 59%|█████▊ | 99909632/170498071 [00:04<00:03, 21306263.38it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 60%|█████▉ | 102072320/170498071 [00:05<00:03, 21151929.09it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 61%|██████▏ | 104464384/170498071 [00:05<00:03, 21792656.59it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 63%|██████▎ | 106659840/170498071 [00:05<00:02, 21386725.65it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 64%|██████▍ | 108920832/170498071 [00:05<00:02, 21710014.23it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 65%|██████▌ | 111116288/170498071 [00:05<00:02, 21241167.89it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 67%|██████▋ | 113541120/170498071 [00:05<00:02, 22049083.16it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 68%|██████▊ | 115769344/170498071 [00:05<00:02, 21067777.78it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 69%|██████▉ | 118030336/170498071 [00:05<00:02, 21474442.53it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 70%|███████ | 120193024/170498071 [00:05<00:02, 19823274.12it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 72%|███████▏ | 122224640/170498071 [00:06<00:02, 19471183.62it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 73%|███████▎ | 124190720/170498071 [00:06<00:02, 17873496.55it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 74%|███████▍ | 126025728/170498071 [00:06<00:02, 16817889.13it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 75%|███████▍ | 127762432/170498071 [00:06<00:02, 16186952.66it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 76%|███████▌ | 129400832/170498071 [00:06<00:02, 15789183.38it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 77%|███████▋ | 131006464/170498071 [00:06<00:02, 15482944.36it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 78%|███████▊ | 132579328/170498071 [00:06<00:02, 15098811.97it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 79%|███████▊ | 134119424/170498071 [00:06<00:02, 14977124.11it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 80%|███████▉ | 135626752/170498071 [00:06<00:02, 14929116.66it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 80%|████████ | 137134080/170498071 [00:07<00:02, 14648969.73it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 81%|████████▏ | 138608640/170498071 [00:07<00:02, 14671163.55it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 82%|████████▏ | 140083200/170498071 [00:07<00:02, 14686569.65it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 83%|████████▎ | 141885440/170498071 [00:07<00:01, 15554076.95it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 84%|████████▍ | 143589376/170498071 [00:07<00:01, 15854513.49it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 85%|████████▌ | 145391616/170498071 [00:07<00:01, 16350344.79it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 86%|████████▋ | 147128320/170498071 [00:07<00:01, 16559020.07it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 87%|████████▋ | 149061632/170498071 [00:07<00:01, 17232449.65it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 88%|████████▊ | 150863872/170498071 [00:07<00:01, 17447581.45it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 90%|████████▉ | 152633344/170498071 [00:07<00:01, 17476767.38it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 91%|█████████ | 155058176/170498071 [00:08<00:00, 19434356.44it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 93%|█████████▎| 157712384/170498071 [00:08<00:00, 21544683.73it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 94%|█████████▍| 160661504/170498071 [00:08<00:00, 23832060.44it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 96%|█████████▌| 163545088/170498071 [00:08<00:00, 25225731.12it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 98%|█████████▊| 166854656/170498071 [00:08<00:00, 27373989.44it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|█████████▉| 169672704/170498071 [00:08<00:00, 27582174.15it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:08<00:00, 19884004.38it/s]" + "100%|██████████| 170498071/170498071 [00:01<00:00, 106209257.98it/s]" ] }, { @@ -1018,10 +490,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:25.923304Z", - "iopub.status.busy": "2024-07-02T12:06:25.922962Z", - "iopub.status.idle": "2024-07-02T12:06:25.927532Z", - "shell.execute_reply": "2024-07-02T12:06:25.927116Z" + "iopub.execute_input": "2024-07-02T15:15:48.813684Z", + "iopub.status.busy": "2024-07-02T15:15:48.813281Z", + "iopub.status.idle": "2024-07-02T15:15:48.818166Z", + "shell.execute_reply": "2024-07-02T15:15:48.817615Z" }, "nbsphinx": "hidden" }, @@ -1072,10 +544,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:25.929617Z", - "iopub.status.busy": "2024-07-02T12:06:25.929294Z", - "iopub.status.idle": "2024-07-02T12:06:26.466020Z", - "shell.execute_reply": "2024-07-02T12:06:26.465500Z" + "iopub.execute_input": "2024-07-02T15:15:48.820188Z", + "iopub.status.busy": "2024-07-02T15:15:48.819791Z", + "iopub.status.idle": "2024-07-02T15:15:49.359971Z", + "shell.execute_reply": "2024-07-02T15:15:49.359408Z" } }, "outputs": [ @@ -1108,10 +580,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:26.468274Z", - "iopub.status.busy": "2024-07-02T12:06:26.467846Z", - "iopub.status.idle": "2024-07-02T12:06:26.973804Z", - "shell.execute_reply": "2024-07-02T12:06:26.973190Z" + "iopub.execute_input": "2024-07-02T15:15:49.362067Z", + "iopub.status.busy": "2024-07-02T15:15:49.361785Z", + "iopub.status.idle": "2024-07-02T15:15:49.873206Z", + "shell.execute_reply": "2024-07-02T15:15:49.872724Z" } }, "outputs": [ @@ -1149,10 +621,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:26.976024Z", - "iopub.status.busy": "2024-07-02T12:06:26.975702Z", - "iopub.status.idle": "2024-07-02T12:06:26.979191Z", - "shell.execute_reply": "2024-07-02T12:06:26.978654Z" + "iopub.execute_input": "2024-07-02T15:15:49.875391Z", + "iopub.status.busy": "2024-07-02T15:15:49.875042Z", + "iopub.status.idle": "2024-07-02T15:15:49.878400Z", + "shell.execute_reply": "2024-07-02T15:15:49.877944Z" } }, "outputs": [], @@ -1175,17 +647,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:26.981120Z", - "iopub.status.busy": "2024-07-02T12:06:26.980808Z", - "iopub.status.idle": "2024-07-02T12:06:39.219368Z", - "shell.execute_reply": "2024-07-02T12:06:39.218785Z" + "iopub.execute_input": "2024-07-02T15:15:49.880181Z", + "iopub.status.busy": "2024-07-02T15:15:49.880011Z", + "iopub.status.idle": "2024-07-02T15:16:02.227760Z", + "shell.execute_reply": "2024-07-02T15:16:02.227173Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e62048d58b1a436fa16544b9ecbd1a17", + "model_id": "7134c3b9c85247698385a933e9c6f4c1", "version_major": 2, "version_minor": 0 }, @@ -1244,10 +716,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:39.221701Z", - "iopub.status.busy": "2024-07-02T12:06:39.221327Z", - "iopub.status.idle": "2024-07-02T12:06:41.264255Z", - "shell.execute_reply": "2024-07-02T12:06:41.263645Z" + "iopub.execute_input": "2024-07-02T15:16:02.229945Z", + "iopub.status.busy": "2024-07-02T15:16:02.229742Z", + "iopub.status.idle": "2024-07-02T15:16:04.294329Z", + "shell.execute_reply": "2024-07-02T15:16:04.293708Z" } }, "outputs": [ @@ -1291,10 +763,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:41.266829Z", - "iopub.status.busy": "2024-07-02T12:06:41.266301Z", - "iopub.status.idle": "2024-07-02T12:06:41.492927Z", - "shell.execute_reply": "2024-07-02T12:06:41.492268Z" + "iopub.execute_input": "2024-07-02T15:16:04.297035Z", + "iopub.status.busy": "2024-07-02T15:16:04.296744Z", + "iopub.status.idle": "2024-07-02T15:16:04.555185Z", + "shell.execute_reply": "2024-07-02T15:16:04.554125Z" } }, "outputs": [ @@ -1330,10 +802,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:41.495155Z", - "iopub.status.busy": "2024-07-02T12:06:41.494971Z", - "iopub.status.idle": "2024-07-02T12:06:42.143408Z", - "shell.execute_reply": "2024-07-02T12:06:42.142827Z" + "iopub.execute_input": "2024-07-02T15:16:04.557598Z", + "iopub.status.busy": "2024-07-02T15:16:04.557392Z", + "iopub.status.idle": "2024-07-02T15:16:05.237315Z", + "shell.execute_reply": "2024-07-02T15:16:05.236772Z" } }, "outputs": [ @@ -1383,10 +855,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:42.145875Z", - "iopub.status.busy": "2024-07-02T12:06:42.145693Z", - "iopub.status.idle": "2024-07-02T12:06:42.443716Z", - "shell.execute_reply": "2024-07-02T12:06:42.443121Z" + "iopub.execute_input": "2024-07-02T15:16:05.240254Z", + "iopub.status.busy": "2024-07-02T15:16:05.239837Z", + "iopub.status.idle": "2024-07-02T15:16:05.575080Z", + "shell.execute_reply": "2024-07-02T15:16:05.574558Z" } }, "outputs": [ @@ -1434,10 +906,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:42.445959Z", - "iopub.status.busy": "2024-07-02T12:06:42.445765Z", - "iopub.status.idle": "2024-07-02T12:06:42.675040Z", - "shell.execute_reply": "2024-07-02T12:06:42.674459Z" + "iopub.execute_input": "2024-07-02T15:16:05.577340Z", + "iopub.status.busy": "2024-07-02T15:16:05.576994Z", + "iopub.status.idle": "2024-07-02T15:16:05.817984Z", + "shell.execute_reply": "2024-07-02T15:16:05.817361Z" } }, "outputs": [ @@ -1493,10 +965,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:42.677732Z", - "iopub.status.busy": "2024-07-02T12:06:42.677210Z", - "iopub.status.idle": "2024-07-02T12:06:42.745827Z", - "shell.execute_reply": "2024-07-02T12:06:42.745362Z" + "iopub.execute_input": "2024-07-02T15:16:05.820538Z", + "iopub.status.busy": "2024-07-02T15:16:05.820336Z", + "iopub.status.idle": "2024-07-02T15:16:05.907382Z", + "shell.execute_reply": "2024-07-02T15:16:05.906874Z" } }, "outputs": [], @@ -1517,10 +989,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:42.748346Z", - "iopub.status.busy": "2024-07-02T12:06:42.748025Z", - "iopub.status.idle": "2024-07-02T12:06:52.686113Z", - "shell.execute_reply": "2024-07-02T12:06:52.685493Z" + "iopub.execute_input": "2024-07-02T15:16:05.910032Z", + "iopub.status.busy": "2024-07-02T15:16:05.909504Z", + "iopub.status.idle": "2024-07-02T15:16:16.136329Z", + "shell.execute_reply": "2024-07-02T15:16:16.135702Z" } }, "outputs": [ @@ -1557,10 +1029,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:52.688740Z", - "iopub.status.busy": "2024-07-02T12:06:52.688263Z", - "iopub.status.idle": "2024-07-02T12:06:54.757637Z", - "shell.execute_reply": "2024-07-02T12:06:54.757095Z" + "iopub.execute_input": "2024-07-02T15:16:16.138895Z", + "iopub.status.busy": "2024-07-02T15:16:16.138488Z", + "iopub.status.idle": "2024-07-02T15:16:18.289669Z", + "shell.execute_reply": "2024-07-02T15:16:18.289140Z" } }, "outputs": [ @@ -1591,10 +1063,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:54.760248Z", - "iopub.status.busy": "2024-07-02T12:06:54.759634Z", - "iopub.status.idle": "2024-07-02T12:06:54.964477Z", - "shell.execute_reply": "2024-07-02T12:06:54.963957Z" + "iopub.execute_input": "2024-07-02T15:16:18.292281Z", + "iopub.status.busy": "2024-07-02T15:16:18.291784Z", + "iopub.status.idle": "2024-07-02T15:16:18.494637Z", + "shell.execute_reply": "2024-07-02T15:16:18.494138Z" } }, "outputs": [], @@ -1608,10 +1080,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:54.966866Z", - "iopub.status.busy": "2024-07-02T12:06:54.966507Z", - "iopub.status.idle": "2024-07-02T12:06:54.969693Z", - "shell.execute_reply": "2024-07-02T12:06:54.969165Z" + "iopub.execute_input": "2024-07-02T15:16:18.496977Z", + "iopub.status.busy": "2024-07-02T15:16:18.496633Z", + "iopub.status.idle": "2024-07-02T15:16:18.499690Z", + "shell.execute_reply": "2024-07-02T15:16:18.499247Z" } }, "outputs": [], @@ -1633,10 +1105,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:54.971890Z", - "iopub.status.busy": "2024-07-02T12:06:54.971573Z", - "iopub.status.idle": "2024-07-02T12:06:54.979664Z", - "shell.execute_reply": "2024-07-02T12:06:54.979125Z" + "iopub.execute_input": "2024-07-02T15:16:18.501694Z", + "iopub.status.busy": "2024-07-02T15:16:18.501306Z", + "iopub.status.idle": "2024-07-02T15:16:18.509698Z", + "shell.execute_reply": "2024-07-02T15:16:18.509149Z" }, "nbsphinx": "hidden" }, @@ -1681,7 +1153,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "038d1dec855f4a5d8a895b8c5ca8a543": { + "0ec7acb06a8d4e7c8cee6f0af1617289": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_252fe402519d4210ba4ba3fef0912a86", + "placeholder": "​", + "style": "IPY_MODEL_b8360c36dca94afc98ec4fb786a3c57f", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 304MB/s]" + } + }, + "252fe402519d4210ba4ba3fef0912a86": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1734,48 +1229,23 @@ "width": null } }, - "189964aceefe49698fa8fa689efdba0f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7f36baa4eaa845949d5ad61b24217bd2", - "placeholder": "​", - "style": "IPY_MODEL_50adf2f382654575992aa00abedb3fda", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 291MB/s]" - } - }, - "32782ba639b74ba19d535e6b9e43df2f": { + "4cb10e135c4d4df6a0102b8fa2c4e435": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "50adf2f382654575992aa00abedb3fda": { + "6b6164dfe4394da88a0985c0358adabf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1793,30 +1263,31 @@ "text_color": null } }, - "55c2a3ff8e46463392cbdc7feacce684": { + "7134c3b9c85247698385a933e9c6f4c1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_038d1dec855f4a5d8a895b8c5ca8a543", - "placeholder": "​", - "style": "IPY_MODEL_32782ba639b74ba19d535e6b9e43df2f", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f75ac16d283e42748e30f48710f7c779", + "IPY_MODEL_ebbc5fc8b0754655bb152b6178ceae67", + "IPY_MODEL_0ec7acb06a8d4e7c8cee6f0af1617289" + ], + "layout": "IPY_MODEL_76e78968a920473d8821422c81a0fcdd", "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "tooltip": null } }, - "7f36baa4eaa845949d5ad61b24217bd2": { + "76e78968a920473d8821422c81a0fcdd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1869,23 +1340,25 @@ "width": null } }, - "9c339ec47e3249839dd034d9f3c0f0bd": { + "b8360c36dca94afc98ec4fb786a3c57f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "d0f48ceb51424194a566927347c5e11d": { + "bc50c73a865f4e2e8076a042331398c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1938,57 +1411,7 @@ "width": null } }, - "e2efb59d0f4740bb8af23c2fd00116b3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f2d6b576288e4f7fbed42581aafbf977", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9c339ec47e3249839dd034d9f3c0f0bd", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "e62048d58b1a436fa16544b9ecbd1a17": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_55c2a3ff8e46463392cbdc7feacce684", - "IPY_MODEL_e2efb59d0f4740bb8af23c2fd00116b3", - "IPY_MODEL_189964aceefe49698fa8fa689efdba0f" - ], - "layout": "IPY_MODEL_d0f48ceb51424194a566927347c5e11d", - "tabbable": null, - "tooltip": null - } - }, - "f2d6b576288e4f7fbed42581aafbf977": { + "be9da5a89136408299b9df5aa61bf8ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2040,6 +1463,55 @@ "visibility": null, "width": null } + }, + "ebbc5fc8b0754655bb152b6178ceae67": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_be9da5a89136408299b9df5aa61bf8ca", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4cb10e135c4d4df6a0102b8fa2c4e435", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } + }, + "f75ac16d283e42748e30f48710f7c779": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_bc50c73a865f4e2e8076a042331398c7", + "placeholder": "​", + "style": "IPY_MODEL_6b6164dfe4394da88a0985c0358adabf", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } } }, "version_major": 2, diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 75e02e92c..d7791c942 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:59.101052Z", - "iopub.status.busy": "2024-07-02T12:06:59.100876Z", - "iopub.status.idle": "2024-07-02T12:07:00.258136Z", - "shell.execute_reply": "2024-07-02T12:07:00.257587Z" + "iopub.execute_input": "2024-07-02T15:16:22.773416Z", + "iopub.status.busy": "2024-07-02T15:16:22.773067Z", + "iopub.status.idle": "2024-07-02T15:16:23.924928Z", + "shell.execute_reply": "2024-07-02T15:16:23.924442Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.260745Z", - "iopub.status.busy": "2024-07-02T12:07:00.260339Z", - "iopub.status.idle": "2024-07-02T12:07:00.277570Z", - "shell.execute_reply": "2024-07-02T12:07:00.277011Z" + "iopub.execute_input": "2024-07-02T15:16:23.927425Z", + "iopub.status.busy": "2024-07-02T15:16:23.927055Z", + "iopub.status.idle": "2024-07-02T15:16:23.943960Z", + "shell.execute_reply": "2024-07-02T15:16:23.943415Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.280398Z", - "iopub.status.busy": "2024-07-02T12:07:00.279700Z", - "iopub.status.idle": "2024-07-02T12:07:00.283630Z", - "shell.execute_reply": "2024-07-02T12:07:00.282919Z" + "iopub.execute_input": "2024-07-02T15:16:23.946374Z", + "iopub.status.busy": "2024-07-02T15:16:23.945882Z", + "iopub.status.idle": "2024-07-02T15:16:23.948942Z", + "shell.execute_reply": "2024-07-02T15:16:23.948387Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.286415Z", - "iopub.status.busy": "2024-07-02T12:07:00.285840Z", - "iopub.status.idle": "2024-07-02T12:07:00.351880Z", - "shell.execute_reply": "2024-07-02T12:07:00.350456Z" + "iopub.execute_input": "2024-07-02T15:16:23.951055Z", + "iopub.status.busy": "2024-07-02T15:16:23.950645Z", + "iopub.status.idle": "2024-07-02T15:16:24.037023Z", + "shell.execute_reply": "2024-07-02T15:16:24.036470Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.354191Z", - "iopub.status.busy": "2024-07-02T12:07:00.353874Z", - "iopub.status.idle": "2024-07-02T12:07:00.543757Z", - "shell.execute_reply": "2024-07-02T12:07:00.543276Z" + "iopub.execute_input": "2024-07-02T15:16:24.039484Z", + "iopub.status.busy": "2024-07-02T15:16:24.039164Z", + "iopub.status.idle": "2024-07-02T15:16:24.218535Z", + "shell.execute_reply": "2024-07-02T15:16:24.217887Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.545894Z", - "iopub.status.busy": "2024-07-02T12:07:00.545559Z", - "iopub.status.idle": "2024-07-02T12:07:00.784978Z", - "shell.execute_reply": "2024-07-02T12:07:00.784416Z" + "iopub.execute_input": "2024-07-02T15:16:24.220994Z", + "iopub.status.busy": "2024-07-02T15:16:24.220778Z", + "iopub.status.idle": "2024-07-02T15:16:24.467677Z", + "shell.execute_reply": "2024-07-02T15:16:24.467120Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.787127Z", - "iopub.status.busy": "2024-07-02T12:07:00.786946Z", - "iopub.status.idle": "2024-07-02T12:07:00.791220Z", - "shell.execute_reply": "2024-07-02T12:07:00.790792Z" + "iopub.execute_input": "2024-07-02T15:16:24.469799Z", + "iopub.status.busy": "2024-07-02T15:16:24.469507Z", + "iopub.status.idle": "2024-07-02T15:16:24.473810Z", + "shell.execute_reply": "2024-07-02T15:16:24.473346Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.793213Z", - "iopub.status.busy": "2024-07-02T12:07:00.792887Z", - "iopub.status.idle": "2024-07-02T12:07:00.798368Z", - "shell.execute_reply": "2024-07-02T12:07:00.797958Z" + "iopub.execute_input": "2024-07-02T15:16:24.475783Z", + "iopub.status.busy": "2024-07-02T15:16:24.475357Z", + "iopub.status.idle": "2024-07-02T15:16:24.481254Z", + "shell.execute_reply": "2024-07-02T15:16:24.480664Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.800409Z", - "iopub.status.busy": "2024-07-02T12:07:00.800087Z", - "iopub.status.idle": "2024-07-02T12:07:00.802550Z", - "shell.execute_reply": "2024-07-02T12:07:00.802117Z" + "iopub.execute_input": "2024-07-02T15:16:24.483486Z", + "iopub.status.busy": "2024-07-02T15:16:24.483065Z", + "iopub.status.idle": "2024-07-02T15:16:24.485618Z", + "shell.execute_reply": "2024-07-02T15:16:24.485175Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.804548Z", - "iopub.status.busy": "2024-07-02T12:07:00.804231Z", - "iopub.status.idle": "2024-07-02T12:07:09.170648Z", - "shell.execute_reply": "2024-07-02T12:07:09.170087Z" + "iopub.execute_input": "2024-07-02T15:16:24.487609Z", + "iopub.status.busy": "2024-07-02T15:16:24.487303Z", + "iopub.status.idle": "2024-07-02T15:16:33.078902Z", + "shell.execute_reply": "2024-07-02T15:16:33.078332Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.173635Z", - "iopub.status.busy": "2024-07-02T12:07:09.172986Z", - "iopub.status.idle": "2024-07-02T12:07:09.180628Z", - "shell.execute_reply": "2024-07-02T12:07:09.180165Z" + "iopub.execute_input": "2024-07-02T15:16:33.081569Z", + "iopub.status.busy": "2024-07-02T15:16:33.081171Z", + "iopub.status.idle": "2024-07-02T15:16:33.088462Z", + "shell.execute_reply": "2024-07-02T15:16:33.087998Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.182718Z", - "iopub.status.busy": "2024-07-02T12:07:09.182401Z", - "iopub.status.idle": "2024-07-02T12:07:09.186064Z", - "shell.execute_reply": "2024-07-02T12:07:09.185614Z" + "iopub.execute_input": "2024-07-02T15:16:33.090386Z", + "iopub.status.busy": "2024-07-02T15:16:33.090207Z", + "iopub.status.idle": "2024-07-02T15:16:33.093961Z", + "shell.execute_reply": "2024-07-02T15:16:33.093497Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.188065Z", - "iopub.status.busy": "2024-07-02T12:07:09.187765Z", - "iopub.status.idle": "2024-07-02T12:07:09.191124Z", - "shell.execute_reply": "2024-07-02T12:07:09.190682Z" + "iopub.execute_input": "2024-07-02T15:16:33.095977Z", + "iopub.status.busy": "2024-07-02T15:16:33.095566Z", + "iopub.status.idle": "2024-07-02T15:16:33.098952Z", + "shell.execute_reply": "2024-07-02T15:16:33.098404Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.193018Z", - "iopub.status.busy": "2024-07-02T12:07:09.192715Z", - "iopub.status.idle": "2024-07-02T12:07:09.195753Z", - "shell.execute_reply": "2024-07-02T12:07:09.195211Z" + "iopub.execute_input": "2024-07-02T15:16:33.101040Z", + "iopub.status.busy": "2024-07-02T15:16:33.100641Z", + "iopub.status.idle": "2024-07-02T15:16:33.103744Z", + "shell.execute_reply": "2024-07-02T15:16:33.103272Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.197818Z", - "iopub.status.busy": "2024-07-02T12:07:09.197511Z", - "iopub.status.idle": "2024-07-02T12:07:09.205619Z", - "shell.execute_reply": "2024-07-02T12:07:09.205180Z" + "iopub.execute_input": "2024-07-02T15:16:33.105508Z", + "iopub.status.busy": "2024-07-02T15:16:33.105338Z", + "iopub.status.idle": "2024-07-02T15:16:33.113464Z", + "shell.execute_reply": "2024-07-02T15:16:33.112912Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.207503Z", - "iopub.status.busy": "2024-07-02T12:07:09.207209Z", - "iopub.status.idle": "2024-07-02T12:07:09.209820Z", - "shell.execute_reply": "2024-07-02T12:07:09.209307Z" + "iopub.execute_input": "2024-07-02T15:16:33.115593Z", + "iopub.status.busy": "2024-07-02T15:16:33.115160Z", + "iopub.status.idle": "2024-07-02T15:16:33.117716Z", + "shell.execute_reply": "2024-07-02T15:16:33.117284Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.211933Z", - "iopub.status.busy": "2024-07-02T12:07:09.211620Z", - "iopub.status.idle": "2024-07-02T12:07:09.330539Z", - "shell.execute_reply": "2024-07-02T12:07:09.329946Z" + "iopub.execute_input": "2024-07-02T15:16:33.119784Z", + "iopub.status.busy": "2024-07-02T15:16:33.119483Z", + "iopub.status.idle": "2024-07-02T15:16:33.240234Z", + "shell.execute_reply": "2024-07-02T15:16:33.239660Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.332913Z", - "iopub.status.busy": "2024-07-02T12:07:09.332537Z", - "iopub.status.idle": "2024-07-02T12:07:09.439546Z", - "shell.execute_reply": "2024-07-02T12:07:09.438879Z" + "iopub.execute_input": "2024-07-02T15:16:33.242716Z", + "iopub.status.busy": "2024-07-02T15:16:33.242257Z", + "iopub.status.idle": "2024-07-02T15:16:33.345325Z", + "shell.execute_reply": "2024-07-02T15:16:33.344837Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.441953Z", - "iopub.status.busy": "2024-07-02T12:07:09.441731Z", - "iopub.status.idle": "2024-07-02T12:07:09.926340Z", - "shell.execute_reply": "2024-07-02T12:07:09.925811Z" + "iopub.execute_input": "2024-07-02T15:16:33.347642Z", + "iopub.status.busy": "2024-07-02T15:16:33.347274Z", + "iopub.status.idle": "2024-07-02T15:16:33.847085Z", + "shell.execute_reply": "2024-07-02T15:16:33.846449Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.928918Z", - "iopub.status.busy": "2024-07-02T12:07:09.928531Z", - "iopub.status.idle": "2024-07-02T12:07:10.007223Z", - "shell.execute_reply": "2024-07-02T12:07:10.006669Z" + "iopub.execute_input": "2024-07-02T15:16:33.849760Z", + "iopub.status.busy": "2024-07-02T15:16:33.849569Z", + "iopub.status.idle": "2024-07-02T15:16:33.921326Z", + "shell.execute_reply": "2024-07-02T15:16:33.920734Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:10.009492Z", - "iopub.status.busy": "2024-07-02T12:07:10.009118Z", - "iopub.status.idle": "2024-07-02T12:07:10.017415Z", - "shell.execute_reply": "2024-07-02T12:07:10.016968Z" + "iopub.execute_input": "2024-07-02T15:16:33.923630Z", + "iopub.status.busy": "2024-07-02T15:16:33.923266Z", + "iopub.status.idle": "2024-07-02T15:16:33.931669Z", + "shell.execute_reply": "2024-07-02T15:16:33.931217Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:10.019396Z", - "iopub.status.busy": "2024-07-02T12:07:10.019069Z", - "iopub.status.idle": "2024-07-02T12:07:10.021767Z", - "shell.execute_reply": "2024-07-02T12:07:10.021319Z" + "iopub.execute_input": "2024-07-02T15:16:33.933564Z", + "iopub.status.busy": "2024-07-02T15:16:33.933243Z", + "iopub.status.idle": "2024-07-02T15:16:33.935935Z", + "shell.execute_reply": "2024-07-02T15:16:33.935490Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:10.023754Z", - "iopub.status.busy": "2024-07-02T12:07:10.023447Z", - "iopub.status.idle": "2024-07-02T12:07:15.333825Z", - "shell.execute_reply": "2024-07-02T12:07:15.333229Z" + "iopub.execute_input": "2024-07-02T15:16:33.937940Z", + "iopub.status.busy": "2024-07-02T15:16:33.937538Z", + "iopub.status.idle": "2024-07-02T15:16:39.357576Z", + "shell.execute_reply": "2024-07-02T15:16:39.356965Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:15.336220Z", - "iopub.status.busy": "2024-07-02T12:07:15.335826Z", - "iopub.status.idle": "2024-07-02T12:07:15.344270Z", - "shell.execute_reply": "2024-07-02T12:07:15.343811Z" + "iopub.execute_input": "2024-07-02T15:16:39.359859Z", + "iopub.status.busy": "2024-07-02T15:16:39.359635Z", + "iopub.status.idle": "2024-07-02T15:16:39.368310Z", + "shell.execute_reply": "2024-07-02T15:16:39.367738Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:15.346339Z", - "iopub.status.busy": "2024-07-02T12:07:15.346012Z", - "iopub.status.idle": "2024-07-02T12:07:15.414442Z", - "shell.execute_reply": "2024-07-02T12:07:15.413948Z" + "iopub.execute_input": "2024-07-02T15:16:39.370438Z", + "iopub.status.busy": "2024-07-02T15:16:39.370050Z", + "iopub.status.idle": "2024-07-02T15:16:39.434092Z", + "shell.execute_reply": "2024-07-02T15:16:39.433485Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index f10d41fcb..6c0dcf672 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -800,13 +800,13 @@

3. Use cleanlab to find label issues

-
+
-
+

Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().

@@ -1196,7 +1196,7 @@

Get label quality scores -{"state": {"acd636f3fd2248ec95449072f24f9e7c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "58e7f42a53f94f1bbd0867761587546d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "7fe63f20c00f4319a0964ba731ec434b": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_acd636f3fd2248ec95449072f24f9e7c", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_58e7f42a53f94f1bbd0867761587546d", "tabbable": null, "tooltip": null, "value": 30.0}}, "3bc90b1caf7040428b193fe64f002cf0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "004250ad803f48e690d7de9d8df2a5d4": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2cda7dd9aaa748a58027afd119786f13": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3bc90b1caf7040428b193fe64f002cf0", "placeholder": "\u200b", "style": "IPY_MODEL_004250ad803f48e690d7de9d8df2a5d4", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "5148cb9775f14bd194e8f971e6975671": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e1313619907446f882ce04af77ca7ac9": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "cec723cee8f249199133eca7dc012de4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5148cb9775f14bd194e8f971e6975671", "placeholder": "\u200b", "style": "IPY_MODEL_e1313619907446f882ce04af77ca7ac9", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007789.00it/s]"}}, "443d77bb1d4e408f9a9d727916a4a908": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9e20fdede857444e8054f80d2f1060d4": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2cda7dd9aaa748a58027afd119786f13", "IPY_MODEL_7fe63f20c00f4319a0964ba731ec434b", "IPY_MODEL_cec723cee8f249199133eca7dc012de4"], "layout": "IPY_MODEL_443d77bb1d4e408f9a9d727916a4a908", "tabbable": null, "tooltip": null}}, "b345e277b7bf4c62b4c637851b6c0214": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "aa5c0233f9e24cfb94a975b0bdddc4f5": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "87cc943b387f45588eebeaa6c9ffd2b2": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b345e277b7bf4c62b4c637851b6c0214", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_aa5c0233f9e24cfb94a975b0bdddc4f5", "tabbable": null, "tooltip": null, "value": 30.0}}, "14f7f9d4ea3e4f77a067725d5a561423": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "df3ac9cfe3ae41ceb9de218f2d91ba0b": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "689cb1d684d746f081730b6005754954": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_14f7f9d4ea3e4f77a067725d5a561423", "placeholder": "\u200b", "style": "IPY_MODEL_df3ac9cfe3ae41ceb9de218f2d91ba0b", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "8d802ab243544f9d9299452347a70569": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9c2434fab6d1467eb5531b5dd54033e7": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "621b8d69d18c4672a45b4f9791a34f0e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8d802ab243544f9d9299452347a70569", "placeholder": "\u200b", "style": "IPY_MODEL_9c2434fab6d1467eb5531b5dd54033e7", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:22<00:00,\u2007\u20071.35it/s]"}}, "39fca7453c7a49f9b90660089493b14b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "301ab18342ea43859b3e69cf6784234e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_689cb1d684d746f081730b6005754954", "IPY_MODEL_87cc943b387f45588eebeaa6c9ffd2b2", "IPY_MODEL_621b8d69d18c4672a45b4f9791a34f0e"], "layout": "IPY_MODEL_39fca7453c7a49f9b90660089493b14b", "tabbable": null, "tooltip": null}}, "6ec1d65e68c649a287aa126587fe0f81": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "40f6e020e4814cb8b0192d390bc249e5": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "d0ee84f27abf457094d435faffe772aa": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6ec1d65e68c649a287aa126587fe0f81", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_40f6e020e4814cb8b0192d390bc249e5", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "1c5deedf3c2d452d9820d81060a3a997": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c90d5255c6884dd0acaca4ca4a0be555": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2b3a352a3891401497f4005cb5fc04d1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1c5deedf3c2d452d9820d81060a3a997", "placeholder": "\u200b", "style": "IPY_MODEL_c90d5255c6884dd0acaca4ca4a0be555", "tabbable": null, "tooltip": null, "value": "100%"}}, "5c44620b24814621897a324f8628f9b0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8c0884f36e9a43fcb603fc1d8a5ac45d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2d192adc8c47477a9a6eecca9e36e444": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5c44620b24814621897a324f8628f9b0", "placeholder": "\u200b", "style": "IPY_MODEL_8c0884f36e9a43fcb603fc1d8a5ac45d", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:31<00:00,\u2007155900.40it/s]"}}, "d13c21dbbe954346b0c709b6d940db68": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bd3e5cdb83b549b9ac1d29639e5d5848": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2b3a352a3891401497f4005cb5fc04d1", "IPY_MODEL_d0ee84f27abf457094d435faffe772aa", "IPY_MODEL_2d192adc8c47477a9a6eecca9e36e444"], "layout": "IPY_MODEL_d13c21dbbe954346b0c709b6d940db68", "tabbable": null, "tooltip": null}}, "b3df8f2c00a04d5b832de9fc959e9aae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f6b0d85730d34497bc3daf8d027415bc": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "dbe2204bfeee42de9c8c9d92d9dc0eb7": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b3df8f2c00a04d5b832de9fc959e9aae", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f6b0d85730d34497bc3daf8d027415bc", "tabbable": null, "tooltip": null, "value": 30.0}}, "6fad649441314d58a415b41b259d3ca9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "46d41e19a14142bbad7d9d0b73fd7a54": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ba6883ab9881467ba869997da1c9ea0e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6fad649441314d58a415b41b259d3ca9", "placeholder": "\u200b", "style": "IPY_MODEL_46d41e19a14142bbad7d9d0b73fd7a54", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "292d8527f19545118904c48eb804b159": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ebe541c7a5ed4c939a5f7b993c9dee23": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "09ba332d59f94952875cd79ebffa12b3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_292d8527f19545118904c48eb804b159", "placeholder": "\u200b", "style": "IPY_MODEL_ebe541c7a5ed4c939a5f7b993c9dee23", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200720.84it/s]"}}, "50d3a7dfb3964711a029df0085e31f7b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f7bb7e722917409d87abfe3e6a57fae6": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ba6883ab9881467ba869997da1c9ea0e", "IPY_MODEL_dbe2204bfeee42de9c8c9d92d9dc0eb7", "IPY_MODEL_09ba332d59f94952875cd79ebffa12b3"], "layout": "IPY_MODEL_50d3a7dfb3964711a029df0085e31f7b", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"f14aada5b0eb4e5ba3b431cb7b518da6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cbbd654031b747468c432f9278836125": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "544233229d944fd690c9e89d8792bbe8": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f14aada5b0eb4e5ba3b431cb7b518da6", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_cbbd654031b747468c432f9278836125", "tabbable": null, "tooltip": null, "value": 30.0}}, "9f4790501bef48328e2bd3eee0152689": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9479cd74f7ae4ab98e7dd6164deed30a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b4d580e3f19a47c6a1dde0776a4e5606": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9f4790501bef48328e2bd3eee0152689", "placeholder": "\u200b", "style": "IPY_MODEL_9479cd74f7ae4ab98e7dd6164deed30a", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "7014f38f051242f19c5f77d4e2e3dd69": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b2ec5e9d337d409f8f6128aa41a5d79f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b8c4edcee9764b17abf836ac1f23e926": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7014f38f051242f19c5f77d4e2e3dd69", "placeholder": "\u200b", "style": "IPY_MODEL_b2ec5e9d337d409f8f6128aa41a5d79f", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007785.11it/s]"}}, "98b4559124704d61a177e8a88a9d3c8a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6d37081f0d674141ab48e998533cdac5": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b4d580e3f19a47c6a1dde0776a4e5606", "IPY_MODEL_544233229d944fd690c9e89d8792bbe8", "IPY_MODEL_b8c4edcee9764b17abf836ac1f23e926"], "layout": "IPY_MODEL_98b4559124704d61a177e8a88a9d3c8a", "tabbable": null, "tooltip": null}}, "e5f3cab627be4d4a8d76b371ca9346f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "16f22dbcf3de44d7b4fd798a1f0b853a": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "eaaf30bb31f7495d8fbee7118acc211f": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e5f3cab627be4d4a8d76b371ca9346f3", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_16f22dbcf3de44d7b4fd798a1f0b853a", "tabbable": null, "tooltip": null, "value": 30.0}}, "80bc86fa44ab4ddb9d7fbfcd03b7da23": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5a6ca2db026040538be2877748a39874": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "0aae918c08c84bed9ae02fe6e0c0d703": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_80bc86fa44ab4ddb9d7fbfcd03b7da23", "placeholder": "\u200b", "style": "IPY_MODEL_5a6ca2db026040538be2877748a39874", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "b27b9980b1934cddbaa61f473c83ad55": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "03565a80c7d34c6293339e9385fd0f09": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "cd8af1b2cbd542f4869d02c1d973e524": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b27b9980b1934cddbaa61f473c83ad55", "placeholder": "\u200b", "style": "IPY_MODEL_03565a80c7d34c6293339e9385fd0f09", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:20<00:00,\u2007\u20071.41it/s]"}}, "afdd70b438584e8381748e28bdc60eb0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6181f2ac640b4d5694a1537900a59156": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_0aae918c08c84bed9ae02fe6e0c0d703", "IPY_MODEL_eaaf30bb31f7495d8fbee7118acc211f", "IPY_MODEL_cd8af1b2cbd542f4869d02c1d973e524"], "layout": "IPY_MODEL_afdd70b438584e8381748e28bdc60eb0", "tabbable": null, "tooltip": null}}, "f3a91f9853db4fac966be7c379809f9c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ce421d89a8e8429486414e8e5f2fb0b9": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "72b5647ddfd149559d4a948d2411a55b": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f3a91f9853db4fac966be7c379809f9c", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_ce421d89a8e8429486414e8e5f2fb0b9", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "9ba76afb638e410e891187e79a458dac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0c57b519892e41bcb28985872ce030c7": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f58763e1734248fda5001bdfeb216209": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9ba76afb638e410e891187e79a458dac", "placeholder": "\u200b", "style": "IPY_MODEL_0c57b519892e41bcb28985872ce030c7", "tabbable": null, "tooltip": null, "value": "100%"}}, "a5072a4bea8140e2808839dad337d950": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a2f21f1a521e4c718452c19f8256841e": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "0d5c01051776423480b74fabbe29251b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a5072a4bea8140e2808839dad337d950", "placeholder": "\u200b", "style": "IPY_MODEL_a2f21f1a521e4c718452c19f8256841e", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:33<00:00,\u2007149090.50it/s]"}}, "d4139e678fc14fc5b5f22a2a0dd84027": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ee454cb23f344e94bf0306f6bd70e6ef": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_f58763e1734248fda5001bdfeb216209", "IPY_MODEL_72b5647ddfd149559d4a948d2411a55b", "IPY_MODEL_0d5c01051776423480b74fabbe29251b"], "layout": "IPY_MODEL_d4139e678fc14fc5b5f22a2a0dd84027", "tabbable": null, "tooltip": null}}, "0b8a0362dd6d4e8faa60de3132d8f2b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f3ecd743295c4f619abad363dcb05fab": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "3401bd6344664b4f85cbb90cf38682a3": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0b8a0362dd6d4e8faa60de3132d8f2b3", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f3ecd743295c4f619abad363dcb05fab", "tabbable": null, "tooltip": null, "value": 30.0}}, "0ea451f86f7b4b05957d9adf3ae7f735": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cf74a277aa3141788d774f9065d2366d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ac2a26cb4281477983b7746968ff3e52": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0ea451f86f7b4b05957d9adf3ae7f735", "placeholder": "\u200b", "style": "IPY_MODEL_cf74a277aa3141788d774f9065d2366d", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "b2f361ba3c35411587bfb94055421b84": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "97090cf694824c77b08cfdb17f057e3e": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "4102b17c3b13403aa9b3d80fb7df75ac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b2f361ba3c35411587bfb94055421b84", "placeholder": "\u200b", "style": "IPY_MODEL_97090cf694824c77b08cfdb17f057e3e", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200721.42it/s]"}}, "ecae615274e34bf59e8e58c649c294bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "22d36bebc1d941d19f0aa385e194e320": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ac2a26cb4281477983b7746968ff3e52", "IPY_MODEL_3401bd6344664b4f85cbb90cf38682a3", "IPY_MODEL_4102b17c3b13403aa9b3d80fb7df75ac"], "layout": "IPY_MODEL_ecae615274e34bf59e8e58c649c294bc", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index fdafb004b..f4716d029 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:18.593560Z", - "iopub.status.busy": "2024-07-02T12:07:18.593400Z", - "iopub.status.idle": "2024-07-02T12:07:20.263944Z", - "shell.execute_reply": "2024-07-02T12:07:20.263270Z" + "iopub.execute_input": "2024-07-02T15:16:42.561018Z", + "iopub.status.busy": "2024-07-02T15:16:42.560861Z", + "iopub.status.idle": "2024-07-02T15:16:44.625687Z", + "shell.execute_reply": "2024-07-02T15:16:44.624982Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:20.266581Z", - "iopub.status.busy": "2024-07-02T12:07:20.266205Z", - "iopub.status.idle": "2024-07-02T12:08:06.109041Z", - "shell.execute_reply": "2024-07-02T12:08:06.108401Z" + "iopub.execute_input": "2024-07-02T15:16:44.628410Z", + "iopub.status.busy": "2024-07-02T15:16:44.628235Z", + "iopub.status.idle": "2024-07-02T15:17:44.748591Z", + "shell.execute_reply": "2024-07-02T15:17:44.747911Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:06.111457Z", - "iopub.status.busy": "2024-07-02T12:08:06.111270Z", - "iopub.status.idle": "2024-07-02T12:08:07.194905Z", - "shell.execute_reply": "2024-07-02T12:08:07.194300Z" + "iopub.execute_input": "2024-07-02T15:17:44.750950Z", + "iopub.status.busy": "2024-07-02T15:17:44.750762Z", + "iopub.status.idle": "2024-07-02T15:17:45.855060Z", + "shell.execute_reply": "2024-07-02T15:17:45.854509Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.197493Z", - "iopub.status.busy": "2024-07-02T12:08:07.197237Z", - "iopub.status.idle": "2024-07-02T12:08:07.200309Z", - "shell.execute_reply": "2024-07-02T12:08:07.199874Z" + "iopub.execute_input": "2024-07-02T15:17:45.857557Z", + "iopub.status.busy": "2024-07-02T15:17:45.857136Z", + "iopub.status.idle": "2024-07-02T15:17:45.860333Z", + "shell.execute_reply": "2024-07-02T15:17:45.859895Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.202276Z", - "iopub.status.busy": "2024-07-02T12:08:07.202097Z", - "iopub.status.idle": "2024-07-02T12:08:07.205874Z", - "shell.execute_reply": "2024-07-02T12:08:07.205417Z" + "iopub.execute_input": "2024-07-02T15:17:45.862386Z", + "iopub.status.busy": "2024-07-02T15:17:45.862053Z", + "iopub.status.idle": "2024-07-02T15:17:45.865756Z", + "shell.execute_reply": "2024-07-02T15:17:45.865329Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.207818Z", - "iopub.status.busy": "2024-07-02T12:08:07.207520Z", - "iopub.status.idle": "2024-07-02T12:08:07.211075Z", - "shell.execute_reply": "2024-07-02T12:08:07.210551Z" + "iopub.execute_input": "2024-07-02T15:17:45.867774Z", + "iopub.status.busy": "2024-07-02T15:17:45.867526Z", + "iopub.status.idle": "2024-07-02T15:17:45.871521Z", + "shell.execute_reply": "2024-07-02T15:17:45.871083Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.213131Z", - "iopub.status.busy": "2024-07-02T12:08:07.212769Z", - "iopub.status.idle": "2024-07-02T12:08:07.215484Z", - "shell.execute_reply": "2024-07-02T12:08:07.215039Z" + "iopub.execute_input": "2024-07-02T15:17:45.873483Z", + "iopub.status.busy": "2024-07-02T15:17:45.873088Z", + "iopub.status.idle": "2024-07-02T15:17:45.875944Z", + "shell.execute_reply": "2024-07-02T15:17:45.875421Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.217418Z", - "iopub.status.busy": "2024-07-02T12:08:07.217121Z", - "iopub.status.idle": "2024-07-02T12:08:41.707148Z", - "shell.execute_reply": "2024-07-02T12:08:41.706563Z" + "iopub.execute_input": "2024-07-02T15:17:45.878104Z", + "iopub.status.busy": "2024-07-02T15:17:45.877703Z", + "iopub.status.idle": "2024-07-02T15:18:18.817812Z", + "shell.execute_reply": "2024-07-02T15:18:18.817242Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9e20fdede857444e8054f80d2f1060d4", + "model_id": "6d37081f0d674141ab48e998533cdac5", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "301ab18342ea43859b3e69cf6784234e", + "model_id": "6181f2ac640b4d5694a1537900a59156", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:41.710056Z", - "iopub.status.busy": "2024-07-02T12:08:41.709655Z", - "iopub.status.idle": "2024-07-02T12:08:42.388632Z", - "shell.execute_reply": "2024-07-02T12:08:42.388139Z" + "iopub.execute_input": "2024-07-02T15:18:18.820319Z", + "iopub.status.busy": "2024-07-02T15:18:18.819979Z", + "iopub.status.idle": "2024-07-02T15:18:19.488167Z", + "shell.execute_reply": "2024-07-02T15:18:19.487624Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:42.390931Z", - "iopub.status.busy": "2024-07-02T12:08:42.390474Z", - "iopub.status.idle": "2024-07-02T12:08:45.214722Z", - "shell.execute_reply": "2024-07-02T12:08:45.214183Z" + "iopub.execute_input": "2024-07-02T15:18:19.490558Z", + "iopub.status.busy": "2024-07-02T15:18:19.490113Z", + "iopub.status.idle": "2024-07-02T15:18:22.347830Z", + "shell.execute_reply": "2024-07-02T15:18:22.347301Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:45.217043Z", - "iopub.status.busy": "2024-07-02T12:08:45.216683Z", - "iopub.status.idle": "2024-07-02T12:09:17.125267Z", - "shell.execute_reply": "2024-07-02T12:09:17.124709Z" + "iopub.execute_input": "2024-07-02T15:18:22.350104Z", + "iopub.status.busy": "2024-07-02T15:18:22.349819Z", + "iopub.status.idle": "2024-07-02T15:18:55.684419Z", + "shell.execute_reply": "2024-07-02T15:18:55.683880Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bd3e5cdb83b549b9ac1d29639e5d5848", + "model_id": "ee454cb23f344e94bf0306f6bd70e6ef", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:17.127732Z", - "iopub.status.busy": "2024-07-02T12:09:17.127284Z", - "iopub.status.idle": "2024-07-02T12:09:31.678319Z", - "shell.execute_reply": "2024-07-02T12:09:31.677670Z" + "iopub.execute_input": "2024-07-02T15:18:55.686609Z", + "iopub.status.busy": "2024-07-02T15:18:55.686279Z", + "iopub.status.idle": "2024-07-02T15:19:09.955147Z", + "shell.execute_reply": "2024-07-02T15:19:09.954600Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:31.680982Z", - "iopub.status.busy": "2024-07-02T12:09:31.680776Z", - "iopub.status.idle": "2024-07-02T12:09:35.425388Z", - "shell.execute_reply": "2024-07-02T12:09:35.424766Z" + "iopub.execute_input": "2024-07-02T15:19:09.957540Z", + "iopub.status.busy": "2024-07-02T15:19:09.957240Z", + "iopub.status.idle": "2024-07-02T15:19:13.735071Z", + "shell.execute_reply": "2024-07-02T15:19:13.734559Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:35.427710Z", - "iopub.status.busy": "2024-07-02T12:09:35.427361Z", - "iopub.status.idle": "2024-07-02T12:09:36.906817Z", - "shell.execute_reply": "2024-07-02T12:09:36.906253Z" + "iopub.execute_input": "2024-07-02T15:19:13.737328Z", + "iopub.status.busy": "2024-07-02T15:19:13.736989Z", + "iopub.status.idle": "2024-07-02T15:19:15.136499Z", + "shell.execute_reply": "2024-07-02T15:19:15.135918Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7bb7e722917409d87abfe3e6a57fae6", + "model_id": "22d36bebc1d941d19f0aa385e194e320", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:36.909119Z", - "iopub.status.busy": "2024-07-02T12:09:36.908767Z", - "iopub.status.idle": "2024-07-02T12:09:36.938376Z", - "shell.execute_reply": "2024-07-02T12:09:36.937849Z" + "iopub.execute_input": "2024-07-02T15:19:15.138723Z", + "iopub.status.busy": "2024-07-02T15:19:15.138396Z", + "iopub.status.idle": "2024-07-02T15:19:15.166918Z", + "shell.execute_reply": "2024-07-02T15:19:15.166433Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:36.940953Z", - "iopub.status.busy": "2024-07-02T12:09:36.940583Z", - "iopub.status.idle": "2024-07-02T12:09:42.990503Z", - "shell.execute_reply": "2024-07-02T12:09:42.989906Z" + "iopub.execute_input": "2024-07-02T15:19:15.169310Z", + "iopub.status.busy": "2024-07-02T15:19:15.168971Z", + "iopub.status.idle": "2024-07-02T15:19:21.253732Z", + "shell.execute_reply": "2024-07-02T15:19:21.253153Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:42.992659Z", - "iopub.status.busy": "2024-07-02T12:09:42.992472Z", - "iopub.status.idle": "2024-07-02T12:09:43.049918Z", - "shell.execute_reply": "2024-07-02T12:09:43.049421Z" + "iopub.execute_input": "2024-07-02T15:19:21.256000Z", + "iopub.status.busy": "2024-07-02T15:19:21.255599Z", + "iopub.status.idle": "2024-07-02T15:19:21.311129Z", + "shell.execute_reply": "2024-07-02T15:19:21.310514Z" }, "nbsphinx": "hidden" }, @@ -1038,7 +1038,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "004250ad803f48e690d7de9d8df2a5d4": { + "03565a80c7d34c6293339e9385fd0f09": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1056,7 +1056,7 @@ "text_color": null } }, - "09ba332d59f94952875cd79ebffa12b3": { + "0aae918c08c84bed9ae02fe6e0c0d703": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1071,15 +1071,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_292d8527f19545118904c48eb804b159", + "layout": "IPY_MODEL_80bc86fa44ab4ddb9d7fbfcd03b7da23", "placeholder": "​", - "style": "IPY_MODEL_ebe541c7a5ed4c939a5f7b993c9dee23", + "style": "IPY_MODEL_5a6ca2db026040538be2877748a39874", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 20.84it/s]" + "value": "number of examples processed for checking labels: 100%" } }, - "14f7f9d4ea3e4f77a067725d5a561423": { + "0b8a0362dd6d4e8faa60de3132d8f2b3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1132,60 +1132,48 @@ "width": null } }, - "1c5deedf3c2d452d9820d81060a3a997": { - "model_module": "@jupyter-widgets/base", + "0c57b519892e41bcb28985872ce030c7": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0d5c01051776423480b74fabbe29251b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a5072a4bea8140e2808839dad337d950", + "placeholder": "​", + "style": "IPY_MODEL_a2f21f1a521e4c718452c19f8256841e", + "tabbable": null, + "tooltip": null, + "value": " 4997683/4997683 [00:33<00:00, 149090.50it/s]" } }, - "292d8527f19545118904c48eb804b159": { + "0ea451f86f7b4b05957d9adf3ae7f735": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1238,30 +1226,73 @@ "width": null } }, - "2b3a352a3891401497f4005cb5fc04d1": { + "16f22dbcf3de44d7b4fd798a1f0b853a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "22d36bebc1d941d19f0aa385e194e320": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ac2a26cb4281477983b7746968ff3e52", + "IPY_MODEL_3401bd6344664b4f85cbb90cf38682a3", + "IPY_MODEL_4102b17c3b13403aa9b3d80fb7df75ac" + ], + "layout": "IPY_MODEL_ecae615274e34bf59e8e58c649c294bc", + "tabbable": null, + "tooltip": null + } + }, + "3401bd6344664b4f85cbb90cf38682a3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1c5deedf3c2d452d9820d81060a3a997", - "placeholder": "​", - "style": "IPY_MODEL_c90d5255c6884dd0acaca4ca4a0be555", + "layout": "IPY_MODEL_0b8a0362dd6d4e8faa60de3132d8f2b3", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f3ecd743295c4f619abad363dcb05fab", "tabbable": null, "tooltip": null, - "value": "100%" + "value": 30.0 } }, - "2cda7dd9aaa748a58027afd119786f13": { + "4102b17c3b13403aa9b3d80fb7df75ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1276,38 +1307,83 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3bc90b1caf7040428b193fe64f002cf0", + "layout": "IPY_MODEL_b2f361ba3c35411587bfb94055421b84", "placeholder": "​", - "style": "IPY_MODEL_004250ad803f48e690d7de9d8df2a5d4", + "style": "IPY_MODEL_97090cf694824c77b08cfdb17f057e3e", "tabbable": null, "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" + "value": " 30/30 [00:01<00:00, 21.42it/s]" } }, - "2d192adc8c47477a9a6eecca9e36e444": { + "544233229d944fd690c9e89d8792bbe8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5c44620b24814621897a324f8628f9b0", - "placeholder": "​", - "style": "IPY_MODEL_8c0884f36e9a43fcb603fc1d8a5ac45d", + "layout": "IPY_MODEL_f14aada5b0eb4e5ba3b431cb7b518da6", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cbbd654031b747468c432f9278836125", "tabbable": null, "tooltip": null, - "value": " 4997683/4997683 [00:31<00:00, 155900.40it/s]" + "value": 30.0 + } + }, + "5a6ca2db026040538be2877748a39874": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6181f2ac640b4d5694a1537900a59156": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0aae918c08c84bed9ae02fe6e0c0d703", + "IPY_MODEL_eaaf30bb31f7495d8fbee7118acc211f", + "IPY_MODEL_cd8af1b2cbd542f4869d02c1d973e524" + ], + "layout": "IPY_MODEL_afdd70b438584e8381748e28bdc60eb0", + "tabbable": null, + "tooltip": null } }, - "301ab18342ea43859b3e69cf6784234e": { + "6d37081f0d674141ab48e998533cdac5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1322,16 +1398,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_689cb1d684d746f081730b6005754954", - "IPY_MODEL_87cc943b387f45588eebeaa6c9ffd2b2", - "IPY_MODEL_621b8d69d18c4672a45b4f9791a34f0e" + "IPY_MODEL_b4d580e3f19a47c6a1dde0776a4e5606", + "IPY_MODEL_544233229d944fd690c9e89d8792bbe8", + "IPY_MODEL_b8c4edcee9764b17abf836ac1f23e926" ], - "layout": "IPY_MODEL_39fca7453c7a49f9b90660089493b14b", + "layout": "IPY_MODEL_98b4559124704d61a177e8a88a9d3c8a", "tabbable": null, "tooltip": null } }, - "39fca7453c7a49f9b90660089493b14b": { + "7014f38f051242f19c5f77d4e2e3dd69": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1384,7 +1460,33 @@ "width": null } }, - "3bc90b1caf7040428b193fe64f002cf0": { + "72b5647ddfd149559d4a948d2411a55b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f3a91f9853db4fac966be7c379809f9c", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ce421d89a8e8429486414e8e5f2fb0b9", + "tabbable": null, + "tooltip": null, + "value": 4997683.0 + } + }, + "80bc86fa44ab4ddb9d7fbfcd03b7da23": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1437,23 +1539,43 @@ "width": null } }, - "40f6e020e4814cb8b0192d390bc249e5": { + "9479cd74f7ae4ab98e7dd6164deed30a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "97090cf694824c77b08cfdb17f057e3e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "443d77bb1d4e408f9a9d727916a4a908": { + "98b4559124704d61a177e8a88a9d3c8a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1506,25 +1628,7 @@ "width": null } }, - "46d41e19a14142bbad7d9d0b73fd7a54": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "50d3a7dfb3964711a029df0085e31f7b": { + "9ba76afb638e410e891187e79a458dac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1577,7 +1681,7 @@ "width": null } }, - "5148cb9775f14bd194e8f971e6975671": { + "9f4790501bef48328e2bd3eee0152689": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1630,23 +1734,25 @@ "width": null } }, - "58e7f42a53f94f1bbd0867761587546d": { + "a2f21f1a521e4c718452c19f8256841e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5c44620b24814621897a324f8628f9b0": { + "a5072a4bea8140e2808839dad337d950": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1699,7 +1805,7 @@ "width": null } }, - "621b8d69d18c4672a45b4f9791a34f0e": { + "ac2a26cb4281477983b7746968ff3e52": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1714,45 +1820,22 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8d802ab243544f9d9299452347a70569", + "layout": "IPY_MODEL_0ea451f86f7b4b05957d9adf3ae7f735", "placeholder": "​", - "style": "IPY_MODEL_9c2434fab6d1467eb5531b5dd54033e7", + "style": "IPY_MODEL_cf74a277aa3141788d774f9065d2366d", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:22<00:00,  1.35it/s]" + "value": "images processed using softmin: 100%" } }, - "689cb1d684d746f081730b6005754954": { - "model_module": "@jupyter-widgets/controls", + "afdd70b438584e8381748e28bdc60eb0": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_14f7f9d4ea3e4f77a067725d5a561423", - "placeholder": "​", - "style": "IPY_MODEL_df3ac9cfe3ae41ceb9de218f2d91ba0b", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" - } - }, - "6ec1d65e68c649a287aa126587fe0f81": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -1798,7 +1881,7 @@ "width": null } }, - "6fad649441314d58a415b41b259d3ca9": { + "b27b9980b1934cddbaa61f473c83ad55": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1851,59 +1934,7 @@ "width": null } }, - "7fe63f20c00f4319a0964ba731ec434b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_acd636f3fd2248ec95449072f24f9e7c", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_58e7f42a53f94f1bbd0867761587546d", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "87cc943b387f45588eebeaa6c9ffd2b2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b345e277b7bf4c62b4c637851b6c0214", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_aa5c0233f9e24cfb94a975b0bdddc4f5", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "8c0884f36e9a43fcb603fc1d8a5ac45d": { + "b2ec5e9d337d409f8f6128aa41a5d79f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1921,7 +1952,7 @@ "text_color": null } }, - "8d802ab243544f9d9299452347a70569": { + "b2f361ba3c35411587bfb94055421b84": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1974,49 +2005,92 @@ "width": null } }, - "9c2434fab6d1467eb5531b5dd54033e7": { + "b4d580e3f19a47c6a1dde0776a4e5606": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9f4790501bef48328e2bd3eee0152689", + "placeholder": "​", + "style": "IPY_MODEL_9479cd74f7ae4ab98e7dd6164deed30a", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" + } + }, + "b8c4edcee9764b17abf836ac1f23e926": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7014f38f051242f19c5f77d4e2e3dd69", + "placeholder": "​", + "style": "IPY_MODEL_b2ec5e9d337d409f8f6128aa41a5d79f", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 785.11it/s]" + } + }, + "cbbd654031b747468c432f9278836125": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "9e20fdede857444e8054f80d2f1060d4": { + "cd8af1b2cbd542f4869d02c1d973e524": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2cda7dd9aaa748a58027afd119786f13", - "IPY_MODEL_7fe63f20c00f4319a0964ba731ec434b", - "IPY_MODEL_cec723cee8f249199133eca7dc012de4" - ], - "layout": "IPY_MODEL_443d77bb1d4e408f9a9d727916a4a908", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b27b9980b1934cddbaa61f473c83ad55", + "placeholder": "​", + "style": "IPY_MODEL_03565a80c7d34c6293339e9385fd0f09", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 30/30 [00:20<00:00,  1.41it/s]" } }, - "aa5c0233f9e24cfb94a975b0bdddc4f5": { + "ce421d89a8e8429486414e8e5f2fb0b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2032,7 +2106,25 @@ "description_width": "" } }, - "acd636f3fd2248ec95449072f24f9e7c": { + "cf74a277aa3141788d774f9065d2366d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d4139e678fc14fc5b5f22a2a0dd84027": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2085,7 +2177,7 @@ "width": null } }, - "b345e277b7bf4c62b4c637851b6c0214": { + "e5f3cab627be4d4a8d76b371ca9346f3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2138,7 +2230,33 @@ "width": null } }, - "b3df8f2c00a04d5b832de9fc959e9aae": { + "eaaf30bb31f7495d8fbee7118acc211f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e5f3cab627be4d4a8d76b371ca9346f3", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_16f22dbcf3de44d7b4fd798a1f0b853a", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "ecae615274e34bf59e8e58c649c294bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2191,30 +2309,7 @@ "width": null } }, - "ba6883ab9881467ba869997da1c9ea0e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6fad649441314d58a415b41b259d3ca9", - "placeholder": "​", - "style": "IPY_MODEL_46d41e19a14142bbad7d9d0b73fd7a54", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "bd3e5cdb83b549b9ac1d29639e5d5848": { + "ee454cb23f344e94bf0306f6bd70e6ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2229,83 +2324,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2b3a352a3891401497f4005cb5fc04d1", - "IPY_MODEL_d0ee84f27abf457094d435faffe772aa", - "IPY_MODEL_2d192adc8c47477a9a6eecca9e36e444" + "IPY_MODEL_f58763e1734248fda5001bdfeb216209", + "IPY_MODEL_72b5647ddfd149559d4a948d2411a55b", + "IPY_MODEL_0d5c01051776423480b74fabbe29251b" ], - "layout": "IPY_MODEL_d13c21dbbe954346b0c709b6d940db68", + "layout": "IPY_MODEL_d4139e678fc14fc5b5f22a2a0dd84027", "tabbable": null, "tooltip": null } }, - "c90d5255c6884dd0acaca4ca4a0be555": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "cec723cee8f249199133eca7dc012de4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5148cb9775f14bd194e8f971e6975671", - "placeholder": "​", - "style": "IPY_MODEL_e1313619907446f882ce04af77ca7ac9", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 789.00it/s]" - } - }, - "d0ee84f27abf457094d435faffe772aa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6ec1d65e68c649a287aa126587fe0f81", - "max": 4997683.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_40f6e020e4814cb8b0192d390bc249e5", - "tabbable": null, - "tooltip": null, - "value": 4997683.0 - } - }, - "d13c21dbbe954346b0c709b6d940db68": { + "f14aada5b0eb4e5ba3b431cb7b518da6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2358,87 +2386,60 @@ "width": null } }, - "dbe2204bfeee42de9c8c9d92d9dc0eb7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b3df8f2c00a04d5b832de9fc959e9aae", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f6b0d85730d34497bc3daf8d027415bc", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "df3ac9cfe3ae41ceb9de218f2d91ba0b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "e1313619907446f882ce04af77ca7ac9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "ebe541c7a5ed4c939a5f7b993c9dee23": { - "model_module": "@jupyter-widgets/controls", + "f3a91f9853db4fac966be7c379809f9c": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "f6b0d85730d34497bc3daf8d027415bc": { + "f3ecd743295c4f619abad363dcb05fab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2454,28 +2455,27 @@ "description_width": "" } }, - "f7bb7e722917409d87abfe3e6a57fae6": { + "f58763e1734248fda5001bdfeb216209": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ba6883ab9881467ba869997da1c9ea0e", - "IPY_MODEL_dbe2204bfeee42de9c8c9d92d9dc0eb7", - "IPY_MODEL_09ba332d59f94952875cd79ebffa12b3" - ], - "layout": "IPY_MODEL_50d3a7dfb3964711a029df0085e31f7b", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9ba76afb638e410e891187e79a458dac", + "placeholder": "​", + "style": "IPY_MODEL_0c57b519892e41bcb28985872ce030c7", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } } }, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 12731131e..ee2b80072 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -710,16 +710,16 @@

1. Install required dependencies and download data

diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index 2f967cbe9..42ddeaa94 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:45.418874Z", - "iopub.status.busy": "2024-07-02T12:09:45.418417Z", - "iopub.status.idle": "2024-07-02T12:09:46.521891Z", - "shell.execute_reply": "2024-07-02T12:09:46.521319Z" + "iopub.execute_input": "2024-07-02T15:19:23.685217Z", + "iopub.status.busy": "2024-07-02T15:19:23.685050Z", + "iopub.status.idle": "2024-07-02T15:19:24.935394Z", + "shell.execute_reply": "2024-07-02T15:19:24.934810Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-02 12:09:45-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-02 15:19:23-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.249, 2400:52e0:1a00::871:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.249|:443... connected.\r\n" + "185.93.1.244, 2400:52e0:1a00::871:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... connected.\r\n" ] }, { @@ -122,9 +122,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.95MB/s in 0.2s \r\n", "\r\n", - "2024-07-02 12:09:45 (6.77 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-02 15:19:24 (5.95 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -144,9 +144,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-02 12:09:46-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.236.81, 16.182.109.113, 3.5.9.115, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.236.81|:443... connected.\r\n", + "--2024-07-02 15:19:24-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.131.75, 52.217.90.4, 52.217.236.25, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.131.75|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -167,9 +167,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 92.7MB/s in 0.2s \r\n", "\r\n", - "2024-07-02 12:09:46 (150 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-02 15:19:24 (92.7 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -186,10 +186,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:46.524639Z", - "iopub.status.busy": "2024-07-02T12:09:46.524272Z", - "iopub.status.idle": "2024-07-02T12:09:47.827762Z", - "shell.execute_reply": "2024-07-02T12:09:47.827179Z" + "iopub.execute_input": "2024-07-02T15:19:24.937602Z", + "iopub.status.busy": "2024-07-02T15:19:24.937420Z", + "iopub.status.idle": "2024-07-02T15:19:26.157450Z", + "shell.execute_reply": "2024-07-02T15:19:26.156955Z" }, "nbsphinx": "hidden" }, @@ -200,7 +200,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -226,10 +226,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:47.830413Z", - "iopub.status.busy": "2024-07-02T12:09:47.829987Z", - "iopub.status.idle": "2024-07-02T12:09:47.833472Z", - "shell.execute_reply": "2024-07-02T12:09:47.833017Z" + "iopub.execute_input": "2024-07-02T15:19:26.159981Z", + "iopub.status.busy": "2024-07-02T15:19:26.159618Z", + "iopub.status.idle": "2024-07-02T15:19:26.162912Z", + "shell.execute_reply": "2024-07-02T15:19:26.162448Z" } }, "outputs": [], @@ -279,10 +279,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:47.835687Z", - "iopub.status.busy": "2024-07-02T12:09:47.835327Z", - "iopub.status.idle": "2024-07-02T12:09:47.838382Z", - "shell.execute_reply": "2024-07-02T12:09:47.837903Z" + "iopub.execute_input": "2024-07-02T15:19:26.165013Z", + "iopub.status.busy": "2024-07-02T15:19:26.164698Z", + "iopub.status.idle": "2024-07-02T15:19:26.167499Z", + "shell.execute_reply": "2024-07-02T15:19:26.167088Z" }, "nbsphinx": "hidden" }, @@ -300,10 +300,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:47.840488Z", - "iopub.status.busy": "2024-07-02T12:09:47.840076Z", - "iopub.status.idle": "2024-07-02T12:09:56.981305Z", - "shell.execute_reply": "2024-07-02T12:09:56.980685Z" + "iopub.execute_input": "2024-07-02T15:19:26.169329Z", + "iopub.status.busy": "2024-07-02T15:19:26.169155Z", + "iopub.status.idle": "2024-07-02T15:19:35.271117Z", + "shell.execute_reply": "2024-07-02T15:19:35.270638Z" } }, "outputs": [], @@ -377,10 +377,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:56.983968Z", - "iopub.status.busy": "2024-07-02T12:09:56.983751Z", - "iopub.status.idle": "2024-07-02T12:09:56.989422Z", - "shell.execute_reply": "2024-07-02T12:09:56.988975Z" + "iopub.execute_input": "2024-07-02T15:19:35.273414Z", + "iopub.status.busy": "2024-07-02T15:19:35.273192Z", + "iopub.status.idle": "2024-07-02T15:19:35.278675Z", + "shell.execute_reply": "2024-07-02T15:19:35.278216Z" }, "nbsphinx": "hidden" }, @@ -420,10 +420,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:56.991449Z", - "iopub.status.busy": "2024-07-02T12:09:56.991142Z", - "iopub.status.idle": "2024-07-02T12:09:57.333959Z", - "shell.execute_reply": "2024-07-02T12:09:57.333418Z" + "iopub.execute_input": "2024-07-02T15:19:35.280475Z", + "iopub.status.busy": "2024-07-02T15:19:35.280305Z", + "iopub.status.idle": "2024-07-02T15:19:35.621923Z", + "shell.execute_reply": "2024-07-02T15:19:35.621363Z" } }, "outputs": [], @@ -460,10 +460,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:57.336408Z", - "iopub.status.busy": "2024-07-02T12:09:57.336047Z", - "iopub.status.idle": "2024-07-02T12:09:57.340566Z", - "shell.execute_reply": "2024-07-02T12:09:57.340088Z" + "iopub.execute_input": "2024-07-02T15:19:35.624478Z", + "iopub.status.busy": "2024-07-02T15:19:35.624094Z", + "iopub.status.idle": "2024-07-02T15:19:35.628348Z", + "shell.execute_reply": "2024-07-02T15:19:35.627829Z" } }, "outputs": [ @@ -535,10 +535,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:57.342536Z", - "iopub.status.busy": "2024-07-02T12:09:57.342207Z", - "iopub.status.idle": "2024-07-02T12:09:59.889796Z", - "shell.execute_reply": "2024-07-02T12:09:59.889167Z" + "iopub.execute_input": "2024-07-02T15:19:35.630446Z", + "iopub.status.busy": "2024-07-02T15:19:35.630129Z", + "iopub.status.idle": "2024-07-02T15:19:38.137637Z", + "shell.execute_reply": "2024-07-02T15:19:38.137007Z" } }, "outputs": [], @@ -560,10 +560,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.892826Z", - "iopub.status.busy": "2024-07-02T12:09:59.892074Z", - "iopub.status.idle": "2024-07-02T12:09:59.896257Z", - "shell.execute_reply": "2024-07-02T12:09:59.895794Z" + "iopub.execute_input": "2024-07-02T15:19:38.140589Z", + "iopub.status.busy": "2024-07-02T15:19:38.140060Z", + "iopub.status.idle": "2024-07-02T15:19:38.143991Z", + "shell.execute_reply": "2024-07-02T15:19:38.143492Z" } }, "outputs": [ @@ -599,10 +599,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.898108Z", - "iopub.status.busy": "2024-07-02T12:09:59.897930Z", - "iopub.status.idle": "2024-07-02T12:09:59.903451Z", - "shell.execute_reply": "2024-07-02T12:09:59.902896Z" + "iopub.execute_input": "2024-07-02T15:19:38.145836Z", + "iopub.status.busy": "2024-07-02T15:19:38.145654Z", + "iopub.status.idle": "2024-07-02T15:19:38.150999Z", + "shell.execute_reply": "2024-07-02T15:19:38.150467Z" } }, "outputs": [ @@ -780,10 +780,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.905627Z", - "iopub.status.busy": "2024-07-02T12:09:59.905242Z", - "iopub.status.idle": "2024-07-02T12:09:59.932087Z", - "shell.execute_reply": "2024-07-02T12:09:59.931495Z" + "iopub.execute_input": "2024-07-02T15:19:38.153003Z", + "iopub.status.busy": "2024-07-02T15:19:38.152675Z", + "iopub.status.idle": "2024-07-02T15:19:38.178476Z", + "shell.execute_reply": "2024-07-02T15:19:38.177990Z" } }, "outputs": [ @@ -885,10 +885,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.934435Z", - "iopub.status.busy": "2024-07-02T12:09:59.934079Z", - "iopub.status.idle": "2024-07-02T12:09:59.939450Z", - "shell.execute_reply": "2024-07-02T12:09:59.938896Z" + "iopub.execute_input": "2024-07-02T15:19:38.180581Z", + "iopub.status.busy": "2024-07-02T15:19:38.180244Z", + "iopub.status.idle": "2024-07-02T15:19:38.184905Z", + "shell.execute_reply": "2024-07-02T15:19:38.184358Z" } }, "outputs": [ @@ -962,10 +962,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.941692Z", - "iopub.status.busy": "2024-07-02T12:09:59.941362Z", - "iopub.status.idle": "2024-07-02T12:10:01.337767Z", - "shell.execute_reply": "2024-07-02T12:10:01.337179Z" + "iopub.execute_input": "2024-07-02T15:19:38.187003Z", + "iopub.status.busy": "2024-07-02T15:19:38.186684Z", + "iopub.status.idle": "2024-07-02T15:19:39.591022Z", + "shell.execute_reply": "2024-07-02T15:19:39.590483Z" } }, "outputs": [ @@ -1137,10 +1137,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:10:01.339986Z", - "iopub.status.busy": "2024-07-02T12:10:01.339664Z", - "iopub.status.idle": "2024-07-02T12:10:01.343749Z", - "shell.execute_reply": "2024-07-02T12:10:01.343244Z" + "iopub.execute_input": "2024-07-02T15:19:39.593197Z", + "iopub.status.busy": "2024-07-02T15:19:39.592842Z", + "iopub.status.idle": "2024-07-02T15:19:39.596856Z", + "shell.execute_reply": "2024-07-02T15:19:39.596378Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 7a346e86c..c750b6915 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b", + commit_hash: "e67c4aeedd6310b5ad112e4c90674400bc877e0e", }; \ No newline at end of file

oY0G zXV4VtGFPWaKYk?s4NQG2tzmrrlR9M=Xn~OTUcZ?;8mEs`hbIFy{G|Vh1KHa~5F$Ej*-L;S9b*}sx(S=g1Ih{dfwE07i-0Rw@9M@5+XUV{$WY3Fx)e;#2z<= z3Vb5%$>gS-z2^>3szi>UAoSQ1X@=)DBBLm-fLCT%`5V|(EAklSIG;ecnV00r)@CLR zg}GsoK9n6n3(Pl=6%jcJEd?&=BNHiaKM17ocwMiz4!7M_Wm;z}^W>W?Cu!uDH{<#D$k zj@+jy0zp<#6d`&*p>3oqGH#;_&*h_TQssi?6{Cg|`?Hy0Wl!i`DayoYK-H*c6g!))ifY2BBYi`COtMlLF!?$&#UZD}Nv<6Y52wjpx0zy{g zgG{^TQC%nogdlC$f@z!*SJZiG}Y-NQH|MOx074L)xA;kxCsw9L^EMT8bvW-;aptaB|Ecuuan0? zhci*Rghr8k6vB8w#>FV%{r|We^@8I41)+IwqkLF*=aioy@JUpkvS_&T-b7J$#W)%u z+BohJ1OAL!&4=l8@1v^l>#M(_`f$(K?sF86g(1NkJH?ev zDoP1s=I3z^Ow}j)<-b>;WS%Iv!0Qd({h~{_KJO2fXzv^<(e6CRgCTJ<;hEsFV{g;j>N_V|O`vA^I&51rTMw6H;-w75$7; z`u%qFW=cpTbg9NjxgnYG(R&0@BWGoMvuR-R?K~Fi_VTQ z^Lw@C06>X|BOmT_HCL5jwW**qM$6RqnR@W-W1aTMX=pQql4^Pu-#@y!(-%lMg zkKeCQJ7x;M|6^3lc*>kW(oITQIaYiXKMK93b?OG_Z;EL_UV&KqHI}2K@gcb}cen|( zFvo1+6PU^kVz!mT>&2Ea$6fKdv`5S>F3gxdF`s$MjOicK%oDHoC&W-=U^5d$8c#O4 zv90IRDudhiF;fXSeI(Ci#e%2>F%P&X)fUCP;ncm?#7uR;>$2@JkGKd^cgMK#>%x68 z+j#pmIu+BtH0|QUcAt;2hh~>zg6PJXk|x10gmDs0!VcYzsRjpa)85!X%B`5nyaTkj zA0x()=!Y@ecq_bp93wjJou@G-{&(PunB83T2QOm+`25f1&lm+)fzBcJ4i~vn*;p5T zRg{nY#PI?>V+%_ox`O#bsuvr0DW)f!tsJ|Cx=6?^Ar#a#V$(RQ{)$+BUJf2AWB=mP z+zpNu(v;K2CU9vo5@VlmDRjxPRr$3}TI_pXFDIuo()gs7Wj7neR%a)DQv1NOda)fS z^B9XGD+`FV70wr!mYJne7>DtJGqYkR@HTBSCw2#SqLr7&F64i2`ziKMGW?>m zX=awQBeo1oSQA@??D`R=2KM!|ez1F8?Dyq~qb@7FleQJ6?uwmHmIo0k!oK~n4|(q> zJ{bF&4=)Rk#s0;wb4y|elPA7|9MY#j(5=`_h{SH(iycK35`lc+ulunRxQUH<6#JHJ z`qjQk?+?9yj}=>SS%1V*ww6V&M4$@L?a$bX+@8jKh%H04`i-t}4XH*n%QeoOb#+eh z23Om-r7C3dYe?KDauA4_X!WdpTpS@{62m4OiH~y(MiwRQXA&Q1xxxdajw0cX8XvouRuv!vp-B;uCS|!z?Gd( zbZWucx9#A`hNkOWi|WOfWm$>w%?Ow_J>v^lNk+UQ8}l%`0;Cz^9VxO}U?|6Kux@|p zl|w~Ve4c~NL)On6zmq0QA>7Z8CuQeA7KPBdZu}NoB;Z${)Ib8GUxWBElr02NIlNJP zF(+`fN&GUZ2DHuMPf>G)E`_}Q4^W_3#SDScWvIZu!kMGI}yzdxKNe(Co zBzBG`9d4G`ca87I2}JgYCuGC}#BisCl31@LEbSk^*9o7^Pm;i(;qk|a?bwp3lnJiz zU}St}JDZ14cWk_qJzkqkl&)>3#`olZ|28xJ6uIEaKBcUzANJewc-(5~9PbMe z>*C8%#xTLJB>F)`xVSOCADN#(cnI;M9WB(h?66Qox@?OJf*;Ss+q+n7cKeNZN-9VuT0#In+!N;l z6>i7>zzuiJz4-b3n)4{$O&IXA_#A%ybE7PG2>w58~H=92vp$xke zmq1KARg;y>PVim<@d*^mA0BFG3%Q1b-)wD;K#(b6FlEDogD?bfWY4!III*MgMh_Mj zn%ERJ=OpMU-C6*?Y>c|XlbID@O+i9CdsLxM{RG1Cd1gWLgTe+0sdfl-s8IqX>w{1h z(z16zjJGWkmJ%#XV5nOqOr=;lR&K|x-M#0}O1fHsDerr zGmN?$6U^NFmTjS0jRXqVI~={RpS?YiVC#<8ch?fSl*Mb?2MM0ecpdOMA*dW)m;9Zu z4_yYdwKH_)*Ry2}dNesG=Vs`IO$|dm4XH>74pcPM<$wG78jAVj>2TArbc>kAQk%;w;KM%hDS5 z(0y5AeH??NdFIvSiB-74zW=#YebJsFYQ>_9*^bbtIMJCCZnq|}1=s$O4T;-IMY!nk z&GKwV)*xqA?T~}JI8@A5Y)iai5rJ(_Y`{f$xw}-I*PcHI;`b&dx*+SJrxK5HDKgF` zR^?K(yOemIEQR@DVomnAaWzZ~+bz2vOuLcziT6y=30B^+7*fgY#5gX8%L8&J0DMwC zZY3+8CgT3n5$N?iu?eSt`uD^&l=%i)%?dswikPH}t#KVE=wHT2^xM-}Vn|y@7~y0L zS{hw(%TSZv(yY1d-rZix+^47rR_opCO*<>~xlKHNjhpHKf9% z2Of+aYS4m(FUlADPcBS72M@9K%rUkgfS#^nY|0MWI#z(bxkeSm`VrwZ;lb;g8@o!Y zFnzx@?y+Od_j`J<^zV$DXjpw=SwrJNO0b7S`hsT@<1Sl?!|i59Lb^FX7?KOLX=M72 zsT-gjH}7eb*QGS=jH_ggyy$40Owh!;CR@%>9k0Ckz;2 ztR=MstR8J_Pw5|m@S=%Ej1eWm@NtUqAZ6YsXd+S#L6cqh!RX4K_RHuGf6Xv%klF=y z&oTzcg#VarTuvn162cc6F_nT$c>7}G3c1~TFEx&$wR=m3m`*TrnK6ynMiK&3{bclE z3m>Jqvqkf?fo$&zdclYvH4}QU{f*uhIhT#39KM3XnQK;8-S{TmCYRqblG6AJ z4ywCWJ*>V@*TeeYi^NeL}Q<-VV-9A4U(2%prIJZ7|=iIm+} zaMIeqy)c$!_D$0$!mME{%$5pz$IYzyPIeSZuqd1}{fY%uPNfQkGxAf3UAv|m_W9NcWHE+%*-&|l40VqP2H%b-8jcoNsda# zm`&tqZb`n0KAg9zYg#Ug;#;3&ouY9&HZW0g00UO`kX2B#sfm*EN<6-IGZQHTu&6w_ zrD>h5h)$Wgjft>SA&s32+nPv8fCYz39ZVbXgQpw4J=wy}6vKobT}{Du)YGP4o$Mn;AImkgT!c zEE6RmvJho+Q7O#PGzYtr#35^*iINjpa9B0pbc?(MVOV5(FLTKF(L^~=CpfHLY9eI@ z7L`+1n($+aVlw;TXL`TLE;hB0VRo*jkC@ljm?GujBJfueTc*(hvF zOuebTMseEYYA=~+>UsM3J?nz0o6N@hHxuQhX3?%Gm$~CGYbtuOyleCbbnbN%DQ~dg zFzgmiha`=>yJMmpU=c9!_e`YJ!J_in2c}83Y;AqiLyyPw$?di$CQ|lb!C~HWQ)gKZ zm0nRIak%x$WFpTg&b_fVL(ZS{sO$T8rlqnJ8Go6&P`gRHzcGlFjH2ada{RS%WfDF7 zGT0@Jmnr<_kVIac2`H09N+UenBdHMZoO+?`yVkiU5tal1Vd~2M=Dv3lWnlvm#zCS_ z(hix3Yvm+L*dburR7v_thVieKMA@ehFg>d$)uilgVdajXRwRwHx9n}52u^aRbQzAs z5XlJ&!;&sY3WjzONv=d%t(e*^$%Q?t64@FCMkkTJ+umbJQeB?+HsGFIq&BI&(st%awP_Q{;z4BP-VEpS)Z43ZB)I<<$$QTq{{#WQG()@a9=2^Pp(cS zEoBzo6P0X|=~ReMre##(quMl6vQqZ+N7G7S4~XN!o~xo9VL)bbLxMf97QK=i{AYgQ z2L;J$O5fpFfVql4%A&1ql01Tt+y)Rwo=may{>hXe8xcA|g8|8Q^61mKFu5Pac7#No z$0whls)J!=4>&s|*;{tR@4rtTT#?|=ITMjm7%-*qb}}8zChP;9)+NubEDcrOCz8J< zG!f%N6}-5eTtjZ^8V^fh#fVcLjy_H{Q4ClNnmoHOV0PiB6vYTx5J3)Y;L2ag@5$bt zrxgZFD;!@=Om^5#?-a^rn~(~zs=}#?DV<~&ne|P|0(s>A;G0rjF8WkWX(PMS$<INA z_$wvONn+pAF7=wsv$;d+F-mMDIw2;vJWI+x%%{0nSxhTU2Q0!J6$&RkGl!qr|_g{Y4&BSH4c|SsJmw zq${2bX@94#qpTQO?B@%X6JY*to!p@J%cfaIs-4bh8}KAQ^m0vmCVPq=?rB&vwGHoU|vhfQ$320v`G{Eld{Bv31%NS-{ur(p==Cd#AKVvVi4!lt#dR z=0@H;Fl`ulx+M}2V{T*xJRO!cQ+ne7rp-+AD=Qi9u6bzz^p=pb8&85y%hIk(-i@e)mfL&pj6Z1`8qd;q%2xd9W%?>gA}yhnF1IjWG3H-BB@ZudWilw|11wk` z;N_IDMK+6TWi!eV)G7xSFXCpx=ZYDvsm`8>RLs9u&R8$&X_J4(LGnTp&&JF!8}mF^ z9iFjHc9Pw78H0#XC~!cT*o^25wQPxJ6Ehqs7A`j9roA#buxTk7_AH}Xd@I|NO<0Rt znN1)nJ);g~DKFnCvoWR*$#8;d=8VwF^6ory%FmGB?VZ{w!}9KNrLGx;1Wi~T;R?08 zWelPCE{*_5@~P~9Vh3dmmeqQAXvP$|*lt8dUpr}u^~LCn@$%jA<1@=IgI!peu}E&Nv1>E-%f-f5CY8hCnwb~LdiXXlQ(g&Y z_R7rpR1Wo3nJdY9*ch5=IcD5Jok@8QM6|^&P3B&*9wKXJPLu87l0K92I*#C=h|2s+ z@<1#iE|YS|O~8zb&)iH_=j+5w%bNt}k}{W3n>0Kn^AMRsaeC&jvO2qGWfBhBNnQLv zcIJIlSncFEV)ze31wgtbDnI-~1z{;3GRZfR-a!O_%v5lLsmo+k-;MZB1K?E8M8&^VL z4I4W6GM5vX{lIp1W^3!w1iqRm<@|KLhQ)@->rN|&`x zE~Z9it(Es1%OqHTd(dG0Z8xL!x0|l_Cd>NU207N>`sQV=mw$WhTkCJ< z)wljO>eWerZ7Xl;WDle5CVktY2|mu02OZBYFetNb^Mt@4jwZIwT3 zZC0_YD<`o2_RwbQZ@X``{#Ls^>xQhW8vCSO)wfJ)gGldVzN`rErz zvd_vYI9w}xG$mBUglGNWZAf-TMK<#Ex9Mz5Qufw=-!TExx#=il0RIpGbMKYy#io|n z`Le`AIv3Wx-2-pdUEyRerE>c=HTybcCF8$BH@}x%l|9JHrYu1GU!aN2vSX!uyb(Fs z$L!gztKZ`H(hIWp{(C*L|ALJC~J;dlzS4rR-n-A9exNinF_V$Sj%TD(vsm(z5Z3p^mm5 zP_iSth1{^FJ^w3nVfaLq$V&_3_h#d(yh6H}2eL;|2G##VByKqs*v$*s)r#J9^n#nm zvY$Cvn&I~8>}|3{tIuasQo#JT2Le8DoNTHCE@$tzk>tv~n!Q;r`drUmPnrDwUr!XC z7~SPAn~!XILi2PMQf@FFy11X+m(crgh^WYNA7r1V)P0w&hQf#0#gs!{J+Lyv%8qQZ zdVmx2d!6k8#lL5NPw3`=SQ{Kr8-bs)DQ}Ho^@c^8?VOZ zTV!&|K8NDGtxP^RvN<0)k&^N`YiLa?sl?MYXB%Y(gG7?qSnm$L(70mG z_k{PVpn8p*`=s5Lci9^D(wtLC^2F!EbGrJWB1G2S2Dv$U%6N^^XaF(o%Gi8_H}w$& zufYv-+S=iDMw6VzPI!IMCg+e1UZGu1eP_J7^~%w@;MG)^bCdsn&G9+R3$NSeL(T2Xk(t^}?;=IpsM1)3Z5qD&Td@!<^3Uc>VodP71FdZIj!(EM8Za%WcVV zJp6M1C z;KFDU4cJG30) zp6-?V4Z)o-p_qT*y27~rx!vs%^ZnplFV5LzMD80$yxNY>{e?ek^~~If+$ds-au4AB zP4jc-^0t|`OcK%Om)uRHBLn}xmt;8`ayQw`mp;mswJ~=orF(O)zjk4MSukwM9YN61 zOpm@^ta5DC8m#&4JSPa>mK$tiGanjl&kZQUmg%+WY{9^;j_l;V+zq9n$EqF7t-vDH z-?nDK8;5wZRqAhX8kF;=7wdl@w>1Gd|3YqKwzg89BXm2M8%1#i76gB`?3ry-*7s~K zZaZx!Z#yx>>jgcJ=3cd9YhP*c1m(%xT{OXZ!QNB3O$oCMKo0nMSjSdcZ#Mg4t`}SM zTdrjmcr}-l_74#ULFSFzkuE|ixc)R353q9qq4;_3B?qZE>rL(u%!t76*8DRU54(%v zg?G8l%y!(!IdoXisUPUQ*-V~JA z$PxAB8J>57CXhh$gN5q6ij*0Rg^wTfshxM!7O5mg<`tKUN^w-4Ev4&oDuAlF&RK*f zs3E^1GSQC)3EPcEj}in5 zE9l-JFOd-S09HO@kIxpf(ZlWXB8gpM0Q}S`?{_x?|$q68M@o!}z8nV)x@U-vANL@8P!iPHI}yj4UI8nIOgc^;AoC2R6VA-vQE z9Uw1|*b600KoJgd_P_7Q!*ACi>s@)a4tU+aKd&K|!vA<)9Vfi@I+r(`a?%!+)d&i2 z=RLM^9`-oz4>D&&stz@t=k4U2Z@tPZ;vDW~3>%y=L%iOnzkfG)xL=r~E9u75oNz&VoGS8<<&jhe`g4vc+)lD}y zEQi-=bInh=3Wh8-7rNs0(|U69Wb|sANH9^9TY25($mVgT{pWE0!=0= zpT1+p(>p>Bzuq@fB3=QbcxY}z)0OUYR9?tD(<7?~V$GtUE zCN4q+Z~idvC#zuG2lHlG1^FN89Cmy%Zz6=L+!ThhZdY15L8xQ?R2O!XB<=YX>wKMB_-t5e3y{;Pp_p0W%C32Es8oa5C zM?R|MPb@3sg;mP@Gn5eomDh}!kKL*YA?p0vc9QazYc0xK5|KZED6b5AI4U2r>GA#n z`(pFYVXTftn^COLke>q2ZfeaKhh-mbGUI8?#A^n z7Ia6sHxTBr>v;Y|njXf2?a6#nR(-)?W{I^eI-kv_%%cR%?sNH*iS{rS94=Y)F!gf& z^nb4h$iI^RGx_nqUe9;7lbYewt^8I0{_#?4*1nVPL>aPCe;ruIU+WBq`cLwmywG{_ z)iAd5Bf9iODFwc;_)~taWF|#61>I=$IzU<50#c?)K@TgtvQ^azJlO6@1@7!deYKJe zwl63ll3^CSk!&>Leo%?z6MBco~6GpQsf!?mM=V|M;aF1Qd%0cH5%jBUDH@lDqu`elyaPL@_Z;Kda zQKQ%zmbpl_f9zzkI^fhSb_nfB(_6m_hu||dpfhe;NZNl>d*o(IdjAdF} z!Q9^l$Iha4u_!cl34=Xg7}xOFCI0E4{T*y9d_&$Wu!OqP!zk3}yHob^EIlvpM=Hw$LP7QsnB$Fkgc1yN>nB8-f| zu;0Ac)1+5}9hSuMXt3MLSO+qzhtk!tS6Dv1qh2=@1UiKvzmWDJA@I@G*v%{|{RYMw zw%rkXfn}NA1cL_lL$NLZ?rqT#ZaNUV@p~w_LWGsaI3L2gafOFZ#oqre4geuI0;CpC z*!OfSlW!KYZlM3gSf+~&%m@G>IDmQWLb}7#mt(uJ^e*b#sCU_ZGd7PwEf8hCXKylX z`LkF7_hUs>LT7mVeXQP=G`b%wgd3@y+u91Z^GNJjJK+qPprW3zIZd#>f$%KpYT=AV z!cL5)mjGoggee>s^{IGIcsW4GbW$0qIRSPF5+eWgxc7J=5H6&xbaA9`1`Q61GP57{ zgYXceeI@`^iO?QIi-fkI?U#n`;CeHm0V)Ox`0!xp z&I{+-kYLkm!d^66;VmJPyAU%6fH=5;oqn+jaQMEE<&0s_U&68EEpWpF;X}d;tbQt7 zz|j5?))eUaTsW1*jBsYnOCdTkhWm}?Z-fh3IgEZQEMedP5r+x1e>nb02w98o^+kA* zrK3W{=fFKyaXJ>e#F-EAmr>P%zmD*fPqd4RM2OFh(^SGVV zK5w>&Q`wPNVL+S>O`94V_nc)oL0pW$@8b-%yTw^L%cX6C0O(05)Tf*YA4Hx%<5+6p%GF z#}-_k6zKuI(&HTXroJpPH!gq=qMU}*gT=*hS14&iMO*_q%ugC&t|IxUI3ta{Fy5?z zpviIWEL&t2yoi&7HNOq@z|^<{W&j;98w`icj+<#`qW5`8+ya&g`v%+@4p|X*jP{Q0 z*T*GOA(p^6bgUa2V%C;8Nb4?QPuyEtqtW~07SU|4j>j!xS>wWEQ{}~>PB7q1+)$P( zG$R0nz=w^`#eJ|asi*zLI4AnL+y7b|a;=AjiMbt@OoNB-$1P(RrXkXwC8OTQLiww> zc&-n?+cFP-h&$s-c&&C6J)!FewGBmav=-caM0;2quOYoiptOUilE*tpJ{UIZC}LT| zG;Oc)0InnM*?~@hq9+XfuDYK{3|9wdK3Ddm7?!dD#)yg)`7umXxy(u6sZ_*=9}`50Ed33K01-l0 zOC$nr!qH061!PUoOD{TVg`Rg(L_rLl3db!TD2Lx`2x1;8_@MGng*!Z%BO1wBD*kBo zK3n7tvIdE~;q)TWYew>4i*STtr6P35;UMHyh|o6fgYaCX=m7VeGfb4h(wk!m9QuhI z;M_4HXQT_lJ7KJe_tOa?!FK@z1Z)GhPZgmcPeTx?hC0cOQg59V8W#bXatRwPBFin2fakQq>R zKY0cA1IeGp6AR1N46_st?ee zsU9%qxd{2b@Cyh;jzwOHST_A|fDi!=X0arcxr7X!@gGI+_=JecQp~cy1Yr$_%dEt` zSjJDBd7h2FB)aUW(CST_-)*YTK8#9^VI_#i{Cf;lv9CN5^?(6^=dF|CGPZNy7i zIm~SL-xDRy0?Dr`0$^ZQv5KLWfe1n502;Lq@`V2V#p6i3g!^N} z=(Y^5uYhx6#Z#?Gu$x4TJcQs_+eGn68hc+U-b7~ADqISlm5N({Mw`R|utAAS$sB?#Jf%W5E@(3K7KaKdK0ek-JW-Qfv3IW z`S3}X_{Y@rp7x0E$nwqKj0h3pJ!-Lchr~NlQ7pyrJ!nuYi$`vLaKrRh#J|^&VCbHB zcb1u{so4ymtV$Dr3uRaibtzE zICjLB_|J?LjOS|)cn`VdlI|efO|phzH2FGDj@~bE1z*PAvxWoeNtixv5A&47fjSQf z!)9rS^6s_*S!+VwK&-cktDOi?DUmnS`wJ3=8~WOISfQ> z?lQeP&X9GK7P@vL)zy1`f{6mppPL!I{G)6KHJM7>Sw&mrsyXFv^+(J}M;c z@XAjT7b0t2jU5jt%cRgP2ni`lhZwD)|}Rm$5*C-qcS@R{!&s z{Vg&?$o&{h>KhbS7;s+l(HcCOF7brju1Z+03iuWfAV5AGeqFMG$O?PkmejY#V#D+Y zoT+gdfFX}KQ{#elPbBEJJsj!$Tr%5^1h>7HtY?_Se8a`L?u%qLLqqu%1i8~oXk$fK zCG!*Rv?D=9P{PONY<0K{S(g>>G+39A@Zg^#fUh$N<&MENAZy}1J}?Til)x@4k9wzy z(I?n|B58u;pTk3*ETM=&X#nNx4<@!npG$Y#PH+QG$^?eS1NP_>ipY9WFIB=s)WJ+` z=mu`;5*V5bL|DJGNP_ZJ!Ja7z=$-^zylq+n!_0&e1>LTdp3s72z(kofV1qGE0K;+< zA{e?cu6PZ2_tMxHHpx#=TZ7WU-kz|!B*Bxxr40=n8UHGw+$8oVi? z1!H9q=$cgtHqdr+f*mm$*lc?OdQZZ%^Yf- zQWKbb&Y2OA!&LMJm+&ZoWsTQ_q9Toa*W-j7>zZyAZlK4X2`t}SO?b)h=*tAABRw1f zM4Ium#5@e|6IjmUVHDM0Y+dJ9A?AKcm}9~5m_9I(;q4B5MXKc`UiltFNrr>15=$`` zaEYJzJ$0Q71-6MSQ?Y*&=b?S#L_%haQzFaj*;mcnaZY^yJ(`gMbO^0J{KYMCE@33| zNMzZP`jycR&&19wgM5)UyVws%Tg zWK9Ifi%xt-1jy@c5};Gx#MUG(XHeoHBGiB(iCaj>b7rcUUSDO(Dok{Tiu}Za?=jjah+xpV z8dt3V79_gYyh`wfn)1XfLipD3L?05WH`XNV&2c7Sr%p($B_a;~F)@;lYkx*{B9GX? zN{|@CGAke=9!u;72H7Oqz_|+(Q`ki`!>n4I*vg93{@vw?r-@KUR-0;n%G$)2g!Z&e zrnp3yIFtyreTOM-_^w2El5Xj~#4arRHaPKM^sz*y6Fnx~3;}3cm?x||k!VYV0tqKc z<<*@|{6I)MUP|0SYUk2rQ)u~BlNl#pOI%0(+wKPKvEap9i5E!Ty$?+SkA0kY)E?KY z?yU(W_&p8PeK5i8{%C?5{@DZ<#FLI9x;khrb!0b%HwjT-ii^|*R=7%;u9W`TXgC;< zyIkN6x7U+;Q+s$+U)q3(?cPK>k}zK0RBBD)cD9x_BXKYMr6XAO)|ra$YMvr_fq>4^ z-Qe}x6-|KOrosLotc#Rodhzw0FB+h40`0m=&oay<-PTB>U`CXbX)`aE8KBI3uurc< zo9((wS>EpuN*bB^9q1w5>&n{*#tEe?jzbYp9=skWeZ_q;8B8rk37f)D*^4^9s z&rOw{<_P3uNSCwOa~|B4DNSPe&V~sb&6T375;lU=0x8RM8)K|2l=TrOE+=^dXJJWWUYc9M@w5X+d#rNDcbhA5uBVLW!bI539-!X zM=5jpt0zl~xbj>7#NG{PtEKkL1nSjDf943(&S1B}M?Xth&h3+aC}y^F21nq*Tq(=- zs@RNL&zGKI?uX_tly2e(h?htgu-f44rBbH%UCh483Zj9!RqPsgRV!uL5C{=^xAl{x~F+aRfRaWuFP;9Fy{xZJ^;v>12+;^558v z@b2%@&ddZ3oRzNR2xMQ7E@JHhwp^65>{*0CSEM(&vOTV`j{q^(rS}-CKOKOMR_^fr zO=%5}w-Hv}mTtE~Y`Q#PdQ-s0SpM)k*#N|5C^S5g_TnhjK9!E+D22UbZ=h+fq|KPE zBjl}gIafnh-b)#drO+PYgY*P5fvKORqd5W%c{1icQfMJ#+S!b2w5zpj3P<3Cjf{DW zcC?d)Gi%_!10hiC%uWEh$fB4D%&aHVaT@sIA!F{OBkIeT{6FI=c;YSli6c zkQm`U9~pZq7~fPjoFm}WLdN{AFsP+$Fv|iB(twz|O05y>8Yt6nodG_2^e#|_&Yv!o zdczw6*-XZ_AThLvl<5#AOzJ8_=Uz6#@NP2XLVqKC8!baF^f$tlJ!Hs*{zj8Mtc!U}?N8o5d;-NIc9-l=b46Y*iBG29aWf*BYCR9H`N@+Y+R^EzXo7p%>ey|Y6Ctm~xlgj-8xOjpU6F=1qd ztPJS`)(w-PgHjvenNc!y!Nx{tJzf^TJq43wueoQtYS}&RS#O3+#3BWYbvzCtQWcRq=4WR5W_xr|XnFc8h z_S`GmyOfnTs!NC}%NWh)P;uq6;E@<+SU&-Bg~zVz~%T1Mjqu*Kpj*+Q^S^-1@YWq50RDOZ`e}1?;l-X#XPP^v{+jW(5jhrZ5)?LnL;f`A!hP(NLd?P0S z=p(<#5zOuH5@KSaLW8U3E7kT+!^j(VwR@V?1iH}H8{rWcIS$=`8xaAS_O zCsZWM&$F=q29pnWq{`9dnH!;Zo_sGy{c4eXC?7pLR?0hYx|ffXuVtZrd zdzP)0_u!tpHp-c9Ovfd1h%nc1*1z+k{3zE&jsGk^gr1v(icQ>4 zI}62H?zz>H{LZpg;Q2x)8^t9q-qTKTjeEYfC+U_tl5{d>lCG7j;uiOR`+5pFi}33P zD|&-WUxfqM+RE1*er%xNCRG&7X+AwSz*-ERs(r1@d2Q;JL$1A&S4aPCMupy4{l>uBb=e8D4}d z>X=1B=nXiL!n!KZ1-~2N=O`l5(P$#V^d1V9K^`D|#lrNyiq{-r{F1YIfT<1^4^*%$ zGDC!UlxrU4Llw_ix({s1YvL6R!TI%G0kFAP(U?VV91KJ-{Y||Ibe1b1*D?;M6;~QE zj6|5&)eF)luw$vhjjImi!Kwwbh#V3?NH#!~qUXae!xYCj%wwYzPMrEKjZxg?s(Q~j zg$>90@Q(`F_mtHbuA8dhzJS7zX`~4^s8O(ZM(1AcyMd}}QaAWup5i@1B$gAg&}2C+ z7Ae|rq6}K9XvNZ?Z~?o2M)#uxmMXlUd#&PuxfJm#Qz=nv6dkxy($*_%SxUhj`n4BQ zJNOhTo*^V6Mm#hX;JaPHbR^ph3=ps_^xlPF5YK6Q6unt^Vr`_)L@rT0cDM3^r3V$E z$fV)AV+yzLf`k2kS5$Juj+{{>upr@=t`jrwIsKcjmK~QA+s!XelkO_IvzV+I7$PSy z+^zw*_pu^`g(B9S->iEc;EI*f8*D`GWPqKu(ix&qthr-o^iJWyaQ=cc1dRUSn#>XI z`l!%zWLth=Ba6gN;5^=Vz&BRPTr;~0du2Tab`Zh+%F!9^rfkHlyD#qOGA5q}N*h!A zfStURCEo=H5S%SI8G&B=yZI@Pa}=GLE0OP=jqqj*B{x@rt6C{vaW+1`y)vEa`<#Q6 zPApq4(NGi(RL$=;1n0uq1jBRTN@td##0&$|Bb9b$O-$&bEM(Ti{BdZ)KBk9~XNH^8 zOF5sVwO|{^aC`eH=W?%d>KG+n5(tk~qDwb7!YHwl8wKGwsS?laQsl}zEPMS?Nn}?P zM7yGXum!?>E&GDO8A_JzEMGTyWzDKR7ppashQaBY^7>H9F3 z8KBI3FeF#Wvg(ci^2m0t*?G!6)}U%sD>sl)qFl))Ng3Q=g-*hpm*i&f&xaICVLa*$3?vh4f9Ff%7A*RZr(ocYZpHcNuJW!8A$9br@&wD?aisfLfN<)=;CIRtN~-oRr8gs~0l=59YJk@lHl?X< zSrCI;@r4>3tg=)!XIW!GJO@Aoz1|WxWq8w0b%;lT+Z!q#l>@uRs()`VBYaf#S%m-`HbB@mFs!NS2P)3^7AkZKp6jIGz_zMgRIW0{a46@DFKy}+9Y)o$Js?#N&$u(ez420lfq7Bh^N!(voT zK=k~3&hS$wRkbBpT+*yBIQggpy-8e?~TH9xBO zP}5h{mdE=OPVA@p!y43^P}LW#AEILTunrNi*4DggutKC_nO6L^LDdEB7OPhAc-1gX zqKdUd&s|d0c3<=?D^)djW6(Ya3J|C>@cvQ7GPogt!Qjvg6}{7P$Rrh-^;d%(KdD&u zs+-V!;EQV2ZX_UB514@gnhrt|-_qmF}LnG0E-JhzJ%mk#bRI@k&Z{MgG zJUv4s#7vf<4h!%8L6yaoJ@S)k5o`6I{H$U+d4maPcg`~+8)Y0)FF(7C<0=2o6{>M zRL@Jzw9QkLsfLA-YQVC)grg0rx~rYR+D4k6U_ejx6|(@o1Jtis-dDcCv;j|j z9rMSa#8RgBF%^#HCx2$!g9xQ6g>nffahJ6HPckVR07LfALXUXc311vhq$T2 z(rxNh3}XcXDubW5t7};d1ZR%lrADVys=?Jg>;yvhshLJl2*jRHvv@&581opW3Xu1^`VCj?yEAHr#ccT7S#=Uav0Mu)gLf{d z3%Ignms!dt80)U6$FK~@LATU8c>D&_SPW6-H>N%ucDbhxVq$Po9R~s)sU6^_N9u6K zmO~JLcYXCR*y|~(3#3lGo1wuIQ(yS>xw;<1S_}+&qn^Tzt@yI4^B>hr2d@x_5+;68 zpXUapU`q|29Pq6*cye%Q?Q3fqY4U8S zDquu&%@>w=ff)`UI9oC=INw@>=LJ3ei6{*_5K%4%Xw)q80#g|_AR*DZ5ttHFh@2QG z)NK!?#5 z^?+&&x)f{v4vihKR%*ly<73lNn%+Rtq1Xf7Rce-4g86nlH#l3X8SFuV9||?ES-i&} z+)He9qCw0ZZBtNnDTXdR!O@yNHkb`?nWSNHy@beoMfHL4lQjkw!~GAK^q(}(EC{dk z>6!)KBQ2a?qZ#u*pmhFc&2ma<%4`kGx4r+6@%K5Jb^iliAqzBTDKD?Z8kXtmfAG>R z(J&3}{{d6F+^jFbD$V5YsY1?RePC{_M*Ba|*XT8x)l^@-)@xY4d;f=^<2Gmvq;c0l zO$1BdiUH@hyW1Qd+o37-2Hu-^o=|o{GmKeW43>Ra(-Ga=!}U7rZfm5Zox>)NHOQGd zj(zz=6K_v~(s!EoRwStZq`65uD+y0qM6=zn)uM|5akg4}Z7XjQywyVM;zWX$!P-$o zukdGqwhy1gzUZVKL1RaC)%HOhDo5vP4|8mGFYOu{8#KU-aBz^e*p{RnDmJIRBhmJy zxKT=N0wuXzquorYXD4Zq4_aJAc#3vC4gO`+&UPlT%Zjxl27R9?jzI%L)OD>bJbqBy z!IRXL%T?_(=DH$XGJ=H&Hkzt?@;$Aomh=y_1E|z{o@t$^vaY?-zM=}a{m!hnk`LN- zG(i`h&ZPNMmbzbP>?AvLY%@n)4UIkQrkg}-DcM6ejK==uWllS(vF;#^H8j)ppd`1n z)tS^?>8~@XyGI9I7ixBs!*uUk5zYTB*KKE+FXOhMI;LyHxjStHKWcRA?Kq{^2ay_G zJy7*t;|-su>E<%4pTmaUSvs93Av$@K&aC+{x~7gK7F6ptQ6rPj&;eQ>*7J1NY3!Rt zI`sJsn_kbAI&^ab4tmv^!Jhr1TT2_;+ReH;s-eQYx+0cIFQ*oWs9V|-3oJUP8~;5j zXb(#e(HI%C8i7tuS!0|tI-Kh=u{-qm61-L9J}cQ(6(f6SgY1cEr~HS*6UZZy!x0CVM2<&H?{n=`T7Qw>4h?V zDXl8qaJ>_w-6{cZqJB3IaxSv_g6Ms|R>1j3JTcrPmkq9T558S#~-{^@Kv(Dwfge! zV^h6G|2t(RXemL&LiNB@AvzIn*?jhr3B zeb4mXb|m=po&Ja~o4S%8qksi*5|-chDt_@Ndkk{ z>AirTdrMoeTj=i&!9>F~H$r#9TmzGz$;c z2?PU@h7;|LB{iYJ)R9SOlP*sCest1tig12>(jZzy=;S1H+5smR2a?Ws z{ByekNRvj8arI_nkp6p;4Vdhi;tm6TNxDcYsPWOHnM~c62^J#Qt(0!W`J_=)y(g|G zIaAr6JWRSq6I^|k)R+eCUnMQ00`GmBw1;M!#!EKUiZwskR4X%VlTEd<$sxIjB1E|+ z+fW3td$NSa?rM;1s+ED>$v3HbyEREJpa~8%P2NOHo9LhX%oGewMlVWO)9Uc#-82{+ znY^3^A9YGbSL5LXE4n1_p~2Fq4TFUTd~#PfAm#u zK$M?6gZG-Ow1*ZXA7Fs}I%hw*`kozd(&sgTRfWm783)N0;Pd#hX5jfMG@@m+wsi(e zi;~^InK9mCFe^}m7xBHow2I`}yw@X20(2L8oWFJsGz03Y6VEN>v+pbR3T%hE~7XoK|bst`Aj zT%Ek1L1kuCh~#8iAu}Uzni9c}Ao;Zgp=2>DnPtTJ55T6!vbl)C)q+Y9szrz*3Nz@BOD0jlm02F!^i9&2c!(3y6hX0@+*yP5}wk9 zs>;|ih3c}dPl`#EuKiQS&|J$0r@W<_SR0q}6U#-q-)O=IpqA|m{?w$*08^q={Xl#B zvbNx(Hicydj1MC|^EA4FN$a?-tX**ku+gWa{B!Gt3-l@NSgHo)laP<2$w?_|S*D-} zAscKprj-73)&S*=1*#pHXn;6lXbWGartGogodugRQ+9tZT7Br3owAT+a)U`_1>W@l zy9Shc0q4wt(QtiHiq;-6!@EA)jYw(2(ph~4?zT7D5k4H7vbP@ZEc|6w3UbJI7M9OR zSmlfK?9Q+Q#ArV1AU*6?ScC4B_!!!fY>NB%gPLn--*lhJMEHYf#TNAvb+jLW&-XYL#0IYHV#F*5CO$3SnH{pjRrk1Vf`#Yzb|fq`Gi|MmA4v#0e^DL(;ct zm%4*Xf7d_Nma~Xw!Kr-i|8K%mk-M)Wa9?EVC@x>WX!5&$kJOoV==Zt-B>%`ksXMuJ zKMYAN4@S>D!&0$@)r?4;$tf~uZ0aV4lb>97f#vl^wwk)wtiH$vscjh=%2zmm;D`y> zElCw}nv-uNk_6Tf%{_obb2GLO%}KWr&86-knj3R~q)$9VG}rJ5(OlEti00ayBbs`B zKDC_FT<#y_cl7nt>73@yJs|m4JR+J)ev&$x)7&%PG_1K_o2HHDG&iJG+JW!UoGqNw zF-?tn2hxn*1*Fkt6x}1O%lGKe79ijTob`d;F==j~+wxQw*u8h!J!izattbuK;YV>A zw!=-5G;D{u#58P&tz~K04$+oWY=^=mlD<_+8n(mR#x!h)PqNdn9lk0^!*;l*C=J_T zpGxxEV_2Fsr(N$cX}ITFJuWSV)9#m_2;T40(y(1ltx3an>2jE8_x6#rVotk?lW8($ z?PlIhYYML1j&+9_7t&@S8l)HZxt7+P`_bc08p~Hr&g3D2{)=l*#`|f=bN3PW*32)3UgB+Ux_-!?TZR$k*f%xYHuN6(`pnetJKa7i3iUV1VX$*Le5HC-mnV z{QPU`rGMYNbxK&eEpVzUaRymWJG23g?&(H`VZZyp^f>UrJ^dzY-u`vv-muIw{V$fD zmz3Txon-+G)#-Q$L(*BM-~e;@5|)0La<~|o&T=3f!vuCt&t$HP z#HjR3ltZuX=`1ITF^73Q(wVN2!kKlw(zj9$9s8!UToQp{M)YH<3y3niTM~2B4@sZ! z&&?4wjY;=m8Obp1zl7YTC`&&JZc>9_eLc>hp3@`u88 zfpGJY^qn*~=2-eWYZ5!<_jL3v3MbHueqdhInCq3gJjeV1m zv5y9iWSN6=b24HX+7WD8oUz}E=;%Xv#wZ3I!F5#`=P1n?W6Vl9GcjX4jm@k!*LSb! z87CbCBT zSu^9#nROd=!K_7_OPN}VP)idX4OCPH?|VmPxNaMP;3)91416)j9Hh<6f+D!Ti`P=9g-@FzXCM z$EnK73I@Go3EpsZT-ITRh6FIerNk_jmtd5)Cs?D(st^2}e((U9H;b%cLs`}^OQeyR z$}E=mP!19z$cqf4H7CnJ>;nvxjR~=b&62actPz!_<%ynPMM@U>db=>o8 z>j5Nz7Q9c)Qh;av*-hB2wI`?w&h{fKC!5-5Z?Zxaks6fEY$^zC5=#%nDTJfa2CfRt zM*by!1I;3`ncDycN{GzcP(O*$Z5d!Cf_EceF!Kr>yKdw&?k?C_9RK1`o>yNN9LuL^k?n^BY_| zE_(|144s@^X3uJ0&%^HWVf&fccg=#dUYgD1mI5mk!%bhFz0VmDOMuz_oFJoiXV-Bp zsmrnK25#sXca;e8_*(X3*0v3Nf6V5C(CC6jFymhKd%md}%ARI39WcV|Im~5VFSDI^ zzisl)dB-W;-7d$8lc8z79D@ydj`hl!<-(@yV1Nb=U$~@g&Uj1SG_oUNPKO-kK`=2O zhw1c^Hm5x}smbGm(y_@NaCXO>2gqmZ*&sJq6O!`^d1vKMdTc`A;E6Z zIn9t$S1v(L&zzw&!I|DU0v5~XQ6hLdg#y@i%<;4W_a8du9-F(ay z5~@+gPSikxy* zJq%RlxDqn~>6#qvKZk)x6!cDnjpv9i=MDwiot#s}0G6GSGniQ=!nB+{R3$AlbEf?B z;yGK>!hTsf4$K_x=j0rv9DdEuVR3kYIXo=LVLMX92{5%d2d(^aH3DkNa##jZ%;7+J zPCu4z9A)lh-jECDkIWIjlvc-z&Dn%;Yn3kVklIamN?s)ORAm zS8HL$r$PHabC|BUK>~LJ zuJ3Z(z+W$oUhvb~oVA`*k-caGgLP=>fBPAty-JQ z-B7_PPoI)&(j&~sT|q~d?3~;vmhJaQV;z8UbZ!HD4iknK<|_EaMpl;R#4v4gleJgRDFk zK2)#G^`j(z+LW75rD(W4cMFZZxhEHGEys1ScAvR6lMd&Oq=Wd!Q@MZA;Fk-zgJ?GS zAGx^DKb{Og3dv!~QE-17VxsB_~0frX7d@)RzF`Gn?qCLw~`=9z>r zb~FpIU68kcRS3ANQ{EKHTGuVFA1iC<(jzaC_LC+3&4ioAmcD{=tOpHx}{=TrVJAgeIh6}Hjm&0=U-)x4>m&@L%2 zfydhdwaIx;t$E7`%bzpyShr9EOz_lh+V9LQss zVFFWm&d~2*-VO%)!U)R{V+LvA)KhuLO%$&Bk!SM$;E`bNxjeL%jALIt$Qx)wfWhv{qr4}^^iU&A02q)dMnU4hTSF=L-Ut2b66FZzld^B#^fbI&wg&R!R%6#2h0bBI*u*HArN*XGY+wFf_a{sQK@FeK-r_e^XLe;V2K&>=1V z7_%PcX5_D;91^qHZE|)_{u`EgG%_YJnAh6M7DRUCyF)=q{vB65ilh1SiLZQMn2k?h35%@GxIN!;4<&o`Sv8ze`!A2Z-M{+c3D31r+|ZpR^+1> zP8_URm5(;$;9&kQ`RMfm2d{6+zd&OT!~F9!xNb}S0vgnA%ST@BaN5^<^7{})!$H61 z|KLDk7o5yLO4FAAmOq`QZGOQ#t@-m}zBipNHMyEUkjhhX!>+Hnh z0v>I(*H@c$R=d`$v-0(3oekJ%)>*r{g2_}nM|PNT7wsxAy+7paDM0%=u=*PBD?nSA za4_;nfh*Ny;)#NtG}!iZ0owS16F6Ne_=~1(c(Y(1mE!T8f<|^ELBnSSTAFR*%YrbL zU2)y(6{dny4gB4~l(P+of+eKYazx1CtVq9%n*t2zib9{6?6_XO$jg|={K^Fp+g$Gy(NiERrTvh)q#AUeY#9SYx3 z;*y|3`8;4lM4BiuxEOq3(I;l?xM50+;s#oV!jpJ zoL$(Ian2wFCKw|)om;q%$i6DC@CM7OHChfB2(pU#?%F2cvcs2J~Qk+`$T=$RAlkJoT1^u?XjL?W27hW(gdEf01aa+(PSK_p z8e~rUD%hNMj=-E&8D>r!-PN4-OO!e7@@R8fV~?U=DQ>rZ=Cq&to6{~EXil3lxM({~ z8zwTReJwVpohvb?RV5Z(q=Qg@O_2jjvq3d2srl8W1WJ;NHZ!yhhyeVkf#hTs&7`Vs zl3&!628WasO=VyPQ5`R@_g%J;RBg76TQz3exH_Zg8MTe8Yl}iC%cgZ^U0;W0U60#h z*7bmGMe$VE8}=48Vd(Q9?`Y91Yu+ugB!Bx<(Rv2yuqz(4B>(uI=S80f8iR@a)CR!y zMv*6|oL=Su#ixswTJdhdPv?sYEzmRnQqfjR^bGu?sEnbD`=*E$-$~I0{PDEN3siMC zlKrI+?Jqrq$bpEvMGO9C*gkN?y`mQko#Z#NVy?bmi!m+$3Z4|PT-5X}9z?QVutCCd zo)VTFeN*IsYULQOxQdUSgKUc%aJ8m)EOxa=&w~2Jjgeu&>PE$H9lujE0ibc);z?lY zI+NGb#_fukKWI*KeE3ReG zJ?>FCLxmUD7jIyg99OT~&$Ghg)$lq+iJtn)b|)5-BY>ISRVxwOHSL8 zVB+)=^b&w$ThA^Dqk7I*R1!tOb}TQs!m>mDn?}6?2p)<00+&rC3mNR++;4*UdrNIO zUm$lGP=*~U83?mr$rY9^ipva8WDjnDs!`_k@Mrn$n_5P9-$Qv}rVb{SDhR^2^DTT>_2Fh0qHyC}HRpVbhZ( zOpd~Mk4m)UZVGwrz)tY@b0ss}fTKseC#<|*GL%7|5Fz$JKF*OUVcd%nTjZgf3#Pv> z89*e2hd-1oKu+7a*iv38YKAx{v@AVDW82%69-~1!7juFeuBH1aE^JU*j(ojy2)&w@ zqLaBe*sx_OI=F~~2V0c}(FAtErKs=3vA>6uj-t59k>+fjJDamT>{7ac@?G7ll*!{T zs?I^6+i&+gA@UAt>q=C4MOxaBL6yJ^2CzraYDMWSD%BWcsY$c*b4pE`{g_w!nY1M6 zP*REp8Z35dMQKYa_JR?mH)w42*ir@ZJZ3AZE215tSdcCV|})jc48T6urh`mFL425JX0Fg?JJe? ziFUINm)>XAE(Zwj94p<7oW*mZu0B;Nr9ye1GwV3y53`OZUoZVcC7E-_tm97)O3`SJ z5hgq>MWZvVlbS&G?G7B@o2tk;88Ot_94B+Qm)|I5|0GXKm55Z)w>JmT8Wu3UBaMkkz&=W6HKO=$^An zi0qQ-T<32c%6uWZZXlhQ)M?vty zGM3|kW+3hg1vPF!+ZL7m!qQX!A5g89m94fw5R=g7gpRb^W&c}L;H)n)4u zDx9;f%!8$+e?xQtzHeYuUD;xmcK;whvkIoH_St7##GdUr+Su;`EDg(D1e~&;PXqPCv@yn?v3OnO6VL@ zp2Z1QD=I(2DWOnVzLBfZ_S$k=PMFY?@^T0C+>lp}l~Peq?#BI|GOT=_4SEKSD?i97 z;obOh+3DeimX_LM8WS2BW>_G|fO3&Jk+U^$DIKa-#U2`YtEhswSGH_YOWmd~NgEKZb9 z|DWh>=*jXwSi3?~4fKLX&y=rZ?EuUup!{5k2pZ0pzeMjfsAa_ynk~ek0$rVrV?R4qWI2*x zrI$Ijf1`?)XlE8zT2(-W2`nbi4E80c0y(q63ATq+?4`JqJ6E7LR2-}6YEJ9ctzwTo ziM`dYLc}M*elZn)uzWB0cY_KC^ADDLfXitEL*U1RigDkoc8a%iK&b+bl2xeOiSGZ( ztk6^4XBC;X5nW=I@L8!@8@1&XAE`FBjH)nIPUzSQQ{}k*Xhyg($&4^%O2r|XEoFwe z8U!;dY8;5VE-x|1E?rh}j8@r*jb??5>&#%*n=4*Wh1=|_$R*tioN&lou=I#It^F}G z^@GPNLMaEQvlZyRbX<}8%N4dXw&G?*AGhypY2g69hT4P7W98OR^Qt2C`>X6Lp7Biu zI`fE|%kB>qYpLABc$MgN6wA`yrqX1~A8jj54P%vkrOB3)9V^k8krHonpD1C#f+`&0GEuYY;EzMk@JTW7kwRxBa)gyJ6*i%YXYl zb6e$TJNz3W+LcXt+u*9Zm9aeZ{CuzSfE9Y~e_V-nDr|#Q&nq8s@o9fnYMgl&$m?Ex zUe&vQ-V|Uri>feZ;8!ol4UTrLLVM?NI z?R3Tnk9Ae|Y3!ZkDhED^7Ar{=ja>EN8H3+pK~Azlk9)YB&$Iz3#&sgP}WTH34^`hX;Y^SF^pqscyrqfJL|A zVc<-+;SE9k?!#L;Xp_|$NqU(wD$(qL4|4X+Y6Ldynbi>V z-Z#q)9O;3m1oa%=2>iKsRv>8p!|?jxZja&3>`B?U=$IddcVWl}i+c_q6Yi^x6oeRp zLUe`*ZJk z5$M9Tf=HbvBGM3|3y#!;2J6&8hKN8g%xCIYpZ}f;e6gA84dr8pPq6n33KE1FG+Ki` zQfm+d>C_QHA(6p4L!=-yI50RYREzQcJ{4wos2U;qYIH$rbx^Q60tqgNP=~1l1)A`X zNP$ilCI~fXh{%7Fh>9FCZ}<*--;fAFpdm6SB2b_U66iwJ>X1NvXrw?Bqz}@C8`K2w zZxT_!Q!9rrviC!>1#5M|;b8)URuCMji3m}LhUy~(!C`8xAXFbrWd7S!SY~0}@RRnw zk?QbZO{gJUrx%0^4B?>~O-OiK)a2P43sP>mo$9~r2N2nmXa&_CQ6xgX50jxPh^ll6JiJsHiT(V%CNu)bwr>hSfh&w)9Lhq;6}no z&$_gc7kGYuQ@Q|joi}2zpYFex917G`BRhgi^G4YF1cpRJLV?nqAHN25AxIe*Aj!V z6oKIQ@)5qi5rM&>5xPiZJ;+Y<5up* zP%48uL>&~V2@chUa|#QAQ#(%I#q$l*hiP@{;1HcIFc{fjsM-)39ug9wMUJi_H3mH# zHhRWR-rto7)G0MVet%OSmLLpVUN@pehwy;N)E*sk<;}{=+xs=s=|h7;149jZ)EorJ zeAU50YK=ZTGTacZMr7eSzmb_%KL5RU0xv#Ys1JAF7(b3z`QPhB8_^7v*)JwRIcP}F zUIUf?-5SF+-=_W>eW{zt+k@$2M%MTJ8==69fiqfytPeHTzM8-woxu=@+KV9=b;?2N z@JIpbN%di&s58_DLbtjaRGp}I4Gjwq(}f5^1%mLvP<60ItI>yq2(*HrV7&n>*u}N4 zCetS(bBhc`%?owO8jT=G5E!8g3ylcT3c`Z}Q6CTjvLB$7^JNz=@_he$$>HFaJtKzv zZOKhLEDiz#_l~gl6(B}oVFq;=>UsodoQYJ2>cb*XXQ>ZG%{>tAnZy}pP`Dsa6B!yB zu0vx{gg_e^sz$vX5>Fi#5*iV@i8mhEWoSrHSWrkP>O;7`Ffv#jt`P)>L~27rL$rax zT6n{&2HEA`6bRf_*984td0=Xj|Hs~&07y|?ecx$8S!Lg4x*HV61;$#sHdip4aYbWd z5;v-=suNc*YD{7hKrn<+5fHc_iZ~D#R0N|GxMGYkam7TV5Sm3zVocm(%;FNh->s@? zW+qtm#^?LK_j%s8V@6L`->!43?sk^{Ij30>M?5)wc)p^P-LmO>39C>Bc z@NLUlkZT?g>BIdgtLK)%1eEEt!l(NmHDP;;5BUVDK=mnA3I9ps>&2#dqZPQCy#m zP_OVxN{X&Ix~}S`8i%`0<7exJs;BKl69p)^>jTqpNl)F?K*IlsU7 z!t>Kdk1Z)`n-#_>uPJ8P@e~ffN}!ZWmcz~_S2^ol2`}6!2PXZrw#ANLOdpsmv5es{ z+b^0@`i%73w+>R?np$B&vc!HiWx?Ql`a_BFPlyAH6}EOwH+4<7*}GNOQT@QO1642E zuHx!Jm^*C71)U3~$q{4)mR>ITr824g9Gjy{GfM3Fx)wx3&*8=5Qtfg;em~7}I6*iL z%a#)asuAc~NjCz)-a8;Zys|iphF7Pbki|HjocF!T;xdDZ<*`K?zNd2(TV;m{=d%TH zI4OZ1AFyxFSiw>3IC8<86(!J0zUkN&VxI4sj_x@&kGy8_3c14Qw`b6OO+JPj*uH9N zf#;SLACblj*PJ}#pFZ=QqeN`cNxJeu*;gO`YHz$2c2eB!bcum$Qi67K`E{+1)P%W zt9FHF^;E}p%dTIl$h~gJ+8)g}=ZYCjm1vD+F%|AHLHeJngU>VG*fMe1qm08l{ ze5@#{V)?G?SHzqZtd((hPd}}5K3%k3c>k={b1P#+wD!xyOP`4ymLT&p?M3;?q8e08 zf36bJp|Q745q4hXUjURA(f?YIo!m) zeP)bgYw#_lVyG+<0%5 z#R$T=ug~XPbzH^K%1E`E!!E;7=;&%t@pPZV9Emq%IB1p0_P9lA!A<8Vru}WYB}zkQ z>_0|#j^8B{vY;I;K&lN&0ecvlQH71$HPi~1buE4*MD;T|H>9h1J61Ih4ChP~&ABP* zgHvh0yfS8_(!O=Z3sYELMe*2_1p^CN55}m*a$(U@N?OU$O-@g(R94v2*dS~rsOYY# zGGgIPdpA$+Jls@ROpePx?5U_{IZ`FMYd9L zkkr^G#Z6DmPxP%Uj-uNEGfv7|ZW>E5ZO9^3+;+f>y$hzz4)2uELU;)F2x)^rZASlO z7y~;xvDV%shtTj@Z+wIDt04aJIC_PNqj;)SQDcEHs35bT!&^3o1xIw*NA9u>lk~gn zyFs+mha0{rZb4f~WoyJcz2c`bjSt?X+Kghs^c0x6rBcOYFAJ=4S(VEH(L8WjX323? zHUn%QoXocBmo(LP(8es!M#L&xcF7VbQbWRJFEz{l$<-Hd$Xc}bu^{jh#o(Y;UEQ`y zwMV%%mrv!KoLO0%(KKwa>d+aZn#?Ap%@RYC^#T*!URTjOkk%D!IhtkZs^dxBBYfwI z=1HB0r&&G3)J3xfCn90S0Cf=Z(&wbxVKHXBvDVXTa6bK!L~c~FxC$&8DdlJumW`5v zK7tGqu(#=Uz&!NZ7&Z#%CN8o!`@CJU*?$z((D=voG>22c37?y>U}>(PyB^Yt$_y!^ zb79NUY>XKuvmN!I;kAwBf_FXmxtL$T`EV)uxM}!;_ z7Js$w7WT+g)}k++EM#$x7TTyYXYAMHG-*a#^@~k|{Q^Z8gO`frz!vDzVGoT%bz3$3 z3`3tBqE_`QV~9Geo{Xv4K~l_kX||LUfBa`LsOY6Xl_ho@HRHer2GlWiX&BT?x+YaG zL?MTTC(RvUZu6$eoePz+%XBo#6?XPg#ZcH_$*8UetV*2;o!B-a^~(tOoxzetFkm^N zqnny#SOzmxQIHdB>X)2JAJ5pd#XruUaZ)Dd$hNWgf*Czo-_pv5%IG+L$uqI8lr(Ir z9s;e$rQ{S)ksJ-SQMt31R0HkMa4M?83}*V3v=Dvd;at%u1@fd&96bn1oXqGpw&F{L zN6vJIEoIP*2Qh!9vy8X@Vg^fJin@xJed&y01&&kG(hOCzQ6WoKAgwi++t|wV0DDWM z-1`f2yRGy;m^V4myXzGknW9_C=n{y0lK=CU~9HS9C;Q z$8r=~)52*F&q9FDuzH7UR?T0Uo02s$?WI01eKvMjSm|%ebL7H$?`I1J6*3-3Dxsr9 z(Q39u|t}4US98FPNN2oUd)7pO6oq!*0aIdNgssYpjkSW_Yz-R;pfVVObybz54pL4|w z&K1!#uKC157S{+fIPRy@dy1#WHFq!gs!CLZZ790$l`0_F{W9)nLM5EQpagS_(#qQ= zmQN5vs)+~L1M`lLpJ%{d=iwj@-4`}($Tipoj!PU~CJ;ot;hGa@j-^>X2&j@N)OXjB zX>R{T;55=%s*!s;gQb{u!X`~@_(Jo@yb8<`SG(bqT>@r+M(YfPT{a+rb;j_`ctC7A z1^OMQ8ZP6KU9lN5`WApN0Tx$@k^2q!ygaMofSI(clEg|G0R8c!YW$;%n`SJkJw__y z7!H$9=P7m!-K2^+Q)hG)&;EGU{s(DooZBu40X%~^GD|w3ZyhwM<^h+f7}8KxJfg&DA~42mcX-BMw@G=BI-HX71r&1#$r(5U?Py zJiAn`=oK;fkAOs@n3w{0ks$!(z-NA&fV6<)xL`SWt;|&5BLE;nWqLE9{S3z9$K~cP z77R6wvo9v^FXmi&nK2j7Cd(rzOZ0uIQfhfAeKC*0+7LROPW?PgMct0`AgSRHKJqOF!#a&P{kQM z6VG-!&q@U1RK$_zHIK@JQ?!AZIu_Vbz$>2Xfq>QlJR7E}y29-@J3-?XOknR7l{gq5 zP#Wlb0N-pN7CqIO#zySfF1UnE5V>SKfk*HRK{Pw`I02t$g}ZiMNkiG&+unsrZWG5w`{i$||u- zNOWM?Gd$_gcYHlOX)%i{Pb?p?fi_r3jw526OyGZHm|0_7X04b-k2Gpb+q!Vz{q(mW3EE-m~r+7f@OF(WN z#{z*>22P=Y6V{zj{U;C902YHzvXScG90|_w6qPuI5-m%%pG0i=gwkk)2%46RT;got zke5d~_^*oZwPJHK7>YIf5wT@+lBO_BV$SZH-P|Zq(gfq+LaHzyZJ)RUu>xMfLICFa}*F7^fJa~ZO?CFpHOXwI}*GG5(QW%s1fjJ3Y!ow z$RoaIRWk_V46YF@E1%9(Jo5TNPT&EvNggPg8=C-yv^l)W9Ik|}IvyA?B01>~^Fsl; zNzf#nsfCAFb_JAA0IZtR%VOK`fJu4?Q)&ggD!`f6WtK5I9TrZ;wCsTRH`CvlE|=MY z1~I6R@l+CXoy49ojes+%;OscD4U=;jAFXMqpW^)UC^0*pzf1wfNH5J$68vnUjW}C5SQRUFkBo}PRT`91Jwx9m_?>l z0AGTbX7-kdW&^{e06YYJ3}!NDJ&ZHB#&T{Ui|gEj!6Th`UvuLa%WMPA^4KX5+<1k$ zB1J+@C`8@>r#_rY;Z&BGl`Q<7V#;TcMUM$HbILh7wmE-u}eNcKS$FL7VWjK zEkH0XGgqTvLSIT6fl8<&@#G%jGJy{&L3bb;6B&^Z2vjmmNWk7%hdvPjL|PKst8gfK z5pFyQl1XRmYGOn~^GW%1)?yA}$z#9@nyQnWVhTtrrX2y#z!wSJD!V|NU9jvvI9wuC zxN2sJfk_H9bMy&e3XEsoOc~c8yo@DB@vvtaI$jO~E0twc+B)zQ zng5N%jh3p~B^UGn$xLxcDJ8KuR$Fm30|HER%x*4$w*9r;!^}OX8);D+Xeq zLOwj~@d|k_NiPW$z$6|UtzEn`U`7%zoxwLw2v@-5n8sAPmQ`_WkJ+Cs$cz*oNy{)` zmN4gH_aj^ybOsqZ%MR#^M~+}P@;F*~>x>T(-Xr z^a3aj-7b;hg4x2eI9R1+b|#5yh##Z?OdySOocfaAq%pR{`06@KJU(FdetUxdY0|{o z17;7&<9y;V%4F99vOvBKavx#cB{L4QNF(3MiWRL1xY8%C7;*EgfuWMyd?n%EY2%}G za4uv_Nw2-NH2szN5*{6A2r8yOm{O8?6-JAVudX{vOddY_nEG0)uQi$BuD;f4DP8~H zUTb(8#)mf?IeS7Eanc1BH$-X9_xb#6?Z0vccNJ6T-P2>{>It*?yCz&cVa}GX=WgG1 z#sd%?@=5Q%p!nL};lV$hGtMcJTpEChLV`OS-eecyXy+s*Ar}r-vVoW5qdl`#bhMxN z;hfjI7cElSX}FQeCZx-~pIu*i={#QH1I=%i!E_tdaepq1tgKgi;}c5xnpLl=#IZRP~H(i2hbVgB4Kn03Jy8l zRMKDKEH>#bI2n?sXSa&(c&<5jZFkaH0WsvLR!KCglLiokgG%lhury@FY#n77rnize zTzk~qzjrU%z+n(w02%@7tSIE)GLZ!&`ih)PWaW!{nAs}o;fu%5T|crI01G0cq77U~ zW|bn%8yOdI`0Au>0d$fic+YGV-Es1TbC-Ujlj(pH+&tIZLoF?sJnsPU^nUC1783{G z+*KSqYFEF)npKHB(keF4S+cW-oUaa0S_x6wdCNJi^cSTRJtvdJl z);5x_n9zCNQUAtCUL7f}JaAsm@YmbsuIvQdQ3AhIGtkM4EFqR44anA2CNm_0u^T7L z2Z)*4YGK#8L&Gy(oqJ>-@I9b`xfgk|K-iN0%L*J)>M0;l5zDkBwS9UcJx=rfz4zw+ zwc8$MNsQQk-T?7TZr%{Fbm!deodCD)?xlXPbM7I#Z+-9Nc}M5kezk9MkLb;wE55(S zym24-fD9b{RcSPTcrs2Z`hNpVuS#ZSsD%HO?FN@!uyec=`VGn7?g! z#}oLBQS(?T9WIXynZav26*ZF?MruG*8IBT}JB}<>fDZUS8Ng6mqRlF^RrK&94wkPI zd5t{297!b8FS(%MfliRp61;O+LE8ehD!0GPR?!{Djhk2fT(NAL$7+$c)wcgoc*4>c*IQQDQ zpA(V`ZP)ozJBh~L{Qj06l6>XwV(MIDhE?ar$Sb4;9s`gAC;yU77IC)KTH; zgXUk+Nql?Qik?%>zUVC9{pPu6oqf?omjvB)ncJ<%|9A+6^rxDnlmDIaO&WmAvlMwj zHYkziuk~C08#Ms?ZARDR>(SX}J2n8_7+;bNtR`My#}+nubX#u`y~$k-#P|c&4ijCK z`Mv%}_Wr>$*A5l0Df6G0BH#JxXI8dmzhmXQdv3? zSUS!EnZa2T>ZAo~7H4qAs(9$Y1%1T>%jWM_zy?Unq6sqH28Kznl}fZh;FC2TY^M{4 zImSg10B@7_mZ~XaxhAv*v4ij+iX)Jq8I4X5nADm8mw-l7M|(tlM1>@cJ4rpsmP>3M zh)P!r$1AcjLI&qbR2iX=&Q%pV-d%VmAlo(}38bdQ2J4d+ROXEav~3f*V}TnsiS}@e zI2meGh9sY^+Y9>H8kUeTNF6Mj8FRA(Y&zq0wbtt(uVu!Q)l#1q2oq$b$sA>)+Z5mq=`2vcv2!C zGZ~O&r5TLIDyjb|Orw>>N#`#Zlmq~!^Y#QK{Z1sEavEE)rpx-#g6P#AqPer^naBD< z@^XrM0nqn}J3{3p<`ZXzLE=|Cu8UK&Kyl-70WlruI7`rz9n-W(T@3i&FtE}`x%uG8oD*5mo$PZ?S)Y*f%Om@zzMG zivxjbI7D=y4Fyy~ur)KOAEQfO4slzoJCp=+qmqG|NI^oAz`r}Dbmz1#G1B|WT-hG$ ziE3;VDkCASO6q$2Iqa)iNK=+r2HPaTbQW9OIhHv7srd(tG1?-hiTNSoLuRlwDQZ9z z5OqV;3Qi@8z(}1gDp(e+vxZb#mLRIQwSq_b*jP?`mq6-_Bo4Gg>-1NqwF0Q~=?^AD zd6V!2!2ARdNE?wXBSjR9$FfjdA|pAj07c25$<@zK=U5{tH=!epI1Pz{Wa9d4o`g~n zqG$(kgm@gsS`LWv1SL=WVMQj)FG~fS;%MZ4CVEth!x`e@Q>ol_MVvcYe6iqVu-+9W z-m6jcDv(1TYh6??f--#sCs?K|rl<2rgm9JPwC-_~HC^{Cx!$5&%yI=eMEGz7%5W8{ zoCJbe0jdUmQJ&Ss&S~4!!mt#U9f5p?YQ>c>iWzK6aVGQ(j-2S z3?$}>%2foE5g<=LrRi4dSK9yfdW}3FdK8|QSNNHQkgGd$9<+Fj{35}|W zP@IgN5TL)ugo<;3H>BElWmJA^oq>!I=@4O;F&{e|Wf||P7Nn3*eKa*)cn)G3EyD5GKnYs~o%i^< z8*>GZpL1ZbWQmEz#>Jk>Vl}BlLGF8ENJq?_^(Sh#WUzhnXmL^j?KzF3-1^BG{slqa zb28?$HLFS)iHP80-1)@wAY;ko5Ogh$Nu!P!B|NCzBhy79un>(#RFXoOLgLqLHx6CR zlb@fo{ko&#O2>mPBLX=;s7z=fIBKEtCYrve!RzJxH+Utza}B3j!jT%e;83YEOjonZ%wJMv^N$3&Mtm+-Qax2 zlkE}^5y>fjR?!V|Fc6_d1Q(SrK!Qjw6h_}@ZBbf=LY~A^Qk#oIDDat_1okp_iO*tB z)hqVBBg8WDe4x)E?PD^OelVO+KG{J63O-Rfk*Mt$hQgV(nW*J7uAaEyTyZirThdsH zzW2?i-o<&MSKek4;uA56+a_%RDb`d-2!Qs;(L=b*Uo zr|W8wtLcN&QQAwNlN}<%jnp?*X;YQdPkpFe1#*f=@Kg#z@x<##MhWUORcx(dQ1}A0 z5@jJ|^(h{oyg6J7s2+<_McpP0`s58^Qd0j&3*BpGEkjerFpq<(LP=m*O^m&meV?OE zx;i~PS@FS@$x*y>wK%aLGxdBt$Fi)es)%c5iyj4*6qXmxR1z%6lrv>21A0{Or7MIM zyYaq{cbA&0I0{%dBy})lS|J(yjpc(_N<(Q~S{$_~KIGTYWX1;YY-Rk*L@>);haEU% zCnel7gJl%%S;*knW?fN?F+feEGwf?Jb`!yq+&VsjD-~d!_zCia;v&*frqrR8hq+u) zsc)=fuTU+8$NcIHmW$7BLCq8eV>#k55V{KG`$**k3kqRYlKy8fjQgL?542WN?XjPj z@Z|-KW7(0~SU0HV$WegWWl?ep7p6-U6i4QQLX~jzq*zC$>pMvolrbn)`%@@u8n$np z@xm;rZgFM};ebhbp14*=ROXixiUMCYc{9k@L`_om6N;G<%`G?gN+r>uWsV0a;d!hi z1az4vgwNnIsZm7~Z>*IjDT7GZK#fmZ=E5R!oU(qrTnc=un#2>zq!dZ0PX_0*IDfS` zE}za>TvJ)#lO#tZETWP;MnAb#P3SQaT1i6n?pg-Mwda_C;IRv4M)36li}v$TrZm_0~xC97=* z4!D{)Sp+41)3_RjnEUQR3iqZmjPr4wuw=oYA?E1wEa z?b(CL`^N;LMmYs*>4=5Wjs{4w7_5heVT(07#PYOXYzp#^_1#w|Fd|_BWv34Ub0~JKw#0<}Nq5(>glP69a2k0@CV{FzgW^hchny}`SB6+yT zrNZiV$Qq-8B9uu6d5=K&UEC(vj}DuG0wxo9Q7{jI7j(adQLaM1B!^XIpf3|Af|Y~CJI&j(rSlUb1THwce9-d`be48_i`46d43 z({=F#G__Q&By8%o_|O6wb0{(BQFB;2+&HJKz(a+?lun$6o}_-l9gU93i7jo{SV=23 z32D%6@CV7pdRtGk7T_8peHSJ?dO!_sO4qZ#$bxDp)bxyV#-Tf;vW@JA>hLtKVuBF5 zIO|0W@mOWZ7Ktqgz%P;={w&hovW+PoP7PO&#qr3$TmWUr;zwg60tmxBN|gkoVQ?># zY0Pgum&uLh`qBa_PYY5bu~-2L1USr4@D%di{X*L8)43Y4aU+$XnEJQrjyQ42f&pUI z_67aAAXaEiB_~>51q)FbO)^UU$25_yNgD_<&~T=lTaG0Sf}c_myV9D}y0kJzMsKfL zWlYBo3o+x3NqI3|TIo+E>ry9vFSO5!Z)^CBahjrwfi!dpCIZy1aWF+kO*mQFT&-%A#>#s=(xvZpC_E}2r9x~Amol4*p)I6f_%v%&J} zxA254uB|Sw9^&}^3n~ALiH5vkWfU`}5)Nf-6y#E5MRAxqmC(I#p-h#DX;kKbt1u@O z3tsn!4Q{y&tND8Mzpb1e!jTy zD=Q8uVAON43CZX;aiMT|H#5`}R1G=}4B=1u z2Fa$&{);w?oJv|*S;`))w-)JPj72)mTk0aUb(KCK9eiCGbFhPjG2>m;lC~Bz9xB$u z@)f``9NIXF0^~oy#2hxP9pA;oN2%pGD|jfJ5-ci3M}77P0;}`8WMrpIcG)5kS4d8RR z&(c#>CapszC)XzjSP<^`JlkCZf_h4P1j%JvS6Ifdbg*5dPe^izk7-+n6ysOQ`|=`h z@~d~Q7@SXkCaGK}=~{94^W|~SjW{6BsT#5HJw`yJKoc(B(6|y)-#`);f zLb${Hgh0jdLgG(QPB`Ci)S|EePe4^)!7dOlGH?yp=U6KeeIoMUcw zyYeNgEG-`No=7tAa~S?ncR)JmWN6gqH@8-zPUjmTHPk{n$K>=P{tIA*4_;F`#bv2^ zRsnAl--by>Gt6U-CP%!321(W-)L^&5rl+u3ka zAy`rDORhl%aTdoZfnh8@kba#PYrNa?WD(r2dE!4{J7+G?qh^XZSq zhMj<;3P1$F05B0GehILOwDMv)1=q$#k4g2y%Qun1!1M6QM#gKYWPt59=Zt01Y2;712TwE5NkhorvyPrk0?T# ziP>D0Nx!9`6Oa`Fl8%d|;}_Eb@MUl|T5(b7OxZOkJ|Pb?mnqFUAh$gNA^=_j;iys% z`U3X{R+c0&i?o4&pF#FB$+bX+W;9RNAr!`vR(Yhb^YK08<>u(%&I=d`L}COrV(BbU zF)A<@N8I$({DEsvS#Wz!F5VQLaSGZDmZ-6{wPrTy?G7|#2JFoMtpR1*<$$PK%az75 zjB0p&l1IvjEo+m29790bz;qNDQzs=yRJ#K${gt^DsftAhTKY3dA8m4=(O2wjfG@yc zpaFxHW`pd593G(}=^CM%l)WU$nC=3JDTQCn%fra!L7~KG=ar6MKI!gPH^xtiJv@=yMpUXXTB;`@WybM;cP={L0ojATpMe9v#J_WnTkGA zhse_&m_Dhh=CiJ7I3zdu~M-Xk#KN_0#j7e zQj{jj=AX`&R6ZzS1$+T1OcU%zw4R7Q$2LHJ(B-9=&uMCbdn4x$In>Kknv#}*B0sL}C^8}=%8AX5z07&w8n|Vp*$~aEg6)I(zyShcg#Zve#+ny0I7*#G z%ct{;ksHxLMKlgN7g!r&NH83N+$~os;5Nyxqv5b?d*osdB~Te>8M&6&0TI5j{7R2L zbt#BZNUDAi-3?Fx*`MQE9&c2h0Evt_gGj+~7mG!ivAd!A-2A1vDd_{#!Ni_189Sum%6MNkM^#bW z{AV#RpZ-Lm&-2q0i##|5O?q^s=@2J7xEw~9#lwkfS1dizq{}5_Ixra2?y3M@3&=JP z<^h==2?{(>Jn|a`ZsM@QvY>Oq05QPWEhE;+&Y*HKs5lkNUPu7KpV{csxkgUrVg^$q zG00qG=$qqOU*!`e&IDgM|D9+AtEJ z3iu?z7-A#_ge-IEWA6flDvR`BJwX_YGT;*?mC6*9$5&RUg>;5`d{E(i5AbhwhL;&;wR=2Usnv10ZNdpl@Z2PY2l!pJCc;>|okRe`|hg3duq2Gigs2 z>sv*u*%Y&0T3D{fS=Qq$>v5K~7~l@VWj)UF-@-fVahCNs%YTo7t;bo`<1AAmJL_?l zADeNn9%osPvrGaz>v5L#ILkc=9#4&UtjAf_<1FiOmaUPg^*GCVoMlGXU_H*V)WPnU z@xtnHmi0JGiHOgL8mz}z0`+bW*2s8c^*GCVoMk=Ea<{>;9k7Mv5L5 z3PRN5EbDQW?ZZ3kah8MYahCNs%X*yU$A)*-<1A%NYCX=f9%osPv+Q8IsK;4;OoV4W z&axh7xx1of^*GB~(tvuL<-bLE*5fRTBZ2hS<1FiOmi0Kxw!xkCI7@=+>T#C!ILmsR zWvzI6J(Vg`;%X*w;n~K8!W8*Bt=SD6*x?Apfap4b#eO|a@7Ozz&d5(Xn@pbcH zS$W4GX{AT_ye$LFU7v_qGH#Jn$yEuEC(K*16jSs)VsSxyeeB|I<(?0>j$OQNFHu?e zlY_&@N{jdD_MhKwCb#47y0GsNOK$3s`={*M@P(yWeZ%cvSTg7n+)(zfC{8?SNw;vq z7ndCLbusGj%Lj?a55GJw4u4=tQ7pQDNrQOf(964sLl3`vAMv{fmh3CecwosfB7fNB z`-;W)Eg3BCy?@DZpWtV?BQ75(COo+0&po_vxEFjqIQ^{2XI~&T9(nn2vB#0Hy6DKu z3*wb=mk$t=$8&AzIJz`?!;(?r(D9cyh*KU~GEf}#@RA?=mwr7Yt|a^Vh9y7g9`_>Z zMB&qy4->z9WXTZm?x$h>nMarO7vFqz$v$Gmr!OBO{`ts~k+Ks##9^PgypOo{sLT6{ zlRkaMGiUph{H;73P>)uWeQ z#E0;tW0nq<7a#fK@&m(bk6rryo&yZ~u!Ar5kLVuPdp-6MZ=@D{%L>Zv!)=p=3_EpMFJ z`LvtD&Zo8fW-MtRD2sO1Hz!@HoaIeAPZ?9(_ms2RUsHE;P20^ieK*%iySZkxy*6di zrRr`NN7vdloOG$Sn|9H)4(;^aw2Q8FXjhW$dbD|BG3DWwrahyF7lWExj_>ep?6&<* zx^3;|T8DN~7dy0zu61Y^b+KJL-c59|L&K=w?HW$HG`iNIogtsEL%Zl&hj!*}+C|r< zbZBVpreSogLpysn?V@WP+Bv&v7hOx*O=+E)YBV=HJW71A!!6Zl;&f~oUF_I$cjKbA z8<;7{%25A(Xc+$84UM|Hv57BsWWT%7QRCq$UdC=maR{;Xsur!YoR327#94OWwJkk` zadnH9%+d3%ZaF$3*I(UoY(jRM-Ew?Fo;JJXl!Tn{vzF5ma`n$zPEE+k*R*^tA@9GY z<>Z81^;pZL2|0UC%jdf9+786rYg-11IX`b1+4f@J^<11fuVqhh=-ifuHL;aNu!(Qw;a{B(Mk8T3>M3tXc^J=;`rC&i=D+W3tEP? z{pO}uTfdpLprw%9^Ueh=UrNX>3u~Qx^T}E#m8aT1pC)cvET2!%v%fEFDeStx7WX;& z=1@`VbMEPuqmujIA!;{$Q`Fwy*hMWzCJioJRO`!=i&{S4tu~C}!avdVKF`9`THMmG zizzA8&-XSM)8@OYCm1|V%uJlLsIr7YX7a4T3?p6w0)=4ik9y*YSd+U+jnZl z@04%a%v_qw*BLcmw7j-59#~%M^Pm;AmEo>v`Sh+avf}wSadWNbe_m00E6;AOjrsR~ zT)R5)w=KuFy+Jg{lW*qv&6`_>C3EDR-?n{dD<0JeTWYWNnk}^s9dS$B4%u;s=G-RV z=PfP8U7YNAe4}svbW80id*9l2k=P<$BwJe=lb$TQwKlY~x7PTpx7Eg{@3z`&diJ)q zL+ivtJLwL(a>&Zs3;Nf#mT|j!MW#$S>!L=FudJ=gH&?dpl@s^st?jj5O}V|+tLJX7 z^{USuwO+lwqt>fcchnA@5ALXq$c~q5BXZT9EuTrA_QF?c3uVNrme2I4?Lev`qTP1p zs@h@p(5jZhlArBd)iORIcf49Vt8Tih#`w*`}MDJHGlwe9aZ^;zzE)}LDrO1{75&$VtH7S_5oC#-er zxv*t?n{h}s_GjN{IW)OL?>B2*dSqQ~vh-MAn=Eg>+3pUdct4u(R;>?H->UUt?0vO9 zOuMhvhcn;qaEExye!H~~3*V`A;e>Z;UHI_+nyfJXfp#}Yw&&IFGDNpMP#dC2f2sYp z;lWxzE_|@oj}zW)|1zijhL_1*OmWOZEn_;U5^a=@n(9?ovtkvrqoBMSC1H2+{O-j0 zcG7$z%>TPAUstnQ^_;qzRadj>YL*&^x|$_TFQTqywJP&>?GnK2YF4DliMpCq zSF;jT?|-M76{2Rfbm|e1(_Oa$Lv<9P-2KC$;#+@jQM&KCDpvo!WoNrq(;oTJU@_`}3i;v6<<|}? zP%M)&!czaU}>&EIjf51P>j>0 ze3l9Ff>IT-!xWICT$>V=H;JpWX0a80Ph4?A!ANH-s}v|=)rl**=e3flQSredk3E$U zNL6oY9XuQGNxKh)rcXjk1cm=1?GnszPO5msLW`aqZw`FE!ti zD`*;(femV?m#GO&HE(LsmUYTDTGZMh$9ii8t5nu*RCTsCJ)ka=QxfWTRtyZ|GF((Xmp~TE6_K`|dPr1mqShsIO7ke49#RvXOfzMR z>I!PblSP}!#ZE;RO5RZ|Pm|S;jWSvChlG>&Uvx_@pUT%0>fffT1xnkd@f7Z9!WKsy zxnkcY#ca~FHaX#`S5qp>+UXSHq?mQte4k6WQ+ zjt2b5H>u)6iNCl|K{PKZm0F?dq(QY{79zz~%lv@)yHp2Os7WhE96_PKsQgxeH5E{n z#%KH~2xRMQFPdL5snX*IE(I!OEtA$7$f?}P#xGY{I%l7NoaT6y6<8}&EupL_$f^>x z5z30?Q361tAS8HvHK`4xlwFEkDH0sWbb8$34Dc=qCgg?<2O2d#%sRM>ekD@5lVz%-=2i-5Nf!@wQTgTwv) zaa*TP$Y5|dDYx?42MUTy(O`{&$H46<73tW9;z{w%Hz<_omt*xdqfw&XzWmV2@AW89 zZA`H&8`0LGpebr85|u(pKg+G4R90f`*2rolwcDA8Ke>{>+rz`3yyNiBd9C76_17*b z)W9tva8l@t&nQ&^wIM6v_^Au1&FLdFp|Dcv(m}~ZuC`rMmYa19in<1|4jD_}s%uip zkFv&6UDJ{FR1-0h4C6|L+STQ#@OvzQr!x)$@y?5P9A8Z5?CQduI^$q*^-FhjEBK~E z;XSKtQeYbP)HL$~kGg@Hu2IrGQYa!_s==>NJ<<0oh@`G<*g;vdHIq`}9@Q)Lwpwmk zF;H@~l835ZqIRsKmnqfhIf_CpYr7Oz=`3dOMU_C1#g>uER+SH;ESRwAv{56!|9uLw zqIgr@n%bHQg;N9S6dST=7}^qnOC=qc(8C17M;dPf#UD{rs5nnGTnjmpOnx?sh(}3j z(+zigbq4iDRc50`HTQsuYM77E3>=H0p@bU+(5T$FWWm6wWGA(g)7i?x^nNNwON@U) zj1n8(oZnAO+567@sJkm)U6avPIH$B8O1Ww98mTl&0cu~fD6m&iC}b{G`!ILd3@M$b zFC&!sf#IBq0>w9ds5(?x%9y1cMEUf0XOyEdgO5#FFtCvERMIMw)Wk*Wqx3X}6)Fst zr~qqg6fULyrCG*g;hW)2rz~RKQIps(sWoj`I(5R?FG{{)mMPxJ0<72q4JN9Idu5Jw zLQ!&!fFABjjcV*Ml=L$U(=z0WrE=?txwHP1D`v2@qD7b2Qu&HG2REznk_!UL?*uC@-a;BE znFd>-%|j>@d9UbH2M;WeWz$Ae9%j#)bKPlx!qx|Q7GiAVK>vnnv-V? z3Ag7rQ9>J?(^jdcsk@ZQrADZVY0XjCNHGxDs>t0xYslIj%{S)?>Dz=Mrn$!YgT(>lZri+vWJGMVfHGqkFyn&D_$k=dFGPUS~vEz&V`JFgqF2>@987f zF%NjgEN!nsV>3v5cb-&=rM;<;@nB+ZL#z~AV%xVj?JWue?>we{?$yt|`nlIZI;)?1 zY3FVI+{-+E>gQe}sr+|4_aa4l@6+!5ZnxZnyFzq-zy6+Hox;L-cOI1!;|^c5cX-@G z_v{^AxF`3Yhj@#Vv^D#Lg$wQ+IasXRcfTIupvR_kpZVo0?hLlwF-8ggI zhE?IZ4R={vMdhEX8^b|2uAcBUF?YkN#_;NYuKux=+YraBe^*^|^C#=U)wvDfHf_!C z_7wd)9ncU(m>>K2ie8=89=4|EUZ3EWgqaT(SAFlb0W-gG)EfTo4!?2KPyY4A+~0S- z#`z0>qI^<&!OWh^eiHUvcF!F{b1&|?BLma;N$)7GyZW_3;kl3AQ#ng)ePwOq%zuo$ zH~h!Qwc~q<(xSDE;Y4-q4I{)ehlGvceqUVcjuy|2ytgsD<2!4Alo$SJuAWA#QDXfY zYa7LF53c=bu4j1OLujO8hy{DUSRLDJFmE`3QLT-O!?F?IdbTZ_Xqr*=R z5S=zdzIRBtZ&#tIYvN(}+^54{pY)-^>yHa_=ZKGRuR78GkdMOWR&dULTSyF6t(XxXB+;vTo^4Z~tn`ce#T%dA0 zE?8AJypmtS1M4cPLIrk=LS6~yZCu4YwipHFvS(D#dKJq+^)kw3ymVz#wJ_hffhm)4 z3<#H4gJEfSx+7K7+FkHX#rpzFWRG*ZgoUU$M|{RcwB?9+ZGj*ny6Y zD3!vB4X6XLk*X>)-@MfZWqtM|(#(54DqU`F_m{%MXlV0rL zEMT`X6%Qi0imh5ZPAj!&Uk*f zW{ez-j2SM?cdfc<`j~VqAe=D|t@gTU)(dNO*cLOM$`?D1nlV~ze0AMb^_5j$Ss%BS z>np3a75#@RYuu_89J_w{ zpxkeENo+qXukZIsg`1h%&s`sGKli>pU(G$T>yG3_>eWdcQ~YDU@XAx~ACpMv=bdu@ zgPs5Lj>~LGgXPTzh_TCl(sSnAuinq!1N=S6-$VR8%-;t79^vn&{5{Iw&-i zIDfz3@0a{N!QYeoJ;mSC{5`|pv;1x3?>YW{#ow>_d!D}+`1?&b_p2`s42q_&TZ*j$ z&;reG4Z{koe~^TSZUyOv6iC ze<&_{9qzwg@e+Kt{2o5vx>b^QtbiPP$4ggq63#1-kKOgs8~uua{&W-9jt5o@=NH&4 z#dVPn0{~qCfYs=|O3Y?jtA$0OYW{vgTxn>4uj^_PC!seo(BMItgJu(uR|htta?UG>T&98+x{Pm}~)6(nTTH1HOv zWvhfS-1bVkR&j9uAgHLsJHL!$D;5pG_#umd+~ToH75r?9hfe^As^w^4(ZsZeXC-jH z4A!P_S8O`In8DhJor#6lH+3sGl@hbaP}FjO7^iD?pp|_+2m(;EnrW{+rWyAPO+4Q! z;Z6jURYtI~e2L>L`9a|8y!9V08WP5MSvfgvCD1D>?itH7UDfvk9J;to0t4XRj-d-5 z0@RnzGTX;4Qbamekr|r85K^?>^XiJzTPnxX7;fuq`%9l3#0lZ1A!ABy*P9C%8_)oF1H%SX zpaU!5#0He$dnTKFc;V%nq=KA&2#4yoHZ8?LpFU6>km(syvqLG7j8|5wc?vTgNc4Sv zdg7q30L)e^QZ~WGTX8+dciG(uE>w#N%BulQJR zRE|Z?Uj&R23ztLOMWWEKB8KkgG3aD*6yus^YdDe9_=#zMn{J8qCu|z_nKl7jr(Z$@ zk;N2H9Hq%n+MI5F3A0lG1gqk4a(hOZ8Kk+I3-k(nmKK|%8d8Nw(KioXJq`r{ijg`> z?F|~~(-V*jn4~Y%@<*q?LRCUy#Ko_!8(c_#BuiR94r)N}CqX8%6T%5LLf2G$kZiif zfuxn=V=Z6N11W0}m4r z2$)R-R*Mm6oQG+QlT$67u^R(P#{G19kN9X)0+y9p;dJBVrEB1}IFo!9H6ayNV)=~r z4Qz(QCs-DuI=T?zFEAgFFN$gSnr@mQ!73OQ!GO6MaaMq~0C=(ca70Q7iD_5pSvdC@ z!kIANr812VgCZ$^(->lk`pc#ZkkE*GQxV=hVr*0p7zDMI9S&d)BSO0b$L@d_22*q_ z=CD^m>#_mKSp>bKkYLQgZ$C<4PqgLFJL?*y|ndA}8> zgN;m8m}JaetEn-4lnkx?>PerWZik~Ve|}JItJ?6TCT3G{hNwd*VfwDMACJd z{~NTmy{?!)ZT^Cx;l@szDQ2@`!-N=3H?_y+(Vx`e9KJbV^P8Wc#!2<7n+JC>4pE&$ z2z(oV)@DCf5);3+`HTE#$9Fa#D|UWu^JVy897Do%WTlj|FDNQa+C|;F6HQhct5{8gYEybG?H}6dDN!s0uHn%itO_&9nR8kgMl*%yVN1%F26Gc3d57;}JH1R-r z^VfQ|Z7e(SpxEYZ?%m{QP1gTfCuB>pkH2}GIB??4d;cH)kX!e-dQYJ`S2u|Rg3Z0! z4&|4Ga_;5LvbI>!hg(h;f9(tP!I53}78^g@@;NbTG#@8iv30ok(nqxW*1>$dAb&t_ zF(FsoOFS*F_1J@J%D=XJMdWi(8*i;PiB13Fi?1BO$HR7R@x{&t+2qI7uHyLP`gIj! z)>eCpZ98f5-Tr(r`zpS8_Iua#7t0>5?zOCF9(-;a;cR$DP zF%;^`_qXjM&gxP9f|y+3;}2$x=_NMx;8An(e#Z}O=qXO<$wzS@AEPIKwkLO)xkq(B zaqu%Q4-lWORKzBzygIZu7Lzqo!Nw^}G?DDU`7 z;wU++mkz3aLwrNdsdJBcxt|y?m?j&$@Nv{FhBRR1S8_q+olv9Ex{lv4w z`63v|$5odXdy9ixw(cc9-@vuC`*SUtL=Vf4x5}3j{i2lT;}-b~=F|{zm;89jU_Sm* zdnGTF0w2Gp^0D)mUTGkKBOkw>^mKq|Eb?*CM|7jP$VBKZS95gJp(FU>;sJcz)s4CG z+Lz=@jK;s~NIs5`QwP}xs;z;{>ms}kD|#x`tfnesA{wL_9&>MM^`UeupHvq`(8OxG&IuUy5W4B z+F1RbcqUH7= zy`kZg+WuB3i1shN`cxuoVx>H|sLB&qi$wMtSylhpl^x=~WUkW`DLUXavX zl6qNEcS!233#UN-N|GPSi$YS}z6te!qy|Z9hottE)P0gtBz2vn4wuwoNgX4ps-#Yp z)WeeUCH0V`E|k=2N&P@lH%sbDNxca*<(x&5Tp=(1SW;^wb+@GcA*qKY^(RR^C8?Js zwMkO{lGN`dwNO&;N@|&;K6Me)qmtTNQcEN?Oj6fNs!>w!NU9{MA5W3wcuBq{sS_l1 ztE5ho)GsCV4N2W8sY@iaR#KNs>LW>ABdHrCb-kn(Na{98{Y_G9CACpfk4oxQNo|zW zNu4LDyCwA zq@Iw}^OE|5q;^Q^IZ3@Ksri!nKvL@^)%6mnTO>75Qm;sAq@ zlKO(A{wS$0OKOXxJW1UqsmYRhQc~ZS)LW8jmel={5|VmJQY$3&p`_MGYO|yslGII- zdJ<~NIa?+9lDzl}N&QYz+a&cDN!>1~osxQ9Qa!%~wMtS$CG}59jh57QN$HaMoutM| z>bH_QUQ!QA>T8nPAgQlQYMrDm{<0+hF3D+%p5$FL|NHazKWCE|zj%iw{%KY35z>ASUm_|~D-@8{%aPO03>J+3|EeLZ~O@anVeujR#@N^VfN z<;d!$zTP37@MzmJ>HT|LP5Q zlW(3n@0H}6YYu(At2k?Ar!L{0Csdzq{b6Bi^7W)!J9QD?ySq~_as9fNyNAD?P`y8H z6D~Wsy4N0Y4>rsr7OrqwwISSoO7)M)JvLm^%6#K{d-fF@e|AY1QCzyCdpPRU>V~Av zl*7085Z8Ovd|3Qy_2t$da<`DXbx-bo*NyS>HHeL;UNStKaz^!u4nJ=Y%5kp@4{tn6 zzOv|@y!Dy*0rKIEZgqQ`=C{DDW3R>34>}DE-wLYl=ES$|-7%@A7wjecZ}8TqY}k>! z^^M=Cemf^W_Y;3UnXaGvmEV)Fdf1W4lU;pI^^y4NaN>E@za%Zkj(Vj>IO&4w?~>1l zZ{(LpexKuT&ih@5gx5^2{v!F&+25@8*dxCE+jr3Dw_p9eEzaEg)xP28i>tp%em-X^ zL&rD0MUV4;H&RT#y=#|vYY9a`0Z-%X# zO^-r)^j6Z3aZ{>GbK(nYnDvp;aP~Cb!iIINQ~!l&)k|~ob3egjGEq+deKDLhy?Sv@ zn15%!?oynlUBM6E`mlA*N!VykFnl8v5hYOmgViiv}J_Yik|4HnPeQ2k}Xf(0>XkC;U?8KUM7fAe=k#p=U{cM-?_ zZe{myzn0pg&?A)DvAm~YkKWNe+;>I$Z~ExHJBNL4u6~dckDs_TFUAe|v??~7xV4M; zVxO&(q@MdNQRusMzwn&fs)KWq>>JKoS)H7a{qLwAlaLqOS-mhJ-(DqWL)7+?)p8|7 zqUhDkoe&P@JRDUk6&NKDeDblBi-ut%B zzZGk%!*cR-Kd~{a{yKMKxMH2`espEcebwJ`YwxXN!zUiB9+wlp>hkiW z_ImgU;yZm_PF8on4b`zZ`MF&tCu9?=YLl9V^W;(%WBu~vB#>{C0||m zc=gGI8 z{%@+)m=s*?D<-`-V6X6;m#RNazCU7fbw@&~TdIFg$V0YPZ%)X+Y-{BpxY|9e?x_Ag z{z}aL1A12POMbss_{J-8)kh3(_+52dLh?O3kGv|j|7b_&@LPYZ@jClYayN{=`q|qo z-yUyNS0=xD{7t!XqI>Zb3nC(a`IqXlguLvpaymy>zx=oApJP&V`;e!b`pTd^#lM!^ z*f0FvKdM(HjfTD7`U*lCP1_^>>Gl7r9-lP$U}tTZF3)ZKPI7hcPBn5-=Nh^1UR!4- z4Ib#S_40(A&~58w30dvF^@@bNxaZd7Awwq8PL|-y?H0*LGpaMhi+#2pEtD&(eM4k} zF*!;03oq!ml{q9yQSHC=iQFCGtpm0)y3v)}2W@?hE8icy^~0PPb{)HBWbXOv(5)wk zUJtZxn3Z8$zm=1p`-x}++F95-BFqopdRyy7dGL-sW$QrEr?|DB*!lAzUB!(Dvq1OD zZ@n*W6TV&8y7wOOJyyR@^Z$pf>kf$Oc>dqRLPv@X0YSxrqGIog7<IZScr*9Oyv4|s94Y#W30c=&U*)v{QkMw*_rLLv$N&Rt?{+^ zrK~DnTC}Nb8m<#k=Y#Mn)Kr0@W+~njzRtx@JPkK}suvS?4(fJVT@%*XXXQO8|9*LQ zt(d#jOi;I6zr~v^o+7J;skolyyYG=@aFWT7EE~#~ANGX|q(?eCxh%cOigjDgfM7UF#2L{7N)I@Sb@ji4VTv^G_u z=#3TpMM7^ArZGq6AGLW22q1JVNU>w zNp>xgEO`~&#J<5M7@u06b8&2%6@-CHt{haJcGV7Yr^vcNZYenq4{>X#tbb8Hku}@| zrN@y|9?A+V3yPrBK4o0B)j4#e33efqqqA*Z^o^$!3RIGk-Glr^i_s=1LndecsaIc8 z+$e7918<5FLEd8JSQD&B=2;wT)|yzr1DhZ)$dv|rpY#xaj5l4-8Ix{Uy#$)Q+C@v~ zY;lN4nrvEWpa!m(^*GBdLu;m@zo+^-XZ?$*rl|&r!@kmIo*zZG!xQyy+WWbQ(+P5J z|BD=3tztFu`oiKNZhmNj5z6GpGt3c`&@i~N$e(F~7UxJGUznrj*(NM5HEmc9`MlD9 z9`p`5^p%j7&NF2gtfJ9;seVp=29wx%v|N%JE<|OM(yZYkb)giFsDrSErD-Ez+F*1U zm$On}_Lp{HJf)(!G9~Q^37|zwOo1Z#BNLYEln-$tS7>d_-J+SBV`$n|d${;*nJLjA zgMJieg8bClZ0bloZDrkP{a8$zYb#7?8d)S6O)XqB&Zu+dT9mw0sZ05RRZ7s2))B6h zeBA6ITCOo+iIusD;B}_q`l*D|%5Xnf*laX47@Oo>^N!N%Rf%7TIJn>nRc0Tu&L+UN4SPS17KO z)f&m?Y&U(Yb9UQlih7?D?WjYKv#{6vC)M`mY>nf97%QtI^JTH@V4(;+=_U8<=Tg}n@|LkS<(gnjCT zKC5vMiLC*>cA2k;J#1>{q8HK3$-cOISZU#URBCH3 zoXd%|g;t^HVOqh>=)sD_* zrns}Fn*U)+eG*zipbLN33-Nu2HQ~W+Q>fM$(~!z;?{#F%1=*3km*Ql2S6@pXT3aHg zEFHZl=jKXhM}}N7C1E11wEIJ*Ym#MVrEu})JJU%mj!;#OtV+qBRPrK=5er__6%$ND znS{{%n&kV5QtRLHIrplZI?R(m&MZ<7Rw|TAarL@vE3 z6w1F=33pqB^2hxo$B4;co3-*AQPMh4zP$DSB#gR;ggq-lHcS3yf{n%rWycA0TqPIA zMd%$mb#MWvbyD)b%hjzcra)2g~GQr^VFEh1@PeL~*e+ns#P8`%|%- z!VMDlry#|YV>m1RQREuPF@ASVcEtR3SK3`YToq0oM2qt`xWl%$*E-Y00UaDq@wJd5!_dXYM#o zspw(O1Cmn4+=ud`%pqdFm-)P;uG?*HL17}i%6p37kdOI$NsBdEgUPd^xe29?s>Zz~ zpi}<=ADw-lYHN3R8+Q(HiXUXC!g_*0%a@x+-k_J-t1#=m3 ztg`v6Bu@`F*92K?uV(J&LOo|=Eyguw9JQH)O{gQnoMCt*HrF&`Bk{U3R!Q7T4xxnJ z&aL9>Nb^F2#QittW5y+2uZq9=ve(iVr-yaSyEH06jn$;My4BFI0>$cG4{hSaLalGc z+9mU$kaggU3aD12pyzls68jpNp@x}-Gm|VSXCw9H=V=r3RXwllX6DvfULvfexse`( z>aL}ifLU0RvfQiJ*2L7Str?~YXAGv^6x%aW7tXHj%vh9|!WxTf75BY4P^@WhhAv|Y z%IN*xK;7;rMa#MrM+Ug?Elusam~m#52K%3nOOJLjufmy88g?nr9q6&{)gwe|H<=qp zW=zE0d+fvN)kRhhbGjBO5@XHbE=*@-o6)#N)%QfMOdm7UlT0nJEI=J`Mo9AkJ@%`i zpZKgFAp4prI8pVKYM~kBkHvgp-FRgX#8xA7wJeV6VJT5nJv9C3KD0<;fyCwYVx$#C zu%aqbQq>xjD6_TMU(ARz!#34w;g1~wnp1<4?$;=x_4oEs=1iSjm^#+0M$qOPHGD+o z7&*+G7-m~7N-AC>jG``}aWBTno*mNyQW!hYJedAy<&?tf6V0&CfcsOy){c@|N8noT zJ0#pU$-G4;BTHP-KCK#{E$v69n4w8H;d@m{h=4_$9P$48Zki^0fEk_L7e;aIBWi21 z8Tg?T6iF5+p|Xp{xobD3ME2!~Dk5RF8A}f*KN5l-NzI9BbBE{>?FP4DuHq=w@xr#^%rMfZxRK&h3{{Vft@_ZJq1j))LF zmzc4zat-T$Y{uU3_D_~++RgEcWsuN=KU>1ZnB{VwBM39QV1r;u367whp9br5y2DB{ z&TAwOR4(efq-OQ^ru2YSvc8-{q2^BRSgZ2C*O>KdA*aOxuYyKqGV2 zhPMoL70Ib)7$7o@NJuk>XjK=Whv}{GErTx0&!|~T8^gN$q(skLW}c?q((gKek%(Sy z4kyopP`oyA=Aayqt-ooCH-N+;Fx1Mi`3c{2^Jaq#`V*_O{N&)s_cU+l5%X@H%=$EA zd1TY~hN<&W*~gq3+7g!9DWxfE6oFmB9i0;Qm$_!j(8${V(!+nBl#S%HGV7GXklQxoAN!wb6oT9|~xgbZ9b3&S?dU9#b$(jo2=Fxgw_nnv&=oNI;59Y^uZfrKoNQ&E2yMh>Z&5YC8 z)k{s%4Co~v*;Jv)H(&;&XlB4~H_Xcoh7>X4N2pc6H9wi-Bz$lrbk?8nq0!glk#GzT_PEz`6aSR9{Hj$!*veW0bvl%8aLT)=8k^J6b)4_$z#^>`=#d;fomBdI zIs{u&>`Wv=zW$<7m<7fMmnd5y&t$?We@4Ah6gH-wtLRzTf;m;tD1k=d6mD~0Y#{jM zS6($s5Aqt|+(itlZkcM3I0jeVdE#&ls(g)aIpG^&!6_zF_>?ZJc>QwXVNKbE94U`2 z<-sD(x2`Ec)Z}+;ivns}pq{2*#6sbGS5}f1)wMuJUBYT{udXHDK>3#~{v^*B1z!WWm0%C|M*lvHYP&@(mU@1!+9U z*HYhItZ8P!IT@$H8DFr>&4avG)%T-chND|gw6J`okp-IjTIY10b>c)Vw1F2TA3;WE z+gPx&b4G>bfE*w$wYA{5j494rIP8+Ml`{11Y<+Lqm1p%3ciLHSu*p0)*wm}hCK=?Y zPwHbzwLtUo+W$X18Lb-lhvV?FS3NuRj zzL6UZyMmuPB{2eT$61c+Jlu3t6GtanZu0itcL{PkxTc9OO)1;XRd`IYoc(u}r>00* z;v|JFHUH7bRd`Laz&>P(yq!%MU@1o#N~2Q3ce(}JRwnS(Z_jT~%H_O9UczUFoCQot z?=Pp|Ay9IkH1ZL?vn*LUh41^@90w)gaT9M^=hDQL)|NwSs?D)v{3qYAzZ(^&je%Bw z5kAjyN+)ywtu5WuQ-mzAz!c#U3;VBbvBo9nP4ULMTCcOHXu|Bk#>GWgq6G*3T0(S{ zKv(^x<8mAc;}}HXEJNPiQMaKV%Q?mDMaaeJy0NJTwXA0K6O)(yKlM+lhQbD}_-|MG zW;b=C{xy+qoY4YnwxBB7sNK1(_)=o07*~<7+5(f7$%P~Iu2thwG&B{9=;F2iPt8)h zHS?ie17ch$-wm~2x4vM+pq;9Tqz#ta|7lIwfW{>$thY1YjE&B*6wr@2+vgf^&u9Fh zc#2Fa=s@{6Lmb&sz=X9G`gA{(dE6xA`D-XfujKZEF5vs>^fgv*Yz36^G<{h- zruc}7!r4rcb6!Ass5+CBZUOCJ({VZT1ZEzNPPz8ZDVmav`EoF~v8VVjvp|Q*mp-D$ zIm=z0*{M*1#k31jnc6#BG%L_@(Gq94E81SNY&96}(vPz(I4tpv5%2#k&48X4Za@c*`=+K(R0ePL@K0TzreQwHwVphqbPa zMK0yc!`D14;u*2gRp{*$@YGu8;vxFjEPHhx94~75q|A?QCE^^GUvvVtPZ~kQsTN$@ za*pppoXtXxH7H?EbSX-8ZR<)I&ukvTd{+t%GZwU8WZ$>oq=F-Ht)SN#sd0dRH#(S5 zqMsQ3(1NX~X{rC;Ql0^Tb5~N&l6{~OJ7JW$=v(^4PpsJ@Lk(-V%AGb z+I#sB@tYYVnroCfwb?mbGi^A?NWN&}7~om1DFqRnl~Q>#1$Q}dZAS*tF+c+L!4`nHz9SP=;&v~C^7{a37=sgl6+sc^%NrltXLnJ0xe#i`)Bi` zn36QbB?bk~LV*j)6!Z_jvV$Vmx*?@PJ1<)6(!@<*dB*Bpn7{00u|BY%7Yh3IQK=Yz zO=dEKtgwqXNkQj|)DUZ)9$AniIVi?MV2QwL%-n_j=36zU96^P82=B^Pm}5-l@kg5C zlu;|jPt(n{!~Y+0se>Qj%CA~MmvP6ic!V{JDK0wKD4-N}CX=(CC|K3oV&xrHvp@n0 zYc;JCYK1k~L#&Sce?|)O>OpT}WAxQ|R~={71Zs|_c-#oyNZ!anKPOCzsYt2AV|=y2 zx7K&E6WANmy9QQQ@wlP&XT9}&rHTnvOwUO6;;CjRz;7*WgPZJovan~+_Wx4=_v;~;@RV7tgMelA)kj` ztk~spK3G}HQ`Vf0l_=|2NBo1saSQ@hU+0v zIGam_A|Cd0Ot{$B&kCD?3onq!)bLJN*K%My@i6~1zxmviI`&#Hx(!VD2-|q+oel6`sWjv z%>&4aLyWrqI0-wl#X6B*``f}r%dJ){*9hWtR{p{o^o;-T0{l(xSq4ACj4tji5D z=qHMQX)Wi%LA{X5KREJ$y z`e)g(O^@Q85x##6r4qkjSbUG+iP7|5taA)9=ttkYuvQmgzsW^X;sUlfUL!CmqqN0K z)Vbrt1ui?P>7*oI3!a(1v<9Vo)Vl)ZzeK`&7P%^N!U8J!*6tlBUfHZT@zf|cthn%x z=^aMzPWSep+Kqd=iMonx0F$G_^=v8QV($>qJ6Ecoe`T2;(>svXet?9F?#a2#WT!az zXqhME^e!nr&9e^DtBdz@cs24GR&&ervR#_#1Dq-i%IIfDkM9@2HnvNH?$ahQMYVWCmP!}8aL^4gvSX-Ht+r2_XU~!vW zF9uIBw1J^I&-&IB!%Nz*0o*tj@`?{%E7AxzTPKQ}hiB(MxY_1H5zd4BJ`P3EaUM39 zK#a?IBQS5ELl}=;gwIc-TlVu4U-;NCRE3e2p8bMoO0Rxyw0bJ^`&C~X1TOO$l;ci+ z+ivUv8#We9Is3v|8V@J>Lxq%9eZ{*#8-$A~Jh_}{0z|1`+f$uV;!9_muoL}*M8AqQ z2#!|HdVIsoxrLSM??DZGeW5N3m6EqTP;$~HW0WF)|0w6a%fPW=oKtEf1lv+Rlrb zyn0d+fcuj?w-yT<*tX#A za$zGZVo)WK-%OTMY#~-Dd>>tdUbR5JHx}aQ;rN!4g5dj_In`%yby1_W4f>cVPO*2V zIIqE&u73|IMIif&KG9O_ndk5>JVg2taiU5n#kAH;&G6EH=vKL$ODOl*8HB5I4Hk89V$l-E3d#Jp3gN^F;R^ifiZ| zHdyDJdU9DjcW_cl(E4dZ@yXN3A+F?=XF(RRwi7xd--xX3ZNq^oCzO`8Kud5HA#nj) z5QlC|Vnb|umeqZ;@30CKk~qW_XYV#>D5mmgX(d)17EITdp|GyaQQv?eHZ1qd!(YBJ z&!bJbr9`n|a&(wd*pe3^svoT#Fa@V2a@7qSVZ(td^PnoQT4+cB?LQ>Xm`B=R$AeOu zO0_e&Q^&GL+j1M0z}7&a?H3O`N5ZnoFwOr z#05;7`#K3v$^$9C%y3t#*c?Cqtj861pJ`GKIAvkC=l2-yC0b3l;aH-ebgi@_1BM5P z4l``G^fbKXhLUtA-+99;)7g9IxFk=-L(H62bfAZ9$qm)cW#-M1bCpYEI~&G>J0-;o zDM49Va?8G##mafMV>%-m5lE&dLp;Q^1vZ$poSF|Chc2=`)N%gQ4K2rN_uFt^`n^Az zw>j}Y{p>?w+j0ZM+9m&OUP`GE0R)1d*!QuVSF#v^Ui(`wp+Kxf@(LTapInUs$)cp_ z5fx}%IHYy$E9lBgMmbY_BB9pd`XG*O#F%x{FL;eqh`@2cB0oy}VZE&qjhHv0B)yqG z!i|Rfj#cB~S{n=p$s@4SbHzQgWM@GIdysgy!Iq=vQ4mQPjuE9ql}$W>C3|59TRt9v zuO>^z`HILC8_WmJ*!NRgx`8eYwCkTpWle+2gUGG6VN`pFZa`D|R@)_db{`7S>7k(q z#lF-PB8}RP73`@9*dbT2VV^_2!Dqt|YPK7iZT>E~mLiCU02^|=M5)hYk27BC2uz99 zyK}uMKQ}H|gne%N<=+$$wnr{C%np0AklkN&+-JL^Q{-!Z5q-e+PQyj)7dCw7%83xH zMX|+4mJ+Gyw(feQG*`8PleWf{6}2;@h)H$X5p*}Rl#TWOHEzkm+Iv4{*3TfCFb*>EhfVw5|T{~ik8aY5FD$vo^J2T;bm<7H`W z=vX&x8G3tBb~N)~pN3)o!-iL3=P)stF?shWKb*5m^~F4L;Umr`I(%$tQTvLV-Aq}0 z8XsJQUA67P;<*#%3p{QNqc&e)@ysr5#~&+PmkJ-a{y3mpwhb0N<9KG}DH3nWrZCPY zD%%d_R;K7L##X2FTu8XrEh*uWEX==1MOiG)xrqz8jpZ?6lyehjye-8GI0}*&camSm7PuJ+|Ov8XZLdwn(mM`r~`;BOq#Ok$qG!sE(JtU)N;Pc+Guvwp@l*c$9^ zB;+1SXuTc#VdFhH9}x5-DgUBko^7XrM(&3=z!OPD>iiI#=EKgd(=WvI5y;lW!;Qg6DOChF_T+p*>`=dcR)e{@d1)PDJNdKm4^ z#Xf#_u>BsMuvc@+rcWpe`k{tXHs^=hG2FoYiC4M`Z)UtNmK`eDF`=2vmTY7dJFWmF zF7Qn_G%1ttCA24XNt;ogEFnqyhvdVn+ab_Q=GAEMln<*@axJuKafBQPrbNd=aSoZ> zyM_Set{%yQv%l%ghBSO?ZB+YU3kTq6hy`@{LRE*h|=i7q1)6=lybd?l<=b_jID zYH-0#&Nw&T^xyc2N79 z+S+j;RKO#^gZEF2qet)+%b=VfVQX*4eF;Yv78$o-jz8^+o#iUtcC`PjlVyjA$DQq1 zqnvrv6mTbi6@+oQJ=#~Nhg~BJ6oG5GA{oQ~)dN=_%9#!a2XG>Qufcmusku=e-z&h) z&{&GAh>sHZfN5<%JLV*C{Tc^7ftMP;z0mc;=mAo$9YdWuxAP#3*2!U5xK9qULs22< zM{>_Ex(u;zHIOL@iVD7pD$tZ+;OzFXQ&HU>hH*V8Rt=Zqy5lXzl@-;}IB=!9J9T(| zoD^T+{3Om(tQ}>4uHzWD@|1LY9=?&VEU2KZVZV%#wd+!*h|>9H9I^}zb1I#P@%GvH z*gFhDBMlvKW`aFm$NA{GdFKTYbRz?9DvD3CpVBA-h2hcWlN!*c=Bf6flobJ0I%1k^ zF@jK~Ua&QW9R5bozMY=4wtIWKxGm~qZg89*e@6kh!1AU4%k@*BOyD$ z#qgFY9n0xJf7f=7$idmN4}fdym@EdzEJc@>)pPAo+swmvej3j|i|{69~QScv)z{}KA#ve0fc$e=&roE)oI5n7u^o%8BnyZuKA~k! zE{r*QUYqYh)2=QmA#y&l&%&e5i}+44&XLodQeWCjlBr5gU3#(|7k_4dVj@KUmUm=>f)pV+S( zz81wd+FcERUYqP864u;ouOMOj6b&CucGzxz20)bI)GC^)Z$<4gOGA`5Zna0??4>lc z^|fvG`Fe2R4*NJgcw?u1lpc&pm7^nhDSx4*xcK!msgptuM*=^6D zvq!9@MDQN_1Ov^v1u`6Fb)i=KP^kH~(|gB(efF9LiThJHJB}SC~%HGXDZ%WAz$`$)WI=p&WB}yLRC`mqF+dCOd+H2hTU)$>$ zfb`m!6Cg6r*cVE~Y6jBttbL6{{Q8>UkR}tOY%qzV=NQs7Uq@$g=7KE2{1|f<4#HZ} z@r&q@M7egeJ_@~LuMeLeFh0cS@9Zn|;OpuG zZO2n}4nDkTub~I4-?FbY(BS&$D0m|qK=XgKcS>pF>@4+Hdm{r-EQ7ZpgB((Rw|}LD zGVVYaIc|o@o~DJWTOb`Ax629zBSUnfv?D^q+U?jmH5-S5{^jf}TR}^A;od@PX-gGl zO5C4NXd7`pSI$?7izfH%qz5hcIe2LJvi)<9eXm44bHcB)Pu90*hgqVviLc| zM9Dwx4RuntXLjrkxv+zk9hJq3=l04v>FdAk*oH8P8}RfWdo7(*_m%yX9&Gsr!>^T? z^45;yPMP537Y@wr4c{OJe~ck4O2NsdMh9EmP~CmT72O5DGRV}pddn>s#~%y<|TAYz*{BJQ62PJB4nD)Z5&5r8ZK>MTgNsT`7FkzL-Gh?cz_lO-bSOqpXH9yF-kXCdTMAD)Ds^}*;kOCH|O;Vny^JirkrbGbIqu|g74 zgO$=^;$X)t8EGEk_(;N0!yM}+d^%jK1lRrZI7b=o3E4eYM>%>)j*4R(YXP-BL-|`I za_&oefS5eqfe-mC^1)hwI5^QULQ=TBp_3eDS(SHF9J?h))FFFWd_C{DATvatWk^mu zMhg%zGf`=Z34omdmxs;h@7WN?)H1l5ik%~62tj{Zx!uuJ{4v)tSi?of`3`)qElI+- z&;d1>QV;2!juLy&H%*;JhF_usY8JS^=$GV3(}V6GInL_A+aIG+8pGjbj!Zq6xB^ww zV%r)WSUEU{@~a(qg2=(9YaNgD*rw|pCOueVgTtx?37M6o*}IY>MfZ)4{W{%sv*WrR z%uRtH$-GL7lusQ?4Yb`A+5~9NHQIPUAb@4j$+smESUcRbZ&uVpx<>A@by zW!G|MuTMBm>aiDuqpcn+d)o0rX9zpv2rs7b_RnKLLkSZ-i`Q=>!Z5V*H2M;#^<*#}YlbU2!bO@NKc_&O6|8vjhE_i)t9RLUXmd>u7F}K|itX9yA39soyI{ zj98v0Riz}Qym2%WH6A)D>YM`}$+~i)68VlF^q}<#)Q=YX*`HGPGDDNU9Pdz-EwHx= zeSA9pM)_BFIE}8@7mk(&iQ_Bqe;j-DVEV31Nt53?U}wk#C)X+1 zEpgIN7iF^^yi`oVZjxhfl~7>KF8vW}8~llGq~13j;bNwnQpaHUM11b9?2@pZr(y)u z^n=AqY3%~!Gfyas`lXc7j5LJSJK(3FMsH2f{cuLwhl-b0I?)|7x-p@&5@nDW$T(KR65{LX$|@b_ z&Rid%glo9CSX04Nl&oTGr1FU#bg!c<(i5P!BgMCM6|BZhzr{Gh%W6mJRv&H2#Jd2^ zcKWiuEFZXDez%6Qe8y$@g!1c)yhaKniX*uaYn#YkleoCiRQX&FE^RJ*nPcm=RIo$f zVE@)int^@0b~UhL*RCbmt83R%?9#QX7yEPV>cY-kyZW&w*RGly*RHPY!xibI1J|y_ z*?Vi(+U&X&EYfc)BBjsPu7U9+fe(9Y?OK9ewIZGL(+ZZY(zhv6NEfZ(k^Wf$G9~b4 z&#YaGv0GMP(kCmbCLOXOy>!NkNa=|c30`QJ^ugNImmRQzMS5RF0_l3St3Uf)?OK|h zuA*G&aTUq87l&_86fd2vqHcR6Xu@7r(L(8B71>Dts$iAQRlD|N&#Fi--KwIyq)%0_ zNQbKEC+STU>7*;wu5Ro{wQIR}=|mM-O9!gpl-^T8lde+{3BRd{GNJUEiiS#;sYoyV zrFIQtXQ{xXr&J`AZc+iHk5sftI!FagNKk@3qawLY zAJncM>;x6*qzBYQ)JFP01&?%oir$l+Pm%Dd2BgbV(4@apWGbDVqV>|#DOjYNQ_!T3 zQ_!S?Q_!S$)2{C9+7ulm{hESBIyD6*J({9i>CF_FbYw{onsavPchugHoSzhX2KP!_V0GL)5FCQqIL% zobI#=sq1pKIGLwcu20w6DPBQRAdwb+Q;=5r{sq!!yF~WWvvx`)mmS6F+&4WMwwEFx z^R-kf0Vl!o$AO=*JhU?Y=@mph!_QbAN*NW@RCxP*0KaeBFNnV5W z3_oMpH?531+}jnNjOEWU{EX#6lJS!h5j9o*B;aRk{MU5<17&|{IY5%!_Y5cl@H004 zXWH9diE_y*PAwZAZAE1|C}FH6lDS9LgPGmT%9)?c{Uf6^EcHXf|0E`bfVDt!e6}7X zbwYCd%_7*_(;B;#Y5)8Beaz*3EHS?L`)GX0BA8nd%%ceARRk;M;llMh%LSGwg1HvK z+>2m|o<&gaB3P*+m|qbrpa|Bk2-dy`Cgn$VofM~J79fd9c}kAAB%Ub7M5&UJ<84JS zM-fadg552OL0`yBrPfN0zh4Af;}&Sy(of7xtF=fd4NU~ z!Q@ORq{*2eF{$*D<&;Q#50!I55~aFJ=E+fr$yri}c@NVtsRNVaOBKQVieOJMg=9K8 zuTWqSOirjoNtCKEnWt7ECg)foCg+&Mq+(3w$ySKTSyzaCdQ8LQL`ja{Rs`Gm9>&@& zIX>~T|Hke9Z`8j3MjiZbRQi9TGX5KN{QamzsbrJmPrVe^&hvW3`J?cJCrRcdnlnIc$b0oFtk&ljK)yI2JKz6f^Ri3w|0rKiDg zk?M6tYjL7xU>*tWpiR{1mJ7 zF`TFBy_641OPBw0{TT2YG=9YG|6%>OM2WqWVNG8#g}we`MU*c80pv0Pkfwnn8jwo^ zA_K8DjEuiYrF$#b>d@BSN)N*sy4PE&%J)%%-K2ki#BHILeZZJQ zt0l;!-}@+?-K2g8ErV+HRXW2P(X75oA2)rW-c8vu@+ej4hXgNZVn1McbiAK3&`n>y z-%>z-G%LzsZ$oR_?JP75ci*bXLjRwOhdO27LHoT#7LzHTj zq=E+L?@*s3*|?NFJPsC4d{bzsvGLDo_z$ek1)e3|)1Mx(^4xpn1cUO4UBs3(Bnp^TfY4eterr z8%3&Yq$~j~Ju?1N${Mbet8`1}_2>h(JNS`S4zs-*D%q~k3z>5+g^y608-An_Bha&( zXvqjA%11MU;vLY}Bjfi`<_KkAqBLJ+i?Tj|K~tfFG-#wL%k74J7yk}FPHQPXwYMu$ zO5;}2!}@@}u`hnIhe{@C?aKI=QHZ)U8b6Vf@Z&i{67T55@8%%t@?04u)xHPXHyo;d zWc&}58mE*qJf<(>lwek1ApORvSQ7w~W--8L8jxl&FliP8m^C1+Vqj7|036o9KN^r$ zF(Rcie}F$UAgy9xQab}ks~F(#ni~971JW!;>~#%Dvl!Sp4LqPlBbDxK@ELhmXyi@d zO5PJ1c}r;I9ifpoghrm=MxNhBp58{D-A3LE8rcXm@=nmm8$l!Q1C6{5H1aOc$p6$~ z8Qo53bbS3ZHRISESp9a&6T`gTgltCmAqS9$x>fr=??+(ExYHDpLUl zj8o#n@?~yS!|-#hiZi!i;RU%}B%r1S=OIzj0M&}Je>Zk&konu~OQB1blfS9$?6#xwRyJhRlCq*TO0+IbSHdY+a| zQa%8+18Cvf=eKc7a3SgeB^aE%#MHv9Fb_y1-^K@qlbB0b;+J&8$ zjr!kD`1$k~XQO_SOS9HRDt$B+g{bu1RFr&))Tv5KZ~m{Id<&R7OBrZGf)YXaO8{3I zF->s=&-iI*>LofdP3Z*iLIZ6Rly<FBUa)PFiB*f|}wyF?GCqx&vV zgAdUgmuS_8Xuu`1e5kbb;&OlYz$V+(n?k26@X-mU`0ktZx?Z9&Gn7cfA=)qlb-hU$ zGf>D)dNl)GcawU~#5mlfT{D$I0RPNXx&pMHrSt{ZI!lQK$eX1M2Iw$b>FRY;_DkM8 zloha$VrRist@LP3r2+yy$ue6h3BKIfoT}*@PPK84(nd>FWg$|1yo`FyRw4;rtG(Uy z3f`p9xm>0Axk@xL_+l=`_9nfUt8|ye&O^Iz(wcb~_nTyz2MM`J_2zTrocSDic|J!5 zFF^ZdYS@0Lgh}=)Q^-20Ka~O+_%v4kCe&YTVxJxE+fe=CIftsD^b$Z zZzGD`3t8GicRp6;Xgu?8fM-NDPV!U11E;&uApf`&C!|}b=Q3q6!1ZOyT!4~gAueGDzQrW5Mb^qWR^ouR>4xr zp((4GW?hYRIn;lRG6&%L8fB(;j%>h*2dG?6ToYI~qjKzl7mjaT>D>jiB8QHxRVD+3 zu0xOK(7JWXG=R73P&}^Eg4P7ql1d2emE1)cq4DwXy-`nWEHB*{Ial(p+;vY%zREz8`5)dsK$c!fzqJ zne^F4)FYGpHYo{VoO`YtINQ3r=$g{U6R0+j8T8pE$WBJeW+mQ`L6bH^W-{p3X0#`R zK1e}68FV5A8Z(2+6Qm@A7Lzg#Kp{>L`>8Ss*clDf*upI9wx9#{Qu-EU2!Q)m48mTT zz7^^A(r;VQV|%InHf0vT*V~lwUh+6MesUeusY-paY(`_GS5ksk(g5cK+e@EpM{n+> zSKCqEUK+oHvFsf%%J))(ol2aRi%(1BGNC1w!=kBH} zpFwVRQ-?H2(Qe92gG}wF`kyNa-kj{mUP!jFw+s2LQlcoSZ1pJ0-^mHf?uG>Hrd7L@ zxsl9zqz_o5`Z-6Xcz>YUsK~SI_!Lm&>?6PZN|Tf^)x%wNA|>y^ls-z2_F%A%(!jmw zi=%XIFUIUBMeXCrt^1UC%37|JAou;sM9N;OlyE&T9vOC-Kv`cX^(kY&QXA~1{mK|G zF0Mp8s4b@HU8j4_EXg88VJnf*!2?PhQg|N37$2qS2hoWyDET0??hCpvBVWKdH8jWz zI{5`;;{}Bs;>hKPFz_$v;UOe{K|Rt@zZdjnI!A^chVZ?h#fO<@KCFxc*7gXp&ZD>^ z&}Mm*bp*qdN0AxGGmqc}DFMLkD7rn5793T^dF9E@dbAF;@3fvWQxu2~JTp@3V;J*1 z>TrycA3FxI%cIcakmx*Gf1I(`$DPSrZAS7LTj}TlB!?Tu5K8{CRtarB<I+I!2X5oe{g>GnI*g0hlu>0qf6#yTe$30gHy*ba=vJ1)*V6NZdk}!o< ze9M^oMdc%4Yc8VnRQlo~mgZD?brH)iu2>Kpl zo=OY8hmDg;zkRO^@=BGx`F$=LKIVZG&W@DuPm~+2xvaQ>W%Fg;mcO_R$w{TbS0GNQ z^wky4+V2Ob1y0`KF_IsCOs#i9Eu{XS)Cbc~KX6eEu5wYyS5e^$bmS^lX;4OE+r+a&qjEBK8s22(R*4xSuXt|@-r9B-F=XT6G1 z_7bJCbV(Ox*hX!y!&KNtYc%ldbr@6IDC~yPOH1?CwHUVKZpFxO4QWzupkCW3^9HP% zZRDBF%zd+AR&As0*)T7*(R~dx{ZXmtCU39e$-5Z!3PNoTO-imt-D|6^h9{KvBU<)^ z?*53RPblgp?93-L`6hDTL#uDXgxfVLr6+e35zP`Y??rEX0_d7Gve zqYaM~xEJ2hxE!_+jo~072O_tJD%{~LgSBoC&CY>P?V(?CAeHDX z6LT6(Fz)uy9TOxCk!CJ@rI~5JnYl-*TDW@?EJ}>l=1(@E4xP7Bm<=AbZftAmR@gDG zEzXXawjE51cT)5(=$IR3I9^M%Ldoo*6;^Ki4_0hc_D~rc56o~I2Kz28vZ2Jgbj5}_ zd6z=%uqyA;bUUhem%g=Q|8SRF9T?@i)ZL-fGwi2C2hv@poen6$%Tz)^y2~_J!Cbyf z#}y^c`?4I#wWm>+Kd%kI<-eTJ{M0o^-nN2;-klwH~7%(<$jO#2}rrHLPkrgejd8^SN!mYM|~DPO$h1 zWH+6zK4F$>Pr3SYpE76mQ;bGBMf}Owl0Uh>?)=Fm)O!Xk$SoWci7NhHo1*g3imcCL z+~H}pgKJ^WJa`7-O{Z>u!AeS}jK8pYr&HPI7#P&^IabVcdib16@Ax-FIh}U@jUh}Y z&lk!-t%P|UQNkykwG!a?#h0?p5Xf{o`-0nE<{z$7{67%EbizLk$g%Ty$z3q`B{%cH zOK#@hFR}MYr>?J&2&nCgBg%c&);ySUEXqJ%3DrneGAoqzI%tgA$Qa387ShLnR2kwGF`jVrn~p2gTG@05yxN9{@}%t_}hCp}1Pxu!H_7 zjs%;jdOuOuso*+GV(dKFNqpHr)?!s!gG3AQtjw1UnRu5UPDc1|5Ti! zs;U*pcX4d6ZdX31)vm}Vn-03FV*z~K)WHCg+|&^OKe(xJ0Fmx!6Hc4mQARe|HPF%n z=^Qk~LyZME+gUg}UUhpbdjoCn97|Dq?J zDxBYXt3IF}@kSXAa`RCKdo$BL)Ia{&%i<{^sJQgRKtsd(xa+ld&=)>x7w|v#QHN;! zi3S{EpK~ce)5{|PyJ0v@eef^JrPPvO+*^vP`*$g>QZHY$7M1r^rvU`}aoMx{)b7A8 z_^A^CYWt&^-%&SzwI{%KfAsHn^weMN4A3%w!SVpLvEev<69CSWbSD5cI!V<_qo9*C zw=_EPB>h$zqjZuQmQi~HtSF-{1@J11&N@lc$|Bu;T31%><8@#5%hfJu!WUg>L%0f0 zrqVqCKK=J|PTBiZuN*3IpH`Gp`vCk=4#{uO%W`U0fDYw>{X}ESgZ2}BRUTsd6S)N< zawk;}M7o`{C=kuuNxuZD{Q+uMKs|TT;tCMWo%B-$2d11iiF2 zgX&gAFF8M(E~g8|3EEzX^ZLCKhUf%^SLOo7SLVFZHSlL;E~jA?Ze?N>Zo+p}(DY}d zR#CfoJ(K15WuY98^VFsc%E8yeRcUcJjH_p~JY4Oeu`Id(mOkIg0fVigj_}oM@r=T& zs-3_(r7Fh#8D&*fYZ;!>gQ_TEBY9Rs_iUt|)zD`fX{QDrRzoc|Qlsi>FMt)*F{m5q zCk=$wKxP|hLJf@VMmnPb-w3rEuwfBuf3Jr-Kd3{9HXE} zu{YpSKf*Q*hF_kB*U>OchkqULpx;!_$_R~-s)s;)W? zAS4Qrx2Rj4rxD4eLSHuFyyIAn;e{k9v^UD-=~9V{wH( zuFu$S^^qW#{;rRykxShhpoCmH*Z@-_mkbS&E|&&3#N5rLQw^ELs}Tk$m-;qR#{hiQ zNF5X=Usc6l`3jBbn^{snZP}0oRNE~4Nc9_|89&nW#u&dJ>2zbr;*a#EvHC&Sk22}F zTi_jW+nKcL?*&OW(a0uX-bA~bp!+vbeiOABK+UFVOMvlBG4z}0L{s#@CMwnpwcSKR zo2hjTU(uRoXxBdaq8VgtA3bh{O7Ej4&5_GK`nWmf#6G[Yq>Ra&4U_R-uH81;Sh z3xJn=2^PPsCN8#qh$KrZwLZnQyzl9+HHV3RM1okqHq~v7C0Tyq=Mv$Nbi)v7NTG#+cbD7nqy8xQl<@ z877$A*qr%iel;4=6m#z?^=hNm3zH8r;@9E|@4e&7?eY#!tLQaKYXhBejh?nad#_Q; zXcTadhD2i&?$PdOlyi@sMuT>bTDAr44h?Aw*|ml zrmhlfp@|aY&n={Qb-4Y z7t}(6JQ^gyTbcvVDn;fq_sskGRLLpGhj-T0r@QExa0`M0#0ciE9tnE@M%;^dpksz0@N$?}( zNwA6By8?Vg)d5=Zq#ya_{e1qGxA0mi@?t(d6L?`AmdOiwje3=q$ea$+E(xyDHxk^V zKP0$A|449v%5_siLVu9?O> zPG}nc7}ILSrZrxp#!ekQVamu4Crz9>eiQzT9*nbH`A)C|K6!q1rwpC%q1M9$e%3>s z3oxapx&Yu_Pjxv!Lae$0z@?YE79hEoTBT^(8@*rwT_nHWY72nwy`fev!dX6y&WrT2 zH#F@<8rVl24REFpR+o!ZzOOnSAgM3bwu_Y87c1pO>eP=TGy19Vz=Ha7H4>_(>_9aFAbKEHfb+C;AXMRb`gtJs73V2%5H!_!8b1i; z(0NK9ggnkunZd~aJk1^qtMokG9*oMKrv^iqbIA~>>hqL6L@f_+ZwOSON-c(>bt=sn z3jM9pwV|lO7J57sOXwx)HcV~gCU5xA<^8C|7D9;rqa+l1#qKMuo6b%|;YXU-MF z)nT5JGyd%(`9m9qs~za}mwBGd+-?LjZ`Z(IBVch{qJD8$pfAy(IL^U+Bzp1^O&JMe z{t|sZQk?|Qa1=W2673qroX<7TeY9EuH;NNRs}tQ`vK*DhC(wud!s*Ru*zhl@;TW{@ zC4D>ww*O0dI7XcUFl4Mc$(7m9;xB&2t6}tTtlCM-pf57G;pShS`i;X9`;vByQ>y@+ z8>cqVlBE05$gyg9*ZA`RP}?u5!g#gwzsg=eUJXE+&&R7x0Z?cSDn3CijW9|MrKS_q zprXakpP-ieSMf(DsAZ7=#fOkGLGAALt!!R&S$v4z$iD*hort`@rL_~)YX73#nuvz) zB0Tnw2Iw9Sx!pzU<1zTV=kfh?j2ByFoNTb{- z>W2V>r$P$T=5%s{>h%%gC(KhD(uxn&Qgr0!oRTi_X>?|~IvvzHA7aCoMms-*sHBm{40Q&;;u#q2 zG2Sa;aK{n$Lo8q>*tJ=kaP5gf9(Vl2Em8KT!iHD?zPHduL-D z&=0fKnQq+PmEGaW;sHJs9XJPOL>dl@)kOex=0d{K=+n8F{b}?}1F`eeQCcc|eg}8U zWy{med1|Z{RR>Y^+yjEB{d^2B=Y0oJM|*O6qAMm3zKBtprs5VQfn_;>jK$2=TV40kQcN`jd4 zn1`0~oIG;W*vVlNXUv#27B2*)1O!s>615B%AO^wIV+q#~vQP_A==Zt+5Q7l?>2`#}Hl?fJ!vsW6WHZf|_*dW6ltw z5JWMp%9KMqEMqbRp$c_a24l9U zG^}2x)&MJ{0T%W$wKBpGi3o~Zu6iTfd^vP@8jaTgH2nG^_zeYsjmiz*O~ z>MNW@6$r=J6>6x)RUjM(R;a;RRDp2hA|XnIa8#qnl~`_BLgZ9}NbrnW1bKP6QjO3O z7RW?DBljm{qCJ=)4z-=YNz@k`4sy0#D< zejdGEh)HHUpX55gKg!%*B}aw$sSPuxUHRU*gYX)H<7+h7ek_1Pje? z^k|8HF2uy8{?!mAmibpf+*;;e3-Rl6|2p&jNQX11LtLo-1wW2YUgqyWH&Lld_6r$c0j zLdtt+ZxnLfL+!T0@;wxPo509z{w44=*be*l(A(|eo4dn5556)x{c|Ax-szub-X9sk zZ-tBqq|A@~1u5w+e{(S*{#g(;{`AjHD37U=(x3p7($KA){z4Rb z*x%0JBmZn3<1AI@^x;hvGGmVn^}xznn%KMVl|5dCdQ7qUC&7 zQaB%C<3gkMam$?X*P_;M{RyF&^|*f~#P;L<1rW_n;J$x3Jv;%UmeY@ap_?qHv?pQk za$0jze3ei6!{Ix5%0CgJ>1px#Px~hYOHQ}TAt%0|j^oIZ7lC`;NoR0V^O)M4#YCU| zwI92pU&x+-!nS8m5W=smV_fDcEYB_RdkpB_zpEQhBFw^r`Ma~Yg<48s=g{U$>A^Yl z`K2`LJO;v2s(Qh{9^&-{flL1OuQRW@Jg5mn9@NAiFVGPR{q65a1*7p3{8EZ}2@STC zR$W4uUP|@<5#QT?{J+Du=(0Z&qTUruNbBju6@RVx{2mGH3o9I-)<5I1c+FpiUtB?W zLA5GAhpBOs1n^G~On%lvVu{dH8>dU|)=A3+O#2bgxl|EqZ+Wq2o8HM|q8dj_kJ zR2cnrz>gEumP2dbO;rAR`tFv03N2jaj}uScUWsGTv(v6}yskYv^@_0YqRMUm6sQK= z@lQ?923nbVBVE3r+T-@@)w65Y4y^ab9X#H=rGM{W*2Oz0cQH}ErG0n(lOgKgLsxxE z*Y9BfzNNl?fdMuWeoMdF{s|E29W?n{TH^?;5bYli-=EPER{y?#V!F4SchYgln=i;a zcFzIbyGK}W>BW6a*bk`x1FUclXx{_W)B~#Z(2u=cInK~<#2Fe+1&*U=^&k7|Q};*c zj}K_bBaEB}l=`uMKE&q7FyH~zcme|+(4{Bn>JO;*Q}oUU6!Z*(>j5o$CcX;KMf1#a z|5TI1*xATo>}=Zm*zcojr|}c<1A6tsKRrb9|6vY7`2w5c^%~H>d)JPABdjyD^d(k| z+Z5*&p2}`ht5;YEw$YGR{#o(l1(@Y?>GN5CNhl^JD2Q|^>=6)@!u|;&V@eUrQrbVlmp7F? z24YPrNi0ulNoB1O#nMPBThmA?rPJD>(1b}Psm)@XUO6kRoiN}4ElX>cOTw?iV2qb2 zB6Co^>HCJJH%d3s+4&Pk$P4z?QT{FO=yOV%-p;I{1%mo^?A@zxf9TL3>FgfqIVtmI z4ZmK)qw#+V#e(gM_yMqguw4owGT8nBB2Id{D@3pKc1MT{MwAV)+rzgY#O@A}FoWG0 zqJIXv6U3Da5>_#z-44FR8SO3*2{PGT(#h*Nw-ex}LcZ{5?BBLuw+MW_6Q0Tb2ERF; z%4Fw<_?XFV1ko_mZV0g~RDA!2+BHI+vGqr@A_czi*x>uOejQQ1C~B1%884&#ne7S? z0a!|*=kTaTg@od$u3GiWS0bYWVgFPH)jsJ14MWZflqVT{6b3ooOT_G*py#<84I9M@P1JNyOS|rUah%+V=1cH8E9`2yDWA4&Q1cAjs+#LGX?EF@Kq^f z4}jQK$Q}eyxG=ixGKws0kA%oqL^Nj=v3tT7uP8>!G8$D>vVBlg!hR?w<=b0KijuRq z-3lr{6_P+W=`Rzixhri2tFaY?(QF|hL@)NkiST)*|{R7pDsG5;xPhd{h8iLT{P zt5S9)hy|sPxI=ME!(fL7mxeVC*`@7X5I>Ybst!e!L2Ww}T-N5Ftr0IR)h}xo!f)53 z%i853_LN1!;tQtNWntb?YF-XK>?o}(XHSGETOK7pN>Sx)A4JIt_IQXr6)-CtrE(SR z84zbH+P^@ws$@@rcwEVz1Tnm_Jqse$H}+JB<=>zhk5a}ec1{T7kcGNbvH4w;JymRe zSt4##SRY9Ps@g*#o>oQM;pLleQ8K)5@+~4qQq^j9Ux=t`c7KR`)ltq!np<69k{Y5p zx`sUj@O=$|J!=ZQS<~(TShtqsu&WlPw@4~b+a3t9thU|LymFB~5q_0Vj{~(D+AV2# z9n3b7G@*{<@ve^K*fUHLcf!P)Hg!c@sw=5huV?o#>C6a5Ix~W4(f3FvLw!3L(kWP9 z3|UlPqNHnp{t`*k8i+4`Ln-l(4JD6%8)Ev4q+#DlRlF7u@7z=pkr8UMvB(-j1_cJd@{n7<_;X-{1+Bi41u3ZAV2HUaY z>+BUt728U(tJ>O~O`N6{;^6XioJ2@>WIL(dr|ra^*6l@{Y>&x1k`i|iQMUsIb0i(? zAoWtdqredzrBu&4V!?`}UY(>l>`qvCq*tb=M%6G}lJ={Uf~It_u?uDce^PYv@;J4t zvz-dB%na*_rDa8DyD;4Ubhd{k+QRekP5j-PFEqL;7tW1d5jv;3V8PphUq9^}CU!mi z<>z=;J1@kWu67NGN|w!Vs%6)NxNO<=A@X*!JE#B1X%E0wue$lnvS_nMX%k**hd!D|Zdg?ZsM1@^|A@tS({#(Mag&h@r4 zLm*xVjhg^_ZuY^(zZLq}1@U*Ro_$c7)3m1#y76iHwlAvvG#%)RTY}S6tslDhX*$*q z-TySz?vJ!j(_tg348Wuz(X-H`0hl!I(&bUuwm#NC)ZASvI1qjGF3lT=sCOyjAf$Jf z77fDaxQidLFaqw-nO zD!Y7QVBS#}w|8mtC_5VjO3)AQ?9?5NWf zZ6;#MT{rA|98NdR#wS%N{mG!W^hZ?MX3G8(3cr~a{{)*hQ|j^dGKihy(IlIx($82R zB=xTN)8Va&Xw)k|+qvWOyFs`K8(SYCO^Ilb$uL ztMy<9U%2P?=-9t+*Y**Xq}M6n6xH~}E+5Dr1S7@iG%_69d-wjuPC?s$v5O%1(J#35 zIfui%?Gg}!!ZF93qwC>zhlJ-i?Z)^>7#FJNLuXs?d$VA=>2DgFnsi4pZS1D$W8wqi z3cuR9=htjrQ{?HJVb1wg4577H_=bi>;@2*rorS*ly|z_*}Nb$ ze=3ZeKb6|fuuJnv2(jtO{T9if@@Tr<45CH^ZVEQhst7E;o9J_dB=-FbyD5C@W}t;P zQP51>Fl-`grrpG(S!*%UthJab%|V(taUvx`LkPcxZlb<~!M2GG5C-xlN;6B0>N?AA zl34!6TW2+5@`d_vx2Z(<9rD_x<{4t5AlZMDyodjWTSXM)H%Tb>Y}`Mhbh9P(tFy(R z;&Vhyo+Fvuo@2L!N~O7WvviV4!R^R|FMMx10KXG(HEl#NUOq}Z&n^d1dmipjUecO* z=>IP%cs>T&OB!Ru{rRYomsDqggl%1bTNsI#mL4y#a|b-4vZ6%R;*h zUT0de&@P-7qnsNlU2%Crh})xAhmPI*;ahQfxX|X;o60W21UQ|#FG3^X57vuNuhS`X zv53)&QRma?%3`c?)2ZST6lyxnGvfUc7>}2hmSVQUJ5NSrS%#at=`?y7M(uREzYHas zPGQSMtXmG-rqj{osDHe#v;z0r8|a4>h`xb#t-!jqfeQW(%?&i|cNB92efk~uZ5yc7 zN=&O8=)g+MrxGuO0#*S!CwkdlQCTW`%Mq#qGDb-fo9oZDV6~o`AxZ9ApO+&Y#g>1UH4U36Q?YCo{ zwCT!rI~Xt2yxtCT&rrD?m>SNIwF6~1LmPKsT%4hxotUK0P}7}g?lZJ{CwgQA`FCRd zilD^1u%<^)lU>k^pf$VfDiF_i;qEtrO7F&yi=e5yF`gnQdN+DR1l8Gt%GyQ~_SmH% z4(vhFD`?W6Shx!BMYq5n8G8|H1+ChPF1msO_hGoKAZs5w&96(Q6L5~lJO0$EwVOl{)4+<=B2zTBqXx1U|eLN&8UH`-qBzd>O%TSSr z@!%A67_D`O@*GBPcWB~aJBtaaf!Cp4A4apCpn^v*o=(uzBY0R@MQ4uSd1DnNIf~^( ze8E)hC~hzQqF;_;R{o3j9>swBi?SUjLgC ze>!VtgnQXpOiVi{`hs1EvYoSQ!`=HFZj^S=)xYtGTKGIV!4B$n9xc9u_MgW+=MH*# z9u`T;MpF%BhuQ#8>7KOP`NVG4{0Sm2bS zat_ws>onEDsJTwh9K^a#l^)u~X+$*o!*yC0jm~(Ta^1K8fav|m{*H1zz@&4XdOg5> zltisasUD&#B_eM*jyM@<#X}4|iTEw$e1us}Vh=^JM^^%TFR9HBPP z>~|U>G$hceK;H#8-@rXG&?!I%1Dr;1r;F*Vgy<3EG^Vt%oJw#vjpa0j z$Q0j6M<-%A1>k-k%jpd9Yka3P&5i98g8Q%7&JV_D1{NsAe3e%zvz08po*) z<%e;cmJo{)Aj$S|#fa+(V8r>jPATw2@tk@PE#o=0AyANt!3~QCLcrL3lr+AR7;adcD;{rFV-@W{0kP3e?c$61u)7vUS7d4@gx$$LM2C@uk|gMWcl1KL_GsI=Bkm`~}%U``MvO6g>UYT=YlUkJ=?h3P^{ ziT!I@rwy%4g;sw@?@~Ekzizb=shxrdKyx(=csDbR(;A*OX`JsMHl=ZDLLm2P5J=`% z2&7PsTBma|!VSILv?85TEAS@QX8k#VH1E;8T$DK&ZG4kz1*0S1q>qK1Z|G&PQw{F2 z>76bRD+@bW=+E>{E4bqoajH?b5T~|Dq+>rcbIJL+$qsRfBRE3_r!GYI4CtFT>0}0{ z2Sl%;PA+Pk$H_)NXOyhgXLQDcm(Ju&fS6RwDSuJ!SNPZ(YjKW+P*+~l5kO0YM0H)9lL0Rb(889hoP~vIpv^T zIhzv(F(sQb4q{eGrx10@=46juoE3XycZPsIE9n%Y^f^$6H>pl3r!dXT;Z%itK`Gb} zFQ-!)?#elx_9nvz6VUHYFAAj-Ih_g!Nsvow$I9h&gYQ9UjF+V#KQtoBclpBON?!GXQNGmmX@b3g`7MFXv~L0Gmw$fh zn5YM|a!W8Ya8DCS+je;oCpkjUw!w6+h?5L%G;j!=-Ly0_$3zY0rv^ox3(S8{ypsWO-5xEl4e!U>jbW0%6 zL|rIRc`OssPHpILc}y$PSmp5-1ScQdXt9zsr-F1TG+R|lSJ5eF?9{fKT@m$oj~-TZ znnTp6gsyUr=2dcrLj+fLA|TdQb_PR0r8Kqu#>pA5iza;Iv@i(qz)^S91?i22o=DG*QV!P1V^F^aFy zsQONODp3Oi^9t>&;rscmmpxxDEg-X)=M#|3N>yhWrkh7DergaYGPb9iu?{8O)Q-CALFhzl5~R_I}1^b zs~S6lAhLXq&ajJyeDC-m-hGc5brQ zt(;Q$4ico*8#$7#np z?VKJot}mvCT~R1Pd^Q{AFQ?i4u`o*!zNc~n&{w1!1c4$nq%H%U%mI68;y|ZB`n_gd zZCXaw)u8?Z`uFPFwJrb3wU^EfL=W9ctp+)xAZ`r8_>mjS($s%2)~2mgdL+inhr!NI zfm^wMHhdIF$ue!|OwWd(zP8e@KBocwF%;|HRtg%0X&~D$jPI>9aTIP-9uLD(yOq+8 z#xy-@xKk4Do};liULKCQek*Msjj5u^2+@H7ttkFT++<0X3;vT^b)UpQZiAqJjM!S_ zF$n_6H-YkWE_@(x4Ihr*FHzaT9=os|2HnBy>K~oKCau>2xYwVtsT%e937z~6t(}Pb{`Wtj*l+05 zL?;Jz8IN1Jud}tsW1LEUJp(S%q@U5A59#U8D2Qa$2?Du9{3nC46EXcu26Z8j_HaZV z_6r8kMY{2e(;@I8mu+mEAnJ2}QwbU!j>1doWhwb2q66p(p z_^lxjwK4=^<)oq0u%=AIR!>fC3Z3qRz+HYiI?gl-pYHU5_%t00`ZVmrgj(Mo;k1D} z{R})QOe31*)TG2S@uVQ}`$8a25qdcjYl}oIM70Pv?-F|)1R^$|Mze6&FR?cVoTRYd zFh`uE!@pq(IZ2skJ3}EV&vo+0vtkzyiea6k)3cp9M)5n^GY3nNs1>6Ob8$~8x&!cp z9JCO3RgdQ4&hKwZI1i&)9^ebm_<5KQKu#SM3xd1n& zlE{xnVJ1Y5Mb4ZA`?$4Qw+V_7UX=Q*-js^!7P*#b=3->MkFqUx+60P1{yJ`R?q7`g zd>>vi#bmgTMlZoqvX7p?XY@KX3!;;G?-i!`OHm2?=+;uq+|o)psK+v=#Q!@xy1fj= zen<(IqhTbuTr_sMQ|LcwK3tBQg?*H41*SLbU9$qc=plCN!ac2Yhf1{Vchrj{Q41L-kgpi$0`nk(fxXuXf7Ajl`={ z`#;1gMC$?c{tt9` z#DK;-4|FL7-+RwzHA*PK9~C@IBH*)vPss%Y6!3XT<_-}OtAGcxV-H588b<;4aAXk3 z;1D-__G|(YctEU3f*554BvVknE_T#Jk|~t5s4pt16f9^cAdQ08jRmAr z@Vte9^a}d75|BZ`BLgxiXxc_JG6VQbQTnzOl2ug#+Y899pzIIH4w>t7M z*w6K|V3cSS^nlb)Vb5n3{~5u)2&<@)Zc`*eaRrTM2q>vw*K7f$6~tRApsa$1D+QES zaB-c0iVEUx7f@M%j}3i(Ku8r;`SF;5Zx!&~m1wBy3f!{QV&LaAzQ=Z!&Ej|nAs-S5MvAmlCCqO_C1yy4U=%wI?xB~hp zm=-9YpNu~#{iYxx15|~#;Dqsm6r@QcUKIvEles;rq(+xtgkL>jDL^mGfP!z%_N50@u{v6}YCZR^XbthJpJbo=;L;?-8l7jh>G` z)Z=7@KV+!0v{$e0s z8pUcN*_>98!hmxg5VJ0LKKZy0W?fRSxR@kxr9gb2n`HVDLawXI@zMfrDcD>}z+DCR z46qfXGEwd;IAFjd1zXEW8c!9(^cnI($vOjGE9hHE;=EIEyMllZ3PLLi_^hD50Wk}D z6=s<<;wV`7twaed$m^ff>CaV#Bv6%EMkTR=GBrdanS#dE1*BB)qXB6Y6g5$T6+AK^ zg96{rVUkFwlCyOLWK}TAXyi~Zp`K{uR&cJafP4x@8;ybr?lusOA_@u^P~4{kAEt5N zEu{dTr7f9+J__V_&t+#bJQf!pH`DR6uI5e06KKd!*-@h26y z9sY~}x&Lx|{CQPzd;CQOZjZmr;LElDng?XYzv20;;eDmM-&XL40rwPKH^Ap8apOEt z;Kq5Zz>V{a!C77oc?T4Xs+S%RD_(m(tIt3QeygCwAOZg>nCdg+gObw*d{R(qu*8W` z$jda^fS3xZ;44yC5nBO&RtgYT0dFx55U3#YFahxk@%%4E8D>aARoP)cVg5~YNe>u^?+1Ieb0wCy(PGzf^?e%G*ZwdO2GFDBDV@?qJXpTp;ekQ zxy6~L#j$7rtvn!^wqf5q4vvi$4~Tvj&u4YtCBc@0ANL99uHcdH zPa!>(xJmY5@a2PAe-B6=13jPYDi50mE7){WQXHy)_mqdma0R_i3mD13CwbtDVOANX zDjUuU7^5K1IRWDo96T@JCj~)&3;0>Vri%h5D){%RfN%vTu9@eb$x0fUr_!kmWY76d zHzdw<4@l`}cs?ulO$jCidF~1LO~F$G<|w%57maxa@cLKO5wbv4;zSErq+sZM0ZSC@ zc_3h!0^S=RHMl}S>&F6CDp>YHz-k5j*)#wC5UJ!NlL%|A0=M)V80_H=$6HlFW3vaO zI-)$EHTSgyZ&Q%|gMb|h<{7Zd13s(nM^V}15$mA=`xNkg{K)ixf<7@~GB~7w_v8mS ztbn)U2RN!A1wNk!IIe&{xCZe3rG&q*205j`E$mqaySemDu@x6IE_gsnf6?<tzL> zgqKiqq^f{Y3MPLmpo{`OivcE-Q*frbfC>uu6Lx4+QgE}DfNvE1Qb$15!o2>9p?p*W z;#5>rZG(e-40zR?U!%Qw7C3h(>b-XANknfIpRo6|EKUQ4auZ6*TK2pgjYhSk7O}L#3mt)b1sq zvx3bAbXC9~%_B-T1#ankGI+~vKO3L6L!*xeq&oU}KI?5C4)$3Cl$;nO5e6xkHdw$A z1>=VZ7^dLDPyr(p)EX|pr(nA_e;mSmFVn)%{mdS>_Sz)-(Yt6ik~gV5I^+Ap_a0R=}GE07NQy zWWZVlyk`J3)-yQ6z2h$SL$##5f0Qmm*uo6C~1K_BFoWBb=uE1U?;4cL!R|`0$;E(}l6!0+~ zNc5b7EgJ+}VBnLY@L3*Exu_}=4fsbv%uS+kMM2P30oNGF{+cze2)OA1sgB#8&kEZm z!FLrLJH)_e`IXf9Q&b!U9}T##V9sIDc&MPx5dn`Cd^F&xf<4DXtAdoZWYF3 zu#o#4?@EexiSGfa(}bSS8hc%W6Dt^VOF&Wufwu)DSMZAgDLvq`Lhgu4YL8ft?+Hk& zpp#!fu!6q~2vJbX7LAMw_>2@79;#sWeF0e%B!3{lmrcnKLvkp%X+SOo{U1u4JPPQK0T%3t)17W?FIHf!wwO+>a$%Ychw48!AIB*r9 zf&$(`0-%xtH^nLpWVeTd31hKFwIclc3lqtshKBQDEeNlrphj$oP)ETj1L`XHK8|SA zS8&aMh6?yJ7bMz9!PUT6a{u|glEFcu(nLYp_yU?Km~21`1+lSb1yXILfH$!KXrsU_ zYpc`OW;>AD|- zWipm>i^f0?NS=dzp4V!VSHg!XcwxYB1y>4)#z+O@s|XmSfVa(n6=M_}Hej5B9^Z<_ zPag1L0}UZRd&F8^L%>7@i)so8SJ1AufXNDC*AXyPLHaNO(-btXDm4HVjcmO0zO#^iFQ|T zKfi#U3IYoY=*_?<9WiMMA$?UPb{PTv75q_Nz(55~B>{sKOsyhdsDe3F1q}Zms3u^f z0vu5#{eP5_#Wh7`jDkOF2^gnfV{HLHDX3mgz|RU2Hx@8a0q^L88VOfWrKy0)06sGk zshNCr3DtKv< zyujci=g}39G0?c=0m%ittK|-veUBL-u()AF=)u4|MVIo5RmMuT>I1^#Ht3fUS}kyk_v4Gnl3-S|1e>%MbuQ77cOYDEJB6+W^E< z;93!%!D%+@@I}!`xMT_zH7rXe-H>ea>l_v(2RL~L44~^0a_y{g?n__ARdvC3g?DLzF8w*KG&X{e^2&pMewf}1O-l1V^I1*<~^v{rC4 zi-5KYkcF&&9hhw7OrPcumChcJOuKqMFSt8{&8*)br|9?cfav$}e6r~k%Fs_ip}e9$ zK!LALK_P>bWGfD<~TB}y?|vNkOWqEKI>IS30|q-W@n#} z)k^Ml6A-E3b58+l75vplzE$C+dLp~O;k&$W zE8fon_9%EcLBKu*)g}ozpn$jaLLP?{giI4~Siz#{0*)$JG9!-M{~lMen?&U=1q)^i zIHe%-JOO7EoLDH}oPu8#3AmtuHwS}d7Zn685%3QKpHxubQXyAV<-u|R*A!g;UBC?m zVXFk(Qox&tA)7l2Mn(#_r+|+}godqP-a0e>qm}euFDefd^xGidk%BcF1w2vEV6%W{ z3VLi2@It|aC;_h&EZZjF4FG=s;f~9jlfkris^WH;4-BwaV7~<%cLj~l9*{m1fZeoA zFdxT=;8+TR_X~)lfKOwDMmz=W4+3EP1t}@=r>G=Q5bKD5L<(Fhk}`P2W|cW68Yw&= zW~K6cUT|6lSOoD`tmC4e-UDX+&ER>x@K6T0DU4yYH4(CUKr+bg`K+i@l3q>)6D|nI ztswi~0`e-@U_gEWGX84(BPs<|CD#=Jg%zy4Dxj!>s@DV*SJ3#5fRYNH8Bkim$h)FZ zRskPL$5t#<~)%EYAd+%NkEtj zaR2csP=2Ydrz$fN3uvI=K?(uiDcGJyKw||sGn1`o!XS#xa)17A?g25YrRTFogh&Fd zi}U&~5%`Quq|jDXc4QaOUcsrH0y;8S!g=`f3h3ei$;0w|a(*UK?5=<}mjmd@0C!h0 ztgO}I%KG2OBa%Wt&&S~$VIYH99D&b@0~q1~(I4jdthLo8c!UDpeGd9Q2FE!8KA;U? z41)^{e9c>k%8wqAB*%L`>k7`JLxc$oUT}mAEd_*oK=dbjJ}>xJ2FF-`q|uM?fauTk ze3ma$8%b}Ll02OS%vP|vtAM!*9`+M3UjgrU2O}3M$TLB}Vg@!_(aij{dzlBsiWTtT z`vVT2J5?gA^un!R60lmq&vOMtD(JmPz*+@Ymk3y|;No%t8x@pUDPXgL4jX+!qLlD? zr6|fa2Ds73%cxVO#db0{!@&K+{T>fUQTBO0%a0RIk-z~3^N$HQq@eO?0fz<1{KF@m zBF<4J5uC^Se*~QHfaGz~^I27{OYmt0AMXk{tDwsx0p}G|c`D#<1TxEdgQhf#Ch6lu~TkKoG!FrIfR_sR#8#N~S^=NZ3h3E2|b^cu9~EfSOK3C z3A2(hIKe4=Xdob^2PA>ip3nM(lO&;^mcc64-;HBo0n&TG{Qi)^^LpW-4DcfW&IZB3 zv51h>1Cl{@&u2|*B*8fqoN6H;H-l(SpmI9_`8*&A6!3hOkGD`n28ER5?Ixgzg82Oe z6l3t^k1i!WAW4??eAbb{qFXCgeGEu^ukyj?2b2L?O15K~tPXyyS) zvW4ffnr@Wf)(YmV7SK*Xg+ByzRIqIg0Pg>~DA}}DRJtj+wN5}!1*O&t=%b*4$+y1( z*Umu-T+4W%vNx9yMTELGVKtsPysfcRzknkOu#qKe99O`bR|1?=u*l?bMuD5@c?N&86{$)E$^3Uw$%3*$oXBMc zvLW10wYe1szC~RDevm^l1!`zyF(Tf>p)MCW8Vun@|OA zHdz@+JMH~mN}q$l5iVP|cmi^JK#G>v^I1#ZiWLQXN)G)iq_Be7?*$Z7aNvW0k_z&F z7Ene(tpLfiyn^?!1XNPc-k4Rz13u5tY98@StEs@Xtd0WLvU&jbusbwp#NYPg1sR^S>kPJwI4_>w&TNMVUB?G*&5CRa!u#g2e``Q&75`Xlw-VnX+MTQZCyTRdEZuO+jK)`ke~) zej`!#DA;Pieg)^MipC)Yb&c&u6tw!*Co0F4xOH?=0qTe?JEOp@qVo#eD!Qn^t)j~c z+$y@pK<3J#9VF44CHe0k(ztFN-BFc4I*E#36LG63T7g?54;8pI@0cjLqdsI#%Six2UGAM8j4^?2Y^;ub&NUMyt zB#|5ns+n7;+#Zm^=JkBm=O&_GK!Lji7FJNIm1q=GP{E{FQi0Fy3T2eI4PRb?YgQ!% z$Gb_YRTQ|ldes!ThSyZ!8d67rYe+qx5;xU`4Dg3W*^`y)w6O=I^gnn$t4BYvqM3r6 zJq5H>kfoP^HVS4L&|X2uerEi2Qqt0-+Eqcx!IDUK1+V%G=%rx900Dg!S@IrFi^FVPg#uGE!BB7%*DF69dL6STIbYj8{M=)rkrcjuDMX3a~XSSLsv* zdwqsXSJHTtsLWIl$0YKbf_p!S##{wgjm826-N%W>Vg=Vslw}IOn=Bf?m*VwDDxlxb zLRPB^b}40B*C^;WQNVfyS;GZvQgGaWC>fg~AWSq$)=4&K%|A{sqZ z!%d^N2Yi;BNIxc5I8M<&CC)$(h*^U@pH(YTf`=)HzfQnN1@$)w7_DIIMgij#%-Sko zd}*HlCDUCygiKVG$2$c~Qt*D4fT;?;-6vqWf=dR>R1kDfG=5XC-+;Las`w6x$^s>M zP3adaaI0gP0=EKwSKwB_Y6WfutWn@rz5Tz<^1#DN~R=_R=ZUyXB z;8wr^1#SiWslcs(qY8Z3l9~I^2_$a_rmFDsaP zLclcz(I*Am1n`+WZW(e%RVtko6~BT~21F~^azQj6Dp+H{69sqv7LDf$4qOuOO2Ljx zW=4Ihq}Cl#d9R?{H36R#6f+M6 zEhrHoNYq2Rp%sTH{Gl1_nJ@gWM_iqE9Lt@tboZuv~bXIJ7@d@coU#phMvR(t^k zZp9Z?;8uJw1#ZQcRItOeM42+&|D^)liZ8D!ZpBwp;8uJU1#ZPxQ{Yy7O$Bbn*HPeB zd_4s|x8fTraVx&D0=MFuFxblzZ!HinK; z;95Rbfos`M3S7%3C~z$cSKwMUMS*MCGy(GayKCAERT0x7tXT?N%jPh6!)<`=uenC% zdqC=Rq35$E{43S5M8S}c0+uU?G70!rDtQq@lKex#r~mT6De4kQ$SJ$iwsDiU|#{zNUb2Gh)+m5C2po63fycm zDR8sNqQK22y8<_xTna)=N%Ar{$wiBS&BIY)17P{BInfixN`;7kFUxB%sM6KSq0Zk$#Oq<`@a=ZMqR17c@;&u3wOa}MsLpkV_6 zT^0Ob66mhr#t)*=t1Pd7k_R?F=S2Fd%Fq@91}JcQ`CtXES;H8}z<6j$l##08W;0rW zo69%_J~x~3nn=1nlE_34NMXZ0pXCm|DGJ<7rzvo!uNew%_LLN7DcIXfz#IUdvHYN! z^5&~bGGoOe1+H036}YLcP~fJzN`aebqym%Z0IO6$j2N|JgvY_D(G-**I;(8L-V%@e z=-Cj5jM#64-w1rQz+vx=_}7SoMl3L5u@P&H_``^^MvxIZjksmRb|Yr_ucaI zOWW}BtJo0yLNx@xo(jS5phECFpAh^iCj`Hs3Bm6nLPQzCujawWFW5oMGJ@ZAgOA@} zgWwm_Ao$HMh%;{a`OPPIw;93j<-o`9$v~Vig5P0*kKasz;P*=)_?-|4e$@klU*Le? z*DxS<7;(u6{!=+1|GgVxkrDVytdG6?S4)T+M(`gc;Nw3MK=4oh5d14S1pizH!9OBE z@Y5p%-$X<34JriRcS5`|g4cWacvXYo`5uC&UrU<=u8xpBsxLRvai_mo;w2*V&1~5ZvWHF6BPNSH)E<|#ab6C z(7R{n2xzYUDrmZITM%7Cx;Qlsd1R!gu_A&v!Bts#lsnlI`CV6!JpzU9JGfyA2 zPbzBXS9IF`pya4MyzVG5xdTiM&pW-xfowD{MRW=ry0Sh+bcvwDZumudngSUeQtz)R zqqC$e(z>wMTKm2Vp%E#O#zlfw{-A?j2Suku=Q#2u%S;(lMW;0CnW^mREn@HSuT?7T zGWf*TcyB7y&&jXZ+&e~f|JOKQYN#GnRylQaapVb+jpn3A;~e-hC>u>F6BU;`7~Xl5 zDw;;g(nJT-$J8+Y?=M;Qpfg{3(@~Q&$Otb_{x4cO@|D~wr8@T&PF3WK4uAHmAnMu5 z#5n&oPMS74Wjr2}Q^PY%zj-nvn|Rq(!C5KkqBBgtepa%&^A)A5k1h(n`X$atf4#dX z1M}wBxc5hcuY8TmrbFp(eaS+pS2|SQy)RkrAiU=(r5B4rlNa&2$&If#wO)Hk>e&7g zXGm4F$p1#&wf`$f``qoXf@oGSsvU2f{x9PU(@&n4GW)-xRQ~>@AiU5%fTygGZ*W(1 zzA`Sk*may;CE3-MFCF-Dli3}z<_mpS+hU$*hZ&zI&LkeV+~*!z$#ulTZuFK_wso-Y^qvXd{> zSb0CY0@ziUFHQJTlP`JsQtunS*-6CCYpnQ*FLl_PjxX8yQj9N+`O=Cn&spUNUt&j9 z2#IbQ5SQ1d@EkMm*}=5#8g`Ei&kA?eSoq5eJ?%%S+K(MurFAT zdX0zK9I9z%ggS|EO6I(b(K+Js;uRi3&r;xAQL~1HXQneboyqD!ihFhf=bhky+yMdKR9MM*Zx|2=zC#FjI6P4DvIXqI1UPWsO>I^`(y|Yg%|P z^_lAgLv0_G4UNvq$L@qiS5M8Q8evvl2&}s7J3>1{qw8R~3(6c_1xANujxG_*t1ZIV zU--=V)IimFWKgghTIa?>ZpNPqo-FZ#&@q)+@!dOD{TtTRRu@;7*;XVbq%nJ zB#IHEe*`7D87DaE2P=Bh+3I}TYb`5>i@2iddal25D34)9zVlq^ns~0zmY!<~&e^dJ zjc-`~epJKq4<;n<$am1zk2uuEoQKm3r`Z_SMI2*eT$z`8uI@Ov#;|8NvBtPUaa4_Q zB|PZ47T|0e!-8=rjkQ0jQvC;=(g*mXR&;ytX=HQ0Pq*Gb#YJ2{zV}?UW8yp#VJl*L zuI)IN#jw4Bp6hmy=W3k5bFE3lt}PE9v@TcvEN4&>dyt!b!|!8japP(_+;bhpw%>+5 z#%A8eH2`~bTW6!@=6u+wdIRD#Zp{S|SDpo)>nH4XZCFceb8TFUvA?x(rN%zi#&uj> zgsrSCimKe}VRXJ!{CHuF!N${O59`C&aoXA$RdMda#*I?(!-lnBKZv-#!Pe2%_NbLl zA2tdJ{f)ymVvA+7<#Uz`u4{lb>{tMIlQZo?i$yg^^e8ZUs%xw}89Nl44U#WmYhvqK zRL>%h`XtW8&n#A@Paxu&h>d&2HNfim2I)}YQ};4Z{VZ8SqDoeNw6aKUeyXvurj6Oci?S=~!@@`VQf1=DBCC82 z5OF1HsJ z$3536?CWP(+EbpZ@EO-NzP@brjYaZ`S;p2xv>pSd+XZG6rTU`yYBc5S1 zu+g1yeNfjYY;9*)W$aI9T)(Sp6}G4|>=Cx2GpgmhJHCt!mXH=^Kc0)58 zsih0`Tz9blne{BHNuwuml7=p0t=#!Q#1*fg>l$F4%Nt7?Xm|OnQ42dfS)HY*!@4c7 zJCfN~sRy=2GOmi){m8hIuJK$mu>q3hM2#H(WY(kx{7h_>#^yq1E2DeZRmix0!fryw z72~q!s)7B33>%4!gN$qH4bPPgTLoE1qdKR0y1#TMe#o}|#ZEu&CPCQZ$GApgZy)1o z(<-*>`qA=Sj=E}h^Vo-8o@;6!*EPV3fuzw%E1#{CFKS1$ApXioRx@H)u(-MMP)J zvc@$8b!1$b@yu*o3-Nq!y^gxj@>yi1^8D+7wGvOzW*3KTm|TqOZ#)MZ*CQ;z#`W-) z=W2|{a_e-|p83xL+xYmW1?wDohWU{H)pO4k8&5xmWqj|s3Sqn%Rz6l7*L5+r=PDWB zbB(|z14ge#QqOe;+X@&qB(>)%lU5=})%yG_Rg#z^^HAHw&lhFlpE<0S*bu;MC2$Bk z0T|c6*aE;h9F;lA^Yv{i@XsGs=Yk;Os)!HojVl^o+#8nV-w(F;#Q0j-*+iI1OhaK~3Ww01?RROUtcSl`rcL9NQX zNS&c|?J?sc!V?Z`+kHUC*6n)@=!si3qx)m+o%Cy&7x`lCseOp{mU)pe){)xh=vJ8* znd1Ci`}Oz;>OHP`#vr5nE-WGCFZ&`mc9MXwtW>+~i;$ok0b#WRkaP;VHNItv1SZk2 zx&dMRX>!>Y8DkF$2pdW3%D%`EG&&$`B5Nk6(DN-*CS*gyCIy5=fKjFO%~QpfJi;g- zYzbv7_agld%LBqrbAzcyCC$qLVYfLj zfg9MNZU44z(7k}L4*@i_+=~pcKL>=Bp!MZm~YBup#rpRw-N?TE-0PNFU3+ z$egiD%&^`F43FQwU*FaP`}J?zzMGp?znEdes9gCMS+b6d85Y4hNpJ()y61o%t))+N z^z5OSVZYPp@-H$5t&SPCffbX_Owu}KZZURq%&>io5_Rg;w@2Ilt-mVC!I)u}5lCx7 zS_ISQ@-O1XyB0I-E|Z1dv`*`j1nih$??49d-Tq;0iC{jB&y2kgj4Sa(&(#LI9~ib3 z+a4HKN$huETsyGQfpP7^4hP0{2|F7Y*EVcvU|eOfmw|ElZ0urSoXfF&fpK-jz6HjW zb&2Q7vea|MS>d_fVrv4U*Ljt?uo;12v#<++aot?&xuVv2uF2SM0HNPvN|=P528`kn zY%yS5-LSWSagE>Nxt3rz0mJrU8v*00iR}ZdO;MdIzSxtkDF0Mz6~pKMW@i98;JHQ| z^jvlDp}z^ufG_;5mr+eSyy#oh$G_)VkMIG#`SgA$K9o1E?=E_-fXkjM0^i7+&`Fp8?sWq$L^b{Q)q#UWk8w!V&Il3L zp{|}QZhy~J9UnoP(5e&I6;<#5Z2vc_&Op#0ca{loH#20T^CI9jW(cbbxPKYK`Uc#% z3}Jl)4&y>tAuD05I^ZZJMC=K0JTmL~_FEp`nJeWlg3R3k9L$64N{$AOl|fiyz(KUE zi`)Mk{9c-73mQ|;5(f?A(oI*lLi zW(R^s;^sQ?=A&vFIs z4~1+kRRL}c1+z-C(o?~vG)mN{Z=ag~6F5kEXM26~&vFCq(;z)@zzv#Nr?)e1`6*(d d0-74g;++i?0kedcLRqC*S0{r^DJ@OX0{{@D?=t`Z delta 379027 zcmb?^XIK_$Q&rtF?tbSx*L5u3A5islRp0f*3SHGbi!MHp?zsFwn!T!2`O;~ry%GoY zkE&FuRE7Am@d*jtyOk+lI<9;9xVZ8O(G@C0cP~@ELfQD}=;-d<%U0-CZu;eTnfIox zb(k|&*^oZ{<0?fDEw$Bb?)ew2y{pE%q(fQxB&_Az5370suY#hCl&vB z@BaU8lD*;7|2(5?s4J1;u6-^G;{Z$Elyb0(LadA)l+0_Y7tYMGf#zj{Y$)RZw_Md- zOewIxm9rGPKh(?=2hM{DCD^55CD=cUyIFVFP_}uLlkHYa2VOqQkjS&dz52!{44Ii# z3N&A;E=9nc_meSLI4HI8K{zuLL{+mEXPupkb5+^g?h&l3bM%bPF0k}vhK+q&T+3z_ zx3M+DBH0C9O0iomh6%FV?B+-tyRn3nDFNaX;Uy5B8!Ta3xKYLkmh^9DWDj<#29_F= zT!Jh;*gRIC2RSb@Ol+qvQpU-~bufWxi|$KN?NZ=XoTCh5W|_{xY)+IEWIxL=3bH~# z^J`5**|><(Y|TU$YwlrYM};|2;~MaCo>j*x5klO())wHH^gzOfCVJSU4jLUNtFn7? zLR{bExNcG1QwH`;V{?i*7#C(3WS@B;V&&Zf*phBDD=1cx?Ve}{TdG<^*?v(5q%{i@ zgLn%Av665-J3XR`QsIN@AJ?}xyCl?w%;G#1uSa_8dat#I?HU@v6ho3T#n`jGstU4- zgI9^wizB&0z>;kvL)d+89lJH&#)=}VAOXy5LAr=-+TD(%GB7Q1d*qx98raeOrwKUX8gmiV* z5H~GIEQZ`S+{F96=7(ZMWhl{C!g56Osq8)#4*auko>jmK_sfVSLeR1L|=o>d*E#D5ml3qrTx_y+$8!4MFhO@4&VW?mE z5$x=i?%^|{rDt$2>G($NfjJBE9|SEoKy}OnRZiovNUY562n1Io`qMgqTSz zpcrBgh8jsc<4%X~Jw3qr@qq^caUI^)fTRk-jQ67d{AZGMpm=RsEsG>+3!&h=b%a_& zsE^OL6KWx$YNhWY)LKG)YPXwE>j~99_W+?*5-RBgN4+7`VCg|p1_))=bJXz+LIzdm z$PI)_oOqa2EhJQ(=0^zinNTf;a@09Ot!sallzk!8%cJKA^@>o2Ru>6%lu(xjaZ~}J z>U_OK%4Ul)u=KtCE)#MyDIEId3ZeEBYQnjzgxW=@F;lM-Y7?Qpui~iJgnE{DgOueH z>Qwk0LR}+No2nf3mQZ`z-6Lh^MgPe_&5sj2B;@a;u+jC$gt|?rPu*S<>I4TfkfZhy z>iyVvr0fNurv3hrP}>Rh?)_&%%_LOh%`b$SMX1(${wCBzL}g5pzCF#6b4X#^M*kUz zJ>fB-Hwh@)QKQfpAyQtp#-7+B9v}SG@+If>czlPgvuk-uDKlbh)_2Q z%aF1&gv#1pmQW7}m9e2xIYRCrWSx^7^^s5~oE1pfazedyS0&UcLWT9JMyL;j+A@-( zE)XhUE=NH^orwi0SqM#n~k{F{*5t~VysAB1WuZbGPAgc|UWqb?GP z-Px3s-6YhMD;#xb6++HYv?J6FLK#y! z5$YhJwuw6v>KUPa`JJOq5lVTMqm~hB?Dj6C<~l+RXcRa$b?5<(@G{!VJ%CsaU{i-WK`Uni8l z-{2vb+C!+m*9wPZVDcLwyM3%P6jNsjHLk(^-!OHEP%Yv)YB!;#ZcZM9%N7ty8(w@Y zrdALtt>>|r9^F4hs6Pqy@p|fnS{bNt1|h${LnNXO6Y7lN1fez( z>U_7~CgQRqgu2((J_%E~g!+8rO%|r^5h_LYmQZ&H^(@yo6_=eP)P!2sW@G9qq0)38 zKrIVfVL*N6J)1e8G_!?$xu6pJbby;#gL2`la9|>YZhOyB@quFI+-0X!R9oVel6_`@2bBu${ z7^Y<}4)w5QhdI&g%#V$qQj1+b)XC=MFb`lbVY{M+K8(=(lL01P z@bW^4mc5x#3cPIW3}DBMjt2XN7xx#<%uYJWP9IfXiU&U2+`iFenJ5HPjCG9m_qBu- zcoC_1+~~@PH9QV-tc@jCaXZ)uXU6=3O7XaeOXJ4WX4{ONL%85dcJU>*GNL&)(#F+c z16LS`Mspm2a<w7Dq z&xTIa{>#x2LwHcd3_V|6mMuN09PtLsa*s(-KGn#PBG7P4>UGVmLh4|FalJER>N3Bg z27Y04bHr@=G%1^zB}KS+JoXBT>$MqGk=0IV&aRw-)quxmZ>bll^t2Id!I)pzmK#El zDkF&raLahS=IhRwUYi{_t|mJ-JA|#4>p@z0!QPnFm`zzEV=K+*26z5ny+Wx; z+WS1;30LPK0!R+t_ zR^|YKJ+Ppp6wej7n?l);`7ZXmf&Sv8tSsBzjaR-zCQ%^`rBocnX5+1m;ANo`iEb{Zbv%8ns=A-LXW2+I_%w zSh|SWK{}&->*Szoep5+cWa>~PKAxB$fUNfHl5M}R1DENsEtzse8H-o9H%Yl_%YDXk zt-+$>ISb(p#Vopl+PJ`~R+L~MTWv)+ljEz6x^VXUYB7i&+bw{dw!)0MihirDAm>4> z2KzCdv|t!)GVB?+ul>H-1RPmCC6=guCNgq~%$Y$`B7&vM`$MIjJN^$|uxVXc*1WbPa$^;1U8e?iSCW+7x3)Hp zLnfB(H?ll?YK<8I-6W>6uCq(=9LRfX_bIi}xbWjvECZeu{iEE6Vqu@HD}h+W^CZk_ zJK_u@HU^R*YsNGS^ADG6zM%>7Q4_YQAD&9#8~#yj-t-&mToZ=)x`Mj6XyyhH6Ah+h zkZ6p?I;99dlVkr*Yk>wlZd7cuc?>Jq63;IxaC*dV_4l{H5)ijhaVS-1Be6S$r&z1 zz5hSlj@`RV%=XWVM*72ZD~vamWp*XAPoWGfQa;k40zAvY1>^Yw8*`TR|9v>f?#;?r z{~c*;pWT`4pj{u>p*u6#?>mNp#;Qkt?Dw4m!G^{&W$d8cX`o3Ux-#|mH1JY5(~q67 zBMqd!o8gDFh38(FtBid^*mrwJfd~z%_&I+#*r$FZL05-^0w>CR-SbIMu`D}eUj~@p zeu*Dq>QA1jk%*~y_QifPJMlnqsW3vJOL^7#vW#beB?*seOAQKh9-Ko*V?yBYu~$ z!aq`h{W_Gf?o+AkvXeu=DHpnV^63;-etHO~sz$LYr1RO|@Pu*xiCVy_kfoy&7 z^{SA-%{s>{poD{^1HctF0ZVM zZsrru)6Gz355g6o^4E8e7ycI4V zVw&=-RyfWyLL+ zQ<}f+z!zo*e>F=eILf$(M; z^=o+q1B69F?H?v+!OL(VN}%K=ds{}(kk2-&DEQ8c)1|s#8Sft2Uj&7G1wli>Q3`Ly zJwO*w&_oah4z>+6!TGU*2Ap{CQ+vUmG*bBx;cs08vAlQO?IzgCXE*f_=y|3uB?+Q< zsr0FW>wH!`NKn96xIau#O7`=bMZ^C{aQHVt9_TlIM13%`{&f}T`n|LQ+#Dlde;$s^ zL_mk~^Lgnu_^LF|$4a!`4# zFc7M~3c8sPKa58Z1jh#p=OHZv7kYm4^RoFGQNkFLFrQ|I=w(w0Ct8FJu%XV`g^Oqm z6?aTp3KTFys?I)}Ru5DURg?x3Ji@mWQvDOD6j&T3lh7Y^0sjPxhM}RtlUT?%#f7)A z*Iuk7EL5R%dqZJweBIepI2Aby%xxj8i_;Hngb_Fj-0g(Pn9lAb+>O1wNrEs-jMBf7 zgo*gR+I@wAxL(Ut;W_N}q5XwVaT=5+{D9MdLBbCB{={Lz!MOg-k;2dT`qd0!Ax?LW z5*Fa=lgA3DV;t)Q;arRlr*L$GY~d|j9x+q6mc}?fGXrF1H4dfDvv!KKRkG$&A0h%v z_6ie0>^o%;oIh8{BCUX^CBlUi_C`Rdu+LKAGaLyWRtN{vVhmU*j7M4mm)8iF&|;X@ z2{&Lqj%}dlMc$r7fhwOLZTP(}Box;Vm`lz;BxC>)^ z+C#$!M6y8a;O+Jcci~ibP?(3sI(0<2j+Q0EF=26<@s8;A&I#dUEKka5;aXY-cK#vE zqUmf20}l#>eJ}&9&k2{)GBEGFFo0SE^)7K^&h0DoCWyQ$Tusfu;6mYItO5NkA0EU zA?}}66cbSnLoFbJ-i-UY=Ha6AI4u?->VT9DN0$^i@#JB7SHR-Mc%hQ_r|1@D=k_I0IKD05s^|c|?aX!2T}*GgC6Z%$?;TMS zKa?t-i6j^<=#7XBKm~6_<8i&VA4U0clm-jMuaNRq$;4uOo1av?7Z)Fwi~qzrT@WD7 z#I0M^;xhPp^B^%<9+`v1_i#C55U;@Ht1RLfG!rxAi>=@uhj4hynA<|cna1`!#?6*Y_Pcg;@F}HAqA(aJeI*4L&TH3{KFd~ z#JZwm0)#06&+P#cIBm2z0SobNEGNX7@thEyvcy&_#PI3jk~ASkED>E}&{UW=M_e2u z-C8KFSrjA%+_YT02=m%>HOFh%T5&V1@F5$;18@sZw~CizubRF~{1RUuy-zHlY_U1W zT`y9B*j7WOpg!{jG2g_$`lA%cB-uDr!gP|qF&*lZ3^UUWrOiOWJFyz z94xFRIqDT~Xm!a(O3m@$)eqEAz{9^tPGXs+*OxrOeaFa#k|b=Xhs`7r6q`bL9BT%u z_qrDVV%tbW;P-xmXMu{XC0Vp;z)%oDMIh$D*#S*;V0CMWhU*{lf0bPHqW~sj%gd32 z=fEl5B%5er)r;^8ypt%wi)@fvRT~7Qy(Our{|0ee^+7OeprjW~@8!iV0l=KW7Pxq* z1kgD0zhGSO{s_q(Y~K^Tp;tJaWaVn*|Sk?z6QN7R$9#%V|c>3*C(Xed2~NB7+3TzN`MuH4X?EB~vF z^e!&n+lebr@4}Ub#&PA3x=AnN@&moN@=?9H^5T8C@~3^Jxww4cK(4&wV6I#|lq)|v zOu8GFj~T_4*Zz$we>#RM-#AXX50|G*;mRYYapjk?QMneM$nsYwa+KB}mH(86M2QW9yR&eFYRa`k-&6N*bD=om~4L5V;ep|WnE!(*Av^?oE zJfd{lBh^zna+|zE(r5}lhREf}bL7mE(zVzlBF;)n;k465>3m9F$m7a75V>%Krbym9 z@CE0+w_bDJd-bhUhQ0T0fUGv=F-#*P-upl+Bi@^>lkLae+d?nviS@bDCaX>PjG22j z4yT05GzK8t-n2e=5F=YeAq_r!0Z-MCeWY2^^-GsUg3Mfn9XOz0ahP0Nc7$OT!ZN?e z93qsqY9OnG)2U5lmvP#&g=_%Lj@OT7+IE&v>;(V>(3*Q>*{6$ao}5_-WJ$7VG}=m+ zb%G(iWd@qz0&&p^p6nyb5g-PVQ)P2#8JN*emPF(Ggn>zeWrHvS%rMy$S_Uk`Wgd!g z7a|7UenNUM!<|Fhv}zMUE^A z%QSkqY!EF|iK}D*VC^LT5^&E7St#c0{z}*+|UU>|APf!P%Q- z?=T-(+ht2BrGvZkWW%Xl_wWuG+A>5gvztD4#^lT9U<~U%*$7%8Lick9aU7NbT8v+g z$V$^JtdKQhKz^g)W{`R!Mhu2n{S@F?qu~lLjVDd8#%bB203;%Ls`uuKER3R`!T}Nk zsvW4IgmbUUULdo>iMM1&u{$=uBfEt(2+Q1;b;U#4%*Qe(F3)==i$$FRl)jN&!u4*v zm376}YkZWQRG>6K;@3YIr5P5#-niZqr=M1X((h&b;{8w>U(HX7@sex$(RS@opdeL- zj{ns2`%}s*0PLvirvjA@C_K}}@1qK($5Q=> zZnFFP5#973=ockH*Ow0WJK~ShUgP~b$WVHCnxD`}=Q2^C>o%Dmco|ks0xPWXD~E@0 zV&PF+{WO#o{wJmY`gt&mp?f(|UhLyUIeXAA1e@i;)(MK4bdi8Nc7LdB>dd z3&iG|dChMCPG$G~Iw0l4_-B3>m9(N}f0h}rwoUNVSHHwSuc!?IB zP%I+gt#E(zz!-U0r2i|dvbbvg<*=uL#{SsTpu3s>6KwFnw*JJm?zZzMs@vJYpQvtV zCoZkn#lIp}eZP2rBjqZC7*P&veeStnaH{`w)MNA8L*x8gQ1_n5*?7!(S^j#O!;)VA zA}|hEWv+i+teMgC{mEd{exZLR?tQMV^goN8X~SxNIkwF28~q(vb64{GuTwYxw#)+o z{9}**N{l*ipML_j&(%Nt4`4knI?vgs_$7a|i?I-HD)g_9(~tN3(bfnmf9$^nr_En* zb}ROZv)k9#{=eY+YklAq*0xr_IeMejZWus}_P9|1394)D1B^JR-uDV9j+u7$3n0cy8WKQ^ zr5YJf9vh|J=l~LAmN5Y&$X<^PAVIcyJeQ_Ud^8(9Y!YD;qEZMyA z3NlaYHc>(5X?3y`WS*9ktstXsZB{{^v03IQW??;VnWxx_yOAEt6fP{{#MO#9G$Nv* zClaDZd^3uH>6?ev2fK>fDubDq6jV!m^sEuQEUOWLMb9$iAbZ7q5x7u876(d>6KFt{ zMoJ~Pds)$@^iO}-EDD{d$nRhAOV65p8dN}1~=bP2*v*uP%Vglq7cEVj})oQe+LjEz_kh_I9jdg;LDGS zF-m4F94J+;7o*grP@)OUTKHM1tRqKhj8XXr)8S_21Q|-VxRvNw?OHf4M42r>sXk2E z4A<*fTKNv&->IS!jY4bT@70ujv_)qa+1n1bH&;#tEt3st0JcyT#mi+Ny3>7sIKHLQ zkERPKB@e6x7A#QPL7$<4Dpp6vd`f1JH`(B`0_6>gwIkrUvB89M%FfuRhb|~-jy(HtzFt)7 zD5Dj}g$=H~qU??tQC?H#6h$NTt}FKzNh6DIC>vr%9^axiP2L^lt0M6*>7Ejud0r1b zK2R<#iZaSPQo4&|l^0KxH83O5&y{nF!brCl$~KfC-xH#DCgf7YM2D}mu8{)ge^Vw= zY9eL(ia>PucRjEQ11W>G8*C&B6wK-a4R^F=4BZqdJp&cW1+JxeG=j|79JHzxRu+QFf%7SC*&M9s!I(h}iKsaIR5h?2%~l6u zq!Ik1QQ&n-9vT63vkI*1!RTP)rhzjlCdW|SCXnilB6MFzQ25^10IJ&sZlZbEjHL%% zL$m?l+a-?)c1s9+fo6ByLxUdu0;#r%|Ai@qscC_W1JO)TH74*Xo>4v>8>rEv^!tLq zCJ88QB2c|Tb2k_xRb4WoRPR((3_)pZUDY|7Q%S_2i<_zLD!qct>8zriKQsaeNePq( z9TI4aQ@Jst)e=-Oc*Wx@&Xy5I zxN(8%t&XneYy@6DMV|vm*{+hnu&t`2)Paj(AcDCXOwGs@tv@)fk1&CK!RoOv^|VTa zdvvlTJh4F4lCo0n;W> zeUfsy-3YTT23_@mA}}P~Z3dZdRb${UdbPmEe@B!03AJnZ(BUbIx(8;qjEe}7=TV1Z zGAvw;Uc^}sUl&)SvxX@6OQ`GPAQzNUkD;8IHwMG|-!p+sl}!Y`H&piky~?Y#Ahn)a z3DygY&B1r0x&#QRs4mHS7*|f7CCsd-Mjy}m>F1R=tpC~SCaF~h!>g#%(5wJ1s-~`l z)5|fOq)Th6(VI)_VewzoRj4KWI~F}ix;nLzmV3*genTW-hYtUeP}o>q!Yd&uCldB+ zte!?p8>^W0yCxs1rs<$JF^LndKVaGb1qW+45=fog2)y_ONHQbi2?u@*b>!3o%sA<MYz1m7T7BC#UngS`a-}+{7>|LBDY(E9|vMT~Y{Q8+g=U)e?0w1tva2jGyZ(Vd7GC z7>5OytWcj9GjDJk7I4I}Lj#ttQ*SE*dMG@yUL7c6-oT5wYIKD44Q#SieMpW{%^tPd zj8e}PbysxL(RkRIB+F*RNyLJV!^*#+mAaVdXUYiXe*;qF=!{D*(}7 zO={S$l4c6}Tqn*3#%OjhT=q&0jmMukj`dd${L)xMaZZ%`Rtz=_h#jf%2iGQyFv7k~ zG;`(5aVTr6NfV)TWJk?M2eSc;I*1OD?-{AtQ53O2*OdDvct2gUF&N~iYOCRjS(?)5 zTRS-WaE@jspPjNm6N$dAgs+}jq>13OvL%`rKHGMgCXUaZSfR;9UrfSvC$G{#{%Xs$ znk7QusNF~dB^x!B_^YwGn!bGY%og8m+1oU8d4$S&nm_mo#vPhdJUj1qYvTEG`;xjlOsOd^~Zedq@C2{@L(ZlG?n?fsb_ucyg28(E%$RE`8M0=fR@CXr}V5o%vf6#$S~& zTA#6m+LQd%vJ&lCzHYEw>s9LH04@GL8rI|krM3q8v=Yu%4ASmlI1|c(wM`k|fem$_ z&mb*&9nYkd!V!8c@{F5p>T2K)qxL+{<07**=pR+#S&P<;zDkAxzj?G?U#k(O_4?Yq z2(8!GJdqeN^+`PqjEmM*=UK`vt@YZuK{;(LUI97fwGH?tKUdIh;uepR&9 zxw-(=Re-PQ`f4E7qLo8KHSOPgrO+5Ha#O* z)_Q$2Al9eGQ7yFIpm@{DciXl$T5nK{Xs7jhVaZOu)`oQPwRR`YciZyr+KoI9-Fs-g z1{Ne~y#}t5>@)D>zS{2SD_>a0-_x|{qlws35LpU;J`i7hIYir)&&CbccKWA%u8+_P zc>BB@hGU+fcG zveb9m(Pcg*uUzR<@^5Q>O8&Us*ILph-)#|_eXYIO>QnM^=xgo84qt2Scl&NL@9`=5 zuYEox4?W~l@|Gh$F$Kr9w|OPMJEQG^K01aqn|xlog;z<`C2bQvJLQUY5MLpvQ2UN| zHq%WXZp+)gx|{E6hoIw37_9PBt+yN7_FVfZfV;Zqi?$_?>-t-JMax|c&;)rM^RzZ- zFW?ch8`3^l3gU=KItj zZ@lL958BS(HX<#kHLu^>!+f$W8xho(M{s2ZUE>h~Ck46rY;88k#`|yo`9VGSx{nqG zEmLu=E!^Y->%2MWJ#UtTyr2Vo!xew`iTQa)5c;?%>3|OI3TnpVx(@|q@V3}m5Hy^Z zE#{{0Hs*HFJs#}CouC;a4q^Vopk$sM!_%OtJUj5K@3x_T2X*5sJY{sx{5gIfD|Cgt zs;{VYTX@_B8r=x~YPe2!o4;z&>!$Dm?KJBczQQT1?hapJpIv7`zlDai{;a(28tS?6 zh<-o{xA zb=P^^VT*Nx)Eq9e*{7XDTXbJ}u(R8B-tlxc)NSLn(_y#ncOJpBM^}vxhW;mYXf{Wf zta@5^j7QKF=wy60@=u+27`}c^_kpi_?}E-dT-Lm-JI~iGc2#$auVA>Y+s9`g73yMn z9va@!&ESQ9_FR|6-!|!$E|ypIlP|h7UT3M_b${_X>mUn0&45j_YN}x+dGH!u>u=P- zy?O1Vn1g@k>sIsxcjpn-hXya_E5wxw-p#WUStht5-|*ma!I``sWVM5%c^(=x3GT^f z_x~E)hX;%45WI>9`w$=O^)tVo!S}qwPM_dMN=}oXMg*tu5)T~}Jb;guZ`r~1c-+?W zgSGq=V`xtBGoFXR8-r``O&o*4QG72aI~ly4=i!e(g5UBL&Rq_^%uBrcR`A9;Od(k* zf!Il-O`uWJjNA0@?p(xGk&fUI0o@-`ePkk=F zt#WU!nX!GiW<;r6y~F*uW>N;|Z(x{agY-9X{pex(VVGVwLLY@v?j8OY>H6XLV((FU z5!ONKSp6bQ2TstV*8>ZoWU?L|IxU2Uv-F2>I%z5gUN&355z}|3>o?$ZI;(GmQV#U< zOnrTPb8VnUpT{VKx990AV%ofrQ(*7Kdi33yLRfF9{xU9itmG6hYqh=uzW!>hzJ?g3 zrE~RvU~p!Oz6(y#hX!JC`IhZ^^yQaAI0x!IxKVW72Cx3E|0G2hFYM#2XgI`KG5;uM z#jnRXD^@(kF)`r}PRq`-`W6@jf6@S~zpPilS%2!QV)#GL>G$Gv#sxh(4qONuT+%o7 z)`X~L3(U*+zc{0BF66B7^`<@vU+;N`vqsoG&e+n2oU!jd;bd+1oO7a+FS&YIuQ}_s zddFE;{E5Sh{lekx`o`58@m;?M<5v+Fh;IK98;EYTG6QkW?s7w-1eKps8G2!6v2H+= z&LD*yG=?~QXMT_&6{nGULmzJqxYJ+|$Z(;dlNX5Z512%RpQ|wyuG={^9x@ zUWGa`hgzGZ1a8>d0xMZdXnl8QnNO&%_(XNWs8$ zYAZ-hHk_tiPiGzqk;AEd45@z1XZU@PVXXkAYlj+|QFJ`8c!r@Q!<++4#wOMWIhh8U z0|2D+LR_hVD(k)s%pYaA_w%yB&7%$DXdZ8)`&xnBV+}KZzD-bbh9RCivG!@40X?~H zJhPn|7$+NOz66M{l3~{@!+VF=kH~4s|u)&bY^KxvHVPa8u`8(I}myi?d(YB&N+4BthC@c(+Se;?t-G(nh z5U{aR`jE*xn%_E}t`erxk*re6kvoz|QmNL)XVI z8|+Yf(eRFDV{z1bLl-#bx&b|%ARZHZ)8{eEZqs=TLIkVtp;N${?-+Vg*&FkO5WZ6N zGVcvt!OO=6C1|8FmIp&08Sc~Q8KFRga+S(Y2nz$z3M$E9G{WJp3<4^Td5=!Q7q=iG z+P*V<{COcDvaO0{pB&xS2Hdb3{lE>05$%qCHw1}@cg(tMM7x;F4JMc&G&bWqmK$Ot zRmb9O1foU~DBa`-n@V_FW^BoIzp!qAu?MBb38EaRv_?7ZZ7PG`BMUWP@*_iia8+fb z`OuqJn>;A$e3D?Y#@Lh6vv>xl1slb%m%(T$ikvox+$)k+_Yk?rQJRxAs4HvAb=Cd(1jp)fN9-Eti??nVVx01 zgPue0!y08YX9%!~u$Jfje3ynNI4wbqiiH7Sq0_{_3e#k+u?9cyTC~7O^TQthLbbsg z3yq6;z-dd2Gm6Tl4W3zQtV=aG^U^B7q9{{S_;!u4DOG=tVZJOe27oJ#0?R=0R^tNd z=pZmFdoU(|PJWhVq@d~Ic}`OcMSDU8T(L}#M_Dz)<12mO(Q=>fQZlAV$T1C7mQu`s7SkFoXTglg~mQK zQQ_s!4i=eBox$9ZJ1+1<@rAbaRNDQpyIswd$l|EqMS4SbX(7)+BWqU;Ck zaHDA$jgUSBfDi(}6tjtD(&a@FgPRUh0F1tCtj55nR#QJ-yuJ<-%?k+sh6M%ieBf?B6G-32FJOwcIFjay{B~1&9Vq#(( z1w>=pUo=tmgQaGnCiKo}71O1naf4xSgz}+vrkd#rWoWg7rR$p-@KKfB)E8Avo6|;B zJCN8437Oi$)R$67fEm3&R8U{*+i34fWByQuN zP3UkA$-bLrGSE!9d04apypLvKjh32%Km;(+bORU(9GjI84j%*46d_>?Oqg%VrW6(= zu8`T_!3CxjG;31C%}6eYi!U<`a6WW5y)mKlSwyFU z-wLi0*W6qqA2Q@y)PlzdZa z-of**R&J&_iB4ct0p?qjwi*jpDa|6j$Jnhl=h5mROlxjNYYJF0*!+aoL%7krnU;ra zllcv$9(bSq#cH0)d*@x7`55n=`EK(HTAmCc=H8TDdQXtszR(Xmj571j!kR{y8}O!D z9ciWr7_P&^s1TuED+*uof4rt9u3**(Q4e1;y|WIMtZY8d_n(^TW^_)Eg#EP`vp4LQ z)ijr*nU*0}?+H`uQoDLjfNoZUPWR0^Q1G~-7;Il|E(vQkFz-{)8Mh~RInpc#NA(dF z?sdaDZOuVg59D>pJ{`_ES^XjbE&5Vu?6Lres&3o{NzRErzJ z?*~pX1wz42vw>H9#9s3u&PaKO%wvmUf?%LBn5D4t3G+;^6M@9zW`3@x`or9xcVhOe zxh!o%u*)TLGoQ8(UN+aIRt!Qi!kt&mD|xp1-7x<`IpOUD%&Sa6up-ra3$oPYvmts*u8_NW-(;Cr6czwt!47KJ zEEM1P`XNV0yJZu{5U{u{6u(#Eg8~SOdrCRLW7$IySYD)_-0DFbVQC;Dssdf2XgPz( zSXGOl@X-m&0x>AQu#67QE3iB$g1LFXh;x=;UW68x zEa8?>%NQPu397|bnvVhfFD*gzK?QW= zkSL?7+=|}+B-!BsRx}ooY)z$g0I!V0YU@Di4#{rq1;6U8#W-PL1+%p_2MZ?KtP~H6 zurM&zZXM4X;-TBe^2QMBNGUHyxxWB%&GnbX1yLtQYqH?Qm|Xw9Q7T2QQx6DWi>(NU@LlO4yy_z4W$V@f(`1= zN5;q)W^F|CT$IR&5bVHy&>9HYbn8oh&U!N^SslC~4^OpL;j?Pix`0*-m{XePuZYJ% zv)5X=&$j~A`+gz_7g*65F`~L+ORP)j{1}7U;FP7-B+7W=0CumiUgOQUX0?^(K_eD0 zZ>@C@AME2dSbI@)?hwJu2mteXHP^vod#%$cg-QqLBWx0=JZMD=bS}D2T9^58*>~ry zC(*Y)a6feRqE*Vf%A;G>Bq?__{t+4vxonFk*5>FFA-L|gm)7GFE<5#u^$1@f;gb~| z-Xj$ZU##d67RlcDYTd@Swp?OE?-=ptiVhk_NN+Ga&}ITTj8zPFB(;i!TU54U z6w{c0W643`)JiJgXwt1G4Ak1t$W0WVqqCvq5MepaXe%LOW|P-2;X0>n8%>+uMV}qS zO2Wh8bGPk1(vu-l1K)?(&=16I=7vJ_g?k#u`+=Uh?7jvhJTF-W1edU7P{h-#VYYPq z@p&Z(U2j*yt|e_3XypAj%m8_m~=5LO-xPOvFxx;t{SJkhpC!0~V;$+nl) zM>3LaMrs~1Qf!xa9(wk-9irtylV+p(5;)O=aV7K3Fi)aRlR|{=BIiLJK!FHJMGcHV_^T~y_18~ALTQHR}@Xga_ z*lzO57(L6jpGFxNraZ7wZQ3Da9DtU2wm*3uzAvER0V0@@)GCNuVndIqh&$F=Zreqx zf|o079VnKa0P(&YIyTsraXi5Dn`}E7!VX-#*;bvZ-&|;$3tlcR6oVQqi_L^JcG-e> z@PvFHc=TRdD9z>u;+F?67;`TGq}(oH1P+@c4emYRYv%4LUo)Hju*J|eGy9TF4KfZz zis2tuY!7)6j$X5&Hzu&T;DSOMI*~xK@i%=fM&0&Z{cy)7q-`8TvxORX@wu%7%`${& zDfO!jeOe{Agb94>=$HW4e6U>?f#<)5>EQgYwwIKh$6SD6XJ-^sz`g?euiO9+FN*EI zaD53ZA-7MYnAKxPoC`BlcJ!63?4&Rqm=kE%0!M(|1mlA2=-V?y&zJOe5!E~Zdkq2) z0{hB`O5k3j{S^cuTjx-I6O=gY}?$P%CmQ+ zwLMP8S*cNH`vWPLt=+?pw(p7U6p41U_95Bxz3gtjf~=n%eKLkzoiNb8fzQ?*X18(k zB{(p{ewi1$^KbU%w5|nijJHc*#zcEAZ}=Knc5n2`rusNLGtIu3x(U?hNny-v`&zz< z&vWb(sKE+5xctDrt|ie|As5@TdBnh__MQIBer_?O-e_M*VLjhsh`l*h4nsHD)l_5G zU3(h1zS&OmpySnD`*LpG(sir-sSwH21lk`{h=PFD!=N4ZH&nrJ*FF_Yy=a%gKla(( z3d99EbD@LMKkVpx$@_u)to?U7mIx~Pis+yAc2s>8&zBt@IdAtX3Ierl(66BcfsH3z z`$242;*sHHXwd_=&8LNH3Ju4*iT~ymcO>2p=BWUt-TCQcQk#tV;VY~ zITPsERWw!xXdFi{E0uMQBa~W!$lAkb`c$IQ0<$9kWd2p$1kuEN0%8TO zaX3yeC{1%Y%F(PS2yD(PbOfoZ#mz!N2}cly)kku$x1${4H1li%o4c`)o8<#E%Rdjd zSK+WvRCA!8Bi#=d$2ib$d+vulYjSOsuFcV(>o}CuZKgMI1i@>~9C=s@wuJ-zGW33! z(27GY*~amhI!td&zNY|xU-B3sn)7!RBR3#N<_E?(&}PgBhY2q3<|s+i3lUv;(a}dC z@w4+436A>`#CcY-V+JM1%RolDTMNYl9O(069J6rGAjcF$z{bNIK@_0?5vv37{n8A_ z4XoG)nT`mWdA--`0zvc%+ccOuk@JkPlQ~a_o#IHR@c=X!^yZcex92!oQY{xS0$wga z$s$Jr7BzW^<234%Vda$$JfDK0YaJtK;+OFDV}Y<@vtv2c0s%9KV4`5qK}RZPZ0u1- zKN(6toN;`RqV&Zj2l}aq{cy(>2l}aq{V?;YqYS>j?Y1KW*Dw3Pk&n~YPaKiRi=p$C z;~^GI{?73$rK_PaY!7-Skv`GB3?BdDP~zCT{EdsbobMbPJsD>XH9YE|#K6(UWdUf$ z-UqgpIo-I8p>ii}bWEE)?H;!X;2_(pImmRab198SBlD6;yB*#&J0%RWi(5#PwmMf* zj1<0w1lX5tcH+%`;j8lhV<7 zf?_}e|hZoO7wQptwH{DW?D@;c)0KquPw*lc`C)JlC|BV8MIj``B88yc_?U(O zIwuHzj5~1g4rfQIDL)p{i_K5@FYI>SmvH07se{g8RCA?2oy)mdf8+`0P~krq-F@0Q zjUSgfo^{eZ=!Zxj=y(ws&kPrx=*d3Ge!S$g)6AEMc9EIYc%P{^Y<1HqqcIX1A(KIF z8>5$DHwL@VTla*Wa%LCWB_P>VZWr1mAlbzcF0@NPvb{>W&@KVV zMn}2OE&<8Lmv^CE0+PL3*@boqNOozA3+)n+?Abalv`av;JL|g|Qu<{!$h^ia5lmq5 zCzG-pPC%>5Vaz3&HOd%5~bm`!kRic2p>>7_K+X)*Kq|J9-ec!s-*a-7+o zH^LQ4)2H1U=^g=AowqAN>KC^fG&}C<45TAn=raSyT{N9H!kY-b7r6MhYg=TxRtk~a zpMG=QqZs47Xf~KP)^&!)V+ca@ade~+EkU%f{S?Bw=J-~iTzzN`Nl zoSEesNy{c`L;N>S5zXLMrscSj*m13`GeTATf!&9$a z-zZhXBW!r*LO!a*SS8@%Zmo?T&$Oz#vEc| zQ#X3F{2E5Zx~aaN;nny*pXuId<3`VPUqfj-cPpAXCc*Tb?BE{iuIR#H9*=XEpmjKy zo9G_z9q3`>zV14dlcPxhA)y~wM%z2!!9X-a!s9cj)FCVjY)o^P1hG{^OTeW5?whDF z*l375fkF!qX`#B0*|NX62cszyTtCiDv)cm@*dp#!*Q^QdQ`k7&CcDwT&uds~in}#+ zV33vCrdjU$2$B03$J#k=s^{Cp<_L~ou}zupMq4(o;hRNnv}N-e?qB9cTQ;xZ##Qc4 zwC10E(4pjxbg4JF{R4T9L9KjbN;2W=yvI#-a2)fFz=L4r{cg06^BS%@>_+=Iuc7r1 zH`>Q}4RsgYXdmY_ym5&Owv|`hXdmY_{OhJ0?c=X#PG@#^iHLjEt{+!$2pIr$}oG5Uun`V2s$~M-x0tbe?384@2xA zqlKq42$OiU;AIOB&7uN9rGuMKL&D*&ejcbao-U(3Vw-6g#ru3Yq@GgiD z{FI0R@;RElZqIiCBK8*Z&>W3ISmQwKoe&8u8|JA*Gw<;sfW9rzPh#gJh8RG`osdp2 zx45S+(i8kp((^=w(mSO+N5v>zSJ88kLFwqqo-H^nSJi{Ygnan5nn#H0=hgHa!PSrc z!c|*TkE_*!g4SsB{JvxibBjulayhiaAbgO&N+Jv%WgZF+bP6^WGxJv}u0BH#xr zkCHs-fLuOU+{Z&R75@PgmExgVF#SL{)X$TSS*e{y8>qam92@94i2I+cp`M*ZQm=Nn z=NJaMmhPe1KKmhBe5Qx$y%!FlcCi4^6&5cJAB^?9LXi)yOe#V&VYGs;Xo=e|%R}>} zRo;1gFqUba^Vn>!rhAT1YuksG&3YbEcCyI&KA7gSJ?NMG@_})#hi2FBhu(>%w#@T< z`7bavZlMRw_47f6oc{(>1xq|stIq!+Tl{j*M$D9URS|;4r}vesJ*6l{Y@bgl@DaRZ z_hOp1@(*&*8DT2DQd$vNQB-=>?vH zw7P*WFL~Zdk(tPp=F<&N5anzy9U!D2Sa92;!BdSp4?I`#)MD==&rX~Ud*VUI`to7p zXPyH%4SeB2&(reZ$yc6o5|lC@x%$^XarNhX;p!)SC3s zKLqAH)4^;-$Uy9$L~xTTgldgQ`~t&)*zaWnV5Byr7nW*{E@TuncbV0SDM4U#gbBVh zhwR7QGGXzEBV;I^)Yb5WP^}EP@Wj|rRU#D7RxAW95A)%Zun@F7M209H@}1U+K-XEt zg5k8%AugKzK*SjOSRj63Xn*yP3N%}R7y+Uafb+0^BI<>pm1I8r)F=e4B=h0@W+5FZ z1?%~bw}Wc@8Zv=q-;eNy4*xAiJmvKXLCeK_*neOMS}x|pq>&+LxtI^jW`vM=_?OHO zv|P-GQ4>PYaxoun$_hct#eCRjdI(xB=EDWEL(pmiJ-x!5 z5UTUF#GU>RYu_Cg)zQ8Gp@Vb~R8+tM%Pz3&F6>g5E%d%%@4YwdB^GS4cV#s8ZVc9_ z2Rn9SMaAB*VsFtzO`^YLXoqZ=jK8m<4&@Ffx@y><-v~7%rHX@MMs+C!nph#Vsg05U}3b z;{D%3>LW)2upu?j1V5;3zAN(r$B}c161q0zAJ~?BJrPhBAst0oB*f6JzXZ^w;pKDM zxs#7)0v=(pcmG$48%pgl?Zt@ftN+CMA-+Tg5OH42M&R_;Flp#V{wP?odS1=Y%*K}Q4>{_Y@WLX zJ`jD!-Rgm;5V#U+tasoO5o?4`ASw#3#Hw2-5EljK1^z(34wQ-unS1L6p0E}g*Sx?$ zWaHw1Qb-O8L@j<(_a!3K2@a&05V)iMD>M)lY*u1z3k$@Bbd?D0Re|S3Haa!7>P5+3 zY67dFqKry5&!Yohh-{w3RZ%xf;sZ00o!Ac~Y)3=S!tbS%AS*eLrgdPo1p`t8Y0e0K zt4$9KM~rEJT0x4?IN(#0O0=WR0^L!OBGntL=ot7#B)FwZAWho92~Ox1NRNH<+sq!7 z5313t^3%J$1K%PRBEe#4;5BRByThyR0%I}NO(8Z3C##&t1g;bsCLA0eNMG{F3D22W zdE04HAkEjzKMkA`_>(B-{b_+^VzCpm10SQtsCj`aNQtd%A$TvWtl-!mDm7)hB(Nj0 zN(Klq&-nF$xu}>YFK~cx>ZzzGkZyY9!q?v#NH;z5+c!G`>83}1d%HNWKhnYn{4D}% zRuZ@h6#+4({P@%93xPC00_T)@C6MlXPN4f+P;4u^k$dH6tQ_-5_NM0hO;!G%pkookhNiqOq zG8APFA+06i9?&+;B*(2Nr-rR0n77a{G@xj|ARHLb!t~H@nJ|B5+d~qC3EU{iBCzix z=}R>WL;Fk4QsaT7fx^{uuw*~`|H2T-BKjogHB$IIdbDu0D-gavHdaEXEz6+(B*}BC z4RDzzNyhlnLkU4+d1^t@yrX0!EL|d*!8CD zx-wk`lB1IT?E6KhB#oHfUY?goF)jW-h|m@`fNc6M2r;4|465)MHEVVv58`h~UbChq zzbiR}Xlf#uFB~kFpn~v9fJP4_D_C8>e4Cb`8p+0rTC6OBq zg0?dvGXk-;Ce%tmP>=@ESu_920Qg4X+QJ#a@56$IA-(=MQnHRFL@ZfWuNMSG2X#jT zX+RlG#4KnrNyyihL7t?vMsN`nn}RfURIzLok4SUq$GnqPG!LqSwO*j;Us?b7wL?%R z%pi?ZeA6Y!nJ+Yw_5=MrA*Xwgff@kJ=@~Se_0jeF2B8)jzhU!*MyvcFy3kk#r2~TK zLSq?h9Ta4X+3Uz*X{Fkd0#(2ecr-HT1fo(29nN(ZBJcf^gJLm}H%H+4Mvl)2qMKLC zAY@JuU2-gg&kKU+l4BWEEDGv?X#yOXM#^GB!jzRk(=mfYGfWsP-X28vsg{8g1kr`Z zG8la@h%P*q!H^R{eAuP`C5SFOmVw>rAiD5a2C)}|=)z+e?7b317aq%C_{|`?@K^>L z%7f^_BQ=eOL2+1fgMw#4oiSQ5S3KW&-Ul4s1%1MDApX`t^u;8Wirx_PDM*6xEmXOE zfre`V7UXEuwz?$0cgcmq8zk6L47 zEXb@Nsc`gB*&73s-<>tCPF%w zDX3kf6f=f2V|tVJADTEoMU)hkNZ}idzpust2FFSXBjp?~#SD-u(Rdbxc{(X71o|h6 z#3-ewzlzD7L@8!aSqVn`J!?BauVl0z5E>AdtVf!38{MV8kPFrNsAbj^P{S$l}=-%w-MtSpn8?D zE@R1L>b>Z-+f?ao%=nt(j3l#XOC8C$5N$GLTVpzUF!0t(;r;#1XNYd{_ zLl07LRBkOCg*>xLz`nOeN{>Po!>)DItI+G{4Z^e1HVXf{7fBCeo;mh_bQTN1JcWGk za*&>44Ts&IHzUjq(fLe zdR>&F!Y(s5-8OnjN+Ygf*ndSzBd%gtdQI99&30>rl&1CB=5Gtdccf2|ZDk}e!z96? zd(sA#<1hRB=JD5;$5IcbTK88{3pN1ud?!80uD5=Z(wM3ke)%k&!mf?JNcW)4TW>9M zBX0s5*Z@7`OV1A#LmvkjZ#7YlvPeugNwwZp2q#B-%fd0?BuC)k^;f1|-SWIau6h?@%5{bU8BY22ARC=wU>+Q8IhdrBQxo zpzl!r5o66`&&l#hzq7%9^f;sJJH!Zq0!#q^#xiQ<_euTcvT`H_(NB>1!sQk+Croo- zEQoG|%pR6?kp1pXscaiGk?ivCkHM23li%&0CP87Aw5RPO|!yn?WWnNYlL+LA>h zWtjMt3qdPL3iC(F!fmKFPL7jdw(;?rrnQ9H2*z3PD*o`jY>g#l@xv4uCZPBR6gG|e zx++=QnHjRfjLNLpvXfN;lFgN&R(ie(qgo)No!ou$mB>z2iOP~ivR|sEm8d1M{fx?Q z%Va02L}m608EV<-n^uBX%f4q+wy%|8;^A*VhV`=3%n>;MB*P5$84wf{$zCyxep_Xj z&6MBBSZtS}!YGQcPFSv;3bGT--{$SZw&cz5e!mR0+(cPyhN~sAo2+7fJ|e^HPv#g+ zk5z#{^hsGUBk=ANp1^`LGRztiQ1+dfAw0f-HKxrl{*nxpfQCMs<~ZHtD=sO$xFMT> znf9eH6UmCnvTC4sD7%XpDRP9qN98;dj2F%+K5{GI3RaPB7{UICcg!4BOLE#Xb zkC=?734DSz$nnNRvUR(Vj-N)Crl5r|vSx6U9W@I+^7zFsI1>?)5v~t2u#lFMbdvj2&So8?!#$mFzS3Bf0*g0%o*tUyd7+4z`0*j2alol4b7v2 zQK8#6Sh`|%owibNh!3Wr+kJSd3wB1U+dnB-h;|4)n%512QiIPU4Y!97;y5%8)?q>% zDqjy_uWF4J!8F9V52IQK(-7xAWON9oA>Jr=)VUDc!d;D#S>ayO!voR}XW|%Pe zeN-@AF}n}l#|6_Bv-@ykYA{_fyAK!V2GbR@`>{iPdhDhuvtO;pNlse~uW8FM|j zYC+|jFORE3zZ=1*<$U*N!Q)8$?cg5}D&S??JPa1MTJF3P{MpK)glxSZ{27S`6m2}h z#(+{uv!Ty!^*Z?2zkfF5z6m~o@&2^jKCpQo{K1kw`;Xs(F#!_C*!VHnh;g)3?q!4? zWlSWdb)ns$t3^n%Go^B2V73>zViDp&OmFEy?pH1$w;97T?je}4T6hRM)7Zf?1Qk|S zB9NusL)=N``w($Mro=mhrV1*7zO_OI()xq@z9BS4Pzmhz51|_|OQ3iC5bEzrAiQCS zg#E6SB!upJEP;%WkO+(y`3I5lRptfJ~ z!ad?c=&Pklh{6zZ2}|IOF$5Kpa{}v=Lg*X6O33uokn30idFdgjNSqTGlofK95ol}* zxrQZBD=!2U^l}2{8i#CP1co*XIg2HrY!UL3xlO+|A()rRaUM6@hP-1K=Q@UzVtGvK z9D<5+d6VK9YFI8GQmWf(O{}K))`EKjac-p5#AZMhy(1 zfp7_FF(l+NLR0@h^MjyaAr=1vQug14++d_0jH)^*U)VM#1QRBcf5_$wUB-o6W2E9I zqU`cN&}=7#bU{08YF%Oiou6lhJjD$AIGf4@I>PqO2Ma?EV*(P6RhdNxR;&qWh>4^* z3{6@ifjwJ89^27QZDn!DNY=^u?hEliTNO)#BOF_-bs%J%3);rbSzw6UDIYfyJ7%bx zaHrkcAO`+C8}gQR%R=~RSsJpC(O!Hh1QmV?mQtA+X3^ykIpHCzVGVN~6ybXlD$i(Ps(FObMmYX9*0=2t_6PW~97LL1{e2d7vE?#~RR`L#-5*__a|URb2| zYeRj=`@Wez5WFF@!q%)n$(GQAXazD%*s(qIGE?BxU7<6W;%e>aN(E4*|9;OGP zg*WljlCa)V-o=Cb^v7r~sOKejwc?ZvHRYIzCjpfmkGxz<-VLeSP2^>O+>gZg$lV~V zj{G>rwQ(X823aCj)~(q${~#$ z6-x8MlcMDx1q!p{`zbq&qO&%>g{X(W3 zvnPe4Y|56uM7D_2P?#(KRj7j>8p|(YEwOu3Ich$E^ABku-zQM`)JlHh-&26Mt>tF~ zhj*@n{Fi?ZKsb$go#eIx%EoST%=QM3lHWtV5;I<=g*TDXY4ZBQ+Ic`fc^6yWIu{R? zU%-jP@nkS)s2q1%Qe-59*(2ocf|+g~CCAJ;nT39)7drnBz9zl=5Y^`~$K9i%R)pp4?6#<4LfsScH1HQ2w=QWZo~9 zzY^N>ugj`P=IjdjfvS<2w_5%}>|@r+&s3=)b>1K!jftnYxs+^?FT>2bb1TWea@UFN z5bq5;_Q+8$>Etkdj>`X@1X+DTJ_0j2z_H4W@*1$_l6(&uVMT`A7h0Cf-(aSrIEVNW znk47T)rPeo=Y`zMK^STd{~#ZP_Aq8d@l~UbKFS|+FATT7%2By|IL}$uVV>~4ZP;8( zfnumb*bium9IV(WtPXGQ@W?gnkOk`~0Nl-YQ#`{q37@5Sh5d%vuEJ?=E_&q!iUwf| zMcTIm!cJn{C1GHMg4x0{S(u~1eNU*F@$@kBU0bD@d!6vG6wJ&H=l;6rRdr~R7*@lU ztDGb!gwZpHo#Z~SIXx@|t$Yq!krj4|yA(KV3fnCxdU{^i4&g4haoBKIte*+n^vYF8 z`&8OFEZt5pm$2SpF??VGBl6A9OB@ikOL(5+pfFUmr!j>xE(a1wHOx8g@(g zY~#wXMM$4M^ly7$a(@gvhO|`<69zB$gyjq5`W_7HV9#50`H8R!JAQZVyt(36T`*UC zk4s?#+Hg%%1ii*<;VqN_KFc-|zH+^D74jA(r32_SbU_fgnm zMjRGeMSB=tM`4MvR#uoe)l*<56=>OULd?*ozT$~BDRpn^PP$4Im^}{^Ybsn1QanRy zU@D9bR;;oVI4I?c#fYll943V+I%1qOFT6&jD6%B8{hNAN+$I;AH&l@xaSAuGe{G%G zq(rTV$CBEuQJ~(w#z~Efu2hUV4x<>LFa?+iIa;naIiI7jC(^GqJxR|6ioUQuNio-o zI8|@v1=(qe5n_#9&rtNiDThD61>R;V49IHh4V0qJhMNxfHBs!wj5N%m`jM?R%C_WW zCq;F#w4MSp?xs-Vgz3~PEfo${f=Rz=qrjw`pjcBOrJVw`(}2tLwHH026obf$71WK))GEiqwFQb*q6e9^NO427fe}j-ix9?W z#&&_@OBJZeT!sLYT@XfD=EsX}AOK8Yz!sKepl>(scRJ9i>j%&UV&No z;DS-q^P&sNE>f()YM8?K!p==NKSYVl`@0JY*`b&wigt1rRJ$;#QBA&K_Onom8{w(Hix(T@%g>69 z2wVOKsxP$uQ?XM3Tu@)Rt}1vyS?)u|WgSStFOPf+oy=nL@fYc9ue1Yad*x)&Qr1;d zq9tbdyPTBI(UmI_^o89n%9$de=I+WLu_~*?JMW<^!?-qHkrc(B%;=-6MYp*t_g4kR zo2+n9+QUshrL-zK5@5*svosv?udlo+42PT=DATG&?ykGio;;nT2q#fe<+~~<-J38d zxFJ&-L~pJLQLaS0I9dVTq+P9Y8yFp~oGv=e+8X6?3}S~=U=TUeMDzy2An#zN(mcp(nxn)F z^315daLA@9>W645asY#;lkcZ2jW{Sj$>KCIbCG*S6j4nW>ET~+V)CR zxcPT#zF^TwIaPFZOS&jA!@qyxxpq^c#(^S=BT!Fw2IEH<0Ld9dRZjg|1c@D_#0&!~ zQQ+2Kv-cpWLzLp+tKCp#bu>Buu9V!1GDS3LIZF8q;omqpjQN=BHKOe#$eVTSSN1XWKft{NgwQDJ&tiq#+brK+}Jf*o485gn7M?4c+} z)zXtzJI^>$yOX)X$f_=?emK=~U>8{3Rh59OagN{zQ@X2+sBIOZaOfK18`#NURSj#Q zh7Jx_72mhe~22zoEK|AWYZYcTr7us1uBB(;4{9G!>oyT4oAMP0*5~* zt0swhm_JR0iN$$qIX_)>2%!g{g&&HK-!(_I09y?&=c;xi9?v{Ze@I=R8Ypr&L+~7| z7O7B4Ke@v&?NkLo=`z&>v0Zdot(uKfMk7M8LSfw+l_jD_W*QL@TC7(M7uE9MM-^f` z^f#=apH%TxV^d?ZYJkY*hpno4RnpC!ZK}Z{R_7h6?no@4diD@Int{bttgaoqRV7FR z7ON{@pQ@!O)a(PQSxBJ-<0(C;`hpl5R#vzxbU3csi3y{*`Mo})a)+PJsR$aO_GOB2 z^@=JD-Glp|f8+>TZmVvgYlS2Dl&ie0EEa(Bfoh|TFkXA{M0H$f<#6Ps>ZM>oaOkb7 zFKRC{Yp?!aRW;%CpQ;O3vOp2CMSf$f!#^M^UqKYk;kDsSweY)0CJLYkyX~Rk)vM%Z^dum>C@I$O@;ctjU{{az~gLkEiqv<(a;cmQQlH#L?mj%cfR`%s%Vk z0r|P%GE4`_Cko$0c-YT#`_6})Y3||-ZjHmI*;*`Q`L|Pv{)nG7ahcIYLR?#gW1~$% zA3*npwhAx(_w5ocv<|nlCR>kocZY}V!!?$|ZE&Y>1x8+q7Dd|j=weUKbO}ey{*bX_ z!@CMQUah)?_qO3|NI~!Lng3oihSe7~^$7=zB)qUy|L{eEM#!dt;Zv|2E)NR#M01!l zG<>zlA$COgG%SZM--V-&MsPi3j1H$GGG-*?LP7YLf6oCZ%n3|quUr}4oa_no^Mr?! z!;`C%K>r?Ipj#S#2r+>H6zqI;!NA>jh3~TxZtv_4Z{RH4dYlMfRsY{h$1E+Jv|m4_ z7FlK!LDNFMYwJl~M`qiS;zqB%NM>1Ur^4}rZAnhY@OrT5S$HpRL5^A$5%!qAq4IG+ z9~Yn$xR+@n=nK_(LtYXUu|||AH#uUZ6Vbov?g7?~B05wPelVm>1hExveLF`KiN$Vq ziTG3e!IS(5dne%s7Na6MJBY=;k614jlh25d*bAR_T@W$T1FJOuhH4)Y+oNl3n6V*( z?&xLpOKRJX3 z5#FSvQLrc6eGuVMOW-~Ep~yS@uL#t@hCu^`cCRC_?O0p=i>UCp`sx98!lwtN>SMJL z3jc?5b|<#U>Rsf6TXQ4H?^EBFSf{AhAwX#l*MRQ4uW%-gsp<*@fMKiJ1OwC5YcO@e z3X_EK zr_gc(K>4$p&H(Xh+_6)QnH}RW6}!}!sY19=tbQUs{?uNqCI`?SM|}fnKm%apL3N(U z;qD>z44fqo0M{ex<49W?0K-nG+ld^W{DQBB+NafJXj7PbPTfW1V1EH)iL6-+fS8MF zRDw)icxzdu9{jIj^Z@s+svDzKaP5Y=kEnvCxA7{Nd`G=cglTe5JpgM_J@2c}h%hOS z)R-88>vZsAHR`|$FZ|mRbrX?8>*s1rY{+4zy-+_!H_+;@D>;mKtH#8XoWoD=)aTHa zQRg>xhNy>OAMtuv|4EISk>&za`%_K#q;Ow7;S1hb?*6Jihpr2mrN$(3m}ad(JIlQ` zn$zgIkUD7ki5z}()F8Xx0dU7j^AgRWubYN$9^rLi;i)0mCLUW|^BcM@%DgqBMGl>6 zX)w!t+^iP+Xr7_<5ae6Q0sQeCUe~JvhmipqbKATo(V%^lom6uYt%m_2mF?n^T!Z%2 z777jCPfk^7%tP{cHQpwNX*Bmlv+5bG!ECDFW;HTKV~)(`YBh*~tuUN88m~dUIF55j zGiYdr7v4%=B;c*HVUngR+A=~@HKRlhd($ku%^7s>RXty`U*vFqfMzLH5A_FWP|KpcF4hdu zoDeyT9Ijc3 zq~1~ua-MVmv|Xk-f^MKkS5!8RC95@vsa5E|MuS>D;?Zg{J;x5g75hilCX3)QHUP9NGy+`v|G>R4nG?=|(T(PqcYUYV-A`WX1 z%Yp)%rbjA$M#gb`Us`ZNgE?;~ayX@FEQ+%CjAklUu}{uw9-*5_ekmQ02sZNRq6R&K z8d9b~t=;n~{dz^yT;$OGIzB32ctdj-t%tvERn|qvyZ9(!*GSWc znkyoVwgMlI^?Fi;c?3{I4*vHr>qF(N ziR(vf48Q>cA?K5(SoB0S|HAr35v%1F&5t5hb<0SkR}--sSw$`ou|C;GBHLo)^;Bt9 zQMf%Uc8;Wba@fk-`p2%3$nJ;BWKV|46I@`dM`S78H$|5@p<|6m)Ydd^4l6D_a)ak} zBFACm>whTH1h48xMqzveD@fI{806)4rth@{6;IlGJ5 zk-ysrw>KI`ZgNI=5>C{yAYl-U>=d~}G$lj#$Ql^qG=sUp)1HxmnDuEc?Ck!L60|AN z!p`JLYkx0NFfh^!`VEZiV<)KS<%q~r7Q*exk&(&n2yOAU(&F+X4_G@hav;)Jb4j1# z1Xp5OnA91Z7e>}XYMR4%!Tuj2&kCIhEM6LUOB8VO@<>a}er~G&T(azVf)mtSA6XY! z2^?V>7=NTTAn1GemdH<{&5r*$@`%W{*{;ZX$d=lQ_+3xv4BhueZWVE*2T?W)RN~2K zO`WSqmqhk$TzL;*d>ZD#F)k^dPGSP8UqS5LUh(=21RxBMz|oZr`NNi+=**Z;wUgoM=>D`1}Myy z+Cty4r6?*6Q*9T1iE2&$xS!Y=ZXJ$VA{zCS6F8%$U=uJ#&3%yb$3#c65M7JTIr$_T~IO5$+Kmj(3PNY0G+C0WOtBv+@MYI#H|B}8*23V67 zjYhN;w=o6y0HnojjCEm7gAH83^m3NGez-;d}Khl=y7bdxo z>4iyhVtN&4Lu7-a-&8>f9B&YK4UH}pHT>JiN)gVDt`uQxLG*c1gbw4PZ7_%TDn)>o z)1#Xs$F?RyYtEKX1iB@EYtmX6Ju7;5ovJ7R4irVBUXxcT51|Nd#B?p%iYb4KugCC-0n2nca1YfekGQ}2Veu@@%SAqSX(Y1w+WM@Ro713U6 z+s53+hPs3jtw!pok{w~Db4(`MCe1KQz``x&rlX+Dm$hSRiVoM?FJ`#d1FmQgb44_c zNs1WrJV(Qbn9<@7evXP6jv0suYD-F~Mn>vloJf9lj2+3Y9qS3V4KaVB=TNqq0f7R( z5pAQtDdtCU*c9A6=BH|cycHc|_PGeRJNn055JfK>9P?W2YYK5;}D*AH#yEWib_^e2Nt@4@F4=w#1Ba7C7(R7js<{YI#Y_ zS5c@fM`C^wE41cZ;DbTCB{07x1CaA%^^;ktg1*^BiVtJw~DPt z&ecdB3@@9;_P~s`%H5Wa31qT8Mncc1~;yecV=!PJ0@3u32=wcKY>>?YA~2d|5r zC4PGP$Jh$=b5>kRF_EnOv34+ZOKcr<=R;xYQ_qny6iT+m&UZo%14zMxlsz!(VC+(~ zJQTqjjvS4BA~vFZ$7An{o!XYuvCqZZpGsqUqlfL^*`&^fpnI{q1TgaY5lsX7@3V6A zHbSv#z#oreX?9c|D-}MCeS~fp{xwsF!JuDb&5N6@ev8Gd;B#UWt2*@iJ=R+^vck`? zzhf(9r%PN*tYs~5iE|@;9h1Dk#WHSAZ$$Gr_E$2no< zI9PST)}?XT=(!16*bQc_jXNwFVaE+|f#T3=(U!Q$=+>-zlIjdw_Qh?-8V;cdp5(-d zICtWfrFLV}x8`+@_ebN*)3nEq#~l!@bpENhd2ZOY*!y0b6A80Sn+Vaj;v&SxJ-R#& zv(JE63I#kZ)_&@PxC-p}7q-5RyN6aGE$j$&Kg4YpRaE{lt_a_=c@fITHOo}twOZAbG5WDFdZ;kfB>Dpwj0KU(!%`UF&*PcwNfv# zR-;NK5U<^Uu_tzfdPP))8@Z~tZ%4X!?cqU2r>b2Fn`#|MhF;qafoU;7n@391w6^5? zCz;hD#h|sth-rqw>>H$IhY2TBB^lZrdg%VG%AHIyX*c7n zAl;8?YeCU^ZIT01oEN#iPD|IYdTV_ke4Dl!nw`G2wmMwisU41H#}OWbc9N|?Z1;XM zu`LI*4rpR`I%~bj@U_}-n0Q2+ie|@PpxZI+Y=Ip(ozb4g^q0IU`}Lw-O6P`+d`O?Y zaY3;9qSo4(3l013YB4EB$v4<1hvJ-}`hD#Wq5&U#q@5w&wt1@kYBuYaTCHfnd*5oS zi?`(;DnFHf)>?_58oy}ginos}2DApmKdz!C*`tmB3nA~=P+eEZ*2Q19wDE#5jb`yX zkTMdA<_iVQ>+pB+LDlGW)t~W4sJDQwR=VEoddWejp(Y2_Ty+C!Rl{fxT{0yE z$Gmlo+5bL1x+3=d2|rz3dwP8&(e*-_*B6`#jU%cs-Ckr_SfS8GV|sX6UE^T7N{8A> zO$(1DLn3rYM=+M`QR`0PBpyp5B6XOkl5vKDD4n$(t>uY29Zl5v0XC)ULYWoZ%+fu# zpw|;Qy0h%MAXoR5UmNK(?E5oKb*{|I-)gRV&pyAAU$3E}gPIrV|LptjA zvg?!1x^wIb-E>-JZ6A8+`ZJtv{d6wuI%I&(nSDQDkWR<0M~CRfv(HmU=V9C?qVc)__Wi~01s?Y&3p}n))!k$~ex9!T%zn3dmX7yEljrI_vj4{}&^=<; zZVPqC*;T$+CuKOzm+3~b>*bX?J9fRjMtFXQ4Z1M)|D2z67VP))HVJwevqhK6K0mo# z=gvOAu|s#8J-2w5?g_&0!G--gjfdGs)G5`Cq@ZNcMcqh*QBbT=aO9FMmkkoUZs;(f zDaUAWQy0{b3N`Ml?lN-;&298Am`h1=(@$alpZC&lU@m1?9sN+Ij5mJzbXHlh4fW00 z|MMmK2bdn4J)G1i)74-dKW3t9+h_d{vLaGX_sM*4sSYdT`nlA|Nw`LT9HCE!1{Ccf zm5EG@)ni_6Zf2x`FLC;41{9^&??aMdqG{lQLBE*+6(s5BR|)8CvOa+Usnhi(RRX%0 zp+Crg=H}=ZR|%+EuArmD#`*(Q0=nBo|CKSi+d_|N1>b5dua$l-HEr^!o&J|9F`Cvv zpNN_6`9`Ivv!1yyGPj%lP?e}Cdgxs+BP=tOn&itJeQgroU+)E>{q?h$qS+XSTpy^X zYtObiANVpzf1iDPZ&;OD4S$k7B#zL#Q#5EgN*~Dl&!}u*i-l`D}+pBL> zNto=@H)n+3lvJsKS`&&|oogC04(nCUl)}aHD#8u{)PTD8&M2=rEkONU-(tO z1v9dsma?98emwfRI>kREyt(T8-kg>m+VK*sV%C&M7d&ey|Gja{WR4DoC{ z+1=L=#r|K}z%T>Bn$W>%H*!EpWB=WMI(Wh}nZbqe=0Z4!8Bhne7$#86qin!7KEhDO z)Z1KRIEy6sw`mHX(-a4t$@a9xbxD5jn!&{DeYq8_bJ-XOJ=omu`MThR=3~F{=HqkH)(;65ote9q? zQJ_E-{+ekxjv3i;f)uGfCrGaRU~nNXhNcaJKjs>C(CUD#3k0f<7aDdUsdj|0<%VIb zZn+2Dx6-hl{nL4k0TVh<#vKU-w1;_X4JpV<5fF&%M?(#={Ar3CS+PVFNDi+zoJBa9 zN=6J1GYLZh#3lCO8x0LTscx3-H(;hfDNq4?JYXnCs-*y?95Q^*D#z`p0h4x#6Xk!0w^}mHL?z7=6Vs zkP&!y4POOWHw>t}&z!);+kz*~EXNz+x_bsx(q~RU_E6Bkvd4H0e5x>@(nN9s)1Mi} zvMO+SiPu2)R|Yy6`H2L)HH^dBQS*0(a)uH8!LSyKG3z%2DioWQjn#+{77%-+WB|6T*ozmM@F z+6nmdr^M;AR}VyY1iJg0IE59>lqh(i&HnM1(JSxC9|CDh? zRB~os@kb{ZFE9dylZ^21g(n>2%VZ-eGczwdbDHrcBhYiE5wkmkV_cbK#1DnL%%x)p zN+5lKaX(fA#f4R{KjR|WcqoC`CB{Qo0-Kf^@1YxjZiVp^BM`mXcoa)u^BN;6B{Q$T z%~Y&8F)aSh`i{fBWB)(pG-Nh3^(k@poHBJJFefRB)LRtZzs^MA2P_z5dR zfL#JAv+`K?gaV)&ybG9LTIrU62-AULG-mGPidO<=nt>Ld4Eh=g`x%8VJ_#6?#4){1WaS%7z64hG{7tt(ZW4JAxW6b6u}pZ&ITvAVVVX*A{5CU3gih_C>%5k zPw+)@>O>T-3C`qcoyg8GB03=v1Ji$ILSvThIGIe*C458}s2QLpq4|=>z*0lPNX*7? z4#Scr15KLz6J~=XLja1fhFS?sY?RO#b;g;ggXKvEnkU%}GbH7LP=paMtV6<3Tgv54 zw}f4c)B0WscI^Kx{S&UDQX|hyXaY_J30fNpl0Gp(f-yi!uL+?bu3$SkAqFwf0*Wve zshAGm5+M_s2)Q$O%t`RGqtd7rCfu-~SLYuR@-a!aXtU}>+IT0pku@_TJHdpagjlo@ z_AaI{JQ;HF&V(?ejvE7oc|iR>nD!>TV4`XFCj?^l<#BsjdYrcNRA?wCzG(@LM1Cyc z8M3JYg*wTsu417TP|^CSPow5*OB{T&&L~c12VRVVw@VzMoj1)vd>qD%dOqae`CN z5{(#h<$WK^Rtz*-@e7PfxF8gPg&%PGk3X~sQZMHCVrwRCX1(t-pQJkMYF9UDFrzxkKj{klzj1>kHz#`S5|%`dmVJW3%A}yW z^tvoN=?*JD%9QjGs~74BlAMJ+#OkI=j}a!zwM$XxZs$+n)h3A#bS&E?MPszwp){!_ z$;hF3Y?t2r@&Ym*sXqEhuF*MmE-^9c+yiw6yke5)of|;wLg~eRK zA$5}3J}D^lO%7&bn8WpxAG5alV^FdM+98{h`#KVuBRCZLE0S@!8bzwrfaV*X!5Zqu z_~anw{4N=ir!cl{l9L~^&l4LZFJYhGYnnWheV*Scc{Qe^=XGD1!1r55U_vtjFNRKC zl82(F4E}-YOePIV_9siTV-}IRgOclz6$MuAq}}0UDVZ}Mxr|ECbzt&tHe~VtE_pBQ zPvF+Xj zIUvmu>_WPnOLiyuFX=0Ux-N^EMeL9MP2#^O`8G$A-!tLzE+)@%rq}y-lb%w=*-ZyAmz{GZP>v&ONsjp%N}UfZ8fW`D-J2VAKC1fLQl(l zf`Kk6yrFk+OEI$l*Lb9qu!i5IdP)Fu?9+TwBuws~gHj$aIY~&0GrO)0OZmVW;bL{l z4^@%(?Qatjq{K5pe(RL-oC$EhTS{Ge z-PJ4Q6O-d}epTf7XOiFI5h-KP;~AbnmnQj@z>6^{^XPtcD43Tbcc9lTOH$N$Rf~Cf zr^A=EDR)@3a(iP6dicjl@+`j}e@c1H)cs_0N*=cEgrvbN#V>p3$|z{MGbN--`U8r! zl2!8d{VDsI^UXh%vYh?zb}q#c%MxfxUR3@o?y*@uU!eJXW08}lje#cO?Stjc@&J3o z_s`lZ&!s-EWCjfYhMsr3;9 zNFKs|OW502lDbVs>Gnl!Tgrbic}lsV*S^q-lb#9U7J%im~fsGC|DckN=lZ- z6+p(()H1}(KQY62!JgBp><|GAJ(sFsUE}7CmTIprqYA#bU67Ybw4H*5?4c?do-0TP0)Lu z^#@;V(=hAPW*~R)v`^d3?BT3Knh!#c+}H`tt~(FDdZ%^9cK|Fu@2|Dfwlnpt^Gmye zp6+JJc?nIK8q%!*Lt~W2MW^YRxgF7`d1DNiFB8pvYqTM=lhUk7 zn!mOaTu({cjtPZw?bBP+G!JR^;BQI`rIT&K*-ZP~H1TZatX^sS;HX32wAl#3&G;@5 z(JyTj#_cc!pbTFyPZIoH+H|JE;?Zf#*tOZXwC~yV&cw7I*md}nwE67nG9zsgyB5w) zqe&n>LFW9lnXIMXEKGamPOrx|q-|wYFm!8L8M|f{r!8UErw7vxvun|bv_0(F?R?r= zc6Gj*X3wHx@4Eud+IwlIs*B2k3BRS?X12kX)Q9|;7EXuF!aoNr(_=AaY}T3|No`Ee zb}u@T<{3MEYuKPZ`RwmsC&mYgN3;2SBA;E-*ZuoSgbS|eX_(aqT5ckgdZdq`swXBd z%(Dr{DDqA(#n=untg4khfDu?*I~{XmpJRNklb(szz@L5sfqwPVSO0tM3mQ1tApI&@ z15EuY!Zh^uuTkL`I~~W*RuDNyi+l z-~=R5>3>jPLK?)SW41tXj0xKGVzd_D>C^cx@uLapJF(RmmXv-1O`s?>{b#1d_8IA0 zump}}rmx0ydent=+toOI9;*TFXg4%Ve}%CJ22|KM-JWcoZEFj0Ez?J_4q{K6^jTCi zNN=Csmkm{ScS)BZhIHWGE4`JOBKffoUB2fT4mS5qKY<}j;rahue0BU3=3}n*q!)^1!5764UXRZhmbkW=r6qnNfzzm5f`KBc2R7O0! zxt6|Djc`9^gkaL|wVf@ran0z(TK+E{8K0Ogj#SThU4veK4#{ZF8kUnP;{f~oba=)F zb{(Y7IK-}+$cz#;FZ#Py_ym}M_Bf}kjpydp!4O<&cwt7buT zQ#0xkledK%IWT`%OE@+qV-M3y!8AdSBW7giYSRDrtj%c3^yvPRAZKZjAm{MSf}F8i zGxjk(eB32`55>aw?e_@Z*WZ`%n(4vgXa*`Z3X=k;!F;Kb3gGLL44Tc9PMN(B#Ow7c z!x3G0SX&sdSk|0{}r%*)6A`wlxY1HnH&C>L}#_koMJ_BYqiZ>`@g{jY7I1t z=~eQ&Vwnj2*Up)%DK1p&o{4!W`+oxtAUTbP+d_Ho%-;XQ_P|l?NxNY^8bNly z%$H0deEez}n7PK5O7UTMChc#oLiU(UM>~3L`F&;tyUv=CnL;fRuFlD%{mE4*T$owg zfnK+-%zS~F9smDW6!;fq_H`Et5!+3)@y<@kr0-g^wRDHl{h1vYS=FKcl|c~tIGK>h zamwLLdf4$Qj6aq+AJfVGe}rMqr#dOWlIdUgzNaUYpU!+q)hg)v{)NoFOqOMrGcj2{ z{@c@lK5a6y#2z;?X~w~;5PvK4B)i(($vli1KmL~oc@paQ4q5CWbK={ll8|a6=?eVg zOyqkYyh(ON<^@C(@BcAV0R>Moi!j@HSYbj7+macI;r7J$U8Xw}{hB!+(LDgg+Ckrx zm0H)GXuf35#EH_fu-M8TntjbgrR=2?T9U1WMh7~1p0%95B8ic?Yn?T@Dr7#}WF=w3 z14f2RbI~D-zN43ODR;`kOmr2iIsp|#Jv$HgqKA6g)1d3=yBbwB@9$?cvD?m^$#J0?8N?)}@ zYaLd#$;z}BZohWP0{TiE_UVakSt*Xft>3_``t+^#?9;r0tWxo};Zw3Ec?zGNUXry= z1RJ*^>$^bVQSaw`a0G+X|olc`0j3b>X(~Nmgq&;nwO?mfD__T%jb>EwVc~ z2%jFPmYpX)qMmPdh4|oFl57KJy@+Xpw6}ij16Lxm_t^=mU9Zc=nyd)cJt4b96h1m7 z+pIo|wCu5>@bj{>e-v)lXEn~|RpAb8TV?kYzrWr-`z+E@l|=Eoy*qjH@Qp8-y{eym z;l4JWkk=!7bTuv=nbJL*CcC!J@&S)Q*;kQG^FJQq0+WYj=hOEhF?(Cy|OKok{S{Q66NIqKN}B**x|n!;WQlMhKmICA&4* zS}VsE`W?@X!nhnWf*)D?(y}cXaw(fG{Om(7{1A#a5C)vezGX$qBO1}&d*`wb;shHA zhtFrXMGR_ya-gq6d%Cu<7g>0fHmDWXv&}=yTiK|@X%z80NWYss-kD1UcV1-gw-9cN zUS(gi5pEZ}&mLtd+&1_9>_I)cn$Ys5K2jOj}+&uk_IW8olPEI>SE#B*xlSfRF9P{f=>*joq z2_(!gm>1K~vi*rEHpg3tx_1TTw6ZN6L49!ju$=ukkvN_&ELY@sV?4B(kuMC6$T?-n z$(SN@FfRi622~W5V~J`03<0Qm?g%_|@i~jCq?Coa98W9{R!zRp%a~Ikkm;0^v#LsD zjwR=yvZj7hYm4-pi2|9|nK_tk7~h~`vU4_L0?$eWOQ#ZIsF7|U{yDTkCHbdo$=&pv zbfWLwuo|&%niC8Ao8%0#;Pzx}mNOlxI>>LHqelc>K#5+oCcS@l@Pd=waw3sUV>tZS zE9X}$CIxKjm-E7o-)$V4^F+8?JtpTUml1}Ir5==jn>{{fvvB7;G3N)Ql3SC5kvX2E z+Q~LP&|zARLKJ1)tQ<^M8$z`qB8KeAafZHgb3hPEQ<$?~5XE{~&OYJp!dkOX>(-lv z8u??+Qlu!Y$fo!lcR`fWEjbhHgj(qVIf2NwDIfwB<+vb))q$M-g32!(%z0@ zPP!mZ*qNLJd;Zz@vYh^y?Wa_It)Sq3&NGqwm}fb^p}A9>dQksW&H;h@+qXI23p^)( z%6Y(hgPkKy(E{P)_NI+CocdT7({oJN!fGBq`I%NZ3TodPOc8O7u{BgEO&>W!coSv1 zFWf!Qnd%8-y;4mFg<5!&X4)d$t<5m467HI3nbrw+t~sV?XD(EW)~21pXMSx>zX|#X z>}c{9kh8j*N(Ifx^G$Ao8gd4kGBJaE-T=z;E36@8w5iaB>w-KPZKCmE&m14Ho?yb7 zcWc67RZ~rt0@L2y_f!Brc=00-51L9WUI=5X z7bPalo3_kjEEiHRpn@*ZA2xkJn8e#-rq1xg5fiEz)54fMB^IwuL>N0AJ7LPRqk5=$ z-gFy5o&qh0W2L4mm=KB=?sds@%#spNUNK?P;c$#CS51BxN5Ts)yJ0%d2=u*e`WdT% zw|7i#i0G7X0v8^b=!TpZWc6bcDpMV+0?89o7qkXiKR3}$AuovED-$BC5l|s;iJKYm zo$rKa5Bp%k%&Bt)zW>d16fOMZ&!%D~yx}iAf&E`hyAUxgtNyX1-_4Hp5NexCkAuD7 z>kOo+O)i}*v_qhZgj@P@8*&UHj>Bci7Tv!{po%*7UkOJr^}y$T`nGd(Rhq8s$EI`EF}x=-OJS_ET+h=b?40A5>8X9J=Jrp%M!#U|V|R4nVfFc7(&7 z?VU?Eg1>;2ez~Z9DO}F-3-`Rq;knIg!>z%&j@8WU{v4Bg8O@Gi!qD-#BN)4P6LW`P z)RsFhcO+@|zOg3+P0y8?)kYT1GFR{X*}2mZ6oCTUG7E2Bn7bI`Y3Mj%6j`}6_mssA zVaOA2Tg{g2UY3iUTLL;O>x}UPZ;$6ThvbzgH4s*qv|5vknKa^M^LOTolSUiX=DxS4 z+E}_V_ZXtvH>3H2Ls2ejcH|qvkl6%aCvhX;bix>_9RVt=^F%3)t^(p3?Zjdc!axw2dWl#3Y*ag36(+!;t4m;^T01PzS2k-PHW+W_R=%-x87_Rn{6>3c+P zkc0PfF>|-50xsAbEAb_$R__hcTzGv&yCz|rjU}6)_J8!GQ^BGo{T=2iPJ0@>FBk=!Nd+YG1j{bf4yTp|!kOY$8 z4v~%FP+UVcyV+!2lWg2waJK>(in~M63>3El#Zt6TDDGY;UU)xq&g}Bg-}OGd7@j|J z&Fp06p8F$X=giDm(CO>o-X&%?X+U6~pr7E3D}g<4Me) z*FT177D-(1@qics%Ul68SA=K9#GG{$T*7IJm|s|yeE?!Ym7Xyk_?9;2F+n<=7W0t- z2@7&!PB=mNQ{^!gEbmA-CdWs{xZ>BtV>+-*s|f-Igb>mhaD$iENZ7W+4PM>G$2i&o zy8%;UnC@6vG$*DIPyQi><%2*N3d3nDo)hl3Am$7Z#0{3kxRbE`>KH$mgyRA0V;-@* zEr+o&3H*r9K;EX^m-nj3X7^d4pEV{w1FUBxkAa6lHfZ&7{v1+k{)vdlrM9%f&9?>q5~c(k^F2iB^)Za~Ga?)_0=c7=1AwMgMsn_A`=>3^*qtF zMj|6t4ixnPjqtksX{acV3}!*GqEfaQ)#plF@L;j%1cQ0W)Rc$8z;y;OS1*O=C^2Jg zl?d+4xP-5%MS~f8tIq`e(eA()CF+_cVmVpFkyICp9PzU>5u7Z#gtS>Ame&^+AOt{l zxc$F}vqke620^O0ks?3rSSUJAI9)FhtzkAK03iV4ISnZj6|gjPA|8$&DIzONEFU8} zOzhWW9B;?J#*0pq-$y2jc9L+^WYHql=7={Skq3F6#ErcUNJPh8>$xHY%j}M`3t;+T z+XW)Htl|=$zF4%Aw8iKZqIx7uUL#6mXp2~|S=8Hw62)Aj6@ z8WGg!&NslL_m~=B@H^31eg(GqzUTpvLi3-9SXN*FG!?r&6HR0B60WfJg$T}aT|%C( zL@WoSIE>M+Ma2vRFch9ZTKcZPMTZ$$I#+kEPoj$~{S6e(#`~;e^(@ZE6+U7c3x`85 zq3-2MZ+yZvb|i7Km^!hKiIa8ni-k*EE@7+2u_uWWo^BGG=>Xw(?PBY~|5wODVp(<| zK*c8H|Mzvr>aMZGnfam5^j++H2K^%oT2u|?dy#+D<3b_6Yl>Acs@#MK3d)?8ZI6DR9OZ?5(qf%pX~HWZDkrub zS*Iiw@fMJj$2KS9Z}|w`0)s}y7L(sc#>YyDI1Q)7HfAxwqUW&+^y9BXy*MJ|oEL&~ zUW4(Fxv{gs!aSLe%VHO?SmX~Yei)!!6bNVw0$vA+;+Qg4qvOia^aU#uOG zCFoG>W|HsLiP+67PlUMfVvqZs+BoQJ><||7SP&2(SmXNVW8afrg*#=~=3=Z1u}p`n zv2X<&w2fP_g@pC?d$E{d#KfQ^D@Nsx#fq1)aiBV$+0pm0XWf9wd}s0BWCcFAo)|7} zxP%4%;$K-V>;f%Fv8=7QLV)thJo+M${S6N!Y%gBS(7T!U2YF3jILDTp5O~G_qUk4= z;Pt`c-Yoheg%K3CMZ0UnK4`X3%yQ-#P|EnR?n0QjoefeS^l(RYI*M85n}9V6S9B6P zF^V$^ck3$NQH$r`+(XQ=vzg-{`%cXC(JHQR!`|X7%3(=g@uaUeD+U-P%b>?qpEp>X zN2`Ay%~Cz$V7kPJ0~uzJq;Mj7A`{!<5AoszmcE7{U_j_>C0NY2<8CT3oVXyKgDO)i?`ZPeDpR;?1?f5iGA_RLh)aWoZ}E+`s1JyaSgA2>|QQD zDB!u?sSsDw=(=HIB}*qxXro=OH=OYNF=F^`C9fM4Ki16s$po?Rn}87j+u@zl#MM?j z>k%`>nN9Hs}ga6mgo+;u>g#+LfwN2kT71nA>XIQ4w| ztT@q{kM_JMF0taHHJ8Q5X>{E+G0XgAdiO>3r)Mm1LJkv3>!N2K5_f#^rdTKtRAZ~# z;$U0BeO|JsRslh1?L#s1@o{ph4+YstJn^VU;^!<4a-)YN0Xe%#15n^org4n}=>M0v z4ua1@dg172;`LUj#;wF1)qW{vd8I)BF#rx`@gOK%%#X5hAH;8Lc_L~n$qWVu0<7V9 zg|(y?%P7kgUSKD=T#M&0(NVI5mBS4uiJF(AQd6yE62&MA8KLZI!gcb&Dkf8RSL;1_R7p8(#Q_!z8|TJhM9EByez?qrPdHMGxbr zOTudNsWpowjj0sYDk{5zPV&E;qJ>86HfTUV!{sBB=f1c@86bea^dx`_J!m;Z{roy|B~=+ z2rqpt*+sKv|1GIU^)yEiH!JlHF)6=TDfp+JB| z0BesM)Qy`;)wa7q+)>JTR-?EqDv584xFIyPxJ}$#mWNEd#p_C((X~Ty-c|mRM(9y* zsQYoJxJT3npY(`p&+>Zx|Aoc?eV_X9ogs0~R5&Y1Tn`$R$m3vU&pCN(W!zgW9}U|V z$Fz;!!T^JCo7ml%s>ct#O&{ou6OP2$+VduFdDLw3#Wiv9R0ih@ar>xM{`fs^4~?$B z7Po<#E#pqyekx>-dvWXdX!V1*rW7FjS=?^=f15w!;A}gmkKbR!O`{pg-o^bzVMlz5 z`^ac_ZW?!AaEeSVyQ2tqDa*aEmPs?}c1Y@mdwWP9vgmo3w=@=2dr29#7-J~A*BU*2 zBy~qIzS3%jky6k^nvK`^Nnh9EVK)0qlUQMP2S_ulc$hAYrQd%&3Jn$=} zw2c*Nbw%opBec?JmQe%*=E2Z5FR3#QF-XgJhs1l6q}O?uz?;&fFg+rT1{Y>XVS2)Y-$O6KHDq7^#LvS5AEcJ}9N0`11GC+PtjQRni~^Q89=L zA5q|_@0cqMv*DwH#TMBPFR{orW0`a#%_~_geM`-+-eQq>`!?xVnjvPFbT!K}I&(J` zfEFH-t`*$nFMXT&vy@>O_4#=Ox+aKs!!r&`S)QOx|8;Rwv_Tv{7`6RH%J3Z8T)sW} zctYxmqHCmd;chr4{Q)k2T9gJ0_ft}aV*_6zV?gf5;7=A5?0-QDGl~zjGQDxvD^ivV z?=4seuQ`Ts| zF1Igw6Cc0$>p}j*zg4^7ZZN$l(|{Z@bIo@-8RG3wp)6kd^~hKtk1u3U8G>^C(ZrVU znsnE#cz5KYif3prXrCdzkbk7qOC3MaTCg3R*2nK=5CectvxouZDsj)`c)0I>J9?AC zWE>7-Kw5kgma!2EYtd#?tPqFi#7DBs2RVc{FHH4ugS>c+4amX05uIKf4*11 z65bviZ$-lU6XSJu5DuCVzkvLHJUhM_6vAE$;}r}Sun-gWLOWK*`&6%qcXp(;qYG=| zJ&`&kp5A~pcx!wUM)UCCRcqqyu>H1p2k;T>u`@n~P&e$4??u8jN8)`*c<5w&XA)YS zi-+q;x8vjI+K~TkM^Lh4u;0zKzWT$ApQL%@~)2S>NO=w_Ow7ehV8J zf>T;`@H+Q|`IM5}D}m)`3CZk|T42^JO=xD# zd&HfU31_HizpS@-#MBK5FDUaFTP>*xIAJIiZRaja>hL`Yo;2_B0|}j2wsR0Eakt|M zOebl~DjI@dX9L+t%6Vl&nGH?d z-CWj)rao^i8^N+O&rG4SWoqSv+I5ueMWO>u`=anpGM4$s=ZWLB8F00AtIo1>3?rud zkFqW}y^D-#3oj{*ps+1EU{Jvxz3wuWr5>Q9^0URCd&u^?p~_Ls+|dsr8Ow4QuyXOW zSQ*pvbFOe^oUFJO&*6Q%Yz&)9b8&N-jOAc5;y8pWWw1w&)5A8kY&a{2s~Xu&mM)d! zkf@gpqa5BEWg}QQG)$8H#AVBzEF0Sxr zc@4WBUaplrV$cHy4)3W78f;>(e!ym#KeIc!R?9|F4okPmMzfmX)OH!uWmKFQ#_W`- zDTg<^Wy4rG`0tgyU@!yUB&zqzQYeRzL$Z;q9ArPs{$S?t#}S#Fau{%oeddsTTqa=l zhkB=EQz(aRzq0$}ozt?8%p86`CtFQ94Es$soz)iGFUnXpK;q!bvKzGeCRf=iQ69P^{vGK927bUjyiL8hc z>-~bg?WVqzHDdOc(ATn+)L@U^${0??@Lhoad)Y~54%0r$MpF(g1ajtnR8&jOw0D~8 zM?2fdr&11o*vXlPb!P{81hXFQIaxT2b7kj%Ysk8bbs~nyOb5XnQa`)G%bmJOEsF+AJH&qeZFD^4t294BYGpcWwFaBhOU7p3xx z$|B5cjYXIQokbWIgGHEwMtJ}&_A*7@h(Q7b)%QeGGURsXW%@v0yf#bz#(@`Tdx@Os zq^t!IN0!TB*A-`hpNGleSQi()HA)^xqu%4?{xsTivivrUc9<@|K%5 zqS3W7nKg2?Q`n*H9~?Zl_EglO4OeERXvZR?1r-CToZ7y`Spj>MIol6)Rlu%zF1pD> z@rX8rAL}TN(F_ynDOj8v4wx$z@!U~VfP(g=w;C$aS^OHnwD@)tMHS^Yv4!F%$}g^! z!rYhEZKHrKkDM&yf)pN9*tKB_cqPZBj*3t;Wp(MQnRnfAtImoB`~ZRF-4(Vhq7Y~1 zux@;(*h0lw(MNHVQXJM#Q9@;K8Ei?tJ4CS-zM?_Kj-^V4JBtj^;dMh!{&(Eb#~B$u zI9jiGLtRUeq+oh_`URpb-j$+|+w&Y-`ZA$p2?WJ2dIE!VV|=q^Qp<3Sh0V;Z?GBpW+5fkHK*| zvP|ZUeSTIj?F9IY03Vh*N$EU3p?JVZCl0GlD~2-Y1Ot<2eEq2TMRO>$bG(%GdC7jXb^4d6Aid8#Xilw?*iiNu=&(OTS9?C&1`q?;G*&Ag9D4ozx%>q2} zhq_8QkHgLI`_xy`Q7N>6@(_b}A%cwodl~IKnl@IN)%3iH680N&QtobMkusx&MarhF zEK)vgZBfoIZIvQsDFbKSwa0Tqm9U$D6VN8YEa3A9Wi_jSh`^$wkwf8pLIvHb2Dp8R(w*w8 zs?4mj;pNJktepTNun1sb#+_`i#h->L;qC;^POU z2Qn)f>aCAAPE*o1!}z-ymR_KFm9jg_+?q3B^&=xUr-C?7L3*8CE`~ z_Kw5m8Ul7Ina;6WU=hIDV&6Rg!y9$!K4mzwJ}PG4t%tn!HuJ$Hhm~;VhwGMXkDJ;4 zd0bh;q6ALY1RM81t*oF#FP&BPVw=;mC~Z;2pGr@>>ymOOUCm9tqwLP& z$re})uymgVKK)49n1v%J@PZFYUkvod(B8xEjna!%dlogqpiH#IU7*EvYgu~6tKjf6*X#Y+L`5e=ctTebbFm822HQ}nd=v;VdA(E%S(?aauzmeh3+N&-D_UiK zG|?wU^@?_vgC(jCRI@+GROb0(kwSHj;WncgeUZm=eH)N`qUYC=?2nySxAvr3R-Sz;G%WsvNo2%Mq zgP!H(x})@B)oM0LO7Wo*m6)MZKtQAvAFfnwti^K}H$ug7%M1r|e59%ii<5GN@mSS# z%0V_k#j-7lgPA>1^&?9Y<_f=>tXf4mY?#K*;pz9P5zKlBKq}Z8#reasA5<)FcR5l1 zn5|;kp2Zd3G*7jHaxg7q@dperb}ZcnlvAR=cc|>~sg35mtsIH zqs#`LQMF-Y7K3V<+;PKB7ghFFJgF9!RZMr#gP0U->>sKO%FO<{ifPG7z;MkS)g3#Y z)UhY3lPtUNc~W;&b#TZVRWc>D_MOU?QC$PjhNgEMw+>XpqMP%zRiCJ@;YurYW0rLt zP~=f(4ph6~a9i~pE8gF3IH=(vIp=yioYZrz`Dl`x`X-Iaz13a5MGij{)KG1UWK9g7 zxWAto?(g74a;UHV&*n42UtQ;0!Jh7h| zPBQJAXy}VJ4N)_Em=S}NaBgbPYJ)r4E>^P~Q~Y(ax)a_jQLhnjFt}8z?qS78Ps!A4 z1Ni8~61As0gZ2nI0(4j8J4wwl+5x~|bYzyAeo}MDWObS~FYk%()hxUI&4B*+`E)g$ z0^@YIA2DNLAQw$xTF(6@8U1n0JhhM)52HnDN0#OU@=3U_pWu!w)v5OAe2&o@@7$nH zti_8e+^kOhdP8D>g;)HXvQ${jvcUupK#M(pZEB1Ue`lPxi zjV?Q{?!Yq1AQhq1KiEw1TAjg9+}B=JZ>FA>c}vYQY5w)Kx&W`gt!COd%@uBSPYuU@ zIi*)WVCV4iq534tq?zMjdZLb_98Nz|Px*S+K^%<3pX$}j9Aq!mb0`P9*J_5_oiK2i z%`z%-+&$l`GimiR{#LVWQsrPyeN;0Ycj5|b1)5aK;i{E}nS-y5W--g;8aTwG!`C&! z6~&GkMmxqMpE?>p9N?@ewBoy^wY4?P7}*R#*L*aNSnj0>Wu!w9fYYs7JRJ`oO*57W z8YpK7-sZ1~u;p>An`&6w{17Z{uDLad-evjMr)LfKMv8P zQu*45%<@%>E#e)JXnHX9`h@+|4{AF+QK`AfI~cmB(7+`oi#5J@qgKP(_;^?Zu=cpM zL6c3{uTL_w$HPq)`n^&$0ZhX-p&xAxRXobpv}AT^4q;xd<{IzP=uM7U0ecEGOy4aI zKBW=i0Tr5Q{8R#kjxi(pkJWUbT~hLR4a1}Y5o|PmtHu>4PSDJy(+jUD8kXtBXRP3< znscsvQ>eQ@(~-6n-7*dA+U6R_yA>LECdWmOuhTHiG0e5G%($T}eK>ffT62WXF{*cH zSZ=KVtWx}Or)DjS$8d$?_GsWW22lojypNqj*Z~dG_|9>dcu1q696lb_Fgj@Zxg45)tYA&)-GD}tl|$>t$9{a?yghc725dVU;MOi z)g32LWMl0omRZFYh_?K!;zDz+c~%kI+9F?zwifvww$lz}nO2x9vqSL-HhySobb&Qm zqtqyH&j?Z@C&-}=TBeg57Hpi;SzAoa^Rb(S{^=eT`ZIfKTe3_mxGFNLUO3=^1GH_K z+=tkYAfS>StsJo1AnotGcUMPiTQWQeL$Eym{7A?hZBL48q)Lc4)seF@LN z>Y_HQ?z*F9(XBM7?dVz_@Hi*Q1})sBbwD;Mt%L!z;V5lyByC&dh3}}eusbcVU64DT ztJ4nl;-l{iw0}9X;a*}Z)(u79)-^?c+aWJUvgl@qfT8 zbdmNP<>j+f%Q6A{2QU3HEz?ZkA21~=E&39!(Xu@B`3GRmTCMJXps&$CYS&YJ_1dIm znJE5)*SO7EBj32Iu{M&Whvfh>ZS;*$=I>fN)Yf*WC%(N)tMx^GZGyeA{5S0|X2A(8 z2G}46zSG%wODp4BJZ|tv3!6N+Ug^bSZJZ+?mA%ovwdSLSzqRljfy*Ej=n84E>-IW0 z^}(gCb<{QU<)b&7=xV$0QL7LgEC;v@KL~YwZ29Q(4!RLEbyR0v9~(aPN)JnFRxjO; zG&OjD1>x`@U6DPXcc{dY_qJ5mm*RF&>EbELm0I04Ngtv43E6~EVQq!~I1^k&W1Sn1bssUCQWgC351a%yShtgoVZ zkGkt8)6^s{{Voosc5=>D|Ir(EIp$3uvwtLMpIuc(RZe)Jy+{tgBqt}pS2dSr|a|^Xk**3 zO+|3)TQ?els0RMDHJCMU*xq1P=R8M4G0o7z-4IG0F4fcEL}AC)Gqj?q z2Yd|)RI}{@40D-1XX+ZQ53+a3^~LE;3{C8L&uQ7zKt1Pd8$)MiwGnJQr@djPHCos+ z*aIgEEmC(0H!P@AHlueNf;;qx}l6$p)5Hir{C15tYv{*dhl-s24so#n6bw*~~C3 zkM*Ez1#+EdptJKoXBwu$L&nQucQk#D;TVHv2p|SP5sPb^+4$r33k{#(`3@n{b%|je zBM~B*KmKW{q0hImIkm#@jIya&ZD83C`B}2QYYk=J#%B7DhSQWy{wBlHZ_>@Z&4wzL z2_(of20cA)51V*?9Ab?J?J(4_7i{LQfG<2?kTOV!0doyJ_XeBeUB?ZB;n4z#)~_+7 z!sBKV{o$;k7LBg`&2XAVr(7|>`BN^#huenxH1*X3(l>Ic2c8;y9r);zH-=vV*c6tC zqkW9aYMX_0Y-!A85E6lqi3pHd;cjh=FCBSWy&{bFo%v|jp2l8H7#X5&YYc(7Q?hZk zqnYNz9AjOEM|eC<4(_KSqdm3CnF=F3px|^maFnrv(QgvbskL|Ep_EHg2XyMD&<&%B z#;fjT!V~5jnJ(($^vVH8e-b)l#Uf)LC!XrowZ`pKfQrq=iFMeN%XQQNXx}f!wStfQ zM`PMJmbOG!j~VkB5Xk_7VF2-G8aFN(^*H*Zu>;d+0fiA1wndYIV4c(UjFDxBli-qZ zBERKi{8?iTOhR6_aYwEfjr+e|7Z|WkFg2x1#u<3}6(c;i;i{j0&B)X>6PQ{zjN@6J zv;y~hG$a-jsVYns;mHq-B@NJ8*EZhR-#+ngmWhxV(G}M?Cl1D6(9X5O!VUof?Gu;E~ly2J0l8me3 z@SsFzn)g<8qLF5hi4)-)r#uH~BHX;eMeoTIOKEDJD$#)!o1#s0vE?(YOG>P8iAdT@qajgAD{1s$ha~s{ zB9~!Rr=)!}TGAzHDUAkoOM(lGxD0o@TkaEI9f|XjW(iL6 zAKMPgPx_ew)-rp?@`5CLCLj3O&;)%PU)l&gTVwD+>CNq3(elD1cXW1)uLR8r5_1pp zeb9{Zq`88VIJhDy!&Yz|EgF?{^IQ0$si)vf_LD%91`QsQ#In6_-{_>4sAg`d4_Y`Q z$sZNXEcL;Y$0qr(j3ocSiky_hvI6-6We`86ET5dT(Hit6ou0&U?YIRJgOE%Q$1Dg$ zO=A23CH++k`20CL=_dyJ{R1s`Zc;hRoa_sjh^ohhLR_;L+T*t}ia?puvhAy0n&4W5 zV*>}{rOT2kTtHgxdrpy?ljb@IuH$n%lay8v3il?Jf@$!^gGqHrc;raZGgk<=Ur36u zf$;g|q?w>9JmzXrZIU0|NLmG8xcjrDJN6K^c$;)W0O6~5e0cCf(jM}Aj7Lf3vsa>U-A$-Dmt$|6`O^)zhKhY%lRB_eqsQA9$|WKKcPdM6VFRrgOuB-^aP$#z8Gg|W$t zS-yAtg~0v@8e;(ZQ=2?XaQ;6gs~>d9ER$94tm9K}lRH|t@vb9!)~YBJ*%^|Pzuxii zB13X378^i0DgS0ODKU8i%WM%4vd~YaWR{NzK-n0i-jxBp`dMR3{30cJpA{%}Yew?k zuh#+k0<8`X%t~I&G6tDpJW+IPksXHlXSE}c;#zI15y=f$x|lC8UGRgk$@@J7=kdBZ z$)^Mmmd#6EPr^0}lJ}GF>B8i(jUX&MkbIW>9&{*qqAi4%jwN?(0HN>wqiL+8;2J-;zw|O~VbCsdRt(Ns!{}yy z)7!0aLRYsG)kO=tnA~vZdZti;;2O^IF-6%5j*u}t4b=-YeOp8NaSK0?-)4|7xPxgh z8M%FNY9rH5mbSnltaD2XKtDG%u`~z(DC37}tL7$=9jGO)o#_CZETeEud(#U>F-GB{ zP}AaCK%joOX%Q=doCwn-1_Bt=Ahm+J$9FcZWv%;B7t;q8SB1Jq;rZQ7dx-GLo+g$< z>>S3?mNr`J?;_wdbkOC+#~p>ktSE@ zh49hwrt8)au9;$jp7#i@oMy6vW{AhmGTi`=#3^%4(9<5lpXQsg>>BMo;|p}VstW^c@q_cGi-h>W%O)dHQ0tqfmE`x?d!{%d zNSnu|_RN9=hTO47r-I>9A?OQWTTYN$wkf><9Jj8M z!ddKN-IQ&_QrIVj^8}+`3g-ze8>G}Gf<`q?@gstYTk!c?v`X1U^51ElVoyBcX-JAK z`Twhkl;b4*Kvc>oQm$V&{&$@oDYG5m_r?Ky`H_QCc9VSH4M`~rfpFij6wbq{Mx@Lp ziVPl`vXx~A9cMF;*N@lX>}e_EEjo-^l+u!+wU91|(;TCEbuf7)Nd3F7DA*GC{FXs>bceiUP zGl}}n-{;G(ddRCU>2bd)VzKJddOuWIA9?Nei*pWqwR zn|7(RL3Qhq+UZ*q$)h7cwzD3Q+K(mpYtu(2pOaJ|S{me$MmX z_#!ovG}}h+c|AP+kb06d-`%y+ni08f+ottnSr9|zgAq*mt|HOSqQ2p9%t(!-3f2Tgn_z=99K9kh5o+iD4qpePCKFdheNbgSBk`>M~zk>YetEr7z}d zub0O1{0hnr=cmGQ-?X6)sHU>m9qn$MwvA1a@i@3?T3bdT#$%rrX%}np9IRTWEo9}O zY?H?Hk{XmT9#^$b+d(;KLs)e>9^=q7mPt9{ID86EJ4!iRj7nqq0wMhz>p~^2W2X(|R+wH3qN+Z8&q{eT&i>2J+Dt`_gXN^3i)o(%`-o(gose zzohM^(J{x<-q`S|Q%|SC3nVUs_H5b!mJu4%Ax5Uw@bF^ym2^AQu6?>EPQ8+*s?E!G z?NQo12HA+8e};9z?VqJhqC%Z`mDZZlcleMtn5HJxNksKN_XO{zb0XmL}=@X>?4p zbh8TMTck5BYB*UJ*xdBtRER-^0vBF;Jv*hZVAh@)(FLQf>7%Gd+V)O2YcF{~x>eEgA_|S~T)dm0nE+-lR!~`=mKFl=|Yw!GxSQj8dY{p^FaHhwf*p;Md^=ePw-@IdJ_g&kOdKM{V~0Y z@|Razq?)xO9eXp3cz~PBH)*@m>3Vx!;gy%t7c!8<0QQDbOSomBR&_spKFfEOxkeN8 zD%}wue3Ncnm#226cE(2rYJ6|Qfu(q6EVJg_v|+sr*wfB=>|LJ>xKf;pZuZN74_$E4 zoCX;q?fIyEvkY^G@O$$NxY>%!Fur95Y=!5dgIZ^-tiwlbdt{g!#UI~gbgIXvww7m@ zJEb>@3@KHVMxXJ5c0UG_rHaVZ40B)hI4uM2J>{yX&dBhwH z!`xNddLW}Y)r{a+#z%e% zgxyYM%%r-lK5NnKxbqg>cKOYsMY~HGI*L$o-9l%;O$(iOw=7!3cQW8ocCO-zdzK9C z9%TGLQ-6Dy0iR>wGT`SKMwVxtocC`~rParK-ez>>6^{DU%7hJ|)oH%Ci&f@#%%yq2p9%W`>|%@_1joJ~s0xLqkFw!li^vmPIg>?TLO=XVyW1 zF5h{fj2neExSl+77~II0Ug?fjt1_pu$P3bDn}{=zyHX65jtO$W)@It+N_@y#hHRs4^rgkbjD@ zwM`bBcH%5j5}d{CHUK!8r9oRL!fC46E z*`ly+`F=S4Zq{2{v#Cm-WHB8q;)EfXD|}vLxo}jsw#vRoP14vQ`)?Wz_Q;ml@uJM~ z$(~S~%?=?5MxOw@tY!9iE5QPOqs6?o*~}wpLc46H6HL17Ht3W#*A|tGP4dEX+h^Yg zI(T$wwskECCxm4mBVk-*_60i#+jq+jv4&9FGh0K_5BARP$#5EP#?3}O1z#s&?Gh2Xv(r5ogU^XOWGmLLI zD>-{Gvl>LH+53nZyfd;{-VShf=$D!8#7y8`HrIa8mb~o6U#~;r*E;+lKb!545Gg#Z zDElSR!06I!mQ6bxfuGB=`?2)WP`H<6w=G^cGF##ZOt|sXX-f7}#@5vf!v@~V_TX8A zW@l$OK)7p3b~6_UZ*9nyw}i0!rR*ytJ>XXMP#Xxd?q|o7PV3m??7E-}eDi5`9TL8K zo}J9Hb^y7$BDXi$?&#eMlMnv;b@m2tDj4ehIvW;=hHPK_)FsD?(QnM?=Lb6D3b&jF zu0VF4f6gH2)9}M4IR)gmE;Q$1T?mH^$eBijxjrbTng}~f#Q$z3$+=5@d&qNc5#e8} zb0&~{BXl{xl5}x$4yT+I={eg;kFUwj8O*XT9+c4*sYd723TUUuP)1p zW0`*uObnRtu6O|+JSk@hA^hj}Ic-Q7Fe|4c5lA*Sr;5>fZSj!BIZK_)H0{^t{Lbi% zZ4n^(qRiadwpgvzBLD4?k~XhJ9Cbb->3KGz$Wkoc;NxwLUBiPW{|P;QB6)Q z;>J&Z%TbVggZ{`F!O+aG;C9YDPzbN`&G&N}FnApnJj&Ta+RVPca+VV^N$+zah!6_} zxtyE)X`4HpQQN_&b9zie^q@noFS=rk0p+{oI$8*)yXUrMAWXVO9vIj6%2n0|swItc zIqHpC=5o{%+Vj-c3UfCyQ{T|`W@`-84Hm-suDSh~2@^O3gk5l-9=VN(^-cYG*6z_f z>wXfR^&DC51qN>*df_H?F9a9)oyE^iwhlp^^tsG4%PYSY!X*B21>7G{)4DdwEJ$+0 zEeyGHSeneVnp|(}keC}U5M0E%q}(Sqf~!2uAJTJKH$WqfVYhvE9q`JGTpuRm94pkJ zile!1*pQt&!AfuyKhMhzYr`^R6QrLO=dx@xp4Kog1}%6}s};VnBzHHkK#Nx7A_fv1 z(3BUo{PD$=x!qZwfN}`xqq+9{Q>Cdn@s8->hFlMPc0+EF2QcF{Hx_No^+Z8-dA@kc zf!vH*ko@UTF2gJjnX7ZffroQ*g_iKZz?`?Uz?XTp`FmZwZ_4=HkMDyn75@fPugr zQg>mKyhW^a?{1p6pJ4+8-qEMst z=BMYaAq4tou{+`1?7UYj^Izx(Bxpf%YkL&c+13*ai}P-~0TpgN+HOML5r+8zY1$m7 zE1obtZ-+IE_yxBQE}Hyg zV&#Iwe9r2kdUo7{)zoe6iKd>bHxwOiwZZFAfEE z%=1QRaRv5xcjJQX7MGvcvY;MI8~p;)6%T1!@P<(C8(aV<`>*2Yh=N`utm<6Axo~#Z z0yq?X6*ulx(3FJz`WH-LaA72fE69Zd2L+xO?s@M8HS#^p15v>phVc>uTsCopTvmG;Puf0$84kavfrqa-KP}{V{tMw_H?vt2AKTz%mSuc{LL`!(X~=rTfBW%!A;Vv=FKjE2d7tY#{~sm zq}PgGR4|yuC%dN>_C}p1*nW>!tSFGNxC@6ccdBhYJZ)`38EHqgHx!Vy9A2=gAe}g( z>$ZY?mg$gtW#Ks7Wlur79$()NAt+pcGM?JmV&7VY+z>q4rtmFMU8P;&o4ODd1r#nM z^n9BZ!U46b_+qodl_VVAq7W`Pxr%$XD!fM8Q`2Dn|F=T;|4$O~{~r?0|39cR|Np05 z`2Wx9#{WN|N8wpgzI{Lb|4;h!|3?G)|Hlt5JWl@KOw9lPwuJxxWGVmu=!C-iWPs?P zE%akFt+eW(T``s>6>ekjU<{TGB=*#16s{(Ua>*+UCSlj&!bJ>(FqmYe$2XZWaXN3x zGgZ7PPs}R(L`->NL*aLXnR_*_${Nh8vS>T6%8ok<6NxI9>@RG~(D7iwvBEhvf&={1 z-emz-+|ge(g^L(O;Vx)_N4}33%0f@zjcQuJl`a)COTDnahc`hu>4TvvToLByD7FE7CtVFU>foMQ*OVi%SDM;{j?DF+vM1X zc&|A23SLEUHo7b*%CzO93i~2E-iRymoQoX!#L;z%;H$@6)+K&Lcb&gMPnQIb|KnDn z`mKtX-=vi9Dsn+Jt&89sk5j2De}uGg>mmaKx$A?A8X&jWqPA#`DcKdx3@W16u@$x{ zQhgKNI%jg%O4}lqLkQ^qr8V&z$aLjzAi5A-1k0(%=IGD=fGV5N?a9R z+*Gugp%G8txVaIAJB4BdF2}9^@Bd7F?f&oo6f;^3o<2m^0L!Y2-q;A1@EhLt?fj<3 z^yk<67GAWw=mSfS4&|UTpOj>e{I|fF=jA~~O0@N25xlTj$^OD--;ts`ej{+wfg-s0 zxu#R1JGy*1}vse13|emeC}9L6`b27)t8Na2PY>=V85#YaU@4dKs%Dy;(X7%zeUJu7hh>T}^i^=IA5}jMWSPdswNYtf?CG~vM;zT$y zK~e{|DQ@Y*N5}RmCdW2Ns-jOZIrK=Pq5X>CJ91p~?2uv^t+-okaZi>cg>v^i|H(BEbJBue;gH~4WEyiqMMc8S7aWtbSMcC$0@v2%t;K#!( z7QvtnrrQ&sToK-Otavu5TXeFR;lMg>c&eD`(kQ{hVjcfc0ma&Z9q{S%#k1Uj1vi(d zxK})sL6;aXd)Nxr;GWNm>)Au7cv~z3(eU>7&>SI55tLjIK=_?i$#3L$eTS0EBz)`2 zr~m3!a*mW=QMY806NF)nOW^*SHQ2ss3EX?L2IFQW-AVe};1ZaYtik(3OJyCWBOPxv%koxEMDw)jE^MhRmp=YL>UU>I_5(hW4S}w~<>M^K=G&LR$b=lv|1y5I& z+$2&Im`XS;P0cRhwDc&q#Fa?%zNkb6tpUr*OTx(SX(LLWliwr8mZUpE_~-Y$w1;N! z(&ko`{OSPdy%+N7K8yMEBTGtBN%^oH{S*K;<1M^JvLEW=#=DeEWFGHY*@EzosOG2;q31S61Uccx6>w zD{&#|lWz0MdVIfR5)r24Nr@K;r~X;8f`pN;OW?uB8hrCZ$$lrczU6aI=YzUClr9#m zw5-QicDQi!+nr-e;WJe0;DXn^$Bi0v{}Ehb-Fa)7H>x~gv_pHHO0^7Jhu&G;3{4$f z+JV2k^{{j4Ar|j9!-!D0XDQ3W0}dcCptLu?ek$-P{n-kX)}>zQudGs;(dtxfv$Ds( z_>_KPS?dsD7@*!VtSS&!IJ03X+&j3E^Kv9x?O3gtzab{DJ(r~1L439 zr4Eh|N_v*Q^@cDyp|qH!A5)dKvxD%Ru@r6`T#0{4Dus@1B`z|R?j&J&YANjWT8Xo> zOV8Ou*tewgIm>**Y{Kh;+5B2;`LaCd;R_C67#w%H^a_pkx?I}JjZdBPs1&}M$z?D;DZSs6kE*@O+VJ+o^L)xa z(qd~Hm%;jr%R8)T8LZ&AX#eJASK)&mq>2vh%9J#^N?10D<%FMErqHS}PmMVLIu`;65TQ+AFy% z4L+vjqB8ho+|r;rL!bZ z82sxMOHW|t5rZBdxA#WelF@jf?1dXTUq|MR)9;nRjyGZiJmOi|FYp;j5>>t_OX0=G zm)?~f41?6zp2p0{rDt}Cig*ug!FAx)ikIvdB{8wktP-MF&~HJ_VP?$Sl~i%FU%42* zTS}t+qRanac|vd2Qx~-GaG4kSJ#}Cx{t#b2?pt(Dv0y2x#G~ZpYENG0?=s2_ROgw6 z7A=jgd>)UWxrVRIv%%B zKYXG*jB;=}S57`RO^O+QFSnUY47_%}N|h%B%8jHoSrM zzAxWEr5q-xm`$T?>?+J2`@z1#?EY&UE6g67jrP};&$MO3Q_+Na~c+f@u^nP8Y5og1wJ(CaQ0iv@egTBAO$ z?pk5Zph^6&TSYtgMtZY`?zok%;+}wyHa1iocjThgnH6_!`NTK*6(&1Anp0l!%o>f~ z-p~oPJ_O%Eiym9i7B3r7(Y)3_6J1AFwEd4n3^`LN-igT-Ppo;odyiY2x9TrMjr`qHRzRYfd~9$Qnf){4(C_Q#6G_Wx0KFRnOb^&h`y@2D8SI5LP~`fNgktaO$5GT33|Q%D*qFBdC1y^)8RQ z)~XD5MFD&K-SKF*$~(?{w5&npJ!?Li->CAK9T!~@)2h;m#5}M*tnw56e{P3La54^{ zV~@%$R$R2YcjX&Ch8_A>cBEOwgDaQW@L8Q=D}SdMUdL7bNu%#%m5)lm*du-A zJ(_wusnW@oM-XIGUJ~%pN12t)d;cS~ILM zDK?wB(Oh3}jb@bzV}pv!Q908Nb<0UvAW3Uw~)Hr&TC zNt2#vkgGD2(@}%HbLyc%LGRewJvC5 z_u-q+zI}in&=XJ>9hl?k(k8IEj;o!k{Q15)by3ZC!|NmcfjOPg+5L0spx^^gVCZ+l zJF*qU#XX0QjtDeFMrk57!qBjgNP{p$7aD2MX~M(84Z=umgkBS2K*Ri}js5pr0{dz8 z@U_z6^Be0pp+(ihPl3>StuWjGLWk%ggu>w9D4jmkDAWXp2I-B!GL#DaZ@D;%we}A`c-B8m zFANDY>cYUzpw6(UsK~IW2w||^s5gddwYcHKahcZs5t`u0V2#EY83lLB1PAFghG3l` zL?2`TwMT}b;G4)BXPzB5K@bpbj0la22nsXk;M$pRjZqsC6cPmH(L{zsYQw^D@VpU^ z1cCp((hvjcykNxOfd5`=IMT>a5W2KrgkwNNRJblU%wPzK0A#(<7!?|3G(;Lgg2Ez0 zfivFNrYghQKQvSW{{zVkp+ccCM5xgQ2Zx4*=|ePHV^9QI7(2@wFI+TkjR5RshzJS} zi_iv#L8H>aPoXX}R38GG5k^FYqOAKP>Qy_9UoHszmzwpkKXhbJ;D4`KC`1~_g-)y- z5fC5@4>N{EXo5mRLL!6)p2+ZtTLt6y2>kURf>vh?(;32}gc0EyZJ5xgk2FLH!whgq5N`N!+a5vSztk6u zf-`0YHvm%q|J9-AwMT}ay;7Bk0>|E7&LB+oqN81Q0?H3)d8Z@M5 zuYsz6amat<{_iS_jB2E4gJzByStsCMq(aXJ&T5J>-&fiA2Sr3iga|_;g0#92p)On) z1?|hI4H8B~1sj4jXk*-XXgf`o%E3P)!o!1t^>BMr6g0AM4Rk;e5fLHK7wI(ciRMMI zv%F|4(?*7BL4}6k;1I1aB!cvPVPU}`QE&^CF)Ua(Q!w7EI(6hFf&YK2I>><5?He&9 zFzmnA%QLG!meU8X*{)Jgr9#T^u^&iU8pWdE7S!AMZrJ`3f6^22zC01 zAlTOn{X{^Nfq1(yJOtcUD2#&s89I?Dp)n!~MwTc8cF%&=6<~}ogc`IOp$F3O9rWp=nD;c;RlFM#!z}|FsXZpw#e1)hA1A2z z7uy7V*~0$C8FgRg{ui|wNeeqzF|$sz@#Y8e($s(`@VzH zJ)nTfVy#_OuAniC5k*a6T%o$FD;kY}OH85$6qGP30s;?+A`HYWfZ#%bD{72JjZ0!w z&>l98#u&vc#u%gD@2RTk=^3%=yuRN#@42qixiWZnb#*;e^=$X@zwf_evRkuE8=k7W zW?n_RP&K9GYOdJ6mKnwiIK6F7Gi`_6%Tx=hYm`j6efspSYg0DODOgogY%teEUttgz z727BjT)UJ<8S?U`N?|oIb?ugZ%&=E8b;*ruwix`zjN|K48aBj`j!P8rz#B6<=1Nvc zqxU@5QQ6D963UUSlswhpKuOC7kIrGe%xVhTz3rA9jU7rZsoWR~EXp7C-b%vlHD_S3 z{MGu~*ah-=S9knU$ufLHH*B^=rYx$6@B5}EcAPqMV7Qf~H7qhOFIMZ4+eIx5cd|8q znXxY#pQ-B>s)gt4%o5+t7oC!4YKF-W_4RmDwiT8J#n5$y2VpHJlr+0o&|Ed|>y{I! zuP@xpGN4-;GtVfn95Pc&u17~&Dwdh&HHxVD+cUKtQaYJYc#H+-7C2}Ot~=FxW|k&2 zEG7t{iIMGRcF(flYr5koj%F1MUooVqq&f^eT`O7MhQ`HHSoUqzD5|dNIC{}g+`O%q zEC&rzVN+EtTd1G1kj^d7UY*J2ORnu{zUBI=QS$Sso4TvGrMyz~^OhAUE7K>`-!(RHh2-Y$h*R z*Bql*M2~ihuBUsRlh^&c63jbzA?l%mvcU?70^y-)s!pL`u$<%_g$J@+@j;)ag(0O; zapJtiYFeA(iVvAm#=w~ej8c^G&DxkJFF^Y?pUNBfw3VF>p6$`bV?W>>$ z3_4c(JgYrAzSQ2Cg(dpl!o0Cn6n~#>E7BpQ{gq|6VsR^=r{NB8bks!8vK-GSxUNDG zP-){t^DCucS9CmZ=83uFh86iBin!yznO(D%Y56K${eUy&SLBd^;$obMG} z_Cz1OzS#`RCAY4Lo@-IFN+~Ue)9e;)W-e!9?dl2!uH$=HONPVRWLuu@c%EK#wAct^ zIB?8&9gTfAU*rw5o_W5iX^L0S3S!4oGX@0H|FGruOct%2E>=9x)eWtnvNWnz$;_iV zdZw?&ddIc(n4ZZkt76FaR$-1MH7p)|XHjg9tvPh&@X_WNyK2HQfY@rKIgdKP!cgG$ zF@IIlaWOdjNWI9VI5~p#YZolbOiSsXR<>30&@_xzb1nIm8FH?D`^Q`QXOo{ttUsqo zO;nf*a#uqCXN0PTqZS<#C7z|zDrn-02NqV;M4U#t?OTS%PJl7)sM5y5)uKD>gGRKz zhyIRi-ZaofY(--#8a``}XFDvk=p`lHV=(WxM$We_nOtJy=wVlUYv%ZzmeO!I-3A{w zvtw4ZHN{5DEzna2`oF6&=S|yES?+E0zppo0m4mH95mP7c6?_~rMf`-gHY~?febX#? zVtrp$&S+CZ%|SUUI7~Ieu-Q2*#VsnlA;ot~Mlfz=Xpkkgt3~@6_Fh7px^&#AqWgDd z4$u0or=VF;t{C;gcN9DCdul;1Sf**HdN66~A~t6BW%Nd#K$G4MGl10+R)8` zvY67SB0l=z%oFR1$*qRA%fVvt`Rr!+FKw5d*w++n7uB@=Jl-}vuey1R%4j!>OiGL# z-@^Y|&@@zFtQ=o=3Z}MRTc_W%(-Odpd3>N9kj^aB-LJv|eLH>E9BLhaRjowsGW7;KJNiuUhF`Aum%^xp8QlbROG z=PkB{n9e??j?t_3F<-|pDWP8QmN6vMg24)5dM@#VlA#xx+=|JbaFO`~i+<$vNa-EF z<;+PLn|8~yQ{I^?=h-jb75#E4PZhZp>#TZPjQIJ?i8bea&3T^`ET}o}tKuCs=Y7q2 zUvu6YHRpZJdB5kNLCtx;XJksXEtVKdsX6a!&il`Y4QkH&WC&Kxc`sdcHRt^og$`=Y z`{rm_&3RvQ-q)P>dkz-VocApQ9yRBE&3RvQ-bbO8|GS*`mUyAf*0w@9cTN4xEv#;Xs zYW`;N_jB>r*|Q(d`Jl!e&nBQlFiN6(!C3&+2ZE^PK^DcBSO8I$MYxq%w{Mt)SqnZv zF27JzbO2q2DC!fX0BAPATFbLM8?=0h*t%yrM2b9_2S6Y&=(%&oUqVP%d2dXTyK_t3fD*@POP5?-080{5ke=fOU zP~hh*QpM(P)(_3<1qVEtVE~o~*$wb1Z+RNG3wF!%VDXh9dh;6l{aRWq6E1g1qn;yO&q zJkWezs<`5j*)f1JrD;q2qELTwF0JVm^A;=O>&5!+S&ZKBKW^vQowHjO{0gIudrA%RRLy~!T_}$ zKe9wbWfR+t5GQrO;F8*GN&z^pei$hQU_=yO3sD6Gu^mt&tPvp0R0gIOq6&iP_lGmg zDS?tE#|fyELJp2heJg-6*KB@Gwt&n7$bvX08Nw_;y)Dt_c@XA4c|sDdry5!StcWa| zlty`8xk`XEN@}o@2hd)ed~y8&qcpWD9_|9{GypdlD!>z<3I(nNI*2qUUTh&=#>4{by9|9tDr;jD>F4Y(f;wPh5fV1 z&m_h@Ee^_&yhiV<;CM|N9I?r0D*%F0fhNe+CI+;DKUdfl$d0fSUXkm9JZ2#*`hbW) zJO!Dj7lJq``U-=iSOUWg?AtbN4K#4xBsBn_sICU?S|PL7l3Ucp0aw+Zz^hJbQLJOd zKTy{cb&L|GNfm)i0$|We9;+R@5_mBd2wPV@o%BHo=x;)&>A?J1BZ?Z|7~uE-9m$ly zBI~|GXGVyV5Yjc6IV)ij9vw~|i^_Wcm|vwT$hgaz@TJ1iWdAVt0J2(gHYjLZp%gk0i0I= zUk`jpD4&8Djes*Cx>z(xStvN<-;p>}1o=jWlqR!~zD@G0GTk zMN@HPE>08N#7pcOtrfCAWni|_Z&Th^)BNg}O@1md_u%^BGKsBS)&Z4Q0=i!UtM4jc zu{^TP0Gz9GPU$g-2z3x0L#C#&H<5}{V6~^`6)Tok1ev)e_ zsN~EI!y&oTohmcgNG0O-EU#G5SyU96W>+M6m){iG6!Zw8yxR~UlmV1_E=dHk=g51) z8|#1}R}_VrY*@jLbLx4FlvX47WNNGP2G@t_Z0*JI_p^=Z)vzwB0ECr0WFE0JfLoV( zkxx#OY}zZf*qw_cD|?nMp{ga2ts29R#1@x?4LYnktdP=2dds~mWq`KY%L>UaPOT`7 zX-`IiL{rA7#ILeOV|b9j@1Y`rTSm7qZI|~V-0NpIWsI`G@aUulu$yBI*czaEGBio> zD(R&DF-)5>Mp>#H-&L`~*!LMDbhXJTE*k-OIg;fgy-@;{lN!g;8#1{5#5yy%W$X$j z-AE?C>csn7-Y0p@azO2pBBYT4O!8`8EwCZ`bQj`@CA}Q-qnl42fbaK-oGj5G4(@5Kkr(drh zK3W-*uhJiVQi@1A@i8(T$DTB^X@g7Yq*iI6DdV%%q(iMqdUIiNK$t|D@>Eiz9wxw3ikcgw<_3`k9cpfn zi8VLK=RGhrH%QG5vL`=P%?xxj~v7Ff}(wrD#@~vp}Xk zHU%kaZjfYWM9mFSbAwcQU}|oV&pCH0ZjfqZO6iUI8SOF;S0PjS|G9qjm#ht&6LbGs z-*48Pf3N57n&8gAUwdo&9C0;SPLWh&YEw~%(IHiyf{utdvHDyIiQ^hR$=2 z0V3j2*^xAUN)zgoOujy%##y2g7kDC`op>)rX^F2A6^QEu+R3ekWhv4co!78%xY+#Of?nd+ zedY|4{5oSCvAfTl0WvzOQg}vDOF)_^%xGH#Tm2$cE^K1yVOS48dsxFUdALTfhA6Ua8!Ww(HM=dex_@!ONlzrzMTbpIIS@s2UyEe;O zEKOfxmNnx-Z$5I)g!Y;LRPDa|etnMm-`RaTi0Sk1?L2GUggN|OAFP`&_x0~*UaV>W z;A#Ia-M|YE%~rw)i5Y3&oI!@0?wia*`b>9ryH}x3&|3cuGuDeUti#lsBf>D;t!F7GeT< zp>#@g!IYEY3y#apyRTE7RDvjhq=3x;KL(~pCAKL6NhW0oT|F-4o_bbT;``-;xMEK6 z0OEj+p(cy4~)hECZq3`)%_m-sy32iKysr|y@i zd{$DajT*i}${Z6b{lb%?>uoq{-ak9lIe7{`QQg$A0bQZ?6iNz_#k|CrfG^C)mF82< z3MB9oD`40DO2YyJG%}2A$o3| zm)|RIFPt*}K=JUM8@q~0{cr6czBO!qFQIIkcSx&q%;4zP=Jn1r9UUoVm*;hfex4QU zUZ3~NFZ?De=KkZVE@E4G-b+pA$ackqw)2nrTubukP;t#c^ScDU-!|{gHgyEjWzkJ4 z=^4xp%|$mX_?ksUFh&w}=;QM6X=jCF=gc?e9oZvC)j%{Jg7C7`CFP1_K|C}utzZJY zPz%w^OgSljnV0=z-rqa60Y@cYW^eIaX8r)N?6Y~D+L)HIr}z5r&v>tUKK;Ha^S_a4 z@!fv$Rl?VMuDE=!`D4HM`}ie~Ycv0)7AG~|k9@P2C43jZb#w3eymj8(w(;Z@9YcXdIt8Y(6ul>XyQR959eKw5bF8Kp8jC60Gh9nF9{!MTvgr z&TUFb0&$j97O7{2Z*$1O@@?9ZGJ!&;8l~fm0?10LPRf#-C6?tv-T`tZub6sPc*U_} z=a;`$Cu_sH)G4LzIw)+)J_0w*Q_&H0uYA`Gi&~RKNRmBw_rrDX3eZz?0{Jm}{o+G}#7ve|f-Z1ZL;>B|j*WG=?YvaY;|Cm4GzPlHUXut591tT_e zSTMbf*wUV#-_}`*(>o&`-+jThe&XxH7xWj?pT54G7(IMJA2GMMV2rqS_=3J-%6@bE zwWYL~BUU_j!!WVE>6rNE@C7}@gICWQD2x&OLY^m%9no}b)`$hYqhmM8W5;PLI*73! zt>`7TR=#*&e$nNP`-g~iV`p>?Ufh4d8JYjd`xqUp8@b@BHo0>yoanh1oICNH3opF5 zR96?u7ZxX9Q0j!M04(f~;F{julri$1Xj&&z(JUoA)>R&pSudRcpykDjBYO4e!XdIv z9J>>6Gm$xWR^RXl!!zH>#u|Ka;*EVfa1IG6D!}gBXY~~wlm*@X=Wb#B6Ib>TZz~I) z7VD0?p>0+^`x(_Q;*5#^8{b6x_)Q4)FZb^+7HJEt|LMEvA8oPg^aa=dpYD^xn})@L zAGB%zm9Lz+;0HbV&$Um~MgRMs8l=IC=7Pcf#fa{#9DT{+CAP~V?yE1YE%*2mK$9aeHrFQUXZ*CfvR8c7wB34*N{Hyq^~ z^&-hv(BANY{H#ANpq}c7i*c(Zw;UEf%%wFP77uy&poKlfgUc7}4@eDcAqjZ6%K$!V zl-VSi1^7Ir7)dKCM$WaIPo-x{4Z~tYAyT=+0c0jAgc|#vVV5ZXy^4CNSy+GQJXAV` zl8*(`jw&&$MF6yRo`ji+vM_A;AjyqJC0|q86!eHKUbthyh*6qe1*`WMJr>z0vTiMq zN<6`o+LtLsIQ>eB7NbH_jK#>88F|%`yT@!*|1@P-wnBF#zcJm6p}zg8aGmr}&FNuT zh+7=8J`~I|#+{@^$m`Jlkh)0E;a8OTh+!(yU~*Z=bRb1j1Luv8RM&vaLz_UEe7`8> zb%44knrT$4SIL4@i`4mKkXSAie?789e4NR79}A$8AeP*?s|%R#PRLrxEl=&fc(@n~ zH;kxnE4pMUa1A6IP^i2&r(l!NL{1ebToom@DNBM9$)yq}kh4Y>W})Pe90J4}6uqmn zas-(NW|O&?2bWBi7UnsCVTc}F$RD6#(LG8Mqk9E}KtZI#kdf3PhMUY>Np4Z*IcVZ% zUBv<9X_^JWC6g(XU~hD1_$GD-?GBPuBY+<Y$D%4UAGHxoE6BMlU{sMt;oBS++P4`wGDrexnU`7M#(pI$!$`}8RCt+82{{o z14kLv^}aEKbzZ;5dxV7uJb7M|Yet^L2a*8Xc*#g{u?SM#D!AU{erZ+tG-Wup!u2M< zE>CX))7Pau6+{w8%{(v%&j~zV!iogoa zUB(kT6PX_hG*x<>a?munQ2LqOA!uGkjvxs0K-`md25l1ram|w}xoKaVJ6xQc+BDd6 zMRXk@x@W zBlcg^?2;p+btFikCQFhPo)7zuYTmLSaTee_oJJ7g$c}(pi8}Rook?MkTz5DWiSS89pd~GJgpFkf)n6K}L&+NwNlnVI^ zQPTAYV0IC5qGc7S(n4H>LV*7mUkAD$+Yrf7HuVA`TC6C~nc4s{VNs@A8AY7dj9iOq z?W6{~lt_r_V(`YWX@eh7qy%cPJ3Z=3xxlP!xqCDs11|zPR2|X9G zl{r-@DyGMBAsxGve`meI)_}gtgO?QFINE)jqLSikuS_wR1e3d_wXT>vBAu2=WXW%< zC}&umcyrCFe%a)Qil7;iNH7Ph1ZtwBcZCT{=F3RagcM2*XDmxkY|1dTC~x7Y5LMCZ zESv-+m~bqh1lB=8ROf_2e?fA1i8(84O?YHgN$wI%hmu*;-5B4H(rjLwf37$QYT3kQ zsjcBJR78*SMAs|;4+|y^$X|gh7_f3+%aH;qi8G+(h(Qhom|;;J;aH-qzKRzRvREp- zg9|B2dY~YzZjmK9363;qIhcE}Z+L7VW)bJVR5=~U!+x?#Dw2uq#rTGl_GD!XHJn!D zltDh@K*WQRP5L#|At-m0)Xx)OKnn~)4#6zJBua(gzlE>N0MtSfCKWKyyd11(KOprQ zS~KixOlrbp1k4QBE3k@r2sjdD%!l;MN|ljpa-(A0j~8mWDbMWfOgf;TCL6P7FBe@!t82Eo(WmJ@?hBJ9}RcJXW z3qc+tZ;EOGVn}9d)Bc45;*er13*X4amvSBW3_y)g6@u~*ewd^m5izB-To4~zhZ&aA zvfFu%Dz2X+I_F@@lc7jbXvqnsoB&l~Fvv(-&NHGg6+`4`sEiDT3(etF#unD6EHn&W%0JaQ#36iF>E~U|U*-vaw)kT;W z{Ln!QN95q0f%Suk2g3q-Th<8*bl7BVW05%|RY$B?6vin^x@Q$lk7baVNX-X;UnHF} zha{oTz?gms0z`5{AvtE7Ab-=QxQazg0Ve~?78JChikQ-9R6iiOO;ZvySYpC=7mgT> zwOZxq;1v>UMJM6{lJS~o5bVPx0A7+QCT4X^jnp=&hwNR+{nAR`OBs!=NYRw!*A?wu zPfT0bH<$8M&E!it1!D@In_i%X3?4Q#J2H?-Nk;u=U&>bvOBiIJ*3)@A%pW2*c(7Ra z;p)Sw04rRqPDffIJWgV%Wl6{aJO*&BEK8@&?lAE{157&v~vdJw= zZI>cV{=yD9s#QR$$&{yZjHHsrI)QVKYFOY0UCoO9qaNlA?#3cqHRx6NL#T#PRH<(v zlUMVFAoRoKpm4)A2arK!wu0VwaGg6w?&qC`o{~QDs${H zs&>vr74STO4wmM;q`j8)LeLI?6W}J3o-81SaxI5~sKokZQqBV1rOJaS8B@=RKO|yn7VnW9yU1Y%yc43q{Sw0IMWtVwQDKzd`5`~E? zhF1{13{A-+jT2nrdHg)~8&)Q$(L7ZFz_>Jf@TpEiMJMVYP+S3@7@9E$a6xkj6UoLR z3muEeO~XDVdC{{et%mOV?w^UC;>h_6>#`1DQB(zVEF7QM$xIKGI|kW9QhD$1h#jZ-6!Yyo(N1ltUC{)EO6JEfA77j4>)(U!OVsv$~R}43kF% zEVOx3#dywByaIS?oRzKD6iGS=~;&`_RIn z?MQ4GgZd=r9>EKoP}KTi<)EK*_F(RwlmZ8(7@t%^j2Gy4usd@9lXr5PjQ5>*bIj~FTxBB2fw zRY_SfiH~BQY3v!&0m-IMnnZzw3b`Rt&5)`r7!9TcW=G-y3vm@V%GQ{03~Cy~o*tc+t-(mAM6*t6Fe*!U)+-A~5ND&7Dqd2uJ1{=kgNRZRcEHMX(bA#Wo=O*Brs1F` zKEZ5)$`_C)T;iCZsPB5LcSVGx5(x^n6h}C&M@^F5vpzyu&4x}@*Z2vNn~Vvz`9)%* zO}krPF?;*MUhPfB#27Q4RrnUs%w^Ft`b1LK6RV~k7;8G=2-}SOJ*lA6DiWELo@wQF zOc{)=3_U!!N$;$=8db_ug{2h-Q?!hDJlhOwI<5f>aFrd+#8rd3C?=gF`UTP2?_sNf z5@nmht1saM#CLrL9ltl=?hIB8m3*EhaHN=jAQ}l`gI*x(iprDH*Tf3$8(2E=E|%D? z%R;%n*P`(tmJ=I|eEQ>iFY3q&4L}=DK1%`49g2-wc(LUw;q!kZ7<<29T*}IAP?6$H z&=|T!!Z{XULda$<$<{3WrcDbz30eg_jre^Es6LY1m0<}(xAc*q7>4z^!m>>%4NJG3 z4nifVT`FmPZ`C0=0$k)$Q4SVAzNE{ByqSVm5Sn6L0X2=nD>;y-IN;fyC?1Kj@7lZ} zSyl>z%p?-uLUja5Kp8%?YY^9 z65f}@e$CZpD_o5D@)!`18S)91Q4YD{%24FsbIbdL7d4(XBsH!3}4h~&v(~*aO@lEyB zk_S$NU>nf?7F4+aJ!D{zjguHYbh&ncZCxwc6pEw>CA(MXu}f|l?G&ed4wpq@t07hH z+)Gz=%Azb$Nik0yLFsU`38FYG6HF%?^f7rCt5)^hFmy51Az9`mUp+}05+owS@T@Ba zmD?C1MKy{|rzbxB7^G{Ndpo(ed3pZbHcnp_GAUYGJDED#L}7k0b`U?9+nBOp%>lME9a6|X=E zP{OiAn<;OHKH>uH_O0q*nU1MVVMQzy3S@!~l4~sP61d(KYM85`za*MplvtRS0)ih#nPH8s6{iU=T`5WczLLpU{ zzMwM{Gy(ussJW~d#P>`v0lE^0FVSnM;>;+;YK9pc^Tx%9!lof3ngr%pxy8gs7o@r= zT_B@^6ikH7o>fto{;Rw+OQYQ@o7}1x3-T+M(q^RJ_W1jv4#F9>XunZ;wQ38)*90(t zDy2jy41jlRW+2pn-00krsszk(`X(j!L+-XszK)a*YURC18I`Tb;Zt6lG`hsXSu^|7 zS4j_rMg73ygR)ecWv5{vN&hicJpBmb01vVTq_l}~f&6Zup+Kqtk%kpaQTcs9awVn@ z8p-;baPmY>4q1xb1-BYHZ9_N`Azv0O^eQwGw^=)+QhZXwk#<;@+%OTi$_ADrGafg9 zUSvXJcgd)phK-L`UV?$GCWk5OFHm9-E(}l%V-RG7bI`zWgaei;MRh%+TDl&PRw_fw zg0uiI@j!yw#7jq5h4sdaHm|yrR-=-hxwKZpnA!60t?EqLrG%^C#V4vHjXvHlnT{kS zu#{oK#PbWAk%G$rIe<5VtDyw4UzDX1Aj1G(X23Eg+ICf!6 zi;5a&lUo!$P8T{J+@$7OPOTxM4Xm!J$yFes2DK3HkTe)1+X#EC%|oIJLBg@)R6G@t z0g{#q1*9~bU^q3BJEzqimNF_$LWi$vjH`f6sTIyK1m4QL%>oLo8 zTo*ck3zkxj5Mq-cEvyCUBxGm1HZz#|ENBBbyogF;4DgBnGOS5-P|;OB+R`^t9FyAy zkRQ4|V1G$$n3Bed2;CkOahDDbN?;Q(fVu%XBciI9RHV`4GnV#DynIUX6nKq*`{D7H zW+Ub7adn0+2HYIc-nRVJdYB@3NU&vKK`>q^cR;L=@hlS(06;TZV?ao9)0i~l#>`Ps z&JtT~-rI;(7jc0Ciz29uL4`erQ-;7niQt)TRF*+{N(STU0?Zwp-j2f3z$z#qZG|XK z4bquXrdVX;T@dJ1=w`HenJB?}Nvco9@gW1`35_m#d9hZ8Clw-Gaw$2ftkzA_ETvmoxe2YrA*r{*gQUE&ro5Ma zxs-=$PJGG9uoqehjtU9yBN@!F0P>)O5V0pN7CTjK%PLaANMb<9u*UUap^qul-68$T zg}kb2Un5EbfUXF43u_k<0QNVw8;{L_eV_!sgm88^%hH+_JANlltV?Ox7ehKOIh5#> zODdS;zJUbLfGp-jAObIstjxfjfiVEH31kr3q^D*y`A6}#!vW$F zgaWLKFNi7-g!W*yB1B|b5hGGAt>th-mSBXWMw^)=j@W0(0Zg6z7^PZ)l&S-?X-sS! z*s`>QWFEp50^AxSIv-nSDS6s*m^DSjl6nbqyk$fzWlXj*&XV6+NQzV^KK#|fzI7>2 zWys_glCsvWGQ{jx7ZqyBoV8@mS~6#;mdsg8=By=iqH2j+GUrRPugT<6Qs!#OoL`Va zT1)0^ayq8elB+^6){;4E$(&6oq_t$u%K;A7k~yWHu$IhOOXh5qL0U`ZY>@<8OXjR4 zbJmhMtAX~lWX@VLXP7`*OXjR4bJmhMYss9=DWtVz&cuY+S~6!XnX{J6xu=}lS~BMr z32jQsAgv{H){;4629a7aXDyktbrxwYnX{J6Sxe@uC3Du2IjIFwOXh5mM_Qc*laf_g zOXjR4bACaJaVxxVEt#_^i?o)^Sxe^JW2S8_nKLe^Q%mM-okUto=3Lm6{8vlnY@1EV zsjMY)esLaYEtzv@O5S5FnR5{}P*PGVYss9S%O9;JbJmhMtCBf`7l$tSMn|EUy}luC zX!}NQanYzHE6LowXv#Tf|LB5)B{2eID3Ec=QlBo_#crNjkmO5KlAY?%3Vam?Iqjn7 z5leF7=`L?~7H5n+qNBJ>TVKfhDrg+NWMez=_sqV7#qz;7^a`FZmh98l9)5J`J|g#3ZtBp-mJSvFc$8z0AH_|nk1yS)Q*;;6O+3b} zop|(>hkn@~&${>VrTpC#-23>=OR||?h^bc(+BX_Fm+ibc_az2SG%R}ze!F|{@t<%0 z_>>GK247zS+4xZny~XgaF8f>6f%c+D+v~do-#TX5?=l=7vfqj$gEPOrY^?mikB$t= z-&l4bAA%E)UDjV7eDqH%4+?Jh*0PWH?xPup9ej~@M5l;x_b^0B>%8BsrnvE%>xZ^= zOj`{;IdR!>ZU0x#oSqSrPPws5aP;?qv2w($6NWAiP8hmkmm&Ue=gJYmsi&-%*+v|6 z8{f1&eMQjr^oEy5PjewQD@?p#@+Hc|;^gy`QFZ%$dt%FD>K=}1dpM@=;h3?9V`lZS zX_GHe_t0>7ti_3wFVXgJUU;n4dHNpC3y-xr&yeSJu6klI?U9BtdxsA%`qej#Z}o2M z>idpw+uFmiR_BGc*y_CSSgZ5GTWoP2?csHATbwxglJHoo^Gx}Cth%~p>R9c*<;H5@pt&kPT? zK66j~qPFOmY4OZZKi@TU|DL)=-BaI02U|D4r`}Pc?y0+!m9F3xMdP&%T09=tKh|(k zES`5=!#84a({&Bsip7p|8pg-s>2n&s9g7ow)o^+&uKQKPDX}=^`i8H?;se)LuDkY$ zhD+k3bLKXDtyA+VAm-iB&`-?$b;Ho=gFQEKaQgg)y~UyP8V1L|Jab;d;jws2v$&&K zeB{Z>37MxF4vEhl`&7db)z^M>Wz)6W-_T&jCyl$Ia@*J5P`S!;H#CfmPq^zh4M#R# zMHN>(&sFN4;SMiZ&@e1MX~lwuqpD9j@!p31V)@ezL#hvsdnY>BRvf#qVNmrqE8b}O zX7<8{Tzt(n3md)@i|rRxZt}fnDmSS-Tm5{RSg}MtpWx2^xu_x6e0?pt=kS|DMdh9| zzil`wzW!=ax#)YM^8Q9IZa6YNVd~<_y*#tH;p-hM-6*F1nOpDiJdIjQ8U{Bvs*A8I z`DVZP8aFMeTyFc4hGVKv9rr4C9$w_w8yki;wRvOZrcQXFa#M3&sO}D3gq<4x^t($d zojP}E!`I`hyuP$@mF!IoN5n_nn=1D*?spBx#7E!1sk#>pv0iXnn{VRTzI$`S=;j+X z1YH?_bNkKJUCD2DG=1~d&DEWN?jeU?c(swsDtDTBsdA_5msRfc`j@I75*kt2v)gas zMozh<`l4pkv*9;OZ>jXG`-e);c3EDT!DlV6yu_QAR~~=I@=DJhx}ti5=A#L^`&C-K zbw#BYpIcG+>DpJS?~*!Va%P0*|EQsIFUuRM-zl_{@|}h!wO?8NPV><_<(sOJ%jxoU zhNmxHS(zCRuB_a1zg3l);jU^py19?6X#7puTDkLgS5@B1^II!@e)%sdM<>13FuwW% zVJA$ zlUH*qhum3tL7#4G7~6bTPIOn{Nl)HcnU(L|S$$X3Zzn)b`eUA_3r%3bwX zUAe2>J1Tdzc6DXb`S5*&KG<-FF+NrxL6J^NShOc(6tU#(F!qs-x+RDE6 z@Y;sM;-7uCwqaZ>?s%iJSFO0K((rqCH5^lQF?ixPv7mitU1b1ITUWV)`^U;X^jzQK z9@OX_E?Hl>hqdb~_wfGu$`xLGt8x$X?ylTJ{GPP< z7D{}q!+&0duUS`3=1tAIs##YBQM0b#N2*y@(&r*-)>W1Jqh?*ztg9vytY%$>>qE`D z!v7nYel_c=Vj=yXv91EFtA;k6sY4GhW)boMMJRV)Jy87UpAAZ<=A&ZWKN~)4an==& z{=C1Kd(#zNMdL>eC%67;KQZxN4Qs0}(EsBGtLk&`y^kACI7F0BTQMSd&RFRj)n997 zRjz~BjT+*rI>xev``Dd>gnh}%WBTp+3^64L28lJ7to*Exc38y9+~W5=e|6^pVqI?K zXz{}zuRL8PgeeV&tISO)0HRf@tffJpRPQ8l+aG$DNej?CGo(g#VsR( zPwrfK!=O5<@avHI*ea|R1`Jsg|1MJ6T{jgMKB^K9_3XT#=TgoRYP*dCgUP>M`HS9l z6ex#D*ew<<2%$U$LSBbL?FoW0F)+E8PDrr+98l!Bn2t_bw z=n%_BB)pJ)C|(i9S;e%(6%QoihY{c8s^PUcgodMGw%+o~(&@9g?i6IMD^$S9d z=9~^Qq$Lp}rr|)U0#B44?^qI8&G0CbWY+r_WSi-A!l|AKJ`Snk+}( zISUUNgh3X(8c<3>TxiqPFbl#80FAL7P{d#I)tP1yx*ADs2J@zxht^J`S2#@leOC$Q z9lY?7Ob(h050YQLf=)rYiK z4e5g-8Wk(K*@&l6q-|63r_WZc-mwa}`2!1MX-DE{o80!e#3~!YSt!?6HvT zLH+H(q{`WnB|vdu5r(3@1br4v5PzESNG7ZM&}YGSYALE?*!jF|!Z7ABaG@0R3S!Ei zW(_47g;KyE+zg8nL_jb_sSryViVate?6?wi!1QrmiKrr{ zNUrm2a-))(Q)697YEsNQxIUa$`zeqFjxnog_cSdpX`Y~OF=5})blZYFTJw1{7-hKI zs4P;-mJzI9yI>h}DzR5epQDXU9EYt)(cvyhdSQ~)GMrPnln06!it4O-TO4%Gs)@C& ztG0F3wytkZICbLq6TT|*jgvR~+SXOux@ue3#1s|kfA`j9i5J>zZ7Y;>H+2ZESi9<` z0h#xzV8h2fxoY5-gbnWll=|%+;=^+{b({743#<711%J2lcN>4V2hYE7$M6xEd#f7T z|D!wFe96Z82a_{*-tb@!=1chWpmQx4a0@y`+ru)2qpql{KKy}V+@n{{h955RBtwMO z=+1}lysUH9!~%sP5S&oHrNLwxYc>hX0cE&Gv$d)=$6J7(ej zgU~#W-m23u#vJ^xq&4^jPlx(B7;@`uG;!$_(6DZyWjA1nEBK~wL!-vvFE}tb_S~?j zPtc?PlGT|kWMCK_u%1Gzt-^x}w;HT;HeBtFYWOk9ZE~Ap-TsTT92BaFZLXLHZ|Jlg ztKD27k7vMzT@BuBY(vMfOG6?NS3V1LCU0Us#baIpGn z@B+&xsM}D_6X+N}ZXPy2MUEQF#4ICmPGRy431O6%vC)|3_Y!2C|EsMxGIU28mz(_7ENi3`I9(fs;X)^$z7wj zGF%e}q~3~R6emy8OKYwXnetF#+|%NqTtV`&Yq%mDI5Cw{sbIs{Oqz|aTX-L=Y9EpT z!=|QK=xtC|!bm44VjeG$TJ(#snO9A*TwcXi^DSRdeZ^2TC<7s-H%mp!gap&_gPuEQ zyqf{mo7lD;&99VZo97oEB-8M)aG@oI1r%Qjwyx*eMQ(xhNR3GuSrnLWtPyzi45OsM z5?g}SkEhmEO@)&9@kN-r+`0$VEq=A4XPCuO0UliZN4Q>W+s=y>&ur`)Kr@e_nB1sX zzdyvhDQ$}N=PU{**dbza+ciUStVuAEI}l$p9Py;us$~&ta0_NZH}U;M7BBOH^^UVF z*M)D~gN+`#Nf>-#khFCOv!%_RE8$`=Ed`cu*jrVImlX&`&61^+be>9yR*AG0rMauh ziI~)&G}+@hhI<3+x+Rw(o~)9ksy-`&ZIp0%D)|Crrz-cYGAJVcon`TSFH)Yd)3-Q4;B8u){U68ar4?> ziDO}xKs-H712f35q4m6iS#1+#Ot(nICM^*1x%T z=838&#R1T@&4$C)cWL(}u1Z>Xf6?W#xBJXG`>6H&-5s2L)IFb`EEX9{4h|MBy6508 zYwgeKvizQ)%kq0y4-`kA)Gf}9r&ET1*$ax**S*~@IQQ{;{fXkLpKKU0>!YFf1s@IF zFs_?u+_hmuFiG8T^AO<;4@Lz0pS-~xF4}D6=<1(r_<2@1D&pyjH;fo2HvMJ82yw?l z8y?HF3(k9ZgV;wrcL+Z@XzPZ1JBk;F-ZvuH`wb~x`4B(Z{+A6iZDH`k;F{sV(S5{! zPD1?0A;Eqf#HybTji2i4M+aTM>{A6d9T#MNARhbC%47Q5RZs@ZYRn?V7^7E18TNdJ zgfgraV}HEzygqgll%e|ft)L8hJST)Q{BTonwdnU#;0}8{N8%ZJ@8O$Z&HceWL&YN_ zH;xEqm4jzHiAiN%=}+GdcJG_ReV`DEHthoL0X^@#YF_e*6W%L$D&fcJ*?F_;QckiW zch9lyHeNhTJZaI)A4hK7(OGOb3-LW`bRM6D>lg5i#5S5!)OBvnuu4Q$9b0!iLjH#5;K@;Z zr4Xzqu^ZC{gA^2nN08mZ83ACx*FX#S)QV8md_iC)^$C<2^^`_WZWYho(;L5Cm(r>y zrtURI5tqNWv3rh?xQDNxXyehs)bwoEcFclZ#OSnL(}*mz!MH|jjqs!HP~NTJ68Qx` zg3re(QD;_6`g;AKVEL;uUP^!-po8!8PV(5)x}(u8MawS%7b)dQwEFhqKEdwUi+-6I zoY?RpqLOuqt@>JMFe~D`mo^T~TE3o#>E0;0u5IAa;ReAjs0Gc?RI3nd7YnAe#drmX zq4~P4F=R}fHIC-uUIPT<;UDyb`ss{5!Q4@6@aNc$LsGn>6}*XnUq1<%JqX^BI%9!v5nb( zaN=W$p7$>pUS(kpcyr^iwV76%X|Qy>%Tn&F+(VGtvcrz3--VUs3@K z9v-}D-!YjtgV(;bX-2=yt}5m7y27TOUsksfoo{}-m$>HC2fEB^d)}s??RodVwJY;X z)x{}o)b~qY{F)V9eB|74S!Gb_Y8l}^7mW*p5yO%{$AkkMgD%r-|zW*iN8Pa_i`XkeWhEQ%4KcDRqr5v>%CWg(^>5Q-YX-5LqC>B zZ`#Ju#{Wq1ig!4AMdsBD_sQX;E}3}QZE%|g0WcRgspgMz>lh%*p}@zFB~a@nP#gVySRr}8z!$DbU*@N%YIvV1~NrVg&l zhnN_AZQio-s%eA&1mhO!i|A8%oe&02a-9FF<0|aE1=o^DSVK1g^=28BNNKSsmYua_ zd|gV5A;b@wfihHZN9R@yUs1H8A5feV{2E4vCY2sr(a~TS zasWgDj(Rq_7Yb&i0!GkQV1usZff{sO0DWYXD+F-B_o?vzor>z=1``HsyGB8eAU2SaF}K&>ne9iM8^uIekKukB~=H>Odw5RBIbRYNK#4S zCoM3J1ek)%JzGHF5d=a>FL41hdrnpNCa}qbTQGR8bTO_jnegT=f7iDm)IcB1^Lz@VJKSC{f^R()tCcipi@-bvPoW zXId$LDdVvfaIcvB=9-~+X-@{+cbJ&+??roq0Wbid^A?Erfqx|ISO0VFDPJ^ydvw+5%oaFV69e8un4ddEICqjNORhs{0j&?i!qf=FvvD^Ur6{b zxzQ+ameOW~btS%jh&XudmSH(Uc``_+89Iv{Pvl5=xXI>Y0X;VJf{D~8m~fsrv+ZEc z0fAh|d#XWb5^yY`L(}t2Ajx1ZLL+ARA7`)53?{N^6S?(N8`!)B@B!QhQ`k{mE+EEr z6a$0J7l_+3*_5_7*Kg6X#pGtgSo@tFw{-uSR+aQ|#>E=3$>?8oj9v{h*MS1Fl$jC| z0g9Ta3rAk;Z<*8(^ z$MXjKdx!myq!R4vJl-{2I|S-wDg+C!QYe|^Hms6p zb5dAb-NY|K?4Ag|0dN3UoLMXY{#J}&-gArCPLkV;qVFlq32wg$Y7Pnj#{g50R}5eu z6gjYL^hx|x49jS%&Vnf5zHyzD98^Rfyb60ZJ;+w<7LDMF#S&?*u}x|&ASJw528$7> zdrF&vNFelGGQ{$4FJ{#Yk><&5M*DPflc)zf{xn~&cz@T+RG`!b$vB$g=37^8ak z^Hh3>B!vR-Zk7;WNTek>tRtX$yh6Sj_K~7u%2WFWn|IWc-I3g5p4t-j;w6t#x8>Q% z@6%}7{`zNAo~(NJ^L*BQv_W7VqzM30LE!?+LK+0RRgA&+c{d)mg-nS;^|T5_-E(~# zsGDV|W+BX+iY8s5q~T*lO)ixTc_q4DuHBx4Zc{3Ri*YpR3VLG0CAs+>PXbUs6G<79B^cAv|o(9d&cQ)a|Y-*wS?*m=FiZ`o*xOU!wVbXKKM3uk?vSpC$acib7rFlnYd#JhOh7F05 zQ^csVw;Yj^$UG2lmaj54O_fQgkb7hUP2%l>fD5<3$ce{l!5+?y<3$F81jY!H!~u-N zre0#lmr$M@F{>=6L`EO0Y#~yY>rz^EVs$gQMG54w1Y2JZ@ySCA zdydkSF}6{qbeb~eZviaQu{;37xQga@m;ts~B3uPr0o{hwrlWX-PooI(60>^frM zduO@^FSprxWqa|Df8KF$Fst*{VP86?ywBCs1_j;vZhh&iIb-jyeC4ny7nF|ZGpSIP@IO4=}x1P*@9=>Snx5W5KTQ40mdi0pl-vSmS*-Xwk zzfKvWUf8MA9xhOKKz@v6i52H<{j7a-&Ahn&!mSO%jWJZn;V(bNrYxNrg$k!d9nu;i&wRQaeTaP@8I1x-osfu$R9hZZ2)@)7;fN&foeS+100r56{^;P(0&r?fBpA^kc;M zv$q!iS9|`Iv$sxc+4J1e%m2kr|Jo0>p7HZZmu}_nwV>|O*H538c|=_H@#~v1ABuV1 z%R|NY{{8xC;_Cy;`-w{qEoa5w{{8w`aoSKme)p5t&k!AZBK`Zw4qe5Rv&-$o+n@05 zPwF@}@rv@k;`{&Mqmn7xVw;pcXvY@^wJi@2r+v!Dk^A!Tp-*2wUku(0>G;oH_eAIJ zNMkne+YdhDl)3Wb@@vZ-#4V4M2QIw0JXZX6IEUm#7VhTLji0{0pBVex&b`Hy1wA{7 zLZql+tEk?BGHIbab`hU;;JgPr^0C`#r*;>0{dm)tb>!sL@(ph5SpJ?^H5w^w-+>@)^{Rut(?4T3+i6eXQ@fq1Kn|`%psCc@Uyq^5+ z=-%at3sr=>dY8`<-FhG?eahbz=gK$V?a$qNiu?QU(@XmDwAVj4qN`B*@^Q#uzP()C z*+;DH%ayK{Z>s5ie@#3uhr{fC<+H`k_VT=AclHv)`*U6<$HzOb**QS0kRP|nUOW88 zo&Ch10erl7AKLiYfbz-W*K!JVy=7-_(S9Ib=$-jE;gOyDimTmu&Q=I_B3Aa&Uy#9yx?Bo|2d1ZoVVt zbZhamK|}ereOr!QK9rGk_rIHNsn33V;rEMv)>m9BKVG^wAK%}vJVjh7fAOy+yT*#g z_UDTib{HQmxpb@XeWMt0LPZdA;QT67QDTW$R1cePK=cA>be8V6U7p_hIK!ve51Hk zPLF2~Dqk*^bVWLEWcdQ|eK}}e_|Y4VIAjzT`anLw&7;aEirX_tOK*JRWZ{kGi-TlO zKRKF7rps}j{lgn)iCf0-#k+D{cw@yqW5w=&ay#ERm}9rerx^Uiy-sxO9eM0RxyC-< z{>^WR#R?xA4#FfSxP^Z(w$P8C8gC;S|FucrL;myf0NQ$DG4b(Af?|( z=_x6#ktkNXe7ZT~eAVrN2t)XHt4dO4mr~b);!OSS-cA%Y(m=(o0ghTS^pQu?!$PM6X@q;$5F{w1Z0rSyuFu9VXKQo3GBpGfH@DZL@3JEZiOlr~7| zT`4^-rL9tWK}ro$Dqq-f8s|vy9eMCiQu?Qqo|jVFi;&)uQco#ul2TSmA4zGXlvYW} zmeOrfIz~$Wl+sC3`h%1vN-2=ic~V*~rJqRY9w|+`T8f`a@dhb9C#B_5+99PiQhHfR z_e*KJl%AB*Q&M_KO23uT4k^7VrT3(CilhR5leN#%ml+t&lbi0&_Qo2z}Q>3(8N|#G%nUw0K^oW#%l$JREE7p3%>lzt_pE2((j~nnv~{C>HAW8LP{5XSBh(;c!iXHEu}e9S|_EYQhHNLw@Yc4 zl(i z%Uh!F#UsPFbrwqxFJ~9^$kd6y59KGuQRUyq-%je^{B0+(d%_=wiuVU++6NbZwdn%E zq_35q$cWo7*fm*dw)={8-za}WTyf#9{eykKS%&EK4lz}f_YGDbTYgx6_@Kbn{Y@vkJHGsScw(+GK9M8sgWpczrZRT~D^4o+--{nSI)6v} z_3no*?kT3+zN1&r_dDfZ%bz}aeN!7R{A2H);)P#b++NhZ*w`tsPAPAeC#-*AS8p+S zVtKeY;+!`-i*3Je(_VZsbZ4hv{Hf9HG+%wvrI+*yCY@e>P5y4(EhyOG&DR~fYk08q z%;w7lT&}&mvE5&d?t6&X{=g+&g3WIE#gc&|-cF@3_W0l{0v=GFo(d*EJRBzH`% z*agRnPuA|(KiGM8`NtV4_9QoSdlzxz&pPxI*AF_fL-bI${Gj}3{PpA+%`m-3PQ6cdg#lDR}py^6%mcO?q%gmw>DHi+}U}ethoslXCa?^OS+nHF}8b zqAr7~u5t0DTOEOaI z8St#jGosX^ai+N8Du&$oy&B`u|JhaLA7iX>d(>VMf*1PUNrU1 zteE@V#-72$3(I%M=l$i3ot=a0MEULbb9BpFdJl~n{Phw!1j5Ihpz|#((=am>)g?e;n#h|r2gGIi}Qca{rvIf@^9j=$Ddj596WnVx$|Dp)n52f zIezv@gSvMVpHApGK&(4_-}b?%2Bvd#j=VR)y$uL*t19oba!~ZpySEjW8QnVs2i;o! zTSlyHzbz~37FennyLwxD@!Y#CUl_AvM2|b@#g%{Axqook9p(NRDfSFj-C3Rzi^Eoz zkB!CYYsyn&aj(0|7scYNb>+XtV*TCnge@mD=Ed%bZ`jgC-d|jIFAc0aCmtr^U-y=O zEjqojeX_KhJ#p7-+vD-GVMBRPMt<%kb_T3EzX>*MEdM(`^7#EM1-}Vyd7%7he56OW z#?is+50#J0h+F^0)tbz1S4>fuvOT7~(I*(TxjZT(KlciXkCxAj#os+vzBm?7{Z-kE zL^0_%PC@qh@=ftq&%RK8 zJ{I}vgIHYmk{p2H-Tdg~ayb$OM|+CNuk>jbT=r`DvH1H5Tgy9Q@#xpf|BS`s8q2rF zV%zOaEeMWw3f|vQ{$uo&SpHVIm$)Ura_`{NUFBQi^Y41Iye$^_o~2~_&v*0-e*ULQ zGnf8Zu6FX~#LK%GRD=IgzBB&8miOdD3GbV)7)zn}+F#{D5Q^9RU5?~XJp04)yOAjN z`Gf}>`*FE{Fz2K4wek0(K5lyOfbXx^EBe*iPs`(@6YlHU*wi-*GL7uB;Z<{OD&n1O zE27!1aaMf7bL|_ijKxC7#!F-I{Z5Tn#p2an8sod|<-n7oDukUM|yot}*YGxBq?VW?Jb&Y%N75-3kU(V{dtjG4Ai|PmT5pVyV-yJxTpIk7s@$mSR zTaMhmuh>3sz+f?Q&R(5@w!<11#~0guGa2>|=#7Jg^7jFqg6+c_{}6vOcOjRpd?R*W z{3fI5fW}wjuh;2~2LwGvHh#al31x9p6VHzRToWTikKO}2MvwE@!Hulm^0`G*J8&%Y z8@RWy>$vq-)W#>HuLIhd5jXMa_jvFL`9`*d@R#9J&Anq_zhJ!G`0MB(>-RA6?-c`k zN1Mp^4{6lmUr)b^MY8k0jr|tw8rV)eeBz+?;_1KM+$q>~SmT`df)h?^tP`8x9oSxc z_V&Qe;;gp^c8E5cCy!{{7@wn@va54&)7ZwT(Paac^Z$>n>kg>mc>Z}P(u-J-jtvzH zsIfQf1x4&#uq!HR>?ML4qtA*_85@Ge#1ad}=-EY+7-KA0qefr9Q?X%vPD*k2Y zLKGbTu=a)qTKgaWCqY^XjZ{!PcA9@V$=Jp4PDepIaHctNy}27jIo$K3 zA+d%Iy7#2Zx>pgAnqiU-h&{-1p%u*ycptpG;b)O8LMsVI_KA7!A`r_7H(&B%U=uV9t-k1=d_ zKoPYYX-aL1*zWHn?HOl4q!Qi9G*KL{mZr^#h7Od~*}f3DPB0|u1dixM%#@!p48f`W z1AXXZ+mcSwg^6NP7Nt^bt-z9$7#8d--TBIZWkV&mM&YD)hG5Aw#qdhY4E46UQThZd z#wj5|6?j!Atm>{(tLcVeI6|ewOeZoqj{OiUVMO zYBvX5pU-Jq6zS(VFam-55F(O5irg6FN6khCInldHu)NuG4XI2Hl4>OzN;{~WoBfOx zDEWt=QZ(Bu*q<&3c{!;~`enZ1p^hG3DiS$d?`Y)EFp%ahMBTYPjhd6pS!hVo3EYR$ z)*HhqYj|K)>S`(LOcQTlOpJ>RTQ&0R_8^;v1vjP0U1(LYCGewa!OTcdV)Y^6lszWc zg;p*z`baU$3|LHsTq$COp^bye8Ff^ZQ7!fF;F77MDmh8HDTaSEzI)#pT5Eh1*Fm88tQhSwTXqXQ5uGHGs?(9)9MeuJT~;8;sYMQZU&d3Q?b1L;!^2*t2; zbSH*lokDSbE+W{hgJNWu!UKnT!&3(u10?Um_97CBe5thsyV~&W@y87sDvTs`1$H@I zdsK6x^tTXr^Q3U;EKo2bR5etim3^zh65hasN}m?amB|wf7(vm6)RnTcs@+jHR@?xu|-H5Y>WSzi$2v!dpm{Puy4W0_D-3A-D6WpSNR z8Ki|5Qflg5p*2xPLKzq-Co@fkZVu$-jWvMR6Fd$YrrW13CI4Ctm2^M?Wy4e5KFLkN zq6D;?uUv{MKQNe)=k!nnQ42T1Tk(UWlt<8W>?Z6YK0Gpfr4z}LQqmIxfGL3C2t`h+7(lss72GKDJeCIIGs6juCneTcT_u~sDwa^S?e)Tdm{=9zTZrsUpC!Z$ z9bduGtK>w3p$2944)Ujobt|$*Ab12;%;8 zSjgh8yc5oz3ukV#^B4wH&`j?A2Sb)dF4Dm&sue_hHei3KHl9#Ac5cP0s zR;edzXntXS86&nr%!A2VLlcf^RYhZuCm3UBVxI8npeFh3Co7yPvH zn`*`w>N3GviQZH*X6o)r+ry1Bb<|`jOcqZb2T;~ZdqD14(>Mpq^GXD!+~(veC69^{%+BlJsjQ;}V@nmZZqL zSa&A=Acso_I*a~sX2!1;ce*I{_t`(lRq4_q%u8F0aidC>Fsmy&sGM1Upm=8es&IwN z;LNJ@Fv6L!j5sm3O|~lj^wd_NoH}tw_cCHJU@~O1rDr9pmQv4Mw)8Q=qp1Zozk+8< zBdTiN^=>~S)@vq<21}*^Mr@n%*(JE4avC1ujgvY=W1*C3tl~$5n}#?Qnr#c?jqpus z3w%sL(!`<0j~ZT3Fm_JW;6fhbpW(uwMHx7i8kAhTYAAwEn3C%#F?IJ{hl2SjdEyw@ zXVBmFscRZzL|_Npr(kbKxo7N)|BG34K7HxHwLR-Gr+R zrTDhhYO1=d{*|x;CX3>P;`J!`5FXa^ay590Ffg^WCnjz7pVjc-P(($mrx{P#DNYC| zGwxNZUPwd7XNWQ4f>C`igqhu{m8IO{#!%_?tfC@g=T-}sLch+>On!oi6VzO4y%g{(TEAvILD(0Y@wW@ZZ9EPN}=<4XR=Tn}Uj{V>^Xo*_a|5XJt8UbBjg zTZMVDG>8b*q}6aoKXNm}(M@r1*>bxu)#uOPDfuz_{+&?l;1~9-NYF0$)AQ1$T|&vJ zSL|!ipS!^|{3<*~y*);p#voaeSa-Q8fi)!f(?eQjtrfx#d|RWT8UhU1FBB@+rG^KL z?HttP%7ey^YLWzh>fRcSG-Ew1787P~an#seW2fW!?8xm~$a{`DC5DDull{A(^r&2T zlK*^Km@tzKKE~>)18dZxi8aHE7g}}x{Z$MB*9ZxXv>N@-8INfV9K0DFLNBo?9#b<& zYVns5Cu}T|9UD{Pf2ip%&A4d9v1yq(Sfuc}OGTP{89GZzwuh%VmyO@*1nwivzhVq< zP?P(v8M|sp@0-S+T5{SgW4xApaK|`aOOCo{9Ihq_#fDIeELof5158G2kA!SGY6B&u zc!Up)n_J77ns!2LebHhkOCZN-;;P>ls{%JD^`Wtsmi+k0*hfo_d}@qQlazAM7%t`J z8vQj=;7hR}bA^ILS!d*?g&gIh*J5(8=)e9o4s)P^Wngc3^=BAeu4G@CE4?=&m;~;_ zCt!pVu#yxT5$Q(}ZEHKz(ax~eXCI8qH6Grsk99J^osW)mOC1+kTsrqjRKpVRAkK~K z)PRFeP4Ib4$gK#EiZhLG913u8#t}-fe<0*_3^bLL4mz6PfCal058tG5BH5hMFGjkM zdl@h(E~X1Q-9eg!Qw<5H8m=mv^vJ`Ms+AB2cM=ZnaJ&m)L4<$@J$fJcC3yv#T%|`o zCRk%WCDo;N4au#H>2Iy-S7l9pnjkpPz|_x?RoCu;B9>P|*{Ob}D2*t3r1l`YaqUtP zdaz5CKxlD? z7c~kF8VD-_`T6c8Dt8htfTc@rME+cIWs@H=|-MkL#Q#Uo}=`3xR_`x zG<&284>g!c)GVQ9%nl~u*d4~iB*+h=OKTgrNCD$bh#d0y)s`CnspljGPsr~BKlenJ z10>%`_U2R6BFIR(iNoSckH8)}*>qeJbgDNFs{e{d>+V0V=c%3(*O)3)&Go?A+@#Ob z#Kd4+2$Di)ipj({_jq~fhHg^CY%y}2DS9lS#}5iKW&71HPNv9)PLva3E-7_NG9A;% z=xZ1;+4itWDZ1-|@fkeNbVMVwg+ku}f~O3HbTPY1i3|RJa&fw(Z|F)fVW?s5LQ|$H zLPD#t9p$#=tB0Z@>bpsa`g|R6GoId-y(y_fD<|6Y6U=ehQquvgV1aFXZdTum5;L%N zr7Zt{nvv1Dktb#0QJ|Eu(u7SsR}1IhMp32A9%R}FHHY@8UxM~^tnVyktPp-WtA~yC?LE26*8aOnv9nVc_%JDLfL3#X|=ljSoH_ldvsh$Mq-ORf3e(P-a4rKpNOTouxuBUp z)$4@UDO#U1VSfb*KWiwL+O3%zO>A_(oYeKaSbqdTs&&DHheFJQ@$pOT-OQB?!^;2@H3EO-v1D6K75a38|xmP<`)87jrSFh zb5eQI;IN{MEz8mU^DTYY8C-QROPU0D3XPgR7!}mrfP)=NakC~y+FQa5n=fbz*Q(!&t5 zeH>W@tY7cSt$bDA7gE8D87~S-7$Tfra?(4@jEIiK6|^g~QLEz8fU0KukmjpH9kcT- zTj3xm+>C{b3l`KC`l874nO38yhLdZVbM0*xR+724Z3&8;+16RYdTAd%v0IWbhcTyF zI@=R-UKr6zTX;*=Gb7kwvM^bK$p+BGUah=o#n@J^)S@3&)`$jXOmS62zFs3=!M8Up z27~I+C||kQnz+&X*5+E&VrHwt%jd!-W`u_MecV2=nOSR;Sowm2=# zhzvy;2`64STj6A6J99ma6_20P+N8`5=EsG^Q`YqM#hce#c*wnr_LgziqI0fV8 z!gjHRWKOop(Ce7(d;e+WLYMDhIp)|%?qmK%lV)E_JyiRcV8a+3tsu6@{p_=rC*bAN z9b70kr(94J$s<1B;{L}VGfHSkYLAqhU`9a6bs=QHoZa2gPue>!f5Nb9K&B6!{~&uvCll?OC62e> zS47jYk9BmCj!raVpP+VB3rIXD_M;pio%zb%Uc#QvjZVxmhe%md@~gn|Uye*yJNi<^ zAMsk}?P>W_P4r$u@1JXeMR5tm5snvWv5#b)lP}I5-lD^3wq%=|KRfbkr`!siyy#TS zJy%ILFTXaijBpu2#?zt(ojhspVZ_d578D&r=q!XXDt2(RAt35;K-S?u7_mB`n)GVP~A(hGB&n;W4v5P02TQyE%kVNE7rR ze)v>pYOxk!TF`1U)^RSrGRBCF&Kk3e-2f_@!z6sAgQ&E_zgSEQzQ?kxH+P^A2m5pL zm&-6VP_C=wRBU`K{r&U1nzoRpyM{V&$FiJx*2d0?1&B-iBvaq5I64 z6!L@BOET^=kJXX>298DSUze4jJNwbN%y;$!l6F6v5$6HN8&<-7n+}+F=;+lbyxV5~ z1g!*>I)pOucn4X1w=LpY2GF>s-wBki6l)1WFS+(A<>%z2Bf5hP8bH5Ps zy@^5a-isZ;#G9fQNcvFrTXPtB*X-_1CwFysCeJ*mu;(pvnno6Flh8KoB6DA@TvKvnTrM&4;L5lcj#$hc}@cx9G zRP(cWtHzn%wbbW5%hJ9p-AYsZ++xm@^cf>p+rfhKMlQ$eWbg=!OX}@zAyW0?7OP5- zLQ7bn)vBj3LGkftFimo{bV$v*S4q0(Y?*-vw`VXbTTDeCqMG(9PkWp6($2j1x>^tv zGPz*##m~QAm3Az^PUpJ20NY!swAOX8t_&ZW&7qolLMF0>_lK;NTCqIB#^L z+27s|#B;yKaONcXLl{}h4(6h<*7x!j1<;o}H#k~S! z%JA%8MQYq!3>Y}I^&~XP@=n9WCkymwS2q_as+HxTmdT?U@7_ODYWMRL=*Qh6qh<%aJVE`#K$N20H*&y7}zcZXV+pxQ1ge(uD{V0P5 zl%l?eu@cuGV!_s%DSWyUF$#WR;E_QdQmtWPdN4(J8VR1}RUu9ZoN)g|N_x^tW#4A!z+gS}O!xIO7V`tzjp4`@jG zy#F>a)hn(PflyL9vOxH8t`R-Qk&H;PXG15wD7$`~uXJ>geV{4d3Me@$&YxCP8t5cl z(pwP2jH`p`j8E_5DJs@J@oOy;UI#coCBysTYb*g2SxNDbo-Vgwm&g=;6&ya!g-i?L zYN#){2c}rCO9VMcEt6eI@sdKn6TU`dN)YryE6!>JUYas1E3Q(*RLiaZPq3RH`{Tb9 zg*W)}zD@gv2bTj3rrgKc$rYX2U^z>>AHaX&nt)Q|ya}#k`a?@4nz9LN!wso&nphjI zT!fFq?EwSGc+>toXj!^g(tz{4#Mh5UVMd`V;vFfsZ@e=FKEg?Uw=Kf&F%NGznJ3;F z@2zfD+I%mT%m0!~kjZa|s}%Et<+4haBDPy_$inYrw*SU!JEN4H7Hoc* zhn;TXPnNtwH~|l#*y2OH)VCsz@3G*eVrCZ_l>#$``Kq|s>UsWb!LB7AC*YOHtD1t( zz0M4Aq7|{|-;`f0I51%z-mIg4m89v1#H8a)_NvINE@d3H7}QKD<){V6M1n459=FtX zpjz+X7;zcH@YMHs+2z(L%VHf;B?%|6_zn@DpE?Ls!i!nuIHj%(|Lj22{^@tiJ(XE} zHb}^;D^)*d$<_o^!kH)XN>Nm$5v8RT=g~u!yz;2UgYNFQ?^l5=Xz#B>)y>t^e}p$DFc#EcMl>PJg?3az8~Q{a0j-X~9Zc4icg zX8Y537M1VNM-i@+iAO7fKVzs3@d!QAKZ^wzN$h;xtd;SSL1Kvd%;amG72z9G#AmEh zd@(EL1LIlgc>A}B&gv(nJ6f?6ai;o^;0=r<{<{h8)a6UXSGwzDy{VBKZiO*lcCq43 z_O-)BA9dIcfBI4>M|(Sr$NS>NFrAvaTlcH%hdiyABSNl(M*(=RiIcHDR=DmZ>x+a@ z`qI`8l)c%m(buJ|)8W%ML!$@^@MaMwdR)^wOotm&@U?rQ`l{E>2(D$<%sD;nV= zW?gM7P9DzQgcfm)%1Fw*YuDX~y4J}sox4yCKhWKnbst||?wEov**f8Ps&YLmrXG`d zRb+B(8GJXs@@scz!bj+?Qi%putYgf>S}--VVo_(D7xmo6Vjvjj;B-!RYX}v5d)~R3 zFghlS=u(PkAq<9b-n9>GX~hdO`8WZ`g)Xtv%i_O9@Ok>Cmaw9`t*r>en1>ggL9y0Q zYB2_1;T3$L{;I7N8!qN4XomPmo$!%*Ny)#17!fA3in*tetEX3_(4-Ph^fU$4wCnVr zYC(wV_A+Od*u02#w?iRgW)hd`cg)9e7Vo%kq~6cvD;OXQPyd2ZqiawByWY=<))+c|9eS8FL39jBSmQ`6a|7=0s;M3Zww)v@PW#6G z*#oZYsX&b;TRZ4B@V5{2!({YygVcM9=&5rP`>c+gh6c`UW}nr^r-_~d*A}Acm+#kA zXLZ^PE24C!aNq%of9j&SJ`(ypRY%jBKzH2b^>ZNcNi)0d9KRM_2CjV^i6&#qI%^J& z75a77t{k{A*LsY3f}}mk!YMI3S7T>)mQ3@l-$3cVYs< zkea0S-&rr|PDy1~Tb*=(!E3BR0ybT1^%t;Ds)|n~E6%W9euEVkmme*r(6#o4{jkAW z4I96;XqaiEXbmUFrdda5Ny}#I5G~m&Ll`?VC|j(0yot0Ky2bTLahb#x94{5z zSGDYKF(BLQ2ib4`w$_7Iw_)4c;H^xPV!CaGh2Tc}j-rS(gVH29SjN6K8;@d~yI z()u^n3L5F+zrxM2STw(C5h=}fv%$^;E@6g1Bb7;jDl~ zrEKUFOXWdxEn{n{;oP68Wkr8pcf@;-{Lvb2Eo(+E%c0OF^~<$5KeQEyImcRxA_L?) zIwyKw+15((39ywCgvf)|4pL&EZJ|K$fpICxDcE*aAg4QC3(pFmUDL z0xn@U7UZSXZC`0g_?X#Ra#Jm#A!cY9Y5PG-zO4=UYHr`UwnThPDkR*hXN%Ty>ou@p z{o&j{8rm>7IT_#BhPlZ}_olXrTCtYRZSbC)d$@%S4xE#tTiVjJq;G568ZCLEjSYsa zGPJYR)Ff>0V4JDs*6C!MuO-h$+v2rk!!EXEY7(K2*;jfJWBW%V-HEjg){>pN+a_vU zmq*y#sbA|YLD+}jwf_=5p?0;}#9lUBWWz03*+6XoQL{x+=boOBpyo1`Td z4zj`Jac-G7dt;;n@isiC=iIwPZ9O!Gmcub>YA)Kb5oaL>O{Jx|qii@sX5OP?Y*=JC zS#zunVHhVXB-&PM$v-CAu+ik)EnnHvwPg1xqUpt|VF!z=^XAdjYS^7moo=h8(>_msB_#_ zshG+;`jqXamK%H8_M4U)_PcGcmP|co`=l}ac;0qVOD?=%8=x_~xNJjI8t;Sy0bGpe zOMzE0l968aLx;szF)rUpjjjoUuG|5!0xnJbkutj5V}%DdY*>MSmnOPzZ6+oDEhdn_ zCDSe2HBS0Szuys~hg>A@+OUFia)7~hNJ~~U+3@)c=gzg*mTI{HHrqTcxmOlr*47Cp z5e4@&KD>{{CVSgoQ|tA>))=1-d*hM+mWQHSB1xm)+geKN9@+XbUYf>Mlj}>3pV)#m z)`X`*^<1i4t_}MlPQH3>+p8sic_~`Vxt(4Mb$Ts?9_%+m#?VBvD`V(eu>b(a(N~`B zM=gm4z)Oi-8dFVfC`Es?mDfmfKiLq72-3j}IZ4Z`?I5q!lDCS<*iN#*`;IcaT8T$6 zT3kT6dTp-#l<8Y%xt0#7k95{WUZJK&y2&%tRIa-m<-pWdUh)Vvb)=+>sW4<3bcxF= zdr;ZZatG4Sv8!=@X*m*)?&m;_zm<{UDLFaRS2S>YPedfR=W;Fu_pt{dFZ^VzxWEgq zxI+VEctg(Q758PJC@yg6n_&60MiSwogm7`EM)Iv7Lsgfjq7vMmsi&qh?3G-oBtx~p z@w7b8)=r8KlY6VUbgqiLQcFU=1GVJe;ZOz_kfzs=ztfTdwdAkWBtiEzq?-{kmgoj^ zp$gp3*`5a1fhs=F#aChH>xc@I7QuqJ!ogpP3K>U*4Wy6tWLOmAJgVspgw_NunH$RA zYsu8cqC=e9vZ;)11Sdx~m$&NpF3@NlUj!Omf^Pwh_Tno*quu%b&u9m}{4=^F-~1V^ zUi%sC#CLw8oVf5ax;WqV8C{dF`UH!(=M$OYj?ZX6zTh+3lW+HocI2x)QBK_J36@lG z7bZ%GD?Py@?(+l?mw85e@J*i4#rPUeVB!u>H0`bcI=;LUnd0V76cE>T0uy(3Mtk#x zonR5Sb)tZ{sx#V$@9B&#&6jkd-nD9h2jVVBR4*>)MB8!%Xu!8}qKD#2PKbG}vWm+% zqr37=oG34@;e@(As4U_FPUuP8zKL?;>dk0pzIQX)moMFfXmQ~tgo)cWK@(SPA`|y) zCW(ULmQ8e2T(ODr;(pEOP`+Ffn7COJ1#hT8+^LB^i3>GB6SrwXjJQb?#lFK)&Ji?|jOG;t>;XyQUl(8O(+(Jp)yCKM#@!32xA1QVFJ0TcC#+b@BMt1qLI zT=?Qkv_jl^iE$EFUZTWt0bKdAO8{}xWpoX`<`PwiJ1(IFals`NEN-_%0dch@IK{P= zs9xM@2_P=CL`~v0OSDj2WeH5&V~P62C6>|7RJ^s^yzv{P2dzoyi8o$)h*4O(Pne1)uR@pBbFBVf#Qw^>kx!@lBVoK&F>8C%Ql0m4Ed40lRFn7BL^ zNRLedc|;}J$mJbA6sOZCV!qTJqQPxs-x^|Qh42^W9dRiwen#+6imX!YO5pxd@#Bl1 z5eX*Rf)Z`SfN_ZrpuED*2)@cxWbN(lgnK;2&(HW7!DA%y$0Z>vQT$BA&xnLm^r4Ns zqqG;Be!0IHRcR-OvXe+*g{%ZK-%2VbehRBcWT}`? zkHCZtu+CCgSt3iggcR0V0ru&r$}VpEO-cCTSYgbeFqV{1yfB(jq6p?(1amEdxfj8T zxjJzB_Hl)dMKGr#m`f2X$*l9GE-)p* zs|Z%I2=)R~NR$)v3KbT?#Dq!`MBxcjcxn}3VvZGHVvY$+_{9{SYz3H@bp_bEpH)mu zl$3;xMX=3`@WyNfGRs9fSR& z`_JttV=s$fuZv*+7QuvX6+n?I5fUgC}BS{6%RZu+!nMN9+g7g@BgA_Sd?x9PgM&0B#rFWs-V*TiI3m(7P?Jux??50)S*scNmt%C#>3w&(yr)$mfVn{+Zh4+vHom17k z{S?y!jHfA0fMfKyhuqOw_;>KXpk_Vgj=B@HsHfb+SzDH0kws))ry9Lb;4sbZ1>~&2*3!s6sNsQH!#a}s$mMl8((OKS zfKIoOUiX!&dkQy=>aW844oP@TwfdokJZVNhd9_aWH@Odhn4?r}fE-p{xMFbLfukJ4 z*SHH^jzlz@&=4`$XzIfl!L@qoNkL&LIg8X~sZs zT&A5Ml}20%Qq=^P9*8I|;Acd_5i$&veRXefOECm)qY8uM9lGuG*C4rinSWJDY8;Ya zgZCYhAd%Z(=pcjQ27~_qEgg)0exf~tW#4wf^`d4+c-tWyZi>1V0!zqjkDq`T5s=)} z+Thv)S$FY+=tV8}d7vnFj$Gp)`6AVilN;-9(WE$3kWQ=P*5 z@0TJh74>AbL8Ri}PDCmqMCEQGZcF%#AA1;-kkQ=bY%TGLu3i}jqVMG#~#ALt)Lr#CCrm~fG_Wq^t_ML^EOV; zyEr{>;`F?S)8qdXu-nt~7EaGQI6cQgdY*ZD-oEL1_onB~+hX3k>3Qp>=bf9LH*R|V ze*``6+7|PsP0xEaJ#X3cykpaI@S^AcK+yAcO)qw9NvQr4>p;vQJx?AzPZd4S4n1$w z^t?~g^EOS-yEHwACVJkY>3N5yXY1EN3-7=EhHB(E`YB<+1!%msI_$j;z=kig}dj+KM4cn%mV zHvyoravOlVW3dFMQQ2{FD}a9E;1JVj%{ZJQrP1?oaxZ}PEp3zrP0&z%vn28 zZXKE?s{P^`e%9Tz_w<@rj#c&TqBV)AWEb5|M9sVDZ6Z#Tc2m6xa%+H@6VTJ$bb5l^ z0l;;l+`(hFkY|2|pRjjdP|FE&HA?U5?Mx{X(dj@oHjpoddqX16KfPGve*O_t`fW9-KgKM;TrrZr6cc$FkgWI@lIoezvXM$N* z1;IRJ$0M| z&iAx&4$K4B{?37UAoFVmTfUYD>)xl%mE({XKNqv&JzbtFj{#_uBo6|hBrf(O2^Bmi z_hj_-ISo<4p=5aku)ulpaDaL9^0HLqX53wgM6BNm&jiMELj4Pr>WRd z#7L(pX(`iQE=5yL)97Vr%4sq!W31P5sN^*Lwj6QYX{x<~v5Xa{`80X2L}!js#7cDS z7_D200>{WB1;UQeycBt+yEvRoi1~P)dCfC4)_})2O5$p*CErA@-Xq=FyhCk?P__v%2LW1EaRP3mfY2H9X0!Fk^Q$b zb^RVy;qKMS^x0@|re`EOg>qZ&3x<|14w~!qbOWl+iWhWXjXc=>g=q0Gkv+3KrR;#1 zg8M{0m!K6d=dZ$7aFX(J4-0BMoC)mRaN+Ee9z`rCX01RCxj|I4- zg2wBaC1X8wcY;o>mj?g@Y``F$pamOH?gZs*fU-|epN-JZ3A(tEY4tXtz7w=<6O?^| zJklWZ1kFs7N4uX8Z4Ro3HXp7}`!~xS)hoz?kIGJ9X}ejD2l#O_;`0;al8$zspz-Ob z{S_@shm=?JHXS4WiY8{r(*W*gzy@E@@GYEqcMDA974_YUL3%}3w!(Z~QJe4Oi2w(` zXRPWrjQA_sv`wB-ChE2tjvv=O*2WfN>kpW0 z*HeFlZm(0qkMd{_=3Ouhyp@K-7?y*VZ*XkBQ7#9bR@<3%_jc&`I{EI9#{(?f!C3AN zdAK{5cT7O}CKH5OU=g+|0TkDvq9dhmh6P@y!#go=uT#)2c|<6)hfD-Up@ zs`hc1w(f#IJWL@E(`6D~{#b^Z)W{spo#8-)j?9tn0L(^`l=On>}@mK>%!yD{2_ zY29uN>|y%68)I>p;`gBGhw1ztd87wR@|XunJLWsksWs>&-&&X=O#U#X?Bx>A_sT?fWyA{OxBLq=jl9fUR3-%K^qbe?gxtH1!wg z&_Z`qtmQ!%E9y81`^uto2T@fPH97=~%A!MuU`kn3N0R3O{33B??Za?&S+wu4oD2|o zgt5IxV0&2ZtTzX#)E^AS{(*5lNEcL4^Bk6+gS7k{!kB}Ub58E(eo*xH+56zSjE@UHTDo!>am88+%4XWgYy?$nX>T?Hina7X1)Z*7 zG}h6WD-gDhez}5{uA_HXz`2fET}4p2j^)0 z(lykOlX@LOawz&bgyhhw>mrZtT$kIs=ZOAn=~xW^=@w0=Z_72Q{|(t!%^n(!>{rp0 zZ9=xV+sj!~Tn-((!Ic!d$u+mXiMTU|mfe)w0bIL@F6L0^-*OvO&dBbNQ>7>AZb44k z-?9&=nSXOr-u%rCYk3PMhlbtaHXBt?^R``pJQxD9{j?r`xZ(Bhzw>tc(UG zxs$4lPbsKr{u(k{Uh(Sl*$ zNGmN6y^-!(Fi$s9ZL8ePW1|?&?z>S#={?1$3Gv}_y@GW_wlfETzbhfhuc|VpLs8{B6 zeS-ewQJ*Kwx$_BZA&>rjg2Bk6c2CikJff!<{XBa5lrx(>lY6Sd9(zLA7B9+r1Yr?n zTa~bndmdT9gdz=cv3lmws$6dALlv}q&T>+ob5(}t%+l}$*T3Wib6Q?Nhk4ZGC1a~! za_^tMlzVz`FP7j~C!u4F;&erZ+2P`r3KTW7l{+QflbzM3^{WAnJ2h$ETQn`}`&KUW z?3L^Zk)K|{67y*AYeYnObmlb{>O89Y2CC1a@7`dE%cIY4xJCW`gPMylj-FX!aJqVnj^Jl1*OJ05{?@3@!8-f=Ho-eYT$M}yuoOXho+XCA$J zFUP1I+BzJ~EkB|-P5K)h5*LmXsB*;z9t7hDc!@j;`^a@o{s>2yN2fl*vh&FK6H6cX zi8FV6!ko>cm!B}Zp~KIdx#%Y!c0mIk(`yyPxI*Y18tr3bT`8qLo~>Ul1Ths z-cqvw6xc=s1JK-Uv^@X@w+;XEtTYD*4}=wNql7>d*hZ%Um39Cnf}qxI6cdE{wozJ; z(h|TJ#N{dmGfP6S(iWMS!O-9~`lMoQLO63th|&s~7ekb00B)hM=WWzEl$}Cqx zq2z5;zP!@feVeebv-|KoVR8|CzXFS%S%IaT zQh{SduAyB;?%%44-1yrS(dq5ckWEN_0Jwx5fpay)ni(Bt2DNbNnR|(zR zPWM$%zp~N+SW;z--*&oMS*fnOL7yr^m_$KgP=Q24!cah>Oci_%LwyppucCAVSYHJL zCy}gz+EpP;qFGfjeiB_$L4|5cXJ8YmVZZl=uYSuu344PX51rFdPRT$p^`d9;&e1JyPR7L_^s);#vfFf#v zv!pM^L=ysS=u-T$PpI-dohBKKg%)7SzWW z-J%QiVOh7xtAWxc^p>c3=5#vL07H>Z4h@w?08JYzO#x;$ z#PFum*@jSEI+baJuBTICBP9YK_HAy2&Kl`NBiNCVbdAwyBXw*HDMnh`7#40MV`Fs4 zNcEaPJ4Ra41QuYV2P$aSREcpnipKp@1CNX9)TEZp@h4jR-CNoiL?cFe)KuxKviw*J zEc+s;TT`&$yEY$CS2u$p8tHyBw9QEMn{!pmnxj#_(ofAXR=-jnFlX`jE8$H?CvoOk zmX7Dm3Zs={A^!;tYoXK*y&%ZT@gS^_DQxNiR9!&6r(arNdVf#FqtK=ADJBXP{6yoU zpy;3IP!#IelfE@(<7WD&wbCFY{~^HWhNBx6dIr#NFAC%&euuLdr0eMKeP zLDF@K5a2L%5x_!Y1;`@30MBSUKr`N`{&=^r9Nwp@weXhoGraKqANmD z20att0C{u*_(atOxI`TVFw-c2X5!Iy!oS^16p}N&pq6|WTu=*pV6`vj=(Lb@k&FV| zqK^WkQ$RGpZE7Tdk@^5M6A!!-PT^^Hft-cnLANT$KKTn#-7!@(>IK>_Bz;ep1^5Zq zsRHaEx6S|?sG0z|)LwwiG@`Te8A=_|MVY62L-)HV&4R^b9NjSC83Zm`v&?D$!M$0g%*HnGNv1tMVccE2`Y7d*iAp=bdsOYK zR0HVV7Y;s+Qv1RUrjgQD5s%`k_k-h0qnZ5>yQI;nekh(sRr*788ZGXRB{_{A^+!9? zsOFJC)(&c2SpsN)rJ6KzP1gWE_b0>?hqI zETp?>@F1n0vv>vuo%f0mCuelRq)oBmZnuQ)7byJ@cqoZ=C4?50uih+}rs$#^ahFa)~YO-Vx#Zttc$LzJ-q z?S?{)yJ`PW=5!v$Aa0oA57#xsrRe0n()g2;-6e5J$8ZD>FR9&dtWqy=+W=yk zm-KnKG9F<32xY9wezKH~=IzBlk$)J0_46fl7>NOVN&7}B6;*~eUYfj5BNb;Su3}F~ zsy$jM=ag`!6kPL5sxeCG_&=3z8Ksnhn1iE~h5)FwDwQ3rltvm=hft@{O2GfruOE$$ zaQzkN>}W2HMg-BnqcMs^XU8a&Xv`P|Uy)Gy7$r>2>ZALUf)fz8{z-8Oh_C;oEeRO= zKS?)EsZ60`VacG3RT=|;FOUw6#R|?`L8OdDjm+9ww}&n#Db*-(9G25P(I?%zX^^D)sXP2&8~lemIQlaxuIM16&=)gH?H3PZk!0w*hz0a7Mo z)b`M)$;uFb_$drpd;@v+rYO7!;{#n-r#9{5zCN7E5Hp*=HTH+!h_w@NwD;4kUT z48;o>GrvXcHVAJdm5QSP7#_bCBEaYb8*t zp%=tC;@kJdUn_BH)_r8fA1xbBesh%=H7gpQWz@c2){hp?#q`@l=DEsD0PiKRtE41& z7&d_x02qBSMi}u}01S3@x|$5z~BYJuX5G|VBG!oT4L!Y1mf-OV?L)!!%;zy)&%qr2ZIfk?w9DpKG= zr5w^QhKkg8A)-~bqI`=;Tc}i3v+^zC*+Qi zq~*#KfEp{{S+CLR6`0`HsMtzO@M|<-C5~+HzQRhTbxvX0@f4<2UB$G`tI)=4l(&jm z;=hBpxJGBc!>W6YDy+uxca4^;Rz`brVC}dB9$EZQ`8n7?hNdd@oq0K0gBLe@u2IH; z`}7)klWP>Q7VhgBu7l>xk873jek`KYDu@t2SS5=OA2MZ#D7qjOT=*~LR7{Qc)BwH{ z;Cn&?-;>`uWez~whrlEz;smf-!@#i#hGnDh3Zf=H`cupPDl?4D7)0O!Eu^E^y z&uMH1f`UJ2e+CXa{-9?Wu<#G$yaoO+o0@*F_|udvN-eK!bs--f$-&cL+Oky{Tx<?NB}~YM4)H^$Zz{DYYKJnMju*S{qY{T-0TDmcGS5zYu^kKEek!p;`H9kvilcM* zHSpkvygt}@Vt2yt?5DV$%5DJnUCNID2X-mj0Q&yK*yo>=?ZCG0hEh&b) z|IAqA0d)N|T|A&{02uuXV~z)vv|#aNSi;9wXtwx)?YIn|I&s{XVMC^j5cQ%ldTh=* z9fDImMjH<)s{tY;2t7tuB~*BfrW{7=k5S+e&OCKQ`5xG?qiEVOay`b_-eXD{u+GPo zjQ|ghGxqHXWpi-Bix%Q-3sL=;@k57<8~vX%um$>fdzi?h%yrt}5SpGLs^f_%@Q=P&4&Gib;Qiv1O8 zd_m8DML%B9>fe;L0M&k12;lVZ$~u7lXSu+?XVK&rwDu1MbMtT~7*O22}raLC_YGw~fb&hxJa|*d z1UB;T|EudPpsPrpwokAat^ooh1oz+0@mow@hURL5+9NOKeY`d;n58M7kEdzw6#8o{PZja1f~ zF*DRZ7h}AT>T@e*wNA}Lk9Xv0f?p_*n&b`E!h02V8#~Wcb?-L5vb?JD+(8FiRR`|G z`~gwxZp;jbyLU0uuBtxwV&*{j-eA zTvhuY;~sXGO7aA|=PuRcNsLQB%kilXiu%+?O^J!=uMYehlk}%YJkAc7^(dyWTHHNP zLNz5ermA{ zOBJv%HFl|k78<%sHH?iJ2XQl&(|&t~iLy(*e1?09T`JLY4DBN|_Bk$YkJOdt=+Q^2 z?TZ+tAKDlkjoJoBt4e3k1u2H+^->F8#K4o#%a|n4oA?q_{gKN3DrO%(<=Q3-1nVUy*~i{ttNhmneEAscdVSR zQeKHEsa}7NNrTJ4K{fCrHlTwl)4!Oz2i3WMF=-E~VV`1l>c>IGuYw8VSE;aTFdUic ztEt~{`}g`R?uZU5GcIN;#N5v@J0Kc<;aGyNnEwaW=C3hpA=-T7nAdkK13aJd9UI(1 z)$Rv2xPvo2%x?H{>2*>wXy#P2y2n2=ubW!{{_rplLe!3L9)R#pU><|Gkia|xF)yKc z8KP$*RwX30q=%nbQa#96I5ooil9|0B3MMxPLaa(|4uQy?!u%a#u@?R*&C!T0(IRIm za};80Q<=jd@})M1Laa$`j)f?khJ`zgIR>%vY0Z%k2h(z4<*%cylRjm?#^-^Iq;8FWvakGfpRM_-ZyVKW54!r?|%x?N2k>dTJhd(N?8hmy;if~=or$%BZ zHY;pKL7XXU4uGf{jM?{09SAmuK~yNhX00t^_Czd0QB3-0YFbe?>swJyizsGx)%K*M zM?_LiRbO(WJM$MelR&q0akD+d?Bd*y-->g$gqGl@A1uL54=9QG_e_l~$turFn*NA+ zmBPfEr+Sw%D?w~2g?^l;{7RdBASRTCHS<(lX|oqZL>ZJaPn{}*(U_<5mo@2IWaRTx zzm+u$Hj;Y(#vI-_CucDYA6vuW}VdOjsA%Gde9YD6Wo4V{7&NDqOO zM`wIcr^=fyh}sp*aS)d(V9kF}^(&e)A)Zt;CqneDWKQw;FsrgT36aT_&DjvSs+dzD zc2z;geo%$0nz6s0;^AE)kBM{#kgc@48K#Hku)Bt(Up=!-bDu_nX7TC6#}mN^J8X>EqXYny#^ z@fSVu_4q|kb)k*fE^)`+QB-(`I&8y*I@sOfRHeFRe+YM7hB@nT9rNmO9ljwP8y>=i ze-1GR=u*F?MyX#@t4)o}cIsArGdT))R-X%K)c|`@oI25fvs7;A7_vVAS@s7|mX1EZ zMRo2R-WT5*#;JUbFwf%D@0CkeiMpWc1LJ4JNF<`}1@{^s;&)^57Q#+L5hz&=D7aI2F)|MTbsU z@^R{VC+>v?;S8sQbM*;2;|dk0Ms()RkL!#p4bRj}coPLn?1q{YVFs$*UCdyFpLfC7 z+)$mnvUu1Pmn_60%$!=U0X=#WVdjJIHq2TOjSZ9FQp2nb@xm}0LX_`jcFFV?T>zr+ zRt*24`^U}^-6O;M&M;Q11Kn_i`>MWnGmHCt)$7$N>Z4K)!72|pUO$cM(%meAUw7tq zH|s;hc8BdxR9Fw#!7xy*>4BOKtIs{mY7iBBqWy=}4lVNa!mu4y3wvR)A66NAV;?%K zT)nX?9ac|!n}HC>=dXhLm~HW+(eOSf>V>-02b1lEYS9XYu6&`@{ zZmDepFo$p9D<{m;Tk0<@iVZ@Qx75}_n9;XXp~0NCW-!L`mdf@!M&g!Q@H@88TPo2I z%%NMV_Yl~D+J<0&Z>bJLF%xg8OG7zd-C>;J_%Q5Ww^VZ%mvX_ywv-=^g>*}u7;a{V zfU*7X3`>U*7|5k+%?QlWr7G=6teK^1!br^ErRtp)ZAW3VU8;tU!dUNA7e|>DAd-(Z z{d6pysxaDYi3eGF{b8n88%LY@5xzYdCB&*~W3Y9_s-0ud3$d!?STr$KZ6Ax8V^!{P z<_d`O)mw*-Ze$Z5gr%R-+!3pA<~b>8n~dw zj>p2jKpFScMaDgK)q%0ti~}ZM7rvmnOfdUo;?z40kV=1O67~p>>>JT>hH*i?n}DTj zs@4D<_Mt~+a4Y35<}+))S&C0wz?EjXsyq#|bGe#5&1?kmY#Lg$T$P`WDYaa! zm~OU$_%YpV4bgfAF6hhE-Wj;9Tdq>hV7a=cML}gYLu|Cd`T|sD6No&s*@)j~v*y*=*n*a;l5@DC$#Zbe z2LtDDhO%?9JYn@*F6Gr+&QNn6i^cP}u=n%Kc2H?NpN-!z-%O9#F^a8H0SmCltWxb3 zpoUfI-~vqVRVx2NG&gVC4v6;u?UzK+;2K`?Zwiq-0 zUp08KSq2aN>{x6D`_T`(=;;i_`6eTAk6xYl2QoYov=}>TY|2}&%8^} zJ8o5EDU0b#(c^CQaw)DVZq;ZRn&4KOv`D%fw!2l=<=Cw7WRMocR^Xn?t){QQigK&3 zE6^af3SDXbmJx4pV|#suL7_jG-if+}_YK1}*R5`^L_gzspjEhWUaTTkp}@uJ!YZuB z#j5gZ6tP$>UyXJzRw>rthHJ6vy$1W_Vs&i|Hb~CruTrlC;=A>Q1!LrzQzpM5e@ImjpTD}1r(RY<~BX+#+YW+qu?7SMk z$;_n+ZbIjrSD~BG$&AL}ckbky&B_psHe)Y*s4i?aCuDd?t;>jayZH}Vm#}YGPkab@ zt@>_(@^!U+3x@c*db0%`b6vIGijC}n^4Vr)QHQo-Z+@U+x8iEY$iSmTQQPoo>7?4Y z4b48OGHyq%lWNp<>_sQlzuR#ea8h;IfsOK{y0(L3)pw$rb86g9Yc&QSPUYH#Ta9yS;Vx{x=ak=W6n0KU??!K*Q*U=;&p)T4{=(JkoO=0}nGrvJ zd+kB*+*I}VVD;WqL-xSto9fse%<-El(_U<_H&wU27`mHk-(JkOMe5UDG-Z(r*oQ0Z zB4zA@<|1`)pIH?m;eKp&i&WkHSU!u?(*0N)i`19>7~MrUj}M*mx0-jrEDdq(0E)h^ z79B_DR6U3xhW~|w$aP=sJ%|Z-Uu8UmwQyezIfTh@U)?!`KDw`J9me(MzS?#e&AP90 z9bqx=2xi6oSw~r;`%!Gg_tovA#8jnY*rxBRwZ}M?{5Y%pejHa1uDU%QBsy>c_rRG> zU|f!}-SF6Z#*o@|?Qs;4HG0&O#lG4liSo-C- zE}X7lriWiDO7D*+95$Utr@vM&&ZDPZ z6UPHzF-s!+>bhB0RlSM^A5cTDVq6cXt5qblA;1ug%^7cg_wh`%xW=cswNF$tdhjmbMl z<-28e|4&)hZowKZq$&jR7lJ@m{MLEfOocG=Q!U6^Qu*J(>YT1F+%a>g;dfx(bhYXZ zmJFk4k0rAnnswC5yJlXGCF;^$tdJ!t?>*cuE>Vl`nX4db-8VNue7cVhen@+a)*gI- zJ7~(;!{f2)`Opl3*!vLI>bcTKaH7x`W=`r*XhKBNCCqUu^^bFaIE-hS{@OP=B=gq{<}WK#Dm zGrKQwOkj_aKJiqgFSui?SZGcnl{HIjVpS*>X5UqxTx$`6JMEKeewJ{*_w-N{WH)?ucp48kWS_M2Y1-}Rnqrn2{q{-%&7e;^u3u= z#rP92SaP<*HVlx)fM=VhZTD5f8s$E$yYgg-tJ{)+MxxBISNUtA$Pp zF6X6QPo17wwMk@^`NgW;iL7c+{hr8b2hldM)du3VFN}yuY?VSdtEbffqL-&t7XsE- zRG&S$c$ggq1dBUDz}QNvn7362VOZQB0>FJ;qnV|BLnv1c4%gG)Os+&hl42zLx)gOt0%}pRfAMkYxO88%G#?E zC$k!P?d3tN<)wn};g)P{GOLnCP3w56gR6H3s8`9XL3+OnRYUwO9F|vMPEr+{+{&vJ zbKty#O?&3#ONdEaohC6{d6R~b!pfyraS?Sig;m&-YdYOfbwE$3MqEjcBQlmBPBMa6%GM=NB>%nN}SrtpmS6BS>@H0)K&n( z?^9d#wbl=u`p1pi@x@G|E~K@5)zmar4(MPL4OV6vbj*C!Catvz!Z)220f7O`sHUW| z3L%UkYy6-3QfJ2&^iMi$;my)p{?M74!OElNrML1TjEiD=bvHfQ#}-Y4K;a!!f(%wL z!lXX6Bf-ti6^IAboX*kbIEqJ*gw zTX_&(lHD2&v9_dDST)bV&35I$WLu%)a#%wl+Ly9|)#{vB!z=JrF>23{%POtw==X=0 z+H5>YuYU8uP#nx*rNp}xR%V^}e7u+1a&bw3x|hqUfV`P=b8ijFZFNKJTWQqim&dAY zTRR!n4p^E?P0fS709}>`Ghl_Plb1#LvQ{x=<;9M*LY2>FO@p|Y4_gus+PCSp5zX^k zqmXhdzx4oOPXTKOM4y6IG{pCU+(%{wtD3rA$O;AOTG6Vhh8DJR>0)H;uNJn-AQ^*B zBZ=`YsFnp==@G`Dw}ZfV_k_Su7gc+TSjiE_cxO~ki&!ZU#-QWFT2X8PEAWPjRX}wt zYGpOwiX12lZwF*7E>ilu`O@!(+mSwOg%1RrGgelFSANrmZuqpkh&A2 zS5Dn8%gx3RR=}GN%rR(PRo3#j^6&{@rCL|Q40)wCRkDUav0dG$iUqKJRy8XGk^0rF5fB%vSqmU~)wdd}gVn8u2&b%JEri%y!?S{zG)QV75>tSte zSAW!lq1%;52&Uh5RW1Z8m2D`YHicLv5QZU@Rj&HD^sqHl0Su|3sy09q*qZw4S_5qI(ygxGxD-(6=Bs=f40PYysYj- zpb7a}VF$RZx*1ke^}LnUKgnh4yHc0E;zc)Vt_~Sc>Cnwetq!%e4Co!~W;Ic+HrSFb ztGG7SNr=O3txFIy+F4s6YPQEfqwMz91&_<_^`TZW56C?otOStiK$!IcV*PK{bBHf} ztuE?EM-0Vf_r6XR&bPR%E`?((E~}|gu=7S|Y&e%yP`d`q~>;6mC>kb-a)D8|nOJ^-#0=TAk1&G|z{c zgr==j>mzYV;^sA1^`orH2&0V(0?liz2KToDJ*KJo{c(#oO`Yj)Wq^3vA6J=Us`mga zuVd=X0IVy%H!ZEk4#Xwxn5sJr>nPbETzHPD=fkW%D)C^eGr~OvqvT^M({SuaC4RSh zAv}M$)q2(twDy?FI|566+7PQG!ox@4()nVDRU6^6Bd}W-Ls+|c(quXi-&-v)pOjN;N8&zaqq;p3Q*NUw z{0Gb!GYXB`s2+|&2^&?f(N-ixz40(A@ffR@&OhA4TQwTLy}CL)2KNSB&Gq=}J zqsC&wZ&ZgSVEa!x4mSlGRf>sLPBnNOZUld=J#HKpE?3*r#r5hoPa9?S6pA$IRu5~n`%{5XD3?O5q>k#>g0Kx zDj1bW{ka+|c2+d@e6G2y%03BQeq8mOguywk?oG0yAR7K@l~U1@u^%2+v6C^+j;n4{ zuxyX33scZdTz;g-ZMAqRVrSIXsTeBG)fWO8sw=N)XaeWTp=wRTeHdrWrJ|?dusR@IU?x6|Y*uS#Teb0UDL##G z{=N{%Q$!_JxcG3!!m6#pHH34IhCs$fs`G4oyy4tCJf5o1IoL6ts=w#pn(|bAnTLti zWiCeRsoFT#D(dx=x+!l_Z*?g3)7q-YJWL?Y(?ngGhh2_yw9v{0)bRPZs6JMi7UQn$ z$9&v0K2}*4SoQSR5e3zp1=u$EJBhsN#{$f>$7;!9+~FTvXodXeyMTI&(2tMR>P1#x z2oyL*E6;)`wZxj|dxq=^?cg0Rx~S^2ZhKnQp$ML!Pqh@E70##0&6bZ#|srBzrLCgp!$X=VRU`Sn)e za&uc1T#uWtxK+5?-d5RG#Xt!qoUpr$k78L=?2VgR&E6Wz3%u?snkX* z3wy?7FN9-f7;orJD*X}^V(*-oll+kYeoDoo&~Q@-jW`E%Err+@CuTHF!pYwRyhzUA zhk);?7Gb0Q(%wk)1Vs9l%y3bz;WlJe-_dK)f;x zQVJ+vAD$^uM`|H$8?s7T0gDGmN4_ zx=rB>#RW8*$)KcweRCO<7T~#@L0JKf*DxqA;KpVK6$K>zi$P@uRI-#EjhV(TwCq1h$a=oqHYxTuq74OK8T? z`>fK!0bD{WCx+M1IJu30bT1jS6VU1vgHQqAH3$@d9;AGX*_uSjT7=mDpr{w058%}k&^^8)pbl2 za7}}U5ZVg7T1Q!;QIrN7biIf9wK^K2U;zlh$2D=@=`g@$1krAhMz{vdq zT551eKqXzkQ3tr3B2PHPDde;OyO47P_}!lbExDSD0#a*m#Q|*AH791gXvF$A1uQMb z;C4YDmtAD05=`!i%9+v(9tzk|iop{BPc<+Fq}5rT2{@v`O98veaT%`##CK`(PRM2r zJ__htiSxt>cvOMGR{;SP8T=5?P=ok|oDMT|8HofeuFhG!3eojXzamzRiLa>4)+)&a z;N3GC+>`>E)nt%Hz!(kE3n;3yWEAjHgUkY4cr}em4G@AC(+C6!7_K#P3K-vjHS!3! zQlCM70VA|VApuYH3@9R?pa#WVLh#xe)mut{ubxz81$1q~8WjYb)u6I~&P`dPnt;<9 z)D%##8Ee#WfXl!y*;H?cLkzsxMxdd9O&T;2khKMCG#6kmh*kpZ1<_W3y#zu9*h|3m zn-F^mgbT2jKvx0w66hwtUIIM@*aO~2fV~7F1=veqfDN$z;_DedSXAsKFjRoO1cnQ+ zm%u0i_7WH?z+M951=vda7V735z3Ct5>0mA zfIZ=tYXHyahCSm~iHber*9x#_{00H`jNdH4p7GlR*fV~o0DH#o7Qho8_g{O3*fais z0DHzC7GTf#V*>0Me^P)w=}PUfIZ`H5%~Gqf7b!L zLhL-x1=x9B39$3LA;2$H!t*sP)%OlyD?U0gqfdWM z{v@Ep00y50Om%7URmepRz6&Tdkn_aDdyTr%SPkL}s5*!>5(*flL1F>VHSiJ;_&aCu zDNOr6H|2Ltl8DM44U!268p3%}2*@~^K`H?cG)N<0`WV(oCm`E+27Uru^j0gnFr$!N z8u$xX6U`b~1duOpWC;*ZaWaE!0t!!MkX-=1_=+q!1=u5($3^4|-P(|Q8mQ!V0FPxs zCuUTf!O4XM6qv=Jh=9`?6cf-zu|^32!{#$6C1Cpkeg9QP$ccrlQcgh75(X6nq+7(4lMMl54l(F1;HB$0lb%BCBKr{d`GZ=d1GtXOvOBEv;x>tzNb1>mJ&(ikn^=v4+|1$bX)@P~ly zHyBJ1@c9mdXaQ&M>d!xug*4WmN~aRwSARR-=RDILz^$L@#Ed)-I9UnE`;@^P0k1Wf zC*WxeYb?-!u75=>CW}NRQ7nTc0tP>0uuQ;#=L}W|*#3gSDgmLd7_1Sn;vIu^0tUPX z!1Zr~5PHQKoxDka-TJKr_Rxg;`vYt2Z~%9Q+ld+TKXUSJ0hzus*dt(p2KyY~Vwd)) z^ngQ*7w~Zpa7aM@9}JEN=o2qKX&e(kUhV--2sjs?!6^ac$Ew^RVR?m__%L?f$9lgC2rJTd>x z6YBu3^tlsb|N1EMrGUUxT*p5GOby-$Se}|S-Vt!IU14dMd=QmQX&L-0U_?3waRNNk zGx#E4k_O)dB+tMaKLqs7$iM^dUhD42?H`}Ne@H-NA9WCUFGFh*JAfPO<;09kL7eO( zAUHpRBm%Z+kW4_Q0<4ijK>UIXQfWZzue&B`M5SmER!Juyc~J&_0=gGtkWl~~834om z1)MCwAd7%xcr6_o0RjdR!1~K3gx*|-N_GPFOS}wn5x7JB^9;u|LL;vOxPS6HF(M*DxU}8N6)q?5z%ZAch@-Vc9s4Q#Dpq2o7Qyv<11klL{0QCr5rOu(V5C9rDfE(M$ zi5W>+aB>p?U0cWJ>u)n59ylQZCA1JgMTuYgEj&#YtT->kj|_TDqu|)24MnP zcV*C#fQv1slNC@+xTw%e`T$)7(ChgC5d!FSe1L8O?AG@rfcpeI{fOfWpwY(x+#UU# z7+%n)WS0>ogw9|vSeiuM5=p)Ne0d#9$Ki!EL2XWQ|l4r7^ zoPpl{2f_1)b_riKm?L1rB-WTGpe{~%K$Zmp=-3B?QE?=5)XTtbfRf8Bq(lxFZ6ZuVipcz)b+U|2-juoDcw< z5|C>(gEIomH4M%PNVAT?1p&u2xFn$W2G+PDVCPl_*9f?{DRdSF>b)T<6Eyf+K>Y2j zaa(}*E(UiA{6qay>o$W24&d&1YZfpO29V_UJE#Inl;`E7=M<*dktv)O*qfwqo_>0$l#NJPL~;c7LfJ|gRcS(US;rI zK;l~r;uUfFr?&?21>7Ql>t8}4b{BdQ*g<_tPDn8>J`UhMP2$9iQTI4GnShZG8Ke;4 z^@u?#0TVSy;{cc8|Cm+MImCGNl!2ds&M^!!3OJ{Mzkp&UYh)2XhmFAS00DEKF~}w$ z)pG`}>_P@I-O|h&37txT`^90pC4Yqp5&9UJ3aAv$>Fg-mKD6 zfS(V8)&eGL&{jY~xW+(J+6y3G82}vw*p2N-U^aEIO3E6Y0bESD^<5=>Z*opI1Y}Rk zpu2z{SsC;cK<+hAX>S7CsE)-MdAsgMfShX>EAy~Me+O`#16@wk=#Y=o2Mc(o!4Ltr z3$n&A0pqGN7%reeH3lOEoX}vjfF9LZW2^&Q@YKQN4~G~lYcZH0fDVK~+oA=~Q7{0L z1thG;V5)#jAq=JoXj7lT3<1>|FmTNha;hPd*#eF>VlY<#9UX&u=L;y=l)*v)Uo}`P z;NNDfu~a~smJF8L0PC-2D<&&NrEePss|C=BGpKj1fCr%r)(Z#^W3W+xcV`Bh1q|)N zV5@)^5d>Vub|GE5vC2*X<$5sKC4hXBz>vQLEbqf$uK@B&0*(Cw=qww6g96(8&fu^H zbp2`IVscbe{vFBSxPYQ#8JrYw`ws@E1;mVJaQ1&-B7^e+jz%-MDB$H}09=1A3n5o0 zu=T0{Ivxk$x`1gj7~B-lYA%CY0(#A7a7V!8g$(Wqc(jPY0|xy4f0rdp9*N4gr3{`3 zuotgMU>>bz{}rt9%mKWbUpO)2@hVP!C7|*;2CoIsxjbl^>#dON8<@No@KJ-00)}j3 zjZXq>E4~nTPG-H|&Klnxz-GmR6P90+6A<`7`psNBI74EGa0V|YX4Krp$vy(kXplsJ z_g}1$Ou!@!QV2+Jjx|yV7GTvs8Q9!_52L1w`-eZtO zfX^ca0Ro!owgwS!yr1*+C#OTW^|_sxu_A^`$SZ(c$Y9X&3&<49prC-n-x(AZknjhC zA_9hJP)q|_f7#;k*UBYCWt<0tQUcl}U{FRt^Mnk_37`{@U|9tLyEUjJfc(=yql$nO z-T+vC)r7ME5M+q0Iy&My$QH@BBm(8q_3zXEW;pD!20qG z`U|isF&HRdYE=e<1|QAxmqs%18mn>o6ECU|U@VV+D{q zAC&QjfMm@WOc0R01%qe-Ra-Hb4B*l$QMG0=RaE@jGMFY{YdZ!r1mtbcV3vTTofym( z5FE~6u7HCY%oot0Gv*(RUnt~WX9kM}^ytE1serE z4x!rhMd|xDo&kF`*(fUHbqFe(104pAaXo|qXOtu3D|yuzzx1E{mB}q9l-nSS&F$h{lRoj zKkuX)wG@Mk0;1eBLMBus%aMJ-?$1Nvj9K$1_NWLRr%|Ze$0N+gv9tj{vN+{$Bfz?#$@d2I$OaeduMEJ}BY{d(TJt6&Yd@~6Be;m-&MNbaD zaiV;37{EILc7~4x4pRwp@jX2>;vB#gd~sq%N&IStussy8eWo zVv;};06KjPg(MO%_6h?}0k#!B1YVF?Coqo4lGFigR&poCXOtm1rGUx!IvpUjOURqoGr90GCH1(x@!pk}E!wszR0|WKdneP;Uk` z1*A;MptgYR$r;oY;7-LLL_qs=3>pZ?>c^l_G5Y@D=YeTTDobgk=+GG0+QI=mP_3Mp zaXvFAw-Hb^D}#0d)&(#K74RS%gD?T8g0Fv_i0q_FU*jwqRNBP>Txo<8bCSCgh$a0- zIIRZyy&S;$eVmvvDlaGZ6HquGgD3&6dWDz_5R$zJgFyl=lw|O`fbcR5h6*5OU9f`J z($8j%B#jF^?Q5~d7zePO;}CP{^s{w1eY}&-zPwQ6L;-v2vBo3;!4?4x`ZL%jz_wy1f#qaY zfkCXX+W~CW9**(-?}jm)zRyWFJpW*DK*0O)3=RpXF^Rzu0Sl)xI3~b<8iNx8mP}`G zO2D$2iTM8ajFA0`Rn7@mG?&2z0pxoO-Em34*~JX52$;Bp!8HMOmNK{@z$#83mCS6!BYWrj3I0{1uWRC*MF>#$Sth$ zTtL6A3|m5Kt$p&W*_vH-g~su5UB{WJMFYt(cA_fKsnX3YDC zlj{n&{hdLG4RHVQ-HX4eZXhZ%lQC!{;CX5WO$7Xvo z#EhZ-TtH}Xy8d&9t$|F!L}gD71|0=l$i*O>z;~)6CLe>Y4&XWrCuXE9z{%YOBrnaN zCxP=+Kv0dueEsj^5H6vg6QgwM{r&`Ss~OK2t-&A%u>S8(%-B?u^@j=|w|OYQMF4k+ zI7Z9GpBF|FxK6;;28VV+WsF0($Z<~0xZQ-4#}mNqZ#*M2PW6O-v;$awvJ-QX|0Lj? z`bqjT9KiasoS5Ot+JVcPEhKLj26F|hi(oKcz>9ti778dZjKN|7dB-zYN&ufF;u)>= zuiYygz*ek6%%#)kPvs12oOC0jVz5pCoi&PfY!J|U34=`n?kr=lMZk@f47LdeZE*^eOHcL1C9kYe{KIZQvH@WcU}9OJ}{Hu1P8EdfcA zc=G+nGa+-+vC0bp2mBbk67b%i!D|6eaxi!+U|=o=?*)v>%iyB`I-eE2`-y;yEw565 z$!Afa^I4(tRltEl4899!7R(?X91!Z(KP=B6zJUG}86*^7_m3w5d@AQt9(n)qaR~QV z5+`P4sKF&96F|p8!mN}8UQ!8P8!<@Z04^Y%6EnW!;7I8E5jacw`*AKTKqd$1?;kQd zQ71isz%t5^6(`3cLy!Zwf*elFnAVh&a|yW6mO&l@>!^UrzcI+~04|`Q6Ej@LI&ykp zA^Ey7C?de8AA@29e*V&>qyxCf(oW1cIgs_s5|~RS4AGlz1qZNxB^{&hKkW3XM1J0- zYdC;2)N*3R@DW@=9RYOoEjpx1`gl?8aXke(lpj@0^rga-p*yxOjJHCXV8Mc z0cyn5wG3K2fQxMF#Ee$kI5||nymbtI6HsA2gKz=6Hv-`Pud9&ln^>irfQOqI^b}BP z3xhra8tHl?1=w~D5MWz2Sb%NWPzEk)n{C=~QDM_&7^4K(mW>r)TQ**RZCNyd`BZ3$ z?p`z~QwZS8Lw1r$V44HC2VFCqsL{sFZBqiy?q)Doz@5JsED(@&4}--566|HLOaMG{ zQl%>e*tM*&0q(!-YSxR2UCkx|b~RfC*wySHfUi#D869;syBxr6`^$+r$@>T_A^kNc z*{p*l=>CfcXE-eBs}FGcF#%Z)GdL-r!Vv~%1av;e;Jkn(x{gZ%>`Jc^z!!D#jI^b^ zdH=g1WKmgf>aJS^_L9ojx-^PWxsAJ0j9t+K0d_@?3B*t)H~^(mVjRGYvz(X_c$Sl& z6L4{cH`kfG5*4@tr4rr)npc6R}&z>t|o{8kJG{C-1?jZ_EX!sc{0f30B%}7CuS`F#8wn^2|4zeNw5HT z4<$p32{`hVK}i7xelRE_ppFMuT3*1H1Pm$(=%~%A>HwExXbp!rrqvc;TUJkiZCL{W zwq=b4*p@XDU|ZHwfNhzpjS$C5-GG^5XTk zP{{jStg=Kv$HEMj3uvw@T_s>}an@KXU~Opz8w9|2Dh>unt^Y=V_qmgLpNm(@2CN_7i5cl?v3?=} zaO6q_cnQ!KG{e^hXg^%Il1egBvG>xH0!}w%jWhy^HfE4s0Q|R785sra(jc<{+wcGZ zx>}bJM1)5L4qd5`oC0d-Tc|t^;Kt^2Vg_8hl72w}_9ZY_K&keuQA|JuU1CWAE_*7J z5n>N~c>%Url?0sW#-&yjVBhN15MUc#TYzmyJpr~M4O~L(QX3OkL30C+L8*Do9Kfw_ z;lzv{{W!U`fLuKpv=fl67lRH0W^2$yI0oU=WjGqB2N>5d!|vV6=cmzjKyx0+cRwf`B9= zS!0p__@*VxrV2Rd(qy`jX2V%!mViXMkU0XLj%AJc0`6#yMFP5yW{sr+?&>Tn1T>k< z8mmju^@lqEE^W!Qb)o{7s020&=r@7E76C!g47Lk6qk&rhoTO42e+d{cmBBs%t~Xlc zppYX|SmlU-UOLZl0k!nRJS700aH*QJ0{)o6;DUhoGZ|bK@K%FsJpZ`^D(O;hib};f zoaeTH`g0lF6JW29hXU+1@kD^VCQJeLnt0{_Jb$eh#Y=}6;d%+Y7GST5cLMA+@lk+1 z1L6eOYvQW_drkZhV6O>R{L;=0uop!l0rr~k5^zvYR$l@3nn)(Vo~$Va*lQw<0DDcO zw*l6lJ>xTqiao(I3$W*NfB<_w2MMs}b4~&Fl*l8%p3eCN=;`b-3JJ02a}fddd@e4) zp3kKO*z>up0DC%D5MWQ|$^z`^TulS|{9(`MnxbM)-#P;9=^G-zp1utQ*t55Z0DJZ} z7huocRs`@&M?9nSdQ4~645O_>c!O{6#Eka~c)EuPn7EKZCjr4!0-hESz(vk53<8&_ zj=3jUqlakNW%PD{%diXSM`SzYDSDjq^mhQ8HPDF}bvAJF?*e=_GZ-eI;Z_DC1nk12HFjYYHLky-1xT(P`0p3ShV~&8s8q60^ z)pd+j7759xTfbC*-5o0g*d4H1fZYM>1lS$0QGne6TLjo0u)Q?x|37ztTU6{0_)CD@ z0s92l9dJ;9-2q1g*d1_OfZYM71h}f|4mc~s?tlvfcn&3a!d-OP0X!eBIx%O8-4KxP zH0$3IF#jxry8>d*Gk5@i_djUPJ=El}s8oViW(UNS(*5@X*5~a=>^fo0lXmEIWZ@>1A$|tfAb@c zZAS;Nez=a&^~e7HqN|f`bbY}Yx(R6fl0i=axj!)IBVgG(29W{+w8j7dt27uaU@V!1 z>(5Xj7qy|o1=yC45@1_4R)B5UcmcL$(E@DCrU8C zHd3F$(Kapig%038UF^h+@t?UnmI)a2jloI*Ze4(DjgWWoxXASa;EbE<*dze1xCv|( z&`cMyL%=2tb_uZU+#|rYY(D`!35)gr)t5^>MC3NL4PLsbnxm4(w(Nue+p^OFY|G9G zur0eNz_#oP0o04HgtJmDF5|jGxC?JOF}U((D{c$0?Yt)-HU(=u6!1`kCjy3KU=34% zS2_mI1h^u#%1a@)*)HR?0Bxx8jsW+M*@+AJNZ<@Lr2*Vlqr>7Pi(T(m0d~DV1lZNa zca?Se!>%_Gfz4EE_99%Wmjk$ee4Lo^M-V3`6|g23gA@XmXpmaKp@OWDPJn+A7n2M^ z>`MIw*wtheU{{k(fL%=v0d_UH1^DZhO`N+K7ssr#%6lU*r}YdBPmPc6M}PMuB>q z+*v^5MhqeZz&Sb<&|Sd&77TiorRyKp0pI1M(pOaA8J$3s0DG1X6kwb6I{{u8FAUBy zOf>9jMhLKL87;tNS2Iou${8bXunXNFKo=Til=6rduWr2PL^y|9W%7*; zVkhkFu(4w$K&;VXffj4ESg*xOE#7H?w?rrd-d`ZGL5q7@eAeQM7I-wCRPbazi4$5} z)M6H01VOiGaY&0jTHqBBN_(rtJ}vHOfv5N>?Wz`dwS!`hv^cHBK`rpk0Hy8FVulto zwXpCK7A4Ns(PvuB)8e@nco~3H;vi<8FNp5LKg99U1&P13_^Jioa3Dx;i$KuZ84&c0 zJp?^j4?)kmL(sG05EtzB(?i>c(xccA^guNPJ)a9f&!9rkGoKLjC?^Cxpb0_GAwtk& zd=THYpa<*_qsQhT=vg-idV&pt9!!IvC&M6a+U;MYqx7T^=zT4YXh9FV0Mauo5X-b! zrv*I|0!WW~K+ppm5cC)Z!~ren_kYCbr*a7Ty&Hml`GvSkHvy3Jt0e^e7zIH;NUc4zYa^n=buoSUbRPPVdypv#0+VCq5NgkWT zd&RG8I59l`_EYxhJ@#_$RX@`}oFx~V5@*_>f*he#^P!w+$-csh`K^s*v4?J&c?eul+8Y0#%e=bPT+NOrX;b!=)IxA}h{2iE2w)3`ol~u>aJrY;u~|G&pR9(_e(|WE0N*s`8n#9s8MO zQ^9FtQ|oNmRJnCKxwgZ+5~wXI8-b#Lv;}YSH7J+*@aU!KsQnqjmCU9;hPI z{m<65>0s2MU(+8DyJWM?s!{sb3@TZAFgy{-9c{C6#PHnY|5y(1%RfgmsyXRl{MBDr znqvQ(M%RB$Dfz#YE5D?uv)N+3R6f60Ur$=0Q=>Cae{env4b4Ma?~Dq|5Sv+5e0GDo zKIRvkHn?#CBM}O+kpW5^%1N}KbDrG3tnErI3KR1Z&r9TEmo6(;K z6irQ`PW0z3MO#wHvkKkQU84A7`csf#82$M|(W(^sL?M3)<)A-F>Cbm!DJT?6p`G-n z2SppupTiWrOMgmIv^@Q}MbWGDCm}_H>CXp5iy6fz=tFebi&S(F$9wM>kt8v$R`H@Ut3PAS%wGDM z6^+xRUYWTOqf1wGUKRMs%!?RZ!7zC*wNI}$C$JJHz*S6b$`YG1FAyslKK~XHi=!T3HaIOIvhmb*YY(8Zo-UMF-$O-woMt=2mpp*c^#ztBcMv z{ZiK;b#Sc9)03}s(E&JpDj>1G-l@=Cu1ubMv5U^AJQnbYL?HpOL3A2VKy1x))Zn4| zVhlmegV?Xm2gKIHRWMUvY*k!5LIYzYX~)TV=uG_M(e35;v-X*5P8XXE;?u5GIv>MIc;Fua63c&d^I+Wz7 z6Iz4=X*A1-lV~^;WrU887cX)~bo!3n!^3)Z5BsfPr?AK{)h;fnuX{kl=an;gtab11 z_Wb*>&UEi?e0hyO92)b*3Du2{GfJ4PO6Y|C!dWev9rSWSkG!2wGhZjPF{vGjGRi(8 zL)4Z883NrEoIt~}lK8zteWTss$k2LIza)M|(mItAb0n!SR@Zykz)BX8r1dt4HIj||4c`5+w1yT}QRg-dMB+QApL4lRWfY#mAmXV*G(MnVeyu9-y{=Vy4RLRnws zR}BmLd%36dda)sY+Ba0wNcgMPuCGtPQMK{L-Dv)cW=+%5XB1=6VGs^gfyZg%qI=Kl z7ft;GwouwOcxKj~qO)DILs7=>r#+}gF8vlz%-u2RORpSh50UOSo7$l$#L9=Y3T!+F?=crhd#sqqw&Z+zR|0TIf~N9HRCCq9%>hh1=cztfA}BNEC`MVITU4V zSwyz}dD$9Y78HFn$3jkJ01Eeq3>{Zr&(oqtka>@5?TwVb(+0|b2=Sr3MX_N ziZVXKhaVPk02Z7RAkiq0e5w(y^%Jumfu<0Kub2>(brlwzY3ngth0#yt1b zasSMo)QCPc8>QiXNIN@v3co`-G#37bbST~}CsYd_hBO-n*Frip^}Z9z4rfBf2lt?~ zuMd|Fr;p*rXZQlL-3Y-6kPeN2_a7bV&_1CZ8e=#iOG0(KIqYjMCp5K>9f~sIq3B3; z=*H6ws#0jZ)b6tlUbjn;mOktozbR;OXeB<>8aLb(I=n8kE-ifnVC2{Y!l5a+_!xh= zC;7g)l-}csJ2dD`vob~L3jyO_e4y5jB)Z`9hYk(G_a{0Oh>zqtv=}?6aoBCPd$XZZ z`4^OP%^eU9?Z&lFhpyvOx(>a>M|&N5@z4o1!wt0Y!hLDs8?O#7`U1kZf}Kvk?*9S% ziVh|G*9m38KC4;b?@p+Ef}{VRVeXb6`???2{P#wDx5w$X2Ru~C;I?U1(Wh_y z+~3cAvU`>Fib-g>)#Tzs1@K28jOn>uBvuBT_Oh<@P+M~18Wd0zU}?H89jS$*;U!>EGWep;IWS*;V!ki|iXQ(0%pF+w9>UtyNs9clk?I8aE?4#ea(Ghi{2+ zms_`L-EgG(a%%su?or`RLf5(zRlCyf(tA#>dkh+ZYQ%IiNF6Bs&NI<@#Bm27*(SBx zhq&jZx_3a*sNl>o>3q1<`*nYeo1uD-Zj&W9R}&K7BP6W{l#+$_i;RGc;i0{wLOTa* zBSQQDB6^0Rk)i#1caMm)OWEWR@;4>> z^o#nfUwC9_%g{k~${ml82dZ4zcNuCt_6T`SI=;3J-ILl%uRKB$#V0ev2crCrk)aVi zJ4JL1?-v@`+1BuiA5xJt@abq!d;erMtr9<^Bd7{o+&*=3ZG7kWAw5Ykt(qDblRk(# zGNe!ZkU_+JI`-=u+P`09SjTR50Yl=4j8=i=-evP07e8bH=_KzGt_~%8o7yX6ar}@i zRQR87+ow~ma_CKQhT(7qp+iB-oKUvq zPAJhTC-e#a9<*MUwGx7d2hC=~wSx{l*yM!Vo1M^PIC0RaleSiJf+yj(LBnov*q}q> zb~>SDaM7UILHK6Sq1y1tU@UjntMKkX_M+^Ap%{EBXm<Mj8DtCZg*}dUfeYL%(q?)jM}f_5MkhqV)B>@d0iD zv_kKi=Q1n3cZqH_qb4efDWa6*YAols4@Y_3zQjkiNlM(wb~beVn=)FZpQZ6i=y6^rb9uaUSM!P6p4V>~)f?LuIVcomq;F0+ z@FmoeorANu3l@({R5~sF6k}A!o4fj@-|Tp0*I4JS)iW+r8jscPP9x(66!xWGW{ma| kkn-9cvo$VFsq71hB%KGsq0}qvP?T|PI@J*s^;^OJ2NVY3RR910 diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index dc4a39baca514695c7760832d1a329f92e973481..f279273aeddb93d8732108dfe2353d9eaaf94baa 100644 GIT binary patch delta 64 zcmeA<%hYq0X+sO6VSch@O1WvenZ9wdd6KC`nptw1d8&nFqM>gjSSK(EDRD2 T4NQ`ajm-?q4a^o_Vax#l$Z8Zr delta 63 zcmca|oAJtR#tn-Z4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW TQVa|Xl9Nr$lS~(1Vax#l+FlhF diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb index 0c56c3881..aa00ed6e9 100644 --- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb @@ -113,10 +113,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:24.117516Z", - "iopub.status.busy": "2024-07-02T12:00:24.117048Z", - "iopub.status.idle": "2024-07-02T12:00:25.333194Z", - "shell.execute_reply": "2024-07-02T12:00:25.332647Z" + "iopub.execute_input": "2024-07-02T15:09:49.406100Z", + "iopub.status.busy": "2024-07-02T15:09:49.405638Z", + "iopub.status.idle": "2024-07-02T15:09:50.626225Z", + "shell.execute_reply": "2024-07-02T15:09:50.625679Z" }, "nbsphinx": "hidden" }, @@ -126,7 +126,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -151,10 +151,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:25.335570Z", - "iopub.status.busy": "2024-07-02T12:00:25.335300Z", - "iopub.status.idle": "2024-07-02T12:00:25.352966Z", - "shell.execute_reply": "2024-07-02T12:00:25.352544Z" + "iopub.execute_input": "2024-07-02T15:09:50.628776Z", + "iopub.status.busy": "2024-07-02T15:09:50.628382Z", + "iopub.status.idle": "2024-07-02T15:09:50.646656Z", + "shell.execute_reply": "2024-07-02T15:09:50.646174Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:25.355177Z", - "iopub.status.busy": "2024-07-02T12:00:25.354929Z", - "iopub.status.idle": "2024-07-02T12:00:25.498882Z", - "shell.execute_reply": "2024-07-02T12:00:25.498315Z" + "iopub.execute_input": "2024-07-02T15:09:50.649040Z", + "iopub.status.busy": "2024-07-02T15:09:50.648771Z", + "iopub.status.idle": "2024-07-02T15:09:50.799686Z", + "shell.execute_reply": "2024-07-02T15:09:50.799107Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:25.528732Z", - "iopub.status.busy": "2024-07-02T12:00:25.528329Z", - "iopub.status.idle": "2024-07-02T12:00:25.532259Z", - "shell.execute_reply": "2024-07-02T12:00:25.531790Z" + "iopub.execute_input": "2024-07-02T15:09:50.830515Z", + "iopub.status.busy": "2024-07-02T15:09:50.830286Z", + "iopub.status.idle": "2024-07-02T15:09:50.833956Z", + "shell.execute_reply": "2024-07-02T15:09:50.833391Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:25.534236Z", - "iopub.status.busy": "2024-07-02T12:00:25.534064Z", - "iopub.status.idle": "2024-07-02T12:00:25.542721Z", - "shell.execute_reply": "2024-07-02T12:00:25.542178Z" + "iopub.execute_input": "2024-07-02T15:09:50.836142Z", + "iopub.status.busy": "2024-07-02T15:09:50.835713Z", + "iopub.status.idle": "2024-07-02T15:09:50.843960Z", + "shell.execute_reply": "2024-07-02T15:09:50.843409Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:25.544841Z", - "iopub.status.busy": "2024-07-02T12:00:25.544667Z", - "iopub.status.idle": "2024-07-02T12:00:25.547142Z", - "shell.execute_reply": "2024-07-02T12:00:25.546723Z" + "iopub.execute_input": "2024-07-02T15:09:50.846292Z", + "iopub.status.busy": "2024-07-02T15:09:50.845872Z", + "iopub.status.idle": "2024-07-02T15:09:50.848589Z", + "shell.execute_reply": "2024-07-02T15:09:50.848046Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:25.549121Z", - "iopub.status.busy": "2024-07-02T12:00:25.548952Z", - "iopub.status.idle": "2024-07-02T12:00:26.069775Z", - "shell.execute_reply": "2024-07-02T12:00:26.069166Z" + "iopub.execute_input": "2024-07-02T15:09:50.850511Z", + "iopub.status.busy": "2024-07-02T15:09:50.850252Z", + "iopub.status.idle": "2024-07-02T15:09:51.372873Z", + "shell.execute_reply": "2024-07-02T15:09:51.372266Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:26.072294Z", - "iopub.status.busy": "2024-07-02T12:00:26.072111Z", - "iopub.status.idle": "2024-07-02T12:00:27.964122Z", - "shell.execute_reply": "2024-07-02T12:00:27.963476Z" + "iopub.execute_input": "2024-07-02T15:09:51.375361Z", + "iopub.status.busy": "2024-07-02T15:09:51.375157Z", + "iopub.status.idle": "2024-07-02T15:09:53.243284Z", + "shell.execute_reply": "2024-07-02T15:09:53.242604Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:27.966793Z", - "iopub.status.busy": "2024-07-02T12:00:27.966128Z", - "iopub.status.idle": "2024-07-02T12:00:27.975803Z", - "shell.execute_reply": "2024-07-02T12:00:27.975266Z" + "iopub.execute_input": "2024-07-02T15:09:53.246075Z", + "iopub.status.busy": "2024-07-02T15:09:53.245483Z", + "iopub.status.idle": "2024-07-02T15:09:53.255700Z", + "shell.execute_reply": "2024-07-02T15:09:53.255167Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:27.977956Z", - "iopub.status.busy": "2024-07-02T12:00:27.977648Z", - "iopub.status.idle": "2024-07-02T12:00:27.981829Z", - "shell.execute_reply": "2024-07-02T12:00:27.981303Z" + "iopub.execute_input": "2024-07-02T15:09:53.257868Z", + "iopub.status.busy": "2024-07-02T15:09:53.257460Z", + "iopub.status.idle": "2024-07-02T15:09:53.261706Z", + "shell.execute_reply": "2024-07-02T15:09:53.261166Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:27.984025Z", - "iopub.status.busy": "2024-07-02T12:00:27.983701Z", - "iopub.status.idle": "2024-07-02T12:00:27.990825Z", - "shell.execute_reply": "2024-07-02T12:00:27.990380Z" + "iopub.execute_input": "2024-07-02T15:09:53.263822Z", + "iopub.status.busy": "2024-07-02T15:09:53.263391Z", + "iopub.status.idle": "2024-07-02T15:09:53.270955Z", + "shell.execute_reply": "2024-07-02T15:09:53.270531Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:27.992803Z", - "iopub.status.busy": "2024-07-02T12:00:27.992505Z", - "iopub.status.idle": "2024-07-02T12:00:28.104238Z", - "shell.execute_reply": "2024-07-02T12:00:28.103750Z" + "iopub.execute_input": "2024-07-02T15:09:53.273195Z", + "iopub.status.busy": "2024-07-02T15:09:53.272768Z", + "iopub.status.idle": "2024-07-02T15:09:53.386175Z", + "shell.execute_reply": "2024-07-02T15:09:53.385548Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:28.106465Z", - "iopub.status.busy": "2024-07-02T12:00:28.106127Z", - "iopub.status.idle": "2024-07-02T12:00:28.108811Z", - "shell.execute_reply": "2024-07-02T12:00:28.108400Z" + "iopub.execute_input": "2024-07-02T15:09:53.388505Z", + "iopub.status.busy": "2024-07-02T15:09:53.388085Z", + "iopub.status.idle": "2024-07-02T15:09:53.390961Z", + "shell.execute_reply": "2024-07-02T15:09:53.390511Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:28.110759Z", - "iopub.status.busy": "2024-07-02T12:00:28.110457Z", - "iopub.status.idle": "2024-07-02T12:00:30.104044Z", - "shell.execute_reply": "2024-07-02T12:00:30.103432Z" + "iopub.execute_input": "2024-07-02T15:09:53.392859Z", + "iopub.status.busy": "2024-07-02T15:09:53.392685Z", + "iopub.status.idle": "2024-07-02T15:09:55.359879Z", + "shell.execute_reply": "2024-07-02T15:09:55.359148Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:30.106906Z", - "iopub.status.busy": "2024-07-02T12:00:30.106328Z", - "iopub.status.idle": "2024-07-02T12:00:30.117548Z", - "shell.execute_reply": "2024-07-02T12:00:30.117099Z" + "iopub.execute_input": "2024-07-02T15:09:55.362970Z", + "iopub.status.busy": "2024-07-02T15:09:55.362388Z", + "iopub.status.idle": "2024-07-02T15:09:55.374161Z", + "shell.execute_reply": "2024-07-02T15:09:55.373705Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:30.119573Z", - "iopub.status.busy": "2024-07-02T12:00:30.119249Z", - "iopub.status.idle": "2024-07-02T12:00:30.150922Z", - "shell.execute_reply": "2024-07-02T12:00:30.150454Z" + "iopub.execute_input": "2024-07-02T15:09:55.376352Z", + "iopub.status.busy": "2024-07-02T15:09:55.375903Z", + "iopub.status.idle": "2024-07-02T15:09:55.432383Z", + "shell.execute_reply": "2024-07-02T15:09:55.431845Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb index d42308ae9..cac09ab25 100644 --- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:34.059784Z", - "iopub.status.busy": "2024-07-02T12:00:34.059279Z", - "iopub.status.idle": "2024-07-02T12:00:36.809187Z", - "shell.execute_reply": "2024-07-02T12:00:36.808623Z" + "iopub.execute_input": "2024-07-02T15:09:59.845378Z", + "iopub.status.busy": "2024-07-02T15:09:59.845205Z", + "iopub.status.idle": "2024-07-02T15:10:02.560189Z", + "shell.execute_reply": "2024-07-02T15:10:02.559618Z" }, "nbsphinx": "hidden" }, @@ -135,7 +135,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -160,10 +160,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.811854Z", - "iopub.status.busy": "2024-07-02T12:00:36.811437Z", - "iopub.status.idle": "2024-07-02T12:00:36.814737Z", - "shell.execute_reply": "2024-07-02T12:00:36.814309Z" + "iopub.execute_input": "2024-07-02T15:10:02.562794Z", + "iopub.status.busy": "2024-07-02T15:10:02.562496Z", + "iopub.status.idle": "2024-07-02T15:10:02.565788Z", + "shell.execute_reply": "2024-07-02T15:10:02.565349Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.816857Z", - "iopub.status.busy": "2024-07-02T12:00:36.816534Z", - "iopub.status.idle": "2024-07-02T12:00:36.819520Z", - "shell.execute_reply": "2024-07-02T12:00:36.819089Z" + "iopub.execute_input": "2024-07-02T15:10:02.567948Z", + "iopub.status.busy": "2024-07-02T15:10:02.567553Z", + "iopub.status.idle": "2024-07-02T15:10:02.570524Z", + "shell.execute_reply": "2024-07-02T15:10:02.570092Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.821601Z", - "iopub.status.busy": "2024-07-02T12:00:36.821264Z", - "iopub.status.idle": "2024-07-02T12:00:36.862716Z", - "shell.execute_reply": "2024-07-02T12:00:36.862142Z" + "iopub.execute_input": "2024-07-02T15:10:02.572562Z", + "iopub.status.busy": "2024-07-02T15:10:02.572231Z", + "iopub.status.idle": "2024-07-02T15:10:02.699550Z", + "shell.execute_reply": "2024-07-02T15:10:02.699010Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.864907Z", - "iopub.status.busy": "2024-07-02T12:00:36.864568Z", - "iopub.status.idle": "2024-07-02T12:00:36.868079Z", - "shell.execute_reply": "2024-07-02T12:00:36.867616Z" + "iopub.execute_input": "2024-07-02T15:10:02.702025Z", + "iopub.status.busy": "2024-07-02T15:10:02.701663Z", + "iopub.status.idle": "2024-07-02T15:10:02.705030Z", + "shell.execute_reply": "2024-07-02T15:10:02.704599Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.870408Z", - "iopub.status.busy": "2024-07-02T12:00:36.870073Z", - "iopub.status.idle": "2024-07-02T12:00:36.873573Z", - "shell.execute_reply": "2024-07-02T12:00:36.873016Z" + "iopub.execute_input": "2024-07-02T15:10:02.707115Z", + "iopub.status.busy": "2024-07-02T15:10:02.706775Z", + "iopub.status.idle": "2024-07-02T15:10:02.709922Z", + "shell.execute_reply": "2024-07-02T15:10:02.709360Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'getting_spare_card', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay'}\n" + "Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'change_pin'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.875763Z", - "iopub.status.busy": "2024-07-02T12:00:36.875423Z", - "iopub.status.idle": "2024-07-02T12:00:36.878670Z", - "shell.execute_reply": "2024-07-02T12:00:36.878216Z" + "iopub.execute_input": "2024-07-02T15:10:02.711932Z", + "iopub.status.busy": "2024-07-02T15:10:02.711538Z", + "iopub.status.idle": "2024-07-02T15:10:02.714467Z", + "shell.execute_reply": "2024-07-02T15:10:02.713938Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.880795Z", - "iopub.status.busy": "2024-07-02T12:00:36.880374Z", - "iopub.status.idle": "2024-07-02T12:00:36.883787Z", - "shell.execute_reply": "2024-07-02T12:00:36.883314Z" + "iopub.execute_input": "2024-07-02T15:10:02.716605Z", + "iopub.status.busy": "2024-07-02T15:10:02.716210Z", + "iopub.status.idle": "2024-07-02T15:10:02.719587Z", + "shell.execute_reply": "2024-07-02T15:10:02.719150Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:36.885847Z", - "iopub.status.busy": "2024-07-02T12:00:36.885533Z", - "iopub.status.idle": "2024-07-02T12:00:41.284528Z", - "shell.execute_reply": "2024-07-02T12:00:41.283984Z" + "iopub.execute_input": "2024-07-02T15:10:02.721398Z", + "iopub.status.busy": "2024-07-02T15:10:02.721231Z", + "iopub.status.idle": "2024-07-02T15:10:07.115741Z", + "shell.execute_reply": "2024-07-02T15:10:07.115100Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e89a8a43528e42c38eca656e48b7da7e", + "model_id": "c943f13df8c04e77aae4c7ca2cbbd613", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca42a9ff17da48fab63132c9d67266dd", + "model_id": "06405b534d7c49db89f3d29b52da1f80", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "283fc6563d5645c9a2d53edd642983d4", + "model_id": "1146fbcfe9cf41da81392df94520265c", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e90e40189b0e460d90a444df7fe6d1a9", + "model_id": "bd1aa12a83f148a6a04b7394dd645fb3", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "018045ff81b24bf7b8b7b92eeb3e59db", + "model_id": "3840f91804d14d5aa30e594b1e1d7fa3", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb1f3f2a9964471a8a7688badac98c84", + "model_id": "4522cd2764fc425b83a55e8426ac45e2", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "edb46e1892c744119cd3f4a130dfb3e3", + "model_id": "9a72b5f7de474f75bb371e057f0f1914", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:41.287341Z", - "iopub.status.busy": "2024-07-02T12:00:41.286878Z", - "iopub.status.idle": "2024-07-02T12:00:41.289761Z", - "shell.execute_reply": "2024-07-02T12:00:41.289214Z" + "iopub.execute_input": "2024-07-02T15:10:07.118564Z", + "iopub.status.busy": "2024-07-02T15:10:07.118178Z", + "iopub.status.idle": "2024-07-02T15:10:07.121171Z", + "shell.execute_reply": "2024-07-02T15:10:07.120694Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:41.291735Z", - "iopub.status.busy": "2024-07-02T12:00:41.291455Z", - "iopub.status.idle": "2024-07-02T12:00:41.294547Z", - "shell.execute_reply": "2024-07-02T12:00:41.294136Z" + "iopub.execute_input": "2024-07-02T15:10:07.123119Z", + "iopub.status.busy": "2024-07-02T15:10:07.122798Z", + "iopub.status.idle": "2024-07-02T15:10:07.125865Z", + "shell.execute_reply": "2024-07-02T15:10:07.125458Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:41.296484Z", - "iopub.status.busy": "2024-07-02T12:00:41.296165Z", - "iopub.status.idle": "2024-07-02T12:00:44.031023Z", - "shell.execute_reply": "2024-07-02T12:00:44.030422Z" + "iopub.execute_input": "2024-07-02T15:10:07.127724Z", + "iopub.status.busy": "2024-07-02T15:10:07.127405Z", + "iopub.status.idle": "2024-07-02T15:10:09.752308Z", + "shell.execute_reply": "2024-07-02T15:10:09.751709Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.034106Z", - "iopub.status.busy": "2024-07-02T12:00:44.033286Z", - "iopub.status.idle": "2024-07-02T12:00:44.041018Z", - "shell.execute_reply": "2024-07-02T12:00:44.040563Z" + "iopub.execute_input": "2024-07-02T15:10:09.755206Z", + "iopub.status.busy": "2024-07-02T15:10:09.754521Z", + "iopub.status.idle": "2024-07-02T15:10:09.762087Z", + "shell.execute_reply": "2024-07-02T15:10:09.761397Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.043124Z", - "iopub.status.busy": "2024-07-02T12:00:44.042704Z", - "iopub.status.idle": "2024-07-02T12:00:44.046699Z", - "shell.execute_reply": "2024-07-02T12:00:44.046142Z" + "iopub.execute_input": "2024-07-02T15:10:09.764089Z", + "iopub.status.busy": "2024-07-02T15:10:09.763785Z", + "iopub.status.idle": "2024-07-02T15:10:09.767525Z", + "shell.execute_reply": "2024-07-02T15:10:09.767095Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.048913Z", - "iopub.status.busy": "2024-07-02T12:00:44.048523Z", - "iopub.status.idle": "2024-07-02T12:00:44.051593Z", - "shell.execute_reply": "2024-07-02T12:00:44.051082Z" + "iopub.execute_input": "2024-07-02T15:10:09.769470Z", + "iopub.status.busy": "2024-07-02T15:10:09.769149Z", + "iopub.status.idle": "2024-07-02T15:10:09.772223Z", + "shell.execute_reply": "2024-07-02T15:10:09.771699Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.053726Z", - "iopub.status.busy": "2024-07-02T12:00:44.053345Z", - "iopub.status.idle": "2024-07-02T12:00:44.056186Z", - "shell.execute_reply": "2024-07-02T12:00:44.055762Z" + "iopub.execute_input": "2024-07-02T15:10:09.774298Z", + "iopub.status.busy": "2024-07-02T15:10:09.773989Z", + "iopub.status.idle": "2024-07-02T15:10:09.776793Z", + "shell.execute_reply": "2024-07-02T15:10:09.776376Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.058086Z", - "iopub.status.busy": "2024-07-02T12:00:44.057784Z", - "iopub.status.idle": "2024-07-02T12:00:44.064436Z", - "shell.execute_reply": "2024-07-02T12:00:44.063922Z" + "iopub.execute_input": "2024-07-02T15:10:09.778772Z", + "iopub.status.busy": "2024-07-02T15:10:09.778454Z", + "iopub.status.idle": "2024-07-02T15:10:09.785054Z", + "shell.execute_reply": "2024-07-02T15:10:09.784624Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.066501Z", - "iopub.status.busy": "2024-07-02T12:00:44.066197Z", - "iopub.status.idle": "2024-07-02T12:00:44.289398Z", - "shell.execute_reply": "2024-07-02T12:00:44.288882Z" + "iopub.execute_input": "2024-07-02T15:10:09.787107Z", + "iopub.status.busy": "2024-07-02T15:10:09.786788Z", + "iopub.status.idle": "2024-07-02T15:10:10.037396Z", + "shell.execute_reply": "2024-07-02T15:10:10.036833Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.292040Z", - "iopub.status.busy": "2024-07-02T12:00:44.291643Z", - "iopub.status.idle": "2024-07-02T12:00:44.466523Z", - "shell.execute_reply": "2024-07-02T12:00:44.466004Z" + "iopub.execute_input": "2024-07-02T15:10:10.039906Z", + "iopub.status.busy": "2024-07-02T15:10:10.039540Z", + "iopub.status.idle": "2024-07-02T15:10:10.215217Z", + "shell.execute_reply": "2024-07-02T15:10:10.214642Z" }, "scrolled": true }, @@ -1058,10 +1058,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:44.469939Z", - "iopub.status.busy": "2024-07-02T12:00:44.468998Z", - "iopub.status.idle": "2024-07-02T12:00:44.473947Z", - "shell.execute_reply": "2024-07-02T12:00:44.473442Z" + "iopub.execute_input": "2024-07-02T15:10:10.217686Z", + "iopub.status.busy": "2024-07-02T15:10:10.217327Z", + "iopub.status.idle": "2024-07-02T15:10:10.221199Z", + "shell.execute_reply": "2024-07-02T15:10:10.220704Z" }, "nbsphinx": "hidden" }, @@ -1105,31 +1105,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "018045ff81b24bf7b8b7b92eeb3e59db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_365ffce5f68c44638088fdbae83f6f7b", - "IPY_MODEL_c08801bd218a4933bad779d4baa0b544", - "IPY_MODEL_0f51dc70dda342b1b6b17e56b63d5f14" - ], - "layout": "IPY_MODEL_e14305181a4f4a1aba92bf0159aa7bf3", - "tabbable": null, - "tooltip": null - } - }, - "0298beb970f94688915a8e32a774126c": { + "038891c782ab46f4ba836914abbfc5ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1182,108 +1158,31 @@ "width": null } }, - "036fc6d42c084bba8e6ff1d651c36d55": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b03695b8bbe44d13a5612d5120ea2a28", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_25890da51b1a4e519cdfb13a8c6a9b74", - "tabbable": null, - "tooltip": null, - "value": 665.0 - } - }, - "038607d420fe468e857b145df291678c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "03cd490c005341539cee9b6bd0c64509": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "060119cb42d74e19bb4e92690f697a5e": { + "06405b534d7c49db89f3d29b52da1f80": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0298beb970f94688915a8e32a774126c", - "placeholder": "​", - "style": "IPY_MODEL_eb0935dc83294cc7a1acad3dd963f608", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_5316135ae1ac4592a7b4239c553c02c6", + "IPY_MODEL_43953f2b89bb41408d6583c47201913d", + "IPY_MODEL_e0adbaa3ffa64032933136aebb9a1f7b" + ], + "layout": "IPY_MODEL_cbb206b9c0d64fe0b479fb7cc9df570b", "tabbable": null, - "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 200MB/s]" - } - }, - "0607d3fa923e46caa07bbdf1220db2b6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null } }, - "078724370bc24c649597fb04791e1a0e": { + "07d79636c68343278e1f779c27aef268": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1336,7 +1235,25 @@ "width": null } }, - "0bade9b66cc6401491c957e37747b252": { + "0d5dda5aa9e840a7969a3facb0c17f1f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0da07d715efe402083873e0661e556ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1389,7 +1306,67 @@ "width": null } }, - "0f51dc70dda342b1b6b17e56b63d5f14": { + "1146fbcfe9cf41da81392df94520265c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_59eec5b7926b48ae8d162ecab0db1ec2", + "IPY_MODEL_c18be04c279b46b48b79ee15e32435e6", + "IPY_MODEL_e32cd0a87f0f4ad18f72cc198abad4fd" + ], + "layout": "IPY_MODEL_505538aed9b54bebb4c3f0107daadf44", + "tabbable": null, + "tooltip": null + } + }, + "131d5d7c7d344158b759f1061ef8d42d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1740b9557dc0484c95a45ed25a63585f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "2694565fc4d4493db595fec20b7b65bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1404,38 +1381,99 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_581a302d09fd45e49c7f87d39dd3c921", + "layout": "IPY_MODEL_f25b6a55df5d45bc80be73c91794168b", "placeholder": "​", - "style": "IPY_MODEL_0607d3fa923e46caa07bbdf1220db2b6", + "style": "IPY_MODEL_e970db685471489481e8d686d97b3cc2", "tabbable": null, "tooltip": null, - "value": " 466k/466k [00:00<00:00, 15.8MB/s]" + "value": "tokenizer_config.json: 100%" } }, - "1372d9e4b7724cdead58971eebb0f969": { + "2df9eaceb2e14e8fa3a19b25c7d76f35": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "360120e496a841119a2a238bfb8c7631": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3840f91804d14d5aa30e594b1e1d7fa3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ef479a0478b644bbbd209856e893f0ed", + "IPY_MODEL_d9d22d6398f34538ac40fff96fa8abaf", + "IPY_MODEL_8b024eb1e7d34d94b81d05d691a3874d" + ], + "layout": "IPY_MODEL_cefce6f533124c4e90d7c277319c65e9", + "tabbable": null, + "tooltip": null + } + }, + "38b1bd4b5ec44d41981a433be46d45f0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_285337fc5df34cbfa6c9ed52458ebf3d", - "placeholder": "​", - "style": "IPY_MODEL_80145043305a4ffabe28cb2a16f379de", + "layout": "IPY_MODEL_ea436305a9d143d09b28e8d32eb3020a", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_360120e496a841119a2a238bfb8c7631", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 118kB/s]" + "value": 54245363.0 } }, - "1820812db0184a3ba2bd25871cc78e2f": { + "3a43140a399d4c6b8942f19c238c99d0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1488,60 +1526,33 @@ "width": null } }, - "1d21e4a5af7c4ae0aadfd84ea1555716": { - "model_module": "@jupyter-widgets/base", + "3ac7761e49b1406fb28de06a9a09ffc6": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_769b1ec27342400b9cb6e41638eaa9d4", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d5eda741ae08481f90152525ac295029", + "tabbable": null, + "tooltip": null, + "value": 48.0 } }, - "20b3e8ae925a497db651bb3e420ccedd": { + "3bd0776bd0b840e8b2a1d15afd1e27f8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1559,7 +1570,7 @@ "text_color": null } }, - "25890da51b1a4e519cdfb13a8c6a9b74": { + "3d25676d34544705a2bcbe334c412efc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1575,31 +1586,7 @@ "description_width": "" } }, - "283fc6563d5645c9a2d53edd642983d4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d66a39fee74f43488e2d84a8afff525a", - "IPY_MODEL_036fc6d42c084bba8e6ff1d651c36d55", - "IPY_MODEL_1372d9e4b7724cdead58971eebb0f969" - ], - "layout": "IPY_MODEL_330425c288064ecea245f9a589f86dce", - "tabbable": null, - "tooltip": null - } - }, - "285337fc5df34cbfa6c9ed52458ebf3d": { + "3edc8ef534274f15b48e90d83188b494": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1652,23 +1639,33 @@ "width": null } }, - "290e38f7423c457a9127d9cd3394ab4e": { + "43953f2b89bb41408d6583c47201913d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b01dff0aaef24c71a79429be65e9ce07", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3d25676d34544705a2bcbe334c412efc", + "tabbable": null, + "tooltip": null, + "value": 2211.0 } }, - "2adec3ea4ab147fca4247ce82a707f41": { + "44b0dfbf62fd4d088800332e97908228": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1721,7 +1718,31 @@ "width": null } }, - "330425c288064ecea245f9a589f86dce": { + "4522cd2764fc425b83a55e8426ac45e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2694565fc4d4493db595fec20b7b65bf", + "IPY_MODEL_3ac7761e49b1406fb28de06a9a09ffc6", + "IPY_MODEL_a588c0dcd0974f8da33c7bdf3d4a35e8" + ], + "layout": "IPY_MODEL_ce7bece14db441b3b9a7a133c7e71a07", + "tabbable": null, + "tooltip": null + } + }, + "4cbe66fa6f75404e9a5e6814de5f2203": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1774,7 +1795,7 @@ "width": null } }, - "33adffdfde344f24ab11355d2abf0744": { + "502164ca582c4a0bbb3b20dc28ae0b2c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1792,30 +1813,7 @@ "text_color": null } }, - "365ffce5f68c44638088fdbae83f6f7b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_757f11a9bf36405e89b2715bc5278a25", - "placeholder": "​", - "style": "IPY_MODEL_e7529ab3626947b3ac4bc76edafa711a", - "tabbable": null, - "tooltip": null, - "value": "tokenizer.json: 100%" - } - }, - "3c023990927b4f04a4351901432e7d8f": { + "505538aed9b54bebb4c3f0107daadf44": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1868,7 +1866,53 @@ "width": null } }, - "465f0d2ce3444dfd9bd85fd2529dc52c": { + "5316135ae1ac4592a7b4239c553c02c6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4cbe66fa6f75404e9a5e6814de5f2203", + "placeholder": "​", + "style": "IPY_MODEL_1740b9557dc0484c95a45ed25a63585f", + "tabbable": null, + "tooltip": null, + "value": "README.md: 100%" + } + }, + "54ed34f093ee42d68b280d3bd01e5599": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3a43140a399d4c6b8942f19c238c99d0", + "placeholder": "​", + "style": "IPY_MODEL_131d5d7c7d344158b759f1061ef8d42d", + "tabbable": null, + "tooltip": null, + "value": "vocab.txt: 100%" + } + }, + "59de812fe1e841889d9b5bfc883f3b40": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1886,7 +1930,46 @@ "text_color": null } }, - "465f57a032274e4dadbee2eb87856ef1": { + "59eec5b7926b48ae8d162ecab0db1ec2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6ef7ce225dc34f17b94e5dbe3a820e35", + "placeholder": "​", + "style": "IPY_MODEL_a99583c6f0e24d69872cbda693c983eb", + "tabbable": null, + "tooltip": null, + "value": "config.json: 100%" + } + }, + "6680b7e73b4d4bb589857c254d0632ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "6833c949ea36409b943f5aa9a751fe85": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1939,7 +2022,7 @@ "width": null } }, - "4ed3c0ca07f04a87b53a6c5d68d36cf6": { + "6c0bd2a44f6e49e7bb1931ece0eab850": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1992,7 +2075,7 @@ "width": null } }, - "52a83385affa4985b26bec259ee14740": { + "6e6cda23dd024255af57333cc9d9bfea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2007,49 +2090,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1d21e4a5af7c4ae0aadfd84ea1555716", + "layout": "IPY_MODEL_80258d9522ac4e858c828ed1eba37fb6", "placeholder": "​", - "style": "IPY_MODEL_038607d420fe468e857b145df291678c", + "style": "IPY_MODEL_2df9eaceb2e14e8fa3a19b25c7d76f35", "tabbable": null, "tooltip": null, - "value": "README.md: 100%" - } - }, - "5376601de0794106a7b8777224bcafd4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "57bc4c06dbfc46c89ae707951f55ed3a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": " 391/391 [00:00<00:00, 67.9kB/s]" } }, - "581a302d09fd45e49c7f87d39dd3c921": { + "6ed70f51e8b34c1094db85d132e5ce9c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2102,7 +2151,7 @@ "width": null } }, - "5d425fc517de40599859741dfdf6bb2e": { + "6ef7ce225dc34f17b94e5dbe3a820e35": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2155,7 +2204,7 @@ "width": null } }, - "5ec8a2f5f2034c158f6d2c361a60ea25": { + "7042493643924b098a59e80ef3a6125e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2208,7 +2257,7 @@ "width": null } }, - "5efd9793a22541dd9dd8f09fcfbec967": { + "7416ceea465444209f091f4e54c9aebe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2226,105 +2275,14 @@ "text_color": null } }, - "60b3d9bfd8e147ef947e8e81fd9fb70e": { - "model_module": "@jupyter-widgets/controls", + "769b1ec27342400b9cb6e41638eaa9d4": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "654d58802fd94f6ead893ddbb0e3d131": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0bade9b66cc6401491c957e37747b252", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_290e38f7423c457a9127d9cd3394ab4e", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } - }, - "66ee22f8cf5b4a78ab33aee929a5fbd0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fad0806770b14f9086fd1b3b755413fb", - "placeholder": "​", - "style": "IPY_MODEL_814ed56db234446a92fe939efe5a477b", - "tabbable": null, - "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 8.21kB/s]" - } - }, - "6f739705eccc46afb2020460a828b56b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5d425fc517de40599859741dfdf6bb2e", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_7a181aaf8967410dae1ce2d0e0d9856a", - "tabbable": null, - "tooltip": null, - "value": 391.0 - } - }, - "757f11a9bf36405e89b2715bc5278a25": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -2370,7 +2328,23 @@ "width": null } }, - "796c15ee275d4dff935fe8e20583896d": { + "7a60c769a9a94e748e35f240733664b3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "7b0e3596ec8d47089daa31da5c13b681": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2423,77 +2397,7 @@ "width": null } }, - "7a181aaf8967410dae1ce2d0e0d9856a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "80145043305a4ffabe28cb2a16f379de": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "814ed56db234446a92fe939efe5a477b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "853f7130b22d49bfaf39b5d7bf7af4ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "8e1a3b5aa6b14adc8ebdc1566696e505": { + "7f092e8ede4e4d63a5fde7a51f92e32c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2546,25 +2450,30 @@ "width": null } }, - "9814c1a1993c40a683e790580ecf178a": { + "7fd953032b654b6080e9188c21e62fc9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c176725128c3447ebe665fa5ac3e316f", + "placeholder": "​", + "style": "IPY_MODEL_59de812fe1e841889d9b5bfc883f3b40", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 38.4MB/s]" } }, - "982a96cc3c0b4c00938e722c374cd707": { + "80258d9522ac4e858c828ed1eba37fb6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2617,7 +2526,103 @@ "width": null } }, - "98b6d5dd0c4546948933a4d8faa4bbc9": { + "8b024eb1e7d34d94b81d05d691a3874d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6c0bd2a44f6e49e7bb1931ece0eab850", + "placeholder": "​", + "style": "IPY_MODEL_b6e52d34f81d421d9eba7c8cc9b7847a", + "tabbable": null, + "tooltip": null, + "value": " 466k/466k [00:00<00:00, 16.0MB/s]" + } + }, + "914b3f22f6b446608a80b2bd86dde07b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3edc8ef534274f15b48e90d83188b494", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d76fd6c68456446baaae1b57ccf46065", + "tabbable": null, + "tooltip": null, + "value": 391.0 + } + }, + "9a092b3142994e27b931efc3d8141660": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6ed70f51e8b34c1094db85d132e5ce9c", + "placeholder": "​", + "style": "IPY_MODEL_acbecbc8b02440768730f4625e2f605e", + "tabbable": null, + "tooltip": null, + "value": " 54.2M/54.2M [00:00<00:00, 239MB/s]" + } + }, + "9a72b5f7de474f75bb371e057f0f1914": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_54ed34f093ee42d68b280d3bd01e5599", + "IPY_MODEL_c5eaef5240de4848ba66275755fe00f6", + "IPY_MODEL_7fd953032b654b6080e9188c21e62fc9" + ], + "layout": "IPY_MODEL_44b0dfbf62fd4d088800332e97908228", + "tabbable": null, + "tooltip": null + } + }, + "9f0bab6dcc71427ba6d39cfcb15d877f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2670,7 +2675,30 @@ "width": null } }, - "9e5f0df62415449eb138994f79e6d9e0": { + "a588c0dcd0974f8da33c7bdf3d4a35e8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0da07d715efe402083873e0661e556ea", + "placeholder": "​", + "style": "IPY_MODEL_ae1e6cca03b34513818a3bd133ae6f5a", + "tabbable": null, + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 9.25kB/s]" + } + }, + "a99583c6f0e24d69872cbda693c983eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2688,60 +2716,43 @@ "text_color": null } }, - "a6d4217338164b029ad977bd11fb8d9e": { - "model_module": "@jupyter-widgets/base", + "acbecbc8b02440768730f4625e2f605e": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ae1e6cca03b34513818a3bd133ae6f5a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b03695b8bbe44d13a5612d5120ea2a28": { + "b01dff0aaef24c71a79429be65e9ce07": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2794,98 +2805,66 @@ "width": null } }, - "b83ba78e112b427d90a55129eecf514c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_078724370bc24c649597fb04791e1a0e", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5376601de0794106a7b8777224bcafd4", - "tabbable": null, - "tooltip": null, - "value": 2211.0 - } - }, - "c08801bd218a4933bad779d4baa0b544": { + "b65073e7ff2e476bb34d75a0faf77c6f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_da42c63dc73b49f8b62f507344120b1a", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_60b3d9bfd8e147ef947e8e81fd9fb70e", + "layout": "IPY_MODEL_7b0e3596ec8d47089daa31da5c13b681", + "placeholder": "​", + "style": "IPY_MODEL_502164ca582c4a0bbb3b20dc28ae0b2c", "tabbable": null, "tooltip": null, - "value": 466062.0 + "value": "pytorch_model.bin: 100%" } }, - "c187a19bc3e941088eef67c273bf61ed": { + "b6e52d34f81d421d9eba7c8cc9b7847a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c9ff9f5312794e5381359492c09edaaf": { + "bc071c694fb7476a8213ad06a4cef625": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5ec8a2f5f2034c158f6d2c361a60ea25", - "placeholder": "​", - "style": "IPY_MODEL_853f7130b22d49bfaf39b5d7bf7af4ce", - "tabbable": null, - "tooltip": null, - "value": "vocab.txt: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ca42a9ff17da48fab63132c9d67266dd": { + "bd1aa12a83f148a6a04b7394dd645fb3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2900,131 +2879,95 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_52a83385affa4985b26bec259ee14740", - "IPY_MODEL_b83ba78e112b427d90a55129eecf514c", - "IPY_MODEL_f7e3eba4cb63469d997967f982f4a1df" + "IPY_MODEL_b65073e7ff2e476bb34d75a0faf77c6f", + "IPY_MODEL_38b1bd4b5ec44d41981a433be46d45f0", + "IPY_MODEL_9a092b3142994e27b931efc3d8141660" ], - "layout": "IPY_MODEL_8e1a3b5aa6b14adc8ebdc1566696e505", + "layout": "IPY_MODEL_7f092e8ede4e4d63a5fde7a51f92e32c", "tabbable": null, "tooltip": null } }, - "cc4d2d6857724ea0a2f3ec85a6f395ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4ed3c0ca07f04a87b53a6c5d68d36cf6", - "placeholder": "​", - "style": "IPY_MODEL_5efd9793a22541dd9dd8f09fcfbec967", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 31.3MB/s]" - } - }, - "d015bcf5ce69476ab54fd8e945eaa689": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_465f57a032274e4dadbee2eb87856ef1", - "placeholder": "​", - "style": "IPY_MODEL_465f0d2ce3444dfd9bd85fd2529dc52c", - "tabbable": null, - "tooltip": null, - "value": " 391/391 [00:00<00:00, 66.4kB/s]" - } - }, - "d25c524b9c4444829470ac057e0fae42": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1820812db0184a3ba2bd25871cc78e2f", - "placeholder": "​", - "style": "IPY_MODEL_57bc4c06dbfc46c89ae707951f55ed3a", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" - } - }, - "d66a39fee74f43488e2d84a8afff525a": { - "model_module": "@jupyter-widgets/controls", + "c176725128c3447ebe665fa5ac3e316f": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e7e2566da91d4dde83d80ed19a91e263", - "placeholder": "​", - "style": "IPY_MODEL_20b3e8ae925a497db651bb3e420ccedd", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d6ea8d2f7af14ef69353a0ae60cf677e": { + "c18be04c279b46b48b79ee15e32435e6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3c023990927b4f04a4351901432e7d8f", - "placeholder": "​", - "style": "IPY_MODEL_9814c1a1993c40a683e790580ecf178a", + "layout": "IPY_MODEL_fabb2363238a4c1a91de78e13b8a0a3e", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6680b7e73b4d4bb589857c254d0632ef", "tabbable": null, "tooltip": null, - "value": "pytorch_model.bin: 100%" + "value": 665.0 } }, - "da42c63dc73b49f8b62f507344120b1a": { + "c33f4e7c33ca4ebd8cb646737881a850": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3077,7 +3020,7 @@ "width": null } }, - "dbc2947dd29d4e92a20d79a1f5606f98": { + "c5eaef5240de4848ba66275755fe00f6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3093,17 +3036,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_de5b529ba5b7466f857e19bdfdcddd30", + "layout": "IPY_MODEL_07d79636c68343278e1f779c27aef268", "max": 231508.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_c187a19bc3e941088eef67c273bf61ed", + "style": "IPY_MODEL_fba1e85bf6444f50b1917a3eb9c35bcc", "tabbable": null, "tooltip": null, "value": 231508.0 } }, - "de5b529ba5b7466f857e19bdfdcddd30": { + "c943f13df8c04e77aae4c7ca2cbbd613": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fcaf1661c4314a27a12b7c20cbce3cc8", + "IPY_MODEL_914b3f22f6b446608a80b2bd86dde07b", + "IPY_MODEL_6e6cda23dd024255af57333cc9d9bfea" + ], + "layout": "IPY_MODEL_de51505fe22c4b5fa21abe797f7a7d7a", + "tabbable": null, + "tooltip": null + } + }, + "cbb206b9c0d64fe0b479fb7cc9df570b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3156,7 +3123,7 @@ "width": null } }, - "e14305181a4f4a1aba92bf0159aa7bf3": { + "ce7bece14db441b3b9a7a133c7e71a07": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3209,25 +3176,7 @@ "width": null } }, - "e7529ab3626947b3ac4bc76edafa711a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "e7e2566da91d4dde83d80ed19a91e263": { + "cefce6f533124c4e90d7c277319c65e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3280,55 +3229,65 @@ "width": null } }, - "e89a8a43528e42c38eca656e48b7da7e": { + "d5eda741ae08481f90152525ac295029": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ebe70157b15a43cebe4c33d29744783b", - "IPY_MODEL_6f739705eccc46afb2020460a828b56b", - "IPY_MODEL_d015bcf5ce69476ab54fd8e945eaa689" - ], - "layout": "IPY_MODEL_e9a141532bb34d7697db7a780d7a2002", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "e90e40189b0e460d90a444df7fe6d1a9": { + "d76fd6c68456446baaae1b57ccf46065": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d9d22d6398f34538ac40fff96fa8abaf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d6ea8d2f7af14ef69353a0ae60cf677e", - "IPY_MODEL_eccbc90dc8114497bb7438d438edc7f2", - "IPY_MODEL_060119cb42d74e19bb4e92690f697a5e" - ], - "layout": "IPY_MODEL_a6d4217338164b029ad977bd11fb8d9e", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7042493643924b098a59e80ef3a6125e", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7a60c769a9a94e748e35f240733664b3", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 466062.0 } }, - "e9a141532bb34d7697db7a780d7a2002": { + "de51505fe22c4b5fa21abe797f7a7d7a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3381,49 +3340,7 @@ "width": null } }, - "eb0935dc83294cc7a1acad3dd963f608": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "eb1f3f2a9964471a8a7688badac98c84": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d25c524b9c4444829470ac057e0fae42", - "IPY_MODEL_654d58802fd94f6ead893ddbb0e3d131", - "IPY_MODEL_66ee22f8cf5b4a78ab33aee929a5fbd0" - ], - "layout": "IPY_MODEL_98b6d5dd0c4546948933a4d8faa4bbc9", - "tabbable": null, - "tooltip": null - } - }, - "ebe70157b15a43cebe4c33d29744783b": { + "e0adbaa3ffa64032933136aebb9a1f7b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3438,65 +3355,56 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_796c15ee275d4dff935fe8e20583896d", + "layout": "IPY_MODEL_9f0bab6dcc71427ba6d39cfcb15d877f", "placeholder": "​", - "style": "IPY_MODEL_33adffdfde344f24ab11355d2abf0744", + "style": "IPY_MODEL_7416ceea465444209f091f4e54c9aebe", "tabbable": null, "tooltip": null, - "value": ".gitattributes: 100%" + "value": " 2.21k/2.21k [00:00<00:00, 401kB/s]" } }, - "eccbc90dc8114497bb7438d438edc7f2": { + "e32cd0a87f0f4ad18f72cc198abad4fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2adec3ea4ab147fca4247ce82a707f41", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_03cd490c005341539cee9b6bd0c64509", + "layout": "IPY_MODEL_c33f4e7c33ca4ebd8cb646737881a850", + "placeholder": "​", + "style": "IPY_MODEL_3bd0776bd0b840e8b2a1d15afd1e27f8", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": " 665/665 [00:00<00:00, 118kB/s]" } }, - "edb46e1892c744119cd3f4a130dfb3e3": { + "e970db685471489481e8d686d97b3cc2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c9ff9f5312794e5381359492c09edaaf", - "IPY_MODEL_dbc2947dd29d4e92a20d79a1f5606f98", - "IPY_MODEL_cc4d2d6857724ea0a2f3ec85a6f395ea" - ], - "layout": "IPY_MODEL_f3decebc5af44971b456d5da642e43b2", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f3decebc5af44971b456d5da642e43b2": { + "ea436305a9d143d09b28e8d32eb3020a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3549,7 +3457,7 @@ "width": null } }, - "f7e3eba4cb63469d997967f982f4a1df": { + "ef479a0478b644bbbd209856e893f0ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3564,15 +3472,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_982a96cc3c0b4c00938e722c374cd707", + "layout": "IPY_MODEL_6833c949ea36409b943f5aa9a751fe85", "placeholder": "​", - "style": "IPY_MODEL_9e5f0df62415449eb138994f79e6d9e0", + "style": "IPY_MODEL_0d5dda5aa9e840a7969a3facb0c17f1f", "tabbable": null, "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 389kB/s]" + "value": "tokenizer.json: 100%" + } + }, + "f25b6a55df5d45bc80be73c91794168b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "fad0806770b14f9086fd1b3b755413fb": { + "fabb2363238a4c1a91de78e13b8a0a3e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3624,6 +3585,45 @@ "visibility": null, "width": null } + }, + "fba1e85bf6444f50b1917a3eb9c35bcc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fcaf1661c4314a27a12b7c20cbce3cc8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_038891c782ab46f4ba836914abbfc5ce", + "placeholder": "​", + "style": "IPY_MODEL_bc071c694fb7476a8213ad06a4cef625", + "tabbable": null, + "tooltip": null, + "value": ".gitattributes: 100%" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb index 9db139a3f..a4fd4545f 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:48.153712Z", - "iopub.status.busy": "2024-07-02T12:00:48.153535Z", - "iopub.status.idle": "2024-07-02T12:00:53.266339Z", - "shell.execute_reply": "2024-07-02T12:00:53.265786Z" + "iopub.execute_input": "2024-07-02T15:10:13.381463Z", + "iopub.status.busy": "2024-07-02T15:10:13.381288Z", + "iopub.status.idle": "2024-07-02T15:10:18.674436Z", + "shell.execute_reply": "2024-07-02T15:10:18.673907Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:53.268847Z", - "iopub.status.busy": "2024-07-02T12:00:53.268512Z", - "iopub.status.idle": "2024-07-02T12:00:53.271688Z", - "shell.execute_reply": "2024-07-02T12:00:53.271237Z" + "iopub.execute_input": "2024-07-02T15:10:18.676864Z", + "iopub.status.busy": "2024-07-02T15:10:18.676521Z", + "iopub.status.idle": "2024-07-02T15:10:18.679999Z", + "shell.execute_reply": "2024-07-02T15:10:18.679435Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:53.273790Z", - "iopub.status.busy": "2024-07-02T12:00:53.273468Z", - "iopub.status.idle": "2024-07-02T12:00:53.277843Z", - "shell.execute_reply": "2024-07-02T12:00:53.277413Z" + "iopub.execute_input": "2024-07-02T15:10:18.681962Z", + "iopub.status.busy": "2024-07-02T15:10:18.681787Z", + "iopub.status.idle": "2024-07-02T15:10:18.686141Z", + "shell.execute_reply": "2024-07-02T15:10:18.685703Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-02T12:00:53.279840Z", - "iopub.status.busy": "2024-07-02T12:00:53.279499Z", - "iopub.status.idle": "2024-07-02T12:00:54.884749Z", - "shell.execute_reply": "2024-07-02T12:00:54.884125Z" + "iopub.execute_input": "2024-07-02T15:10:18.688033Z", + "iopub.status.busy": "2024-07-02T15:10:18.687785Z", + "iopub.status.idle": "2024-07-02T15:10:20.393053Z", + "shell.execute_reply": "2024-07-02T15:10:20.392456Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-02T12:00:54.887464Z", - "iopub.status.busy": "2024-07-02T12:00:54.887081Z", - "iopub.status.idle": "2024-07-02T12:00:54.897463Z", - "shell.execute_reply": "2024-07-02T12:00:54.897041Z" + "iopub.execute_input": "2024-07-02T15:10:20.395802Z", + "iopub.status.busy": "2024-07-02T15:10:20.395334Z", + "iopub.status.idle": "2024-07-02T15:10:20.407068Z", + "shell.execute_reply": "2024-07-02T15:10:20.406544Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:54.899593Z", - "iopub.status.busy": "2024-07-02T12:00:54.899256Z", - "iopub.status.idle": "2024-07-02T12:00:54.904661Z", - "shell.execute_reply": "2024-07-02T12:00:54.904214Z" + "iopub.execute_input": "2024-07-02T15:10:20.409159Z", + "iopub.status.busy": "2024-07-02T15:10:20.408835Z", + "iopub.status.idle": "2024-07-02T15:10:20.414421Z", + "shell.execute_reply": "2024-07-02T15:10:20.413846Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-02T12:00:54.906699Z", - "iopub.status.busy": "2024-07-02T12:00:54.906445Z", - "iopub.status.idle": "2024-07-02T12:00:55.370547Z", - "shell.execute_reply": "2024-07-02T12:00:55.370054Z" + "iopub.execute_input": "2024-07-02T15:10:20.416521Z", + "iopub.status.busy": "2024-07-02T15:10:20.416054Z", + "iopub.status.idle": "2024-07-02T15:10:20.875781Z", + "shell.execute_reply": "2024-07-02T15:10:20.875260Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:55.372729Z", - "iopub.status.busy": "2024-07-02T12:00:55.372455Z", - "iopub.status.idle": "2024-07-02T12:00:56.373788Z", - "shell.execute_reply": "2024-07-02T12:00:56.373190Z" + "iopub.execute_input": "2024-07-02T15:10:20.877916Z", + "iopub.status.busy": "2024-07-02T15:10:20.877560Z", + "iopub.status.idle": "2024-07-02T15:10:21.631226Z", + "shell.execute_reply": "2024-07-02T15:10:21.630744Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-07-02T12:00:56.376073Z", - "iopub.status.busy": "2024-07-02T12:00:56.375890Z", - "iopub.status.idle": "2024-07-02T12:00:56.393884Z", - "shell.execute_reply": "2024-07-02T12:00:56.393321Z" + "iopub.execute_input": "2024-07-02T15:10:21.633680Z", + "iopub.status.busy": "2024-07-02T15:10:21.633336Z", + "iopub.status.idle": "2024-07-02T15:10:21.651564Z", + "shell.execute_reply": "2024-07-02T15:10:21.651138Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:56.396057Z", - "iopub.status.busy": "2024-07-02T12:00:56.395720Z", - "iopub.status.idle": "2024-07-02T12:00:56.398930Z", - "shell.execute_reply": "2024-07-02T12:00:56.398478Z" + "iopub.execute_input": "2024-07-02T15:10:21.653547Z", + "iopub.status.busy": "2024-07-02T15:10:21.653247Z", + "iopub.status.idle": "2024-07-02T15:10:21.656414Z", + "shell.execute_reply": "2024-07-02T15:10:21.655863Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:00:56.400749Z", - "iopub.status.busy": "2024-07-02T12:00:56.400581Z", - "iopub.status.idle": "2024-07-02T12:01:10.956584Z", - "shell.execute_reply": "2024-07-02T12:01:10.955969Z" + "iopub.execute_input": "2024-07-02T15:10:21.658634Z", + "iopub.status.busy": "2024-07-02T15:10:21.658142Z", + "iopub.status.idle": "2024-07-02T15:10:35.825662Z", + "shell.execute_reply": "2024-07-02T15:10:35.825086Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:10.959440Z", - "iopub.status.busy": "2024-07-02T12:01:10.959028Z", - "iopub.status.idle": "2024-07-02T12:01:10.962902Z", - "shell.execute_reply": "2024-07-02T12:01:10.962374Z" + "iopub.execute_input": "2024-07-02T15:10:35.828473Z", + "iopub.status.busy": "2024-07-02T15:10:35.828094Z", + "iopub.status.idle": "2024-07-02T15:10:35.831789Z", + "shell.execute_reply": "2024-07-02T15:10:35.831277Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:10.964878Z", - "iopub.status.busy": "2024-07-02T12:01:10.964705Z", - "iopub.status.idle": "2024-07-02T12:01:11.664747Z", - "shell.execute_reply": "2024-07-02T12:01:11.664181Z" + "iopub.execute_input": "2024-07-02T15:10:35.833874Z", + "iopub.status.busy": "2024-07-02T15:10:35.833468Z", + "iopub.status.idle": "2024-07-02T15:10:36.552465Z", + "shell.execute_reply": "2024-07-02T15:10:36.551895Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.667592Z", - "iopub.status.busy": "2024-07-02T12:01:11.667207Z", - "iopub.status.idle": "2024-07-02T12:01:11.671960Z", - "shell.execute_reply": "2024-07-02T12:01:11.671464Z" + "iopub.execute_input": "2024-07-02T15:10:36.556106Z", + "iopub.status.busy": "2024-07-02T15:10:36.555160Z", + "iopub.status.idle": "2024-07-02T15:10:36.561881Z", + "shell.execute_reply": "2024-07-02T15:10:36.561370Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.674352Z", - "iopub.status.busy": "2024-07-02T12:01:11.673986Z", - "iopub.status.idle": "2024-07-02T12:01:11.769978Z", - "shell.execute_reply": "2024-07-02T12:01:11.769317Z" + "iopub.execute_input": "2024-07-02T15:10:36.565373Z", + "iopub.status.busy": "2024-07-02T15:10:36.564458Z", + "iopub.status.idle": "2024-07-02T15:10:36.658752Z", + "shell.execute_reply": "2024-07-02T15:10:36.658223Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.772290Z", - "iopub.status.busy": "2024-07-02T12:01:11.771936Z", - "iopub.status.idle": "2024-07-02T12:01:11.785262Z", - "shell.execute_reply": "2024-07-02T12:01:11.784787Z" + "iopub.execute_input": "2024-07-02T15:10:36.661210Z", + "iopub.status.busy": "2024-07-02T15:10:36.660924Z", + "iopub.status.idle": "2024-07-02T15:10:36.673696Z", + "shell.execute_reply": "2024-07-02T15:10:36.673268Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.787484Z", - "iopub.status.busy": "2024-07-02T12:01:11.787145Z", - "iopub.status.idle": "2024-07-02T12:01:11.795270Z", - "shell.execute_reply": "2024-07-02T12:01:11.794713Z" + "iopub.execute_input": "2024-07-02T15:10:36.675623Z", + "iopub.status.busy": "2024-07-02T15:10:36.675445Z", + "iopub.status.idle": "2024-07-02T15:10:36.683122Z", + "shell.execute_reply": "2024-07-02T15:10:36.682702Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.797390Z", - "iopub.status.busy": "2024-07-02T12:01:11.797080Z", - "iopub.status.idle": "2024-07-02T12:01:11.801551Z", - "shell.execute_reply": "2024-07-02T12:01:11.800973Z" + "iopub.execute_input": "2024-07-02T15:10:36.685019Z", + "iopub.status.busy": "2024-07-02T15:10:36.684848Z", + "iopub.status.idle": "2024-07-02T15:10:36.688952Z", + "shell.execute_reply": "2024-07-02T15:10:36.688536Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.803467Z", - "iopub.status.busy": "2024-07-02T12:01:11.803275Z", - "iopub.status.idle": "2024-07-02T12:01:11.809289Z", - "shell.execute_reply": "2024-07-02T12:01:11.808826Z" + "iopub.execute_input": "2024-07-02T15:10:36.690791Z", + "iopub.status.busy": "2024-07-02T15:10:36.690602Z", + "iopub.status.idle": "2024-07-02T15:10:36.696393Z", + "shell.execute_reply": "2024-07-02T15:10:36.695933Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.811355Z", - "iopub.status.busy": "2024-07-02T12:01:11.811010Z", - "iopub.status.idle": "2024-07-02T12:01:11.924674Z", - "shell.execute_reply": "2024-07-02T12:01:11.924087Z" + "iopub.execute_input": "2024-07-02T15:10:36.698276Z", + "iopub.status.busy": "2024-07-02T15:10:36.698106Z", + "iopub.status.idle": "2024-07-02T15:10:36.808722Z", + "shell.execute_reply": "2024-07-02T15:10:36.808237Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:11.927078Z", - "iopub.status.busy": "2024-07-02T12:01:11.926676Z", - "iopub.status.idle": "2024-07-02T12:01:12.029810Z", - "shell.execute_reply": "2024-07-02T12:01:12.029255Z" + "iopub.execute_input": "2024-07-02T15:10:36.810751Z", + "iopub.status.busy": "2024-07-02T15:10:36.810575Z", + "iopub.status.idle": "2024-07-02T15:10:36.915062Z", + "shell.execute_reply": "2024-07-02T15:10:36.914621Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-02T12:01:12.031968Z", - "iopub.status.busy": "2024-07-02T12:01:12.031613Z", - "iopub.status.idle": "2024-07-02T12:01:12.132022Z", - "shell.execute_reply": "2024-07-02T12:01:12.131402Z" + "iopub.execute_input": "2024-07-02T15:10:36.917156Z", + "iopub.status.busy": "2024-07-02T15:10:36.916831Z", + "iopub.status.idle": "2024-07-02T15:10:37.019921Z", + "shell.execute_reply": "2024-07-02T15:10:37.019441Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:12.134167Z", - "iopub.status.busy": "2024-07-02T12:01:12.133985Z", - "iopub.status.idle": "2024-07-02T12:01:12.235981Z", - "shell.execute_reply": "2024-07-02T12:01:12.235470Z" + "iopub.execute_input": "2024-07-02T15:10:37.022021Z", + "iopub.status.busy": "2024-07-02T15:10:37.021843Z", + "iopub.status.idle": "2024-07-02T15:10:37.126427Z", + "shell.execute_reply": "2024-07-02T15:10:37.125880Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:12.237957Z", - "iopub.status.busy": "2024-07-02T12:01:12.237775Z", - "iopub.status.idle": "2024-07-02T12:01:12.241026Z", - "shell.execute_reply": "2024-07-02T12:01:12.240474Z" + "iopub.execute_input": "2024-07-02T15:10:37.128724Z", + "iopub.status.busy": "2024-07-02T15:10:37.128301Z", + "iopub.status.idle": "2024-07-02T15:10:37.131483Z", + "shell.execute_reply": "2024-07-02T15:10:37.131015Z" }, "nbsphinx": "hidden" }, @@ -1392,7 +1392,76 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0b4a72514d5e48d9a5de944d58a5949b": { + "026a8d3d502347a290f99b02f8529d28": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0fca732af2064a0da3c0e7502ad49282", + "placeholder": "​", + "style": "IPY_MODEL_fb83ceda077c4e68aad24838206b09cc", + "tabbable": null, + "tooltip": null, + "value": "classifier.ckpt: 100%" + } + }, + "0c9f4529a42246ee88b9b31ac1212ba1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_86ca63a102954afa94795c3e61c20880", + "placeholder": "​", + "style": "IPY_MODEL_e9b3119fe459439e8146cddd6880a9f1", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "0dfc7f988177492e852e1a1e2e2e3c55": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d16b4b3c59ba49d9a539a98b5d9ace25", + "placeholder": "​", + "style": "IPY_MODEL_8712797b65e1448f8a93f64e6e581af5", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 502kB/s]" + } + }, + "0fb76291c4d34ebdba9ffa24ed513e0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1445,30 +1514,7 @@ "width": null } }, - "1879dcf933dc47ef80752de84b1be173": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1e578c121b074fe689ec2d0de3438add", - "placeholder": "​", - "style": "IPY_MODEL_65678011a834483f81123fce6a847a0f", - "tabbable": null, - "tooltip": null, - "value": "classifier.ckpt: 100%" - } - }, - "1df8c0f2d29d45779e226e08c41aefd8": { + "0fca732af2064a0da3c0e7502ad49282": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1521,7 +1567,7 @@ "width": null } }, - "1e578c121b074fe689ec2d0de3438add": { + "1b8db158701349408c5bdef740765f91": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1574,7 +1620,7 @@ "width": null } }, - "2073d888478c4148aa6af8f01d1a55c0": { + "23ef0aabeddc46538e90abc8e42e36c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1590,17 +1636,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f9d1becbce8d4a0e92d1291261488c36", - "max": 15856877.0, + "layout": "IPY_MODEL_5d471f2ed23e49dbb159b32a3c2d406e", + "max": 16887676.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_7fb74483ba564e44bf8528608dd0c00d", + "style": "IPY_MODEL_511baacd1c3e4816bbe0b13284bf3fdd", "tabbable": null, "tooltip": null, - "value": 15856877.0 + "value": 16887676.0 } }, - "249f3be21f17476cb286f35a4beae4ea": { + "299896a97d41456cac583393c5611a30": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1653,74 +1699,7 @@ "width": null } }, - "280097d7d8744e47bd5924ed20469bd6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f1379d86855941e4a6388b556616e327", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_40a775a4588f452f8bf5d7fbc03bc9ba", - "tabbable": null, - "tooltip": null, - "value": 2041.0 - } - }, - "28c2d54c60d54e18885566cb5f99ba0a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "38e4ea17dfd74406801cb8ef4fa01bfb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d3b969344b34427e9067d2e51d96dd59", - "placeholder": "​", - "style": "IPY_MODEL_9ad076b10aa949c583d59ebf787d18d9", - "tabbable": null, - "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 501kB/s]" - } - }, - "39378c4365b84e2ba8a40f5da8c49a6b": { + "3c2e1352b6dd4da68e969419d9ecdaea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1773,48 +1752,7 @@ "width": null } }, - "3b3e7e9c2d3a481a8b66e18c87d96abe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "3e0efe779a52444991f5fa51e7b53a87": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5740512f403c4e52a3ce092bc9b6022f", - "placeholder": "​", - "style": "IPY_MODEL_9fbff358e60544609b6d2a561cf6f85c", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" - } - }, - "40a775a4588f452f8bf5d7fbc03bc9ba": { + "44dd265825e9454e868ab092b351a312": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1830,7 +1768,7 @@ "description_width": "" } }, - "4209fa1ed28448c4a745350a1042e490": { + "4526bd738aca4e52beef9dbf333987c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1846,40 +1784,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0b4a72514d5e48d9a5de944d58a5949b", - "max": 128619.0, + "layout": "IPY_MODEL_6a601518732a456897e40425c7447332", + "max": 15856877.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_54ae4e63d9c84d078262621e2d8e350f", - "tabbable": null, - "tooltip": null, - "value": 128619.0 - } - }, - "4d17c45bac644d16890f974d9bf14252": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1df8c0f2d29d45779e226e08c41aefd8", - "placeholder": "​", - "style": "IPY_MODEL_7a9eaff0d94f402eb6726bbbe282d200", + "style": "IPY_MODEL_8063b86fd1644501bd7a35a8f8c91e8d", "tabbable": null, "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 121MB/s]" + "value": 15856877.0 } }, - "513013579fcb4448832ca2cf5f6e0fc2": { + "4a111d06ad644ff3b7dfcd8f9a93b27f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1932,7 +1847,7 @@ "width": null } }, - "54ae4e63d9c84d078262621e2d8e350f": { + "511baacd1c3e4816bbe0b13284bf3fdd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1948,7 +1863,31 @@ "description_width": "" } }, - "5740512f403c4e52a3ce092bc9b6022f": { + "562674bc865d4b9c866de03e113283d6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_026a8d3d502347a290f99b02f8529d28", + "IPY_MODEL_4526bd738aca4e52beef9dbf333987c6", + "IPY_MODEL_72f97078b8fc4a6d9b507a102418aea1" + ], + "layout": "IPY_MODEL_cdbef2f290304f54be4b5de93eb10cc1", + "tabbable": null, + "tooltip": null + } + }, + "5940aac0c62e430f99781eb883247639": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2001,7 +1940,7 @@ "width": null } }, - "5fc9fb12e7584385b6dbcc71ea004933": { + "5d471f2ed23e49dbb159b32a3c2d406e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2054,43 +1993,49 @@ "width": null } }, - "65678011a834483f81123fce6a847a0f": { + "5fd920ec2856445d98a6f7e8f0229c4d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a5ba789c57d244a6afdd80d9221598a2", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ba0d6ceeadc14ee192ae1dc31b4e4186", + "tabbable": null, + "tooltip": null, + "value": 128619.0 } }, - "65c231ce763c440980afbfa1c4eaab86": { + "60b559ea05d04ab2aefd425629200a9b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "68b7a074f4a5491984f03ffaed29102c": { + "60d05f62061f46e7b7f871ccf49bce6e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2105,87 +2050,18 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6fe26799ef5342b7ae1161bc8ef4ec8a", + "layout": "IPY_MODEL_795a99fe1a5a4fe28962d2835b6d0806", "placeholder": "​", - "style": "IPY_MODEL_ba779947520844b1b4e9e788604b7ed8", + "style": "IPY_MODEL_ef52679868914ac0a1bac86a98893d61", "tabbable": null, "tooltip": null, - "value": "label_encoder.txt: 100%" + "value": " 129k/129k [00:00<00:00, 11.1MB/s]" } }, - "6b1e51bd7dfd4e7e9b96650627dd4347": { + "618905a858344a0488c8d600292e4cc3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "6fe26799ef5342b7ae1161bc8ef4ec8a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "73fd95beddfa4d59833b8b729dc2ef1b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", @@ -2197,80 +2073,65 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_513013579fcb4448832ca2cf5f6e0fc2", + "layout": "IPY_MODEL_7b05aea7bfdd48ddbe010dce6c119c9a", "placeholder": "​", - "style": "IPY_MODEL_edbc3e1e1e514aeea602d52281c3dfa3", + "style": "IPY_MODEL_d1a86a88d50a4bb2aee04ac90b0d069a", "tabbable": null, "tooltip": null, - "value": "hyperparams.yaml: 100%" + "value": " 3.20k/3.20k [00:00<00:00, 790kB/s]" } }, - "743444b6670b4fa99cc0396e4b32f5e4": { + "678fc99edb84497dbac731cf9a1fa79f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b95daff336854c7aa1ca5dbdf574af6e", - "placeholder": "​", - "style": "IPY_MODEL_65c231ce763c440980afbfa1c4eaab86", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_df22226a67914b8a9d0693b25dffdc60", + "IPY_MODEL_7f009f8c42734f8e9fa9ea99cf520312", + "IPY_MODEL_618905a858344a0488c8d600292e4cc3" + ], + "layout": "IPY_MODEL_bf8a89f9ccd04b1a857340ebae05002e", "tabbable": null, - "tooltip": null, - "value": " 129k/129k [00:00<00:00, 10.6MB/s]" - } - }, - "7a9eaff0d94f402eb6726bbbe282d200": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null } }, - "7ac998d738294e6c82fb7330a80ef819": { + "6816c11c8cd945968d5b0ce5adfb87f5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1879dcf933dc47ef80752de84b1be173", - "IPY_MODEL_2073d888478c4148aa6af8f01d1a55c0", - "IPY_MODEL_b182da74164e4c6da9b9218000f3b471" - ], - "layout": "IPY_MODEL_7b71cae594754cd4b51bdf0cc3937dc8", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4a111d06ad644ff3b7dfcd8f9a93b27f", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_60b559ea05d04ab2aefd425629200a9b", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 2041.0 } }, - "7b71cae594754cd4b51bdf0cc3937dc8": { + "6a601518732a456897e40425c7447332": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2323,7 +2184,7 @@ "width": null } }, - "7e28797dcb31400985bcdae5b8c5fb36": { + "6b7cf1b25d364dbab4d57e7ee83bbf50": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2341,23 +2202,30 @@ "text_color": null } }, - "7fb74483ba564e44bf8528608dd0c00d": { + "72f97078b8fc4a6d9b507a102418aea1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1b8db158701349408c5bdef740765f91", + "placeholder": "​", + "style": "IPY_MODEL_c0c1b2f629d145fa8b43d2ac3c1494a6", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 176MB/s]" } }, - "96e34a60ae404938aef646ab844636ca": { + "795a99fe1a5a4fe28962d2835b6d0806": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2410,67 +2278,7 @@ "width": null } }, - "9946d2c65d6146e5a82c395bc8875caf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_68b7a074f4a5491984f03ffaed29102c", - "IPY_MODEL_4209fa1ed28448c4a745350a1042e490", - "IPY_MODEL_743444b6670b4fa99cc0396e4b32f5e4" - ], - "layout": "IPY_MODEL_f70f441fdd5147c685302ccf8dbb2370", - "tabbable": null, - "tooltip": null - } - }, - "9ad076b10aa949c583d59ebf787d18d9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "9fbff358e60544609b6d2a561cf6f85c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a93eeb28cf894cdba2553c527a3bb1a7": { + "7ab9a8d4e3e64a0f90d15eb2e77ce38b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2523,7 +2331,7 @@ "width": null } }, - "af604b66416942c3beafdb0f24f5fb27": { + "7b05aea7bfdd48ddbe010dce6c119c9a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2576,7 +2384,91 @@ "width": null } }, - "b182da74164e4c6da9b9218000f3b471": { + "7f009f8c42734f8e9fa9ea99cf520312": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fd00c0575ed7437ca5aabc5c08229d32", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_44dd265825e9454e868ab092b351a312", + "tabbable": null, + "tooltip": null, + "value": 3201.0 + } + }, + "8063b86fd1644501bd7a35a8f8c91e8d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "84fa5f8c2433478c9dc01b8a41eb48d5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8531becaffca44989779b1d37c65260e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_85e7b3e36e7c47129e29727d2f646f2f", + "IPY_MODEL_6816c11c8cd945968d5b0ce5adfb87f5", + "IPY_MODEL_0dfc7f988177492e852e1a1e2e2e3c55" + ], + "layout": "IPY_MODEL_a3dbbfce424340438202c2a4f54314d7", + "tabbable": null, + "tooltip": null + } + }, + "85e7b3e36e7c47129e29727d2f646f2f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2591,41 +2483,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_39378c4365b84e2ba8a40f5da8c49a6b", + "layout": "IPY_MODEL_d116ac8503dc4fa19dbf254269e57e81", "placeholder": "​", - "style": "IPY_MODEL_7e28797dcb31400985bcdae5b8c5fb36", + "style": "IPY_MODEL_865a2e4e16fe4c8fab1e00a4c6cd135d", "tabbable": null, "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 133MB/s]" + "value": "hyperparams.yaml: 100%" } }, - "b4c75c905856457c980a958861d26460": { + "865a2e4e16fe4c8fab1e00a4c6cd135d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a93eeb28cf894cdba2553c527a3bb1a7", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6b1e51bd7dfd4e7e9b96650627dd4347", - "tabbable": null, - "tooltip": null, - "value": 16887676.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b7930e24a3604c3783c6342017146161": { + "86ca63a102954afa94795c3e61c20880": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2678,7 +2562,43 @@ "width": null } }, - "b95daff336854c7aa1ca5dbdf574af6e": { + "8712797b65e1448f8a93f64e6e581af5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "97d74ea76f524faea7cd1c349d25ed2d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a3dbbfce424340438202c2a4f54314d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2731,25 +2651,30 @@ "width": null } }, - "ba779947520844b1b4e9e788604b7ed8": { + "a408ccf89ec14eceb847510634138186": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_299896a97d41456cac583393c5611a30", + "placeholder": "​", + "style": "IPY_MODEL_6b7cf1b25d364dbab4d57e7ee83bbf50", + "tabbable": null, + "tooltip": null, + "value": " 16.9M/16.9M [00:00<00:00, 190MB/s]" } }, - "c5e86d52ed71402dbadb1a831d3a99dd": { + "a5ba789c57d244a6afdd80d9221598a2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2802,7 +2727,7 @@ "width": null } }, - "cb4111006dc844c69976ab2a2fd47bf5": { + "ad707c12b59d4885be4f947c794ca0ea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2817,16 +2742,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3e0efe779a52444991f5fa51e7b53a87", - "IPY_MODEL_b4c75c905856457c980a958861d26460", - "IPY_MODEL_4d17c45bac644d16890f974d9bf14252" + "IPY_MODEL_0c9f4529a42246ee88b9b31ac1212ba1", + "IPY_MODEL_23ef0aabeddc46538e90abc8e42e36c0", + "IPY_MODEL_a408ccf89ec14eceb847510634138186" ], - "layout": "IPY_MODEL_af604b66416942c3beafdb0f24f5fb27", + "layout": "IPY_MODEL_3c2e1352b6dd4da68e969419d9ecdaea", "tabbable": null, "tooltip": null } }, - "cbf0488c8e70416aabb7cb8cffe59c43": { + "ba0d6ceeadc14ee192ae1dc31b4e4186": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2842,33 +2767,30 @@ "description_width": "" } }, - "cf0f8cce4150428582199659b7ecc31f": { + "bc115d8e973a43f98d22e93fcdeb0d0a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5fc9fb12e7584385b6dbcc71ea004933", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cbf0488c8e70416aabb7cb8cffe59c43", + "layout": "IPY_MODEL_0fb76291c4d34ebdba9ffa24ed513e0a", + "placeholder": "​", + "style": "IPY_MODEL_97d74ea76f524faea7cd1c349d25ed2d", "tabbable": null, "tooltip": null, - "value": 3201.0 + "value": "label_encoder.txt: 100%" } }, - "d3b969344b34427e9067d2e51d96dd59": { + "bf8a89f9ccd04b1a857340ebae05002e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2921,30 +2843,31 @@ "width": null } }, - "d5743decd00649e19fbee18925104825": { + "c0a1f533272a4f6cb65d54cbace4208e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c5e86d52ed71402dbadb1a831d3a99dd", - "placeholder": "​", - "style": "IPY_MODEL_3b3e7e9c2d3a481a8b66e18c87d96abe", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_bc115d8e973a43f98d22e93fcdeb0d0a", + "IPY_MODEL_5fd920ec2856445d98a6f7e8f0229c4d", + "IPY_MODEL_60d05f62061f46e7b7f871ccf49bce6e" + ], + "layout": "IPY_MODEL_7ab9a8d4e3e64a0f90d15eb2e77ce38b", "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" + "tooltip": null } }, - "edbc3e1e1e514aeea602d52281c3dfa3": { + "c0c1b2f629d145fa8b43d2ac3c1494a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2962,7 +2885,7 @@ "text_color": null } }, - "f1379d86855941e4a6388b556616e327": { + "cdbef2f290304f54be4b5de93eb10cc1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3015,30 +2938,7 @@ "width": null } }, - "f5b3c5a8ab94472a8c12d10627c4a3b2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_249f3be21f17476cb286f35a4beae4ea", - "placeholder": "​", - "style": "IPY_MODEL_28c2d54c60d54e18885566cb5f99ba0a", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 820kB/s]" - } - }, - "f70f441fdd5147c685302ccf8dbb2370": { + "d116ac8503dc4fa19dbf254269e57e81": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3091,7 +2991,7 @@ "width": null } }, - "f9d1becbce8d4a0e92d1291261488c36": { + "d16b4b3c59ba49d9a539a98b5d9ace25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3144,52 +3044,152 @@ "width": null } }, - "fbe4b54842f84d388c29da52e5a714cb": { + "d1a86a88d50a4bb2aee04ac90b0d069a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_73fd95beddfa4d59833b8b729dc2ef1b", - "IPY_MODEL_280097d7d8744e47bd5924ed20469bd6", - "IPY_MODEL_38e4ea17dfd74406801cb8ef4fa01bfb" - ], - "layout": "IPY_MODEL_96e34a60ae404938aef646ab844636ca", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "fc94797f46734484ae1adb8c6aac5095": { + "df22226a67914b8a9d0693b25dffdc60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d5743decd00649e19fbee18925104825", - "IPY_MODEL_cf0f8cce4150428582199659b7ecc31f", - "IPY_MODEL_f5b3c5a8ab94472a8c12d10627c4a3b2" - ], - "layout": "IPY_MODEL_b7930e24a3604c3783c6342017146161", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5940aac0c62e430f99781eb883247639", + "placeholder": "​", + "style": "IPY_MODEL_84fa5f8c2433478c9dc01b8a41eb48d5", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "e9b3119fe459439e8146cddd6880a9f1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ef52679868914ac0a1bac86a98893d61": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fb83ceda077c4e68aad24838206b09cc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fd00c0575ed7437ca5aabc5c08229d32": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index 58bbdaa8a..0a658abc0 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:15.541042Z", - "iopub.status.busy": "2024-07-02T12:01:15.540869Z", - "iopub.status.idle": "2024-07-02T12:01:16.706079Z", - "shell.execute_reply": "2024-07-02T12:01:16.705546Z" + "iopub.execute_input": "2024-07-02T15:10:41.435250Z", + "iopub.status.busy": "2024-07-02T15:10:41.434904Z", + "iopub.status.idle": "2024-07-02T15:10:42.616974Z", + "shell.execute_reply": "2024-07-02T15:10:42.616367Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.708528Z", - "iopub.status.busy": "2024-07-02T12:01:16.708127Z", - "iopub.status.idle": "2024-07-02T12:01:16.711112Z", - "shell.execute_reply": "2024-07-02T12:01:16.710676Z" + "iopub.execute_input": "2024-07-02T15:10:42.619570Z", + "iopub.status.busy": "2024-07-02T15:10:42.619310Z", + "iopub.status.idle": "2024-07-02T15:10:42.622452Z", + "shell.execute_reply": "2024-07-02T15:10:42.621992Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.713182Z", - "iopub.status.busy": "2024-07-02T12:01:16.712867Z", - "iopub.status.idle": "2024-07-02T12:01:16.721179Z", - "shell.execute_reply": "2024-07-02T12:01:16.720739Z" + "iopub.execute_input": "2024-07-02T15:10:42.624524Z", + "iopub.status.busy": "2024-07-02T15:10:42.624220Z", + "iopub.status.idle": "2024-07-02T15:10:42.632638Z", + "shell.execute_reply": "2024-07-02T15:10:42.632176Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.723125Z", - "iopub.status.busy": "2024-07-02T12:01:16.722823Z", - "iopub.status.idle": "2024-07-02T12:01:16.727946Z", - "shell.execute_reply": "2024-07-02T12:01:16.727497Z" + "iopub.execute_input": "2024-07-02T15:10:42.634681Z", + "iopub.status.busy": "2024-07-02T15:10:42.634369Z", + "iopub.status.idle": "2024-07-02T15:10:42.638869Z", + "shell.execute_reply": "2024-07-02T15:10:42.638430Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.730061Z", - "iopub.status.busy": "2024-07-02T12:01:16.729738Z", - "iopub.status.idle": "2024-07-02T12:01:16.910261Z", - "shell.execute_reply": "2024-07-02T12:01:16.909774Z" + "iopub.execute_input": "2024-07-02T15:10:42.640929Z", + "iopub.status.busy": "2024-07-02T15:10:42.640599Z", + "iopub.status.idle": "2024-07-02T15:10:42.823237Z", + "shell.execute_reply": "2024-07-02T15:10:42.822755Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:16.912657Z", - "iopub.status.busy": "2024-07-02T12:01:16.912383Z", - "iopub.status.idle": "2024-07-02T12:01:17.280864Z", - "shell.execute_reply": "2024-07-02T12:01:17.280305Z" + "iopub.execute_input": "2024-07-02T15:10:42.825617Z", + "iopub.status.busy": "2024-07-02T15:10:42.825349Z", + "iopub.status.idle": "2024-07-02T15:10:43.193502Z", + "shell.execute_reply": "2024-07-02T15:10:43.192923Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:17.283183Z", - "iopub.status.busy": "2024-07-02T12:01:17.282742Z", - "iopub.status.idle": "2024-07-02T12:01:17.305912Z", - "shell.execute_reply": "2024-07-02T12:01:17.305342Z" + "iopub.execute_input": "2024-07-02T15:10:43.195821Z", + "iopub.status.busy": "2024-07-02T15:10:43.195490Z", + "iopub.status.idle": "2024-07-02T15:10:43.218270Z", + "shell.execute_reply": "2024-07-02T15:10:43.217850Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:17.308190Z", - "iopub.status.busy": "2024-07-02T12:01:17.307876Z", - "iopub.status.idle": "2024-07-02T12:01:17.318887Z", - "shell.execute_reply": "2024-07-02T12:01:17.318342Z" + "iopub.execute_input": "2024-07-02T15:10:43.220248Z", + "iopub.status.busy": "2024-07-02T15:10:43.219922Z", + "iopub.status.idle": "2024-07-02T15:10:43.230680Z", + "shell.execute_reply": "2024-07-02T15:10:43.230226Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:17.321139Z", - "iopub.status.busy": "2024-07-02T12:01:17.320805Z", - "iopub.status.idle": "2024-07-02T12:01:19.303196Z", - "shell.execute_reply": "2024-07-02T12:01:19.302567Z" + "iopub.execute_input": "2024-07-02T15:10:43.232767Z", + "iopub.status.busy": "2024-07-02T15:10:43.232457Z", + "iopub.status.idle": "2024-07-02T15:10:45.202083Z", + "shell.execute_reply": "2024-07-02T15:10:45.201442Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.305724Z", - "iopub.status.busy": "2024-07-02T12:01:19.305235Z", - "iopub.status.idle": "2024-07-02T12:01:19.326596Z", - "shell.execute_reply": "2024-07-02T12:01:19.326111Z" + "iopub.execute_input": "2024-07-02T15:10:45.204518Z", + "iopub.status.busy": "2024-07-02T15:10:45.204239Z", + "iopub.status.idle": "2024-07-02T15:10:45.224676Z", + "shell.execute_reply": "2024-07-02T15:10:45.224228Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.328751Z", - "iopub.status.busy": "2024-07-02T12:01:19.328411Z", - "iopub.status.idle": "2024-07-02T12:01:19.346909Z", - "shell.execute_reply": "2024-07-02T12:01:19.346408Z" + "iopub.execute_input": "2024-07-02T15:10:45.226664Z", + "iopub.status.busy": "2024-07-02T15:10:45.226491Z", + "iopub.status.idle": "2024-07-02T15:10:45.243690Z", + "shell.execute_reply": "2024-07-02T15:10:45.243258Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.349172Z", - "iopub.status.busy": "2024-07-02T12:01:19.348833Z", - "iopub.status.idle": "2024-07-02T12:01:19.364109Z", - "shell.execute_reply": "2024-07-02T12:01:19.363523Z" + "iopub.execute_input": "2024-07-02T15:10:45.245491Z", + "iopub.status.busy": "2024-07-02T15:10:45.245321Z", + "iopub.status.idle": "2024-07-02T15:10:45.259517Z", + "shell.execute_reply": "2024-07-02T15:10:45.259087Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.366447Z", - "iopub.status.busy": "2024-07-02T12:01:19.366041Z", - "iopub.status.idle": "2024-07-02T12:01:19.385525Z", - "shell.execute_reply": "2024-07-02T12:01:19.384972Z" + "iopub.execute_input": "2024-07-02T15:10:45.261615Z", + "iopub.status.busy": "2024-07-02T15:10:45.261244Z", + "iopub.status.idle": "2024-07-02T15:10:45.279892Z", + "shell.execute_reply": "2024-07-02T15:10:45.279369Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "59b4478dd8e7455d94d80c6cac5956e7", + "model_id": "850cf9c5dd514c5c8c5878e12d30f5ca", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.387568Z", - "iopub.status.busy": "2024-07-02T12:01:19.387355Z", - "iopub.status.idle": "2024-07-02T12:01:19.403995Z", - "shell.execute_reply": "2024-07-02T12:01:19.403416Z" + "iopub.execute_input": "2024-07-02T15:10:45.282051Z", + "iopub.status.busy": "2024-07-02T15:10:45.281611Z", + "iopub.status.idle": "2024-07-02T15:10:45.296424Z", + "shell.execute_reply": "2024-07-02T15:10:45.295992Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.406166Z", - "iopub.status.busy": "2024-07-02T12:01:19.405840Z", - "iopub.status.idle": "2024-07-02T12:01:19.411828Z", - "shell.execute_reply": "2024-07-02T12:01:19.411266Z" + "iopub.execute_input": "2024-07-02T15:10:45.298329Z", + "iopub.status.busy": "2024-07-02T15:10:45.298157Z", + "iopub.status.idle": "2024-07-02T15:10:45.304307Z", + "shell.execute_reply": "2024-07-02T15:10:45.303885Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:19.414062Z", - "iopub.status.busy": "2024-07-02T12:01:19.413631Z", - "iopub.status.idle": "2024-07-02T12:01:19.432239Z", - "shell.execute_reply": "2024-07-02T12:01:19.431665Z" + "iopub.execute_input": "2024-07-02T15:10:45.306200Z", + "iopub.status.busy": "2024-07-02T15:10:45.306029Z", + "iopub.status.idle": "2024-07-02T15:10:45.323469Z", + "shell.execute_reply": "2024-07-02T15:10:45.323042Z" } }, "outputs": [ @@ -1447,23 +1447,33 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "160374201c2049b98c39d1da42e6f09d": { + "07b42c7871184a77913db05041f70f6c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e5651455523845919804bfd3f20d32fd", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_da3ba2f2d038490c8a65361852a477f2", + "tabbable": null, + "tooltip": null, + "value": 132.0 } }, - "23609831ef654449b59fb8c4f8a2bb30": { + "81406c4c29884619bacbf6314e1bb90e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1516,30 +1526,49 @@ "width": null } }, - "2c61a80b080b4e158a20edb5c4a1ac84": { + "850cf9c5dd514c5c8c5878e12d30f5ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_23609831ef654449b59fb8c4f8a2bb30", - "placeholder": "​", - "style": "IPY_MODEL_ada4493def764ffa859a5d6ba4d315fb", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b58f2ef4defc4008a4fb84b628c0df08", + "IPY_MODEL_07b42c7871184a77913db05041f70f6c", + "IPY_MODEL_ef016c3dc0df4a9a878a4f9644a436dd" + ], + "layout": "IPY_MODEL_f8cacbb114a946fb8b37956128a62704", "tabbable": null, - "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" + "tooltip": null + } + }, + "a43777fd323b46498d1b65ddfdcb03d7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "430e528b6e30444ea44c9f7dacbfcc30": { + "b58f2ef4defc4008a4fb84b628c0df08": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1554,65 +1583,49 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8addd7af612b43d395a8dfcfeb6287ef", + "layout": "IPY_MODEL_eae1af9f890445fab406fb6b04a570ff", "placeholder": "​", - "style": "IPY_MODEL_bd9b705b24884f74a14e8bfdd7ee8634", + "style": "IPY_MODEL_b7fb3ecf4b354ed5aa31f23e9c2d7f20", "tabbable": null, "tooltip": null, - "value": " 132/132 [00:00<00:00, 13162.98 examples/s]" + "value": "Saving the dataset (1/1 shards): 100%" } }, - "4d30844fcfff423583118cba2ebebe1b": { + "b7fb3ecf4b354ed5aa31f23e9c2d7f20": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_92d343740ab348028d512cbabde596de", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_160374201c2049b98c39d1da42e6f09d", - "tabbable": null, - "tooltip": null, - "value": 132.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "59b4478dd8e7455d94d80c6cac5956e7": { + "da3ba2f2d038490c8a65361852a477f2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2c61a80b080b4e158a20edb5c4a1ac84", - "IPY_MODEL_4d30844fcfff423583118cba2ebebe1b", - "IPY_MODEL_430e528b6e30444ea44c9f7dacbfcc30" - ], - "layout": "IPY_MODEL_5e818fd01e87406a87c87fc7bc810095", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "5e818fd01e87406a87c87fc7bc810095": { + "e5651455523845919804bfd3f20d32fd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1665,7 +1678,7 @@ "width": null } }, - "8addd7af612b43d395a8dfcfeb6287ef": { + "eae1af9f890445fab406fb6b04a570ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1718,7 +1731,30 @@ "width": null } }, - "92d343740ab348028d512cbabde596de": { + "ef016c3dc0df4a9a878a4f9644a436dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_81406c4c29884619bacbf6314e1bb90e", + "placeholder": "​", + "style": "IPY_MODEL_a43777fd323b46498d1b65ddfdcb03d7", + "tabbable": null, + "tooltip": null, + "value": " 132/132 [00:00<00:00, 13503.28 examples/s]" + } + }, + "f8cacbb114a946fb8b37956128a62704": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1770,42 +1806,6 @@ "visibility": null, "width": null } - }, - "ada4493def764ffa859a5d6ba4d315fb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "bd9b705b24884f74a14e8bfdd7ee8634": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index 61c4891f1..cf7301700 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:22.152510Z", - "iopub.status.busy": "2024-07-02T12:01:22.152333Z", - "iopub.status.idle": "2024-07-02T12:01:23.345486Z", - "shell.execute_reply": "2024-07-02T12:01:23.344925Z" + "iopub.execute_input": "2024-07-02T15:10:48.203913Z", + "iopub.status.busy": "2024-07-02T15:10:48.203743Z", + "iopub.status.idle": "2024-07-02T15:10:49.370874Z", + "shell.execute_reply": "2024-07-02T15:10:49.370326Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.348223Z", - "iopub.status.busy": "2024-07-02T12:01:23.347674Z", - "iopub.status.idle": "2024-07-02T12:01:23.350818Z", - "shell.execute_reply": "2024-07-02T12:01:23.350357Z" + "iopub.execute_input": "2024-07-02T15:10:49.373236Z", + "iopub.status.busy": "2024-07-02T15:10:49.372955Z", + "iopub.status.idle": "2024-07-02T15:10:49.375887Z", + "shell.execute_reply": "2024-07-02T15:10:49.375403Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.352826Z", - "iopub.status.busy": "2024-07-02T12:01:23.352642Z", - "iopub.status.idle": "2024-07-02T12:01:23.361928Z", - "shell.execute_reply": "2024-07-02T12:01:23.361407Z" + "iopub.execute_input": "2024-07-02T15:10:49.377883Z", + "iopub.status.busy": "2024-07-02T15:10:49.377688Z", + "iopub.status.idle": "2024-07-02T15:10:49.386512Z", + "shell.execute_reply": "2024-07-02T15:10:49.386078Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.363999Z", - "iopub.status.busy": "2024-07-02T12:01:23.363568Z", - "iopub.status.idle": "2024-07-02T12:01:23.368394Z", - "shell.execute_reply": "2024-07-02T12:01:23.367822Z" + "iopub.execute_input": "2024-07-02T15:10:49.388331Z", + "iopub.status.busy": "2024-07-02T15:10:49.388162Z", + "iopub.status.idle": "2024-07-02T15:10:49.392743Z", + "shell.execute_reply": "2024-07-02T15:10:49.392198Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.370691Z", - "iopub.status.busy": "2024-07-02T12:01:23.370280Z", - "iopub.status.idle": "2024-07-02T12:01:23.560449Z", - "shell.execute_reply": "2024-07-02T12:01:23.559925Z" + "iopub.execute_input": "2024-07-02T15:10:49.394895Z", + "iopub.status.busy": "2024-07-02T15:10:49.394722Z", + "iopub.status.idle": "2024-07-02T15:10:49.580391Z", + "shell.execute_reply": "2024-07-02T15:10:49.579904Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.563109Z", - "iopub.status.busy": "2024-07-02T12:01:23.562666Z", - "iopub.status.idle": "2024-07-02T12:01:23.933479Z", - "shell.execute_reply": "2024-07-02T12:01:23.932844Z" + "iopub.execute_input": "2024-07-02T15:10:49.582895Z", + "iopub.status.busy": "2024-07-02T15:10:49.582500Z", + "iopub.status.idle": "2024-07-02T15:10:49.951559Z", + "shell.execute_reply": "2024-07-02T15:10:49.951015Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.935860Z", - "iopub.status.busy": "2024-07-02T12:01:23.935411Z", - "iopub.status.idle": "2024-07-02T12:01:23.938217Z", - "shell.execute_reply": "2024-07-02T12:01:23.937776Z" + "iopub.execute_input": "2024-07-02T15:10:49.953780Z", + "iopub.status.busy": "2024-07-02T15:10:49.953420Z", + "iopub.status.idle": "2024-07-02T15:10:49.956065Z", + "shell.execute_reply": "2024-07-02T15:10:49.955645Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.940195Z", - "iopub.status.busy": "2024-07-02T12:01:23.940017Z", - "iopub.status.idle": "2024-07-02T12:01:23.974114Z", - "shell.execute_reply": "2024-07-02T12:01:23.973647Z" + "iopub.execute_input": "2024-07-02T15:10:49.958088Z", + "iopub.status.busy": "2024-07-02T15:10:49.957749Z", + "iopub.status.idle": "2024-07-02T15:10:49.991460Z", + "shell.execute_reply": "2024-07-02T15:10:49.991052Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:23.976287Z", - "iopub.status.busy": "2024-07-02T12:01:23.976112Z", - "iopub.status.idle": "2024-07-02T12:01:26.051828Z", - "shell.execute_reply": "2024-07-02T12:01:26.051244Z" + "iopub.execute_input": "2024-07-02T15:10:49.993592Z", + "iopub.status.busy": "2024-07-02T15:10:49.993200Z", + "iopub.status.idle": "2024-07-02T15:10:52.000228Z", + "shell.execute_reply": "2024-07-02T15:10:51.999641Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.054329Z", - "iopub.status.busy": "2024-07-02T12:01:26.053806Z", - "iopub.status.idle": "2024-07-02T12:01:26.073654Z", - "shell.execute_reply": "2024-07-02T12:01:26.073152Z" + "iopub.execute_input": "2024-07-02T15:10:52.002802Z", + "iopub.status.busy": "2024-07-02T15:10:52.002295Z", + "iopub.status.idle": "2024-07-02T15:10:52.021391Z", + "shell.execute_reply": "2024-07-02T15:10:52.020959Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.075978Z", - "iopub.status.busy": "2024-07-02T12:01:26.075603Z", - "iopub.status.idle": "2024-07-02T12:01:26.082158Z", - "shell.execute_reply": "2024-07-02T12:01:26.081661Z" + "iopub.execute_input": "2024-07-02T15:10:52.023564Z", + "iopub.status.busy": "2024-07-02T15:10:52.023238Z", + "iopub.status.idle": "2024-07-02T15:10:52.029818Z", + "shell.execute_reply": "2024-07-02T15:10:52.029240Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.084369Z", - "iopub.status.busy": "2024-07-02T12:01:26.084032Z", - "iopub.status.idle": "2024-07-02T12:01:26.090027Z", - "shell.execute_reply": "2024-07-02T12:01:26.089524Z" + "iopub.execute_input": "2024-07-02T15:10:52.031965Z", + "iopub.status.busy": "2024-07-02T15:10:52.031647Z", + "iopub.status.idle": "2024-07-02T15:10:52.037297Z", + "shell.execute_reply": "2024-07-02T15:10:52.036772Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.092307Z", - "iopub.status.busy": "2024-07-02T12:01:26.091888Z", - "iopub.status.idle": "2024-07-02T12:01:26.102686Z", - "shell.execute_reply": "2024-07-02T12:01:26.102114Z" + "iopub.execute_input": "2024-07-02T15:10:52.039441Z", + "iopub.status.busy": "2024-07-02T15:10:52.039151Z", + "iopub.status.idle": "2024-07-02T15:10:52.049413Z", + "shell.execute_reply": "2024-07-02T15:10:52.048911Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.104843Z", - "iopub.status.busy": "2024-07-02T12:01:26.104499Z", - "iopub.status.idle": "2024-07-02T12:01:26.113923Z", - "shell.execute_reply": "2024-07-02T12:01:26.113353Z" + "iopub.execute_input": "2024-07-02T15:10:52.051475Z", + "iopub.status.busy": "2024-07-02T15:10:52.051095Z", + "iopub.status.idle": "2024-07-02T15:10:52.060097Z", + "shell.execute_reply": "2024-07-02T15:10:52.059640Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.116196Z", - "iopub.status.busy": "2024-07-02T12:01:26.115857Z", - "iopub.status.idle": "2024-07-02T12:01:26.122959Z", - "shell.execute_reply": "2024-07-02T12:01:26.122462Z" + "iopub.execute_input": "2024-07-02T15:10:52.062179Z", + "iopub.status.busy": "2024-07-02T15:10:52.061837Z", + "iopub.status.idle": "2024-07-02T15:10:52.068765Z", + "shell.execute_reply": "2024-07-02T15:10:52.068314Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.125128Z", - "iopub.status.busy": "2024-07-02T12:01:26.124796Z", - "iopub.status.idle": "2024-07-02T12:01:26.134864Z", - "shell.execute_reply": "2024-07-02T12:01:26.134300Z" + "iopub.execute_input": "2024-07-02T15:10:52.070862Z", + "iopub.status.busy": "2024-07-02T15:10:52.070545Z", + "iopub.status.idle": "2024-07-02T15:10:52.079842Z", + "shell.execute_reply": "2024-07-02T15:10:52.079380Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:26.137332Z", - "iopub.status.busy": "2024-07-02T12:01:26.136913Z", - "iopub.status.idle": "2024-07-02T12:01:26.152852Z", - "shell.execute_reply": "2024-07-02T12:01:26.152376Z" + "iopub.execute_input": "2024-07-02T15:10:52.081933Z", + "iopub.status.busy": "2024-07-02T15:10:52.081594Z", + "iopub.status.idle": "2024-07-02T15:10:52.097277Z", + "shell.execute_reply": "2024-07-02T15:10:52.096807Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb index 3baceeb0b..2852ac72e 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:28.896200Z", - "iopub.status.busy": "2024-07-02T12:01:28.896023Z", - "iopub.status.idle": "2024-07-02T12:01:31.827318Z", - "shell.execute_reply": "2024-07-02T12:01:31.826688Z" + "iopub.execute_input": "2024-07-02T15:10:54.880751Z", + "iopub.status.busy": "2024-07-02T15:10:54.880594Z", + "iopub.status.idle": "2024-07-02T15:10:57.696869Z", + "shell.execute_reply": "2024-07-02T15:10:57.696388Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:31.829957Z", - "iopub.status.busy": "2024-07-02T12:01:31.829648Z", - "iopub.status.idle": "2024-07-02T12:01:31.833462Z", - "shell.execute_reply": "2024-07-02T12:01:31.833002Z" + "iopub.execute_input": "2024-07-02T15:10:57.699412Z", + "iopub.status.busy": "2024-07-02T15:10:57.698969Z", + "iopub.status.idle": "2024-07-02T15:10:57.702504Z", + "shell.execute_reply": "2024-07-02T15:10:57.702065Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:31.835341Z", - "iopub.status.busy": "2024-07-02T12:01:31.835170Z", - "iopub.status.idle": "2024-07-02T12:01:42.989836Z", - "shell.execute_reply": "2024-07-02T12:01:42.989362Z" + "iopub.execute_input": "2024-07-02T15:10:57.704607Z", + "iopub.status.busy": "2024-07-02T15:10:57.704218Z", + "iopub.status.idle": "2024-07-02T15:11:08.972759Z", + "shell.execute_reply": "2024-07-02T15:11:08.972290Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d4c59b0bfa86424a8c95a71f890f5454", + "model_id": "76447603597c41e58c504ba366dedf8b", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ffbe85316974d029eab626642378580", + "model_id": "74d7207adb634a9a9648063cd4ebf05d", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a9f98ff0f0446e7b89c4fe4fffc3418", + "model_id": "24554a44a66045a29398e71c18b39f2f", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "39838b65ab134d2a9a445437586fec98", + "model_id": "52a2b90360f7460f9d5e8e206e5b7b47", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4d801b30b791427d9103f41505cf1a3e", + "model_id": "1eca5328aef44e1ca18c8c422f647377", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d1f1b12cc3545b0b78b6f64afe61ba8", + "model_id": "c8ad57476e81431f9ef31378a786d5e9", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "495daf880acd479da7fa63fedf1e1368", + "model_id": "4761c3ddf1a643e8bda01b752e44ad8b", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96b3b9a948504544be06e5692d10926d", + "model_id": "8d04c2d222424f08b06b6508223878ed", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:42.992144Z", - "iopub.status.busy": "2024-07-02T12:01:42.991695Z", - "iopub.status.idle": "2024-07-02T12:01:42.995507Z", - "shell.execute_reply": "2024-07-02T12:01:42.995062Z" + "iopub.execute_input": "2024-07-02T15:11:08.975154Z", + "iopub.status.busy": "2024-07-02T15:11:08.974702Z", + "iopub.status.idle": "2024-07-02T15:11:08.978606Z", + "shell.execute_reply": "2024-07-02T15:11:08.978061Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:42.997511Z", - "iopub.status.busy": "2024-07-02T12:01:42.997189Z", - "iopub.status.idle": "2024-07-02T12:01:54.313084Z", - "shell.execute_reply": "2024-07-02T12:01:54.312563Z" + "iopub.execute_input": "2024-07-02T15:11:08.980647Z", + "iopub.status.busy": "2024-07-02T15:11:08.980365Z", + "iopub.status.idle": "2024-07-02T15:11:20.198567Z", + "shell.execute_reply": "2024-07-02T15:11:20.197917Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5191d0744a454151b8fae157e5a21ef4", + "model_id": "ea88c13811944930a76ece93362f7e4c", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:01:54.315561Z", - "iopub.status.busy": "2024-07-02T12:01:54.315315Z", - "iopub.status.idle": "2024-07-02T12:02:13.013990Z", - "shell.execute_reply": "2024-07-02T12:02:13.013360Z" + "iopub.execute_input": "2024-07-02T15:11:20.201174Z", + "iopub.status.busy": "2024-07-02T15:11:20.200947Z", + "iopub.status.idle": "2024-07-02T15:11:38.612541Z", + "shell.execute_reply": "2024-07-02T15:11:38.611926Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.016850Z", - "iopub.status.busy": "2024-07-02T12:02:13.016410Z", - "iopub.status.idle": "2024-07-02T12:02:13.021208Z", - "shell.execute_reply": "2024-07-02T12:02:13.020777Z" + "iopub.execute_input": "2024-07-02T15:11:38.615766Z", + "iopub.status.busy": "2024-07-02T15:11:38.615417Z", + "iopub.status.idle": "2024-07-02T15:11:38.621062Z", + "shell.execute_reply": "2024-07-02T15:11:38.620540Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.023194Z", - "iopub.status.busy": "2024-07-02T12:02:13.022869Z", - "iopub.status.idle": "2024-07-02T12:02:13.027182Z", - "shell.execute_reply": "2024-07-02T12:02:13.026649Z" + "iopub.execute_input": "2024-07-02T15:11:38.623170Z", + "iopub.status.busy": "2024-07-02T15:11:38.622849Z", + "iopub.status.idle": "2024-07-02T15:11:38.627084Z", + "shell.execute_reply": "2024-07-02T15:11:38.626551Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.029208Z", - "iopub.status.busy": "2024-07-02T12:02:13.028904Z", - "iopub.status.idle": "2024-07-02T12:02:13.037801Z", - "shell.execute_reply": "2024-07-02T12:02:13.037284Z" + "iopub.execute_input": "2024-07-02T15:11:38.628931Z", + "iopub.status.busy": "2024-07-02T15:11:38.628726Z", + "iopub.status.idle": "2024-07-02T15:11:38.637629Z", + "shell.execute_reply": "2024-07-02T15:11:38.637111Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.039783Z", - "iopub.status.busy": "2024-07-02T12:02:13.039463Z", - "iopub.status.idle": "2024-07-02T12:02:13.066102Z", - "shell.execute_reply": "2024-07-02T12:02:13.065500Z" + "iopub.execute_input": "2024-07-02T15:11:38.639743Z", + "iopub.status.busy": "2024-07-02T15:11:38.639336Z", + "iopub.status.idle": "2024-07-02T15:11:38.665352Z", + "shell.execute_reply": "2024-07-02T15:11:38.664931Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:13.068543Z", - "iopub.status.busy": "2024-07-02T12:02:13.068350Z", - "iopub.status.idle": "2024-07-02T12:02:45.178356Z", - "shell.execute_reply": "2024-07-02T12:02:45.177789Z" + "iopub.execute_input": "2024-07-02T15:11:38.667332Z", + "iopub.status.busy": "2024-07-02T15:11:38.667160Z", + "iopub.status.idle": "2024-07-02T15:12:10.330212Z", + "shell.execute_reply": "2024-07-02T15:12:10.329611Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.801\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.690\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.468\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.414\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec86bd0afa46422aa85bf2778e427f2a", + "model_id": "860c6216e3754afa972fdf5b5a0980a0", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0b406e9eaf143599fd4e302b57381b4", + "model_id": "bf64e375efe14d25b7e951f059b16c23", "version_major": 2, "version_minor": 0 }, @@ -840,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.793\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.642\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.570\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.471\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bfd46491d1764708be24b2103e5e6cb5", + "model_id": "8259ba9a3539477db64cbdd68592e635", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1696a28972cf4c1c95e3e3bf755c8d21", + "model_id": "da2c01112d1f4e749b0ca2c79b09927f", "version_major": 2, "version_minor": 0 }, @@ -898,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.822\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.668\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.476\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.531\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "32f22fc4e23745929d001d9647682786", + "model_id": "26d250d79c2447489401eb9ab9ace7df", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "846e19cb26a94bdba7b363dce398b69c", + "model_id": "a3115a3594ce4aa497f8a610abb0af9e", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:45.181036Z", - "iopub.status.busy": "2024-07-02T12:02:45.180584Z", - "iopub.status.idle": "2024-07-02T12:02:45.194402Z", - "shell.execute_reply": "2024-07-02T12:02:45.193957Z" + "iopub.execute_input": "2024-07-02T15:12:10.332761Z", + "iopub.status.busy": "2024-07-02T15:12:10.332362Z", + "iopub.status.idle": "2024-07-02T15:12:10.346556Z", + "shell.execute_reply": "2024-07-02T15:12:10.346082Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:45.196378Z", - "iopub.status.busy": "2024-07-02T12:02:45.196060Z", - "iopub.status.idle": "2024-07-02T12:02:45.659461Z", - "shell.execute_reply": "2024-07-02T12:02:45.658926Z" + "iopub.execute_input": "2024-07-02T15:12:10.348951Z", + "iopub.status.busy": "2024-07-02T15:12:10.348618Z", + "iopub.status.idle": "2024-07-02T15:12:10.823258Z", + "shell.execute_reply": "2024-07-02T15:12:10.822713Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:02:45.661921Z", - "iopub.status.busy": "2024-07-02T12:02:45.661522Z", - "iopub.status.idle": "2024-07-02T12:04:21.084670Z", - "shell.execute_reply": "2024-07-02T12:04:21.084011Z" + "iopub.execute_input": "2024-07-02T15:12:10.825656Z", + "iopub.status.busy": "2024-07-02T15:12:10.825310Z", + "iopub.status.idle": "2024-07-02T15:13:46.428675Z", + "shell.execute_reply": "2024-07-02T15:13:46.428018Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "683ea97790a64507b71e617e6bb1960f", + "model_id": "b66bf1f268f64f16b0ab04fbfef16cb7", "version_major": 2, "version_minor": 0 }, @@ -1144,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.087384Z", - "iopub.status.busy": "2024-07-02T12:04:21.086898Z", - "iopub.status.idle": "2024-07-02T12:04:21.530187Z", - "shell.execute_reply": "2024-07-02T12:04:21.529650Z" + "iopub.execute_input": "2024-07-02T15:13:46.431322Z", + "iopub.status.busy": "2024-07-02T15:13:46.430773Z", + "iopub.status.idle": "2024-07-02T15:13:46.883257Z", + "shell.execute_reply": "2024-07-02T15:13:46.882712Z" } }, "outputs": [ @@ -1293,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.532970Z", - "iopub.status.busy": "2024-07-02T12:04:21.532489Z", - "iopub.status.idle": "2024-07-02T12:04:21.594306Z", - "shell.execute_reply": "2024-07-02T12:04:21.593726Z" + "iopub.execute_input": "2024-07-02T15:13:46.885977Z", + "iopub.status.busy": "2024-07-02T15:13:46.885501Z", + "iopub.status.idle": "2024-07-02T15:13:46.948513Z", + "shell.execute_reply": "2024-07-02T15:13:46.947996Z" } }, "outputs": [ @@ -1400,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.597613Z", - "iopub.status.busy": "2024-07-02T12:04:21.597278Z", - "iopub.status.idle": "2024-07-02T12:04:21.605873Z", - "shell.execute_reply": "2024-07-02T12:04:21.605434Z" + "iopub.execute_input": "2024-07-02T15:13:46.950792Z", + "iopub.status.busy": "2024-07-02T15:13:46.950469Z", + "iopub.status.idle": "2024-07-02T15:13:46.958869Z", + "shell.execute_reply": "2024-07-02T15:13:46.958422Z" } }, "outputs": [ @@ -1533,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.607881Z", - "iopub.status.busy": "2024-07-02T12:04:21.607595Z", - "iopub.status.idle": "2024-07-02T12:04:21.612387Z", - "shell.execute_reply": "2024-07-02T12:04:21.611934Z" + "iopub.execute_input": "2024-07-02T15:13:46.960882Z", + "iopub.status.busy": "2024-07-02T15:13:46.960564Z", + "iopub.status.idle": "2024-07-02T15:13:46.965390Z", + "shell.execute_reply": "2024-07-02T15:13:46.964852Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:21.614443Z", - "iopub.status.busy": "2024-07-02T12:04:21.614030Z", - "iopub.status.idle": "2024-07-02T12:04:22.120240Z", - "shell.execute_reply": "2024-07-02T12:04:22.119680Z" + "iopub.execute_input": "2024-07-02T15:13:46.967456Z", + "iopub.status.busy": "2024-07-02T15:13:46.967155Z", + "iopub.status.idle": "2024-07-02T15:13:47.465450Z", + "shell.execute_reply": "2024-07-02T15:13:47.464898Z" } }, "outputs": [ @@ -1620,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.122526Z", - "iopub.status.busy": "2024-07-02T12:04:22.122160Z", - "iopub.status.idle": "2024-07-02T12:04:22.130544Z", - "shell.execute_reply": "2024-07-02T12:04:22.130091Z" + "iopub.execute_input": "2024-07-02T15:13:47.467701Z", + "iopub.status.busy": "2024-07-02T15:13:47.467369Z", + "iopub.status.idle": "2024-07-02T15:13:47.475692Z", + "shell.execute_reply": "2024-07-02T15:13:47.475239Z" } }, "outputs": [ @@ -1790,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.132648Z", - "iopub.status.busy": "2024-07-02T12:04:22.132322Z", - "iopub.status.idle": "2024-07-02T12:04:22.139582Z", - "shell.execute_reply": "2024-07-02T12:04:22.139132Z" + "iopub.execute_input": "2024-07-02T15:13:47.477736Z", + "iopub.status.busy": "2024-07-02T15:13:47.477444Z", + "iopub.status.idle": "2024-07-02T15:13:47.484538Z", + "shell.execute_reply": "2024-07-02T15:13:47.483995Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.141499Z", - "iopub.status.busy": "2024-07-02T12:04:22.141182Z", - "iopub.status.idle": "2024-07-02T12:04:22.871798Z", - "shell.execute_reply": "2024-07-02T12:04:22.871228Z" + "iopub.execute_input": "2024-07-02T15:13:47.486504Z", + "iopub.status.busy": "2024-07-02T15:13:47.486124Z", + "iopub.status.idle": "2024-07-02T15:13:48.236887Z", + "shell.execute_reply": "2024-07-02T15:13:48.236330Z" } }, "outputs": [ @@ -1909,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.874107Z", - "iopub.status.busy": "2024-07-02T12:04:22.873751Z", - "iopub.status.idle": "2024-07-02T12:04:22.889160Z", - "shell.execute_reply": "2024-07-02T12:04:22.888693Z" + "iopub.execute_input": "2024-07-02T15:13:48.238951Z", + "iopub.status.busy": "2024-07-02T15:13:48.238743Z", + "iopub.status.idle": "2024-07-02T15:13:48.254003Z", + "shell.execute_reply": "2024-07-02T15:13:48.253445Z" } }, "outputs": [ @@ -2069,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.891280Z", - "iopub.status.busy": "2024-07-02T12:04:22.890945Z", - "iopub.status.idle": "2024-07-02T12:04:22.896314Z", - "shell.execute_reply": "2024-07-02T12:04:22.895869Z" + "iopub.execute_input": "2024-07-02T15:13:48.256077Z", + "iopub.status.busy": "2024-07-02T15:13:48.255753Z", + "iopub.status.idle": "2024-07-02T15:13:48.261132Z", + "shell.execute_reply": "2024-07-02T15:13:48.260713Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:22.898366Z", - "iopub.status.busy": "2024-07-02T12:04:22.898042Z", - "iopub.status.idle": "2024-07-02T12:04:23.354782Z", - "shell.execute_reply": "2024-07-02T12:04:23.354256Z" + "iopub.execute_input": "2024-07-02T15:13:48.263200Z", + "iopub.status.busy": "2024-07-02T15:13:48.262806Z", + "iopub.status.idle": "2024-07-02T15:13:48.721823Z", + "shell.execute_reply": "2024-07-02T15:13:48.721244Z" } }, "outputs": [ @@ -2202,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.357430Z", - "iopub.status.busy": "2024-07-02T12:04:23.357055Z", - "iopub.status.idle": "2024-07-02T12:04:23.366373Z", - "shell.execute_reply": "2024-07-02T12:04:23.365890Z" + "iopub.execute_input": "2024-07-02T15:13:48.724484Z", + "iopub.status.busy": "2024-07-02T15:13:48.724285Z", + "iopub.status.idle": "2024-07-02T15:13:48.733522Z", + "shell.execute_reply": "2024-07-02T15:13:48.732818Z" } }, "outputs": [ @@ -2230,47 +2230,47 @@ " \n", " \n", " \n", - " is_dark_issue\n", " dark_score\n", + " is_dark_issue\n", " \n", " \n", " \n", " \n", " 34848\n", - " True\n", " 0.203922\n", + " True\n", " \n", " \n", " 50270\n", - " True\n", " 0.204588\n", + " True\n", " \n", " \n", " 3936\n", - " True\n", " 0.213098\n", + " True\n", " \n", " \n", " 733\n", - " True\n", " 0.217686\n", + " True\n", " \n", " \n", " 8094\n", - " True\n", " 0.230118\n", + " True\n", " \n", " \n", "\n", "" ], "text/plain": [ - " is_dark_issue dark_score\n", - "34848 True 0.203922\n", - "50270 True 0.204588\n", - "3936 True 0.213098\n", - "733 True 0.217686\n", - "8094 True 0.230118" + " dark_score is_dark_issue\n", + "34848 0.203922 True\n", + "50270 0.204588 True\n", + "3936 0.213098 True\n", + "733 0.217686 True\n", + "8094 0.230118 True" ] }, "execution_count": 26, @@ -2333,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.368851Z", - "iopub.status.busy": "2024-07-02T12:04:23.368495Z", - "iopub.status.idle": "2024-07-02T12:04:23.374119Z", - "shell.execute_reply": "2024-07-02T12:04:23.373635Z" + "iopub.execute_input": "2024-07-02T15:13:48.735985Z", + "iopub.status.busy": "2024-07-02T15:13:48.735796Z", + "iopub.status.idle": "2024-07-02T15:13:48.741485Z", + "shell.execute_reply": "2024-07-02T15:13:48.740930Z" }, "nbsphinx": "hidden" }, @@ -2373,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.376452Z", - "iopub.status.busy": "2024-07-02T12:04:23.376105Z", - "iopub.status.idle": "2024-07-02T12:04:23.576168Z", - "shell.execute_reply": "2024-07-02T12:04:23.575585Z" + "iopub.execute_input": "2024-07-02T15:13:48.743854Z", + "iopub.status.busy": "2024-07-02T15:13:48.743665Z", + "iopub.status.idle": "2024-07-02T15:13:48.944292Z", + "shell.execute_reply": "2024-07-02T15:13:48.943813Z" } }, "outputs": [ @@ -2418,10 +2418,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.578422Z", - "iopub.status.busy": "2024-07-02T12:04:23.578237Z", - "iopub.status.idle": "2024-07-02T12:04:23.586182Z", - "shell.execute_reply": "2024-07-02T12:04:23.585742Z" + "iopub.execute_input": "2024-07-02T15:13:48.946415Z", + "iopub.status.busy": "2024-07-02T15:13:48.946254Z", + "iopub.status.idle": "2024-07-02T15:13:48.953697Z", + "shell.execute_reply": "2024-07-02T15:13:48.953257Z" } }, "outputs": [ @@ -2507,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.588296Z", - "iopub.status.busy": "2024-07-02T12:04:23.587874Z", - "iopub.status.idle": "2024-07-02T12:04:23.783615Z", - "shell.execute_reply": "2024-07-02T12:04:23.783030Z" + "iopub.execute_input": "2024-07-02T15:13:48.955509Z", + "iopub.status.busy": "2024-07-02T15:13:48.955356Z", + "iopub.status.idle": "2024-07-02T15:13:49.147359Z", + "shell.execute_reply": "2024-07-02T15:13:49.146829Z" } }, "outputs": [ @@ -2550,10 +2550,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:23.785886Z", - "iopub.status.busy": "2024-07-02T12:04:23.785554Z", - "iopub.status.idle": "2024-07-02T12:04:23.789936Z", - "shell.execute_reply": "2024-07-02T12:04:23.789389Z" + "iopub.execute_input": "2024-07-02T15:13:49.149499Z", + "iopub.status.busy": "2024-07-02T15:13:49.149335Z", + "iopub.status.idle": "2024-07-02T15:13:49.153453Z", + "shell.execute_reply": "2024-07-02T15:13:49.153029Z" }, "nbsphinx": "hidden" }, @@ -2590,7 +2590,71 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "007a3563b0514e35b0a7409f1a0e8668": { + "017871e5d3284cbbbede907271767d8c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_837e8c16ffdd41a4ae0f4631055cca57", + "placeholder": "​", + "style": "IPY_MODEL_9c4688aea9f9425883500665dfa60bf9", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "01c7b5f2e46d450e9f0ee68d2e6e7184": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "039cadb1d8a94b7d86d6d048e6ff9a52": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dce15d5cb91b42a780b136a398da089e", + "placeholder": "​", + "style": "IPY_MODEL_05aeaba6e4c6492d92f32a92995787a1", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:01<00:00, 8784.47 examples/s]" + } + }, + "04cb19cb27ec48d8bf07ddb3dbafdbf7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2643,7 +2707,41 @@ "width": null } }, - "0205ebdad1d64de8a1bd7d1c741d5fcb": { + "05aeaba6e4c6492d92f32a92995787a1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0b15bb47dbed43cbaed90ce690a3f879": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0ce0d8843432485ea62b457bcb0faf43": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2696,7 +2794,181 @@ "width": null } }, - "052177a0e94b458eb71c811c3229f857": { + "0e4e51c9e4794c289497fc9034858879": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_459779a55e494dea92f7ce2871528083", + "placeholder": "​", + "style": "IPY_MODEL_72292d3faee34bf1949667097765550e", + "tabbable": null, + "tooltip": null, + "value": "Downloading readme: 100%" + } + }, + "0fab8f6f053241e4a76c15c455304f10": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "135c72d3fed34ffc90902f7faf2f27b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1361429788e54a748edb03027a9cab6a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "13b268a2d27e4d6c855f5088b35b9247": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6a1591c936ba4e709d74e0ec278cefd6", + "IPY_MODEL_24566f0819db4eecbcd0b9831228c09f", + "IPY_MODEL_78981540b49a4ac4b4674a46e0b71bd0" + ], + "layout": "IPY_MODEL_d9e695fa0c8741669e44cf4502f36d47", + "tabbable": null, + "tooltip": null + } + }, + "1711a1f1944742ea8f38809143bc3cc8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2b8b9a59fe67431c926345fe531d94ed", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_964944a145304bd58d1fd62856054c5a", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "17a61e4c38a64575bc341e3504130922": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_75137f463796461d87f332d1e82c826b", + "placeholder": "​", + "style": "IPY_MODEL_3a2f200c9979405ea1799e8b257d2962", + "tabbable": null, + "tooltip": null, + "value": " 8.85k/8.85k [00:00<00:00, 1.51MB/s]" + } + }, + "195a744ab07644a29d3e3e02108d0aa2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b31e5df3571f4ac28345bf70cd4e947c", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0b15bb47dbed43cbaed90ce690a3f879", + "tabbable": null, + "tooltip": null, + "value": 4422102.0 + } + }, + "1a4cf1e028c44677995965a00cb4aa35": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2749,61 +3021,8 @@ "width": null } }, - "0826f288ec1f413988e02f4ef8849c28": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "088b60ea8401405ea27804efbb34b231": { - "model_module": "@jupyter-widgets/controls", + "1a67cc49a0714635846f10e32202bddc": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { @@ -2818,40 +3037,43 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_40e0d768badf43758c95df2555d1a977", - "max": 40.0, + "layout": "IPY_MODEL_ffc2140e49b04844ba200898835e603c", + "max": 8845.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_29258ac24b084216b09e806906959044", + "style": "IPY_MODEL_97bd081fbc2c473fb8852e3e2ef1391d", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 8845.0 } }, - "0a39ed2c512b4f33865071d90e585e06": { + "1bf57cd82d684206a95f2c1e179d96ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ac0dd2e3b9574dfba4e088b07dc9917a", - "placeholder": "​", - "style": "IPY_MODEL_7d2cf7fe1d884127b2ea048061916752", + "layout": "IPY_MODEL_c639afb167db44978154a2d4054f1d40", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ccea9ba4e771468a95236d2db5bf0264", "tabbable": null, "tooltip": null, - "value": " 5.15k/5.15k [00:00<00:00, 778kB/s]" + "value": 40.0 } }, - "0b2e2b62fbba4e8e919185c698964e99": { + "1c413b52ad994cc6818c637e38897e16": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2904,7 +3126,7 @@ "width": null } }, - "0cae058fc562457bbb502b466cfdfcab": { + "1d882b744195442f85a86d91cabda0fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2957,7 +3179,25 @@ "width": null } }, - "0d1f1b12cc3545b0b78b6f64afe61ba8": { + "1e4c7bfe71e44d6dbf26a8dff0f90509": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1eca5328aef44e1ca18c8c422f647377": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2972,42 +3212,39 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_b1d5272979684bff96c71500a455d400", - "IPY_MODEL_6b095fb924ef41358579ec879bf0f9fe", - "IPY_MODEL_0a39ed2c512b4f33865071d90e585e06" + "IPY_MODEL_017871e5d3284cbbbede907271767d8c", + "IPY_MODEL_195a744ab07644a29d3e3e02108d0aa2", + "IPY_MODEL_5b344538fc3b43a98f559b15bee9a5fa" ], - "layout": "IPY_MODEL_72961bb987d24298bfdb11eb59546963", + "layout": "IPY_MODEL_ddee9f52a482431d83538c9f941bfbe9", "tabbable": null, "tooltip": null } }, - "0e97775e042e4049bb15d621385de0b0": { + "1f029c9a5f7842a19a17b9c9f75ecc48": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d84b7a5330814c309bc2e3a29fd936ef", - "max": 26421880.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f4bfad9cd0da4d31a8d5c783407a73c8", + "layout": "IPY_MODEL_f039d6ee9460446c9cbfff2777f2bb15", + "placeholder": "​", + "style": "IPY_MODEL_5795395d630f42f09192fdb4e89f8465", "tabbable": null, "tooltip": null, - "value": 26421880.0 + "value": " 40/40 [00:00<00:00, 66.57it/s]" } }, - "0ec179f1c53a4547a89ad81af9b56fa0": { + "1fe8a37c7c4b481eb559efba20e116ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3023,35 +3260,70 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d90986d0f1bb4e8ebf632742ca0c49a3", - "max": 8845.0, + "layout": "IPY_MODEL_cfdfd821fb03474ba31a8ca207281b52", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_7004a3b5592a40ee8328f00432adfc79", + "style": "IPY_MODEL_2130bdaf86db48658ae28f45cec5400b", "tabbable": null, "tooltip": null, - "value": 8845.0 + "value": 40.0 } }, - "0f8071081d82450c9dd7fd9a927b6b1f": { - "model_module": "@jupyter-widgets/controls", + "1ffe24ac54fe4cb49579cb75ed7db9b5": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0f83d37dd1394bc7a4f3ffc18b97b21c": { + "20fc3b9b9a4b48d881bc11004e753c5e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3104,96 +3376,23 @@ "width": null } }, - "1262e1266fb7438b9a905b26b4129cf0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_eaa35cd4c5ae462181de3ad1ab98c2d1", - "placeholder": "​", - "style": "IPY_MODEL_9bc90fd3c0264b26b5bf1c99f4b9caad", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "159251baf5e8425b8c9f8d7acb9abc55": { + "2130bdaf86db48658ae28f45cec5400b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "1696a28972cf4c1c95e3e3bf755c8d21": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_bd7060172fb747a6ae92a503a3922356", - "IPY_MODEL_5779a50d69944438b953e80eb37bbcac", - "IPY_MODEL_359bb8b698c94cf8b32d650da4752723" - ], - "layout": "IPY_MODEL_be6b57b7d12a497fbc96e8e89b08f15a", - "tabbable": null, - "tooltip": null - } - }, - "1a9f98ff0f0446e7b89c4fe4fffc3418": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1262e1266fb7438b9a905b26b4129cf0", - "IPY_MODEL_0e97775e042e4049bb15d621385de0b0", - "IPY_MODEL_56000f81200d41e0bfa6f6b08d883916" - ], - "layout": "IPY_MODEL_75221b9dde234f55bcd73f8bba5f3fa5", - "tabbable": null, - "tooltip": null + "bar_color": null, + "description_width": "" } }, - "1e5f76275f894a96a2d651d0a386fdf9": { + "23fe73f9bf064ef5bd44fb6b0e295456": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3246,30 +3445,7 @@ "width": null } }, - "1ef3f8ae36734efb86b54939cb9711d4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7b58054c0d6a418194fc7e1f039c639b", - "placeholder": "​", - "style": "IPY_MODEL_864d773ce416475ba6a1e506b36063dc", - "tabbable": null, - "tooltip": null, - "value": " 4/4 [00:00<00:00, 1198.12it/s]" - } - }, - "1f5d840cd56a4162b5ad826e1f2902c2": { + "244159f1aa444aedbd2e3e9382b4326b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3322,39 +3498,31 @@ "width": null } }, - "1fb6203f0a3f47309b3fefc7cf4a8522": { + "24554a44a66045a29398e71c18b39f2f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7e502dd5790a480887b970c2862e5170", + "IPY_MODEL_be2ba8934caf4f6eb77db4a243219f26", + "IPY_MODEL_b36cf6fb19144ae68ceeda6d14a0c88e" + ], + "layout": "IPY_MODEL_5d925a37f7f24e27bf2be497abcd3368", + "tabbable": null, + "tooltip": null } }, - "1fdf0f2ba1e440ccaf02e74fc4b28520": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "21a5ba5f675d47938f0e977783a004ba": { + "24566f0819db4eecbcd0b9831228c09f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3370,130 +3538,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2d5192bd86654b04b8a872e4841a73ee", - "max": 60000.0, + "layout": "IPY_MODEL_34591a8e01774b23835f7bec29c46ffb", + "max": 4.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_1fb6203f0a3f47309b3fefc7cf4a8522", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "21a93c7e2dfc4569b325ed80637cf469": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ff08f8bfa09d42838688c6f725adb306", - "placeholder": "​", - "style": "IPY_MODEL_aea843e930ac410596a0f4cb4f6520e0", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 65.61it/s]" - } - }, - "240d9d285e4c42e9a24006d9d2988868": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1e5f76275f894a96a2d651d0a386fdf9", - "placeholder": "​", - "style": "IPY_MODEL_416fa493cc9c4ff6ad13e8b6e6aedbaa", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" - } - }, - "25f2773590254b58b3ac4b1c0a886c35": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f1c238f4a14549229bdf80d577253ccf", - "placeholder": "​", - "style": "IPY_MODEL_f0b50dd1b20c48b6911a694433d48e05", + "style": "IPY_MODEL_618936f2cae44a09978936d5100bc0a5", "tabbable": null, "tooltip": null, - "value": " 4.83k/4.83k [00:00<00:00, 623kB/s]" - } - }, - "27b9b136f3bb4b1a80402df468b5c136": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": 4.0 } }, - "2865f56223344611aa17c9ec66a2d090": { + "26d250d79c2447489401eb9ab9ace7df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f9978a29787547e3bc5e59bde742651c", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cd6bbb8ca872405fa17b2571965191b3", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e452cfce19364c6fb6b50db4144baec6", + "IPY_MODEL_cb9288f8e40c422ea8a86e41ea2ba6df", + "IPY_MODEL_76f7038f04f14f28ba39042b9a0b29ca" + ], + "layout": "IPY_MODEL_e09e931dcf844151835a19cfddd0f459", "tabbable": null, - "tooltip": null, - "value": 60000.0 + "tooltip": null } }, - "29258ac24b084216b09e806906959044": { + "270f6bf2c3e643ea8dd1a756817909fe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3509,25 +3588,7 @@ "description_width": "" } }, - "2c4aaf8a7a84451d93bb9c185069cfe6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "2d5192bd86654b04b8a872e4841a73ee": { + "2b356c8f0be148beb637a84faef5510a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3580,31 +3641,7 @@ "width": null } }, - "2ffbe85316974d029eab626642378580": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_62e4f2d9c4534ebbab778993d057a978", - "IPY_MODEL_0ec179f1c53a4547a89ad81af9b56fa0", - "IPY_MODEL_5c88bd64b2f44f8ab2aa0198c89462d3" - ], - "layout": "IPY_MODEL_44a3e2129c4243e4862df806c2d8c5af", - "tabbable": null, - "tooltip": null - } - }, - "306584df97934511bd6de92ca49b025b": { + "2b8b9a59fe67431c926345fe531d94ed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3657,7 +3694,30 @@ "width": null } }, - "30c3b868ba4b46ea9bcdb05e1c6d5613": { + "2e0923cec6894e0bb926973c7f50f3d3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c867ebcf347146abb7ec6115797953c1", + "placeholder": "​", + "style": "IPY_MODEL_5b97df5a1e0d485f8420059c34c61136", + "tabbable": null, + "tooltip": null, + "value": "Map (num_proc=4): 100%" + } + }, + "2f6cf6dc5a4845588c6a21a1c21879bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3710,7 +3770,7 @@ "width": null } }, - "3256f32acb0e4cf5b5de459cdd30f479": { + "2ffdee00ac1f47dcaad4ccd7cac60390": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3725,15 +3785,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5e25f06738614802b41458873966a7a2", + "layout": "IPY_MODEL_f7d5ee1b172841528a12c8d5a15409f3", "placeholder": "​", - "style": "IPY_MODEL_3b900abb40be48198b5e1814d396c692", + "style": "IPY_MODEL_b40cdc4a6ff640758611c963c4a3014d", "tabbable": null, "tooltip": null, - "value": " 10000/10000 [00:01<00:00, 8596.66 examples/s]" + "value": "100%" + } + }, + "3145d8f1a50346caa657e7b6ade92792": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "32b658a174274a4997618547ef7ef447": { + "34591a8e01774b23835f7bec29c46ffb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3786,57 +3864,25 @@ "width": null } }, - "32f22fc4e23745929d001d9647682786": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7684491703b342af8b42512fb30334e4", - "IPY_MODEL_fe1973b9b1fa4957b9894f465a0fe87c", - "IPY_MODEL_c9693739925b43cd83cb4e68fc01ecc9" - ], - "layout": "IPY_MODEL_dd1415dc221544d78c38cecb125e95de", - "tabbable": null, - "tooltip": null - } - }, - "33c9e2d67e4e498a9badfc73dd036c12": { + "34eceefcb9cd4c0390182a3da1170a7b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a2e082221e6d4efe981d8286fcaa40bd", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_422391611be34f58bd16d29a7a790f7f", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "33e89871cb224a0bb17051bdb6a4736f": { + "3557a3c3c9e84bdd905c60c08626cdc2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3889,7 +3935,25 @@ "width": null } }, - "3598bd0162e44744bf0e88509c1fcc05": { + "3a2f200c9979405ea1799e8b257d2962": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3bbe5464e48448959dd872a56942a7f5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3942,39 +4006,16 @@ "width": null } }, - "359bb8b698c94cf8b32d650da4752723": { - "model_module": "@jupyter-widgets/controls", + "3c7b41e10bf74f948457cd90e200afe1": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d44de894f1eb4db0ad9f986867905216", - "placeholder": "​", - "style": "IPY_MODEL_91eaa66c2eb641e689fc8028aee35c80", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.56it/s]" - } - }, - "372ab13ae5c8452ead0d7884763b3fc8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, @@ -4018,30 +4059,7 @@ "width": null } }, - "3746c22618f54648a82eac82edf13ec6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0826f288ec1f413988e02f4ef8849c28", - "placeholder": "​", - "style": "IPY_MODEL_7fd76734cd4b4cec9bba7cdd76aaea68", - "tabbable": null, - "tooltip": null, - "value": " 29.5k/29.5k [00:00<00:00, 4.49MB/s]" - } - }, - "3851a48a716b467ba9b981bd35b1822c": { + "41acfac3e7634989b2ccc6a4e9503ea6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4094,31 +4112,30 @@ "width": null } }, - "39838b65ab134d2a9a445437586fec98": { + "4398abd231514ecb8426aaa36111102f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_bf04eda28d94482ebdbf589d87951c61", - "IPY_MODEL_92fe75b0bd1341e9878165f8c906fc19", - "IPY_MODEL_3746c22618f54648a82eac82edf13ec6" - ], - "layout": "IPY_MODEL_5d3e10744606438787cdfd6315052b40", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e1a517549d364c30bc66ec975d022bdc", + "placeholder": "​", + "style": "IPY_MODEL_9975419ead304f1bb35f1f16ba689746", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "3b900abb40be48198b5e1814d396c692": { + "43ad8b951bf64a8b94108e8d800b7f93": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4136,7 +4153,7 @@ "text_color": null } }, - "3ed339ad73774a5cae0f763c178cbf4b": { + "459779a55e494dea92f7ce2871528083": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4189,25 +4206,80 @@ "width": null } }, - "409b5a2567834d99aba6129faa130451": { + "4761c3ddf1a643e8bda01b752e44ad8b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a685a21bed8147549eee85cbac9f5358", + "IPY_MODEL_53e6c8e4d9e0441a8b68c5ea09e71ff1", + "IPY_MODEL_726586d1a8d9496886b7c74c9ae9216a" + ], + "layout": "IPY_MODEL_86c15161ec474c7ea837b47a73b55834", + "tabbable": null, + "tooltip": null + } + }, + "47b85a2880604d3dac3aeaeda306130f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c61855779cd74bc097076c41da0eed54", + "placeholder": "​", + "style": "IPY_MODEL_5b1660aaad334a86aba0c255fa391c8d", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "483cd57673244db39d3524fef0835d46": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_492f8e571e174d8f92e7b18b8e13bc62", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_94e97c6d2fc14e229a4c96a99d543e33", + "tabbable": null, + "tooltip": null, + "value": 60000.0 } }, - "40e0d768badf43758c95df2555d1a977": { + "492f8e571e174d8f92e7b18b8e13bc62": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4260,41 +4332,7 @@ "width": null } }, - "416fa493cc9c4ff6ad13e8b6e6aedbaa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "422391611be34f58bd16d29a7a790f7f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "44a3e2129c4243e4862df806c2d8c5af": { + "4a3fd3d2d678476d980c97d8064b895c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4347,7 +4385,7 @@ "width": null } }, - "46552aea691e492084a7278f7a059830": { + "4cb21215cfbb4594b1f5a243ec53d04e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4365,7 +4403,7 @@ "text_color": null } }, - "488f0ba8d1c24d58adaefa23ebad9e9f": { + "4f03fb7101fe41f5becc6ecfabbba17a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4381,57 +4419,80 @@ "description_width": "" } }, - "4957f5fe7164427d8b712200dde5c3ab": { + "52a2b90360f7460f9d5e8e206e5b7b47": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e6fa033915fa4a5987c2c8fa374502e4", + "IPY_MODEL_ec5400e36f274359b296a5d6e0e33a39", + "IPY_MODEL_7c31f8bf76884e7683d9da37b70c4cc1" + ], + "layout": "IPY_MODEL_ddbe3a04ddff46c5bdfab6de8a35f7cc", + "tabbable": null, + "tooltip": null + } + }, + "53049123e6a9407b8314d7f6b14f99af": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ddb1bbe42c9e442091cc9c4122b5de26", - "max": 4422102.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e85e1e91a24f4199a9a4b3e9abe8696f", + "layout": "IPY_MODEL_ad18fb7ca0984499bf68129daeec626a", + "placeholder": "​", + "style": "IPY_MODEL_b42a1a7b436f4dfcab7ea91187e0b743", "tabbable": null, "tooltip": null, - "value": 4422102.0 + "value": " 40/40 [00:00<00:00, 66.30it/s]" } }, - "495daf880acd479da7fa63fedf1e1368": { + "53e6c8e4d9e0441a8b68c5ea09e71ff1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_240d9d285e4c42e9a24006d9d2988868", - "IPY_MODEL_21a5ba5f675d47938f0e977783a004ba", - "IPY_MODEL_513c96503e024d098d8203aa3b604a38" - ], - "layout": "IPY_MODEL_306584df97934511bd6de92ca49b025b", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9a4fcd9b64a048d28bbf5ab053b0a6d7", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0fab8f6f053241e4a76c15c455304f10", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 60000.0 } }, - "4a57dceefdc148afb5c7afa8adec5114": { + "5795395d630f42f09192fdb4e89f8465": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4449,47 +4510,66 @@ "text_color": null } }, - "4aadbbb3e859454a93556ff943f76e5b": { + "5b1660aaad334a86aba0c255fa391c8d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "4d801b30b791427d9103f41505cf1a3e": { + "5b344538fc3b43a98f559b15bee9a5fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8dbd4d3399124f6d8275c1d0fdfe9983", - "IPY_MODEL_4957f5fe7164427d8b712200dde5c3ab", - "IPY_MODEL_cab386a86c594ee2885f6d1679103b3b" - ], - "layout": "IPY_MODEL_625866dbaec44035a15f3927c4b770e5", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3557a3c3c9e84bdd905c60c08626cdc2", + "placeholder": "​", + "style": "IPY_MODEL_a121eb4e9f9e48609d3a4b697bab56c8", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 4.42M/4.42M [00:00<00:00, 79.2MB/s]" + } + }, + "5b97df5a1e0d485f8420059c34c61136": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "4dda8d782c00439b8a9eefdfe211c961": { + "5bfde149dd324016b5d45c6bddf7fad2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4542,25 +4622,7 @@ "width": null } }, - "4ec1fcd52f8c4d51bb3475d2f3c24732": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4ff41db514e041798fc3d0bf13325104": { + "5d925a37f7f24e27bf2be497abcd3368": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4613,12 +4675,81 @@ "width": null } }, - "513c96503e024d098d8203aa3b604a38": { - "model_module": "@jupyter-widgets/controls", + "603bc1c8b13349f78643f74115dd3fc5": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "618936f2cae44a09978936d5100bc0a5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "6719b46ec6814ef5869cf0982143c632": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", @@ -4628,39 +4759,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9009c4b403d146b493913cc05ca55a44", + "layout": "IPY_MODEL_04cb19cb27ec48d8bf07ddb3dbafdbf7", "placeholder": "​", - "style": "IPY_MODEL_ac82938d47c543a89ca5def5e546d7da", + "style": "IPY_MODEL_951638e8145c4431963a7a3c2f7e4b09", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:07<00:00, 8644.15 examples/s]" + "value": "100%" } }, - "5191d0744a454151b8fae157e5a21ef4": { - "model_module": "@jupyter-widgets/controls", + "67307a5269b545deb5492d878a0da28f": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_fffb62594db04599b3628dceafda46f1", - "IPY_MODEL_2865f56223344611aa17c9ec66a2d090", - "IPY_MODEL_d4be07fa12674628ae93c0119edbf6e1" - ], - "layout": "IPY_MODEL_052177a0e94b458eb71c811c3229f857", - "tabbable": null, - "tooltip": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "51bb6dd9acdc4544b4d17f4f020b1764": { + "691ecad63a504dffa766ac43d3b78fb4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4713,7 +4873,7 @@ "width": null } }, - "55923d8f76544ee5b5e53cb28dcbbcc5": { + "6a1591c936ba4e709d74e0ec278cefd6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4728,64 +4888,51 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0f83d37dd1394bc7a4f3ffc18b97b21c", + "layout": "IPY_MODEL_b30172a2a47d40f0a77a14540ecd18d6", "placeholder": "​", - "style": "IPY_MODEL_8be082c69bad41aa815fa99c34e5a9ea", + "style": "IPY_MODEL_4cb21215cfbb4594b1f5a243ec53d04e", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 65.15it/s]" + "value": "Computing checksums: 100%" } }, - "56000f81200d41e0bfa6f6b08d883916": { + "6d8a49f522924aaaabb198187245001e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9eace039e9d343e1a0113042a3582776", - "placeholder": "​", - "style": "IPY_MODEL_409b5a2567834d99aba6129faa130451", - "tabbable": null, - "tooltip": null, - "value": " 26.4M/26.4M [00:00<00:00, 120MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5779a50d69944438b953e80eb37bbcac": { + "72292d3faee34bf1949667097765550e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1f5d840cd56a4162b5ad826e1f2902c2", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_488f0ba8d1c24d58adaefa23ebad9e9f", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5c88bd64b2f44f8ab2aa0198c89462d3": { + "726586d1a8d9496886b7c74c9ae9216a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4800,15 +4947,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ed5bd36e898a43dc9c5cb8e283abbead", + "layout": "IPY_MODEL_c3c495d6cade4b65834b43700b7d4655", "placeholder": "​", - "style": "IPY_MODEL_890e03a51fce4dd185e5a42dc5da23ed", + "style": "IPY_MODEL_f5501a6381964ea3b5684ee5ac2a2990", "tabbable": null, "tooltip": null, - "value": " 8.85k/8.85k [00:00<00:00, 1.48MB/s]" + "value": " 60000/60000 [00:06<00:00, 8666.45 examples/s]" } }, - "5c9cad60b37f4313a6899ca1a71bbee0": { + "727d6b8ed0214669ae4a1c260b9eb2ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4824,7 +4971,7 @@ "description_width": "" } }, - "5d3e10744606438787cdfd6315052b40": { + "73b0c283a1d549d786b83bf092b5a247": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4877,7 +5024,31 @@ "width": null } }, - "5e25f06738614802b41458873966a7a2": { + "74d7207adb634a9a9648063cd4ebf05d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0e4e51c9e4794c289497fc9034858879", + "IPY_MODEL_1a67cc49a0714635846f10e32202bddc", + "IPY_MODEL_17a61e4c38a64575bc341e3504130922" + ], + "layout": "IPY_MODEL_9cc09cfd956141a89cc11fdb073b27f5", + "tabbable": null, + "tooltip": null + } + }, + "75137f463796461d87f332d1e82c826b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4930,7 +5101,77 @@ "width": null } }, - "6095c69a4f934899a783495c289c15a3": { + "76447603597c41e58c504ba366dedf8b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_efabf4edc4f14504b06bad7e71659998", + "IPY_MODEL_bb9b88f5052e43e4861dcbd84c5aa196", + "IPY_MODEL_e5a63f7446074676b31de8da1ee38a2b" + ], + "layout": "IPY_MODEL_4a3fd3d2d678476d980c97d8064b895c", + "tabbable": null, + "tooltip": null + } + }, + "76f7038f04f14f28ba39042b9a0b29ca": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0ce0d8843432485ea62b457bcb0faf43", + "placeholder": "​", + "style": "IPY_MODEL_923f0259202b48ac847034f1d10cd3e9", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 63.59it/s]" + } + }, + "78981540b49a4ac4b4674a46e0b71bd0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d4d042ad47854553b9660b36249c7347", + "placeholder": "​", + "style": "IPY_MODEL_01c7b5f2e46d450e9f0ee68d2e6e7184", + "tabbable": null, + "tooltip": null, + "value": " 4/4 [00:00<00:00, 1262.77it/s]" + } + }, + "79afef11e9d74259a71e933ac999caf5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4983,7 +5224,7 @@ "width": null } }, - "6102ff846ce741a4aa1c83f94cf213b4": { + "7c31f8bf76884e7683d9da37b70c4cc1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4998,15 +5239,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0b2e2b62fbba4e8e919185c698964e99", + "layout": "IPY_MODEL_1c413b52ad994cc6818c637e38897e16", "placeholder": "​", - "style": "IPY_MODEL_a2c46edab30a43d3ad1274496e23dc19", + "style": "IPY_MODEL_954cd8e3b16c4419af3905829990415a", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 68.42it/s]" + "value": " 29.5k/29.5k [00:00<00:00, 4.33MB/s]" } }, - "625866dbaec44035a15f3927c4b770e5": { + "7cb2c22fa5f843e79a58ef3c50f8eab3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5059,7 +5300,7 @@ "width": null } }, - "62e4f2d9c4534ebbab778993d057a978": { + "7e502dd5790a480887b970c2862e5170": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5074,15 +5315,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4dda8d782c00439b8a9eefdfe211c961", + "layout": "IPY_MODEL_691ecad63a504dffa766ac43d3b78fb4", "placeholder": "​", - "style": "IPY_MODEL_d89ee00461d14abe96f3f0cdcfa3da61", + "style": "IPY_MODEL_135c72d3fed34ffc90902f7faf2f27b0", "tabbable": null, "tooltip": null, - "value": "Downloading readme: 100%" + "value": "Downloading data: 100%" } }, - "640d5d2692d64656a67cd09cee644495": { + "80bc3ab0f2e34cba9a1c31d7411ecbc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5098,108 +5339,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_007a3563b0514e35b0a7409f1a0e8668", - "max": 4.0, + "layout": "IPY_MODEL_c932cb59d82141628dd6ec40fc18f737", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_ded2f3b115fb46e48b5699012c011fa5", + "style": "IPY_MODEL_b4c1e3125e1f43ae99fc53aa3a9c0505", "tabbable": null, "tooltip": null, - "value": 4.0 + "value": 60000.0 } }, - "645dbaa1cd59415da2cc2b69430972fa": { + "8259ba9a3539477db64cbdd68592e635": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_33e89871cb224a0bb17051bdb6a4736f", - "placeholder": "​", - "style": "IPY_MODEL_159251baf5e8425b8c9f8d7acb9abc55", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "647ec368e3354d458cfed043dad850ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "683ea97790a64507b71e617e6bb1960f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e1aad335fcff4496842cc4f52b05fb6a", - "IPY_MODEL_be4ee2ccce154754b1f1e8d49134ee62", - "IPY_MODEL_edb3baaca40743c08801b6e9bff25752" + "IPY_MODEL_47b85a2880604d3dac3aeaeda306130f", + "IPY_MODEL_1711a1f1944742ea8f38809143bc3cc8", + "IPY_MODEL_df9aa39a8a3a44d8bb0fd0ea66fb7093" ], - "layout": "IPY_MODEL_51bb6dd9acdc4544b4d17f4f020b1764", + "layout": "IPY_MODEL_3bbe5464e48448959dd872a56942a7f5", "tabbable": null, "tooltip": null } }, - "6b095fb924ef41358579ec879bf0f9fe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3ed339ad73774a5cae0f763c178cbf4b", - "max": 5148.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1fdf0f2ba1e440ccaf02e74fc4b28520", - "tabbable": null, - "tooltip": null, - "value": 5148.0 - } - }, - "6ca7ade3edba440ba7226afd89340130": { + "837e8c16ffdd41a4ae0f4631055cca57": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5252,60 +5426,49 @@ "width": null } }, - "6ccb235983834c00ac32be1422c16641": { - "model_module": "@jupyter-widgets/base", + "8555aae383d74d6c851e5296a2f76824": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "860c6216e3754afa972fdf5b5a0980a0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2ffdee00ac1f47dcaad4ccd7cac60390", + "IPY_MODEL_1fe8a37c7c4b481eb559efba20e116ac", + "IPY_MODEL_a972ac7ecf4c4746bb430a3161226786" + ], + "layout": "IPY_MODEL_73b0c283a1d549d786b83bf092b5a247", + "tabbable": null, + "tooltip": null } }, - "6f3f698e19f14817b2b2fa6c67e55a47": { + "86c15161ec474c7ea837b47a73b55834": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5358,23 +5521,72 @@ "width": null } }, - "7004a3b5592a40ee8328f00432adfc79": { + "8d04c2d222424f08b06b6508223878ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ef2ba57d7cd44e3cb5973e6264f86666", + "IPY_MODEL_bfc4b95406a34e3ca4e93013d10e4852", + "IPY_MODEL_039cadb1d8a94b7d86d6d048e6ff9a52" + ], + "layout": "IPY_MODEL_ae9e474ca22b45648e40de05673fbd79", + "tabbable": null, + "tooltip": null + } + }, + "8f9cbba88a5d4fe29d7de8c5c017526e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ca783c0a89cb4d7aabe9391975acb8ff", + "placeholder": "​", + "style": "IPY_MODEL_43ad8b951bf64a8b94108e8d800b7f93", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "923f0259202b48ac847034f1d10cd3e9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "72961bb987d24298bfdb11eb59546963": { + "940ecb97d50847cda5f82f56b828ce1e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5427,7 +5639,7 @@ "width": null } }, - "736ef5bce23e47429bfcb196fb8b85b3": { + "944cd1ea980e485caca5409c07bca11c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5480,30 +5692,41 @@ "width": null } }, - "743f0d74186d4b7093498a547ab9ac95": { + "94e97c6d2fc14e229a4c96a99d543e33": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7b5cc6d910b546ce964b6b2bbec05343", - "placeholder": "​", - "style": "IPY_MODEL_b3495f2ab71b4f848590703a190ddebc", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 68.82it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "951638e8145c4431963a7a3c2f7e4b09": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "751475cab3c24730bab0fbab4d5284f2": { + "954cd8e3b16c4419af3905829990415a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5521,7 +5744,7 @@ "text_color": null } }, - "75221b9dde234f55bcd73f8bba5f3fa5": { + "95c819d6d1bb48a4a1718e13d4b708d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5574,86 +5797,62 @@ "width": null } }, - "759a332c02e64ce79a364cd518bc163e": { + "964944a145304bd58d1fd62856054c5a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "96a4611a123247198b0f710c5bf75bd7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6095c69a4f934899a783495c289c15a3", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5c9cad60b37f4313a6899ca1a71bbee0", + "layout": "IPY_MODEL_41acfac3e7634989b2ccc6a4e9503ea6", + "placeholder": "​", + "style": "IPY_MODEL_1e4c7bfe71e44d6dbf26a8dff0f90509", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "Downloading data: 100%" } }, - "75af0524012d41858477feaee9949557": { - "model_module": "@jupyter-widgets/base", + "97bd081fbc2c473fb8852e3e2ef1391d": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "7684491703b342af8b42512fb30334e4": { + "9882b87501b84979bb5ca2b3e35d53d1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5668,15 +5867,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a61e85ca959b4dafbe01836e2add2005", + "layout": "IPY_MODEL_1ffe24ac54fe4cb49579cb75ed7db9b5", "placeholder": "​", - "style": "IPY_MODEL_4ec1fcd52f8c4d51bb3475d2f3c24732", + "style": "IPY_MODEL_cbfecf1072ed4e2caaa31da07e0da756", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 60000/60000 [00:36<00:00, 1731.74it/s]" + } + }, + "9975419ead304f1bb35f1f16ba689746": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "7b58054c0d6a418194fc7e1f039c639b": { + "9a4fcd9b64a048d28bbf5ab053b0a6d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5729,7 +5946,23 @@ "width": null } }, - "7b5cc6d910b546ce964b6b2bbec05343": { + "9aee6db9bd984fa5a023e92a81ba822d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "9b19aa98c0d64d849bf08b178b9841e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5782,7 +6015,7 @@ "width": null } }, - "7d2cf7fe1d884127b2ea048061916752": { + "9c4688aea9f9425883500665dfa60bf9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5800,7 +6033,7 @@ "text_color": null } }, - "7df97d24399f4f8a980e937ed234ad3c": { + "9cc09cfd956141a89cc11fdb073b27f5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5853,7 +6086,7 @@ "width": null } }, - "7fd76734cd4b4cec9bba7cdd76aaea68": { + "9dfddac443ca480cac3f02058d42e8a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5871,7 +6104,7 @@ "text_color": null } }, - "823bddadbc6644c283f25f9cc6a18fc8": { + "a121eb4e9f9e48609d3a4b697bab56c8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5889,30 +6122,7 @@ "text_color": null } }, - "831b82774fb644faa4c010b37968f99b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_32b658a174274a4997618547ef7ef447", - "placeholder": "​", - "style": "IPY_MODEL_0f8071081d82450c9dd7fd9a927b6b1f", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "846e19cb26a94bdba7b363dce398b69c": { + "a3115a3594ce4aa497f8a610abb0af9e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5927,50 +6137,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_99c1a284b0e04c9a85ab4ee83de08fc0", - "IPY_MODEL_759a332c02e64ce79a364cd518bc163e", - "IPY_MODEL_743f0d74186d4b7093498a547ab9ac95" + "IPY_MODEL_6719b46ec6814ef5869cf0982143c632", + "IPY_MODEL_f7496bea7da04557b5e943e71814b3ad", + "IPY_MODEL_1f029c9a5f7842a19a17b9c9f75ecc48" ], - "layout": "IPY_MODEL_c5594aabc34c40e2bd69e47ea0624f4a", + "layout": "IPY_MODEL_f34228361c9a43dc9eb3dc33677084e0", "tabbable": null, "tooltip": null } }, - "864d773ce416475ba6a1e506b36063dc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "877c0b06e48d4616b74e70c7b9e6abff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "890e03a51fce4dd185e5a42dc5da23ed": { + "a64c2baabe354ca8a0f8f64ae6bc4ede": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5988,7 +6164,7 @@ "text_color": null } }, - "89956ffd6bd842798b8752c8f8fbcef6": { + "a685a21bed8147549eee85cbac9f5358": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6003,15 +6179,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bd6988dd8c6745d9a63c01e21b21baa3", + "layout": "IPY_MODEL_cc7be77366654c01991ea476f2293f63", "placeholder": "​", - "style": "IPY_MODEL_647ec368e3354d458cfed043dad850ce", + "style": "IPY_MODEL_6d8a49f522924aaaabb198187245001e", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Generating train split: 100%" } }, - "8a3f12d334d645c8a42ca8a0292075a9": { + "a8e1731ca82b4390af97a6c9c6efcc51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6064,25 +6240,7 @@ "width": null } }, - "8be082c69bad41aa815fa99c34e5a9ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "8dbd4d3399124f6d8275c1d0fdfe9983": { + "a972ac7ecf4c4746bb430a3161226786": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6097,15 +6255,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6ca7ade3edba440ba7226afd89340130", + "layout": "IPY_MODEL_9b19aa98c0d64d849bf08b178b9841e7", "placeholder": "​", - "style": "IPY_MODEL_bdd6eef2f72d4192953e456956c77bd9", + "style": "IPY_MODEL_3145d8f1a50346caa657e7b6ade92792", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": " 40/40 [00:00<00:00, 59.36it/s]" + } + }, + "ad18fb7ca0984499bf68129daeec626a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "9009c4b403d146b493913cc05ca55a44": { + "ae9e474ca22b45648e40de05673fbd79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6158,7 +6369,7 @@ "width": null } }, - "91eaa66c2eb641e689fc8028aee35c80": { + "af859482bc704e13a4993c698c9ba181": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6176,106 +6387,154 @@ "text_color": null } }, - "92fe75b0bd1341e9878165f8c906fc19": { - "model_module": "@jupyter-widgets/controls", + "b30172a2a47d40f0a77a14540ecd18d6": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3851a48a716b467ba9b981bd35b1822c", - "max": 29515.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4aadbbb3e859454a93556ff943f76e5b", - "tabbable": null, - "tooltip": null, - "value": 29515.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "94beed83d4f34e37885835c7ee53b3e7": { - "model_module": "@jupyter-widgets/controls", + "b31e5df3571f4ac28345bf70cd4e947c": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c9e6261cabb7413784e072a38690acc3", - "max": 4833.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c9722775eec64862b3c787b4a9da67b5", - "tabbable": null, - "tooltip": null, - "value": 4833.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "96b3b9a948504544be06e5692d10926d": { + "b36cf6fb19144ae68ceeda6d14a0c88e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e451b86b3b73462bb7d10f31b67e7f35", - "IPY_MODEL_ff2c7db5dbda44b8b24ca18490cb3473", - "IPY_MODEL_3256f32acb0e4cf5b5de459cdd30f479" - ], - "layout": "IPY_MODEL_ed769dcb47ca423fb840b23690485ebe", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a8e1731ca82b4390af97a6c9c6efcc51", + "placeholder": "​", + "style": "IPY_MODEL_d2b86bd7871548f5b1e0d2d312d2ef24", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 26.4M/26.4M [00:00<00:00, 109MB/s]" } }, - "99c1a284b0e04c9a85ab4ee83de08fc0": { + "b40cdc4a6ff640758611c963c4a3014d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_da9f7efe26b243e7bed64d6bd9746699", - "placeholder": "​", - "style": "IPY_MODEL_bff32efaaebb4a2789332d03ffc174f6", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "9bc90fd3c0264b26b5bf1c99f4b9caad": { + "b42a1a7b436f4dfcab7ea91187e0b743": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6293,7 +6552,47 @@ "text_color": null } }, - "9eace039e9d343e1a0113042a3582776": { + "b4c1e3125e1f43ae99fc53aa3a9c0505": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "b66bf1f268f64f16b0ab04fbfef16cb7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8f9cbba88a5d4fe29d7de8c5c017526e", + "IPY_MODEL_80bc3ab0f2e34cba9a1c31d7411ecbc5", + "IPY_MODEL_9882b87501b84979bb5ca2b3e35d53d1" + ], + "layout": "IPY_MODEL_79afef11e9d74259a71e933ac999caf5", + "tabbable": null, + "tooltip": null + } + }, + "b76a6f2e44334c838c6350a5d3bbb530": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6346,7 +6645,59 @@ "width": null } }, - "a0b406e9eaf143599fd4e302b57381b4": { + "bb9b88f5052e43e4861dcbd84c5aa196": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c0e6653485214e6295a6c309d77804a3", + "max": 4833.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4f03fb7101fe41f5becc6ecfabbba17a", + "tabbable": null, + "tooltip": null, + "value": 4833.0 + } + }, + "be2ba8934caf4f6eb77db4a243219f26": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b76a6f2e44334c838c6350a5d3bbb530", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cd71b5032d334106871d93c7901358eb", + "tabbable": null, + "tooltip": null, + "value": 26421880.0 + } + }, + "bf64e375efe14d25b7e951f059b16c23": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6361,16 +6712,42 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_89956ffd6bd842798b8752c8f8fbcef6", - "IPY_MODEL_088b60ea8401405ea27804efbb34b231", - "IPY_MODEL_6102ff846ce741a4aa1c83f94cf213b4" + "IPY_MODEL_4398abd231514ecb8426aaa36111102f", + "IPY_MODEL_ef6365260ca348ed903c1353490fc655", + "IPY_MODEL_cfbabccb48c14370b2a05af8b6c6a2ce" ], - "layout": "IPY_MODEL_7df97d24399f4f8a980e937ed234ad3c", + "layout": "IPY_MODEL_20fc3b9b9a4b48d881bc11004e753c5e", "tabbable": null, "tooltip": null } }, - "a201453cadfa4bccaf26c6f446b4e7ee": { + "bfc4b95406a34e3ca4e93013d10e4852": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_23fe73f9bf064ef5bd44fb6b0e295456", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9aee6db9bd984fa5a023e92a81ba822d", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "c0e6653485214e6295a6c309d77804a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6423,25 +6800,7 @@ "width": null } }, - "a2c46edab30a43d3ad1274496e23dc19": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a2e082221e6d4efe981d8286fcaa40bd": { + "c3c495d6cade4b65834b43700b7d4655": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6494,7 +6853,7 @@ "width": null } }, - "a3af79b4c771458595c44a209a768d66": { + "c61855779cd74bc097076c41da0eed54": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6547,7 +6906,7 @@ "width": null } }, - "a61e85ca959b4dafbe01836e2add2005": { + "c639afb167db44978154a2d4054f1d40": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6600,23 +6959,7 @@ "width": null } }, - "a955b675afa4453385243c0af21d7bb7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "ac0dd2e3b9574dfba4e088b07dc9917a": { + "c867ebcf347146abb7ec6115797953c1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6669,100 +7012,84 @@ "width": null } }, - "ac82938d47c543a89ca5def5e546d7da": { + "c8ad57476e81431f9ef31378a786d5e9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_96a4611a123247198b0f710c5bf75bd7", + "IPY_MODEL_cf26e95faaa94e23b57c2179d5e9c64e", + "IPY_MODEL_d6650de0045c4384af30511ccab8a0f7" + ], + "layout": "IPY_MODEL_d0b1f8cfd74c4375af09960b50139328", + "tabbable": null, + "tooltip": null } }, - "aea843e930ac410596a0f4cb4f6520e0": { - "model_module": "@jupyter-widgets/controls", + "c932cb59d82141628dd6ec40fc18f737": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b1d5272979684bff96c71500a455d400": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_372ab13ae5c8452ead0d7884763b3fc8", - "placeholder": "​", - "style": "IPY_MODEL_27b9b136f3bb4b1a80402df468b5c136", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "b3495f2ab71b4f848590703a190ddebc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b3f80c6a23394b5dbcb460b9a19b34ff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "bd6988dd8c6745d9a63c01e21b21baa3": { + "ca783c0a89cb4d7aabe9391975acb8ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6815,30 +7142,7 @@ "width": null } }, - "bd7060172fb747a6ae92a503a3922356": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a201453cadfa4bccaf26c6f446b4e7ee", - "placeholder": "​", - "style": "IPY_MODEL_2c4aaf8a7a84451d93bb9c185069cfe6", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "bdd1580a8e924b29bc29315b007f1f26": { + "cabee28dffcd4272a0c22c2d5c770596": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6891,51 +7195,7 @@ "width": null } }, - "bdd6eef2f72d4192953e456956c77bd9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "be4ee2ccce154754b1f1e8d49134ee62": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_75af0524012d41858477feaee9949557", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_877c0b06e48d4616b74e70c7b9e6abff", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "be6b57b7d12a497fbc96e8e89b08f15a": { + "cac4ab6d168f46baa1c90ed6d428997f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6988,54 +7248,33 @@ "width": null } }, - "bf04eda28d94482ebdbf589d87951c61": { + "cb9288f8e40c422ea8a86e41ea2ba6df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0205ebdad1d64de8a1bd7d1c741d5fcb", - "placeholder": "​", - "style": "IPY_MODEL_f0fcd694fc07412a83769122e48dd5c1", + "layout": "IPY_MODEL_1d882b744195442f85a86d91cabda0fb", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_eab0669b062645d4ac17d3d2f88de120", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "bfd46491d1764708be24b2103e5e6cb5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_645dbaa1cd59415da2cc2b69430972fa", - "IPY_MODEL_33c9e2d67e4e498a9badfc73dd036c12", - "IPY_MODEL_21a93c7e2dfc4569b325ed80637cf469" - ], - "layout": "IPY_MODEL_4ff41db514e041798fc3d0bf13325104", - "tabbable": null, - "tooltip": null + "value": 40.0 } }, - "bff32efaaebb4a2789332d03ffc174f6": { + "cbfecf1072ed4e2caaa31da07e0da756": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7053,7 +7292,7 @@ "text_color": null } }, - "c34dc272c24c40c697da60df89a38f25": { + "cc12a064eb004d6390ea2006fbb725ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7071,7 +7310,7 @@ "text_color": null } }, - "c4fcca6ce699447399e56dfc004afddb": { + "cc7be77366654c01991ea476f2293f63": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7124,7 +7363,7 @@ "width": null } }, - "c5594aabc34c40e2bd69e47ea0624f4a": { + "cca5b38ac94f4b60ab4f617db8198ff8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7177,30 +7416,23 @@ "width": null } }, - "c9693739925b43cd83cb4e68fc01ecc9": { + "ccea9ba4e771468a95236d2db5bf0264": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_bdd1580a8e924b29bc29315b007f1f26", - "placeholder": "​", - "style": "IPY_MODEL_751475cab3c24730bab0fbab4d5284f2", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 68.93it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "c9722775eec64862b3c787b4a9da67b5": { + "cd71b5032d334106871d93c7901358eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -7216,7 +7448,56 @@ "description_width": "" } }, - "c9e6261cabb7413784e072a38690acc3": { + "cf26e95faaa94e23b57c2179d5e9c64e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_940ecb97d50847cda5f82f56b828ce1e", + "max": 5148.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_727d6b8ed0214669ae4a1c260b9eb2ba", + "tabbable": null, + "tooltip": null, + "value": 5148.0 + } + }, + "cfbabccb48c14370b2a05af8b6c6a2ce": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_95c819d6d1bb48a4a1718e13d4b708d7", + "placeholder": "​", + "style": "IPY_MODEL_ec1afef96b264d888ab90ae59248acd5", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 65.10it/s]" + } + }, + "cfdfd821fb03474ba31a8ca207281b52": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7269,80 +7550,60 @@ "width": null } }, - "cab386a86c594ee2885f6d1679103b3b": { - "model_module": "@jupyter-widgets/controls", + "d0b1f8cfd74c4375af09960b50139328": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6f3f698e19f14817b2b2fa6c67e55a47", - "placeholder": "​", - "style": "IPY_MODEL_c34dc272c24c40c697da60df89a38f25", - "tabbable": null, - "tooltip": null, - "value": " 4.42M/4.42M [00:00<00:00, 53.3MB/s]" - } - }, - "cd6bbb8ca872405fa17b2571965191b3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "cf4a395a6d57451092b05d86340035d6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d15f06ef2c454254bee0f59957e49d4b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d2775a5c6f9b4c439174c1b5f34446b5": { + "d2b86bd7871548f5b1e0d2d312d2ef24": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7360,7 +7621,7 @@ "text_color": null } }, - "d44de894f1eb4db0ad9f986867905216": { + "d4d042ad47854553b9660b36249c7347": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7413,7 +7674,7 @@ "width": null } }, - "d4be07fa12674628ae93c0119edbf6e1": { + "d6650de0045c4384af30511ccab8a0f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7428,57 +7689,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d82285fcd9b84504a28fb9c9b1ad268f", + "layout": "IPY_MODEL_cabee28dffcd4272a0c22c2d5c770596", "placeholder": "​", - "style": "IPY_MODEL_edb2e6f6c0214dae8109a703bb56a3ba", + "style": "IPY_MODEL_34eceefcb9cd4c0390182a3da1170a7b", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 5257.56 examples/s]" + "value": " 5.15k/5.15k [00:00<00:00, 871kB/s]" } }, - "d4c59b0bfa86424a8c95a71f890f5454": { + "d73ad391537b4ea7a7841fe7944270c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f2b0f79e655c4494923cc58f73494551", - "IPY_MODEL_94beed83d4f34e37885835c7ee53b3e7", - "IPY_MODEL_25f2773590254b58b3ac4b1c0a886c35" - ], - "layout": "IPY_MODEL_d75e706d71cc4c7d8ec28bc9b0e5e02a", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1a4cf1e028c44677995965a00cb4aa35", + "placeholder": "​", + "style": "IPY_MODEL_a64c2baabe354ca8a0f8f64ae6bc4ede", "tabbable": null, - "tooltip": null - } - }, - "d72826ff906b4e3e81e8eb6291d1cc9a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 7362.49 examples/s]" } }, - "d75e706d71cc4c7d8ec28bc9b0e5e02a": { + "d9e695fa0c8741669e44cf4502f36d47": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7531,7 +7773,49 @@ "width": null } }, - "d82285fcd9b84504a28fb9c9b1ad268f": { + "da2c01112d1f4e749b0ca2c79b09927f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fc1d8341e6624186a80fabb78e1c9a60", + "IPY_MODEL_1bf57cd82d684206a95f2c1e179d96ce", + "IPY_MODEL_53049123e6a9407b8314d7f6b14f99af" + ], + "layout": "IPY_MODEL_2b356c8f0be148beb637a84faef5510a", + "tabbable": null, + "tooltip": null + } + }, + "dc0ef01ff0b14b05bd75d62108c813b6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "dce15d5cb91b42a780b136a398da089e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7584,7 +7868,25 @@ "width": null } }, - "d84b7a5330814c309bc2e3a29fd936ef": { + "dd1800dbb9064a41a02af5ec0f1f5b67": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ddbe3a04ddff46c5bdfab6de8a35f7cc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7637,25 +7939,7 @@ "width": null } }, - "d89ee00461d14abe96f3f0cdcfa3da61": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d90986d0f1bb4e8ebf632742ca0c49a3": { + "ddee9f52a482431d83538c9f941bfbe9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7708,7 +7992,7 @@ "width": null } }, - "d9f443785177406fb3840783c441fddc": { + "df9aa39a8a3a44d8bb0fd0ea66fb7093": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7723,68 +8007,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e186a328526e44f2abaeb0fbc2e6a273", + "layout": "IPY_MODEL_2f6cf6dc5a4845588c6a21a1c21879bc", "placeholder": "​", - "style": "IPY_MODEL_d2775a5c6f9b4c439174c1b5f34446b5", + "style": "IPY_MODEL_dd1800dbb9064a41a02af5ec0f1f5b67", "tabbable": null, "tooltip": null, - "value": "Computing checksums: 100%" + "value": " 40/40 [00:00<00:00, 63.93it/s]" } }, - "da9f7efe26b243e7bed64d6bd9746699": { - "model_module": "@jupyter-widgets/base", + "e03718c7114049e881b5ec8c92d14511": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "dd1415dc221544d78c38cecb125e95de": { + "e09e931dcf844151835a19cfddd0f459": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7837,7 +8084,7 @@ "width": null } }, - "ddb1bbe42c9e442091cc9c4122b5de26": { + "e1a517549d364c30bc66ec975d022bdc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7890,76 +8137,30 @@ "width": null } }, - "ded2f3b115fb46e48b5699012c011fa5": { + "e452cfce19364c6fb6b50db4144baec6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "e186a328526e44f2abaeb0fbc2e6a273": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cca5b38ac94f4b60ab4f617db8198ff8", + "placeholder": "​", + "style": "IPY_MODEL_8555aae383d74d6c851e5296a2f76824", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "e1aad335fcff4496842cc4f52b05fb6a": { + "e5a63f7446074676b31de8da1ee38a2b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7974,15 +8175,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a3af79b4c771458595c44a209a768d66", + "layout": "IPY_MODEL_cac4ab6d168f46baa1c90ed6d428997f", "placeholder": "​", - "style": "IPY_MODEL_823bddadbc6644c283f25f9cc6a18fc8", + "style": "IPY_MODEL_af859482bc704e13a4993c698c9ba181", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 4.83k/4.83k [00:00<00:00, 612kB/s]" } }, - "e451b86b3b73462bb7d10f31b67e7f35": { + "e6d9862771cc40a5bc3f7cb24e629eba": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e6fa033915fa4a5987c2c8fa374502e4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7997,68 +8214,73 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8a3f12d334d645c8a42ca8a0292075a9", + "layout": "IPY_MODEL_3c7b41e10bf74f948457cd90e200afe1", "placeholder": "​", - "style": "IPY_MODEL_4a57dceefdc148afb5c7afa8adec5114", + "style": "IPY_MODEL_9dfddac443ca480cac3f02058d42e8a5", "tabbable": null, "tooltip": null, - "value": "Generating test split: 100%" + "value": "Downloading data: 100%" } }, - "e57ec2490fdc4028b01834557f09baa2": { - "model_module": "@jupyter-widgets/base", + "ea88c13811944930a76ece93362f7e4c": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2e0923cec6894e0bb926973c7f50f3d3", + "IPY_MODEL_483cd57673244db39d3524fef0835d46", + "IPY_MODEL_d73ad391537b4ea7a7841fe7944270c0" + ], + "layout": "IPY_MODEL_7cb2c22fa5f843e79a58ef3c50f8eab3", + "tabbable": null, + "tooltip": null + } + }, + "eab0669b062645d4ac17d3d2f88de120": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "ec1afef96b264d888ab90ae59248acd5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e6e9051f4c2a49228c32a25f39a57f4c": { + "ec5400e36f274359b296a5d6e0e33a39": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -8074,110 +8296,89 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e57ec2490fdc4028b01834557f09baa2", - "max": 40.0, + "layout": "IPY_MODEL_944cd1ea980e485caca5409c07bca11c", + "max": 29515.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_d15f06ef2c454254bee0f59957e49d4b", + "style": "IPY_MODEL_270f6bf2c3e643ea8dd1a756817909fe", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 29515.0 } }, - "e85e1e91a24f4199a9a4b3e9abe8696f": { + "ef2ba57d7cd44e3cb5973e6264f86666": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_244159f1aa444aedbd2e3e9382b4326b", + "placeholder": "​", + "style": "IPY_MODEL_cc12a064eb004d6390ea2006fbb725ed", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" } }, - "eaa35cd4c5ae462181de3ad1ab98c2d1": { - "model_module": "@jupyter-widgets/base", + "ef6365260ca348ed903c1353490fc655": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f6079e4e10e545668732bdada9c65e44", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e03718c7114049e881b5ec8c92d14511", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "ec86bd0afa46422aa85bf2778e427f2a": { + "efabf4edc4f14504b06bad7e71659998": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_831b82774fb644faa4c010b37968f99b", - "IPY_MODEL_e6e9051f4c2a49228c32a25f39a57f4c", - "IPY_MODEL_55923d8f76544ee5b5e53cb28dcbbcc5" - ], - "layout": "IPY_MODEL_0cae058fc562457bbb502b466cfdfcab", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5bfde149dd324016b5d45c6bddf7fad2", + "placeholder": "​", + "style": "IPY_MODEL_dc0ef01ff0b14b05bd75d62108c813b6", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Downloading builder script: 100%" } }, - "ed5bd36e898a43dc9c5cb8e283abbead": { + "f039d6ee9460446c9cbfff2777f2bb15": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8230,7 +8431,7 @@ "width": null } }, - "ed769dcb47ca423fb840b23690485ebe": { + "f34228361c9a43dc9eb3dc33677084e0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8283,7 +8484,7 @@ "width": null } }, - "edb2e6f6c0214dae8109a703bb56a3ba": { + "f5501a6381964ea3b5684ee5ac2a2990": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -8301,90 +8502,7 @@ "text_color": null } }, - "edb3baaca40743c08801b6e9bff25752": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3598bd0162e44744bf0e88509c1fcc05", - "placeholder": "​", - "style": "IPY_MODEL_cf4a395a6d57451092b05d86340035d6", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:36<00:00, 1626.06it/s]" - } - }, - "ee5d45a366aa46a4a7c3de67f844b7ba": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d9f443785177406fb3840783c441fddc", - "IPY_MODEL_640d5d2692d64656a67cd09cee644495", - "IPY_MODEL_1ef3f8ae36734efb86b54939cb9711d4" - ], - "layout": "IPY_MODEL_c4fcca6ce699447399e56dfc004afddb", - "tabbable": null, - "tooltip": null - } - }, - "f0b50dd1b20c48b6911a694433d48e05": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f0fcd694fc07412a83769122e48dd5c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f1c238f4a14549229bdf80d577253ccf": { + "f6079e4e10e545668732bdada9c65e44": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8437,99 +8555,33 @@ "width": null } }, - "f2b0f79e655c4494923cc58f73494551": { + "f7496bea7da04557b5e943e71814b3ad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f8aafa4a992b42cf95a5ace2356676d6", - "placeholder": "​", - "style": "IPY_MODEL_d72826ff906b4e3e81e8eb6291d1cc9a", + "layout": "IPY_MODEL_67307a5269b545deb5492d878a0da28f", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e6d9862771cc40a5bc3f7cb24e629eba", "tabbable": null, "tooltip": null, - "value": "Downloading builder script: 100%" - } - }, - "f4bfad9cd0da4d31a8d5c783407a73c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "f8aafa4a992b42cf95a5ace2356676d6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "value": 40.0 } }, - "f9978a29787547e3bc5e59bde742651c": { + "f7d5ee1b172841528a12c8d5a15409f3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8582,33 +8634,30 @@ "width": null } }, - "fe1973b9b1fa4957b9894f465a0fe87c": { + "fc1d8341e6624186a80fabb78e1c9a60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_736ef5bce23e47429bfcb196fb8b85b3", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b3f80c6a23394b5dbcb460b9a19b34ff", + "layout": "IPY_MODEL_603bc1c8b13349f78643f74115dd3fc5", + "placeholder": "​", + "style": "IPY_MODEL_1361429788e54a748edb03027a9cab6a", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "100%" } }, - "ff08f8bfa09d42838688c6f725adb306": { + "ffc2140e49b04844ba200898835e603c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8660,55 +8709,6 @@ "visibility": null, "width": null } - }, - "ff2c7db5dbda44b8b24ca18490cb3473": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6ccb235983834c00ac32be1422c16641", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a955b675afa4453385243c0af21d7bb7", - "tabbable": null, - "tooltip": null, - "value": 10000.0 - } - }, - "fffb62594db04599b3628dceafda46f1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_30c3b868ba4b46ea9bcdb05e1c6d5613", - "placeholder": "​", - "style": "IPY_MODEL_46552aea691e492084a7278f7a059830", - "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 32831e810..079f8c422 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:27.356934Z", - "iopub.status.busy": "2024-07-02T12:04:27.356523Z", - "iopub.status.idle": "2024-07-02T12:04:28.474290Z", - "shell.execute_reply": "2024-07-02T12:04:28.473753Z" + "iopub.execute_input": "2024-07-02T15:13:52.731591Z", + "iopub.status.busy": "2024-07-02T15:13:52.731198Z", + "iopub.status.idle": "2024-07-02T15:13:53.826850Z", + "shell.execute_reply": "2024-07-02T15:13:53.826290Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.476781Z", - "iopub.status.busy": "2024-07-02T12:04:28.476419Z", - "iopub.status.idle": "2024-07-02T12:04:28.493512Z", - "shell.execute_reply": "2024-07-02T12:04:28.493079Z" + "iopub.execute_input": "2024-07-02T15:13:53.829437Z", + "iopub.status.busy": "2024-07-02T15:13:53.829016Z", + "iopub.status.idle": "2024-07-02T15:13:53.846142Z", + "shell.execute_reply": "2024-07-02T15:13:53.845712Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.495747Z", - "iopub.status.busy": "2024-07-02T12:04:28.495323Z", - "iopub.status.idle": "2024-07-02T12:04:28.552204Z", - "shell.execute_reply": "2024-07-02T12:04:28.551635Z" + "iopub.execute_input": "2024-07-02T15:13:53.848204Z", + "iopub.status.busy": "2024-07-02T15:13:53.847818Z", + "iopub.status.idle": "2024-07-02T15:13:53.884392Z", + "shell.execute_reply": "2024-07-02T15:13:53.883872Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.554311Z", - "iopub.status.busy": "2024-07-02T12:04:28.553993Z", - "iopub.status.idle": "2024-07-02T12:04:28.557548Z", - "shell.execute_reply": "2024-07-02T12:04:28.557017Z" + "iopub.execute_input": "2024-07-02T15:13:53.887171Z", + "iopub.status.busy": "2024-07-02T15:13:53.886837Z", + "iopub.status.idle": "2024-07-02T15:13:53.890668Z", + "shell.execute_reply": "2024-07-02T15:13:53.890246Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.559563Z", - "iopub.status.busy": "2024-07-02T12:04:28.559241Z", - "iopub.status.idle": "2024-07-02T12:04:28.566506Z", - "shell.execute_reply": "2024-07-02T12:04:28.566080Z" + "iopub.execute_input": "2024-07-02T15:13:53.892601Z", + "iopub.status.busy": "2024-07-02T15:13:53.892297Z", + "iopub.status.idle": "2024-07-02T15:13:53.899797Z", + "shell.execute_reply": "2024-07-02T15:13:53.899259Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.568485Z", - "iopub.status.busy": "2024-07-02T12:04:28.568190Z", - "iopub.status.idle": "2024-07-02T12:04:28.570814Z", - "shell.execute_reply": "2024-07-02T12:04:28.570270Z" + "iopub.execute_input": "2024-07-02T15:13:53.901915Z", + "iopub.status.busy": "2024-07-02T15:13:53.901601Z", + "iopub.status.idle": "2024-07-02T15:13:53.904220Z", + "shell.execute_reply": "2024-07-02T15:13:53.903685Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:28.572815Z", - "iopub.status.busy": "2024-07-02T12:04:28.572491Z", - "iopub.status.idle": "2024-07-02T12:04:31.525677Z", - "shell.execute_reply": "2024-07-02T12:04:31.525153Z" + "iopub.execute_input": "2024-07-02T15:13:53.906153Z", + "iopub.status.busy": "2024-07-02T15:13:53.905838Z", + "iopub.status.idle": "2024-07-02T15:13:56.829546Z", + "shell.execute_reply": "2024-07-02T15:13:56.829019Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:31.528465Z", - "iopub.status.busy": "2024-07-02T12:04:31.528045Z", - "iopub.status.idle": "2024-07-02T12:04:31.537314Z", - "shell.execute_reply": "2024-07-02T12:04:31.536783Z" + "iopub.execute_input": "2024-07-02T15:13:56.832266Z", + "iopub.status.busy": "2024-07-02T15:13:56.832063Z", + "iopub.status.idle": "2024-07-02T15:13:56.841280Z", + "shell.execute_reply": "2024-07-02T15:13:56.840813Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:31.539264Z", - "iopub.status.busy": "2024-07-02T12:04:31.539089Z", - "iopub.status.idle": "2024-07-02T12:04:33.395993Z", - "shell.execute_reply": "2024-07-02T12:04:33.395329Z" + "iopub.execute_input": "2024-07-02T15:13:56.843320Z", + "iopub.status.busy": "2024-07-02T15:13:56.843129Z", + "iopub.status.idle": "2024-07-02T15:13:58.717626Z", + "shell.execute_reply": "2024-07-02T15:13:58.717017Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.398417Z", - "iopub.status.busy": "2024-07-02T12:04:33.397878Z", - "iopub.status.idle": "2024-07-02T12:04:33.416211Z", - "shell.execute_reply": "2024-07-02T12:04:33.415751Z" + "iopub.execute_input": "2024-07-02T15:13:58.720164Z", + "iopub.status.busy": "2024-07-02T15:13:58.719607Z", + "iopub.status.idle": "2024-07-02T15:13:58.738219Z", + "shell.execute_reply": "2024-07-02T15:13:58.737654Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.418164Z", - "iopub.status.busy": "2024-07-02T12:04:33.417840Z", - "iopub.status.idle": "2024-07-02T12:04:33.425514Z", - "shell.execute_reply": "2024-07-02T12:04:33.425080Z" + "iopub.execute_input": "2024-07-02T15:13:58.740165Z", + "iopub.status.busy": "2024-07-02T15:13:58.739856Z", + "iopub.status.idle": "2024-07-02T15:13:58.747692Z", + "shell.execute_reply": "2024-07-02T15:13:58.747149Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.427421Z", - "iopub.status.busy": "2024-07-02T12:04:33.427245Z", - "iopub.status.idle": "2024-07-02T12:04:33.435924Z", - "shell.execute_reply": "2024-07-02T12:04:33.435472Z" + "iopub.execute_input": "2024-07-02T15:13:58.749890Z", + "iopub.status.busy": "2024-07-02T15:13:58.749354Z", + "iopub.status.idle": "2024-07-02T15:13:58.758107Z", + "shell.execute_reply": "2024-07-02T15:13:58.757568Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.437900Z", - "iopub.status.busy": "2024-07-02T12:04:33.437577Z", - "iopub.status.idle": "2024-07-02T12:04:33.445125Z", - "shell.execute_reply": "2024-07-02T12:04:33.444685Z" + "iopub.execute_input": "2024-07-02T15:13:58.760206Z", + "iopub.status.busy": "2024-07-02T15:13:58.759870Z", + "iopub.status.idle": "2024-07-02T15:13:58.767460Z", + "shell.execute_reply": "2024-07-02T15:13:58.767003Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.447029Z", - "iopub.status.busy": "2024-07-02T12:04:33.446852Z", - "iopub.status.idle": "2024-07-02T12:04:33.455323Z", - "shell.execute_reply": "2024-07-02T12:04:33.454897Z" + "iopub.execute_input": "2024-07-02T15:13:58.769386Z", + "iopub.status.busy": "2024-07-02T15:13:58.769213Z", + "iopub.status.idle": "2024-07-02T15:13:58.777797Z", + "shell.execute_reply": "2024-07-02T15:13:58.777350Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.457305Z", - "iopub.status.busy": "2024-07-02T12:04:33.457003Z", - "iopub.status.idle": "2024-07-02T12:04:33.464266Z", - "shell.execute_reply": "2024-07-02T12:04:33.463800Z" + "iopub.execute_input": "2024-07-02T15:13:58.779615Z", + "iopub.status.busy": "2024-07-02T15:13:58.779445Z", + "iopub.status.idle": "2024-07-02T15:13:58.786751Z", + "shell.execute_reply": "2024-07-02T15:13:58.786316Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.466390Z", - "iopub.status.busy": "2024-07-02T12:04:33.465996Z", - "iopub.status.idle": "2024-07-02T12:04:33.473134Z", - "shell.execute_reply": "2024-07-02T12:04:33.472705Z" + "iopub.execute_input": "2024-07-02T15:13:58.788616Z", + "iopub.status.busy": "2024-07-02T15:13:58.788445Z", + "iopub.status.idle": "2024-07-02T15:13:58.796817Z", + "shell.execute_reply": "2024-07-02T15:13:58.796328Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:33.475300Z", - "iopub.status.busy": "2024-07-02T12:04:33.474982Z", - "iopub.status.idle": "2024-07-02T12:04:33.482977Z", - "shell.execute_reply": "2024-07-02T12:04:33.482536Z" + "iopub.execute_input": "2024-07-02T15:13:58.799200Z", + "iopub.status.busy": "2024-07-02T15:13:58.798774Z", + "iopub.status.idle": "2024-07-02T15:13:58.807454Z", + "shell.execute_reply": "2024-07-02T15:13:58.806894Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 8395c410d..5204560ef 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:36.240740Z", - "iopub.status.busy": "2024-07-02T12:04:36.240404Z", - "iopub.status.idle": "2024-07-02T12:04:38.828958Z", - "shell.execute_reply": "2024-07-02T12:04:38.828416Z" + "iopub.execute_input": "2024-07-02T15:14:01.500489Z", + "iopub.status.busy": "2024-07-02T15:14:01.500322Z", + "iopub.status.idle": "2024-07-02T15:14:04.113035Z", + "shell.execute_reply": "2024-07-02T15:14:04.112481Z" }, "nbsphinx": "hidden" }, @@ -96,7 +96,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.831414Z", - "iopub.status.busy": "2024-07-02T12:04:38.831139Z", - "iopub.status.idle": "2024-07-02T12:04:38.834207Z", - "shell.execute_reply": "2024-07-02T12:04:38.833787Z" + "iopub.execute_input": "2024-07-02T15:14:04.115579Z", + "iopub.status.busy": "2024-07-02T15:14:04.115125Z", + "iopub.status.idle": "2024-07-02T15:14:04.118367Z", + "shell.execute_reply": "2024-07-02T15:14:04.117915Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.836176Z", - "iopub.status.busy": "2024-07-02T12:04:38.835855Z", - "iopub.status.idle": "2024-07-02T12:04:38.838727Z", - "shell.execute_reply": "2024-07-02T12:04:38.838306Z" + "iopub.execute_input": "2024-07-02T15:14:04.120314Z", + "iopub.status.busy": "2024-07-02T15:14:04.119999Z", + "iopub.status.idle": "2024-07-02T15:14:04.123081Z", + "shell.execute_reply": "2024-07-02T15:14:04.122619Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.840549Z", - "iopub.status.busy": "2024-07-02T12:04:38.840377Z", - "iopub.status.idle": "2024-07-02T12:04:38.923955Z", - "shell.execute_reply": "2024-07-02T12:04:38.923459Z" + "iopub.execute_input": "2024-07-02T15:14:04.125041Z", + "iopub.status.busy": "2024-07-02T15:14:04.124728Z", + "iopub.status.idle": "2024-07-02T15:14:04.163294Z", + "shell.execute_reply": "2024-07-02T15:14:04.162806Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.926011Z", - "iopub.status.busy": "2024-07-02T12:04:38.925614Z", - "iopub.status.idle": "2024-07-02T12:04:38.929422Z", - "shell.execute_reply": "2024-07-02T12:04:38.928857Z" + "iopub.execute_input": "2024-07-02T15:14:04.165499Z", + "iopub.status.busy": "2024-07-02T15:14:04.165073Z", + "iopub.status.idle": "2024-07-02T15:14:04.168687Z", + "shell.execute_reply": "2024-07-02T15:14:04.168240Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged'}\n" + "Classes: {'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'card_payment_fee_charged', 'cancel_transfer', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.931544Z", - "iopub.status.busy": "2024-07-02T12:04:38.931095Z", - "iopub.status.idle": "2024-07-02T12:04:38.934251Z", - "shell.execute_reply": "2024-07-02T12:04:38.933726Z" + "iopub.execute_input": "2024-07-02T15:14:04.170669Z", + "iopub.status.busy": "2024-07-02T15:14:04.170357Z", + "iopub.status.idle": "2024-07-02T15:14:04.173526Z", + "shell.execute_reply": "2024-07-02T15:14:04.172982Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.936534Z", - "iopub.status.busy": "2024-07-02T12:04:38.936327Z", - "iopub.status.idle": "2024-07-02T12:04:42.537806Z", - "shell.execute_reply": "2024-07-02T12:04:42.537162Z" + "iopub.execute_input": "2024-07-02T15:14:04.175608Z", + "iopub.status.busy": "2024-07-02T15:14:04.175312Z", + "iopub.status.idle": "2024-07-02T15:14:07.867281Z", + "shell.execute_reply": "2024-07-02T15:14:07.866722Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:42.540458Z", - "iopub.status.busy": "2024-07-02T12:04:42.540268Z", - "iopub.status.idle": "2024-07-02T12:04:43.423626Z", - "shell.execute_reply": "2024-07-02T12:04:43.423064Z" + "iopub.execute_input": "2024-07-02T15:14:07.870054Z", + "iopub.status.busy": "2024-07-02T15:14:07.869647Z", + "iopub.status.idle": "2024-07-02T15:14:08.750932Z", + "shell.execute_reply": "2024-07-02T15:14:08.750350Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:43.426912Z", - "iopub.status.busy": "2024-07-02T12:04:43.426508Z", - "iopub.status.idle": "2024-07-02T12:04:43.429416Z", - "shell.execute_reply": "2024-07-02T12:04:43.428926Z" + "iopub.execute_input": "2024-07-02T15:14:08.753892Z", + "iopub.status.busy": "2024-07-02T15:14:08.753472Z", + "iopub.status.idle": "2024-07-02T15:14:08.756403Z", + "shell.execute_reply": "2024-07-02T15:14:08.755906Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:43.431781Z", - "iopub.status.busy": "2024-07-02T12:04:43.431407Z", - "iopub.status.idle": "2024-07-02T12:04:45.304891Z", - "shell.execute_reply": "2024-07-02T12:04:45.304275Z" + "iopub.execute_input": "2024-07-02T15:14:08.759587Z", + "iopub.status.busy": "2024-07-02T15:14:08.758650Z", + "iopub.status.idle": "2024-07-02T15:14:10.695173Z", + "shell.execute_reply": "2024-07-02T15:14:10.694552Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.309001Z", - "iopub.status.busy": "2024-07-02T12:04:45.307874Z", - "iopub.status.idle": "2024-07-02T12:04:45.333199Z", - "shell.execute_reply": "2024-07-02T12:04:45.332708Z" + "iopub.execute_input": "2024-07-02T15:14:10.699111Z", + "iopub.status.busy": "2024-07-02T15:14:10.697727Z", + "iopub.status.idle": "2024-07-02T15:14:10.723548Z", + "shell.execute_reply": "2024-07-02T15:14:10.723039Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.336771Z", - "iopub.status.busy": "2024-07-02T12:04:45.335844Z", - "iopub.status.idle": "2024-07-02T12:04:45.346004Z", - "shell.execute_reply": "2024-07-02T12:04:45.345452Z" + "iopub.execute_input": "2024-07-02T15:14:10.727082Z", + "iopub.status.busy": "2024-07-02T15:14:10.726140Z", + "iopub.status.idle": "2024-07-02T15:14:10.737117Z", + "shell.execute_reply": "2024-07-02T15:14:10.736707Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.348315Z", - "iopub.status.busy": "2024-07-02T12:04:45.347931Z", - "iopub.status.idle": "2024-07-02T12:04:45.352195Z", - "shell.execute_reply": "2024-07-02T12:04:45.351669Z" + "iopub.execute_input": "2024-07-02T15:14:10.739972Z", + "iopub.status.busy": "2024-07-02T15:14:10.739233Z", + "iopub.status.idle": "2024-07-02T15:14:10.744512Z", + "shell.execute_reply": "2024-07-02T15:14:10.744100Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.354318Z", - "iopub.status.busy": "2024-07-02T12:04:45.354009Z", - "iopub.status.idle": "2024-07-02T12:04:45.360212Z", - "shell.execute_reply": "2024-07-02T12:04:45.359737Z" + "iopub.execute_input": "2024-07-02T15:14:10.746541Z", + "iopub.status.busy": "2024-07-02T15:14:10.746363Z", + "iopub.status.idle": "2024-07-02T15:14:10.752732Z", + "shell.execute_reply": "2024-07-02T15:14:10.752214Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.362212Z", - "iopub.status.busy": "2024-07-02T12:04:45.361899Z", - "iopub.status.idle": "2024-07-02T12:04:45.368332Z", - "shell.execute_reply": "2024-07-02T12:04:45.367912Z" + "iopub.execute_input": "2024-07-02T15:14:10.754855Z", + "iopub.status.busy": "2024-07-02T15:14:10.754542Z", + "iopub.status.idle": "2024-07-02T15:14:10.760876Z", + "shell.execute_reply": "2024-07-02T15:14:10.760354Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.370347Z", - "iopub.status.busy": "2024-07-02T12:04:45.370035Z", - "iopub.status.idle": "2024-07-02T12:04:45.375916Z", - "shell.execute_reply": "2024-07-02T12:04:45.375352Z" + "iopub.execute_input": "2024-07-02T15:14:10.762917Z", + "iopub.status.busy": "2024-07-02T15:14:10.762536Z", + "iopub.status.idle": "2024-07-02T15:14:10.768287Z", + "shell.execute_reply": "2024-07-02T15:14:10.767766Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.377933Z", - "iopub.status.busy": "2024-07-02T12:04:45.377533Z", - "iopub.status.idle": "2024-07-02T12:04:45.386285Z", - "shell.execute_reply": "2024-07-02T12:04:45.385744Z" + "iopub.execute_input": "2024-07-02T15:14:10.770234Z", + "iopub.status.busy": "2024-07-02T15:14:10.769934Z", + "iopub.status.idle": "2024-07-02T15:14:10.778237Z", + "shell.execute_reply": "2024-07-02T15:14:10.777705Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.388235Z", - "iopub.status.busy": "2024-07-02T12:04:45.387909Z", - "iopub.status.idle": "2024-07-02T12:04:45.393341Z", - "shell.execute_reply": "2024-07-02T12:04:45.392791Z" + "iopub.execute_input": "2024-07-02T15:14:10.780199Z", + "iopub.status.busy": "2024-07-02T15:14:10.779892Z", + "iopub.status.idle": "2024-07-02T15:14:10.785104Z", + "shell.execute_reply": "2024-07-02T15:14:10.784582Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.395404Z", - "iopub.status.busy": "2024-07-02T12:04:45.395057Z", - "iopub.status.idle": "2024-07-02T12:04:45.400341Z", - "shell.execute_reply": "2024-07-02T12:04:45.399863Z" + "iopub.execute_input": "2024-07-02T15:14:10.787024Z", + "iopub.status.busy": "2024-07-02T15:14:10.786715Z", + "iopub.status.idle": "2024-07-02T15:14:10.791931Z", + "shell.execute_reply": "2024-07-02T15:14:10.791409Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.402359Z", - "iopub.status.busy": "2024-07-02T12:04:45.402038Z", - "iopub.status.idle": "2024-07-02T12:04:45.405437Z", - "shell.execute_reply": "2024-07-02T12:04:45.405020Z" + "iopub.execute_input": "2024-07-02T15:14:10.793948Z", + "iopub.status.busy": "2024-07-02T15:14:10.793644Z", + "iopub.status.idle": "2024-07-02T15:14:10.797169Z", + "shell.execute_reply": "2024-07-02T15:14:10.796651Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.407623Z", - "iopub.status.busy": "2024-07-02T12:04:45.407307Z", - "iopub.status.idle": "2024-07-02T12:04:45.412091Z", - "shell.execute_reply": "2024-07-02T12:04:45.411668Z" + "iopub.execute_input": "2024-07-02T15:14:10.799179Z", + "iopub.status.busy": "2024-07-02T15:14:10.798916Z", + "iopub.status.idle": "2024-07-02T15:14:10.804228Z", + "shell.execute_reply": "2024-07-02T15:14:10.803755Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index 62a8a980c..9f74b4e12 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:48.475916Z", - "iopub.status.busy": "2024-07-02T12:04:48.475349Z", - "iopub.status.idle": "2024-07-02T12:04:48.903298Z", - "shell.execute_reply": "2024-07-02T12:04:48.902818Z" + "iopub.execute_input": "2024-07-02T15:14:14.103983Z", + "iopub.status.busy": "2024-07-02T15:14:14.103826Z", + "iopub.status.idle": "2024-07-02T15:14:14.532907Z", + "shell.execute_reply": "2024-07-02T15:14:14.532306Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:48.905906Z", - "iopub.status.busy": "2024-07-02T12:04:48.905515Z", - "iopub.status.idle": "2024-07-02T12:04:49.030978Z", - "shell.execute_reply": "2024-07-02T12:04:49.030445Z" + "iopub.execute_input": "2024-07-02T15:14:14.535883Z", + "iopub.status.busy": "2024-07-02T15:14:14.535387Z", + "iopub.status.idle": "2024-07-02T15:14:14.663925Z", + "shell.execute_reply": "2024-07-02T15:14:14.663366Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:49.033125Z", - "iopub.status.busy": "2024-07-02T12:04:49.032895Z", - "iopub.status.idle": "2024-07-02T12:04:49.055963Z", - "shell.execute_reply": "2024-07-02T12:04:49.055416Z" + "iopub.execute_input": "2024-07-02T15:14:14.666105Z", + "iopub.status.busy": "2024-07-02T15:14:14.665873Z", + "iopub.status.idle": "2024-07-02T15:14:14.688697Z", + "shell.execute_reply": "2024-07-02T15:14:14.688145Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:49.058382Z", - "iopub.status.busy": "2024-07-02T12:04:49.057963Z", - "iopub.status.idle": "2024-07-02T12:04:51.680557Z", - "shell.execute_reply": "2024-07-02T12:04:51.680002Z" + "iopub.execute_input": "2024-07-02T15:14:14.691372Z", + "iopub.status.busy": "2024-07-02T15:14:14.691132Z", + "iopub.status.idle": "2024-07-02T15:14:17.410594Z", + "shell.execute_reply": "2024-07-02T15:14:17.410094Z" } }, "outputs": [ @@ -235,7 +235,7 @@ "Finding class_imbalance issues ...\n", "Finding underperforming_group issues ...\n", "\n", - "Audit complete. 524 issues found in the dataset.\n" + "Audit complete. 523 issues found in the dataset.\n" ] }, { @@ -280,13 +280,13 @@ " \n", " 2\n", " outlier\n", - " 0.356924\n", - " 363\n", + " 0.356958\n", + " 362\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619581\n", + " 0.619565\n", " 108\n", " \n", " \n", @@ -315,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356924 363\n", - "3 near_duplicate 0.619581 108\n", + "2 outlier 0.356958 362\n", + "3 near_duplicate 0.619565 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651929 0" @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:51.683932Z", - "iopub.status.busy": "2024-07-02T12:04:51.683392Z", - "iopub.status.idle": "2024-07-02T12:04:59.515985Z", - "shell.execute_reply": "2024-07-02T12:04:59.515371Z" + "iopub.execute_input": "2024-07-02T15:14:17.413158Z", + "iopub.status.busy": "2024-07-02T15:14:17.412638Z", + "iopub.status.idle": "2024-07-02T15:14:25.265742Z", + "shell.execute_reply": "2024-07-02T15:14:25.265250Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:59.518078Z", - "iopub.status.busy": "2024-07-02T12:04:59.517894Z", - "iopub.status.idle": "2024-07-02T12:04:59.659289Z", - "shell.execute_reply": "2024-07-02T12:04:59.658739Z" + "iopub.execute_input": "2024-07-02T15:14:25.267894Z", + "iopub.status.busy": "2024-07-02T15:14:25.267556Z", + "iopub.status.idle": "2024-07-02T15:14:25.428084Z", + "shell.execute_reply": "2024-07-02T15:14:25.427532Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:59.661683Z", - "iopub.status.busy": "2024-07-02T12:04:59.661350Z", - "iopub.status.idle": "2024-07-02T12:05:00.957856Z", - "shell.execute_reply": "2024-07-02T12:05:00.957311Z" + "iopub.execute_input": "2024-07-02T15:14:25.430688Z", + "iopub.status.busy": "2024-07-02T15:14:25.430400Z", + "iopub.status.idle": "2024-07-02T15:14:26.733556Z", + "shell.execute_reply": "2024-07-02T15:14:26.733008Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:00.960128Z", - "iopub.status.busy": "2024-07-02T12:05:00.959785Z", - "iopub.status.idle": "2024-07-02T12:05:01.381421Z", - "shell.execute_reply": "2024-07-02T12:05:01.380807Z" + "iopub.execute_input": "2024-07-02T15:14:26.735854Z", + "iopub.status.busy": "2024-07-02T15:14:26.735515Z", + "iopub.status.idle": "2024-07-02T15:14:27.149306Z", + "shell.execute_reply": "2024-07-02T15:14:27.148705Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.383745Z", - "iopub.status.busy": "2024-07-02T12:05:01.383267Z", - "iopub.status.idle": "2024-07-02T12:05:01.392315Z", - "shell.execute_reply": "2024-07-02T12:05:01.391863Z" + "iopub.execute_input": "2024-07-02T15:14:27.151755Z", + "iopub.status.busy": "2024-07-02T15:14:27.151216Z", + "iopub.status.idle": "2024-07-02T15:14:27.160230Z", + "shell.execute_reply": "2024-07-02T15:14:27.159782Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.394282Z", - "iopub.status.busy": "2024-07-02T12:05:01.393956Z", - "iopub.status.idle": "2024-07-02T12:05:01.411562Z", - "shell.execute_reply": "2024-07-02T12:05:01.411139Z" + "iopub.execute_input": "2024-07-02T15:14:27.162273Z", + "iopub.status.busy": "2024-07-02T15:14:27.161949Z", + "iopub.status.idle": "2024-07-02T15:14:27.180092Z", + "shell.execute_reply": "2024-07-02T15:14:27.179529Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.413543Z", - "iopub.status.busy": "2024-07-02T12:05:01.413221Z", - "iopub.status.idle": "2024-07-02T12:05:01.630162Z", - "shell.execute_reply": "2024-07-02T12:05:01.629562Z" + "iopub.execute_input": "2024-07-02T15:14:27.183621Z", + "iopub.status.busy": "2024-07-02T15:14:27.183436Z", + "iopub.status.idle": "2024-07-02T15:14:27.404912Z", + "shell.execute_reply": "2024-07-02T15:14:27.404293Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.632639Z", - "iopub.status.busy": "2024-07-02T12:05:01.632236Z", - "iopub.status.idle": "2024-07-02T12:05:01.650528Z", - "shell.execute_reply": "2024-07-02T12:05:01.649988Z" + "iopub.execute_input": "2024-07-02T15:14:27.407504Z", + "iopub.status.busy": "2024-07-02T15:14:27.407113Z", + "iopub.status.idle": "2024-07-02T15:14:27.426425Z", + "shell.execute_reply": "2024-07-02T15:14:27.425957Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.652709Z", - "iopub.status.busy": "2024-07-02T12:05:01.652303Z", - "iopub.status.idle": "2024-07-02T12:05:01.816760Z", - "shell.execute_reply": "2024-07-02T12:05:01.816173Z" + "iopub.execute_input": "2024-07-02T15:14:27.428485Z", + "iopub.status.busy": "2024-07-02T15:14:27.428302Z", + "iopub.status.idle": "2024-07-02T15:14:27.595938Z", + "shell.execute_reply": "2024-07-02T15:14:27.595360Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.818813Z", - "iopub.status.busy": "2024-07-02T12:05:01.818633Z", - "iopub.status.idle": "2024-07-02T12:05:01.828263Z", - "shell.execute_reply": "2024-07-02T12:05:01.827827Z" + "iopub.execute_input": "2024-07-02T15:14:27.598162Z", + "iopub.status.busy": "2024-07-02T15:14:27.597979Z", + "iopub.status.idle": "2024-07-02T15:14:27.607922Z", + "shell.execute_reply": "2024-07-02T15:14:27.607375Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.830285Z", - "iopub.status.busy": "2024-07-02T12:05:01.830099Z", - "iopub.status.idle": "2024-07-02T12:05:01.839416Z", - "shell.execute_reply": "2024-07-02T12:05:01.838852Z" + "iopub.execute_input": "2024-07-02T15:14:27.610002Z", + "iopub.status.busy": "2024-07-02T15:14:27.609825Z", + "iopub.status.idle": "2024-07-02T15:14:27.619372Z", + "shell.execute_reply": "2024-07-02T15:14:27.618837Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.841444Z", - "iopub.status.busy": "2024-07-02T12:05:01.841118Z", - "iopub.status.idle": "2024-07-02T12:05:01.878960Z", - "shell.execute_reply": "2024-07-02T12:05:01.878541Z" + "iopub.execute_input": "2024-07-02T15:14:27.621551Z", + "iopub.status.busy": "2024-07-02T15:14:27.621164Z", + "iopub.status.idle": "2024-07-02T15:14:27.651909Z", + "shell.execute_reply": "2024-07-02T15:14:27.651479Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.881007Z", - "iopub.status.busy": "2024-07-02T12:05:01.880679Z", - "iopub.status.idle": "2024-07-02T12:05:01.883255Z", - "shell.execute_reply": "2024-07-02T12:05:01.882829Z" + "iopub.execute_input": "2024-07-02T15:14:27.653825Z", + "iopub.status.busy": "2024-07-02T15:14:27.653548Z", + "iopub.status.idle": "2024-07-02T15:14:27.656169Z", + "shell.execute_reply": "2024-07-02T15:14:27.655741Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.885223Z", - "iopub.status.busy": "2024-07-02T12:05:01.884900Z", - "iopub.status.idle": "2024-07-02T12:05:01.903469Z", - "shell.execute_reply": "2024-07-02T12:05:01.903010Z" + "iopub.execute_input": "2024-07-02T15:14:27.658186Z", + "iopub.status.busy": "2024-07-02T15:14:27.657882Z", + "iopub.status.idle": "2024-07-02T15:14:27.676913Z", + "shell.execute_reply": "2024-07-02T15:14:27.676456Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.905390Z", - "iopub.status.busy": "2024-07-02T12:05:01.905216Z", - "iopub.status.idle": "2024-07-02T12:05:01.909303Z", - "shell.execute_reply": "2024-07-02T12:05:01.908869Z" + "iopub.execute_input": "2024-07-02T15:14:27.679075Z", + "iopub.status.busy": "2024-07-02T15:14:27.678723Z", + "iopub.status.idle": "2024-07-02T15:14:27.683007Z", + "shell.execute_reply": "2024-07-02T15:14:27.682466Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.911113Z", - "iopub.status.busy": "2024-07-02T12:05:01.910943Z", - "iopub.status.idle": "2024-07-02T12:05:01.938117Z", - "shell.execute_reply": "2024-07-02T12:05:01.937659Z" + "iopub.execute_input": "2024-07-02T15:14:27.684997Z", + "iopub.status.busy": "2024-07-02T15:14:27.684696Z", + "iopub.status.idle": "2024-07-02T15:14:27.717340Z", + "shell.execute_reply": "2024-07-02T15:14:27.716802Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:01.940161Z", - "iopub.status.busy": "2024-07-02T12:05:01.939837Z", - "iopub.status.idle": "2024-07-02T12:05:02.252666Z", - "shell.execute_reply": "2024-07-02T12:05:02.252098Z" + "iopub.execute_input": "2024-07-02T15:14:27.719447Z", + "iopub.status.busy": "2024-07-02T15:14:27.719135Z", + "iopub.status.idle": "2024-07-02T15:14:28.089427Z", + "shell.execute_reply": "2024-07-02T15:14:28.088856Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.254862Z", - "iopub.status.busy": "2024-07-02T12:05:02.254429Z", - "iopub.status.idle": "2024-07-02T12:05:02.257607Z", - "shell.execute_reply": "2024-07-02T12:05:02.257069Z" + "iopub.execute_input": "2024-07-02T15:14:28.091789Z", + "iopub.status.busy": "2024-07-02T15:14:28.091461Z", + "iopub.status.idle": "2024-07-02T15:14:28.094696Z", + "shell.execute_reply": "2024-07-02T15:14:28.094164Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.259719Z", - "iopub.status.busy": "2024-07-02T12:05:02.259383Z", - "iopub.status.idle": "2024-07-02T12:05:02.272004Z", - "shell.execute_reply": "2024-07-02T12:05:02.271534Z" + "iopub.execute_input": "2024-07-02T15:14:28.096729Z", + "iopub.status.busy": "2024-07-02T15:14:28.096462Z", + "iopub.status.idle": "2024-07-02T15:14:28.109432Z", + "shell.execute_reply": "2024-07-02T15:14:28.109008Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.273862Z", - "iopub.status.busy": "2024-07-02T12:05:02.273687Z", - "iopub.status.idle": "2024-07-02T12:05:02.287267Z", - "shell.execute_reply": "2024-07-02T12:05:02.286829Z" + "iopub.execute_input": "2024-07-02T15:14:28.111301Z", + "iopub.status.busy": "2024-07-02T15:14:28.111131Z", + "iopub.status.idle": "2024-07-02T15:14:28.124546Z", + "shell.execute_reply": "2024-07-02T15:14:28.124107Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.289083Z", - "iopub.status.busy": "2024-07-02T12:05:02.288916Z", - "iopub.status.idle": "2024-07-02T12:05:02.298453Z", - "shell.execute_reply": "2024-07-02T12:05:02.298027Z" + "iopub.execute_input": "2024-07-02T15:14:28.126667Z", + "iopub.status.busy": "2024-07-02T15:14:28.126240Z", + "iopub.status.idle": "2024-07-02T15:14:28.136518Z", + "shell.execute_reply": "2024-07-02T15:14:28.135974Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.300283Z", - "iopub.status.busy": "2024-07-02T12:05:02.300116Z", - "iopub.status.idle": "2024-07-02T12:05:02.309664Z", - "shell.execute_reply": "2024-07-02T12:05:02.309126Z" + "iopub.execute_input": "2024-07-02T15:14:28.138549Z", + "iopub.status.busy": "2024-07-02T15:14:28.138251Z", + "iopub.status.idle": "2024-07-02T15:14:28.147091Z", + "shell.execute_reply": "2024-07-02T15:14:28.146561Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.311452Z", - "iopub.status.busy": "2024-07-02T12:05:02.311286Z", - "iopub.status.idle": "2024-07-02T12:05:02.314989Z", - "shell.execute_reply": "2024-07-02T12:05:02.314531Z" + "iopub.execute_input": "2024-07-02T15:14:28.149207Z", + "iopub.status.busy": "2024-07-02T15:14:28.148904Z", + "iopub.status.idle": "2024-07-02T15:14:28.152652Z", + "shell.execute_reply": "2024-07-02T15:14:28.152121Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.316924Z", - "iopub.status.busy": "2024-07-02T12:05:02.316631Z", - "iopub.status.idle": "2024-07-02T12:05:02.366687Z", - "shell.execute_reply": "2024-07-02T12:05:02.366234Z" + "iopub.execute_input": "2024-07-02T15:14:28.154967Z", + "iopub.status.busy": "2024-07-02T15:14:28.154533Z", + "iopub.status.idle": "2024-07-02T15:14:28.210134Z", + "shell.execute_reply": "2024-07-02T15:14:28.209520Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.368916Z", - "iopub.status.busy": "2024-07-02T12:05:02.368532Z", - "iopub.status.idle": "2024-07-02T12:05:02.374010Z", - "shell.execute_reply": "2024-07-02T12:05:02.373493Z" + "iopub.execute_input": "2024-07-02T15:14:28.212907Z", + "iopub.status.busy": "2024-07-02T15:14:28.212352Z", + "iopub.status.idle": "2024-07-02T15:14:28.219047Z", + "shell.execute_reply": "2024-07-02T15:14:28.218594Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.375995Z", - "iopub.status.busy": "2024-07-02T12:05:02.375691Z", - "iopub.status.idle": "2024-07-02T12:05:02.386423Z", - "shell.execute_reply": "2024-07-02T12:05:02.385887Z" + "iopub.execute_input": "2024-07-02T15:14:28.221050Z", + "iopub.status.busy": "2024-07-02T15:14:28.220676Z", + "iopub.status.idle": "2024-07-02T15:14:28.232201Z", + "shell.execute_reply": "2024-07-02T15:14:28.231649Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.388574Z", - "iopub.status.busy": "2024-07-02T12:05:02.388272Z", - "iopub.status.idle": "2024-07-02T12:05:02.563243Z", - "shell.execute_reply": "2024-07-02T12:05:02.562691Z" + "iopub.execute_input": "2024-07-02T15:14:28.234255Z", + "iopub.status.busy": "2024-07-02T15:14:28.233933Z", + "iopub.status.idle": "2024-07-02T15:14:28.446890Z", + "shell.execute_reply": "2024-07-02T15:14:28.446307Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.565412Z", - "iopub.status.busy": "2024-07-02T12:05:02.565240Z", - "iopub.status.idle": "2024-07-02T12:05:02.572732Z", - "shell.execute_reply": "2024-07-02T12:05:02.572280Z" + "iopub.execute_input": "2024-07-02T15:14:28.449143Z", + "iopub.status.busy": "2024-07-02T15:14:28.448684Z", + "iopub.status.idle": "2024-07-02T15:14:28.456578Z", + "shell.execute_reply": "2024-07-02T15:14:28.456036Z" }, "nbsphinx": "hidden" }, @@ -3760,10 +3760,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:02.574589Z", - "iopub.status.busy": "2024-07-02T12:05:02.574422Z", - "iopub.status.idle": "2024-07-02T12:05:08.693945Z", - "shell.execute_reply": "2024-07-02T12:05:08.693406Z" + "iopub.execute_input": "2024-07-02T15:14:28.458595Z", + "iopub.status.busy": "2024-07-02T15:14:28.458296Z", + "iopub.status.idle": "2024-07-02T15:14:35.258258Z", + "shell.execute_reply": "2024-07-02T15:14:35.257674Z" } }, "outputs": [ @@ -3787,7 +3787,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 917504/170498071 [00:00<00:20, 8347158.96it/s]" + " 0%| | 458752/170498071 [00:00<00:37, 4495236.08it/s]" ] }, { @@ -3795,7 +3795,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 9601024/170498071 [00:00<00:03, 52614403.72it/s]" + " 2%|▏ | 4227072/170498071 [00:00<00:07, 23242348.53it/s]" ] }, { @@ -3803,7 +3803,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 18481152/170498071 [00:00<00:02, 68746962.66it/s]" + " 5%|▌ | 9306112/170498071 [00:00<00:04, 35527365.93it/s]" ] }, { @@ -3811,7 +3811,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 25493504/170498071 [00:00<00:02, 68028252.66it/s]" + " 8%|▊ | 13926400/170498071 [00:00<00:03, 39660501.21it/s]" ] }, { @@ -3819,7 +3819,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32571392/170498071 [00:00<00:02, 68946396.69it/s]" + " 11%|█ | 18644992/170498071 [00:00<00:03, 42142752.96it/s]" ] }, { @@ -3827,7 +3827,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 39845888/170498071 [00:00<00:01, 70065798.28it/s]" + " 14%|█▎ | 23166976/170498071 [00:00<00:03, 43171320.39it/s]" ] }, { @@ -3835,7 +3835,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 46891008/170498071 [00:00<00:01, 68706053.96it/s]" + " 16%|█▌ | 27688960/170498071 [00:00<00:03, 43778497.63it/s]" ] }, { @@ -3843,7 +3843,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 54394880/170498071 [00:00<00:01, 70657768.03it/s]" + " 19%|█▉ | 32276480/170498071 [00:00<00:03, 44429066.54it/s]" ] }, { @@ -3851,7 +3851,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 61505536/170498071 [00:00<00:01, 69454102.48it/s]" + " 22%|██▏ | 36732928/170498071 [00:00<00:03, 44053184.08it/s]" ] }, { @@ -3859,7 +3859,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 69074944/170498071 [00:01<00:01, 71043124.94it/s]" + " 24%|██▍ | 41156608/170498071 [00:01<00:03, 43037571.10it/s]" ] }, { @@ -3867,7 +3867,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 76218368/170498071 [00:01<00:01, 69909000.72it/s]" + " 27%|██▋ | 45481984/170498071 [00:01<00:02, 41764924.25it/s]" ] }, { @@ -3875,7 +3875,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 83230720/170498071 [00:01<00:01, 69743647.72it/s]" + " 29%|██▉ | 49741824/170498071 [00:01<00:02, 41917563.82it/s]" ] }, { @@ -3883,7 +3883,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 92930048/170498071 [00:01<00:00, 77765718.10it/s]" + " 32%|███▏ | 54296576/170498071 [00:01<00:02, 42876516.22it/s]" ] }, { @@ -3891,7 +3891,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 100794368/170498071 [00:01<00:00, 77921748.40it/s]" + " 35%|███▍ | 58884096/170498071 [00:01<00:02, 43617372.00it/s]" ] }, { @@ -3899,7 +3899,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▎ | 108625920/170498071 [00:01<00:00, 74716954.24it/s]" + " 37%|███▋ | 63504384/170498071 [00:01<00:02, 44261340.15it/s]" ] }, { @@ -3907,7 +3907,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 116162560/170498071 [00:01<00:00, 72297265.09it/s]" + " 40%|███▉ | 68124672/170498071 [00:01<00:02, 44684329.80it/s]" ] }, { @@ -3915,7 +3915,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 123437056/170498071 [00:01<00:00, 71923065.09it/s]" + " 43%|████▎ | 72810496/170498071 [00:01<00:02, 45260580.12it/s]" ] }, { @@ -3923,7 +3923,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 130678784/170498071 [00:01<00:00, 70005310.34it/s]" + " 45%|████▌ | 77430784/170498071 [00:01<00:02, 45420696.51it/s]" ] }, { @@ -3931,7 +3931,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 138280960/170498071 [00:01<00:00, 71572260.33it/s]" + " 48%|████▊ | 82018304/170498071 [00:01<00:01, 45522327.94it/s]" ] }, { @@ -3939,7 +3939,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 145489920/170498071 [00:02<00:00, 69364483.36it/s]" + " 51%|█████ | 87097344/170498071 [00:02<00:01, 47025132.26it/s]" ] }, { @@ -3947,7 +3947,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 152961024/170498071 [00:02<00:00, 70793844.72it/s]" + " 56%|█████▌ | 95223808/170498071 [00:02<00:01, 57144332.93it/s]" ] }, { @@ -3955,7 +3955,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 160071680/170498071 [00:02<00:00, 69055168.81it/s]" + " 61%|██████ | 103972864/170498071 [00:02<00:01, 66190969.00it/s]" ] }, { @@ -3963,7 +3963,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 167608320/170498071 [00:02<00:00, 70630354.07it/s]" + " 66%|██████▌ | 112721920/170498071 [00:02<00:00, 72211785.85it/s]" ] }, { @@ -3971,7 +3971,55 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:02<00:00, 69520911.78it/s]" + " 72%|███████▏ | 122159104/170498071 [00:02<00:00, 78777574.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 77%|███████▋ | 130809856/170498071 [00:02<00:00, 81070781.57it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 82%|████████▏ | 139427840/170498071 [00:02<00:00, 82534573.38it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|████████▋ | 147685376/170498071 [00:02<00:00, 81824762.39it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 91%|█████████▏| 155877376/170498071 [00:02<00:00, 80442403.29it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 96%|█████████▋| 164397056/170498071 [00:02<00:00, 81837467.80it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:03<00:00, 56242831.52it/s]" ] }, { @@ -4045,10 +4093,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:08.696598Z", - "iopub.status.busy": "2024-07-02T12:05:08.696059Z", - "iopub.status.idle": "2024-07-02T12:05:08.763426Z", - "shell.execute_reply": "2024-07-02T12:05:08.762929Z" + "iopub.execute_input": "2024-07-02T15:14:35.261268Z", + "iopub.status.busy": "2024-07-02T15:14:35.260525Z", + "iopub.status.idle": "2024-07-02T15:14:35.329313Z", + "shell.execute_reply": "2024-07-02T15:14:35.328751Z" } }, "outputs": [], @@ -4070,10 +4118,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:08.765613Z", - "iopub.status.busy": "2024-07-02T12:05:08.765285Z", - "iopub.status.idle": "2024-07-02T12:05:08.805806Z", - "shell.execute_reply": "2024-07-02T12:05:08.805281Z" + "iopub.execute_input": "2024-07-02T15:14:35.331778Z", + "iopub.status.busy": "2024-07-02T15:14:35.331348Z", + "iopub.status.idle": "2024-07-02T15:14:35.379236Z", + "shell.execute_reply": "2024-07-02T15:14:35.378772Z" } }, "outputs": [], @@ -4107,10 +4155,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:08.807933Z", - "iopub.status.busy": "2024-07-02T12:05:08.807600Z", - "iopub.status.idle": "2024-07-02T12:05:10.199005Z", - "shell.execute_reply": "2024-07-02T12:05:10.198447Z" + "iopub.execute_input": "2024-07-02T15:14:35.381457Z", + "iopub.status.busy": "2024-07-02T15:14:35.381124Z", + "iopub.status.idle": "2024-07-02T15:14:36.847060Z", + "shell.execute_reply": "2024-07-02T15:14:36.846506Z" } }, "outputs": [ @@ -4184,10 +4232,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:10.201199Z", - "iopub.status.busy": "2024-07-02T12:05:10.200858Z", - "iopub.status.idle": "2024-07-02T12:05:10.987916Z", - "shell.execute_reply": "2024-07-02T12:05:10.987295Z" + "iopub.execute_input": "2024-07-02T15:14:36.849295Z", + "iopub.status.busy": "2024-07-02T15:14:36.848928Z", + "iopub.status.idle": "2024-07-02T15:14:37.628043Z", + "shell.execute_reply": "2024-07-02T15:14:37.627421Z" } }, "outputs": [ @@ -4202,7 +4250,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab730d681373436cbffc495350a9abe1", + "model_id": "edeb0eb92f8e493694db63fbedcce068", "version_major": 2, "version_minor": 0 }, @@ -4226,7 +4274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e44decacc70f4d08b59475e297136aab", + "model_id": "a53db32e879b421b9c5a2cb90345b461", "version_major": 2, "version_minor": 0 }, @@ -4476,30 +4524,41 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "06e95a0f1df9408095248eef0924c604": { + "0ba1b4f02e98442dbf83a9d402d61603": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5fccbfa0a7a94b55a6825fc52ecdeee3", - "placeholder": "​", - "style": "IPY_MODEL_9d67c6a8b80b4718975da970d5ba6be1", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 725.51it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1245fefd15c748ca9a6c437e90990634": { + "13f79e5c34544a20b6e43544e002e0d6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "21919d64401941e69bdd1e7fdeb39391": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4552,7 +4611,30 @@ "width": null } }, - "22612fb7095f4323876a32fa6832ebee": { + "22dce5e6cbbd456899db36ca71231b83": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_246224e7d6e14ee996208e5a901506a3", + "placeholder": "​", + "style": "IPY_MODEL_669c9d816b274632945703bb33ea88b1", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "246224e7d6e14ee996208e5a901506a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4605,23 +4687,7 @@ "width": null } }, - "2ce33b586399430db7231ec582a8ad1c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "302d670260304f5d973a1863227c2b38": { + "49bd46daef6e4afaae2104d6fddc5eff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4674,7 +4740,7 @@ "width": null } }, - "34fad403248e49fb9d7ed5541db4875e": { + "5f4143d1143347bf8d67acbd62e4c7a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4690,53 +4756,7 @@ "description_width": "" } }, - "37657cc47549425e81123fbc00061dcd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_57d53163a3e24cfb8adf32a3c2859334", - "placeholder": "​", - "style": "IPY_MODEL_d6c64d036d3c464bba338c11b7d7e118", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "3f75258f70194866856b4da554e4dbeb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1245fefd15c748ca9a6c437e90990634", - "placeholder": "​", - "style": "IPY_MODEL_4c9fcf59ee52451aad0a525849ecf86b", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "440b53038a3d4c4c964a83e8b710361f": { + "5fe4a241c042442299e152f3905b32e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4789,7 +4809,7 @@ "width": null } }, - "4c9fcf59ee52451aad0a525849ecf86b": { + "669c9d816b274632945703bb33ea88b1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4807,7 +4827,30 @@ "text_color": null } }, - "57d53163a3e24cfb8adf32a3c2859334": { + "752199f5b1064d09a833e6e140acf999": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_21919d64401941e69bdd1e7fdeb39391", + "placeholder": "​", + "style": "IPY_MODEL_b2c4bbe171d649419f7ff67ac4f27ab6", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 781.88it/s]" + } + }, + "7f367a2cdd5445f58aecb1320024dca9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4860,7 +4903,7 @@ "width": null } }, - "5fccbfa0a7a94b55a6825fc52ecdeee3": { + "945bafb17f6c4017b07c073239844118": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4913,30 +4956,31 @@ "width": null } }, - "6bdd7248294f4094a2da7c7af2e67e50": { + "a53db32e879b421b9c5a2cb90345b461": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d6941ea7ad6a41efb80f48dde9923682", - "placeholder": "​", - "style": "IPY_MODEL_a55c5a0d7aca4c16a982994a5595ca08", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ae7e7acb2667481d93c3d5d070d947f1", + "IPY_MODEL_d03e8f0da10e418392f2df6f61dea5ed", + "IPY_MODEL_752199f5b1064d09a833e6e140acf999" + ], + "layout": "IPY_MODEL_5fe4a241c042442299e152f3905b32e6", "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 811.85it/s]" + "tooltip": null } }, - "797a5104afa24ca5b172ddc308a704ec": { + "a6d4bb6587dc4b0ab299cde66d887195": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4989,25 +5033,30 @@ "width": null } }, - "9d67c6a8b80b4718975da970d5ba6be1": { + "ae7e7acb2667481d93c3d5d070d947f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_945bafb17f6c4017b07c073239844118", + "placeholder": "​", + "style": "IPY_MODEL_0ba1b4f02e98442dbf83a9d402d61603", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "a55c5a0d7aca4c16a982994a5595ca08": { + "b2c4bbe171d649419f7ff67ac4f27ab6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5025,31 +5074,30 @@ "text_color": null } }, - "ab730d681373436cbffc495350a9abe1": { + "b7a191fc264f425c94ccbd4b2e6ff5bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_37657cc47549425e81123fbc00061dcd", - "IPY_MODEL_ccd3930d3b25423fb8d520dc87205752", - "IPY_MODEL_6bdd7248294f4094a2da7c7af2e67e50" - ], - "layout": "IPY_MODEL_440b53038a3d4c4c964a83e8b710361f", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d1fa249a3b3741948f9b90a3eba494cd", + "placeholder": "​", + "style": "IPY_MODEL_df530a10186c40c8b9ba0ace062c0018", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 200/200 [00:00<00:00, 806.22it/s]" } }, - "ccd3930d3b25423fb8d520dc87205752": { + "ba0f29fa569646e89dd03db3974a4a00": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5065,17 +5113,43 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_302d670260304f5d973a1863227c2b38", + "layout": "IPY_MODEL_7f367a2cdd5445f58aecb1320024dca9", "max": 200.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2ce33b586399430db7231ec582a8ad1c", + "style": "IPY_MODEL_5f4143d1143347bf8d67acbd62e4c7a9", "tabbable": null, "tooltip": null, "value": 200.0 } }, - "d6941ea7ad6a41efb80f48dde9923682": { + "d03e8f0da10e418392f2df6f61dea5ed": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_49bd46daef6e4afaae2104d6fddc5eff", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_13f79e5c34544a20b6e43544e002e0d6", + "tabbable": null, + "tooltip": null, + "value": 200.0 + } + }, + "d1fa249a3b3741948f9b90a3eba494cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5128,7 +5202,7 @@ "width": null } }, - "d6c64d036d3c464bba338c11b7d7e118": { + "df530a10186c40c8b9ba0ace062c0018": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5146,7 +5220,7 @@ "text_color": null } }, - "e44decacc70f4d08b59475e297136aab": { + "edeb0eb92f8e493694db63fbedcce068": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5161,40 +5235,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3f75258f70194866856b4da554e4dbeb", - "IPY_MODEL_e621caf6c19d4d638ba32cd7caed9a15", - "IPY_MODEL_06e95a0f1df9408095248eef0924c604" + "IPY_MODEL_22dce5e6cbbd456899db36ca71231b83", + "IPY_MODEL_ba0f29fa569646e89dd03db3974a4a00", + "IPY_MODEL_b7a191fc264f425c94ccbd4b2e6ff5bf" ], - "layout": "IPY_MODEL_22612fb7095f4323876a32fa6832ebee", + "layout": "IPY_MODEL_a6d4bb6587dc4b0ab299cde66d887195", "tabbable": null, "tooltip": null } - }, - "e621caf6c19d4d638ba32cd7caed9a15": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_797a5104afa24ca5b172ddc308a704ec", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_34fad403248e49fb9d7ed5541db4875e", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 05afc2f2e..46444aea9 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:14.883207Z", - "iopub.status.busy": "2024-07-02T12:05:14.882732Z", - "iopub.status.idle": "2024-07-02T12:05:15.976658Z", - "shell.execute_reply": "2024-07-02T12:05:15.976156Z" + "iopub.execute_input": "2024-07-02T15:14:41.637741Z", + "iopub.status.busy": "2024-07-02T15:14:41.637272Z", + "iopub.status.idle": "2024-07-02T15:14:42.748122Z", + "shell.execute_reply": "2024-07-02T15:14:42.747575Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:15.979210Z", - "iopub.status.busy": "2024-07-02T12:05:15.978822Z", - "iopub.status.idle": "2024-07-02T12:05:15.981689Z", - "shell.execute_reply": "2024-07-02T12:05:15.981162Z" + "iopub.execute_input": "2024-07-02T15:14:42.750701Z", + "iopub.status.busy": "2024-07-02T15:14:42.750299Z", + "iopub.status.idle": "2024-07-02T15:14:42.753136Z", + "shell.execute_reply": "2024-07-02T15:14:42.752592Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:15.983805Z", - "iopub.status.busy": "2024-07-02T12:05:15.983602Z", - "iopub.status.idle": "2024-07-02T12:05:15.994757Z", - "shell.execute_reply": "2024-07-02T12:05:15.994295Z" + "iopub.execute_input": "2024-07-02T15:14:42.755371Z", + "iopub.status.busy": "2024-07-02T15:14:42.755052Z", + "iopub.status.idle": "2024-07-02T15:14:42.766462Z", + "shell.execute_reply": "2024-07-02T15:14:42.766035Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:15.996851Z", - "iopub.status.busy": "2024-07-02T12:05:15.996526Z", - "iopub.status.idle": "2024-07-02T12:05:19.883673Z", - "shell.execute_reply": "2024-07-02T12:05:19.883072Z" + "iopub.execute_input": "2024-07-02T15:14:42.768527Z", + "iopub.status.busy": "2024-07-02T15:14:42.768199Z", + "iopub.status.idle": "2024-07-02T15:14:48.317038Z", + "shell.execute_reply": "2024-07-02T15:14:48.316439Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 964629f99..d639cd18b 100644 --- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:21.944164Z", - "iopub.status.busy": "2024-07-02T12:05:21.943684Z", - "iopub.status.idle": "2024-07-02T12:05:23.029911Z", - "shell.execute_reply": "2024-07-02T12:05:23.029367Z" + "iopub.execute_input": "2024-07-02T15:14:50.408143Z", + "iopub.status.busy": "2024-07-02T15:14:50.407965Z", + "iopub.status.idle": "2024-07-02T15:14:51.502266Z", + "shell.execute_reply": "2024-07-02T15:14:51.501686Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:23.032775Z", - "iopub.status.busy": "2024-07-02T12:05:23.032157Z", - "iopub.status.idle": "2024-07-02T12:05:23.035645Z", - "shell.execute_reply": "2024-07-02T12:05:23.035092Z" + "iopub.execute_input": "2024-07-02T15:14:51.504987Z", + "iopub.status.busy": "2024-07-02T15:14:51.504521Z", + "iopub.status.idle": "2024-07-02T15:14:51.507736Z", + "shell.execute_reply": "2024-07-02T15:14:51.507307Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:23.037598Z", - "iopub.status.busy": "2024-07-02T12:05:23.037330Z", - "iopub.status.idle": "2024-07-02T12:05:26.140141Z", - "shell.execute_reply": "2024-07-02T12:05:26.139387Z" + "iopub.execute_input": "2024-07-02T15:14:51.509847Z", + "iopub.status.busy": "2024-07-02T15:14:51.509517Z", + "iopub.status.idle": "2024-07-02T15:14:54.665499Z", + "shell.execute_reply": "2024-07-02T15:14:54.664870Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.143157Z", - "iopub.status.busy": "2024-07-02T12:05:26.142519Z", - "iopub.status.idle": "2024-07-02T12:05:26.174588Z", - "shell.execute_reply": "2024-07-02T12:05:26.174022Z" + "iopub.execute_input": "2024-07-02T15:14:54.668720Z", + "iopub.status.busy": "2024-07-02T15:14:54.667931Z", + "iopub.status.idle": "2024-07-02T15:14:54.700443Z", + "shell.execute_reply": "2024-07-02T15:14:54.699878Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.177140Z", - "iopub.status.busy": "2024-07-02T12:05:26.176847Z", - "iopub.status.idle": "2024-07-02T12:05:26.205277Z", - "shell.execute_reply": "2024-07-02T12:05:26.204606Z" + "iopub.execute_input": "2024-07-02T15:14:54.702890Z", + "iopub.status.busy": "2024-07-02T15:14:54.702662Z", + "iopub.status.idle": "2024-07-02T15:14:54.732809Z", + "shell.execute_reply": "2024-07-02T15:14:54.732249Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.208173Z", - "iopub.status.busy": "2024-07-02T12:05:26.207802Z", - "iopub.status.idle": "2024-07-02T12:05:26.210662Z", - "shell.execute_reply": "2024-07-02T12:05:26.210230Z" + "iopub.execute_input": "2024-07-02T15:14:54.735364Z", + "iopub.status.busy": "2024-07-02T15:14:54.735130Z", + "iopub.status.idle": "2024-07-02T15:14:54.738210Z", + "shell.execute_reply": "2024-07-02T15:14:54.737649Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.212655Z", - "iopub.status.busy": "2024-07-02T12:05:26.212352Z", - "iopub.status.idle": "2024-07-02T12:05:26.214801Z", - "shell.execute_reply": "2024-07-02T12:05:26.214383Z" + "iopub.execute_input": "2024-07-02T15:14:54.740326Z", + "iopub.status.busy": "2024-07-02T15:14:54.739950Z", + "iopub.status.idle": "2024-07-02T15:14:54.742624Z", + "shell.execute_reply": "2024-07-02T15:14:54.742090Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.216825Z", - "iopub.status.busy": "2024-07-02T12:05:26.216567Z", - "iopub.status.idle": "2024-07-02T12:05:26.239503Z", - "shell.execute_reply": "2024-07-02T12:05:26.238989Z" + "iopub.execute_input": "2024-07-02T15:14:54.744773Z", + "iopub.status.busy": "2024-07-02T15:14:54.744389Z", + "iopub.status.idle": "2024-07-02T15:14:54.767963Z", + "shell.execute_reply": "2024-07-02T15:14:54.767405Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3fbed235b41419c8dcc7c6dc31f69a4", + "model_id": "0af6d2097bac4b69850c70d9d5904db8", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55f5d02e58414e189c4d35720f6593e4", + "model_id": "5e3107780da94917a2e6e00a57affa5f", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.245285Z", - "iopub.status.busy": "2024-07-02T12:05:26.244763Z", - "iopub.status.idle": "2024-07-02T12:05:26.251470Z", - "shell.execute_reply": "2024-07-02T12:05:26.251055Z" + "iopub.execute_input": "2024-07-02T15:14:54.773314Z", + "iopub.status.busy": "2024-07-02T15:14:54.772881Z", + "iopub.status.idle": "2024-07-02T15:14:54.779377Z", + "shell.execute_reply": "2024-07-02T15:14:54.778951Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.253486Z", - "iopub.status.busy": "2024-07-02T12:05:26.253192Z", - "iopub.status.idle": "2024-07-02T12:05:26.256606Z", - "shell.execute_reply": "2024-07-02T12:05:26.256082Z" + "iopub.execute_input": "2024-07-02T15:14:54.781444Z", + "iopub.status.busy": "2024-07-02T15:14:54.781005Z", + "iopub.status.idle": "2024-07-02T15:14:54.784548Z", + "shell.execute_reply": "2024-07-02T15:14:54.784032Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.258538Z", - "iopub.status.busy": "2024-07-02T12:05:26.258279Z", - "iopub.status.idle": "2024-07-02T12:05:26.264446Z", - "shell.execute_reply": "2024-07-02T12:05:26.264008Z" + "iopub.execute_input": "2024-07-02T15:14:54.786545Z", + "iopub.status.busy": "2024-07-02T15:14:54.786237Z", + "iopub.status.idle": "2024-07-02T15:14:54.792562Z", + "shell.execute_reply": "2024-07-02T15:14:54.792035Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.266379Z", - "iopub.status.busy": "2024-07-02T12:05:26.266007Z", - "iopub.status.idle": "2024-07-02T12:05:26.301431Z", - "shell.execute_reply": "2024-07-02T12:05:26.300735Z" + "iopub.execute_input": "2024-07-02T15:14:54.794631Z", + "iopub.status.busy": "2024-07-02T15:14:54.794246Z", + "iopub.status.idle": "2024-07-02T15:14:54.828207Z", + "shell.execute_reply": "2024-07-02T15:14:54.827507Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.304129Z", - "iopub.status.busy": "2024-07-02T12:05:26.303754Z", - "iopub.status.idle": "2024-07-02T12:05:26.336384Z", - "shell.execute_reply": "2024-07-02T12:05:26.335710Z" + "iopub.execute_input": "2024-07-02T15:14:54.830484Z", + "iopub.status.busy": "2024-07-02T15:14:54.830261Z", + "iopub.status.idle": "2024-07-02T15:14:54.860257Z", + "shell.execute_reply": "2024-07-02T15:14:54.859690Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.339079Z", - "iopub.status.busy": "2024-07-02T12:05:26.338735Z", - "iopub.status.idle": "2024-07-02T12:05:26.455537Z", - "shell.execute_reply": "2024-07-02T12:05:26.454922Z" + "iopub.execute_input": "2024-07-02T15:14:54.863007Z", + "iopub.status.busy": "2024-07-02T15:14:54.862546Z", + "iopub.status.idle": "2024-07-02T15:14:54.984115Z", + "shell.execute_reply": "2024-07-02T15:14:54.983484Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:26.458378Z", - "iopub.status.busy": "2024-07-02T12:05:26.457687Z", - "iopub.status.idle": "2024-07-02T12:05:29.464168Z", - "shell.execute_reply": "2024-07-02T12:05:29.463628Z" + "iopub.execute_input": "2024-07-02T15:14:54.986796Z", + "iopub.status.busy": "2024-07-02T15:14:54.986226Z", + "iopub.status.idle": "2024-07-02T15:14:58.051483Z", + "shell.execute_reply": "2024-07-02T15:14:58.050808Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.466470Z", - "iopub.status.busy": "2024-07-02T12:05:29.466106Z", - "iopub.status.idle": "2024-07-02T12:05:29.522164Z", - "shell.execute_reply": "2024-07-02T12:05:29.521722Z" + "iopub.execute_input": "2024-07-02T15:14:58.053810Z", + "iopub.status.busy": "2024-07-02T15:14:58.053595Z", + "iopub.status.idle": "2024-07-02T15:14:58.112425Z", + "shell.execute_reply": "2024-07-02T15:14:58.111852Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.524149Z", - "iopub.status.busy": "2024-07-02T12:05:29.523825Z", - "iopub.status.idle": "2024-07-02T12:05:29.563088Z", - "shell.execute_reply": "2024-07-02T12:05:29.562637Z" + "iopub.execute_input": "2024-07-02T15:14:58.114567Z", + "iopub.status.busy": "2024-07-02T15:14:58.114179Z", + "iopub.status.idle": "2024-07-02T15:14:58.154327Z", + "shell.execute_reply": "2024-07-02T15:14:58.153741Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "c8a16553", + "id": "50482bad", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "fae60230", + "id": "07405bb8", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "9569bf2b", + "id": "f375f11d", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "570b1222", + "id": "ada84c58", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.565181Z", - "iopub.status.busy": "2024-07-02T12:05:29.564854Z", - "iopub.status.idle": "2024-07-02T12:05:29.572447Z", - "shell.execute_reply": "2024-07-02T12:05:29.571983Z" + "iopub.execute_input": "2024-07-02T15:14:58.156555Z", + "iopub.status.busy": "2024-07-02T15:14:58.156257Z", + "iopub.status.idle": "2024-07-02T15:14:58.163817Z", + "shell.execute_reply": "2024-07-02T15:14:58.163319Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "a87b6fe0", + "id": "13fb70ab", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "26953078", + "id": "692524aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.574436Z", - "iopub.status.busy": "2024-07-02T12:05:29.574108Z", - "iopub.status.idle": "2024-07-02T12:05:29.592051Z", - "shell.execute_reply": "2024-07-02T12:05:29.591598Z" + "iopub.execute_input": "2024-07-02T15:14:58.165738Z", + "iopub.status.busy": "2024-07-02T15:14:58.165567Z", + "iopub.status.idle": "2024-07-02T15:14:58.184376Z", + "shell.execute_reply": "2024-07-02T15:14:58.183834Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "948c6a32", + "id": "c63f4c73", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:29.594121Z", - "iopub.status.busy": "2024-07-02T12:05:29.593804Z", - "iopub.status.idle": "2024-07-02T12:05:29.596796Z", - "shell.execute_reply": "2024-07-02T12:05:29.596261Z" + "iopub.execute_input": "2024-07-02T15:14:58.186438Z", + "iopub.status.busy": "2024-07-02T15:14:58.186138Z", + "iopub.status.idle": "2024-07-02T15:14:58.189362Z", + "shell.execute_reply": "2024-07-02T15:14:58.188854Z" } }, "outputs": [ @@ -1622,25 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1e8b9b429c6a4df5b632d8335fdb02e7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "31b4169790de40918177589ab5b35e53": { + "03bdac28b0744e63957425aef141438c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1693,39 +1675,31 @@ "width": null } }, - "3e0c64c5666d42f5a0006507f8bef3cf": { + "0af6d2097bac4b69850c70d9d5904db8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "42ef207d69534acdbaf463021cfc93cf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b05e5e24c03d4b8393b6fbc900c09846", + "IPY_MODEL_1a648d3a4fa24399916eb3b310f94eb1", + "IPY_MODEL_1d20270beb494280ba9716e526d53f5b" + ], + "layout": "IPY_MODEL_162ace65984b48448d278f7f058df9b9", + "tabbable": null, + "tooltip": null } }, - "47199e38a1de47d2b40f863611c9c287": { + "162ace65984b48448d278f7f058df9b9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1778,25 +1752,56 @@ "width": null } }, - "507bd342f43644e28c3e257c443121b3": { + "1a648d3a4fa24399916eb3b310f94eb1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2fc72a84067d46678a572aa49ebcbfee", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d390dd0db893478398a63bc195614ea3", + "tabbable": null, + "tooltip": null, + "value": 50.0 } }, - "546f976ecd3443c7ae6b00cfbd3063d7": { + "1d20270beb494280ba9716e526d53f5b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_03bdac28b0744e63957425aef141438c", + "placeholder": "​", + "style": "IPY_MODEL_d92d13f5147b4721964e817fcd0e6a7b", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1078393.58it/s]" + } + }, + "1edeca25122644478494bfcfeda4647f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1849,31 +1854,23 @@ "width": null } }, - "55f5d02e58414e189c4d35720f6593e4": { + "28fb5636d77344d0af4d56b62998af86": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f01dfba04fcd45ceb75c23f67e8886c7", - "IPY_MODEL_617a9f7f01f040228329a5ec756d97f6", - "IPY_MODEL_ed7c570506e6416fb02ab5e72e3ceb03" - ], - "layout": "IPY_MODEL_829e711fb4284e36a06bf2a1d8c1d975", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "60269b97e2604128baf4ce6cdc816ee4": { + "2fc72a84067d46678a572aa49ebcbfee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1926,59 +1923,72 @@ "width": null } }, - "6135cc292d2a4431bf055b0d0936e234": { + "46803bc4f974489e87287e18bb02597e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_60269b97e2604128baf4ce6cdc816ee4", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3e0c64c5666d42f5a0006507f8bef3cf", + "layout": "IPY_MODEL_ee2eaee4046a40398bf58b1b4a9e3ebc", + "placeholder": "​", + "style": "IPY_MODEL_65acdb25c5c948c9848681db00fdc46b", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": "number of examples processed for checking labels: " } }, - "617a9f7f01f040228329a5ec756d97f6": { + "5e3107780da94917a2e6e00a57affa5f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ba0bb27ac92840eeb82c447bf3772478", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_42ef207d69534acdbaf463021cfc93cf", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_46803bc4f974489e87287e18bb02597e", + "IPY_MODEL_d31a1289a39a46899da8b556759347e9", + "IPY_MODEL_6e15813423a14f6c9174690b8907c81c" + ], + "layout": "IPY_MODEL_1edeca25122644478494bfcfeda4647f", "tabbable": null, - "tooltip": null, - "value": 50.0 + "tooltip": null + } + }, + "65acdb25c5c948c9848681db00fdc46b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "73aeda6759084147870440cb627c1d38": { + "6e15813423a14f6c9174690b8907c81c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1993,15 +2003,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b60d3a9c115940b998532778042a9156", + "layout": "IPY_MODEL_ac3767a4657d48b4a0218f3167e710d9", "placeholder": "​", - "style": "IPY_MODEL_1e8b9b429c6a4df5b632d8335fdb02e7", + "style": "IPY_MODEL_f0f4c38d78db4623a4f8710c6f263222", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1101879.42it/s]" + "value": " 10000/? [00:00<00:00, 1603327.22it/s]" } }, - "829e711fb4284e36a06bf2a1d8c1d975": { + "a9d39a3ea28d44c682f45f076bc8bd67": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2054,25 +2064,60 @@ "width": null } }, - "894752d3c3ad46abbf5f852be62b9157": { - "model_module": "@jupyter-widgets/controls", + "ac3767a4657d48b4a0218f3167e710d9": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "94d1e49e872241989a3aaf081a4914f3": { + "b05e5e24c03d4b8393b6fbc900c09846": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2087,57 +2132,57 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_546f976ecd3443c7ae6b00cfbd3063d7", + "layout": "IPY_MODEL_d697f27b641d4fa4a8cece8123222bff", "placeholder": "​", - "style": "IPY_MODEL_894752d3c3ad46abbf5f852be62b9157", + "style": "IPY_MODEL_eddf7a5ddfe14a858eb2826e37c0b231", "tabbable": null, "tooltip": null, "value": "number of examples processed for estimating thresholds: " } }, - "b3fbed235b41419c8dcc7c6dc31f69a4": { + "d31a1289a39a46899da8b556759347e9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_94d1e49e872241989a3aaf081a4914f3", - "IPY_MODEL_6135cc292d2a4431bf055b0d0936e234", - "IPY_MODEL_73aeda6759084147870440cb627c1d38" - ], - "layout": "IPY_MODEL_47199e38a1de47d2b40f863611c9c287", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a9d39a3ea28d44c682f45f076bc8bd67", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_28fb5636d77344d0af4d56b62998af86", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 50.0 } }, - "b60c569797aa438d8c67c0007154831b": { + "d390dd0db893478398a63bc195614ea3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "b60d3a9c115940b998532778042a9156": { + "d697f27b641d4fa4a8cece8123222bff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2190,60 +2235,43 @@ "width": null } }, - "ba0bb27ac92840eeb82c447bf3772478": { - "model_module": "@jupyter-widgets/base", + "d92d13f5147b4721964e817fcd0e6a7b": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "d554d48cbc1b4a5caca9da8c04018917": { + "eddf7a5ddfe14a858eb2826e37c0b231": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ee2eaee4046a40398bf58b1b4a9e3ebc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2296,50 +2324,22 @@ "width": null } }, - "ed7c570506e6416fb02ab5e72e3ceb03": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_31b4169790de40918177589ab5b35e53", - "placeholder": "​", - "style": "IPY_MODEL_b60c569797aa438d8c67c0007154831b", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1638080.06it/s]" - } - }, - "f01dfba04fcd45ceb75c23f67e8886c7": { + "f0f4c38d78db4623a4f8710c6f263222": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d554d48cbc1b4a5caca9da8c04018917", - "placeholder": "​", - "style": "IPY_MODEL_507bd342f43644e28c3e257c443121b3", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 31db58268..6e9b55b48 100644 --- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:32.646814Z", - "iopub.status.busy": "2024-07-02T12:05:32.646634Z", - "iopub.status.idle": "2024-07-02T12:05:33.799016Z", - "shell.execute_reply": "2024-07-02T12:05:33.798421Z" + "iopub.execute_input": "2024-07-02T15:15:01.547795Z", + "iopub.status.busy": "2024-07-02T15:15:01.547635Z", + "iopub.status.idle": "2024-07-02T15:15:02.724422Z", + "shell.execute_reply": "2024-07-02T15:15:02.723868Z" }, "nbsphinx": "hidden" }, @@ -68,7 +68,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:33.801518Z", - "iopub.status.busy": "2024-07-02T12:05:33.801117Z", - "iopub.status.idle": "2024-07-02T12:05:33.979293Z", - "shell.execute_reply": "2024-07-02T12:05:33.978808Z" + "iopub.execute_input": "2024-07-02T15:15:02.727054Z", + "iopub.status.busy": "2024-07-02T15:15:02.726599Z", + "iopub.status.idle": "2024-07-02T15:15:02.907470Z", + "shell.execute_reply": "2024-07-02T15:15:02.906926Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:33.981747Z", - "iopub.status.busy": "2024-07-02T12:05:33.981411Z", - "iopub.status.idle": "2024-07-02T12:05:33.992581Z", - "shell.execute_reply": "2024-07-02T12:05:33.992150Z" + "iopub.execute_input": "2024-07-02T15:15:02.909852Z", + "iopub.status.busy": "2024-07-02T15:15:02.909658Z", + "iopub.status.idle": "2024-07-02T15:15:02.920956Z", + "shell.execute_reply": "2024-07-02T15:15:02.920549Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:33.994624Z", - "iopub.status.busy": "2024-07-02T12:05:33.994295Z", - "iopub.status.idle": "2024-07-02T12:05:34.203292Z", - "shell.execute_reply": "2024-07-02T12:05:34.202749Z" + "iopub.execute_input": "2024-07-02T15:15:02.923032Z", + "iopub.status.busy": "2024-07-02T15:15:02.922709Z", + "iopub.status.idle": "2024-07-02T15:15:03.157261Z", + "shell.execute_reply": "2024-07-02T15:15:03.156698Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:34.205578Z", - "iopub.status.busy": "2024-07-02T12:05:34.205242Z", - "iopub.status.idle": "2024-07-02T12:05:34.231392Z", - "shell.execute_reply": "2024-07-02T12:05:34.230966Z" + "iopub.execute_input": "2024-07-02T15:15:03.159542Z", + "iopub.status.busy": "2024-07-02T15:15:03.159306Z", + "iopub.status.idle": "2024-07-02T15:15:03.185836Z", + "shell.execute_reply": "2024-07-02T15:15:03.185396Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:34.233560Z", - "iopub.status.busy": "2024-07-02T12:05:34.233135Z", - "iopub.status.idle": "2024-07-02T12:05:36.181908Z", - "shell.execute_reply": "2024-07-02T12:05:36.181255Z" + "iopub.execute_input": "2024-07-02T15:15:03.188049Z", + "iopub.status.busy": "2024-07-02T15:15:03.187618Z", + "iopub.status.idle": "2024-07-02T15:15:05.211831Z", + "shell.execute_reply": "2024-07-02T15:15:05.211148Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:36.184389Z", - "iopub.status.busy": "2024-07-02T12:05:36.183843Z", - "iopub.status.idle": "2024-07-02T12:05:36.201856Z", - "shell.execute_reply": "2024-07-02T12:05:36.201294Z" + "iopub.execute_input": "2024-07-02T15:15:05.214216Z", + "iopub.status.busy": "2024-07-02T15:15:05.213865Z", + "iopub.status.idle": "2024-07-02T15:15:05.231692Z", + "shell.execute_reply": "2024-07-02T15:15:05.231165Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:36.204241Z", - "iopub.status.busy": "2024-07-02T12:05:36.203963Z", - "iopub.status.idle": "2024-07-02T12:05:37.598285Z", - "shell.execute_reply": "2024-07-02T12:05:37.597675Z" + "iopub.execute_input": "2024-07-02T15:15:05.233970Z", + "iopub.status.busy": "2024-07-02T15:15:05.233542Z", + "iopub.status.idle": "2024-07-02T15:15:06.669686Z", + "shell.execute_reply": "2024-07-02T15:15:06.669077Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.600758Z", - "iopub.status.busy": "2024-07-02T12:05:37.600219Z", - "iopub.status.idle": "2024-07-02T12:05:37.613480Z", - "shell.execute_reply": "2024-07-02T12:05:37.612921Z" + "iopub.execute_input": "2024-07-02T15:15:06.672583Z", + "iopub.status.busy": "2024-07-02T15:15:06.671803Z", + "iopub.status.idle": "2024-07-02T15:15:06.685525Z", + "shell.execute_reply": "2024-07-02T15:15:06.685058Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.615558Z", - "iopub.status.busy": "2024-07-02T12:05:37.615275Z", - "iopub.status.idle": "2024-07-02T12:05:37.682573Z", - "shell.execute_reply": "2024-07-02T12:05:37.681981Z" + "iopub.execute_input": "2024-07-02T15:15:06.687638Z", + "iopub.status.busy": "2024-07-02T15:15:06.687306Z", + "iopub.status.idle": "2024-07-02T15:15:06.760352Z", + "shell.execute_reply": "2024-07-02T15:15:06.759817Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.685019Z", - "iopub.status.busy": "2024-07-02T12:05:37.684694Z", - "iopub.status.idle": "2024-07-02T12:05:37.893897Z", - "shell.execute_reply": "2024-07-02T12:05:37.893417Z" + "iopub.execute_input": "2024-07-02T15:15:06.762567Z", + "iopub.status.busy": "2024-07-02T15:15:06.762336Z", + "iopub.status.idle": "2024-07-02T15:15:06.973074Z", + "shell.execute_reply": "2024-07-02T15:15:06.972522Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.896031Z", - "iopub.status.busy": "2024-07-02T12:05:37.895697Z", - "iopub.status.idle": "2024-07-02T12:05:37.912159Z", - "shell.execute_reply": "2024-07-02T12:05:37.911619Z" + "iopub.execute_input": "2024-07-02T15:15:06.975381Z", + "iopub.status.busy": "2024-07-02T15:15:06.975014Z", + "iopub.status.idle": "2024-07-02T15:15:06.992441Z", + "shell.execute_reply": "2024-07-02T15:15:06.991996Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.914291Z", - "iopub.status.busy": "2024-07-02T12:05:37.913990Z", - "iopub.status.idle": "2024-07-02T12:05:37.923838Z", - "shell.execute_reply": "2024-07-02T12:05:37.923277Z" + "iopub.execute_input": "2024-07-02T15:15:06.994338Z", + "iopub.status.busy": "2024-07-02T15:15:06.994162Z", + "iopub.status.idle": "2024-07-02T15:15:07.004135Z", + "shell.execute_reply": "2024-07-02T15:15:07.003687Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:37.925873Z", - "iopub.status.busy": "2024-07-02T12:05:37.925449Z", - "iopub.status.idle": "2024-07-02T12:05:38.005405Z", - "shell.execute_reply": "2024-07-02T12:05:38.004805Z" + "iopub.execute_input": "2024-07-02T15:15:07.005979Z", + "iopub.status.busy": "2024-07-02T15:15:07.005810Z", + "iopub.status.idle": "2024-07-02T15:15:07.089012Z", + "shell.execute_reply": "2024-07-02T15:15:07.088395Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.007885Z", - "iopub.status.busy": "2024-07-02T12:05:38.007370Z", - "iopub.status.idle": "2024-07-02T12:05:38.126166Z", - "shell.execute_reply": "2024-07-02T12:05:38.125636Z" + "iopub.execute_input": "2024-07-02T15:15:07.091284Z", + "iopub.status.busy": "2024-07-02T15:15:07.091062Z", + "iopub.status.idle": "2024-07-02T15:15:07.217284Z", + "shell.execute_reply": "2024-07-02T15:15:07.216745Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.128463Z", - "iopub.status.busy": "2024-07-02T12:05:38.128096Z", - "iopub.status.idle": "2024-07-02T12:05:38.132029Z", - "shell.execute_reply": "2024-07-02T12:05:38.131380Z" + "iopub.execute_input": "2024-07-02T15:15:07.219493Z", + "iopub.status.busy": "2024-07-02T15:15:07.219260Z", + "iopub.status.idle": "2024-07-02T15:15:07.223285Z", + "shell.execute_reply": "2024-07-02T15:15:07.222834Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.134113Z", - "iopub.status.busy": "2024-07-02T12:05:38.133792Z", - "iopub.status.idle": "2024-07-02T12:05:38.137656Z", - "shell.execute_reply": "2024-07-02T12:05:38.137186Z" + "iopub.execute_input": "2024-07-02T15:15:07.225147Z", + "iopub.status.busy": "2024-07-02T15:15:07.224971Z", + "iopub.status.idle": "2024-07-02T15:15:07.228887Z", + "shell.execute_reply": "2024-07-02T15:15:07.228428Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.139628Z", - "iopub.status.busy": "2024-07-02T12:05:38.139306Z", - "iopub.status.idle": "2024-07-02T12:05:38.175873Z", - "shell.execute_reply": "2024-07-02T12:05:38.175335Z" + "iopub.execute_input": "2024-07-02T15:15:07.231022Z", + "iopub.status.busy": "2024-07-02T15:15:07.230634Z", + "iopub.status.idle": "2024-07-02T15:15:07.267559Z", + "shell.execute_reply": "2024-07-02T15:15:07.267094Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.177802Z", - "iopub.status.busy": "2024-07-02T12:05:38.177621Z", - "iopub.status.idle": "2024-07-02T12:05:38.222062Z", - "shell.execute_reply": "2024-07-02T12:05:38.221459Z" + "iopub.execute_input": "2024-07-02T15:15:07.269540Z", + "iopub.status.busy": "2024-07-02T15:15:07.269232Z", + "iopub.status.idle": "2024-07-02T15:15:07.311391Z", + "shell.execute_reply": "2024-07-02T15:15:07.310918Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.225715Z", - "iopub.status.busy": "2024-07-02T12:05:38.225497Z", - "iopub.status.idle": "2024-07-02T12:05:38.315625Z", - "shell.execute_reply": "2024-07-02T12:05:38.315082Z" + "iopub.execute_input": "2024-07-02T15:15:07.313490Z", + "iopub.status.busy": "2024-07-02T15:15:07.313161Z", + "iopub.status.idle": "2024-07-02T15:15:07.408862Z", + "shell.execute_reply": "2024-07-02T15:15:07.408302Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.318154Z", - "iopub.status.busy": "2024-07-02T12:05:38.317969Z", - "iopub.status.idle": "2024-07-02T12:05:38.405501Z", - "shell.execute_reply": "2024-07-02T12:05:38.404891Z" + "iopub.execute_input": "2024-07-02T15:15:07.411502Z", + "iopub.status.busy": "2024-07-02T15:15:07.411209Z", + "iopub.status.idle": "2024-07-02T15:15:07.496801Z", + "shell.execute_reply": "2024-07-02T15:15:07.496253Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.407826Z", - "iopub.status.busy": "2024-07-02T12:05:38.407489Z", - "iopub.status.idle": "2024-07-02T12:05:38.614829Z", - "shell.execute_reply": "2024-07-02T12:05:38.614370Z" + "iopub.execute_input": "2024-07-02T15:15:07.499171Z", + "iopub.status.busy": "2024-07-02T15:15:07.498817Z", + "iopub.status.idle": "2024-07-02T15:15:07.704826Z", + "shell.execute_reply": "2024-07-02T15:15:07.704295Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.617073Z", - "iopub.status.busy": "2024-07-02T12:05:38.616735Z", - "iopub.status.idle": "2024-07-02T12:05:38.796547Z", - "shell.execute_reply": "2024-07-02T12:05:38.796035Z" + "iopub.execute_input": "2024-07-02T15:15:07.706982Z", + "iopub.status.busy": "2024-07-02T15:15:07.706641Z", + "iopub.status.idle": "2024-07-02T15:15:07.893000Z", + "shell.execute_reply": "2024-07-02T15:15:07.892303Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.798843Z", - "iopub.status.busy": "2024-07-02T12:05:38.798472Z", - "iopub.status.idle": "2024-07-02T12:05:38.804480Z", - "shell.execute_reply": "2024-07-02T12:05:38.804052Z" + "iopub.execute_input": "2024-07-02T15:15:07.895600Z", + "iopub.status.busy": "2024-07-02T15:15:07.895219Z", + "iopub.status.idle": "2024-07-02T15:15:07.901308Z", + "shell.execute_reply": "2024-07-02T15:15:07.900873Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:38.806348Z", - "iopub.status.busy": "2024-07-02T12:05:38.806175Z", - "iopub.status.idle": "2024-07-02T12:05:39.020330Z", - "shell.execute_reply": "2024-07-02T12:05:39.019866Z" + "iopub.execute_input": "2024-07-02T15:15:07.903351Z", + "iopub.status.busy": "2024-07-02T15:15:07.903038Z", + "iopub.status.idle": "2024-07-02T15:15:08.118284Z", + "shell.execute_reply": "2024-07-02T15:15:08.117695Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:39.022452Z", - "iopub.status.busy": "2024-07-02T12:05:39.022256Z", - "iopub.status.idle": "2024-07-02T12:05:40.077777Z", - "shell.execute_reply": "2024-07-02T12:05:40.077247Z" + "iopub.execute_input": "2024-07-02T15:15:08.120578Z", + "iopub.status.busy": "2024-07-02T15:15:08.120236Z", + "iopub.status.idle": "2024-07-02T15:15:09.203021Z", + "shell.execute_reply": "2024-07-02T15:15:09.202483Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index dfb026440..e3e8817fd 100644 --- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:43.484936Z", - "iopub.status.busy": "2024-07-02T12:05:43.484760Z", - "iopub.status.idle": "2024-07-02T12:05:44.574684Z", - "shell.execute_reply": "2024-07-02T12:05:44.574061Z" + "iopub.execute_input": "2024-07-02T15:15:12.510036Z", + "iopub.status.busy": "2024-07-02T15:15:12.509861Z", + "iopub.status.idle": "2024-07-02T15:15:13.631469Z", + "shell.execute_reply": "2024-07-02T15:15:13.630838Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.577417Z", - "iopub.status.busy": "2024-07-02T12:05:44.576983Z", - "iopub.status.idle": "2024-07-02T12:05:44.579868Z", - "shell.execute_reply": "2024-07-02T12:05:44.579405Z" + "iopub.execute_input": "2024-07-02T15:15:13.634301Z", + "iopub.status.busy": "2024-07-02T15:15:13.633841Z", + "iopub.status.idle": "2024-07-02T15:15:13.636840Z", + "shell.execute_reply": "2024-07-02T15:15:13.636388Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.581906Z", - "iopub.status.busy": "2024-07-02T12:05:44.581588Z", - "iopub.status.idle": "2024-07-02T12:05:44.588930Z", - "shell.execute_reply": "2024-07-02T12:05:44.588511Z" + "iopub.execute_input": "2024-07-02T15:15:13.639070Z", + "iopub.status.busy": "2024-07-02T15:15:13.638755Z", + "iopub.status.idle": "2024-07-02T15:15:13.646413Z", + "shell.execute_reply": "2024-07-02T15:15:13.645954Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.591022Z", - "iopub.status.busy": "2024-07-02T12:05:44.590587Z", - "iopub.status.idle": "2024-07-02T12:05:44.643404Z", - "shell.execute_reply": "2024-07-02T12:05:44.642882Z" + "iopub.execute_input": "2024-07-02T15:15:13.648424Z", + "iopub.status.busy": "2024-07-02T15:15:13.648104Z", + "iopub.status.idle": "2024-07-02T15:15:13.695570Z", + "shell.execute_reply": "2024-07-02T15:15:13.695113Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.645347Z", - "iopub.status.busy": "2024-07-02T12:05:44.645170Z", - "iopub.status.idle": "2024-07-02T12:05:44.661922Z", - "shell.execute_reply": "2024-07-02T12:05:44.661404Z" + "iopub.execute_input": "2024-07-02T15:15:13.697840Z", + "iopub.status.busy": "2024-07-02T15:15:13.697478Z", + "iopub.status.idle": "2024-07-02T15:15:13.714358Z", + "shell.execute_reply": "2024-07-02T15:15:13.713787Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.663786Z", - "iopub.status.busy": "2024-07-02T12:05:44.663593Z", - "iopub.status.idle": "2024-07-02T12:05:44.667360Z", - "shell.execute_reply": "2024-07-02T12:05:44.666837Z" + "iopub.execute_input": "2024-07-02T15:15:13.716418Z", + "iopub.status.busy": "2024-07-02T15:15:13.716235Z", + "iopub.status.idle": "2024-07-02T15:15:13.720328Z", + "shell.execute_reply": "2024-07-02T15:15:13.719874Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.669486Z", - "iopub.status.busy": "2024-07-02T12:05:44.669101Z", - "iopub.status.idle": "2024-07-02T12:05:44.685613Z", - "shell.execute_reply": "2024-07-02T12:05:44.685195Z" + "iopub.execute_input": "2024-07-02T15:15:13.722265Z", + "iopub.status.busy": "2024-07-02T15:15:13.722093Z", + "iopub.status.idle": "2024-07-02T15:15:13.738589Z", + "shell.execute_reply": "2024-07-02T15:15:13.738172Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.687438Z", - "iopub.status.busy": "2024-07-02T12:05:44.687261Z", - "iopub.status.idle": "2024-07-02T12:05:44.713068Z", - "shell.execute_reply": "2024-07-02T12:05:44.712511Z" + "iopub.execute_input": "2024-07-02T15:15:13.740448Z", + "iopub.status.busy": "2024-07-02T15:15:13.740273Z", + "iopub.status.idle": "2024-07-02T15:15:13.766807Z", + "shell.execute_reply": "2024-07-02T15:15:13.766364Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:44.714998Z", - "iopub.status.busy": "2024-07-02T12:05:44.714828Z", - "iopub.status.idle": "2024-07-02T12:05:46.561058Z", - "shell.execute_reply": "2024-07-02T12:05:46.560413Z" + "iopub.execute_input": "2024-07-02T15:15:13.768717Z", + "iopub.status.busy": "2024-07-02T15:15:13.768540Z", + "iopub.status.idle": "2024-07-02T15:15:15.660293Z", + "shell.execute_reply": "2024-07-02T15:15:15.659737Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.563695Z", - "iopub.status.busy": "2024-07-02T12:05:46.563390Z", - "iopub.status.idle": "2024-07-02T12:05:46.570695Z", - "shell.execute_reply": "2024-07-02T12:05:46.570276Z" + "iopub.execute_input": "2024-07-02T15:15:15.663110Z", + "iopub.status.busy": "2024-07-02T15:15:15.662673Z", + "iopub.status.idle": "2024-07-02T15:15:15.669361Z", + "shell.execute_reply": "2024-07-02T15:15:15.668883Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.572666Z", - "iopub.status.busy": "2024-07-02T12:05:46.572452Z", - "iopub.status.idle": "2024-07-02T12:05:46.585257Z", - "shell.execute_reply": "2024-07-02T12:05:46.584820Z" + "iopub.execute_input": "2024-07-02T15:15:15.671496Z", + "iopub.status.busy": "2024-07-02T15:15:15.671115Z", + "iopub.status.idle": "2024-07-02T15:15:15.683951Z", + "shell.execute_reply": "2024-07-02T15:15:15.683520Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.587355Z", - "iopub.status.busy": "2024-07-02T12:05:46.586953Z", - "iopub.status.idle": "2024-07-02T12:05:46.593328Z", - "shell.execute_reply": "2024-07-02T12:05:46.592850Z" + "iopub.execute_input": "2024-07-02T15:15:15.685932Z", + "iopub.status.busy": "2024-07-02T15:15:15.685735Z", + "iopub.status.idle": "2024-07-02T15:15:15.691990Z", + "shell.execute_reply": "2024-07-02T15:15:15.691571Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.595350Z", - "iopub.status.busy": "2024-07-02T12:05:46.595021Z", - "iopub.status.idle": "2024-07-02T12:05:46.597564Z", - "shell.execute_reply": "2024-07-02T12:05:46.597149Z" + "iopub.execute_input": "2024-07-02T15:15:15.693946Z", + "iopub.status.busy": "2024-07-02T15:15:15.693759Z", + "iopub.status.idle": "2024-07-02T15:15:15.696269Z", + "shell.execute_reply": "2024-07-02T15:15:15.695843Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.599508Z", - "iopub.status.busy": "2024-07-02T12:05:46.599184Z", - "iopub.status.idle": "2024-07-02T12:05:46.602546Z", - "shell.execute_reply": "2024-07-02T12:05:46.602058Z" + "iopub.execute_input": "2024-07-02T15:15:15.698086Z", + "iopub.status.busy": "2024-07-02T15:15:15.697916Z", + "iopub.status.idle": "2024-07-02T15:15:15.701287Z", + "shell.execute_reply": "2024-07-02T15:15:15.700768Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.604583Z", - "iopub.status.busy": "2024-07-02T12:05:46.604261Z", - "iopub.status.idle": "2024-07-02T12:05:46.606854Z", - "shell.execute_reply": "2024-07-02T12:05:46.606416Z" + "iopub.execute_input": "2024-07-02T15:15:15.703245Z", + "iopub.status.busy": "2024-07-02T15:15:15.702979Z", + "iopub.status.idle": "2024-07-02T15:15:15.705625Z", + "shell.execute_reply": "2024-07-02T15:15:15.705105Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.608809Z", - "iopub.status.busy": "2024-07-02T12:05:46.608533Z", - "iopub.status.idle": "2024-07-02T12:05:46.612540Z", - "shell.execute_reply": "2024-07-02T12:05:46.612106Z" + "iopub.execute_input": "2024-07-02T15:15:15.707758Z", + "iopub.status.busy": "2024-07-02T15:15:15.707334Z", + "iopub.status.idle": "2024-07-02T15:15:15.711674Z", + "shell.execute_reply": "2024-07-02T15:15:15.711211Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.614617Z", - "iopub.status.busy": "2024-07-02T12:05:46.614295Z", - "iopub.status.idle": "2024-07-02T12:05:46.642333Z", - "shell.execute_reply": "2024-07-02T12:05:46.641923Z" + "iopub.execute_input": "2024-07-02T15:15:15.713628Z", + "iopub.status.busy": "2024-07-02T15:15:15.713453Z", + "iopub.status.idle": "2024-07-02T15:15:15.742599Z", + "shell.execute_reply": "2024-07-02T15:15:15.742060Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:46.644398Z", - "iopub.status.busy": "2024-07-02T12:05:46.644076Z", - "iopub.status.idle": "2024-07-02T12:05:46.648349Z", - "shell.execute_reply": "2024-07-02T12:05:46.647909Z" + "iopub.execute_input": "2024-07-02T15:15:15.744764Z", + "iopub.status.busy": "2024-07-02T15:15:15.744458Z", + "iopub.status.idle": "2024-07-02T15:15:15.749091Z", + "shell.execute_reply": "2024-07-02T15:15:15.748548Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 02d580b54..cd94e39a4 100644 --- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:49.390201Z", - "iopub.status.busy": "2024-07-02T12:05:49.390029Z", - "iopub.status.idle": "2024-07-02T12:05:50.506272Z", - "shell.execute_reply": "2024-07-02T12:05:50.505689Z" + "iopub.execute_input": "2024-07-02T15:15:18.624231Z", + "iopub.status.busy": "2024-07-02T15:15:18.623753Z", + "iopub.status.idle": "2024-07-02T15:15:19.807437Z", + "shell.execute_reply": "2024-07-02T15:15:19.806877Z" }, "nbsphinx": "hidden" }, @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -105,10 +105,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:50.508865Z", - "iopub.status.busy": "2024-07-02T12:05:50.508468Z", - "iopub.status.idle": "2024-07-02T12:05:50.696756Z", - "shell.execute_reply": "2024-07-02T12:05:50.696292Z" + "iopub.execute_input": "2024-07-02T15:15:19.810010Z", + "iopub.status.busy": "2024-07-02T15:15:19.809534Z", + "iopub.status.idle": "2024-07-02T15:15:20.005847Z", + "shell.execute_reply": "2024-07-02T15:15:20.005329Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:50.698941Z", - "iopub.status.busy": "2024-07-02T12:05:50.698699Z", - "iopub.status.idle": "2024-07-02T12:05:50.711704Z", - "shell.execute_reply": "2024-07-02T12:05:50.711226Z" + "iopub.execute_input": "2024-07-02T15:15:20.008548Z", + "iopub.status.busy": "2024-07-02T15:15:20.008063Z", + "iopub.status.idle": "2024-07-02T15:15:20.021462Z", + "shell.execute_reply": "2024-07-02T15:15:20.021022Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:50.713503Z", - "iopub.status.busy": "2024-07-02T12:05:50.713332Z", - "iopub.status.idle": "2024-07-02T12:05:53.318405Z", - "shell.execute_reply": "2024-07-02T12:05:53.317873Z" + "iopub.execute_input": "2024-07-02T15:15:20.023553Z", + "iopub.status.busy": "2024-07-02T15:15:20.023228Z", + "iopub.status.idle": "2024-07-02T15:15:22.667041Z", + "shell.execute_reply": "2024-07-02T15:15:22.666472Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:53.320633Z", - "iopub.status.busy": "2024-07-02T12:05:53.320318Z", - "iopub.status.idle": "2024-07-02T12:05:54.676476Z", - "shell.execute_reply": "2024-07-02T12:05:54.675931Z" + "iopub.execute_input": "2024-07-02T15:15:22.669429Z", + "iopub.status.busy": "2024-07-02T15:15:22.669046Z", + "iopub.status.idle": "2024-07-02T15:15:24.080473Z", + "shell.execute_reply": "2024-07-02T15:15:24.079910Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:54.678848Z", - "iopub.status.busy": "2024-07-02T12:05:54.678408Z", - "iopub.status.idle": "2024-07-02T12:05:54.682336Z", - "shell.execute_reply": "2024-07-02T12:05:54.681800Z" + "iopub.execute_input": "2024-07-02T15:15:24.082867Z", + "iopub.status.busy": "2024-07-02T15:15:24.082524Z", + "iopub.status.idle": "2024-07-02T15:15:24.086566Z", + "shell.execute_reply": "2024-07-02T15:15:24.086070Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:54.684325Z", - "iopub.status.busy": "2024-07-02T12:05:54.683937Z", - "iopub.status.idle": "2024-07-02T12:05:56.558099Z", - "shell.execute_reply": "2024-07-02T12:05:56.557479Z" + "iopub.execute_input": "2024-07-02T15:15:24.088468Z", + "iopub.status.busy": "2024-07-02T15:15:24.088287Z", + "iopub.status.idle": "2024-07-02T15:15:26.051644Z", + "shell.execute_reply": "2024-07-02T15:15:26.051027Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:56.560538Z", - "iopub.status.busy": "2024-07-02T12:05:56.560208Z", - "iopub.status.idle": "2024-07-02T12:05:56.567803Z", - "shell.execute_reply": "2024-07-02T12:05:56.567265Z" + "iopub.execute_input": "2024-07-02T15:15:26.054487Z", + "iopub.status.busy": "2024-07-02T15:15:26.053807Z", + "iopub.status.idle": "2024-07-02T15:15:26.061647Z", + "shell.execute_reply": "2024-07-02T15:15:26.061203Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:56.569739Z", - "iopub.status.busy": "2024-07-02T12:05:56.569446Z", - "iopub.status.idle": "2024-07-02T12:05:59.160999Z", - "shell.execute_reply": "2024-07-02T12:05:59.160450Z" + "iopub.execute_input": "2024-07-02T15:15:26.063701Z", + "iopub.status.busy": "2024-07-02T15:15:26.063447Z", + "iopub.status.idle": "2024-07-02T15:15:28.644430Z", + "shell.execute_reply": "2024-07-02T15:15:28.643824Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:59.163107Z", - "iopub.status.busy": "2024-07-02T12:05:59.162773Z", - "iopub.status.idle": "2024-07-02T12:05:59.166191Z", - "shell.execute_reply": "2024-07-02T12:05:59.165684Z" + "iopub.execute_input": "2024-07-02T15:15:28.646593Z", + "iopub.status.busy": "2024-07-02T15:15:28.646407Z", + "iopub.status.idle": "2024-07-02T15:15:28.649931Z", + "shell.execute_reply": "2024-07-02T15:15:28.649426Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:59.168252Z", - "iopub.status.busy": "2024-07-02T12:05:59.167849Z", - "iopub.status.idle": "2024-07-02T12:05:59.171322Z", - "shell.execute_reply": "2024-07-02T12:05:59.170794Z" + "iopub.execute_input": "2024-07-02T15:15:28.651842Z", + "iopub.status.busy": "2024-07-02T15:15:28.651670Z", + "iopub.status.idle": "2024-07-02T15:15:28.654914Z", + "shell.execute_reply": "2024-07-02T15:15:28.654497Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:05:59.173235Z", - "iopub.status.busy": "2024-07-02T12:05:59.172937Z", - "iopub.status.idle": "2024-07-02T12:05:59.176035Z", - "shell.execute_reply": "2024-07-02T12:05:59.175500Z" + "iopub.execute_input": "2024-07-02T15:15:28.656734Z", + "iopub.status.busy": "2024-07-02T15:15:28.656564Z", + "iopub.status.idle": "2024-07-02T15:15:28.659904Z", + "shell.execute_reply": "2024-07-02T15:15:28.659358Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index 7ce8a7f2b..a35b0cd70 100644 --- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:01.378322Z", - "iopub.status.busy": "2024-07-02T12:06:01.377923Z", - "iopub.status.idle": "2024-07-02T12:06:02.503419Z", - "shell.execute_reply": "2024-07-02T12:06:02.502819Z" + "iopub.execute_input": "2024-07-02T15:15:30.956908Z", + "iopub.status.busy": "2024-07-02T15:15:30.956487Z", + "iopub.status.idle": "2024-07-02T15:15:32.095214Z", + "shell.execute_reply": "2024-07-02T15:15:32.094654Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -109,10 +109,10 @@ "id": "c90449c8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:02.505878Z", - "iopub.status.busy": "2024-07-02T12:06:02.505606Z", - "iopub.status.idle": "2024-07-02T12:06:03.484637Z", - "shell.execute_reply": "2024-07-02T12:06:03.483911Z" + "iopub.execute_input": "2024-07-02T15:15:32.097678Z", + "iopub.status.busy": "2024-07-02T15:15:32.097267Z", + "iopub.status.idle": "2024-07-02T15:15:33.338055Z", + "shell.execute_reply": "2024-07-02T15:15:33.337365Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.487478Z", - "iopub.status.busy": "2024-07-02T12:06:03.486983Z", - "iopub.status.idle": "2024-07-02T12:06:03.490372Z", - "shell.execute_reply": "2024-07-02T12:06:03.489937Z" + "iopub.execute_input": "2024-07-02T15:15:33.340749Z", + "iopub.status.busy": "2024-07-02T15:15:33.340321Z", + "iopub.status.idle": "2024-07-02T15:15:33.343719Z", + "shell.execute_reply": "2024-07-02T15:15:33.343229Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.492668Z", - "iopub.status.busy": "2024-07-02T12:06:03.492302Z", - "iopub.status.idle": "2024-07-02T12:06:03.499701Z", - "shell.execute_reply": "2024-07-02T12:06:03.499223Z" + "iopub.execute_input": "2024-07-02T15:15:33.345667Z", + "iopub.status.busy": "2024-07-02T15:15:33.345338Z", + "iopub.status.idle": "2024-07-02T15:15:33.351615Z", + "shell.execute_reply": "2024-07-02T15:15:33.351194Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.501657Z", - "iopub.status.busy": "2024-07-02T12:06:03.501478Z", - "iopub.status.idle": "2024-07-02T12:06:03.984496Z", - "shell.execute_reply": "2024-07-02T12:06:03.983911Z" + "iopub.execute_input": "2024-07-02T15:15:33.353788Z", + "iopub.status.busy": "2024-07-02T15:15:33.353318Z", + "iopub.status.idle": "2024-07-02T15:15:33.838412Z", + "shell.execute_reply": "2024-07-02T15:15:33.837799Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.987155Z", - "iopub.status.busy": "2024-07-02T12:06:03.986711Z", - "iopub.status.idle": "2024-07-02T12:06:03.992050Z", - "shell.execute_reply": "2024-07-02T12:06:03.991587Z" + "iopub.execute_input": "2024-07-02T15:15:33.840873Z", + "iopub.status.busy": "2024-07-02T15:15:33.840457Z", + "iopub.status.idle": "2024-07-02T15:15:33.845948Z", + "shell.execute_reply": "2024-07-02T15:15:33.845370Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.993958Z", - "iopub.status.busy": "2024-07-02T12:06:03.993639Z", - "iopub.status.idle": "2024-07-02T12:06:03.997330Z", - "shell.execute_reply": "2024-07-02T12:06:03.996906Z" + "iopub.execute_input": "2024-07-02T15:15:33.848087Z", + "iopub.status.busy": "2024-07-02T15:15:33.847762Z", + "iopub.status.idle": "2024-07-02T15:15:33.851505Z", + "shell.execute_reply": "2024-07-02T15:15:33.851083Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:03.999294Z", - "iopub.status.busy": "2024-07-02T12:06:03.998989Z", - "iopub.status.idle": "2024-07-02T12:06:04.886721Z", - "shell.execute_reply": "2024-07-02T12:06:04.886183Z" + "iopub.execute_input": "2024-07-02T15:15:33.853551Z", + "iopub.status.busy": "2024-07-02T15:15:33.853155Z", + "iopub.status.idle": "2024-07-02T15:15:34.718833Z", + "shell.execute_reply": "2024-07-02T15:15:34.718192Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:04.889094Z", - "iopub.status.busy": "2024-07-02T12:06:04.888730Z", - "iopub.status.idle": "2024-07-02T12:06:05.104977Z", - "shell.execute_reply": "2024-07-02T12:06:05.104560Z" + "iopub.execute_input": "2024-07-02T15:15:34.721211Z", + "iopub.status.busy": "2024-07-02T15:15:34.720852Z", + "iopub.status.idle": "2024-07-02T15:15:34.944154Z", + "shell.execute_reply": "2024-07-02T15:15:34.943692Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:05.107009Z", - "iopub.status.busy": "2024-07-02T12:06:05.106744Z", - "iopub.status.idle": "2024-07-02T12:06:05.111011Z", - "shell.execute_reply": "2024-07-02T12:06:05.110475Z" + "iopub.execute_input": "2024-07-02T15:15:34.946483Z", + "iopub.status.busy": "2024-07-02T15:15:34.946141Z", + "iopub.status.idle": "2024-07-02T15:15:34.950453Z", + "shell.execute_reply": "2024-07-02T15:15:34.950017Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:05.112841Z", - "iopub.status.busy": "2024-07-02T12:06:05.112667Z", - "iopub.status.idle": "2024-07-02T12:06:05.549544Z", - "shell.execute_reply": "2024-07-02T12:06:05.548895Z" + "iopub.execute_input": "2024-07-02T15:15:34.952518Z", + "iopub.status.busy": "2024-07-02T15:15:34.952202Z", + "iopub.status.idle": "2024-07-02T15:15:35.406704Z", + "shell.execute_reply": "2024-07-02T15:15:35.406148Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:05.552420Z", - "iopub.status.busy": "2024-07-02T12:06:05.552234Z", - "iopub.status.idle": "2024-07-02T12:06:05.880895Z", - "shell.execute_reply": "2024-07-02T12:06:05.880300Z" + "iopub.execute_input": "2024-07-02T15:15:35.409869Z", + "iopub.status.busy": "2024-07-02T15:15:35.409486Z", + "iopub.status.idle": "2024-07-02T15:15:35.740831Z", + "shell.execute_reply": "2024-07-02T15:15:35.740278Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:05.883106Z", - "iopub.status.busy": "2024-07-02T12:06:05.882705Z", - "iopub.status.idle": "2024-07-02T12:06:06.240971Z", - "shell.execute_reply": "2024-07-02T12:06:06.240404Z" + "iopub.execute_input": "2024-07-02T15:15:35.743697Z", + "iopub.status.busy": "2024-07-02T15:15:35.743347Z", + "iopub.status.idle": "2024-07-02T15:15:36.106871Z", + "shell.execute_reply": "2024-07-02T15:15:36.106275Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:06.243379Z", - "iopub.status.busy": "2024-07-02T12:06:06.243189Z", - "iopub.status.idle": "2024-07-02T12:06:06.680772Z", - "shell.execute_reply": "2024-07-02T12:06:06.680290Z" + "iopub.execute_input": "2024-07-02T15:15:36.110205Z", + "iopub.status.busy": "2024-07-02T15:15:36.109829Z", + "iopub.status.idle": "2024-07-02T15:15:36.549166Z", + "shell.execute_reply": "2024-07-02T15:15:36.548631Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:06.682984Z", - "iopub.status.busy": "2024-07-02T12:06:06.682675Z", - "iopub.status.idle": "2024-07-02T12:06:07.129389Z", - "shell.execute_reply": "2024-07-02T12:06:07.128744Z" + "iopub.execute_input": "2024-07-02T15:15:36.553350Z", + "iopub.status.busy": "2024-07-02T15:15:36.553003Z", + "iopub.status.idle": "2024-07-02T15:15:36.974053Z", + "shell.execute_reply": "2024-07-02T15:15:36.973378Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:07.132269Z", - "iopub.status.busy": "2024-07-02T12:06:07.132092Z", - "iopub.status.idle": "2024-07-02T12:06:07.345651Z", - "shell.execute_reply": "2024-07-02T12:06:07.345066Z" + "iopub.execute_input": "2024-07-02T15:15:36.976911Z", + "iopub.status.busy": "2024-07-02T15:15:36.976726Z", + "iopub.status.idle": "2024-07-02T15:15:37.190142Z", + "shell.execute_reply": "2024-07-02T15:15:37.189597Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:07.347943Z", - "iopub.status.busy": "2024-07-02T12:06:07.347569Z", - "iopub.status.idle": "2024-07-02T12:06:07.545897Z", - "shell.execute_reply": "2024-07-02T12:06:07.545303Z" + "iopub.execute_input": "2024-07-02T15:15:37.192342Z", + "iopub.status.busy": "2024-07-02T15:15:37.191989Z", + "iopub.status.idle": "2024-07-02T15:15:37.390057Z", + "shell.execute_reply": "2024-07-02T15:15:37.389444Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:07.548054Z", - "iopub.status.busy": "2024-07-02T12:06:07.547721Z", - "iopub.status.idle": "2024-07-02T12:06:07.550610Z", - "shell.execute_reply": "2024-07-02T12:06:07.550172Z" + "iopub.execute_input": "2024-07-02T15:15:37.392297Z", + "iopub.status.busy": "2024-07-02T15:15:37.391973Z", + "iopub.status.idle": "2024-07-02T15:15:37.394998Z", + "shell.execute_reply": "2024-07-02T15:15:37.394453Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:07.552606Z", - "iopub.status.busy": "2024-07-02T12:06:07.552209Z", - "iopub.status.idle": "2024-07-02T12:06:08.545283Z", - "shell.execute_reply": "2024-07-02T12:06:08.544691Z" + "iopub.execute_input": "2024-07-02T15:15:37.397009Z", + "iopub.status.busy": "2024-07-02T15:15:37.396673Z", + "iopub.status.idle": "2024-07-02T15:15:38.375549Z", + "shell.execute_reply": "2024-07-02T15:15:38.375024Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:08.550100Z", - "iopub.status.busy": "2024-07-02T12:06:08.549675Z", - "iopub.status.idle": "2024-07-02T12:06:08.692703Z", - "shell.execute_reply": "2024-07-02T12:06:08.692222Z" + "iopub.execute_input": "2024-07-02T15:15:38.378310Z", + "iopub.status.busy": "2024-07-02T15:15:38.377935Z", + "iopub.status.idle": "2024-07-02T15:15:38.576337Z", + "shell.execute_reply": "2024-07-02T15:15:38.575768Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:08.694865Z", - "iopub.status.busy": "2024-07-02T12:06:08.694525Z", - "iopub.status.idle": "2024-07-02T12:06:08.829794Z", - "shell.execute_reply": "2024-07-02T12:06:08.829310Z" + "iopub.execute_input": "2024-07-02T15:15:38.578422Z", + "iopub.status.busy": "2024-07-02T15:15:38.578242Z", + "iopub.status.idle": "2024-07-02T15:15:38.716353Z", + "shell.execute_reply": "2024-07-02T15:15:38.715888Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:08.832030Z", - "iopub.status.busy": "2024-07-02T12:06:08.831714Z", - "iopub.status.idle": "2024-07-02T12:06:09.569943Z", - "shell.execute_reply": "2024-07-02T12:06:09.569367Z" + "iopub.execute_input": "2024-07-02T15:15:38.718767Z", + "iopub.status.busy": "2024-07-02T15:15:38.718383Z", + "iopub.status.idle": "2024-07-02T15:15:39.383126Z", + "shell.execute_reply": "2024-07-02T15:15:39.382541Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:09.572191Z", - "iopub.status.busy": "2024-07-02T12:06:09.571856Z", - "iopub.status.idle": "2024-07-02T12:06:09.575442Z", - "shell.execute_reply": "2024-07-02T12:06:09.575034Z" + "iopub.execute_input": "2024-07-02T15:15:39.385201Z", + "iopub.status.busy": "2024-07-02T15:15:39.385018Z", + "iopub.status.idle": "2024-07-02T15:15:39.388752Z", + "shell.execute_reply": "2024-07-02T15:15:39.388195Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 12c6da264..e7ee45271 100644 --- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:11.678697Z", - "iopub.status.busy": "2024-07-02T12:06:11.678521Z", - "iopub.status.idle": "2024-07-02T12:06:14.408240Z", - "shell.execute_reply": "2024-07-02T12:06:14.407674Z" + "iopub.execute_input": "2024-07-02T15:15:41.499853Z", + "iopub.status.busy": "2024-07-02T15:15:41.499683Z", + "iopub.status.idle": "2024-07-02T15:15:44.231209Z", + "shell.execute_reply": "2024-07-02T15:15:44.230660Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "id": "4396f544", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:14.410934Z", - "iopub.status.busy": "2024-07-02T12:06:14.410443Z", - "iopub.status.idle": "2024-07-02T12:06:14.735244Z", - "shell.execute_reply": "2024-07-02T12:06:14.734679Z" + "iopub.execute_input": "2024-07-02T15:15:44.233719Z", + "iopub.status.busy": "2024-07-02T15:15:44.233290Z", + "iopub.status.idle": "2024-07-02T15:15:44.547799Z", + "shell.execute_reply": "2024-07-02T15:15:44.547256Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:14.737835Z", - "iopub.status.busy": "2024-07-02T12:06:14.737360Z", - "iopub.status.idle": "2024-07-02T12:06:14.741543Z", - "shell.execute_reply": "2024-07-02T12:06:14.741013Z" + "iopub.execute_input": "2024-07-02T15:15:44.550457Z", + "iopub.status.busy": "2024-07-02T15:15:44.550003Z", + "iopub.status.idle": "2024-07-02T15:15:44.553889Z", + "shell.execute_reply": "2024-07-02T15:15:44.553463Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:14.743746Z", - "iopub.status.busy": "2024-07-02T12:06:14.743385Z", - "iopub.status.idle": "2024-07-02T12:06:25.921071Z", - "shell.execute_reply": "2024-07-02T12:06:25.920486Z" + "iopub.execute_input": "2024-07-02T15:15:44.555964Z", + "iopub.status.busy": "2024-07-02T15:15:44.555530Z", + "iopub.status.idle": "2024-07-02T15:15:48.811407Z", + "shell.execute_reply": "2024-07-02T15:15:48.810907Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 458752/170498071 [00:00<00:37, 4550205.38it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8200886.72it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 2686976/170498071 [00:00<00:11, 14867624.00it/s]" + " 6%|▋ | 10780672/170498071 [00:00<00:02, 58894029.31it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 4915200/170498071 [00:00<00:09, 18176569.25it/s]" + " 13%|█▎ | 22380544/170498071 [00:00<00:01, 84273722.65it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 7110656/170498071 [00:00<00:08, 19525356.25it/s]" + " 20%|█▉ | 33783808/170498071 [00:00<00:01, 95827715.47it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 9273344/170498071 [00:00<00:08, 20138060.31it/s]" + " 27%|██▋ | 45383680/170498071 [00:00<00:01, 102972274.05it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 11468800/170498071 [00:00<00:07, 20583296.62it/s]" + " 33%|███▎ | 56721408/170498071 [00:00<00:01, 106415655.53it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 13565952/170498071 [00:00<00:07, 20618122.34it/s]" + " 40%|████ | 68288512/170498071 [00:00<00:00, 109377801.86it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 15695872/170498071 [00:00<00:07, 20684064.34it/s]" + " 47%|████▋ | 79790080/170498071 [00:00<00:00, 111060852.43it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 17793024/170498071 [00:00<00:07, 20210099.70it/s]" + " 54%|█████▎ | 91291648/170498071 [00:00<00:00, 112242317.21it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 19857408/170498071 [00:01<00:07, 20157298.26it/s]" + " 60%|██████ | 102727680/170498071 [00:01<00:00, 112875530.06it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 21889024/170498071 [00:01<00:07, 19580366.36it/s]" + " 67%|██████▋ | 114262016/170498071 [00:01<00:00, 113610104.30it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 23887872/170498071 [00:01<00:07, 19689752.59it/s]" + " 74%|███████▎ | 125665280/170498071 [00:01<00:00, 112903553.22it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 26148864/170498071 [00:01<00:07, 20522936.05it/s]" + " 81%|████████ | 137396224/170498071 [00:01<00:00, 114077850.23it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 28901376/170498071 [00:01<00:06, 22488420.49it/s]" + " 87%|████████▋ | 148897792/170498071 [00:01<00:00, 114231113.69it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 31260672/170498071 [00:01<00:06, 22666713.87it/s]" + " 94%|█████████▍| 160399360/170498071 [00:01<00:00, 114421071.55it/s]" ] }, { @@ -372,535 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 33783808/170498071 [00:01<00:05, 23420171.66it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 21%|██ | 36143104/170498071 [00:01<00:05, 23367837.44it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 23%|██▎ | 38567936/170498071 [00:01<00:05, 23628433.32it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 24%|██▍ | 40992768/170498071 [00:01<00:05, 23729287.62it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 25%|██▌ | 43384832/170498071 [00:02<00:05, 23481823.27it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 27%|██▋ | 45809664/170498071 [00:02<00:05, 23565140.27it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 28%|██▊ | 48168960/170498071 [00:02<00:05, 22124551.71it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 30%|██▉ | 50429952/170498071 [00:02<00:05, 21597165.25it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 31%|███ | 52625408/170498071 [00:02<00:05, 21122055.47it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 32%|███▏ | 54755328/170498071 [00:02<00:05, 20674704.10it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 33%|███▎ | 56852480/170498071 [00:02<00:05, 20193072.76it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 35%|███▍ | 59015168/170498071 [00:02<00:05, 20474965.70it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 36%|███▌ | 61440000/170498071 [00:02<00:05, 21428625.80it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 37%|███▋ | 63602688/170498071 [00:03<00:05, 20984454.80it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 39%|███▊ | 65732608/170498071 [00:03<00:05, 20040214.69it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 40%|███▉ | 67764224/170498071 [00:03<00:05, 19617119.66it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 41%|████ | 69763072/170498071 [00:03<00:05, 19368566.16it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 42%|████▏ | 71729152/170498071 [00:03<00:05, 18942200.76it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 43%|████▎ | 73760768/170498071 [00:03<00:05, 19136506.47it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 44%|████▍ | 75694080/170498071 [00:03<00:05, 18546539.77it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 46%|████▌ | 77856768/170498071 [00:03<00:04, 19310897.00it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 47%|████▋ | 79855616/170498071 [00:03<00:04, 19370411.60it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 48%|████▊ | 81821696/170498071 [00:03<00:04, 18841681.57it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 49%|████▉ | 83722240/170498071 [00:04<00:04, 18578900.08it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 50%|█████ | 85590016/170498071 [00:04<00:04, 18310455.55it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 51%|█████▏ | 87425024/170498071 [00:04<00:04, 17994534.24it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 52%|█████▏ | 89227264/170498071 [00:04<00:04, 17969991.24it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 53%|█████▎ | 91029504/170498071 [00:04<00:04, 17885343.83it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 54%|█████▍ | 92864512/170498071 [00:04<00:04, 17966202.49it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 56%|█████▌ | 95223808/170498071 [00:04<00:03, 19459620.88it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 57%|█████▋ | 97583104/170498071 [00:04<00:03, 20637975.50it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 59%|█████▊ | 99909632/170498071 [00:04<00:03, 21306263.38it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 60%|█████▉ | 102072320/170498071 [00:05<00:03, 21151929.09it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 61%|██████▏ | 104464384/170498071 [00:05<00:03, 21792656.59it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 63%|██████▎ | 106659840/170498071 [00:05<00:02, 21386725.65it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 64%|██████▍ | 108920832/170498071 [00:05<00:02, 21710014.23it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 65%|██████▌ | 111116288/170498071 [00:05<00:02, 21241167.89it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 67%|██████▋ | 113541120/170498071 [00:05<00:02, 22049083.16it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 68%|██████▊ | 115769344/170498071 [00:05<00:02, 21067777.78it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 69%|██████▉ | 118030336/170498071 [00:05<00:02, 21474442.53it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 70%|███████ | 120193024/170498071 [00:05<00:02, 19823274.12it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 72%|███████▏ | 122224640/170498071 [00:06<00:02, 19471183.62it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 73%|███████▎ | 124190720/170498071 [00:06<00:02, 17873496.55it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 74%|███████▍ | 126025728/170498071 [00:06<00:02, 16817889.13it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 75%|███████▍ | 127762432/170498071 [00:06<00:02, 16186952.66it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 76%|███████▌ | 129400832/170498071 [00:06<00:02, 15789183.38it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 77%|███████▋ | 131006464/170498071 [00:06<00:02, 15482944.36it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 78%|███████▊ | 132579328/170498071 [00:06<00:02, 15098811.97it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 79%|███████▊ | 134119424/170498071 [00:06<00:02, 14977124.11it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 80%|███████▉ | 135626752/170498071 [00:06<00:02, 14929116.66it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 80%|████████ | 137134080/170498071 [00:07<00:02, 14648969.73it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 81%|████████▏ | 138608640/170498071 [00:07<00:02, 14671163.55it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 82%|████████▏ | 140083200/170498071 [00:07<00:02, 14686569.65it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 83%|████████▎ | 141885440/170498071 [00:07<00:01, 15554076.95it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 84%|████████▍ | 143589376/170498071 [00:07<00:01, 15854513.49it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 85%|████████▌ | 145391616/170498071 [00:07<00:01, 16350344.79it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 86%|████████▋ | 147128320/170498071 [00:07<00:01, 16559020.07it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 87%|████████▋ | 149061632/170498071 [00:07<00:01, 17232449.65it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 88%|████████▊ | 150863872/170498071 [00:07<00:01, 17447581.45it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 90%|████████▉ | 152633344/170498071 [00:07<00:01, 17476767.38it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 91%|█████████ | 155058176/170498071 [00:08<00:00, 19434356.44it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 93%|█████████▎| 157712384/170498071 [00:08<00:00, 21544683.73it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 94%|█████████▍| 160661504/170498071 [00:08<00:00, 23832060.44it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 96%|█████████▌| 163545088/170498071 [00:08<00:00, 25225731.12it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 98%|█████████▊| 166854656/170498071 [00:08<00:00, 27373989.44it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|█████████▉| 169672704/170498071 [00:08<00:00, 27582174.15it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:08<00:00, 19884004.38it/s]" + "100%|██████████| 170498071/170498071 [00:01<00:00, 106209257.98it/s]" ] }, { @@ -1018,10 +490,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:25.923304Z", - "iopub.status.busy": "2024-07-02T12:06:25.922962Z", - "iopub.status.idle": "2024-07-02T12:06:25.927532Z", - "shell.execute_reply": "2024-07-02T12:06:25.927116Z" + "iopub.execute_input": "2024-07-02T15:15:48.813684Z", + "iopub.status.busy": "2024-07-02T15:15:48.813281Z", + "iopub.status.idle": "2024-07-02T15:15:48.818166Z", + "shell.execute_reply": "2024-07-02T15:15:48.817615Z" }, "nbsphinx": "hidden" }, @@ -1072,10 +544,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:25.929617Z", - "iopub.status.busy": "2024-07-02T12:06:25.929294Z", - "iopub.status.idle": "2024-07-02T12:06:26.466020Z", - "shell.execute_reply": "2024-07-02T12:06:26.465500Z" + "iopub.execute_input": "2024-07-02T15:15:48.820188Z", + "iopub.status.busy": "2024-07-02T15:15:48.819791Z", + "iopub.status.idle": "2024-07-02T15:15:49.359971Z", + "shell.execute_reply": "2024-07-02T15:15:49.359408Z" } }, "outputs": [ @@ -1108,10 +580,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:26.468274Z", - "iopub.status.busy": "2024-07-02T12:06:26.467846Z", - "iopub.status.idle": "2024-07-02T12:06:26.973804Z", - "shell.execute_reply": "2024-07-02T12:06:26.973190Z" + "iopub.execute_input": "2024-07-02T15:15:49.362067Z", + "iopub.status.busy": "2024-07-02T15:15:49.361785Z", + "iopub.status.idle": "2024-07-02T15:15:49.873206Z", + "shell.execute_reply": "2024-07-02T15:15:49.872724Z" } }, "outputs": [ @@ -1149,10 +621,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:26.976024Z", - "iopub.status.busy": "2024-07-02T12:06:26.975702Z", - "iopub.status.idle": "2024-07-02T12:06:26.979191Z", - "shell.execute_reply": "2024-07-02T12:06:26.978654Z" + "iopub.execute_input": "2024-07-02T15:15:49.875391Z", + "iopub.status.busy": "2024-07-02T15:15:49.875042Z", + "iopub.status.idle": "2024-07-02T15:15:49.878400Z", + "shell.execute_reply": "2024-07-02T15:15:49.877944Z" } }, "outputs": [], @@ -1175,17 +647,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:26.981120Z", - "iopub.status.busy": "2024-07-02T12:06:26.980808Z", - "iopub.status.idle": "2024-07-02T12:06:39.219368Z", - "shell.execute_reply": "2024-07-02T12:06:39.218785Z" + "iopub.execute_input": "2024-07-02T15:15:49.880181Z", + "iopub.status.busy": "2024-07-02T15:15:49.880011Z", + "iopub.status.idle": "2024-07-02T15:16:02.227760Z", + "shell.execute_reply": "2024-07-02T15:16:02.227173Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e62048d58b1a436fa16544b9ecbd1a17", + "model_id": "7134c3b9c85247698385a933e9c6f4c1", "version_major": 2, "version_minor": 0 }, @@ -1244,10 +716,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:39.221701Z", - "iopub.status.busy": "2024-07-02T12:06:39.221327Z", - "iopub.status.idle": "2024-07-02T12:06:41.264255Z", - "shell.execute_reply": "2024-07-02T12:06:41.263645Z" + "iopub.execute_input": "2024-07-02T15:16:02.229945Z", + "iopub.status.busy": "2024-07-02T15:16:02.229742Z", + "iopub.status.idle": "2024-07-02T15:16:04.294329Z", + "shell.execute_reply": "2024-07-02T15:16:04.293708Z" } }, "outputs": [ @@ -1291,10 +763,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:41.266829Z", - "iopub.status.busy": "2024-07-02T12:06:41.266301Z", - "iopub.status.idle": "2024-07-02T12:06:41.492927Z", - "shell.execute_reply": "2024-07-02T12:06:41.492268Z" + "iopub.execute_input": "2024-07-02T15:16:04.297035Z", + "iopub.status.busy": "2024-07-02T15:16:04.296744Z", + "iopub.status.idle": "2024-07-02T15:16:04.555185Z", + "shell.execute_reply": "2024-07-02T15:16:04.554125Z" } }, "outputs": [ @@ -1330,10 +802,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:41.495155Z", - "iopub.status.busy": "2024-07-02T12:06:41.494971Z", - "iopub.status.idle": "2024-07-02T12:06:42.143408Z", - "shell.execute_reply": "2024-07-02T12:06:42.142827Z" + "iopub.execute_input": "2024-07-02T15:16:04.557598Z", + "iopub.status.busy": "2024-07-02T15:16:04.557392Z", + "iopub.status.idle": "2024-07-02T15:16:05.237315Z", + "shell.execute_reply": "2024-07-02T15:16:05.236772Z" } }, "outputs": [ @@ -1383,10 +855,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:42.145875Z", - "iopub.status.busy": "2024-07-02T12:06:42.145693Z", - "iopub.status.idle": "2024-07-02T12:06:42.443716Z", - "shell.execute_reply": "2024-07-02T12:06:42.443121Z" + "iopub.execute_input": "2024-07-02T15:16:05.240254Z", + "iopub.status.busy": "2024-07-02T15:16:05.239837Z", + "iopub.status.idle": "2024-07-02T15:16:05.575080Z", + "shell.execute_reply": "2024-07-02T15:16:05.574558Z" } }, "outputs": [ @@ -1434,10 +906,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:42.445959Z", - "iopub.status.busy": "2024-07-02T12:06:42.445765Z", - "iopub.status.idle": "2024-07-02T12:06:42.675040Z", - "shell.execute_reply": "2024-07-02T12:06:42.674459Z" + "iopub.execute_input": "2024-07-02T15:16:05.577340Z", + "iopub.status.busy": "2024-07-02T15:16:05.576994Z", + "iopub.status.idle": "2024-07-02T15:16:05.817984Z", + "shell.execute_reply": "2024-07-02T15:16:05.817361Z" } }, "outputs": [ @@ -1493,10 +965,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:42.677732Z", - "iopub.status.busy": "2024-07-02T12:06:42.677210Z", - "iopub.status.idle": "2024-07-02T12:06:42.745827Z", - "shell.execute_reply": "2024-07-02T12:06:42.745362Z" + "iopub.execute_input": "2024-07-02T15:16:05.820538Z", + "iopub.status.busy": "2024-07-02T15:16:05.820336Z", + "iopub.status.idle": "2024-07-02T15:16:05.907382Z", + "shell.execute_reply": "2024-07-02T15:16:05.906874Z" } }, "outputs": [], @@ -1517,10 +989,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:42.748346Z", - "iopub.status.busy": "2024-07-02T12:06:42.748025Z", - "iopub.status.idle": "2024-07-02T12:06:52.686113Z", - "shell.execute_reply": "2024-07-02T12:06:52.685493Z" + "iopub.execute_input": "2024-07-02T15:16:05.910032Z", + "iopub.status.busy": "2024-07-02T15:16:05.909504Z", + "iopub.status.idle": "2024-07-02T15:16:16.136329Z", + "shell.execute_reply": "2024-07-02T15:16:16.135702Z" } }, "outputs": [ @@ -1557,10 +1029,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:52.688740Z", - "iopub.status.busy": "2024-07-02T12:06:52.688263Z", - "iopub.status.idle": "2024-07-02T12:06:54.757637Z", - "shell.execute_reply": "2024-07-02T12:06:54.757095Z" + "iopub.execute_input": "2024-07-02T15:16:16.138895Z", + "iopub.status.busy": "2024-07-02T15:16:16.138488Z", + "iopub.status.idle": "2024-07-02T15:16:18.289669Z", + "shell.execute_reply": "2024-07-02T15:16:18.289140Z" } }, "outputs": [ @@ -1591,10 +1063,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:54.760248Z", - "iopub.status.busy": "2024-07-02T12:06:54.759634Z", - "iopub.status.idle": "2024-07-02T12:06:54.964477Z", - "shell.execute_reply": "2024-07-02T12:06:54.963957Z" + "iopub.execute_input": "2024-07-02T15:16:18.292281Z", + "iopub.status.busy": "2024-07-02T15:16:18.291784Z", + "iopub.status.idle": "2024-07-02T15:16:18.494637Z", + "shell.execute_reply": "2024-07-02T15:16:18.494138Z" } }, "outputs": [], @@ -1608,10 +1080,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:54.966866Z", - "iopub.status.busy": "2024-07-02T12:06:54.966507Z", - "iopub.status.idle": "2024-07-02T12:06:54.969693Z", - "shell.execute_reply": "2024-07-02T12:06:54.969165Z" + "iopub.execute_input": "2024-07-02T15:16:18.496977Z", + "iopub.status.busy": "2024-07-02T15:16:18.496633Z", + "iopub.status.idle": "2024-07-02T15:16:18.499690Z", + "shell.execute_reply": "2024-07-02T15:16:18.499247Z" } }, "outputs": [], @@ -1633,10 +1105,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:54.971890Z", - "iopub.status.busy": "2024-07-02T12:06:54.971573Z", - "iopub.status.idle": "2024-07-02T12:06:54.979664Z", - "shell.execute_reply": "2024-07-02T12:06:54.979125Z" + "iopub.execute_input": "2024-07-02T15:16:18.501694Z", + "iopub.status.busy": "2024-07-02T15:16:18.501306Z", + "iopub.status.idle": "2024-07-02T15:16:18.509698Z", + "shell.execute_reply": "2024-07-02T15:16:18.509149Z" }, "nbsphinx": "hidden" }, @@ -1681,7 +1153,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "038d1dec855f4a5d8a895b8c5ca8a543": { + "0ec7acb06a8d4e7c8cee6f0af1617289": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_252fe402519d4210ba4ba3fef0912a86", + "placeholder": "​", + "style": "IPY_MODEL_b8360c36dca94afc98ec4fb786a3c57f", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 304MB/s]" + } + }, + "252fe402519d4210ba4ba3fef0912a86": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1734,48 +1229,23 @@ "width": null } }, - "189964aceefe49698fa8fa689efdba0f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7f36baa4eaa845949d5ad61b24217bd2", - "placeholder": "​", - "style": "IPY_MODEL_50adf2f382654575992aa00abedb3fda", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 291MB/s]" - } - }, - "32782ba639b74ba19d535e6b9e43df2f": { + "4cb10e135c4d4df6a0102b8fa2c4e435": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "50adf2f382654575992aa00abedb3fda": { + "6b6164dfe4394da88a0985c0358adabf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1793,30 +1263,31 @@ "text_color": null } }, - "55c2a3ff8e46463392cbdc7feacce684": { + "7134c3b9c85247698385a933e9c6f4c1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_038d1dec855f4a5d8a895b8c5ca8a543", - "placeholder": "​", - "style": "IPY_MODEL_32782ba639b74ba19d535e6b9e43df2f", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f75ac16d283e42748e30f48710f7c779", + "IPY_MODEL_ebbc5fc8b0754655bb152b6178ceae67", + "IPY_MODEL_0ec7acb06a8d4e7c8cee6f0af1617289" + ], + "layout": "IPY_MODEL_76e78968a920473d8821422c81a0fcdd", "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "tooltip": null } }, - "7f36baa4eaa845949d5ad61b24217bd2": { + "76e78968a920473d8821422c81a0fcdd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1869,23 +1340,25 @@ "width": null } }, - "9c339ec47e3249839dd034d9f3c0f0bd": { + "b8360c36dca94afc98ec4fb786a3c57f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "d0f48ceb51424194a566927347c5e11d": { + "bc50c73a865f4e2e8076a042331398c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1938,57 +1411,7 @@ "width": null } }, - "e2efb59d0f4740bb8af23c2fd00116b3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f2d6b576288e4f7fbed42581aafbf977", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9c339ec47e3249839dd034d9f3c0f0bd", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "e62048d58b1a436fa16544b9ecbd1a17": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_55c2a3ff8e46463392cbdc7feacce684", - "IPY_MODEL_e2efb59d0f4740bb8af23c2fd00116b3", - "IPY_MODEL_189964aceefe49698fa8fa689efdba0f" - ], - "layout": "IPY_MODEL_d0f48ceb51424194a566927347c5e11d", - "tabbable": null, - "tooltip": null - } - }, - "f2d6b576288e4f7fbed42581aafbf977": { + "be9da5a89136408299b9df5aa61bf8ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2040,6 +1463,55 @@ "visibility": null, "width": null } + }, + "ebbc5fc8b0754655bb152b6178ceae67": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_be9da5a89136408299b9df5aa61bf8ca", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4cb10e135c4d4df6a0102b8fa2c4e435", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } + }, + "f75ac16d283e42748e30f48710f7c779": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_bc50c73a865f4e2e8076a042331398c7", + "placeholder": "​", + "style": "IPY_MODEL_6b6164dfe4394da88a0985c0358adabf", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 75e02e92c..d7791c942 100644 --- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:06:59.101052Z", - "iopub.status.busy": "2024-07-02T12:06:59.100876Z", - "iopub.status.idle": "2024-07-02T12:07:00.258136Z", - "shell.execute_reply": "2024-07-02T12:07:00.257587Z" + "iopub.execute_input": "2024-07-02T15:16:22.773416Z", + "iopub.status.busy": "2024-07-02T15:16:22.773067Z", + "iopub.status.idle": "2024-07-02T15:16:23.924928Z", + "shell.execute_reply": "2024-07-02T15:16:23.924442Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.260745Z", - "iopub.status.busy": "2024-07-02T12:07:00.260339Z", - "iopub.status.idle": "2024-07-02T12:07:00.277570Z", - "shell.execute_reply": "2024-07-02T12:07:00.277011Z" + "iopub.execute_input": "2024-07-02T15:16:23.927425Z", + "iopub.status.busy": "2024-07-02T15:16:23.927055Z", + "iopub.status.idle": "2024-07-02T15:16:23.943960Z", + "shell.execute_reply": "2024-07-02T15:16:23.943415Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.280398Z", - "iopub.status.busy": "2024-07-02T12:07:00.279700Z", - "iopub.status.idle": "2024-07-02T12:07:00.283630Z", - "shell.execute_reply": "2024-07-02T12:07:00.282919Z" + "iopub.execute_input": "2024-07-02T15:16:23.946374Z", + "iopub.status.busy": "2024-07-02T15:16:23.945882Z", + "iopub.status.idle": "2024-07-02T15:16:23.948942Z", + "shell.execute_reply": "2024-07-02T15:16:23.948387Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.286415Z", - "iopub.status.busy": "2024-07-02T12:07:00.285840Z", - "iopub.status.idle": "2024-07-02T12:07:00.351880Z", - "shell.execute_reply": "2024-07-02T12:07:00.350456Z" + "iopub.execute_input": "2024-07-02T15:16:23.951055Z", + "iopub.status.busy": "2024-07-02T15:16:23.950645Z", + "iopub.status.idle": "2024-07-02T15:16:24.037023Z", + "shell.execute_reply": "2024-07-02T15:16:24.036470Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.354191Z", - "iopub.status.busy": "2024-07-02T12:07:00.353874Z", - "iopub.status.idle": "2024-07-02T12:07:00.543757Z", - "shell.execute_reply": "2024-07-02T12:07:00.543276Z" + "iopub.execute_input": "2024-07-02T15:16:24.039484Z", + "iopub.status.busy": "2024-07-02T15:16:24.039164Z", + "iopub.status.idle": "2024-07-02T15:16:24.218535Z", + "shell.execute_reply": "2024-07-02T15:16:24.217887Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.545894Z", - "iopub.status.busy": "2024-07-02T12:07:00.545559Z", - "iopub.status.idle": "2024-07-02T12:07:00.784978Z", - "shell.execute_reply": "2024-07-02T12:07:00.784416Z" + "iopub.execute_input": "2024-07-02T15:16:24.220994Z", + "iopub.status.busy": "2024-07-02T15:16:24.220778Z", + "iopub.status.idle": "2024-07-02T15:16:24.467677Z", + "shell.execute_reply": "2024-07-02T15:16:24.467120Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.787127Z", - "iopub.status.busy": "2024-07-02T12:07:00.786946Z", - "iopub.status.idle": "2024-07-02T12:07:00.791220Z", - "shell.execute_reply": "2024-07-02T12:07:00.790792Z" + "iopub.execute_input": "2024-07-02T15:16:24.469799Z", + "iopub.status.busy": "2024-07-02T15:16:24.469507Z", + "iopub.status.idle": "2024-07-02T15:16:24.473810Z", + "shell.execute_reply": "2024-07-02T15:16:24.473346Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.793213Z", - "iopub.status.busy": "2024-07-02T12:07:00.792887Z", - "iopub.status.idle": "2024-07-02T12:07:00.798368Z", - "shell.execute_reply": "2024-07-02T12:07:00.797958Z" + "iopub.execute_input": "2024-07-02T15:16:24.475783Z", + "iopub.status.busy": "2024-07-02T15:16:24.475357Z", + "iopub.status.idle": "2024-07-02T15:16:24.481254Z", + "shell.execute_reply": "2024-07-02T15:16:24.480664Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.800409Z", - "iopub.status.busy": "2024-07-02T12:07:00.800087Z", - "iopub.status.idle": "2024-07-02T12:07:00.802550Z", - "shell.execute_reply": "2024-07-02T12:07:00.802117Z" + "iopub.execute_input": "2024-07-02T15:16:24.483486Z", + "iopub.status.busy": "2024-07-02T15:16:24.483065Z", + "iopub.status.idle": "2024-07-02T15:16:24.485618Z", + "shell.execute_reply": "2024-07-02T15:16:24.485175Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:00.804548Z", - "iopub.status.busy": "2024-07-02T12:07:00.804231Z", - "iopub.status.idle": "2024-07-02T12:07:09.170648Z", - "shell.execute_reply": "2024-07-02T12:07:09.170087Z" + "iopub.execute_input": "2024-07-02T15:16:24.487609Z", + "iopub.status.busy": "2024-07-02T15:16:24.487303Z", + "iopub.status.idle": "2024-07-02T15:16:33.078902Z", + "shell.execute_reply": "2024-07-02T15:16:33.078332Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.173635Z", - "iopub.status.busy": "2024-07-02T12:07:09.172986Z", - "iopub.status.idle": "2024-07-02T12:07:09.180628Z", - "shell.execute_reply": "2024-07-02T12:07:09.180165Z" + "iopub.execute_input": "2024-07-02T15:16:33.081569Z", + "iopub.status.busy": "2024-07-02T15:16:33.081171Z", + "iopub.status.idle": "2024-07-02T15:16:33.088462Z", + "shell.execute_reply": "2024-07-02T15:16:33.087998Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.182718Z", - "iopub.status.busy": "2024-07-02T12:07:09.182401Z", - "iopub.status.idle": "2024-07-02T12:07:09.186064Z", - "shell.execute_reply": "2024-07-02T12:07:09.185614Z" + "iopub.execute_input": "2024-07-02T15:16:33.090386Z", + "iopub.status.busy": "2024-07-02T15:16:33.090207Z", + "iopub.status.idle": "2024-07-02T15:16:33.093961Z", + "shell.execute_reply": "2024-07-02T15:16:33.093497Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.188065Z", - "iopub.status.busy": "2024-07-02T12:07:09.187765Z", - "iopub.status.idle": "2024-07-02T12:07:09.191124Z", - "shell.execute_reply": "2024-07-02T12:07:09.190682Z" + "iopub.execute_input": "2024-07-02T15:16:33.095977Z", + "iopub.status.busy": "2024-07-02T15:16:33.095566Z", + "iopub.status.idle": "2024-07-02T15:16:33.098952Z", + "shell.execute_reply": "2024-07-02T15:16:33.098404Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.193018Z", - "iopub.status.busy": "2024-07-02T12:07:09.192715Z", - "iopub.status.idle": "2024-07-02T12:07:09.195753Z", - "shell.execute_reply": "2024-07-02T12:07:09.195211Z" + "iopub.execute_input": "2024-07-02T15:16:33.101040Z", + "iopub.status.busy": "2024-07-02T15:16:33.100641Z", + "iopub.status.idle": "2024-07-02T15:16:33.103744Z", + "shell.execute_reply": "2024-07-02T15:16:33.103272Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.197818Z", - "iopub.status.busy": "2024-07-02T12:07:09.197511Z", - "iopub.status.idle": "2024-07-02T12:07:09.205619Z", - "shell.execute_reply": "2024-07-02T12:07:09.205180Z" + "iopub.execute_input": "2024-07-02T15:16:33.105508Z", + "iopub.status.busy": "2024-07-02T15:16:33.105338Z", + "iopub.status.idle": "2024-07-02T15:16:33.113464Z", + "shell.execute_reply": "2024-07-02T15:16:33.112912Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.207503Z", - "iopub.status.busy": "2024-07-02T12:07:09.207209Z", - "iopub.status.idle": "2024-07-02T12:07:09.209820Z", - "shell.execute_reply": "2024-07-02T12:07:09.209307Z" + "iopub.execute_input": "2024-07-02T15:16:33.115593Z", + "iopub.status.busy": "2024-07-02T15:16:33.115160Z", + "iopub.status.idle": "2024-07-02T15:16:33.117716Z", + "shell.execute_reply": "2024-07-02T15:16:33.117284Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.211933Z", - "iopub.status.busy": "2024-07-02T12:07:09.211620Z", - "iopub.status.idle": "2024-07-02T12:07:09.330539Z", - "shell.execute_reply": "2024-07-02T12:07:09.329946Z" + "iopub.execute_input": "2024-07-02T15:16:33.119784Z", + "iopub.status.busy": "2024-07-02T15:16:33.119483Z", + "iopub.status.idle": "2024-07-02T15:16:33.240234Z", + "shell.execute_reply": "2024-07-02T15:16:33.239660Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.332913Z", - "iopub.status.busy": "2024-07-02T12:07:09.332537Z", - "iopub.status.idle": "2024-07-02T12:07:09.439546Z", - "shell.execute_reply": "2024-07-02T12:07:09.438879Z" + "iopub.execute_input": "2024-07-02T15:16:33.242716Z", + "iopub.status.busy": "2024-07-02T15:16:33.242257Z", + "iopub.status.idle": "2024-07-02T15:16:33.345325Z", + "shell.execute_reply": "2024-07-02T15:16:33.344837Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.441953Z", - "iopub.status.busy": "2024-07-02T12:07:09.441731Z", - "iopub.status.idle": "2024-07-02T12:07:09.926340Z", - "shell.execute_reply": "2024-07-02T12:07:09.925811Z" + "iopub.execute_input": "2024-07-02T15:16:33.347642Z", + "iopub.status.busy": "2024-07-02T15:16:33.347274Z", + "iopub.status.idle": "2024-07-02T15:16:33.847085Z", + "shell.execute_reply": "2024-07-02T15:16:33.846449Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:09.928918Z", - "iopub.status.busy": "2024-07-02T12:07:09.928531Z", - "iopub.status.idle": "2024-07-02T12:07:10.007223Z", - "shell.execute_reply": "2024-07-02T12:07:10.006669Z" + "iopub.execute_input": "2024-07-02T15:16:33.849760Z", + "iopub.status.busy": "2024-07-02T15:16:33.849569Z", + "iopub.status.idle": "2024-07-02T15:16:33.921326Z", + "shell.execute_reply": "2024-07-02T15:16:33.920734Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:10.009492Z", - "iopub.status.busy": "2024-07-02T12:07:10.009118Z", - "iopub.status.idle": "2024-07-02T12:07:10.017415Z", - "shell.execute_reply": "2024-07-02T12:07:10.016968Z" + "iopub.execute_input": "2024-07-02T15:16:33.923630Z", + "iopub.status.busy": "2024-07-02T15:16:33.923266Z", + "iopub.status.idle": "2024-07-02T15:16:33.931669Z", + "shell.execute_reply": "2024-07-02T15:16:33.931217Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:10.019396Z", - "iopub.status.busy": "2024-07-02T12:07:10.019069Z", - "iopub.status.idle": "2024-07-02T12:07:10.021767Z", - "shell.execute_reply": "2024-07-02T12:07:10.021319Z" + "iopub.execute_input": "2024-07-02T15:16:33.933564Z", + "iopub.status.busy": "2024-07-02T15:16:33.933243Z", + "iopub.status.idle": "2024-07-02T15:16:33.935935Z", + "shell.execute_reply": "2024-07-02T15:16:33.935490Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:10.023754Z", - "iopub.status.busy": "2024-07-02T12:07:10.023447Z", - "iopub.status.idle": "2024-07-02T12:07:15.333825Z", - "shell.execute_reply": "2024-07-02T12:07:15.333229Z" + "iopub.execute_input": "2024-07-02T15:16:33.937940Z", + "iopub.status.busy": "2024-07-02T15:16:33.937538Z", + "iopub.status.idle": "2024-07-02T15:16:39.357576Z", + "shell.execute_reply": "2024-07-02T15:16:39.356965Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:15.336220Z", - "iopub.status.busy": "2024-07-02T12:07:15.335826Z", - "iopub.status.idle": "2024-07-02T12:07:15.344270Z", - "shell.execute_reply": "2024-07-02T12:07:15.343811Z" + "iopub.execute_input": "2024-07-02T15:16:39.359859Z", + "iopub.status.busy": "2024-07-02T15:16:39.359635Z", + "iopub.status.idle": "2024-07-02T15:16:39.368310Z", + "shell.execute_reply": "2024-07-02T15:16:39.367738Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:15.346339Z", - "iopub.status.busy": "2024-07-02T12:07:15.346012Z", - "iopub.status.idle": "2024-07-02T12:07:15.414442Z", - "shell.execute_reply": "2024-07-02T12:07:15.413948Z" + "iopub.execute_input": "2024-07-02T15:16:39.370438Z", + "iopub.status.busy": "2024-07-02T15:16:39.370050Z", + "iopub.status.idle": "2024-07-02T15:16:39.434092Z", + "shell.execute_reply": "2024-07-02T15:16:39.433485Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index fdafb004b..f4716d029 100644 --- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:18.593560Z", - "iopub.status.busy": "2024-07-02T12:07:18.593400Z", - "iopub.status.idle": "2024-07-02T12:07:20.263944Z", - "shell.execute_reply": "2024-07-02T12:07:20.263270Z" + "iopub.execute_input": "2024-07-02T15:16:42.561018Z", + "iopub.status.busy": "2024-07-02T15:16:42.560861Z", + "iopub.status.idle": "2024-07-02T15:16:44.625687Z", + "shell.execute_reply": "2024-07-02T15:16:44.624982Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:07:20.266581Z", - "iopub.status.busy": "2024-07-02T12:07:20.266205Z", - "iopub.status.idle": "2024-07-02T12:08:06.109041Z", - "shell.execute_reply": "2024-07-02T12:08:06.108401Z" + "iopub.execute_input": "2024-07-02T15:16:44.628410Z", + "iopub.status.busy": "2024-07-02T15:16:44.628235Z", + "iopub.status.idle": "2024-07-02T15:17:44.748591Z", + "shell.execute_reply": "2024-07-02T15:17:44.747911Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:06.111457Z", - "iopub.status.busy": "2024-07-02T12:08:06.111270Z", - "iopub.status.idle": "2024-07-02T12:08:07.194905Z", - "shell.execute_reply": "2024-07-02T12:08:07.194300Z" + "iopub.execute_input": "2024-07-02T15:17:44.750950Z", + "iopub.status.busy": "2024-07-02T15:17:44.750762Z", + "iopub.status.idle": "2024-07-02T15:17:45.855060Z", + "shell.execute_reply": "2024-07-02T15:17:45.854509Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.197493Z", - "iopub.status.busy": "2024-07-02T12:08:07.197237Z", - "iopub.status.idle": "2024-07-02T12:08:07.200309Z", - "shell.execute_reply": "2024-07-02T12:08:07.199874Z" + "iopub.execute_input": "2024-07-02T15:17:45.857557Z", + "iopub.status.busy": "2024-07-02T15:17:45.857136Z", + "iopub.status.idle": "2024-07-02T15:17:45.860333Z", + "shell.execute_reply": "2024-07-02T15:17:45.859895Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.202276Z", - "iopub.status.busy": "2024-07-02T12:08:07.202097Z", - "iopub.status.idle": "2024-07-02T12:08:07.205874Z", - "shell.execute_reply": "2024-07-02T12:08:07.205417Z" + "iopub.execute_input": "2024-07-02T15:17:45.862386Z", + "iopub.status.busy": "2024-07-02T15:17:45.862053Z", + "iopub.status.idle": "2024-07-02T15:17:45.865756Z", + "shell.execute_reply": "2024-07-02T15:17:45.865329Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.207818Z", - "iopub.status.busy": "2024-07-02T12:08:07.207520Z", - "iopub.status.idle": "2024-07-02T12:08:07.211075Z", - "shell.execute_reply": "2024-07-02T12:08:07.210551Z" + "iopub.execute_input": "2024-07-02T15:17:45.867774Z", + "iopub.status.busy": "2024-07-02T15:17:45.867526Z", + "iopub.status.idle": "2024-07-02T15:17:45.871521Z", + "shell.execute_reply": "2024-07-02T15:17:45.871083Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.213131Z", - "iopub.status.busy": "2024-07-02T12:08:07.212769Z", - "iopub.status.idle": "2024-07-02T12:08:07.215484Z", - "shell.execute_reply": "2024-07-02T12:08:07.215039Z" + "iopub.execute_input": "2024-07-02T15:17:45.873483Z", + "iopub.status.busy": "2024-07-02T15:17:45.873088Z", + "iopub.status.idle": "2024-07-02T15:17:45.875944Z", + "shell.execute_reply": "2024-07-02T15:17:45.875421Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:07.217418Z", - "iopub.status.busy": "2024-07-02T12:08:07.217121Z", - "iopub.status.idle": "2024-07-02T12:08:41.707148Z", - "shell.execute_reply": "2024-07-02T12:08:41.706563Z" + "iopub.execute_input": "2024-07-02T15:17:45.878104Z", + "iopub.status.busy": "2024-07-02T15:17:45.877703Z", + "iopub.status.idle": "2024-07-02T15:18:18.817812Z", + "shell.execute_reply": "2024-07-02T15:18:18.817242Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9e20fdede857444e8054f80d2f1060d4", + "model_id": "6d37081f0d674141ab48e998533cdac5", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "301ab18342ea43859b3e69cf6784234e", + "model_id": "6181f2ac640b4d5694a1537900a59156", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:41.710056Z", - "iopub.status.busy": "2024-07-02T12:08:41.709655Z", - "iopub.status.idle": "2024-07-02T12:08:42.388632Z", - "shell.execute_reply": "2024-07-02T12:08:42.388139Z" + "iopub.execute_input": "2024-07-02T15:18:18.820319Z", + "iopub.status.busy": "2024-07-02T15:18:18.819979Z", + "iopub.status.idle": "2024-07-02T15:18:19.488167Z", + "shell.execute_reply": "2024-07-02T15:18:19.487624Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:42.390931Z", - "iopub.status.busy": "2024-07-02T12:08:42.390474Z", - "iopub.status.idle": "2024-07-02T12:08:45.214722Z", - "shell.execute_reply": "2024-07-02T12:08:45.214183Z" + "iopub.execute_input": "2024-07-02T15:18:19.490558Z", + "iopub.status.busy": "2024-07-02T15:18:19.490113Z", + "iopub.status.idle": "2024-07-02T15:18:22.347830Z", + "shell.execute_reply": "2024-07-02T15:18:22.347301Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:08:45.217043Z", - "iopub.status.busy": "2024-07-02T12:08:45.216683Z", - "iopub.status.idle": "2024-07-02T12:09:17.125267Z", - "shell.execute_reply": "2024-07-02T12:09:17.124709Z" + "iopub.execute_input": "2024-07-02T15:18:22.350104Z", + "iopub.status.busy": "2024-07-02T15:18:22.349819Z", + "iopub.status.idle": "2024-07-02T15:18:55.684419Z", + "shell.execute_reply": "2024-07-02T15:18:55.683880Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bd3e5cdb83b549b9ac1d29639e5d5848", + "model_id": "ee454cb23f344e94bf0306f6bd70e6ef", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:17.127732Z", - "iopub.status.busy": "2024-07-02T12:09:17.127284Z", - "iopub.status.idle": "2024-07-02T12:09:31.678319Z", - "shell.execute_reply": "2024-07-02T12:09:31.677670Z" + "iopub.execute_input": "2024-07-02T15:18:55.686609Z", + "iopub.status.busy": "2024-07-02T15:18:55.686279Z", + "iopub.status.idle": "2024-07-02T15:19:09.955147Z", + "shell.execute_reply": "2024-07-02T15:19:09.954600Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:31.680982Z", - "iopub.status.busy": "2024-07-02T12:09:31.680776Z", - "iopub.status.idle": "2024-07-02T12:09:35.425388Z", - "shell.execute_reply": "2024-07-02T12:09:35.424766Z" + "iopub.execute_input": "2024-07-02T15:19:09.957540Z", + "iopub.status.busy": "2024-07-02T15:19:09.957240Z", + "iopub.status.idle": "2024-07-02T15:19:13.735071Z", + "shell.execute_reply": "2024-07-02T15:19:13.734559Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:35.427710Z", - "iopub.status.busy": "2024-07-02T12:09:35.427361Z", - "iopub.status.idle": "2024-07-02T12:09:36.906817Z", - "shell.execute_reply": "2024-07-02T12:09:36.906253Z" + "iopub.execute_input": "2024-07-02T15:19:13.737328Z", + "iopub.status.busy": "2024-07-02T15:19:13.736989Z", + "iopub.status.idle": "2024-07-02T15:19:15.136499Z", + "shell.execute_reply": "2024-07-02T15:19:15.135918Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7bb7e722917409d87abfe3e6a57fae6", + "model_id": "22d36bebc1d941d19f0aa385e194e320", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:36.909119Z", - "iopub.status.busy": "2024-07-02T12:09:36.908767Z", - "iopub.status.idle": "2024-07-02T12:09:36.938376Z", - "shell.execute_reply": "2024-07-02T12:09:36.937849Z" + "iopub.execute_input": "2024-07-02T15:19:15.138723Z", + "iopub.status.busy": "2024-07-02T15:19:15.138396Z", + "iopub.status.idle": "2024-07-02T15:19:15.166918Z", + "shell.execute_reply": "2024-07-02T15:19:15.166433Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:36.940953Z", - "iopub.status.busy": "2024-07-02T12:09:36.940583Z", - "iopub.status.idle": "2024-07-02T12:09:42.990503Z", - "shell.execute_reply": "2024-07-02T12:09:42.989906Z" + "iopub.execute_input": "2024-07-02T15:19:15.169310Z", + "iopub.status.busy": "2024-07-02T15:19:15.168971Z", + "iopub.status.idle": "2024-07-02T15:19:21.253732Z", + "shell.execute_reply": "2024-07-02T15:19:21.253153Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:42.992659Z", - "iopub.status.busy": "2024-07-02T12:09:42.992472Z", - "iopub.status.idle": "2024-07-02T12:09:43.049918Z", - "shell.execute_reply": "2024-07-02T12:09:43.049421Z" + "iopub.execute_input": "2024-07-02T15:19:21.256000Z", + "iopub.status.busy": "2024-07-02T15:19:21.255599Z", + "iopub.status.idle": "2024-07-02T15:19:21.311129Z", + "shell.execute_reply": "2024-07-02T15:19:21.310514Z" }, "nbsphinx": "hidden" }, @@ -1038,7 +1038,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "004250ad803f48e690d7de9d8df2a5d4": { + "03565a80c7d34c6293339e9385fd0f09": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1056,7 +1056,7 @@ "text_color": null } }, - "09ba332d59f94952875cd79ebffa12b3": { + "0aae918c08c84bed9ae02fe6e0c0d703": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1071,15 +1071,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_292d8527f19545118904c48eb804b159", + "layout": "IPY_MODEL_80bc86fa44ab4ddb9d7fbfcd03b7da23", "placeholder": "​", - "style": "IPY_MODEL_ebe541c7a5ed4c939a5f7b993c9dee23", + "style": "IPY_MODEL_5a6ca2db026040538be2877748a39874", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 20.84it/s]" + "value": "number of examples processed for checking labels: 100%" } }, - "14f7f9d4ea3e4f77a067725d5a561423": { + "0b8a0362dd6d4e8faa60de3132d8f2b3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1132,60 +1132,48 @@ "width": null } }, - "1c5deedf3c2d452d9820d81060a3a997": { - "model_module": "@jupyter-widgets/base", + "0c57b519892e41bcb28985872ce030c7": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0d5c01051776423480b74fabbe29251b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a5072a4bea8140e2808839dad337d950", + "placeholder": "​", + "style": "IPY_MODEL_a2f21f1a521e4c718452c19f8256841e", + "tabbable": null, + "tooltip": null, + "value": " 4997683/4997683 [00:33<00:00, 149090.50it/s]" } }, - "292d8527f19545118904c48eb804b159": { + "0ea451f86f7b4b05957d9adf3ae7f735": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1238,30 +1226,73 @@ "width": null } }, - "2b3a352a3891401497f4005cb5fc04d1": { + "16f22dbcf3de44d7b4fd798a1f0b853a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "22d36bebc1d941d19f0aa385e194e320": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ac2a26cb4281477983b7746968ff3e52", + "IPY_MODEL_3401bd6344664b4f85cbb90cf38682a3", + "IPY_MODEL_4102b17c3b13403aa9b3d80fb7df75ac" + ], + "layout": "IPY_MODEL_ecae615274e34bf59e8e58c649c294bc", + "tabbable": null, + "tooltip": null + } + }, + "3401bd6344664b4f85cbb90cf38682a3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1c5deedf3c2d452d9820d81060a3a997", - "placeholder": "​", - "style": "IPY_MODEL_c90d5255c6884dd0acaca4ca4a0be555", + "layout": "IPY_MODEL_0b8a0362dd6d4e8faa60de3132d8f2b3", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f3ecd743295c4f619abad363dcb05fab", "tabbable": null, "tooltip": null, - "value": "100%" + "value": 30.0 } }, - "2cda7dd9aaa748a58027afd119786f13": { + "4102b17c3b13403aa9b3d80fb7df75ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1276,38 +1307,83 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3bc90b1caf7040428b193fe64f002cf0", + "layout": "IPY_MODEL_b2f361ba3c35411587bfb94055421b84", "placeholder": "​", - "style": "IPY_MODEL_004250ad803f48e690d7de9d8df2a5d4", + "style": "IPY_MODEL_97090cf694824c77b08cfdb17f057e3e", "tabbable": null, "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" + "value": " 30/30 [00:01<00:00, 21.42it/s]" } }, - "2d192adc8c47477a9a6eecca9e36e444": { + "544233229d944fd690c9e89d8792bbe8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5c44620b24814621897a324f8628f9b0", - "placeholder": "​", - "style": "IPY_MODEL_8c0884f36e9a43fcb603fc1d8a5ac45d", + "layout": "IPY_MODEL_f14aada5b0eb4e5ba3b431cb7b518da6", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cbbd654031b747468c432f9278836125", "tabbable": null, "tooltip": null, - "value": " 4997683/4997683 [00:31<00:00, 155900.40it/s]" + "value": 30.0 + } + }, + "5a6ca2db026040538be2877748a39874": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6181f2ac640b4d5694a1537900a59156": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0aae918c08c84bed9ae02fe6e0c0d703", + "IPY_MODEL_eaaf30bb31f7495d8fbee7118acc211f", + "IPY_MODEL_cd8af1b2cbd542f4869d02c1d973e524" + ], + "layout": "IPY_MODEL_afdd70b438584e8381748e28bdc60eb0", + "tabbable": null, + "tooltip": null } }, - "301ab18342ea43859b3e69cf6784234e": { + "6d37081f0d674141ab48e998533cdac5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1322,16 +1398,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_689cb1d684d746f081730b6005754954", - "IPY_MODEL_87cc943b387f45588eebeaa6c9ffd2b2", - "IPY_MODEL_621b8d69d18c4672a45b4f9791a34f0e" + "IPY_MODEL_b4d580e3f19a47c6a1dde0776a4e5606", + "IPY_MODEL_544233229d944fd690c9e89d8792bbe8", + "IPY_MODEL_b8c4edcee9764b17abf836ac1f23e926" ], - "layout": "IPY_MODEL_39fca7453c7a49f9b90660089493b14b", + "layout": "IPY_MODEL_98b4559124704d61a177e8a88a9d3c8a", "tabbable": null, "tooltip": null } }, - "39fca7453c7a49f9b90660089493b14b": { + "7014f38f051242f19c5f77d4e2e3dd69": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1384,7 +1460,33 @@ "width": null } }, - "3bc90b1caf7040428b193fe64f002cf0": { + "72b5647ddfd149559d4a948d2411a55b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f3a91f9853db4fac966be7c379809f9c", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ce421d89a8e8429486414e8e5f2fb0b9", + "tabbable": null, + "tooltip": null, + "value": 4997683.0 + } + }, + "80bc86fa44ab4ddb9d7fbfcd03b7da23": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1437,23 +1539,43 @@ "width": null } }, - "40f6e020e4814cb8b0192d390bc249e5": { + "9479cd74f7ae4ab98e7dd6164deed30a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "97090cf694824c77b08cfdb17f057e3e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "443d77bb1d4e408f9a9d727916a4a908": { + "98b4559124704d61a177e8a88a9d3c8a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1506,25 +1628,7 @@ "width": null } }, - "46d41e19a14142bbad7d9d0b73fd7a54": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "50d3a7dfb3964711a029df0085e31f7b": { + "9ba76afb638e410e891187e79a458dac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1577,7 +1681,7 @@ "width": null } }, - "5148cb9775f14bd194e8f971e6975671": { + "9f4790501bef48328e2bd3eee0152689": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1630,23 +1734,25 @@ "width": null } }, - "58e7f42a53f94f1bbd0867761587546d": { + "a2f21f1a521e4c718452c19f8256841e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5c44620b24814621897a324f8628f9b0": { + "a5072a4bea8140e2808839dad337d950": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1699,7 +1805,7 @@ "width": null } }, - "621b8d69d18c4672a45b4f9791a34f0e": { + "ac2a26cb4281477983b7746968ff3e52": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1714,45 +1820,22 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8d802ab243544f9d9299452347a70569", + "layout": "IPY_MODEL_0ea451f86f7b4b05957d9adf3ae7f735", "placeholder": "​", - "style": "IPY_MODEL_9c2434fab6d1467eb5531b5dd54033e7", + "style": "IPY_MODEL_cf74a277aa3141788d774f9065d2366d", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:22<00:00,  1.35it/s]" + "value": "images processed using softmin: 100%" } }, - "689cb1d684d746f081730b6005754954": { - "model_module": "@jupyter-widgets/controls", + "afdd70b438584e8381748e28bdc60eb0": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_14f7f9d4ea3e4f77a067725d5a561423", - "placeholder": "​", - "style": "IPY_MODEL_df3ac9cfe3ae41ceb9de218f2d91ba0b", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" - } - }, - "6ec1d65e68c649a287aa126587fe0f81": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -1798,7 +1881,7 @@ "width": null } }, - "6fad649441314d58a415b41b259d3ca9": { + "b27b9980b1934cddbaa61f473c83ad55": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1851,59 +1934,7 @@ "width": null } }, - "7fe63f20c00f4319a0964ba731ec434b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_acd636f3fd2248ec95449072f24f9e7c", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_58e7f42a53f94f1bbd0867761587546d", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "87cc943b387f45588eebeaa6c9ffd2b2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b345e277b7bf4c62b4c637851b6c0214", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_aa5c0233f9e24cfb94a975b0bdddc4f5", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "8c0884f36e9a43fcb603fc1d8a5ac45d": { + "b2ec5e9d337d409f8f6128aa41a5d79f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1921,7 +1952,7 @@ "text_color": null } }, - "8d802ab243544f9d9299452347a70569": { + "b2f361ba3c35411587bfb94055421b84": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1974,49 +2005,92 @@ "width": null } }, - "9c2434fab6d1467eb5531b5dd54033e7": { + "b4d580e3f19a47c6a1dde0776a4e5606": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9f4790501bef48328e2bd3eee0152689", + "placeholder": "​", + "style": "IPY_MODEL_9479cd74f7ae4ab98e7dd6164deed30a", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" + } + }, + "b8c4edcee9764b17abf836ac1f23e926": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7014f38f051242f19c5f77d4e2e3dd69", + "placeholder": "​", + "style": "IPY_MODEL_b2ec5e9d337d409f8f6128aa41a5d79f", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 785.11it/s]" + } + }, + "cbbd654031b747468c432f9278836125": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "9e20fdede857444e8054f80d2f1060d4": { + "cd8af1b2cbd542f4869d02c1d973e524": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2cda7dd9aaa748a58027afd119786f13", - "IPY_MODEL_7fe63f20c00f4319a0964ba731ec434b", - "IPY_MODEL_cec723cee8f249199133eca7dc012de4" - ], - "layout": "IPY_MODEL_443d77bb1d4e408f9a9d727916a4a908", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b27b9980b1934cddbaa61f473c83ad55", + "placeholder": "​", + "style": "IPY_MODEL_03565a80c7d34c6293339e9385fd0f09", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 30/30 [00:20<00:00,  1.41it/s]" } }, - "aa5c0233f9e24cfb94a975b0bdddc4f5": { + "ce421d89a8e8429486414e8e5f2fb0b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2032,7 +2106,25 @@ "description_width": "" } }, - "acd636f3fd2248ec95449072f24f9e7c": { + "cf74a277aa3141788d774f9065d2366d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d4139e678fc14fc5b5f22a2a0dd84027": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2085,7 +2177,7 @@ "width": null } }, - "b345e277b7bf4c62b4c637851b6c0214": { + "e5f3cab627be4d4a8d76b371ca9346f3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2138,7 +2230,33 @@ "width": null } }, - "b3df8f2c00a04d5b832de9fc959e9aae": { + "eaaf30bb31f7495d8fbee7118acc211f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e5f3cab627be4d4a8d76b371ca9346f3", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_16f22dbcf3de44d7b4fd798a1f0b853a", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "ecae615274e34bf59e8e58c649c294bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2191,30 +2309,7 @@ "width": null } }, - "ba6883ab9881467ba869997da1c9ea0e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6fad649441314d58a415b41b259d3ca9", - "placeholder": "​", - "style": "IPY_MODEL_46d41e19a14142bbad7d9d0b73fd7a54", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "bd3e5cdb83b549b9ac1d29639e5d5848": { + "ee454cb23f344e94bf0306f6bd70e6ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2229,83 +2324,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2b3a352a3891401497f4005cb5fc04d1", - "IPY_MODEL_d0ee84f27abf457094d435faffe772aa", - "IPY_MODEL_2d192adc8c47477a9a6eecca9e36e444" + "IPY_MODEL_f58763e1734248fda5001bdfeb216209", + "IPY_MODEL_72b5647ddfd149559d4a948d2411a55b", + "IPY_MODEL_0d5c01051776423480b74fabbe29251b" ], - "layout": "IPY_MODEL_d13c21dbbe954346b0c709b6d940db68", + "layout": "IPY_MODEL_d4139e678fc14fc5b5f22a2a0dd84027", "tabbable": null, "tooltip": null } }, - "c90d5255c6884dd0acaca4ca4a0be555": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "cec723cee8f249199133eca7dc012de4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5148cb9775f14bd194e8f971e6975671", - "placeholder": "​", - "style": "IPY_MODEL_e1313619907446f882ce04af77ca7ac9", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 789.00it/s]" - } - }, - "d0ee84f27abf457094d435faffe772aa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6ec1d65e68c649a287aa126587fe0f81", - "max": 4997683.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_40f6e020e4814cb8b0192d390bc249e5", - "tabbable": null, - "tooltip": null, - "value": 4997683.0 - } - }, - "d13c21dbbe954346b0c709b6d940db68": { + "f14aada5b0eb4e5ba3b431cb7b518da6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2358,87 +2386,60 @@ "width": null } }, - "dbe2204bfeee42de9c8c9d92d9dc0eb7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b3df8f2c00a04d5b832de9fc959e9aae", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f6b0d85730d34497bc3daf8d027415bc", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "df3ac9cfe3ae41ceb9de218f2d91ba0b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "e1313619907446f882ce04af77ca7ac9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "ebe541c7a5ed4c939a5f7b993c9dee23": { - "model_module": "@jupyter-widgets/controls", + "f3a91f9853db4fac966be7c379809f9c": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "f6b0d85730d34497bc3daf8d027415bc": { + "f3ecd743295c4f619abad363dcb05fab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2454,28 +2455,27 @@ "description_width": "" } }, - "f7bb7e722917409d87abfe3e6a57fae6": { + "f58763e1734248fda5001bdfeb216209": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ba6883ab9881467ba869997da1c9ea0e", - "IPY_MODEL_dbe2204bfeee42de9c8c9d92d9dc0eb7", - "IPY_MODEL_09ba332d59f94952875cd79ebffa12b3" - ], - "layout": "IPY_MODEL_50d3a7dfb3964711a029df0085e31f7b", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9ba76afb638e410e891187e79a458dac", + "placeholder": "​", + "style": "IPY_MODEL_0c57b519892e41bcb28985872ce030c7", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 2f967cbe9..42ddeaa94 100644 --- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:45.418874Z", - "iopub.status.busy": "2024-07-02T12:09:45.418417Z", - "iopub.status.idle": "2024-07-02T12:09:46.521891Z", - "shell.execute_reply": "2024-07-02T12:09:46.521319Z" + "iopub.execute_input": "2024-07-02T15:19:23.685217Z", + "iopub.status.busy": "2024-07-02T15:19:23.685050Z", + "iopub.status.idle": "2024-07-02T15:19:24.935394Z", + "shell.execute_reply": "2024-07-02T15:19:24.934810Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-02 12:09:45-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-02 15:19:23-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.249, 2400:52e0:1a00::871:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.249|:443... connected.\r\n" + "185.93.1.244, 2400:52e0:1a00::871:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... connected.\r\n" ] }, { @@ -122,9 +122,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.95MB/s in 0.2s \r\n", "\r\n", - "2024-07-02 12:09:45 (6.77 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-02 15:19:24 (5.95 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -144,9 +144,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-02 12:09:46-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.236.81, 16.182.109.113, 3.5.9.115, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.236.81|:443... connected.\r\n", + "--2024-07-02 15:19:24-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.131.75, 52.217.90.4, 52.217.236.25, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.131.75|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -167,9 +167,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 92.7MB/s in 0.2s \r\n", "\r\n", - "2024-07-02 12:09:46 (150 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-02 15:19:24 (92.7 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -186,10 +186,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:46.524639Z", - "iopub.status.busy": "2024-07-02T12:09:46.524272Z", - "iopub.status.idle": "2024-07-02T12:09:47.827762Z", - "shell.execute_reply": "2024-07-02T12:09:47.827179Z" + "iopub.execute_input": "2024-07-02T15:19:24.937602Z", + "iopub.status.busy": "2024-07-02T15:19:24.937420Z", + "iopub.status.idle": "2024-07-02T15:19:26.157450Z", + "shell.execute_reply": "2024-07-02T15:19:26.156955Z" }, "nbsphinx": "hidden" }, @@ -200,7 +200,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -226,10 +226,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:47.830413Z", - "iopub.status.busy": "2024-07-02T12:09:47.829987Z", - "iopub.status.idle": "2024-07-02T12:09:47.833472Z", - "shell.execute_reply": "2024-07-02T12:09:47.833017Z" + "iopub.execute_input": "2024-07-02T15:19:26.159981Z", + "iopub.status.busy": "2024-07-02T15:19:26.159618Z", + "iopub.status.idle": "2024-07-02T15:19:26.162912Z", + "shell.execute_reply": "2024-07-02T15:19:26.162448Z" } }, "outputs": [], @@ -279,10 +279,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:47.835687Z", - "iopub.status.busy": "2024-07-02T12:09:47.835327Z", - "iopub.status.idle": "2024-07-02T12:09:47.838382Z", - "shell.execute_reply": "2024-07-02T12:09:47.837903Z" + "iopub.execute_input": "2024-07-02T15:19:26.165013Z", + "iopub.status.busy": "2024-07-02T15:19:26.164698Z", + "iopub.status.idle": "2024-07-02T15:19:26.167499Z", + "shell.execute_reply": "2024-07-02T15:19:26.167088Z" }, "nbsphinx": "hidden" }, @@ -300,10 +300,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:47.840488Z", - "iopub.status.busy": "2024-07-02T12:09:47.840076Z", - "iopub.status.idle": "2024-07-02T12:09:56.981305Z", - "shell.execute_reply": "2024-07-02T12:09:56.980685Z" + "iopub.execute_input": "2024-07-02T15:19:26.169329Z", + "iopub.status.busy": "2024-07-02T15:19:26.169155Z", + "iopub.status.idle": "2024-07-02T15:19:35.271117Z", + "shell.execute_reply": "2024-07-02T15:19:35.270638Z" } }, "outputs": [], @@ -377,10 +377,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:56.983968Z", - "iopub.status.busy": "2024-07-02T12:09:56.983751Z", - "iopub.status.idle": "2024-07-02T12:09:56.989422Z", - "shell.execute_reply": "2024-07-02T12:09:56.988975Z" + "iopub.execute_input": "2024-07-02T15:19:35.273414Z", + "iopub.status.busy": "2024-07-02T15:19:35.273192Z", + "iopub.status.idle": "2024-07-02T15:19:35.278675Z", + "shell.execute_reply": "2024-07-02T15:19:35.278216Z" }, "nbsphinx": "hidden" }, @@ -420,10 +420,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:56.991449Z", - "iopub.status.busy": "2024-07-02T12:09:56.991142Z", - "iopub.status.idle": "2024-07-02T12:09:57.333959Z", - "shell.execute_reply": "2024-07-02T12:09:57.333418Z" + "iopub.execute_input": "2024-07-02T15:19:35.280475Z", + "iopub.status.busy": "2024-07-02T15:19:35.280305Z", + "iopub.status.idle": "2024-07-02T15:19:35.621923Z", + "shell.execute_reply": "2024-07-02T15:19:35.621363Z" } }, "outputs": [], @@ -460,10 +460,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:57.336408Z", - "iopub.status.busy": "2024-07-02T12:09:57.336047Z", - "iopub.status.idle": "2024-07-02T12:09:57.340566Z", - "shell.execute_reply": "2024-07-02T12:09:57.340088Z" + "iopub.execute_input": "2024-07-02T15:19:35.624478Z", + "iopub.status.busy": "2024-07-02T15:19:35.624094Z", + "iopub.status.idle": "2024-07-02T15:19:35.628348Z", + "shell.execute_reply": "2024-07-02T15:19:35.627829Z" } }, "outputs": [ @@ -535,10 +535,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:57.342536Z", - "iopub.status.busy": "2024-07-02T12:09:57.342207Z", - "iopub.status.idle": "2024-07-02T12:09:59.889796Z", - "shell.execute_reply": "2024-07-02T12:09:59.889167Z" + "iopub.execute_input": "2024-07-02T15:19:35.630446Z", + "iopub.status.busy": "2024-07-02T15:19:35.630129Z", + "iopub.status.idle": "2024-07-02T15:19:38.137637Z", + "shell.execute_reply": "2024-07-02T15:19:38.137007Z" } }, "outputs": [], @@ -560,10 +560,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.892826Z", - "iopub.status.busy": "2024-07-02T12:09:59.892074Z", - "iopub.status.idle": "2024-07-02T12:09:59.896257Z", - "shell.execute_reply": "2024-07-02T12:09:59.895794Z" + "iopub.execute_input": "2024-07-02T15:19:38.140589Z", + "iopub.status.busy": "2024-07-02T15:19:38.140060Z", + "iopub.status.idle": "2024-07-02T15:19:38.143991Z", + "shell.execute_reply": "2024-07-02T15:19:38.143492Z" } }, "outputs": [ @@ -599,10 +599,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.898108Z", - "iopub.status.busy": "2024-07-02T12:09:59.897930Z", - "iopub.status.idle": "2024-07-02T12:09:59.903451Z", - "shell.execute_reply": "2024-07-02T12:09:59.902896Z" + "iopub.execute_input": "2024-07-02T15:19:38.145836Z", + "iopub.status.busy": "2024-07-02T15:19:38.145654Z", + "iopub.status.idle": "2024-07-02T15:19:38.150999Z", + "shell.execute_reply": "2024-07-02T15:19:38.150467Z" } }, "outputs": [ @@ -780,10 +780,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.905627Z", - "iopub.status.busy": "2024-07-02T12:09:59.905242Z", - "iopub.status.idle": "2024-07-02T12:09:59.932087Z", - "shell.execute_reply": "2024-07-02T12:09:59.931495Z" + "iopub.execute_input": "2024-07-02T15:19:38.153003Z", + "iopub.status.busy": "2024-07-02T15:19:38.152675Z", + "iopub.status.idle": "2024-07-02T15:19:38.178476Z", + "shell.execute_reply": "2024-07-02T15:19:38.177990Z" } }, "outputs": [ @@ -885,10 +885,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.934435Z", - "iopub.status.busy": "2024-07-02T12:09:59.934079Z", - "iopub.status.idle": "2024-07-02T12:09:59.939450Z", - "shell.execute_reply": "2024-07-02T12:09:59.938896Z" + "iopub.execute_input": "2024-07-02T15:19:38.180581Z", + "iopub.status.busy": "2024-07-02T15:19:38.180244Z", + "iopub.status.idle": "2024-07-02T15:19:38.184905Z", + "shell.execute_reply": "2024-07-02T15:19:38.184358Z" } }, "outputs": [ @@ -962,10 +962,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:09:59.941692Z", - "iopub.status.busy": "2024-07-02T12:09:59.941362Z", - "iopub.status.idle": "2024-07-02T12:10:01.337767Z", - "shell.execute_reply": "2024-07-02T12:10:01.337179Z" + "iopub.execute_input": "2024-07-02T15:19:38.187003Z", + "iopub.status.busy": "2024-07-02T15:19:38.186684Z", + "iopub.status.idle": "2024-07-02T15:19:39.591022Z", + "shell.execute_reply": "2024-07-02T15:19:39.590483Z" } }, "outputs": [ @@ -1137,10 +1137,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:10:01.339986Z", - "iopub.status.busy": "2024-07-02T12:10:01.339664Z", - "iopub.status.idle": "2024-07-02T12:10:01.343749Z", - "shell.execute_reply": "2024-07-02T12:10:01.343244Z" + "iopub.execute_input": "2024-07-02T15:19:39.593197Z", + "iopub.status.busy": "2024-07-02T15:19:39.592842Z", + "iopub.status.idle": "2024-07-02T15:19:39.596856Z", + "shell.execute_reply": "2024-07-02T15:19:39.596378Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index 56f3b4982de97e84ccef583f6d5f701af07b67c0..02c17e98369c58e0252f31e571be6a624d73a094 100644 GIT binary patch delta 62 zcmX>tep-A(E~8<7vSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Rvazw5fw_U%=6Q^|TmWhL5*7de delta 62 zcmX>tep-A(E~BBDQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg=6Q^|TmXE)66pW{ diff --git a/master/.doctrees/tutorials/clean_learning/tabular.doctree b/master/.doctrees/tutorials/clean_learning/tabular.doctree index 23aebfda45202106996b07828a5a4612cd4fb161..04d9eb002612f96d1f7905958b4af269cbb80f3a 100644 GIT binary patch delta 64 zcmcb6i}~&?<_%{!4fB&NQ_4-#&Ge0v&67+m(#(?6%u_8a6AjH1Qw+>gjSSK(EDRD2 U4NQ`ajm-?q4a_$G delta 64 zcmcb6i}~&?<_%{!4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW UQVa|Xl9Nr$lT0`Nka{vGU diff --git a/master/.doctrees/tutorials/clean_learning/text.doctree b/master/.doctrees/tutorials/clean_learning/text.doctree index 8402a8744ae32bf861682dc1609ac54306f36ccd..12d81867237edecf4369d74a6c74dce83e96de77 100644 GIT binary patch delta 13390 zcmeHNS*%@E8O}bpgtkBl>7Yot=UC>_Va)@H#u_z&NTERztZnKVp<-!i38J>Wg_0&} zjKLGxDWoDHh6f3$HhM@@Xdjf|gIIaM2N~jsk*GI@_)^7hpM9^rx5cwCzMeE`lXi8l z^{@4R!~g&H@S!D#4=p+R(?xV=u<>*9cD0@d?WM^oWSxw4RJ%xYVk8?w93>)fu81b; z*gqD%areWUtM~Mm?!C7EiM=1}|7h=J^@;1NW&OW@aZUfVy_5Z~?_J&hz}^e{7w`Mn zyIXj8|IhoI4y^1yGPSb*lPT6;dEnCeFI%z2{;BTLd$;d#Tej`oy=B`Dx96U;JGkBb zUH7kE67Fz!-JZ5=+qM0!tG4XxKlH}R{#Ooc{=dI-(Vx2g^B*`kJou{zFX{j8fpvX> zKfia7^mjkFqAwp@wYc57*}1>>pn8`}k3II_vDLj`1<#bnYyuHUYv+;(Iye@*kBajC z-;O^}UPNA6e(ai~)93bx5=45*Mbtsq=#9-hGV3Laj%GuKpDQkEJ~#c$Yv=YT6)O9X zlMPwW=nUnS#cYLSgee(@pDQkEKKJAae9lMe9A(ZRWMQ0gM0m}uh*1fdeH(0XQS-U2 zKf7pQj~hX8QlB5-$CK`2i?dM91j**wnU)V!HEJUW2$U!ja4R=x|BbahQ z$i#+$78f-qTl(wE7Iv-E%*(7}5?W*}z2}-HB6TL2TH62X@q@)R51m+X?1JBa{%Dt{ z7);7|RECdH>4iy_M8l~uUf6om6VqUw{c zSIg=bw|7?5Q%_Zc`qQ1xvy+wz&og6LdHfeej5EYDAB~DJk^1r5I?D&gKhybD)hnDs z)c-tEZR{1!AnW%}b=KF9e6w0SNw_hV25p%0$TQ)Q7z*cc-sd0(_y4i8YeClsqG?c8 zWUqx*&M?YBMaqegvSP!S1_#cW_)gW!!DXrx4T9q%Oe5924vYjJ5)Z))9$Macw(5@D zHKh9F{toFCwjG8+>aRXob-RWzX`)32Ay<--CKzT?q{%z3vmb1@V`ArmZUdX-Lh>w8 ztU`J^YCV%dH#f%HICkAI@l-XLREUo87zslzN|&YP90Ay9eNMGI)p>7yU`1!q;O1{v zN2=2P^)L2!Xs@vSFdkJudbGNHtzKvE{P6lcTUYJdx2Cr$CP^icIRPL7IqevAK5477 zb~yUr?rvx2f+9l=E_$PS`RK>DjZRJN=dV_yug$I6x~9JM zP-jK25Rt1N-_W^c(i0kUCO}YTU}WmF6(G{2B*-ugrhnKZC%9EAts)mv2u-A8KKR7B z2!gVdYxnHgfD_zG01njby`X}asHEXRXhuK{wR`nHfE#@TqU#;UtDAa-GlUj!=bfm| z>k1rBsq7ReNkDF_lSvtHXOdt5GB|!+^OjT#;;ocALXwOKRyt51B6W&OBlFxs2PSAuz>bGC0F6n0ComVm7 zg+Pzq;Vqz5!BZKH)_HK^y3Sn-x>`^bQgTA!odhE`6HBv564ANjYicHzwIni`h>IXj zo^efraArBDTw#%i3Cay0UOYD-d}-tXh5;4#-|$wY>xC~>SFII<&H*1x03*uVK%kI0 z5ihg_4>nW)QXuv8qqEds(DeH8=d0y|w_mQlTuqIBH#HwsqqCLA`Kw1?9uT{AO|QP^ zRJFWUh|CW`*v*gv#uaDWfbXfZG$w5l<=!~r`0(9^($`Z8vZa&eUPR&HT&;obF)1Jw zyn!OPf9V|Ud;RjxbCcBg1SiY1h5kTRy|yV)nK%<%reOxpXkXzVsea`*(7xg!hT8Xq z-&TveDrFtOsWJu*QPtU>mdh6 zcT6l9K-3Qdmxv)qgu1`@2!eXLiHr_E z%!)xW0Zg6~h{aI&yC?!F5>{9^GeIe}j={PZpS=lM@p}5ySqcaP=BWotWeNgl#gkI( z#eEVgq7E3Q$_(xbhj8`v=X7rA6%IjL3Hb&#h@2yRs7Q!q9^RQ*uo2YMa+-4B5f%ZydH@IynPcSEp;bIr*mkIRPcNFd zdJVTXo_3o6yJZRPIf(?aLlu_6_)gM{zvk`5w}f9R6K5P}>3QZOu8^5z{_4Vgk;ZZE zIaRIb6`~3);4~3TSgs(sOn3sK1C<++GIxry_NCg`wNnJ>vJSt%xu^#@3>;n;JsE{^ z?o6?33cX&mkuhAUH5qml#1>FJ=WO8z8%k{zat3yVqon$Ut2&!{#bXTRZu`}p3%bfc z7py=9g!n)uP1gejJL+USZ8F2^r;Uw7839N98_I72*NrfFMmmu|b*8iKGi6vI^bfRw z%s}vcgsU@Bp;rZxPG_k@;Q-L5YbR*8uy-qW+i0iTRe@kjCqz~tzofmV4O&7X$pML{ z<*w^hLiYtCZ)g;ZMvJ;o2Lli1wGn!Na_%`4aSc8b1X^QhL=yw6iG&P4kqO1-{q20hK3Lg{+1id7tHK#V9_!&%ZL^)gRz0AO`@Jwge z$5M*UhhO2!8o?XFuUm+yK6&fJ>Mr2a*dwOVW)TDh5d2F5?QxTki*at31;vm3qrPe0Z4w$hn^ZU=&mRL0JTVNi=|oYjXsE|H{8&< z5t%yr5QyoDhf(v@uiVgC)XivVDRi_@H9%pZ1_UKM07+5QQe|fAh%E~yt%glhMhetZ z#v(jOzCtg^YL5;?4f8nHZ1xx$Gg3)3&7uHzMK3K{1?ahW4d(Sg>*l6b9KF47tyvnq zH*3-N;w_ykyCgc;Fb%7olYkj9zzD~E;)W$SV8)xEG(}G!IKm;4l5#_hga4zPyC%U~ zI8*_%U8B)Ex;BKujzhfZ`mv1@m#?+!n4DYbfkMC@ zsKwESqp%yGMu(FI)HKx1v>OTy$mVsH55D&O>PyvB>0#zWsgRy$IL*?H%>u6%Mh1lj z20AT3UIfYWrftauXH9tr=VXDzru70#)|T+uJLDo#3Un{BB;Z@jBzOW)IWqdpR!dXg zT`ChkApvLD2|yLy0mZb`5-CbZ{h>Q1W?P$0GL1Sz{mf^fcZDO&bXsoP0lniev;ex} z)n}RN71lG8wrj*Z^e?e!B>@#j z(PT($ikT+k?EstuYgX8GMidG=#ujHA@9n&=2gy~MNy-eWRH0MTK0*r+RL2wA2s7=K zNoiv;lx5%zMF5(*P055Fu0f-p`}$kYR~OH%P<2MPl%ux-iVz+xXOhsxH-Q1JEQgt3 zN_!6-^W?~#MSb#@m8ox=>RhqTtP^uY4wiw$jAUXtqD5dZ)FK?_*=kD+W3x#I5RU5O zH=;1xRT=$2c0S(1$jr=V;H_|9_H<=e3abI!mGzzXP00EWhdP_bqlNKkVLVzmd+IeF zEsRGC|><`YMrk8#rR;2nt)Ph2nMlDSy!o8=miOymT4(z zqQ+Z}x&x$?N(?a?Y;EA+gQZQ31`|R`AG|~Y(Wn>+p%28DDt`N%nKDzFvtxYm;XE{x zwyS&n|61$&zwi71KD>YN!}}K>{?$UN*uC4f+W=SSzUWSO_vh2b&D*xz-F;+QcR#XcW%uOtRo$!iTwcGhskmb7nvJ`= z-+ArI?$`I+{$Kr@OaESVFMe=eed79JX?JqpmE9NiUAC~lS%0rr_VMn{hgNjo`_L5= z_ifqfHs7;j^H#U>-n1jQ9r2nqo$ir+%e%Yx$^YpCM<0LaXRABOSZAEzl9?n};3fs9 zq)Nhg9i2|yH;+HqzKEQfIC|aT(-(At6U^D1ac@qT?7ZTXvtXmrOsN?ApKD#zeD3tK zuUyb!hUb8Ac$7-WU>%E+rx=xB*6=9$pKD#zeD28;_*}9i2||ta_^C*=#0eo{);Xz2 zogF0Gwy62srr%t8VFzC#Lgs9!XToRgac17Klsr$;M&JKj+oGf7)WsKelBbzx9Gz7{ zXo@S*${6pW3)TcPNVIiP6WNkKe&E6?MK4rBqAX}3D77K-EF9$|W(3#W{?mP}Yu27x zcJz|JeED!CqhK-zi95yVNo$R?TF{tdR^GGT_Nv@Ieib#;xoj+eNlA2xhQuOCIAkML zefmtXw0?0*xvZZ4NztqCD9dA&%9>kEWC}pBa@jM&2(HB$X1r9r(~lOf7oC>fQ~j?4 z#rlqRL-%*}`=`sb^&^iJiz;nZpfV+<@#&be zH8o>PYotFuNbKex76*#fC$?BVaP_CX*Q{t|Vt=+k4#qQl}IxCWOR8Yy8 z_gqA+td3qgZAjFP5yA#3RxKs1#(Q1b`{vWd*NW+ud-E%>>-9H(cJqxlef$%j-rVwF z&sp7aes;yCHTA9g%Vnra!_%q{tt+pqh&GxDu89v!1SKp$m^~5N(nK9;P=EZTp|vDi$)|zrIec1hkjiw?QJ_%Yy=pM zA`k}wCt+- zkF-OE?--hrLh84lFRr-Cpm3`YcpzQ`LM&JmM;^EqA?n_n>&Lc@cWi2amleYc7Q&*I zquN-JwFURsM0$IVBep8*#FKX-fH{S-(OT3iovmR_{f@ou8=4I8+aWumHZ=vLYT2^x zOqZr!c&b?SA%rpqXLt#YdkK`|h+PaO`G}IKl(H&GQg3*oUm-++d%DiMQa%K z%YOp#wMNkg+gJWvEb1hk7%nk7OF?`wK|mRjBDMH~#1x+?pNv2u7?&&qmo+68!J*(n z9YSrV*j(!6_2I2!i+iB*hl|Oml;bw1q(v577*Hu73Te*BLL$pOP0AEWG zaR9!puNPNNG9x+H&?hl-z$QBtY>JWziFcdpJB}3-eLAlULBSw`bArTBAbesbc;=|e zT-!L{g#n*at{FfnJW3}poLt0ZYYJuONrpx^FZT@(gb!G~HHwoj6}rCvt%6nv6v8Vo zV~7$d;PF9hT8BuoQ8CO^{5JL5yY%()Kruc1mZX~xsg`Fd=dW%#t+PzmmQjt|SJu;f zY(a;)m;z6((Od$Ti$WuGNGN-n=qzT`OhtHz!V=V6$s1*0LrT^w9|;&zk{<5qV9|lV z^Z{56hEkMX?SW2iB7UV9bY^{0oIzdN#`X0J%3HtJt0($VzR-8pv8cx4S0fjVj}4}w5m@uV#jDFFpjZR*np=Yg(@DmfEb)-F?# zvVw@^j<}>q$V%(}TRvBuwS`jeeW{?JmZ6dKl{ER@fw2`8fd})+L&9eZN;Vea*CdWB zf(2j@(vS^(U^}4qJx2G14UAu;m8{d(duBp*fLrYL4JTi6BYV9MX*_&tw z*sEInWWP+zD9x4&OY;%c_G0I+9)4~zHA-{mnPOShlGQu-tKi;Qfb9;Ndx3i670Z%n zmL-?WXU^$d$)Tq9~ zJ4d`6n5qE*w#4A-=dUR@w8ns%Lh3EomX}OwsWXMk_n9mBSl9&5bmMU()5g^|KQT)b zNy81frsdQ)AJ1KYde$mMmz}2cU0~$!R5?K3DHDWH+#tmqy#vE$c!qbmzoBt3Ibgt+ zAO<+BH`B7}NLX)9;I=IkzD&ZRs2Lr(jgbe-&KAo?K=bS@1DUIU^F}3%nKDMAXTA~J z^N5{5{Nn+T7#$bPQ`A+elT=WqQRe#em&TU$w*9>P5j=WJ0E3M1Oo#1{Ccy{CHcrVY zGY5xwSD@?0+e2KSYnj4ttee3DstHpH7Wu0T`k z6Qg1lh@G~y211ko8x5VoTHwJ_VQH`u{s8q=dpP!4!G?wvH(3h??m!E`(#FG~-xpO&6!p3r${SIPhX>Jz;mbFa3oB|E zF%Z-qmnPsd978dXMpF<2v|~ev+r}rg53o`y`i$OCK$8*;7HuWXQl`kygCG^2?N1gAx^gLLh|_x_@2+k3!(aF)aNo>`0WLq{4iC0k-tj?{Cn zr4*C{BahDXk}9w&L@DxNP2&X)f#qbmO*uh~YTd&Ng@08FJsXw{e76^{-58&3aqi^7*>x1jZuAVYejLz|YpmlhW2oWVDJgC6a;7&xea~(WO=+oLP zu@7%s&Wg{L+nJA|wzqlC>fuL5W-$keH9V`w*OjX~F-ec&@12B3R4Sv*&Sn~!E|6?%dBYX_YmiFSzf)me6B*@2Cp)rDHt1BgN{|y==@}< zh=RXAi#S-nwkYanzgTYQv;@)jNNelcwu1W5V>J#8o*98hqS9oA9w1lLDVW+h)CVt> zHQL0UF$imzsm!gH5v3MNQ-ppkndx~3wE1n89M#6c|3WEoX$3mY1oq3hnNG#o+osEvQ*?^YA-l|BdJ^;qjv6+FdVzu=qrtLnS%ACvW8_m{ViW(}iR!)Vqpnl+4O4fCcOqglgf)-aki zjAjkZe;^pl*+;X6^G{|*vxd>EVKi$PJ{%g&8b-5*!OUYcYZ%QM_-NKJyx%{XH4K?> gjAjjU#xtW?!)Vqpnl+qf7W98KYv^It@YJIJ07n8`F8}}l diff --git a/master/.doctrees/tutorials/datalab/audio.doctree b/master/.doctrees/tutorials/datalab/audio.doctree index 9d2401ea672f5b2817c4bb596964f0a250c06c7e..ae82961b712fa89791c5adcc24ef21f7bb11682d 100644 GIT binary patch delta 8790 zcmeHLONgaM73O;XQ93>*Va8X$x!2KQ5R>V8oO-!3Vgw~IhS@|U45#W;MNkqQ7cL}> z;38lI8Bl+-5Zt(sNQfX@Fy!Gn$-+g5St!wsgxSo_mC^6-|Mp~h)GplL9-3+B>U-)` zeXlyNK6CojXHGx+`#t?wN%CFyh~Kv*WEYz+E!3D;2No5oQ7uNrT2ZmqtDyW3uI)W> zZEu0^cWtiiEpPwznpkTs%CmT))@%;aio}*U*=$1F+;e$&bXa+nolBLK6Gfa2*Cu3~ zH_0f)X?*tO<>}4-6T_2>wU62ttxGJD$R!`eWmT$UX*q`GHvIPT+M-){eDv$X1M5!R zJpT2$lUqo9cn+Nsq z!eSj=4cS)fYbX@7N!6raf|%yXH`6x1eR=u9V%4cR-f?}Yx+O=(s-ImR&aSl2lx=Y- zGbKsvEUqAChaXEd?yv>yC!~Os;@3;#^KH_C|qJ_DMlRvB~zm_RkN-J z|4_pnws3sSZ3~@}Qwx5){d>cwK5f47x&7}veD?9j?^>T_D>=7P$&e*QHX3D$VQP-7 zCfQtlZTZ-Mv$i_d!mSl1N->3yvKAYPcgCx_`S#z3Uo5WluH-BVe(@_`y>$NrpTGCM zOFaZ0UEMpYvk%`jo_cNhp>?ODFk$hN1 zC&RgwNx+up6ofQ6e0p)w+5|3mt=Y~^tb7io>1>=>Pm(#4KtBSuX@O;s>#5}5I}5BxbhZhEI|dFadt^4Mwcr!++zue3M(v|X{I{2bCfs$ z_i>n1M^|@F%^%Wi)oE#b^SR}@iDf6&O_VCi1y>~yB!e9oQe08dm?YM0n z<=Ka%3BOLT_sPtiv?j%mK=DV$U%s;3+q`ri#C6>%p0+m7DL#|!?Y-grI%MU5y^x_{ z*q1=ow31S%SddCO1f^(>o+CC656)AjsK^#2sjQ|Q^R^wYW5VY}5SEA);9H`21v$c5 zm9qKx>yv5sYR*mcN^ZUErQ!UAkkn0Xmf^H9wNwi7PsYX_@S$qvsvNd*sOUb#R#K`0 zO(2L(<`S$fNK?9TNNvlr&JEqdoE|2R@~m@Q_JC)14j*21I=bOzC)R+H(oV+!3xjyV zq$?cVxDtqhy}`2x3Cz(q(TI``$dteq7YD>9UE`shO*~t{13Z3jBg6MA^sCl3r8Wyu zj-kvN3P1Z3@?@v__}1Cs;<{UWHndL+C)WyDV``-tN8M##kbb> z#;{&P0R#af2Bj@QM`CeVY73--cP;779lwHjoKXeF))3eQ2G}@evSPSccvvEy5B$xG z!*3QV(-PbwW!Vdu8W7AZf>y^-~{V{PEw0^LGb-ldlzs2BJ1j z)2b;l!mtp9YqB+KRmwKepTPsf2SN_=PADh*@1-hR^^~IQ$Zh!G=3jd!ez>^Oxu$rO zL>+kgqpL&b#15@(-RtTm&aulJVk!>r4#=KoXlYcEj6s%+yankh1HZo1$}(kc3O>b8&&8(TmIaA?ewXzMkB{6tJh<-E^;0MN<9og^ zy#I`9IRJJVB`nnpO-!w^p(;+>I{xtk(;Z!3er;GoFayCiJj@nugP#tvF%mSaTg<`x z?8U{$;37_7&A~ERJaWVs00Xbq3gehQ@1r-n+wtWU&p7Rsp859AFXN$x^ou~y@Sbvb z%~XS2X#{9<$Y3g}4dUOFhtUHJ7+C`*3}x|po^!#wO^bq=glUN_|7kVA_> zBVAI;FwDBm!=0LbZlYInCfmt}hI1G61$`6S0_Ow~ql#1yMQlllb~T#{>8m#8SwdE5 zqB-b&n4WRI;1Md03i`q;M8(6mT0ZuE6d&bI50yt(_m1lZ(|R4vOly~htKC7woFaD< zklh62e+^wGUhO6zB)bX7ZUVBKfV?x6yX76Xn}GbsKz zzI}7|$(y?ie75h>749dTTCyQJBCBl*tEBoG3q?bY04&PDU&4C_gX{xKN~_zxvAgr# z#TDLMpSX{xpL+On7ax4+6AygmqH19A`02)@#r;Q4k9Xc!-Ziw+8Uf1>jko@@+!=z` zr9|aYb)Hg=K^dcyQ5rs3=Mu!J3+>p!xuVHNq;+nKbbszn)t3krJEag>lA` z;pCkN9>laVE4=P_?rA{u(zD%P7tQG7YkymwADY3D&e`#)zb{Yj875(*sw8xxVpPzn z2(M-h0G-BX9`D{iy>V;#c>uM>TD5V-3WI739LOpdD^s=AnY04xpo=KAFng36+=Y&T zw!z2(vC_fu^vrEDo;QY7bq&%+$OEp8SANvp*UMa85>;gY4R6T4C{Ta{>8*%)I{4u38tJok%>p0eabCPI1%-&^NJ^-x|Pt0ZMcXwaz% zg!|=UXvB60$+W_bU%0irct*(w-y&P6R#3E-r1hkdj<|M?NRsfR5lVibAbZeADWwoL zC(tli?-`6#K>>Lj$3OpZdDm}F?0jc&rB#~acnrZ|h%R5H{LO)7>Asc>3m9lX!uA3l8u zP962E7D?e(xG)2ruaozLK|&LzPNSn@^Uu?miQWh5R%P1jyp zzOWb?vGa;)jj(0KwAXjL`v*n70*Xpj$;3863oUdZ5a2oYsC8MOkm$yMqrg{O%fXLW zoHo_NUWl;nYt=-kXK*2UB38jAsj{4PG7%K@D9u`dSDCcpt{dC+?*21Qi6gBove%+0 zLY%(-R?h6wc2(CN9uWq_=?LkE z8>9&B2Lgo4StKD=SU&KLdxCXF-HB%vCI<%_s;fd~8H`DXZS9psEA;rQN*K3))V~l@6if22R>k%>>DB1ZS9qA=(PtBA!63RVigrZ6cTXi z;0gHv>wRJN@{HC6JZ8)a&==vMl_`gUze#8mdU|*^!hMwJZ-Jqas9Ul*z>_EmLnXjx z;t2qUUNj=c70VhC?VJLS-~VTK|7op{v^0(^6K)3nKpQelB0N&XEHj7A=}4O%I3({~ zX6=-xVv|!qp4K8b7z`)UUaH?==uJlj*k*B~*Eq5A_CsIbA2 zfF_^{`9qq)JvL&;@rw_1=i0Fkcy`+-yY~&Y7G>G{QVpasGK5fC8PSU?mE_(W2O?%o zKbKqy+!2hS;tRvz!rpK>FlQh5^{YES$Eb<_F;kX;Auqk7Ds-&@$$2SMTC=Pbc7@SK zRJWqpe?Qyp??n$v!QE<1%`oF30Hh`ZnP{=0PCvM@ywvs9B&B64Y+;oNEE56#6i3nY zFagahyAIz78JSHR=Bk# z7L`a=jG2-+At{Z=!^DzfRGL?iSi5>pu<^;744t=_&m4><=H=uZW)kGSYGt-jnBy!% z*W3ijx3LT^X5*6`vaFGoSh1{L?5=K?AloI##^uXOA+}49Shq`%?Gj|W1bJ&eb-a^p zmmu%Be%UTTwo8!h5@fps*)BoeVePVAf*jlJY?mP0CCGLOa=4fKzb`>}diuRP&!6}o DgA(mt diff --git a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree index cb14f0ca88c9c59ea995e6bcec9cadf09dcf8163..68b47c79e824aa39cb8ab713f45d420a56ceb1bd 100644 GIT binary patch delta 1957 zcmeH{J8M-z6ool+W8pQZpu~WX9D-ef*)y}})rbKt1T~h5N|@Ict5hO33flPi0S6m@ zLoC9nw6L^@R~x}wB?K%?BPlK7T!mnooY<#d%wFHDwfFYw`1b1f%HSwpjxIe^_w>As zCMZiflF8P2ZhQih1(3DiIbf7hg8}`l>bF(Dy7{%X3L?3vQc@hFiZ&{1Y~~_KAQgS{ zXuW#cscO$|REyv1taN!rDJ7&u<+!tefs!NvNeW8M!Uj9lp4?%#$Li!|@Pb5wqID3h z*Gg(`mGEc`iND&}HTJ5i!2}f1Si`wRZ;azI1yF#T3>4Zw+UDbcO|(0o*oUgy$ogk? z{Qwq*Y`%H?h4t&~Gxy}kIzUw91A?ZY1E5#M0_~FB8JjW`WR8SED$GfFFF6UBT@-1= zb#a4DHWz-eoZCxFI{&^taga?)De9!!~A*iubqJ;UybRsI)C}`u)11vT^ zLoC8pT3A}dXd~pS5&{;cP*PgNNsypXTpu7T+nu@hoHOUl=Hkfa;>g1CL9WG}Tk|*X z-JY_Rn}{)`;H4HWXyBb@fxNtVc8yBPD}MMT?Oj=-`5~ zN+;9YU#%W>qS}*d)y(JGiYQSU1m`6Z2#Sh};GK_2Ssha|y~d8UN4MDQP@iikNC7+u zKTkqN1 zs+-5^2X?vcU=W+>0h?+bd}PD5kQ!`Ro3d7jg|4#MQ@~74crRgB*!7`)k$_gll#Rxm z9VzQn)ZP^pT;(0I$D6a?+2MAf*LzXbB53XiJd__3S7jum=v6?+T_9|WX|D^ci^8`R z2DR(lK45qIF2-nF)?9d$QCj6F7I%}^f=tT0YG#LOAFVf_yg(w%rf|YUK9}<;k(RiP z{Y?%^NJ@~`NkXwI6*q#-Cc5Br2okpCys1w8n?mz-g^7OW>H&l1?5=8CD1Tr}oABY23^{cqf@E5M5?ea+PY4xz{n#L#V@e5b3&0U%~cmCpB zS7i5~J3Je|G1)A9VPpRxZop1A!yD{LcL$2L{VwhaZ`UPnhj+h=e|-k_yZFaL^RtVl Koj=lhcIXGjXiiK3 diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree index 5ee4aa04abe1fea490a7075ac01f8a09d30a9cae..f228ed7f37bf05663342a6a2aef9c34a0a2d5016 100644 GIT binary patch delta 72 zcmaELjpNldjty5h4fB&NQ_4-#&Ge0v&67+m(#(?6%u_8a6AjH1Qw+>gjSSK(EDRD2 c4NQ`ajm-?q4a}NZx!PH|7`L->F{LU30G5aqKL7v# delta 72 zcmaELjpNldjty5h4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW cQVa|Xl9Nr$lT4dgx!PH|7`L->F{LU30I%m35&!@I diff --git a/master/.doctrees/tutorials/datalab/image.doctree b/master/.doctrees/tutorials/datalab/image.doctree index f82d8d8ce92248037156cc1f33d191177ab83073..d3ee2c7ce167270230aa9f485d801049aaa600e2 100644 GIT binary patch delta 32957 zcmeI53#?_=S>JW;@zdxUgN!ms6efb6?rn}?t8wo9^d!C?Y5ee!dc$@`*DA1dE% zZdK7%rE5%VZ0&rIHY8!|LNv~aAcc-f8DWJv`<2T+^TwGqT3aJj>21`qE?q2zu7WZq z)v0mS>1W>ZKeWyzYW>zO#2+UPR>EMGm+6kj#(sk8{GIeh?@X*SQssjMB_Vd}E7mA0oJ zJN1$2z%yrW`~Jy+nXF?dRpCP2XkE(~q>C=w| ztUdRx;7;neSl-Y>XN#Zuj=MLF7j3w4bj|5|U%Y-p>eG+C_`1{o?)mr%uODbX@v6~f zr$71C|GfIZwWt5}*A8~SJURWPul-`r&nE4;J4ToN)C<3``pnYs_3c-08NK%ObuYeY z{}0bT_Tq1E%$yO?6w=hna(A2HZPBErtjZufo+lQo9(w5Pw|ryw;H=_C<+rJuTIy7A zQEOjHe8^YQ#6m6JYUrW)Wt*e8vte}m!)##t*sDiJ+7Dkbx_nbxx!lK_+rw9k-qicZ zVoU#G^^tE|zCqs0y1nh-=#E$VX}6p0ORpaN!s;V?K1R2z{h#BO`LA9v`WG8hSFiyD z7F8;*ttHOrSfylK>5JN^_Wr9!9~pXR{+ic}Za6qgp@>3CStQx$+G<~k$V-+#y)SGR zV1^!=zcgisH;r-mj+UFHy0ozej*Yk{FaJq*McYSj8gWOvAK%jc?9HQBEq}Y))i>Te zy5kjpyxi93-!S5u?zOPk%^%qR>Miq6A7lS!h2buVWPL0M`r3IdWL^2d|6*;^GO7+d zG#`u6>krO?lCtC^ypFXFL3`V{V5A6DB?19$aYct7nm>R0=pj3+jHw#g6o#y&8)z@%{`q;{;JQD% z@Y>Da7~M4g%K43ZMt}E)UpN2WKihbC<75gS_>mJ2#QQ!_&t~s^;Kbsu?|tBb2kXm~ zcG}JON9-0t`J%}GfBDZ3e1Hc^QCN{*$Peh!EgoKdKzU^rf9C9?zwan_bkrho)dpp zhw+`4TlKd*jMMVX-`aldziqhxAJ%gI>(h4UFE&p6eP6DVf6GI8{5?DGdi*_C{p4?T zUPb=;n>qZ+6>t2%;qiade>{(3?>qDlC@6gFn_^r-nVrWbgG)lN+FwCOCVMqBM7r0_f(<%`W@rL z^C$l8=Ep|Us`8a{O(`-SR<(9UCJ=+uu?#`__NwEX*UX=|di=%FH1=C=AANND&RMTv z3a$3pE4D=YhL3GuJ}s*z0p5H{svymps!Y0!2DsALCZxGMwe|k33050c(TGawN|aTy zWdcJA3Ja;K`Te(#KR=q*EDVs6BDqoL>I`5WlBFIprT4yl;oj{d^V$10|K(`NYWv)i zV>wOzR$KXRc4T|&A8%jP?moW#?b8^d4wXrg^sKQQWb~p=Ch5X?Z|CHNT60-q|vOU97?UnD|8n=J-eVaEOlf{pG=dBOC>-vWtIySpr2h}8%ie#*#JW3$+ z=xw52)s^$*{MkRxiyA17TR!fg_ReQE56-B`}DxhRoB1k*sT5T-SMI6z|z`N@7#WU``82H?P=3Q+)UIN{Q?sx`wJnQcTSZx zl)!QmyseUOvZ-PbsdF1V3U^X<0XkEjZGJR6QTDf^6rnao?SrsV8&x`O8Yya36r!lw zM-Fe{nAzKA1D0cZ_?NfeJ{z!{vb)`Svva!K72T=AVpt!_k|pU>2T#2j>%v)C7EZL! zT{k{7f9!`g@7bCbg)fq70{2dXJ-znESix_bvMfbCf8@=0-qBjuCAYoS7MROz&nTD6 zpW~Br^D}9SE3JENy7u@xx8=+Z*z4}xsAP%Oo|rfSc>vpY|g!Uv8v`&^xr#{a$W z0{3hJbY5Yryy_$KbD!M#^k`CpP!a^07Xb#*Ia_%v@IO;<#3Y=zZj&SiOzZBR4p}{a z_4Xpy-7pERmR7;)k;AmYbmKYp=s^}ujP2+@ZXcRI8HpW}qH^SMo1=;bTvW0aBoYOe zKu9d|nh(wIyg&O=rC?!AQ%NtO<7(q#U8g`Q)j>qZ`JH-h^EXDbe$%DQODQJ3R`p7@ z`>y}AebX(%uee>Ad_l2p+3rHu>~>RBgh(5z$i-@pf250xoYDZN+LG+Ana@scKRP;T z`;STN^`YK7O6kAv>4A;A=yClkgB-~oH-UzX1kXU02LzVji%@C;k+2mk?);JG$M4^o zQ3f~g5JgmVrc#4TNd{XQM^qKQ!P$1VJ~f)vkzXaJ;lWnJe^f+TeAm>xrh@YG(T|P4 zGMe_AlQv=c>ipK;HBLRe_edIZg@C#?gT+h^` zgb!*Jv-hx8)rdlr4!l;oFek)jAZD5%6zRKcf)1s3mhA2t?g$98sJlyi07Z8hyT z7gn6>;m>W}d`zho=Sr!nRUrHk|FnsKcwivi69+p6{8*x>fi^^Y{6yzi9~+(QKN`K) zQ--eN-lqq4OfFF5H@*al99Y!~iUh;gWolGup;7~3T4{z03eN+fgfEj+%`$-D4DRNbmM0@|ATzg>)3p+-U(NbMpvxHmyR!iHZu>rHK{P$a38k?%J zv_VA$YIT}w6Ss*pCIrdT#Dq_r+PZIR5@Z4M%lW|6QWaBRQ?OMAz#Xd6%)fr`_A|Md zR9ctHHBpff37$=v8xWM+H5j{@U-i_^)1x7Si;{EJZ!dEG7i+aQ-#EVUR_k|*JuZgH z1{b2hq+gcUbx>120$DXx?dNCCZ2l7^l3UaAw$qK)wZW?sgEi|OmC zXv0S@_}DCxacL$;dz~G>o#Lj?ER0Mp)7q!(_*GN< z3YvxtYiSXCNAis1fWSorRQxWoANLK^}+aWDI(aatknLm8Z&c7N>6Cp&TT0p>qo*nH&B}yrY zJCzHfSzXG2?Zr}N1E#ALe|z|^Hg7yev|1rY62ZE0lxbG;X>1u9nqu6dlB&b2`3o=Q zNI2AHL=0!imrFg-*AgX zj=oK^EV_-P(gOTNybBml?VbOgN-0DY1^DVGwtsGvakMcs(WR#ntMT2)XYh_90f(jF zUf}31jSZNs+V-yTTPOWyGr;nbirxyBkuYhK77cgI1{lK}I}#M!mB%+ZH}X+I^e6*C)5a+0f-w5OTcN;{HouC{Fw9_Ox;}^GMMk; zUDKp74l2~vm54u71tBA-p9<}xReBCS0v8II#VxsZhJJ*n@r|e{t0=q-(ZVfjubZ}E zJm9gY&qM_q1uD@MbR&uqU^>Kcm2wf8$(vrIUGkj`7+o#BZo79p6NM>ut$ptYFOrSP zgL4SB)O=P;0y8%!HW_F)+?)$*0~XT(4OEe6B$A1)EMcx4ikGtTgRf? z&0eSCI`?gyxa3s*tFj&0!R`|zJ*|2mG6&_Viu(rFt?2Tabm3fq%rt*dI1tK-!|(`^ zE1E9?jbJ%{hl1yrlvHNXu{pjLR0Y9RRMgO3!gKgdD?Xg_+X5O6Shejxe0F^2RPCp;Qpk!+YRmgaM>Ujp_OlGm{*&H#h}yk)C*>R?A~ zBz1s4d?DvoKDC3V4Ou?_n>%vQCI@2MXFfhYe66|R-Vfd;#G8aTcF%*9zzC;|+12gy zA0Ho?x_o&gd?$Q0DVB}Hw*BhI$Jb8qb6O;@&jr+JiR!b~8O)X@cZ*nj zuI(Z92`U5f!4+12*-+~}qkVa<|9+PHsxRYlX)-Y|nTT3b|BO{B+7t_Xxm6FkKiC``@e16u${?No#UXA8N4 z3Ds*-C9+J~#lX^}+e@wM*?`$%k?g4Ddd;e?O6#(nGq$c`qo}I1A&c2MQfNWQTVJZ! z!mBMC6i0z32**kqxFW%oQU*C0&8COo9>1Z zK}^VPqq0+^_>jx4EJuU2ucEHo(Zf5i`iov#vMQuZo#w1k2l9$Tg;3LjqZSnN_b-s; zkma23hHReyiJcqTueY05=bU$_ed!O!SMPv9ie2p&jt2os0ge7R(*fL_-1>A#iIH$~ z4@1?#lIeibwqv6@PJiI2)ZTBv;UvrXNea78C%4 z5CqLe?&`8p7j&{}4P7*0om$L+RRXm;@S7W%sr;^qHsu6iYymF0R&cOmkX?0?TjI5p z1`;E4c3pbwH(k;K`t_~vrQy|Qb2V zdMyR_M8l|}#2~60Ei8x&e!wHIr{6E6US_od_OgaY2oW;YrWmNGA`Ly-0CeuxRm zt}-hH8$dDl8�RWGY_}ty8Sb7pS?5MZv~_^y|GdK;F}#9ypqP{j!1UxR0P5*fn#p zXd77BvS@3N=aACrHq{_SG{$5hJSYXoL9`XC0J;{6!aG`tkc3Lc;d^DxSOjo|Lzi8y z=+szsW0WbOBO~|`mJ%TaQv#^Hyl8meE*N(nHt+%X1i!xZw%LGfnN!-*#cJ-J%(Yfh z#Cbe38Wdw5xs?F4q^?OZh0H1GHr0gpTy|EJ-sqAMZvp$g)k{4V-C-Aio{Ay^imxK) z8HzoHLrN)%j@#+P6a!}ClHKn&+vT(SerxM;`eF#fJot{!rYSa2e8`M062Oo4$kN^F zC<92r8wdrQl70mPFrh{z&+kMQ`^8P@ePUY`$%FJfatT<0nPK{9PRWBfvI|`bA z<#~t>L39~x;BfYl9RpXg+?(&9%zC%%qL&RVYFVIFL`izDO${}L@&H%_7ueK{5l~2T z0DnZQ29_Hw69-Kv2I*g-kBeZ&6s|M^oKm*5LYML@x=xu5&uS(jXF=L;=&A~|2cS=2 z+Y`?tOjs6Z{iajrUjwF>a>ty)CZIj|DgYL_M#F<5*nq;(l6nToLwhc3BD!phUIJzh zf60=T;DDEzJ%{U}{E-n=)uKAnw#p<4AD;m*gnIN0sD%(uC}Bvft3K9my6l7?vjMZq zbjBzbf{lbcG&k63?`23pE z3C6;JlQh`yMW!Q~L_G>QjK`wgyo7xRY%gL3;>}*uc7@o-)vn)FyI!q$8ww*LI?j~k ze&`&Lr@~H!($LHdEBdQkstpX=5JdK*w}BJe=Xm{BHSXh426jETC+QRMBLD=X>UmpS&TUE`%Yo0pp@`z1VM8ucS6`9U(J(JW+H#WSNx%neq+n}K|nIH=1!DS9qO9(^!N z2svam*Eh2Pd!fUSfjf;i#5^a*aR4Dvh0ykg9%7)5Jz_+o)n7p$^qtdwJUMVX z_h!1BYhHQxLUSq8^{*_nFMM7(#vjv(e88x()e8dVw=5l-R`K(+b3o=`PoJo-g7hN25 zAXy@Y*3r17I`9j3ZT<4-TZ`Dh1JYrGE6Kzx9bhD0S%zK5Wt}r%lexpJgdeb}vzy&E ziBL4?q!g-9m>N=0ChHKuk&%#%c3i;>qK3EzQbe$w!6Iq{N;aY*eRXs=7h3OhK6srA z&5v|LQ->T!`wln*j`V!`EDf2U@98nw-jS=!0fV{ee%GyPxAZGn3_c0cmr)K_59(zJ zBgQlbRe4HCcIdx%a*%;>$BVv%azq_k?CQkw1Z$~m0^oKk}-i?_sD)MbMsN7F9rmnN2^ePGFVfu$U!Eg3yKA( zEZh;;LyHyV7JQPVK~<^tNwn5W;5cBpSr%r!mODkrvM^)j914L?MaiG>KWWvXJyWR) z=)#+>qM0%MOeZQ&%hMDDE5m!hyQzp%(Z{-|2r;NYX!K^Lk;<%rBjHw%Rgl9livls2 z1@Tm7ME!oV%erR3Y^MlW6lP2$QI;X+VX_6L5!rCnG$=$Zid(vXt4SCdmHB0waEi-X z@bM(HaaIX9&wV!YPN2!OH;$BbRp{A_?>^bOl!s)8Jrm)`ffu^>FV=?c?hE?`KGRlh?r3 zxl0~5v@C-Ea#RK(M3GxWnQ^3rkM@Vt#;ZueuCZ!acvl*{hsL9ISXN8G*5$APv&%mAfZ6t$OoMdHK3ugzrNZDv z1suw3H(#wG;U5A#M$-k3DIXn2W z=}v&!EXvnd(j(_fD9ioatbHDImvxrdka`3CBfcvGoZ&&`mu6z9$p!}HyTY^M zL52=yugeTv$$dP?z>Z}Var>8F8XukwEUj#R{`Yt8Za??P_TB5j&-LKvdhl~GmAnsa zt_MF~Hgei2L)L?z>%q_U;OBbqbKlJAdhl~)aHfAeay|ID9{gMne)i0nt_MF~HqN>p z{9F%yt_MFCv&ieg&-LKvdhqjQUtYW({9F%y=6UMDNz*KvSPy>YZ$?@Vey#^U*Mpxu z!=~%O&&Bu4tp`8XgP-fcPo{JCcCXiipMUMV>3Z;UJ^0!8-(gt~e&%maT@QX^>G-d@0-b9%wsmbP!#uf5jV1+|^%B#thO7RD2y@^LK(0ilKWX33xi3y`Kp?4S)87)6LGmuP8 zoXU3%>+TZBq#Q>#!0!&7Njc-y2{ zc3(Ag(dpN|d2(oGYA>o==49m_ua3U6|$7IaK;3_M;5a3O-QnE zsR&A@rgN&Hi%zSL{=jXU9C;hA8XY`Fyzwzj^fUyD|J4=%i4!89vyGrcgg6&&BBT0IbPEqyJU1@|22y< z{l|URylVLs`7~4e+QXxpUh>D?fws?IJo@;)Yxdnrcd`Tba_#&NFB$#%#M_m?%koNhe)3t|+3fmF)7& zz(w=tr=0VKSMB59H9FG%#C4++o5tDmCw%C-(Ty+l)6QSphprxddG(Ux?XO)kx@h?= z-63Cp&FH4yOO^-y>}y6JU%hVcUKU6EV=wlTYv)f~$GOh5YLsfCNu+C2l&Yju$hzc= ztyj+5#bFFxG#`u6_a2^kvbHQ^r7Y>Bs;l6rMY@iOOk3G9E>g+BMW^IPkIgr}Z*-@d zTCYttwesaq4Lb%g{C94aa`*=J$-|H@|1&*Wb~8>q{G#x9>l; zF>e3+mo|>HANt>;-S&0QY#ePLy?o3_oUfu@&E6OcfTuLsEs$imA^K+?7PIS61r6C!a^t~7N1?5KQo2* z7wS^m_U2EGjvVGAsYU58bXvFP9vvNBe1f0Y3%w}YD_^y7^bpr*A*FAB`R_Nb_?NT( zQ(xKm-S)BH+}N7rC%%34@o&H5JMsLdBB z9=UVpZI9e}=`a7;tozS**dLFazmE?-^57%)KXT`>cYNVw`;l_={qxs-b)$X#q)cE{ z8ADY$BLS=i%&da;mZTHo{2Olb?yYI0R8Y|iz_m5rs!B)aU1b8m%ercQ>8Exc9Zg7i zDb-%rrqR@G=Rv=^h%Tiba!GShnY0Jw85v z|B;?ZyueCKid2quIx44K3_|XWTmpI8!zg@IaP2U^LroJ=5ZQP6{4C~zqS4JXsU_~#lTWR6{aeDr9(88j3Rh%qi(tC_!@?IZJt ze{%D-t-+>i9)5Q7W1|Q9Ur`}XVDl=$1SBT{F4;R}UOoapG;)wg2bp zt;?tVPZipmPj6i_sY^SPEYA-`mR)uZwriV7UG{@Pj-*nLJ zvESc*(`>+Y*`B>*>xS2OZ;D;9c$=oKgRh-eG2dZX`B0|R)J;+nFlpzHKe326&!=2q zi7ykXRN6*2BG$19nzYe!)sgwFH*NjyXlfNruBz#F3o=Jlg^Ko}EOjjk2LNi%esOc* zrJop0`%Tj{Whbo%OkerbSat#T=|9}Qe46T_sq8C*?J1F@^cLzuvlg zYXUWpgbsylV&Vj(FX-!xsw17K((N;Uy>+yEoCzq}SG5J6H^z`5Dw-l8OCNn{teT7W zZ9P4j4H&H#PcrK_n(8iheD_anjoYW6-oEBKt(TE^Y&P&{%_8*h15>$B zXf3=n5$IM$m2^-vmVmm{9l)e9&_Fm}E$U!uE%9xtme)qO;B0gcC_G zYo4T*p~A07p#OIJXilKFZ%s(6qN#NhDtn-<=*Ef45?f^qp~ac^!cHpQCI7Gu#UHu| z5=0dFkC^N;&HU`n_UCZaUdy#?YMOXm#M~{*#k-FWM#pk2m_s@yYh#`^MW-0zn4j zVN!7-kYOlsPf2nuD(RJ)pZ!XPE+&M!swyB@0h|K2>4M7DltwoW>TN!HX8h=ATB;_w zOcK^mm@<{#d1;FVrdHS{%4JDh;K>2A?eXWfe`Gdbwd#WHkDl8;JQYe5sj3MtvUr_* z;1L`s5r|wDO`Lz^6&(U=Vh!YKBAlO8!D|N!Q=|r8lR;JsTm_?>C=F4lY)LJD@yl>kae66yyngNG-l83;U0i2MWQRj1gV`|x={Ibhm% z70&gW^(z(b{h!&S!j&s=SJWhs%(z-%$zU`kv2JRsGdoQ$&|#qkuAB~#-QN~?1F!5L z!0dO{a(|f{IK=aTHye09u`F|S1-684rLQtZi8A*y5toEHB_l2SAEGT0MlvXaCy-H@ z2@r#1);2}xi4&V zuBzvNW<1u{6>}l284&U0wx-Aw5uQE+*0S_-`Mxcx+<;lpIqQJM@_Z|Ia+1-mEj@f7 zWVV;CQozArqh%Zb_ImuAV_-jHojNLbSY@ISkqHvWSOC&WTJT0Kn>f028geF8%&bfT zm->R|kd}dr$elH>7gNl_3Hw}HprE5R(vTZqYy_O5LTHyY9^Q!AJCsC91Q~FXF=_<9f;}5e9f!FNhPTX1PT;`6e&x=%$YW$2_02A zwFW+|R(Z&4jV&xJChcDtQ=;IRvdd&$fg7;x+xD&FTPFQxTM%+NO_C%IuTv=Q3%-Mv zCc!i2o|JK%TfWLV6Ro5a3!g(#(8wB?(N$zx?#x!pl2FsnG0q2WlYqd(6bRZ?uSv|r zf87GKk8~8}Y``pI(gkJGZ#EYN*}f`{ubHBu@S-rdz72>HWCp-4$%d9LT3s%|F|vvp z;t{F@3;~P}mMmC6y=tJEUf$rMftxWJr&kIIod)brQ!!44_vG8E(n(p9O~0`D+0ksk zZikF#19sy|w7vG7<5{LY?M?>*8huPgZqcQq?FFR8R6xcP+l(s8#zg?^i*iP;DogLpwf$OzRPXk}s{{YQ3BR5-H;twDqd%=Ip_!5B%5-3=NFHTP94F#a0igK+ zZkvzjuKXe!?Q!b;_M0-to}BiZUd{&|``}p4N9=C~uG9|&T8T&D)Y(!jyj-NT5t?mRJ?@W)FEoTe*8yi`J(@LFMfDw_%E zoLTptWyp4ikOxdJA7=Km+pjz>+{t&o`?W&cD8zNQzlZjMX}dAIvVHbf#>b~x*uqoM zffL#kk`CO~sf0C$c4L^aOfv%#pZ$dooZ0{C4se$Po*VYZVbOn-et|Q`pWHb*8+f#; zef)LXubD)21PpG_anX~5qM}w+LV=~CL|nEc*+U80L}cap7vwoa2uv^2okq#pRfO~% zIk*;a*ffgC3mqZE23g8i^c+FL0tggy#n6#K7|ID>3B#_YX~xv^E0P18$r8= z$|?)EC>_itD_9!QXvvWz*X$c_4cK0WV!u@gg*>Vw6fbSx@WgmJEfTXLLEZ9}yF{sE znAv$x)u1vs(8kRH?K5P zVPtZtT2_GpRb0n4&mP!*X!O9qn>tX_27L8EKR9q`<@pLY@Kh;Rz}H++md-OHfJ|b# zMrRSpo6KP8+>|ZmXTC!HpF;e>V0ygQk^PG% zU=s4$!O=vVLy>G(M|DlFRT&qhZ^&$h%wkdbNS{#?8J>nH<6wx`SOcFEY-3$mnZYJ7 zh5@(A5)im)A5{(aX6W+ z*Xjam%4xq<(LVm4$3M>OMp0s+Ihijaeln1!bjh%iG63BDa(G2;20b^HX$SEX80w2M zI9L&2KSgY%Y)_yy(Khg=6nbEuAS9|iO##gwBiZ(`C%^*TQw*5yCgBY5dQC4Y-827X zeEm*=YwQ-w(1Mu+w9~mTlqOjuxV7jRoN6Zh4YU9$`|hnlE$QAEy#L)CU?~Uu@j% za{O6S(g}2vfrLhsP7v)P!*T=?p0i_A1Q$j6ZcNwwbck}ms%p>u@zxt>b-&rgXh5`Q z#yf{56k%`)aSSht&X=f#3eS0>;lEVn1+B+83d(??Bc6osMg~LB+%T^24z`k+YTZlt zo??~9UWOQ>DWsjiCq<<*i9$|T<@WkbXKqg;h3PX4n{>&}jT3H9*V-||*7zWG6`BKy zxnu%J7F%XG1f~%9l&2~{uA=JYj8NFXSTgo5(<+8Uw4iIU1`PxBBsB}c58#CyQB^VK zW_qt?^;;&(men)2v8=`Y$PT38Dh-k1id| z2q5T^e-~>5c3l^1Lw37Sz#`Uw{B%?`Vg)jGLHxk53W=ft-E$R|${E;&#EOScnwy z;XyS(QZRUgeF#(#&HOY1`*pQPm<-rmAYL&WFu5FQybx@D*R0YdE_Y~9lkg->*1D9Q z!loT8oHn7>-NbiL*}tr2Xny)$klo*K4Y=lk+BbA)d({lSw)cr*N0S7V(<4*=WLZ*R zlC((nunK4_fXba_XiB$6hYs?qQq~2a3X&F@fYty}cjH!PZkk3sK~GTWsC67<4&Uf> zT?=g)cXy1+Le@24oBlo*wE^49nOhFDsfOx+VU{FqcA*5a7Ds($NDZ~+v|VB?NuJ`< z0FOZU6rf-5O6}-Xm?6fZVf30M@5Iz z*g}c4#JaTnphCxsU`hjXT+v&gX@#=P)RtZ8zktQPX0x0`&IYWC6*@APvKxwBD_2Sx zOcauXDzo0gp!%bzA&NuLqq#3B%>3-1?jTJYS`9&dvQNR)`-++ayvBfIs{KJZaDXrL zVndJCg_&#$ay6fe$a>Q&cvIId&E7Tml)xF?&RAT@xlr9$S zj`d{|p-1SsnBGLc3^v#;dT4S3nS|PfI62d^S%pVgCRcQm`F?xScKWk^g9qbm*U*lw z#975o4M-98D0dVNI6%quJSE_$k;RHW8M3x_d8z|M9p^Fdjt-Qw0na=D8VAnq1*PP` zqje(XDOnzV7!*X%7N}tx%y%1{+N#aqT^~Y%j;cGXOO}jKauh9I+zaN)42R zpBAt?$;{7v3AN;8!1T>;VItgbRI3g)qBW6B+5&(vZ~A5JYKgF^f+ufCf6KDws14{@ zK#ci@+0qSpq!@VaLU~@v9=8b9Us`|}IE~T`N6exIZ;#Mqv?Wt+)6P?iB$r;(U1=Mz z`~tH$ZgXk7-tU%rCFC%X;Q?AQN!y6*AZm~5jP2kel|>sqh@83u#RJQCIo0heZ$oct zf594X)B|BhUg$f-z4CVXwD;-iw4nH}K>0x%K>N*z1MC~J)|HP~&5W{G_BRCssZbMT z8frr$)*zYk3B$Oo`AYL}7rQrXBA^)347p*$5bsgrH2;f3vt=QzUR9CUUaPb=i*Oq- zyHJsIl*qJZZ5{F>Aso&!b62ITnxx5MAKqqq`-T1nRftxUW(MDE57A1N+%y0;vky=p zU2U82e5lmWkkH7QSTaz=pYu)xB5U8wAN&HcE;jjkt#;iF3$eWhSIQdWAY? zofW+qTn!_I#CT25&%JjCdWz6Tti$A8MU3oiv3f&7=XajjH4iyeU98%@x%(u6Y~ zr#+~g;01%}^R@=rl4YY6PNd(kS+<(}maEmd_JR}fyMDDzi;xV!iVFG$_KkKF07Rlk z0|Q5rDYHC;{i~u`?*R99aE{Feo@}qaF(Ct; zKw@O6Ad6|nAqE1DFyv>1)^Q?J?_3lW1SASx_<9SpVr?l>ych#l`|2B$9F%x5Mi%@@ zz-XetL;NY`vHSovY^R|PaW0RWkOCq50OgTD ztgr1-LGP6&kJAD%ZZJ@#Fa&Uf0U|=^8x+NryOtvY-akV^8vz$P)&d1CG z1}OhN2JQ=K0#Z=)hjq>uJOo`H9Vde-l)x}V{8k7CfC&i01G_oWF7Wb@(Q|*Yqu2!L zvzC*^9o)6u6|2}Wg9cl}PO;hqYYnMiOHwsjKS3YOq_&&M4=ieD?6gNX-nh-Ay6?r% zm+voP1KxO`M`Lo)b6|y95RNB*X6qqXM_ zBIyf@vg<|C^&;tdk@Vo6^8r=YdXaQ>eRaJ^x?Ut5JutBIdtd~xUL<|#CD!#K>3WfL zy-2!VBwa6(t`|vrcXHQ@q_YzP%bcr{6Mj9*dXaR!Nc!SS&+A3f7vEo9FOseoN%t-J z9$2PXFOnWuUtKSft`|vP^sm2JFOseoNnd)0b-hS>{`YPTtX-}bN!N>{>qXL+Twgu7 z;I>{QJ-;KnUL;*Fk}iJ5^LmkVvEqDSqiwxNx_^mvy-2!VBwhYZH|s^x^&%<1f@|^n qk=Ki)-QVOkux`0tB+b9QZ@oymUL@^VTU{@be*2Ovi=-dB`2PcQ-kaP2 diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree index 20c2362aa2fae6d0cb397eef17122b9b1c8f4976..53c0d5503ce9c59079bb47639bc51226087df8f6 100644 GIT binary patch delta 62 zcmZ23wOndLBBNn`vSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Rvazw5fw_U%=1GiGxdB)%5+wit delta 62 zcmZ23wOndLBBP<1QAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg=1GiGxdCeR68HcB diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree index 12c4a09fb32544c3b7787345fe5455e50cf400ea..d9266e104e2b93bec80241c9a780254d7676f762 100644 GIT binary patch delta 68 zcmcb(j{V{~_6_?u4fB&NQ_4-#&Ge0v&67+m(#(?6%u_8a6AjH1Qw+>gjSSK(EDRD2 Y4NQ`ajm-?q4a}O~b8dgn$(VE&04SUmhyVZp delta 68 zcmcb(j{V{~_6_?u4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW YQVa|Xl9Nr$lT4f6b8dgn$(VE&06wo5TL1t6 diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree index 0f4f6ff70b7d133de7ac9930b3ddb699148f4d45..c1b34d420539c2920e808f30174d6036c0b4d12d 100644 GIT binary patch delta 472 zcmZ2EnRDf2&J8;_4fB&NQ_4-#&Ge0v&67+m(#(?6%u_8a6AjH1Qw+>gjSSK(EDRD2 z4NQ`ajm-?q4a_#bleebJil?WRlw{_m$HW&GBo+a+CnpxAsK?Y!zo^C}GubL5iU#J2 zO)scq)SUQ|qQt!7wA3Pyw+ivowZ0X4%S;CWbvP~&BrP)7cyMGR2)Lj7XuCE^e delta 494 zcmZ2EnRDf2&J8;_4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW zQVa|Xl9Nr$lT0_ii^4AQJ z>4Bw;k}T>V(f*gx!XQR+Mq*xiYD|1VW*&$;-Jq0Fnpr)jcKSs%CYi}r8BsJcTWoqk zB_rqL-I>jk^RvbH(o;)HGV{{oiwhEqQsXDrW-ClSl4Zr8oS2uKniF4Al$ckXmRdAf oHb;K4A~4(zWU=r;Tn=;iKdy z;u9?tFiiK*f`mS3JBTJt+dHjAu>U%vNdMW+)JOl+PLb;Pr&`7CbMDEy4=-H*^+zu= zgniduYkl8ZYp?y_=D_>69(e!O(LYS7PdeP~*B@fFnq%0y=`+VSJd<0*uv~0-D)vni zTLe=_(-<}|t96skqqUDFcWiUK;l4?8#;eMe)|fVYczvb7A56Mwc-87pG9RxKue~0d zGQNGyk7A)v6Qd7Lx^BE<-F*3Z{P6n0;Gc@lieR>)vr<(4bJCO_FTGEhshf4R+uerp zMEIvp4mGBVF&v}AOWZrlm_EMM-5V1Zep7bcc%yZ$>}whHqN%!>_9%Z!hr^dPRRzAL zZSIO)W$MJ1OR?#>R^^(s9`3j>b9m3z>!tbdw9QrH@AUM49lhaqdJc=dYh_LrB(J{; zru27oct+oE3o{C4#icjO%AY*B?b(ytV)AcU>g2Xb<}U{g;u5cGTe2T_ImsSeow#F7 zSFVf{kf{Y^S^*g?Ak(vDgUNnU063K8C?@;W0(zk=PfshL7s~SVXrOmPR>Wf0MpUDO z!Hd%yQCnVa%xvBfZKHrJPcNjHrx(id^g@a`dNMbmFi$b$ouioS4`q3JdJtcpUMS1c zV+Hg=*{FP^g>yl9xtF5$|AkNiKR)xwV4-6TA1gOgbOEMGZf})7OD%$ zDNIk)8RV;_k>xK`8d9h>X<%1rUvgQy`|i?J@WBpR;hLd&Az zi(RNK8h+G;)|O^^tTmGrWH; zNMFnh-;=}g%x)o?R-;?QU=32l+YXwYJu}*$o{47*37t^ybWvply4yu-qhVt$u&VL^ z|IJ#|nDIiy;Z`(D)L?W+WOd0yEzxiUqXp4$j}H#c`Dk7wz2c+!(eNb#xW^s>+zvH6 zLRG{z1e7%yVe2MTIR#=DeJdr4aAN-^6pwt4ZbB^?s;U@jN?U&`ns3?+q(0PuR5b-s zy%b1wZ$XPEsBQtX%UfWXkcYwST^&+z?qNtlFUyWa9;-o3+6@`YrXF-#P#f;paCrU9sw4!O<>V&qWPJSsrc9nK)3|CZ!7v{1ajF#3$j4Sx&VYDHJYBC z1ibr`5Y$K#a`9ObSd7j?^%+;+-yl7`d#^VU+>ZD>&@QYOZ>$Vf-((YK>0-mC|!iiYf@uyj&-8_?a6>WKy@((diR z;KFubaMKR()v*H@oZbQJw7U^4jGQ&>gklWsL<=$$jWjM^Ak*`~PAJCird*}4i9@Bw z`qA9T>FItnFWXbtx}j3}E=Y9EE}*t=7f`ETh!$o{&2$TmECl@S#~`+;&9H7SKL#l| z(F`g1xEWG{Te5R%iZK$Tf1!-+?#GcH(Z0|EG+uceGGr}6jgdm#Zh-l45rDs$kq#^d z@QaH9eA<$1aGnM~zej>k836DzOJLtVG=N%efX#y7APKWX?_O!}`2b2pWU2;HbB>m5 zo=9u|Aml5t6g5W%Uk;*`(eUh2R2L1eEJaN@Ze(gE!1e;Wk!8T{!)2MshaevV%TZIN zZbW?Kp}tD(#Pv=4v}t_I+XtHHxd&jIlD&jaxJH2^&FJOFpB1+%%|%>kxq;4jw#aP>L> z-n$NfUtb3vj_(KHPxk}x*m?jyd;s#>xB<*=|6UGov`1dvfW8yu?DkFo?(GENu}<)C z;2>HhTJJ-5^HoC%o^#)sdTuj{VOOK1F zu}hB&Pm~@PFO?n_aKR`&E`tAp5v9jP>2XndT!gJqdR&CBSnk~&1$@-l7%MtV| zf0lMv&-QyUo`>ZVMrSHjO^wv(s`%-DQ0Hw|ncTd+R*Wqvn=bD89KAI|t1DnW`UUz% z@a5KIQTqjYc=8kTuj?J+~b(=C#QLj7|zqSrZNq?v6 z6NaxbLmIoTPO0wersX*{(Nv68>N*xrS)V9dQ9}1Ap_)tN4~-hOZ@SXTv0bKm)G;hB zI)1237jOMrxg|AlNZAof*cSFx?pr$Jj57J9r0e2BA%=$KilK4o$eB^cxK>r<03TQw-t zR8DmRYqqUvo@JUg2^h{2qrX?C3K>#Lto(uUx45dhj;DLfv2B;?%;a3g#2m{Yszz*6 zNY^t&A~qR`#$(Eq)P=Lk@mQh=A5B!9QgCrTGC(X|KCavlC%&apZfUk=P{OI{b6I;! z^K6T9>bU8eRl2^e1>gP>lQ|SSrp;whtYF2d4#ugzqsn<%C&MC~_>Rh`PgUKK_Oh(h zRz2qXy6vZi{-L}UD~1?G5WhAc5xu9BjTo~URh2FI=mT^1?5R%7v6w`&nQj`cLv3u? zD(8;tG3J`4LAV|*z@j}Qt2IMRn=$!D@!4^uN{s$mxiQr{eR6wjut?3cIg&+dduLbM z%I22kUBwn|o_3M+&3T|&lwDMANfZlH$m-rHzC5ES3Cd*wm}_}H^Qh%pmZ!?E@LlG+ zuA|HSG|C9ke1m(sBYU1wOU1U-*5uHWt?OZz<+o&9HlSzv*p*Adqr}wYex$lS^BHkH zOZP;(%wW)wZ^w#KNVjBB2GEkTWpmkyPO(6 zNy#N~HkP<1G$CfX`-|*jwk6@d;rqd^NqyC@?65xh`%A*mrFSkHk6=b!%q>%HN5M8G8`tNYM==$nkdCP+ z0~u2%F-%fpnpqvG`*PW-lsK5GvIMdpJ)*0=#<3n8IxLE>$RQN&pI7O~VwKD7u_Ei% z&DAcJLFmY0VWvCs{)@`K(ji$oBuj^6>5wcPlGi?*ONZqD?;&ZVF3y~M_O|~5Z)%tP delta 10857 zcmeHNYjBmt8J-jN3lbHyL~*PnCtz)>k?elE`&}>z34}|;K*CKTYO>$0fRU5LKyZWv z@P-!Zv^`TTf}Q^9j3E8zI8!hEqn!b&?ggyCb; z#4a^V=3$IomzfT4@DlC=4G$!?4!VAFV9K0{x+<+*zBPerCH`c}mC04BKZj3K<(qHC zrcDg3`DH9^V{-hyDN`r9)-4D>Hcgzob5Yo;kqv*1O-=IdvuRk(M9W=OTD8s^8-~2* zs(ARXQ8lR{4-KNZ$=)5`OD^+nlmo9<%$ykY5;1w|j}=oVTEy{+?>?#rP1jjwZuLJk zX!6X)x(bU-%-GZuyGYl;&1YgW^G8)tH9FaK>YC)vEtjd`$r+pKCf@HG{w}!W`+d*I zU29c@7A7vc2&bCw`DFFLpNcC=_sTPGS5!TGXz=kvgE93}kvcRu#roZ-;Fj(53?_zg zkDKVj_3^oDdh%_gh)pkIqeX12h>hpkMiaxN2ytl3115&`BDm02fTKllp{)SN(zwfW zJeJ!xpm|#8ygaf2bri(L%GDjoHjCH_aG}HkTxcu6g%a~{DmJ0902u1d115$;TLF%z z{VTwQwgQ|{1Q*&y3xH-3U}!7ASw(Q6tpH~i!G*R6H#)gANw_u(T*(*uPkZ9G!SCFfx@HyU-upA4iN!)Re--5J0H#{)D!vYrpnf~fg20p1sW z0=!*%u7|petxrp)%ER`JsAd}YE(caB6=BD|ji@%7b9^IOoRzA}-L09?KaBcYH-V_L z6hzfC5Y^8>RPScAbW-YO7=m!zWISVb!AtCG1 zBsS8c?%4_8yE_3+jU^x!UnW4s_M~2b-+?m4C4MA1gA<*--gS5TdL0Z!yv?x2&%9PO9BH-_R5PX~7 z2J80RgAkH~Z4i>r+8`u&ac)d4d6cBXzexFZ^FxS7vQI4r8LvGA5fV#KO9W`#0W@cq z0Qt$Rbz~`!pI!>&GnVC&+Zpl`I~Dn~5g>nM8SL9nkD$eu!)8HnIta65|1LG)>k$-> z#MF(Vwmg(7o(Q#X6yg=X9koRRz8ys?qvo;OQDf9Re>-Z;n?{9Zl57{K8(R+Q&Mwb- zz8m5(atCV7=8ecteRQ21+pQ$;*aKp&>HsnS-UDJ@=>Re3I`U^3CzG?Bc^I@jy#loS zV+CkA_6Ve@Zza$ye-vsz)(O@R9|iK4I)VIyPShF&<&DSSG~C^V+9T^bUBLF*0r406fcWU0K>W;ph;Pe!7Wjb)d?MF9i`sHO|LR%v_7&NF+$_bb6|?2@NwhlOeD%i_wQ|-0G?F{| z-~mK(KBwM0fPNp$(r@Y;x((y`SUq73o9oynWS(isUw?(VufIs=rlAITbXmnrdCS-6 zooc`dgE>ZARQ@>PtUYxUPu<^);X6aGROj*KLzf^&Oxi zdpfi|B~hu^3hi)HEycQLm{>P3#Vp{SZBjujgX+5J>JCk{rnCf#>#k3!!M_k$U3vH%+=RZp0$kW=^Slo3zYAYahbk1}dIF_e!<++xrV{URi9FRKk zh4yYNUP6K;YYu2DN=q0xtTo6t4roSy+7?vf z8Lpw?M-7j<%wUcqD79Qd9Mdy>k~;LUc0!3W4Q{5tN9D|BI`=I?((btG+f{>ju9s4t zAB&Zwly!$SGW(K*GI3bb<*P4hm)9CLBfc<&X9(3$4yZ(1p6A=PP7O=X=&u!=yIf$; z6s}+{F@$aDDm}_WsNfDUb% zm6VVzJEAil_<={bYck7KTbtoBYWg;|ZEQ@IU0fw!9T(j8eXa%&Av`R&&Mjv9ZeSTs zTH`D^{%38vRC`HEuKc<7&o~X#ropYi#KdNnVOZR7n5ULH^O@)PPE>}aB!nxtv;yT5 zPFG@8Ee~9^TMMj;(Pj?kLY1Ggaou;-A71#Wy7$uS)>d1n`tu7*6{^EbV%aA5gl%Gv zJHk-=dwShQm52@BRz({SFR&T5usXt2a{U0?Y9SbyMUIrsCvP&*LPg z7aDOa$f}|5wEoW3$UU6YLmPl8Aft6ad`RGy+ zOqXNDY3gjSSK(EDRD2 h4NQ`ajm-?q4a}O~a<;$aWCUU+AZFhFmXk&QJpf-W8z%q& delta 80 zcmbR9NqEjD;SGB@4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW hQVa|Xl9Nr$lT4f6a<;$aWCUU+AZFhFmXk&QJpg|t8~OkM diff --git a/master/.doctrees/tutorials/faq.doctree b/master/.doctrees/tutorials/faq.doctree index 6cd8f62cea8c22a0af8b9beec4b18b46e8ea89d5..7a9a5a46958388d7da7b87be8aa496e77767d32b 100644 GIT binary patch delta 3863 zcmeHJzl)tk6y*(DB$6QFM$7l@5-XSNotZoL-oe5~3&AC^6b)g1ij650gJ1|63pF50 z2%idD8MX@;0zOih6#fI6M$3SOHo-yyX$;PLyDJ-!oziBTSKW8doO|y%=ev33$jvK9 zzTG;k&nBPGxi@ItCXP-jrOHKo&>S*wF;GPyXuxDG0Rvrr@c7D&2Z!JNYJgBF8}J-M z!bn~mB!=v>^MEHkuKsptTC4_UfgC*g25kxzfZ#aP8eCm;a=jwBdhFu8 zYqNXTCSU(Bn3~uGK_JdZ#L}9X5YU&HVl8ES<$`*0HhiRBTbgF;A!le$_7+*Jmq0cq z?Hy?>VSM6GwLE+HrMfg3bRd*$1J+>Bdha!rXk&YR^;n|bZu`gg>X%9X2@l^>8-4IC z1>>nr_1Kg=SHqD|=LU~KQb|R$vBsEK>mEi!$+>cH?YHPw=%hp=hKl6`(O5le>~Bq` z5nOS_U;>*lHsUr?jmZNf@PgwPch&N^@sm0_JNbdyoD8}5gy-)l=z@-&uvD(#Bv&8(9*)zFq{i^0IySrgv-)Imp|d+$ZO|{BdiBi4$roOH`AqL_ z|5e?Y;p*wN@%nA`)X?SDR(<^VVhq7InFU*dk&Ku~Ohs3LDJF@TX5m+Lc4=y)Mn{%1 zvqj8p9+(|PGQNqj%Hkq-q?oM^+BBF{K?A2k(B?aTOF@)HBzWGC}sg~SmBk%a<-TUhGrCuJ*r?I@F!`|*5 zRaWn4AFb|QL~cu)>xX&scILgeH!kkIadGc= z>w65@lkXjW=hO#lAsEh>OOXU*qfm@gG_w{U#;mm#K~C<%gQr%mKiG5X(vYC0k}bG| z22Bv3MOY{cm`Wkyg)hGSq+4`dU-@(Qva2p@Tbys5it#}(gJY{Jxd3IOlj{}fEBnsg zyE?shb@I(G18GYk1um8}8tYT6D1{`8H&hCaZ=6xjOova^Tf3H_Xdx6wL>X;$v5_UG zx%yNp$oT&EYH9lLYju7ykO3!RgUvP~f=N|7mvq(vs90FE{p&~dd#mk@@bE)*c<6xF zapkh|gF~u;sYr$fD;x`@wvn1LXoAd}=nOs}JDWiuW~7)>b=5nY3fUBl<7uOQb20?X zreG+Zt!?Y`AwrB*dlNAjtT1c&-1N)O)%v6l|Kc5`yTCIlYvcK|Dh-Krh8V;c7f`1V zyaUVZHQLx_q|JOx@hK#Jov?9>CR{+R-fdEHlKCBe4s5c1G-3BWg)xSxkoU z=daYh@wH#o-szE#)#XWlggK`k_y(t&chmti+IS9!&>lazcH-#j>C*>?Rb&%F0Uu2+ z$k`^;)f13062$r~d+X_G_9jcyTfeE#CTDufTW#2{y#D5~!$)3v`PE~+tKC}Nc}Tx{ z^x$~ymU?#Rl56|kzkmJ?De9CgM+w%WE+u&@*zBYNvqhdQXKA5Ew53|jnSuk1l4r^# z(AG$m@NYG843(=kRin1ngALIYVMjS|U0l(I8aosCajwP=_(F}@In3xKiAflpM2R6s zFKvisUS_e?KkloycP(pf8f~tEK#0Y9fnpsu@?fkD9?TeTtLMktch!^ApU$aElc8i5$J%Ks)Ie&JZR=au7HCxU7OUyb zF=Myfo8aknWyW{!s{;UDdwodLCQ*7D3L8_Kr+6$tQB$h6Nz^x^2<$e@XJdVx(AliX z-VV2_jLi18yS2Laf{!)XVrV<#eqZb;3|WhWwc WB69OLXKU#F-x0~v-G8fVPy7QSbyqq7 diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree index 5d501d388aadcea907fabb1eb566d75a25a73e2d..ee217aec4f1536cf23b56143a888de9f2292a1e7 100644 GIT binary patch delta 76 zcmZ3wlXvM(-VFyh4fB&NQ_4-#&Ge0v&67+m(#(?6%u_8a6AjH1Qw+>gjSSK(EDRD2 f4NQ`ajm-?q4a}N9a<+fuWCUWS?H@Urw*&zI*$o*s delta 76 zcmZ3wlXvM(-VFyh4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW fQVa|Xl9Nr$lT4dGa<+fuWCUWS?H@Urw*&zI^|%@d diff --git a/master/.doctrees/tutorials/index.doctree b/master/.doctrees/tutorials/index.doctree index 779172c32b58f71822d48d8cd3bbe0b207b3ab21..6128b05bd7783b8439254f326d9d6021e4b93e0d 100644 GIT binary patch delta 62 zcmX>saadwQAfsV^vSmuSX}X!dak6=msYRMua+-Ong=M0ld18uzd8&~?nuUcyqM?CF Rvazw5fw_U%<|f92TmWGm5@Y}X delta 62 zcmX>saadwQAfus~QAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg<|f92TmWgjSSK(EDRD2 c4NQ`ajm-?q4a}O~a<;$aWZeFilc|#r0Gb#Vxc~qF delta 72 zcmeyif#cf-jtzS_4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW cQVa|Xl9Nr$lT4f6a<;$aWZeFilc|#r0JC=(i~s-t diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree index 4c4cceacc95fc57cb9002ca3dcd48024028193d6..a456524f4b2abc2556b2e1891232cea362f5a7e0 100644 GIT binary patch delta 80 zcmaFyo%zLg<_(uPZS#{YQ_4-#&Ge0v&67+m(#(?6%u_8a6AjH1Qw+>gjSSK(EDRD2 k4NQ`ajm-?q4b1dY@{^18i}Op1l2a!aGHY$NgjSSK(EDRD2 c4NQ`ajm-?q4a}N9a<+fuWZeFdlWB=00CLqATL1t6 delta 72 zcmbPwoMY;7jtvJm4b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW cQVa|Xl9Nr$lT4dGa<+fuWZeFdlWB=00E{#kE&u=k diff --git a/master/.doctrees/tutorials/outliers.doctree b/master/.doctrees/tutorials/outliers.doctree index e375832607bcff876fa950489684beb88ba3ad54..6df76bd38e2256e1e649dd344838f993821ce72e 100644 GIT binary patch delta 2238 zcmeH|%}Z866vi8{FOn!Kg#x=-wCPRHe4j}x=wo5@(g#IFTFlIyQK3>$2njQa7J-Z6 zwJz1Fs9gmi^{ygn6|`~JuJAADFDM!{p~$+cc3!xv``mM$-#qu>etW*`#*1YQ*OuO0 z^0MLCN;=?8ep&XacJ8&47?qDYps`fj9CTn15y4Uro^u5n)a3Nalcz{uP1^&_bo+(j z4uD+%m5hR7V3^cxrgq0}C{YkMLLL+^TS%qW<@Ywz&OfJM|8VW|)9e@7`h{e*hyDsV zUE6K3S{r|K zy;L;W_V`mz)7`K4mdsw`E5gOWK0DMuG*O#)wTVRbwQi8)M7 z@m%HJ8eyY17`%v1qq5vf{U;OzudR2WEaoC;pW+HCf{ip~$8a91)Wa-P>GpZ3#^Tlp z*|C#@sP0P@X&oJ{WUX2SjYgq0420%6BBO#akJLi+d3}0>3^bHBVCFQ|7u{o|v3~n1 z86{(7v9+evT}RKHuO2yY@bLLi@*V%KE<3%}o=P!wk~Fvc8?AVEjdT|q&yi#0y;!X0 UVm%k@`M2N9f2n6Z)w8qd2NP>`hyVZp delta 2188 zcmeHHJ8KkC81-UIR4f!_wGc8{L=YGEz8@w;kyr>WCO#0sP5AE11XLCigdoA7r2!wC zI9Q1yBB%ikvYq||Tg6T*3k&~%;N49u#K>r$VVZ$+?|FRRnVXB2-gM?Hy3l+ELlwRE;#5Dz z2QjW>#AwYi<_DObn5s_JHxE=BSHj@@cu#$PeA`^-Kxy>@&GmK~J!o!wUTGe8hqmj1 zKdx*(bi4CA`%q6I>>4`TT!g{$Dwizl`X#6XTa9b!y?-*01xYd2UT8~zGZ}$M!MQal z9#4R%=DkNZ3PDT!@rQb8ZezIA7>CJ;$@%8|!k7E&I?tOvRO`adG z3QYi_jYg@~-BEP8D>IIA_n2{SB6B!m>+_M5XnU)A7|o$x8ln}(mTQkhGCq6hWMC2g625p4ZPB_pY zI9s;U%c#=Sf@H=jZh;stmnLi84Jo~-U#@lZtE@sJhq<|D~zetR12 zKR^ws9Z6Lz&&*V^a(g4R3sw+LIWcKGQW~llS55>%{EDxl#c!v+BOg1Dw&b6NP+#6S zjh?qZUtKSnS2>9|HhgZZc4Fx0@v%^_r@x~MVvOZ_C4U`JDJyc>j%QE)Z5F9K8$~09 b6<90aS^gjSSK(EDRD2 Y4NQ`ajm-?q4a}OKa&CXh$#{J_042c|rvLx| delta 68 zcmdmUm~GEtwhdc24b6;73Nkawiu5fl4b4+bQc_cslZ?%b5|hmm6U|Z$%*_pwjm^wW YQVa|Xl9Nr$lT4eRa&CXh$#{J_06WwddH?_b diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree index 19a3e019ff6addbaa7c5d967b3b66618f47fccf7..40513c4e3594ec9ee52d417405b342848910d1d0 100644 GIT binary patch delta 7680 zcmeHLO^an$73FDZx=rFoQt57!zW3cOk_e{g+UJ~oz6{zT1_>n5&Y**s&^ezP6g$C4 z$e>Z|74 z*IsMY%^NS@yz%l|?;IDGlFu*cZ<;-oY}BrrRBdrS2pcllBB?q@K@caU3|TTizIps7 zH;+#cA3}T>v4!{u;-iS$5XTVnGap0Tj<^GHC*tFXPar;t_!QzU#NCK(#65@;h?9s@ zh)*N#Mcg;LdHnPjzBHJuEICmr8_R-4i0q2@PAipiNP0Z~`sr8Nr>*FKOdyb%ES_r$eu+OzETLv=^{KAC24tM^VIytw|BdLof)d&5OUCwO5~zr zP?42L5nUH92Q$0&eD~e0X^Jr$&7?%MY}vTPN>(qebIJ($_8Z+VCxd2|N=fPQZCtbQ zlstweJL_eP#f`Y@?%D8CcYV^#J5C?%A8(}{#jiVh{i*Kw;47m?nnWy>oszD|m{lo- zgqFtnWf_CVsgx~O8w!W$U9mYEVK|qP6@~H2tKG@jTTk_GPKJ5=9TioD4{VbOBIiQZ znt*pQWM(+~=Seg3c=cNywG+R0q1zjOdT0OexchAP&|pb$Rj!g2k|jPN>1u7}B8$Qo z@7EJ$NxJx~6#hoO0;oc|5P$@ot*-~_LC;D~rFd&N0?4tVcOhZiRq=9FPk(gg@%)Ki z4$ZWSYR)~^eRfZu72>%U_rCYc&gILy!;W{6HGm}96658uP_S2ma{)yp7gl|0;u>$i zJzo8LcgO6;ef{Oh4_eLILGDMt_VtU8fA!0cJa(})s0UBS#>4GAvpat9v+l&uDsIHH z+<*V#9g40hg=nOWg);}2t%=Ht6ue?nXVhE~F%dsOz|EBvuRDH51P(alNCMoT-5UCC)keY-_A3 zlZrNn+10K7`K_rACIu8h(h!qYE*9ymwd{iO&dy$VqW|k;iY->WaEV-U98310kgV8| zlnBdmcIDmfFO%WugiBh72Eey7bTf<;g!HYT<3mL!)$r~`MO(1)RRqAK& zU+;jyR9K1(>EnuH>CsRKjpBeKStCWi!l0E=8wQ)yEPz4n0csr1k_C3Lj_Vj~Bu?WW z9_*iNCLYgxrvLoZ#{fpE(I{6~5a<@Tx5a|9H28Z}1SLHubnm>xsR&8pP`9%(6)2Kq z*Cu$V_`Oj@SjCcEg*G72YOWGKyZQy zQ%O)5K8FRxdA%6UOIy9HQdB&w6or!&jx{g_{*&4`7UOHLcBkev{@ckk>qG)H3gU)3 zVSpX7{bw6%Y3%>M$q1E;}?B@?<49k95ET>>8z$TM$9a* zOpJ9kf%21%7%UlNpqw3(v@0~WMdBeew%T|QU#;;yc$ylII!9yvV|Q}esBI05AhbZr zNQfJ#99OZ4DHkP_1f9vaBE+i7T)rvkbz*28A0j5hsGK5cbae^Fsp9#>8i;NKsl(N2 z=vSywC@SM5RNlMY_DY8~@-F!BiD@J4ybd=|0p>&nBZ8>$JziVzSO(gQtyb-`37ixM zX)Sa+918?=1{qdQMrWyv$!l1ghJ;ZtDY66_EEW@(73$j?SMd~je1Fn!&;D)t-%Xm? z7kKQCPCowaLMO)GKiQv~Mp+^3VF!}ZSYg-+u|^L92djzb<~+7Nd+men=`AS6`P0HF zISDM<2oA3q0u$oZ7Ih1YZ%!}{B=Lu&qCksO2tKOps6sAOtHX$myI)QtKQGsdKRWmS z5P5R^=QsMh&VmW$8um&G#lRp~cqf=M4YyLXgZJ|Zn?A0VXWLC3f?lI*@*tQR!+MA| z8&8;S5n-dYw7}lDmwG$?=z4d)X~>p)sO88uM)3by(AC7Y9O!~WHb!t?gtq>{b6A9K wjNrxyZj9i@2o4(~xG{noBZz;EZj9i@2(H`EjS<`!!8Mb+G=ei2!9U&pUt+qt9{>OV delta 7687 zcmeHLO{iU073S*GCbqS;%}Z+Y&bcQ~Tfsi^*4}&VKZ~Y_MG9@S9Yn;IbpO(TXrgH( zgHY6gw4+AE<48IYK?OUhR|GYsb7?1%Nx<9cYogF-hlT^9A1)jc_I7lwb#$L zzHi-wJJ%2HTz~ub$AytsFJF4)+IRN77dCS)B}HXqNG1eR$XZIuSk6S065FVMcyRnj z2ggUS55YbR+k$-r_EFe9uw$_4nUBHlh200cANFzB1F#2SpMX6Cdl+^CwhcQ8I|X|L z_DR^Iu+x)+<2zsYV&{vbnoBNDTV^hfG*?GiRzZd`_s{=y=Lf^5ZeG~gp8V(8oez$6 zibROQDV8O$a@t2#j8D~A$5L^b-#Bz?f*apD*Tt-gPB}UiHT&p85?Okqe9>7ucJs>y zPtBfptlv5FldjqrZLw0KEKEK-8)7YZV$imRVrDmv2Tt{GUf4PLyS?TgXT~9AqfAvb zOUbSzubDkrStVCrY=84F&56lBuQxAlHLlpoGH6vjR}o_-XRI|sJFB_Be7@c8_pdc4 zCl7q8y*3)xVpNrsh$<`L19`(SSS3ng#j$_)a`W({`(AT<)D65L_2XyS=Q_RCzsdav z7aG^8m^?+1Lbb%i(naAF-olkgrb-j|{npl)@L!rjcBCr8HlDK01?kKc5$68Q*PBNs zZ-1@*(t3`%P`>WBQ^ZwQ=4Rr&)c^b%m|Hl38 zll|Vy%@ds#>{9gB>MA&9;nC*RTY*m1Lz#1s6&7_#L5C!Lgi3$Xq4}|4*Yq$MbLo{m#NUcd7a8zCTNJ>FWM>U)sHK zW3Ss~R@tJTMdreUn1at%S}#oC;K?m&HGuJHt+xAHe{JrY+&SIe7`--7FtrQU&wTak z7oYpem!5w1q8(_|)zbrs$Hd_ zkrX_+LhZM=#zEs>3g9b}Q;i-evN}@CIVY}qGWuEb^HJw)N}g3z4q(;Vxl&@m$WFcj z;!!T+ZpiTrcjK_$asU2>#!L6Z9tLLVlKt58^^ z5`8XGparuRF;HCYP~&_;V;INTqMOMHt0)>GjbY>UqC?lam1^FhgMJSI_j2bj5oCyx z73LrU^3r?OnEN(Z(#9{a<`YhE4bf!5#+InH5>e-@nSdmW%_FRJMvCaN@maWpvof%b zxEgI9a0QhK&i-*U9`M{>x!A~dz1v>=q7nU5mzwP_OZoUWuRcliIikH6UzJ35{wdBX zy@WvJD^Sl`B^%gRFccZ&*m?FUV%RLg9b&s_xF?n&I8Z%a1=Uc`uAUys=Me;lf?I;1 zDIjK}i*d=Y4-%{zgfw8bTBy3i5zADeeIyljf>U&ngqK-?a8MX<`T~QQFj%>QCIDz{ z$#X_O)lh2zC_-4q;E?D3w~x0kbVH8&GoNWc--)E5)6nfnW;AW~PEvMLArw^b-Y%*M zg3MZKos0vzGKGXcjK6?j(ONGzuZ$`vErW7iFbVdUD!^chx+dZ*=FL0kd4|LBfaB>T zp(l0)(PMLd&THFmrp|vME z%gbz-1?nbpj0GA@v5Az>I+!q?DMzzl!(mBzHx`FVxQec!-d#OCkklC+=69M? z)a6*DB!*Tlg{L>75vJokduW~#?J_2Zo-pOb^W*goNYjv9XKKipD9jLNh{lEK<5)zg zeO73`MKo+OivpD_Sg?#SK#lrL(L$Hopr;aNAxFCy09zHJcT-T<7@YCIb`B!>5{~F( z!10t$L^t4ep+tZ4LVK>0NR`vlR)Ox+0$3zYP|ndIsX{LTX(2W=s2y)EnUhyOXzU{1 zDcJK=aH=U3n6eQ zLQnO7|3>@JKAm;E#Df}L1oc~^!j@u%2qb;R#^7{O0s$O0-TLLL|JQ9SOYfg<-x$3% zRHjuN4Rz=0>9wf?+>aP=D6ARm`u)_}{`WNdvka~-x|?NivkY#Q!Tq}hlPy!qP1DWvjg!rjOfAyPlGDsnEi4la%@b1$%u|gF z(kv_t5)BPZl8uec49pG8Hvi=OZ7OfZ%D`Y#P?T!NrK@XXU}U0eV6JOmq+n=jWoT(- zWIS2cJ9P4yFn&gp$#Q|gK(aC3d$NH~_~blC6+KfuOH*Gb{bB_m$jnnP&@(FL;=(k< zL_q_jaeV^(+naOyC?NV>3OY z$?qehc#zcs-LcnJExp7#RzU%1fRUN6f`X-yo;h*O0jZq)*2R4U`(D>;HDmAOf&g1+ zlz?J76CTTJLqdVETuE6BuOK>hSNp}I#_rmXYkDL`=Y~-K$@#n4hnuI?Nh$$zC~0}~ zUyLGUF-LLUJV|Qoy>&5HoRYyFJ0(LfVx)L@^SXqf`J7XWm>C&BV6tL~$mHDrJgu6H KTQ!-UZ3F;UKzW4# delta 1480 zcmbRJgscAv7fS={RECW#M>q}5j7kbJGs=qeEiDbrQ%q7)Qd z<9~LDab}Yr`npc`^@vq7(K9kO1Y$Ei3qu_RLo+=?3nM*414}(aLt`BUV?9$n5OeZH zk0>5wrIX{L_S&eWmsrOtfHWDI`6?(7=^8Ty4MS6d$%bAIll5KMN1(q~Gj+30pWw=< zH<`zq4;mq$_|1gJ?S1cal*R1|V&ip#Up#8O-UlYL0{=!`(jrqnAZ$2#%71!(6%*%V zfv_-11z`3hHe+s|{63tAvIwL!bDktI0u8;)C-+2ii~w(L-jxtEe=^@cZuY6^%!~{S O46SmETjiMkZUg|9Wp5k+ diff --git a/master/_modules/cleanlab/internal/util.html b/master/_modules/cleanlab/internal/util.html index 00b634411..6e8353c80 100644 --- a/master/_modules/cleanlab/internal/util.html +++ b/master/_modules/cleanlab/internal/util.html @@ -643,7 +643,7 @@