From a368bd5f07e2ebd468ee549b9002a714f73eab65 Mon Sep 17 00:00:00 2001 From: elisno Date: Tue, 2 Jul 2024 15:35:50 +0000 Subject: [PATCH] deploy: cleanlab/cleanlab@c915f776420f13284807e915043326eda337d0c4 --- 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 16378654 -> 16378592 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 | 1264 ++--- .../tutorials/datalab/datalab_advanced.ipynb | 326 +- .../datalab/datalab_quickstart.ipynb | 138 +- .../nbsphinx/tutorials/datalab/image.ipynb | 4066 ++++++++--------- .../nbsphinx/tutorials/datalab/tabular.ipynb | 138 +- .../nbsphinx/tutorials/datalab/text.ipynb | 172 +- .../tutorials/datalab/workflows.ipynb | 1200 +++-- .../nbsphinx/tutorials/dataset_health.ipynb | 34 +- master/.doctrees/nbsphinx/tutorials/faq.ipynb | 648 +-- .../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 | 488 +- .../nbsphinx/tutorials/regression.ipynb | 202 +- .../nbsphinx/tutorials/segmentation.ipynb | 974 ++-- .../tutorials/token_classification.ipynb | 158 +- .../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 -> 230171 bytes .../.doctrees/tutorials/datalab/audio.doctree | Bin 333645 -> 333647 bytes .../datalab/datalab_advanced.doctree | Bin 203507 -> 203507 bytes .../datalab/datalab_quickstart.doctree | Bin 142186 -> 142186 bytes .../.doctrees/tutorials/datalab/image.doctree | Bin 514366 -> 514364 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 104195 -> 104189 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 176667 -> 176673 bytes master/_modules/cleanlab/internal/util.html | 19 +- .../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 | 1264 ++--- .../tutorials/datalab/datalab_advanced.html | 4 +- .../tutorials/datalab/datalab_advanced.ipynb | 326 +- .../datalab/datalab_quickstart.ipynb | 138 +- master/tutorials/datalab/image.html | 46 +- master/tutorials/datalab/image.ipynb | 4066 ++++++++--------- master/tutorials/datalab/tabular.ipynb | 138 +- master/tutorials/datalab/text.html | 2 +- master/tutorials/datalab/text.ipynb | 172 +- master/tutorials/datalab/workflows.html | 414 +- master/tutorials/datalab/workflows.ipynb | 1200 +++-- master/tutorials/dataset_health.ipynb | 34 +- master/tutorials/faq.html | 6 +- master/tutorials/faq.ipynb | 648 +-- 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 | 488 +- master/tutorials/regression.ipynb | 202 +- master/tutorials/segmentation.html | 10 +- master/tutorials/segmentation.ipynb | 974 ++-- master/tutorials/token_classification.html | 20 +- master/tutorials/token_classification.ipynb | 158 +- versioning.js | 2 +- 178 files changed, 12627 insertions(+), 12638 deletions(-) diff --git a/master/.buildinfo b/master/.buildinfo index 90f554846..7b7a7503f 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: 9a84ce39768e5004457710fb533df182 +config: fc87451fab9a1cf1e283b90e1b533322 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index c30a94616428769f11ed97736d8a922cc21688d6..6d0cc54a74218534bc63d592699e16b95e45372d 100644 GIT binary patch delta 115 zcmdlWxj}M61fxNwaj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF XahgeTs$ufx4#q%E(sgliFJ}Y*sLdfs delta 115 zcmdlWxj}M61fxN+WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Xv9Xzfxq;c{4#q%E(sgliFJ}Y*kdYu2 diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree index 525e8b8576a8df844fe9a5149479df3c496c89da..310065927e5336f495d0af1ddbbea24b16f6198c 100644 GIT binary patch delta 1440 zcmX^3o8{ndmJJDv2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(tuIvXQQ9vZHz2W))_C7Lx4)>71@>!^l5bfTM^U-R+w#IsdVdqqBSS zCjloW@^rozW@9BsXV2!1lAUDP*|YhZOaljbb{eU5krgvNn=7(92 zO1f(|JDT5MCFoA=r;;v`Qev)3aja&*Q|Ua!Zsxh^%ItoQ`$o?MqIuz6Zupe*_J zH}u?>AY1q3h92q7!plTl$TdM{^P^WHT;%KC_%&UIJlzjW)VH4#V(cf=a*%dmImRBc NLOyOgu&kTH2mr5`u71@>!^l5bfTM^U-R+w#IsdVdqqBSS zCjloW@^rozW@9BsXV2!1lAUDP*|YhZOaljbb{eU5krgvNn=7(92 zO1f(|JDT5MCFoA=r;;v`Qev)3aja&*Q|Ua!Zsxh^%ItoQ`$o?MqIuz6Zupe*_J zH}u?>AY1q3h92q7!plTl$TdM{^P^WHT;%KC_%&UIJlzjW)VH4#V(cf=a*%dmImRBc NLOyOgu&kTH2mo8PqJ01W diff --git a/master/.doctrees/cleanlab/classification.doctree b/master/.doctrees/cleanlab/classification.doctree index 408e2a9842f35c538049657b7a2641c34055bdd7..99944cc06caff2b335a31c7bd65dde753f771f37 100644 GIT binary patch delta 4130 zcmbuC-%C_M6vvslcU|4w?r!3mB1B<7v37M=-K~rSDGE_nYf-kWyK=+S{80*RJtVml zMUiolAuuYm2rLkh`Xl0u{v2$J~F+?48d&i(Es^P>voU{taoq&d4%;QGRc0d zcPxvX6ZZn2ji*?po=b+rm4D6(c(;js!FsE=kan!Mek(bI^#-<+Cs=QE7iqxl`WmSB zklAe1ab5%QqojAAIJ`W)^+K6I#-0gw9e5B$VpfYXkVZ)8?u7QWolig8#RZ5AkfN3h_O7NI849LsFQQCOcp- zPwGef`xcF`v-i!pL6D(nYlBRNE=F| z4WA%t*xz`u=!_@7Fzjt8@sQ7dMEAVI@Y`FAosD}d>0wVdi&Rr$5?M7gY3t8 zM>EM;aWC-Mc#2i(Ib=Xw`RA;FcN@qTthaI#X~%kNH9;kJT~wX{Yu1D7HdBH*baaduf&UZ;d%$EBx~k z`f4rO?_|R@)B=l@bSLf_Hj8iq4>_XXihxT^^jj*Do$jEW>rrfg4QEn?wan5CSiVJH zrYDoqHcwNb>>=$rj7q6sT^5oCt+VtzauQYo+UIG^j;vr}i8|TUUvvt`1Ls3FadveJ zX0e1@N{6?y)FUSu!gz=qmTgj#1D^!EoMOZ&_`Iq=TQb_uiQBOCZT{jU2rhN`AA diff --git a/master/.doctrees/cleanlab/count.doctree b/master/.doctrees/cleanlab/count.doctree index c9bf9fb3fe0cdf991e74d3683da44bc014be610f..3528c15900023e057921c247fe0e9037fc30e12e 100644 GIT binary patch delta 3514 zcmb`K-%C?*7{>XY??IWf+M087L6|E|LU-KeJZscK8B~y<*d(>dHde&ESdtellmaWf zN$Omji;$9T77S4i^5O`}i$xb9b`eGGu8Xj|5Mf2&MP~>4b1t?}9&>>PTu? zR=`v0YKM4Co9B5O+R#9if@1G23BXCpB(% zt#6L#xgA|6dY&3yCXzQt%f$O(i5tXLOsV5Ua_wS@xMv~#nbb;HtGj$;7y4%PO9*QV zQxEznq(|v(X;9A(F}wEeRmnt_(ArC(Fq&5TJ}_C8T&x=2?ql!q_P+@wy>BEZat6Wo4oJuxc9N zcKFyRyxdI}fgVZdwxjt^*%6s=NeB?w!?dt7_6$Z`=LC>XZ~S@Ux|R8NTC(KhVh=p zR=5o^kN7S8S7qD==mPDvX!E}on}7a4iaq$6ui)RRA49ny%Eh@V@=`7ss}T>ipn1^$ ILrph*2YO=~2mk;8 delta 3514 zcmb`K-%C?*7{>XY??IWf+M087L6|E|LU-nLJ8RTJ8B~y<*d(>doSPT(!lH;4N`V#L zBy}#%MMz0E3x+5Md2s~g#iEN4yNIH8*F{)fh_E8?qO$}2y6ED4>ks%m&-*^ld%kRi zthJD}E}mvmA~<@bW6baIB?2QIa>}1b1(I?w?hV98r9iSxO3AVm_e$*vpU*D^B)^tn zc|4`Adc|YfJkQzCh6bwS9hcFh!XmxelJjX5h3wb9y6@o^)jj()OXG|{^Q>>K7HOo` z(ns{XBSnd1+IO8uUi7~qX1gw*C3@yUkBQk9Pn0;i6^%to#7QSlJ|Rx}?96Taq{hvz z^~@1Hx1;Ms&r^fTMDpfviFiM3On7s^eEj;b?W&6X4l@mDw@a=T5B;BM$>A~2PUhMi&etgee6Bn{x^Y!Mpp2lC(y(5 zD2tz*5F21w3mI2D#Y&h?Gi!t@;Q;dzt8GK$phqa~L1X3My5Pk&%e0VKSy|{jteS?n z9X{3zFL%>Lpi2@u?P$JSocfT7PS}YEL+xl@ou3tAs(C}Oz?Y10tp+Usy1+O5uavUy zIdI<>R_oA~f9!l$fK3o)CA5n1%_v)aZm8H0Hf!np@+2R&qVbt7_6$Z`=K<>XZ~S@Ux|R8NWK}rhVh=p zR=5o&9`RfFugbU$&;?qn(dK_GHuwC06npSBU%|gsKZbHal#6o}i IhnjBq4o*4uuK)l5 diff --git a/master/.doctrees/cleanlab/data_valuation.doctree b/master/.doctrees/cleanlab/data_valuation.doctree index c1ea3660a6c75ee1c906fe7f2a914f0ac8b35e76..d9c7dc16c805dba1a4dc630c9b1c7bc22ba6853c 100644 GIT binary patch delta 469 zcmca~p7GLo#tkuy2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$&-5+Z<4O5QP5%YRi>RxWa*szkF}8;je(ok-B`%dIEh_&^DXXvW^y!# dY<|n1LVCDvZWJsHA;&%kVDRv7E=`MN1OSdcjb{J= delta 469 zcmca~p7GLo#tkuy2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W|MmuZ<4O5QP5%YRi>RxWa*szkF}8;je(ok-B`%dIEh_&^DXXvW^y!# dY<|n1LVCDvZWJsHA;&%kVDRv7E=`MN1OUdIh?f8W diff --git a/master/.doctrees/cleanlab/datalab/datalab.doctree b/master/.doctrees/cleanlab/datalab/datalab.doctree index 8e70ed27cfec1005999b1b7f5ed54bd2f4daa65b..e6d075358462f6ae715375b1d1b9eda316e6640f 100644 GIT binary patch delta 4158 zcmbW4T}xC!7{{5>Rd+Y-l$CiA84JD8MW^*;cOxW)!qR+WTl7NOb8OdEYcW%6vo{i! zkficp2Lfr6Qlc$lhnOI$i-^)vJndCwU3H;1)dvu2PJV*kWX; z?!HFQd1T-y=-hSgyu6cHiNByvzgR2pGwd#?x{}P1?*fuJ!J3D^0=E#!#Ms-BpFmEV zJTN*0HIyT;R?Gi^Qqoy&J~K{7cJ2g%V?9hfsd`3`h`dI)@T4Z9QA z^c&FWe!KvZ{ZBjOr;L%zCD2#$JOq+?FAm99D)zO7&An`ex}b%nUU}Te9aPV%XTp%P z-l(1_WZOT^0nZYWsb_IZ2fcBth_z=|3b4EjiyvGNX)G)%%(L{JL$ulSgt&P!MH<&Y zJQsY(gP~;>X~Xu~6sVK0d?pM#Wt(@H$~O^x-xm_Xi*RtU$m{i3{=>gW5L5Hbf5_}Q z$ZBrgL7hBGX$p9jiUC>WNAl^lQrN$VPi&+4*wJxO|I7wh?J&z~XaVotPgOj>gNsG_ zi;*H4xN?lnLN|DWDbOwaY#S|xX0>7LQ3)@N&^u7@|8#9P{q2;F&9#1dTmC_53M|M6 ihiF=nR+=?qf}OfSd*r)}3w99bu+_itwcI~VkJEp9weCs) delta 4158 zcmbW4-Aj{E9LG6Fr*2DIS(z7+G3bRZdd%EQgrrbdTl0lA^kTGUb2GJu)zX%|kuXA% z$_INO7%eG9vWOkx1W{c?)GTGotIE3SLT{=+K&bQNUvR#Ez~_8^-*bMy^PERtu;>ex zXFj4t++Bn9-HttV5ofsG6>~&l&Zx_+);iTL#TnhJ#9S^#tyT6#>gpVdQ*p2>%Hp!D}sD&+U++B{4?QGos9;j9g z1=;e>W+&pDl!wH&YOTW+Y(nh;iY+9=EFJ3vy2v1JoQzSV;xN0`cMEhrIDHCq-tTvV z&fc>=(0TOyG0?ed@PfRP6_S5JU-(j!yiYT_z;QKIB;N%j+r?UkzXG=q$@Z|fBR_$h z26=FF2*_!W{@dN4(?50*bas!=fX*5HA?RGbR}7M=bQ~l{GtFSm_{2NV`RNhx&DD%f zU{h~Er}c3jBoj}A@>9l0_A==;^8ARX9V>jE|NmCuA>r)=;h-S$mH-~WXK@FGlHEb@9imjB2v;>Xl{^B*$1 z4zlW7cTh8rQJMjsrD8x<`H@n3y&Cp!;^}R)6gxUD>d$R})h4~Bk(TlP160BDo48mc zQHd1c<+kH=7P`T`xhTxwZlQJ`;XWR_@ZVs4UTlxSg)l$e@oU}knNn_=Zl-UXY@TFlk!F^hW}a$cnP_O9m||d_YGjaRVPTMHXke0T QY;0y=ZeX^#oTZxw04kpmLjV8( diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree index e389b61733b67c8446288cee8b70937baec23cd4..c61e1726a137e9e3f68a0dd531d56bbfaffb0371 100644 GIT binary patch delta 63 zcmccfmGRD3#tngt2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o SmPy8GCdsLW$(tJ)qbmWqloW&j delta 63 zcmccfmGRD3#tngt2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S SCdtOeW(MX4W}6!sqbmWuv=iz8 diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index ad6540e85db82dcabdf5cfbc2853e3c2336ef5b8..9733d7a3b0e305f0d356700d5fd7e04bdcab5781 100644 GIT binary patch delta 61 zcmZ2yxXy4x4x>S)aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF QahgeTs$ufxIgCQ$07)PcDF6Tf delta 61 zcmZ2yxXy4x4x>S`WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{IgCQ$08Ky@q{45LA&aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF QahgeTs$ufx9>&$W0AHRHBme*a delta 61 zcmdlUw>@q{45LA^WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{9>&$W0As!qiU0rr diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree index ee9424bc77832547cd5c4b7219c5cd27a4273ffc..528b04c6db0f1976fd580e54892692416710cc8b 100644 GIT binary patch delta 80 zcmX@Gg8#q@{tao2hMC5tmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o imPy8GCdsLW$@-IH1(ch2F}Ckw1YxG_yBL|m)w delta 80 zcmX@Gg8#q@{tao2hRK#G<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S iCdtOeW(MX4X8Mz31(ch2F}Ckw1YxG_yBL|*jN?@ delta 63 zcmaFxf%(A)<_%|94U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u TOp=X_%?!*9%r^gFP5A@>+;|mi diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree index 9f7146d41b4a0b25bcdbc380bc99b71a0c3cfad4..f5f903facb8e11d18a8165c5791d5c789a7b4240 100644 GIT binary patch delta 115 zcmX@AbyRDEAEQC0aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF XahgeTs$ufxI>sG>r0Yr(zRw5%-Y+7J delta 115 zcmX@AbyRDEAEQCCWlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Xv9Xzfxq;c{I>sG>r0Yr(zRw5%#q%Lq diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index c59a3cacc0e05a26ac07f22d9cf8794243c84335..b74cea2a01daf7e78379c3cea127bc1050ba7330 100644 GIT binary patch delta 5615 zcmbtYe`u6t9Opat-lliHySp*A1NEldoVn_5KfP1OW^?4>bjMZ`o4fXIZ8_ALvmH*| zZG-q<(`^6Euzj%OUES9KZcaFRDiR=SWZlj2OYkmnnG(1vXEJSLlnxzE13R&3t*A&eInyW*VL z)Qzo7G&dFSr_Ezfz)6v4^@}f(72;9LZD;^aw0>zI$WGyX-Xm@e?&Uz+XN&Kc@Kt3Q zcqmZ}W$vKe9j`%)v(dkyW0&!zt~8XkEo+MK3kHACJq{aRE+#Ege4d;`2XF{pY~9XR zdQXAr|KRuVt-+UaKwsUz1Wex6-ZWf=Ym2GWQt{~UU2K6`K1*<+Ydg=5Hh@jgEN73o z_|(xfSOnWfaKb+Sr^MeA0kLZ9RuM_}!ZUGj;y$)go<8|)0j4{B(%?Vd$_I;}(IWGm z$)~5TV=3_LxgBB1IiJ2g8-S3)alH!zd+&MV$1Sv)zxn2PW75*@g9o%?FcEZ`(z0xgk%)XfGfAH?R~*(xE6V zAn4(eMXNUpDP`i~8eS}ADaCP$_SF=WnHKs*?F-NzsE^JfG*L($TtT~_Bo}ZDuc98@ zhtfV)K=1({2YBEeiIrz|UuNtqWG-x4Hu}tafXvX1k z#}@hv>!lXA(S`EPGxQuzF%zet($($+>tNf^93ho?7H+k@N6j$}7W#h+b8VED+ZZ1C zFhuLrwFE`MUOhJBQDYsnz7Wr)yp2qGCQOye>ZVu@X0k-bs%eWF>7h7WRYNrmP%93X z8oo^W576)EP7H_Q?O}r66zw8YVaQKL$fw2+(jeRuhr@oHWS<0T`fv`tUqGg&M(GfI z0Sk!i_l~-LB!^STlKJI7A>F?H$8$IZhe&Nq(-ZJ1WI0@F?sYl`oGh=`TqfXE`U7% delta 5615 zcmbtYe`u6t9Opat-lliHySp*A1NEldT)FDrxpqI)vDqATINh<;#MZ7|+fGHDIP-Gq zZX+})%q<_|2}4wYJ|1~bv&SUlYrO%GYo;q_J~7PHcg*5iXs zO%W?_WkkI5$E?z1nh6`MAE0OQ5^-UR12 z_F*d%ZLMYeN!u6{a7rXQ{NnReow(nA6B>Y%9bXs-vPXEI@rWCl-5lurbnzV%zJ>w= z?@3ldnLB8A_sh`YZ1ivF*fsoiZw^Y^mNiBAS%cs28;6ZA7n5mIe3qI+2XF|U@7T(h z22O$L|KRuVjm%IH==b(50h712H;y#m+F~lZRNOyw3tOO;FA%)dyOn1TN5Cd%mb1rP zeClWpEP`z#IANdvqvG$0fLOIxPz^fr%!%chUre9H29C#OTi*&w8(#F z^69CoSPDFQZd({~&ZlqA1|XzxT<^fZ-rFAeQ9G^XZ@#|=7Uuy)rMOsc@Ys(g`t^34 zI5dy_oW>Fm-rFYE{(1s>HuMPduF1`R_G0~E^X??n+b%IsYseFE+RaD*4J<{HbSSFJ z2zs~_(CW2v%9^;ihF3~iO$nT$Yc&OBzMXzidjs?w)JJC#nkb|Wte_oGk_$LSR#89h zLunr?pnGrtNf^93ho|5^lAvT{)ghZ6}%XOql9bx{u;Tm?;n)YoyI;w4V}iRSnfTOdU8} zYWOng-%r1zJ24!JH%170Q*?=ZogqISC7&8UKpD6v4u}0XDLe_(^r0epzkp0l9j1fu z1uP)4-#hB+ks?l^K<1bKgmnA%9WUY(93r(LM-RiNP~dQ>xmRdEd<71N)EpI3fA3q> S`d6vWgA{SQ(*K>_D*X>c0ZZoq diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree index 63b48dd5ea788625f5b6b17c63d7a263dd373497..eb0e18a8e6e19bcbd9e41230759541cfe8b754b3 100644 GIT binary patch delta 2660 zcmex*o8{|mmJMNy2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(!34nMv0**-6~oH*GQhm`NG1R1Njdzlcjs}d|?(A za&)Foc90R?>?;vNmc8kl9b_(&ZtrGC#bkDJT${7mLDP$@D9zcdrrobVmYtK;wD~5- z`*@P0863QmeIc`@0xifopv zjHMuzYt=6&-O-yFoA;2eYcgYV^X7tfWzq{Tpw5EL#yt((q^D@c=8VY;+PEj5xTU_i zc7{0_;S4olbNH;uWYkX^4;60iUV4TMKZA7sSk);`vY$6^JhYLF6tVG8-DZK4yn-a# zH(Bss!Q}Thc^03%MMm&KHE%Ax!z@6K)wPrPKJaWdeCt9+5DWf;=+^tRne?Q!S@7Qr zPSRZrOit5fco;>uFXLdmM@BIT)W035pS;REcDlU~qv+&_S;E^*L>PCFk%t`>A!cmv UlVJQVM7sBZ?X~TKI*d;k0TKosHvj+t delta 2660 zcmex*o8{|mmJMNy2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}Dj>nMv0**-6~oH*GQhm`NG1R1Njdzlcjs}d|?(A za&)Foc90R?>?;vNmc8kl9b_(&ZtrGC#bkDJT${7mLDP$@D9zcdrrobVmYtK;wD~5- z`*@P0863QmeIc`@0xifopv zjHMuzYt=6&-O-yFoA;2eYcgYV^X7tfWzq{Tpw5EL#yt((q^D@c=8VY;+PEj5xTU_i zc7{0_;S4olbNH;uWYkX^4;60iUV4TMKZA7sSk);`vY$6^JhYLF6tVG8-DZK4yn-a# zH(Bss!Q}Thc^03%MMm&KHE%Ax!z@6K)wPrPKJaWdeCt9+5DWf;=+^tRne?Q!S@7Qr zPSRZrOit5fco;>uFXLdmM@BIT)W035pS;REcDlU~qv+&_S;E^*L>PCFk%t`>A!cmv UlVJQVM7sBZ?X~TKI*d;k0cq0$zW@LL diff --git a/master/.doctrees/cleanlab/datalab/internal/factory.doctree b/master/.doctrees/cleanlab/datalab/internal/factory.doctree index 37616e9af00d2c1d41b9bb2cb48556b71bf7d7cb..85037b01580822a460645678540c5f164c10ef26 100644 GIT binary patch delta 1118 zcmZ4agL&l-<_&&~2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(!pKTbW4KHTj`o!Q=(v0-HCp_zID$yM;WBRJnOXtMR>gw*KM+`8CCvMi)yhT>zOx%1*Cx}ct@9JG4-OkMq4Y#tB6Rwq; z?X7w<$kAE2Id`!u8(BIh=Pu^moUrP%GU>VnACztWc}|&(lmOH@`R6&w&9^VJ@sMoq hX2A#lSV`73S@1#M=CjX?gh|&4$>98(*Zrzy1OPnWU26aU delta 1118 zcmZ4agL&l-<_&&~2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}E96TbW4KHTj`o!Q=(v0-HCp_zID$yM;WBRJnOXtMR>gw*KM+`8CCvMi)yhT>zOx%1*Cx}ct@9JG4-OkMq4Y#tB6Rwq; z?X7w<$kAE2Id`!u8(BIh=Pu^moUrP%GU>VnACztWc}|&(lmOH@`R6&w&9^VJ@sMoq hX2A#lSV`73S@1#M=CjX?gh|&4$>98(*Zrzy1OU+yQT+e_ diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree index 63d258cf665c1711ebb24826e589a54088bc7d52..1277c51c10f3e9aac5be348250ca2c97750582b2 100644 GIT binary patch delta 120 zcmcbsd{=ox0HZ;saj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF eahgeTs$ufx2F7{JhNSA!pZtJbYI7!Q2@e34sUze7 delta 120 zcmcbsd{=ox0HZ;&WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V ev9Xzfxq;c{2F7{JhNSA!pZtJbYI7!Q2@e2xX(F%y diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree index 26478b0b09c008cb16af6379bec2c4b9d08a3d0e..cd20e44623269cf9008ddf8a8a6f220567e5fafb 100644 GIT binary patch delta 1107 zcmeBu&(!;#X+s2~L8fu3WsyaJzNwK}qN$0wNs>{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5zakDRz=|P2MP8u-S>TgNYoSX`2&x6uHUK`G_1%agz&_cqa4732%0g zG+`mzgw1?%s%+%y=G%N;e-zw=F==`Jw2OOI>wCqr&gvfThQC5=4I z4guPmcX&jQ8T1YT2Aj|MJ|R721Jh{SW`_U;W^!F^uvso-3wcq#IU!n(tP~zMIU!nh ivt~NGExE4d-F$k94tb$9*&%>;^64cqo7XM7F8~0G8BX~C delta 1107 zcmeBu&(!;#X+s2~L9%5^xoNtYzHzd7lBq?SS#p|rs)c2up?PA8fqANtL7IhyL8767 zNwTrAnSr^1+2#&LDRz=|P2MP8u-S>TgNYoSX`2&x6uHUK`G_1%agz&_cqa4732%0g zG+`mzgw1?%s%+%y=G%N;e-zw=F==`Jw2OOI>wCqr&gvfThQC5=4I z4guPmcX&jQ8T1YT2Aj|MJ|R721Jh{SW`_U;W^!F^uvso-3wcq#IU!n(tP~zMIU!nh ivt~NGExE4d-F$k94tb$9*&%>;^64cqo7XM7F8}~QKSU(} diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree index 5b587082174b323e2c266b2c5958a777c0f12fa2..109f688c6cab0ae023961740693dd186973d3259 100644 GIT binary patch delta 61 zcmew$^+9Tb8ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{GNxZ#09vaOb^rhX 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 eca7a379fd7dac498ceeec488479f1b7c1b57a28..65c73b33cc8b49cd30bbc16c2876ea1286c72b3e 100644 GIT binary patch delta 2585 zcmccdp5?}SmJKnC2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$&-5+?~$%a(Wq{-1am$a)&X@+mS7f|?9Q4`jdoJDezTCQbF-q6 zArr~ECMz0k*lc8al}w$_Eqcic+6|jStUr@!r@DP7nRZTa{7I&rnXW6zwDXhuf70#T ztmxg#OnO{4<}8_9A1yrjn-1UR#r|taPeYR-CP-|y3GE`ozcAgK!pR9|pzh7}(e)JA z`zrP|>0u4`^T9-Y3hcg<{Fnl}SEnx{J+?MC=A7puCzZ^c+)&58S*1din>^hM%-AMR z=;E4Qtj;Jh*(%pzaz%ye=GiSr$aIW=v%qGH36=aLJ7)94`5|m1>jFmj%+3Fnm2;4% z({2MZ>HgeYxJ!(TTBLB-s?CXq(m6?YsiINAdoJDezTCQbF-q6 zArr~ECMz0k*lc8al}w$_Eqcic+6|jStUr@!r@DP7nRZTa{7I&rnXW6zwDXhuf70#T ztmxg#OnO{4<}8_9A1yrjn-1UR#r|taPeYR-CP-|y3GE`ozcAgK!pR9|pzh7}(e)JA z`zrP|>0u4`^T9-Y3hcg<{Fnl}SEnx{J+?MC=A7puCzZ^c+)&58S*1din>^hM%-AMR z=;E4Qtj;Jh*(%pzaz%ye=GiSr$aIW=v%qGH36=aLJ7)94`5|m1>jFmj%+3Fnm2;4% z({2MZ>HgeYxJ!(TTBLB-s?CXq(m6?YsiINABAUV5^QKhNgNZP56n2M+jRsO!^ukr(|e^D UIktO=GfI=?`I7CxR@oCq0G($bssI20 delta 2592 zcmaERn&s_jmJLCSHp!ML<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4X8I}l$;JA``K3k4sgoz#8*e_yIFHeQR8xRDOQv(GF!E0pVksg=ci-gI z%3_-%SP!v~t6O-oKnTm`_gt2&BAUV5^QKhNgNZP56n2M+jRsO!^ukr(|e^D UIktO=GfI=?`I7CxR@oCq09^hE00000 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index 092051f73a0bf8bdcc7e3ec3846e6170237899b8..f9e818534fa2439e6e837504c9ac92cce61e886e 100644 GIT binary patch delta 2523 zcmex0mF3q|mJLCSHkrnymPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$@(ez$;JA``K3k4sgoOb8E-zwIFHeQR8xRDb0#-1aZP4rsV7VGW(C%N zEM)1NtiWov`2&{@E4jLrH{TZU^&81dHo*7;oAA-_Bon#5fS54Rw3mE0Z2n;Wi9-8#TCXJE zesCCA+C8Vx4c{G?Gm{sJ55k=`*SO6kGaWn#ci;TSJBv*JgLHfPy(81_Al*_y85G!^ z8gidJyEi`wFQdR-%c$$***h8J=g+b8$&9TAJ%i10$+hg{rIZFemB|<8v29+MJ(Yt@ z6N=0>bCez-S2xi94K=)*XV$rr?SF^~tDBWM$@D?D(dIe5Ic((XzBa{*T)XvDHeZ-W zUg)(=Ua&@RbM%USvPy}z%?s9SuM}VR9;-fH` j?E+95Px!{M`S5ok@=e%0_3v{st9D>xYP%9A;|)dtPoyX- delta 2523 zcmex0mF3q|mJLCSHp!ML<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4X8I}l$;JA``K3k4sgoOb8E-zwIFHeQR8xRDb0#-1aZP4rsV7VGW(C%N zEM)1NtiWov`2&{@E4jLrH{TZU^&81dHo*7;oAA-_Bon#5fS54Rw3mE0Z2n;Wi9-8#TCXJE zesCCA+C8Vx4c{G?Gm{sJ55k=`*SO6kGaWn#ci;TSJBv*JgLHfPy(81_Al*_y85G!^ z8gidJyEi`wFQdR-%c$$***h8J=g+b8$&9TAJ%i10$+hg{rIZFemB|<8v29+MJ(Yt@ z6N=0>bCez-S2xi94K=)*XV$rr?SF^~tDBWM$@D?D(dIe5Ic((XzBa{*T)XvDHeZ-W zUg)(=Ua&@RbM%USvPy}z%?s9SuM}VR9;-fH` j?E+95Px!{M`S5ok@=e%0_3v{st9D>xYP%9A;|)dt?Qakp diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree index 2b989d648311e3274b408616e7f401572dc33fda..3a6450f3394a7b7139dca2ece4f71b369df3eace 100644 GIT binary patch delta 61 zcmZ3axkz(E6r(|=aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF QahgeTs$ufxE=CDq06U-&-v9sr delta 61 zcmZ3axkz(E6r(}1WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{E=CDq06)MHMF0Q* diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree index 128c530e91564b15d39380e86a21d554a8e0daca..aa84ee3f969343f9d6d83c5679fb8a5d17402f4c 100644 GIT binary patch delta 2466 zcmbRCjb++5mJLyi2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(y?vUy`nCvg3-H$?`0XBwGj6*g1Kvli22b)@l}Vbqh}xNMYH$i%XQ1 zEE6_&2yA30Pv<8IU9tkJbMp-;b~Uo>oIJxZhAfSn*E*f2D2Q1%`+78z=UreFgCdD_ z^L($>Ok}wpZi2L5FNJQX3^+-l8-znTDRe__*m(-wz#Da(nVgX9n(UA!H~D}Q-{xI$ zb!4RnkO|70MN;=0zFy|lzcE8Wpr@-#tg*z#*dtzxV znX#qd#=ZG+)oD(0QcL$_JzuWP$*r7Z>fSw>f3sP?C|SC@Ci{g8Y!;pQhmT~JZ+2Ya zL6)7}lizP(-|W3fo{eNXfmK=8^PM+?~>kfP(s|xFyyzYR&=B(p7Wcj&k zbHGJ0vMRu?%?dZ|$g^{@UM|mOmB&(KrfHCFmKT$G$%>`v1}u!+n`6F7k*9q-Q2P!& Ra%={*k@&YSHfAhl2LQE#{Yd}- delta 2466 zcmbRCjb++5mJLyi2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}CYhUy`nCvg3-H$?`0XBwGj6*g1Kvli22b)@l}Vbqh}xNMYH$i%XQ1 zEE6_&2yA30Pv<8IU9tkJbMp-;b~Uo>oIJxZhAfSn*E*f2D2Q1%`+78z=UreFgCdD_ z^L($>Ok}wpZi2L5FNJQX3^+-l8-znTDRe__*m(-wz#Da(nVgX9n(UA!H~D}Q-{xI$ zb!4RnkO|70MN;=0zFy|lzcE8Wpr@-#tg*z#*dtzxV znX#qd#=ZG+)oD(0QcL$_JzuWP$*r7Z>fSw>f3sP?C|SC@Ci{g8Y!;pQhmT~JZ+2Ya zL6)7}lizP(-|W3fo{eNXfmK=8^PM+?~>kfP(s|xFyyzYR&=B(p7Wcj&k zbHGJ0vMRu?%?dZ|$g^{@UM|mOmB&(KrfHCFmKT$G$%>`v1}u!+n`6F7k*9q-Q2P!& Ra%={*k@&YSHfAhl2LK8L~5$)(u{a2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(ySfFOsfnvSCZkbc+Z^{>k5%i%7N;sJm`*LyGWbP1bl8a&?Pu_T>_0 zB}?~aJ^@xfvUE;9DEE{+jVtYhH>WF1ktJ7m40#$sp?u8#0@lxq9y8WA=QT12RsKmBtrsewFJ+R#-3E%vA7*EEg}@ysbEfOgo>Ho+s1J z*2;V`?L1b!gG@UE>h;OAGq7<3>2_{5Y>^|YM(Eni*`>`+PO7h;{N96W^V^B$9OUU% zm?^yZ*{tK_>E0Z(B$_P$)=%D_&AqvBRU?^x2I*e2Nl%_UKkqLR-aO~hX|l=*pv!@} zHLqVM&)=KZ-;E>F&+G3BZa(`|f|EQy>%SA+y!3q~5$)(u{a2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}B-RFOsfnvSCZkbc+Z^{>k5%i%7N;sJm`*LyGWbP1bl8a&?Pu_T>_0 zB}?~aJ^@xfvUE;9DEE{+jVtYhH>WF1ktJ7m40#$sp?u8#0@lxq9y8WA=QT12RsKmBtrsewFJ+R#-3E%vA7*EEg}@ysbEfOgo>Ho+s1J z*2;V`?L1b!gG@UE>h;OAGq7<3>2_{5Y>^|YM(Eni*`>`+PO7h;{N96W^V^B$9OUU% zm?^yZ*{tK_>E0Z(B$_P$)=%D_&AqvBRU?^x2I*e2Nl%_UKkqLR-aO~hX|l=*pv!@} zHLqVM&)=KZ-;E>F&+G3BZa(`|f|EQy>%SA+y!3q097p$0RR91 delta 61 zcmaDV^HgR-2%|x=WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{7RHA>09j2EX8-^I diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree index 9db6baddb918ccf59709e7ac3f484b400a3a976f..661cddf54dba2b37bb07a1eca890b34b72e1cf72 100644 GIT binary patch delta 2662 zcmbuB%PT}t9LGE6dd_{o4EXjgcS%@=#!u{Pp;Pd-@f4_6iucN(l+BGd8 z@%w^4RZ?V8RiaTbB#DYwR04`DM}i^M+wY4+Ya=jDzRXkfs&6iF=_L+Y&BaVR8J(np zQ~(p{*Bq8EgfhM`9Yw>hu#|-&>2i4%bQjxD6H79>0OZmoTxv##aiQ6$w{9wsRzY&X zcGWDh;}o6gzE8sjcfq-51?L7q%Cq6UfBqeum)&d^EXto2dhV%TwBWvJII!UEXd1UL zwS$(p1=`W}j7okA5A7GIO=7@QXBTSL23&T%qS6|$*5g4skHatKop9AxXP#nP+JHBe zL!GAqPyGq=CjuLTXLih+)#!)Y4mD!}kE8eF`A|GR?7-232_C|fpf;>VQ?!&^cjDY1 z?hM*nXNSFA5!HJPru7rl8OP|f?uN9B&0I|K6kS3OA9)zMUC2RKfWt>y6^7RINkV>5 z-vCW-ZK=$y{}dQapZ>GwWP delta 2662 zcmbuB&nrYx6vw;f`85yo#w?6q8*J1t&l@xEO++aclnp5wX=WG;Y-Tex7{5ws$l|7H z-pY?Gg~9QNvC~ zr>P)0U^4rf!_p;C$(LlKX!sS9c_@-DSLH!}nFTemG@~7$R3ve!86CidW~1J^sYY4_ z$qC!FbI6V}bgt_@3me=C=k67p8w9Dyg7^M;cW_>Ivz@S{e46Napn1uJ`=;fP^kY>b^*F>h9-A1*7@#{?cn@6Q%P*}{YkM~|g=2vCCBuqw^aQhMEv zbAPyU=wOo-_I5>7?@>r-C#W-y(uC%Mtc%S;O!5p}P7fb>7{6UCKv#gnM@ub+R<$WY zeo)^4Rcqx*D95l>yR#EH*D%H&qMBN*A$Hn+Drn!OS14MkkfXbCbFy2q z0C_sg!@0>yDUFklmk3SH*eAR>B66P;S#|?Wh#^m7eY5anetzD~UrJAsmDCz1*EdUS zZmVu2)9WDJ7iw29k?VSi&5n(|J@NCf&ur3IL+} z&&qfT>^4}lj{>_7uh*x*?&BM`QDAq_)<&{Qqu$LEcJYu^Z8S|*sOQ=oambZ}oRX+% zvO}BT=7iH*$khGd67S~ASGJMqa*%G1yRXT#+hIA+=8w->$jTu=e*<+Jzvbm5FVqz3 z*(Oi;%DtKSdl6Z#&zSDV!l<#;l(CVlLM3B6Q2RPwa@-EKS$g|=A;tn8^7T&#>gV2m zSDtaZ204Z_P5S14MkkfXbCbFy2q z0C_sg!@0>yDUFklmk3SH*eAR>B66P;S#|?Wh#^m7eY5anetzD~UrJAsmDCz1*EdUS zZmVu2)9WDJ7iw29k?VSi&5n(|J@NCf&ur3IL+} z&&qfT>^4}lj{>_7uh*x*?&BM`QDAq_)<&{Qqu$LEcJYu^Z8S|*sOQ=oambZ}oRX+% zvO}BT=7iH*$khGd67S~ASGJMqa*%G1yRXT#+hIA+=8w->$jTu=e*<+Jzvbm5FVqz3 z*(Oi;%DtKSdl6Z#&zSDV!l<#;l(CVlLM3B6Q2RPwa@-EKS$g|=A;tn8^7T&#>gV2m zSDtaZ204Z_P5c?Y{IBe}X8$;CgzqBT)ykVyqmwAGJ1mx*d;{|Hyo3o9*!wqW69s;*OVc1PrY6tJsWvHk6W{&Nd(1cuJZa`)Vd{C}x}7cZF-S`?)t{ z#Ph_vlQw_7nL~Q{wRz%Qc3$#atniU_v*+tgWX5g6XO7M5zeteLSOBGr4gWYcSO31n F2LSD?LY@Es delta 2643 zcmcaQh2`oLmJJ?^hRK#G<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4X8MyInN21yP-oq|i7|@RfK=_1A6jKip3W(>c?Y{IBe}X8$;CgzqBT)ykVyqmwAGJ1mx*d;{|Hyo3o9*!wqW69s;*OVc1PrY6tJsWvHk6W{&Nd(1cuJZa`)Vd{C}x}7cZF-S`?)t{ z#Ph_vlQw_7nL~Q{wRz%Qc3$#atniU_v*+tgWX5g6XO7M5zeteLSOBGr4gWYcSO31n F2LMp7C?)^^ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree index c419898b25f16efc5ff580e8019ebb3e35652f2a..380c4711532bbe67c477c19de97da4040d525ed8 100644 GIT binary patch delta 2919 zcmaF)oaNBWGrkyWLE|Y0zo5d>ZpteY#h zjm(%5C==KmTI9t}dddXF)C5Pi$pr>1o8zkdImk1?-%Mb0QL_vudAb|AMK?$Ea&VEQ zd$Z__r{sm&=Jj*+$*4&{Atta!VDsX|JID*~%>ru@Ne`*b3%8b#6+6>5-`*!mx_#gh zV1gt6<}D{`$go%OW6NaAE4-UOUaTRb)Pw2PyEdDb^i-kvv43;NlT03xb#7Mtn4?Lu vE^tWkd$MkKmu4h0DFU+~>-J{~jBm(G$&=*+*thqoF&-v83vCBBcr%y)7!ZUm delta 2919 zcmaF)oaNBWGrkyWLE|Y0zo5d>ZpteY#h zjm(%5C==KmTI9t}dddXF)C5Pi$pr>1o8zkdImk1?-%Mb0QL_vudAb|AMK?$Ea&VEQ zd$Z__r{sm&=Jj*+$*4&{Atta!VDsX|JID*~%>ru@Ne`*b3%8b#6+6>5-`*!mx_#gh zV1gt6<}D{`$go%OW6NaAE4-UOUaTRb)Pw2PyEdDb^i-kvv43;NlT03xb#7Mtn4?Lu vE^tWkd$MkKmu4h0DFU+~>-J{~jBm(G$&=*+*thqoF&-v83vCBBcr%y)(|BgI diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree index c1798c93a596d34249e04ab9cf43aaf9dad5c426..91d4dabde54d73fcbbae5606dc785751a05ec74f 100644 GIT binary patch delta 61 zcmaDV^HgR-2%|x!aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF QahgeTs$ufx7RHA>097p$0RR91 delta 61 zcmaDV^HgR-2%|x=WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{7RHA>09j2EX8-^I diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree index 0c798c3f5f04e5f5574be29d33fecc16090830de..78ea31d83dfa4d1c058c5f72d5eb23b5b0970b1c 100644 GIT binary patch delta 3427 zcmbuC-%C?r7{_^QXIgU(Tj|6SB#4M;Z`+*PMdX#yE_6tWg;8f~G8z4F;)06&wVFPxRUc_nY2 z3NYU7?(@fe7o7DjuUhYE@Pu5dkB8Jqg!^4wb#v9L`h4LAf5_eC2}cfw=@R>8oDz%= ziCWX3VL2VVXR*^6<%bD^Us_U|jf2JLv=|Lo>58orkS#`Y^lp1UP?JvXu3FxNIL?t$ zb5}7=GCoe-bnoI(^JIsI8Pn12&)_eJSbSAP`k2 zms`Jr>wMpS2h{W)SD>TMQQgUBpyt%6RWN5dd;`=pb-gvNNyX@4@T0CgHv;y0E52ad zsdnsH(b!xjT9S6{IVz`c;t}Bd{}c!PxqJlZdi@0F)xR4`;-KoGPTT{vByGSB)+Y#F zcjEfmE*#Wz^XCRTXiZIFo1}txRbu@0)AJW%X6ktGxD=yLkK1WsVilx$la=IT?-op3 zr< z%GpEg8}y~F;Ta8AWTJ^ZuYfINiXE&Crsa+kEWZuHGhg~xt(-Z_E@A~pjh}#Sc379L c&IFTS@HJfaTw{qUB)R^RW0OxGvi7b20B+VDy8r+H delta 3427 zcmbuC-%C?r7{__bGp#v?t#o1u5=2C_SC?n=BJ#>;7dj-x!l-rTT8s)OO{mCU3;H>! zm$8d#m}V3dPM_QxL3t5%C2Tj*jgk@FbdxKHE<`)Wo4h?=|A5c){qa2Kg|m{gtmG^i zALCkMu&?2)>T+wIGY#I5s)an=-o`+kClKVG?s^{bdU>FZAJp7#m3z2Km)S4#lwf>F z)D#VxmNUTnRtE)@A4L#6ZB1-84;I2xV$^4&tM)EHwh+$JyY2lzO$NEUa(NTtI7>?P zJ;gl9_&A-b4VRfFiSd^}M#i6t$Wbee)LSY{YwGu4(8tV!`bufkX9o&0mXh=j0#OZe zx$P^s&i5U6K~43z0v&ypYEM1~H8ZDH!JJ9$Ca75ydTU;j3gN@xM_qAY1nl*8biuq+ z{n*o@sijo3#vS@|R7P6tG2s0F6es<;as=pl{RG#wzZ**8q^hAV+yk}7?Z6J!C-C2J z;rjY6oYZsc=LS1yOJuN3QeM0&Hvjq=`STGAbv}Gjg3+hP9kej93evpUMsl)u3#M(* z%A150qj&Y&#L}EXy&uPb*WYMPpFm447xfw&#ZLkw9iuf>BY^X0?R8Xf!g? z9mrK%^m0)n8g379?#v4%>c&A3tRy0+L5x@sbhkRX-E?`r=MVV&KF{<0JD#b?*)U87@|n=;9W4DX**9M0TEXK(b% zaOcOPSkK~p+sKl3$Z+>(Vlv#t39}6M;$5ckl!FppUS}NPPbtawWzrJAg{WJ6L_dR6=5!9fqd1_M|C=nWt<3!-dZ?>{shQYpvFx0^1{_xu5$-{*P0zvua#Q%UJ7 zDSiGdu|*CJ$zSn~u#z7!90S{kuEYC*g;+A!BiZi%?!=x-6uicFXj0_g@WCZJE9XZiCK9x2pkjUfVMbIvxA>DR)vf zD1hk{4qjC56SKiGZ9N(Sd%Sww2|Cj!#z3cW;1ih8$5YRgS4*?uOO4p<*oVF#l~!lO zOmdSr$(u=Y{-h3Y*H3U!D1H;kc#Zxs+^swkp0aON&LccVvz zJ3lsr^(^1FiyUcQg}W~kQ{gU-+f}$1?slqhH{G+Sa66~oBHLM?QWI64EF+&>nV$EX z==qCf1GJsvJr){$^>7^oi_YFwvKO~wG;Mj8go-ZEt@j_4la#IQHDY}>f49<`ML&4G zHTw5bhXrxx#rf+kWLxneKS$L%E%_T+$*Nc`@f%o7`AFtutRzirW_O@|XL7GQm|3#% zY_3Uh$+ahcm7fi;VQebpb<*W52bfV>-^(<}8Nq~9IKmc?mqbPb{Y+;DIu9mtvUOvw zFU$;*Im#X*@%?wml^Anu11gd4VC4bRNr@?T2%6g3lctrXpR#@omp)IkU#0+yvIZ+D|!7#Ryf7`4SA<&d+X5B0Wr#UfQ&88I@t(6ipY38d< z#E7oCsiwZvz|5b12(ga3_+ z&#;v#b=q?pvnnk)-H?{5PAyF~XxLIic{!_1W(_HE+Sl&Q6)DaO>Y^1Wr)#Doh+ zK~HuG#s^mg3)b@?VMmya0;~qBRE|f)?I9#ITBWB*qgJDJ^Zg)_Cg(T`$K)Sqx_)%= z_O1Z5A4XpfKzk;pB>?S&oyUo9Dl$P6N;+@!Ce0J)6{X8TwI>1{%5j=Dh|-Hlrv0&& zsV4$B>bn_JByrwLxp2BZDjREnkKON2s}_E5erYF$5IQjGa*pntwUq^Rf2P#L5!_c;{^9S z`X$x49HuX2Wjt5an#{iEtkByKJeC_EQ%gazQjc3}9Kw@|9?JkBr5;N&=3o3QT#s{H z3+Co8MJH4cx?3rBH!7of`{hGNf*@Xc5U z;?+eNY(PoPI80{nw?VAhGGNdr8Vf(KxtG zkU1aAnQzn-`Oy*>X~}ipiew_m>m<7q&WN457-o{}#K!F$=FYTIgg{fOnRT-i)aj&jrA#w2S}P?qY38d< z#E7oCsiwZvz|5b12(gb=ASC+(@bUL;;k=<`hPS&yXl#)iRMC;c30VGY{aX5~vKG1Z1=)#@d zerP|8xbBDcY-Fn++OfM&65muDw;{MKH$cfLK)za!+pF#3lZtN3AR(o0OAF>+{48FNcU%Ki zEg`UOV4B$LqQaMI*?0zBPmCR|#4cChlEB3Qhx%~|j3h^_|>gt2YTEBN+N zqxhtu&6z451vY0BOb%qE4bh9i=DYx!e${3<{ORi;#9UxeW8PC8p(J=TYWi0Rx+7y& z$Y~S`Vqma~%%H~T7nF-o&{t#BD?+6Lk4D2APW)Qq$k>8-6t7nY-EJ8ax@X02#s(M{ z-<>6Fh8JD*Iv6-Z93)J*X!1OP<6{()i_@PH2(7?-K>Q*nw1V4W0{xnzZApsdo>jv5 z8w7nzGlVVQRF}ccD1XA6SNd(Y`B8 zj-C)E?umh^rJ3-;HLi9ELum~DCCU4{no&1nv0m&LSZ{++PTY~AFK0nwB93JP!b z<$cObmhR1a1^5SG1lqXB)WOJVwax|81w(#vFJ!m(7 z40uFFI`|k+wYfI9j;z3`+PpN(jD;LOCT-4*t|#4(z{Hodxh|falN>u^Cii9WPL|7j LLYC&meOb}~!F@t7 delta 1044 zcmeC{X6ox^+Tg=zkZhS!ZkleUZ=7tNWNMLSmYimuYGIjZXr7p2V4iAZkY-_FkZ5RN zl5A{jW?*h$wz-D!0UOD>CU4{no&1nv0m&LSZ{++PTY~AFK0nwB93JP!b z<$cObmhR1a1^5SG1lqXB)WOJVwax|81w(#vFJ!m(7 z40uFFI`|k+wYfI9j;z3`+PpN(jD;LOCT-4*t|#4(z{Hodxh|falN>u^Cii9WPL|7j LLYC&meOb}~6+1Uv diff --git a/master/.doctrees/cleanlab/datalab/internal/task.doctree b/master/.doctrees/cleanlab/datalab/internal/task.doctree index a04c21af43503128738a66ffa0524fdc6a20906f..d08ddd540af16c258f1750acb4e10bfb46ae3fed 100644 GIT binary patch delta 3026 zcmcb8nECc$<_#W<2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(t(}A25@wYjU85-{fno3(3;B`74_|*;WcoUc!}6y0wWGX`5GaFDAp< zM2qar&-qzdNw+glBVu!jsI?$DI-@6BsXr%Kjeeu z0;9)d2CSU9Fd0cU(PAePIWEoKyw#eUbo(ZQl0dYrADMQ#IFM`SE5`~l?fmOvN~S;0 zx+Rcl=Lt_;GVPq;O|G5Ye%@rHyoX_>lNT8AZSD-xU?DjjZGIT`fpmXvW{kW~Mz}r< ztJ>TZvx#*3CWCZ7i;E@G&bNsor0d*lm?A<}Y*cN2pKd~?AJ1kwlI~J)= z3zOMMPBTFJLMA`35x+kyS!8uuFuj*vETDHx6+YQO{KiC)Rcl;$IKf~PdejmqXmA%mv z*#CYXCwb|8GOs7!=B~r!*bU05Al>ChJ;+FRAl(KR1VG`xx#ApoWm?E)gA1z#0Uo@c A5C8xG delta 3026 zcmcb8nECc$<_#W<2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}7P*A25@wYjU85-{fno3(3;B`74_|*;WcoUc!}6y0wWGX`5GaFDAp< zM2qar&-qzdNw+glBVu!jsI?$DI-@6BsXr%Kjeeu z0;9)d2CSU9Fd0cU(PAePIWEoKyw#eUbo(ZQl0dYrADMQ#IFM`SE5`~l?fmOvN~S;0 zx+Rcl=Lt_;GVPq;O|G5Ye%@rHyoX_>lNT8AZSD-xU?DjjZGIT`fpmXvW{kW~Mz}r< ztJ>TZvx#*3CWCZ7i;E@G&bNsor0d*lm?A<}Y*cN2pKd~?AJ1kwlI~J)= z3zOMMPBTFJLMA`35x+kyS!8uuFuj*vETDHx6+YQO{KiC)Rcl;$IKf~PdejmqXmA%mv z*#CYXCwb|8GOs7!=B~r!*bU05Al>ChJ;+FRAl(KR1VG`xx#ApoWm?E)gA1z#0fiZW AVgLXD diff --git a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree index c418ba7faefa82b5b6b1f0c8a882c0981d97fc52..d8510b862a681e4c8875ed39d8ed224ab95d8b3e 100644 GIT binary patch delta 61 zcmew@^;>E~E0aN{aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF QahgeTs$ufxtxU(b0BR2tZ~y=R delta 61 zcmew@^;>E~E0aO8WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{txU(b0B$c5)&Kwi diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index a3827a6629a21ea891ca2c35f93bb59179e57ee8..2d9b225c9d76b02e86f619d6a7f6a3eaa683a946 100644 GIT binary patch delta 1233 zcmdlng>A3CkC3csdVnmW(dH#gkJ!l5I{5`>BUw6)Cw~wy*!+iUA}e{?H-8Wa zVIoiKABUw6)Cw~wy*!+iUA}e{?H-8Wa zVIoiKl> z*k{z(HpvMVqOnfmSYp7KAR;L$)(o~Gj^^%eX5`oJ-9O-Y-*e7A_uRX8cMsP#J6zjr z|48aRDKsxCH*$J#SV(wUSg(j)=^<&6*7UT@Olwq#H7(Sd7M>OvnGq3{9-7rFBeQ2l z@PwR|xHr`We7W&w@m zAh(U{VcgSB$-jbrd8xyU``H|krliM!o^NID0?7pvM`7gFnUgWl?lUbBB-I&lU}^u( z1=F;b<&6P7&*Z0qd7dbk0g@MI7lGuXxg)_mtLF~@Jx?qg3wo9;jsrb6EUg1QLzce@ zdYV@B0zI#;OaVQAS=}G>Jh-+W=vln(Dd^dLLni3i;R|1oys&vTNZ#6(4d!`&M?UCD zyPANceGlq+dLQcf;X!n-#DDV$tnGE~3s&gRA#@JyKe7q*tohyslFd(?0?C5Y=vg-9 z>@v{P_S0|RdYaCoecS3%1?ai@@>(#@)8)@W&+%6`f}Wz{F6i0zdN$}8SUnWM7jpzD!llm@)^$j{qc z(wdflu9fYm7|hkwf#!p*ojcJw5KZbrp8-#5jxsIkK?$I1zLiQq^js+I1S{+lPW_F$ z^1Mho1v*y0K~s!7Iv#zvrVnicUAsopJD{si426TPH)1IkbUhwNqe1k*AX){Y*M|~# zz2~xc0`E!u`$(E$e8Y);V=0H_D{Cj?EK3%6zjs)Ez$?cimM(T&I$4Z|>tfAW)CXge zxO@^tqwJQcqY{VAD&$~hyDD)|k-2(oN+9z*%-)wHE4#)`UwWEKODYQEwVvl7l-zM>Jvzh85% zA4{oRbnWjlRbd(MkP=D>FwQ#L)q0-7(U!@+A}hB$RvpMuWpu#!iQy`mR5<`_Hs^+U zaf7_Y_A0t+JbP!mNvHwfDgw~+I zUunQOW@Q-~y%ZP>u#VOhKB?4@$23>WVE^(TZIz$Vt_^FiY&QNqr)$igk5GK-bZJca_ZLi1&VjM8l&VBeJ!dtBeWX}8I!{>&M&PU|OO=Ag;52iVb4aoBxjPXEDPZAQ zHsyzoU>L_r6;r*f;aQ?Yfo;Z8>UETzcv9H~7J^ruReD;m^f~5=5)Gb(tX3)CSutNx z+(`8nHxtxAk?p5$$3$3)^fUgG6W1kRct^DbI0J=beYq}B-P;Na+#%p0f@0JfH+bF@ ztCsk>BXBK2^>d>H9zqIu^PG3p8%^8@LoA!Fn#B{QK+!Zuz3+w+OQo@GAc1680HRnu zWSD0;+!eF}ZI5auZn#suX8bk5b+kJKgcNAR3wEj3!7(75Dzgu(Ch@pLeQCG^uR>ud zGP#o*0-~ym<|XFVs2b0%R&Rl`$(7J6sk%Gr`Y<=6YAmXjYrY)QUJEe(tGs#@l2NVe z;Z7{k$y!sU`zwdi&KMK>9^yzXy8 z&85H^bpGv#)cxvRYqP`jBj5?cQmO{*ME32g`=hHbSu^jC)(g913omw0)h}Qu*Gf!5Q;Z$#rq2Nlh6liJuC;HA8e=#gmzM_@4@+k5)+@g*@bg=we I#x=kH04c2_@&Et; delta 12890 zcmbuG`B&6s6vof}F2H~@fPnj!iiV1eB3o&qxRe`i5Ge@CqTzyKN59nFE?kVWmc0(rU+3pKJkUY0}4oKeGmJQ~4e@7nZ zNxK?>q;NZ9n}6uBYiN+P5t(RDzzXFRlgiEWh*|^c;6(Bj_nA?}DDKuV;gv!PP@SPs^XX zLC=Jn=vfwidp+oxeK!`&bHM#F(DTcGMuDEiwP!%jr%%v(-(SxzgPuvx(H`pa;u+@2 z6+{VGak*TjTF^DYlLA0g^QL7W+R02^LG+O?-3QUR0TcyRctjAL16>a{q4B_LkNmvN zB`s+w=z6UU6@$5&+R=Q_wPOcb2cpR#^cnD^<|xymP)Yz@^Q=??qG!TsCs<+MNa}0c zl?$WkB$unqIUGbnO&N?|`npaTE!<-sngDK-XgfX*7r)7(}Z;^!iW& zulHOLPvAX?e;-LRjBhy6XAI@Ad}ZxmoMp)Z@AnSN4|wG`#L~r%oj?}j;ksCJ7WKl| zWWMx1#iHz%sib)se#CafoWPxDQ%~bZjwoEA4i`xuQ;>L`PnE{K9Xv~ZjR(exoWPvFoG-_oyAc8dNplnjIpBbAeqI0BbuN1#H>Ve$X7JN`1fnh z^u9A;8W2~?~Ca-#Z)BGW=0oR_TR2Q`8D(h%3xLbu}O&su&E}%8Y z4^VtK$E++zqn84M0oKvF!Z(c?aAH%%4E8Vo(OUT#?b?X8%4Xx=bGpX-*(k-gUgzwN zP`09BNU1w$`Z2wgVxacUjq`SYWgy6k^OF=!w2YUX(`v9%fcCCKg{4XnF!`LbLka|n zst)@9bALgGat4frrPMX_)N@v2*hh+mV`nMLzzCc*WvP-M1Wq$&IfoQ0pSu%*kOCH- zZBu?|4~B8HOfl8l8lELe4A^EYrCvwbNhg$DU?F(bX{DP5OP}K|E3x2N$ZD1HofY#H z#SK+&aWg>;7TNylc1(n&NFU=rIdNSAhPPLnfiqA@){pCg)x9mSz#Re}qHCO5;|9;0 z`l%&;?g(5>Q2pH~frpR+-aPkR^+qE%!Vt@+t7h?pDOfblQSZB<#8PQYD@Y*46@VyK zOAYfZhr5DSp!E^W#0_?;SB<|WxQ=#*fRF+WdBHCAIyeS|Q)SK})g&I5s4oqd;8iFr zMJ9J}LqJr8XkKDojjHjSYV{U4n_LOKlB&C-u8(jtsvuESr1^1NTP@J|ukz|uNJh0T z)SXyjQ?$mS)lStAy{ZEd-^tbq&n)-k@p?ber%4JiwD)R5b|+j0$2!^0#CuqglkoEZfl>ok-kYt z0Shm@r@cf!bvT9!n@R729x~3UBn1NblA<3qVN1b{%z7Ji6~6V?*P_?`7oBC`^18nX zHJ1Ww(D}C`TKBJat<8?m4}&KROQ{;L6WOn~9)PaCWX-%kRxjv+Exg!0RX>NJTsKW$ zIS6EZO7#M;UP7{F4mhfx0WWr(dO=@;?t003^Nb2zfE%8d+|oOt&EEZ<{=oPS=Mhi! zIv`((CGo|7^_S>7RH&bah10w}hJq{2QlPo{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-TvVKZ_ag0osN|QG+9y1`-5TM4O&A*sB*~rp4`TcJG$s$}u z*NS4mYA0*T_PZQq6M857%V%6l?z4?R084B!8lW``` zUa+5o<g0osN|QG+9y1`-5TM4O&A*sB*~rp4`TcJG$s$}u z*NS4mYA0*T_PZQq6M857%V%6l?z4?R084B!8lW``` zUa+5o<ZVv2!zs*ypOg@r+)p@B)V Xv9Xzfxq;c{4n`w((sjM!;N}4U*F7NX diff --git a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree index 65f50f9943eb04e7f64555208946bee284ccc731..985a7f25db079a7068b6f67b15472e1e2196d158 100644 GIT binary patch delta 3237 zcmbuB&r4KM6vsJFM;d?33=YM!A2>mlF2Xl&ej9>P5LBCi1Q$`fnP#*=93l;7QR9F` ziw5&iR}_k{axpHf8ys59DB@s?NZd$U7lP2Lz(Jx#_c@DM-}C-}&-r}cx%b@j@;lD_ zj&o%cw0nVx*n?=MH>8J+P%siq=tfja7^##N(={WY8DS$DO-5pgKsuOA`ICGO+T|&8 zu#)Bj4V^90z9aI$^|XORn#(?~+1=&hx;NfPt)Ozhs*wFE-#rzgx%sk>le{D5c#qDz zyH;p4AMbhLpmfXfcJj}?b4?_g?}pE}(#SWLw@A($KXX0OL2>4IV`^xaLQa{@<;OHI zwpt>Ad}-|xX=))K0)`{08&!2yrxhqYwm(wtT<%mlF2Xl6&U-V4pcDkvW+1^u6ywaikwA1&3bUwj zz@kNi`KT)jMOe8Q7uF39EoKyPs6`}hq^*lUa8=+S(W3jD#jNjnf57K_zVF<7?s@rL zcYfEsHU?TUmYR-a^xkmN7>z_tJ!u;0Xe<#l5-H6{hcq)9)e=FiFBuN&nxW}@0ovs$ zYp7!K!G_KjY2Pt<=y}pWBCXYc-|Fu2@Q^>=NUfmqpsJAlD&IR3p}F~LfRnr#YoaH_ zySvtCG@s~s?xJ+d_IC15y$ekwn(yfsTWRF$tJ@@Jj-R_}bWof*-k2U9p^!6HbNLYs zjIEbQAYa+ILYmqcZf*9H$h`GIS|pO6$bylIPF9&84JC_F@(uV)-(} zy+ro&0369rStmbNfz6V|LoN)_TB&O9hyge~s$={ydx_^0@s9 z9uV2%>>W#R!3pnw15cgslgpqw;qR`%rW3wegns!ARzJI@k5HB$>N>y3;jiN0?%hWi zqfJ@?=SuKHezVxw3rw&sJYNB~{BMt4Aj`C$;AO=s_}KdrjNtP6 zlLz3(FTbFT*2FA|0=Z_=0cKh52wL$&6|NKIwF1lkVB{nz!4w-mtpwQ168LbZU8&Lz zU^{{f*X#t;Ai diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree index e629f7a5a0195049c11d50ad50336b9af328d9c1..228d56cba87ca6fbcbb298664fa6cb3add22f43f 100644 GIT binary patch delta 15725 zcmbuG`&X4!8pqdu-}AnQi@*^O8AvA4N@i&uk?WKf)Jz~-5TGES9=RwOqLf1mO?0I& zYN2_O-Jp#!8X&X0sZd}1UyLFo87&*BF(00)`eU}=vn2W>i9%V&U^t*c5vpEp;d z+5S{n18PoM{~YLZ{A)8o%?TTkfyddllQ(66no~F51ATtWr-PcEH4&iZ;jK$R&Gv1R zK+S*Frhu9s?YskO=GK>jnrj*@AUR?829SJdUm@u8)%|Nh&BF%*K(hSrsOJ6;P|cD< z=w2x}`U6;7^RZ;GLdqxTIixr312yM1SAyj0r_O++Z!3D2`F{BpsF~5$4zB0F+tI$Y zoIeL@R)5_9`n-Cv7u2k}vKQ3+rt7-ACLcPNEpx26z6=!IadW!7s4;8SbpHd?WVg`E z?8fcgpyrf&6G5Mk_qKqV4SiXdChcMDJSIv}8Usf^3OBPZQ1xRE2KEsh9Kaaxq9W!- znr~rgV1@I2nFgv33SfR0#}8yXt2P=!&xVmFWq~L*`W8A zGSWt|EudM8aa zsCai8%aB)eUT@T!z<~E610DV>+XyH~P8zPp(DQOA51(~kgw)U=Q#fB{HkkOZ>M>;q}<3+&;3 z1z1(eDzE^>RJV);Ytm2DBx=80{!0MolI6U+j?I<7fp~4GlFH|7WP|wKDt19SV}mEb zf2@OS17~jnRW|Y#tPizrVT(O6asJy~%#EL_XU!N-g8$oKjOA-TVO~5xO_@rKM_HzP zv$#qxqEw*Ee*Yx%;BPgvbC?4Xe5t=Y>+Dv3q)04Xz(tmf_RX~}79@W#y82zPUQ~UZ zZ3p(6(RlCQVqNmaAD|xm0B62@e5IB~Z{A@&1He!#Jrp+@tSOhl1|Sh&D%yAn7UdYY zZ)uai(%~g-nneA6z|x&V!^4zW0BiK>`4Z7CA)}Qjv{&0Am8I0*or)5i~O-h zf?W-4M1d`!qH?7TxWkNePp((?fL6I+y=mY^rEHM&Zsgs&lzdK{S@_E~C5F*;1J?sWW1B^C@om1mVB@_t==`b38^!5cJP^&cexy|x7F zPmAv;xmHY^r;kwEF_hj7Q&R@Yvr87Kv&jC08U!3+1{*z2-Tx4#%Wq_=o_xhD^)kkj z;2jo-&83JU)l1$p3D3pAL~4|(utI%QUXmMZ&>z**5P7S{UR=CVZ%Nf*YC}0d{|`dIP2ZzXdJ=xbuZJ#g}qg)tE=Yl@PfM zB9Jt3HEQJRb0RpJ^Mbom; z{Yb+Z#fw`Gn%pSpZPUsACiHWtb&aMGVR%;VG~u-AHhAk9jm|}Y5c;~!)EtbZLDpNQ zm1s2t>rd0}nYzJC-({h(&*X8SMSG)Pbr3;3sn?`^kIweSv9T;r3+vahAp!*OA#a;} zdC_UpbgB!|Hh^27-W#q(p$j8eKdK1R%H`*7*F?2;ymk;2r>4o;4sfEP@1D_ypnV|N zf%KOotrP9Wj9$f)d}nI#&0z%Co33pcDZLptzoVz|Q){%-5J@8{wT1);<%Mn9050BF zEwtnd?M3<8IWs2_q+Nb8GfZE*p}D*CFLY_EDU9^%XFeTm*w_>tZWtd+>UoGv%%F~BWmu+1m* zB4Bhk*sGuEQ&8QrEjsW6VCOoQ)5TW(2&((szw~U>@9eMiL{vBZtZqkjYtHMZQQaY( zI`HcVSDoiy(t)p+UFCt=SbxHJd z7;1IbUA<2JHsi^v`FXnblMW1E4LXTUrt zG}LU8Un#=_Ifj|H1G;gU=3M@b)qFwzf^edg9d5oQKVggi)J5`rx#pq#=0tNCu_w*g zUp8GqNCdIVd;e7ue!Mx!{2SMowq?(n-;-a4Uxy`-tUk82amhWk3#g*N&Iv9 HzVH74PQ|qZ delta 15725 zcmbuG{a2M$7RT3lp8GtP7lA7vGLTN9mCVw-D6dnBsF^^vAVEPuMcxEMl5%K)iL4Yy zEi`X(8njW;1Znw_cWh7P_)<27Vp7;h=t%gMq>K@%$zaU6&&_G|%7@}?4ynO7JT^3>5f*xwW?lA@^ zl*98juj2}uwZC2US)xkQ_FBL1jGR;0Q@Q;vJCmlR28S?9-tq*<;tx|z~ z$3yEu^0Pnu*G0OUONxu9tcNWOOZEJ%8{pm&+~S8sxvsjY3`dj7i&?OV%* z^Ppz+H}#;;YnS>!&8n;WK+SJEZ^&!%5erzEeZ`G3P;|%5new8>tXb3j4^WfcLNBwf z+j~IGX}yy{pO5x6gPQgI>6j+%W$XebO5qv4Z1<{`(*>~m1@ z?hKYHujshms4tEI??nbW>S?wUR4qos-OFgQ{~<*bESjO=CMjl+R

$qM+MBIZNwc`R}O0sN_PF^dBOkjNkbWF6W2)4u1~g98ds zQNk**07bN`j0I}aPt+u8zg+%H0LPMLy|<1nkiUU=ZIF`0=Wk>~_}waYQ95ITC&7QB zgKRx#Zva&`@)oQYwQOO}xntt|_q&-3KV8R~FrEbex1kuz*M7=8cy6*Xof?j@H2G$6 zmR>}uM3?>EDdxuCY+~mz2PF6sUwPKit^8=ASUSH;ED`OS>zyn>{$O@}nDKDfm?<&ED*-S|Pyy!oUxS~9(HhxH5sL#=dETxh7KTmc(^M1bjN^(8+=-%vReMjPG_ID z&s4l9JznWY#YKR-sOj`%6-o0DN<;0nVzm%yYQ{^t@G5It| zL6%*$vqt!MHrT2o>S%c--rAySgWP3~v0FE$&jhLt&cmUJoU1_R2Uols`g7GAH zy9HviDYQ`akoQc&a}h9+8l@_zR3Da?gY>%*Ht&_!GbZ zW@zt^GP%-`!)gyYOBw;9KTX`Ql8)o3dKe$cMp|5V;H{ zYp?3=I*pN#xX14=g&P+E{HX5*Q`xuy zq~VC-!7YbOE)?*#>C`|I`X$u52Gi(ZJS$gn+nt zv>Jl-r5U}ZZt&7~T4?AuxgBiQULQ~$L=boCGig7dv%PU_l=*AH13ETD06#wLZId@I zJY$+ky8^Tg;MS-2M`_{c!U)!fDucCh`MKLUQLUY%9RkIvajLchoT%vgr?g>c9|(3Z zy_KMKpxv0-r?`{%91XrXi~##mv@K(#H{<4a^<;i}wRQ#~Y4jScJ`O^8L8~^1i}zIv zE&Wn^LB4j5%t-`klb_5C)7QH+SEv5PPHmO^;v4!RNVWXVKlrZxntYcF!Bje_=|7=6 z`$>xqf7Ds;s}BMvBMKa;=g6%>7-r= zjP3?|?Q?w^s(Y?k2YvwTSm$!O)S@3nb)Wf{o`L$E_q866>ZY92ZK!U|1^o=FJFG(o zejVYgbKYeg_EK>aT7)Ss8X7o56(?b0u!wF>!O2Yz+n)ZN>o_sHv#=;vV6 z>h8PxF8SMxC#vRW>H5z)Fl>@w|0I7QI4YD;-K}fYr*w_xSj=BSY{cNF?#WK=eO+)Fm$*(N7`q&*gjG F{{vRp88!d_ diff --git a/master/.doctrees/cleanlab/experimental/span_classification.doctree b/master/.doctrees/cleanlab/experimental/span_classification.doctree index d925561ac89bfe4cc95b52dba363d77cbfea666c..3785a403012558e420c0251de5bf7eccf4b87186 100644 GIT binary patch delta 960 zcmX>#f$7u)rVVL~2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(yG!@{_J>@_wb1&6dnJnMt+}q;vAAHvY+5*^9{0T(|6-|p2S;ba9E#O~b^h7{;tD)Wl;P}{s;=_XmBRk_(wHN%jc ykV@Tb7j7Uxmd?p`;li86GHlt%(|w>^b~9_f8V6arHy#f$7u)rVVL~2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}Bxm@{_J>@_wb1&6dnJnMt+}q;vAAHvY+5*^9{0T(|6-|p2S;ba9E#O~b^h7{;tD)Wl;P}{s;=_XmBRk_(wHN%jc ykV@Tb7j7Uxmd?p`;li86GHlt%(|w>^b~9_f8V6arHy{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5h!EgQRQPsBW-%E>jN&c{)3}3z^B$>9RSGzm%0Mos-W=No>v+-A6_M zZB%yw>)t88kclk2H=mR0Cr>BX-j}kX&=7S)a`YQa?&Rj*-kQhQ$_M}|WmDS# delta 1121 zcmeBrz}oeIb%PJ1L9%5^xoNtYzHzd7lBq?SS#p|rs)c2up?PA8fqANtL7IhyL8767 zNwTrAnSr^1+2k6=gQRQPsBW-%E>jN&c{)3}3z^B$>9RSGzm%0Mos-W=No>v+-A6_M zZB%yw>)t88kclk2H=mR0Cr>BX-j}kX&=7S)a`YQa?&Rj*-kQhQ$_M}^&PfLV diff --git a/master/.doctrees/cleanlab/internal/index.doctree b/master/.doctrees/cleanlab/internal/index.doctree index 8ebe260a1eed4b88569d6551e17a83ce47dfb000..1793b713bdf3a22434053fd778abc1e1e8a46dd6 100644 GIT binary patch delta 115 zcmdm@yhV9K0HZ;saj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF XahgeTs$ufx2F4y{(skLeR`37-yY?ZI delta 115 zcmdm@yhV9K0HZ;&WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Xv9Xzfxq;c{2F4y{(skLeR`37-qq-np diff --git a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree index 6513a315db91ae3f007c6102553f900ca3a92195..27ac4a2eaf2dbb1f3d72224609be2dcd8fcabac4 100644 GIT binary patch delta 472 zcmcaKo$=Ci#tmtV2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(yG!GLx=rGNVMyW>x0px0pDesIif$(_FKSOr2#q94zF; zrMY1@nRZs2%q1h`J>)Ljywhe98|khE>YOasz_Iy@^LKG_bjM7-BPqPOE9N~JA@z_O ztb4M21N&zAq)1V6?2n$z*C0B%FGgUqMnyDPAs4-wuOU;OJi8~TN^O=|z{5el?z1a$ z)X32tGnxM`+vd~fckqydn~C F2mrk5{SE*C delta 1662 zcmaDdi}lGY)(tU?2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W|MmuzmTr!A$QPbCgwHlWa*r2z}-l)#?248d&#iwqkQLPbrBCHlI@!e z(y1YShfJM&rE18C3y__kWX_Xr=jM;{Vx;Q=`|*I%e=>DesIif$(_FKSOr2#q94zF; zrMY1@nRZs2%q1h`J>)Ljywhe98|khE>YOasz_Iy@^LKG_bjM7-BPqPOE9N~JA@z_O ztb4M21N&zAq)1V6?2n$z*C0B%FGgUqMnyDPAs4-wuOU;OJi8~TN^O=|z{5el?z1a$ z)X32tGnxM`+vd~fckqydn~C F2moJI>^1-Z diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index 99da55a29114db878a1c42d45197d0fdc5c01461..d19197906f26db39429934745676c5682c508845 100644 GIT binary patch delta 1900 zcmbRDmTBHwrVSa42ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(v^|vXQQ9@<*1$&HBuf$kKVAWJgY3s5@oz6gFWFa&*qvoX>AbhC>(X z&e&WaxPgfrJ7;cwDKeQXJ7;co6+c3zotLD(kg2m@?l_q`<&|!d>Cb~ITgkN3USlV@ zc50CkpPLuzUL@0{)`rVS*ST5H#RO(f<%PIWCW%yf#U3@-%;e&B8I-fn6YEqjSTYJcgF^F!nZcQ>>zP?|4GwUQ(F7iSRr2E30lVrNQ%Sd#y?c!Dn Ubf>LkA|tJRWXavkx^*Ta0DcxlBme*a delta 1900 zcmbRDmTBHwrVSa42FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}9a)vXQQ9@<*1$&HBuf$kKVAWJgY3s5@oz6gFWFa&*qvoX>AbhC>(X z&e&WaxPgfrJ7;cwDKeQXJ7;co6+c3zotLD(kg2m@?l_q`<&|!d>Cb~ITgkN3USlV@ zc50CkpPLuzUL@0{)`rVS*ST5H#RO(f<%PIWCW%yf#U3@-%;e&B8I-fn6YEqjSTYJcgF^F!nZcQ>>zP?|4GwUQ(F7iSRr2E30lVrNQ%Sd#y?c!Dn Ubf>LkA|tJRWXavkx^*Ta0Ava=G<9iUS|gIopKh8)XcR>k6=;bsRO7d`z^V9_TFt`Qt60!D z)Gv!hB;8n%alliEq}4^*8$}^eCW;k;S{LO-ly!bHCUtin|A5c){XWn6o)^yBcHP@{ zU6&oLFSFF=)Mi(wWoBf_nbx&ddxo4X*=46w%E^#qizH{s+1ZY@Id;o_tHYV@NZVKC zs7tG>s;_l8iPvT(57ARevK;(DC=~(8P{{@)msk{FpK@kI5F`yAnwV1rNfC|vM9T~bZ_+sz-(3WS8X$(SC!0dxCGQ}KX_4GlgjQ*;uKMx zI*&qb8kOw!3;=uFaQraPt^1S}s9D{50H}HW%r@~|O_RLG!^KY#+3livz9bk)5}iwF z?2QZ^NS7SBt0Pn0Ga#5Axw||PM3U;~TDozd2DGd$KVA4_GFDid){H#@Xx5AsP&Td! z5!NF$W1DIEq#LBUnI=v90LP!IY5fM7txCqvY!wf_zt5o`Rj@ugdVY$xsq_b-24!5q zW_A)IowzdN1==L>CH2}$h|e6t-doXUEgavOi>^h1aHY8tg(-b5q+w_CQA_Z`ifCD9 zF@l7J&mrI>iuIMEO2fjO?1tm)b~&;D-Wh=&I3G>E8u>)4&@kZ2_**D}>FdxrxRC^f z^K(W@8_@&6MhqC~YeK<#@I-ZrtsZHWlcOkxHMXG;@%_o)jsAShRbug8R9V2dYuA_mP;`hacFl0J|(_8=SHCN7b38RrA6a{1Yr}5hx8S!`8JlDj(08> zm+aq#9bv+l!m$Q6Xv6W#gjw|-R8$}UOCQ2^|Xj!jZ&YN}*Ql9KHvlUcGz=ERZ; zdre|ZMQyd+K|EF?xrZJ}qD45EPa^}qBM>Qfjba_?R#k&&dMWUR3Ph4IeI%UK=XFf!T6(~p564r>H<)| zZu(_S7Ep6~?Ep|SZvz67OC>9ij55i0gyDfX>3{^C<$d$mvKlB(JUjn3>A&q z6AkLsN0SYEwPZZI7c9>Un;K{JC9rt*Q7SrHgSq5 zP94V~H;qE}xkrFKX0{vyx^=ZB12rq#4+Ax?p57(Ct7)94C0P6vvALWy!y5-9NvvZf zjkvZ+1JWgp{TedeHw%JkV}C*@h$NLy)O71e6=+$VK05c|=LlhKS~YnepjkDUP1$=@ zkgy)9n%qv4KD$7g+iBd47jXP3n#tcFvsK9G*`4CS_x1VeM+L0U?!lkpZOV57P@Vi< z#zxkPkw#pZ@d6!^_>y|81;lF%VsDetCp8?;Nk>=0K)Bpgj+V*8PNZUI_Mqm##T8Mr zo_que3!j7ENf;Y0MCH20IVpnU?B+pa0=zT)J#appeAV-bR-iG!mGQSw4Aa)2vv4CZ zGUw-w6xO4=fQ{%kGTeXywcv?rWNR%_%dHb=1*`8sLE`(9zZ-q|n6Jd5J;)_)N4~WY zm$8kPQKrg1HxY*jK$tW9}U98^cN6Mwt_n4+v9u<#$NG6@W(J4 z8bMS%5&u>)uys$7N<85ISDAW-dKOm>sGmn?W;d^YjoyKSL>GF$^2c!LfBt1!Mq zMp(3b%d27S6X?Y{P_&$~PLG%Nh2S>v-8nx3g+CvHRV*7j8QK1G<{Fv;Y7A diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree index 1fe52a61ea654e1e6f6f4ad2e71b7cbb36a23db4..15616494596d46f72d9d437a5e6daa5837b53623 100644 GIT binary patch delta 1179 zcmey>$@Hs}X+r{|L8fu3WsyaJzNwK}qN$0wNs>{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5hAOzev|q$P>0%fO$O`)&X@+E@ejr3)n8QlB{!cAP z>6`B>2a*vwAf3lm#e_(YkwTvE$z5g=n`$@Hs}X+r{|L9%5^xoNtYzHzd7lBq?SS#p|rs)c2up?PA8fqANtL7IhyL8767 zNwTrAnSr^1+2jd~zev|q$P>0%fO$O`)&X@+E@ejr3)n8QlB{!cAP z>6`B>2a*vwAf3lm#e_(YkwTvE$z5g=n`ZVv2!zs*ypOg@r+)p@B)V ev9Xzfxq;c{2F5cyhNSA!pR6Dty?F-TT5bS(WFnIQ diff --git a/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree b/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree index 65b5b0691369ee6705f18c617f0d607daff80712..0fa6623961108969c654e79505e916569622eeed 100644 GIT binary patch delta 1898 zcmbRJif#5QwhcjyHkrnymPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$@(ez$;JA``K3k4sgpmts%_rFSjT8UswqI7QIq#DHcuT%ptr za>poi!#Bk_6uQAqrH_0!Y)(+WOribvHCHfG77xaHZz*)c8H4BKhXF7}OKgreiDD(! z7u=iut-3kM(Ve@w+{KG@zicuT%ptr za>poi!#Bk_6uQAqrH_0!Y)(+WOribvHCHfG77xaHZz*)c8H4BKhXF7}OKgreiDD(! z7u=iut-3kM(Ve@w+{KG@zi{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5z=G<)rJHd_g2+a@7?6$=jHVNVXEBdGi{UUo2$loVci{J<_|vVE@b=FSW)GU61dd$WD+ Pb~V!N1?8KnDP4>Jci{J<_|vVE@b=FSW)GU61dd$WD+ Pb~V!N1?8KnDP4>JK^`c9 diff --git a/master/.doctrees/cleanlab/internal/neighbor/search.doctree b/master/.doctrees/cleanlab/internal/neighbor/search.doctree index 9ae25bbdc95e03fd91f22ef990f2c5dd5f093b50..8d7701c5a1ee43acde424cd6267805842745db12 100644 GIT binary patch delta 518 zcmX@{m+{13#tlA<2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(w5!H}rhtP5yg(&pEEmF(o{ j{3hlvPL9sx%{TPsFp;N|-N?a%be*xx$(wIgy<-LdT{N07 delta 518 zcmX@{m+{13#tlA<2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}9mmH}rhtP5yg(&pEEmF(o{ j{3hlvPL9sx%{TPsFp;N|-N?a%be*xx$(wIgy<-LdmR*#_ diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index 641b566cd83d4973248bff0526bd6a750d25a1d7..c5f435c31bd39a78da6973d8f1ec3056f49c394d 100644 GIT binary patch delta 719 zcmccgg7MM|#tnXq2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$&>3C?~|@6UesmtMW(rASO?TO`A!l4cki zi^$R4wfPL2J{vhY`!?_7ts=|CeVae>Ph%p-&i>7A!YyRk*}u6{w3AFbFG>WEZs+De zsSl*vx4BUEGwF5#(@+0qWrZ@*?E~wKP|hXO&KA{fGVMI4af_Lp6i~j|UpJgA*OqVg z)judgj-A<)?>P%@-sNIKmc0<&v)q1YlV`V|81H7QVpB5h2I&?rjh83S?)lQ}ncki zi^$R4wfPL2J{vhY`!?_7ts=|CeVae>Ph%p-&i>7A!YyRk*}u6{w3AFbFG>WEZs+De zsSl*vx4BUEGwF5#(@+0qWrZ@*?E~wKP|hXO&KA{fGVMI4af_Lp6i~j|UpJgA*OqVg z)judgj-A<)?>P%@-sNIKmc0<&v)q1YlV`V|81H7QVpB5h2I&?rjh83S?)lQ}nZEWx)1@c1j7@d@?`tGDv=qrGVtHeFsS1cAN&u zCv)pS@|l9Y;8_+e`%t@6T3wOasqk5TzWh~?JoVyMu*T0`Dg~XN6z$UPl%>VLfxhob zoZ5ZizV@!!0y+oFkPkv=F1ua>?oK${o9owTuTz#*1i%_^Y}lyX7tVCF$_qM2y+=Uj zY|RGHIsMjt(3#%o(C(CbHr)b!_k7E>`@$#k{o89n=ansX(AoLUHqhDI+6X$&?zjLt zr+2;&I&FKt)b5nl_Ce5Bv#(yePrU_=vhKhudgOiQlO-KRpmWLLUlw4_vqz6XPM^FZ z_Jh6Ecl`r8ySpnv=SLq8fzGK@O`vnSuOD=-_!M;(_n!fsN6r<1bv6#%1Id4-A0$nL z-dp!j72qtbXpx1Nx)vf+RxYcC+YJz@R1NE8^GISGLc5fWcVeXeZX>iC=IE(E#w?iI z{Cjj5As^kq zc0%{kEmpU(R7z}PgV0nw#N>}GMnq3C3;leZoissph)^U_BQ1=_dF9tq6SYn-3pfv<96~X~ zn?dzHr%y5ywBcQ$Vp2uh1ha{s9P%2dh**ZLjh`~($^gMcTG?PCju3cwwLhXft z{$e~1TY1zH#ckLP6)KuYi!8hs`Qh+rjbNywxCD99&PcPM!}| z@F>Q`0)9Lix=l(b#^n-jq>&=thHa@(3{qpHMxiMfsqA%r2(}xkqLM2heq(zK`Koy& zHV#S%57zL5DOl~#?oJiY_3?Phc$Wvj3e*_X@g8>>p%iFYCqD?DT*qDutvtbN!QoHt z<4qVU4oGem`=YpoV$O2|Hb0~W>Bmr|@OUB(4e|Tf4i4kaZ@9$lq4$|pG>r3DYP-gL z*tv&;Q2h^lm-Zxt=Poa&P2+r6d;O%cH?=F(pIheMrQ+`?9xGJ0mA?IxU(}w!qe!Gi zYQ{2Ay3ea0z#^VN6A$6_&iCv VEUbg1204_3Y^*|3|C1dv{0C4JTY~@q delta 7751 zcmbuE|4-C)9LM*5pT`Mzad&(N0gLL|Sk%!W2h6zBWl1y?S!k5tad7fQjzTNMsX0uY zn<#$LcZogQnZZNH+jmkW0tsf%zLEX`U^|{0R{qy7xc)T93*X#X$ zy*}^Vjn0@xXG{YtncbUT^X`(3xz9Sh&gvz3zFe=*S(}&d$#!~b?9STf?7qA_yC>V8 z<8?T4?M{2H(#poPLjhZ%yvPk4B1JG)XI9+$7KE;s#Sts52-zgyLFemFlxug&fb9$IloGJ{Wq#%*ko+J^0m)(eHjupKI0cfA zJAEMeRKafWEQ^+YsNE^8u1M`v_$)tP_6kUzd|?Y%<7Y3Hg3eEhc4&9X(&FDh-*+WB z+I``^_O9LxItR;;4?<`zyIuwEPB_~e>sD*8Qv-6#|L1%Ak1L!=n?L6q5 z-hL2t+ID@Z-6^f@gP^ZwPn~w3dJ7t)Z{N#$5ak%VhGJ29UmR6d1T zidjMgmc*eee1Vegu#j;Lb3p6CCH{{g>c)8--7I5PGm?y3eC1*B)aYgD&{y&SA6?J3 zL-*1xR<*KJN^E0;&{RC&*X?9k(62Zyk#>~DlA)8Gg8VL0d2=GtdY3Ztl7;%uvKFZG zbNF$|PJ{C}d5=Yi$sbvah@NB?`uQ3=VS?%qp-7}gS{RS>%CDs+YMo#fa2`TAgkp#{ zg6e%vpJXOz!@EMoq>8o)W)nZ%XF<52RH_WJwRjE@gPHuvdGz8)CVxa+y~g5%+6x2y z#dsXH@~AnA+prrdR5XzmS$HqTE9Yy?^n3zeh3(le9+k$og5&w-7K3PZa6QfD@O-d> zhcPY`@MF=?ZBjxpE|qX2jTG@VY)ge=kQyU33QfUCWv}rAu-!-%m0SVw>sw>U@8yx$ zI4B`JSi|?HV6{KJGgUm($KxsEJstonP-9TX``l%OQlO=sd_Q<{9lI^G;yAAbhd;TG zH)5#RC%IYdiQ*QDImZpy{E!-?A48SGV~I30#P4A{IE?3f!zE@9z0a(oew@cr+g0wz z&OIE2>VDumv?n1vcX&B%8t22>>nD}Hp5bw_Mw=Dp5DzV3Q~Ddg$ioFIIdOq~T{7s%5&IYBsb^D{|hvLh>U z^LpuC3haI)mrjB1_lm(3=zgIxn>>GSzM%Gk0(;}Mq*%#KUyhsE42t+D&~0x{X3qHF z=d<~m>sb!6?B4vr?;P2FHr?zHx|5kKdx7cObh1OJ@aDeA*%a8nI^L9reEV0&3vVvX zDInW^z0CuQxfNNMtjA`35bw_Mw=Dp5DzV3Q~Ddg$ioFIIdOq~T{7s%5&IYBsb^D{|hvLh>U z^LpuC3haI)mrjB1_lm(3=zgIxn>>GSzM%Gk0(;}Mq*%#KUyhsE42t+D&~0x{X3qHF z=d<~m>sb!6?B4vr?;P2FHr?zHx|5kKdx7cObh1OJ@aDeA*%a8nI^L9reEV0&3vVvX zDInW^z0CuQxfNNMtjA`3ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{dPXG<05X{o00000 diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree index 832e3df261f5a7f920122ed3b3b66ceafbb693b4..f66d3920af64fa36372b03adb6369978d4053b67 100644 GIT binary patch delta 115 zcmbQJK2d#x52Hb*aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF XahgeTs$ufx8pb7Tr0WXcaO4I6qk$nu delta 115 zcmbQJK2d#x52Hb{WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Xv9Xzfxq;c{8pb7Tr0WXcaO4I6i$x$4 diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree index 4260a48e930ffee621660a1a8bb50cc9ac14915d..0f49c851a448dd7b0f179975e5e2ab483767b300 100644 GIT binary patch delta 4067 zcmbtX?Mu^96y}thbDL(qv8Z%{t6?mE?)5jCV34v&PGJ-TMa?l^Dtj>(v<5X;fmk8j z;0{3@MD?kh+z2{Llx(3Q`C%Qs_EY-*gATjP-enESQdck>mfTh;Xp@4@pe%=6?k-!mJrr&Xu_#BlXOx?q-Z(QM#hj&D;Ss*?au!Q#IXL=H!xQ$ zf_vUv2L{%D!>dq(Gv209Lv5l?p@yYfNrf6}hQ<_XI5pg(P(%1$SfPe&;bKV>fe#Uia)z5+U|i>+|3B@sN%1i zU=0-)qWxN^BrViTuOESzI0;#b&Qs$dkE z@wnNEl5fo@B<&i3Eo9aj#h`s8-dfG(Y$HWjw2u9e2R7r;RW08)q0lVeTB|M?$!0u1 zOAAJc7xFXNUx(_`&*qQt9jKZ_+aJULeI%XK5-C>AG|^Gy(+UPA#lnRjff&}m`a0%} zM{&=a??A^oZ+H}Ha13-P)XNs5|&AsaOK(H<7HGX`Pj;n z^ED{7M4G9j=3cnM&OAR*xKZa^L{Ko-tHT6 zvM2i)r(A&9{V$Ux7_^sm*Mo*Et*Y4Ek9pMK|5K>h?%(aGYulh;ORH_$6*Zxz(>geb zqevUUNHo!{{OvY)gjxVE6&xmB-vGN%zL&>;)k6a1dU4Z5Eg5)+T5ObSRMt9zY cQew&)=*J{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5h=Io1|-URCd^WmB~Y%ES=NWH#6~1o}*MsvhK}}%0Jmj)-~DCs&eyV z?FVE84oGK?-bW_#>|`@~Os1WaOlFg5=QVR}(sgb&wDMvm&yOm056Sf7I)@pgyA&Lk z@15_GX=kb1VlwSK==p?*a+kxJ0|`@~Os1WaOlFg5=QVR}(sgb&wDMvm&yOm056Sf7I)@pgyA&Lk z@15_GX=kb1VlwSK==p?*a+kxJ0{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5!~5A<}hCHk3=-tj4^QnPmGwIwvQxHAF&`&0E?3}*&w&-rM?3}(iR^knrb_&RRB;C%!YfB*mh delta 1181 zcmX@z%W}GxWrH81L9%5^xoNtYzHzd7lBq?SS#p|rs)c2up?PA8fqANtL7IhyL8767 zNwTrAnSr^1+2%S%A<}hCHk3=-tj4^QnPmGwIwvQxHAF&`&0E?3}*&w&-rM?3}(iR^knrb_&RRB;C%{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5z;we$sVKHwa`*->k&EfSF_qL0XF^r?MB3r88x60H5mS2^>1Cg1 ZMK&LhCf|t7dFLlJlB|6@FvZ+r1OWSB*q#6Y delta 739 zcmeBL#@e-vb%PJ1L9%5^xoNtYzHzd7lBq?SS#p|rs)c2up?PA8fqANtL7IhyL8767 zNwTrAnSr^1+2$HXe$sVKHwa`*->k&EfSF_qL0XF^r?MB3r88x60H5mS2^>1Cg1 ZMK&LhCf|t7dFLlJlB|6@FvZ+r1OR6Z(PjVu diff --git a/master/.doctrees/cleanlab/multilabel_classification/index.doctree b/master/.doctrees/cleanlab/multilabel_classification/index.doctree index 6c53cf87f5c5559d39a8fa57e2490235aedff523..01a98c3bc23c6001b56b06c36cc47c5abf59ce14 100644 GIT binary patch delta 137 zcmdm@wnc425uZVv2!zs*ypOg@r+)p@B)V tv9Xzfxq+E}N`7*&esO+jQF5yO{F6Qrv0GU1DTLpR%K6)8$bz%XLH-ObVdMNi{DBB delta 748 zcmaF+p6Ts-rVSp92FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}7P*Imytqk?A)x$#zZN$do+Up1qD7jR}(lcqAvk7ZBcD$&t-Ui3t*$ zm+~zn-OE77gWa(Cy#O;=UWfTWeDfv|ITo^fusK2UKbbC1kd)cXEz=iHmc8JxkLTpt e>{F6Qrv0GU1DTLpR%K6)8$bz%XLH-ObVdM&%h(bC diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree index 5e76907d91ccd3aaa557401b896c3a32f2113eb0..8fd48f83669a4c8db7d0d709999121b896ee346d 100644 GIT binary patch delta 466 zcmbQRl4-(9rVT!f2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$&+gsH<7N%fhTD42ByViY2193*_eqeoj@yvCeLBZCr5Mn=1uHzEF|mP b?7-8@O0urW4m>HF6NO@V$HF6NO@V$yg`#z-W+ZTxwZlQJ`;XWR_@ZVs4UTlxSg)l$e@oU}yg`#z-W+cnNn_=Zl-UXY@TFlk!F^hW}a$cnP_O9m||d_YGjaRVPTMHXke0T WY;0y=ZeX@~0%IQ;y8M{;aRUH`{~&q* diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index b347984bf087e98b7b01eed2beccace996221809..609fe8416a44ed3a660d140d61d6ef4c3cf45feb 100644 GIT binary patch delta 1676 zcmdlyiF5NL&J7-n2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$&)J>-;u6qp`hR9CrtKCWa*qf)t!ld@&?u-a&%X1KEw8jogAI@n{)YD zNO$q(g@XS`*EM;eVBKb4Q6@5V{uMt&rp{dHLNXEo$hAGPtH`wTiGnhjI=3p%BU2}% zdMcSZMYPtFsk2ab11rfXZ*!orF)zuwCI=elY*uhI^dwp5=Ekl`WaN*=uC&c7CpC$Z zY#%VC`%ey#XB*Bi@UZcRM!AG_k+imn2 zXOZWG=>-Z*;@g1%Q6oT>^$`7aD;a(B$kM;vOM*$8n=GxXB*Bi@UZcRM!AG_k+imn2 zXOZWG=>-Z*;@g1%Q6oT>^$`7aD;a(B$kM;vOM*$8n=Gx{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5i7KAX_w6HJrH(!BW|b1f5DI)TeNx(PNvT3Dv!zZr=I$0 zGVNTeRY#_s_jQ(%X{Ut&xncX(D2+@z+08h})OpzA9qBqZKeTpbCMSorY`$f`mvs9k zKeTSy?CtEsN{*d5n~gmyxX9D#9x^A09G#(?GnOymB}?bzjOEIkw`>j3CrkI{-ltEw z$&;I%Dj7rnpc+QU2 zN3eO`wgEP??o+TS3+Qg&x?^CoM6`nPR(>}xY0eiYLT*@+KKpOX_q3cZNympLEJ$;O zddAjlm<)!FrV**orSwS!B(L~2CLt3BW3C~8HWFGoQ z%D)e|$XpH9KMs;6J?JEMHrr3`pyyv<#t=&_*7q+HUfWLFnGzvUD>Q={Ga?Stls#>d zvue{!H<-%G56Ei7v^DOdqPhE8^eo{l`e?tg^`S3Pz_$K3h#7;f2BGg@{0R4nEnGKf L1G}8$W(Ix%j2+0$ diff --git a/master/.doctrees/cleanlab/outlier.doctree b/master/.doctrees/cleanlab/outlier.doctree index dc847d1b66c87b01e39558d029605c00f6117a1c..7310add5af4784b42f1a827f6b98f8a5db0e4c0a 100644 GIT binary patch delta 1462 zcmZo@W@~6>+u+A&kZD|MS!7Y5Z)#+gXli0^l4O)hO zlWgB)sB0&u?+hVN^MaGIn|*e*lNG%#lNX#++MK!n7Fk|~=)QR{g^663D{a1fB#i>Q z&mW5=&+g3&PKvUT=jsW2xi%YLI7oWZ0!KASchT(*L$d6iCdSCRy_lELj6BQ1+La_2 l)Ah)*efj}rMz-zD?u=O+6zG5M&ls;uj((%+u+A&kZhS!ZkleUZ=7tNWNMLSmYimuYGIjZXr7p2V4iAZkY-_FkZ5RN zl5A{jW?*h$Ho1=R2hO zlWgB)sB0&u?+hVN^MaGIn|*e*lNG%#lNX#++MK!n7Fk|~=)QR{g^663D{a1fB#i>Q z&mW5=&+g3&PKvUT=jsW2xi%YLI7oWZ0!KASchT(*L$d6iCdSCRy_lELj6BQ1+La_2 l)Ah)*efj}rMz-zD?u=O+6zG5M&ls;uj((%cayAXdf+Zb{mqk@c8HLr)q3()@lw)t7BVyHPBu{0-n>cT0~^`e z4L0j5WRo6F)1kH-Y*trV!$h9#n+;S?QetzPdJvg51B1_{EoC9k-(cGp z8l_U=_sJ%kC~^EH^Hxf1e`2|h65I7{dnvKq)BYnRwjXz5VkJA`BsSZ52ys!M{b*2O z0NL7wCf`{sxOv-RIUx#-2-)`5hh!tR1M~b{Zj!ZuiVDZ=`}rAN$S58`#mx2u6-GWX z!+bhWulV*ydW;onWH}sE!m>=)iDp!uu3y3^u>D;q<6ZKzJcT0~^`e z4L0j5WRo6F)1kH-Y*trV!$h9#n+;S?QetzPdJvg51B1_{EoC9k-(cGp z8l_U=_sJ%kC~^EH^Hxf1e`2|h65I7{dnvKq)BYnRwjXz5VkJA`BsSZ52ys!M{b*2O z0NL7wCf`{sxOv-RIUx#-2-)`5hh!tR1M~b{Zj!ZuiVDZ=`}rAN$S58`#mx2u6-GWX z!+bhWulV*ydW;onWH}sE!m>=)iDp!uu3y3^u>D;q<6ZKzJZVv2!zs*ypOg@r+)p@B)V bv9Xzfxq;c{7RCs1_Mm`P+lrgzQZwXo2ONMpD~vGEH~T$!ZM&0=xePXmn%JLd~%3$Y;TjOhWIBc?g)Q>~Ps#R)i;2-9A{o4u#gjrCqxzZeyv6(F zb3A>wo!C2l_l^-g&pbSb_Y}@Y7Sh;^i!3bQ{qogmc{WDO@%rRWqG#eog6KK$Y8TNn zJavWWsn5(4J&m(TqUY*2^+eC_bJXd)=beqTdRs;IJ(N*4LaVTU>FPm~@Zg{_PuRB- z3qg5UNtB>1xzfs7c&bM!gwo^6N&M#gN5FPUS=xk-Kwj1$Z*nN5JY!-jA)QtN*8dPW zY-J`G99L596p?_g-Zt|6R%YWvX&KUD8}q^^6I;Tcip(po22yQHF*O)FAF5@mpkf{S zKn}qUHykWrr-jfvZs+Q5RsbWl%t*RM0bOc<)ELFzC6Mh|iys)drVPVe8%y9D8szKs zn-N{C+@m)MrcM^apLLE6@yR5!!`cKZ$B)*$07!wD86Wt|5<@-QOtB~U70q)jm#h^Q z23W{~H~;-r*a{ynv3`{L?VL|{=Ul8{h)5s86&SA;~bqv&qX!`pNE13usHIp>~x&b`aC=H*%Q z{h*?TtK0jkI$af&VRu`VC*lf6+#R0kkkcJ%SKS>u)riNVhMelIaAl=Sb*nBhqI|_u znl)X+8;??Py+}Q!auo3fL`p(zAj>#zgeOB%_gVv6o2B z47ZVvC?;kubrL=2t~@1@8?FP9Or(17q_C!c;3?TYF)=w>NFP&J61@AK5@yziZrw0iww@jaAMHiBQ+zjQ^>Bs@5*WC;6K zVgaxPm3T4Qk}H1J#FJ5_07_0MefZ7!kAU^GlHG)kKwj2n-r!J5c-qKTLMo;BEdL>L z)WVD~IH4q4DIz{iy`$#`EzHV?QZl5a7UqIaMwZ2&ip(po22yQH5j7Y)AM&zQP_~YJ zActUwn-1o)(?aN-uyb`UGr)+K=}Fhfr%4Tv8l(8T1hPH7_<@mY${@_QusFVCwf?J$e&hjIbX3S?AaQpGq)0tc^1pezfKVKnnDj@PWT9*44tTBzuBi(LBd;$y#7x zfCbEW^WR^Et?=sRRl9!zmq}Y7yHOV+mJZ}E SYyA#%(sDM{fc#9S4Zi@oM42@J diff --git a/master/.doctrees/cleanlab/regression/rank.doctree b/master/.doctrees/cleanlab/regression/rank.doctree index f768c48716f2e32dab6d91e8d3cc888598de9db9..70f105bc19db8a836e1cefac036cf5e857326d92 100644 GIT binary patch delta 471 zcmaDpi}Cp^#tm_d2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$&>pS?~tzPfwSS}7rr8LG)GUqCoH@c&dm?Rx04aF55(g(t4gI9lH*dR&A+{K7y;2!k^lez delta 471 zcmaDpi}Cp^#tm_d2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W|R9E?~tzPfwSS}7rr8LG)GUqCoH@c&dm?Rx04aF55(g(t4gI9lH*dR&A+{K7y%GojW7TJ diff --git a/master/.doctrees/cleanlab/segmentation/filter.doctree b/master/.doctrees/cleanlab/segmentation/filter.doctree index ac53ae7c756f20c8bbc33687c9f932ec4d283321..c31aefecacc0df8f28ec38d46cc6d7165cb3ee1c 100644 GIT binary patch delta 517 zcmdmUpK;H9#tlA<2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$&+gsR~eG3Nq=%6x6I@oW`oI*Z2XfqG4+yX!sZ_404DOZLM^W&Tet4y pd}X1@J9vaQm$7eSq0E@gJ9zH1QDlVpW(|?EeB>LkIbYe=2LPNem*@Zh delta 517 zcmdmUpK;H9#tlA<2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W|M0eR~eG3Nq=%6x6I@oW`oI*Z2XfqG4+yX!sZ_404DOZLM^W&Tet4y pd}X1@J9vaQm$7eSq0E@gJ9zH1QDlVpW(|?EeB>LkIbYe=2LM4UlNbO1 diff --git a/master/.doctrees/cleanlab/segmentation/index.doctree b/master/.doctrees/cleanlab/segmentation/index.doctree index 02170fd1fdb6cabb5d56b8318986edeb74be6948..7f51a3dd7cc75582687b7e79d738d3cdb47bbf65 100644 GIT binary patch delta 120 zcmX>jdq#Fc1fxNwaj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF eahgeTs$ufx4#ql0LsE6=PoBUcw%M1to*Mvry(2jQ delta 120 zcmX>jdq#Fc1fxN+WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V ev9Xzfxq;c{4#ql0LsE6=PoBUcw%M1to*MvNeIf+_ diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree index a046f0cd0ca918847ef0433e31c0b1f024bd0fbe..6df3d9ec139ac203f4a9d833110055512245bf52 100644 GIT binary patch delta 695 zcmX>!f%(t`<_#W<2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$&)J>ACay}p4D^nWhNOWa&#UiUDJMnqRky_|5?b!f%(t`<_#W<2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W|J!zACay}p4D^nWhNOWa&#UiUDJMnqRky_|5?b3CUz4n9`bBO=|III$oS4Yc8a;U{YY|yGeJ1;J@lXCBD7^V7n-VKU zMu>0j zf}0P??BSRuRHWf%fXQdT%nd=Zgw#ZoA~kPJ#C5TW*FVYu^q` HfoB*2w4pD* delta 1010 zcmbO`n`Q27mJNQ42FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W|Qj}Uz4n9`bBO=|III$oS4Yc8a;U{YY|yGeJ1;J@lXCBD7^V7n-VKU zMu>0j zf}0P??BSRuRHWf%fXQdT%nd=Zgw#ZoA~kPJ#C5TW*FVYu^q` HfoB*2?)@eo diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree index 50eb6a3616959a1a21e20d9f5baf7ba6dc2d39ed..3b1052114df1cb4fcf0ee01ec775dde4be01c351 100644 GIT binary patch delta 475 zcmX?gh4IuC#tlA<2ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$%|_kN!K*_E_>`|PUa;nBwGj6d7B(fQIiw+R5zz{=96V*)aC@foy_Ff g+rTZfd8^P8vH~e;GD!DVk@#S8?9SZ$HY1i1010N0ng9R* delta 475 zcmX?gh4IuC#tlA<2FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W{YbWN!K*_E_>`|PUa;nBwGj6d7B(fQIiw+R5zz{=96V*)aC@foy_Ff g+rTZfd8^P8vH~e;GD!DVk@#S8?9SZ$HY1i104W-a$^ZZW diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index 8f28ac885f50663e8c197d00656d6826f952e30e..aebed1a36080259c1a99c25d241948bd0e49a407 100644 GIT binary patch delta 120 zcmca7cTa9Z2BSfyaj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF eahgeTs$ufx8H^hk4N29dKY1UE+~(=b$GHKg$0T_G delta 120 zcmca7cTa9Z2BSf;WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V ev9Xzfxq;c{8H^hk4N29dKY1UE+~(=b$GHKCha*J* diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index af3ec093e2b0e97b93c4167d2810333f859f84f6..8bd786af7828f2c0e2c22b1f3a839e6a9c7fd2dd 100644 GIT binary patch delta 687 zcmZp_#@v35d4mU|L8fu3WsyaJzNwK}qN$0wNs>{bg+WqcYN~;ykwK!dL84irg+;Qt zWs-54Nph-T^5hD}U!-fg$R4@*H`6i}a&%rNU6Uez)8;77>8vE{+^onyhlylelNI^f zHWv#mB2(vSQB4kV?9AUhS?(kmF{sF&zxlUb%7 delta 687 zcmZp_#@v35d4mU|L9%5^xoNtYzHzd7lBq?SS#p|rs)c2up?PA8fqANtL7IhyL8767 zNwTrAnSr^1+2jhwU!-fg$R4@*H`6i}a&%rNU6Uez)8;77>8vE{+^onyhlylelNI^f zHWv#mB2(vSQB4kV?9AUhS?(kmF{sF&zxlUn+a diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree index 432ea29bbdffa6c70076373426d8007ade48698a..016d95944bd5c04003fb025c5027facd1776c1d5 100644 GIT binary patch delta 998 zcmX@}mgUS_mJNQ42ARgCmPHl?`ld!^iKZszCP_w#76wU)si_8*Mh1z-28m{g78c3o zmPy8GCdsLW$(!pKnaR+#k!dXx$#zZN$P_=_-;|MO@=w+xa&*T`*1yU>nO9VJvm|>g zDm)K7hGFeDiGnd1QqF%mj(eZ-m;&cf)30(K!^_A0tuAOm--4E|Hl{ zf$LYviKLKccS5W9=EUY=^6cK6&>B@tp1qwvgf>U~R3gvb$spYlzgJk2V|V;^V6LfU F1OO1eFCG8@ delta 998 zcmX@}mgUS_mJNQ42FaEw<)-Oo`o_uTNv0NQX31&hsTP)rhUSSW2Ii?o25A-+28o6S zCdtOeW(MX4W}E96naR+#k!dXx$#zZN$P_=_-;|MO@=w+xa&*T`*1yU>nO9VJvm|>g zDm)K7hGFeDiGnd1QqF%mj(eZ-m;&cf)30(K!^_A0tuAOm--4E|Hl{ zf$LYviKLKccS5W9=EUY=^6cK6&>B@tp1qwvgf>U~R3gvb$spYlzgJk2V|V;^V6LfU F1OSN;C4K+^ diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle index df411c7706ed3f3c14d2520bbe45047847aa3bb9..e221920a785c8e680631994a2d874087961651e9 100644 GIT binary patch delta 374871 zcmb@vb$C=q)Ia{c>yh28WV4p_O-O(UQXGQ21TPj6+zB+eBv4!f7Z{ubDHIJlMT)xy zclYAOt^8)@&R*L0d4GTG@b-D=J?HMsXO7Oyxt4_&9x890f2hn}k=V6ZztVk6r6>GU zv_#rZ#Y+}%S2V3uV!O1C9TQ6zO-w76m{uaKRH^nQOSdc5sd)R2McPlh)KN+MY^=(i zva7mwY2UtEx3;A>+MK^g8Wt^HrdXn@P$K$o#w2CCv|l=Q=-PM2q|yl)J=5{O-hqzC z-*lS;7LqUE=^A*a-JVF`>E|g9vI`ebaxQS&Q^CV;?BL+{6|it7;6C2L#Gi>P$XDv( z;olb2^H~KIU}_0{gmlKFBH&e;s~DeFpdc3uimZJY%O|vVb51_3mCe+vL&t$LCTZLE z=$O{4M_Rjt-u*IqbnV!8dUQK9M(*n(E^Y?D{(=F#oNG67GeGM7rgq?(@KAwZBS4?x zt<4c?v2WkBUfpL*l6S?Jyrz|v_j;rF)K(R_!XQl@TNrh5fQH4x9sI&5JKrd!4Bs%p z%_Si*VnDJY*2JIgT4L7Q2XZvb?sQKPkoz*ziN=oQcST2n#V<3>e4DmPt{`~Tr9wf} zW_>w(44)Pq0bV}KRPYVjJNStObo|lQC3tDuXx@{8P2k|qc8%rpyn3z(Xt-EcgwHMD z0gEl&Jd#NkklVL~g`ZZaEXaPAY0*y7h$KebadKO)A)=MR_hXp00Iy zPihQEEN_qH_jtn*zvren}vPm;ris$*}S3 z;*qe0igF%)eY^u?pL!@m9le<0*A`YzPx0^@Qi}3P9Tfb?E``vzZq5sGxkg?-J;A~` zfcrrY2cOrz5ML=K9+|+suVZ2UQIwU7Mvb^=!7)C`BokOaT4Lf|2+ETZOFfZa(8%xZ zqU9II6z0zq^lUtykWh%<>ouSzi70a7w$wKvSp>Tx!p`S*wSe@^BV<))OwxbXhDOFO z|4X#^oj&7#8ga%XK|nquHG%IPQ;ZWerVja6bfKZCPZoA`aZ#Y*wfY6Z!bGQZ>e?ft zBmb&{0qeniH#zEN_`Tj1L!xBfJz|&g?t9mggFL~lrTr}J+L=)aQd^BI+6ySKV5jY zHwNf*k1TjNP~`bT5BjAXzqf!DH6)3@B-Qpk9KgxPD3i`~Er`qH!+IA`%}!S`$*!naO{p!v&tyV`J|7HRL~%wT=eBQr^Inb*l5=p4iMO)%kX124}< zX|*#Z{dZbGAsPPfDM^TE5pMn!fjdjM zqzhr>x0{69@kUR$7la#C*+{rOge&2RAlxd#g?uv;ZZqOCCn%FN7Q#*^KNcBdCEPv2 zeYg@$xWj}C3yUG#?}WQnSm0(7uEo(r@>?F^mL(<;ZaLwO$SM+U8R0I4S0>yH%w;M; zNr7ETe%vxs;7$;(TufEcbPeG$hEyZmCc>>QQj2i;gxhVZO}LeWla~>=4}@#dy)OA} zHQ^4Gtw*@6gv*TCCa{YMn_Im;`SCj8RI?ip?oYy9eb|z4TL@R#+L~~G6K+Giz`Z0K ztS)d530GlM3Te5QaE{1!gqu&e1)0^`6ZSY^tMBeWxSfRSe^20U5w6*pj^wvbg!`7$ ziEujzr`alS&k479VOR3oTf#j)+=Fnl3HNqTFTy<~oNRJtI$@6zHg2WBT_xP$w;ANO zH-x)1wjbf{6E3_|f5NRL-0%hh_XpvwCk!CJZ6KWC@Ib=tBAmLyAi^yo+#gK`WfFED zVe_jFCfsAfZFo9_aB~Q^V(71g`-^aSdj#$_;ToP4xZQ+nb3ok?XVWaggnIu8zFH>waRFiHS`dr43xtbkcb;(X z2sbWgQV0Co0m6mE#dX9SAl$9IRh=<+kZ>a}3EU;ZjcIhM3;yjN!d)D;{uj)xBizR+ zhcc=~@+E(%$sOafekrQNbN|#CG5pnEit@gB9%**BZG7%J6Q4Ifh?-;ZyRjk zitz4HE2&yPlVa+j+OB#nZ@zWz`)mFDkcCV-c9-JyKOn9BU?iDi*E zZX(2(k?~;nkb)unsFCH6Du?)KBT8_&#DEPX+pe1D|(oNOJjY>q1|ICw^OH@r;#+pIXezKl)cx*$W=<(;`9e1Y)>q%x1-@v=$V zPM?PK$A+mkuOz|kG$9Siyk()k8On2ojx^^z&2VF&HGI%L*kxAp-~vOnm(-xiZ30%W`98x+@b~#J5)?!Dq6laY%0nD}I_yv` zmPAhK;vzxVTzKimCdgkRoYKWCOk ziC1ZALXx#O;c(`5%&?-7^Z4Ol1b=jP1aF+(jb!#Fkj9so(_V>JaNI9p?oWK5Ek*g< zIc?CmcqR9v;hgyQ+|XgO4b2~3f?i}o2b{{N2VU)Qk5`+mz*g2C5{h)fOFlY>awK=Y z8MqGCkKl*Qw{s`>m-A!MY!Jt-A(Ud1mUra@^EVfU@b2GZxdgt?3OmSon5@T!!Mgzr zj10qdBeAnCPsfj27)AuZ^NDwkz= ziTL=*fO!7JiemiY1@TCcPyCeMTk<_u_u~zd8>0Btta5>z^$(SNBAS!!t8~EW>88ZV zv;$2}CUnNhJtslx|Bc_hvI@U$RSE7b|6tWWL3qo8B>8uIBn(0`{_nBaBU}sO?V)IC z!bv+2C2Zq0Q8e7}{st>?I;Xz4Xq6rx%RgOP1jU7wJBrjv@zFPr5CAzASL2g@=P$KsnRnppAwTZ8?7W@e!!WC4TtHk=#E#cWMyG zd2lI+Z+yN#U+?@NP}hQl_;xCTzj$+Kr{YhYw{u_kh8K!(@5%ok;r}b?_61+_vjkIlWz{*- z;0}NNLW%2nL(EPK<5tU=5HmnhB~At7i*t=tK+(Ra z5zZ>dO`~#_s>rd$at1`GEvTHoSK(+}LwR*>rQln*yAHQYCInV?GIv235e{g=T@i8- zwrj=RqYav;qHY1J2a9y#uFHs0U_fWiz~_9m!sPDUOhv#@06`4}r%E+cK+u;M)}c&;^#G@~b2iv!4)>=`a7)5wPR1s94~VcJH89x4 zB~lMu+RxRef|fkOHJ~IoA(HP;i)8Z)+!NZM@l|dd_3YE@+%u)1^$^TTwN*HA*rg zU_7$t0Ii-&!r+n75~UDhWbSgb}vgksPE5)9y)*Q?l0s ziAFk$x9j1Q$C7aj-o=^#q=^$OdnOqguq7bsAB&ueBm@)xmW&A;pwerJJ@AkKXx#cB z^+<*a#C|gyK=Q%%!Jy1aX&BUgk+idd3TxA}mlqdW>o*EhIfdqxeB-=}D~+#RqCjThRtZ>Pv?Ukpp)(l9r&Pvboeov!hrG zX={2mr;T(q&4V@_r9;ugf>YwJZqlDASmR#OZ!{&^r%U%yvSeTBHA?z2qz@?>*x8$8LBo}}c_5z^hX!MxGZ5md6cankWrhC`FZvn{fvr)k@=Go3R)3t-r`2oY? zP)vvB!_qYR4XQeY4C763os%Eu8 zjf>)<_V;CGFT`J!{?01FfE&_9)CL7^OBXTPpwk^RO*8gAa!=ZiO3>gTvlm7^k{)1{ z;PGG5LLjr1b*728rjAMqp6^nF^FHuQ0%pGFiF3a_FE&_ zc0oGWx0&n^JzKl2tSAMW+(~wZf~9wn)uy8z>n4k%WKd5TE`2fB#a^-@3}ybm_iPE! z(O-t^H;h+(u{R3)Yh_O<*>%0lMaj7vW#uUOaI@?kMR*Qn$LQIk+hiT7tt#%6>FC*ZyJZek zEn|779h8-!l3hD03!>z^ld>S%O?yUmnriX)MVY@^i@Yk^K*4Tamz|?+_urO%qB5Mi zD=Uw#Pv9Z!&t&f?g73A=zZBkjBO5@wb@?D$6D+iimdbBa1hZWJl9Cpsd>w6jIat1z zo?RU(Pp9(6>EvG8AT3<(FCGg;$j{TZIvDNe~uK$;D++A)Gp>G^6d;4AJ8}rKwkapD$v%`-wJjetkMnMXesApBsIb27IFi~ z8Y#6xcU$=(Ha!G<*t@;FFE!fVo#g)XJKjY;j9RK?Pq~6Ru1{Zir5{j62~L##O9d+q zlur=!fH#NABYqeVAV39p?hIAHsUzk7K$njZmAN@qRHo-7xtc07ahlx0kn_Ydi52@# zfm>$F3(AG5^6dh7{10HM;ku>rh13fvD@4Idt&}HGFN|9&Z%+mPvQa*RhF|V>`7PRD z`fmAaHd8hPd8=hwklbXD66Bi-8DQced3Up*Sl&%}Q?`ZmJE|IlKasoP{yXx@R94eN z`FNTi>mSS4(X)M?%TLfrv%yz+jSwN1ma7z5G{Z_670>C}l5RzJ8`EZ?0my$V*MXgN zh6DrGA-NW0pEoxI3(6}F2^tTopjgN1N`hQrZ$&jcP)*^Vd~<6m@C_V12Trc7Xi1&; zp@AYY@cV6eNo@csbiE%6lA9@H;Ez57W`a^p6_XiFfsr6WD#QuOXE#^Kf&<4Q>)^{_ z17^d??G$+oQRoM#2Jd!Jgh|ll$-HugaH#CANDoF#+D212oYYUzjbWzqLl*{M_JByZ zXpmwyLqPur#RDGbDfzM=-hE|KVuu9;N@3$H2lFlnjc-Y~{EK`6E1NsqESm9uuV z@-ek#)fnY_laMNPD=O2e$S0~P*U|=)YA9z@@~2u#|Gx5lZRH)oZ*;lvqAdhKitL-wetz)^^_RtEk?f8|--L!4X^}U7709qAPRB^4a)3e%DFWB%AQubDcSRaauSI zdieL>%11O8p1e_hpn3BqR8@>hTS~9;=goVA%AYqmMwLHr+L=_%sj-hbRK?kD9tbx* z;N$`-y_uUU+*W#+q++<5fgiDf$1AEnGStz1hO6R1R-W1kTrj90>`_I9?(y7p3Ae9}l&3abMKj26}ZXRIodQG(#{s!MXgjjyMu(iu@+PF3|5 zQUj#VP~`@U0xQi_wUr8Dte>rFA20w;jW%HIJXI)MGe|JXg+dZ% zjdD~8RJrL(RSb`35!t#dSA~L=6G95Zoy$}OsDST&Q=!Xccy<9VR;n1T>JtIYt5vA* zCMdtg;4(6O1^`~k!MTy(3>dB}_ zfjz1Z)EbEgRO1*?svT4nV<-%fSChb>b%xkL`q3mg7-$btgJ*SysKHdqT4BW#s)eCw z?c{utaQOPNDmL&r4ChP|D8IL&2F|&zdLd{F=iFAI+fQV{OS`K&Em#s(d7w(BYv0np zR65%B;4@W4Ve){c*DCa+5b5^fjS4-fLdfI~s_kllj8p`*ix9}EkwL9!x6ke%sa`l6 zRy?RlkU(aX4|-1#hE_stnXI1x^3zr5F|ry#XOz@XU~Bat1ITNIPIqV!l=LIG0O2ZU z8G;oswrS8ZVJU_;+Xg+-3MyPm5AvHar*Dwoj3fI6ITb>K4MT#qgb3uIu|aiI0(pLF z(3`+N@PIv;08%%pg22m|pA@j1#*({4)xPZ(RlB_} z$VekZc{u1WjhkCXgZ`ptubv88L7h6|Y|u9fw)9$1d+K%bgP=M}!3-JCf(~gIy`KF^ zWyapI!sB0px`g@7P%AXJG+Qbr06(O1iy`<3)!|A+a2yR{b7ZjJ*SGA!eqZl(1^W&1 z$QSH4$esehYiPI3xZpP$LElm3f`?FxX_JD}XgH0m75s^Y$mFKMSE$7XrUauqA|$=) zr3UY&q@!K%8cJSkADm0cDIJ3svMQaQsWQREJ%XFkaaH|-R~BXT2?@=fJAyU9x;xqc z%dQA+isBPa+Yp=)^aGF(K~A97RP7CZMW>qJ{NQpl4V&Kz-cDo6`$)9ghsVJOsD(~G z2}T7EvF7Aw!B!fpf4mA#VYuQ!(s1|JkWi36Llq5|e+-^3#$WxQkXC`$mqKQLhb*HB z$)^Em(}$c3C6aG(hG^-m;*JXON7|ROaTaWk_+l^|zQhypB+3U$N5hL9)@*gc_kkT;U62^YCT z*Hgcw6$-6FO|ZFW=uL+86M3LNNI0Eg#!n0Of%{5^ZlSK2P&)Jk^=_BSq5GM=TcviW z-@Bjcg!(h}Y0JUXJYSZFl$So4vg{`@UGD%799k};wF z{M|EFB(uhgWYWaY+7w}Kc4${tpTFF!@&PnmT4B!Y(4jiN=kJ41>iLwFp{BqMCCr`# z98H{RC|?))h0dP(jiQ4)ZTr7)aKM}W`0>KfpV525qKX|hL{dMNAG059hd;U1teMn|g8 z(*-Eht7dt2U(6PWvZVl>GW*A>{fW|~sG2=d63VFkiSnSV+Mg(wYOA+Xp_evP`wJ3n zbG1KFTBNA`iSn(T+CTSg=&bf9%Gj=If1RQ{kSUFafG@!Re0rCwSULjcf8uaV{I}??ccEu&Q|;P z5si7Z|FUP{+3L~MH+$!*m(WS9{}Q#H>ON}3yR;Cw|@8i*Ss(Sx#eG-w&Tq;6g$&`<9rfN9S%O|yD9 z93ZKVCK#@|tZpd%Q54YQx_aIZgY4>cUkAtCP+yfI`*paZUi*K72oPe#tUshGxc;tM zCjY;{G=Rh>Y8fo|Slx&FKf!|tZ>1V(jjn?j;mZ%|Q5x};X4M2YFGUpwu#x5mLR>}JSa5oOjf_M{u=g-PJ?@m%2(gT+&uENy>cziS0GZ1D^>f zmIL8V z;kr1*a=>T#np=Ta!9eWa@vJ5VyL10}O%9tyP&7N>mkXK*wiLzL;D9SHYuaNmG}knN z-(C5xO^xfC-9KWDMK?9Iu^4~dW_8MzyPCg$M1l$TH9fHeA0BF!{D41-J=XYs#2+u7 zXewec5}#`rZYzn=)$WC+Ia`uDdm}k%h?^M~BZ9VSVxNX+dFurzJ%8U(7mkUv=0_JhDm~##{{;MnOcRc)ZZ`fK! z?ceyqGJe{UEUA9kHJ1b#0P)&v{jpJL3zm;5Pg6~pBe z51}Z9K1>whXVJ#VM^B^(mz>{^tZm>yE2cW)Mu&NVh zgmvqO%?y0g3w6!HSl**U-CBbi?<}>Tu0>cL!*y@0IY^B%go3XZeOB16W7rF{Zxe0; zcj^{wSyQg`VwPmr)xt&59iQ$ z_;IS1F-z(|gfc(y$i!Q{IzZd!ei$q(pO z5vGReXSEv;B=la??#5Ae?~?XpFgk9$t6h(_Xt3iG?JtbU0kSkB_O{_fImq7Ibw= zX@l^7OU>FyzgDgOqo1^{uCQM}@;lM5PhH(q;?`u{2&N>h3(?s7u|E463;IlMD(GWu zp?l1>Q>g=-ORvk}@D$xVtER5>HQs9bD)Pd!>I+lm&=+$@)?6O!FBS692Wx8{6?mfQL9|;adZqD-Io>8uZO^ z`>GcGa~toa8NIIOk)gNu6wM(nF!6YLzO-}WQJr1pMb zg%5`7*G7QdS;cg4*-U*=sX#uOt)D^3$@BH`65;Ieh5AC2RORZEDEadeeHtZ?F4O06 zLfeVU^^l%zyi%V_!Q^Z8W$4-DJbf=pp4uRG%ig4)LlMes(VwIZEL-)*DZ;xQ`VRDL z_g(tdl)SV@KaP@p_UpS+^2tFFclBXWoQz}o910e7N)#vkv?$JtvtqZr^P)H{FX~TG z+%i}6=t&=9GusV)H+pu~ExlWUd_!J!JAOxxp2Q(de!j1NLtD3bte-;9etDvQK*`t7 z_2^DMX}jU2eiJ1J{4EacexqMV&*s0?FQDY`_xh@wf4qwy^ff75;%EI7I^L;&^fC0T ziZh4~lN!*=jAXpx3IqBy4SD(Qja}{F@{+PR8$l#AT zU%ZH!nkcG}SJdG5cdefcm8lura7_tAIXdj8k_I$W5(&1IHlUl#gzQt+Pyw}u$Vr{3 ziUpDk{_wn9+29XPUo}HZ+F)u8gFmX1YZ?48nNZi@kIB$v(R3pk8T`rcx{26rQ!|4< z8HTnn_@ghbjX2uCw&G}a)5LB|+Z)zW8QOO;_?;^0X7D?;Y!A^Q6MGrjD+C?CWf)rc z4Gxia(8o84?k@)#QYo1>#L&jygg6jhA8L?LpS&C;`lQ%6(I=NCias$-5q;7)Ta;kQ zbWwHVOi_mmvqaSw&Jhiryg*ca-Xc+T#bUABp(UcBfBQ`|^sg&LLw{H;j@E6R*said zakSSPMME!z;%G0nilepMA$GIv6b*f4w`k}=`$a=GD63AyIjMst%@=7Hm`cw{S(5|WxeV2of?W-Hn$KeQ>T+?`z zwk_1y*qpkBZ(;P?H@J-vJ()<_PVHb!q+t1-jOc?Pejo=wE=ZpEbUxZSS8q z`j^Q{myG9V+vuxC^sy5HX1;Dj&wvy1@eLz-NrRBJ?;595<)1w_PNLl=yfP+JQ$P7^ z%%C<)|7JwbmXmI+R1qgQ(7OlPsFeV6DPKf;im(|+jL%pfo?4Zg^=o>OlYe@$PJ}TXsbfVS7l6S`$NdM@+P#~BIK|n6WS3F z@{r*+i;{2Zi`_OSo6z={G-%t%gtj7t ztkBeiwjzYIwJ@Pw93it?nb7u_kYC%F&~}NCvr$gk0?&&FDA4-Cf(ZhH=*q@AuA3xq3tmtw+}TrCCEc$d;DU!32l$b z`5_}rXnRb^tkI?^^sIB732l$b*@%fIv^^%|`AH^})P$TnMI5?nwh3h?Is1B=32l!F znai8d_Lz?MX@;pLg=-8&U?0{GQFnv@C z=kMytPsdyoO#BC_QPb@J7-O3 zwI$@@^Cq+(CS;3?CbS=>Rzh7GQF+6zib1*Oh8P8*w@qkMOgav@D@H-7`(h-T9*L3o z=80&quFu63xcXA;Hvexi<~qC;W6tLZ_CD{D4LXh{G){HjAg4IES(X526^k#ozo(MOioiWj?g2{|_#?(98%x1JHCg)GM z%>L}x`po|9-(0{PPdmPeHT$zav9KsXR=haK--%{__HQg|_GkZ)pUnR3k1K8VXaB&m zX1}5DmKVD%O)~pa{@2Q8f6Av;H~Ulmd`)q*8g;~Os(RvR`3=ne@ zi`_o7F#8P+TZ@KnmMR*0YCBQQn;py2Ja7s@z zdJ*G#+?)^?G{B5rXGdfx+&su!E%5mq7R)rqaoj?XJGx6vkdtL*cr1b}2}m*>&`x@w z0`o?g9|U~u1+E`y9?Ni30Cj5uc8oSNd{(p4baMycdA*Ng%;nY}85r7&10zT%}dOq0tNsGAOluhYL+snkNc8Gg4%V@)XaQY{4_Ko!*QH?#a{BP@{s5P8t-VsldpXpnI~1SCB+7X`}0=I4xRU=TRv zi1|3HVIQ3`I~i^Y5feN*Yt{kBTXWInm(H45%|Jjt+AZC{wsYun`bRFAozQT>{Ep$y z>xg&ews7`!GunQWNQt;5M#_>qOp$^B!HWCndFS{`IlJ( z>S!$`z@W$GhYUf2Kp+CS#u6VyfPSd*>}IxD;E-2lDNCfhGiQK;kw}A|-@na8qGHcaFcA!(}8YW?r(OTf9rnR+uTZG>`yok(PRJOKxHK`inRQ zL}PSf3y<$YYTzA}rLi#8!s?-x&TPgefWbg(umt1Tr40BZ{)Qe*d~B`>u4*j|-#zqe zNior6(+&2}Te`4C6cul3ghdX!nl1JpP|^WWSS2u;9irGc%y7dRO?-`E!DtKm^e3KU z!1ySCINgu4RH5rq=|l^|-C_bATg1|XMI|H__y{ackZ{VPg*A#<+${dV$~`G%NfEYE zuw{A6WsWShuxgS8{gDd1>Y~4yWkJ`l2^mtwg8sM+A$L}_ETUwu>K1fCoSdy#)3Tb9 zUu%hN^XiCgQ|npK^+M7%wt;1jS0F$4wV?M5vnICG!_yg-1uU~CR^`#5@KgnM4>mEw$Nx#!iluIZf(33XeY>aL97{#IuUa_Y!tfRK|A0E+%>|Z4 z6mM#-h2aZE{{!#TVoME{H5gk7sj3DG6Rh>&o8K)BSY~gG@;TQM3NF_PD+c8oEelwa zf}s4?iL(OqxNA{P2`cTj6bI=O6lLJk&6XAnZx?efEKPv+l!d$*)f-maZRryvOa=q@ z_$PzcM=Vw8WKi?CrB}eq9`FyMfi+K9sxl~#0U(m{j$DX(>3K^k&4rB1mMN4p+_3aw z$OXT2PO#8sO#w&#wxAc^erJF24dL|{is+X2EQ4tn-+yRfsK*i1a2WI0a*xes!{N3k z7Jm^}_PHhSzbgun1ozl%nhFx*ti|E)Z_xC}6fy)r0nt;|dl{a8LV`C1DV$Xev}LWu zU`L5HhfR#{C~|b~+gb-clvyt`WZJY;YZH(?=)Mxx3A5Hv2)p{_M(Y}yS{=>SiGkbc zJL(XNbqIroA^<=Dp4#*&C^#!=KAkFWy~37HEnv}_)><@`vg?bfRKFo( zDzyMznjj6+8(Dj^X-ELrLTHG%W*9h7zPVKiuQjux+hKUe2b#BFOrT%k(w3~*@+&Oc z#yXfvaIh^yYCse^uh~+dBeZm4jkAtmL4vgbJm1ASnIYV{{?;DwpI@wJ0}la+`c`Im z8_0jj-U4rpux87+EBMaz-+`|N-)}plf(K#X$XM%}z(I6ht*vLCid4V`hb_Uu9Ruw2y(~6#ZxM%AI z`>eCB#=%2AWKeP~I-S+ZW`%7xS<$=RSHQ|GR)!B5AZ5uw8h9L9mj<2_zz^UA^)Fbf zfQlYzQ8;g#wLP*M{Pw4{lpGyP?X${osFgop)dZts-BZ>=LFhQ}l5kvl+j^ei>WY78 zh8daonEb)V9uKV#k&K|pGb_WZst9K^gwL&C*(^64fCQNV_IqWG#r9eB+IomX$Ifr9 z=w+BIF#f$&f@Lf6#hMpz{6Qq!9}Jfxuu7YU+cx8vDlWCbfCt7QF&gH`Y%DKNlfJi< zwyk*hxxuzQjKga~ZFkswG8!(|*wCA2SHNnW?GWSeF$P;QqwQf_gzX8|-eR%sWgI@+ zYJ0<`J&nj}cH0yjf#)2yOL)#%%le)cLsSaZv=ry2{w8ms(v9`E$o=t z@wUM8L71s9B1DMSe?X+*f4wOuFKN?bktz*OF~YbqwsUyilT@&w7eTJT<4HD>$9yGQ zb%uQs%7xA_y*g_kcLu1h4zzh-GlKlTO3A_IrM5U&sg`Yzn#q-&!OLN`U~tG(C{lRG zvFgvZaI8CdL$hZqTW7Y2_v7C2Y2oM;+j?xllI?8h4b3a?Lwg%~L-Pte)XO$mfsRf4 z*<=hC>4+tf^e04d&y290_yKXz_o_$R!tu7L<9Hi-CG!d_I??tMmaxk-n-8yflV{ng zGsoCQTM)?4v4z0l^KG@*5*a0r9|hcP?Xm*&hOiQ>?&eW}1{=^D7u)m>?f*PiXp^lb z)GxB3k8M$}e#)`EQX*}huCT?i?ddv0B*!v5pOG->w#Bxd)$RzWAXs+}eVJRn9R+sS zWNR3dY_plMxox{_=de3o?z7GM0kcN{t=Xo8Wscfr_(KJBIbx$*E$vBLUmTskp0+VO zc|;~4*!H3gJr78&vG0Y|E|T@aG3Mi>UFO`+ki# zKDKROi$991cxr2b{ge92mW%x(d1pI-)8+7c+g^N}_|f(U+y1!q=2MYT9JfMv&aAv7 za&h3Z+V_fT>cR&h!siFaCKpTu$?=gYctsi+7Pxo#v_df;IWe*(9IuF6E#X$c5Z?!0j6y-=kMiuM?G2k0i_Ax^ z0#PL*1AooZuT5|GTgk}Ffv3~={?*Gw9>Zej%SUcy_D*j&y+S0~w5)*3Dn&BkEA)}W@zLEUvMxUM>K8cUHM`py5h0|HoP2J z37en47C8-{kGdThh50)7BI}}&;q1pkyX8+JtKi?Cyoij#=XGx*&jo(@Nl?#euMfUu z-H#Bziy#NzOm;2Y#@UTH-^oRVK@z(GSz7Y?>pz{l>P zcC-MjfJHR+U$N4gb@nl=6NJ;=6@D?1zV?g?HBPm!LOfoFHq-13l>Y zJgyjsDC}VP1DLM{JBH)0uWLYo)lLmejdNUJQ1!nkvl1N)e}@ratQGDH^(o?T$v|pp zy$&q?$#FmMG(%8ZV9%0{<_xnI>N^{JwZ+=t@+3zwHMd{bslKS|P)P-uRLPFOYd0X^ zHg;&>*oKa^Y&M<^zczL#87|QI2X}%etsM+&DeAvgs1;kJIOOPiX4p5q+dK9!`X-{I zL&Gp{Au+bV;4Y4x5!+1qiEO3=Bl;{c-s`V0rlx3Gx~Gy@#h zs05z}ISvLa0YoTIS=HY++;N)@zIlY>P{4!ZRM-OB{pwi6up+!#;MgWa;87DM2^)22IaQA3QJ>DbF)2#it!I9P5bAw%qik#ikqs082UGe`gt z%5YZo({dg8bnwbc9XlA+f4R)jI`GO5Ai~PgonsqEW(Bxty(5Wbj$GiF z170q=AqN#3N6&y2w>!cqcE>+O?8IG;7={b=NLq@WKk9xc=zXWK1-Klp47ls4ILf`_ z;wbA*Iw~@bGV3DxBf6RUSSeSg;> zV;mbK@;CJG!gEJ!mUS1KDE*5g6y)R;wt}y%UE|>I?;Y1={uOxM7so5M2}8i0l^Lyu zy(G@&!ZHpo$eq=M`2$@%aZU()D~@Au4$Ra#bEH&8Fgwg?0IpD{6{dwdJJNwJnw)Z$ z^&T4pp@)OrB?>6uE{pRxb?5v@=PByLp$=zn7Gn=~q=USv2?L?DfU^aQRWU$zm8hRU z$uel$m{eFa>+%HWpVX}ViaW1S8?-Cs9Ke$0c$m~KQL*5Q%u^oTOmeQ&GR!j+B?122 z(3wt!yWG^7rV*riU z$?Aih4q;aW`(-*WQKeIUbv9xQD{ynHQvox_JM*Z|D^7Cy^Hw!Q6ztSgXAbKKXsT1f zq*>0Dbc9c{o#R=tZnW~C@5H3Wp|2<|a%NM&u*J@8A>4W4s+Vr9b9vyN6H6%m*63hZ zV4YLXvRK`7W`OJKoeb9@Ufpvp6>5^yjn1c1aOy)>bjKe$uLiCPf?5s3w>sajq`^Js z6fot2Qw2}%c6!yo_-j`qRGf55sYKS(PL_&6pH=GC96j}`{-F9xG5-9FDw+9?5 z5QWMBwm4r6t3Gm~9U+O+0#BTK0v;TYINkr$S%Ou93oo1}s05q-cCyrG*a69JoW&Vt zqiI82Q_<5iF1Ju44E*H$Lnc_G(l?f1@Z-g9zVG_T5D;R2cu?xPN6oTb;rcUh$B0nN zQ&lbo=ZO7b%@7y*TZbe@3Tl2x6nHAkwd04(@{8Vekjhfq=wf&p5~~G~$A>V?tHdPv zHdiRfx>C>z(T;qaAPd~;a-nhpn+T5bxS|=#34)#T3O!Ae8tLVsq_7K>69`ZbW8z(C zk3q2iPH?e2%T2KJ*4_|y`T*_p&%w=Q=>R%-rMwGm_sIb3l3ZxpK*#}=M3pL65zhu! zbA4lZe+1{*@OrLrc&&kJ3pLu3MlSTWFBz;~6LEy{&0H+sg(EuNoOoXi{<-M0KvWN; z%E`h9`lY$h?UVN|D_qph70;RperSboA$(a!*8_zh`}`iRU)iRfB_MOS*8t_exRy$V zMS5p{*JNRlhOGy?7|H;M?9h)Qm@-{x-%srOKFbx!uX-`C$BQvDbD|g> zsgqs3St`JL&hA1@ac8!x2}@0Z0dP$L6$@Rccp{1p&2^!2f{?X;bD?sAkR?~TdNSlY zTHCt+tDIQ2-nERSoS-NPFnpg2l@mmwnTK4coFJq!--XHvLVmj_ruvb~E>upCv)NZ& zsGK0={yQ#IP7t!{Ll-J12q}5$LgfS@i@b87a)OZ7x2~FOafz11o#?v8@bS(i@W^ME zhUV*|uVRwseG{b{z`3Wf4uj@9^pbHiPb5HfKu_3I<@VCC#s|9@lWOX$srQBALI7>4 z6G5jK+&n`TpfDf>ffL@dxfL9DQYcZ1+TA}=qJZ6hK2XCt4mZO?ZJ=Q}cLvDsx3KcmV>v2Dpf-7~l>cEI-_qD(`8whU=EED6t%aCPA7XVP9QVzNj z-3cmWUUJv_QKB3D74vUyE1Xu!eL*5fH?XWb@X7_E5m+{3sM`W)N^*Y{Oar5;xY4(X zv6W!qn(polQx--92vLXKmI7+dcZY+iZa2g1NgVj!dIq9;W)t<|*~V^$EhF(_&!%pc zQik*m``L{acM?YzTDlpEDT30wwfh*uCXw_twRNMf{Spbjq`CJpM)JJ&ZiadWA_<04 z2TbYeW+6?riAlhmFM=TACo6*jC(&rq>A!@EcC^77(d>9hzg{b zKH1%rVHzevWlwjL;-&{IKGVIMrHsMxvu>J;lriY>=dHd*u+Sp+W`-#a z3j}jkx{ZN9WC@5e{w+JYcY+_>kU;l130+;_qOI;WETsTe&yPz>f%7}us1zW}!ts6X zAuM~LGw!8ADG-0uJxJ=8amNYwRJ!!EKJ8{I2F|$qQv~w`H!22*lpij-T@2eGVpHT= zbSM8#;qI`>Eq4%uXV4PZ1LQTcTS4l$m^Lu|sk=GbLi!yg9H6&<6cD^}qbh)CsDA54 zRRAHcesrTMfRKm3y3-jFgGgL)y{jWEE%&TvC;q~!2mR3rBFs-V4=Ml%x!mhP z1ppxz74o23pO9VSJg5L5WMYB`6##_nP{M->07Bj?<3R-gAr~ikPys;5(^WmF03hVH znw~mr(U=Xgt_cNz6)gHIrZFtpz~f^u9SNCT*Ka66=JKfSaB?$GAy$-cH*ct5RvXW1 zhDixIAOoV^vW?>|3VZkeynUqx4Z3(3zI!F!Z283UR`(hUvE)w{Lcz)nvW9OEko*0G+?DjD4P_X=*Qv=dJdv&0}5l;$G4)dUQ z+K+e`vKhgR2jB8N^o`I)S)SjdVAr*#IuJCQ z09#J>T$FPgg^HrfB&4S+$76-R@Sau@LC%{qJq#b&@(-hd;lMMJVGG~~KuhEnj}{J} zWJ)CV-b8J@ih=h^L;VG+s))VvHAp7YmR7?fHkzI22&bThD%KoRJ?qo0x)T0z|sm z*d|me#`7;8RQ!_3Z}T@+F|dhaVO`G4a+}2OqgePv>P7byi7a^vFTb8!rhAYDj>R7vYf)2og(aAm$%{% zz#73RMZxiKNUSJwP9YJydz`l}D|UJ@Z-j97rBw+LJFAoz-F+eEXj;aL?!FMxUQQhG zQF-xfUXnMH;qD8uS4vG}uNSqv=m`-rLS9`Fc3^!k+G>)sNy*;Utg{31$iJ`V-fiYZ zS91xfsfCy24Q(Q~_^gO-O?iV@-Vog#Mf{dN%?* z?1wf~cr^#5Tg6NQYcsrYAh}$D!mwLk?=87th*kr=%>p+sM24s!N?ZI_?*L(w1$T_` zGL(e?!7UV?5}P^Bi=GlAKI%KsiwZIDh+baFY~)J;F$$jnylmuSs8$ftaB%BsR4i;B@8gOlFni1~u`o2j7VNON+0>8fj5Y`xw zd^bt~i^urNFzls70MM(EPX}_kM43V6-KaJ&ub{8GU?-@I_g$3{DdFp)J}4K+?WKGN zIf0yB#)m%Xjcp36mGhwmg^-~Ye6I!Npth243q`nCP3*R|hS)8wme?((j_(ES7E|9h zf#GGv|LDZ7S zSw5DRaRduhi4Fy+uwX&>aJ280Fdu-&6WG%V^URa{<4vEDZCf}TAeeBNf5)e%+mY7~(UL}74^KVzt)%3HCqDF`Ar1)G`k4>? zy=Fo>U-(uDVFItd@_dMpm~-;-N6!5QYA-6_I|0F{&@Ca9QP} zH6SdpkQKhPMeUJ+)i05R2VGHv=ngU2hrWx$x~yRAgtCRy&=DPlN>U>1=a?u|k`nT2 z!Kjyvu?A9SMn}M@MWeiIB?J0&AAa*`Plc#b40kG#D>_5;l;T`!q_Q=lQ1ME13aT50 zidRCuYY!vB`P7S zW=8q9_aP&qP>D*;CXI_iB`P6zO^QM#Dj|nWi$WzTAy?0eLM198XU>g^VGYAu`)(@m zvx)T9F_;j=L<>+r_s= zF_eJ*dyxfUy=_tG!zY*F>YY)`W!#g074iG_MKKof5Pds>RHPco5lY4oGd}(?I9#s8p7D)DHkAJVAd8*O3`*hNy^7 z7c92rVbn&JD*E4`ELAi%7^J@ZfIE%8XLH1v(`xn%>YC(?$)Cm6|!m-v*tZLT^BF&r&gFa&)2P(ww z>)v`nC!J+AEeHytH7Jg!fV7Yx%9cG!BmioK2AvmG_IG&DVUqa>sI3t}r0JiHOM7h) zeue!Xru1P(U6B2uL8UquT%PHJDp6)7Hh^DagPsb#JWeQ|UX~^Wkw%9Smxvt=eM=`R zra*355bnTAO$!EO1mO-WPbOvsMcF@W0n}5v+#=w86C2pEWTJuT}U^xFO%QE(UvpoKvz*dbTt zVyL#*R=4q=+0^8^G^i7;ItIv7o(b!M3Mo^b!k_{2#jFh*f^gH0%UFL)5YBse^5u>o zocHkLt=&NbXv44p|C;wSI~4RKW!@w4#CcBye19Pb_vbjHoGU>%`Qga}w}Wui~Y^|^|txYd$qkag}dJ*>#X zrNgT9;0*)`=XDD1%bq(Z>afI0A#GWYtYE@KO#*>N*};K?I^_f~uN*q{qa}DC4P+i; zt%JpB)Hcn6hZEcvQke&ca@u~!1ekXd!3r?j5^(DioJ%+ZdN?^`91lRqns*Ey1b>jfdPAwBG$ zUjU*^ChWxU!aueQ@nz?$hi-t~mXJmEPyDzt08u(uCUr^U*~=~=9()swwI5j57xKG@ zJSNtO48`}noQgwsP*sBU;mS(1jfTL3Q6Z=7uMdWUkM8n(Z~xSg1cHO&06Y-KiCG~N zeC$^&2ly1WFMVamEW&ioiV^{zZwrZxwx5P%VNmiUqzkn|u`2ih=sw{7I^+Xwlrf&w zbq=Y(4mqlOL&*D(5JEFWMf3$6%;V>n?y;4Vx@WM~PKu@W>*Le>;!r(>bLr0youP}f zqN20IJt%fl=m@<)|4E9TV5p>cMVO*Uc^T%d!|rrcv}Ng@3c?fxK}LdC6~z@NRLvtF z#WjLf5P~5@HNeZ;lszPg1Dg|~kguq*^MhMfWNm9H8bNw3#UdBv#lv4j1t{=RocWzaoL@rKZaSxNA^8^Bt5DhPgxgP`IS za3?A(-;ssu6cyl1lw!W5oOaO)LSz9eWHr|QZ4-BR6r-SA=l_IKH&E{mgX0xUqU4dJ zAjIIXf$_Wi3k(X%X#c+;!DhuSiO}s7#nE!uJFDv(;10dgs4ZV^HC(J7nTo9vqktU6 zk#aFwuXTrYxr$RXeRY-v;EQ<}Lw6_j8+oCrTq8PG?u zL#nRx{S<`l+fO*1`zv11>_$iuGDxvhB655Pv91M9(J;k_auKONQh{GKyvNRsE}up^ zjaA&Xf7Ak~Ql->6ES;)2jYAITHeGR>aQ_5hMzOhb6_wb*<@NmKc<0j>D5|1l@Wmn- zx+hbdA&mMtbib1geOSpcm9u;W@60M0`tEAQC5iQ}wF>;&_dOW%m0VQjH}c=!8x%(g zMUFe5m?OaRVCdQR8_HtNNIn+q_ z8AWT!XgH(gXBGI7p?l!>qr#77ilZ1py}3f3?s9( zt}E=mt$0GEWkpGaC3h8gT~3Uz-0oS&SN}Xz_(*E?ey(tk#yAUxY z*YNM_rH$j14WuH5B`EW#kS}WBxoVth1;}?x9g=0pMyblR?hd!5PuUJ+hI}RQ)Eqh+ zytL*uTIt5R6cu*{{FLoC1S=kQR$0vY{SaCQZ@no;**{#u{1c&WVgv}H?1DGAe2E&*1J0v3)|M!KLTPK{R{qty>!{ZLNwa1Oj~-%eJpc0>-J zO;Zx)37>ExW+*9>mQMg@W+{(IMCQy@en(S=Y^=)pO3H@CCnDNKO6<*UvyV*qeR+s1 zU4qWOJOD9EmHQ{m25w%JWjHWP0;i>z4+Ku%H2}&uTPVUUvx%E*jNFSzO!Ti4=)gHXcJ7hsH6lp z!ICjb=Egv=o$~aCauQ*k7Eva#6;qWJLH)Dx4q*_;0s0*g32CN!Eho|we8Y?GtC<6O}avKU6y2#u+L=D;9XOvOH-;?B}_Y?$Dl6pB-^+whAa)RU^}x~ZY^{Dt8?s%{XH z5qh3xuRY{Ri+kfxBVp2l!u60RE!A6u;-uv^jBXu@la||%-60ewEw>@2OK3Oy%}}cM z9{)N}xV(4hJi-o?6(s`x7#&(H(ds@vw7(nv-k2Wx2ihk5I6w3|sV?c&~TdHEC!qq#X2~gldm_uJRAD8{axRu{Vn6R z@Jgj`AG$M-E`utuVrPXH%d6mCfeqW!s4^JOhXy-iNqC`3$M8)r*mxt(!Ze)0|@7`t4E zbiJ&DZy05=Yy)7+x`%nQoHt?OfM0O6ur%3fU`)-hPI4W>>%cG!3?mVS@9KtOU>Kep zRX+^RDDz};!?5ZiTd&}-GH3Z|tFW*}1jq6pauZltWEi1)LXeZv-eNUjh85z%j@xa9 zj9bF)_%O;TL7ulqQrHPm)kaeoVKsoGyflYVCgD8qy3{cI_7pd^r5RzTi8?6E3Zu-v zIfFsDVP%3rXG<7i5rCuAEDWQ}c6r{jjl;eY45l;-BkV166ith;XTqaKv-7{>l+-ljIzLb{o37PNf zZ7mMFL|7#GA1I>-hTRnP)?-N63Hx>RAG|scGA!(I`B}M*47(v%{WiMXtO8-{*f7G3 znf*t$K=`NKq>M$$NP)Jj?y}U zaa$Hc{L}50ZM{l&z7)<6&x#G^I%@5{k>TH*r8r>DsfALIQZ*_uvZQ^#N|^`$Ma!}1@HA2!zeL6WJ9sV_*il| z46o4eVZe}UVR(gxC!5{~izN@AFvf>GH@$H$tO~8R#H}}{7hxR;b5j&i+eb=aaogXTaJ=-!pK4;m@zNVlZcPlwOK&{c&m4}I-gt6( zdN^KsX^U~rXd_26n<8ycEzypK|=S9Mu$HT$$;_UyF@+Rn-=atIJIXjtQI>k zE4&HG&2X}{;NI+T45iNPVAH(tQo{Wt&ZYgDaDNW&?{bWz7@)g2<$9rPVyVmd{~c8u&A)v1f7lhhSXU=kqg z*cN_SX!5gN;iHB6LiUF1h5Gs(p{vhwGCY7~od_?YF$B#2NN6tOboj(-B$)9q=5Bb$ zN)Bak_i4B*q1*YiQgX=cQBBt6c{t%b+Tm2yOg8s*cpdogBK!t{g%yCX(1BTx${*&| zz6o#WjGAcnF1#X*!}rkoLwFIPqe2oJ`At1o=cwx9<4`8|uJ!y>Rh;k-O$`-c6I=$B zF792fsp?LXXd`>pNL7c$`K!DjvzF>4!M|}Dh(fI!em8GUufc1bGAl$#en*QBZ@ znc=z=)f2mm2ML1Ug=N7rRk2dZhjUb~>@Jych>dxw=QP{@9!d*Tze#$Z-dJ^?SbX=U zD#}6wrykZqwNEPkmsTqKZzI5~)~Yk;&e(wtsvAV|3p%M>C5X?uscsS>ihHP55r)ht z|3-FxhN`~2kKU-Cs*5WUnK4*(k3@unDPho173rR!V55Y&BUIj!L9QCD`lUQX=8skR z(i(-3WW#usuSDd~r1BBjGDVe47ywH316V&v5{D{tRFBJ}jDO~;ey252p^QHls47TA zoEWj4$~fzuEv9?~+-j>zc=NgHxzu3qFE1Al_+f?WV7U~s;7iq0-eIg&-6~JbbpA>; zmM|yc)^TXF>I-*b;l)?VDoba*d&7=BD#}+Ld7bn*ruuhOTir?3NW%1=L)|f}s>AAw zs=ZWz71^plXn9BVim>R!86+LTuqONi_W4g$e(q>xd;wwpvN_HXzA)J}0&iXkg5mBFpHm5PwB3~>YGLDs%T*#SIN3LZo|+qB`iAE+BdEVj|T)hOaSqCaF38bdT!SgwqyBys*GT;@6|LjKLu z$eb%jMl>g^&~O#M*zmk6G)al5E_H~K4Y0K22)q#3N#zflvLZ65%I3I_awAToE?}D_ z0#759!KlKBW74mwam4g0L?uUTd|pKk$aB7PL{VMO$fh?AyUJ&Wa{q6S3Z!G|*V zb6`Xc)%o$FcqksjkO(}VSq7DdN8k~cGI%;7qBn73Ryr<1EFf;15Yde00EPla3Vuc# z2o06bwG3Yrf#+Jvplopjo@*(CjmskNTuT{zwld-ynvWj(uX8O0Uq_svStLh^fM<|a!dxD+ zj+Y1Xed?9c(bm-YQnk(B292vdG;gS7JrdMjZ2y{C0ql@YZLr_ba@cS6YRc!dc;4}`R-Nb)2s!}- z8AtH1n5{QEm#=nXijOsXSQ9~|8tBV;>vHQSTv)4y;nvVOv}@ZP}?#>Z)v6JvCt{h)5U)!XvpVTxqFxcj89; zvWRAQuWT&&kdvZ-aUbQMCvX2s<~DJPxv0@NZ`mwnIHb zmg>|lqEw$l!fuM60p!w1Rz|n|>aR%3_#AE=P@f|-MJ#(Xo6$$p5q2L}_n`p{W?%e( z?Q!;KwTii(QNJNcFc=UdQ#9qj&#EbB&{3GdZ1kxI?(poAx&f`x2!I1u)JZZK7IIbX z%jV8h`$N(-buYq@ozrT6OYOp@PSFg9Gi7RD*^R%vtNxCFhdn|EHaAe?2km}QM>uou z$C^E{vONDxy-_yh*)P=uw`GH82=4}0Ua2Xk(*6fZ?>FjtgbMjD3PY6Y(vW9B+`))J zeTt=j{p{Y3|Ag7kK_iZ%qi0KUV#N+)ec1C4>Tm3~;QxRILhHZOJ9(2{R9~~M9B6

|tBuavc!<@awFmS{CgV>oSqV3BAw}UwvF>q^|uOSTFteAmt*rM4g4_GEN)~vDLf+QqB zb`;Mh4ac6!jSI2 zXjQstC}TSTQVGzd{XlRTQht^{2z8B)EQ-Ys(h!DrHW2t}u+>qpj3F8^q-r-*<42`d zw<}V6dPyD2T8`HIN;5;oLH1u!_hN0vYA(rRso*hIdIL)|lo1~nx}@P9SHKaUaAu9B zYx>g`wD<~X9Y~$8F*`UMX7iHTXRz}NG=#+n+gdLxsrjfPTv(`C>*R14hAq}aI^yq! z&op)I_i+n4ga)wot2EIpZ<*%0-PQ1)?5?nRxhAum9O|$oD>e2*r9uiFRQ<`yf7=|#OFyK2)vI~m6>#SyzWK>-)Xo98RSr;|Gq4~nN z%bJh;_o}8PY8rmJsln~o!|?vLrkC`0m-`yLp?(}cg)#nG+}RdVH>;&RAQ^GB`r6GB*Aqcn+*>^i+m+ha zl^oW~p^`t=sO`b>`+3%2zR_CZoo+ZEtsUa%upZLmw4V0snH`?3&11dH+B)pg6#u3y zzg|xt<~hEGH*5c$)`b-(YUAwo2OJYZlC;O|uN@m7!iFG<(nDU-+n%iL>4aJiOxJE9 z(eDraGPK(WQxD`&ghM8+8*Iqew)8~~yb%O+;+4eachQ!R2yswP__C`u#eQp*l*$3> zz_jk#WXgd90YI%4e&m`B)>d~$jcgvSB`k@c4E^Dc5!w|r`TIl6Xvr(BXUoQFza-T` zf4EzsT}&2=V{jx z_5N(Wc8~ofOJ?66G8SnEO2zME!{XerStvG^ip*{N*==a*|IN|xW`OYKUM3OG(U ztX-{jqWO3M&|;l-xTK7WUu)Nthe*hJt)W~*s&CQ`kcdp*qFr4cBDc3{2TM4OcW8&x zZ~$w_N9rs(?Ji&C?D$rDm}aD+avJT^wv=QkIiMv>?X1=o4(AVQKiWThlRF7iMiuCA zLc5DFQ{@a^oY8v2`m@@_RDgiX2ypd^HjCQ();;&25^TPuy+y4I4&Z-Bi&s?EgXX^W z8yEcj>9O_-HeuNQO#8R=eCsRiSi*4&uA{miwKd@TzqCIR83F?2Nu>HYN4nC~q-JW5 z$N+d*G4c+Lgc=Y42;1$b0baNlD?3$Kgu}FjUvVYZfYazNap)zbR=P6ggdIznObM|GW|{^n3P0j^9kgM zQD#&zEi$?ii~6-mZ8rA@A0H?#hzuq4b9|ZYWt5NG0`Ze{5AbRnIm6Xqz7*#< zgBg$3ab}*gx&*T-ts;pNKZf|u?0l=p3wHN=aG`aiqqD<&xYRx}%@KbCIz^fYVj@fo zYulrX8#~h_(*9)@Hf~&GSNQ~|Q@6<8E~uU2-jQF}EsliK7dG~Z+)a>y=XL5IxdipV zmJN&~Y!7jW%Y!1TQj4E5H1bQS_|OrNgu|n}_%0(ODd+Tg-s~}vIEI?f4wppEwOjds z5T^)zkY;6MbG9d_jxYQ?H8QO#7SAWjhGmgQ>|ZqiL_3EV;IJ$5j1&Hz{Wdbv1AiS) zMsBZfw@i{jMY8tm#@1xZU7|1y;p?`(>_v2*E8AW4!jI)#YF)W>!eCdH-!ZZtEO`>y ztD2R5O@}CVf}gjQ2UuPs4H7@5kHTQ~^I>L8)DB4yQ(Dy4%J}jY&e-@dsv0{~6zU6Q_oIAjidte*e-?}*|Bj-JJ0xsCZ0}mguP{J&UD9w^eckv9 z_`6x5yAoi($^FNddNbEF-7bfp^4^}Bd_t<_yrY$-P49wK6 zAt+zU%NAR7gykZhwMJ8&IG1y)Kx)2y<302I3tjSSIEzU@wYv6_|f} zE1@~9bbSRO&vv?rB)!U@fS@Tf~OwZPpRGRiu(f z7*Alf0ifBg8z3rh$4=cmk{koz(Jmcf(G4!_);$)5pWaJU;sESRbT?^+GXO>%)D;Q_ zWruaMiQ-o|syj(Dp#d=Lq^_M{@c27&1q6JryG%8K`Db-q1OvAV1pAYk!T^Z-QAY`r z$n$QwWYa<2s{|du-K)ALRPnFf(Df1GH@!s`f9h@BK1b$NR^-E)-qj5t8dA@DIt)O^ zT|xQ--Ap3N;D#~IohP@-}VBLEiWsR5Xui{_29)iKdf5?vU+mE_))LKwF>Mer73}-#nG46HI zpP|-*!d>4_F!;KXp4P(-fZLVzUhdg;dt1`fXZ#Y9Vtud4rpS__w|>Bk5Looeb! zi3~pX*Z)e@K}evD0n{Zkcu|keU{oW$wKZM~)>A!F1%>`QstyK(+1kQIm7eOA9n^ZV zPn@pRTZh?6Iks5rfKnl(RBZ~t&r9xfb9(o?<)#=T>vNk3o6 z@iduilMPe#-KZ83o}nKt80^i`GomKnWb1oS8GOvsFB1$l7LaxDpin=J%D}6sex+b= zq`7_xQ3rpw&=03FsL@70NHAE|j@(21*j`VG`p7LYuao|3!N9Gnp0FG(+jBQPxu4$C zL%%{W=+&F-8P@dC|3K}dsut__3kLTF=$8}ip#C5|Wp|P5Z}kxUNx@*$aQ!MGgF_?q zy!2N-y(pmQ}m6g4RPIc{R2_>vRQh< zB{5!&PiE^MQyC1Jub(3rR9!@F26?6WQ3B=WVsZ!hY>B>syv)SvEz{H1H3vZ3<@#gP zCV6aytwAjPQg8oCDD+>gr)<>lYP`ALRuv;Rk^9CSoAtG*#xP);eu}8ZiaW{1kg`jE zPoPA9s~=2k(|LRJ`0XOzE?XSXFCwDMJ*ZzGxI`V%6OM)QnrV7e@)33CH1=URC-n1) zTuM&r376aCQD4*3=wxJV>^-BOL1ggw2mM28(^&u9@FPm zZ=UD}3kD;ekz2*4=lX2INg32ZW9I1=9Vi_}e*<&g+SWX(yd#e4IbIOtzt`^;j;F@o zL?;@5~(=bgUjtIlsj%Va3G`ju?6KV0OeHyTH8^02={<1{OkBoBg7 zouYRL6Eby=u0gO#E0!1h(la`kuwBe0o!dV;gla;V*MmK7UDuD542<@JegmWXRFKv4 zY((^F2RV6aRCKzx{hHzpq{Zb2KCotX^dOpz7O+0MldCYx;?&OIu{heFreu!d2m3#Z z{z2|I;PYkCKMCokei7|NIM0l#FJQ}0Bv*zS>!Jf``sV;M!2ES|kTBKZo1@ReCm4BhueZxOJHgA`K*6n7d`OJ^S?F96Te(SIG5R9+1UnnV9C9cPY~llN9b*0v#`(8%OifWQ!EP~Eg-5L75p#hkBebs; zGoIGaEe!Mez@VBjW@n~u+Smt9)sDGEFuQrmm^N&gIi)k3Qa{F*jgCMypK1_;-?@@z zhL9N&L-6;2sH-WK(>JCuThT1VleJ30*v)gNrSyc9$d~|vTqXfk75<2h`9-vYSBWt_ z2%l2oSe6#3p3QgSk=ErJ1<<5%%q!U#ShMCalsPyO1O%-nx}l-%W7-nz2)T7(%jZh8 zByBzF5u>zUTYxAdgkwH4D5ewPqe)yxFXqO0vnm@>MuTZ4g$I*R08usv$mX|sLrfx} z!Y+Il)0+KxFQqg5bR=e}Fy(0{Nv4cglL)3P4Op1RyOc_7>FboK(BsFLeEaJ~_FRa9 z4e@G>wNr4t8FPowDR4$h-^NsD#g$UOhM-?#2D@`Dvkh-z2wQFv9w6RSF}%?I5wl!) z%AeoIR2E5xzhdyye%zM~{TPFv>E+2h$5{8ua_>qx%qzA=ZTacS@Yu8BX-P!v zWrFJJ>0{fl4>M!=0Q;`peJEi5O~P&5iI268p?*k=twsp=&UJsNZ>kAa=f+YZ$XNk= z*}>w}4d85vJtXvZxpnMGp{b2+WAU@mT*2+z$L<$NRmWHz;jpx|L|0~9oa)JDmZqwh z<#~b&Qx1-OWw$zIOGXWFxIy4IH1@pE?(d^)0-POV6JT6PEPmyZ%hF+dtSjM?noR(B zHZ!&bZQN>MoH7D@7jd z>3;0jgjp9?$(HJA3*g|}*igbDM=L-eTj7}Q3bWtGijz&?_E&6x+-vNNin}80G{80P zHgV?4kY+_zOPf{+W_!ftQf<+SvJ@P=;%-)w75OY6u7+^1)#}6z7oFXT260z}SxiyK zSr;rCM#YU0FKmyA8$lRu$T~|+ugFFj;wrP^ytoQ1FCg9*Zkgi#rmlr-vEl*Zy%bi_ z-xBwA1wI51YaaJaMOoNK9pm&ccIT~$_8|xu6-nlRCx)AA$LvbI4 zNSlwwtrvCO>s(xma)?x&9l9Ln4DGJPDaeMLnl7U-L~-K{hn>6^R~Jq=roVB3)Vpy3 zqPg^c7-#L5G9SmS7s9yw8fR_xzdeo9il_P)aa)9D{&*SZCq|;R|A?zDl$7cce^F>g z;U53D$QE5W{<$bk?-Sp@9GVGcbs)``jkp#!04@f^Zy_#nGOX-yP}h%77qq4Z$BQG> z@J7xmj`N&H6Pr$g7q&k;|* z|2qCLwU`wb(=9A_e|!a)zB#@&wW~pt`sgjvhr*$)@rx?c`g>M#KYb6(IT*j3DhvWt zgQLgdAB&c=??n7P(SdFLKK@scTz@{k4|PyJ(j{XqgxrnaC1bG{58?@vdEPP*syh7n zFn+plPo=-aKcqH?x-~L}!Jyybtvisdevc>Yt#eukRTcXE5noLhSLuiNcf|VG>6y@q zXi6oxuDR+2B!xCOlXR;WRR?oU8B;2LhCVZEe;2J8^xYIqsYGBuU zCRCHFZ&#m$*TU+Y`X#&(Nw@w94TZN$9Fg!=7+~_)gmYqC+hA&f2VuoRswvpAEFqt| z)PQ-tV9uI^Bf|JPew7d;R+g4*PMAh*!G^~f9rCr>7LGp}5om$de3 z-I;iEEa8yQ>hTi^2ZVJlJe{!6i&mwq_uYibEW$B!62$(L5G5M)m^%rC69BwE5bt}z zFyns0W8&}@w!BEVM^zu@tpv5-CTtVxx$`bzBelg8>odI}&@Is@kFQui&qVvfi^_vq zgsKkXt0aC9);7y0Q7x?PY|X@HgpO9)Gyp^0#O?%-iFx@&T^z$H5*2<9r{o*Qkd#P> z56y2JuZVf%#jYCN+Oh6kd-$+189L9>ritz>+nCtie&ci)kXXphXC}I`$&YiYLb@rj z0zpbE3gOruW*sKq=nT$I%y&Tr4Yed5BneUi4+|2{5;_6oPy(GBCw}XQ#RoQ{R1Jq1 z+C0&XuxG;aMzl)YAQk^po5Zz5>9y?=&r_w})**4HRDAc&iR*~PAMBDCK$U)a_r$(Z z@$Y*k67B_X1sZ!NQqF_(ya)Rx4wZ_J?w@FXcoIMeZ^EP%vvq{k!xE3UVaYr;$H;Ms zXJ{4Jktr2Pp73ZwVs9shQ_y%yqMI}RwwWRScAb+rK>GXog2Z#u@7IeHuRGxHgwGOh zNWX2DChn4c{gx*lkbWP!DxXO_L#@hwy|7>C+_1=>_1T*c0$=`^=Yvq4azHt^9Z3heTiL zZ|y&ctEAt{j!E2*H#;YRWVqd2lRlH4J64jP|5Q2YzV!T%XA(E?p!)1~(g zS4)~gYq~`&@8<^<;d1Sy6sgmi*E7TqA{rzOr|Fva!F5=sBIyw8KV?(~D^MnFwBHX) z1B3-DB3xL8zIGiL9Gb)kojDF!sxMelS_#@lB^5fbUNc8|!U0{LQ*x> zZ%L8|Q&qWDi!ED{RDoSfNTP+=YQX9yJgUL=Bqsf1zr5qZ4P7D6kaXRV<(62uC7tCy+fS?ZuE!qlD5BdyAx=fhqasZ3*kqg`&lhm3pP2>Q}W+nNsM`M!w z;qN&~GYO3sQA)TeuQM!3s)a3<4=ft*OtJ_I-1#lhY-Cu#7UJqFw>FovNw2HONw?cc zZQSMLkVi?5mE`2K*GV-h%E@hiC2e;VFE|-mi{yQGLy&BU;9teiNj3qP?PJi(Y~gY> zLyCB+^fxRO?_RBA@NkpC-Ub_5(~R&V$?V57ZG>STEi0^08{!B(JJ#uVn5i{T&M0Hv zacoGGfe;CRXWgSSd`FUY9E*xJ5axqYsZbJQaK_C$$)ztc7^(|%+Mi{pEsWvUT*FNV z`Gu?b20Tu{-(6E+cwtQz8G^*SKQ%SrDGvU^)8>YsMe=S-!*?RNx3yt|=sh zgpLN>8RFUAbv7Ij$&=j-;legudK)^3B8K)e{3BkNJizd`z+E`V5Fwsk8e$kI-fcd@ z@J=Kr7$u{b*r`o7Lnr^_gAO7yqnT97K+krWT??iI( zd;>-Z;xEiwWVkGnLl+x%iDc5}20SjvU+BKv&|4%QtTf^`&A_20pk5Ei^Uej8wuejtgQ6#Pl7R4KxG(>ghBBqs0$|JCIK}! z)i{YJo1`l}yh<}B3#imAX)_@uOh% zqJ@#rR6emPY-OA;n>%~k&bX&MtY&mDrV`eeK5^O5*@%~}xgS~8&A6>RT+}^`RR{w` zE0-GVpWDU&mek+q2jTsVbDV^$W7h{7ec4M_gFpN;$aqiG!K-2AX+^v&H%J*_^p^3U z>u6&&;i6`ZH-?hscyPxPdQUg*w6V{aWo6%Kw(+cBZ=O$YZCC=s65LqvpenBGrY$fE z2|g<|E)mI;&y2X=$~!LO7skni@u0O*oS}G)v6+Ll*ZFC!v6ir0_x0szn$8SytCe2O zafMbJjFklMp_`2bq7ts}Fm5Op?})v|A{%eE&)8h%U3$1YZP%G0Y*ns#$Ub7!ddNzi zea^T?h|uDq(L;zZ^qTPmRRqgpV*~c2bzO`C+%IP+%q=ti<7jOUU){A@)cJeH1W_He zA5q%_U|CItwZK#3UNK0S@Z6Xy1iAa0aRp(#f;P0Sg4J^RIGVN!su2}TG^aj~8N<4| z!gx1Rkl_1mWm9KCQsra%AfConH>t#Ed03z+M7*%IfoY8WQphvu0~%Zb`Sve&UwEQ4 zc{*E#@`x}|E`Q;Wz8fIqF)=h;5M??hwBB8BIzS`Y_0xh4;DXIT54L(l{Y+Nv4Z4dv z*(SpGJ^1e0`KFJ&89Uj$1B+Nuz<1X?n0g5wG9Hj?>S%v&aiPf6mYr~PtqMc(OreC< zfdGUL2ybm~>MO534QXOJ?8LVCHt=Q{EliZ*Tzk_HSkuyUk}z>a8biRRjp?AH%-~)- zll{?t08#c4H0RLD+=8i_qan8+U=k~Fnr^0zLK`o7nr7QypZ@_31YK{EUj7Hlj=rYh zLPJUYO;gLk3Q*Lcg!Uv6g7(8r;Z7{NZv#K}?J$!s^O|GwhjXJ$y9hPUB}BlQtbLCz zt`I)f6esH)hK)Bh63I=IOg)Gd3oB-raNZ=5g}-N;P7=n1oFc;1=M>qM&rF`|>Cnt! z@aKHf4qhETaCniap&gl;m2^Yy3%zR=RCu$+^8CeJ2pPMkiGq-2mE7L%tJvV=C zx+73B-y7 z*W6j?V7H%{=pA=eH&fy}a}|v8H%|}*ylRtG&?eCQfHq_*f!FoSrGmkdhUQhoCjDz8 zGbO$=*TMV{Ge)wNol2;gFw^5GlfulD_|80UUA2wDQ7whRyi?|JP&LZ@gjx%mqHT>a zFpk_9yTqF*@twI2ZYSBALXpW#b;?W3X0l6Er z+LgO-YFO??`{6lZXH1XKIZpSf8gKWJXI9F zeIT`8*frREgF1JbKGci>jkyIhA7Qrta64#5n)9jBtH+q-DZ4j&TSE2(>Eq3mc+R}? zk4-dR5aO?$V*bkR%4ZzqpQ&a_JZGMF_6+k)!C=H}^9CY=D|5``L2{S*W{i}|O`zo> z^ClvL-KBIRG%qo~6AZGKnhCpmT$3A@neS1Xfnf!WTZ_R`q zagGwV*W8r0tXcxbBewc@bBx?Irk*epB0B?)GXq2g?Eju@3{`$GV^C*qQjX`%ggdBQ ziNP1lp3X8#+GVr-8v(M5Ty(|UPjK4R>Fe zlc{5dOK)rykoyOD2DR{=8RI$grjhxlxi8Vfb^8lvf-*|SkJP!;&aaZmQz?T}@-#u< zx=Zp>q8N=TBvZmUkMmA00X!*RB0SYruVnit^?)dig?oAEm%Pe;5fET17^^4m7c@Kr zk_k?Vqjag2jIp1&Rn)1QOlVphWk9{;hJ-CA%-a*x!O2sdS*-)OKlVdtvNxfTNH7dx z+@M62d_~qQbc;-`LZj4)sjDP=uwQCLcZLzM$*BaC{y!J9B=<5>p*d{*`1$jRBEP$u= zPOhU`uacK|(kg&$x*I)U=lYZbq9+*Pl0t0EjU^~gHujfjS5Ug8bhBSiaJWj!TWV9W z1#gCLYNj|6o60&w5jr*1Nx@hlT)jT^QpEBD1T;($>%ov3oN`qtEj%pc9+fu+RUQKF z`jq-a%5qfZ!!aosm6@}xnwUZgwj*_4bImCV;nh#4(+vS(JlJz(j4KS!N@-=kA+Qbu zQ=0$JsLYQFQ#6DIBS8TMc>Y_+wW&?Y1V6jm3LuOaEU}DXDTKK)0`!9B!&96@KhwJ; zWf;L`xH!G1rJN@28SMfa=g|Ys%sP zVY@%1G_${Qq@e`HaK4vPU(|8^$8_68n96YaNlG%oYf z{4?bP)v-%)mjUB0CsyRTWe5CH3AZ#=H~OkKNaIH^JMkf zsTg;eCuh}7Jt03`*SSGzW%=RYh*Z3=$NztlCKbaj^W>(yR1CW;=dz^!LR1<$i&PIe z_Oh;N>La_`w`@WPGDw(Eqc*A5fs#+VR3#zSTZi+hEm?LxhVfn2KUxJ2{ZfMoqg5-) zx6rG9D#lmkiYgqFih(Y9QdyFU0hoF6*0@x>G0BtbC!}T)0xzZI+(vZ53L8=Vs#FZL%!yW8o$61x+0Th$h~#*9yD7CHktma5ECa?^o>tp_dqD<0 zd2nKIIG3unp8+6%zo)E(nYV3P>U_thrRw)mF^V$R(!2Yqxs)i%(p2(O6lFeCR}L~` zSm5{6CL+Nthem&y-V5|!gPuIJPe?Di4sp)k`VKXc*nP-XiyeBCQ z11fWd_f2USP?;zDq@`IKeB+`t45-YXzG|9=0hM{Oq*dBN!VrSHbXx%APXUz~22}nW zI(11KMqRe}52y#5GAOMsTb38Mgw-CDR*S7Daq?#Ej-)Brya8#KWD$l8O#504h}0v~ z*2`TLJe`z=F_*cF-!4qUn9Dr*`Qo$}qO1A*^E8aP%%A?bF>P$Qlwgaw%rNHiRM-P) zft0|@yk4cK(i{&1FMF~sXVbh{@iY9!QrG2ibC}z)e<|T_nSGz5X&9B63)1n&G>pp3 zldsFtt_z`mx}SzonfcSF57RIzGbVlD>(^-m$|3Zpc*?UN{jaob)I~e2?@v%}fHBQ= zqB0-4r(1id1C`QuI67>Bfu8Blgs155m5y9b+)_cfES0V+>}Vtk}OC;{7)U^X?JpCDid1 zk8q4Jn1kWz*z^VRd48C?ARPlSbD9U2rU#L0K}1iU3ID7~zb!jA_UxN<>TvZ_H0F2f z)2%M!-KO+pvYlI_F-xDl84XQ$riYhDkANYWSBjcGvOgW8F>@7`oKDAR%sg55Z2Dis z5&+{UQ=*{pUO|e%3>bqsfwnB%7%+NrU#gaQ{pSHff{>y&Xz@c3bNhSk7M%+0{a%iOA-&CkHl%RKpV zVFrd?=E=9E85nw*CzUHQF!VA{s@G&-=w+Vt+LRGYSg+v3O4cM)VTYC_ltA{ejLU>k zsTIW!_I#fqu4BNIvl$qKnUgzkIpa(FgD?ZaDhY;z**dTnhZ9^`!{-_Dum}8(B&>E| zd!A+d|2})y=SjvznPtJR8P;{y`p+^jCNt;$>Se|zLa;5al0J7afHB5oJ|}vTDityb z8~9czZ}4@?{8kvmE%(ey_N!FPA6_IZfREKOyAT~aSUgzE)Ge7 znHdIVUJ9EcGPeq!Sggw=1e)QVRl*ar=Er7YU}nznqA{}q!La!T+T2%W7dAIF)0t(~ zP3#2M(=)ddR^$rYvRY#-j!ZYGYsm}`kgvhI6=aHQuycB4TCeEV?3+2x{$&6wz9&TW z%N#@SQ4#^44gh)6!4l!;$*;Od<=Epwts2F}VHDw1pG zX08#*)(bPo2nY9TY32=YIop-5GFONSnzAMHm`JwXojFq^-yh7}CX)M3W_}}*L(gR{ z63Ngc0tDHRGm_^yjv+B4G%V><(?w1_R z^o^gnC1OyYV(DMM9+87%2gDRd=(PF{x~S-=j@k|}>W&?{bWz7@)g2<$9rPVyVmd{~ zc8u&A)v1f76CmDK^7<63SS8E;3r}#NN>&zOw+6FKg7ZFEW92Gg?fnRaGmf&MTGj=E zvGKgxnppz`gPj3cbM3czj`E>aRxVWsf7P)u7+XJUA(6qU23gmrI%pDGT4xjh0z5im&}T@v+O_PCb#M1Ia#X- zoh5p+Zo3+1EpU>3>{rdQUJxuoLMd&W<;FJ6b#;Y=mRX}k7jn9d)n7r&_E~MkqQrMy zvTE2rMg;F(S*>KA?CU;w5Q2xI*wi=c6oD~f^^@5K^v}XiknkSy`M|8t1jQx8v+9ZD z_hYj56Bd`}P0Z>FffKTl?Pmv=Hw4$&rE_{`XJ=Vx_1cELte@ou+ZSai?5Du6N@W1n zvZaeZ^Y|faC^#<8!azfu*2ra9zZ0r(<(F9l3DRJ;@o?gstd|ZBTiBKa->UHahO8D9 z@sAGMt5=0s$m&d}Aw=!L*y5qJ*cZcHJHxKMS(H;0R+Rd%;6T=WG18xMC~KCO?=?D} zyt}US7-E zrL*4!S^+{?zZVr;p-q+SUZVY9_sRZ6sN`bR?6T@|J3AVdogo@mEp7HD@xsl>?8PEE zQJ1|{B-5g^w}|b)3Q4wiPZ(_P_A}Yu)g)(c67Rmuw7t7I+xBkrT-&?;`PtjVySJLy z-d)DjfJrJ92mJFswAOE^9)dymlH+!?kSm_92zqJ~@pr`Kesi5jT4-X{H>4L0egZL&$9 zy(N2t(3Bh4-aWb7_HOYW>D@zL;?6?kzUYOJ8|pbR!SIcKlkO%gC{nV#VFr6~W*tNWv> zZ0>odn#_1o=fRBaa(r3eU^iDeWLcB6oHD{1#Q%!i_CZb^_9#1tuy*jjV8+WaYa?@W zzISFX2M@w8ycgyiwOfbme}PYi(xRN|gwfvrir2qs&K5_RskTK9A&STUiae)f&NL?( zSNTTq2zJSM#3?x{+O3xURO3t zJ=~22zRvN64Zr0qvcFjaLhW#2#hZ2BO#e^L9FiO@OLsfDL9>rJlvvS7pe5TvY=D9(#W+A?1Zc``u>8dulIT&zSdf{W2TyT(+C%fgQSCW%;2IkiHkduWax#z{Z zk<)Ug_{vYeUz&>-sW`&;6}cmW4A!Qg`$Xhfq9R_3pIoD%!FMqNg1rY^Fx2Q5}miNf~?qehCHIJ z3aswQd547PvFUkMLjT53T$jkd$POi&soF^)xZL7SV;{UI=&pS)AQ4Lf7;pWX= z{`@kK&0W>ct#n@-UnuO6H>M(&jZN#GhoRNoa{a+)P~J6Ki~JvDc*4|SdBqstTdF?y z(Rofnh}z@xeirrXGcE7)ilX`!OmQ|X!Q@r4{EN_OW$qj34Y6xDRw~lgB=Kt@D zS?-R!T@DN7uXknd%v(cnWm3no<%yif;+ zh0tnWUZV=E=xC!1wtPfiS9bPj-p+E6V|7pDRb|oYCY@R6&e1+>qq>PZv$#C;Wy6l= zb+I3~;7VR=wxwpiEA%^&7fWzAR)9Ke%`?ZgY{+Wy-jZU7HxLG#&il!Ul}rl4 zQ<7)%4w1wf2uIH4wX=T&1IPeB3V3~FkRMxo6v`6( z;P%rzJe@8{2OFN}U2~C>i{9joc9fG1{>a0V?EHl*@ABG-Y-z6fc&mdy9bPH_xOgGo zBmc7fy_KY9Uhlw)_6^TqoBi_%U-;l-F{#P0_I)4uu**ib4)P~^xmDIR=XG!UX{8p~)e8~uJRzD*D07)Q@ z7YJXd^Q#lwvXxLE435e_?I?4xMCTKx1fQTb#N;~>n!E%6sGQq^h9N1RuxIv(#$rQ$ zRU!?kjsl^VIscL1(kV5cFp2-f<#<{?C8+KvfEHQ#lLVI+Ir%foV=8fZ`I`w7OdA)+ z%7>V#dX|aR&BvCMS~n|^m1X5;F=OwB6`5Pp{CL>kB!7^DY)j^5`Gk)cNo7HC^L&&2 zQ!2o`KRUBxPlx-#sc!i(v_>%;zV4O(n-iA-HulSZT0u^JGc^CPNPamsAE&ockdIxT%>H2hYiBum=~zDQ1@Wg5XY!NX? zjy|}&&2uelMY2l11@G_ir!87rc8aHU+FE`Wx(VuNsV0iZ?QS_Q)TAo5cnOW<54PkI zM*F-~+$nzK3}Is|r7m18?C}^29=z|F?+?xsEktu~%{Z!dy2VkDO`T`Sswm62;&aPS zLQAt&TZ%p9r=Hs_I>H(LzeyacMsMvmnSP3@CeTh9I^av zKgg@&md^0mQ46JAV_rgJ8iyAaCXc0#pR^QKU@N?my;;aP%US!$Bh(jg{JiBVVfCBm z?RC*|+)-v=x?&+jK;kHyuUhI7{0h&z{D$S6U@+#EWi?R;uWnns?VqPI&fvm*3mz5X zwXpr6Wet%*@MB9?st#KJYQeKAoI%8M%PPAW0LnoM@hzFp&eyiWr@pli2GLxJZ+=HZ zvYWVE0aHI%b_?+f{w6cn|BnUipW;dk#<6}kJGw!*Yrz~3mfI`Sk2Q5Ez@?+B7W^uo zZ^0E>o{TB@RVeOge8D9L-ag=~ z#DWtdIo42s8*);sfvB{Cp3ZV|b5_9t!g?Z?X6_Gn1Hr$j0B_lGGstXil_s@CK{uLt zw__42`%u=}1G=`h*}{#s1rw=CHx7DK3*5UD%yX72aC47>fmCuF>xbS2c;KCDvRS_Z z%0U>ezB?E0R%1uzHxGcH1{YMSD3kkZY{3;OISCMkPAC{5$T?0f=tfXl!GeNOtlgW& zz7R6AKq)JYEuLeo+l6xrX4%gW5Y^R5R$BMcf|&%@gTsf>Y~`|o(+)f2VNjB5MOXIi z@&e-e6X5!jC&4wmI#JLZ(pFNmz@)sa)#?Jm!V=F~yt6;0r){g86pa0TkRUSWhi{`|Se! zge|Xtb9W0i*s;&p?xxHu{H2bx{&6oYPsjVOT>Th)2D^d&nVdXys zd9rboIusU@sPzDM$3jZPNKOs&da{jm3%%K)nT1}gteINDMmZN=piyB~xCv~`!2-fM zA~x3~xuup?F8rEAVhXhPDDdLo0)RSPNM26;P+tX}w=U=ZzJ zNZ4QGC|?E?GQvose`w(xd1~)bzc63;%s#<|tL#?ssPLzD4OgF6}fPC zbs=R@gcJWmTll%Cg)eo5Yl-4}MHd?E7oTwk)#3^l3I=-<3fB-B1Sb_vp)we3D#VZK zamQI9go5H<> zxgf&yWF@|ZKJatL!e;{OUZ27j_G5v=z``H!IFH1pY)oM>VTXmYIWw`)6aE-q7*7~x zO8|fXS{K&qZ5uyUbs`?_;oX>VdLa({Ik{GI3n_7HR(@VM3}!DWBz#Z`FfZN{lPE#v z7ljvPuLa?23LQk!@J(SQd1MZ)HWl6?Y_X$H)iJe4WB_|vF)|W<-Bb82L4p54aRJ5t z!s~=ZbSnz>)~5=qFgVgM0(PD#+*uAnUVu=8X~P??7ccnkeBm$UVZaceGDDU4LB_>G zO0*T5ULf>(A?4cwHUNeImDm@`NE2MVS!g6UkPbhi1KD@4aIpO<0nGcTA|EJ1n-_&q z6>)O?bxDhO_~CtF7d&?*d4OccBI($vlyq||+J`6pq~vAyqQ&BcF+N2N#0%T17hRSe zDJ%{sil-S|3zqltt{Zf2SkysKyP+z&ZohESSXyAEXo{8!Uaz8xR*CkxI<{!BNaiNl zWEhuR^c~GQTQJM(yBJ=qSy2V1_*lc2-Kk&H724($rMt)~-cwLCSafp6#zlSX?`OC~ z)$Xn2B8(@(lP}vB8E7u-lQII?fx3ll znW0ZnAW`D>BZ?}*%RWVTok?QF2J|mlT@EOQP}TV9;gbW3zOcW4lnu)=u_yp44lBAM zNWB_aM4RrSrVxrDluRmrbkQ(^8~FcNd+YG1j`w}|yK#pYkN`>0;_PNOvN#kdrAQ#Q z$+{-lxVr$wy+DTI?k*YJDNwX6F2&u8worJVIdgXT(BE~n*ADL=$;>%BbI<+A$eA-U zhxi5tB>wpmr;UszD@;jcm5jC;)NyR|KK}0canY;z=(GvZKQLBSybeYCIM7bo6-D!% zw8N}u6~j=F^KSsGkDca5!*Puq`-KalSMk+UvozX;j~1_tmf2oSv0!6#Z%=e(yUHIc z_E>AMrFkYr6N27c3jcW04(T{lJ6r72M1MoOi z#7X1T*_i%Z2FQQ=|8Bq~x@;aJfO&5nU;!Sj^?F=yF9czy?QEDG_;_qn8 zD1NVx(H9w7B`7=x?{$bVGPom`_pnpUQ)fE1J?0fNoOd)`otP`Uqeaz^fm3Zb_w#KM zvx|4i8%<+0um+zvWpuk3XFhFeXv}klo&$)OQPAJl-D55Xo3vl>P@Jceu> zAci?V#wxvNVvHZdT9oaJ`cogFd@Ggp>aKxc>864Zl z_CYAgn}nXoW1aB(gjhL4OG5xK0CcuTJA4QE!*29g*bYLZ!7Yuk+Z|~1RZ47Q+Zz?a zE*{UxiQVkTfuldOV_{!i|JXo0ttj?28{2pUfI(PT8heUr9eY>A!s=>{@}0`q8a}#u zNUWNn=_ZuXPOls8c+SXJ_>?M93Q8Dd;r(Q6Y}l8eB0zPaCaad; zY>tH|KTh|XaBP3xb9{Ej!m3{G?u}isbNJ}wJ+Uwc%iTSFFcubJbI}Edtkx?$61$z@ zaTQGVsX;x6Ca;IxL`Q##U2JnRfGk~u>l9%NSG0ZDT~~B*R9Sue=-1ej0>KKr<8*AH z1B8+bv19Ea+;chhG6`p2i``&*>p7+S{037p=ei@;v1N79GoLtbeBx%TgcM|dJ67a` zeC=EIK~L?2g=p2oSk{f|+~z2ubDS?8{wVejhB~+2CoU0r)RqOKkf$tN6_>y7tJpdS zK0oS*qn^dWaTnJXwS;p-UdA#kxgmHM@CGut3gj)J-DdpzSXi0P(a_k(F|6?bsG)eN zLtJm$4Uf3IbDiTZ+ffGN+~OA5?yVfk4fi-LD}z^FahG@o*S+Ix-|>!t0n7RZj<{Ey zI5=~WGr+!jakd})NtNgx7}tkYe8+}y@QBSZm>&{%gwX&E8poBeGU(GR?h&tpUM=F5 zGBTLcGVUT>Q`Hy-~pc5bKUEH)`pRTlb9{O=}`P z7!?N}svs7@vj@jbhWTU??JA3d`D8BYk{EZAzk5#|2U|e6yKA&@@G^mmmg?i6yWyg( zjByiPX!PgwxVjEB`bTyg%SR)@W##B;XfyA z(3$e8Gdd2or*p(NOtxxa%#^sWTJ-Lz1#wMy861|zr7>(v;$#Tf6jvYD*&Mfvkv`%8 zN^snIPbx#VgK^*U<~wuLN__QktN8~0W;I{EGgkAxy=XPxuFF>Qjs4wfz7E&o=J2Mz ze>-l2C)L8L7jb8)eV0yo6-Vn1Ui2nzC!aLu@3?xrmSzg#hcj!bE_B8_YQ;x6a6+OH zwc~wJx9GS)Jisr$HCuI;qYywvfa-!9){URUD{WW9_#-^y>5b#Fc{u`G#1G`}mb8hV z%`k;Ttz9?H16?~9?_V7h*BCwO4Mjih6#s~Kzb8H7g$!@x|6gPb$oF~gy)!W0gBQ&{ zF1`mJjZ?(K7@c$P)~fhFbu=2bH{Oq7JZdF?L9~s`j?C+(KKe7GpFd7K9Pi{pt=;m7 z#o7x_#V7C*c$|yh%Uk64OYyt;=$dQs>v)r8-ie3zA)JUk?!~XB(V7SG&3J(DXYnx4 z$^Gv5M?7o-<)W8f#82iEl>ZeE^EuqzVISi^ve}#)s@+G?Y4v)b2yfXs+xryjAQ*K! zD65To`^X+KsCbCKEC$v1$!uSsj3MhD2f-@TBT#n8cKU**vK+j!zU+-1Kr9TB6*EHY z3YKNs14O$fGTSGaFtE65`=c7tPM5`^T0&WCI#yoQN+u?nxYka#!getT7zQww{ZSSo zV%%0NTSQd(YnW^qBZrUSGM00^EFAoix|7Ta;rFh7*wjTPbrr0_^S+T4u$nCYFT8esg{Xs7YTz%-x4k>XUTdwLil_>l>slLGJL;SwwL_gdxgxE*db~Ym1+4F z*>rNh$4=RHhWRi{vlEQw9h9v?`}A<+=hUBM^KJLsPtD*pK|*aj^^lBV!fMJd3!0&I zu?YiE+n;5&PxD%`w?`k2%e+w3DH;C|Z9FRb(O$3>eLNxCZL?^fqhdhqYrD5tL2%GH znTHc_;VMAaD>8;t_^n_F!0OOct5y&W5G8mvWwOswa2f}c zU+&Wfy-i54y#x9*m4(zh;rK5@be0jhX60FqYceJ{qat}ig3aWxK#@>vyP^x;&< zg!hm;ZzXsmPj!Or)dB4_CKS=>n_ilPaq!0SfR8u2VMthOyBGlV8-o~-tqS){Nq|ob zaD}_1B}}ooa3TkMXevFS8ABHad39)`IYxrRa}%TtBQp-*?F(}Q+%P{u>nK=2Ywnbi z1b-*N0!$X|IdQ__S0fT$IMV2!;}T#g8W9;QrzT9~6Z}0R0bUk!cbm>j7-0L(38IN! zX#28+fSQ#F9&S7rbZ%vWFVdtY@P}Rv*qqRe%~lkSi zeFU$;uCLg%8m(M1gazs(0IzD*U<%DvE-s!VSuv~D3Za-Xe zJAq;H=~MqScUfW}zIG?UiNP5r*O9kH-CiejhEqusSPoya0>IIv@TipVFo9t%)dHfS zm3#N2gdBKL@G;mM_4p&s=yNEVznI#~ zPa@3uJ7G4w=>i(l`X$=_IE6kTtr8?&`2rtF#seG@ODPp}(JAo@44sT6E{V^-h|2@F z#Bn^Ak)DYRr+k0X%Wbd3zrKKr9KrUhI(W5rB7DY(a~FkQBEtgCPlR^*Cw5}!8Y}>P z@x1zpEQb|X0T2KIwa0_q$1>XOMJ?Ukk{KPLWe$y zU-5Ty`X?Ud1^aGbB7AC)6U={5BAiXcMb8XQ{GCsGK$b{ea}p&?O-v-OIZ5=%x-hJuve>B$$|_9s#j5vJB z?5pIx;aFl<)ZaPL8P8deXl9nvin4N1Vsi)T4R@9$!oC}h_s?sr-Y{uh;tQVl)Xmnr zi8yf(FW8Qq*1JP@C;IYfm+VjM%&^G=&~zT&rZIY2;b=7612K*Y0L11n~CRngnbXJa*ukH zc+`!iHN3Hch5l)Y*1WUA?s;#89s0ovD-y_u^NKsA9*0}ArI@w>+;h3Hk5zQGhWt6?#SQW^_9FafA@K7`S%P<+bj$^TSrg< zs9i_-9>GpDe|T~q6wyh}(3yN19$uRYCs?=YEI(_ze|fK!cflE5c!uNh z&PkR3MvBkLkTYzq=OA`x%99yhb#Zx*=0XL4;!_Lc48v~@Vnw0+0Bi9+W%ALa_*oS! z?EqK&}#m6m{FJNqRXDyMlJo0kX8!o5P*Q{ig{^ct9W80<2Q2aY0xaS7u zlE2$14`%f`uNwJqQvB2{a)xzTobFC;m9rcL#pNBjL#`pk-`gd({mdQexIug5FKyR5 z&0Dh%`e`V>@j*Gm3T{0C?O1JJ@1%SpDSp{6%&vFm zS9vGa;(t0LUqOn`J}+OySmn1~kTWa^#G=dc8>D2vtIREZ)HV4%HnXQA)ZW1t-@75N z76^9Y%3Jas4#1_;eL2IlBj9M>QA++j=d^8~x{(`wmroEImVf6~} z8~HL~o6CR789oor=_BZ!{3L7flRwBukmBnJ6s+xGw4H)wp)g0iv!h}XDgK1Bg0&a+ za8*RIN`KFt7GLbeTs*F&U^!5TlYWMe!a$_|=%-+9jw9P-Z=6#vii8QP@yB0pS<8lZ-olj zuDL`Wh_6c&GufVL;;@}mVE{;+)L8)=D|X_Du8Q3xeA7*_frKl1C~8Qk?Wu5cgfQ(} zMGF#68=z=o`@}eY9<9i;2MH!`aP!Bd@roRVx&RpQI4@BFt66s9ZEDJUjF$55tEasG zHc;N1Op5j--K|uG*mk}M%Ib+GW-6S~%Zz@3cvZIItt-%9-$Cb(x0NbbzL;W##E}&W zSY5Oe*9=iKBsMrbTp=W(;}}Id5=tg0UXifX6vbT<`b<~EF~|V^(-mFKl6#=GD-5;b-Zx|r= zda1ugPai1!(N=RzJ1n`c=)*7yAt)G7W`e%xih#MV70#}<55fdN$|!WLT<(CpoR!XK z>w8z<%{`TNr2cVBgyiWlxK*syL%}$M12c$RcU5l5g&AHBx3W_$;95@a?9`YQn6nh4N>@?3-3fuDNq=ql5)}J8`j4 z*_cQ)D@=Kw{GK173}S=dt<5r!Oq-s-rE4nM48Hkh0#(~}VTDggkN8czf5OUey zD#sGifA^=?=LRZwc)|5l6$oLwyFkO#4Y>#1@kSq}W(MFWgYqrW{`J|8{#cc)JkLP= zGe{@AGgYa8rUwV+DfbcLSBjK_oFHspsccKMuKZrPo`LxF-;}+OdY00k`vBs~F-mhS zi^^NgQ0`%u*;wYl-dvle(gzfu<%ElxNUZxBrVTV=9?j$T*r|dz(-J6sw9}wr* zKiaPJK|wz$+oAElDxGoSHf2Lr+5oi@lu2D~)n4UIhAPf+I=oo!j{|;Evb?|k1OVSd zI$@>r__*>R8=ZKp{#7~HcAa2g^2|~``s#wE5%ImK^ki@~;?Q`~Rb>$voNBn?EhWn# zptBDwGHs=7+TS8g=j5)YbA z;YHImudTXCes}XxsTc%bKS0$RWd^I<(YEHnzW9Az6&WmXlA~`LsQAu8+)(wC?OuZj zI0o>=qyg}6qT=Mg-c$vvUU%X(&8a}@7E~brR#c#?t*PMK+Nun!0)w;&ARiwp1 zqMM=q$SFeQL&y2gBUG4C7DRB-k;p-?roDs8o=6qmS#^j=^Pvls>S#AA#k3wOhR&L3 zY%osmqk2X94DJDC%XbzdEb6CXnES;5d6d+Q@P2xy0!#Hm=? zRDyy5rLUzC!Cp!gCe`96t?EiW+q+qg)|K<}hPZvH%9~iCdxfeQt9%3q0T2veQ;bgd z;}F#`0(oq>%9E(>;z-pUV&uJ}RnCO-k?|_!7ZlY1ub-?UFCejaDy_VAt5t&;hNhen zs;9SVUmK2h1x$jkh7`TnJ*8ci$RsObb zaUkAVp!k^y-PQADqLL`oe6It#%Ckcwb(jWih|RmYB#GI#&%K{v8v%?V}&0c(L?>| zL49^F^?Zh^zJfw0PDFKG;?$&H!=vSDZm5=`RNrA(O%Dap z6-F3V7=7&`sG-a02V|%jR*8ICFBLWCt{44~bWxjPe!tAAOts1OK36nf-50ORQnS2> zC3z9#bwUGk)eIBgkUx*EoSK!V-s^}y4C?2NGD_4dnZzi=2TRp)wpVuqG|KRyD)o9h z%3$;`HNz_u4&~VQ>aGm#$mPYO)Kho{^08`$B~%>BjB)C943(A3`+9qFCSAUvRPb)0kE9s)$ccTTQ**U(W$0)YGd~cY8QJ-sl{bA%UR(d zBab!ecXcMu%jLSdHzO~6>JD@pl+w{B>XQs>uPLQF>N+^|tvZFLwCXQ)eKtzpp>@sf zxNYvIv8S3s%RllyhO6u~O&KOdfFK2()=%S!!<{rU?Wv#LaMi#z1=FDy1`T1z z#dVQ4`dg}5Y!2ze$zn%o3fvw$z3$}*kI75d3W8BqIH)5{Gh9*mhB!r zrQzZR+;MW%<26oL*GJP*Ab5tyeXIH1QE-|nY zmyMcEcu$;WrGP`hWim|VoWaok5KaK~kuG#B*VF3TV9Sf@#{qjE|%YSL|183xpHUPLw4Fnn(i02H`ziv~V2 zPt=XgyESmk78lhW)Le0)(Wn!eo_uuiSxrZV!2~G>rTxxig*Tc^IwoIrRkM-zu&i4e zh7s{EZ#0E?&20_K+FCAet9u&Q%E>uq%>!lzFCS`7GK`2h2IeQ4c%H$p&oq;4b{WK> zxc&i63S(aROU+E4f%6-U?Q5$rFqp~E|8c~9-)XY=!l(YNVc1r|p`84nVcFBd<<$wa zX*`3g_F7g3fsWdR3LS~sll(@NOL@BqN*mK`PI6QE_8L1GQeoK_e#@-u=LJ^e3S!}@F+*y ziq(}lfZ2K4Yt)s|+gywEy9>1}ACwiH)JEg)Dz%g8cmat=S^$GaX*=+(P|6ss?IQ<7 zpwXDkS}&Y5Rtw+e;`%hdiCTs+#3v}xB<&gST{4iUJ68(_qHt0DV(kdNUi`IG3$tq6 z-D9h@ECUHkVGM(5$jVRdU8&I?<_8ir+qDc!L;{TK4!R!Evh0)M7_2&~UCcAkozOBoUIEHH+XoU@@T>MU zU#`m;t?g%E;9t&YlWi}TRJk&I`@FV*FFfrML*W!<{blVah8|mVQ)|G-VfbLX+hWKI zAMN4B3!Uz2Ls?2csf|IkTHf)%A0BEWY^Oj(rvRva9c>lv^+XF#PlOSw``FSjK6$3~ zwS5K$i(YHtXjx9Wv+uR9SjfHBs&V2+?K!@G>S3?5j2!AX>MSFN3r;%A$icx=XBj#C z?xnMg94fqZ^B6`BoNCs!yi*HxD{>6LKiAj6QDsD&rIAf^thTb?InjZ`xvzAVfkRAd zt8gvaT7`SqPB)lg!V3gg$`(?N~^>@BS?XqQ4V&{vFrx0LgU=dx)R

3s>ClJ6$^#zadUd08nX;?|3UZEjw6*mO}F3+1jX~J zb)&wV&$%CU%P5~ovvdq^2mc}7ud{Uwp9A@)s>SnlXDF$FMLLF&-9Jbf7VB8L`hTF5 zF0*J#vQoz|XY&uZ+*LZmmz!_ITHP9|sa_j&4CB9lkQ%*FXX5H{4c18+8d44}%hAvn zWnI!aqqa_ieDUp_I$a=2+vo}(3q7wJ$|^HK#egbw6CYeBAL~~qs(Ql(UR=Me$2Xg6dHr3biCCm>r60?csw*bk_HF-_TZ=qbXlk}S@uZR@=1{(Xzte@q@p)4)df8YNL8Vpo55?cB$ z*Yqy<_#u5;e`=CiSM<|ZO+wpSK*fOCjp}ydT|I9N<9+>iRMx#u^`2BcS6}L1QmJpf zrCRpJCGYg>>93sx24113>X6i@E=>`NJN;H7>oaP#?(wnar8ah*R^ju*$KotlVIiZW^j7%h<<;Bx>#Be;B$` zYX|>j7{bZAdA(6ch5p0I$P0bQ#mMV!wwtkp{@TLZD5egT=4*7PaHHxOThV*_1C5DP ziS2@ovst}k(n?(ba`DUy#2HPEOSO+nJlqL*d^S! z#2Kyo$_)-{?`#}HwK%!AaWU1?Hv^2{(%Af1qn_boIGk^GJ$54q!Kz}|t*JH2?Wxrp z7>#Qg<~pr_I3dN@n>zNod}Ce8@_d=Glp4n{)ab?Ln`$H&XWWCj4-z*-S!wlz$ZNcj zVP`!zo}PWo6o5j;nOv~n1S88#zThXL2~|urIw4o3*bg6^Xl%mZUKSLF=`_e%iM(bT z`62Tk(~OfG1+&nwna0bucQphI16T}$A6tk7;r8>4AMJoc`-R3`Y#fL{L3rCDW1lY~ za&oEh86mP~g>n0r5b3kZSn)+frmQvoN{E;?7&m_jk$W4Bh+$9%rWuKz9&>@^FKY)n z;Qrf{pkT=`<|w_rfkemx0S{a+$qI00|2R-ALg3s**+mg>2YY=icKy=;rlC1 zMNq+Ur{ShaY_5=mPOiEO6Fi>HT+zv!lG+%JGhOwz5FIZ8O1E9aZO zaR-)*SDB6w)|nekv+FP^lWSdr(cYg;s|3f$@}xefjaykuboHpI&~`x5=woQ~gBhlX z1Q$&P9CgCfk)?r$ya@6-q4C0E7u5DQ6T?a$ATW+D!5DMelI6$|xoDb-r(7{TwWEbkxn^QmlMRJW#dbGLV;N>CA#VX17y~*~7o|kw2@g!A4N;we z#r`}T6*_WqVVO@-kUNczY?x&5pwag&llD5(=m}9$usw}_kR;Wo zw!p2VNlW;<*E=Qs;z|?j@13OK6G*;G8cJ1%5A{#-;M3lUN;2^Y|{>5QMvE`>mmW>j_MMgx@oYmzgX=%3ek}WoI zbV|0^WV%bT#U`8GlZ$wSF13@Ld4xFMWEp>Vcim)*P5K2U-{6(owPA7rpWvrP$(#Ao z#z4w6Xv^VN`M;L-+9ysL1ij z3{MK5f%K;x$&v}l>m5K>vMI?7M=V&Cg-?Rzcj-p`=k8^;6;m*D?LF}ZVVo|F?p7& z;2J)&BN+zM*RW(yG7P4#;q?cS>yYs9;pAst5NY{>m-|K7Mr#+qtG_ z7#_v{A>XwhlijetZ_4g3R~3OmX}`jLsEvh1K-`PQyLJlFSkpP6ZJ%j zQ%;j>&xn+ML=A~OQ#kcJ>zzW>Q`0vEkz~^cq&O3C=f$Kf^t8QU_)Od&1a&YN{h>>l zj`CM}a}yD*QERudR_MJxg<)XIZQ**57qQ&sr6Wk+U(yY%@_%0h;o$AaREqGF1jF^o5B+ysBo8ry1EN)SWi@)@KjelRL!pARP}-Z>L04vmhV zodVlNi1@hE+!R={z(qgKODSqhqoekx!1rjmyUK$pWw0)QqpDeww}OWWZ^$~6A&wYMqoT{G^kUSQtCM(4Y@`>1Sq`vmpMA z!JmI&bH(~o+O2d)S2q>cMf19tYva!K%wmCH2hI*KOPvHyNZ+20>V=rUsHUymL_7F9 zwtK9ogLwey(F1W>WAjdi+QI>>uALr?erjfBs4|ehoOa>%UzwwwK_&6+%m+8xGnV|Qi}`N`2ZfS{<2l{U zyNTecp62C@#qa24UTeGHh$}wiTQi(5{sf)<&b*Yd_|X35=d8t_k21qi2v5-LXlCi( z#h4qh%mAdDGe3=^8Ys3S}wcrR9%_mJyNtIYPm8E@G_)A!qMUgQYZ&-a+&pyDUE^q~0{hW-lN0v2h9 z!w9pMqC|l^95*MCrjk2YX!&utUG<|m5a0aG%(9f?GjQkXbN!s~gR|xfg!X~UW)o3Q z>zn3f@Ijqj_nru4S?EF8kCnHn$y!KErk5 z#JJ^@+8eNO>pH2NwLa8M-9oH{15!CJFx5}xyr5;n)LKMLX_M6YM9kP0G<}O!sXIyf zJFQb)h&Mb9O?4u_zm7dhXRL;Aqho#OSY7~u1tzlSz#u*I6txq*LV{+;ks|Ka>Q(M_yPe{ANX^?}Y%5z(#l` za-zK4-|2O?YpK(S^3L9;`IkSW%1eHnI)W(gX>b~+yaSEW#t`KVY@T-L3zP>wxbSr? zysCZLU}zv{ReIYljju}GdZcy!0yR=_1mG^j5mE1`G;h>(S!ylZy?5GOFJRv?HjQ)1 z_i<^QOKz5>aV}{{OygYgD@7XTlCVINbIIr=n!b5T8t0O?%xRoUKF&_#T=Hc>8t0OG zi_$ok{HBs#`wdBRBw7v}nZ`9>YeuI<5iNiGf#Ur-HH~x8$<=9`i`F_qwS4R6v|^%V z)yXs^tClnGq%}g9Zw>av8Ryex0t?!d2VG5TLN0pTPFus&y$!^G{1>TM=6h*y4D}QI z_(9qeBIV`hl>Pb_X<4MgHhxF7@brBe9J}}g@3Kp8PNcf+l-`%&u^4K}00bkutAbZ( zeL_MX^lW2D>uR6$FB+;&3Qu=Idum`)=&?=Zt|;qC+ZM>fH$ByM)pYNd9)sTbrr%`D z+P9`W5SRIrgu0RRpFFas}P4X_9_~Nro|4)GWOn8~rgjphfxx zJ1G7|t8|7ZQxIbeR<%iId7aGVt!|&bofJPfG@aqi3kQM4=?ued#O3`Mo_>TBe?*$j zFy`hUgdNkfSe2jHCH*2PKCFBCI!4>i?UBy%MI(;7p;!7gQhacq^c9T75Bruy`521# zwTF^p2d3M;nie;TN)Kk}s5qJbicWt?WPTc#zLimCoh*F-Yw>19`Uz6JRGrSy4{#F9 z*QD=a7>9c2r{~d*sZMfwrW?(`(i+w+eYvTub-bT3W4zE-t?DF5S~Ar z4$mB~@UoxNPmr+iSUSAse}$`lP5*^l4?dl)VCY&w@K|JS4buy|u4Fi)cI`8KaoUx1 zIIoQtg23>b*K!aM3_aMF(pp!d7i&wz7sU*R_mGR_jb6OA+A@U>TXQPT_< z2!VE*XK-5iwnaua29t8^=Q`dD;Ix84D*{i8LV9+}SjMUl3#2DTT{B>0^$Iuaoxv$2 z;kyh@AzKDz%mx?2aSAGMa}^c%k~-rjBG>|L1{?(Z3a9Dm{YJ)&wdDF2Q${C(=arsu zj$Gf#$oP$f+p=glAtys`yCQI-;*5h17Rle0XN<62a=gAO<1C>zY9tl)z_^SV>PGBl{FYq}5GC#zK-KboKM*jg8#tjcI+J9A`(#GBV< zR1?nqYN!mOwr3pnx4o4Ez&u*VoPW(QxL6chbunYU?es99y(Ogj-lC*N+|O9c@M%`g z*@dq&-0*?784h(Vq^{J;{AfEVTJbrwRNu_S;5*p6Ugkk!-*W+(aMbfFys&;IykvTX z^$jzpf(r24W|>@_J^EE9JhZ&RB`q`Iq2(3s+&Xhh9SC1{&*WVC#Mhbq>cMq=MJCrK z+*4*I6Y&QbGVMtiYo<6Y(lWWW<4SraoN4(AFUibo=?vki0($*pQDy^jT~kTZj~+tH zjU1M_gQS1;edZFVmjWW#XPu0HL>(gCLCk^3TNJ<_k$i}E+^NAA7;W)ORw`aE}+|<)Ic3H6QuqHhacd^gv$?AFp7Xhv_Zsn9Uj3{Z1 zM;52V3a>2AzuMN$;{5BGch)!L_sDu#oDxGDW^qb%4$k6~*tK~UQ6e7DD(fsUXfp}L zc@;)+7DZ%n{-Tv;aawBGk={SwDT~w6&@Ndw3BIXU)=wUS>EszN7tI@-#rk}wjLte^ zdy}Kd$_hoj6!5L{H8EK~+wRYZ1Gt!&#qcZ!S$m?jnyfk~#Pe%ElzF4b5!X{>4S|iF z2Vh~w3U$_GCV_fl0KI5?cSdCC$DQhg%0`OaaO32x07uk3B*h;`rDy42pEsekKQk-c zW?D-D+IE-4lX9}UzyS+TEWTQpb&5@-p14V6)^LW`6U08a!H}#u?v%pOaam%91@a(- z9{rh^r9w|zX9wA=I%1Wcs46r&gg&=#Zj-$kPJ1AxC>3S1dI;c6wA~<9E-moNuxzsP zpU?=6%pS`wRtuaU&3?gZo;@A2r|~pQU9!i235|(ev)eKZtT-s-M?iimvzZUhl$ENIHR~l`Ym3;;dEhAOIzBs!bAMG(Ddn6SfUmTWwgs&@`MrT*? z(Owg?(-}Ve#`&CZdbT5OJtG^ABjg%^ua;!9d{Ffh*3@O$2jD;zB0~z!uFp$0ZBOoC3}B0`wC;#N5SK>olsb}g8De)ZuXx}RFJYK*(|$DI6(;D(tsD) zo*d21t#a=1rfA}t^EV$A`Q*gGW+Ni!^njdkwV1pz6hW^Nj2E}e8DlSaMBmiUZkxl} zJtnrxIc7T#fj*}VI;qQZLZzdU{qU^zIro7K9wE+gu!C@HSPpz9;}MRJ%sJ-_Vf$`5 zp$-u0dgf@!{R6#odNI@mOR3yTeHC(PpX1kILi@b*Eb^C`BF>lQL}s-Q3k}zXNg^b= zP;I18r!^Xd(omsREYyZ;BO~<@Qk~c&(Hlj21aNY$?L@T?wspV@`{wL%0*x95ymS9@5FF+O3px56-1}y>?JDj&&-)`Gk@Yudi-rx zjt6V;_j0&OgEr;oEMzSHK|zimYw?qdb6_I!5gJjJ!!Y&CN&i!MPG5!w8S?hBF5krS zzR!ts10LLLp!>v}XKWR!7ls9)mtDZ2`p?M8bcJx|!kp%w5Z+psqi6|X_lr4K$o=43 zIfEP_%)XzKKpLr|k8|pRBJj%e9)3;5Z79=Ni0ZbL7iIy)%0KQv+ZVbk0~a;+EVKCBDj!0&P= z6H%`B&#fV%PLHP7t>SXw)0mI2k0SRL5&exOcPvRaOrQG;xgVR7%c*8*M($S9zH4)G z2QnJhnhxlM97qA(XG?RX2XD9@EKjIIbI21t10_y`XepSzII{o{w+HY5z5 zp4*X#BcGLv*eur;4_uJD$lXHHWlip7HaBdG04NY;<<)Y++I6`hgx=82xiE?O2)pda zJwmR3-J1)`i67y4`>A!}kK|4zz2>7+xpu^XpPtWEl63ul&;8!^>V*Zjb7zA#XocE% zKR3vBf5U=Dxw}bS+50MY389kwE;oXRFi()jImjPQc~jXmEkd0$1~))PIIje)q) zO4raeuP-ZIf`)*uC;p~KUSnc+b6?8YJBo7tHjZ+hDbG7^yB`pxaAUR?f}iP*lV>Md zhoVk~Jl3J(m0yb7(X+>uj>zj&>slzQFu68vVa%JwP*WzK%JavrNqGqZ!OvKqocF{L zy?V2pU-tcDMjqn|S;SH7w)d_pUY3~`$YPqKMS4_uB(F9$=H!jF7hJ;6^Yg;nF!av^ zXxoB3hP9}Z8|4p1bD!9?!Z#M??E(&H{?feZwo~A6Ccdx>!WWk1^m?X%i<;&yU@Uo8v;6(Gzlgx= zTjsYSRrGS3e1;e5oZ8y8%MY`?WKv)9uybhsL?SqnV0a&SS?&Q88qQj&kWHW1-v5892*KVo~|PwFxU>4nEm$=~h(z4 zB^)vXdLszkX64s*gRuURe0Vp13Ey3se~N@#mV*i*9I-MV)@EG7#?|>|=zsC4&H4Ap z^;VpJmxS}T=C3DV^!EJ8Wb|-*Z+<+e4|hJ0AMFm|{F0V z!hF91wI`FaMx%8DuDD=z3B2KlUmpV+29~`1~ zwNb%Ya(#D3fiDr`-8`y>(+j8?b}cIKCKct>8mfj_>!=#iH&8Wn+(gw7SW_^Uh`ntm z%|B�XK}+?Ja;s0hh4H{sLHhatXKmxgdn7rSFM?6C@1ytpFAaT*7}}C~zYc&E-bH zZ-md~+Xby%$@SBMT#~NrMZq@=3y-?{6sDq6bzA$QNoVQ}LW^ArebE`$!WxUq&vPr> z3&kUyci~!_>k$U@eT1w-ox+Di$Y*s63yIGpH!NhClK{AQ6w)!@AEn0^y5L<+3b$IF zeO$}J0EW8x8Kf5;*tYO3A>2n)2s^zm;i!nhUL>sUT*x_ZPS?V9M1Ur}3Y(Ge+rEX9 zY<|)7d`-AMe92G`r*4H3KK}}?moG2D1&!dpr@e4 zQ^DA`rxmgser`c&kFJ$DIpM9-3vZIjG+}O&y0lQi;3piwtVvGw@Z?p6<)rr1T31LO(eT_2g&D*Fy|xq=nZMEHX zG2{;+w$x=7tsrXh%r6p=uxm-t0^7X|gFTk}e2E#8rcg8fR!z-#e0tGGV#ed^ioPbi zylbc$PhqOY;;mGT9k&-H5j8H{SJck-#s&+H7R_`Nyrk2~i-WItqgSVj7T7KbcRB~m z)jqa8D^!1fbgBg$=TSMW%n!%?RX9(mfr1ug|1(Hxi|@Ho)*Cpm_x_lv%1G#7kzZ2M!75~&T^tr zrAx6hHQ>^Gk7EA!5p{~;^RApJ7uGMn$MDevi)a@GxBusm!v?L2Szmrs>@4;~r&<@o z<`4HWFS=u}N$X;h?FPI)ptvEb9aG#E%`~TYp=m-mb&xxJ3qCQg`4YI*9_0Ltw#5vK zSSTWfz_!lZR3iy5yg*eZ?&wleM8SUlyon? z;R&?3DTCGni>EWx96}C*4PO8dcF+|E)P^u9r&!|-VP$zStf;?@`&1UEJ40AAf?ijS zD(>k7*9RsP=hT8QVoq_sBZR9L7T@v|tf8&yh4sY@6S-W2N?a6Q*igL5_NqH&{l>-^ zj)xg6@Lc2YfB$FhxeQx;e+ocgr5l?U*vepjppW#iW$i^r4jdxsJ~68`8? zVkW;k_?PT*gm8ag3DmtUcwB=L*o?jf|J{UM|J1Cc&kP1a>iO!TrA} zfgN#MaFcIK=8@kI3@k|{xWbr{Sca)YApQ*s$t(56aArub3$F&0Browr^H!Gx;w(eS zJHSV2DJ2YV=&bPMND_K#$U<|;53WEsJg;OgQNY{clJi86U#d!WkoyzHl)$#2E!Z@! z;cc+1g}8GPaT6D78< zFvX2dmarV!BY0S%r$@6ZSM}?Fe?40=9TwzW*8AhidnJQyS0x6(9!{JVam@1)7Z(~$ z|Fh&fDjq)au4F!ZGz<)lO9iEe1T-3LUwW9o+s3u@7$0@5MG|Ow>3#5x+NB40TwJ%b z+?{6Y)uePEk5I2!DI8nJ;T~#UD&iBkhL&#O@BS(-9nRw>OG&nn?*O+`$I{t+f(M;S zH@cv1ad3BSuhI&J<{eDgA3ZbQ^uxQpD|M|+6?s`+8eqF3krfMd+1JezPf?ZLL6=MO8r?tmu6!riH(N|kO@Rd!RX z3OqB_s=)87OW~wqPM2}>tO;5!uqHUauvEe0_F8V$u-D4chdc$hb)_wNbM>q#J;L7& z+Fr`?wO39R1CN)~LL>cC>euWql{-<@W*sTL&#GF2hi@M%-C|D#U302b&WjXy)~aCf z?^Xp*yjJ=*FURcLpkSKq@B5|Ye01!SQU?d5Ndo_w|3_&xf49$@(iuGNi}$4)8J4Pm z_@5fu0My;JY=L06^(l>^TjaWq9iz(pQOIgI>~qgClNQ~71Sc7H-dygFs*anS(H{3Q zz3pxApgSv?qe&ymI?$6m4|$aR#88VYDA6d~w~S#ffy*BfT-KXDRu%e{{bY}_nm6)B z1L~FSW0cB*SEqW5z01;{16baT5n>pC{<8gn195q?8kNoQKx4Kx@<(xP%Y4zgFnEHE zYF8$Pk2QgOc&elfJJP74L)o8jP!G9l>REQ*pGGx_WoACXC3RVlGrjAUR0fBCa%nFn zmq9DaMMs;&qM&<~l4azvB+? z9$r(nn4!8`LH_Zy7`nAgUkl~Bg!@0ehvCr@PbW{=9OFwk8ym3;4V9hLlfqCJB6S_tE7wpqFzHbsby2ombJB zapi5e1m4(ZYWWHm3U+)}c_zdAbWTN3*Y6ANe>%GjJ>FRUMGeyKP30eLuN|a~M3tw@ zJEPrO%bjpkP5DiRD*G7Rz?g@NhS^s<=Cg_2E1*5#?tbv7$nv1k$^h%#zV$1b z!7epYT2;FW3s{uU3ieS{0Rt#5!47f7J|1^s#|n7%5 zR$`UwX{l8QtI8|h^E%i%yuxCVuu&BjlX#D}B3z$fMVK_H;xL~rWx7@Qk{K1%?o>&a z7F+KwSz2+7H`K6ARyD`fSiu~(R6OU^?7Y7s*O4NOJ!~yl{ZW4u)1!r!g9QPIbny}oxr*M{D#jrA`pQoqPX97^izif)e7JbT_%tm9=IBB+GN zP|oezI9FP{^}S1_#r0RZRa(3?*`x9+UdDMomHB+-Y3^TH#aA92U_}^N--;04u(F2N z$KOq@363|nCMauREkfF=l5fcI-|Z>~G7KjyPR{jD!RSqw$^|I5RZv4*)3wskb}izE z-74EPM!~y-ym2di=728tgU@xfJ_z5y ziW*hf7B3!F`IX&2Z+eZWZ2LbqF{Di6@s3ZZeBwayj!mib=I=iFu`6ADgPRJqWO zrt(}`*@ussmRH8`(W5IXSJ~49qt;e7arvLTdvN7J`~SI~vAuGHD|d~7c4Z^Mk9g&s z%E1B%KisYS$pONHk1Ds>Ls<2!5?0^+h|~V4)On#-wPig}Z9$do0}9;Lt}5IMJ)1A{ z#v^K1-SME&@`hFS9B8zlan(_0F1mDZt18$!2T7Oe!{A5`E>m8IDsV3@+ObE~CVMVg z)4S>|jbYcmRULVx*a20G`CRTXRhRe#Z{n-|;G=)Zs~%bIs;h4C*&Z3H?(uhTCs(;U z(ZU3oRTl*``Y5Zad7$7reQZq}TeaWzD(~Ip$GT|U%&J;w=4Ch_q+(oEDyr(#&;!33 zSLN%7LPhHvPI-FH8MpbNsx!mszXX0Hz~gl@ht#bts3ue!pm`0a!1sGs4r$7WNC?+P zYj43m!II5G4h=`8+h*25#XDx!M~6EPZG~2J9vXy7x6f>XrtW}$4?4l+n9f5((Cm&w zn<8`Pp#f;~_L(}Ha$rH1p#lNgvwLPO)M(F4IOl24%x388?nCRLse5KNL}7bo2BXE@ z;Brydq4km7o|%o1aTn0)v3urtn{(7dn)a(a(faN~H=x;jXM&^eo#~63em%4?I=v4- z_xFG@MEgPDiXKDjpeFlf)J(B-MsV!*zOzNoO$VOqAn)rs5FAbBFG9 z3zkF*Md3!HD8dk~lZeD(X@tlaX^`l`B88H0lN2@DwPJM0eI}j# zahO;it_#%(g$7FTKN9g2?e-0w9xoVcb5YhERz~Y!u_{p3^cNf(>G6q&_r4CkhXfO7sS;P6tXeh$0PAku+3n!1EiU zxq{$Oqd_Yc=|y4TVyQ$Z)k{SYdSj$XEHnwjO-3yeY`oxCllJ|2LCAlU8jd>89X2rN z-wQ?Bc}R#Z&K>3!EEYwEN_C>hNJ*p->}AxN#0Ky+QMgtb90+x)T?nHvqTW|?`5K>$vfS~QmG*vJX>!N>420T zJX~su5Q@X_WS8mL4ng3lVG$AGVY)DpL>!@)>cwFZ;RdacoXaCIpzaH+>)|ZrqGf_0 zkzObXlRynL2_+-~%>|^EhKqFiFi|MVzCVmeu~ZQJuf;}4VO{I@!r;jNDEN85=@IDC z`e9AmNZW~0d$iA0HZCu36Vg~OiIj-7LZjXk3Az+ULVXPf-RdJEB1B>-ZnR`{mP7D= zG-l}f;bH!G&-F2*1(p9{8+~Ia^V_Hdb^n1qd-YTQhc`+;PaW~^D$_Mqwn25?Ro4mn zccGAA_2||?IuU8^LPI0Njo@k8FsQl_MzJnTEDa0OORG0t@W-0OCNlhl5R-`kC zM8a?h_P#Ofks##X3l>Vz>b=7T221~=Snt!rgy_3{!`y=-L}8I4Jy;K{Ceon zx*i=C7%YK)EIiz35F4TDNF_S0DN-+yh6|#Xy6B}yVNKYM?m8i0R}d}8CYUngis_gM26`NCTh8=s4*)9LH}Mh z*H8rgd&y|tYrKCd?a;g;c_r~VJE8A&BOP&utm@PA1*=U~K^h5Y&%>aTm4=7v3^2|UnqbJI142mDWVJu7 zYrzJ+1RT=@r1S=fSS;2`!osCG=x@bRQ)HMHPhK=^w;)&t0vZka2qBD`KzJ#5Cdf`2 zBT;y0xB*XARqukX_+LvEi@2&5^zS92L4#2!TK#xfgJ7*LR4NihKz|HA2~C?8+CdTY zq0syrw4pkj{c$RcCye0{VUb#c!Ke*~_AgW%2~C1lYBCx_VF+syV!`&&AOKV*APbr| zN(>Sy95e^r2XxnA5r#;U6xuv!eL#T!D3J>AUnLSVM4&CNp$`*B{7)5&h>QrAL`1^4 z2$}>vG#o~gI6|il3k%bU4H04g(*Oql$5_@{$HoXM|J^Xc&nu1L-yJga^VI*Lvq-37 zA?RS`H2>iL5^c!qVYH3~MQGu$KZ7tFo;JXuQfZ`E0{ysAD*6A|dlN7z%Cp})4amN4 zvdwgZEGjV6(zUcJXwcuBWP=?Oy)({V(L%9fWhs z%y3}V+G#D5xAm-JsjRcAkzrNL+Zn?(3TS}d+Fo0hu(tE#Y!1~hm!+m)PR%G8MR(jn zMnj3y)85jjFo1uLC%3b&V>zXT$_T9P{l!Arw>ZXnPfY8x8T~mIhD=I(os#b zMw%^4$$8g4HIrx1Z9A`9CW|7fg2hNJXgSl?3}5@oC}Qb%7xeUIk65*mwN5SMP1VIp zL#eRxHk()dIG9K9jRi_EgO>brc;}$piznwxH%* zbS7-Zw2p0WV>C1U^gND&yp{Jp={X&PEQfl-gSdHV(O}S^Q=2Ac66OyUwo*38;@Xr$ zR82-VX$VwbRotmfPbO6?VOuveR9*)Q(6vp|%9(7VX_qA=@14F7ldQ(DjI3s|#9)|4iNF<(&CT$Y}|VoTeOg4v_$Y~pme%5Q_cEUwYu8FIPkR$22b zvFh;YL+W&+POF$w%7Lbc zwnQ~sY!ll_+w`v@tufoQ(`vq8itW-K4dz$DQt^`I(^n=~R@&WSlSsEReGOmCOe*)j{ZT5wG^Aj?XlC)rsvUbj#%tV}-i+OVIZ zOlGnLoFHk-%waR?{A;Qf!_PI6wX`mc)o?G1X;X|D zRj(#vnv`mmD!P1c`p{$+%?b~hVWOa>RU@0tXG|PV84Xu~mQ%c^XGukoT@{ywgBMce zDe>@R=#M|}tKMU0c712)P$dkH{u1cF+ zn!0Jh7zsA8*!KVN)9Gg<<61ub4=9TZE@uD2*;z0gH;*-iw;<#4AKO5KH8c5K9{XhN z2-%Zq1<$+TNaGugMLv$JTY1aIB*#d}2U~I10~exbvFK>{>2aXg={y#?k+GBlY8+N3 zTB)ySc^iKwqG*%j)<`D$%U!)(N=n03YUeo4IWc~y5c8?Oz+l0BI}9GRGzI2&Eh znaqRKvQA_ztQ#$DX3`jAcE-x$qrs_&$I0=o9Xg+lnB9%77sn3EB})UF7z<7T^_~4s z)g5o`8Pl=GVj9c_o}1_f3j+Jz5@PD~gNLgX`g~kaqT3la_pJKwtN#0{|GvW8 zU-jSb4I5Pb_f`M>*Th(={`;!`{y!rIRsa3Dt*ZX}b6ZvY_f`M>*TxO1{`>z7N>u&# zRsVg}e;c8J7v{LooSN-?<4;)ne_y4#0?=7*Z<#Xo&n zhd8;XE|w^-m_{{GH@f~bFp80l0zsQPeUx} zTb*d-CpXXU>vjEdv&7Ozw(JH#%IKCw*VnGo#Ksfr2NG_}Weq@BygqQK5`@hA1S6Cy z5Hhzj;e!-3*Dww6KOoQ2V9DtaB8zm1o(Az{LcuAKpg`~)RRv9%$vJ6_!~$Kl0BE{o zYABZL1c6E)t(=N!Hh79;OtUgIgOPql{jiZhnJOaW;JHBFy0!|SI&Ha@4IbNc!JYud zgIYKQu-PCDbLlidC`SkHr2}&VX_zHOY}%HV6`(_#5Gc{TqvrPw$GOpCGQ02DF>lQ( z2k&B@N)T5LKwe330N@x5;3U8c!8?NS%DLd_HIR^&VuXn*X|-VFR1H9f!l=uFLNOu*V*4PY!|AOn(259e4+qd9R`w*Ji8m`2HK z7%E1S6Ch5=)pw}@HE4lCD*)Rh%Fl??iDQHFWN=xQm-=Alc zdYZg9UI$?KJkLjroxv$_2~9K2jO-($84MXrThz#GHq*@LH|Hnc_;~@*PsZZ(T8LQ& zJ5F+hZGpL9`^k|YmD5xvnoN}<<*NxHrRRaGu;&4qX98&?w&|q)>EIkBv;c7aasYfj z*sI1QKQL#L3n??ST(Yl#Yvu|CRbrQNVw;%PJ4|hhZrBtDUtfP(GOl4V!<0jGvMcHo zKb4AnB8&7)N1`dyfR|Yk04ACMf3&4TR7uL8%-zvJp9Z+a?Q0j1=n~nk5lp&dKC)52TU7Dr ztomU|Zi``-bxCPpX(wsHWS1t_&M-mOWX#}&uX*h{QdV?BCUUUPfpzcuY?B$NOj1yrK0sPtq9jqJ zpnO>oB}g_4+6!PkIjnhyyv0J!P_#mjL0SvCHba6A*|=HG)s-wzKt_pUW>AAnG4O+# zy@FX5(`Z55bx-}7ag7EUq$k{4-=&r*kO#oV_9`a@YahEBn^eI`qn{K!<;&|xRRkqz zWY{1q&Q81JVM;t0%cZSrOrk;nMx6Tk7PbQN2S6!mWM%>G&5%FBNY=b0YFue4ZCtHU{hOLpQ)*n3FOjkg^3Z?=6jA4ChBQr`HNb-jbcv%NwU|AwwX#lbY>O(L+;frfJ5!Ho2pv#DSkT^h!oQnJr&P zs61k+p!~B`sxH}XzzOptShCKLq{xDp4wYxXa133Qln&OBT%Ocl*C0>80jh{HmM+69 zWu_ZBVI-eoV1WKDkdg_Q4s8bzuAa{u(v%A(TTH{e{+jyJlW`48bma(8drY!$QzMC0 z%X1SZD*%%YJ*9v(f(A*R7pUq`0RuIe%Sl5suac8)Yfi?sC8`ndx}0@^iBpReVXG*`{9GGac|IU_N8{Mc_JGenj#kA>L%%*tZt@@!Ot-8vIMw+Zknk0)L^PBAVtmT;K zMK}Sl+zOfSk#v?xqma&!#6@~so^^v zKo|HTNr*NwdDWmg6ipd2L)yP+;6ROOJ|om2vrbJB?T>1@ASSn%l}Wm!JrhZAsXHPg zXuKOT$z9+api(G45qN>A*QME-tX@XB2MVN7BP;DtJAqnUMBg0YoKWA_OKGxHWLCum zo1+e}iCMe14Bnm`HEh@rN zP>K|LmCzW4JN+Qwxr(0sgsf#*^Ndsn`RGo9xIai%?>;wP%=iFXM>Efej zjcX&GzA0Z5RB#_~!Yu87urI?+H22{SGuDio#osO7nsKw=`mxy6?&|RBz*YNv)2nZK zY<354 zQy@kPNEMJ>mi>SabOTjC+4>A6ERP;{mPZxt-&1v57F435Er1OPn$alUi8)eo60#ddykf9#8^MK6D!jm$O>|{ZlO~mj&rba5oK1Y-;VCnW>nQtf1=n znB<&C+NXddfdA7hD~q<2BiQSL$^-mk5XQ68D#fMza`16y`M3DTL**6o6q!y_YFeeX zAQk4Q|C^E9D&>7CT0`Be;EJ(l`B(gjDX$2WlXx{1pe>!yBIRFDN~p4HaeY~NJk;o7 z&hk5X-7$0i*`5Ni6ctyAKA@!;K=*))s2R$312+bR6V`&~SoB%`4ZbmU&W1t2{1_b6 z%cC$V!Ejern4;Pp<$Xx+qJU!1D={a9y)tR?oI5T^7^2J8IaUijBVHXc_fYZViuIku z_yg~5D^4CVx4Tfb%{gq}Q%vvJH|O+71YeF6GmCRNgg;J-HE+$i|LYFairJrD-%)HU z&Uvlkf@=?%du;PY`0gNaQCqVtzAnZ z<)YixIX%S7iMf5m9bfTMG-Ljxxra1=p`U*>=dk^s{m7)b-%0FqXmD_iTKQgoBCcsM zcl6gD4&UyWmUCa*=bZArh;gmvUj6ll1_dwNIAHFImdOHTg%v=dpncI{Rgl`$Jj~~* zhvgSr5@Vj8Hj~vW@}zVy^Pg%rm-lT`h&D&LPtf`pDb(rGC{#*Cro3ZONgf6Rf9A)Y z<-hMzzq!0`!^Rjnbgt2o0>uCMnontd-0<2Ao!cYP^!0Et_Taf4!rz9kciS*tZ+O}N zJRZK^We4+qE8CW@^%(J^A#)G=kFN)LjNx@HQ)~|!83q{&gvnao1wi^>BqbmmCi4bz)y6!N@34Alx3$LPKN6#%Dp8{5+ z*<3Hn#siv71y{?853Yma{#3>cFBo%HaKV4inES|2#g;bnhIv~)oqKUdvHKLni(A~9 zyHL!0@YZ48KYw!Toa4m_EfCi{c@pXzo77$flAP5}H4Y-qL!S(cRY+Q0^L z5VGjZLYL2EX4pB8ikXsqC zQok7`$D+hNDc(9Y{Bj;u;$`vUs3vXk^dGJReiYp%aM!;!oHs(tRKRE{>;|sT&Ex8k z88#BIW~N~n0V*o-lYxuIry2mP8Kp4FLg~Fj#s~@~NA--|3`!e4BAWqksk$8XwloQ) z{q#AC*~dLu6T+`G=;ItSO2gGec8F<9%ox}=>LVJ|uk2PUBnL2l?|`Yn|9i)h!-6#?_r zY02o8eW0Qu>RX8pNido=$z#;1mTocf2&g7X<_f?}nby+;RJ3e~VWcb}nIypD6q~dk z@{&j^1FfnSyr-XKv?VD6ZmK*#$z>E^Mdv1Kh}&gdk;O5yp8Dbf#$UHGT9W(0u-ACXsOyz%dl{vFM^mO z<&3iP5-=3xkO30pEl`X#i>?j^&^GcEq2;0TfaVlW-6b~SoQiBz7A94bacz<~YKgHg z%{z3sSplBai8{Ne3X;_TyftuYh-CN|I4?yll@K@@V-QsmvBY#rGu8(!dLTBNgwgNH zbmX+U>qF5y8S_*=LcO8eme#e@iKaNI;Q$}S_ls*cquHR)DIp5ofKjoq$c!Zv&O!lD zHv~p<1*Ar1T9*9BNt!bQka`jfD-(&T)f%pM2OW;Gl$1ZEx-&#KHVZ1OEy2bG)k##- z8S#^$f*SVGtp+IdPQyf(B*QA_$h}I*vq0n=>WCHq#-sm{jAS*XXp+7HGYO;BU|>@0 z3EPdNENbi*BwI*c7MN|SnM($qOJQziH zHJffR!?PJsJ?7o*oo;bY*0hTqkVIoB=mq>G*y7Mcz#Zhmyh5U*#`n7AQ5Ahss~T2( zW+22zC?@P&bf=yLQ7o&ilOQqYha{f)`xyF$Le*WOKC*;8*I?lXiYyE1YOJO{X;27L zk8alyAAd)TOX9r+>m~8`Ek?XA;+{Un=~GL+1}mQoFdI=ZJ)mQ_7l#J?@&6@Wo$(ji@<61>htpWu$&7 zkktU|HkgnkwV5P5!gD~mVm-j9ld&r6m_s2WSzAbpCbxj6V7eI;OZMY70bN8YrqQ4Z za4M!zAEa)Hh3ATchg+5V8MV*xGq47bN1k>hQ5$MQ-XeVwg6I1Ceni@mb|#W zw-*4^$r6a_mjA5i8!;N2msgB=V@6ZDRVwgS%jgTFg$zd27 zY!o_HuULCleVA@#MYrsVi+>`{NX4|A7IO;o714Eq=){(yaN}gsq9Bsbh&sewqQs(Z zp%hwjZcQTmhiZu8q$qGm=FwlFoq>c$CBjAx0;NL3lDrNqyku}uu%BEO;6xO5YUz-Q}oAYg+sq97gA^dGzhhDp{^URpdv z?SlySgz1=G(JjmR5}00bEwlG3;+9#MUMO?8AYe#=c9YOLWfmk^Cs~x_9kD@q0mX++ zrVdFvup!ALJ~mTKZ)Ol-6JjKVe6ZS6l_3L1mxjXw?Tf1RXr9yoL5aq*ODNyBZ^DF# z=vLEVYJPO9{&Md-H-?hplvPL~m1MCn!${NyNnm7NW-$Z}TTqa`DUwcQ5j-t~k7TSv z=GjwWLH_=UE5MtnA4BsH>IkI)!s$=SA zskJc;`=;2q@6SJYgk4D}=I6me$_7^gB_A9akdh{?%^Hj=N(+^Wn9z#1cJ;hF6H}r) zs2Rgi%#dux>lgj5i~yVG(}=}9kYxO5%3q{@qFkQ=xnMP8H{lY5_mtoOE{ROjqeFNk zdBw=4!`+Bej=qEplA;R*YBS{Xf)U&Vz`;v}LBqm@E0R3j93UOCA@Nr6Qo-vq%`*6A z(F>xQR>e=(h*MHAP5b+om4u;_gk{a39mz-xH3<$-6BbL#%qXVfG2*~7n=aYQFy2VM zC2kwE5o;@JI%*1wKtAMN(Xr9UzGTVIC~52!8y6yqm*JqvsD=mW6E2XL2K|J;xb_0# zm49szha@4rL=kY=Z&}X_Ry?w_3j`pkI6}>w@U8jin*!dN5j=5na3ozi6rj; zMwF|xBORRBppQ(H6Uq~iR!W#ul6pvWQ!;1zofcqlVp_+;F_~tSwep)hKYl;m%;=CZdDi2A2Z`cz}~hzbbG& zTa#JwA<0op%K>3uT*JPG*yStHO&mRUeu^!gno+C{CXKNaz&YxYk)MY|7Y1#f#wWIe zI1>j9DhMGEvb#}sVUlquc%w^^g6cJDAy_?$@WI2x_J!^XUKHnuw0Uqbu;yy28G7dM z0u}O3 zfwPqARMLfG;^o0&_I6w-$!MdSZmO3P*Q{S?=9TwXwiX{gG5??gGG?8s2XhQvCqv8| zgH5gZ~DAf|va%iuiHbpfZ2BXh^1DC#yJ&6X48-#i+gpf0~ zjHWBpyD?!{w8AY8cdbc;R{>i_L>+Jy&ral$Qeh}5mg5B#XH-Z=^E$yP+o5k+Hqczy zOjuk{R`QTr`?S?DEoz}FC$hn?*vz8&y~NDz^SifZ@ThgB*wb@XpCtNH4hBg4&6FPE z$pGyT7ANqEY6v5EXs5*h!YXx03T;L8PP1tfGbo#(zGGgQ66%l#)z{{V|z5TO9BIBWZ~xk z#3u{E;TOR1fzkw3A;^OAdP(6lIm>Wu!1KzmqoNDoBJ4I?%+iJGb)8&K;j{vZFtbjh z;-iFwLaPVExeF&C8zFA<&_@^7sBh!oS&L}T2)45&KUhvlQ&1;Ra&p@|0gQZbW>Rn! zHw;wwj_QWmFdMMsZI3_sQI(4YgB8Uh zSXOXY?D_=NOlzYX4HV&+HsxApiP)O3ZJ#+1esW~YTSG7TCILlk@fbe%wJ zpv((B*)h;VZnkLyi|&?YHP8S%L=47svpV)MZ!G6;VDGG2-a8rnOceb(e)*xbsO@M| zu!W=k0B$B)L^UToK?NHFD;JhLViTZ3T$Xxi@P}q(X&v1Dc!RAxladH-1&T^yl0Xhq zNe{e V>yAg<`5WW)GGif0a{a$?e; z?$}K*<3mTkU;HQ;-D;>R#}*Vqt&u+hEOoP zsvrVb>Zr>^o)`4ZU@5RYaU0^=O$UYFf=w8p016_hN?8@)jREy5^t`sLG_?k0mKc?`oE%Vc*s)oK@OlyT2pHFcGP?kGu_ zoT7TpYC$RMLUf~=aL#HtH5u6^MC*Qt?9?6&ZSgPTL(DOW`3c3g@f*mTKjK3LStc!T zSC3yiPgcs5`w)2(EcmEW%q=RA0rAP3(klQ*vc!7?Kt=Xv0s~Y8#C1uLye#2C!Vv|+ z>jdrn#X<&fsbwA6Ho0tmpe&|cAMxSOG9Ai!%c0z(1pep1q7iv6@L$-OvA-C_dl4Tf z2RJ-rMPym~j41~Sj{3Zq!k9S)CkUqpQ_YrDlIa%$n0O!QUQ#mvTi`pz4V%FtTP-cJ zn6?AR2fn1}X8m}2*S|Hi6->6&C`+v~%!=8D9S$lorw~Pw;d4p=ljUGwva<%DQUuFL zZ8Wso*Mh>BG|PQdM>U^7(c`nQuj?b`wUvq0r9&g)p+sWLs!&@5?Ftx@C@s%Q4ynQb zl-3kjcktP!+T^YS35}B&yXoBCi;n$mwu0r(8D4d1jHe?GB>)x!0q8{iE`anB%`vxSw=W5{b4c8;oz+cNnME)kF83Cy#;=D~$;01!w0bqdAXv&!VE9 zERrAKB$6==2U=EKyS}^8pJxCv2r=`zs7Z*)gr~ni2W0rn{%qu>Md_Df#Q@{BRg4U( zbE6JY{1;t;D*NCH!cv32-!uugb&iZ&wq;z%1XZ%wp%hD|s57|i(M$S;yUKtS>6fz+p^raFdH2rkgT^bxm#SV1O;fwBU!1UQj$ zFgO4d+rzOXLt&T;1nVi8kD^HRUG@|<7NS&u>j)DE{<=xNb`{-f0H=*4{P$ z#s3Et1WOz%7CAO3g8YYQAYof30y9h2b0);Vj!j)pv@eD&z89U!1G0cW8g^JKUT(0s zOYknK5@5w)Ds6&=W4YsPWuyzM2#lpvY63XriJl|{*qO17y42gmeLGZZ~;m8c=vY=XtSwY_M?n5U|UO4EUu z)haMID*O_&rL3%J13CvDE%X0^t-A(C8tRv+pp=2UH3|#`$|*<)?#_ahHLjC^=KuE@bTs{7aZ* zI6N?vrCO0;P4(7(Ay`|YTSYVfYlAi*s!j3q`wft{^qkc?@OfnF45dOxOao(80cJ_s z41Cr^B2f%nqRnVZ45(nBC1aA%D&u1@ozpBn7BeuLc|BrYnb{PTu8n>w`AR}1kp&YU zJ#&jdD9~+mL+ak+qQ>MRlS2<(#DqivxdZu;3Wp5dkj0(O zpk}pdNiv`?K30q3PdYmxK6WeC0iHO6>XHWzu2Y!XBa((Yk!=y{iwK)!T_6U*`yqJ* z$33>5B`KHum{N43YEW1`ra`gv{`sm1+=+PI@DgG%${a7E#}sYBv5U!s&6R2Lz2W2} zTa4f&AYd{e*oZ-b;1r=WHQRDJ$j+$+LcnI7hvS6k1XeSSA9i-&2YJ*L)NowukIbeD z1j%_(ZMr_9swT2YvF5@CTO8GYVeey%=3!`ZAFMhfQ!&}_iJ>ZD4TChoflZ_WYU1YC-)8Ut1C?2uts% zpP86aP28*|ZZ@ThRueZy=vlSHF2|=xEYZxT20)P>8RDj&1&LiHF2{km$-R4 zS!9xHHF2|=xLHlyY|5{7tBIS_Nsg{2ZZ>6%`bes3;$}5*vzoXWof240+}wY@Xf<(j zKRKz@#La5rW;Jm$$R=(EFRmtT?wc`+0#;4jtR`+Y<G`6P26ls7p*34RueZT zwyLcrZdMaF-D=`yvwYWT;$}5*vx#w{nz&g_-29Jx(Q4vmHF2{!OIkH?v#AVROnzK7 zakHAZSxwxmCT>;}H!G7xtBIRmpZ)rc5;wh92QB`9}j2|^JS()HmHg0na;i5w_Br8rd8ps0pl2ridbbE4Z(3zOo7j_-94 zKOBBkJ8`wPHkxiwy0Kgd}>h_@$xa#x{H52y{MbmbnLVN;-F`^ z^wUo->f6q5-fw;6vx}1Am&Z)&Ena(OQ4ewYvC|I!pIl(Z!_O|_?{@FuXBRK5O)RVE z-m7;mPW|`COE4U}*1Xqc#%CWa_CEXIj!pNA*HzSH;f zU3x#?8I|8nnRtb|pN9SK_BnCl721Bz^S^6$p1z;+{O_8bmzL*ssCZ&A<*CNH1N?^< zz3UstHhVXA<$Z^@ZSCi~X6O00*z7$2yJqM4x46%Fyc_?^W+(dhz0ZjgukgQXcAg=h zui1J2cg@Z-_j8{A-IQi0TKhTC|E}42_I}RuziW10W-%xwWyknEji^L6u*2+rXDo=QbW74xiIl6aMl? za~h8f#XI+kJNAlCJzqW{@!Q73!gELew(+RSYd^iL>Dp~>Z8XA@#@t%I?VD~bU*+Xn z8%KvHJn*~5qxW6~7VuZNO6o=K@QQhjL&B4m&TBlT@}$!rZah#dd7*J&<(FgL558dmGh--<2FWD7?n)3(J?=zOeDQ%2UT|=Fa_#oOD~`pr$r&E8o;ObYZ#*je*15fWFJpe+cwG4H$G2DZVp^;f+}6h1 zdA9E_ZXCJyhAAQYe$$bA7gu&AzuD1r%0RKVAJs(ZCm*@y>ee= z*HTDbcJ0KK+{$4q$}i~4ZH=S%-W7$JAL$Us>*v9XrcCa?`5DZ--Aic~^O&3|!s#?GEJ?NL7Tt z+Rj*A-q)U7-FQU!v#(Y+jtRvbZjj;ZK}aAAMtqsGI-D|FdizNM$vmj_FS4dubId-pz9NC)fDxR1;CF!kf| zJ&b&`d=J+?TE2(#_B6YKzqxGO({v9FpOkOm)KAK{@Wo?gU17}Q`&=Mgp4a@9F1qjW zau-efOZm4oPn7Rt@)PCzIQ6glzRYX?$jjs^ra0-z#u3ddhzeVyY;jestHAPXt{?Bm zHrSsUzdtR0A02ncP>#D;2QMInB)>YNI zazxd-s#;f}h4=rbb>(4QHMZ;k6OWdKR}k_Hp*(m)Uvc?A8kP2YzZGl#(fHLqXI=aB zuMQNmZ@;#aDE+hX%;tv<66gP`adqVd4*aaqs`%{v;IqaDu0BxPTOv6{jd~cjG^5T` zh4ajce!{+D*>PWc#$?ayD^^{x?5m#I5dqcjJ{Rcr+=_$6n&h&P;-^1fcJ%(wnf&6i zVd9o6m;Gk{hcEoavNQI3#Czoz%g!Ak4j!~*n0NX;%ci#!+F477d0(tpc5A#ZZcand>zinN1;)w$!tA->^fad}R8#NCjYlZ^grU7x4=gge{ zvsSgR_(4jgI8+8ysUZ%(q6te?rr_vm!GwV6pIdqbTphI;82O+r%w!d4H6iVTpB?5j zm~%B#(Jim*PIz!qQ7uAHg=^)s<)_9qI&kt9RjfX3c?V9+*|5afI-FsY@P<%;Du+}t zhi1uz<+*~U$%dd4qBj_IGw|v{KL^z+WY17PQi&ff9G@5<;E>7NlKKql7Rlpj!uJ6C zDD*gzkJY7qdcbs{{B!}dIg!nWfd9CbyS)bSh-Aa5$;q1kCIme2YD0krFv5b9$AafQ zXPIKh5wm)ST*h$8WT`M;P$9H}Hb=MVwt@{EhNOhZQ7=WA-p8BtqviJ|YSS*PCAl2* zmQbmw4*b_9KZDmwQQ91DFsWvjR+Q5)5`k@J~-QZWs?!kIeV5=R}qe9$PUiR-jV z-VT^4p%H|7$<9&Z9F7DhYrwz9*pkdH-kK4R;MPV@nugb5#hE=`P~JSckD3)wjvku% zW>sWi8jE^oM!CFsD*BP~ko&c7IJDN%srGKtk?>)rp>Bqc#dPu-)#)>k<%Bc}5GJM} ztG8ijGhnxe4<5Ko20B~#OCjbCh>HSJ9QY<7I#pqPF2D|#hgv6ZLrMxOMh-I5kcmaP zu6|1*8Qrw$&$eV-(|(RC)2%%e#RV9%;T(Y$AZ@~jX;a8P3tzECIejtvhg-m=4qgty z8*C)51$_kUOKCf2uz~<@hcQxtlG;^5u5GyKD00s#nPVJKTXuu&8oW@B%1q@ z2UK^neK*qTZdTpR_Ej|g58BNSP@k ziYx`PGMM01$eM5}WO4+9rO6w6mT#f%d~(HA9a0L|afnsj49sI`1FBP)!X2o^RnxE_ zJOzu!6_YV%3AOe5zB7(|aRq<(dq=*w^2k>3J7Me=vT&?{O@}uE0zKV={S}TZ&CO>6 z9k3Q!QWV`Rlvz-q!8ivicp9r-;}@8N;@Zcc&q67`DFMTm26v^x{Xy>wLA?P79Jg+p zrsTQ}i7I0n&52LmSUENs-KZg;DOSYIn^(3=IyuGGEENj@+I6TZVOxej%0O3z);yaD z=~E3SQ&4TgL@k9w%Z11`2lKlHNmkZ@#5}NPF@3RTO~p88wK45ddrTEmcC2)gnyVRkEoY=n-f=n)#UGv%==z}jge+6>u6=5z zq=8MtUx)()LO+l2nAb2h z3PB;bxE6&98)#gE0{U)Cyz>6a!Me&nx>je|6^vd``(;cOT45VfZxhxuj13L$M#$m| z@au*T6*C#65!A~Yr*82Xy`nm&S)nFIMbv<7MpGH{(iGq1{oc>F^sbG5C~0I35ipt` zki;QlWi8##!xIWOr|(5FV6DvLnL_bjV5? zs#k|*A$WyW9fo2>tUar~m$&A%>)@!wlVsT3I)+!;)iaO;=F%!uz^0)<^g1xHwTYt= z+HJ*lB(J~ZIb4J&yu@CVD)m`0rrvotxw4ighwts0nQ zmOy1|nJmoGN`$Z*;!U$)7GS{3=5tMUua`^QObX!lUQ2RxT6pR0izq z@G8UK3llpY9j*!gw!&KPLM5wX$p;yz-<^Ly^wN1OF#mhLLx5Et40d!j(HKYFiO=UT$czgkw5O-&fg=8Kccq zb1nM)Ngh4%+?4rDy6ESEDsiYtS@o6}aq6N@BH3%zN!9(Wy1zy2sMY;#AEltWzg73Q z>i)JrW#s>`{cTgrt*wOelMQXXPUo)rMZ3hmE6mqFZFsm-OD{QL)iDV%`pC7Nyi=YG zzj!3^-%sW}U0d7VOJ2Nc(1A2E@Yhp%df&f(m3Fw8vvKt>?}3f0c77#(wQ<$38S6K$ z=I;S-{l+!Fm?T<0a_V=)FVwa7e4_?$7Uw*EYG?05ZSCd*5`V0CG`s+vTCO`{ZKu}% z{p*tTe4yxf^?N;MTyV@<{vPx$IOd@*&rJNi;`;L&9#X!k<==71L)*_2mt6MnFt5i~ z4~@TEeE8)ff^~y> z2zRJA%v*l{x>7f>;iGlK#C=b!dnVD^oABg1(OQ5ITCcxsh`Exx-rMCzpSqqUqmdPw?5NV z^gE6dj-0rDz?sQBAu>m&+5l!`x?twyE#-7O2M_}PPsZ!Et^rSj<0yqJh7Yk??lX%Q z0RnaK4=z3;zX0dC#qH)WBsCq&nz!r$X5cCC1LZN2^2CRA zVwo0n>GC8sXR8`_9H0c2Y_7kD04uRqH;avD0zV-9Y-=DTWYGpUm&qxHNIbM9gb5_0 zoA$Zgwc5yreUyMACcL(OP!b0YT;~{(F8-N<0!RjLp@oI7=kWB{o->*Ls3o$gtEP!d z4SU*gF}w>(PIWZh)(S>J4E$hbFE7AuXa!Tzh`{ncNa?{u;Hk>mx}Cug#GMq{)-kPS z1KT>f(ZpY74iy`B&+9I(DXt$fk{3Jnm{FJfXwWs+j7SZ#bqPnoF~N&SyWBI7CK=xc zSEZ<$8^&X!$Gcdo1Nhi@rgZ&8cTZ+tBQ$1Q=J!22=8frl(GD>W6~<%N{CvwXqR%_) zPpZzf>RiK*Rh?@mjywDOv8R3exKqym|9Y+wEgIvc+HRQHQyhCn=Nj>w0UOqSGY!#u zvS!18Ix)Uomu}t%CvUiZT_A6m8X~ljz!Bc z-JFw6tLdDL#$c#M+OS=sQ@Lyy?}|IiS8Hy1>W!;9B(1!nD5|M|FajiJW(i>^8CAEr zem?6uV&Ic2W~O3JvI9NoPcOc4Tdnxsy_<)5_rCwe)h)&H2M|x*{l@P)h{3zx80H=R znf!L=yL?;vw-kGR#<$ldHeb>|W!sRLAMwQpHh=}j!OSe`da zKqZgTWMy4b1FH-+m{7(eiF)E{1t*Qxm>S`$+sx)1v_;4C=m?&G=QVU0f6J=?l+lifx)7Ik2#GLXc@?YcpX52CTl77r zwb2cV*`w-hv7+6UL8J6covl^`1#<+pG@Nb3US*img*qc=>b8|p6pRd~DJU4dVt8v; z&$}ZrC8kHx`|5dweWHe--R$h>cU3Ok&2));po)N?g-tnw@b;Bs2=iAs_ zyn@j5BJ2!=fTc%9&4`_4^NvmkM^iNb0J`ce9V9TlG(Z5DT8bm%*tV`R4>dcFDNz7i zY}tNDJD5)~ty;l+ifL6iOGGfAYNc6d!5M}1kxuKm9N|7pF`f+L%F<+*uUEJ_*dSk- zKCYc3AO_Nxcy4picSPIZ-gT= zx@mB~#H>>>trD5Pamz(w)aormk|5_5jB?GA);K@~+fuWjR-JqrpuDbx;lKj;1p@A_ z>k@sH^UzclZp~#`xmXw!iwaPFSb6HB^MESH zY`C1+%iFl4er94yOfNN?K`}$L87LN@NYuN_$WY+?8Pj`X`bR$*k?Sx3pT-O}^Te8o zECXX^Q_{iDxL`gQw65wUN(*ojWC5yx9#MeoXi|j%V3AcVop>!{0~|~+-&pI(G5}d( z>VPzum8RmbH>YiKg-p93o}M+YXE5JlS}lmWBet9o*J?1|UO8e*=VSrvIj?6dg3;iC z$$25LtdcYU3MZrG%+S?A)Y-5Vjn#$yHEU(0Vv=ERX3Hk2#PKHo`8v6Uv65uK9=H<+7RA63#URTON9Py0g2j6VaBpbg21fRPie6-crXau#h5#@G2`U8_ zX9%m0519A%Y@By*q9(G@Al@F+ra*_V#8ank>G$o5+^ip7rYXEtBc0LIy0lfXvl)yC zU^%X?D4>jZl(b3lUDtF;e**pabP%J8NbfAZU5J0Q>@9Bfuxqb{ihr(kqy7F%4&f4KA)>A6(%p zj5ap|FoiJ-I>F4c+K{S}acx$7CVkU@WeIQ%_5?>}wb0w-^<;9kN#<8BpRw2#mNoQQ zcl;_6kQ`|efgUqSsxpimxnsHk^hM1nE*pQ4ceOFK1&~N|!@;19YZq*sDn?we_n8?9=iRc*9= zE$OO_7VvXMT832{t!kr1`2MRlTGdAT8so5PqYa8NXZ~-u(Ew;3kVtxeY`Jw>o5TYZ zVcr=XwhsNKVcz~XOzG!!?zQ!`Zzn7<{QRwb575(xr4^AlXX`lw%}gE6vN}yS>oOEO zC9Om&bB%cR9kmv1#X?Q@!V^3ts*i?QRkUNvgu$hwgy1KdgR&xIGKlsZMd zqXswztBL0UHe}XveXIne|PshPGnf<@}njyf%x5S5+ZSDO3;B(JNUA2|JH@(zVZ=E|O zu~1z7*;^YDFN!%`ii5-tK7Z>Rp>`}DB(6BTSS$Ye`CFq!eM>%m|BJVNDB83_`uym& zoy4RIimk#LDzI^LqvEx9bQ@?u46+Jp5)ve*w#bzX_)@>qdW)mG^Ko_;K5lq!#~|@ScTWC9 z_WH;k#q-7W-H{&XQ9MsvDSx5#EPh|S(iW-n2VJ^}M|<*HCBes8j}PlC)LwkNLAHC1 zy0fQP-K#iyzADcRUpO~^03T=eE?yvZkC3OGw6nVydLS)!?#jm%H}32smdcOk^x@-? zx9#jL`t{-CgP${guIW?!t{7Ouflhbs>>=9pl?T4yz__P&4iGoUk9W)Y<-hAM<;NDO zq9rcthxDmzcgnV%!^Bte*~H|L+c zV*m#p>Bq-e1B>)$4Iht|W4Y-8TZ>oacQ4BE;s4_JT0X9n6PW=9cbxqA3;E=4YP)L1 zF-blaLU++3XygZDPe5%(R+H#Z-`5L+m_w9BEz+r%_Ev|l>3 zc#SwpzT^qRi$4u&Md=!naWlga^0zJcW6cgBbtHOBWNeA=xQ7fgAv)wjNN(V3Im zee0q{QW`I%N2T;rDcvTeUr6b%Qko&9FQhb2N=u})R7(Gl(rPJvB&Els^q!P{E2U4Q z^tzNjm(orty(XoPCr?4VLyBL>FSkmm-OrHjl~QjhJujt$q_k8@ij*Fc(vebnPD&?9 z>2)ccC8d|806f|JuRh9Qo3JC{iO7Wl!i%Zu9VVJdV7i#$4K$F zQaV*i_etqoDb1JC1yWigrOTvrkCdiK=`JbVBBgm!x?M`krF5T^{!2>hq_j#(&r0cG zDQ%L{CMgv!X*cCt7p;)u`|`_wO6ea`S}CPgmm+PDQa33zN~u;#MJWxJ(i2j$rL;~; z$4TigQaVFQd!%%}l>Q{82~v7SN>@thb}3D{L5eR)@m484DWxS++AgJ4Qu<6vk4our zDLpTx-BNl@N}oz;hm;mbX}6Rvlj3Ii#Uf3@@HKY1dG1(U1P)k~L!Lk?(f! zmYiSwWAK}X^gFt3@PKz-RD7D_o-?7?vIXZo{Y?k;<;5;yaGDEC-jzQPgZse@H$^v>>@vOdm#8}S5e#0v0uecFT1Mv zRJmz+ZEyTF#qF}yF;j{UB*e=f?wlxX9#<^;Xy;(>PuCW&NJz1pH}SgSl7yJJY}dpJ z6XoXXi>Hc{m+u?(VHF%IoeHq)Me_0}lw{w?)Vtk8UZAI5%+uD2E78Tcr=P>1Z%EP_H z_yfCi5EuV}`+IkB@pr*tj}d%ei{NVGALnZRYZ%|JOFQx9xNd#Knj;6a@kTTjpA4@i zZ%}Z9eZ1uI^7|}*DZ1)iT8XREUD|qw-d+4_Lezb>tyZMwS*jSla$6gb{?pqNrHxuA zy57e^vFF3L!!_&b`-%r9FgNhsd< zpgiG~g*z{nrte^J^TRZ-=(e4M#lIdd{zhE)YZgi@VOM%=GjM?sUA!$kblvmC7ekRlzYWF1UM#*8 zihcg8_~}P;qeilH9fe;@oQTIzgqoeacpqH zBb`c3eKRjnVoUX}nrvAXSF|dNM(fgy@PwD!l%|DZwq5C}Q2e-k>H1K-p<^k0Jdc6% zRYKIi_738XuG_mc z)c5Hr-unZ;J7hRN`Prb-k>M$K9?hb^eNLYmF??2w_Fk(Yr3K-|_ADm9{V}~%Bb2}O zY42?xT6#S^GJ8IkEgunkE_;U&bZ}{NczBIoI@s$vy!7MBCX@wDO}sSnKbjaOy7uVX zE_j@0MwM8<{YP$U2Y!U!eGd?Jid%m}EuneH!yfHi7dG+b4|wo#W@$tCOaG~6-`BUd zH`XqpP{{Ke29$<~&zJV?A#O>%)86~>VI?j6_0;Qkb`Tu~lzKPp>f2g8d3wJ#;)TB~ zZtv|nqBJYK;J7nNDY5axzHP);@Ad5<&U>$K+hDtS{;1OW@Eql=T^+pJN0%lCm-Ujz zmNtclW}RFbDrR5#&H-Y3svjv_$2C2KD6QaK%C^PmV@vJDX~X*U5-*Krsn4BIvcmi0 zLPNdZpIBm=`47(3T8D@B{(Ri!{rY&@#+E)yh=#l6e%qqhS2*KJ-w|C`hbky~SZQ)X ze(vt^K+J6^`XkF@WU*fQpLwq-k-GYwfgZaYFVFvyHvKPqdbj^TK9>LGrpp?8P5dx<+P<_$mo+V;U-`-!EC6H@FZHonR3fB%wFZNoVOx{D`| z>(<75X_B0!`yLhJ>IQTZ7Z0s%>uvp6i9JLfZn$*Yq2BgOOMeT`PIg7_xN{=g@$6*H zAb(#M?Cb5lW3DLW65{PPEF!+c@X#x{lN-B+i->5rva}}gym!JcT1LdGmc@SJ z_^UZ<)FVwJ;=!xsh~RT~F;3_GDvN6RiX#g(ZNz7T8H88;veXbBW>gFilctsWiqqEC zbQH7C+SbjxY-;Jh!ozp;-8M)}y{o2&xT;HSKk-UUmo~v|K0K}T=Y(*l@2a_V$veXv zPTJX7eEv|;6iYT1Gl8T0m)CPkuRmNI?7e?|X-Yzh-Nfw+w+$B?-2n%S({}boO?sWK z+BT!q7#{w`2^`*@t35&}E4a^=^(Ayf|A$Mw(g?Br`Pw=jXj^aZ)SF6du=0pkH>-49 zi{PZA9}RwfNW&Yo-5P#8xQ(}GcInOVytn3-z8#+DH?!oX+O#)&erbEy!i1Mge%ptL zDP5DD#GZw`iNA`{%aw;GEv`K>(CEH>TM3oGzqLD;l)fuQFRN`WmOQ)Lp#0EB=huJ^XI@?hw0) z5gmEDC6fmX4_;gUd*y5Mk8p8b&dhg4i8Vjvl83F3ugyR5^P{#6_eQTQb!`!R_Md#x zIjOkg>mc>dpqAianMDI!=<5d}52*svEAvBi#H7Zo-3-eP%{h>fuzqA6-( zA+ekqdyM+EV2fRO_3mk~Km zDkU&s-^Bcb{0udBBpl1uP+<(I-(ow{wMPwS+WHP6Zl4gYsel<_YIY4;)u)Clt$G7f zDRWx*8YU-aW4J`4Q&-6@sw#W_E>zjGw2Z0a^vI8X{{vc#^OLppH}YKeJe|OO31%Hm z#z~=7B<~B^2er)HBiZ4HE@oqg&O8|z+2NGZxl$Nqt`2ch&2HJ{?CUDGG&U={g~llg zQrw|Xe4itxPxy7A29{y)RqLrrDOW-@o0@i0I9evpY!5P5kt=0~NEdJYKLw+npy2p) z$Sr?28&MV)9G4Bn8_I@wN!Z`mD;v~Vl@?w^pQ#Up!bBMfWg^uEOPR*(ZVuGpErK6B z+ZjR?9qc-KYss#n6SyzclCvM_?nv&6Sd}=Z@?!)&cO>U0B7SP=3yb9^Ln%2>vsY;t zYkp4l*Ej?z4Fj0_EE{p4NYbEo*rR+NXsAe06DkK%?)ysa6m=0xgW*N?ag8S>4kpNC zV_4;qsgoV*E@427RM4n|kyR^^p?;XNx{3Y6$pGt4 zPdATOAGt-j7&_4BefH=w&BcK2IdETUyd7avl$!xTyTGMd9)?f{HJRXL2vd`^Wh2Zb zx^I>8lrcE0h|=h8s%h*A1mPjc zRU)ORAOp;uWq{s_u4Pmyqq@+qgALFiQ!;);i@Syx-0j7kv1rAmSFKsZl+RW&oG8k^ zwTi(@s~G2IZ5;6U5$AKs#E&1psbPqvqmA&e?oACtCVkr&ryB>u4Wo6mc#&b8c&^x= zuKj4xJcCy)!z`V^eTf@BFRN-bs<;o%{Bv!?R+S=UMj0A7Fq8DIuAzyVq{weE_oGUO zRsSC?9;t7D4`z`l*u7A7j*W!sn9z7Xl$5b9tdVNay}mRcSYQeqL-!(@+TPTFV?9BU zro*9YRbv(-q=+`?G#sSv6xTJPs1XlrZNOs0GTEPTOMQZ?X-1sh)?THADyM}~_%3LN zAJHa{f(GC3GE|X_qYQ{OkSs+Zm9THfJ!4;jj*K>JR4JH6Eoq#Gp@MXJtT1op7BP;5 z7^l30irXDQ2qmXCp6f(OY6HcloMR%(afVH3s zp2CY2sLNafoD!Ge$}-0pimpJD<_jwnWU2K+Lu&{6`Y2+s!e@?di!m#YAF~HyQx+Sh z<3RQp0x=GBGxo{hv~@#}v$}@&S!%$M0h3{P?a1dsxaJ#2EfacCDeVxBv;RzKU$c_x z8@H_x=EY=T2IR9k{0mi~^0&gim;!?cr+FK~8%UK`7qG(vgj44Ha0G>}HJzo-YYkY< znH{qT4#BHth%{xr0lPktDWO@gH%`J~)KZg#wFC=S`X!oP_ z_ZV=J0Nj^Yv$-ij5fXgCAuW?9eCFbaM(P^Y{}-Wd!7gDsBM{|e%0WX1HOUSlb#Fvt zbyxYlS-&ah-2}7vobPg)#<4G*JVp=yL|d6x^P`3x=awn+K(1c+qj}^+wgIAhoz; zz^0Om!`o|=gdb`JNYkzuCh6$hZA^h{;!*6JtI%cUL%a3=b=9y~CvabB!F5BRgPPoT z)6i8*`rJ13)RI%~8WObR+$&WdP-db|_b3?3}B#dFWl$&b^&`3cq zvB;}in_^f8MvoVA+M@G4)j)`)VE_@Qzumn6pawj`A@ZfVJ zd>s?SR$anY{TqiuTx{J#DDJ#mnw)}+rKE#SM!0UlE+t^G)i{xCPFt@;xspd&Fe$Fa z%R1c)n$QBz6Rx@&-BdOy$J3aql@Mo963(JbTHRtLC#>Y?)`zGs$UE5RCgu1VVWHf# z0&=Qroe0UjtnrT4h*#x|{+dW}Opy9Hs`~mpP|3#4#N|d)x@DEk%GyaYIqsTNmRuM=aE?%I|#f| zG=N-d+XLfq)r`qHf%{4?svEIRizMN=1xE{xb^WBz;ZT*D$>&=*`@wSQSl5Rhe*V~h&_zpu@l^+6%CDu;#lIT1W4>(&lpHP#=5RjdSfGY_e{a* zPY@+sm4l>%O^om}oXLGksaVg0PF$7Usdj_z&eEl3Mug&mEa439-M<7I57+gP3@wdt z>&&#TwGn65EKVGs5suGFiHbbwTHSi?($ls^c%A=;EakR0IyumQvgV?XDH5D99{eWJ z)lS9^6y2v`_;!YzeDx#ylf$!AXe@w zdctHh(wkP!s24^!7jlytbTh)o3ySoqyAkIj`dcQP26 z|CoF!Hy&q<@h6RbWEfuGg=Y7v?@Y^n!N`pA zhKkw7Laz-s;D=R$Wg59lku$}}ai-|8gdRUG%5=@Yfg>5C8adNn6-}k2PIHae zOfft98b(aEK50^#9=c+D;^!NWXk@lf=sQsGl%KZ&uB<>k6dl>XT}sv$=!l!~{GRMXb33$hrnH}7jw_ZM_iF_UZR2yZ2HupM zfmJMJ<^R)+jLwa{=u{M1kg>{$Eh<+F=ipA!rA?k>+y^y>^lng+_H}IFB4w-=-imn& zJIdsc?)GZVthEmp4#bR~RCWofcf^7xMrC#{Q+b-VrGX#47}~&-X6H9{p{p&RC&x|2 zw4A!Gf$O0(<2|iHK4d+)0*&Ib%`=QHlrXV@hg!B#x~7^6^z)B6X$4t0UomB{gALZI zhO>63l`ZTtx-MxDOtsT7f~UT>&lm}-3LHAOR45s(@TQdY4O~)(fX{1}a7!!}$LhWs z9~?hsVrPg=*xn$Vl8?4>QN@0>=l{_un(sq7yV3Hg-O#*|`-=6Nc4s&7q1-1;oK@SJ z^@|Y?54btPyb0zVD2>@~{8P(hn;vI`bA9B~STlrm2aS6*a@T7{f66t=-qI||K9xzG zq??<%(zu%qoasYKDApjcN*W=R$l6QQLIFQAl1HX#3>W= zV37P%ziQ@2*+ZLEP|vGVvW$B)MkwbmO_;sUUV>XnzG0lLqn%%)jeE4WcVlV(L`xMi z{%OplUXv`|($-tX(K_n%3;a6X=B-4V@1P&Q@3;GP=euG#0ght;FM~S;wy@z98><{5 z<=hjFktw1JlrXq?S&ECcIZ^hU=B`ry10&*VkntW(U6@ozb6Yj3Y_<_mvntIQ#>Ub@ zHZ~8CqK#r$nNl!Aq$sly@vO=&Ui4x1ln&9QDI+Gj3}rWncA?SFV9yJ!V%jhx@16=7 zuSQ2}?<5>j!~)AaICR0|tH&b5Q-#}mmU<)_K~%x(H`Q|rnv3wiuzqifdmJ4^h6d5j z66%KyvzVxMP(x;1G}QZ8E~NO z#@O|__T!hdSH|f&puPk%D4#mFWr#HBjq#?Mg$EQk8hVF#f;ohJ6717c8vhS`V?im( zDAUTHVnbV(q=*wOU8EWL#`7u_5hIS;y<2!wAIgaO3k;g7F8+g1)lQ$=x@5B z5(-r@_l4{(oeDHvPzkIC=(vJ(wSp<@f0KI&X*~(oS7jC+wwxF=0kiUzqt6L(9 zsdQd#Q?Ax#VGWt<+m@uL>1|ykETh_3iQSZh+3Tp;L;>IBVXd^)xO9Ef8C8lfN`g@a z()eDjeCXTJt=yY^s5(}jK8F}|5eZI4(6B`kw}D#~vPqC9pyEltn4fRqbyOqDP>>#KY?vr+4jGCP=_ z77C_QQ`?88?rLAMaGm_z$#hm_5No4^wNa~!eId6s_;9PzMV87DUG4V7TTv*lDh+yv zb!%ET(~Tl^WxSJd_N^_CP;w8`aka%TPdBxlY@HuBuMdb)!v<^I*mS;88rsj_emr{qD~bFaaTmW?+Z(gbUv zQ&q_`!ERXpV|E#3Iyy?hgH4y!68zG}zG)_W_HnGG8^x7uT}o;;R5K241?q94l(rom zX=_MpXG-}UHTEBFiqd+k?HbS(w-{gP{76$;VN;}eNv0eXMW6s1+TGD#+B?QRA$YWXw>wS4L-stE@m4C#;)q7Ly^GNhrLZ9qxRCEwjJo{+a$&2Yxq}yMpX*O`lZ0=P7n; zwvT&7s@Mb#PZ&Dcg<7mfBvxUq=?_&DUN*pfW}V4Z8|liXFe#c$2s_8rMQBp^t&n-6 zsRKpF*<+~KO(vX50!Ozi3(qmAGcH{e@6J|FL&umRVU~C}s7yE{!NlHT3MFGkXMZfh zIIbC*VZtiAt|wk%DE#`Q({|L8yBn`Bh}V^+L)%3?z}1rmsq^!j=ZL?c2X@T|f96;w&O{|g#=t+72QX!olL@f>hp@vb!d(SFlT z9hFUhQ2VD+C8_iwl*vxOJEwCGnQ+vIWGQO29j|Fb1)E(dq*d2iH2Wx&m~7Ji6m2yyV zUQ3$g>%PfLns>#7 z=Rho&O)&kc$yvkMIYWJ}H2zv#S;=_aWYvlnr%>kFZspV}!~PVO{m_I* zk&KI_Nt%;w!ucxW;v|u-{9_7~t{JgZ{ZFQ-$Y1ibm~ddo?4p;1UV72^R^3A=+G=v6 zS`E++oK?9=F|yDZGxBN{VZ%!Ezan#6b@!*0ZMwTitDcxHYV3tNF?Q-+SC#f#jtR&4 zERDlajM5v8ot+#k!Id%&ck`8EbM1m~BIQmG-l02je`5(#ON%G7s%Z*tiu-Yr%s52ka=iEj4>P-_-s@ITs_AI9 zs01mbq#0W6bQb$3e1;fI6I{%A1bxA7+>cz$)9{4$0*t%GWb`DuX|GV)+oYFvu)WvK zjCT>3%-f!>wJ=2!7TBuO&V{h7KRwL9YGe-blV^hr5v8Q#US^z)alt~0v1_kD8r#h- zJln?%i+wWy?jh6H98c#5V&_-4v^kSD$7AQ0SK5r7-^-EkIQ+5X!?NaBdNI-NY+}oq zaiRxY93;Ba%*D1Kn&4Ye8C!EM%xfTqdZs$_de-_|p4)=~A7kBUA5-BaJ(A@vmA`|@vK#(w(LnMZi3v!NN!_C=E0 zVwC!Z(!4Q!u>lh2e`UTj57trRAJ9d7k6N8Po5HT*_S(BRy{V`bI0iwyn#hKe;nlC2 z^ks7~JWLVBErmpz@s1$lT?T?P=cjJ2QgkabmN>Pz7{dgQenHg5xu2K%q-bFqGmhYx zjP+G5BY48dF-V-Ev@>HdQ_12LNESK1gE?JC5u5M=4L+OgM?f6U;au{h!MB^^Pws^_ghCrBWpLGfWJ@B*jfNzti{;EaS<_R5;;N zw*IB)_!DR?Wx5$h-^_^?zSKDRSPdTSnTCtb5ITd^>u$V{>Hv4l{g?i{Xlp(ivUUEy zo0#f7s5F64N;m43FkY#)X3- z)EBXXQp|XdLXcrvIDW69cuOH`gd-Puht`=tYK2*e5L<>at150%qg3t#%aBVNe8`+;c;}Qq)0>oL&eY{f zRkxTCdG7m!zy=?#_oo-0_5ggvRi=UIu*@Q%WWLcUFKqTD_SE)+Tomd|KO)g1B|3o(__6IXw)dRVLD*W1u1XpRm4)Z;&44%em)n*Jx z#4h!1g0}HV*=5E9B$mtDuH>K1?~C9ByduRpCVH!HKpfj+#(OEuE_5peWePJ^aj}W> z`qk`er;D!-fMeOHq2ObrGl|ahZ5(3|szTeuAGyV27s($qs@@^L16S7g?dG7g)w z)l4bns2Oi$3A&Vd%v{HTUX8$L;6{L^^@ zPMen$ec8NPN3S~?weR=vKyjchP5pH5be4IZ4hTQ!j=yHcS9EDKFAndGU;5LGS8n`= zSC(+Zc~|gcd~XPnkX=Ubn66q&o(?+Kd&jvkA~ zqig_$d^H;{-iz-ZCqED_j>)I~G~-0>o6a86yhr8~9qBAs5ph$(I0}@t#gKfBh-uuE zY;WnhQN%R0YG4uK>jaLQQEf;k`$|;9f<6h{SNhFr#^V%`BvFOrV>74eXyQ(cPT@u| z@(GHM+oO&Xz6W?>#-ToNOnh8hC*$J6CA;Ih|gE*9d0*jr9n~yl6ao22zev8YBg%G3e!2=061On$8>~ z?R{e|=>R10G;Wk&#*n&700n)3%kzB?XD=kKG6(H={*T$tC%Jz#e~;Jxh020In+MW! z1MGI|XR#(DiH{K7EmiRK1@RkGLTn}vDWQY~Q-g8-l(dP?;xBD=vS4-M zOza);Vd@(QN?0?_gSvd7_(>0)Ew?qY*m+7*srTeb81-N z8q`d2#@264_ZoyuU+F+ip);oNretTMNng<18rXh6tz|(RpZ5X^;n%lrN20Oue`~s+ zR#BEr-9_nTlm%wia1Kl#mv;=O&vWq2*06e(iTG4{i5geIm7cQUGr*mb@M%@2M{ZKp z`W8$nCi4==&u&u5h88SZ%)<&WHWI_fIIrNjUkar&&XMS>?v{#0 zK6CEeOqdgs*|$ZufGMar`?disEqKX=>AZafj;lc8rk10tP4O9YT1$A|hpjD$x|oOk z-@rIa$p3x@{i>}6JE{L^hWHkm@GW#HDWHQG3?{R7xu;QUr&gwrxh0+Hc?zm&*Xdu? zROC;i37oz^lQ;!~R_Jy5_r>_?XW5F%XB5GeEASEn z!9CAMTuf(Vvz3~G6b>ha!> zGG@Rm9}gAAXF7py@TD?WheJ@pX?tIqjId0>vVsLmJdSmjvPN2RRb0B3WXac(kO>#m z><>6f?&jxYODyd>Z{N&J7$-*Ccxc z^TR|-CZ+#|7nz4nvcU1r3&ZgePw2QQV2r7TN9f0ZxagTc%kM zoHK>v3{V16m(1~%(DBLmf_Av&8`?Mva!w@K*Da^nqPxIx0w+EVL`$(HoiPhX2Yowh z-?ZPFWBE~;?C)X?$!^_DE%Pc=?$y>`Uc=*k^8&+DdG;y-SD$5)#c{jx}P)$<% zHI^*hMycFdi?a?ec%7w!fKAt10tD=xs^XK$g2SrxFbqF#-&snrk@k`QVUwkXPT;*?85Xx#T0$96NYfY<)P zG7_KlU5DM`KA%q1bqBOD^afnnZ#yjE_`2!_OxUXyF3QOWutrKXcUiisnbPu~EQd6D zfTwkYbY!=sw?+!xE2fMnDqY@ZS*4?=Sy+rO{$iO)}0t=bcSn;zMMIXCU8m@}MAes00(4JT{mS~_US#d($lHAx*pt)Wu+mzFOy z(!f_194Lwc^jjrsWohFZOC^nT`K?&=m}D$t4VN7MvDDN^(I14{<7&Emv~<*xJ3d+P z;*iKac+5INE847@HCXEIU`40fmxsmUx~YovtD_YrQ`PRzgPp9Iy7$smCo5FtR0gWS zRaE`R%LQDq zRaP^4SspD7?rm@BAb-(P;A%@HOyfl=E@6fp(~?fX)(ct^ldF%GM0?;-L|F+ui_B0YlfF0X0*T1#T$V`tB~ zX|;t~IN37F`h%8yS4ULCxqa$clO5FL-TKxTEw_F{D^?q3IN!*M3CYQXFRd_0PI@%8 zR@REOY;GN<pCrYyp46EouQo--yAUSjt*8hUQX8SWL=;o z&&60_(wy6(*6W1o+xqd z!@<@b8biyW)>&E-?bxg(XN|DpYbxeFI!YJ-Cu@ziBK+cHrDW?`EqQ*tup7?Z_O*4Z zmh3)BG(C47q6pm7HlN}b+IQZQr&??41nx_l(yWc7XVa|xR9xyi!-@}81xeaG%bKR6 zz0O!PaJ5@0lIB=Dqz2=27hI^(R)>_YG-950yP67IU_GIxau!;DS5v1JSx>5|)k`p) zxkL}W^$#@_zTE2Nz#WL2Io$RjU7bYnGaN zp9+n!lpi-(7whQ!Hk@4H{<+N*`Mv$*s^4bOUEsbFx(oZ}WQ%kwmHcSmfo;t(zSgiZ>jxF>qvyS<*^yWwR}HPmcLv@ zdVI@@l?F+CO?^j9PEJ0$D;mVfv-gE>QIija594INY^$UtD;cew9Ms&|W?}iv5MUMa zl#_d8;UJbCMB57Q|9fIX1G^rxk65qA)-UnM;TT42+Y`|hk))9ytSzMtIl?hAKB|V? zNc!@bHCSU!dM?z>rOM}8FKWqGdDgvJ^1w?gA_`{c^jfH}Y6naX`;CLKl+oQjLk7PS z?j1M|u-;p-%5xG8fIAXN;<{r#T0=F`oKIGS8G>{$Lq^oc{B<1U^;+_72^mW#=h~cP zxUYL%5!c{i!?lz-${yD&c9CJIzf~ygGP`NU42O3ZIzAPvsMGrki zZ+{tU74V{~>fk^b&W|&BRec#GiVIx&CRjeLkwi!+Atcneot?i_QFc~ga;L?R^@+vI}Ef3I=cfy52m|<#!yhcj~)|O|hNx}$3 zNVg+pEW_SD_N%#Y2V{GSuPb-JeerxX*TuT17YOdcDosIQavLfB3)GC9sDqYrbfubH zSNd3A?jv&Yo?1IfTN^@qx_#2MM)CoXiG!xn(uOZ(5}8p=p1QPX%8z52Optx%g z)r-p?(Kf394f)nb^iW*+2r=tbR&m*5Ojo|?5#_}-k5HGm;}I<4f=B4-iU7`hwPTD6 z-|HCT$Co-nw7Ad_oZ>b|(8N`a$izL4b45XMiz7NJu5d(oaere>2w&a^Ox)avg5ugn z0C8s{`XnxF1Wnx52r=TOMidveCWsGs-OBqp4+{ic= ztr7Pzf=67&2&h1^q7~v+MU0cUQV}J@eTrzQxJ(g1+@u&2 z!Ph9F3UP-blpropgo4HGi6|hhP6Vg8HWAf}I}-uKg^8$1+?I$IimMWViF*=JpSUD3 z#)TYP%gw*Mg!I65Nu8Yk^PfaE>4CT~(aTTwpQO(C8O8=JvNFC#)-~~S13$xH%=EBX zg-C}xj%1insV*5>%N~KkLLm&789|u1oD@ipG=c1*l5ONrhjos0`grUYx}PY%jqDd8 zhE@pw`=S%>!Nkun9!il_x?M@!k12lq@G~sw7;Qs|He$fIL_R35@H33B$`o09yF24z zQt|UEeunWFiTp8hk(Dfd#^YyL(niW}BkwFD#z&BQ9s{K}eugEzr^9XKD2FYM)bxvA zno+fOatJ$#6jsP8F!Lp{Vw_S~J@%{=R*%4h4Y1BqSXm-VxP%neS|Rr7sO~=k6Sv8x zB>m^NqL@QbY;KZcQ8cM!G0deH=2i^zD2A1AbKnZa6|^ZyPQ@_iVwh_&Y_5AT)Uz1o zT?{K#3@cp>Yh4U$TMQG{B6J~)Cxy*LV8VJ*l8kc?lwhLpNGVC?VwklUrWC^-7sqge zsSqlhR!Y)e#jvNvu;<0FTnSFPu;Aath_8xaZ;D~>iebhuCzc{SAv#eEvlhd|L=g3f ziIE~^#M~mNm=c00yj=>n3CjO3@cR(`x{e8loRs`6&Az9gqkae z!V{+O)GEZp94o}c921!Eizz(W3NbP33b75ps+gE4DM_1)Vd+IMc5W$2bGQ9F@B4pe z?f7@rPyf!^^Y5(8e`oD4nl)GW*p#G0Mf23GBmd4i_V27)MY2+o?i9oB6~i7B!yaka zTvbnoVnkCh%u)=Ki($56*pp&dPBH9RG3(!4aosrb&Qfd-Fy5jq0%XzC9&$&QJ1T8I zHR~yN)UBl@J>?!Q!n2}44w-w(8+2xx*$cF6`lFZJ$3&yQ^($NknU$1-W#G1s#4aF)LRbKS*3fuQ%UwN%gw}U+TL(C6UqrV&$Dts_F@54hTa`-5eI2=({Qlle|P~sID-(Rk&+e2yn z5y>B*EB)n}x@**N0JI?-sHg=#GcoBMO&b7?-Lwm&GAIjD^(5CG2qiA#XIRp9${ry5 z=^o+bLI^xgl?KW?b%*HEK)GgF5nDo%8h|8O;dT>~Y~&sf9h{*-@!(IRgY)PN- zV~>B5GP*mX{-qXC!zd1OktxDl01@T_h%grbBQ1c4askAA z2M|#%fQWJdM3f7ls6a%yz(kY_Alxm0h;jkmgsV`5xj;z;BFqIQ!dw6m<^t@aRf%#J zj^OmX%hU5FZz=Eb^t{E>^A1nX8$3PFSUpczJx6+ap00Y{+v$0->Un3U=Z&47_jP)X z;q<(#)AK(8=y^-0=N+9sDNA@zJ#Xmryr0wac23XXj-EGjdfvao1;q-iizn(X6dfvb3dHbg4-J6~_Z+cwRk8Pfw_ilRLy6HIx((}yI z^S({b+crJ#+LrRBP0xEaJ#X3cykpbz#rt~Rt}W%=nw~dndfuz)d8?-9*`()Di+7Y{YuDUOjKg^c;=P)w8MUc@fgHp)O@RUCL&xT92mgxnTj<_P%cqx8oJER;v7 z>`1viz`&7m51P9G*8vEu01j-tIUOSs~q6xjtVkCV7u)6sG;*3SN;}FwfURS($ZewMW0|u~vfMgklc@GTH}SLKw!ObM#cHf7 z@g=QGMkO!lUNUOVr+3LXO3J7D&F*Ex;mAWtKYApo4UpHBF8NI6e)gkWOyX zTFNWM>-W}+we>G@1FR?&zI}# z&XY7BoZIO3e2nlms=5Ge*hcdf$Rh!sECB5+xhzDmbe1MB#KG@bdcIJe3NZW|c@}`> z8=QEarNl+@9021YX6d^aN6KgErV6?&ktYH>y+ob>P;V(?JC@3mfCcGM!!2r}mq!46 zrw8p8`7M*b23WQX!fsKCpAEQ}{{_j;A>0<5U})*;p!rQNccAKI*iZY{ z$?+chMT>`s?CGJDvJ+wo@2~V)hF0vS+3R5x`^mN*s@P9or9u_^>0By&>V66*n8AKZ zA$d5!TauFi25*o@17xY-%Z<#Eu@SmkOD8wV{Q&|uVUX6+!c8c*mj2oVWv`{)o1vez zbY(Ns>Zi%c6jfbuqMd0lvbE&71!C9I^eys8&Uda*AI(18fPP7rJE|9k1s|22!O}Ke zP5}5Z9r5{Ea@{HqQ3XxdDhKFtXvJ0t%At2#G444uK0}@YV9S6F=Fre>ocVB@Jd-l^ z!?zFEj#18`>)T;RIn?Gmc|5?u@8qv3`XI;=-$NZal=i(mjiSIW`TrmY#9 z??yMwl&}Z-H`B#E@^DX<g>b1Bg1p*xn-; zvzrul6y|f2(vHegJ;bSgQpkQZamIeiTa6~h{e~s&CawGpp5rEc{td=_lai02Vgs!_ zCeHwHIW8{-Salp*O9PcXAuk8mc0yhaQ12wRg9f^Ml39A3l2-yVsbJh`c?Gci)AAC4 zZ_eOBl!3f|XN-PF^9|&G7SEInwDBxsfq%#gfTjO|+F#J|Kd|b&AkTB~uP2 z^}Hg-RXZ(OzV|(tzWQJ<^KE`XnV$vu^9Ki-UqUG-F8B&4i0^(T$1E%~KU0q^EU`aR zx(c3TAx!z1>R!dt_cMKS6;=IA*RNtR{+TLVW7_a*h(vy-z1Oe`|4jKR=yV;Uv4%!n zhp;tt;5u5mhW@z@&NbBP1|qRFH0K6bthD6@q8lrjZeX3aQs_;vSZUNv2(yxO6E#>< z|AY`L#rz2&R$Be1$fNsz%I!VG!=t2a9ZTSUwqoe?J-HV3yCwUn*@I({{VIm88IdjS zs&dg3XQkh6aU~^gbIt8;YGF>jA<$D@8oyavzX1<2z;#cJRA0pOaoWH$-uO~46KZXM!A!!j87@3 zX~8-&S>zDDaEOi>;VZ55hml9c%fyQ5X=0UbG_isonGo+=shXKt#+xzh7ig6kqA$=x zGv?_9s$-G6dGbHMb>EE|%IqmYrJl;ceDM!1^(jYL7O3n3-L@bKzd-(0RC9p_S>a-L z(P}FsuBS{ZeByfYlOb_Ejgc|)*V7*|=K6XHQxJTu$J@1VRO=~6f!kb9aW+IA>*a?pQ`8<^ys#m*oeTM#JQtxNX zx$7BhA(P%d!(iae&*x}MCed?@ekMJC&Y8_#$URkIPrV>)8~)}g2f`xDwJK>J_e?T_ z2}K&_V)e|V)w$f#Cn{)}$8u8gxT@?tW@+>{*T3v<<~08e9cEIKmyE4_$-RI6Qts)= zy;z15k))0hj&xmy+2P)iN)$c475=g5k?f*2t#1T4?$@IE@6fbU-?eh37q4V5i2U>l zmY7NLuMziT(wWy-s57bh8>l{$*1W+Imr0-BaEtoBgMOsepXTQubz)2_W^ zjZ}Njy`1$P7L`f=ea|`%`iDng%s<@A-~QoVx_&@BpGgBhFiYkKm}e%v`XI-u9ojw= z%?%yqNE7a$L*f>VLREg7&x2sdhnL8tu#a5V#E)=n8#?^VnM*#ie(!x2ty*7Q5x-NFUiiJD2vvva6w$yLI%OchU7aFKE6PC$##v@p z2VgsBhlA1+Am2fW2k2h{v>#|j38e+V(Gp5)fKMfqW&lkcl{NtL9hLq7j~ta+x^?94 zgaY4FxRcT+zb854H)+aEs4Wy<+xX0h3v#v#*y4=Z_RukBWtit4!F%&Qc;`HHq;Wn9zCg2I zTVU7o9-8f<3_^)VE=mGGb5|$=C$g?+z#e+7f><{QJwRjKlsJGBZs0sX72TD=01Moe z$pG)%mA(MoJs9lvfRqE|=BW$-7~#p8r#&I_00np{{QxF=DeVD{dnug&T)ffm12ohd zqj7)^dvisuK3r~y4;MJ$ql^Qsb}5#!q?FPH*sW5^D1a8eXy7%9_f@(A?Dd7(u90JD zr6WMB3O1Bh8sK5{)zaX6hkpq|weP5L8C3L+mX(1D-qBxW(1>@`uB_4>U}IV38-U<) z=;k|`Uk>GVQ${&7a5udw2SeITt^H8ZZrbR_na}*da)dm}D=`3l$^+X@Q_F+4oi3F} zf$bFFkIYBZ*dOH{(JFs*-_k5%zqX+(VRTfX*RY-|`SCaL za1OsRlUi0)0%=~W{veK3~OOHB?cY!Lp$Wx_O9fDritu=>TkQRgB*u zx=~fBsmrBLRUynq6~dqb8zqLJfQ>R$@Hq_i*{FRrr5nJ;Y8W^h$ttK*9l~rhqdLaV zMp-JTR72?uY+MbDn2pZWz=+w%FPy>1a8z`LmV{$C&d|ef%z!iWWlhv~hV(TtnrFyV z6E&QnS`jF4hUP?|KpNiQgz=@(#|T(*8jYx>3bycq&okROthr3~N5; zxggIOSd!+HM}+EK2eafkwW|Z$cuwo;z(AkVvpVR~b81x=dVNmo>oWFNUDWiNTg83;OYKU=oN)C;b#sEzlDNO;UH^SIHrE`s-x~EjOF}nVgk{c_L zx{H+F7@d7U#~Z_l9+2)!H2MK`{1Q?g(E2Z7-Vey|B|7wg>NkOQ9?-HT82$(JSOx8x zDzP5o&7-6T5qQQ@w-&W*j(;`cufevALLBmda+)fARF)rWgJoYNb!!S1{1K)vsB4?S z3LlWI8QS)M8Z_ssRy0SWUeV9ZF;=hWJunyX%qZziN8DV0T`ot*-p>f5RihzarXekq zIw5-nc_p466)}ah9zfOA<1ig)fw_H{9HY^t!xS5h3Vx-r(NOfSbSN72{7Q~3LHm_r zTY`3y#x!+wDIbK zTAz@)73RYes@EFbdP4fvn2k^9L2IR9#e&20k&Q++Dspz-q86W>Q=K+&G)Jkc0Gnv6 z0555|0Qs~_fIsOxKr=p;#L?-03gslP!7-OA2P-5Vd;u?{9^O?z-ASQsA?ZA|5MUb( z6yPk)6yO$Z6d;QvfM$Fcm4P>23gw)ugsq@j%UV%OAK-=5t<4LlIaI2h(gbH9UD`ol zHjNhGCg}w*&<=oRygmK#VNn6RJ5|N=M)WlHt%c%wAF5J+CS7|-T0<2Duu?MtE>M2~ zcF}YJ))N6V<3O{UwL}p)tJffeQ043pop`m{u?TfjS^@P3$`g{-k#`4x@2QpmS13k+ zJv2&y1GEI7*)<_&SD;f7Io)fz*=zYpP*38~Z((0fR>KmYw&5L_grwc{S%4!{p(DU{ zY9hcR>MOu;nhMa27msqsi^}neD5xb@)N&oqg9~d}cB!Bi8(kNY&d>`1(#W$Dz-6i_ zzzOOofRRQ3H2X)$dE32Y5jj%}YbkKQg|)EvRr`X^qlKi`WDsBjeH7p+1;zkeq{ad~ zpxyw@#1rDAlXyH_C})v)GOWt6Py8ZO56lsbx|eQSDg9nSIRJ8uT-p8Z)Q2(TM0!bD(wKbQN52+1E6~!IQLDI+6OLh z6DfTV%5S2Yec|*r(e%CuS~k(iz9_zls`Z2DO|-NhBEwCT(+}<3L~Z*s=i2^CY(U}T zD)Gz;I&&H_ZgTRZbxE6WDKNa-OX@NJll>*>2f*dMB*Or-=Pc<4ViC=!_<>4&7xB0V zI`2hYhARFPHBj-OQv;Ps>O-bnBWxkph{GfO-DWg^P z6Qy-DfA6Cp${&Wc^DT84jsbj2`-Ur(Rfad-n!HcL6&Gi&VoxcmGg2w6hpUSt5P)bA0!4XO$0MuHY%8gXYAdRXkQm2th;Q!RGABm1|{gvq4NG^>=RG_yb zF^bRV+$g0AjT(g&?HO$yrG%+jy>*W%I0*skV;Yo%!1^(5OTyScCfyjNDus-OC4(|r z`4RwpL3C&|R&VC2K+0&;$gHh(d31HIQiGDmU@6Tb?|Din-5Z0-kRA(j&7-g8DN*#p zSS>|N10z^$nz*@qB0R6WgF=+7( z{*H@ax<1HVN_)na@);Tg0vcXL7Wr5Vpuv`8Kh>} zkd<(>TsZm9QDW7s7<@8O=g)Hfv~&)pUmls}DANIamcg#(&V`3z6KDZ|(HCQc5sw4F zVArG@^I*F?;uWdne8mfC47)$op0AWe8e@-tgqRQ0%A<+%vB_bDM*_eAY5`#E>jGfd z1sJkG`2qkVUWJaq5|GBQS4~~01R{x{uSCrk!ZvyQLutlBIA=D3aJsNi@j)7v5J;~U zDt<`A9D*tQ8*U(MqMq8_0&`gVjZz6*Fo!UD_>B^TG;E?W1uas_BMoDyOnnw1Qe`VD zu!t>-l~ zcsi9>g$bTc<5uCU29NevF|Bh7(~hMut@>)FrLRUC)9L+cW=U8BZ;?*t)?n35r%G$F z{H4>fwaQ2@j;NiM!6SAP5xTJtH zKdx8C`m>1At06-CV3l+nnm8#@6kV7KuKSdmim7p*8p3x1d`D>Dee&O+%mPT=fLVN> z+%_sx0Tyh;^tw;^DwwiKnFs9cCWJ8eY5Ha*1;8y$Sq_kzrpyH>w}s1ny9M)sEAppv zThPLtR4pBDd?yW0hjMn(?R0pFoz!uwvJl|PR%HP|Tn47gP8ywopx`|Hl7Ul>^YkJE z7QT*Lw!t56qo&{CU~bYjrMCArbs--b#lcfNZQHKImsp1vJbkw-v%#6N9qrmiLEphB zx!edHM*=}OzC}yU(&z7#G=Q}4m7f7R{Gj{<@bU*`pXXWOiN{@o0`WtqZ!5JadZ#j! zj+LGq!LBtQWd~YZIy#uS=St_|x`B~)&zX=}vkaxbFCvF#<&RH6?OW6(J@ssi+ z!2X|V8h{FG9SzCMX)?+#r!34_^BQK!Y4b_za{$sGZf zoxXTrauANy(TS!F#yjXx9uIQ@sR0ifSG zF7Wmon!KOZpJ!0-0<&DYpv=HK1?~Qe$agKR`Y%RrE%{zVbiS6pzle#lmg-+pQUNYs zQnmt&xy-armmzvB(G?8oS_;oXwQK2A7GpiGDl32)uPVy`Mqgv>-8E%p0B?ECO|i)p zKOzp+yu(e}dL1FlJ^FNA=@WEMog&ri@DyY1jkhNAZq=hZRoAOg*g1 zT=m6>cN4(+ZUU`Rlqq!bt>RhaeTmCcQ_YCvGRD+!q*YIp)|7iqairK=IJ`7dt_;gG z(>IFpty<*P3`n{)gT|ZeWg-=&m8jkeeBaq%)@G{p7)m$OsmIDHfVd~h5`eo;m^Sz? z41t-R{e`94OinpaS~e}t!J3gxH*=Jw00W*Xq`m<$V+CrPv4Wy6DUsweJKTlRpDMVH z@0sEOzU9wgMA=mRxsncW{y8emrhYF_X*T`!!Y<>?D#$pqidq_r$>0`eQ(zuUHk+h8 z*iJUZ{jID~Yi_n4rJAj$eHV+CavqgDd>p$ph+Kf}mwuGiT{Q8~7i28e*-im;bg-QS za4li`1K?^2+a-X_jB$xh4?S<-eM*jp8xbhe!Xw%EmX8lZ!#?JR(&8)H}9Y`+0p z?ru8+5bMF1o2TsHuiReDT^ud0Rs=21J!c`nZ>EJV0%4+cbb3-nLl)ReWqS z0Wwq&RLb@Zux%;`^R+Dm_LHw|9ze~~w%Gu?O4}9#M3rG6m9Z@X)~u{;0l=xUEUalc zmU3DJ?fh8EML(7jQ{EN=PPD5NUZ#-B+nNHrDsLMkUaj!AjRH8~ZyO5`5n%HNXcu5> zf%hF22iO_`oD8t_2Jj06=g%}h&^8Rfrh+a(wjscN39^j=2(Dlo0kF7&Z8*TQ3QX%3 zY#R*hK(K8jK!u97k^cOyhS?oY!^Mw?Mr1jq`Ge8Rm~Pd zM*J}uXyYr{#9ITGD=}wOWm^+qV=AMER=Qo;HWZ*q74*VN+pE|T)fWVAv_o|_+L5WU ztt-V>wYh;hswxY(QWb`1rN&`gGChnl9jZaYRvKK*mH_a3HK@T#Vb!4qE3K-|nYq=u zj@~tFiO4)sgRzis7PcasTk}Q*2{pOE>6$DvG=f=HMsP)W5zN`YmMsZV&edWpsy2oM zub|Yn4Og3gYaE(?YaAuEvsIu|^=)-&UzE+2j`+3n2H(_3+i3NTi~1A6haU=UgL|Lt zjHPJ&gmxvt*el958Q@ZsZ7M+PI;d4RWZeOo-hhWf0>|Eue)vFc`C2-KNV4Xtj{(-D`e zoAmp9#u{kPHZ`$^IaEK1@W4bzJ}{L>K@9ArmS$ya%XqG%CR)Eu-K&Y>wyBo2%>EFk zYnc%cm1?6ewyCYP8T!>>&DnL#A%My1G8|Rc?5B&ro3F6qOATIn%kU2<~`YjDg{gy^WH!(Y?TMf+= zDBxK`E}%^#>_ywu$wr)|YGcQc1HQ;|z?ZUgNzys8Ter~uxEZ%i6>NfewoR>W!Y$3( z)EtVKYRdH{ZH66io0{5;%kpfFkwMor$L_dI`Lw`3ibqFUn3Eu~v^1k2*0*HMoUJfw z+tjjF*m3dbLTj#JS!+(q)P|coqYc-PsI3{Un^e0I?5o{KZEtUORJYrj-pKi+E%!o; zcHFXK?YL#d+nWQBwzfU@bfyki#M{(}4y^K`16B(jmg&end#WR5@-|g87;AZ(S{#fe zgl922VGG%&`gP(`o^)c^B!u(r3o*NEYuXsFrj4OqcQRWg>DxcFYgiX-j@#6X&Q5>k z=n0J+J>ezMY^FMOF;h~ZUD%2TU2p){rhL1y=+qTUew(`9m3yI4D8uQYTz!IWI6`ex zqq}kEf9{4O4bRjpcq0N!Y@M1KX8Nf;-OT`mpLfUDtW({3uz1)5hb+Xx%sg7J5k1Ng zW)_6-Hq1H@O%0RaO2e!R@xm}0LsaZ(cF(eg4gisOC5!*z0Fby_Sa?Ke|2f7%b+9Ln zaC6kRo@U7;bM$((0+Xmz!?DVJPc%%cx`&&^@uSJYaI+yqY&dLRtwMUi4u*beLod{H zUVZ6h)_|zo8|^=@e%GR49}L@hwX_cw`+1eQFZQAH%GDRU(s}iyujvPYd|8!$KeIi4 ziy74qMQu@+`(d(eQLXx;ySAun{c-HxqFN8YMBJk848SDaqB=yN#4YMCEm}lk1K{jA z)v`!z05R&tC^LsjHxL~ZqpA+XREkm21CcdG6&r-|V${w-n8Pu+lY@C0qyEsM#1K>& zqjn6zjE+%7hjQA6p%}{;m2((IB1SD4hV3&(B^r)76r=hMhaISGI0iUIbsB-07^5za z;C%H*a)uKlv46#=mM$*kqKj>*I0_3XMx7jG=7NB+1Mo~pr_mV5OKQVt%+gCL-59Ky zOKS2M%-~DvofhrKVza%ZMvcW-Z&a7Ynw20@j59OpSb9}yoY@)=hV=Q(%%HZ8GYcVn zdmKvmq-u=E*7Zs48joK1q)Jaf6F;e#38?v#$~Vzm194%ZIX3$znk@-x;3p^ghuPxY zvv=>{?)^jh_Lzfr9#L`_e&zb>H?tx{hDlfho7IF#SlF8>nO8p(v?(mi(r)9B3OmE%uH(8`nJgYv`s=~ zgrk^FW}7VWjk~JxOf#SQIMpnHpX3To!_eJT>!#sE^sdT39oyYqwPCtBB-vdm?niGF zhktmM4NKvF`VW=_gh%_I5}P%`Ee92C!cZpNDy{UtQCph%#Fs zHcnxE0V=Z@ME?0~#IX6Sd38Rvp#7@!0@66`U%REH&~VV63z1k-z$Dzp@h-lgVf@ogzu zyGwOk#%X7k;Z%e3Wt_L%%y=Q7poHCR!m`nCoQGF9+evvX#X=1k->3<~|j^iI?> zw0{VWxu&|k7X6IpYu4e!`G^WzhXRkNi|eo&kEp8aQN$6odOg~GM5Ww-6Rsnw?*{CX zN7S_q*dRGyR+VNW5TC85Q&Tr$M_#NBZN!GSSk>5sQ=!G`+9sSIEmmDN<3wsPJ~)bf z_nhju#jL5SoH9Mt#x2-P&Z!(*vFV*to42BE2h^l!Gp{NVZ5C564?Oi$q0!h|n2yJf z$SJm&RUw*e!;bhsUEF3)&h&uVm>F*x^FOpPVgHccxD+|6`frEw7ZtM|1N}w4*^ch{ zqT24jcD7F?*=c51hj(Cy-lt-B;F!nAz%x6MJ8{kQN^RYV#=laTW032W8XJQh>6Q97 z1}6irRQKPpUA|J+e&<-tU8v@Rnz#$w-UoGJ7k1kZs<6{OjTU}Td3WQ4j9-?$_TplAzG}D^Yk0mIz85yn zSI75arq5Sd_F5BDp}ro#7{9=`p3ymH)S`oCS%_-~QS@!K>;yWe`XLN7+yERxuG?zg zAxy*DD)V8ih}&xTVN8kJ>ds;G(QQ@d2#z|p)y^Ym)@_ydD2qi$F*k0{JH{H}$FL#a zR=1B4Q&o;*v%al19_Lt!6Ra}q1jd=G?uaLJ4xTjeb*PgVm-ni|NmTY;Ek0@H)G2lF zjE>hSjM5WT^%R!O6SeXbE*rM0N~f`_#+^bzTb18wocS~Ah#&KpoHk3Oz}xh+DE&(D zqHKouJ%-@d)zfJCZdK|G3fisI8O*^Q>g^eH>JC--ENb4Nrk=&7utR+~iv_tuwK<0w zaZnvOhbj-M1m`i_2UWy*Y)Q*hnF~0+nCH!c$>`N_)K!7&!pFYN3~-M`=>zfjK=cK4 z`cZhYMNb{Ycf?V|QM|W+UW`>0F5&t)7T@&4+=|7`Hq44x)%`Lw@tn_P)VTnD@Nkq{ zpxRzBb3=^2h9klJD`s|t-B+-?EKpysVfB>y)2xqhpX+8rb?HyDG{Ud0o8?vYt7z~7 zHR3A9b%DBi73XyeRN`x}h)WJg)-N)wOV0rjA^mZ-$;B5(3QG1<*RFAmD1HDCN*)P; zLc<|YUInFY;4)x=T6Y5{Vvp2?8)g@;XS6(h_IavNx$c%yeQugLJf5lPH!)40sjs)p z!Yc7!*b1Mi%C}KL>%VXt=9wD(7iRx6wdgh`!Q;O$d7r6*x6JVWly&VEtl>hcLm+=q z2xP?%iMP$v2qQn$f~=)g);n0857osxW^Og=4$OO~*4@F9VHD+YXx>A!o?3g?EZ}iS zUA~JIa!3`phm*!bYWY2L9Yme`W;Dd7`?&l=+9R~~&;y)DQ_fxhUAgH&3N_Y_{;^6u)z-*gc#KoJwIO zRm&cu_<8F7V{?Pf*Yl32I(&0XbM^QMju7)yrKe`s|CIRWQyiEcsko=+xc}t2Ynmmz z$P<7upt`r}H|~$(s=j5m^dhGO#@i2`YQ+!tB$kZ7i0>X|?DDHj4+bu;{cZ_sra^ORW9OQx#1%v92op9D{IL zjec%+`@ibS^#VH_msL_td|{^11-8~@)l_9)nsxqD;FXuC{j!RCi9^d3HRYAr!-qU7 zz}CEE>r6i@gPQSna(Y$pADm}jQOVw$rPS1aFr%)h;P+-8_4yyOhZkir7Jl_qsd`VT ztk%EAjkPPP;0N@5mN!@!SJb!ywJHOHqQ_Yi1P zFxrG;S}QgB{bW2bJS)aWjrwdRRt-Lxg*{2fxamE2g%zlFe!}kLRv~d_HPz;`*~^O) z-HFQ``=26P0Z9WpL`m3iN=h^PF(>DV20{|7eN zJ9Dd8ZSjARH3g`MmsLrHdRWyEp5bK`QP(}J<_H&zZ*72Zd0Q=1fdp0+gnK5i+CT&( zu`;Q939KRrCrfB`_u`6H;tTF^A+w9BO$n_)q})$vwbCg86};5z8MCvgc8RQVzgV>= zkyQh#-xFCKAlfIk+CjYbfe}v=TV)W=;b}F3=;LYChk*5!)fZ1L9%hFC!Qw6uFt&;+ z;ceAJ7#0tNfU$*A_zVmS9pu|Tv~M5UOWE{%Sev`9Ia-pZ}-p>tDav?{7U(^$R;zfWT|)LK9A?Kp1S?_bTd z>S8*}N6koU<%SM6(Ew$pMaMi+?b2D%5I*UxFbE7_W;H#%RTNDjmrIx&0l1~L@#za`9+Gn=9L!=0>s;R_TtXc>MWwCld>eK1Fd|jdjTt#TA1C+r)w-c$O|7UeOy4j z&u+DZPW>F#B#31ttWtQ@!K#UH6JM<0RqC#x-faqAp3Q+6wU=6Rr4P(@vDX&#l*D>HXFSX5hoI!Q=z)&2@ZKc8+ z3syFr_kSIx+ey#QU7 zA2VQ;s#kzT#qw4OWfj1VwMtbiXw8DSR1jMd4_e%8+lZEhtg%SBRmge(vA3}GJ4C-C zRushdBHTx2C98(IUepQ(>QULMtwt2H^6Fw_?5`HH${`tpP9ur&E}~WiSQ!w;pm%`4 zc=v|DPzS2L#jO+wW4tq~C&jH)2xHK3$rgw$U=`l`unMa#fmSwzG5k$*RWj%aOJJAd zK`*Zcl&}gSjFE2*fk7{5n&9Rzq(Nt zYnDf^rOr{`8Mra!c$#70N~lNWtklrL=w(xB%JVeC5K?zy^eU+P<+<4y!b*6hfH?-O zhsseAM;;!zPU^3USh_rX74ZWB7mYD2t+rIM%7SAcM?qi==XyL<-z(#o@>I30f*JBu zMOU$gLlmlN&4D;v)fx-|m9lC;HSC6QYEd<-y;dlx3RkzPBiye#%vh^#RL25XJFkWn zgh<00)@X=JHLN8NeHvO#)uEbJV}w)HvX(+@t7T1xs8ZWn?L}Urj92@-)aAyT+Nhd! ztf@%p*~A*9n$_jfR@Alj0|nQ!4ng?VxAs6}Z*GOD>J6~A)~eqcz|ghIBM8%Pt*Q`& zmC81hQqe(HDTHB2Rh73P4n1rQRRBY3sp^f;1h%H3y4DC?#U_SMUOc5=3pzi2jt#PRsu+MFvNNRvAMJL9O7$#tGoKq1w#?%-rv)eYfZLs9Z|m1Pumq*BAIJ_s)!Wwo6*9Id^s3XH~5pEcYn zjqs?^ICQ=kZq-Hj+-U3;#t7CyogG!yk+6nqSq6a$+z=?ciF)tCF2)s%Qp=ZH`1s5y z94fi!77!@4J_HI}==G82)4}-OYK8f@g4#F+r!pVa?J<~jA62p6V9EHgXxB&ea4c5o zN7ZMX6#>y;66{Jm-s+?CkMi(VO(wJg#D=VX7ThJ*Q*k9#3Ez zJ_E7u>e~#A7U${@feba3*DN%FbLCcbX5mzfv*uM%vv38X;9`13jW<>HB~}A{N1}*Y zumn3N- za3}A0QGu%8rkHf9Q*k_@o_ZxN7rv`fE3HnRtWg-dDh=?Bl{kES$J3hDKrc>c)ybY( zpAuRr)RYz2N*}L6?|)Yws~w$gZ84S8+=Sbl)i@pCY(v|7E05VXbE>RsaOmNI&ZAsw ztWy6UgWj(}Cq=95YcZl+bUrnAtyN4HCgp!$YvuY+`3=_Lh!d@fY{vQ5=XE&XMys6b zaaPS!tcp6j9^K022B^9l(0#wImlGQ>pQF{X%^1y~jX0BwR$De=Cx}-4wpayJ$R?Z` zN2{}2uuZ+*WK~2M74?Ncj$RPSwqS0vavKQfh2z(yGFz?e?5dI7;!S|3RC-fMzhs2i zJ16EOeTtw`p?j4Sy$Ifa0Dt%6$RYb#-Or*#D6fT%79#T8gGv>sV*wx&oHPdfL`B1chnYOx2+z5cT`48 zcp-#FkOR1}jhvW~58i^2+=Ku>u6^g82LV@eBJ`4%aqK>;v~mEK(8h`3H7`zXCm{Vx z1|0;ndBq@Dz;_Ko1dyjgRN6&ADc2V!p+el>8FUxW{s)6F0d3;3FAT_eCYb-e)F)J+0xQ@0X$NrqHO z@8VLoJA`{|rxQauC+{M_-$AI8k-;7Zu>M{rW@N_qBVgnK0j)JSETD=m;FtqkhF#=I zhd70t5nvZ`p1>P2J3(u%=8}Lk8vN-1HtU)bGhQ@d{hI<-mSAwZND`M_WR_A)?up9T zvJ4&y_`M8+#{!;cU&f-;+j(^;zztv#kBP#Q?N^${sw~Gchm4Ftt8Kf03 zUV{t*0(F+m0$ys6O@Irpeo?8uLh#}j0e=Cbv_>8QlNzx`egS_rWKc-JXsuCHz+*iF ziVG;BK}nYoycS0FmJ#5iCsla?J({sbB?0F&s4AdabJnOK;EV>f1r%w)8uc9DGVo(I z)f?mx18Ne~dzfM%_ z8NX40J>$0suxI=>0rrgFDZrlby9C%Xevbg2@Hqe4C&Zrd2L;$O{)hm3#vd18&-haU z>=}PnfIZ_c2(TyoWd?lyWzYDlqGHeZ8v^VZe~ZA+$Nswx;2rL)D;JF4~0{n(?mL$b!|L3L*(|t zXi!2xcf}f|1dLqFpp1Z+CHnlUoRE`CS*3yi{}l`>2}r++K@|b*S2L(4VD}mZH3TeK z%b=Ek5$hP#DMsf%KexUPTsx%`$U<^b03;lzya z{hVwF7=M^SxPX_g6HIywv5V|S;O7gq2nTQ-1D%*r`7{?WSU}7L215l5y~tp=0D4mw z%@|3*#dUPO%w&|P?6|^Ui~zisOL@i#ICho61OeXH8T=+7<_3ew0>0c~5GCN;U48vC zO-NIHRXT$Jzg^qyKIfV30B-$UCuZb-z{yHLfhPjVV9Vz5ELns*E~2^jPq0LQ;ALg+1GbaJ!+yY)K= z@Y}wBePE5>9l+h;c4EfjkDR2oK>KHpK~O96hV zxsHDXm>RqhusRKEyd&UZyF$`3`5-FM=@|SgV03y0p9OekVDMGIR1M+;q{ze?KLqs6 z%)kS$H|y@mmo+}$e@H-NHgyoW1w(5RJAfPO<;09E{+ygdKtLe|Nd@fGAi039g;^t| zfcQliq}G7eU$`b|MJ2E}tE3l@B9K8w0pTSWWEMa^>|uCT0jEka$Sxo`-u#A!uYkb> zu>Nuip_jvRd(cL3{` zaAL;$N-j<>CFE2M24w_HtI438fU&h0R1k2vHiJq6p4VkiMZkl445|s3(ttsYfFz}C zL+LGa7+OnIRyAc%M*zKv4vl&Y+#QeO@dg+B#$O2M_ekhR2?V%}1DqJ%Nay580p!3R z`hx__8q8pbfQdsG3==>vq$A4+0riFxa2YNkgGR8*XaN~VF&Ha=-2J1F@d9>_VK7m^ z_3*x~?}@lum$hZwCEvC4J=^Qn1{;X4zB#1ObhAXu*{w*z>(?4j5h zN{$!D`uhm{yao?Cfb|b^j4$3ImvW6qopht+S_a1j+|=Nt0D9>jHk}rbcRho%0?Z8z z&I?GpiNQqy#{uZ%@v@MHvxyPGWf0mt-r*#n8YjYbWmRn z;tRN?K|%p`7kU!-hx(L!Sz=t0IDoq}sS`8C-s9xt0>(UKkkTc@>u)Bh1x(Q(t$?hL zSR=iFS5FvZ6wvJ{gUkZXYmiky36nLlJHW-Cs(|Ib4lx!!W0jl&Qa@*qOTZ8f@(6gK zK|TTW9Sam%KmdKT0-%t90~)xB2+57FRY0Yf0Q!6dKyd+fCzm9^K0*zArve&f9KgL^ z&WRbP-*R#V0i8ZEs3c&k3!l3{2~~vHMOG)kUKEZcO+Xs89Kdzdabm`!Z(Kk<0d*3x zeggp)HE1ZHWg^ySOu)r;+|{J1sC@TimF5EOcrj=xV6ZoX)&epnVbE5EzaBv3F)yb4tTNC6TE zy#Bb-lbVbZm0mSjWr6^5PXWt+6R@@pgUJHuYa-By5xd%^55e@J$2Pav}e=V3m~u(za%> zT0reK4Au(h-;Tk00ra^N)Voo@gJ1@m1%!q$*lGjZyz%bFWSgjr=+0n=fEQs5Vgz*W z$zYd&3cVQY7C^o@V8|Z=R`+ADPXKx0p!2^2Lg-5<$a6?QyI~BD2x#PDa7@6zV;Gze z5IBLsDFL^CV{k^m(@6}@X+X!Hr&E|*5S3$53@!aPe|S$h4)ba!Lv!S1<+Sr(6+Y%a&2MoUcg5UJ_;DVlQljGu&ww?fWG=` zypLfG*LR1oUGd;`pj`q6gwH zwFHdxVU0Qhh9qTBPe7Jr3>pZaPw&wBYba!U3RY<>U~@_aO$D4z&7ipe^6~;xTMEdV zo1z~+h!1`4pMFc>UgMs)^5 z3AlK6FRH<0xTq|u#bBg>A+;Hd60ou^gE0b5)MGGCz|Q&%CI}$+HE8Z{0+P4D`47ls zA-P(yN|b==Z5T`wpxQE+As}me2D1e0=)ho(fC3#E%o9MLDnhmM0bF`{2ZS_79tiM>X7INF zavX##j|m*4N>AXk?Eof$pMD8*8P6QTR=l9tYRV9*e<}ON0i68CiSd`GP~JNMw*E%~ z=c#~&``Lug4&Va5Ix(X(t|0076DQ>EQO@u~K+p*W9wnS2gHJO^Ab`GJ1uGH>n1CBj z0GXOKa_pK!W? zGBOFc@{mCm0p%Yt$mSAa+vQ6jh729^m{t58z;@<#Vn#MRO@})23h;ZyAiscO|1c;h zpz>=5g#|Qxqt|~?A+z4HN`Qc!?->LNX!L{5C2aC#5-chY zaxw@JVAs)=z(%U{H9l#AO1nFND-CmEPI5SbPo&=jA3cG79|y30zY_HD2B(iL!07{= zbfZ{729W|96lE|-K(68ph6uP=n!zvuq2(Bi5J1kUU5uGVGSHHK8!+tipktTNsK zZ0JNMW}K_X$&&<-?}FxcY&HfygFGq#N9 z!rn1{DRow-{6wKn~O}tW^cr-BE+U5$d05&sn3k1Gs-&b)BfO=pRn6FW~lf20;S8 zd-09xMgrz0XV65z^E3>a3HT!egBAj8D_Rp+jO$-KQKD~ux3h`fi90wkV?U>u(T{SuxlofIe4=W^57AcLjrJ0e4n0*bd;*mEKs(WT&W<+rZ#=0bO@8a0{T% zTB4dg1Y*hXefT0Gz&--=3E02eA9Mh>JX#)YX;W^ynWB$rU2t# z2Db!c{KDW4fiq;5{RqN+2e4TWDaN194AD<0JT6JsA4E9)sie1y$6aX&NSc(vGXV?J zGk783U`7V71ia76;I)9qxf#3_FgUL#pMSg;GQI$-d=x<6_(b=95>Tx$gD(Q;6Q9ud zCg5OE2Hyp=2w)HouJ&~6A68@#pMZ<|V_;<_2}Q;3AWs6g0MuU#L^Vkqz#W#VGpiJG0GCk2i5bVcaB?vL z1$#0mE+EMO1|TyMIS9Kac>I58)=I)R@z z=~@n8{W?y}7&V#=swaRxor+FsK!7(5m%VE=atN2u#EBVIW^smQ0^TlU&_ckc)eKq@ zz^$)%#*B>&+B$$sZ12R3Haj^vSOYr$TeOKuXHluNnL((4JzE*{5D*j1pr?R`+Zgl~ zP-Z)Wegc~4dLsm|o!I{d39(HZD!{gEgaF&JQ37ns#tN`4n;^ipY?1)mvM2(N`24F> zxEGDebRy5F3*irvz$^!F7tV2FMmskrD*@;BFjy$y&L0ex2*|OQ!Eyl!bQP{uLhMS{ z3b3o$Ai%C>vjDrAXaROLI|SI({7wM(z~UKQbiKQ6;L=;~AOB6?Pv8(`*l>z1KjZ-J ziz80VSbva{j|<3tguy8Rm5wqvE1=tP1{VmpxQ-RNj?1EAS9+DeO|l|g8E+bq8v>S< zXK;(aWzv{Xp9V29pK(`8u`7BYz^>;Jfq9haiR&KQ^3);RI?IU}e&;y(xqvs<8N3nz zruJL0__u}=mHIx%DQC$^%9fa6~n1PFkiOtQ0t zfTQ0Slon9<2ZM3~>UnUT6cVhQ$(xcv9|5a$9sLE=$jBOz0v2hF!2)cvh7q_Y3veUH+GMJ zA>@K=0!0w_O z0$SE|alN;M*nM=D0B@FoU0LISXxLr!NHjKgV~wYxVRuoi0J}$C2(WwPAD;g_1MDt& zD=K!6d?4_dCKbGtQpbOC0MDT>PRvP;Bf$IIDZS6dD`NxJkMG2c^mVv6S0W+sSxOan z3D8G0!$$y|m6Aqs0rp;+O2C=MtdUkgU{eMe1i(`%m66#7SpT~<$tEhc<-P*!di@FT zpulx06_Q6lEqw-+-vQj%f=wpD!M1#=+{?TBZfMvrt%R~W67dp8N9e=nBla67P zsiFcmtkkD71RT;}wtyC+SYw`mM7oRx0-j7@jl}})XpLn8!d>H7Wu=h2I?oyb&8D%& zdI50WN|tRB0Oy|swh9<9nZb4e{!t8K1f12t&GVl-0RBL!kUvCa&xSA zuqSvn0pWT&`wFn9v%dg)I_I$g)}K8m@{5W+p9=}F=W|g3_Ixfbz@E<~1=!QMi~xH& zmlvR?v&*O?#GcPp1=#bsh5&m$*A`&U-g*M;*&8Ilp1q9)*t55p26X?!p1v(b#h$)x z2t1_?zU*e6673zp8+=D6X1rg*$sq!!EM?GDK!7fwy8ydH!$l;H>R5P+^YjuGyO6#D z>_P?*SW8&~PjHrj4q&qeJ29i)7ET@}AjvicBLy_xp*PjhLU!+Dm2m>*?`AMj!2Z1q zCJTV`URp0x1$^DlV1|I2hZ)Qka8rYMWoiHCrg$G?l?9@5M1#cwsvl>KWdaK7)~^&` zcgGq5b_c8%V0XYK0gYWdxYVse><-v2!0vz;0d@zt1=t<%hXA_+_6x8(;E({j1CExZ z^PitP;Do5y9dKHJ-2vwW*d1_@08gO=kGU~d9KiG8suOc&*bM;%&*KApkyoN#h>?Jby+r`O$SIZ$;(kO$HwX z?A73tfO?NuK%g!4e0pet5pVx$~p~(3YehH8X@442BQSn zhL06sTQ)&}ZP_FNY?%u`5DT$Qn=Zh%Y?c7qvbh3m%jOHPEn7t3BaH$4Nz+_k>HzN2 zQ?*gJVaP1ai z8@gA3ZP@_=$Ehjbe7MxZ1n|HwJENv*j!72VvXcUA%gzX}Ejw=mtbg0IOQK?%_9p?Z z_fZb6_qqeP3vW6xc&z5++X8Gm?+J)a$r=v@Jk;PZuRpG1cqUdcMa3&UgJ%LFw8l#T zx3yWX1=x1JBf$M*cI7fY68KIX)(GC2(P5t@i_5O}n-II+9|G)p%_G0X+R3 z&&XArOZ9R9_fHZhX8h*Q$;kw4$jcxlfJ>LKLX$M2a<~Yqq!*A?*O5tpU1?SUb~QN! z*wy3|VAqmcz~Le;t|p%lyPARoPEgz8m1K<~4&b&Gb7IETKu!)6@TmlYgvh1~fTM2e z@-m`fmsp+w|NdncQb|I$%booOH-b5$;&kpTM_ z4p&nl+jJc*1lX0f7GM|BPJo@IBLSXY~mxmfO{gmOOSf;{@2% zOq4=0_2WV&JAfM-<-`no@l6+CS2{}oeChM?Z?2FBy}8Kw0uJ?Iut>nMz6_QMNTsb< zA;30kwE(-&bpq@{Hv**5{b857g;i8SpV)HjRlLI-b`}ot2i5|_LM`5D@lA{6TKucU zOD*tpK4o~L1)g`O*i|i-YJnFG2(H)Sq!uf**h>ooiO00Kro{yGji9?9RTF^swh#k;^o_9lx9%_Twrv*KMh8R5p1~K1mKRx4w zC_PgILC?xS&;v0LPqd(iRS?^)1wBZD*g-9pX>n8wdf)6VKZ--p&(IL_ zw7^fUE{a~!QTl}pG5VnZf_@Evpqu;dv8n}FW&Sy`S4}6kd(36 zRo@S@ypv-0iT)J_rHD=Hz2#RnirAW8*z8{WI5(bl`(L4csf!iq+q?T5l)CvBuCk_# z_4eNMD{Dk--OsFm_tu}IeyK}1#o;Nt|5cMiZD}yai)>&2GtRCurh=V#+>TXOsVb>s zbE-zfVt!%i0t14@Q1>rM>UGN%+^!wJ;zey&c&oTnu|D_&5*J%AHH!WHXQd+EyMB)5 zR+s!@6O)m6+>Z^4Q(IHV7V_RJ(MD-v{br9k?5&ceLBVl~;0{ICtbS@^dYE?d*YpR(uG&ngCK+Ng zspJ{JuKda>5W_>1+`Fuo+xzm*(adT=1{i()7nZif|0Wh5xcpz{%xO#iFXhi)Qq;Me zv0kcR##kRuTE#P>vdw;QAv;Z+C$`>q6_P17o2vZm1`ojfUvRpBvj6MtM?WWJSFtG6 z8xL}_9b8Iwl_e9{onKiHvAaLB{NCozQD3z)Q*0`HbOeU@rY`RE{nKA`QU^NxZ{cq; zLtoa~gI~BBmKhts!(Z6EM>l!2Z~a2mebT)BGb`+>QVnN79qC^(`ggS|oeHqG6pE$Lr>b=R zRg2<#DE^lIm7r(?`WHg~b`k4Ep@j7BEJagND1iP|q<;nJUu*hTg#NXof0s!wJ%w&j zC<%r3QRoBxJ4OHQ;$I2l0tHJ`awGazo&GhYe>W)MEB(ty(MJ?|K>u>P8)uDei^{IH zSGKYvMu)tpH0p9aD-B|FV9NOnX~Y{@Is%_^U$`T%MkM1dQ^< zXO?^u>*Jsb-tEfb$p^ux%*ta4e|jg#H`bp%JL4N$J3Tdcgg!_^Q1c+Js0+Ta4RCbK z;ul*T9~TYwi!GIz4$ep;^C8IY5jbS7ML(*5R5)s$_lr%Hf)3V5CCee=@KwOr3@U$` z*i@=-npkg@9Shp)x=#G4isX!~h8+nyW$~e;O*vx&@Y$ieIb#E{oKpG6mV}M9{bRG` zqSEvuTQnT{=?XfY>z_I4gu+nn-~3}|*UG<$B8gMtABQrgaYEJcAsfvq`%=hV!-)Os zN^Lp^Hr7nFOt(}l>2Pd=&Uy(8XnAo-AGk4dD&nKmJEu<31 zdBe27Jdk{)wH%kbqD%`)SYD;$b6 zf+xj`7cnO)LznQ-klx`Tor`o0i3m|0J}2{W4{H3pY8H=o?ma!9e;?WH8=3U=HU4pE z{8uMbKR!Oj!E9YZC-etCN}|~zFDLZ3w-ajNThDUDgMf({XZR=2*;ZEo@oM3DA3a+hnC=wp4jiv6bd0#ZI-R2gR@o+H+hYBon zLKEQoS+fps`m94M;q6(6(!;B>4xN>df@5c9k;a8N9;#@LSA|sLB3ZrM)BC*GQYal= ztr%nAm|6RHJ_(P^#(H;?#V=YkPe<1-#?HJSG!B;yFz6sa8#x?h-;xGFpPe+$PMwRa% z9GVQrw#Ef_;;JuS6wgl=M23IH__lA7QW=!(jySO@HTx|BPRF3^l8aA>IA zCl=o5gtEf7pl1GXD#)QoWBW3)V&;{M*;M0?h5g;<2ECeCDv+-3j6Y99i$jUdI-!ej z>Ze)8i%uxuGKJg|w!Zq1#zhx|Ml(3S)1LLNOQ0q?=03nroN?IQ z=kcrGQl_ViRHI7b1lmJfq_OQgiBkH~Wv!9F5D14ni`t<`<^aL)2bZ7#63F%P0TTZABya#DE z5^jTZXvTdflnZ`>jJfV1>0Tcx8%h`6#uxbcu{{LA!H*7&hG!og>eMlz9U5;qAxlE_ zdphh}A15@UpB;)c;-TmWb@;}UOsYz7gEa1Qjb3+1nT{^+jn24i)b29Y;%e4d=dRT0 zb-7LH=$hZi9Sy>v>9`m+*0`toyt$mgW3@Zj|4oZ>fpiPN`1dGMIMn@w6B>f!sb+pi z(V^v73C4N1+2PHWDi!Ijfw2Ln3fhmup1V%yI%c?LFL7zFLoaY$twSwv5H_~BFE4%L z)yYM-9E?BTfNM!?O0&QT|&6S@q) z0-6nhLjfHsmyt8O+y8izE`>)W)j#>$l?CYThtUBZ0kkiHjPq@1yHi+2e&fq0D`pkcC&zb*h1P&N&omH0h?Bv9DvMg!x)e?&p43``;V! z-5zJ&CihVx8$M=KAIiSTr{K?|`tOp@jo{L;Cj*88Sd^8~8k3oyJ2Vdi3e-JD^KQcu4;NzMUf@ zeDe;@>)Ruwt1p-3JIpS0M9w6>_FsMu_3a-zFnB;@FW-v3z55nXfj8gQOp8F5@UXu5 z1{d-T?=z^7Z;!Ca`H9oH^ z-M4>O?}*^ueZmHW1|w(xu)%h&2#=s)&`_gSx67797c|l%Xrek;?p^kTlRbiFs)qo==9zpj>$H&&8b1hrxZ;znQl$1JjFv{-|5ggXLYgm`i0l^X7Y>jUoL4on1fwTT0 z9kV94X{q=@tw5FE@{VazXye<)59&;c>C_Crry2aYXF}r#^&*y}%YgpD0|!Kebm?gq z&^Lb2VC7fgT~422@q=cPPKxfK>TvS6X}p5w#1C3Sh0lE3F}-qCc$dg~ef*$k(n&op zMQ~b1d3MAPIzT8{w?6&RufaHp3=JP(mwq^Y&<#q=p!)eg&E(Ct+=?IcP`#?~&e!!g ze$YpxM$$FFNqA7ut`_{@Izfk$Pj*6`;4DG2-SCp2L#5#zL5KFjErJg1hd%@zx(P=J zIyd3C||0*YxbF~vnw9W~Af+qv5*L|ae;JZMx z`S4kwLl2^zkQ-hKG@AzZ1UfWzM-?Y{3LXhG>mTT%o5IFRyl=1H z)E|o1>UF5u4JYJr%L&cFEA=|{JKm+YbKoU<9omC;<#njmV<(j3i4!`H_vCeIr&uR+ z=2sRG7dMCC+(RjmMvqWi~ z&{e$CZX9v<`SJd4#NEy#4iI={O2ky%oKZG~*_~Neg`NPp;mFP~mQKLIjICMPB2_=qjLbdT4wN9-u z$qq#tbwd)DjS_f2TEFDH5-&#U(Bu_P=p$Z**6a;#&gxL} z4Nhnz?!s#J7`H!-5AK7t|4lF?$9yVxH*V$W_nh0|j+zdo$9*(ozWd>!ezL1jiuvHY3)d!-k#f}ibjRnkWpc$5Nwx@t ULuuC7p-AJ}Y^ozNvU8FD2LPS=Q~&?~ delta 374544 zcmb@vcYMsr{|ElvzMHZ&o9%s*h)B^Lhv+3r^wT2IOAtg438xFf8>1(BCj_Ge(L2#O zy#=Sm>FxYx=KbE~K9Aq;j~%~zxXiq!Jm=M>z2Ebmy?gnwWXYAslB_i`U4H92w0w^W z(Pg_->|DN5_X=IQSL{~lx1`b)le)%K>{ceGd!DlW=wN zYV*!nq`!6T-hX(n(tYzxwn6-6F~3zVQ{EmCgZ`VHDe0Uvs9V=w17~Oc7M)6n!(oJEn#43ak`C3?Gnbk>0n}Ng}9ip;Z;=1F3H`J`XwiI zj_#V2isVXV{_Rl;<&3Mls%nVJ4BGqbFt8!R*Qb~2qi zh4BnvK+n`5Q2ZiY%nayh=9L7FL)}U-BchDV19u|IR!tjX0!fcrnL%~+A7a7mOcUUp zcx*xuJ-8RAGV(&11A{_&vzaYdG@y8aMT>Gug99xcrBNh|DZMs~c~C+F5*0}j=5@Fo zTzHYAOAT_v-DXtJ#xyHwVx~ulnJ%3*l3x9~b{jT36O1`qGKM!B zJxex?f?SX0A=a%a3qN9-kqGKGl|%)y>+W@{&#;)hZC4^B<) z#oXx}#w!KJ*xN+`<)c=ipdiIrnxAP#V&T?9m>N4mq_Z#5-tqvq>2CGvcFs&e$uHs{CXbR%uMt_9l-OmKCR4neRO!cowk5 z^3=%Wv==eW!()g=OGbo);>#gQY+~R{aYpkqSqEgEgj#tXZY4a{1(}%0U{EM}B1E=0 z|2*B`F%6Q?0PNM~8@w7pu3QNmNO8t6DK68`jsO!4#4P;GpL~w%0$sYA{?j|>7kp z!(2HXLwQDUp!E|Y;=qyOEXhpofbfPo?AqCxiXY14yeb$BXtO7nSz1yu&xX1;ptL~^hmk0Ms$%^68yJdf-A=ow5{_dsT0XfW5CSZ@fPo%uhm z*bR+(R-nq)TbbexR_yLfYOj)@@M(H5b0^9AlY20wubUWqQiv+>hYa152F%X%dK}lK zx^y-&=R1XggVFOX{GYlKH}3rK<1U>-nTy>`AnQfCfjQ7K1b2Qi8h8g5KG*A*nF%gt zU{4bs;bwL7W1@Rld1Qo%_*o!sz(d~Yb@jgeyLLm4%XTE}@~AhGp82q&2EuV4ypHOuw z*Cy0GLhb3mQo9Lt;YuBnw?LSVrQd&_CFhe&`I&ko^A@41tZG20{e(KvDUMM42-PN) zrE&>XxL z$C9r}=7lzWN#;sIy|_1=P)`U|J#ZwU))4AO1WTPD)ZntCN!~_6O&>ptP&)`Uv=>9D zZ-ja@eGZ`(V=7&8avMv|A({UdJeOp?CsdnUK&V1OHBrqc)I*j!yogZ82z7YdVnXE+ zYSLwv+D@n|vK*54H=(xPVX1S3O7}}zN-{4JvPLmWEg_V$?sAg1jZmwbt|8Q0Ld_Y! zmQXJUsabEp^kPuN%GbaDk_bo&Jb!&ms2F~DWTpA&JgM*q4c9!YAvDU z;7mHnJVnS4Q_d3V6`}sATu7)rgvwvXQr8I8_XSIRC6x3lOZ`QtaV-X!@KCmtQ1zl} zm@##mP)nP?uwd#qp(;eQu%=`34k7auyRDd7PpD{9%WzCxB2@EoEVYSHBLn^-d7lWi zFlIqXT=FiVl3P|Qg{en`dOPnZp>7bW?(V*kIBzeZKI-*RbPpDahiXB&{49+`CsNhX3b%jt}PY*1M^9~S7SSqk8rk)dO ztE5D=I%cNgp!&S)Ova!xyke$!QMfodDhmB$NoieQp} zkr}_)zy!_f!-VEGW}a=42PF6J(Q8m@uP#w3!)G(ui(HI)a3(V%PstQ4$dG0A#?uCd zIIyS`6E>s?6TL(w8kCaUE0yU8tV~F5lmB;+lr093JNB_^*0vxFdf#Jaj;X-g{j(grXjCQ? zq%^k3(Bc-WwdI(~%mJn>Q#`UJG9;cIG1r@?PGkyEF6NK0&}Ky~rt>%#DuSoXA4S@8 zjKw0R5h6lCR>botR`U;@nQ`N4^Ky}*>Jpn3kF3Q^7*~_GirKrVCDVOUbw)XvVOC5^ zW+FG2U{0^7CBu^_hWJU+dXsB0Wv7&9PA`mNO0F%-^jlv8v4iHIxw0!jth&f-$`1ZhmG5WVs5Gw30af5ofU#OtI52Y98D~XeF0D2EOK3D!e%`) zeq0Ua(!9EieW;YVJu3*P`Y+WJpCC3_jC?AGsOlh7hM!rRIX|v~5w97$Bqt3T)NK&% zgIT@tq>mL8j%;KHiOWjMn1Lf)$XBJP`of=W&MaG89i_b{Jx4vKB5x(raL#Pr6XwpG zIlMKbq+)v@Gk>mJf~R@RsAmYkJ^l6E3P^P|5W+@gG#JC!=La*|dA*s-^WvEAAQ+7V zt(gk*yGZad0M~80J%%~8P{kx}*Mi2&(1`;27fQcDihPR#Bl--$$&57idWK=m0CUL1Bb2+CMGk9Y!ru6XAyuX>g zOJDHL5x?HNtRe3r@sNV`ejssg6S0D|9bS20iP|wsx7A@@Ee~c=mjA{nc^l#4hc4bS zOE>Gdk>(#pxv~_GVXCeSm*JHL#;`M`*93u>@tp&hahr{JZ2n0PYbMSf-sse$c^{eRy7Y8Knm ztHIE<6@MBgFEi8EwnHN{nlskBn2qZqnfI&W(a@-5_N`Mf#gRg$*ZR813~$h=r6Xxk z>*c(Y#6N$ONR#0e50<7Rvw58zWL46rGdDij}WZ^JkBCJ>B z7`(Pf6?K?SlO#;UCIvAZ@(QG9>@;{J$Ie1LZP3Q1Ow+A0CTgJuxr~vw3l)oz;l&ef zEXfP=Y>STPWbS4e*^$7Vdx^|v`9lwnW$|iBzUofA#i^yo?DSAzukz2 zUcBhSoJBLuw!4{4+vU7dM4#T?ID1J6=I`x#WLLZd!vNU#uFNmZBy7W-{5a!(D49vz zCk1DFPQ{3L;e-(I8h32%b((zbv@(1a|CFrcq(Hi?Mh|(=8pnqOnl!xlo`A0 zGgu;-EoRd844o5?!Xf#uLEKhG#!TNi7?_n$rOc;2sUWw}Y#C|;FRO4HqrvO4%hQ?9 zdq0754uq6mFf8xQK0XiZSFu!3rE;BOqEhv)D=rB3AudgdrDSp}w7 zhB9XTv0>m_5Mp}W$)Sw%Xa;!MZHJ7XndT|QI8UbMEjudUfz82B#Z1JBVIW(Jibovz z0IIIvfdGyV1xp_6kRi3rVj@osVN9oof@B?%YW=Ac=F6E3MsPL-oOPl)nlq0|5%(S>XglT+v1hC*D zhyeXppwthAgP7YF(!lK(S7pqr^Mjb?m(qY@%{3YG=ebwHvSm8OFefiNc}JOMS4#6f zlfT*cuY_AE;b*r7^jJ=enAt$GKQgzjB#7~jsCNNV<7zeD^}LL$sXU(_4}$@<8wE2R z9tDDe#znHc`0H(W2x`q=(0H2CuI{kYTgMEg6OU14WWcsX5g*BT-3Ah4pBm!w(*B*!O>hN|W`@!5e-Z>;Y%!ua|p(bFD zmb|}FM}p1U^WNh0dWoo<&w{|x-FdgLN-_IjXb+x-$@yl4@qKu6#6E)o1eONQRca=N zU?49Bb9a0gFBFHbM)SHLEx{HOc}1Ap%9*_60ICL(!QF0;w6M*5-XW7m?a|wLLOOOi zw1<0m%dk=>?&rl~WgkDxOTcV~o#1uC;lwj+`1m{<*1N*{g41i=;LXEEx_OKDO@hL| z9`P2-P&n)r?>(mbf8f=`<+gs|oxpUOfPWQxT}u(4ht<;BpMM6^?m)hnP942KqKW4K zDJA$)_}st`@4Ro%r$Cq?bDLPvS7U zJ3kEbdaO6UIySK+h5rJF?*{Yl;`A#c_%)?ID-F$|qrj0r`8#+=*xCHhIQ|x&QOG0? zp0y}a!_(vWLcZ?+&0g|jVeBNnLBKl#+fC)ak)yE50{#ge3iXTlYCZ}hmhiV?`gS&7 zg3A>xW79va;J*q(^wZP)Rk&W(4ZcBw!gcrgS8(|;5BWE7m{7#G;(DW=^5;|72J;9I zk2bLK1wYMa86m14E<}1B`48XsbrP)chHvrRz5q4a1SFnFm4fi^dJTv_+Qko4UMEmM z+v&ARl{o<8FdX5eon6E$E5s z)#@iu;BqZe1Q)TlhYb`w$6-*a-~$c=h6vi@`aMSohT`&fM+rXT^c(4d+c?}kMo@^; zr;ZoQ#5k5og83L9PGji?S%P~wKVr6EJ%wq!HU`MXD%^z<=Ij<~s$?#tK3)Ts?icg~ z@$ck8aN&FbgOmcImkO5nZf%5<0{bo#yuf{+{Yt?wN{B(L1YMDez_qo4Wt0%c^@5F< zi{l%qxwyMYpv7Fg-y-n+h!oao3apkV*n%O7cTjU7-6?pB1^KpHkVC19s`-LF7~<1D z3N9cL1g(&dz_tejdvK^dB-nvvI(<~Ifs!Hpanuzl>pnOsn2Lqze@3vL5@PpR!8D4| zm00v~p`b5rzSTv+O3LOJToMG*N?-3PJHgz)PObc?8-lg8%@4gT$i~v^?g=(iO5gpy z;1I3yPdpS1#?4oMOs)KJPXtG3n}7PZU^Q-j<_p0(O6hOB6l|kae)Kgn! z{!Va)R{FLdS?PISs9mq|S3zUnpOoRDv^mHQ6H38vRmB=uQ7BBYB0Z25SH4sD1dm25 zLxps+O>>Blx8Q-TS-9{L4nre^Xvcj7j*S#L@B(2)IpKAjKDvT%51z;?|1Rtmh|?Pg zUrSLqx4DokL^`(?Ud43fPQseF-l*=v`?y|WPhmTZx2LzTDh@v-3(;op2t3wLIF(|z z`tvF%8d`@4b$B&WVTAAjUIiH^3v)1&d!`Fd;Pml~a1Lh1v{1;yp=B8>%!lQ|3Ygib z4Z=vwREJH%uQ+VGMHqv_DS5(1IJ~-DD8}J!D7=Bg-MfW@k-lNfKA{8C%?pH4Vib-! zDr|_`JAG1Uz+ur@A>M=RxFEcT*|~pJ7>=t2+z=kb)y~}#KE(94dqO`U7CC*=M7BLiSFXV_V;Xb=)9M)v55~9WdD4bbNgbwi@ zfuG|=fw)a+OVKHcO)$c43i_`RNI-%U9sk{bv{r9;yPb%K*d?A0wzm~&K*m^s5jr}F zj?rqu-tR6Vo}J%QG!3hzVP8=&wpP+WQL|qX;aru!r7(7wXo`n>cqa`( zQw^G&Rxl($kYe!C79fT*#)`UO5#EhwML0K+6`^CM$bv-}Ia3rx(Yc5fVq%9(hdbtp zN@AdUi$yhm0Voh|ULjh7Ic>6r<+Q{)QB$n!VVgvQa0ActL@TgA&D7zjeaRmI0VG7hgM-b|;vrhs5qI~W@XkJJ&L!V=A_FGf$}(Lml%+#6f!NmFqt z-(Qm8>8mM-?e!=C#J3g;!Oj6g=YUGB#M3BsfRP|Via@M^^Mjgb!J1ZLH9IowZ6m%a z^BtE=DaE6P&V$oBi*qTusRy7Ae9%*jch4aAcTEtK^bx0^Q5z)X>4IS9U~zAXQOg5e z3V?Y-%`khocpilp{|Chhi_*jouw_q<7B9v=+w)Ix5VrWJ@!~i-@oU51r;49bcn9LF zHe8e?o`c7TGPA`qFdql!iu+-YE4@hEhGM})q(zT!YJ(*=C2ClGh4|qpO0PgD4dGd* zggjEI94h&Q)mE;AM1sZ@_0nYC~jm?jm!RBAhLirke#4uAfuv_=8 z{re{M>NmUhn!#X93rRf?8z-@Yr_&@FPDqElw=)tiiq=)(m3pRMY4#F z6Y>bMHbg#LO8qPDJNOmrzW4rN-S@^@i4?ojg+`=mR5QIyKi%y zvZzo3{a@rBNUwH_!&` zP*xHq*ODIPft-h(Rj^hasZz*ByEc%9bI~P@rN_DG(B{&v{uJDLTl^zI>iYwJfH-c& zK920$NxHy~a0v9hr5RMLbc7*&qy~yP1F_K&p6V;j;j0 ztQp@-m!?pn6wi?MXS)VSnJr!FGYYIRN7_livaw~Jw2RLGxNCF-n-)p~;KupV(Of_N zeWCO=hZ>PBT|kElo99TQIpJolkoKTM0X&I!C2tDYjk6nX7}naU)G2cLn!oPSIDij@`4zbA#9ZzesEo@ez4YZXc3xtVXp z-%=G<_UH>~EIV{S-5cpKuG*`&(s-^w{731YK+>t9Su8`JQIpAahFR90tMQlD*M18q}4!WsdOI=3R9%$oK8L21qo3T*i~1i0lDo_V%Mg!>c4^t5UOOZ z#!n2xTgg_k(=fc#N%lm^3UDn&=22qKK$%C0V+YG@Vz$86k+L2BY;^cUS)7!OUY;R) z>-*RI*pAU4ahp^Iic6Fi!-{KV<^5R47?UT{&^gA>fP5%(a~xLO?-lHOfmg75hh$pL zJ0!x*P@5>^SRFJ#B$lorqWEY)M% z7~$!!vYrZ72@L}LexvJx(ZB;J*`e_}!AbB}uwNwSzIwBt$If>xejYm?wEKC~@x<-t zQOAKWzl~hA^hm$Aa#q@Lzx$2k^rfusm&Cc!*am)|Iro^>%I_~uU&9jo4s(61aiU)V z7qxcw+sH+4cJW)vMW=W3TS6;zVY*ZYmnHkP;@XuC_FGqm(jvqy`{w(}fw3S|1FNj@ zYsLB|C|cvk^~MWZ{mN22n(?|lMEFxU)k+Wgz2-(7ze2y?x&GMZuHPQcy#S!A~ak|f8{?PZ05xf`fKaJBtdX)b?&Q}Li z^$$_7Zo9gXzsGIMHub+D1zD}SsiFN3|3jP>-?sJl==NlLe~)e_cl1W`We`YY-;U;RsU43m+ZgoUx|xKi~Q$szOd>6O1{EUNI@YwCbE z{^dE`<{$mH{zBi0+@2MHuCKt$8&LG!pBpKni~*6fhDH4+a!ytNk}A_*3|5B(IN%74 ze+Um)oBAAzVvIPf%zW;7IFh* zvLY~zc6ESfbcj%oa2$r3121zEPk<{h^;dcdL_H;p8!U%}2YNb5%QAuVog}()pr?}* zRSEQTl4}hE_i#+FXcp+%jVRj$dOAtlgg{Rx`QACuGwN;a5$Ne86MF@EI!VVqfx(8n2Tl(3ELmG-25NIusc|{O!Onxc+)zGYaSS0)XUOfeYyvfQSK&5NW9E{lHCh5LkvL zb9V})aLa>0A;lc(k)slbdKM^#zdsEe;QPW7cu^oi*${|CLPwRfu=r!(I61Ef4wlF_ zh*0PZl%tP8ir{CtytW?-s~hA`F&%D_Pm-c=t4od!AQ!<2A@VFf3UwvqO>wy%W#sR0 z{f?F7=zK^K+*wuLPeZS;Fl;j$*xpP&9jr9(vVfFieH#Fq%YVT+(MgS@B7Zosh1`## zOPBUj)C7wbscfL{aD@_T(r+LZ&W`+ZVG10)?O*U;m<~6w#d7P+2{%=Kcu2F1y-dEISR$w zD$bI@>>DflDfv7hVF#QzC(oqg2Kkv4z9^L6r5JoTcC4`5MR@{e>`RyBS--%^*DLa1 zx^9enj1{iCF7Lu|V!bJ+_%OyRQ@vaAf?v@|_8oacj+4*#Xsxs3f&8CeVPeW7d0&nR z=@U7{8wjsD%0895f5kAbp2=f5PGVomr~HD8bbcjoLpL1sfat>$KMC7q`h2BykN$As zH+dgAUG(3+QZbZML!>}K*JnG!IH7{(i#=q_{_v$(F_7b6fK1_gUxf%|8Kv}aV4z|S z*SKD(pqtD3!zL=lR9X&(1u1%R9J~%zOsDe0{?MvdoTKZP{b8n2v5Dg#&Z3|=p+U?z z*{Zlq%fUChVlBr3bSWr4z9JmFbt~lDxR)Mk#L5 zdc@k&ik6%<>okRP&(p&?1>;`nXcO&QHCn5Xqo=5u1U>#I!Q*5oG! zD(7+qQidz_zdsfLLBROBn!?ZD~>P(+nkLH;aP^Wf84c zqq-uHh>rciatoAKX?vI#1?+4QVS<|$Dc@IfyW;w7b;uQjp|4|jN2&y-y#yAsHdD^x(#xU-3sMEFkW@0G;;QMsE|5{wIbe^J`G zF0qiO^7N#h0#y{3x>}~%#C59f3ROPcHa`vvf!tK55Q1T&T_%w6RyhvV(W&@eNjex+ z&uN{-3l2}4RoyvmYdO8*DBUU@ms%-YwTrV9KSG6G^xzc&JW)zji5m^nrB#FJb^(pR zsDY1+AVX;tg6|Dg-9fJkDh)`fr;>vWd_yzv-JmK3LMo{uJr6ZVK6$7yyOQb+o$x<5 zx>;h8Li7s{sqCr)ZmOyZ=b}%ld&S;bLp7ARtr}LTql%!FRk7@r9!ytTjqDdJ^&28t z+qeIZtb#_WQXW}JK9O}mBh?IUVWx^#(Jk*90Z;Q1XlbTe$w_&!g;&bOtvD%FFr=;O zI^7B?4pi%*3Sht3oR^>)BlHX)QC(DXD5ngFAP;cEhO&<;pL4rg{Z-Go9{1M}70qD= zG#@pB+eWJrL>?}~#;GWGbB*BY393wP7^^)~RpduC3fF|_3Fu}X?+booV1d1ss3Ha6 zZe^VctX`_3cy#A^Zy-k(?i=CQWh%5!z;JNsO4TJ1?=zUWGs_BQuU8%Mom~%h7(BW` zg@&nfAA6}_QLd^xpN)3RQ*HBOqt1OQv@9kC%3W8vxdOdzs2p5$;Vo4Q4&nPf)#*UC z?9ErI@d7qF@QvyXS769T)khwin*Lc;ibKf%s+y=`Q(MZ_E4gZZYV|#?>{pGto`@@8 zP~SFzK_#vA!HDwe1N5THA^O1*^rFeO->QNGep62qu~9{JH5xNX>h)N)+aC;=m{uPY z24{;v!$xX~3lu!MmV@|FYJYHZQkntwYpkB{2l`~D`9t*|>fS;&I+6Ycnv1doZ?c;i8bfBvcY~Dk7B0=XyUj6kL4P4QkL*m zDM+)AYg!VlX^NZ19|3~CLp12fkWnLnX*!L^KXw{4m$|}AOq!to6o%)`8k3X-{pQwq zd@i-)ioaXyk1k|aZh(0O*^i@jCvZ6SI0NdcziOtk;dbb0r6hd zjcKm&bcQ!Az16n0)_6KYT3e0B`yxAfTN~EN+uDO9Z?zR&G@Cdcx^&lgjLPq=@ffvA zvR5Nh`)SZS6C%*}R83ot!XdH_dJIX@i-&0vxoFZzO-D}=VnBE+O~dDGQasLUld_Y% zHn}#{YZKjcuT5HHc{x}<%PYEej#q{&bG@Q3neSC}{9>=@3$wkViuws6sym>@K%lhn>-g3fE% zXzltz*ZtUNREr=_zYl5^K* z`y;%vEl&&T$00Z~f^KpMiYY-ZE?SETvU2`AaA8n)uI$q#LCckFYl}B~!4kFvz30r* zZAZ{SuHi~My<&de6@<>35HSz#35w@%U5A6x1HguxZPjpFVbBOpw(57i)p+-V9&yz^ zJP4Y_HL>7HP+yK6{qvyd96RuS}Lt(fIn>lU&(|7TPwPS(vt3kAD3+YSGC{f-s}2HiiQ$?5KeVmOm7i({ExFfQ)LsV?Tye(es9Nwz^ z%7LBVuJz2MbD?${r=9ls+MOJNd!M!{*BJ($(xL?&k>d9=+T$F8woohOqEQ#Lo(cHY zMePT!?4!$C&oo)%n)VV`HuQ!TElLTP{+1RUb|=xNx3%ay6cTOtKs$>Q{>4jeCRc6B zYi&HI>StfHshrMIzH8ATdQz>uH24e$_M2buT2AY4RKa~X?esSV@8rr>atC+e5H^Gb zuaL1$+qrbGr*}t{4X(sBJhXgp2B!yUt>74rhX##3*J;4wDkYW0uodIG=aD#=*Nyuhw5%n z?B~79adh9fXh)=Omyov^?k=sXDL~=3ayoQj@@BZKlCBybh4(7!s^HL4Ro4`UeXHvR z;P6fjT>%e;Ikj}hFx|7R4qY3!85$ev8e+OzBi(2mK5W94TN$rgi_14{uDgxXqgv^1 z;`07&b*Y%nXs=7g;fszsbS>>>I3ZCt9ECf&u+7r4{E@EGe){Vr8ea7g7SO+QNbxSaDEWIyY_X_kpeXIU*f z&g+_E5PbCo*lWOo9@zeJdqx{oKeJ{)`)(wc(>LswfTAze#?hVqX;4n(3 z@9QZ6ckA_hDbBR(aUa;>){`!94L{TMyz0?W(jNf+o- zUQfD!-Z>KH(Vd@`)I^08fgKtscAY8a}gcM zMh}qWU{JdLA@2fMI=*LpkdvXO`S=!}I7R%bKs-|<1q;XMi+p~P0Jn_QPo#L*j;gf; z`Q!C-d~Olcn5FN^9@hIbL9Y-Z9muBY_fX*^!^BMe2a1Qxh=*kOX1ab9A2mNc>zSH~}C$DO4ZU)BFw#W{3qaAJyCG z?2`b#9o74RYESiLf#kUUC8Zb`1Wr4lKSQh6C+GAwiWee81y3&ORlxdAUuN~Si+Wl$ z5Rgw6LvOJA61v;`#5KJQYOd(tQ@pPn^IqQx&by`Wg?)oKO7LCQQJDKwj)L&Onn&nJ z^Ogtto-_hth7iCvnxW*qJ`oiEt(Sv1rJ({C{#5^%!bcDYL?Ab5+ymF`@LKPragX=< zR8Z24`2ORa{*%vqLu5%6#ank&tu@GrzAXcH#0K<6^}9X@&jZ*M=3FyqK-LPq5vB_a zXo-0NoD&&n1`kgY5H-=A&L@8GD2Ml@h8AdCgB1b{L+KRV8r&W7C;h8bc76 z_EcXN+)x@Q?)W1LByiJD=zGItwV@}S=MWM7LF~{+BG_ARFw=^LjOhomhCWi_ai%gz zG#h;PCg5=k773!kW)sDs9W>;%fF(l>!zG+cfyp5rpL!H&sD*thJjOtA*BwC)FKtMs zaVNwT_yi0#5Pi;|g!RfA95fcgvOTM0K)*M00XD2^K%W9#faR(i&~FP|fYCJ!r*QbC zreP}%H`O+vSHl-zLS4go9LCf)-VUOhKS2K)_;oG%_rZmGb zMrl;}b^y2@rzi_Wd4|Qb9!^kJb>|rYI!IcECjm7I4CO$|6mezve7m77#rM0sSB92A zdCow-Vd@WK3k(Bfo{l~2fTv@>Ibo=cJ9eeh28zqiJZ;Ef{WFHz6tZIgh?u-DLcady zk|72Ae%I@UB{&q^HjJk5eGgMMu*76c04M%2pl{RG(tn$V@P@Ek-F6QRBe3UQer#Cj zyXPXD(Xhl*!$UefjfT6Q8OWY1;-z7g&&@;B{6jizCW4qqV>!6?ts$F^bpjxUx8EBM zQXJPojJE;_JYyhGRxy@^-T1~FIyQcwh|rr>V;p=eG+w9ZSu+!jErBi4C<5swBiFqO z6vhk4huKy6YON9dJjw;oN^hhXfd2z8hZ7CP{uJ{52jv&IP{EjohN?XH++swJ5-)%R zyK$Y*&Idf)r~pC>CQlIdIE*ws;eU`yHJ8B_ZXpsr8#W}ApmrJW2ARR(C|RjHbx1& z+1iMnpAwb|M7iv zB(FKiJAmBzMGA0YqVcWoAgXW14s*_##jxpQqa6J{HcXgmL{EtUd^E!tPO(Jh@X)@i z2(6;xmKlRU1Ta!)2?K%SbGn7YzkzWY`aNtgY@u-}oq(X{N~skdT4Y>Bu?a+sjAFZO z$>m1$J1YR>tfJ|w2xmO}zS`KDPOCkDvIs33D%Uq^K*2*(Z#ZDHaRYW9{1^vR+=P-d z+MA59(>7y6WJj=Whmm5>jhG}2$>C{eTtU%AJODP(|FuLkByHJkD%oX zBh7^;*v9as@f)4yMgtHn)WE^7jp0~7*>8;K7pDR2@y>V@hfyDle9T+vuf|-TI|4*9 z?xc9WfU1q(KH9V$`&2oB3HsbHzz8r$XrlQPnbf^2G3~<5FYq%Rpln_lV7gCdlksr1 z+=M>00$_v6bd0k35*kxHrS4&5u<03=-e52tq-;LRXnIR0J?D{iET-w$120)k*YKFL z(Pi2}$%{V3luS1Y_W;GCOk(gT+GJqI<0cU%^n=YS*kHQK?@@*n=avTk6+Dn0&DMdbrb2wObt_Aie(dK7^c*vb>toZ)m4Fx zMJ6pM{JW9}Y+qrDgf$wN4g^w}k|@s)9MeUZ*$??@|6vNkvXfU#ecPLQ(0RNE_r6;R z$0wN3i4y=Tb~d4tC;)!!VmgawjAQ*w)5IujHP|GicmhW>iJa+-m)#3vOlN+9U3A;? zcoTZ50${hvrqh_yGE+_EF^4^8n%sEKn>N=}m)6H}^Gq^Om}ByXqZgSP&~@Y5MN zbg&x%`Xp8YHuQE%LDQ}1F#m3~Rr$YZ5!+1lp*q`yZoJ~G`Z>q+T7sl`zQz z5g#k@ct+f$_YTt*TD>Enl3?>CbhEW+4{~h9G@}CYcboKB-KPDfOV}L6hfH&6)n3r! zwiQ-6X<|HH0(zb>ajPulS<^u5n17x()uF_PovxbDM;M$U4qY?Vqg4a~GQiz8OsgNUA|0pt{UkPN{0`nhvB+h$k+Dhko9Ip7esVz25;%n1VY#RQ1(-GWL zj(;#6#NpIWrk!+4;||-;nL~KIe6Xc$+xnm?-@MHC0nNLaV)k~Fz^VfC2H&m0@ct|d zip!X*!pUM(pO+6S$joDKxYOU9B}QQbwHZBg%7=%7%*}A9)tL(fD7<1cum6Si0BX+$ zVy$N1ugdzta|fGwGvWmVxXf#P)}I#!AQ<*Z-5|Glzwcec1Jr?Cj*BA94TOlPrqRE^ zR9Cn^#(dUy=f%80WY2O~Eg==mzJILek)|tLRndH%jsxsbUHLH^fTKuV15U1No`gIH zsH>WHQCq1ioK?+?)+zaLWeqdMPcnMMsteQVn_t)vPVb)PipWmr@^zzEKx}rAAAEMwyg-C>OlGJBh2|&p zhT{+;FPhQnARpGeYDN!P@?o><<{DW2%uVx5oE~z|9D?b%hvr78WjOCCTW<9;b1j_z z?3FnLr>owXFZurT>xXz*5{mUV!&VN`@!!zi_0SbtZ1^-!o*0c z#rKCEKh)zvm%v>%ixn&Wnv3OsNr+{d1kt4;Ej@joq&zKDWe4gO3U#UkN+-;}`KxN{Gv&{)yOsJKhR9IZ8%Ghvd!T1v`Wz}|7P z-eKKN(WGYdL`zG`HZ3RvEx@mAlr ziV#85+*XFFt%Ck7t)D1HZ(JALXl7M`XC15*b1|yVnFn2r8wA@XSVbaM1^v5N5B!RG zg1cGe6r-JoAuIIjY2C+XdAZ)(TJS5pq$gXAzr;&=f9q9_mmUMHM}CDDb*h!-erv23 zi2BiIj*TzFtw(=_jnyNq6@E!2heliPalC9FV?FjOymbE4n(g~U0!vS_GMuwso<`%W z2w*qNm|?Y1C<50d=eB0fvfkzNFm{ghpznI{B2@rZnwMGL`sM}J3mhlk7yS|^c}uMM z6!V3blcc5ALXMM~E3Emypp)X2)(*aJO9A3!1+;IpE@!!bmo{5>^T=2Nv$t5QQ@mM0 zi1Wd@URFPJVy~nL*sXT-(cm6y5Z6Grz1{|5_FGF(yh89aAO;2ElJnsHlV0qHr@h#l z&st+CvFBbzlSKOAC=on+-TH)M``AtEVNNfL&^rMx+V!rt!RY(m)DI7=Ldv$m_t#bp zy!z7Go??n4ib?ruML)`yTgnK&b+Au@Yd=_T34fZp7k;(A_I(7wZZaQW=(*{kfv_Ln z)`p$`;T4gs4!cZ24}5G>d|&so&;=9FsY7Kuo+=ekgXfn z>Q$XhMAz-+1JYDXm52bi-(Wk-pu4+z{V@K1ki4)&FVWBuOHf0uxB|E^K8%2Pm4yj zLnjoV?FJRp1{k!<_LibsAU39h=~rx0h;}@#KyW3vy%vhk+5{X&#`8Ac_c_3Wimsu& zU~5M)VtD{;@aQF*-!A}AJhUQ~!J8crqnRj#x0^U3`LO0+w(We@NYYz2iUkWo-2iiL z+m6r)vjNt)XWPbcAbx1uRx$eJHv6X?H?P(W{;r6zqOU48IOYPx$IlnVV`X~g{&fKe5dh)(!utf+aK9KQMduF z3Xclx4>>*T5!?6rZV8OCI#Ft;a0S9ZeSbT;SeCd#NjWV8@SMU=_CL{fnxo{1eFvwn<<0E}xW)#z^tMp7wVmd=Gtxr!Z4P$j57Ej$ z5nT?CrZ6x#$&TLBe6Sl~c4vDOov+UT7@Dm7S9Y@(iCGmbO1A$=H-yXp_B4ACNFVLe zK+z!k3UqZio^AIHu}@=XTi9WQooaoLtPBx0x|Nq$QI~E`Eu7D$x;+>Te?cpNJI+Kx60Bx-zT zuTSft3H4oKfHn)u;E6AGIpWJPvAU>aiCuu|L!otnUx$@_$-UVsyHaN9k>~Yn(0lkxbm*+ZNG%6?mx+}IOVxMup%|;YumHiTuoV+P{Y8!-FvH>See8q?ZmEb>(Zv=H ziq#*nV&7H{nr#TFtN6o#rgGv(SK2u!HbMlYe+S1Y3L}xax=s#sVJ+d{Ym(z2r6(`! z;-J`zK*T{B*Nvw4a!_o9hzOci1?}@G%i3~5}`QB+X*<-L35Sh ze?hI?(B0c20rWyBr-U*8D^$j7!%1m-8KH z&5z9mPakrOq*&?@Jo`n5mR<8lopcNrcv#Fo<3PWiM9@2&chIc*i3Sn+W7emj{)z*w z`U&F4s}8&GiymSV5OWL7Q^lb!5tvaXqz7zy*CC@bQ8LJFZ83txNhLbMl;@5%bhGCV zlpuhx@PpG12_I?-~UL}Q|zXt__KT`M@za-T#W zR(7K0K8Y@??nKLd5PvP;l$E z6r`^X=>w;=c1BR5%)NVC1~WQ3H_#19sSvG|O+4N{_Wi(rKJ*F%O?x^ij*zidRRWNstfd07an38g&u1Py zE8H>OSxDhB1R&-FI`c@*Yqgu^yea}~`3<$8XC{);p5rvaL5#CKpXKt-9A`gDE>RNJDf^5dcN~h6g?XhZ!|o5=FZ{b*BSu5*4PXCpK@x1tZtZd&OAD% zAo7R@v}!?ht#H>x=Q7Sj$yc0P>Da)IX@&2uI%zf*9t~L`_{+J6{<&kqK#Uz0Mrhwv4P6zw;W6X2s3gRznQ1LaTp$J|$ zJpGUJUpnP*02|*q4|B>H`_Z|L%4w!RBpc0_7#_W#^Sxi4Xg^E_!R_B^*}y6ehmCkH zns*{UczGspp;v^2mt3)n;u4P^P&K5k-{}TqVjh3?J;`~63%w`748iv*7kW=Zq6ajt zI~1!^!ra|Zml&e=B&#`Cz0C{z!0w9u1z0UOCoVY|jtuv*oD<>2?i1;1M2nqL*2TSk zX1zHH!;7`|%YLWe&{3%QNF zu)~_T(8`mfR*!depdB5MMSec~@u0N}Jt&NCN- zSEr?#xm^|IpkFUnqzEMUYp8{d`nl@(zO;gf%gGW}&i2DxttqtkdZKFyL@$GKge)WF z{&b;V^UZ2nPYw4@a0z`M93etXVHad@;8Yjd3=%UmpXRDd>$_z0wt0>Vy;&m4S~t%{ z^B9|$6k%~&xM>SrXbVS}6J)#47LG*EFL$9W9Et8*?P@}2<0*3w+2!mJFV$w3zk;w0 zYVLJ$tHFeQE}BEn^m>BB_7hb)l`@-QxOc*yoXJ0|!>(Zx`CykpgXAxzN^*L}TB$hR~UgUG_t?>>nlN ztz!2j>0kSm03C(yU)hL&yC;ie;Bu@q2`(48SNZOwV5!!f4EDkh8%S^NP5>puZVf1I z?xxs|Af(aY?(>jv*hc0?KkKs!d<}3@oSz~%8Q(&zaJk%l)_1>y2tX!B&6BUD_q*KR z`H0vb>h}FJb*!BUApSv!7?vyHu1u$1769~X?pA@Eo*{Y=W2qm(-ba>O(p?uR3Vw)m zKNF(xK^gZk5enB=a$n)0aBO8ay3ujpz=FvxR2uE$Lg@fmega5 zHEX~Y)5j5+p}vWG0mX-mUZFA16qnRv1485o!_Y&(*6zhb`+vA8&In;3Ks+I?otx$r z$q#^=9o&mBBf~qn_xrv!{edG)a?@;RegG`&;@*uJY2Dp@P#{cP?hBV$S~$EEO(w)9gk6M@wB-xHn;zG^^=*h*!Q^f@`#(} zK3&3)?V{TZ(+k~)D3t??ue#q#kd?^t<f`#|pYP_U)@YsC=FCq3n zb??UEh-Yr};~=YGqZe*;>D4Nzc;()Md;^|(?MBzdt%AIdZ24QC*z)tfu;qJyW6MW= zXUjk3g>=C9btNGaDRx~%Wr3Oy69{bF8Ld2rh6cgCfg$L3Jvjv!l_3=SL81VNaM3T^ zt%CEkA!BKID;V-p!G01{YzjGmM`B`^qxKN=@^BT5b%$ta#|i>XJ^`|ZhMdNXJ}VJ& z2N`4asgfZS`wXJX#5tkC5WOmN(XHUo?R@y-p##-IDp9;3L>%;h=qTUeThM?b&m zyPXsAj20@UZR8;M_v(;;{vXz^JG!Z(*&lZscNo@Tt}oi({$_S|w$CL4 zY{9RM;?17!@Nr{MJ(?r#2{?z0w6S?6&TXT22N4d8nfLbOkhdqOwY|+VSie6AqjatO zV#0jnNDy(t3>cxD68m;}QSexHCN%{2i)shAW}i+35$+cu$O3V4?D<0wt|#Q5$@gaO zP6s_8j8+6FJ}WwQCg^8Mi$VZsdnw4%5jDFduDu@{b}`6@c~uJbhb_MZ-LMteExkpl zLoR6BqWT^XcqgbW#Wf1z2>@)$@Ay^OfYHHU&X|9>7vw8yZ|B3HZIsFFm++Lyt)M)d z+-e*|Ed{>{qReas7Q7CB`Xh)mv$X&WeH*k_sDqNv>N=^FonvB@?NrL>4pC8BrB113 z+-|r=G`7K{nXN^PB8T9Ml15^jgDI0+3)DTAVAAAP06-)0hOgX$aSw>M_8iY($_kW) z%kN&n_aw)0t8y@9ife%yQziJ39ZPaA@L|LKD+X1oW-w`%TNL~?9`Cv3D$a7huM>R2 zUR2YZ;9y#VlJNLMH$60%vImYv_$5lsuwV>}z$@>c@ZdvAicx=wwiK;tG4Rk zGZHJkhFE!DvU;iwcBM=)zGM{$Phx|AmsmYY_%G;cQBrUot#9M9xe3Q9{l%~kdFjEp z?I~0v^vev!ZBL#|%npvWeyjs%A{k;Ug!hdsU`Lw-`%vbds6$xRKKP?VZ&RmW+=k@z z#&-$EZAhNX?Pe*bY7fiXTRnqc5w;-(6EEAZ@MYyy!+cqRO&d7X{l1tcH8~jk4P}}n@Z@)-emob9J8PU#?xkSdS>wt5 zH-m9!jVE_L2*#Z?o-BD3j9b?{nejLnch-1v_{(72S>wsS-UQ>JNuJ#OAsBboc=FMw zU}+=_=j=lAXw|)t6^F~$Kxw6rs*)A$atkrHoDz=^3o)+{Mk`_I7qJwyRYPz)owM&= zJ*0#N_%*}md15RD@qScFZ42t)x*>b4M}>BwA-ycE_nso8j-+irRfxHgZmBK0SREab zKp2^0AsgYKHUzJpa}9hG8-knKJUK2tWP$b0R2cM)1)N&h6P%5CvKYv2XNNdJQbvdu z0cBQ)klyT>jVzEQx`wu9-LgXnlRE(ff^tFvtgku731D6^Eb1pyNPim0EXG=fNGCyC zH3=C?a8gKR79h$g>)j7v-i-tUz-$Y^xmQRYk}7T7D~%lT@X9U;5wR)CcViniI0Z5U1oQzg(VdyJOB z;{_&N3)xDrF9{?74%`l*%vLS=8{7+NPnfh@@{fNQLYbio`FB1JSy_>y`#%ezOiL}) zhrA3asYv-J-h@!5qL%X8ybDRHNclbg4w*o3DwgsO+JsK6NPd5(P|8FT%O6|jAIeY6 z`odDT(7`nO7eHk2(kIk`(91|9WN^+ebR?}3#_seFrR)S*ihfZubO^C%E_zCxP|D=h zQvS7ip~V#`KPWU*XMM-Sr1JR**c#$?<5!WPLkWGAfW*Ak-pAvWyX*IZ(Ady!)=wn> zQNAK<&G5oMH4o+Uw)L>f6grPE`4dn8Q98Pjx}>q}MW;}A=BW(Bdu07<`$9q2&_~2N z5uqaE!`#8HICMKzCA>aLx7~MReeNoH!2J=SC#|m(VG!0;oZs!66q-QrQXGKK?~YFo z9p_`cb~yk}yrg(|8OuYb6Q*xwlt@^(H8e_RJq>OkC67ZpQEL_J10$ZV3F;0muR=c% zT`j9^AL_&o+A4cO==;!6LR&>e^adQ(2idUb-y3DJ7Iw15)@$SQ6yx9n{IW$Sds!uW zR_@){8%msIL4?A=ih{6!13gAU*$_|u3gUt+;i5JCK`M~CtSnHayAjqgGo9DpXDmU?C(KTdvF-|Y@ zhk&{=3~?psy(~yZ*;bTF^MLljGRloHt~I2Ks};QVhbUyf61p<8)Vv1!u@)Yb?~rp4 z)V&ODM=J_!S-3{#1gE2AvqcTDJ~|oUrimG~3TykOkqbPGksYO(sAw0s?uys+2gb{o z#Hv=3jPeB+vz_q^_H*bwYs*G^O&H~43`_^!Q9_ca-%@K&2 z;-tD|$+k$O!g6JV3wK|TTBmk_HF>g=6{$W@nq*@oLPv^ZYbxL&xO`C!Wd|gOW7m}2 zf(3^o#(x;pOm;-V%WfrGTTy!ZTFWRi3sm|qstjAnPZ|a2tyEC4;BP&JfOeA=n63S}Jp82bR>4_PM6a zk>NfUN8L2fQot}K+e?@#@B;drsPDr{jz-vv@d{6`uoUoeCH4?vS;tq)aJ`)uJL6kR zvCY<6-qu+!+eTn8X1{EvxJ$)Dh)00zlsyYO*4Pi+_sUYOce;Rp!<|H;?#E@6{RXa- z(H9j`)WGo5vX-_Yr6p%%_~9Y0grJ{gl=vZLY=3xhUWNg|Io3~?WVpo6lN+wc8dz_2 zjKgkzD8q1;TWUMPuA8#Qw01OtnNiYU!5taiG8F29Io~x8(>^|s`AF)m^GxjXThmW##)CQ1jFZbfXQ2;NCE|zpQ(nW4=T$!$N%9<(a ztg|>B992agO_&aH06rZYSwoK7eO%B#0_3>e$CHQZ$<3PB7$Wy0Rx9)gmrH{g&?@ET zM)*-B$4xI@&ao(YBO<04_F@yAv7;2hb0bJHwK8@;|-V)#1T@u(rS4 z20yYT(AqmlPMA}fQ9U4Jh`b|ByW;_A$%uIw=H)fhnk^V1CrpL81Xw*Xm^)G)g&$cH zlyPOOoDjM|fC8)|%8?^ZgV*nyujDK719$?BwUgz9smW)k$fFM&r5|^c= za>B8-&rtH&a>~BRX8`p)Id+F!FP`(|gmZ77p%yKWpQ73uucMen@_iB)$E9*Y2$0WQ zN|(tg`#GNh!oHDzC2={tN=}$we}*!wk^d;UgIeq4gh_({1tsg{&m@%5o8_A;;_Ga- z$|+Maq_JAu=$ry_JW$Auru|-Go8Abo_sJ>SJxF6CTs|njE>+KwBk~<2ZES?b$LI`V zPs(>m3~YZSGnjW;PT2PW)Imqd7~Y;En$$)ZcV143dxC|R3L#IV4WB31GTZwtu^SyHE z^G|`M&;>A}T3C#eIP&-*Ff7mdxf|n36;Op(kgQ8cm^W)Z3Yzaoni*D3*!eO?@TpGKW??wh z;nGZR8HQ6Go@~)B45vCgnb|3<2|-3)!so5AB|XDt5w^z6D3S2z$S^$F$Qcb88-^zv zdGhg;Fg)4FlXqu_;h{O6Tt7Dq=Tbb`zbp(-HuB_}#bJ1|ktah|gyG3Xp1i+03{N)l zWaaf?xL3fF|7;3V6U-1R_A6GpE6m}GaFC66hW%wd6pYy$_MWEp32fQnFg{)D4l9m^ zgYc_S`)Yq%@>@xVh@=Gke03oEvld9i#a7biAk zPmmiVmxqPe+uUV%ZA`=Pr(Uq(LRfoyn~z^yARB!(tU~ijg!P7=*TN`YY$<#ZHkKvb z2wQ0VXn<#J^=p`P#BayVun%@9*XFxnf6>4I!Nyx}P7H}`=U>WlxL*nXV_W)$*Mh$u zhqV#Z+~RrIajWS7LY^e_37Bm!IJ^#fV~ZvK^)~E?)g=qU?1i=O!cqtmYs|ZZ;WfHc zW?CKI1v=X((%q5Bx&HZn?2?VbhnZf6`NK00#SMYqX>SE#8YPw?+@@>itDsD?EdXqB zSA{preXaQNbYe&q1;(HG2)(N-`a9tBZh!*g&V0nL=qNDm%tz>1SAjQTKSES}MTqdb z79on~Sj~{5P-qA)ie+(dDPSiGv4;;Gp#zq0iq63@P$)N6YY81WV_*pC^=WoWj2+OeoDLkuauu zc(K6hRjqK0EAkOK28Ux@k&ns9zMGM9>h2G8HKJ24FAv zrdfCz6-Bt}1{hqV7QvaIipN1|xA0uln%HJu7Kgv5%@oEl1gZ~*285pxbX+O!jd2E&IrdiOdny%tnk%@>r5P| z?W%Bp_PTejKZJc7{?O5^ypT=dc&66sOY9a%*uFLVqM*DfJHw|7N~`vLc)FmpHizj- zGo1*p!LpBsH=q#%%>Pi(SInvKxm8FQV`0>t@b<1YWpL+7xTE!b1N*!>VJgK>d$xZT zPWTeotoPy5Sm~?qK=|-H{2GCU8Gx|1fLV`-AHP<=4zF*InrQMaypr`C0D%Gs?Hsdh z;5YeTjcr6HADc3IfV$m$66D5jTm&AJRrp z=D580iLnvygyKsQB7P+n-zF)7GRNh`ml`6f2xhn@HR3U?_%+kgDc`&mh0ltJ6-qyp z8}X7>I&8>~ct*4TAEB%;;txUhQyNCxBNpGaaRgz45AB*pOcVtFtwqEYt4oHLEhA2& zBV+s9MG)3SxaQ|{h;S4jzU~rn-D(1Y_xKTtyG5)Z416)~239^bqOQ2k71Sr9lOqzD zIxylci3kUi!+^mNlpBO*HgYH(7U3;a$%>H?zg2|D?9mavgn`IsBJ0OS_zFZ0PN*1> z%@ZS12%|lL2w?pr2^>6UMm(yBGCr0@yrs2HsapS<6X7Hfv17z0D&nkpI-lsIBp$C9 zMm!T-#l0mJ;sHM`i#Sjrh0OUT;tB6BR!7{ZNX>NoHexhk+QY5m;HHSBgfq`V-m)=~ zPQth6G2WmM9C!Pk2tUH?n>UtTMBx!)csj!UcM%WJ(ZcCZ5vK?f zaL%&N`e%MH$uY82us}{Q+$C}$l^{pk;}+?U4ID0dMq$UaCyhbY$vl}MD~n~6DC{Ti^Za%`$gh9Q5pQzKQfo<0C`b7rm%ic zBrXV+f!okXTo5dSC&MCp5*7pn0nj5=5tjnDj*DzUa{z+@0}DT;4Tc7bp|gh0i^NsH zGPpHA5?2AsV8h}_Tm>wHua`%zrTOW>Uqs9*{4Vl1%_2EUBs|>{iOYax(E30mE(4Z9 z`H4te1}uYZ6^YA$W$@#@NL&UigAb1)aT%}- z?mdZ|K=ne)T1Gg2u?)EDk4VavfK>O6LIA8@(EG2*UNkjtD6>&Y5r!3Z%Fb16mWk&v zu2okO&YGH+TfE`6ztV-!P?m$TEex%xv?Exfke9jDQ4*%}n6*7CF`_2ghAE|ZJiY2F zAKA0gRo>pLbBL0#%JDDI0Js{e{GFx|ks2U1{Kg?Cq>4~3vc6aO4?+_ol^qEF_&-oq zs+D+okF#p0Q7)-4tKC{9<-1w`!D?)*a)rc7lki{A)zn1gpM(ZxX64Gt3zSYw_NkgL z>poA}8`h*LXWL<0*FM3W1H3dk900 ze+eGIHanWNU7c zb;l~}Rzy7ZkL_(|_-&%{QUy4u0ccbzLuw~1X&TK}a zY7AVNr(9w2K{FR9uSw=HY?1P7l6i1&4>-P9Nmxn6grd7kPNAsk25&I3CNTLeR64tIA)mTt!qaq6EN>4OA~R=S%0L z*sh!^$#rrkK`ubK+FIYofNGC2NYX@?eadf1npg^i3GMFSYf zl!u;GVmAU+YUX%a`I;nw1afnrsyp*Nqr7Nt*Z-VR8mY8KpSfl>U%(McIQk{29pF-cUNQNfT8=;q)z~ujKAu+)@5O18m1i15|#{ z<~L=ey`(qR%CRW&VfR=wNcZhiN>Z;aM z01gmtA8jVsj@7Puy9z6QuXHYRGYiTGW~{ff$_d<^Rg)w`SzSp*mDy6pPB+yPY7JXJ zkZ1tx_E61`h&AtY7YP$uNllf6qE|p3aX>ym3Z(JewKW6 zluGKZp@vp^#`! zY?U`VuuK`t!n0I_bKB_T{sWHkH6aVg=|++R5yylFa#iLrVdDZ7ZA>W9L*xKBWKwOB z-0!%Cs#PS@lhluRM1RQvJeesU+f~3WX$&mPWT#2g{%KtiQ9mQ&mrmQNh%?#$AQ=EQ z9aK{!XSt}8iZ-15U!a~{RK(Hc7f^uSl8OS@PW3eR$QjgP8-BtDkU2F=(h{H~?CScq| z)hy9IAZUuJFJX@b<>rgZfsi&^Wwfz5!e%A4&1B_sRD>lI%knsDyF%r}UQ~PN0O#kb zR@$-Rppo7%X}${M`0(W3uT_*!CY-DOur4cJqf)Z`#VW$BpMT|dgiT9SS=LYT*uPK$ z*@ERNLWmwSNP#$QJ+(r$P*f8ew3^b=1w262G?$cpeXZ&dVXR=L6$|-WR9@aJ*fi3Q z#cV~3z0FiE%*)Z%8(-X2x;vfiP8F67xURW}K$>q%AL3P3T0@_>Fns8Sq6 z6FYuJwM4SEf#+2Cg%)mji_fd>*@($m7gf*A$;+y2d-3g)>nb-%g)X;LEu^;t@2OTw zxbojrwF$>7EHZPV?A|LCq0jyd5^1k?Vjl0!BY(wf)mq7X9{#DCR}no5$biW16=)f; z%*-zUe*8;?0X+rfv(_I~3o1%Y5IO+Xd{W)0kac@Hsyo_p>xN0rYM%kk&Ndz5A|Znc$lyHIbrShb7hx*RJlQ|YU&6=m-KHGR@G6x2uAsbtHs3)%7sT{GFIYPH;1K_8E5%vYx-US)@JoqCWh zTjpZp4Nch5-2Ei58KP3V0QsxS1(R1+en07|gKm-+9o@q-#2%+BY zFZ?-7y^Np&UX3v$1@|Ph5w>BpnkGzNxKpB@Pi5dg&ca~k1TupoU(p%-JxM)L(!t!R zYQnUgYx3+g^v$I!bIt z4VY<%Ita>_sK-lfq0=|&S=KAU3>6NmSE}s@9qng;W^2?#C6(O!PE8o)e1-~Lr`A`9 zOVy3)eiE0jH>>AXL@zhDs0T_=9k;8y(Vzfp%SY-z`tPb(MQz`$K149v&)N;zt8Oky zRJvbXN|Q*izi=o&p#D?=1NMY=$JILt6JsvS^V4c?Sa(J}p9(NtciRnqzN9W7ENOBE zp0{yqIb%Ul1l+lyUPr7HK$JRmV)veQPhI99j=r8eQXdzaHXM1XekvLd9D1qlLpZ|1 z+1CD~t_DB;t^S!x6an%jcBAd1-qI>w$&~I=t&>Hb z?DE#?5iqw()LJ49K@JYp6dbEZQNDKana~gAMR6?RC$%F?svWhsLWB^?jmh>E`LWFN zXqdm&je0}a#QIDv>}pXgj17%iQ6W}{;tEUUQE4=zX~336MtQ;@Wz=~Z4GG}7Ix0}I zVAq%^$`XvwlFFv3?b)s5C@+|j6m^D3NB{u@kxP91)1yLM*`aBjYO(Ux)qJ41Fe;4D z2l7?E7tub>b2bn2XQ!KbxPw>2sHu)@;7FfLrayw>dra>X!aQ3<5vPa@(J@?kizw^& zM&Mk_C|i5h_Nb3H{Mt53Ybz$hIz*`mq9RxfYu&ArGdtZWim+ZYW=vFP@gkjFm#Cf& zoC_=I88y@T9uc8>!-if_y9i?Nymoz~7Kqwl8~R60Au_l)AgVHz!NkE)-$)GNheb^z zGUzlsigL|@>mX-T6ppk6<6!4XqDri00Elvu(3Pu}M>S=8f&+cw*GW<7m2F;%2V?b% zqYhiY+yIDnwz6o~-aDiA+KI`VyQAv4i%FjoQQy?HS~!NnMX|PPMptJ`9HO1r=vS?M z*>hdKBOCHKsxr&D(9*qZ+(1WG&_1dTEO;E%ql#IM>Ne3XgdV_B9$z$(imVWT4INDjt zW-~Inql=X7tLQaSwutG`Az7sE8|_=R zG0Go4?Ts#@Du`q69@^OhY9EfC4#Sgg(^EB(;Tm1y_NpUCwnu; zbj?niRpO-%`wR`?I1ct_^~&l$bZ4%ann%|2F>KbDpnsNT6`>M@yllQnLs&TES*tbH zNN1dG6lxso*zMDK-t3QtnpH$G5bqelPLWF?d)`3f#QfWu3C(Pw=`9iRXrmcVA|!$W zf~L}xTL`y0YyPogDfxMROx9h4pD>Nn;#d88Xcp7NYzX>d&G&8`5Ijd}us`EEKQdY~ zfh1x>hJem+p+qx{Fv>&4O8`&CYFdcuhNfR>CP>L=lQnat!9 zWY%^ z4dU%1)TGFuqGb)4LURpf1!E3?BS395BrsFGX|D@K!g(})n5`zxawS+A=ZcPjQwZBt! z5E@`%0JX^sp4Xu>7!jm3x5g_WTB=8Kl4*aS>Y$&((iYA~XsKS=MyVzH#3^d6d6=D~ zAzR`Qt@e&&P2FR)gkufdnnuKF&C{&eiCXK2tKx9sXp)xly*ADu%b=Yt$?+tGY?JlV zv|Xqc5uT|XDKYpyTT3_{!R2_JqwPjz@F`!5U)%4$x9|DtX?OgS00k1|x=QR}gFA z;4m$D)NB~3EteS7EzzzdGUz&nZi&B+)8b(rUJF;h(prB_0=7)lHl#MhHB+?rrNS3a z*Ai~p@oIcLL;HxzV8CqcOo>6|dE{o0U#1-)pOpUW@Cr^niAb#3lN$*7{95am>~Dh{Z?b94Gge zB`37e)S7903Y`q|3dr|idrxZ#8?am(kABiVpf-)-a_taFlkb1lQr9~b7qpbUbk6?M zC2doQLD#F~nf3f@+9OmQ{PT-Y3%q@_zeS!=&AP2!DWRm_(-J}&acwmIReMQ7Nqk72 zTlIKEpIg0tti=s+u8m<&wX=!seB(214&l@d>YyR>aMlF~2jE}B%r}-b56^eRQ9Z{C zhJyFnU6SLe_7BksN>I%|YQK}9D%(VAaFK!w>UYke01gZ+=!rkRUPFl7`KFvZ?zuqTTLA$@?hUXIuksr ztBWPL1tBlPyw%`9ecfrosXq>IyCB5}FLtMlf`t*fYJ}ZIGfFkciPGV%WT8>QBdtzF zxCOznB2#s)tXrFu2AIsxs78)%wZ!e`eBGZ8;@fKtbsOES zcOIOkYe~ug7|}tuT{0m>YFXQlmGsy7 zL7)D*UQVKVo(|KUvJsOfN9Z!Vt=AN9AT2K5_kmS2bOUHMTF83sO2Lcj^V2$l`+S{0 zP01X^5B7bn`$_CLVBuojFOqbVmg?*XN2O8qg>1?36gQ~0Mi)TSKL?l!#_x2&lBo{e zqbPyXBNE#tJ9Twwt+NFSyqeMxx_+B&*fiUinrvB# zoj2ROx}FPrek7p|(|^*P=bm6ky_kbit9fk`^G>qPf9zwbOBEC19CKN6iJtB;=ZHc= z+bS_*X)WEvFs}~`s2*dKc$}yebCY0qv*aer;j4vA*iF!U+F9yGa&HK2l z&=`Wl2Si;-F`eEq4cW3LsUECFDn{-uot)YoQlnyO5acpQP?h0NUCeJ%LwK1O)6LKN z_QbJF%}_m?ZpS07i`5FDQNx&*qA{>0O=BpNa3lx_T1D!I2DgoAO|T>6)`=}C6=(_C zde|*SZoReuQHDvL`SgI84ulV8aUDG`jqzrl>r+R9VH$-86HowAHq{W#Z`1miL_&p~ z`yr+!`|ECMNBHG%%p%E@C!ZjhGGa|2n6fZpVeaozUD={nsgt1F&oKqo*Nf=75XBp^ zF2|UA1;^_#w+X!hXSC=|OjTCwn)V$8{~j~Yg=?9ue;q^Ea})3Y@g_;0QuAjF#&qCb z+cX zR$?{P>8>!tJuZ)Gi)NHXVB-~c-BncN(;9KrBnMk1Fm9;S*)6LVcS$mfiOM+hibehC zxKYv%w#CE^BMdi0ouy?|V!3bbyR%Il)3?JAV_bXcI>_cPp&Z4E7?~f3VSso?($^IC zoisiQYZ|w%lKA#v`?$RxVsd-mxO0;9Wdq}$OWjM^khlUL@dt0G#C58Woa^7*_hx%9 zW>g2yxpAe0;6k5OCO|>llDPYlbjoFMze;ihZHgQ1E;8P+H}0w=($a%*pCpks9f?~f z)p(Dyam^|qQdM^FVw^p+xe_NMn{!%*h{6!Xnb#USaW}3u9JkGQZ3Ag{;%Z2ZrtgC| zbMKV(C~lo3jKlA7=EncWlQ^}w@dCQ%ahoK~{P`lzPa2Qb`ZKPoq@*;5`16uxWG?al zNMJg*_-9gSTA%p770^rw3xsrEHtb4VKR90_eluYu*Ie1*psX98A<>!?5-**khIg{~ z-z6DehsB#^^jF76NHR7{j=wI+n3fvvE+so;#GB_Zt@0`$W7TvV^0kPs!_HJq9|%ty z$9E@;tjvDX1rTbFctHTx^@$G1x_wr1FJlkPJP^NxDhvWtfg?xb zA4yGT@A3G%Qa`ro$N1l+3dWOa6Z5?)J5=e`N`C2yBFEa8o0fGMLB&Pv1EdXo~|2@4lOO~K~H2?f*@ z2h8gQGgl=XmW;3cw+X@00BXUegvrzvtbdg04x9ESY$I9>LjYfP;zWWs^UBkB32U(C z9g5dS6U=L^$BrlLmt5c6QwejuXjRI3-brv{k+xYAAoiDpXsJPux}88c3Bc+O>z%2d_>|Dm^38*`ckjDGcJ0Jg1h1bSCVT@Vt>N64wjG z|I#XPHBowXo5XTLpT~>e+AeX2P<+>piED_(ALx`=gDU-$u8F;c;@@{qBwPsK5+wIb zq#Ok2c@OkX94r*C>zinOoIpSbZ^AScv$cnnLlO@=W6AtOcf-ddo~Bh`dnT79dBDSQ zi9PLX9zny2iO%-;Y&BJUcAlBoPx$-loW!%j^Sk+pS8eb)?(4*B!n5_F#GS&^Z%N{Q z;dxPa#rw8^19qTvnXT82p2!(HcPPBJ7Tj<_fiG*mFj8_83eRL?n9V*{V{8}*6 zP4^QQ3(vgY5@yWDZ=j#RY{siYp@v9^4I&7;9{+$RH3Vy z)jiY?BI_j$rRkdYyn!rBmUNK4wyBxP3gt-~toOb`0FpVMf*n|3xJZQN$GZobD~8O;r1?96yn%BAjF$?Nvmj7 zK-nRwlwjIed>8i9)!Turyti2~%ep0<6$C2om9&<`MnnWeyiaJmoKkq=!#dDwP|`)h z)RhCgxPBWu*-=Ri2=hJ;u;KoFU-oEJQg!%eX3{i5GewjVZoX^u3zBN0KKa0*;f^Ge zV0Ei@6RkzW1Z)zHzSy?j{FG$th0k|4l6tw|v-86wA6I-1eU%hi37;$ePCA0V0Xp01 zdkW9Tu-g?~TYSnnx#^k16lUkmTg zHP>VJ{sXLQsoyKS@7hLxM0hIN>%SMCk2>nl2v6vuPZX@|O%Huv0jEnJy@&7|)KBj& z{QLL;dcE*GI!HfGc%L~;kE7xrpm3D_fbiaXjQ(rkxqh5JNci`kzY=-eoh0(OJVk$9 z;IVC*{)6zljWhMU=b1EH|4#UO%sl;l;n`)r{d$*p>C;r+Gk`Wr&IyLRe-v){YdLA0-;92k@NaOVIbglO;4DcafoKu^`Z4qqOqU!7X?4i)FJta;78KD zk|zs)pY==rR`4T3Y9*9B>zh2n}xDj)eHKnLJv<^K-%&l zc@SHsOU6?rZ#*i)vWVo_Xx}VKn|#80T?$};V85b3Y(ji8;fvg6K3e#gkQ^)E#3U#0 zqfrs$(!x1I@O zMTjW7C3_M^QD!35*vFg6HCR&LWIqV+n>o;lsjyu*y-Gs0{2?20rh zu?&V~II`jao{noK&q)?Um{OMfo$!qQI{7cbM=F;lml4KNVof}}Ul~ePC1+rFBaS40 zS)E){Fx2;J=?xG;8G<^NUCwoc7VDE;1-e}}CASka|HJm=y;Qnhk8gXieL2HjS?v=J zUi^?VTl0PLbu)RkH@T@m{>4FZYZZ!s#KXzS?#Ol2+2m6K_pI~DwFK^6t|VWgayLCn zuE!p?tc@XY`{WLW(p$+Ng?7k|{@Xid(<#53oFGWx{*Y?)fCV)c>|)K6|&%39Gt97_GviaO{VJT<@BZ7x7aq$E9s+AFf_tQaa&t+51cW>) z4A90!8!iZ{ZK^e#q0#$d2?B5l;($9_dC-)}c6i?KWVdq+gs(U8#g&ruhu(~xXxffN zE-Un5qcSz_Ot}D$#`eoIw71^fpKD-f&5qkTR)#_OhA@KVBfuZl7rUjcp|`l2GN_T^ zkR4NAGI_JiW(Hi2i0wF7(?uH@)j`)@6VqASp|h@2Ol&)qu3bl^u9I5XIVMKgPNj_Q z5EZ3W>XcfBP=jDqbHfS31diwRX=ONID>As-#$bJr8bB0^zq4s!Y{ryL(S+OfGe~Po zsxF2Nk~W@qH_WhJ8~*|g08LMlUj74Rdv61NREO75QeVTQ3a|ncbughl351~SP(!#K z+v{8C$94}f_%g4V27fp^!myK2^ISp%tj^kY>*NUGqYZJQ-eJgCLy(l*IKj}3&?Gq7 zWm65AB3bxnhT#NZM8_#2OkGZqUHaPK!JZ7x8UlaKHf$HG0}jr!kbOJfu!csqJwz@w zVAKaev#@u$VU1MR16CR)km|ZULqKPkv&xW3s~Qo2$-gsHWlMj{@M6mrse{?!HHI@4 z;3L2hR$H(dq}}VGYYp{%#b&T^pTYWFM21i$@NU214oxd1F!_)Hzq`#FV(p^_!Xgf5 z(Bqhaa)FWOy?DZaC(1a3;!_5~1ec@i`_VuNNzC(Ro-uTm7#t`!5TZ_U6sMmJl#s+c z@2E=#{OlUnf$ddtE#zG@P(l)O20!1hIOLYM$(FeK4vuk14XOm>zgl##@c~%}?;jc{ zQ4YBdrad-{k!m5}DOm?ypBXSNF|SL_OGAI6Hk!UN+?G(X-WUjRNV!Ba-x??p)_C54 z_XfN+!sVF%mw~X5!BKAgZJ@;9`*TKysOH*iI zFjAfJ0;7@a5|wF2knG@H2H6s`vy2#)m|Igqu90vLg=?c_o)N=FaFnl1M(W_TywFJQ zusbv~u9oERZ$hz2E=TvKMnYI(z`Wj)`1M*FmlK`BfHp=-L}E^SdVAv*N&JFNWbv1F zHc}!Ia|Tzs8Fxqwmh?1!W3>*TZ!hC}sz31WYn&t%zO6sCU)VX&c#S$@oif;nad>$H zXgbVj{V{A%4L250rB{wJN>g_BriAPXGR7JyVTgI|+lWDcxd}9%XWU3+u&a!2gvJF%jN`)@!akZ zG3~gK5Re#foLNJvfPFubjluILBgP`;CS_Z097t4R$T_2jy@-;2(P;fffaoIUT{7Z# zSvVKJt46{-Np2Qt*DQ`A{|2?YgoQValvu=^!RR~2u96(@?imS3M>tC2eIq3nG0%Jc zp>dkTp!l(oy4v~u??y^2V$NXEGh(yr;Bu}-)E5-UrJs?U$$-O-AOIcxkO(4J|NUoZ)Pom*bBZc6kI7+9QDHyVtTSZ{) z6hhPDDE;cB)F*5?Vczbb3`v<}&%W-4TTVZPrFat>i2%b8#u-W?QZ9*_g)UJko-|4w zn9?)Fondt0jxa1Xh4PK~zB5u93SolN*<^jnd+RIMoWB~wFvFu^u_0w7;ruv95n>Gk z#u{EvI8!MA00OKOEd=H@NJ*nyHx{%ZL>LB)FzihjuW~^UU>FQ(monIqo9oW5DHvQ> zFkFBhDHvRsClB;ZIY|jFJR_wMxRs;<6h_bC_+k4u-gFQqEsq#rl0#mBMDXF~l57Lb+V z$|8=X{7!4PfJi4r8-+nXrC?ZL&aLEJ3WgQt$%YqF5{Rvlz2Bqj1LLly9G765-=JD* zLjlVbY;LDyQgwFwjLwaHIGv4wNMEH6gm#Zox>DsrC?9C^GzEi7avjxtnXr(eiJ;5-CRAOUpC_s6z(ZA^&LGGN|#du-*_@yZ)(7|1*~G!=h9bK`sFb zFf8#ul3W{GrH=Enx~%}hNP{JoIV6=ZS4Myy&~#|3oz&0tEJ+-saO!bt3c+l7-zdZl28IbT07or6NMhi}? z#C6N|`=t?f!MI2WRSV8mN$Vjw#GGnr7%iBa&jvIt>=d0|4tV8DpMaw1zmfaa2OS+~SOblfrvQSFK} z3=hnSR#}iczClhtv-<`j|>bL8F+Fn>+J;@bmt*;!RBn5(s~Af0RA4L z5~kg>XsP3Ei1I#cCa5(f;rjsHwN{lN3MiibdwdXxa=@GrVU=$1uP=*(*KW z+~6BFNXIb1{O!xe=@CretS&m{~YxJ$Rh?gb13te}LJi3?$~`lVkGi4W?Zj*)@649el@7#Wx+pG-){$iO_gdu}>L2Ik3y z^V2c@r$iS0e52OcB!UJ2B!0|9Vup8@iCf%DAKgACM zbzTxTlQ|##ml7r&HY=gmk#r0o%%y4nb2y?2agSjNzeKIynZwFS+FpozU`e$IsVE%(6p&1x5m?zgLGB9K?Pwt4!xGy#5b()N+ z70|_(A%i>XGmaB=0oazz!v;-GhIE(`R^?@2v|vtod4WZ|6OA%3S}=d>*f9g61@q*q zE*Tgtm?yn@WMH&lo~+cj0^(Sk$Z$>@wZ;!%Dmos)qfgE`Fu zi!y@AwIId$odzFQW!x0~8hg4ngE~_E95?vgx(u`5c(*Ylg;=*@AYvhIu<)IhktbL%4D!jQKqSV+8Z$fEO7UBbXggoMDr3#d=c~W!e$yZPwk)MzE`0W=#SYAuooo zO85$Hb;!IS@%U**rrE%bmu6y&U~W}UXJ=xNV4i$2Hxq*d^W>YdObimtlk#Pm7$le{ zm8&u_NH9-&ZOjZIELCt~C94uV*}=sLC6IG8^CDqDYDV#cJwIkjH!)!1nM@29%*pM) znE8$MV>&=sIl*u+TRZmrP=X_?|147+`+(o!gq1dI&(qBR^`W+2k2BAUEDL|nG%vB% zeVU2UgE{w?FETe0qDXO-^tywgb}@SJS*a)Sbjl*^-J7Aj!Phx!w`34ET(VrPSE)30 zc%Co^K2^!;M0D(ohYE+m8yW*9)Mq$l69&j4O1`Zi#U;vYer| zDJ#5&cmvkCFiW}tJF`cY`F3vg-dSU;_rGR*4~XuQHHzS)1OPySPb5DRKRj!Mq{x$_ zvSvuhE@QI>Ny%puvhbS&T)mSgXN{MV0n@VvOUc!xS*xUE%eh&jBnS6PP`)!l# z=LCxoP|6x+JF|_Yj*gJfJbR?ng`8?-_E*rnZFXyEQR0VA+10EcBZ7C2>=q(V_FXSL z1Hq$EZ0wzVlE4_T`iN|6^v%YPW$+&HS^w;>C5j7%X4jFDKaR@YM_5;$H9osD1dPj0 zv7Q}Z-q1>7jhva0ZKBm{EB2ypwli#-mo2lN0>dg50a)|q4*tyjr|iLCJ3qUl)GrTT zoc)$ih0DLm?oW^gvyFx0YqMY2*lb{%6MQSfkL$CWRl+~oZL3-tVj;UDp@tB(8)Nea z*JMkFI(CGe-)B<}P?%Bb!kqot_}w~gNfQrdPnYIW_^QGi?P0ki6nXb#(EbRn3C0TxZ zTyObpAA{w$s+63K(r;g6S$?}I$MV~zd6wV$7vyY}etV;l<+qEPSbm$<%<@~O7CD!t z0qgVjImZZ9Xx7jpsM|d!p0HPF1`t)u(wF5(ljLW0ohsQd{gj+q%v8n3i|wB~q&Xa$ zoU=#LU+Gj!4NRGy6Io5Hfm5q;(xn=xv(6&@?e!MvCvUV!pR+kAJKF3WF= z_gH?b-kWnv(p24}Ih3e4f}nuL_^BdFf=`cf`bs7^=7~iC!=L4Rl1y-_V{WpffL~m5 z%_g|eJ$JdJw>-~Wvk7{7=YB0IU{6)cZ--XTHJfYQ8kXPwS~K^&q=1w4bH5_YIWer) zV0fd*&8lLvlZ`o&pUEbs zf8bM~tU+#7!g%lh#p~NRceAa?RNX9>5W?gCMV{F_ce0&`ENh)hxQqXPAOjl2MhWy& z``mB-i;C^sG4~r08T`BE`v14gtn0%N_@!rV-~U#FKsgQbcy1Osj{xSEdgQ)+z=W@T7q}g~eckzG6D9xpL zfaB%d_L5}|y_S2_Mieanm)u=avg*y;ZG^Gy|0N@LxabxS+5E$1CAH0Dib^A7Y}SL^ z!PZNK{Xi9#|1kGwLe_V=N+@}hyMb`LsxRhcnAed_R}OV%0k3ksVf`Pu^Q>>tfKb~V zP!qu)a|ySuFn9B^U3SjUZ7wsq;HE&{Qd{J4);^Emp)EW z?QNZ0@PiO}e+xXy-SRe*Dpk~nk4N5K!uSMvq_T;A-Ta|tmArY@-%f%$b@Lulc0u0d zs@MxtUUk-HNVf)XF*2`r04jmUoo$+*rz3R7D2fUY($2}|A9&p)Pm5ZGu;zK4?eICf zRbESHd_M1#cftmr&?T>lD?UB@=c(QCX)MXRE&O}E$$88dpWiRa+bQJhw=8dJFg`yX z&%29O3wKWERTA)@oX=ZO8J}N0%IoQc&p+Pfr3v|CZSn`W;B#Z8{PqHlcR>E1!h4sH z{3600mLL;T{^{(%_S@z8!UbLaek@&7(;9s~(el28?3|K+SP)c}kELMo_#55{R+d$MY*Qow89!7Pe!g58I$@ z{aa)e9V<&++_Nf&(xE1hQ36ZCkTJ=ksxIXfJthh#_8o z=yxjr7duwoT<^`=oXI~(601KPKAYdh`q2a+1N^{TQ(J={n}0dqmo2-RZyxOZl23_k zhk(N&`&RxqcPZl4q|fN>->?nVzS-n=?CKo^SVHcB~+p(^lyGeFg zQtzcF%dJ_^#(Lm8?F)*SDWt&s-Pc+L6A5E^GYa93IA-n3Oz{O(#Ch<}(1I3@s4Jhy zf-@v(I9LEIRTfkwj9koY0$^Zt!6{oLlB_Ere2VWg&ia@FJ3@08007l;Q{WJpR6zJv z+Gh^)^#zrQ9E7R~fF8zzhXRoXX$9LVQY**O3n<}iKTFaqyI_Jq>sx>%j%@gy)>m1^C&6 zx6pM=!A0SjG_C+YtN9lG8DFrDrqq_~fUdxoRXW+qAKFbVP+I7(o>@Q$7{pKo5~N~J zfjjh`T>ye0!DR(M2?D%cTyR==9$F;|Qnp4Eq~~`98)*WxU^DjK@fHOr-&8Oj(MA1t zgMtuR(-R>91voBn|6^YPel!g^A1JVOz~|AU1l<3k72dXIW8GEuiFX z!D9>K(T@xMq%uaJI#Bmn!G3}9otFh=0>dHi3;sZtzN4opOCY+_*|bLxps$DNZ^EQO zs9Ja*XjVD}70)d?Vv4B}Yu=&27t%(VMj{%wG5n6C$yZc>{dm(7ssa$)Q6Q?B zWoljtpQQ^;PXq;wTxlxtz~{%UrZmD_7>V-%Mj-q3fa#!3xi|`Xa?rHT`i{gr3i2rH z{}A`o51ZavkMZ)DsUv)S#6)S8n3oW+#pbz*iG!PCCrm|7WuqE-v(U39!te%>mcp@e z(`CZUhUe{Z-gL~C6EIvd5f-R8$fnDt+5`u|^Den&Ix7+wb;GpM`r6|lFK?Q>t)DY8 zp7-26(>amAwg;wFL;@j?Or5D3X!*P8ut*^CnQ4X91OTNV1-%wbXU8kCY#%oHjfpUR z;u?JY7H!CC-lFy=eK743g)jWaL>Lxv0{cFi!20Q?Kwu2(bG^MYggX|_bZ6_QHu7VQ z9SZR?bC*r{nK|FWOSERiQ9WQzmBJ2OHgKw5Xiu}xo~-vplPjzLs$mq|uT|KEMo2v))!j6%L~bE1{l$2#4! zaG0p%CVdJir$V^$Jo^_`VTWfotpUFbEOf0Ta{GI9;Uy|J0T2d{Gjp^3s;~<|WrcGJ zN3b@p8~Q@%v_iS4GB$svxnk#*7EZUGARwxv9k;ixWrfoS4hKgIBiZuBg{N$O6$d>@ zj+GqQ?j?o9^&h}>Ne_ZYczL|ADWor_D1ixiS&Nm0goPcRwRi_w4F72Ex2plSrc^)`w;9 zDkPi^|18n?-G%l96W!uiG#7FYqDNpae6&8S-tj`~kB9@(AT%(+wkCq+R3Y9=dGS|_ z4?B3ea4JcY6T$XOAtmHHXE3Ln*MeY}J2B$c4KoizsU#ocN#WqJ>f|e4{B^O%&ftSERRIe8w46i7T2bG59{AXcdt` zNK(;6DuaQBBK))*_u5XWHk&^ngMXur)9a?NGx_w%h zDENI((Qbk&|ApcJvVBEY2?tlqDA;eGEb?So#3Jr_9EG$j~i@UqCu(WOWZ96;RHXfIH-d;F^WGHtRHX#|-*At%STq>T?K-huK;1W>ui<@>> z(^RM=)UE^yFPkqMGpi<8-TA`Vgx5=fa3SfRiz9_INOVAFlMF+D79OUv&IF_=7CN9P zkM^XMwn*)h{H<6RDz+BtF$UHvUEYGQiX2# zM4T|6w4X>Sgv%>f-|8@4BpAAmvt zRhQi(d@x%$&-{^+7}juM1ME;Jyg*33ED=(?4&ZDQpb$WDqz07<3n|Bgr;QTUk_HrvF}0=jIN=_WdTG3H6^TxtD4a^!S@BvVbhqLLX?r9jgS6Fbp^Rcu z$Z7)s8(^DxLb$$=HNQm*g{w$6RV)+Qk!aCMVZ8a>6!SI+dpdKyQL@X_8*lBQ>5YJu zg(D||v4C;z+tM1~GO5c-+lv-p1tM|00~8y*TDajeklXTpV4P?NFi3*l4> z>j4^*vFeo&j#ja5qRCs~B1#4$-w8|1XMlmhIN}bDe->`0th~op;S~z61eIsw-BvML z3Ug%39=9%9Z$OKi>CB_zS>>1OWP;>4V^OpD)&p|N_ zi9}svV=PGqQDV$eirdH73SWE_`qexw6yW=Y7#Uql7+@_|=*J#ZP$J>G_jlJSp~|EO;3sMQctE_B0*F zLU0&sARaV3W~M#oP`@mRSxC`N-yyr;K`Ua85hvJoeT<$6ums1z5ogx)GPlHF(q<96 zW8RULjM^Ww2<}Uok@8}wD9{?zV$(sik4lgcxT2%T ziB6CaII4?is|8g5sjG-$TQrC<0*ikZ(R@veE!(K4D2u7Sy0?hpWf?%oDF#Tk=DdNT zJf`Ny(G)c!7NYYIQ6uxGo=n*!^h_+W!JiUDVhaDl0AK*mx;z2Td{VfD2~ROmx+?`i#4M5 z42^(|oKo*^5^ZB>F2*7$V;C0OMX>YsAU?B0w3-Qr?hNwv(bFQDR|&?`Y}Ka%?WfAD@$oaFXFRmlqlp_nc21OJ#YMYc z7L{0X(NkAN$4KB24XfwPb42#zO6$S9`&d{Cxh)d#cpI?gT@l{~bsZey zi!xK|w?WS>{e#e|$0FME*z9SzAlq0EJmQJyFN*H8&OJ5}Il0FBqk!i$;|^QC?;BBF z1Yg(m#L+KAt1Wq((AQU@-R2KI3>XHmK@=7PWsA81Fz%BGHj8gU(U!3k_5wInc$rmf zPl^GMEj!OP_NoO`U+NIMkxB(s_?Ba=lD7IcF0p?w)t`2YHUCgI1_@|3E3ksQ)Qw%l z)V#ia>@rHh9erYZ(+Y0w8#|6My(s~)mno}n*f_SBw))=9VxKV5hqjExl-18|6?=uw z@-^6r9~(>YRJ^-$Y$npbE)T>(onn>d`wt8NB1aILoi()S8#@Mi9^Mokdyxm>gdwpD ztRZY4AA5-T{XQY~I`jLIJQj{)Y{GMuvB#J&MI8%+$R_Ngjh$x);qPg&Evz8Cl@&{| z6ozs|WqxQ!RXIf?dQI77M z<6NtBIm@+*-FdE69$evCW%X6ARZ9NkTBY@k*wsv{UA`N;2SD-cSF!h))*ALE))g!s zPkR@8ocX`%b8JgS{BgXv>D1!agTZoJow#TK;^to?UE@4ZlrYu@5A=*{L)SaSC;)H~ z;M!r|dU2B(ZLIcXdCw%#S#tMctNA@H>&ZAZH%7ugqogq zjC*3TooC(Rf+${me+P>J_90_<7Y4;OWMp_HiW|a&Jtc841Kxxi$l~nP5H{N#*OX%7 zXRNFVez!kvHmw1I5}Kel>HWQN;-NSjhOY8d+)Re%rwehX85N)SBkm*<&b<-0i&4G& zUfdame(;01?M(RMVO%Jazxj)}6U^^7FXP}S<0gFcRont5J?-x}Yo=Vz*SI{l_k#X6;9!2escf~#3{bQ0~(_T+N6V zp{Q23yilExcrpiF)jB?e=;cLVJjIQ|%KruR!`*`7Ln!=?2?YZ+xEYpJ@vYGK8D=X7 znV=Xc{cv!2d?m#^!H7~9DLTemBmBwU6YDz1N7}R0@Y-JSI@%W4-UxcY$^r4=4jk62 zxOgocLkT*$G2RWok;S*Og#O7+E5cRrgXtQ8Lqpgz-WdmL2fWe;$ryk5fLET;Hn$(8$eDRb|=0$z+>sdc$mPS#7!Q@!vy{$e(@yU znfd>h=kcrTAzb+(ey{lxknd`Csr?#1$NaAFJqBF0mH>0olQ`Zc;Z17@2l5k~n^LvZ zGB6)2-dVIRA>onP0RJ;tkZ&Joi!{PaEwazdC0E2mYZGiyp*SJIY+%fjBovviB?QI# zqVcV=?a=l+32w+)o?w2jK)baGh1`-$4@JUwD>VOITQ_t|ld#5oF#zf;g&0t*5_eBd zfP3%R%I#7U%%6KOa=_P*(h{0c41Z8og*F&s1UM`wA(CPO$O627WoU?f^AePB@X@8M z7p^W&@Ur2Z#mtsD8&)*@W@N%EYcBd>e1g!Hi%O>@Od=UR&rGOGGBll^Fwp!H42&Ln zppFyn`=EXCb>Mou@>o06XJvvr7OYIT1m{$N8M?SK!2>B$63Asx12-i!qiZ*=CTy`e zp$=y`xbe1xZbX*6Jqe*Cy7f?kJ&B$_kyr5Q4{#A9I1g19|K^qmpV(o|MdFz_`$q_E_e$(Y zF+vzyx-XvJAd%+M022TL02@@eT;PQd`zC&}KqUixyzrHP#N%AQ#G9KZ!ov(BEG}=E z$m~XDqWpG=*SQ{p8}Snln?J`i8Ea>>>s>+}JS`%T<{M*1IS9(OpAr{Yb0XwMB|ax2 zl5uLHZpyaFS8)g1IzLf3x2?UJ7Yju%P9!q{6vqM z*9ksYRhF1Z2;Z(s^dnLC(I$~^jWLNlWn3b&4VTf>zzKoJZc4nnXK$_v+?NVMZ5LkTCjW~@D% z2zR-&5;|Q;gljO_=#@WA(dE}nmaV^@2;YxjGqk>G^kw++?Zit2Vb3Fz+@qf)9)+V? zjOH}&OklwujM3VUCfHq{Ot4j7Ot5^Ocmz@0L2I!y#T@cmCzxDETnCrCifO(A{5=ZF zSuXIw+uX%I#6lj|71txud-#fn6UNIMiLFWMjuzs^B=uz*@oBDT|=24ce=f!(Owl zVv6TJ07>QMga^8b_qd`P0@&sKi%?9l_YR?carOjO2 zLM)~@>x)t9!AOFx>9_H!q>12k~CsC?8>&n2c390C4)wK@nwofIhMiP z6!BTYpekKVG3{efc4mn66z|B`vPW~EKX5uI$rq2K)WNR>;(fFX+)Kq{2!mDS;_;LW zu2zU?_CB&Ym{KL)NEpP95K}CKuqYEoid)j!LBbd@oI_(R@%lLNNJ<^pPZ0BH8LXZp zE+h=Xr&9X^b+y=$mVtYX_!q)p+YD+;eEf@;;^;Z&k)mgdXAlM-=8DHswnB^f;`6i~ zY5qd-Cc;3sL~Q=0DV!X$mx^hgj9Dx8U18F}ftA!cc)d#e)O;OaU;rER*>2#wp1SgW z8^r#!M(9#29zhr^-YgzPX#yv=h-vO}VjE!8HnDY>(rw`^bPR?Iul4ksJ)d3esD`%1E0Wv zX)>O4M+|39S=Z42p_t;_GOLcVM?c000i(fK_f*`2(AxG)TtsN~d?l`;>?^6S#Z74K zB;=iVIccgVAH)+W8Tfq^pQL3l<%@Wv1qzrHoD7_ovbDBEo{~} z{3T# znCvT|xPXvF+3P2v?h9iZNva3~-=-4UM~MN=B!ehcaDaU*^Ifh6xKofsWrZFtiSa`3 zgCub4#8R;jzA2E*qI*P&#TJng4d-QWK_>~U(y}hGR~HE^3mg5tn`9}8 zW_6eRO^TKFk<_>5GAtP=X=wg(Jbo#Z+6&Bd*N5QlRUB|Qj{BXW}{GnFP$ z64fSAoV6xV_UR=4q}b~eNfYz=BdEPQnv@~2MX%HQ``}eslK1wUI9p02G-ozVfH+BmSmKRao|CMyC35{Csif;gtXw8g8>Em7w!yYHBo(9y z&>hKr3gq1`@}JR*hWFfnsTJB{hzZ1khmzhD(;gF2?PJMUM^4;%ZzZ<&<`3ySKWQ|& zQ6{!RF1AuzwB?h%$ENO53(|eXmeTeVlA1sJW2kol#~elaONlqV(@2^@Vc&qI!grfWYY4mXEv0aXouwbwT59yB z_1a2dlOmfsCP?Z|L|qjsg?DLe>WFY@bK35De5}|7-S}B*2Pa3Q^|)aIOS(#JDa2v> z3Im7%u$IWQte5mK;W@OAw1f!aJkXSSe~@$)P+(@&i)A35`St~_FA6#P-E%`=>#G3^tkjf z9ho>RKO-GtzP2#%c)`_nkG{6DBP2sJd)Y}6wRSWWt93FJOLZ|7 z3v-p7C3$_^WdkX+vu>cQC(7`bIila2`+ML|^<;372s`cX)lfzTst{k<0rTw&G0+&G zmk@W-tclF1q?b))u-%^3$ByPE88cd%WNg;jB;)foCe<8iCySw#u}i=`J3Ko?2Acy| z@vOs*;=K%)VM_53gNhDE40gi7KgleKpglVQASazm7n7iWM43cd(oIG&i(nKw1gG_u zy&+SH&HaobEbcF(SS`l@c$9Rm34>)fDf|vQdasF-)khcCdk5ksu`(Ke&7fdF=}VlT zi&Tb5&$^(L9j|Zx=+5%`5^#^XDMIE-^i@-4)K^ux><(quM+_GWdyHyW)>}u|VOAgB5ybkM*Y>pYZ{&h@tnnDAtvKeUH=Zvh9(7bXE`jq*- zhkcA2UvbL`4SFQAMHPR^Jn;4_vTfvvZsI*zHwrU0p<+PICJfI!ku{}Y$Vxl!lgtML zxlzPzGig`Qn`cN!+c5@u=WuJ)ws!>XchF; z13rA@S5IzBo@{>dk(c}k76L3gbTR@~9l8X_j}wNzP2~G{$o0C=9lvTSH;#N;o6GNz z$D6fnxp7sZ*AO{eEy((vfwA(Rh*p0Q%Z>BI zBB}fW#n}g_m)j7AVMEx@cD&cz;fI0g@)hR$jBnf9D(Ap69Qz^c!nQ@^l+&pUxz2n{ z{sMVlyf#x#^D3Mvi=eCx8k8fa*ccDxbGeOfvvcLUtx@|w3*At9v3wb0ZF7HBNoh$=9T3$^Uh{wq(wimG|Gsnx za1>BXhCXkV+u@VTCNt@oaT`d^fm;ax+lM9%TYS|On#DL%RB>v0H!VuelJfZ zs#^88yaAo6`k|njTdt_Rjlz~)me`c4xNX5%-HNZ@S=|&%g$Koh9Ps7pz);6ROV0Fe z*(+dioVCKOj*8hxJ{xGFGUwxf!5Bu-v9+QQrIAIQ>_Q?+du&~Z?OHyzM4~Du57en8wH$FkXkby3tY-^Qzbmo#KA^{bhnu)(U{idH<{UwC{U#h=!^ zYHs4RevrcaPd8&=2S=Tv`!sb!TSPSCo!+46hS zlT)xkk!E%iVnBsg`>dkmSV6I)1OOzsV6)9voj8N7b1I7u->U>O*mDdGr&GcOdA&9)oFqS*fheuc8E__bmdVPN}CVg6-I z7#Pf=7zB%Y~}7~`ey~j6AO!S@{5AzcoJJy#Z#sd@vmDdDR#fIC_dK8MHKUD zD4T!|-c$;3v4fIF*MbSiyRNbU_IFYiT5`TUM4R56bmUt=!bYutsFuG&G zk#YA{wxF1ffo2Bb&3?*o8xF>*nUb=X55n;+l()!G+pmpsFByRS+bP$R=&eAd5AmAL zp-R}(zz)mz!<4Wyn~jc&RF0-tyJv(zm0guCXl(=4R2R7%P& z$DtxXwZm<+%51`Ywcf}bR~by?d!#A@XohHpe6$tR@FZK=n%0zAfH}F!8=NVl_c=!C zcNQpVzSqk?sTAUV70M~xqydGDG6MRIR{li1Q1V!%`O^o)K%=pnlrA`FoDx2G#*S&8 zlav$_iEmK+$;$I^nHDogtv62z_uQ~i^%5oQ!e*m?FH^#z8yh|Lo04WWVXTbefdPt= zwZCh%%0px#QM*-1aq1VKO7WL%%HJqVg)JMmQwc977)79`yQvw3?p4wZ<1B;m`;|(< z;LAZJ%~C3(gD!`aG>6()2CI%Lmk4W)Zi=iyMu3(>CI^I_X)6{-a z8G`~JD;@FY$I1xvDKMgQ0LZ7VawzWcObL%r3?o$UsnIb$d!h6&fBJy=ZZIbGU4yGR_>VoK?n|!=ElH|AR?3u8+&!GBkTtpeKdETWUYMY9PA63{X_+rkCh}*xnaEG?u4+v& zb6{)uy7iVV?%PilL}NFMQ8NIjs9S4C>^ea82WQl^(W=(wFK8in-2S$-$#<@5~*sP`A>bYlvNLXT6*6NHIHtsMD0dexFf|V_}bR|?J9d@Emy^vkJ)I1swawX zSLBKB$yKnG?38PX8=kFJ4fN!q9}84(ov2{{pehxGqVK93p~@@KWJwE;RQ0lDxu7}| zRpt*82qgRl>WAYdsgfwhjDMh{O;tU&;8>+iS1tSjQStm5)#x85bnzDzobh8?4^5t} zqIe$vhk$41sMh~DtB?h%^MsZ6Vim>w?jNi)OH?!i{y$JkmYZ}XSgE2|y7>oQ&MKAW z$8|MwjcPT~Rgd*5ih1BaSdH1B(sBK{7ONsDJSvNqX{~9DGXGH7qINceJ@DP_DwPis zA1?92l1r*8TB#XS45&f&oR?X5M`f{huh8+!Fr73aW3#qU)gthlK5vkDdk z*bMPJbs;Hs(@qV?G}zQt4(jGUT=aHRbsc9eY8kACi9Va*7lFE$4Htd+lX@6Q9nneM z%bH8Q){UgPl2bWA290emm=NoD}eEgugofp?qb*`zW)3y}n zjR6${Y7|lSr2A@P8)+Y^`w^LUKUc$fFjkK1uhp-K_;=o$v{w94y^dt)$kQ10e#%nw z8%dpLZ%S?Kq^TjPhut(2NgL@sHA6}2-`=LY6B}v{l2lz|O*cYvODm00*%fUxMrC(v zr|C$HZc>=$LvxPrFH+5Ripd$!3Y1Z4bDn}72jR&gypz^^lQZ@5v zmCrz9pG=L$i=#PdgvO-zQJO|hTq>&8Y$66GouNUbHLT}pZjjWsi!|_Zk8POlziMDJ z85{LpWdeKtn`SNPXlplXYKeXd_GpSIX26V6Ffg}t;H5lv=SRX9x|o+BFnQr>s2zpoW8?W7k zUiO^GemE`&xlGWS|9wluPR{2X(|MzS@j5%~IZ;dVT7Y*zt3&0}v^L0I8sdrfPtrD_ zurMPE#o8Pctw1hwv}E@E*L3YtnK|nL{2W#zF>&#`c=F2M~L)Zr7iyw&yU%`zM3_Itnh%X+PZeYhW#)?;a+Wm`LbbvTxr9@__t$PnI(iyr?jP35RN&g zb!Woam$bK-u;Q9F73#uI?rJ|UzwbTL9%I4{&$X@XA^h{b_78t5l_cS4Z{3nQMmZc> z>GI5%1A!b#2!LASsJ6P-4!|fhT=&5V!uH*Du}$fip{SMG0NgQIHxmFkH9yYL)i-~2 zjibk+{a&QAV-&u(LRSbK9CsX{n@nd4NhsjrEguxUKxd0~3EOyJG+uYz%}8|ITpi6l zSF9cxTnxA|jzDn9D&1j*aK;ARoVrwcWF4zN+I>W~inoaSZc1B+(pKpDQC)%gfQ-M# z;P3q@mYR51bQ&CeLf3)Dw?kP3Wo^)eAb198cUDKSZ3qaA=XPI@J*UgD0(E@3q&sW2 z5-{MLpy?%7bklJ4HQjRysCn289gR0-AS`a_#!;+u0_%JA$AseHNfVg-4@V|lWAYyynm3P=49sE7%Ais|@wAOkp; zy>y};wjBq!XS*SNwf=zlnlgc65RztP$OOP>NrXQk{TsO5jUi+)OHXlv=O1{vv-K6` z8yor#CA{Wop#Yy+sNX{|K7Wq{x4viF)x0*qas8wH2jay`^cBviLt3U6j@Y1|ZO;mh z?{3o%wB(}Qcj;5P_QAXN>1{~#(jol~7cRr$i~1JUT$FcB537=l>M**l|4O3EZs~t< z=28d0&|k3Qq7fg!ad1(akEZCkPkOkNhDDh6MGyDcGto`4$?$0oHqqKL`4g$k-99;* z;u-lL+PLwH-T`}gB=7uj#WGMR^-Pi-hVcS9a7yhEkUY$h<9@PDvJ+A2lfdMDM3cQk zlJ}8R-|*y)L{WzB$wZU2y^@WJbnTlwij-PDF!>$P!`hhSYUdy5Hk@yO4(*TrQYFve zo&TT7Hg$W8!r0jXmWdBF53EGGQ0<2GrW0}Jl~p&E`5;< z%k6CHPp^`vlhhmUli@3MY^s`P*hQiO2ScF)a=+-@8@={4P^>R7#*D$3PtzGVS$$5pVWRofV^Bf7CDc5&li@eYnxAwwe5No_sCfjQ+tsj> z5nR^Yu!6GsZ9NQY%om(7z7aT}k6{y2{e^ypWt7zi4=}u>t^QK9VINcd93i#zA7c!S zXdd0u3>n|1VhyE+xlX`?eR4mUY_Kr@$sP034EgT7Be<}_aFPdM@1cg(O!%(Ku!jkM zA8v55f$;EH!%Zs)S57j(W8V>6ImKWLJr9qbVYmf`h*M@8szE3C>s&*Y9fVoS4DjXk zBe?qt4)4{khF#3>jjIfnz!`7e%;oRD)v(wae!tvh7{lO~>^GdI7_Y!A2BFN2_dSuT zq*#o9I&Me;)~sgGisLYMt^CE{gKwWT(CnZ14&0{ZVt*U_=z`%gLwnyD0ETx64!28?i`mR$aptyAC;{|LU-*vmC%(RE!>-ur!hYv{E!Q}gSP)b=agu92P zur^gQEM+F6JpSmET8bTKY%^Vdkm>`*nX=fVx5x!4tP(|=P zH@v!iYBYERuJ61LOeK9Ms#|KOA5bF)jsTqPK&WSQsvGLEJf#lq+B5aO3$Sk`N@cC_ zQ*0`0jho_AS!>iJrn1)9LXyf_BW%WGtx=f7>HPqR{4YkZxb%39;@ z!c^88dsT41J%^@RGg|f;mCAao)nihl87+UE%Hf@vmdaY^l$uo5I_n(fw0!4CY7wJl z*~wHXt(G(Hr8YuW?+o$4>6cPx0SmI~dOfuX^P}6{)YVku*&qzae=~i`@F2B{@xxCa zr9NY%y!w)3zwT9PCev3Nf8@0A{8Q=)rqAxMNNdhWb=M}XFU7+#^pb%HrgGPL_nGfk z_eL)^1hlDfPy3OnDY}90J0jQGVn>wutX)gw zpfvN3h2cigY5o)g7c2SS!nD^!@~^RJ6puPAiYh*BAT0xfB<%!Y&_|v|u`!2but1Tv zgJL@BmY0^x4OeybX&Daa>L{fbo|&E2%Y0kLfHkiz+m3kmg0w~fT=doMv^zFj^ueJt zSO;SK9o~E-Z3l^tI+pg{noFI0CT%xKRh>)gPca6AE<~ur^0q(PaV^~z1-4K3z^T{L z&3#_eCEO&}s2f1B2ZkhlMoHjt$1Ez%EhjvaS(O7|zJS?=j@ zKMX5=U(fW#B>L4W9j>xvQ;*e4H|l4$w<*Km2I)&k23z0s1EjV;8>hqOKDOA#rs;b~ zbX4PB|aT(np%F9K5bF{Q@C5W0XlH=f^rxiDKUV5AFxbB*bZfKAJufW)7 zj&H_rJ1%P1Jj2-U|7ei`%j#@~v8^&-S)GjzXp^zLE*G`wmSOA+fBu}&u|AjDMv`Il zJ@2I%@kC8ZO~xzYWwZuU6%nZ!Mwj(8Ed#D7XRD~q$ndu1qBrtQsVfRIoJi_{3KPOF zLro-m56ghhi?ayUBQju{GaFqsF=L7OFCOsFX&EQ&xu($0&7k=-3Lp%_GZto?a^Z*+ zt;;Yv;yxQpq~C8eX?AOEMj+AF-`g@ClE!}6NHkRCOZ9Yo9O&~$D~7iF9WVkWpOJWm@))D%J_w( zUV5AX9|&MG;FlRm6zh_#<*${eHN-nVWOU-xje1#R!WPNeG#}jAGP65vA7D@spxWZr zHkreST6Z{Q8k=CEOQzAdJGo{Of|7o{)3U z{2`gNPsdVW=6Un|r6Myk81;}O_~6wsnMcg;LWl*pl9);HI15F)qcw`mx+uW;XHS%I ztI!(Pmt+pL1Z~WeXHq;WMr0^t?gw{hxw+CCue7N1}gYuLFNrQ!Mfun6`3O_Ug)q2Z8$V@ zt|QdHb$sT}6b~6dUyVK_X3Eg>Hd%gVD~)NG?x-?2D}Y=2*wi*_lNGc;3O|e1LI7-% z`R!mL(h{!>&B|x2pm9VNt=$8MmN+3Y>lJOQ?COv;o#7DMIg4WD!Kl=hcv6?Fb`%pr z)-r;6XT1l@;1()7#bg!IY88Ooafi691$DsAuIaMc+e7G_nRV9=!dHb^otZFrXchvA z@sVL!@P6eqUOXlX4q%+dp_8)8D84wzsvu}amNjlOGwZ%dj226?XukdX4Q<-;tbHy( zD*IBh+Ox#v%-t| zX794$O+%W&*@#LbYCLgB_Foi^2#Bb$hbVg+=mwpN%cfX>WFagQvIFSUs>ZeA>|Cb$ zHnMDr&-b#`_m^k8f)yaCDm&L~1!91JcT&^v@0#p449Z%4_7w9`vXZk0(W*e0n!TG* zfmcR0#gNFVpigGD6K(Ymve{09Hs)n7GFx!QnAP}EezqrV^;3$nUo+B=EX}6a?!s1o zpe(yDg)4@#Jxn`b@%-W0u@1n4-ErYKDfgdz#dY}k=`+0UGldc5=75}9M${QX?sx0hocqjgcS+72M)Y@zoN-LPVd|XIOu8sJ zhgHq8^qeh>e^+Mb45HYl4ytH}Bb|Ma@SL`$2)(@oou43>5|)*k}j^(EjN*AZ9`v1lAo%d}poZ{I;(KV)=%Jstb zNx2C;-WIIZ=RULME#vt8lAcSsHyE+hGWXoK$ICNveP|luXrUTa9L{yc+U(qMmgvji z{%)9;pWD1G#n8-P{k|}lVjJ$1MtMWfyk{1z@vTL-Ll-#=Cfd-CcU!o!IF=K0`Bdvh}^Shb-# z2Xco}@`dVLaKORb9W;goy2Aj#GhbE=MhQ0nv5h|bT<)JdE}C;87nWPu)YlJl``dES z!7p>$*l^Ja?{d9K6o1bBNTPeK^59h~TWqRL-gFB8VpP39V@5KR+&SL^MLOqE+?@eP zsrZI-Ua}oa2qo6dqqv<4P;&9adU-To;$h3yHpqhy@i8hvQGR(8H*2#fh5mUD%{L4T zW$%&7M>oxzN@be4ct^9mz2@&Y!0TG&wI==eN!vV%bwF0~z`(pv^D9SeS=-<|xPg+@ z{W3uw#iAFB@*p(t9ED$H%PJ!C&JqUQJLWB=Y=DWK@|w~zh>glSL>RRHnVLaS_q7ad`_U?O<_y-d@^Pa1@*T&~zzvE1Z($?WbiBsm!Y(I=G?En@y>M0Bznv+E&o% z^WZ}ltR?aV)V$-gI+&ZDw~{bO%%b**v$ONwQA~@WAH|}1Ev)QNWG5RBEGW*q z>&i}MaNxMS!{*N&nC{I2y5Moud0VYm&EmKjd9d|}Q8EsinRl6sF7uh4=fEYlS(*oX z4cPy`Tb8$rL=UaVgY&p-YR$?#*oMONR-FG^UII6K;~SguE|Cm}ao$A|UAHA~0g0-& z=D`(ZEW(@JdA&F-;{p5fes<(i7o5yHO7fPS&YMp1Ho0V)vix;9&xcHfe6Qv8C-M~E zG-c>>J1>z;g*HAir4D)Mn=2 zBNhLamw(56N$~EH{JC!21I6_5Fx6!rC>Bh}rGeh*@C z0Vw|fNqsjn-;zk?xWJ^bI}1%3JFz&QNBZlH)h3OtT5Hl+*?N=4`fW66taWYvB%+%m z+fBHOcIF$O3bJ?S!;S`4c@6jG!^Ryp8hIq&mDQs=7N5x9PE^|JY(8vxU^6&h$^V<= zt$!1goVBZ3(dD51h8=^phKP)N{cJ7 z!#kQ3Y$081e5(R)3ZL*DpbH+81!mWJEEbvpEHOIl))v7Tls(I)-Qa z{wTA^#smHKYhQ2FX8RBiG(cq&g-4ec&@BFahv$zU3@>=ZgC(rB9X&kp{gDL;4!mW! z>y&~j3cE8xSED7>6kK;VXzi=`k+PYcc zYLek$^THV=9Ir3jVt%KABz5!Z@Pm4SNdx%=dGcceHSpHQLx`7JKPz z8+7MX;Rf@?WABZD<@BfK7X`lqw@SAx3PBaqOFePy*}|n(yxaKm#liv$2=lHK{%#3j z(4U26=8s>?K=}j(PdI?C9Gr~|G0AYUJqK4KGRyQbm??kVPE4*X>bA3_6)R2A8~a?!x-qG(4hI;pG(j!8s^>)mleMUe!q^k(uN7|Eov(Mh9= z1aK=mlX`Aqk*W?C?KQVZ3-2_U)a{Fk&N-uDr$xQd<#k0A`(jx4$=dC)L-*~Gvo^N@ z{=B|uvibAi>UA3$W4Nax1Z-o2i@rhw;nl_x9~R zRFuc4_qW-E}Mq zK3N3sp&1k`JX=)70pfe-i^iBg*Fk_NH*LA%E*(s|S|oKvv)V*^;mc2oXg-T(1a-kF z&x=|+aa@;vEE>dVev?gc85hITtctBjbcJ1UBFXCMRSX|OVI@54Qw)8Sjm~aZtbl8u z8M&RCno=(`D~3B@+0=n;i(7ef(a}AM;q(ccD(zJa$1~YzNT1@#B=5OF#bQ!%R7`OX ziq%~ZvKI=-E%Cs0B*p%Atin)|xYz^D|E<^uXKIQ+auuM`EO<1Yyii-~sscu#luOb=djxB~`W^9K1@x^EDxM^U>m&T;1vu#pXLZ+~{O6&FxgY$Hi*yiviMA{eQw|E)>sj zLn}X)d*O-)#Y4CVe$YWzd%aTJ} zT3h>)V_eL>j`6=YT}$?Jc(`6knIl`GN0X90T-o}~O6GF0gUw6$+<)zZOEz<9XF^Iw zaCrJiV~GwOj3pj*EZM;ETiv6C=2}~5w*e^Q=X+ilHayz7a%#IOF7Y;BZA=g3un@b` zv07Ggo0Ddgp@e90es&4b;-}n_FKkD_j>RQ|xL8VgNi$B?1;a{ia%t70OJokL#w@Ch z@}Hk(lz(_l$!>f0zp?X;|82F<_}@#5N+cX!j}=CJx~wdD%pp6hEosHIO8455!(5u* z))Jb}6*H38q}_H!qrH-SYWJ3iZ8$|{9xi!EsYnJ6-#b=n@Ue&azumV+iTC@<(ZeTjlXs_0WnQTAIP(<<$5OTV ze)V*_(q?GV$kGnnMYIQEG__wYUE=jzKkLXL`YO<@i$WXeC z3ByuLJ1{ue*`??0AnaXI`WMBF#n^bacq_O~rdvjpZZ}`;jGbbzQ<`IkK*1lwmGx;n z&`@U=Tr|G4yAAIs_t4g+x|DWxgzYwPG5uTf#bGF+0gFn*G2A(Oi>^;_00{1^hA$1S zFLSGzP1!kEARRlCs@WH1h5O7YeP9Lp30_!AaZ-)dHi*{H{FP?Lu30kaxcRcu)fT|v z{EAYF?P}j}B372Jv*aDck5-qi2RuAyU8xtv8i)}vn!SR4L~ZF3x?aml7yXY1!Qd^W z>N+4RcRb@z>1=x-+Urc|Qzi_(S}JmdaNLv9BRmLWpOt=S24S>kSx0LKCwP~+I6yeN zNg3Rmcob(fD?7x5KeZ@(4n~e!2bQHX;V*)+xfDkrjeM_J4d z=-?Zk1P|<0cFE2te~0L@Oh&F%qOv25`~~u|jZ6c!RhPlXjX)pCWo3>KZpbZT^--Q* z=EnSTfI1 zRPUhP==p|#w&>}GvLA9HJ2#enrF0?{=gK;vomsI0;N8)h*_%jOVf7AMMT{-5Y=@X504l+F%=MZBZ!-%xf4dV0*x3$c%ex{GD6 zTv3Czd@r2-pbYl?G5W{DUX&fN<)X6pWhtEO_{!g9M>+GswO`BNJ`^_ZNM1SIhr&if zEz6&hVj+&@a4$Za`o*a{(}{~#c$-rDHYjiA!KGFPmK(vMgG^vw`Q>o+35&2Tq+E-MWWya98O=Xf=867D?H__aC6tf- z0gV%=9sNpVc!Z=};lXMA?~HOS(RgN|Ne5BICb^!Mm~^nJto#$v!Ilx_&?K(Qg4}V~ z=<*|^N!%uw5N=L1AxxfJeuxxHo?%kHU}kv@eDi~8>OYp4QkO0(KSmmA*hZ6@V{1)d z)|<;;5;fcIEzg0^doT#&4w)(zA2H>1IA)@L@OXJB;oy9}96klj7SsMwZbwqfZ0klib~RE*xQ6Ks(}e1%(tSJXy$WMQ-3HWuoplx4Z(rE4CFi+F#*`qDNP>!%K!$w6ORt z#bsngJ7WrlJgEfc_{555RvhB7>IyfK`s|kqI6TcE;)KN&u)UtG)OlG&ZxYq5sE8ra zqbn;`S#lXhuc>HaXDql2SM0Ym{+PM7Vx&E9D+ZEd8bP@7Ud0d|gkSDg902lo|C0)M zjlUIFzNmQ2q^JH>p>p9};-0SR@+v==ZRNO&MP(R3;C$E0w@whI`BuKSf>77E@()`G zyS1)#b%anDTIp&Hq2{N`15A3GZk78iA$-xJ(vHdhp-*K$ChRe=aubv9qpM7l9NLr;jA81E0`J1<$>L2z4~{^0x6SH`u63+xfKs;2YKq!zo7D`p**eP) z*>9Z{fM$27@&a@6~w&GRXiSAw`q0*xNykyinsVRV^LWdS!Z&RwB<#PDgU}rsk+!R8_(c zL3pE}vm>0)+OAa_Q1;$gd}O_MR(o{!XOLany~+#m_s(j77VVwofrfUgYUaQZVC#?m zxvHaiMQ~B~s!`zq>PSI|jvu1YMW}-W+VF6tQY%o0tChiOl}Z!F4?zoVPZ{I?Uzyfc zSJlTiN~`8L1O$Z%f1>pi+c&JJh63*8K!Iyi3bo@xZfNPTf%*9f) z*i$tljyD|7@*Z}x)MCj?^rG-`Z~qXjMjfHmL8}Kt4g64*AVM1%q=^XOM?|Ouk(k$U zc!rg~COk~12~&qf2tvaIf-sd5jE%1f4Oc_EhQXCMQx*^NtZlV;JkO6WP=)A%gLPpl z$QKqAp^OMp1*VH%A#C>&?KLQi-B|52#`eVIIbkl%mQxo+v=AT(>ra0maOkcfy# zzB)W2Sg8Vo6R5yQ!azGpl}e`$RpXbJ#$;IesnkK?e05l)K&J{9goi03g83nE#ZQ=0 zpw{Zb@Zg%^yLf&&wNl3q3*)N=Ay7Uz94t;1t_}`TtJK;MHIAM;4Kn?Eb!d0WSib+i z*A|R`Js(Y+Io#hjA}BaCLK6w514g2a2#ruj1V?B!e630u5{`pD%*e9xWjc*ksn)83 zwS33}#;N2-s)9q+;m|T@{_n$m@UG9pm+}0QPBu;{#&g&6^iaTBG~WWYSo1>GK8bqi-tFC7akay(ye`tv~gKk+knOz zZ74r9C{(9~-XH+;RR;5wDs6aVxGr1?gz@0hHDFW!;lI#L>*-$j#h&S7c@_WKDC&sD zP-ma$1o?nL-Fx(xL&N`@397!&{qG9XDjQ4N2B4AGX4LijccBpfQcVjK5PR9$m#@+3 zf}n%wg25m2mEn;BaH86k&fc$g+c5GoLa2ZbtwRce(sBt)PV z@PoBFwA;4E6Tf&nW*pC75E2;*Jqo&YjLdWD>H0Dkl@IW5PnD)e!h zu@3Q^5#RS;>qS%gj15Moo=vkw4hM#}XdA@;pRNXDeQ;2iDpDN;!=O$Zq!NTjf`4QB zJM{b@%>ObB1pAMX9r13h^2ECYW1*}4dn>8KzVAK%VVs)pb8G)yYbxk#$g0c4dVc>d z6q?fGax*l!ZHKU-4ujE4_wTi8(Vyps5B$$s`4^`Lp^^*39l&Sn!qwm_LpACU7=pu9kzjHfa0f6% z1!>WO--ml)*Uae>E5FD{rtANI?7a!RRdv<>-w|Y<=kcBkGAIJ>;q1u?NUfYvGb=US zXG+TiEVV2}5h75L>E17j172`KQBY$$gIQTp3TX-CxlH1iij`Sv`hTyz_dVQu!+oB| z=k>f^zu)hFUUq5i;q3L@`#Y`Sv(~bUTEWQVZO?U(_42;$nK{oj3jw2UT`FzST484Mn&a5~n?>|0xLL#3^E!JJo1NjP_b#pv*9xYWi5R8% zu827qKd-w*-ZZCsuAlYunWC(DYz>F6V68}Itf@)67qb~B@h!S)()Kz>UH1CS1IFrZ zv&qFCW-+n~Y@Jpn%j6~pU{TXGC-3|DVkVZ2DhRm&YxynAtK?oWTF#VQ@<41g3mKkC zpDQo(>X)Z)889^Qb<~b8&K#3w^PLGwnGkixX2wH| z%ILc8`36FVn=NMDoMW4T{*5KK(l>5a&obq#LN3G9qyKcCZe|M^JCDpBEjw&on^UzF z*!t?Yf6hEDU4gB->K|8Y>M#GC**)!KwSsARb|L5H*4@eHqQMUB_hV*ak{R(bm+S&2!>yIPLkGXJnCX4Uct(z0foD zLNRCN+1inOqfUwT;biWfdg=w<#ZTmHDgVa^y4z2l)lR+NX;we=wprJ69NYehd&Na& zxn~p*c?+2W%NjaUk!9Ss^dhHCBUsaR6?<3({Vx(1=ifDRPHK9E?kcvNYZXSR*7EKO zpHGvy_Jen4_OD5PDY|&1TK$u```0*H#>jZ4%~on;vKng~ONFZyil%StMnKO^MKbYi zjfv&?{Kq;;Q^(m8QNscvjc6?XvQF8FUn0gf&B~4k*+pru1ULW&P(ZhUTq2 zGmz7$#2Ruj?_(AjKUz+AifWU&B^;xxQbQ18Q12a=O@s|Qn@1FLEt{;YEWc{OgcVJK zVgDQIOtg=kn_jXq zRg=tGZ9iZZ+gJt8s30TN)G14PtEuB=jTo=nR2@?o!kgR`B%==)V3pL zbsSp6ykuitLd<6`W#!kZTs7{@`J(1_jSS=CfTZgVy7EycIjSjVZ=+vSyHa z=IWk)gh<^qeHqEkry4sr zcF;U>kcS=1&HF4!PBxDPGvkZse#$M2*QJIgGrq0+Oe2kcn#gnSBzx`TS);}pRx^Py z$C|-%fsTyzOGlJo0rY8Y4(&ttL*1V#R>}T{fZjKVcCre+(`wQr56srOTZJ#?%1-vj zB}@9HE4-CTy)t*S3}xw4&o;$OXl6`96fwjjkBKL?5IeZ7Ag07()@K-6*&=Iurf6xb zv838I3rtIsaU7XGB5lkn@I#9^i)6r@L~FMV$8szNsHPPoZ7f}Zt9s;|S*KQF8X7&m zc`h57iGa$a_D#>rXL2@HmyCg>HHUDU#6lAN=JhM>9us5UgxLRHmAsOplpkF)_Wvl|%aP_a%ley-VK4O-pCZ9iR zP@4VJb_`25Tr|pzQxHQAW;oU|vw#&QHg{NFzToOckpYhoj|o-u94w|TCU$h?SP~T5 zP1-49pfoXbuqI@(n02)RhI2P(FjtIXE_#jH3T%t&y?;Dx3{7VGlrzs!-?(Vjfd^-r z?OligTE57bF^YNAc9)dwnu!qXIiBVkMm)4C*ujHoCxn_Q;V zd{txpf4T^&#`>zUz8NB=YOJps>zjEAs>b@Nu^taZ)mUFO)>n=7%{)o1?2c7q{XRSf zRbzeCSl_~x(rVIFjrITOBB&bctH$~m+Wvo=vHl#jY|@$z!QX#0>nrV2jRCPVN3{vA zyn0rb&yj984Eve?_qiPu59mDm)j6~H`ze33gIDML>}n(Rdhngcf7a%?)N3Fd<{q2Q z_&~4($qoiIQ^a@U`5H}I|Dtg>vwGc?Ut@4XAm zFoGO|^0jli4yFOj0w5|kZ-LP$Sb*NLVDdt=zd(+`ZB<|mRBz(klhetpJpmGG>iLOt zJE!xyPOOA$X=Xl`DFSlOXYq6sK;acE%JN3It?f6?1Pp0|ng%hX+c=SPK;rT7^By?u zayW>s=1w@*!Y_;G%Lbu{E8Or3hUXYg9%vNd0!A|*cSZi95!SlNtYc5y`kUqqs{wnN z1&*GxysS@54H#;ZUU9P)Bmgdg4*CYs>(DL`^7z+qA7|X6OW)DF0+0_-bH#uNi*>+` z=~$=efvR+zjDV&r0s`=-8vJMGlz$bT^9Z|@%-D#aF>W$b0r

#Z%{w8Vh2rnV;JS zCxD|6k7mXM8g7D9HF5xFik5g!mB0TZO>YI97>~GLB(4H@f$QN9&gyv+(41v}OA8Uz z5%9P|C$-u=D-2Pqr4ujtvssq76N*K;jHX+^&Exzzu>a1iR;EvPH8POw#L@0l+W|5-pG|q!zyLJwkKEZz*<>*kD1gJX2Tp?VGoQ=lw4KL<$K1QTu9 zIykHNC2TwqSBfLg)IEt=aehLTk1AHC&_sW2*Afg&q>F}pm!lHaQk2GI-y zN>a>gT8=O`Fj-E9)k08%SwgwQ+3)4mrWaT?Cq8iXnSf~s03?*q1?kFOMQoG})GwPDuWPT=vullY_^1BIUfmFsy9K&&h%3j-{%8RPC;b~}s4v7(wFABo?{X*yvH z8E(zE`Dp!5=ZU;Ah$gfhiE?6(VAq#D9vE0L4-i=bIo4iP4`L&ksi&r`+F}e%WEq91 zJvndgXaO}7w?n{Ak>Tj(iA?|np9f8dl;GvO7!*!`rVAb%X@PB+zzn}YEQO!*G_V^q zvpHJJ)?JTB66^*(1*j)b5|C*c>k!{4M~uS$bxg4eY}K@L>veYQWUe7zi$I`W>P0p8 zpo6t$+gO%emLOLz2WpJ4PnyHP(13FiVM$w>!bcpp2==D{@|rEoVeQC)nam+w=|${f zB0cn7yJ04=Gs&IPYR*&`jjaL#Dts`L8CI*T+L?W8l3z+qDm^?Z0v+v@7TF5 zo4pw@9s*&`0WxGpb1V`~So*Rq0ia;kGZv#V4_=GTW3+leheXnfr&tXe0O1{uK#z$7 zVf`u;iXy@hf=IMV1dUvF!GvGW4LjeKHQsL)~4DdsVq0+ou4EHBC*(cSkE&YDpW7R#WAx07SCm3o* zZ-#g`uw5EpMB)Rm%cxTpE$$aA`=E}vyb7E};su?Y$aK#eyvIhfuDv?B>)Zp!I(9SY zR*u!8XcYN~rX~U+I~3Ay0YF?<*K)aXB!)=zhV5h!eQX3B;DD}egR3J{Yz@ z;9C_sr&Ta$@_=mZA$O9#S;5z39|LtfU}*9yA)7?t{s35d{LMc7%>9!6*c>Kgc5@bFB=JP#p0lZarA zjWHJ*sl#YcRI>`a4K@1Nxu>Kn@qX_ORI_KEo!gZemUS3)ghqpCB*rU?>_=!XXgZL& z1hz(zK`f9?hOGj4uECZD70SN|$?VYi7#msJ?FI%On+MUCVE6?7h*CrF0>c8$wICZt zyj?uN5FMJxSv|FSu2EZoZ>*C&@xt8UX=Ge>ePEAB6~s&$NRLLI5eA?dXrUHtdS1fC z1$oLgi##Eb3( zcm*?9^TESJlgP_R9Dw17&SnFLxAMfe@_r~Cn%i6}%O-b{-%@I6 zMOvC-s&FyGYe8@}E|_|4qBd#QRnp6`cRd z+^gHAeiLPQ9sJhZ@t-rp>uf+N{9O~w|NFJSY+nmZ12;&HtR9x0!8?c1+LfqpvSX06 zCWp^g5{6d1E6h%r`S-PFeVi_kiU6y&Wf5;QWb;v@AC*l$=V5Y>T0_F9NEz@fo5#-Pm;Q&Xao2l6=>GeV$cJ& zT!#*zyNDsT!F=R(u`lNFPi8e!up#||Fg6}Lrv}alpbu@%kUfL3!;5&>wRwiE*5J*G z!7LRxt4YUfF=`U|=2g!ZuXfb4eddj92e@dw;i~CN>&VjMf)@b3fr;0XT(#s`!aPjk z9mbQSBuKn^n<8$uHie0&fI^wu_J!U^L zc^-e)2aimi|I)YB?Y1#gZ9ijnuVCH7^9TG7p58%CZ1Z|&1rW7cFyR;TGhQtzt^u!T zq}aoePAU_&es*fIf8nwvBY8I3`YYZQZvCfyJpav3wGJuu3_R2%EHY&`U?htBKZh?X zTLiNjKe5tX;S+~;Snz14S_46XtpvBLtz`g|6&Uu65%$#iRyhY;|DPQn{-I%MW->gbAhg)MCR#RdQDlm=^qL<5Wo^k`8d zcF0CP5MUQsphyB3eM7g>UEu@&eBy#fhLf@q_>e&JbWc)Pky(zkk9d+zvc-UDQOn4K zk1&*YQ%;2s{K2#Zx15)<)w!hwu%GJYSiTiF04`WZ@Q|h+V=8-EBAGIjM`ON@Z!E6JWRcG(86J#$#r9S<1!J+%O&;I${3wx)UUl^d?-EU!+=qc%zUwNYXNowK5#GA*c zW7;fyvH301p!XUV9JS9DaNz)T{TMpg+kc^aY(vo8qK>d27=nt}h3fh^y&Tvxg){0JitkJ?24szLe z1E_dc_{7xD7XJKuYG&ufql41>3oq=Vu6tn7=-{IL7Uj-Ye?5V-AAfhzf-k8*ewVWk zJ+SD{C#q8W#iM_|cJY;M)WO}kes)_S*S^1SbkMegkmp>%*?T$*X}5(uwfo{_N2ufX z;{gLtT>K@y?s`uC^oqrQ>fUDDar>$FsQ*x-Nop`NgWilD2fT;8?KxBP@{WV*4M0hf zyr)^GO)xmRMlw&dW>Cht9BZs3!X}$robob^9T|!y2(*>FZn8rN?A->*M2-pU1HP|P zjZ6B)kt4wKk~o`cs9Jn-Mb06*bG2IBFHPpMJWG|ANAqOl*jOGj78%^wx+vz6kDyjH zD4U`}k)WU4jTiv{E0T}X&;i_0oJ8sWicme|Aoa6MMF~Q|9)W1XF6HMXjh}6KUNEV> zqy?_PQf>NlvpFP*UtP}|q9pa|{$-1;G^!|pIW{;;7a$R#13KW@LY`#FV3fS3EcHGx zL~=P&F@V&upu`#1fZfRkP6ATIhZv4ew2o~er68sts*tmo>~Um&BvzyVI@1U<)CZH# zkregGTw^mq1+Id}chsQ2*B>}mZyqT^@;Z&UC)Yg`%>v6 zNt#rkmPm+rySfI5ZBn;*tyJs8v8a2ka<^6ZVv?Ocg4hyomH1L&x&TQ}KeR}KJOIVA zo|C{3`Ity6vd;rGD-itBvnvHIK(S-YB2%SC&e3d+EYuED4J1F&9|ZMK;K$ROA#Br# zVPyBukZY8jo>F1~tA$RkYbr8OW+fjRvx3DyY!kT?E%a_1buzfQMAk9H_PVx;f)EJ% zB<7`}4M5X8$a%yo^44Qlvq)+P#5PT_;YCSb=R=4I*8MYnX+aN!C*x|McSiHNVLZ zB(RixusLRyogr!=OWH-96wR0zSxb#L<$f|#vWQCr7qf@XvHHcM;0N zn9Z|jl0A|r8@CYbxLNYmXA1->f$k)9rYJd}vm)G41&Cy(gg=pGa^2N$6P1w2P*WF< zQm3UWFpMG|dyH1y(snpzQA~9$;{vSd#*pwE_!#m@S`5JS=~}Y;z2yrku7FYzSe9NHBk}B1R;+b6UlSR2Y!0Hn42+ zrxHAEt@_pJTl$eGKIx@gwJEC(B7!IbSdL|$WUnm92q!*ufT%R%WHD3d(bkD5W-YzF$x5`R^J zZBE_u$>PZs*|Oei>bOrAcS$qLshd>LwJg#Qv2pm7fTSZ=Nz`+(5Dm(37aod_BG1&z zND&j{6k;lX)oWzlBoxyP^gmM|Y$$oBNk`45j2{R04N)!Z*;0_pK}=NZ$IZgKF=DQu z+mqQ&8dE3rwGtTm5zr`{S%B`TTY+IIPtgWsZxUg%&N+y361Jm9kaf`I^cbZ9L_;Sr zvF8_=goMU<9*!ryMY?QCwNTQJPeKqU8YdZ>$nKkhCZFT05 zM811Nu$G>w4m#L0n*r7(n9L*s3^ASkSfXAK@h~Ei=$pEC!RofF$hVwqQ497wqh#Ak z>KQhOR*sb9!Psh-tMIwPz>v`GlKeWkP&5p;E$Rvov}alb^b%b_^Z`lSaYu@|6Qe>r zxlmFl`IliAD&!fJ1g0@?2`OM{HHk4$%$TOY5FO18X&-e|glUYOc>>-G)ES{*7~zL8 zhz~6wiEU0z`>r}QU5Ra&g?_`Zx^8KgF^KKWCi;-*TP8$7s*adCg>y*TZb`8-#0~;e z7-qzR5Nk((naIBy+1`17qQIz1r(ZmCKwQ60(`8SfjS?k|V~!Um9-3f%$!Lvqy8)3o zTXoGYe8iP4%c-+&5k( zlKDy$nUPLpn4+<0X&8R)LMlU#cke_tdP?lKwUM#P^Xx4`XT&(W!!ieu+JqAZUt>6kVfe zk;Wa*ctQC$l2|_q8D)kl*AEv1Wbw`s;f?<#zv%eN!&d`BT;~);nbc%bLm@jk(`+i*MEYBK3 zZ2%4L4gxdSjv|Gt$Xfy%>c17K!@LC;L^4y)4oig9Br_CwJ+5MO<1$5rWAhq(lrv*5 zMfq|ltAfegC*8j%DNj%?Jn9!kbq^}}Tv!B~sy-z5Pu^*{ljV{JXrblZ6~37x^G$ZL z+6r$K35hjqTNJZ#i3cYk3AOlGW{Oe+LxK_Qq8he<8MpdHOdLar_!h}8$4ombBQK$ZDZmxgy&$);I1ARh0F>;s~^pLc~w30vV!PWC}6}e5_Kv% zSQtrMgUJdtjoOkV_dq@d*5lw;MQ+C=oFyqrS)r`0z}Zm};7Fa)WX_=uM#-@D>i0ja zt3AXv#yigWKiFGd)yk=ZIm4Ff#svxSwyBAGGyeZr(Ok`#ll&${X1Zjy`%M662U z5(GXvSYs__*+!_Cv)Z|7qRZsAP$Z^NxD%_jQwr4^HNaoj_CHd#mMs zhY5uF#_E8Nz8FlqLLu|pdC*`Y!C+3rc|-mWYyjvz4!xkUsoXecU@-M2br&^{UFIHX zhNOZW?oSFWk{3y$S^2eTMPRseGEa6J?j^vVJ337q6|;Sor#?36h}=Qd_&&>nmy`*9~`D>hicl` zvo1zC1~6dT<1^}trF8=&QxLf#Fmhs_X17PrM^_{_0_H`F_ZBM^@^K0<<#~GqYvw)f>{8PiWMI-ORT6> zU~8+(-&d2Fn2Bs-aR7)xP0Lg04*k$0^#wyVws=SRCCDb^%e!Wg ztE8A9#qSsmnBcOM6pE|wIWt$KhGHzm3XYf!as}lS?*NO5fnQVxPE(eoM7EL5oAn@( z<+LxYh`nS?JuOY%8S(}&!9DULP(_-`XHt6^a1YQF*9)TPq%dI(6{C<%0ihaLyozK8 z@tAEE8m1q4$!d@+nY?5qBb82uSv4hsPttGNnCQ6y!mJh^gQ^kJlV5I?tmO=TKT|VGn0L+0z)I( z$0{(4s$R-I28_z4wpNZ}o8pUOgK`Qv^eQaxV2E+BH5Hj<0}!Q@&05UdO!A^jo&}&Z zxQXNkW^l?;^$awlfU}7FG}!lO1jT46;mo8W{$F3LNT$Tu!PoyaoZthTIdVd~oLb-mgn zRv6WN27i6QV-rm@U<;P}2oU>o_y zCikxIRD&UhbU0ZQGDZ{K0H{OADfZPk zZ#xkONKBY1$w{V>laSJ-`5f7wuywP8`Pll*CNXUJ91I`DWY(d$SX+TJn$giq4y!?I z#dIW*86+MgRs~6dy4WOULR8QTqKr+Y2sKebF1s@}9jxF!!7N}+DDa8(6K~jzE&bwL zGlJ|R|Cj*PMG_;WBq`MM`FLUa*9!pDKGH>3l5CjZy|OtQJeLO+iab);eB^$p!AN7_Mk=tPi{41ta|`er z&?>}t&rxiueb?r<+>0hc8Y%=z6BPkF8lgy79tmG!BI+a@6ZdCRdGrd5qmtsue6g-b zPAcu_C0&OA+VYUtXj%qs5paes!UNgEDES^K#F4p|OrH4qh%Le24jWlN ze4I62*P6{QgYL&_1M*RpT-vKk9wy{*GTNEBxF&c7gl@S~GfwB^{%K{mNFJ0V%ViQA zJNX;uoOtrl$DD9Zg>M!)op*n2R)4@ypZ!WC}q;Ga8qRu?t#uq7SRNO;(u5Uki~$ZiJ`3KtztRkRhL z3|72g0D-_r$E*>Aie$7TF}ivk^2dNmmPmX@D`+yhzNg zN#>}o`qfPQ=ZXB(reDo;)m!J+j~MHj&1M(KAeL@4T*2f4LIg63Rmr!=u!}kwW2I`6 zQQjgKhnaPmUCG_iYIao^jjb#n6+YG8w4dn;FBJi%LeUVF-Um;EJC!2Ul8csz0m)iz zvBYCN#hQ!DHkZR&8yc)dwLv>3A{QNu^5AUq28m`5XVS#;3o9f>09UFTqt>9BVN}kE zQb)RC5?F_TzsNxbW+92Urn*@5C)HG7jdfU4;Vy>I#=4k8nqtm)MZ^&D2x1b(JOEOP zY@Q+L4dZ~GA>q4=u8ApB_F1H2jjGh>{@K`~HgV&_>|;6clOmK+XPUf=c+bS92@H$C z91jcYUPCt-?Gsr?#VH$;DeW_unE<41Mlb}^%lb}rW>6XUKrpLB`4wx# zU>4HrBi0W;Wg)Z?@RX5F4;DgSZBs?Pd$acR9XT$*`v3{)yfzT_LzZT8o3| z7j?|Heu)i)uC&4kP)k%9*SgQJC03W&qGY2L`*%;+|(oM_?i@ zCv=d-G76@AoEiLsi~@KaErkN(*geRoOiX<|`a+j9h5<14VkwePF}5_G2{0c+9ikbb z&cB?~w4)`n$3qtGXsE)8tV7eiOgT(-q!pF)zFvkvbvCRlKuOw}MI3TLdtD4}FG?B= z*w^;~D4Fbfq5B0?hXB4nE!seALxf)(2TqdK9MuGLd}P=|#JY-G7tod~Xl^kYx}gy<7|_kbI`Ba7nBqUqmI&c-5U~ zbF;i=n;Rx1th&TpU}HjO)-iukMFYh=p9h*_#o5Ly#M(r&ZT?*|=Li}&sdrju(8Vg7 zG&Eanh7~^9UQ>OAw~FkE82^YLMV%tmF%t+wg&2M%s{rYsNdbMztpY~EPES%-!;uWF zBxV<^HZb-q=uV>e21NV6}I zkJ;v+4;!Edo?9_4qNEiulQ1gSYQVn`Jrbq+zEVbKI+<|@tTKisu|)(OQh!*Qpdpkq zU^~R1U|IwRoTY{wUmh$i&b)Yg##%}o9bPnYhOj;&m}3?e&p(;j32ulBpAdP-96~K% zmzN!o0Zr@&CF`r{%CSgSSt@6kuf3u7mZf1X4vZQai!dofU z|Jbewo<%%8Vq3)Z4wQ!uW*T9ecZ-f0BMY(_#6R#Fl(H#;ek+h{8C(Q1KN1&tR}{5u znuJH3H)t;`xp<=(cSKbZZLG6p=(rlQn9$|cSa?k49ebfG@kZy4ywF5jIXDe8iIt!j zh$Dt=BE)rU9%KY@o3gUde&!z2Ow?`MJR;*pG}8FLS#p^gCYuKfKQpNig2YG~3#+-bsNxjudEUYj}TbV>Ee8dne9Oku7exFEXBd@isuH9VU zzZ$ZMpSK#aSq<52W_+xMZ0;jkv>LKm4cTlNNZTryr5du?GDfr-vRMt;Y@Hk`wt!Sa zHbGicLpGyG(Q3%%@GzZea*Sg&WV0HwSq<5&hHO?tHmf0<)sW37q_!HeS$0TQLpJxu ziB>~4t09}!kj-kyW*jhD4cYvRpx3=PkZQ{Pz~9vhHUO5 zp0*mYxtZ)p)sW3*!J^fW&7?XK)sW3pHDt4OtY|f4GjxhoLpG}+o7Irbq#)61$fiWM zRzo(cA)D2Z&6a_+)sW3<$Yw&QXf4^)+3E8mMl)=F0YJ`PdsSrK(*1QtR9H zAyT5qB9~^4pnX>&+x`WN-ni8H#Z2iGs{ ztENqwF<6~CX~qbpCeG-k+Dw{}Ru4Y8bg&vZfo8nXN1dd7e@V{1pNtB${VfYP2`+E(o^=P!Di24tRIIzY|* z;*8Hw9~-zWIil)b^+guh@BW?dKgG;pp4*#?9Sl zfBe?X!N+givhf}@X85wv!Qa%a&RjM2&gG+LpE7(|aLVx8c4gE{D>*##jN5`U&uF+x zC)oi7#m>2C%B9*lxhWTDV`~ri>NzdX>H9cm?Bkrdk8_!QoU@vrn?B`IeIJa&b1iP1 za;dS8`@(ar?lbpsUwE$7eHpp0bF(*ASFLLpzkm4hYQWrv6I*>7w|TqcW;^>h*Xq8o ziLLGn&$YTQY+{T1_%`9mRyT(2ZgJz3OT%-m?z80mTHP0(YjvN!kNd)N(_7u>?BmAp zT&w%secTtGYjvNukNd)N@qN>qhNd2l%~r1xoow|;Jsdc#?+j12zH?vwqPOUn>G90a z|Fdi8{(W_gzOTNCPPS&huinw4?y3E8r`lR-C8+J!HW+PXJSx2ni{8Q4H5?fa&kL_> z_)1JZc3s2qG1+-u!-+9@_PmC##^l<04QIw=zv~;$h{1+sWG|wR}GiO z^V^0A zdmo^y1>1Q*k7pak#MdW0+wjHaZCcYXK((uD7}o6EDX($vD|HPu@lU>6*DxU_@2_h( zJtjNWms_%DL&Fh!d6HJ^xA2_*J3idn`i7cjPww#sUw{iE;%5w14aYS9p?+i24`-`} z;qguPsq%a7QRNSD$dYoSx<6NL)bE!xe0lE!&8Q1EKG!h3StEw6r4bu%q;)^Oq5O`w z+)#ep70)*u6SE$BW5X9?virtzD^A&1ZpHkK&A+y(!tM&6{q>~{R{YcPn;O0xlZ{Ku zPpQ4BJhngG)ch%#sL!r^ffhb;Tz+fw{$36(pZ=$EGmf~e`4qIH{#&-02i|pC!vXP^*mzs{r>kFT z-atG0a^d|yY$$)ZBR4mEaqkBr%hbEP1veSZPljJD{NBKvmx0E_dGG!-}%HQnwcb30#&%2s; zGD%=%l6JqFm-_Kt! zVdCB|Nxdx@dFO6#7#)BAJu90x%8MGs_5Ia1?W13* zc~`A&@w|8snEq>BAl_x2ESi<<^|@PPsK*A1SwH&pRz16R-SJAE7mmKhiL=`KVg(2l-%f zrNg7;0e;A%<;JD=lpA;Mo`y-yf7|tlYNZW97C@ey`lNPaZ1^65HNu`IM_} zm=t%&hfg$I(MrK+rizpmva0eGDW)YY`I6;9Rh2LK3RUGRaxv-Z{~YD()Zq2MHC)i9Q-Sp7rcEZ zsNVeH@(cET`KfLTN2>)tTK?;OUw$vMu(!J6vgH%@c{$j0+43`~Lx09?ql44#Tt2gn z`klIUbnwZ_<%P3yOwEAxoR`Q#LRG>N!ZWJt@WR*YCWyZ=mJtwoth+iM8*ka~7{SDQzY4 z^%cb>G`0HV6`j&X#x|&p;E?B?vH_%Ww+-^2TfV317PWq&G8r}0Edk8nQX0;nD4}V1 zRMMbAPS!LjPU5OHE9dl+;#HKsFsP<$QFA;?A!VvyQ^u9T^rT{^Pz^c9n`%ELGF8LA zzQRZ+v&>4dyQt6ymBC24;!$kLFpfI z+EZ7E3P==-WKbD7P1DsWw=5nI?0L7In&S>72=ro3svVPxoN6ePjic>{>bH)HNleejOLLvJwjXxz`sqx_zEb8qtA*o!NzXPO)G)0I6miKj4k)SPnyfQMzGzeBCSUO5`qxoG0!n!%aV|zvO|QT@EM^!^H6{(U zJtz$d5-3B}fUM@^G^$Hdy^m5ZdO)E%aKKvDr|vc7FbZ;-id;qNaC+45qW+d{Yf(Xh zsW&MGX@MG|tR{v{Aw8Gkd1lVcQ{mCksH5i;!({$zGb`>))g&{Px;lDAzIzK27)%Wc ze`I(EswEUC$3mU9fHm_l z9?ndPl2AiIr;Y^G>L@Lj*Qg%H+9GQb6O?jR!GaF6)>DEZk+D=puE4Uc=M7=4@m@07 zMvl|RyKb|oMioLvPe#vC*N1PeOmd@oNX+WS3c^g=dU=Yy0ulewsJAo>ekl0 z5^8m8YrU&hx3=omwr|XZwA5W}=b{qyd&pOamE`;{Go%Tg=nKAzTmD)SoD z6$ZvfO-9ePw4BIzd5ijk`D~;RSGXxwh+lbf<&QhpS`_cn9LGfn_9!8WCX1v+4s^@$ zi)f%oj-n7(yh{|~*%LObFWB;_jXL@ZI1$WPS&P`Gt4OpmMi3_z-O2S zwbPsGh86PpBE<;#7a7%eEw_+0T*EGy$n;sm45*~FE>)A&EVN-GlS7x!P@&Z`v(&%L zd74I@XeyXfVLd8qS6hK=F|IhC%r#Vs)zs0e{2H`#3P)3=QlmC$fx5hw)YL+YqOM!U zR8MWF>l;K$v6QSgi&S=}=&XYfPT^!19VJJ}X4`jy1t-m=rnAlfG^m(fp#B&(CX^J< zp+hKqM%}O?YST$`2LxAMQirmU$k>h2iR6=+ss+bv(beRO=eAdWc=O%^DEla%ZoJi8 zR;M~0s=IBWB~iJPLeF`_p+;R%qgFT;fVdE5g=x`TEl%#MjNP&>dh+nhwpsuye7^LT6xcL&9yk%#p zg2{XzX{lF7OhAE%TYrnb$bD5iqsJ*lCIe4{KD6}S%T zc!RF?tlILm5mChiDzrOjwHe!D*>iaVjk4)QlN#Iw2hiD$2~4*27tO)~MGefH>E<1m z3YfMbs~a{PaxxVStZGKE=}E<8L+!O(ol2CZPo-H3Yw8%?JdNcNi-D`F)a)$-0;=@g zL7yaY&QrhefLZAFiCm4y`m|nKHG()K(=?ox1}Gid5>)~%-M}1{VZmkz$QE-&D$-MB z-HGb1)+BEi>Z*szYB3`$sjFJm=BqF^Th%_Q@ab|g(MEoCNxyW3ms0vmbt9EFWZu!r zc>dnwsvBE%W2c&>x*#58C*ied(3c8=U>avchCnC-GjYl5p+D2{o z+Uxy-KYx4GNBe)y*x#WSt{T-}efRydJFA0#HN8{t^;xTo!_=bn_l*wLtY5YBQ#E(} zs?oC_U4I{c_Xm%zUwzrs)Ot1Tr-Qz%exk3r^Yd|4-!ax)l3E|^FxKqeU!C@NQZ^$ET@_@9CLVJ+7bK zBT)4ZYM)cl54tUTaQnB^#TP#`I_Uf9gXdnN-udLg(ZNj@KU8yc>ZNk$jQ;OGJ|LWY z(K%}4p~2|c9}fR{@Zs>ale#H;$=cCDR$qJbFr^hCN1e8|Q*Tuq6^stfD6Z9dtHX58 z-uI)mKdVuno(g&PlC{H$Z~qbG9rvtl?5Q4md+lg-&l78Zk?Im$_~cqOSiLa(=c9wM zTi0ISTg`le`*yy)7JtI@dmyhH6&%$^?Y|ZBgF}M@I;0jxuXOBDLAU?@D@la>fIDwJ zA^48^^tqSXt2@>PM-I@NK_KuRVJR^zXFTe)U?5WGsby<}qd)tmX+dz0n!fR+acasB zmrv-cH(?=KJShZ2j6eI<#G67$#CIPFewzBansn*%BR}I=0*y$#BUt%p@W62Oz_%VA z9b8=se%DF$I|lNS*MmJnY8~`~oC$`(wTz5|G01dr*5fPlP$Hi~R*xMF`KI&m*siMA6v!ivdHCXi>g3lM8(Y5hu-8MKRf6=UJUryI zG}YAezD0F)-Lx~Bi&mNSaC7^P7-3r!S;O^~wRH#ci(jx+s{gF8QalidWX`_&-LnsWHJ!}a z6bG}Wrfhh)M;g8u)J)&CflZ(sx@giq9zfI7(MofHzDD8pqbWEhai*C7Qltz#1{{jN zn}9#^el`FRfaX?|;_^DyI@c^@fJ(Rp6WvQxyrPqHoIt;L=98(~3Jjgd)`O~^#8EU9 zU7h#h!^6`CK1%dxG&KCgc*g-gWPOub>YArxjSDtCkJT-W`N`ID*aLD{{rn=TbOCia z>$tXW<}4?E1Fet~pEA%7Yzqa{&0GP@1I7cLt}tBj!}6ZV4DILzDlm**AT;t^R(g2E zSX0ZK_{BrMe|Dr&J9fcwJ=^s?L!$^XSO-JLBa8YAP$n0wshv5sZMwoR7Y{i2nqSo6 zi%ssBR$63*vFNlk1y%TD;)<NyceYSb@>JaYf%6b;YJ^ywQsKlG9I5+U=(oJhJwJM_=2OdMbJ-NqYKy z-iQA9jK_L~51qZ}8;|k#IDb#@_auMo_*>84Q~dpczo+^8C4ax-@7Mf2!{2ZC`z?RZ z^0$G%-|_c*{+{FSdHy!?w~4<$@b?0LFY@Fr3$=FMbTn57f(2{|Z-kHe z9^9d8nPv``g%wFKmF^0ru6t=w=UQ9mNjL%!L9$}rW@pna)4{<)x@z6Bf)`?k9Osx2p`1&{jKoQ+5tJokgm9n)Sh z!|<^*JzGF>GYz+3WbLS)5+ii1YVUzmU8lf&&%Wsg}kY$o^^|H zH-yMG8J8|Kwu?H>6(?u=dOlyk{fgJ4VA)#m)R`g^R$%L?_qNsPH5It(o9joaiI2|h zB0j(~>sD>)ly)>+2qu6>%=a0CHXvj>cqx!`O~lE@@toWM%k+aNO#+ zI33V;w=Z6ws-e38VFMC&>K7}>Y)~_xVi}qTcLYex&11POSh$D zm`(m-ws<4HI-suq(8PCA+UEKL<-rB3Xy$x8cuux}I)#Le%g=Ol1bQ=L7bC28TF03L zY&2Ii^9B5Vcy)_(q3g5aW?5vTp&5bB3%*}8HDKI&(aCsL9^YL7kCpEFxtyV^O(qjJ z^08N7ZAP=K66lV#u1vO1riSb(hPzN{W*4#aL=`B%-2i$4xEEgXt2 zN@kuLalo3U=}DnCtC-h{YR#!L2L|i^J@d{~I+w>atAm=&5?O#(*R{R8X2=5Xm^j;` z7p^^c*4-&)P!jjJ)*$L>0%uL)030>(<1O9QNx_!E2REN*J^*VR{ux^7f(S6d1h7v7 zD9Gn*)_Gv(_o7r**nfjz3+iu4O|Q^7tq|wQqf#%noMRO}S*Gk-ukb=qM%DRa>)Q<+aAi+sn^eG?2F6-cErLVoB>N#>%yq(pc+TRo&_F^JuFQ>*FzU9<(t^(UIxMRcN zTe_u*Z(y4#55f5UgsJ6% zo~O#x!8z~PWZx<<4@>iOfJ01V z-vt8Bu+tC`FgS%flxIbwuf_+vuNV1%hA#0mx|^|>ENou}p(h#T-#DjfVrhA7H9DIh z`!142hFQzz?ITJUSPRMHBKVcirCy1(+AbdTWX`(sfz+#ITej35-7G-stc!^da_E1} z)5cp`vrR3Z!PHa$FICL3UFWPUL&)$AAD9$iDkDa8RFEDOHmD|ZM`CAbt;`)224}YA zgsbo=1x*qEe}S`G}w`hY`hs1&4Nf= z1Rr5cL_YESEW^Pz^H{5_q95$IdC9G*v`JI~q8>uHjgSJo!b0>zdPD{SC*!NDo<=Z> z_OuFYqdl!AnXQ^HNF@;*YqSEhN-wgjvE|Yv5LfI%dFC{+5l&?445i>2He26t9E5e$ zA7o!#ir`i>+sYfJZA-vXysOoK;PMTEseoL8&td^$dlW2)?G|ZQu>El{IwmtV)ih_G zP93;Jrg^20n5C%$&)ae&I}0&pI#PU&5E>mQ3V~}`kNuY|Nn*HChfAD_N3jz=D;k#z zzRe+;#iiDqM7L;`Lrq2j+(gJ6vH0q&pG3$Z`un~|CTj{ru+fXTG(xkiuiXN4ESa&| z@uxbyBHw7{b=*QCuaCF%8mo`bG!Lc%$WpKoMxPY_v+4DM}1D0Z?LD&)_0Ch*=pN2whr81_YO5owQcIwZ>I6B z5LTs+C)vT+S|dXiAd7SvhXS$D4m}k6`cvm@wK_MuNo~4hYoH!IcWd{y{L)fSUA8sd zS)P}vJy1>r_n)~nl?pjz)F=MdJ4V>!b@CB8RLii8@rDj8Pa6-f_yBXqc*9cjezf)M z4o!CokK@&Z%eG#mZvGaGJYzf!X~rl#@z!Nq9o6%kt^NMrd#i8ex89k$PEEUc=^=f` zjU((XGfs2HX-2JX9a`?Y)78?#)=vNZ4xDuU*3;EV=Wabiy?_4Jf$I0gtsVbsU3rX} zeBRb?|JVBSPv>pT?(I)l{^>J%@tE&yJ?sA`uey86)*SFw?&*(lTp{w4xcx%`H$6jnUFUH@WgSRewdExZbW9o_zUw$ODMJ?!7IzXNI(aYaZ zXAOY*(P5<;_5MdMPgHXc;qdDpzkHTDb|BP86FPKNuYCOSm(=!yIXv;-T-d5XT{)}N zSMB)s%Moi!XvTU#2U{`M)i?CHzlU$p)m z>b_O_hWfq1(b%@70d;>ZO;r2IweYduNpU!BG>46k4eFw{q)J~>V}z;X~ zojP+rZW%X(b5HJ9I$gCL3AOCL9sSe^ZMa~4{PF?n{x+p=s)vMv#n;ty2XG7RAEKUa z%Pr^i<8bBejs4XT`*XN^m^@(r(itik1a->`gW9Xrj}Pvq4rvGTh22Xd)U7WJYF9qj zPCeL;pZ#PkH>W#n>#YuI&*6R2pc5Y%*-PEho}bO^*>rVKhf<(^)`!Cj+Z@!Tz9Sc3 zY0uGB`845!r*z`b8^z(-&u=?Gy)TEuKH;ry{czi0 z_5IGJW7Q7%$lE%XzN#MjhzrvX+1^W?*M<9jBV8yTSncAl&g6&>yuaEru+&4H*_AsM z_u$-n%xWP?c*o)RnKPU=^b{~sONig;o^22e)z)a zJ=7Qb!1t~$9CmxFdr#HacV|2Gi$2`qkKkPQ(X_?u%i)t9Ih?L@xLOXqP8^2c|G<76 zu1s-wO~2B4s*AK>`f)pZsRR3SVP=}cd#~9!KrNTUaq^ia+_Kyv(EU3tc*h`wke&;n|m*H%wjl@yqR0 zYB1-P%DEotUA@#La@c17reC}*hhzRt3nmXKeM|kUrs=+2L%8sQ%;fpQ7*S^q^LuA%CeayW5N$x(;Z zLUotndrK{os)G!H@Ok5gap7wRlw5VYP|wP%hVM3HIEV9Pk(oZclud1r1!s8ln|=py z$?D9du&wjtu%j#(*DZgjqiUSFYqT0RLVm4t?xP!aO;o>>!@K3Rw=dr{Qk^i8!?X<2 z`$v}Ms%vFnoHeTSef1^j;kOTDxUaru)-bhh*fs5xKAM~RN<+^6$6Q;jo3pE(+9>Dl zm;S%@)On|$Wfbv%>B^AdaQ#7yyC2s=b(wg>SEJ{BcMSJkAfNWRF{R7Z zBw6z=Jh*g`YAg3ye^@b2X=6(#s5UZeZXH`XMg2`ynwxKYD>Ide9jY$(p9RrjEOx)Y0300ZNKn^@0>QR;}^cON2qg!`jt@M6Y3X2T_)74 zLd_QH*Fr58>La0U6Y4{u?i1>lLOmwbtwQ}qs0N|_DAfHz?G)->q28S~9r6!Cek>=I zP#rIVdR(Y}LhTUh0HGcgN)zfvp^gyh2BD4Jy<}6{=pSzX`QWs4sjU>S>|6 z3bj^3iWrPjuGl@p-vU*uR@(8)FPoS5b6P;ek4>N)byVUxm?Ia zLcK54GNG0TwMwWz3iYT^e-r8%p`I1$MWJ33YKKspgnCD)#X@}~)FVQ5xEShAq52B7 zOQ_*O{jTkF$U}r&DJKsX>i0q&E7Ti8eNCvBg~|zaw@_1s+91^BLcJ^0T%jHlN(r@D zs1-teEYupIwhHy6P`3%S0c!ep8im{}Cx0WYGBX7wX$WJuK7@CJFh0kXH%yl2G%6S}4>~p`H`! zE}{M;)X#-_N~m85^%r17r9Q}tYuayfR9Cljzk2bYZKU{hORLpgW43hi^_*@V)e>PkJ;FW7WM={e2>oH=MeuB;xlZHPMl9Svxxi_Z?SyGCqDjzrC}%XL4z%y6?6&oz*Ayu1>*YCzPIu zFK@qKS9p1Z`qs)e9n{80cXkRkoK$)`x*RNMGjKR1|sX6fzdWN`A?rEXC>0)E~xZqoeY8{>ED@lKom>hTk{bqZeaTQcHG z+xlnPw^Q2tyx;uEy#5=x7R&-(;-LN1t|#fgP|m;TywXXk^vuqlYR_S%-oY#9m429# z<6iZr@lIRYuIV4l|4!)-(K)r}qV1i7=@*nHwEt#SpsYC$Ge>`o>kIOH*>(D|)TVuP!Z4sjv0kJw+t9v(>qMc8>^BKP`PPC1lTF z`fPc8NKT$p`g=sG)kkgbqn1tCJy_j6YiG}(?wZmg@s+6_SLUDIeMoTqb*0}mI~iPg zeQ8&G%#%i_^isW>y5&83YxRQCL(Q-BSlFX|uxwHJ=NtR)?4r63Y3xwHut#5Y?rXg} zsOhhF>!5mUZtN7SuPZ$e|M;D=c6JG_SEbkEYbZ6w_efh5Z5g009M`=|Fm8oRSGil2uLQMsls-+Vo;@1BrtW*4 zp@~L0UiG@0{`%1ytS?vGUFx3_vS)C|%F@)B9DQ%;xR{)=sx&Pk)#}})wn2yc%XiIL zUHWT$*R>DGUGL9qwAJbjyIj#$N2u!`g3J761P03lP#-;1`n6hl(XJ_?!RFP~-`_PN zczA7TP)f+2YFAKtQJwLFUEPCUKP(eAJo(E$p%?Ccd3o+_OklP~_Fba70c`O8v1CO7@6)DV-io+)jJ$?R`S zzlq7iH^?XnA2#Usr8gr|-Twy`oZ9D0H^rBp+gN%&Cb{%peAl-(mwKv`*WA2+@S7J) zx5U?f_@`1SCb_;PCQseU`W&%2=H=1{F?mvB>5iCex4nsj;%ukjy&a`j<4bSsDy@yl zHM>jOB2sa-SMbnl;Y!A*P#fp&XdBFZqs->=w`56@7gL+}FtpNdmsZA4`SUxaw_=h@ ze~Zb|UrWnk^7_A*ejAaBv+aXv|6t>Zo;CDi-ucR1{r0cF>DFGs{C}3Njej%#!}5D` z&s8kH(m|^~DV-SI@bm7CP5o1!YWz|Bt6^=*f(c6`Ie_Kh=QGT*WB$1(X{ zr^Z<^`LiyK@p}fnx-~K#R_eQJRPEBVu4=zhYOj`!Vkm{SlDB#^enm|_q-hwP(X(-2 zN{)L4GkP^LjD%G0^=^DNb#L%^pGJB)JhQG}<0j55=->EpN)7oHLp(IKbQsillIs3z z#`$G~8ZSwycmJ@fkGit1)U&>(aj5$Hh{j&Q(L)+lc%hdHcf!X;H4efiGoY_}YgA*; zz^!R~G``Fp+BxW6+sH=BbLh-rY@Ew_Z11URYa9EjH(nUfT^)Qdzxl!N#v|f;)?c}! zbMV}V#*NW4g4LrM+sBu-jom&(ZC@~;Z}8!PjVvhg%;3#2jn_B(sp6+Y)v`kxI|h%B zYh-AIjCsg`0Vf${;juiae#pRn>iT{I_g9D3(u6H~Bl~c8l}8>ZkLaZ4U%ESPVcSfj z9{q{QU_15XDVUz_J(vw(_u-B6;>WW0cMj$sDcu*garz?mv|A=N zPKzHQ!cH*usK#H#*AWm#sr0x(z0^6c4{Wd29m$q(!ZD2qKjF`}i;S}L_{PCdSE-wQglPu{>fQGcquIhW%x zcFi~G!1KS_h!hx}q>F0R#6^R8t9xD?)JP0et|s&>WhF~Q_qBdbh!aO=9 zFFH9^^%yXio*Fo~gBo++{7%8U-)=-04SzW2s9l|d=g)7vCT=ioKTr+6bI{=6{tFtv zmr@)5!fJpWw69urA)op7x7jAUOliC@C1g*vI=!Qd`v2Iv?!cO|=f6)&j}$FL5=4s@ zR$VRXB1-g5qI+8OF3~+TT1Jfs(N+kHtF2&HlvRSY?2^2CZxAIcR*m21%)Kv>@BaRI zbLPyPHfPS9Ipv;LZCi$4Y(HNwI@iY2LAn_!MrnR3h1K-+r16#f9HmE-GBB$!`OpYs z5JlGYEl*!}z&4kdngPd$Dbd3~iSaL6o|2pTI#A67bX@D{8AEk6aTn|f@A_4riTZTf zZ4IPNGc%HPW~u!wVOA=)OP?V2XG>GNIbcX04)e=;acqvz3*cVD$rHW^s=q)kMNLLP zH-7Miiq4&v5v#Hg)WBDe@vg&>vaS^H6Txf0J7XGG})?nOH`qmAc>`Bsr`EzN*8#*uS>M5Jk` zUlUqYQg)UKFNbkeDbxUzFoS;(rH%G;qR4}=mtiY2Fw1hnXVN$1!W8@lgDY{huyukg zEse=&TR;sO*JZR)gT$r9uk@=+{$G*Z?u{8Ywd^L`%;0)drSnT7wp%jb9&kYc+cIitxw}7y;TRj25vbuHb*EdS{R-3hj~U*Q+fFeIxbo-IGJ+(J zT^ZF1(2T~=H|(7crk73a6HkpjLTJFfsFo|589&jt8bvx|VN!Nq2D}xfAopq%@X;S1 z7cUHQRK?fifDj*(XE#H_$bJEV6#vS<2zIF}6pnq3K3#B-=L3;9OM_X?-OMO2{c$J* z?k6X1{IfmryiNhdq%uc%ju2_M)G&)bHiK_xl$ECaEJVaKwa4G6#hJOJHXlpJ@abZS(ivNh2C*J1oM*iK=HJ-0C zE@~;!@faC`oP00CR|y zPpnXeSQ1}83yaJtZ{=iEP&1*pn!?n~t58KX3nO(#Eb_SaOo?e#v5M#*)ii7`XcW_; zy+jHhRk1t;zd~PJE^LID8_mPA8HZ8*DS5Gd#T@5o#EKcX7d1{mt$y+`)~A;>jb73$ zXCqWmP$XD_as^bTysBf%rveY_^X^7iB+h~dOi16rQWQU=l4tD5KnDu%3EAa%7;kGV zZg(L&W(l|(SXyf8ZG<<#iBU?+*mKpj31CriBi24lU~zL2ckhbcu|E&j+Iyv>5ndVB z5OYLjt%fX^fboH~sM8N9x{0ryiNMs7(&7V)sp6XAXN1H#dD{RZ_9wWo7$;2mVR03! zsal*`-iW0wGsOlPeKaQYEwp@Ypg)D|f-(24EX4cIOK7qE)UXJxtZHmW;jcBrO7E*0 zlXWko{Xs@7FFGDEb5D5F?&{#W^s`+(J!%-|=mhR1p&jtJMNrtYZWMCFSXL@m+lWEM z^pACnjWs$?NWtEf;P2)W`uK0BQmW8AEf z508L}32#%iIEDQrYu5AEabnH!MWJSG7n^85tj2Q3m~A01k+Y6*Hd0@}+;TVs-_ zKc~sdGWrCn#vV%e6d@fZ=M9Rqt5qwgLv`acV~R#*m5NWVT1_2{=Vl0vS@ zvy520{ZB?=^Qs1^BC0-DjA|y^y;Z1P*Q%v4cLX}BlTw!`Bi01W!-btcBv*?~#K506 zU#PDjNNpDyTNa@5)s65m#c{Rk5@=q0uu(INGnNR&1nx!b^=3~G3ZfmGd>y2)WyU=k zg~yghvRp--lg2C;65=#0z?dJamCu{*cdZoKC#Vv1rY;uUB6D^QqBjNAmnyEc)A3oN zY7iS!uv0Y$sq=axrgmoMkrv`!EkK&F(fHuoY9!AO@}-w&gS^%KuS+DX5SIokgn5~h z^Sq2|RabSr&9+=7|2ShMjVZ76K~4M})J{4cF9gSV2!)ZLFz_xI7wi(OH>^A3>?dU> z2US!1tJPj%4Bn1{ocVC}=lziLxzX5h_dfs=V3E=e2uUBUfQ1#`4EX&Z7+h-F*HjIX zjM!8F?nOM*vSWO!s}}kD!^Yhj1tY$<1dD)@1gC^C-KKz==t{P5pd`6jp80h-VZ`Ll zj7Zp8V}zd?6g!}L4Kme$bCY}=hD@+iOIcse9zl?ddb99R6JFcW~wm6%#?iDrR z9lIbjoT?uOYm*| z38L_%n9()L(w0v)PpRAQVo-BNJVgky6n?|FT1Nwq!1cmKi!t=&MDBXvmT`$r;9ka!NBE7{j&T)4z@5wBYD;<1j6Vtoo`! zI%`9&kq2LROXn=cXIfr}9IUa{$h5Oco1PeZYO!CljJ>tss29f0S`ZzIF5}wx{bLUj zVk7AM`&te>&Y!;(BZhfy{A(Oqfc*Dik>oKS=hnDiGE^5&7e=L5kScvLPSOEM(_@mu zmX0TOk%E0B<7eXz5pCXX_La8g7{3Sv$2`6Sq}p+l>7IztRWhp)(w73Jz6Ek)oJ@77 zh_4CSQ`oc_j8azzQw>1|>_Wj8UN~)hV)B$8JDIMFY~0G5LMZZzTvBp%HQf^YkK9aq z!5FL88Qo z&ogSoehsd&F{!pAtqBDCbRScQps1C1G6hohid((1y#gtrpdL7QdnmsIkeYa zOc_7ZZ*~SLAi(qth=i(F5}BdGTB>U9rV`TTKoiE{?Ws^X+}^>{l9f&EsM!p=-c77* z8m|+$7xDhi=PD-n9gK_pAi{nSwjT=BDM_J!!_oJuW`eyJWCZCHm@0-jZuVS22b*cuH!3FJrky< z{}7y|)eTJ0eLw z)RYXfz7L|J`V42OVOP@$m2#rH3ELi5cf$X}b*+-*+Y<#g-Dmgzmi07E&i}Rg!!rn&4G& zMQ||r!XBJrdZTf%g1KHXxyRm@OVjEC4IHH4=_c%gab=LE5(WHQuaHz}rU|o*h&(ad zgkuCIqivO_R>As(rQo@CGPgfCN~jL=aC^eN>sO=>-Upne@cE`w8il2q=2O2o1^fil z61ng{ltOgV(9oH}DnSO(i%rQ|LatSGrTT@Wu}kfJC!x9TzAe&pxSl&@|0R1+RJ)cA zl-}En!MMWov&O^H8nnuUH6&LPm8njHTh}j29S>saOju)=0q?lgIt+ma^sZlo4!5uG zNY*;&*kfz$^_7rVS3u1NHpWHTzR@lsAq~2IuaOH~&%s`(jf6_81@<(TlF~NkDhJC6 zcqxi}Bp0DAP0{Ak+w9FFaqdf52VihR;&BogKDoZD6dVVGuSvNav?h9c{gM*Mu%4m_ z2`f;np1Cympf0qgx!H}GsLRs&el)lXL~?Pr2{RqDC+sm5*Vwr-B0E~0Av;iZ2UNym zzX>iJb7GU*OXC#B)l$(UQ$ZEyLug1VUsr^DZCq3ubi@RIlPi;}a};*O2XKNp_+Mck4UQm;Pv8 z>?zX`Ej24^NI=uVRCo}C6MH7VKHW0bcco7k(f1pEF&);@@EDq%l4}6?dc>zmP==aC znmSX%VUWwF3nrW?bHnlmC(1!J92z)K*?Ks$*!HXFlncjU9C4Sl6uG9tOzrr^ZWm{y zny_mC+>3;4B#g)R8jeF6oSZx~xy5#E>O!;jHZ4WpW7d&|Tr-_jS)}0WCT!nuYIKYb zC0peHm1*5gQ<9dCkevkC88sZz@QLl!6vOV0X)eyX#%D~(z1-TFW<_EW;KN?a9}v?% zY5hGDoa`y5F(>1?YkAs!9}P%3YoC*y9tgt@+)IKx56@l%aib*zr&bR|TNvNrfD?t} zI%YR1JKgj_i^Kv8+a!OPaPGdjS!qh%fIA>tyCwpA864F46=sLBC z^AHbHV%mjxQ<@?dCu98(2O9SrCS|crSUdDY=Ll!#S^v5Mv8}>9eC5j*Ob9oHSDI3_{K=&G2!P{PEs>B zGp5SCgfVgpABxLrQG~WVZ{bM&e?+$qEow%$G7rpVDH1$Ve@}ZtySa5^0(pFH;X)l| zVpPucHlNhe!4n6p(q#6E#pyp*S zlF|;RmafuaA2a51PMa$uV)BckExpv^mAhrklE%X#t5u?<8y$#5y{u);7>NI0>X)GmM)cOE~ED(8S7zXXqvDD3@tjh?1+dgv%->!4`pL_>r93|L_z&(ArtTOo;s| zLH?hH1T<<{NE%qhP9+vCx8YigmM+r7AbaPE<7V_$yOt%@DRD=2Gv;G1kX5rwOMe;_ z59Ry0=KoWZK+p3IEdrH7B)nrr&dL#MZNs84d{Zu*o>n!CaGK-pKCMtTn9g(#B?ZacUMF z%#CP5t+oylCQ&utvJ&RLlJ;7?ht1&R@e|tO4%oUMoTyJ1y%82^Ov??!1Ya0x%U$&V$}H0#@%|IpH4pG3>o_zu2Qjn$ZhIwzr1)7#nWCM9<; zV~*u4`JxwQPB%I~{l{{WM<+9;2&TY}Lx&!=bfR17$P+#hW^^u+r{ryuV1B}%U`QDI znz2X_6dv5SF0^x^?58aqBuuKBm?eyZFDlRf=wk9mxv*3$Ja0*4b_JyQ?A@;l_ z+?3D^4Hpyhs%2q3x75B6?FeY4&71>Am~qy@P1-Tao@HKv0iWfv(m}nwQg9$jQOLpe zK2q}7+_CVDxj5L~i>`mh4TSUK&Di1Nre{x4(|AdzBJBmJ(=g=mRAa&#D@eLHB{xs| zOz%dfJMBwK*QVw64%$?S($1ivX*0}l>bVR~o-xaORTG$2O)tqhCpSy`YQRl2z)Mo* z+4WsQ>d#t6VvI7KvdedHps)rV+$s4a7E2`;{@28v_wC?I%fc|2D=z*oHF9)^vSbLu zEcE?SvwfZsD;~m%$6InSnBgn4;1Oxpah{2Pk2d>Drj=%_G`Mv<>`kl9+MFY0$K*Dg zr4iqxLmBlprvG{~CS*<|PVb3x3fPz{X1mQRLtz~{l$83C`4e2L71+b(Z)q|%n>$nX zdix%B*cLOURp4G^PYB_S%B)1^4;I2qzD*b=Cg)j|fP)puoVN(1&b!5M6)4}Nq`wL({&Z}-hx7i}s1an1Uq?Q(Wm>!A{;%^Z|Qr((Hqxt2W(6HE>s85RJHF3#9Ug z&B^q`U0YGfeAqljN3Qox6T}xoqiHqVE%ki9-BB|fIp8>-7mn5ooyCa`xxDhG@IN}2 zqQ7pz!c{(I9;&1InaJG!fl+7b_rl)B`6tZS(gUuYUS%EV>O=u8yVRhl)37OHPnvNy z!Q|W$Vkcx)lcG-x-+&21L8S3#&A9H!IPbCB^C?NumM*2Gv~#)nKA^+w6dfrJPz8JjrKeRMb}eXs$-G=Aa4%x3otK`}E35dS((MR76UM6lZmU&O;In#QLgb zw*dNjx|=iAsE;aOv&mWNB%86-z>K`;tC=Zgn7oWvw*=>^*7$Mxgc@&sB9xuUc0rY) z$oAbzNk3(o`)LV92N62RT?&3-#i0%WDoo+)cjIb7D8WUQvUG;JI}zIJDJ^_w#yP(bu>=vrVP8H z7XrEP-ds`2_JII^U|ZK0TNPcMBoALp;R1*WO3-2mF~ulc>Q!1A?Ps|rxNjZ6Hg#Bl z1sl%CrP<{zy+i`BT`fVev9Mmb8EZ~_t~da<$?VI`HI*&1aldOT)|mF*_K`xWTGG`> zb`RV(2o0#)$Af+;ho6@V`Z&_Ss+cLKRsi`S^l|%PV43%RL>Z>>kP8ob zif-B8mjZh9cO<=KtFx5W(Q;B{Q77IngDlu8?Ki+f`mM7C21+ER=n|L!$IJ%BxGobz zF`_@@I%yqJhgptk1&AA~U~#A5^_gzc*=}M)abj+poq~J$>oZHlPCy2{DgCC0<&4G$ z$I(T)-ple*#U#r++mvi*g4MIjQt%fYrVm&adX zpcz<`GcNKary$5+q{U%#rXgOoP_YAXUFPNJ>aJmIMuxi0#XIo78 zQshiahE^tO^N&UsTObk)ot>X|15t6zRu2Yw(1C>z)BdRZb=dKFkT-R59H4dH(fRqv zu|)@eOFgqt3=6Icn(9Mey$85S`xaZ^+K5Q3Ei{b`nTT(x)9Mc{DV<$v!HSWSp!boa z{D5M#DsX_KlxDDu)6xDOFkbjdIvktFcHSmuh`T_zKe<^#F1+y|!_!=>+ zBD|l4gRA4h42F$gj0Mh-AluDG8M`n=hSRa4C?pvw^?R%}|B|Iq!g2kTffAhwM z+>XrB_zO5;P`><(YoGXvdl20}hL3J8Y_X)$?ah|@6rPgaNPN#-jGVW@5N+RL38ZP; zFk!rwDsLAPMzVu3lJJ>#KMHfVTfNmg#FPeHbCF$k;ixFPPq-sJY?6Wfy`92l3i63v z7I+dOgofenrcVTRIub3nw8W4sK5W`+`J~}EEps7c=19s_m3sNWTC5^T+Awx$59KuEX&0Scp%lx zig2Ov7b6_hZ^xz`v%pFs-~Q5P)Ohi9Cf(?h>nTvZ%`bfww* zGON;Rf86g1D`$0WOK2T%ZRy8F2K1U6 zpf2mCKeS-8h{+;jLdK;jcu_~3jvj(56qhdAb0i0giM%%k_8HNh0G%dlDrQ32Z>38n zQIJD1G@U=~PqBb2tdzY+s#z`3D5wbTDOITMSCl9Ae?aa@*2_c|I!2YB7Uj|M(R1;Q6w>whZKN19#G ziqXT7JfuVR+O;%Nb$Z_oFvvh1lI`a){k-y zVb6cHzZG4GAnuo>!P~4>&iY=%+4~HsV1*AN=u${UD^By76(3`|Qip`erLcU8*0wt) zRkmU%3Ucff^gS|iqLz>otPT6vI@p@5J0-mhwn86!Y(_iqhQ1&G+`f3io{pSl1C#ebe73{ zKzF8%HGs}Hn^uT&0;f7kevPe|!kNqt|GaKi+}-X1z_}q!HOd~64zC?wF-!Q0 z`P&9cr~z)5wl4)8#fv4w>6X9UZ0_u7#i#?0%W%S3Y}(t3ZG}S*EDLF1Unp{OSSocl z>1tmqj1qA5s>Y!HqHkJxp|A1QNokrt5ZoT#=sV2O3tw^qT3^o z+1z<{quX?(7-YctPK}F{HcHq9#<^ALdeMUdmyjn6P<pZ?i|rab)l!}sPe#IXf8hLm?_@N1ztm?68jKfQJZpjF z@Y$KT@Kz?h2z8qVzOxO}1Eq7*M3WKpl)B8Y!od+iS`(V#N+_$e6f?`ZR7b%dv1a5K zU$)Lc$}k7}hLFQt(MaH4(wccz;Uyr*8YUf%wti4a#J-SP^;vTqsmqO7&UCAlQiSH- zoa;bYb+8draG_|)jz#za&}1=nB9k$r2(8|A%uzflGXZa3@m02oi>=Lcx-(Mp5@;FV z!e!Rw0=h4^N&=>?fN=#htg;>!u+bXp8Ue4ySjPiW!*y08&{FHI4k9{WgSD)H%{E#~ z3)nYS#ix@PH`Y?Ptcf>lmQk7yz98Sb#ab0hZXbL>Zr*C0r3J@sw~o+));Q}BE!Znw zNLBC>zPTem36a?kj@;m_SQ&&hpd~m;H|^f)i^Jn zjr^}4vCgKLIoQn^b5!JtAZGF!c$X_@pS70s@Mmi;EwZ7nt)VpQxOJh5OYV}eLCkOR z2mhb|xv5m;l(nOdmMw;Y{Lkw><rUmuatvK4`*p)Y}aA6TFCZ(oX@sI+?>h4;rXu-z!tgCeN;wGAv_s-Dj`_^`` zf7%=L@xHa54k)hbXIh&}(SKP-GAe!e+j>e3{r1RuL=7#?uqLR?=!(Kph{+nE5`H$L z=Q)@357w&EFsrbi9%8scoXyE53q=s0((&hQVG7C=xR-=N6SQFH6Dw)KH&40GzemG% zKC_3C8(o0i#3Mgz#rkIi$MI~cl+bA8q9{}5!_mGo^Be<^)gyT5J(FPqx@LL^z z=48Y0;K)@6thJ@NE;cBcz$ITd8&+Q&eBo}}pmEW!)<)8G4_gTh?@-QGPAcYQYoX!4 zl($uo4Bj@hi*tSS!5SpF7PmFg@aC0me$w<3qDhqsVM4?6QcdY?DOB=vA-r@F;Ug-E z;J1~$UD}3$CL$#aG_099Sj5kUoiPq#jP=!ms1HmF$D%4@)F5#!h6dU?Yov=6ZDX|{ zDul_VnMKTmRc*8AC!2lBY!YN^t`oSIw5pnIk{T3NrLa_@h7CrDNpUr8SS)d{Rj@5l z3x2FE%Hr65-`mC)P=kNhvxRE04I0>B_n6^oLmNgT2Z#J%!)WB7dlOp)Em^B(w&7ar z>E^aLEvRo{!(7k2KCMLma4@B{4VH|9`nI+jS`Pc$*=DM->RSU1JJ?`W1ec0m548=@ zQvJ}$wo)VE+^?kcva{`>MtT%x8}x0eux>(%T-MF*wiqoK+0%v>OgMIJZ=qcreBCEk zVzc_$;EHf;p#ipuT5#z=dzGY;gY8w2j)&WD%+I_TLu}nO-d4kGbJQTAU{@)9xNVI_ znm5u`T}yCwv@Js8;)|c(kFgmwQs8*odM$W0(uSp_D1jim!qUD;xn0m>ifGB-eK4Tx zFP^!Q%XH{!VqXlX>ClLsGi?1-TB?f;DUGy# zsSUQDxq2FGm$YE@6*j!3!?7J#*ja9+Hvr53!C zAgl_OOAw4Kyq)01qkwODhqZGsjY^0RH5#^7<>c3g|S zFWFjX!JyMNET_1%x@T>H1=XNF#rCHb+wFqwH!ZfxCEFk^xao>5M`PG`&2~czF1>E+ zuQ9y2WrOdzX%Bi558IU_zdPv3Uk+H6opOtPw0*!~=&&A>L^Ok9edJAVmN-ex5P3AivEBOgzkps)<(_kzTyWh)D4q zBNB+W7=ek07(+ey6-Ka#Cm4}HyuTRg#g8wB7UP!}QLcD)5ec@52mVmJcySSROBA31 zKedP!igy-~jd)}ctm1{m&@TMEBGQYu6(KJ1up(H*tBR15cv2DR#CwXNj{KNnXi0ua z5m}2@6u~K;Py|i9pNL32o){$(il-CNQ1Nae(jOBbfL}}mCZ0<~!q+Mg4<({aM+I=; zClZm3cpeeS#oLJB6b~Z;6R#p7Yw;u^BE@@%NFW|V40Yz05RpzigBXR{h({2?BVIs+ z^u+UrNO)ZZ;@v~g#G{AERJ?eI){Ey3!6M!|1Wi122%3215H#_`VW<KbFxN~%S}s{b^r^`8^|iuyAQL~sxerB@ciSR( zjZ*xS#LsYaBfav`mnbH>ij#Z>=?#8{>krUPWac9}Lr}&>Iee!dFMcBNGhBa$a$3v# ziiwU85ZS^`akG&8#%bZ`9jpJ?vbWcwXGb$)*yykz6Q*qFIXHgEC&%K#(NCQ zL13b@SvWB)6%i%IehiBz56d~N`%YkHrhRwjTdcshSd_ldx2V2IKFl#6=9~|6&4(3q zF2Dtz;{prk!yNKqPWiAXmwc#OKCEay%rhV6oeyi34{MVT6WSqUA(SOXFVsU|LR(_= zrl?~D85O1{MsLZ7+45mZKJ0OROe(~=2%&gcvabqP{`J5D-LR)?#=pt{U~I)=^iezi8@KzvQG5Rzb>P2IN&k&X{%_RJ-$q3VV;ZAB@ok(M zb?U!SXa5^@@0+L?{r!B{U-__y`LJ{ii&Eue%ttim!>su*IUkmp4||di%gTp6&xgIV zW5OGW(Z8~zjQx`jdz%mYHy$}PW8-8L6Kk<#p z?>7|_(*&>t6&zN9m>Ll2gQ+t@e}}4cld%$`L*3-gy0i49n_SLaOc3D6#(WW>KTXxU z%f60V)VR$Q)*X!Lv_pUtdeL2O?-x554pW=4;gyM-5s@QcsJR4$eVQU@c}Dn zS#-Il+{aOym2;?aFSIR|X7`eZ;FIc%Uh-JouN2x__SJ2rQN5A%HZ|*4bgH*pPM0V> z>@Ank=^oR+edKEH!ak$iJ21Nu`l}S&7bPS~v--;Gb-EMe-XGa~p=$l*O67zp1?S(e zkP+-B3L%bwv!ZVZBQ#w9jwbb&tLf4xu0I?J8{O(J&(>Y0&;gK!FrcCo*vtt1ahf#% z98c*dkcz=`1gVPNsXJVU>-ZV2ze}b8a!FkrwkVO|F$x?g@6)}Z8w2HP#f1}zoK%NH zZ-d#5(61-AL6E^wiWmfbiB=6lJ9p9HL2}8q!sdd%FwATOJCvfV#lZB*KjNoMXW@7V z4^zZDcSqDC{0KLN(|sKv(p@9h!N@t28V;6!(A}oVgHgZ++BjGac31rvy$u#PLcf>p z4wn1W6^^IK?|f^xD5cv97ezRs+)Q|8`mgwbyQ3Bn-^~FjZ_wayxnwWlmI}6(L%^0g z6hGC+;KyaMNbpD_UY?Gqt20ED>MQ97!Bj`E`zjJ=0MpN(kDu1d1=CBq9xnT`MS>_E zsKu&o6#KJ?+^I%ps6hC$h%~A|xU;~-cn1*fEWig92zM5kc#0d~p$ddM3rv_>0O8I8 z{2Qdg`zjFrEMjk}K=`x3E~o&1ixnYvVh_&1%Q*wD=9ckd&cJIq125$aypl8Uh&Awl zHLzP};NfcEMVx{CI0G-?47`Ff@B+@jKAeGoxo5cRBnDoD8F&q5VC^@s!W(!2X5jUgfi>E|N^D^5HSpTYz}|S2fmPMOlaPTG zbs6jFGG2Bq<5kx(R?=m>J-zh53E`{8+ZWf-GsBfJ0N|Fo3U9<$eJD zrZH{LG^Tx;#K)8$aLoYIR>P8xPXBW|LcnEgJ^cwH^S#nE1Gnm0oZ zL5h?a=-@l#I#cc>ikJye-J$rI==?kMdM4E54z-!Zkt=3#Wcnx1Vw0(};(~U%<=WRn}cH_{Y++|Uj@@4Q{Y>hHbP8+n^Ajw?0x5|4t z4!qtw@h$rXA)v)&g1l8>a!wU?f?5QdbkT=F#Y3;P)l?;RA-*Fvki4qTBQ2 z(EyF3_o?LPPs!1;16WQ*%fkWu z=F7tX7S5OJ>CAL$K9ZlN2lJs?r>XJ+H25?vSRjuA$XWo}W^!H#&uBAEUxEMnVV_oVtF2bWihk#Un0*0c25OemdcZX{kl}12vBbsWBZrMQ-Jvy zP(nI2HOM0Y5)GiGlh1N_62Qvk$Sj=-uYfm{PV-kV?d=MrODFwGdAe)5Xl+UsTD$vM z>`J*O;Ym$5>aq%rO{d2JXB z91IuM%PIr88|0z^ocfC(PJXM?M$C`zfU4b?LbcaGS}C+;4XTzxMPua2t|_8w+dqS; z*%vY`k!y=*HY-y6fpU0Ac_&6j3Vn%T_DO5yslXnuMPpMaVjW}m*U1xr{kUEp2XK77 zJVDKIu494k^!FX9>n?kaqK06J-2gcz)A0@RAb?^UVMdcFY9rJonVxKfB$BC5Eb>gI zE3q)R$yA->;ja9TH}gv%&wp{DaSQU~*C091Wcomy%dk!I7$itjL6gn$@BmI%#TV(u zlq;Y~B(*$HRU&)n!e+TYT=BA7(5-uD@fLXmz_Tq-qdnAjE1I~6Zf#{+gKenX9@?`F zBHTkox1+2*G<&-|%2n+3>dV$ct|#kL*Q-L8<0|62Bk{&pz-jdH9{Mp(4hPs52M2x+ zx$Hp8_RxeK5XEj@5H$5DwKYJ37+lS3y@pigqBt|ZR~I3dY%+;T+emG_~9`}+&ft3`5f z-kWhA`%p{{9XTmSt9-?N0^jkUzTv}@ahg%cq2yEO_#CQo8Y-AWaiZM?X8A~@)n5E}= zd6la;ZqTP(LrrF0r||RgD9W)Ky>e%VSekeN2U)R{b3tATu;ik=9KiEe#y0s8u|rT!If2kLUFN52(%wR%RnXfme1d40{b!)xuvzeiuLyt_O9U zg^4X>rH6-o3jdXyZea5Lm3rNPC-5uny@AGVrPLen1h!I-Ki?!YTcB=a0_(VE-AB|6>_rTNy|MoQkKo@V(jSkYt%yv=l#)ox)1hvO^fVp13+ZJr z=k^S^aEX+i0rf_tkxSTSWZE+$lp>KDnYe^SCb;T}blJob^e}VVdzo2uTg@y;qZ#Xr zM5=0$+XZmPd`Uq2dhaTznbrG=Kvji)OlvL3{V`=&^Ze+=t(l{P$v zOK_EP9z*O`sml}ScoL0&g5i@y=bym$OQJGQ#YPqlf69^4Q<#$^Dw-vCa}~#*`Z-@w zDX;J7LhY_hSnufzh|8C;4}1F5*2yQk$ssZY0tAY_4P$ zfXZ9B1bCPGknXb#r8l=OT1@LPZo(EHP#2fRn;8!E$PQo=NoX6DMr*8ABq8 zVm@;xm_NgCBvF+wT+-Apu%StG;S0t|5;^B^-h*;D@<0x!dz*t{4S{^+NW)ha*2AwN z>y1H*_?@Zr#P2Od$gPS_5!IWcQw9S3ty6jja4n}8P|NBug|xx77Q0(oSDm1)1(e>{ zO5I;T=>d>aKp6xuu%P0Hjnz2?mF56v3o5O2Ut$X>O@TBkq_hTDSV-v)U@WB6&}}ET z!rL%F_6IVY$oGG>URm1|x;h zQ3(eKae^GM;p>Ew(&%3mggGNq8%=Ol!T`=YgVRRkT$CXIi(Hhc03ThHz5qR3860v& zE;e#?Qw9KxcH_uj-H@k^eBG6P0Mp%3Z5ySyD;)rwi=urt8c`ITVWZPUxgZx0PB+|x z6P))@CV&>~$+;Ljl}^C!dn%&=T6m#?mnqy!=>m}K1^Ha2BHl`SfG`zo@mA{NZ2Pu1 zIFD1dH;O$@jfn;gpmv2d zjmSnw(}<^nyZ|#~DJ6h{t6D12ixP?hw!`0*fWAGYHYHJ^r?j;sTKAM*mP8e9k$Wj6 z6rf)zV0UR|DbViHjZ#Q(mwbH?8ApwMkS>na`k={i^wdY`1<;~2>KsSwN<;bLD6=$F zF^-y-L4sp6s0^BQjP{p-@*Sh+Wt3(B)qSCn$7rlC5*(uozDipF$FdOrF$yb-@{ZAt zvPug8b6HMT(T`ci`YCM?dDIV*J{Ie*v~fKq+Swri?f#HJ*UO`^Ym5VnX z5(#ekL+Ou^TYwS*&^drBu_}NwyBmNWIz|=BaX}-?L1&Lqayd@$q8$3`7&R=p#o>tt^&7jeFd)k!wP8h8~Rv5=?L&+AiC`hZ3#q+-p~^jG^~gc-q8GtO#7pv zQcd?dwi1%9C;v*Q!+IK8N$KpmUNp>b4-G5+C;lHs!KxS^uIBnV>q%D`C9bFTm6fgl zTPvgM)>EbmzORBZ*3+CS=)v`rrh>q#sMLCzSQUM?o~~3ypRFgKAO`v%RNyEXg3uvH zDI*ADhgL!}A8?1t#D4Roa;xV?UZNkMwR_3@11h+e8Z<_0_tMJ7=+nLQ zR0ZvtAWv{MVV37jko8__+ms_WHig#gr6(%Zuo<(gZie!#l++AjwbCbnU8mB`q5Icq zcypz;?f_A9MBbni%`u*CP>~Q+;s%9}BVjMJ6*Jv$J)w6s_wwBPzmsGD6 zs``>vw8DsZN$IVW22KAGN$ca7c2vVr4b`Roc1J`tuOT*Yvjw#<@I30kz+CDj>e^Z< zi(Tpo0w{QV1|XGw65t?R6#$#A08M#!vh_OMH~F00kNqsQ6y9iz2A)^S=1R_Xs+n4} zK~AS>umGEBt^nz@Re%&aB|tLW2k_@jo<7(*%FAa*Gb|9*Qg)MfTct7fIJ&e&k}Mi0 zKn|@CAeQzE5Kor@n&vJo^YU5!IX9oJLMC57eOt-}H@LBCDX*y9kH{&JnhWrl1`4o` zW(#nYHVXh37oh10kS&aeSBD!xxiZ;Kp6!s+Mye&iJL)7r z8jTUaMg{>c(_Vn4;>cH@f&<^Yd{QdIW~!y|9N86k9+js^mHL!)?UBD~WbUo_ zQj2biKf)2+U{Eg6v2L)%m&m<44BsUh+g%w7kk%c>`w~^}p^OCB&;t|CCHm9@6V@df z(32y7>#690)#}BOF};-Gz}~4?@7~Ok(pxEqNEF%*_93W`QWc;_AK2oHw5bp5-$i=d z2WI{v)#wY0cadiIh3jyUF7!q6i&V8Avc5>m`@vnkNYDGBo)_uI{>-_tztXw%MbQo4 z-PL{|v%*6sOdUUEgZ?7k9foyNsLKEhc7;|9fXP$HJOK6COa%vG4oju*fl56`amodm z_oObv6dwwSP^uT)fGa5SK$!YeYBq>Dw+@1AQt5*VdJkq=vI?BSF}0`C=y13TsdPS^ z6O@gB6jNz_1pMDrdK96I0cbY_0!yWzhcKt}PzDi0mC`VibA~FT9Y2cZ;9srj)j`id z@*D=Y_an6%hS}yL(J;6UA4xY{83!<7ICA_*4~Hw`0XmF8_k5%yBa{ki9`A~(dBl!X z96@jzsRRPl9I3SbpE7ohRJ=ivMk);fka-mHcNRsw0vXr&&)J4Y*()TrLN9TcF41Gs}C^l(&n(C=fA=DZ#g z_YV4^hm=4Wqx=8>K3_UHM)6X)8mJ5vsmfT)bP04jN~ubbV=*HpkY}_~jvkIhVF-_d z;w4a@XeF5Tj>D9kK%d4blK{q#hx#PYt?}r*1gbHC!HNm!k_1vFUV$5=fPYh(C-GN z26;rmf+f(vD9#KW+Z-7Bv@HPoF$@4*SdIROhAMJD`jg{)#T{XErVjtA!}^Z4ChQXdM9bqaTGW;0)23Whi=q;)8hfXdsAS{Dxg7LeGo;zPlpBU_3#6Q-7}KKfDpANX%tYLQLNC#GmFS8Zg%0eBDD+)j8f)O{ zqXR3@K7&#o@#sa-SLnb%s=pj&>L86<4nZ8Goy%cw57L?y7*`*cD-{tASb@S1Qp5^$ z2U9Akk;UlR3bc>HcDHq+1XSo53dRXo&UJLQbQ!!-dr(d(zF*)F9tP1~u|L^yFL zy6!X;OHk$ltV=-MPLtm*j1cAlwz9&nVl0bv~abS(v{kW7x} zl?E!MJ;3tws9-YHxd1OInO0nYE1FC`7a@aWI&l$IN~UhVD)DZ~LKM5bQ5XD>NrITu zep7m=Vos)wzbPBRTJ4fT-&`t~kN?Ha250s~r6YKq}|`Arc|G7f zZtB=6qcuLkP>NRGfgRXQZg=7K?52pjX!&mXdKV*LHy!)~<7qebxrd>%o1FjTNa;_E zr`I%`daSHdGbj~}3`#{)KqfL^!M>+%Pn4x#`uhp<4tuIB2lnzQOzC@a z$U^h8XjvAfjx4&Dr7Qy&^b8%3D+kZet6BILZ0JT71-($>0It43e`e8umnc7rp1oww zMcL@nfk!$GR;O9zLiu?!t zn?=+9;mn=hps883{SEy3+Z1*tvpC&&17FTUK5x;d;-)zI^tjfilZKC+G<@>p(G$jR z(4V4JZ&4Li0dH#b4%221&3cD%oIx62QAlSo(BjmkeLGDQZVxp!0m#WzXNP9lz9grvM{sM zE|Pf}*asC{aL7yrw#+f}0ze0+%-;ZtIx}|LIr9v#RW6wq0m57vb9Ku+3+$>}W)-eN z1uE{IDXtNWFO}JVOr?Xp5l(T>j06ZSnmGetf6>f20F^v4XS?vVffW`sYXt>+WDcY? zUYTB4*~OEeXXYYs?NUJ%ugrzO4tixq1Jv-&oD1-icjgj+I>i{ADweqzSo7kU3)Jfi zrYvM@%A(pHnFHwX|8;d1;8`P09H(fDyOfre7I!FG{BSAmRtmJx;HAY5c!9&+7k76% zUO3zicRjRdQrtQG`_F7L9~{p^X7{(VyR(|)Z8FJZms=qtmwQ-?nwhxV)0w#3kj$n( zih$|0Jr1i~nay$#A2OSv5Y4ifkq`&6nB5@?d7D`vYI>XWfWjDWvoyp(Z?ic>maJxE zO8(ucW<%%g5)HIqXzE~^<1QQybx2(it_>;~cQYj%bh<7-Ah zy!7R?#@Wmci0#ORA;#kkerA^}T*3WhsDKXGni58b_31jpzylI~=5Kh4;enr75F(Ag z*#siO-)sc2OUF`XH*5OPTd&56K$JiSl#rxT7|IC8eYsmjWk{lg$gXd22U%}#A#iNb0=Lc&NY-Rfkn7N%`L&5 z`=bQ+O;|~%11Bwn<&zfb=JlsPfUi&ir7#;;s4=BD%kxsE4`NA6V>VitspW*sh{48Pi&IaO%i@~Pt+mhvux)>vvn8JKFRFJ;W05M6^Ytd=?z zjPbNop|U3Z*os1H;>XrkWle9jsjL~8jUI$R*}S>>l1DIt*uARvdy~ zWqX!0n42Are-@WWYlg)zC$F z)}R_jdV^|F9g_in;ID2*LsY7P4%?tM*D#~>-||~0z^}|WsMm#I`q1iTB{jaL=>?sc zHO&DKscJc@yAnechySZ)=-9J+ugGq=Xym`RpHBjX^GVc&)@Iu@9ckDZr}hF)?WIz6 zG#je}wawHh^+IiI*&Ebvb1WRp$ z`Wj;P)2Uz6q4=-q)Ru-wy;aXlh16&DxWLx+v72sCC+eF6bZfnRpo_!*o1oS;Fuim^ z`}~o(&z}nF`2VH0K^1I>^?+wE8ghSTYs7s$rxDwlvN3k{4Qg^@?kle*m>3w2CfM~i zs1!|c#=s*aP0g_ozRk=D5Nn&UW{&1KNo`O|n&aGpM;Kag4NF>ZTGp1_DbreV4M|#I zX>U-`t#}yCR@^tuTXXyVZq4m0-p1?$mDO!nC2L!3bsLncEvvj}i&eZqwQR@zeX<>v z?*>&gl*?QgilvQbFxs14?5^xgUD;VxY=e<{)xk`tuC(XgHrtzBbq_X-gbEI7aS)bg zxEbu#t!G%L@Fz%mM8iq5R8qR%? zD8d}9i(5Y&#jPJsqiMJ0QG}TR`MyNpEX>PjFdq7t9BF1(yE~gj5q{noL%TzD>B8bc z7o5ovi!^g-z4{(_^dl1M?wd+xU~l=RniwX*<%U@c;)P*0fT-Bj?3|VVViU6!CeQ&V zzr+!d-J+?RZmEM^apLD&<`NLayP?x&s`=f_dJs>$VUo>M9lN7ahS}A+?kM(y`r6&B z3Q@TS8vjALwJ6vVL;gW6?umWrgYxNxz4e0{)(boB2W9rcp2himRLZ)QdEp8lBo4^_zlFz2D#J^;((p(;9%;ktobWscu5E)UhB-_ce{16Xy>S~UR`G3-A$AniRV&ojxoza zWEqRtjyt|~DOfXAAjGq8_NxhkX>2?XPL!e(5s`C>u#TMd2 zTNsapYRx2b7)1Wb*bo=0b(1k33suf37~F+w*%Y*Pp~^889nAUisDV?lB(^K}RI><# zHPvhdQDqv=ciYw6X=X!+XVYN9c2#jYMt{3nHQj6p;W2|X+swcP$ab}F2ChZ7t8_Ct zpD~kFj?6S$0Q%0tCEs>6WERJ+X;DO(O#w&YU1W%B%4`gge>T@Qcs6TZosDaX?W)Wi zE_2EpT$93*VTr?R5&gD{G&E*WW=CN2hj|=-S&uj~oCiB_OP4jUlwOt*f*gF-l z0F8d9LKmQhck0jrOtg2Z&_cBDotmY^_l0QKJJo&>r=4De3!8W9$s)6|$8%L+v02jl zxn6u}O7Jy}XGB<^=;-kNxWs<01}!#&@vO`eGrc;s*!0c(MyqDgs)@Vz>=fP&yDMqb z!=o+rm!R*btD#HGM*3+Q_c_cO_c>K@saY#U=b!fD>8kirbmnw5b18awx_Y@3C!Fc3 z@iH`Zy4s>es^w_obY(2ZUW8|Av?#d(myO7`0+WBb`o01UpRU4InjO6Pufj1;P$L~M z!4q{2j|zi%)79;j7!N#2vkEr=TUFOpC~&K~unKc_tE#dZMQl|oSEGNnsS-zRED)cZZ&YNStF%e_d`qF4{A%Ehyv=%THKPkRrPha6mzR<>u`PM zR^jW-(GbtqV~<^?+HWwcL9E<>J!P57z7c2hWoqL_wDP?gzsbz4iflp$zE@$J%)A?bW~xcM(DkNzw+s8ZsrvneGnJ`c{$+aO&)7+Kqj&gsj)D+FcEjci>ey~9 zj0?(l56+SoRQEj?#|!uXD5m8M^?47PGD8LI#i?_K>b4h}Gt{BIW)+CU`^@SPA^WiA zW~k-+u%KqBZ~HLRGw|^Pbj~idV82-g;@W-`y-F?l8=X__0LB{*2o50EDz)zbX5%X5 za}Wz?l^SvoGh>yya}a&BO4UAuQ^zXx=OHv}mCAdV#e&0_BCAyVBdpTn2=>rb>h=+a zza7Q?x=O7-%CR(mv&!JV%|JlZ-40I+96W}bG~Z(wm?i4Q z4AW^<fK2*SPeaICQ&6$nJv|;^QdO}DJaINk6N@o zjp@OcAnDch(>N8~Qi;xB0^Cx4&tOkmqk_-k91(X0S2=4`hO;<1GU|&57Pg#)y{F(S z7TtA9bvTDyr|?Duy7Hx}a2~%2yi~)^V}iX@ug_y@yi}1Fpou4oE|`_zlQ`Q&oKSYF zHW$sD5W_FxOtM?;x`@4JxB7O)ETT$ZGV37R`>I(_UA$zLLip8Hv#hFi8AH8Wxh`W| zcdM(HajCXjd0v4rTxii0y<;LfcI^`#7KLqZxBBCXSqv#CIlH=cg)2kxeSlE%5C|07 z4FctrQ**B2c44<#a}AeUpVawl=&I{#?hV`oMPJ7a>UA~sI_Bqf_3egPPRwj`Z=en?sRIOZSAjsr!Vt)l9|G!B zGBiu6Y`3r!zp4wjaHBi?7Hs*d*4)DSU^KzwhH7^k%kqZ0bQ??IhAME!jPWF=S4Or& zNz~NUyZqIOJ27GE03`;eo{XkbZ?c&USakBX@f z3&VUzJ$Y(Yoat|+S4008nG9ExcT}P{PG}Q{^Wz=0;2Bo@{WzR|?x=E4%=MmksFWH{ zlBl97M%Pkho|?5Zdh$Mrx_4qu9<}u;rW6+(34vltsIkw?bO@uY7P^>fs_b*K=6}k% z{2VRcsD3=hN#VGf_yP;)xccw{yU}r#>YZ6qO@4{J@3;zkXXa90UYcDzkCQEPza&wi zJ;qm5YhU5qa$FUBZ|1?T%V@-LHR?SEecfy9P{)gsAch!kc<|>HZpG^gE?mH~1_64)_uA2M>7Tr}}zhHfH87^9W;_=dtQLI7)Z$WGLZHArs^Je@9#0#4salKCVyi7kyTn#&h`$nB5fIsvSoI-#C$Z{4z}|}LYZ5L8R)qn< zphD_^r&R)BSXD`t^s;Ir4AZEEu&O|+WPKyU`}s$O_v%Ue)jAcF)XIZp(I%-?^FKD; zOlqY^%E645k4m1*@<9oClUa4yxFTw0GAktrEX<1537hH0%<>R}2Luu*xYv>HORN@?}*B%dNiliHqo ze|eSC8h{k+6`^W~kCj1%rnXY5XQ`~bS}|u`Pqk(DocyY0YIF{-mSXDf)K(FMKcP zRUP5_S*PA+~(^!?Ym{nc5e9+Oc>XZ-qF;*4zwdz3(^|iv1aLMnZ)v!S9 z&Z)9tLB!(YWmYuA(QMXeh!8((EX0!HR!KbNZ&gG1b8!pr2KsaPcm1t#K&MJr1=OtU z7`0gSEW0%dVp2(~s9GLibwKz>Ned4A1FgIW{~l;H(=9sS@>D(Di_57DIjo9EX_~_d zfyh_VDz9R5U^&LB^`)#Lszpw2@UWbiSF!3#PHQkk`_d?WZ7wXpSoJcO6|S4oXN;;; zz{*OiKE2xPYo%6mb6eSY-7gPYJtU9S74SzHlDz5*qRO!SHY^Pt{1jK5jHAXHI%D}mHR)Ve6@%bjARTijTuI^kXljH z%7ie6w=D!lwg&_THAw9(W~D+HBkQfqVpbZ2F}%2!39?2YIG^q z!^lxV7_H)}Old0@!Wgnf5E!ij>TYQ(Bf{vg?0Ej(@1*!#Q ztjtKjXa%Z&%2+uO#*o>wM@DNzuvGvl7%f@@7${$rp)9u#qeY#Efhwo&mBsGMqqPhI zBh*D@FOQv{hY8=nDQ^`+7-NND#xRvqn<`jkKrmKAAuvocJs!=fggyR|YEual;E~!= z$r=PvsIoN!;&5fFKWmgxF~3>4J@%>vzgcZ`dI?pyid6;SK2>1RP4!O|D^IGMSk*Xx zMVI08S9#@8!^)&`SH)bvsTx$ZhIx{Lk#w`C8anJDUN)?T`EpbBu4gq;hpJ&?ys3WF z!{TmP9UXmBjjeChn^nWAtaI(%?Wu01URO}{tzq@j=*1qawg&53sv0$|iOA5sp*2)B zt%W6bQ{`%8RZ)*?SpyLct8E>C_`Na8?otPv;Z1zR(TY^n>SC?kRO9NRnKxB}5KPaT zszQiW4g%(uR9ix;G8!t^wBrD|aFf3|tKX z+j^;rjd7-81D8Xa@e4AQ}(f#|HTKynOHnW;ROl)SkAW}59rb1lk zf))6nIcDu%)jHCutERU=FYZ-$BCX1JvKYI=Ue(>OnyBY3aXQ|sGIq5>)!|l_fpDSL z)@g_@t*zq_N7`5yAZE3-wnEfuhq-`a+F9p3_PRHQ;=BvFx4o4BQXLGloqpfBtZv@*lF#_AiUX`ye27G2`t0uyCI$J{^+IGRPa3jV- z!2aQs7HLg_fYqxZVDLH!*xFcS=w{XT+)I;b;}uVQKWI}c^>;U`F_OKza~0>iV_Rb5 zORM=ktO^LD1=W;)PpcckXhv7Hf7ioA>RV6D_`M448KJ)S!gUO{sf#+^8{0fLth<^W zg-vCz8?8%9Ekesys*TY&0deaT1R7RX-HBm@*7fl?pceME3PGIdiwhEtWm4ZMWv4nY z*lMTF_rpH6Q@!nn<;T~Nqai|u;Bqqc0Gw=gs^>$j-pX?zF1>cDo&zlf;X4#3h|<4X zJrQ0w6c;Y>2cgY7Re@pHuV)OhN+EoD5Jq~ZIy(%TRJXyLjQS=3q3}fzDAx^vA{(j? zF6?4l?obF6IS~S-HHAPy__cf(rXQC!*W;s#91i-M55>j-51sCtjU>Gq?lHx?y$j)b`%RsWIJeu&!RtOjb#D46w8-5O;L(D^sT_rkMk z+kMo4(HJSNr#c?d#O}tmR8YOf;8NR1&uc^1=txid87B7|Bdq*KPeiHVxYwGDFYZyfL$z0}z z$rxd-p`z+G1!vx8YT{~K952hf!#lolP<@|bHU3XIqo-Qsbve~k(rIWQ7nW1io`%cd zYYHXiR+FY#tvsfwxM{d#nx+~~#|6+dwRpPK8={cHMZj+}un$gCQ)XBTAqvmLUBWcA zZnjlR1f1a_x5&9rKC3p*wSqj!i=L4$$V+|q-Bn8!pNEyfc^a!r^Dw12M{})Q zPz|4tp}MMk7h}uySb&S0tIB@?uA2E?y^xx>0J|gKu;)`A3o-Yus_qM|RsXqluD1yN zd{wPoWJN)stdUx;mHM_AJNi`>yu_O4aZv4Cf&DPkQp+FV(o3!O5ObDVBOtOZv-*0H z!#|^WdwY5vS%wLDQ1x7iD~$Zh(Z>hXz~$KMS!-Y$FZFHO^#GM^1uEc?%%z5{uu39~ zS~_E-{a11xS=s-SzuqdGLZ7K3>v7%mWfjhr&s2`pxbEedGC>zpRMlC7 z>*il)|M4}LozK*g_2~WjYcctrsZDEfMtY{oti!qCnL4up8_Jt?m`+@LF9;}ihe)*n zvy=7msRbKw8^Frl@JEl}jaD}Hbja_*jhbP+rdNLSYdMI$bz)BP2LkeT95g;Tpi61Q zzBnoS1PjFDIuG5HFZP8Uf|&!P6j$OedsG16Ii(U{NCmnFM@j${>q?H*FYX6%gHy zfv5AR%2RbA}QEn$BcUO2FQ^49W;dvYbI#0gcu$C@sy* zWd`Kw(s*;2Nfl8Ud6Gdj0rb`r2C9YtyKS`zJfH5qhT!{NSgU$ll{9q6%pk+L^ysH4qgF$xzRTDDkDWG{`2E7GL^JLJ6*B`f@Tpyx8 zVnpReQU?75WJcL7GU0muGhj5Q=b7Dy6+g1AhBv;m03-JaXraMD0hM$CM;zdCiah2Jr;w8Z>_W~ESV~qTXu;K-7m!YaOAcVO zt~fE{Swq&pE?{YK1~&^Ob=gH`Ey?7LsGKUp;GTd#OEY*V;E@KVfDAgz69I=ccrIXP zIWFUsfcP#=-U`{Q!3P0Rl{n8Q0rx8~_$t7^B7+|S8fXx|u+w3NE+dhE#nm{AXJI=3 zai5N<$|RYn%+@L?1q9b*jWhz9)?kocz(@@;2?)|zyahbhz*m54Ob8d^F9dJf(HsgC zfETm~kW5divI4p^<~$Vy zoYtVSfQTlnQB}Z64QdD|)RZ-9JHTb&FS2Awh(nAz%@{Ngut|f)0{oh@Ml%8Sf@mqg zUJz{r*h?T(fV~7<9fa6RAY6dG1iA>Ymq1qm_7dnJz#j140_-IaEx=v^{cM2s7hli# zfudqBfx!aoB`{Qgy#)RcU@w7D0_-I)R)D<(CJ4|=z-3GpVlRPd0_-I)Q-Hk$W(%;F zz&ru=5?Cm}p7Bcr*b{!a2Jm!E@0ZdHSS2dos^J;Eo0^ONgE4sQ^3A3jubX*97?eL%3O{ zk$dL=w&H^mGkW*sIXZ*(vmjEX0?fc^rupJgymz`%111_|hSp1}|TF0P}~1tvp9W$Q%-!v)YQxM=za z0Y@$~7$v~#DuXcswqIi~PQd3|3?>LTeOurEOcK&a-<3`waGQEK;x6Zz?f`E6OebdK zjpbw|Am1Yfa|FTE1D`NhCV+f~qtq1wwm)OA zN$+ z1Z4fnV7CDBD2@X5I>2Sr`^IFyLyTt{928LC2WuP_&^umyD&(ku01pPo1e}S_;Dms5 zcmWw%P6;4K&9 z@?KOnWnl180Qp!)o=*ajWMc3|z(ftc2}qTNHGT-_<;}nYFQe-2fCF~^{UHI7mDI!J zNDI{@b^tfl(}@{b135XVfT9H$Bp0wvgOma~6=aRn0^%29kX8d)f88`mFDgOBSS6!? zR6z_f3!pc%VW+nMa`O)0BYCpGAJ!zQZ)v_ z0{*DZpqzjUH5gP7@U#|#N&;eQGx$xw___?L7EM~xHk954hoRL)WmzKzH3j5t%%C;{ zcl)DwBEj`9wQ(-|#UpEPN+7^(?Bm4nia0072squ3K|cZ0`ZE|HVDtb6zYDlBkilR9 zwFeP!87?8@9UoQ<6F{$*1N~deG{y_C+d7HBQ~FGh z@lC5t)x>W7bSGvUfD3(;FjK(52@I5guNuq|uwf!=%o9*&5`zT-UTCn0ThFfbTTEt^ zC8BajgJlB9`92I;A%HyY1FRB2Z@>er5kT+01FRG9$6N**9N;pZYqH59M)P^BvW36} znjH`KC#FKS6Sz+xRIe$w19-aZqS!7mTG8-@L^vOa1?+p9xn*#wSiSG3D~if!4(1i z+ZbFEFiwMi1jOIY8aD;dyZWf*HUa)9K+T&BVhOm2aDUvF^pL%r{!qZlqYNGksQWhq zOTae`o(PzCj5VGKsC%5j3jyC4xQtgq=wk&i^o@YAry0BxFzzga4+6%YXYj9pP8S(` z7Lef*gRcS(TxRfH16qHHZ!n36w|#X7_0k}|fEyYl6kvB@5(0~9&aJ$~Wh8X~cWH7b zX8du7lT!*9evd(Fmk>|*9zg5U3Ls|$0O7x=G7E@!%)nc~84Y{{6gOET zn*&_NaZUUkV$6NQDggr0!hHb@$su5X2Dt=~g9B*f5kMbo0LUkx`b!1{1nkqmRY*uq ze6s<0iU^>OH~02;ZE}%wB1}g=S(+Xr+Er7gL0IU@d z8_Hn4fbcK|8*P9eZ^(HC@@y8B!JQdw74R&Q!FBwbI|W?n$>1*m%X>4} zBcMbd0xn~pkj8yk<$!?JzcV-_puUU25dj~EGx%FT&?pAS1>79N;G}@ZV;P**fX+XU z$1^!ADn}+TI4|J&BnB4+q?y9tvViXzToo{F25Vdw&~h$=8v=UH2f)qCEg|HL10!)q z!2LxGVg+BaR4voXHLvzJXpo)FC4;OFF}S^ z0vfGnjW+^vY+&$Czy}RJ2pF`DHU1S~Tk(YedG|2hZD$SFcZaZD@q+%7o`3-R+GzSG zXGrV-*7tN`Mva}EoK(Om4U!A+g6j{oE2RMP1wz+2#CkOo|Y6a+Jix90pz3vS%L*L zOUR&{fJ})QRM3FdA9*W5o=T!Z-bny{6F`1R0ICZ3q(OB7Lz1yZO#uUvGpH>fYYGN+ z1w2a$fb~~T$d**B(m()xZU*%>5^y3dgC+vVr3o~e381ge0JIQ5ADRJZC19pEgEkEK z{)c>>K&73ikbe_^_5z9pFz6s)Ne%{`1o-4)5Fy}oZU$WhjLXNs5YVy!K2wWox(Rtw zkX3pJ@GQ!pmw?nI8AJ(47|bAA!20qG`U#3UhJbwS7|ar|v=f8b04}|}i-t3qD=G&xm@lAy1Zyl5a7Tm10=jo* zjimyp6Og<=g98G{u?!43B!E6N z1aL$^LzmuHj}h6y=cTEva?%03(Vli{xg!0}Bu=PI>_&^oR-ODC?asU_b z#fcfE@LV#Tf4&L1jeANI`9na+-wZs8J4J?`V30sSk~0hv2^e*WK@tJB6-f!4C$o-S zV~rFV;QYgOrIK{|h!dT0#WKhu;3EDui88VZDEokc zuS`K?wmbH7F$@9K(-l$_R+lpsav|Us$7jaa#Y}*w5dXR1_7L zMyF8K-M=@>l`n zG70+Q1?;KIV4{Fq^%+bSFkDwL4Zvlbqe?v+v&u|TA;6U?CuW?|$#V!`gTc?>tyq7) z16Y5d6Ejk@;pD{vnuao1N&uTV{wUs&!3ypPB3!~MNq-;C>1zbUc4n|nz@x4VHVF99 zgTW>NXL>W(BA|X>2HONgbaXMar|bsCdnqOxQX0-mm9a9hBg)ePnJmAAgC#N~U$q6)|`#9XWkepXpCBJ~J8Wa>z z^B>kIEFjNK21Ny|xy2wzK-Jp}N(gBBfI%q+y#8KmQbtsUJYDRz2x*d0&ae15F+5aC;w1gU%pe} znvOwZ0e@v;&{TkJMGFFM-2dY5NBZaQ);7^Qaa$*54EEs?LIrHi&LB*{?wkxd3OJXW zK{$bB)bz*s8FX;~H{GC^i__B-G)6jriyZC5 zjGK))c`Sjqr0?67!2}1e{v;=6C%cTPL{3r$eD9DdnBf4^cC6jHU61;}Np8`52RgdCugrLyRh~7+e+b<{g9U0*sFgZV1TynZYdrKR<$S*8yzS zJ&LU&tHbmY3J*)r{Ra_Fe=O;(<8fD70+J_Z@I=7ej0~O$*q@of3jyzZ7`zhjFeigI z0{Z7p!q*?~gpAC`Djx*gEWqGj0lyVw@L9mhLJYnN*k72zcL7a{GKdFfb-MNU$}@;h zz{UO1w<42-qGESY5(2nW#iNT=StF?fxWke=F(XTLPEIMn)=xv=G!^i*A#0>}02h$4 zB<(+({=GJ*XQuSElwlt})`#hw)d8Hr*NHjF{sizNKs>__pYwx$paWPxrxP=#HR0sk z0?xG|;4<RkE8Wjml&q*QpDW?P24mIe@!xh7&VdyE$12 zIK7L(TmiTKVz5Af-);tr1tic_xRwdAD_tqTu4at@yPEX^>}oa%u&dcBz^>*`0$-_V z9d*4sZQ#;d?_d8--%9|$#9>_=XUh*bfcxT*6Ejxt=j5XT;AoLL^SFQthZ&p_5OI{j zSpqJuV~MWgf~eS)UM6srtjJK>i%PvFU{P5HHwbJbjZt-I5M%Nfx1|)jqF4cTJr4*h zp-hilci5K44&l~WPRxMoMymFyfY(tWKRK^A2CQKpp(TQr2u$< zL=`S0jSx71q)O8Zuq(|Zz^=wyfL)ER0J|E00lFF^kN}Uzwg=Z)-X-Kq0w$FNbkt^5 z5n$U{U4U&_EdjP=bp_a#)fZq}*2n=a!#1s{Lk!!r76NR`S_`l(Ye(QN&5m{6YrC4AGQo2cYY%_=xW1tC=F8T{Q;N9l-rF zlVUE;P{o(iXFKUew_FV7322_1!9oG=G*}{_V-eO^E})sNbd`XCC0JvvfVE{j`TApn z5P03BnY>v*nQ{!a2`H+;4gq#!cL_+TTfaxZf#0}{{Q`Dsa0tMqd+btGRyisv^|bNF z1=wA9N`T!(X9d_@bU}dKMVAHGU35)AvuZA`_l6L=k8Ts-%`&JHYs89%-9-;XV|@f` zJQfYRi{b>>J@QO|-6Jn~{__m5yX1|i*gf)|z-5|LH*}Bu>j0iZpPiVK{EYzbbI0{Q z7q7GpSUt6hnNz=J4_y{7N;y;wvh)<^BTfdIJgYpbRs()La7KZImp_>i}+SekW%9Xu-*a1lXs*qNVBn zgNrQPj#Y|_N(EhHDFOCO2o_)ue0c%3S(OC9At{+vMSy*!S6zV1w!D@Q+mN~fY(we` zuuE-30Dov?-=tJcQwMPCn>#V1dmpxnO`EXX5C@OWdWm5#eT`G0*bOB9=GMFVGkuGD7fJdWPW4?e} zT4RxbZmtomvQ)@zoo9uB#*$2a!yG6nG7xp zc%#7;0hM&A*9BCZ!&z<$s5h6v9Rcv(ORc*nz+Mv%T|(?dfp53jQ^H;oPXyR&;<*5O zO}r9duZgz;>^1R0fIR~~IlyJui{h(8jDr2x)*k}wH4z`4O7*F+)#_L}e%U{BU$ z0_-)BQh+^KU1@~aiz2-Mdrf2#V9$7O0rmv<70^vjXMX|qbPg0?Pv=}V!1}Z2L|##` z=W_u8_IxfZz@E>=1laSrgaCUwmlj}8=duFybaojPgxK@BvH*KNR~2B-=Nba+*;`wH zJ$pk0*t55R0DJZ})_{Khu%~Y`QL(3QO9GE*gDQkZ&{=?8qTwQf2X*kc&T-DuT~zEsdI_)#=|kWqWeNJ5v-EWUo7LZm8MQZX^6vuR zOqW*S5CILg>P>Z+ke%CDWrTp)I~j}?0N=V)$T$J;tV>{`fG>L)Oc7A+AcN@wu4^!> z4DJ8i6t5$!GDlPnX)s?vm7}b&NI-tw`lSNw?pPtf?ts+-><(Bbpn+>Em%34i-2qz! z*d4H4fZYLZ0d@!cCBW{0y#nkGI3U38fWu|z`se2k_*+!$4mcse?ts$*><&0bfTvJ` zhuoNp4&eE4*@-zb?3#f5C-wcu4I%SSa|yQv#GPdjE8w064+K=Y$Qq9Yl-3|lz>X`d z@k{{x@Y0NV3E36xYBb*y*1UcUdE$ zfI)Zl{7)j}ODwA-74Vy`CWU|%_gN#gfMBhWPQVupG77NAC5r%bJavFiFwK7+I=kcj zM8)p-00DN#=M-Rfd>#RI$LAMdcYGlMcE=YL@W7=zzPJ#(<4Xy!J3d%|-SOoG*d1R< zfZg#`1lS#4UBDnc619S9{&NS|9bZ>e?2fN5!0z})0_=`&D!}ge76cB^2Dwfzh}I6^ z1<}@tQ8M3uwvt8<=sANP0`k0P&|AQ=w+x~+ zp!1KvR_P}yt27uWV3am%uz>R#3>9D-{)YhDvQYwT%f<>|%UtksD8x2xvH;t%X##A^ zW(u$^n=Qb$Y#xD+GzR*onD}v7fm=mN|qxMAPC}DZs5uSR>$VJT7v* z0621{ESm&;&|s^8rn-zj1;CFqWpV8kVjH?!fNj}60(fW@KcFY$JO>FpBLLUZRLBv@ zVq11hfNj}H0k&mlY=HG|n|5AQY|}0g;CdhUaWz*Rz+HIViNXIgC*Krc+j&PoTx!<1 zC*Yn24|)A@9dJxdO)*8qGb4j10;09Xa{)KCS+4}xcD^OR{bP3GGCmN%y2O5=?=TzLY@j4^?noI=2w+ze6!xO53i zG)X5a2Me)EMgcy$jw}M~N__;_)%Xdps|gTb*OF7f!9p&sCXW!in*0RLQrqH{V2wf! z;I$J#WorA5Oou`C1r`mKXu{EblMCHtTl+ zyfB^_oMlK^I{&fdb~VE!k6q0O0d_T`rI0MWxsY)V;KojHVuro=CJV4DohAU=7oY!T z3W@E(Ma~v*peKWQ0*>e{Z=rxR+KMFtY_paNunS!!z%FzxKswzYcBva!MI}xa7tCJ8 z+u}7yECPt5S{%~iu@-nefzrNc@mY%_S}f9HsTP~GSg*xJEtD2}w793mUs}wig@Ll? zXmMMM{aWnPVzU-2wRoY$axJ!K@vjz7wYaavcP(aVu~>`4TFld8KK)?~)xXx!6TFcnv|n;X%-^1rT&c z2SFFM5OgUDK^K@1AGM&9E@E{4f}pJ)g7#Vn+7J>#K2sG^##P1_>4v6^E00gX?MoR~ z%4=2Pc=0CB$b;{p6-^bFUHPSo^YU8pGt1|NhuQwG;y1m=9?#_1pEmqVv#B~;r+X1z z_Y0mGGK1;HU+DB#yP2;2g{pd~A58K`j6FG*^1 zU0ov{xck4+*_7`Pjknu4RuxVYSIFznpQ8b4Od43Q<5#vnOvIyz{YC)?dUg}4h zxMU6}m=+n_zhuZzx_D?2$2dIY$5z<+&t;F}=IpldUbQ)GTmdgU`o~&(Rl{^~*{8b> zd8t(CP>CJ?aga0Z`B|%ADm>T7nP>t`nUG(lirJn+ZTP;;Gc7u0-!F*;D6t^%V>YMh zGG#)5x|cpKYx>gvxA&-%uF_<{Y-p7p%{%xjn?mgGU)hWB9c)?61r(3)sz{SzfPuKS#4oe|esV=Fu-yrCxpAQ|-=z z>B**KLt~0LoqqRM)zD4Xxdc=2LGL(!RmU4u-~N>iCU*Z$9(V-I^Vu?MVLwSm(qeB%s93|#Qp+a=nLD92x zXhhKfIwYk-XF3>kh(oH&NJ7D2I&`N)B|4<0!wt%OfeyRq@R$y*DIp^rUQ+ZK9p2Mn zKOO$1!xuVSqr)CLR43&_6!M@@Q987sLoGVwuT01KbVxym+obrN4s|J-g$_CCP@E1; z>Clc2Z%E}h9TK`5`NXyKz?Wy&`^FVaOy{c!foh9yoVP0U$n^4r_X!CbwjZ%axc;XcK(Y{tT86 z(qh%uY;k!K(|K(|Hr4Rt@@y!BPHz*^sWersbcoSeZi2r$*v#@rhMj7XUtG?_w8>4# ztF{cc@+YRf3knSnZYPCl_>N+nAHK0u&_6CareuBpxWI&TQLGk^a^+DA{o{Nx(%E>h zJ{v<&iy)4x^Zs#laT@f^9#;k5?F`EvS27iyg^@-^L&U0;+2ev!k+q1CnGk!`%j|Jw zJn8g{*hf_)AnrHpEP$ER#0+t1)TCf{QqR@s=Xx=)ZbLv^QCN33ATB65ornixqOL@u z3iFQhQNe+6De?K6T7hxCIjHmX?IT&Jf5xR8`j14^?Z!IyxWKsS)$`I-tdTf14jjsy z&Iwh)H)}Mj=uaVcbtCSdi#3jt{)%!qaL8TH30=k4Wi%_+*a>B6;e`C#I-zCwV2p8e zbVT_l?uhbF$EJ1Bm9sGtUuV%DcIk=Fvgpt?e2+zkvM+Z+-S7n#&0gcvD>~$lZ?5Q2 z@*_@Y5k9h_nK!ewwjtVg%74uv{}MTfb2GYy6T0u^gqkLELL2dQ5!Q<_%HAg% z)RqKUvb!rieHv7*JYDk}rQmm4`^tU-ciKACa*z``0aw?Wy?_I29g2ZpYU84NbM9wN zYP3{T#=ZF<9Ll%I35|k>XU*EewX+T_g->T4$_U@hI&?}x3htYk#TaL2c&MU&FAAsz zg?v2SQ+qz!P{5z=NsQrezpOnuAA{dz4#gO2;G`KXtJKdouY2_TXW5%%pgR_0(IIGX z=r=f2HvV$&ef6w~kKY_h+XlzO+7WWV6+0AT{C*PUsDtMp`Kni?{4=>brFia@)9*Iv zPlE4W?K=26oc3}k#t6SB`bGNrWO5%U_B<$M2D%|KDt(6*hsMFpt+C(jS^4?1VtMKI z$Oz0F-}aO`7QR%q&(w?FPH2)Zh1|Z;&o3A7qx&c$Uttgq4JhV>Mg~#HoqEahgBkqj z{>ms{6NE!4n%JQjV`|0tXi?bdV!mo~`8*li0q*BkXTN_*zuoW9;?R^qPAKIN3c2(8 zy$C6mfo{-@p;JIO^ckKWji>JM4PTtk8T6gf9>Kk#cBWWxtrPNrr$NmE;bM?OF~*if zWX05rnSE7*Dun{wXZyVvT{37Em2v3=v^bRLloL7!7k`>%KIeprUZ9YB%*Ge*)4AyO z&}a--c-qh2RS7kMH$2T|!pWTueU;F6c(v24GJMnN&}s>-g?l>9p2H2D4mE+dIS$1b zpW&?!E7%_^*!}g;i}u+w(A}xA65ivq-?{hj8fP4K_kQ?dOzMpDD6LZB1lnI*jIsGU zf}Qs2|HblaHu;xBI!^?cs|mh)Ek}9EI2=67GvyQoq%jP__{=9)xJZ8^zIX* zUxrO=NVm>L88{o#u8khS%a9I@f_otyig&{a)r3PK&4$2}kPc0`>x6RLr;z*i46hEA zX-)Uy#%H+xv7H9N=Z_8zgR>tUYTquQ9U5skAxlDax;pG@PbV~`H-+4@>%W?wI>QdC zp#!!b?JZ*^7Pzs)U8()6;B^_kQCd#?)S=y6OvXL9@y$Is+3O3LJZ8GX0$(=`4*Ebk zA2FQTM?`0AwK_BaeWF9zaVxDui*eg;d~nCLeZ8SlP5RNmSc4mD?Z#mjwhA4(ic1O| zdXBqb9eQ@p2{pytIPJrTTrB8b4d*sVuU-{tmew7S>dnyh zLG<&9(YY|xI8+g@=<84%-p$t`7hcNOp=?zt0yY^+BGv{fE98oYLc* zd*P%v`O_z(A76}~c!S-zxkT*D~zx^*DAfO7g{loktdqns2=S=>sNu3ni zs)POBrgL?V?2)H?Sbw{)yt<^kNx6`|;T@xUMnw(`?-WYa`j_|b(W{WaJW|pBZDN$y z-#^g5nUnV?i~S>dM)`+FL_~Is4DZo#fPcTpXx}bWW{*x`QBh$7`f&DMQQ>_<`^0od zb?U>lH`UTejoOSn{Z;+zZ|WwC=+-kVIu8^9ijIF0;68HwO{?}E!buNLVSJ}U}$&k!Ag-gmhSL=Kj zJwx>x(K=gQ4L*e=_kdE$@IKLz-NT~8Lwm)9Mij*_SkMkh=6;uC$eXc zQ0PQO_P5h>d4$xV^mOXp=(g!H*tDKUNHo#3y`nHM>Ry}I={!UFd4voBRKu3H_Ej(2 zyiSxTWSB?DI5jNzZMGy6Jwj$sVx=vuGgQ%4E02(6R7k4msIbT$p?$-;h4$&yEi&4! zW2HyPc}h;&C#FN6@aWJMp#$ucOCBND)c4@GS#sR)2)R!>$!s0EDzudzd4znWq_pbb zY%{&8U-oSxuOA*ECF6tnsHp?m`KWzm(Sv2;hqMAy*%!A>-^Lp~ufhHf5?!DoYZ?vNeM8gwY-I49H|?in=O3BL?FR0{qWbZ9pmG3d}< zcwo?>>u|lGL%ZN}L5Ir1*@6z4@UmcgHCPG%3L1vPuYwK*E^|Tw%bif7RZi$%_)gGz zo!3eT-V!vM4KE2g6uZd@x#1TphFY4R&s*J;R8X#u5f;!L!);%p=I!PpxFVq zInbe6@NZyjcc-lIc7Ki_`i0pj4)+Awb%8qUga#jRLUrMZK&Se`1%dHyN5{8OK`#0g z+IWr^{IzR6g# zuT$H{IiZtJ5>jyV`&n<@?`OU9>Jo%AF@89}>-73f{LEg7?a(H?K(AS%^iJq9UYR$x zx{LmJce|pWn{uAU8}Iy6x^V@sy6aF}ZVI^zR($VYHQ6%K%8Pf_jb-i@f4nb}JJW0; zCGbu;|Joj7+^Crtr>hBSNiHKRek|*i*nME_`-=5EuI%{q{_v5YlcZj?Gt@bBG|~wr zj&?#d@ancstvZ%M?hkuE1dnYDD2o&7lg$aG&E{bg+WqcYN~;ykwK!dL84irg+;Qt TWs-54Nph-T^5(6K*OveQjs_H^ delta 63 zcmeA<%hYq0X+tZcL9%5^xoNtYzHzd7lBq?SS#p|rs)c2up?PA8fqANtL7IhyL8767 TNwTrAnSr^1+2*Z`*OveQk|q=j diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index 88e05bb1b8e87a04fe5367e92486deb76c3e894e..aa947ee925428e07bb6ed5390bddf84db6d444f5 100644 GIT binary patch delta 62 zcmca|oAJtR#tlmt4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F SEt8DXOp;R#lNVoM%mDzW(G+d~ delta 62 zcmV~$$rXSw2nE1LMgE8ZQcfhx#k&P*0T#n8%{UW_V$t^!ytF${+1T~WHpGW^D>Z3t R(7t1sp(Y&8EKtZ3bp5Q06Vw0z diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb index aa00ed6e9..3c2c0a9bd 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": "2024-07-02T15:09:49.406100Z", - "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" + "iopub.execute_input": "2024-07-02T15:24:50.264127Z", + "iopub.status.busy": "2024-07-02T15:24:50.263741Z", + "iopub.status.idle": "2024-07-02T15:24:51.435602Z", + "shell.execute_reply": "2024-07-02T15:24:51.435065Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:09:50.628776Z", - "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" + "iopub.execute_input": "2024-07-02T15:24:51.438162Z", + "iopub.status.busy": "2024-07-02T15:24:51.437743Z", + "iopub.status.idle": "2024-07-02T15:24:51.455082Z", + "shell.execute_reply": "2024-07-02T15:24:51.454668Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.457364Z", + "iopub.status.busy": "2024-07-02T15:24:51.456857Z", + "iopub.status.idle": "2024-07-02T15:24:51.773985Z", + "shell.execute_reply": "2024-07-02T15:24:51.773375Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.803385Z", + "iopub.status.busy": "2024-07-02T15:24:51.802942Z", + "iopub.status.idle": "2024-07-02T15:24:51.806575Z", + "shell.execute_reply": "2024-07-02T15:24:51.806033Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.808623Z", + "iopub.status.busy": "2024-07-02T15:24:51.808291Z", + "iopub.status.idle": "2024-07-02T15:24:51.816291Z", + "shell.execute_reply": "2024-07-02T15:24:51.815728Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.818342Z", + "iopub.status.busy": "2024-07-02T15:24:51.818171Z", + "iopub.status.idle": "2024-07-02T15:24:51.820601Z", + "shell.execute_reply": "2024-07-02T15:24:51.820168Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.822635Z", + "iopub.status.busy": "2024-07-02T15:24:51.822329Z", + "iopub.status.idle": "2024-07-02T15:24:52.333801Z", + "shell.execute_reply": "2024-07-02T15:24:52.333291Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:52.336000Z", + "iopub.status.busy": "2024-07-02T15:24:52.335683Z", + "iopub.status.idle": "2024-07-02T15:24:54.133804Z", + "shell.execute_reply": "2024-07-02T15:24:54.133179Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.136490Z", + "iopub.status.busy": "2024-07-02T15:24:54.135807Z", + "iopub.status.idle": "2024-07-02T15:24:54.145523Z", + "shell.execute_reply": "2024-07-02T15:24:54.145014Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.147631Z", + "iopub.status.busy": "2024-07-02T15:24:54.147304Z", + "iopub.status.idle": "2024-07-02T15:24:54.151216Z", + "shell.execute_reply": "2024-07-02T15:24:54.150782Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.153210Z", + "iopub.status.busy": "2024-07-02T15:24:54.152897Z", + "iopub.status.idle": "2024-07-02T15:24:54.159804Z", + "shell.execute_reply": "2024-07-02T15:24:54.159399Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.161777Z", + "iopub.status.busy": "2024-07-02T15:24:54.161437Z", + "iopub.status.idle": "2024-07-02T15:24:54.271636Z", + "shell.execute_reply": "2024-07-02T15:24:54.271152Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.273415Z", + "iopub.status.busy": "2024-07-02T15:24:54.273245Z", + "iopub.status.idle": "2024-07-02T15:24:54.276027Z", + "shell.execute_reply": "2024-07-02T15:24:54.275583Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.277845Z", + "iopub.status.busy": "2024-07-02T15:24:54.277663Z", + "iopub.status.idle": "2024-07-02T15:24:56.177124Z", + "shell.execute_reply": "2024-07-02T15:24:56.176537Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:56.179941Z", + "iopub.status.busy": "2024-07-02T15:24:56.179406Z", + "iopub.status.idle": "2024-07-02T15:24:56.190491Z", + "shell.execute_reply": "2024-07-02T15:24:56.190044Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:56.192341Z", + "iopub.status.busy": "2024-07-02T15:24:56.192169Z", + "iopub.status.idle": "2024-07-02T15:24:56.274113Z", + "shell.execute_reply": "2024-07-02T15:24:56.273673Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb index cac09ab25..5c8935803 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": "2024-07-02T15:09:59.845378Z", - "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" + "iopub.execute_input": "2024-07-02T15:24:59.158944Z", + "iopub.status.busy": "2024-07-02T15:24:59.158774Z", + "iopub.status.idle": "2024-07-02T15:25:01.944133Z", + "shell.execute_reply": "2024-07-02T15:25:01.943585Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:10:02.562794Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:01.946518Z", + "iopub.status.busy": "2024-07-02T15:25:01.946245Z", + "iopub.status.idle": "2024-07-02T15:25:01.949591Z", + "shell.execute_reply": "2024-07-02T15:25:01.949140Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:01.951916Z", + "iopub.status.busy": "2024-07-02T15:25:01.951599Z", + "iopub.status.idle": "2024-07-02T15:25:01.954525Z", + "shell.execute_reply": "2024-07-02T15:25:01.954082Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:01.956541Z", + "iopub.status.busy": "2024-07-02T15:25:01.956225Z", + "iopub.status.idle": "2024-07-02T15:25:02.013993Z", + "shell.execute_reply": "2024-07-02T15:25:02.013542Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.015897Z", + "iopub.status.busy": "2024-07-02T15:25:02.015721Z", + "iopub.status.idle": "2024-07-02T15:25:02.019309Z", + "shell.execute_reply": "2024-07-02T15:25:02.018806Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.021419Z", + "iopub.status.busy": "2024-07-02T15:25:02.021114Z", + "iopub.status.idle": "2024-07-02T15:25:02.024406Z", + "shell.execute_reply": "2024-07-02T15:25:02.023893Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\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" + "Classes: {'change_pin', 'cancel_transfer', 'supported_cards_and_currencies', 'visa_or_mastercard', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'getting_spare_card', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'card_about_to_expire'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.026552Z", + "iopub.status.busy": "2024-07-02T15:25:02.026250Z", + "iopub.status.idle": "2024-07-02T15:25:02.029388Z", + "shell.execute_reply": "2024-07-02T15:25:02.028932Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.031230Z", + "iopub.status.busy": "2024-07-02T15:25:02.031049Z", + "iopub.status.idle": "2024-07-02T15:25:02.034449Z", + "shell.execute_reply": "2024-07-02T15:25:02.034001Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.036228Z", + "iopub.status.busy": "2024-07-02T15:25:02.036061Z", + "iopub.status.idle": "2024-07-02T15:25:08.385948Z", + "shell.execute_reply": "2024-07-02T15:25:08.385389Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c943f13df8c04e77aae4c7ca2cbbd613", + "model_id": "98baa2df749f4718a19b8ed6f6b64516", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06405b534d7c49db89f3d29b52da1f80", + "model_id": "697435a323a84bea9cc7bfb42e0a5e62", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1146fbcfe9cf41da81392df94520265c", + "model_id": "f7905c4a4ded402ca686067d0aacc292", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bd1aa12a83f148a6a04b7394dd645fb3", + "model_id": "4745e1e3131d4034bf9a596bc4fbddbf", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3840f91804d14d5aa30e594b1e1d7fa3", + "model_id": "48016ee17d6b4133971e8d4b58046f98", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4522cd2764fc425b83a55e8426ac45e2", + "model_id": "3081197287a941cea971e1c24a1abd42", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a72b5f7de474f75bb371e057f0f1914", + "model_id": "f451a7bbcccb4c0ebd05353c6b2b00d8", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:08.388716Z", + "iopub.status.busy": "2024-07-02T15:25:08.388336Z", + "iopub.status.idle": "2024-07-02T15:25:08.391310Z", + "shell.execute_reply": "2024-07-02T15:25:08.390833Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:08.393273Z", + "iopub.status.busy": "2024-07-02T15:25:08.392947Z", + "iopub.status.idle": "2024-07-02T15:25:08.395525Z", + "shell.execute_reply": "2024-07-02T15:25:08.395074Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:08.397396Z", + "iopub.status.busy": "2024-07-02T15:25:08.397223Z", + "iopub.status.idle": "2024-07-02T15:25:11.020595Z", + "shell.execute_reply": "2024-07-02T15:25:11.019993Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.023619Z", + "iopub.status.busy": "2024-07-02T15:25:11.022845Z", + "iopub.status.idle": "2024-07-02T15:25:11.030391Z", + "shell.execute_reply": "2024-07-02T15:25:11.029947Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.032276Z", + "iopub.status.busy": "2024-07-02T15:25:11.032103Z", + "iopub.status.idle": "2024-07-02T15:25:11.035851Z", + "shell.execute_reply": "2024-07-02T15:25:11.035397Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.037827Z", + "iopub.status.busy": "2024-07-02T15:25:11.037491Z", + "iopub.status.idle": "2024-07-02T15:25:11.040763Z", + "shell.execute_reply": "2024-07-02T15:25:11.040318Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.042952Z", + "iopub.status.busy": "2024-07-02T15:25:11.042502Z", + "iopub.status.idle": "2024-07-02T15:25:11.045617Z", + "shell.execute_reply": "2024-07-02T15:25:11.045190Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.047754Z", + "iopub.status.busy": "2024-07-02T15:25:11.047338Z", + "iopub.status.idle": "2024-07-02T15:25:11.054029Z", + "shell.execute_reply": "2024-07-02T15:25:11.053482Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.056131Z", + "iopub.status.busy": "2024-07-02T15:25:11.055786Z", + "iopub.status.idle": "2024-07-02T15:25:11.280111Z", + "shell.execute_reply": "2024-07-02T15:25:11.279635Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.282450Z", + "iopub.status.busy": "2024-07-02T15:25:11.282132Z", + "iopub.status.idle": "2024-07-02T15:25:11.457525Z", + "shell.execute_reply": "2024-07-02T15:25:11.457055Z" }, "scrolled": true }, @@ -1058,10 +1058,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.459928Z", + "iopub.status.busy": "2024-07-02T15:25:11.459576Z", + "iopub.status.idle": "2024-07-02T15:25:11.463148Z", + "shell.execute_reply": "2024-07-02T15:25:11.462676Z" }, "nbsphinx": "hidden" }, @@ -1105,7 +1105,56 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "038891c782ab46f4ba836914abbfc5ce": { + "161fe61da4db4f7e8074ff8edcdcb759": { + "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_21cc8aedfd874b128773266b548130c0", + "placeholder": "​", + "style": "IPY_MODEL_388e3769071b4755bf2c6825a5643904", + "tabbable": null, + "tooltip": null, + "value": " 54.2M/54.2M [00:01<00:00, 27.7MB/s]" + } + }, + "1643bc7cecb74bc190903adf9229b34d": { + "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_7aa30d069f444999834643e94fe81c9b", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8429e83ba7114915933be895d5f6fe3a", + "tabbable": null, + "tooltip": null, + "value": 54245363.0 + } + }, + "16cb8599d6034cd1b810cbf99f703579": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1158,31 +1207,60 @@ "width": null } }, - "06405b534d7c49db89f3d29b52da1f80": { - "model_module": "@jupyter-widgets/controls", + "1a2f8352992a4e8d8667c62f94d7e0e0": { + "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_5316135ae1ac4592a7b4239c553c02c6", - "IPY_MODEL_43953f2b89bb41408d6583c47201913d", - "IPY_MODEL_e0adbaa3ffa64032933136aebb9a1f7b" - ], - "layout": "IPY_MODEL_cbb206b9c0d64fe0b479fb7cc9df570b", - "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 } }, - "07d79636c68343278e1f779c27aef268": { + "1f83772d100c412884fc22f69ab7e498": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1235,25 +1313,33 @@ "width": null } }, - "0d5dda5aa9e840a7969a3facb0c17f1f": { + "20199ed9391b4fd3af9ffba1bd110646": { "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_b1a6862286e144a5b4c7e3b53955c909", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9588d449e7b24e6db9e90daa5078369e", + "tabbable": null, + "tooltip": null, + "value": 2211.0 } }, - "0da07d715efe402083873e0661e556ea": { + "2059ff2a4cfe4817941404aaf9b91a27": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1306,31 +1392,60 @@ "width": null } }, - "1146fbcfe9cf41da81392df94520265c": { - "model_module": "@jupyter-widgets/controls", + "21cc8aedfd874b128773266b548130c0": { + "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_59eec5b7926b48ae8d162ecab0db1ec2", - "IPY_MODEL_c18be04c279b46b48b79ee15e32435e6", - "IPY_MODEL_e32cd0a87f0f4ad18f72cc198abad4fd" - ], - "layout": "IPY_MODEL_505538aed9b54bebb4c3f0107daadf44", - "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 } }, - "131d5d7c7d344158b759f1061ef8d42d": { + "229a059bf66a476caf93f93617098541": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1348,7 +1463,7 @@ "text_color": null } }, - "1740b9557dc0484c95a45ed25a63585f": { + "29ab7c4b24694381897b57b84ded9af9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1366,30 +1481,7 @@ "text_color": null } }, - "2694565fc4d4493db595fec20b7b65bf": { - "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_f25b6a55df5d45bc80be73c91794168b", - "placeholder": "​", - "style": "IPY_MODEL_e970db685471489481e8d686d97b3cc2", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" - } - }, - "2df9eaceb2e14e8fa3a19b25c7d76f35": { + "2df28735813745f899e2ab60996be667": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1407,23 +1499,33 @@ "text_color": null } }, - "360120e496a841119a2a238bfb8c7631": { + "2eda31c15639409c917ce8f30f06fea9": { "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_499e72220e084b96a2fac6d7d24f3e36", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d398c27b16594cdabb17dd18d91923db", + "tabbable": null, + "tooltip": null, + "value": 391.0 } }, - "3840f91804d14d5aa30e594b1e1d7fa3": { + "3081197287a941cea971e1c24a1abd42": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1438,42 +1540,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_ef479a0478b644bbbd209856e893f0ed", - "IPY_MODEL_d9d22d6398f34538ac40fff96fa8abaf", - "IPY_MODEL_8b024eb1e7d34d94b81d05d691a3874d" + "IPY_MODEL_c4ab79f47c1f4a5fa7a952d2f4243909", + "IPY_MODEL_f717bfe4b6324a74be7f067e29025df4", + "IPY_MODEL_cfd42f9c1e5b4b6e957253d074ff0a96" ], - "layout": "IPY_MODEL_cefce6f533124c4e90d7c277319c65e9", + "layout": "IPY_MODEL_2059ff2a4cfe4817941404aaf9b91a27", "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_ea436305a9d143d09b28e8d32eb3020a", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_360120e496a841119a2a238bfb8c7631", - "tabbable": null, - "tooltip": null, - "value": 54245363.0 - } - }, - "3a43140a399d4c6b8942f19c238c99d0": { + "3090f81fd63e47748b48b354dff579e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1526,76 +1602,16 @@ "width": null } }, - "3ac7761e49b1406fb28de06a9a09ffc6": { - "model_module": "@jupyter-widgets/controls", + "31425907e4ff47c1a17ed0a9935430d6": { + "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_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 - } - }, - "3bd0776bd0b840e8b2a1d15afd1e27f8": { - "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 - } - }, - "3d25676d34544705a2bcbe334c412efc": { - "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": "" - } - }, - "3edc8ef534274f15b48e90d83188b494": { - "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, @@ -1639,33 +1655,7 @@ "width": null } }, - "43953f2b89bb41408d6583c47201913d": { - "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_b01dff0aaef24c71a79429be65e9ce07", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3d25676d34544705a2bcbe334c412efc", - "tabbable": null, - "tooltip": null, - "value": 2211.0 - } - }, - "44b0dfbf62fd4d088800332e97908228": { + "31829b18848e480eaf3d67d16a966abc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1718,7 +1708,89 @@ "width": null } }, - "4522cd2764fc425b83a55e8426ac45e2": { + "388e3769071b4755bf2c6825a5643904": { + "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 + } + }, + "40a195a274ab4351b4d37e7880f857e7": { + "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 + } + }, + "40fe431dbc09475d9cc4e82668dce66c": { + "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_31829b18848e480eaf3d67d16a966abc", + "placeholder": "​", + "style": "IPY_MODEL_b2d0af45a331440e91bb670c810aad25", + "tabbable": null, + "tooltip": null, + "value": ".gitattributes: 100%" + } + }, + "41c9e037f3074a5d9b33b7014d85cdfe": { + "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_4e0a66ed3e51494baf8c4960957f2abc", + "placeholder": "​", + "style": "IPY_MODEL_2df28735813745f899e2ab60996be667", + "tabbable": null, + "tooltip": null, + "value": " 2.21k/2.21k [00:00<00:00, 435kB/s]" + } + }, + "4745e1e3131d4034bf9a596bc4fbddbf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1733,16 +1805,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2694565fc4d4493db595fec20b7b65bf", - "IPY_MODEL_3ac7761e49b1406fb28de06a9a09ffc6", - "IPY_MODEL_a588c0dcd0974f8da33c7bdf3d4a35e8" + "IPY_MODEL_e7f2051c6f3e4cb4afa29879fc4e25d7", + "IPY_MODEL_1643bc7cecb74bc190903adf9229b34d", + "IPY_MODEL_161fe61da4db4f7e8074ff8edcdcb759" ], - "layout": "IPY_MODEL_ce7bece14db441b3b9a7a133c7e71a07", + "layout": "IPY_MODEL_e1ad008614fb43fdb5e809f4d8511a82", "tabbable": null, "tooltip": null } }, - "4cbe66fa6f75404e9a5e6814de5f2203": { + "477e3bda05594962bd8ba0f1e3b7bb31": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1795,25 +1867,31 @@ "width": null } }, - "502164ca582c4a0bbb3b20dc28ae0b2c": { + "48016ee17d6b4133971e8d4b58046f98": { "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_cfe55f8de045405f86e66d60c9c1ce0c", + "IPY_MODEL_5f8acf7619d14a2494c2bd74f4c2d794", + "IPY_MODEL_ef11cb19d76d48a098b132ab8531c488" + ], + "layout": "IPY_MODEL_77ab3ba493da4bb68b8d39397523e2e4", + "tabbable": null, + "tooltip": null } }, - "505538aed9b54bebb4c3f0107daadf44": { + "499e72220e084b96a2fac6d7d24f3e36": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1866,71 +1944,7 @@ "width": null } }, - "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", - "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": { + "4d8b4c898e4d49c795640c857d8f9648": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1945,31 +1959,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6ef7ce225dc34f17b94e5dbe3a820e35", + "layout": "IPY_MODEL_ab29117829274449a8621cc4708da2b1", "placeholder": "​", - "style": "IPY_MODEL_a99583c6f0e24d69872cbda693c983eb", + "style": "IPY_MODEL_e6758addea6d44238157a9fee26b6f01", "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": "" + "value": " 665/665 [00:00<00:00, 137kB/s]" } }, - "6833c949ea36409b943f5aa9a751fe85": { + "4e0a66ed3e51494baf8c4960957f2abc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2022,7 +2020,7 @@ "width": null } }, - "6c0bd2a44f6e49e7bb1931ece0eab850": { + "4f6ef66a05864b318215d64d3df902a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2075,33 +2073,42 @@ "width": null } }, - "6e6cda23dd024255af57333cc9d9bfea": { + "5042f028d3a24a99a3cb8dbb370a2d51": { "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_80258d9522ac4e858c828ed1eba37fb6", - "placeholder": "​", - "style": "IPY_MODEL_2df9eaceb2e14e8fa3a19b25c7d76f35", - "tabbable": null, - "tooltip": null, - "value": " 391/391 [00:00<00:00, 67.9kB/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "6ed70f51e8b34c1094db85d132e5ce9c": { - "model_module": "@jupyter-widgets/base", + "51e4ce1aa08a4875b0078386841ef6c6": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "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": "" + } + }, + "5b6cedd62c3b4e2dba4fb6cf6ac81919": { + "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", @@ -2151,7 +2158,7 @@ "width": null } }, - "6ef7ce225dc34f17b94e5dbe3a820e35": { + "5dfa8539a6c948378badd6686cc253dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2204,7 +2211,83 @@ "width": null } }, - "7042493643924b098a59e80ef3a6125e": { + "5f8acf7619d14a2494c2bd74f4c2d794": { + "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_5dfa8539a6c948378badd6686cc253dd", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c9d0db8230f34b468c783b24537b3a87", + "tabbable": null, + "tooltip": null, + "value": 466062.0 + } + }, + "66867813190846ab8aed312ab6908a43": { + "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_4f6ef66a05864b318215d64d3df902a8", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b71a8e9e851b4881bb5145531e49ccd0", + "tabbable": null, + "tooltip": null, + "value": 231508.0 + } + }, + "697435a323a84bea9cc7bfb42e0a5e62": { + "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_dd67fec157b9489faa0dc3ddbd95ef35", + "IPY_MODEL_20199ed9391b4fd3af9ffba1bd110646", + "IPY_MODEL_41c9e037f3074a5d9b33b7014d85cdfe" + ], + "layout": "IPY_MODEL_b42bf80fe5274a5e908a3c39822c2629", + "tabbable": null, + "tooltip": null + } + }, + "71581c894a0e453e992f58ce4ec00848": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2257,25 +2340,30 @@ "width": null } }, - "7416ceea465444209f091f4e54c9aebe": { + "71c0454905f044549aa29c3961bc512b": { "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_16cb8599d6034cd1b810cbf99f703579", + "placeholder": "​", + "style": "IPY_MODEL_8e1757765b51437c9c3576da0b2a84d2", + "tabbable": null, + "tooltip": null, + "value": " 391/391 [00:00<00:00, 69.1kB/s]" } }, - "769b1ec27342400b9cb6e41638eaa9d4": { + "77ab3ba493da4bb68b8d39397523e2e4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2328,23 +2416,7 @@ "width": null } }, - "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": { + "787647896a374c4baef37ba555b3d758": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2397,7 +2469,7 @@ "width": null } }, - "7f092e8ede4e4d63a5fde7a51f92e32c": { + "790e13d2858c4cf4bdf4bce6bda99ddb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2450,30 +2522,7 @@ "width": null } }, - "7fd953032b654b6080e9188c21e62fc9": { - "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_c176725128c3447ebe665fa5ac3e316f", - "placeholder": "​", - "style": "IPY_MODEL_59de812fe1e841889d9b5bfc883f3b40", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 38.4MB/s]" - } - }, - "80258d9522ac4e858c828ed1eba37fb6": { + "7aa30d069f444999834643e94fe81c9b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2526,103 +2575,23 @@ "width": null } }, - "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": { + "8429e83ba7114915933be895d5f6fe3a": { "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_54ed34f093ee42d68b280d3bd01e5599", - "IPY_MODEL_c5eaef5240de4848ba66275755fe00f6", - "IPY_MODEL_7fd953032b654b6080e9188c21e62fc9" - ], - "layout": "IPY_MODEL_44b0dfbf62fd4d088800332e97908228", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "9f0bab6dcc71427ba6d39cfcb15d877f": { + "8c2f5adef8594b9d94b856fe69ecef0d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2675,7 +2644,43 @@ "width": null } }, - "a588c0dcd0974f8da33c7bdf3d4a35e8": { + "8d50a9bb17184cec871c72343c762cc2": { + "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 + } + }, + "8e1757765b51437c9c3576da0b2a84d2": { + "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 + } + }, + "93b428a1e9984e3985970ffd82b85429": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2690,33 +2695,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0da07d715efe402083873e0661e556ea", + "layout": "IPY_MODEL_8c2f5adef8594b9d94b856fe69ecef0d", "placeholder": "​", - "style": "IPY_MODEL_ae1e6cca03b34513818a3bd133ae6f5a", + "style": "IPY_MODEL_40a195a274ab4351b4d37e7880f857e7", "tabbable": null, "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 9.25kB/s]" + "value": "vocab.txt: 100%" } }, - "a99583c6f0e24d69872cbda693c983eb": { + "9588d449e7b24e6db9e90daa5078369e": { "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": "" } }, - "acbecbc8b02440768730f4625e2f605e": { + "967764ca5ac248399e557033cfdf7a08": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2734,7 +2737,7 @@ "text_color": null } }, - "ae1e6cca03b34513818a3bd133ae6f5a": { + "987bc5a0a5174818895b0e59be32726d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2752,8 +2755,32 @@ "text_color": null } }, - "b01dff0aaef24c71a79429be65e9ce07": { - "model_module": "@jupyter-widgets/base", + "98baa2df749f4718a19b8ed6f6b64516": { + "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_40fe431dbc09475d9cc4e82668dce66c", + "IPY_MODEL_2eda31c15639409c917ce8f30f06fea9", + "IPY_MODEL_71c0454905f044549aa29c3961bc512b" + ], + "layout": "IPY_MODEL_c4843a0ecf8947089a56431c777139d7", + "tabbable": null, + "tooltip": null + } + }, + "a215428c7e0d4fdda219b836792e0bea": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { @@ -2805,90 +2832,7 @@ "width": null } }, - "b65073e7ff2e476bb34d75a0faf77c6f": { - "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_7b0e3596ec8d47089daa31da5c13b681", - "placeholder": "​", - "style": "IPY_MODEL_502164ca582c4a0bbb3b20dc28ae0b2c", - "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" - } - }, - "b6e52d34f81d421d9eba7c8cc9b7847a": { - "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 - } - }, - "bc071c694fb7476a8213ad06a4cef625": { - "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 - } - }, - "bd1aa12a83f148a6a04b7394dd645fb3": { - "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_b65073e7ff2e476bb34d75a0faf77c6f", - "IPY_MODEL_38b1bd4b5ec44d41981a433be46d45f0", - "IPY_MODEL_9a092b3142994e27b931efc3d8141660" - ], - "layout": "IPY_MODEL_7f092e8ede4e4d63a5fde7a51f92e32c", - "tabbable": null, - "tooltip": null - } - }, - "c176725128c3447ebe665fa5ac3e316f": { + "ab29117829274449a8621cc4708da2b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2941,33 +2885,7 @@ "width": null } }, - "c18be04c279b46b48b79ee15e32435e6": { - "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_fabb2363238a4c1a91de78e13b8a0a3e", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6680b7e73b4d4bb589857c254d0632ef", - "tabbable": null, - "tooltip": null, - "value": 665.0 - } - }, - "c33f4e7c33ca4ebd8cb646737881a850": { + "b1a6862286e144a5b4c7e3b53955c909": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3020,57 +2938,25 @@ "width": null } }, - "c5eaef5240de4848ba66275755fe00f6": { - "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_07d79636c68343278e1f779c27aef268", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_fba1e85bf6444f50b1917a3eb9c35bcc", - "tabbable": null, - "tooltip": null, - "value": 231508.0 - } - }, - "c943f13df8c04e77aae4c7ca2cbbd613": { + "b2d0af45a331440e91bb670c810aad25": { "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_fcaf1661c4314a27a12b7c20cbce3cc8", - "IPY_MODEL_914b3f22f6b446608a80b2bd86dde07b", - "IPY_MODEL_6e6cda23dd024255af57333cc9d9bfea" - ], - "layout": "IPY_MODEL_de51505fe22c4b5fa21abe797f7a7d7a", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "cbb206b9c0d64fe0b479fb7cc9df570b": { + "b42bf80fe5274a5e908a3c39822c2629": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3123,7 +3009,23 @@ "width": null } }, - "ce7bece14db441b3b9a7a133c7e71a07": { + "b71a8e9e851b4881bb5145531e49ccd0": { + "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": "" + } + }, + "b876cea56c70415bbe96c6d929472c03": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3176,7 +3078,30 @@ "width": null } }, - "cefce6f533124c4e90d7c277319c65e9": { + "bb8dedc70e1e42f985ca245b4c1626d0": { + "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_477e3bda05594962bd8ba0f1e3b7bb31", + "placeholder": "​", + "style": "IPY_MODEL_dc42abfc89cd4333a4569bc9139f85a5", + "tabbable": null, + "tooltip": null, + "value": "config.json: 100%" + } + }, + "c4843a0ecf8947089a56431c777139d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3229,7 +3154,30 @@ "width": null } }, - "d5eda741ae08481f90152525ac295029": { + "c4ab79f47c1f4a5fa7a952d2f4243909": { + "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_3090f81fd63e47748b48b354dff579e6", + "placeholder": "​", + "style": "IPY_MODEL_229a059bf66a476caf93f93617098541", + "tabbable": null, + "tooltip": null, + "value": "tokenizer_config.json: 100%" + } + }, + "c9d0db8230f34b468c783b24537b3a87": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3245,7 +3193,71 @@ "description_width": "" } }, - "d76fd6c68456446baaae1b57ccf46065": { + "cc575b26e89c48eea0b3767999b7957a": { + "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 + } + }, + "cfd42f9c1e5b4b6e957253d074ff0a96": { + "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_b876cea56c70415bbe96c6d929472c03", + "placeholder": "​", + "style": "IPY_MODEL_987bc5a0a5174818895b0e59be32726d", + "tabbable": null, + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 9.30kB/s]" + } + }, + "cfe55f8de045405f86e66d60c9c1ce0c": { + "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_fe6d4bd14f1d4e9c889f127632dfd090", + "placeholder": "​", + "style": "IPY_MODEL_f1cd701a314345e8a946773e606e7e5e", + "tabbable": null, + "tooltip": null, + "value": "tokenizer.json: 100%" + } + }, + "d398c27b16594cdabb17dd18d91923db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3261,7 +3273,7 @@ "description_width": "" } }, - "d9d22d6398f34538ac40fff96fa8abaf": { + "d62a7bbd2b814718bdb6e5c898023432": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3277,17 +3289,58 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7042493643924b098a59e80ef3a6125e", - "max": 466062.0, + "layout": "IPY_MODEL_31425907e4ff47c1a17ed0a9935430d6", + "max": 665.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_7a60c769a9a94e748e35f240733664b3", + "style": "IPY_MODEL_5042f028d3a24a99a3cb8dbb370a2d51", "tabbable": null, "tooltip": null, - "value": 466062.0 + "value": 665.0 + } + }, + "dc42abfc89cd4333a4569bc9139f85a5": { + "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 + } + }, + "dd67fec157b9489faa0dc3ddbd95ef35": { + "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_71581c894a0e453e992f58ce4ec00848", + "placeholder": "​", + "style": "IPY_MODEL_8d50a9bb17184cec871c72343c762cc2", + "tabbable": null, + "tooltip": null, + "value": "README.md: 100%" } }, - "de51505fe22c4b5fa21abe797f7a7d7a": { + "e1ad008614fb43fdb5e809f4d8511a82": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3340,7 +3393,48 @@ "width": null } }, - "e0adbaa3ffa64032933136aebb9a1f7b": { + "e6758addea6d44238157a9fee26b6f01": { + "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 + } + }, + "e7f2051c6f3e4cb4afa29879fc4e25d7": { + "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_a215428c7e0d4fdda219b836792e0bea", + "placeholder": "​", + "style": "IPY_MODEL_967764ca5ac248399e557033cfdf7a08", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" + } + }, + "ea06e6ec819142448a354fc1926e13af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3355,15 +3449,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9f0bab6dcc71427ba6d39cfcb15d877f", + "layout": "IPY_MODEL_790e13d2858c4cf4bdf4bce6bda99ddb", "placeholder": "​", - "style": "IPY_MODEL_7416ceea465444209f091f4e54c9aebe", + "style": "IPY_MODEL_29ab7c4b24694381897b57b84ded9af9", "tabbable": null, "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 401kB/s]" + "value": " 232k/232k [00:00<00:00, 38.1MB/s]" } }, - "e32cd0a87f0f4ad18f72cc198abad4fd": { + "ef11cb19d76d48a098b132ab8531c488": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3378,15 +3472,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c33f4e7c33ca4ebd8cb646737881a850", + "layout": "IPY_MODEL_1a2f8352992a4e8d8667c62f94d7e0e0", "placeholder": "​", - "style": "IPY_MODEL_3bd0776bd0b840e8b2a1d15afd1e27f8", + "style": "IPY_MODEL_cc575b26e89c48eea0b3767999b7957a", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 118kB/s]" + "value": " 466k/466k [00:00<00:00, 35.2MB/s]" } }, - "e970db685471489481e8d686d97b3cc2": { + "f1cd701a314345e8a946773e606e7e5e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3404,136 +3498,81 @@ "text_color": null } }, - "ea436305a9d143d09b28e8d32eb3020a": { - "model_module": "@jupyter-widgets/base", + "f451a7bbcccb4c0ebd05353c6b2b00d8": { + "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/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": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_93b428a1e9984e3985970ffd82b85429", + "IPY_MODEL_66867813190846ab8aed312ab6908a43", + "IPY_MODEL_ea06e6ec819142448a354fc1926e13af" + ], + "layout": "IPY_MODEL_1f83772d100c412884fc22f69ab7e498", + "tabbable": null, + "tooltip": null } }, - "ef479a0478b644bbbd209856e893f0ed": { + "f717bfe4b6324a74be7f067e29025df4": { "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_6833c949ea36409b943f5aa9a751fe85", - "placeholder": "​", - "style": "IPY_MODEL_0d5dda5aa9e840a7969a3facb0c17f1f", + "layout": "IPY_MODEL_5b6cedd62c3b4e2dba4fb6cf6ac81919", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_51e4ce1aa08a4875b0078386841ef6c6", "tabbable": null, "tooltip": null, - "value": "tokenizer.json: 100%" + "value": 48.0 } }, - "f25b6a55df5d45bc80be73c91794168b": { - "model_module": "@jupyter-widgets/base", + "f7905c4a4ded402ca686067d0aacc292": { + "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/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": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_bb8dedc70e1e42f985ca245b4c1626d0", + "IPY_MODEL_d62a7bbd2b814718bdb6e5c898023432", + "IPY_MODEL_4d8b4c898e4d49c795640c857d8f9648" + ], + "layout": "IPY_MODEL_787647896a374c4baef37ba555b3d758", + "tabbable": null, + "tooltip": null } }, - "fabb2363238a4c1a91de78e13b8a0a3e": { + "fe6d4bd14f1d4e9c889f127632dfd090": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3585,45 +3624,6 @@ "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 a4fd4545f..b77ccf17e 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": "2024-07-02T15:10:13.381463Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:14.546953Z", + "iopub.status.busy": "2024-07-02T15:25:14.546784Z", + "iopub.status.idle": "2024-07-02T15:25:19.407981Z", + "shell.execute_reply": "2024-07-02T15:25:19.407476Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:10:18.676864Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:19.410926Z", + "iopub.status.busy": "2024-07-02T15:25:19.410231Z", + "iopub.status.idle": "2024-07-02T15:25:19.413722Z", + "shell.execute_reply": "2024-07-02T15:25:19.413173Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:19.415856Z", + "iopub.status.busy": "2024-07-02T15:25:19.415529Z", + "iopub.status.idle": "2024-07-02T15:25:19.419947Z", + "shell.execute_reply": "2024-07-02T15:25:19.419526Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:19.422117Z", + "iopub.status.busy": "2024-07-02T15:25:19.421696Z", + "iopub.status.idle": "2024-07-02T15:25:21.022232Z", + "shell.execute_reply": "2024-07-02T15:25:21.021605Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:21.024570Z", + "iopub.status.busy": "2024-07-02T15:25:21.024378Z", + "iopub.status.idle": "2024-07-02T15:25:21.034610Z", + "shell.execute_reply": "2024-07-02T15:25:21.034162Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:21.036772Z", + "iopub.status.busy": "2024-07-02T15:25:21.036453Z", + "iopub.status.idle": "2024-07-02T15:25:21.041969Z", + "shell.execute_reply": "2024-07-02T15:25:21.041413Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:21.044032Z", + "iopub.status.busy": "2024-07-02T15:25:21.043619Z", + "iopub.status.idle": "2024-07-02T15:25:21.448517Z", + "shell.execute_reply": "2024-07-02T15:25:21.447933Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:21.450756Z", + "iopub.status.busy": "2024-07-02T15:25:21.450428Z", + "iopub.status.idle": "2024-07-02T15:25:22.369181Z", + "shell.execute_reply": "2024-07-02T15:25:22.368692Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:22.371664Z", + "iopub.status.busy": "2024-07-02T15:25:22.371311Z", + "iopub.status.idle": "2024-07-02T15:25:22.389436Z", + "shell.execute_reply": "2024-07-02T15:25:22.388994Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:22.391511Z", + "iopub.status.busy": "2024-07-02T15:25:22.391097Z", + "iopub.status.idle": "2024-07-02T15:25:22.394237Z", + "shell.execute_reply": "2024-07-02T15:25:22.393731Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:22.396091Z", + "iopub.status.busy": "2024-07-02T15:25:22.395918Z", + "iopub.status.idle": "2024-07-02T15:25:36.049207Z", + "shell.execute_reply": "2024-07-02T15:25:36.048690Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.051828Z", + "iopub.status.busy": "2024-07-02T15:25:36.051442Z", + "iopub.status.idle": "2024-07-02T15:25:36.055485Z", + "shell.execute_reply": "2024-07-02T15:25:36.055009Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.057411Z", + "iopub.status.busy": "2024-07-02T15:25:36.057242Z", + "iopub.status.idle": "2024-07-02T15:25:36.769000Z", + "shell.execute_reply": "2024-07-02T15:25:36.768447Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.772680Z", + "iopub.status.busy": "2024-07-02T15:25:36.771734Z", + "iopub.status.idle": "2024-07-02T15:25:36.778368Z", + "shell.execute_reply": "2024-07-02T15:25:36.777878Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.781861Z", + "iopub.status.busy": "2024-07-02T15:25:36.780939Z", + "iopub.status.idle": "2024-07-02T15:25:36.879710Z", + "shell.execute_reply": "2024-07-02T15:25:36.879109Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.881988Z", + "iopub.status.busy": "2024-07-02T15:25:36.881620Z", + "iopub.status.idle": "2024-07-02T15:25:36.894208Z", + "shell.execute_reply": "2024-07-02T15:25:36.893742Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.896273Z", + "iopub.status.busy": "2024-07-02T15:25:36.895955Z", + "iopub.status.idle": "2024-07-02T15:25:36.903547Z", + "shell.execute_reply": "2024-07-02T15:25:36.903009Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.905350Z", + "iopub.status.busy": "2024-07-02T15:25:36.905182Z", + "iopub.status.idle": "2024-07-02T15:25:36.909462Z", + "shell.execute_reply": "2024-07-02T15:25:36.908923Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.911557Z", + "iopub.status.busy": "2024-07-02T15:25:36.911268Z", + "iopub.status.idle": "2024-07-02T15:25:36.916706Z", + "shell.execute_reply": "2024-07-02T15:25:36.916183Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.918893Z", + "iopub.status.busy": "2024-07-02T15:25:36.918579Z", + "iopub.status.idle": "2024-07-02T15:25:37.028414Z", + "shell.execute_reply": "2024-07-02T15:25:37.027859Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:37.030519Z", + "iopub.status.busy": "2024-07-02T15:25:37.030189Z", + "iopub.status.idle": "2024-07-02T15:25:37.129978Z", + "shell.execute_reply": "2024-07-02T15:25:37.129407Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:37.132132Z", + "iopub.status.busy": "2024-07-02T15:25:37.131812Z", + "iopub.status.idle": "2024-07-02T15:25:37.230271Z", + "shell.execute_reply": "2024-07-02T15:25:37.229796Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:37.232307Z", + "iopub.status.busy": "2024-07-02T15:25:37.231996Z", + "iopub.status.idle": "2024-07-02T15:25:37.334860Z", + "shell.execute_reply": "2024-07-02T15:25:37.334301Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:37.337103Z", + "iopub.status.busy": "2024-07-02T15:25:37.336778Z", + "iopub.status.idle": "2024-07-02T15:25:37.339748Z", + "shell.execute_reply": "2024-07-02T15:25:37.339340Z" }, "nbsphinx": "hidden" }, @@ -1392,53 +1392,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "026a8d3d502347a290f99b02f8529d28": { + "035ed0ac88b843c7986fdcc6053c74ea": { "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", + "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_86ca63a102954afa94795c3e61c20880", - "placeholder": "​", - "style": "IPY_MODEL_e9b3119fe459439e8146cddd6880a9f1", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "0dfc7f988177492e852e1a1e2e2e3c55": { + "05ace592e52c4898960ac3a6834e81db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1453,15 +1425,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d16b4b3c59ba49d9a539a98b5d9ace25", + "layout": "IPY_MODEL_2a8eef61906f4e02b64d2acfa416df0a", "placeholder": "​", - "style": "IPY_MODEL_8712797b65e1448f8a93f64e6e581af5", + "style": "IPY_MODEL_79f5954a72a84d65be69edeb832c92fd", "tabbable": null, "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 502kB/s]" + "value": "classifier.ckpt: 100%" } }, - "0fb76291c4d34ebdba9ffa24ed513e0a": { + "09f9a3d816be47d78e516de2e21980b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1514,113 +1486,87 @@ "width": null } }, - "0fca732af2064a0da3c0e7502ad49282": { - "model_module": "@jupyter-widgets/base", + "0eca11d8b9ca420cb317292d15607da7": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_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_eacb5b752481476099c10dfacf56c89d", + "placeholder": "​", + "style": "IPY_MODEL_f2d5653b2c4e48d493b970861348256b", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 483kB/s]" + } + }, + "0f2e81acb0c846a1bd10715932ea462b": { + "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_53edd2fc08c34cd28b365529e38e14de", + "placeholder": "​", + "style": "IPY_MODEL_3a43941544024cce8e00981fad13ea69", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 164MB/s]" + } + }, + "191733153ce746d1a8ff2b837b57e9be": { + "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": "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 } }, - "1b8db158701349408c5bdef740765f91": { - "model_module": "@jupyter-widgets/base", + "1a33cb48eeb1453993c6eaa84fc502fb": { + "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": "" } }, - "23ef0aabeddc46538e90abc8e42e36c0": { + "1c477dd84702413bafd29643b995393b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1636,17 +1582,40 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5d471f2ed23e49dbb159b32a3c2d406e", + "layout": "IPY_MODEL_b911745838c24922badcd8ce2b74705c", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_511baacd1c3e4816bbe0b13284bf3fdd", + "style": "IPY_MODEL_41f5c12b350f4abbb8805e5d672190d7", "tabbable": null, "tooltip": null, "value": 16887676.0 } }, - "299896a97d41456cac583393c5611a30": { + "20439df6f4ec4c92a86ed7db1713c94a": { + "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_a687a32ad4e94ddab8899265482ec95e", + "placeholder": "​", + "style": "IPY_MODEL_ee82152228b343f5bd122b8ee22d3ade", + "tabbable": null, + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "28bb50bd3b734769b604cbc0566fe965": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1699,7 +1668,7 @@ "width": null } }, - "3c2e1352b6dd4da68e969419d9ecdaea": { + "2a8eef61906f4e02b64d2acfa416df0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1752,49 +1721,72 @@ "width": null } }, - "44dd265825e9454e868ab092b351a312": { + "306f5f497ce647e09871bff6b0c431fe": { "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_6de7c25d6d174a3e92ebb9f11d808ecc", + "IPY_MODEL_76f9849e481f47fb9422fb236d93ee5c", + "IPY_MODEL_c41ac92cd3a44fa7b86ae4c7444f9d57" + ], + "layout": "IPY_MODEL_a66d028f4eea4d9cb7a62da4d3f9af97", + "tabbable": null, + "tooltip": null } }, - "4526bd738aca4e52beef9dbf333987c6": { + "3780dbf7cf624b0fbcc7e46393e604bd": { "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_6a601518732a456897e40425c7447332", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8063b86fd1644501bd7a35a8f8c91e8d", + "layout": "IPY_MODEL_28bb50bd3b734769b604cbc0566fe965", + "placeholder": "​", + "style": "IPY_MODEL_035ed0ac88b843c7986fdcc6053c74ea", "tabbable": null, "tooltip": null, - "value": 15856877.0 + "value": " 3.20k/3.20k [00:00<00:00, 796kB/s]" + } + }, + "3a43941544024cce8e00981fad13ea69": { + "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 } }, - "4a111d06ad644ff3b7dfcd8f9a93b27f": { + "3af2e6f965024e0faef7da0c7564e4bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1847,7 +1839,7 @@ "width": null } }, - "511baacd1c3e4816bbe0b13284bf3fdd": { + "3e86400f85a2412dab304636e64fc255": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1863,7 +1855,7 @@ "description_width": "" } }, - "562674bc865d4b9c866de03e113283d6": { + "40119d252d344b50ada2deb76d102ba9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1878,27 +1870,77 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_026a8d3d502347a290f99b02f8529d28", - "IPY_MODEL_4526bd738aca4e52beef9dbf333987c6", - "IPY_MODEL_72f97078b8fc4a6d9b507a102418aea1" + "IPY_MODEL_6d2f84d14e3a4939a1c606b1769f2052", + "IPY_MODEL_1c477dd84702413bafd29643b995393b", + "IPY_MODEL_638ba26f976e403cb8fbdb569fd358cb" ], - "layout": "IPY_MODEL_cdbef2f290304f54be4b5de93eb10cc1", + "layout": "IPY_MODEL_f0d5fce51b134affae64c7e5b3ff7cde", "tabbable": null, "tooltip": null } }, - "5940aac0c62e430f99781eb883247639": { - "model_module": "@jupyter-widgets/base", + "41f5c12b350f4abbb8805e5d672190d7": { + "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", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "47c5d5c584084493b0861ab73b489d4c": { + "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": "" + } + }, + "4b23757c53804abf826b76129c71f84f": { + "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 + } + }, + "4d42b878a1484c2a8c218ceb2a870ce6": { + "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, @@ -1940,7 +1982,7 @@ "width": null } }, - "5d471f2ed23e49dbb159b32a3c2d406e": { + "53edd2fc08c34cd28b365529e38e14de": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1993,49 +2035,7 @@ "width": null } }, - "5fd920ec2856445d98a6f7e8f0229c4d": { - "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_a5ba789c57d244a6afdd80d9221598a2", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ba0d6ceeadc14ee192ae1dc31b4e4186", - "tabbable": null, - "tooltip": null, - "value": 128619.0 - } - }, - "60b559ea05d04ab2aefd425629200a9b": { - "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": "" - } - }, - "60d05f62061f46e7b7f871ccf49bce6e": { + "5b0eb572f4a44010b0414f2c88c3cd1e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2050,88 +2050,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_795a99fe1a5a4fe28962d2835b6d0806", + "layout": "IPY_MODEL_969d2813a5c04cacb6fc3a37ed2dc5fd", "placeholder": "​", - "style": "IPY_MODEL_ef52679868914ac0a1bac86a98893d61", - "tabbable": null, - "tooltip": null, - "value": " 129k/129k [00:00<00:00, 11.1MB/s]" - } - }, - "618905a858344a0488c8d600292e4cc3": { - "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_7b05aea7bfdd48ddbe010dce6c119c9a", - "placeholder": "​", - "style": "IPY_MODEL_d1a86a88d50a4bb2aee04ac90b0d069a", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 790kB/s]" - } - }, - "678fc99edb84497dbac731cf9a1fa79f": { - "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_df22226a67914b8a9d0693b25dffdc60", - "IPY_MODEL_7f009f8c42734f8e9fa9ea99cf520312", - "IPY_MODEL_618905a858344a0488c8d600292e4cc3" - ], - "layout": "IPY_MODEL_bf8a89f9ccd04b1a857340ebae05002e", - "tabbable": null, - "tooltip": null - } - }, - "6816c11c8cd945968d5b0ce5adfb87f5": { - "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_4a111d06ad644ff3b7dfcd8f9a93b27f", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_60b559ea05d04ab2aefd425629200a9b", + "style": "IPY_MODEL_191733153ce746d1a8ff2b837b57e9be", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": "hyperparams.yaml: 100%" } }, - "6a601518732a456897e40425c7447332": { + "5ce0ad1c70004fae993ca54a47dea1ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2184,25 +2111,33 @@ "width": null } }, - "6b7cf1b25d364dbab4d57e7ee83bbf50": { + "6339c8e626034d7c9fa8cff1415bcad2": { "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_4d42b878a1484c2a8c218ceb2a870ce6", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3e86400f85a2412dab304636e64fc255", + "tabbable": null, + "tooltip": null, + "value": 3201.0 } }, - "72f97078b8fc4a6d9b507a102418aea1": { + "638ba26f976e403cb8fbdb569fd358cb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2217,15 +2152,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1b8db158701349408c5bdef740765f91", + "layout": "IPY_MODEL_5ce0ad1c70004fae993ca54a47dea1ac", "placeholder": "​", - "style": "IPY_MODEL_c0c1b2f629d145fa8b43d2ac3c1494a6", + "style": "IPY_MODEL_4b23757c53804abf826b76129c71f84f", "tabbable": null, "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 176MB/s]" + "value": " 16.9M/16.9M [00:00<00:00, 62.7MB/s]" } }, - "795a99fe1a5a4fe28962d2835b6d0806": { + "65ccc00f12204532a50813004d34c322": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2278,60 +2213,53 @@ "width": null } }, - "7ab9a8d4e3e64a0f90d15eb2e77ce38b": { - "model_module": "@jupyter-widgets/base", + "6d2f84d14e3a4939a1c606b1769f2052": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_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": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e617db8fadf64d708385495d20c7702e", + "placeholder": "​", + "style": "IPY_MODEL_8920ef4b3a4a44c59c581d700192ac74", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "6de7c25d6d174a3e92ebb9f11d808ecc": { + "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_3af2e6f965024e0faef7da0c7564e4bc", + "placeholder": "​", + "style": "IPY_MODEL_a1ecc93b0e124f82a9d890e1b8dc92fb", + "tabbable": null, + "tooltip": null, + "value": "label_encoder.txt: 100%" } }, - "7b05aea7bfdd48ddbe010dce6c119c9a": { + "746cf50561de4f28aec72152e2a74e40": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2384,7 +2312,7 @@ "width": null } }, - "7f009f8c42734f8e9fa9ea99cf520312": { + "76f9849e481f47fb9422fb236d93ee5c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2400,33 +2328,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_fd00c0575ed7437ca5aabc5c08229d32", - "max": 3201.0, + "layout": "IPY_MODEL_746cf50561de4f28aec72152e2a74e40", + "max": 128619.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_44dd265825e9454e868ab092b351a312", + "style": "IPY_MODEL_9ab4d21a4d204ce9aad54b4aa19ef440", "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": "" + "value": 128619.0 } }, - "84fa5f8c2433478c9dc01b8a41eb48d5": { + "79f5954a72a84d65be69edeb832c92fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2444,7 +2356,7 @@ "text_color": null } }, - "8531becaffca44989779b1d37c65260e": { + "869fc446c9a34743877fe8b5536872b1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2459,39 +2371,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_85e7b3e36e7c47129e29727d2f646f2f", - "IPY_MODEL_6816c11c8cd945968d5b0ce5adfb87f5", - "IPY_MODEL_0dfc7f988177492e852e1a1e2e2e3c55" + "IPY_MODEL_5b0eb572f4a44010b0414f2c88c3cd1e", + "IPY_MODEL_d2c10c0d82484b1d9d529d2ef9dae7fb", + "IPY_MODEL_0eca11d8b9ca420cb317292d15607da7" ], - "layout": "IPY_MODEL_a3dbbfce424340438202c2a4f54314d7", + "layout": "IPY_MODEL_09f9a3d816be47d78e516de2e21980b0", "tabbable": null, "tooltip": null } }, - "85e7b3e36e7c47129e29727d2f646f2f": { - "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_d116ac8503dc4fa19dbf254269e57e81", - "placeholder": "​", - "style": "IPY_MODEL_865a2e4e16fe4c8fab1e00a4c6cd135d", - "tabbable": null, - "tooltip": null, - "value": "hyperparams.yaml: 100%" - } - }, - "865a2e4e16fe4c8fab1e00a4c6cd135d": { + "8920ef4b3a4a44c59c581d700192ac74": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2509,7 +2398,60 @@ "text_color": null } }, - "86ca63a102954afa94795c3e61c20880": { + "968ea4aa03974c94a22690152df3a0b0": { + "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 + } + }, + "969d2813a5c04cacb6fc3a37ed2dc5fd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2562,25 +2504,23 @@ "width": null } }, - "8712797b65e1448f8a93f64e6e581af5": { + "9ab4d21a4d204ce9aad54b4aa19ef440": { "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": "" } }, - "97d74ea76f524faea7cd1c349d25ed2d": { + "a1ecc93b0e124f82a9d890e1b8dc92fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2598,7 +2538,7 @@ "text_color": null } }, - "a3dbbfce424340438202c2a4f54314d7": { + "a66d028f4eea4d9cb7a62da4d3f9af97": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2651,30 +2591,60 @@ "width": null } }, - "a408ccf89ec14eceb847510634138186": { - "model_module": "@jupyter-widgets/controls", + "a687a32ad4e94ddab8899265482ec95e": { + "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_299896a97d41456cac583393c5611a30", - "placeholder": "​", - "style": "IPY_MODEL_6b7cf1b25d364dbab4d57e7ee83bbf50", - "tabbable": null, - "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 190MB/s]" + "_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 } }, - "a5ba789c57d244a6afdd80d9221598a2": { + "b911745838c24922badcd8ce2b74705c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2727,7 +2697,7 @@ "width": null } }, - "ad707c12b59d4885be4f947c794ca0ea": { + "baafd2ed9b6541319c792073a72f5e7d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2742,32 +2712,40 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0c9f4529a42246ee88b9b31ac1212ba1", - "IPY_MODEL_23ef0aabeddc46538e90abc8e42e36c0", - "IPY_MODEL_a408ccf89ec14eceb847510634138186" + "IPY_MODEL_05ace592e52c4898960ac3a6834e81db", + "IPY_MODEL_f9493a2b41bb4830b72c769a6924862e", + "IPY_MODEL_0f2e81acb0c846a1bd10715932ea462b" ], - "layout": "IPY_MODEL_3c2e1352b6dd4da68e969419d9ecdaea", + "layout": "IPY_MODEL_f65ea041bc1b48afa70ae6281b147089", "tabbable": null, "tooltip": null } }, - "ba0d6ceeadc14ee192ae1dc31b4e4186": { + "bd27c6867d9a407d8723ff685bd632f8": { "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_20439df6f4ec4c92a86ed7db1713c94a", + "IPY_MODEL_6339c8e626034d7c9fa8cff1415bcad2", + "IPY_MODEL_3780dbf7cf624b0fbcc7e46393e604bd" + ], + "layout": "IPY_MODEL_968ea4aa03974c94a22690152df3a0b0", + "tabbable": null, + "tooltip": null } }, - "bc115d8e973a43f98d22e93fcdeb0d0a": { + "c41ac92cd3a44fa7b86ae4c7444f9d57": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2782,15 +2760,41 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0fb76291c4d34ebdba9ffa24ed513e0a", + "layout": "IPY_MODEL_fac867a2e6ae475dba8300fafb622e27", "placeholder": "​", - "style": "IPY_MODEL_97d74ea76f524faea7cd1c349d25ed2d", + "style": "IPY_MODEL_d7d5493534b94ec28876947b06bbca4c", "tabbable": null, "tooltip": null, - "value": "label_encoder.txt: 100%" + "value": " 129k/129k [00:00<00:00, 17.4MB/s]" + } + }, + "d2c10c0d82484b1d9d529d2ef9dae7fb": { + "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_65ccc00f12204532a50813004d34c322", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_47c5d5c584084493b0861ab73b489d4c", + "tabbable": null, + "tooltip": null, + "value": 2041.0 } }, - "bf8a89f9ccd04b1a857340ebae05002e": { + "d749fc0d9649484f8df326a2bdb58a4f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2843,31 +2847,7 @@ "width": null } }, - "c0a1f533272a4f6cb65d54cbace4208e": { - "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_bc115d8e973a43f98d22e93fcdeb0d0a", - "IPY_MODEL_5fd920ec2856445d98a6f7e8f0229c4d", - "IPY_MODEL_60d05f62061f46e7b7f871ccf49bce6e" - ], - "layout": "IPY_MODEL_7ab9a8d4e3e64a0f90d15eb2e77ce38b", - "tabbable": null, - "tooltip": null - } - }, - "c0c1b2f629d145fa8b43d2ac3c1494a6": { + "d7d5493534b94ec28876947b06bbca4c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2885,7 +2865,7 @@ "text_color": null } }, - "cdbef2f290304f54be4b5de93eb10cc1": { + "e617db8fadf64d708385495d20c7702e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2938,7 +2918,7 @@ "width": null } }, - "d116ac8503dc4fa19dbf254269e57e81": { + "eacb5b752481476099c10dfacf56c89d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2991,7 +2971,25 @@ "width": null } }, - "d16b4b3c59ba49d9a539a98b5d9ace25": { + "ee82152228b343f5bd122b8ee22d3ade": { + "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 + } + }, + "f0d5fce51b134affae64c7e5b3ff7cde": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3044,48 +3042,7 @@ "width": null } }, - "d1a86a88d50a4bb2aee04ac90b0d069a": { - "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 - } - }, - "df22226a67914b8a9d0693b25dffdc60": { - "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_5940aac0c62e430f99781eb883247639", - "placeholder": "​", - "style": "IPY_MODEL_84fa5f8c2433478c9dc01b8a41eb48d5", - "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "e9b3119fe459439e8146cddd6880a9f1": { + "f2d5653b2c4e48d493b970861348256b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3103,43 +3060,86 @@ "text_color": null } }, - "ef52679868914ac0a1bac86a98893d61": { - "model_module": "@jupyter-widgets/controls", + "f65ea041bc1b48afa70ae6281b147089": { + "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 } }, - "fb83ceda077c4e68aad24838206b09cc": { + "f9493a2b41bb4830b72c769a6924862e": { "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_d749fc0d9649484f8df326a2bdb58a4f", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1a33cb48eeb1453993c6eaa84fc502fb", + "tabbable": null, + "tooltip": null, + "value": 15856877.0 } }, - "fd00c0575ed7437ca5aabc5c08229d32": { + "fac867a2e6ae475dba8300fafb622e27": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index 0a658abc0..0f238c16e 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": "2024-07-02T15:10:41.435250Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:40.329888Z", + "iopub.status.busy": "2024-07-02T15:25:40.329681Z", + "iopub.status.idle": "2024-07-02T15:25:41.468704Z", + "shell.execute_reply": "2024-07-02T15:25:41.468091Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:10:42.619570Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:41.471552Z", + "iopub.status.busy": "2024-07-02T15:25:41.471147Z", + "iopub.status.idle": "2024-07-02T15:25:41.474151Z", + "shell.execute_reply": "2024-07-02T15:25:41.473613Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:41.476320Z", + "iopub.status.busy": "2024-07-02T15:25:41.475903Z", + "iopub.status.idle": "2024-07-02T15:25:41.484450Z", + "shell.execute_reply": "2024-07-02T15:25:41.483895Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:41.486500Z", + "iopub.status.busy": "2024-07-02T15:25:41.486077Z", + "iopub.status.idle": "2024-07-02T15:25:41.490609Z", + "shell.execute_reply": "2024-07-02T15:25:41.490062Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:41.492643Z", + "iopub.status.busy": "2024-07-02T15:25:41.492320Z", + "iopub.status.idle": "2024-07-02T15:25:41.674964Z", + "shell.execute_reply": "2024-07-02T15:25:41.674459Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:41.677160Z", + "iopub.status.busy": "2024-07-02T15:25:41.676819Z", + "iopub.status.idle": "2024-07-02T15:25:42.040724Z", + "shell.execute_reply": "2024-07-02T15:25:42.040194Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:42.042978Z", + "iopub.status.busy": "2024-07-02T15:25:42.042640Z", + "iopub.status.idle": "2024-07-02T15:25:42.065518Z", + "shell.execute_reply": "2024-07-02T15:25:42.064981Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:42.067639Z", + "iopub.status.busy": "2024-07-02T15:25:42.067331Z", + "iopub.status.idle": "2024-07-02T15:25:42.078083Z", + "shell.execute_reply": "2024-07-02T15:25:42.077538Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:42.079921Z", + "iopub.status.busy": "2024-07-02T15:25:42.079754Z", + "iopub.status.idle": "2024-07-02T15:25:44.012829Z", + "shell.execute_reply": "2024-07-02T15:25:44.012212Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.015522Z", + "iopub.status.busy": "2024-07-02T15:25:44.014996Z", + "iopub.status.idle": "2024-07-02T15:25:44.035612Z", + "shell.execute_reply": "2024-07-02T15:25:44.035047Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.037621Z", + "iopub.status.busy": "2024-07-02T15:25:44.037319Z", + "iopub.status.idle": "2024-07-02T15:25:44.054645Z", + "shell.execute_reply": "2024-07-02T15:25:44.054093Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.056426Z", + "iopub.status.busy": "2024-07-02T15:25:44.056261Z", + "iopub.status.idle": "2024-07-02T15:25:44.070971Z", + "shell.execute_reply": "2024-07-02T15:25:44.070438Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.073135Z", + "iopub.status.busy": "2024-07-02T15:25:44.072826Z", + "iopub.status.idle": "2024-07-02T15:25:44.091424Z", + "shell.execute_reply": "2024-07-02T15:25:44.090905Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "850cf9c5dd514c5c8c5878e12d30f5ca", + "model_id": "ffa60db8cf7d41c485a3bb13eb857c21", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.093259Z", + "iopub.status.busy": "2024-07-02T15:25:44.093091Z", + "iopub.status.idle": "2024-07-02T15:25:44.107694Z", + "shell.execute_reply": "2024-07-02T15:25:44.107176Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.109743Z", + "iopub.status.busy": "2024-07-02T15:25:44.109410Z", + "iopub.status.idle": "2024-07-02T15:25:44.115034Z", + "shell.execute_reply": "2024-07-02T15:25:44.114606Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.117072Z", + "iopub.status.busy": "2024-07-02T15:25:44.116754Z", + "iopub.status.idle": "2024-07-02T15:25:44.134358Z", + "shell.execute_reply": "2024-07-02T15:25:44.133820Z" } }, "outputs": [ @@ -1447,33 +1447,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "07b42c7871184a77913db05041f70f6c": { - "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_e5651455523845919804bfd3f20d32fd", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_da3ba2f2d038490c8a65361852a477f2", - "tabbable": null, - "tooltip": null, - "value": 132.0 - } - }, - "81406c4c29884619bacbf6314e1bb90e": { + "0d51225159934baaac34694b19a43d65": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1526,49 +1500,56 @@ "width": null } }, - "850cf9c5dd514c5c8c5878e12d30f5ca": { + "2b1e6fc670724ce38421474a588fa03c": { "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_b58f2ef4defc4008a4fb84b628c0df08", - "IPY_MODEL_07b42c7871184a77913db05041f70f6c", - "IPY_MODEL_ef016c3dc0df4a9a878a4f9644a436dd" - ], - "layout": "IPY_MODEL_f8cacbb114a946fb8b37956128a62704", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d8843a6d5b2347648570fd72914f0670", + "placeholder": "​", + "style": "IPY_MODEL_dc282718b936425ab24a89562150ba2f", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" } }, - "a43777fd323b46498d1b65ddfdcb03d7": { + "3a0a9f8d4e1b42459e15f37672591429": { "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_6672342c648a498e8e29b58fe5564cd9", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_50f19109358b42a8bd1e48290795156f", + "tabbable": null, + "tooltip": null, + "value": 132.0 } }, - "b58f2ef4defc4008a4fb84b628c0df08": { + "4b2b2a28121d445c839398dd3d1fda37": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1583,33 +1564,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_eae1af9f890445fab406fb6b04a570ff", + "layout": "IPY_MODEL_0d51225159934baaac34694b19a43d65", "placeholder": "​", - "style": "IPY_MODEL_b7fb3ecf4b354ed5aa31f23e9c2d7f20", + "style": "IPY_MODEL_a5b5d4b4892e4c63a5e61e1415c83270", "tabbable": null, "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" - } - }, - "b7fb3ecf4b354ed5aa31f23e9c2d7f20": { - "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": " 132/132 [00:00<00:00, 13229.66 examples/s]" } }, - "da3ba2f2d038490c8a65361852a477f2": { + "50f19109358b42a8bd1e48290795156f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1625,7 +1588,7 @@ "description_width": "" } }, - "e5651455523845919804bfd3f20d32fd": { + "6672342c648a498e8e29b58fe5564cd9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1678,7 +1641,25 @@ "width": null } }, - "eae1af9f890445fab406fb6b04a570ff": { + "a5b5d4b4892e4c63a5e61e1415c83270": { + "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 + } + }, + "cdee337a805b485c97ee06232ebd5b25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1731,30 +1712,7 @@ "width": null } }, - "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": { + "d8843a6d5b2347648570fd72914f0670": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1806,6 +1764,48 @@ "visibility": null, "width": null } + }, + "dc282718b936425ab24a89562150ba2f": { + "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 + } + }, + "ffa60db8cf7d41c485a3bb13eb857c21": { + "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_2b1e6fc670724ce38421474a588fa03c", + "IPY_MODEL_3a0a9f8d4e1b42459e15f37672591429", + "IPY_MODEL_4b2b2a28121d445c839398dd3d1fda37" + ], + "layout": "IPY_MODEL_cdee337a805b485c97ee06232ebd5b25", + "tabbable": null, + "tooltip": 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 cf7301700..f24727539 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": "2024-07-02T15:10:48.203913Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:46.607904Z", + "iopub.status.busy": "2024-07-02T15:25:46.607488Z", + "iopub.status.idle": "2024-07-02T15:25:47.726756Z", + "shell.execute_reply": "2024-07-02T15:25:47.726167Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:10:49.373236Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:47.729301Z", + "iopub.status.busy": "2024-07-02T15:25:47.728883Z", + "iopub.status.idle": "2024-07-02T15:25:47.731886Z", + "shell.execute_reply": "2024-07-02T15:25:47.731441Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:47.734139Z", + "iopub.status.busy": "2024-07-02T15:25:47.733736Z", + "iopub.status.idle": "2024-07-02T15:25:47.742361Z", + "shell.execute_reply": "2024-07-02T15:25:47.741931Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:47.744536Z", + "iopub.status.busy": "2024-07-02T15:25:47.744028Z", + "iopub.status.idle": "2024-07-02T15:25:47.748673Z", + "shell.execute_reply": "2024-07-02T15:25:47.748258Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:47.750739Z", + "iopub.status.busy": "2024-07-02T15:25:47.750428Z", + "iopub.status.idle": "2024-07-02T15:25:47.928460Z", + "shell.execute_reply": "2024-07-02T15:25:47.927961Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:47.930585Z", + "iopub.status.busy": "2024-07-02T15:25:47.930309Z", + "iopub.status.idle": "2024-07-02T15:25:48.243872Z", + "shell.execute_reply": "2024-07-02T15:25:48.243309Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:48.245977Z", + "iopub.status.busy": "2024-07-02T15:25:48.245615Z", + "iopub.status.idle": "2024-07-02T15:25:48.248189Z", + "shell.execute_reply": "2024-07-02T15:25:48.247774Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:48.250260Z", + "iopub.status.busy": "2024-07-02T15:25:48.249944Z", + "iopub.status.idle": "2024-07-02T15:25:48.283563Z", + "shell.execute_reply": "2024-07-02T15:25:48.283148Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:48.285612Z", + "iopub.status.busy": "2024-07-02T15:25:48.285304Z", + "iopub.status.idle": "2024-07-02T15:25:50.230973Z", + "shell.execute_reply": "2024-07-02T15:25:50.230329Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.233409Z", + "iopub.status.busy": "2024-07-02T15:25:50.233125Z", + "iopub.status.idle": "2024-07-02T15:25:50.251378Z", + "shell.execute_reply": "2024-07-02T15:25:50.250838Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.253418Z", + "iopub.status.busy": "2024-07-02T15:25:50.253081Z", + "iopub.status.idle": "2024-07-02T15:25:50.259217Z", + "shell.execute_reply": "2024-07-02T15:25:50.258792Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.261072Z", + "iopub.status.busy": "2024-07-02T15:25:50.260808Z", + "iopub.status.idle": "2024-07-02T15:25:50.266744Z", + "shell.execute_reply": "2024-07-02T15:25:50.266289Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.268802Z", + "iopub.status.busy": "2024-07-02T15:25:50.268478Z", + "iopub.status.idle": "2024-07-02T15:25:50.278692Z", + "shell.execute_reply": "2024-07-02T15:25:50.278234Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.280493Z", + "iopub.status.busy": "2024-07-02T15:25:50.280327Z", + "iopub.status.idle": "2024-07-02T15:25:50.289428Z", + "shell.execute_reply": "2024-07-02T15:25:50.288895Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.291436Z", + "iopub.status.busy": "2024-07-02T15:25:50.291131Z", + "iopub.status.idle": "2024-07-02T15:25:50.297777Z", + "shell.execute_reply": "2024-07-02T15:25:50.297230Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.299787Z", + "iopub.status.busy": "2024-07-02T15:25:50.299609Z", + "iopub.status.idle": "2024-07-02T15:25:50.308846Z", + "shell.execute_reply": "2024-07-02T15:25:50.308401Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.310782Z", + "iopub.status.busy": "2024-07-02T15:25:50.310611Z", + "iopub.status.idle": "2024-07-02T15:25:50.325913Z", + "shell.execute_reply": "2024-07-02T15:25:50.325466Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb index 2852ac72e..7aa81809c 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": "2024-07-02T15:10:54.880751Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:52.913028Z", + "iopub.status.busy": "2024-07-02T15:25:52.912856Z", + "iopub.status.idle": "2024-07-02T15:25:55.694684Z", + "shell.execute_reply": "2024-07-02T15:25:55.694130Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:55.697195Z", + "iopub.status.busy": "2024-07-02T15:25:55.696837Z", + "iopub.status.idle": "2024-07-02T15:25:55.700483Z", + "shell.execute_reply": "2024-07-02T15:25:55.700023Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:55.702500Z", + "iopub.status.busy": "2024-07-02T15:25:55.702164Z", + "iopub.status.idle": "2024-07-02T15:26:07.555107Z", + "shell.execute_reply": "2024-07-02T15:26:07.554548Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76447603597c41e58c504ba366dedf8b", + "model_id": "2af7057d9f9e4382b8969af056a70b31", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "74d7207adb634a9a9648063cd4ebf05d", + "model_id": "fb1361d5c5c24b5c861d4294a90ba506", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24554a44a66045a29398e71c18b39f2f", + "model_id": "4f579184d87043939e2f168b2dd1baec", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52a2b90360f7460f9d5e8e206e5b7b47", + "model_id": "1ae5db8cbc4445e2b177cbb75cf548c7", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1eca5328aef44e1ca18c8c422f647377", + "model_id": "954702277b7d4c54bdadf343528f7419", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8ad57476e81431f9ef31378a786d5e9", + "model_id": "e7654577166b46889495c83a9b0ff4fb", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4761c3ddf1a643e8bda01b752e44ad8b", + "model_id": "c9a8ae89a73f4e29a42513776b40e081", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8d04c2d222424f08b06b6508223878ed", + "model_id": "8b40f010ec8b45ae9bb41931dd82974f", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:07.557271Z", + "iopub.status.busy": "2024-07-02T15:26:07.556985Z", + "iopub.status.idle": "2024-07-02T15:26:07.560864Z", + "shell.execute_reply": "2024-07-02T15:26:07.560308Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:07.562941Z", + "iopub.status.busy": "2024-07-02T15:26:07.562622Z", + "iopub.status.idle": "2024-07-02T15:26:18.751429Z", + "shell.execute_reply": "2024-07-02T15:26:18.750809Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea88c13811944930a76ece93362f7e4c", + "model_id": "4b0baf8a26df47d59be9d019531cbf27", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:18.753921Z", + "iopub.status.busy": "2024-07-02T15:26:18.753662Z", + "iopub.status.idle": "2024-07-02T15:26:36.659431Z", + "shell.execute_reply": "2024-07-02T15:26:36.658833Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.662110Z", + "iopub.status.busy": "2024-07-02T15:26:36.661747Z", + "iopub.status.idle": "2024-07-02T15:26:36.667344Z", + "shell.execute_reply": "2024-07-02T15:26:36.666923Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.669291Z", + "iopub.status.busy": "2024-07-02T15:26:36.668978Z", + "iopub.status.idle": "2024-07-02T15:26:36.673079Z", + "shell.execute_reply": "2024-07-02T15:26:36.672555Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.675451Z", + "iopub.status.busy": "2024-07-02T15:26:36.675030Z", + "iopub.status.idle": "2024-07-02T15:26:36.683889Z", + "shell.execute_reply": "2024-07-02T15:26:36.683372Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.685830Z", + "iopub.status.busy": "2024-07-02T15:26:36.685513Z", + "iopub.status.idle": "2024-07-02T15:26:36.712057Z", + "shell.execute_reply": "2024-07-02T15:26:36.711539Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.714189Z", + "iopub.status.busy": "2024-07-02T15:26:36.713888Z", + "iopub.status.idle": "2024-07-02T15:27:08.085740Z", + "shell.execute_reply": "2024-07-02T15:27:08.085120Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.690\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.590\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.414\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.391\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "860c6216e3754afa972fdf5b5a0980a0", + "model_id": "c1182fd93339476e85a73f0d08e7897c", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf64e375efe14d25b7e951f059b16c23", + "model_id": "cdc60c956874436f9c4e96ec9e00a78b", "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: 4.642\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.769\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.471\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.425\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8259ba9a3539477db64cbdd68592e635", + "model_id": "fbbd08fb2b71411b97e8aee71a57b844", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "da2c01112d1f4e749b0ca2c79b09927f", + "model_id": "8a55c388532e4805923cfc19ea79d6f9", "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: 4.668\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.589\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.531\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.496\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "26d250d79c2447489401eb9ab9ace7df", + "model_id": "56a210c9781c496fb5787a5b0e41aae6", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a3115a3594ce4aa497f8a610abb0af9e", + "model_id": "c992da0389a7424a9e7d4fb9bcf57de1", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:27:08.088336Z", + "iopub.status.busy": "2024-07-02T15:27:08.087813Z", + "iopub.status.idle": "2024-07-02T15:27:08.102193Z", + "shell.execute_reply": "2024-07-02T15:27:08.101748Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:27:08.104234Z", + "iopub.status.busy": "2024-07-02T15:27:08.103814Z", + "iopub.status.idle": "2024-07-02T15:27:08.570021Z", + "shell.execute_reply": "2024-07-02T15:27:08.569491Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:27:08.572683Z", + "iopub.status.busy": "2024-07-02T15:27:08.572172Z", + "iopub.status.idle": "2024-07-02T15:28:43.786883Z", + "shell.execute_reply": "2024-07-02T15:28:43.786321Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b66bf1f268f64f16b0ab04fbfef16cb7", + "model_id": "1f428038b90b4831b764456ae8104f11", "version_major": 2, "version_minor": 0 }, @@ -1144,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:43.789396Z", + "iopub.status.busy": "2024-07-02T15:28:43.788846Z", + "iopub.status.idle": "2024-07-02T15:28:44.231298Z", + "shell.execute_reply": "2024-07-02T15:28:44.230774Z" } }, "outputs": [ @@ -1293,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.233896Z", + "iopub.status.busy": "2024-07-02T15:28:44.233519Z", + "iopub.status.idle": "2024-07-02T15:28:44.294651Z", + "shell.execute_reply": "2024-07-02T15:28:44.294159Z" } }, "outputs": [ @@ -1400,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.297029Z", + "iopub.status.busy": "2024-07-02T15:28:44.296671Z", + "iopub.status.idle": "2024-07-02T15:28:44.304958Z", + "shell.execute_reply": "2024-07-02T15:28:44.304515Z" } }, "outputs": [ @@ -1533,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.306975Z", + "iopub.status.busy": "2024-07-02T15:28:44.306659Z", + "iopub.status.idle": "2024-07-02T15:28:44.311193Z", + "shell.execute_reply": "2024-07-02T15:28:44.310773Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.313177Z", + "iopub.status.busy": "2024-07-02T15:28:44.312780Z", + "iopub.status.idle": "2024-07-02T15:28:44.809240Z", + "shell.execute_reply": "2024-07-02T15:28:44.808660Z" } }, "outputs": [ @@ -1620,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.811393Z", + "iopub.status.busy": "2024-07-02T15:28:44.811216Z", + "iopub.status.idle": "2024-07-02T15:28:44.819548Z", + "shell.execute_reply": "2024-07-02T15:28:44.819000Z" } }, "outputs": [ @@ -1790,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.821446Z", + "iopub.status.busy": "2024-07-02T15:28:44.821277Z", + "iopub.status.idle": "2024-07-02T15:28:44.828159Z", + "shell.execute_reply": "2024-07-02T15:28:44.827741Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.830046Z", + "iopub.status.busy": "2024-07-02T15:28:44.829875Z", + "iopub.status.idle": "2024-07-02T15:28:45.532924Z", + "shell.execute_reply": "2024-07-02T15:28:45.532367Z" } }, "outputs": [ @@ -1909,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.535268Z", + "iopub.status.busy": "2024-07-02T15:28:45.534928Z", + "iopub.status.idle": "2024-07-02T15:28:45.549825Z", + "shell.execute_reply": "2024-07-02T15:28:45.549359Z" } }, "outputs": [ @@ -2069,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.551906Z", + "iopub.status.busy": "2024-07-02T15:28:45.551577Z", + "iopub.status.idle": "2024-07-02T15:28:45.557082Z", + "shell.execute_reply": "2024-07-02T15:28:45.556637Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.558956Z", + "iopub.status.busy": "2024-07-02T15:28:45.558637Z", + "iopub.status.idle": "2024-07-02T15:28:45.945267Z", + "shell.execute_reply": "2024-07-02T15:28:45.944621Z" } }, "outputs": [ @@ -2202,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.947598Z", + "iopub.status.busy": "2024-07-02T15:28:45.947419Z", + "iopub.status.idle": "2024-07-02T15:28:45.956445Z", + "shell.execute_reply": "2024-07-02T15:28:45.955937Z" } }, "outputs": [ @@ -2333,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.958622Z", + "iopub.status.busy": "2024-07-02T15:28:45.958449Z", + "iopub.status.idle": "2024-07-02T15:28:45.963062Z", + "shell.execute_reply": "2024-07-02T15:28:45.962495Z" }, "nbsphinx": "hidden" }, @@ -2373,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.965091Z", + "iopub.status.busy": "2024-07-02T15:28:45.964919Z", + "iopub.status.idle": "2024-07-02T15:28:46.137736Z", + "shell.execute_reply": "2024-07-02T15:28:46.137194Z" } }, "outputs": [ @@ -2418,10 +2418,10 @@ "execution_count": 29, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:46.139960Z", + "iopub.status.busy": "2024-07-02T15:28:46.139791Z", + "iopub.status.idle": "2024-07-02T15:28:46.147323Z", + "shell.execute_reply": "2024-07-02T15:28:46.146797Z" } }, "outputs": [ @@ -2507,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:46.149214Z", + "iopub.status.busy": "2024-07-02T15:28:46.148915Z", + "iopub.status.idle": "2024-07-02T15:28:46.321937Z", + "shell.execute_reply": "2024-07-02T15:28:46.321507Z" } }, "outputs": [ @@ -2550,10 +2550,10 @@ "execution_count": 31, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:46.324150Z", + "iopub.status.busy": "2024-07-02T15:28:46.323864Z", + "iopub.status.idle": "2024-07-02T15:28:46.328076Z", + "shell.execute_reply": "2024-07-02T15:28:46.327547Z" }, "nbsphinx": "hidden" }, @@ -2590,71 +2590,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "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": { + "06215a9cafbd487ab2aabd7e76896c2d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2707,41 +2643,7 @@ "width": null } }, - "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": { + "095395d6aa1b452fb58afcbbc125c095": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2794,181 +2696,7 @@ "width": null } }, - "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": { + "0ce0648da73f406bb87655fdbb7d5b96": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3021,59 +2749,7 @@ "width": null } }, - "1a67cc49a0714635846f10e32202bddc": { - "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_ffc2140e49b04844ba200898835e603c", - "max": 8845.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_97bd081fbc2c473fb8852e3e2ef1391d", - "tabbable": null, - "tooltip": null, - "value": 8845.0 - } - }, - "1bf57cd82d684206a95f2c1e179d96ce": { - "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_c639afb167db44978154a2d4054f1d40", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ccea9ba4e771468a95236d2db5bf0264", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "1c413b52ad994cc6818c637e38897e16": { + "0df705c672fb43bbb9e3eda00edf190d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3126,7 +2802,7 @@ "width": null } }, - "1d882b744195442f85a86d91cabda0fb": { + "0eae775299c340c282f40d2c11b33d8f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3179,7 +2855,7 @@ "width": null } }, - "1e4c7bfe71e44d6dbf26a8dff0f90509": { + "12840034e5794d6f899a765d58ab3d47": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3197,54 +2873,85 @@ "text_color": null } }, - "1eca5328aef44e1ca18c8c422f647377": { + "128b18a312b94a7e8f83f6ecb1cf88ae": { "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_017871e5d3284cbbbede907271767d8c", - "IPY_MODEL_195a744ab07644a29d3e3e02108d0aa2", - "IPY_MODEL_5b344538fc3b43a98f559b15bee9a5fa" - ], - "layout": "IPY_MODEL_ddee9f52a482431d83538c9f941bfbe9", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ec1c6ef5a124498093928eb1908e2659", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_892769ffede940149b9dd4ef371e6c6b", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 26421880.0 } }, - "1f029c9a5f7842a19a17b9c9f75ecc48": { + "146f9e51cdfd42749198b0e0c23d202d": { "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_f039d6ee9460446c9cbfff2777f2bb15", - "placeholder": "​", - "style": "IPY_MODEL_5795395d630f42f09192fdb4e89f8465", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 66.57it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1fe8a37c7c4b481eb559efba20e116ac": { + "15c4c2eccdef4d22a0be623db1c181f8": { + "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": "" + } + }, + "15f53de19f414e4b8e01003cfcdb4910": { + "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 + } + }, + "1a0b4e1286164abcb03053f6e5bbc01f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3260,17 +2967,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cfdfd821fb03474ba31a8ca207281b52", + "layout": "IPY_MODEL_f0a39fcda0744e718b1f43880054db22", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2130bdaf86db48658ae28f45cec5400b", + "style": "IPY_MODEL_cb63a23d1fae4acc8bb3b9a248ed90dd", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "1ffe24ac54fe4cb49579cb75ed7db9b5": { + "1a33cdc766a747aba50b7d78c6640621": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3323,60 +3030,49 @@ "width": null } }, - "20fc3b9b9a4b48d881bc11004e753c5e": { - "model_module": "@jupyter-widgets/base", + "1a5455d51be94b3eaded7bfd47cc0457": { + "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 } }, - "2130bdaf86db48658ae28f45cec5400b": { + "1ae5db8cbc4445e2b177cbb75cf548c7": { + "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_b9f07a6e2a8343d4978b34e4580a3d43", + "IPY_MODEL_ea09ecc214a24d27ac4f9a9984295435", + "IPY_MODEL_492b6ce45b6d440ebaad497ad655ad65" + ], + "layout": "IPY_MODEL_90fc60fcde7f4ac094170e750a73f244", + "tabbable": null, + "tooltip": null + } + }, + "1bd4198065174e84ab1eca765b96c630": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3385,14 +3081,63 @@ "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "1c5296256b6443e3ba9e455d23cc69c1": { + "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_9ead80d683a6491081632b4f3b9c4a32", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ed1c314562f64740a4bdcaef37ccc36b", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "1d0de356a8a64954852f43aa35806ea0": { + "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": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_86be3dd24db14a8898fdbb06836f6a72", + "placeholder": "​", + "style": "IPY_MODEL_95465de05450430197a0f59c479dd7a9", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 6181.68 examples/s]" } }, - "23fe73f9bf064ef5bd44fb6b0e295456": { + "1d5d7a8cecbf484c972e0942d6a03995": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3445,7 +3190,7 @@ "width": null } }, - "244159f1aa444aedbd2e3e9382b4326b": { + "1d6d3c5bfb63408ebbd5b2eeefef7d5b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3498,31 +3243,30 @@ "width": null } }, - "24554a44a66045a29398e71c18b39f2f": { + "1e6a5b63ab11465f9bbf613c7e547b87": { "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_7e502dd5790a480887b970c2862e5170", - "IPY_MODEL_be2ba8934caf4f6eb77db4a243219f26", - "IPY_MODEL_b36cf6fb19144ae68ceeda6d14a0c88e" - ], - "layout": "IPY_MODEL_5d925a37f7f24e27bf2be497abcd3368", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_558fdfb2b93b4227a1cb1db2b5e4c0c6", + "placeholder": "​", + "style": "IPY_MODEL_e8bde9b4ba7b4598a05bb9a50e01884d", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 40/40 [00:00<00:00, 69.82it/s]" } }, - "24566f0819db4eecbcd0b9831228c09f": { + "1f0336efc4e541d2afd7b3eba8bb28f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3538,17 +3282,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_34591a8e01774b23835f7bec29c46ffb", - "max": 4.0, + "layout": "IPY_MODEL_2903dce2463449dd8f64d52f8dfc2e79", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_618936f2cae44a09978936d5100bc0a5", + "style": "IPY_MODEL_1bd4198065174e84ab1eca765b96c630", "tabbable": null, "tooltip": null, - "value": 4.0 + "value": 60000.0 } }, - "26d250d79c2447489401eb9ab9ace7df": { + "1f428038b90b4831b764456ae8104f11": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3563,32 +3307,57 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e452cfce19364c6fb6b50db4144baec6", - "IPY_MODEL_cb9288f8e40c422ea8a86e41ea2ba6df", - "IPY_MODEL_76f7038f04f14f28ba39042b9a0b29ca" + "IPY_MODEL_b6247fabd3c9465980d7afeb4bbd7321", + "IPY_MODEL_1c5296256b6443e3ba9e455d23cc69c1", + "IPY_MODEL_d414eae569544ed99ff808903e53ca34" ], - "layout": "IPY_MODEL_e09e931dcf844151835a19cfddd0f459", + "layout": "IPY_MODEL_85c28b4798464f5a8417d3b2a042048a", "tabbable": null, "tooltip": null } }, - "270f6bf2c3e643ea8dd1a756817909fe": { + "20f24cbda083496a82c777048b6e35d9": { "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 + } + }, + "21cb86e6ca264dc18a3fc1d6ca53b8c8": { + "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_0ce0648da73f406bb87655fdbb7d5b96", + "placeholder": "​", + "style": "IPY_MODEL_6c3cb9635fc043b49f8d58655d66e4af", + "tabbable": null, + "tooltip": null, + "value": "Downloading builder script: 100%" } }, - "2b356c8f0be148beb637a84faef5510a": { + "23558bd264264033b2d9782455420348": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3641,7 +3410,7 @@ "width": null } }, - "2b8b9a59fe67431c926345fe531d94ed": { + "27a10e327ddb4b9093bc5cd059dc47ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3694,30 +3463,7 @@ "width": null } }, - "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": { + "2903dce2463449dd8f64d52f8dfc2e79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3770,7 +3516,7 @@ "width": null } }, - "2ffdee00ac1f47dcaad4ccd7cac60390": { + "2ad38f3365374d2ebdcb347fecc7f7d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3785,33 +3531,91 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f7d5ee1b172841528a12c8d5a15409f3", + "layout": "IPY_MODEL_1d6d3c5bfb63408ebbd5b2eeefef7d5b", "placeholder": "​", - "style": "IPY_MODEL_b40cdc4a6ff640758611c963c4a3014d", + "style": "IPY_MODEL_93285482bf7a47948bfaf617e6560e62", "tabbable": null, "tooltip": null, "value": "100%" } }, - "3145d8f1a50346caa657e7b6ade92792": { + "2af7057d9f9e4382b8969af056a70b31": { "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_21cb86e6ca264dc18a3fc1d6ca53b8c8", + "IPY_MODEL_6052bd16878a4d93852135e9889c44fa", + "IPY_MODEL_b54fa885be544149b2d737f701302ac2" + ], + "layout": "IPY_MODEL_772715de5a304144aa16418d39998969", + "tabbable": null, + "tooltip": null + } + }, + "2b7d5844fe304275909217722c7a1a24": { + "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_50d4841ee3f9430bb8d68aa6756d0196", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_502bd5601d62495fb79895cb6330ab83", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "2bb7e9eb83fe4497b90616077d472f4b": { + "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_b1526e2e5168453cbfbd347cf38ac67d", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_15c4c2eccdef4d22a0be623db1c181f8", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "34591a8e01774b23835f7bec29c46ffb": { + "2c0b9d7bd4f0471997bca535de0865ad": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3864,7 +3668,7 @@ "width": null } }, - "34eceefcb9cd4c0390182a3da1170a7b": { + "2cfc559f2c3f4eac9c1f78da68485085": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3882,7 +3686,102 @@ "text_color": null } }, - "3557a3c3c9e84bdd905c60c08626cdc2": { + "2d67f6cc14674d09b09b18b656234458": { + "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_fedc3692795249689353eccb515a9530", + "max": 8845.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_eb77dac7bc62412fbb597ff597b6c989", + "tabbable": null, + "tooltip": null, + "value": 8845.0 + } + }, + "2dcfa01772b042d1a18560ea0ffe622b": { + "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_dfb04d3a63764eebb3d552ee4ed183f2", + "placeholder": "​", + "style": "IPY_MODEL_ab47dc6735ee4fb097e7a3681e03b017", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:07<00:00, 8758.15 examples/s]" + } + }, + "2fe3b5eee64448a9ac18e339494c4892": { + "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_374e196ced424d1c8ed2148c7cc59a33", + "placeholder": "​", + "style": "IPY_MODEL_4ed6c09296d547b6b724e0ff1423ae0f", + "tabbable": null, + "tooltip": null, + "value": " 4.42M/4.42M [00:00<00:00, 109MB/s]" + } + }, + "305281fe64904e5e8ca2bc65aa7315dd": { + "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_90d02f25013745e0a5fab63dd3a30607", + "placeholder": "​", + "style": "IPY_MODEL_6d30b5f8bd744ee9b5e0f9ace75f28e1", + "tabbable": null, + "tooltip": null, + "value": "Computing checksums: 100%" + } + }, + "310f996b72454ddeb46aa59742828a71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3935,25 +3834,7 @@ "width": null } }, - "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": { + "3222da55bbd24fc9a61751756a156f00": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4006,7 +3887,7 @@ "width": null } }, - "3c7b41e10bf74f948457cd90e200afe1": { + "34f47aeaa29a4cd099d2f0993d0d6cd8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4059,7 +3940,25 @@ "width": null } }, - "41acfac3e7634989b2ccc6a4e9503ea6": { + "36e037e60520443395cc77903fbf80e2": { + "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 + } + }, + "374e196ced424d1c8ed2148c7cc59a33": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4112,48 +4011,23 @@ "width": null } }, - "4398abd231514ecb8426aaa36111102f": { - "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_e1a517549d364c30bc66ec975d022bdc", - "placeholder": "​", - "style": "IPY_MODEL_9975419ead304f1bb35f1f16ba689746", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "43ad8b951bf64a8b94108e8d800b7f93": { + "39309087b22b46c9b493bf4992a41da3": { "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": "" } }, - "459779a55e494dea92f7ce2871528083": { + "3a4b4cc3e84240c48bd636935fab2f51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4206,31 +4080,7 @@ "width": null } }, - "4761c3ddf1a643e8bda01b752e44ad8b": { - "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_a685a21bed8147549eee85cbac9f5358", - "IPY_MODEL_53e6c8e4d9e0441a8b68c5ea09e71ff1", - "IPY_MODEL_726586d1a8d9496886b7c74c9ae9216a" - ], - "layout": "IPY_MODEL_86c15161ec474c7ea837b47a73b55834", - "tabbable": null, - "tooltip": null - } - }, - "47b85a2880604d3dac3aeaeda306130f": { + "3aa133a9e18a4c55834c6b51537682de": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4245,41 +4095,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c61855779cd74bc097076c41da0eed54", + "layout": "IPY_MODEL_f9a76dcea2324e3a9d9de0674e2e43fa", "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", + "style": "IPY_MODEL_dc09a5e9d9a34e4888bb48a8d57e87a7", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": "Generating train split: 100%" } }, - "492f8e571e174d8f92e7b18b8e13bc62": { + "3e820559a2b24284ba9afe2029142d45": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4332,7 +4156,7 @@ "width": null } }, - "4a3fd3d2d678476d980c97d8064b895c": { + "3fcb5400e6364ce08bf65fad0810c61a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4366,210 +4190,26 @@ "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 - } - }, - "4cb21215cfbb4594b1f5a243ec53d04e": { - "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 - } - }, - "4f03fb7101fe41f5becc6ecfabbba17a": { - "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": "" - } - }, - "52a2b90360f7460f9d5e8e206e5b7b47": { - "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_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_ad18fb7ca0984499bf68129daeec626a", - "placeholder": "​", - "style": "IPY_MODEL_b42a1a7b436f4dfcab7ea91187e0b743", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 66.30it/s]" - } - }, - "53e6c8e4d9e0441a8b68c5ea09e71ff1": { - "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_9a4fcd9b64a048d28bbf5ab053b0a6d7", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0fab8f6f053241e4a76c15c455304f10", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "5795395d630f42f09192fdb4e89f8465": { - "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 - } - }, - "5b1660aaad334a86aba0c255fa391c8d": { - "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 - } - }, - "5b344538fc3b43a98f559b15bee9a5fa": { - "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_3557a3c3c9e84bdd905c60c08626cdc2", - "placeholder": "​", - "style": "IPY_MODEL_a121eb4e9f9e48609d3a4b697bab56c8", - "tabbable": 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 + "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 } }, - "5bfde149dd324016b5d45c6bddf7fad2": { + "4017e76298ac40b38081de747f8fc4cc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4622,7 +4262,7 @@ "width": null } }, - "5d925a37f7f24e27bf2be497abcd3368": { + "409e1ed6db8f49b18a518d7fdfad5fb6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4675,7 +4315,30 @@ "width": null } }, - "603bc1c8b13349f78643f74115dd3fc5": { + "492b6ce45b6d440ebaad497ad655ad65": { + "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_d94f3c1fdf5c401e96d6819b4179d2ab", + "placeholder": "​", + "style": "IPY_MODEL_dda13c05045f458785bc68041fee393c", + "tabbable": null, + "tooltip": null, + "value": " 29.5k/29.5k [00:00<00:00, 4.76MB/s]" + } + }, + "49a533c6ecaf487b84a0ff78339cc685": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4728,7 +4391,96 @@ "width": null } }, - "618936f2cae44a09978936d5100bc0a5": { + "4b0baf8a26df47d59be9d019531cbf27": { + "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_c4cd8eb28a8c41aeb4f5021bc0c08d3b", + "IPY_MODEL_be9b65965fab4cf7aa90e105e3881e8c", + "IPY_MODEL_1d0de356a8a64954852f43aa35806ea0" + ], + "layout": "IPY_MODEL_d52ca08d27924dd2b2bbe5ac7a54db61", + "tabbable": null, + "tooltip": null + } + }, + "4b0eead14d324e12a33b2892275ebfac": { + "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_9586a95094484deda56ab744317ea1da", + "placeholder": "​", + "style": "IPY_MODEL_885edcdabbbd4bc9b9f7adbbe49d7b71", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 61.14it/s]" + } + }, + "4ed6c09296d547b6b724e0ff1423ae0f": { + "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 + } + }, + "4f579184d87043939e2f168b2dd1baec": { + "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_d78de8b0f7b447dea71448f103aaeb26", + "IPY_MODEL_128b18a312b94a7e8f83f6ecb1cf88ae", + "IPY_MODEL_65791f080fad42fead67f26e5eb9754a" + ], + "layout": "IPY_MODEL_54ed5f61afce4a71a0c2578066595ba9", + "tabbable": null, + "tooltip": null + } + }, + "502bd5601d62495fb79895cb6330ab83": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4744,7 +4496,7 @@ "description_width": "" } }, - "6719b46ec6814ef5869cf0982143c632": { + "50853ac2e6cb46bda181bddbe208896e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4759,15 +4511,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_04cb19cb27ec48d8bf07ddb3dbafdbf7", + "layout": "IPY_MODEL_49a533c6ecaf487b84a0ff78339cc685", "placeholder": "​", - "style": "IPY_MODEL_951638e8145c4431963a7a3c2f7e4b09", + "style": "IPY_MODEL_7b4c55cd9513498aa91be74b3b85459f", "tabbable": null, "tooltip": null, "value": "100%" } }, - "67307a5269b545deb5492d878a0da28f": { + "50d4841ee3f9430bb8d68aa6756d0196": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4820,7 +4572,7 @@ "width": null } }, - "691ecad63a504dffa766ac43d3b78fb4": { + "52facea6cf664b71b4519a25366ea06d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4873,105 +4625,139 @@ "width": null } }, - "6a1591c936ba4e709d74e0ec278cefd6": { + "544f80d6b8bd416c90752e013a500795": { "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_b30172a2a47d40f0a77a14540ecd18d6", - "placeholder": "​", - "style": "IPY_MODEL_4cb21215cfbb4594b1f5a243ec53d04e", + "layout": "IPY_MODEL_73fd68751918467e89cc16c9e0562921", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_86c4fe875d4b45fdae915826bfa54ee4", "tabbable": null, "tooltip": null, - "value": "Computing checksums: 100%" - } - }, - "6d8a49f522924aaaabb198187245001e": { - "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 } }, - "72292d3faee34bf1949667097765550e": { - "model_module": "@jupyter-widgets/controls", + "54ed5f61afce4a71a0c2578066595ba9": { + "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 - } - }, - "726586d1a8d9496886b7c74c9ae9216a": { - "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_c3c495d6cade4b65834b43700b7d4655", - "placeholder": "​", - "style": "IPY_MODEL_f5501a6381964ea3b5684ee5ac2a2990", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:06<00:00, 8666.45 examples/s]" + "_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 } }, - "727d6b8ed0214669ae4a1c260b9eb2ba": { - "model_module": "@jupyter-widgets/controls", + "54fdb625585a48d3a536a808c7c52093": { + "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", + "_model_name": "LayoutModel", "_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 } }, - "73b0c283a1d549d786b83bf092b5a247": { + "558fdfb2b93b4227a1cb1db2b5e4c0c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5024,7 +4810,7 @@ "width": null } }, - "74d7207adb634a9a9648063cd4ebf05d": { + "56a210c9781c496fb5787a5b0e41aae6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5039,16 +4825,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0e4e51c9e4794c289497fc9034858879", - "IPY_MODEL_1a67cc49a0714635846f10e32202bddc", - "IPY_MODEL_17a61e4c38a64575bc341e3504130922" + "IPY_MODEL_96640dfd8c924e69aea47a8e395b564a", + "IPY_MODEL_2bb7e9eb83fe4497b90616077d472f4b", + "IPY_MODEL_d3dd1e811fee428d9c87b01b54f9622a" ], - "layout": "IPY_MODEL_9cc09cfd956141a89cc11fdb073b27f5", + "layout": "IPY_MODEL_d7867621beb2472ebd0b9a1429d5f921", "tabbable": null, "tooltip": null } }, - "75137f463796461d87f332d1e82c826b": { + "5a1e3853e4354222b9159b4b5c75171e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5101,77 +4887,7 @@ "width": null } }, - "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": { + "5a58bb79eb564c4899ea598ea50d0627": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5224,30 +4940,7 @@ "width": null } }, - "7c31f8bf76884e7683d9da37b70c4cc1": { - "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_1c413b52ad994cc6818c637e38897e16", - "placeholder": "​", - "style": "IPY_MODEL_954cd8e3b16c4419af3905829990415a", - "tabbable": null, - "tooltip": null, - "value": " 29.5k/29.5k [00:00<00:00, 4.33MB/s]" - } - }, - "7cb2c22fa5f843e79a58ef3c50f8eab3": { + "5afff76b270f4649b9c0d802eb9618fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5300,30 +4993,7 @@ "width": null } }, - "7e502dd5790a480887b970c2862e5170": { - "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_691ecad63a504dffa766ac43d3b78fb4", - "placeholder": "​", - "style": "IPY_MODEL_135c72d3fed34ffc90902f7faf2f27b0", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "80bc3ab0f2e34cba9a1c31d7411ecbc5": { + "6052bd16878a4d93852135e9889c44fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5339,41 +5009,40 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c932cb59d82141628dd6ec40fc18f737", - "max": 60000.0, + "layout": "IPY_MODEL_3a4b4cc3e84240c48bd636935fab2f51", + "max": 4833.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_b4c1e3125e1f43ae99fc53aa3a9c0505", + "style": "IPY_MODEL_6c4e444daf724ae993ae806d939ccea1", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": 4833.0 } }, - "8259ba9a3539477db64cbdd68592e635": { + "65791f080fad42fead67f26e5eb9754a": { "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_47b85a2880604d3dac3aeaeda306130f", - "IPY_MODEL_1711a1f1944742ea8f38809143bc3cc8", - "IPY_MODEL_df9aa39a8a3a44d8bb0fd0ea66fb7093" - ], - "layout": "IPY_MODEL_3bbe5464e48448959dd872a56942a7f5", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d4229c1699a84c50b8786f96f822af38", + "placeholder": "​", + "style": "IPY_MODEL_15f53de19f414e4b8e01003cfcdb4910", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 26.4M/26.4M [00:00<00:00, 118MB/s]" } }, - "837e8c16ffdd41a4ae0f4631055cca57": { + "6b890c9f41c34c199f4422339b9a7115": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5426,49 +5095,7 @@ "width": null } }, - "8555aae383d74d6c851e5296a2f76824": { - "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 - } - }, - "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 - } - }, - "86c15161ec474c7ea837b47a73b55834": { + "6b978c7a0d62417fa0d91f45e66413c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5521,54 +5148,41 @@ "width": null } }, - "8d04c2d222424f08b06b6508223878ed": { + "6c3cb9635fc043b49f8d58655d66e4af": { "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_ef2ba57d7cd44e3cb5973e6264f86666", - "IPY_MODEL_bfc4b95406a34e3ca4e93013d10e4852", - "IPY_MODEL_039cadb1d8a94b7d86d6d048e6ff9a52" - ], - "layout": "IPY_MODEL_ae9e474ca22b45648e40de05673fbd79", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8f9cbba88a5d4fe29d7de8c5c017526e": { + "6c4e444daf724ae993ae806d939ccea1": { "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_ca783c0a89cb4d7aabe9391975acb8ff", - "placeholder": "​", - "style": "IPY_MODEL_43ad8b951bf64a8b94108e8d800b7f93", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "923f0259202b48ac847034f1d10cd3e9": { + "6d30b5f8bd744ee9b5e0f9ace75f28e1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5586,7 +5200,7 @@ "text_color": null } }, - "940ecb97d50847cda5f82f56b828ce1e": { + "73fd68751918467e89cc16c9e0562921": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5639,7 +5253,7 @@ "width": null } }, - "944cd1ea980e485caca5409c07bca11c": { + "772715de5a304144aa16418d39998969": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5692,23 +5306,7 @@ "width": null } }, - "94e97c6d2fc14e229a4c96a99d543e33": { - "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": "" - } - }, - "951638e8145c4431963a7a3c2f7e4b09": { + "78f28a8c417d4930886931708429e1f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5726,25 +5324,60 @@ "text_color": null } }, - "954cd8e3b16c4419af3905829990415a": { - "model_module": "@jupyter-widgets/controls", + "799cd6b93e0f4d0286080d1655cd9063": { + "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 } }, - "95c819d6d1bb48a4a1718e13d4b708d7": { + "79ad2adc81c04b66803bcf992b75007d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5797,23 +5430,7 @@ "width": null } }, - "964944a145304bd58d1fd62856054c5a": { - "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": "" - } - }, - "96a4611a123247198b0f710c5bf75bd7": { + "7aa6115a89d841d1b7d7edc93ac4abb6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5828,31 +5445,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_41acfac3e7634989b2ccc6a4e9503ea6", + "layout": "IPY_MODEL_c1b8508edf42452ba2d2219aaffc4440", "placeholder": "​", - "style": "IPY_MODEL_1e4c7bfe71e44d6dbf26a8dff0f90509", + "style": "IPY_MODEL_78f28a8c417d4930886931708429e1f3", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": "Downloading readme: 100%" } }, - "97bd081fbc2c473fb8852e3e2ef1391d": { + "7b4c55cd9513498aa91be74b3b85459f": { "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 } }, - "9882b87501b84979bb5ca2b3e35d53d1": { + "7e3f05f2d52c45518e7fa6ec6779cb4c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5867,33 +5486,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1ffe24ac54fe4cb49579cb75ed7db9b5", + "layout": "IPY_MODEL_27a10e327ddb4b9093bc5cd059dc47ea", "placeholder": "​", - "style": "IPY_MODEL_cbfecf1072ed4e2caaa31da07e0da756", + "style": "IPY_MODEL_12840034e5794d6f899a765d58ab3d47", "tabbable": null, "tooltip": null, - "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 + "value": "100%" } }, - "9a4fcd9b64a048d28bbf5ab053b0a6d7": { + "7f7f3f87082943e4a0df02dcd27f337a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5946,23 +5547,31 @@ "width": null } }, - "9aee6db9bd984fa5a023e92a81ba822d": { + "827d7da7658b40f6857044407fdda547": { "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_305281fe64904e5e8ca2bc65aa7315dd", + "IPY_MODEL_544f80d6b8bd416c90752e013a500795", + "IPY_MODEL_a1f4f066adc94df989e214551e96e664" + ], + "layout": "IPY_MODEL_3e820559a2b24284ba9afe2029142d45", + "tabbable": null, + "tooltip": null } }, - "9b19aa98c0d64d849bf08b178b9841e7": { + "85c28b4798464f5a8417d3b2a042048a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6015,25 +5624,30 @@ "width": null } }, - "9c4688aea9f9425883500665dfa60bf9": { + "8672ba9a0b95494e85eb4e493ebff06a": { "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_cdad743f4a2e4284a382dfde60cd6ce3", + "placeholder": "​", + "style": "IPY_MODEL_ef9027846b204180af89c953b7afc317", + "tabbable": null, + "tooltip": null, + "value": " 8.85k/8.85k [00:00<00:00, 1.56MB/s]" } }, - "9cc09cfd956141a89cc11fdb073b27f5": { + "86be3dd24db14a8898fdbb06836f6a72": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6086,25 +5700,23 @@ "width": null } }, - "9dfddac443ca480cac3f02058d42e8a5": { + "86c4fe875d4b45fdae915826bfa54ee4": { "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": "" } }, - "a121eb4e9f9e48609d3a4b697bab56c8": { + "885edcdabbbd4bc9b9f7adbbe49d7b71": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6122,201 +5734,123 @@ "text_color": null } }, - "a3115a3594ce4aa497f8a610abb0af9e": { + "88d02b42470e4501bb3cd1e3e6ebf7eb": { "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_6719b46ec6814ef5869cf0982143c632", - "IPY_MODEL_f7496bea7da04557b5e943e71814b3ad", - "IPY_MODEL_1f029c9a5f7842a19a17b9c9f75ecc48" - ], - "layout": "IPY_MODEL_f34228361c9a43dc9eb3dc33677084e0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3fcb5400e6364ce08bf65fad0810c61a", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d0384f03015b4b29a2fb529874891acb", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 40.0 } }, - "a64c2baabe354ca8a0f8f64ae6bc4ede": { + "892769ffede940149b9dd4ef371e6c6b": { "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": "" } }, - "a685a21bed8147549eee85cbac9f5358": { + "8a55c388532e4805923cfc19ea79d6f9": { "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_cc7be77366654c01991ea476f2293f63", - "placeholder": "​", - "style": "IPY_MODEL_6d8a49f522924aaaabb198187245001e", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7e3f05f2d52c45518e7fa6ec6779cb4c", + "IPY_MODEL_88d02b42470e4501bb3cd1e3e6ebf7eb", + "IPY_MODEL_a27c3d6d92f64500b703f8534a213527" + ], + "layout": "IPY_MODEL_c6bf48673c474f28b05932dfba8d075f", "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" + "tooltip": null } }, - "a8e1731ca82b4390af97a6c9c6efcc51": { - "model_module": "@jupyter-widgets/base", + "8b40f010ec8b45ae9bb41931dd82974f": { + "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/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": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f87fe7987a7548d391653382cc0cac40", + "IPY_MODEL_c05105f4332440beb07521cc771bd4ab", + "IPY_MODEL_e46b79664a5b499091764978698553f1" + ], + "layout": "IPY_MODEL_6b890c9f41c34c199f4422339b9a7115", + "tabbable": null, + "tooltip": null } }, - "a972ac7ecf4c4746bb430a3161226786": { + "8b8994fc668b4dceb9c06453fb39bc0e": { "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_9b19aa98c0d64d849bf08b178b9841e7", - "placeholder": "​", - "style": "IPY_MODEL_3145d8f1a50346caa657e7b6ade92792", + "layout": "IPY_MODEL_79ad2adc81c04b66803bcf992b75007d", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e8c7dbc5d3214dcb86aaab520f395f74", "tabbable": null, "tooltip": null, - "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 + "value": 4422102.0 } }, - "ae9e474ca22b45648e40de05673fbd79": { + "8c42c7196455418d88da9ac9a371e461": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6369,7 +5903,56 @@ "width": null } }, - "af859482bc704e13a4993c698c9ba181": { + "8d585cf69b3f4724a623579d5335bfd9": { + "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_4017e76298ac40b38081de747f8fc4cc", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f457d8bcb4404971bf2ad18e7efff0e1", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "8f5c14e14fcf4f388e2f257aa70609f8": { + "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_34f47aeaa29a4cd099d2f0993d0d6cd8", + "placeholder": "​", + "style": "IPY_MODEL_dc5e03ce2bc744b4b89300005f223fa6", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 71.56it/s]" + } + }, + "8f6be0928cea469a9271f1c136a90fec": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6387,7 +5970,7 @@ "text_color": null } }, - "b30172a2a47d40f0a77a14540ecd18d6": { + "90d02f25013745e0a5fab63dd3a30607": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6440,7 +6023,7 @@ "width": null } }, - "b31e5df3571f4ac28345bf70cd4e947c": { + "90fc60fcde7f4ac094170e750a73f244": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6493,30 +6076,7 @@ "width": null } }, - "b36cf6fb19144ae68ceeda6d14a0c88e": { - "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_a8e1731ca82b4390af97a6c9c6efcc51", - "placeholder": "​", - "style": "IPY_MODEL_d2b86bd7871548f5b1e0d2d312d2ef24", - "tabbable": null, - "tooltip": null, - "value": " 26.4M/26.4M [00:00<00:00, 109MB/s]" - } - }, - "b40cdc4a6ff640758611c963c4a3014d": { + "93285482bf7a47948bfaf617e6560e62": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6534,7 +6094,7 @@ "text_color": null } }, - "b42a1a7b436f4dfcab7ea91187e0b743": { + "9517d2ba00ed4eb094644c417a85d507": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6552,23 +6112,25 @@ "text_color": null } }, - "b4c1e3125e1f43ae99fc53aa3a9c0505": { + "95465de05450430197a0f59c479dd7a9": { "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 } }, - "b66bf1f268f64f16b0ab04fbfef16cb7": { + "954702277b7d4c54bdadf343528f7419": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6583,16 +6145,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_8f9cbba88a5d4fe29d7de8c5c017526e", - "IPY_MODEL_80bc3ab0f2e34cba9a1c31d7411ecbc5", - "IPY_MODEL_9882b87501b84979bb5ca2b3e35d53d1" + "IPY_MODEL_f1ced5ef687547f290b64d20124f82b3", + "IPY_MODEL_8b8994fc668b4dceb9c06453fb39bc0e", + "IPY_MODEL_2fe3b5eee64448a9ac18e339494c4892" ], - "layout": "IPY_MODEL_79afef11e9d74259a71e933ac999caf5", + "layout": "IPY_MODEL_799cd6b93e0f4d0286080d1655cd9063", "tabbable": null, "tooltip": null } }, - "b76a6f2e44334c838c6350a5d3bbb530": { + "9586a95094484deda56ab744317ea1da": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6645,109 +6207,30 @@ "width": null } }, - "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", - "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_4398abd231514ecb8426aaa36111102f", - "IPY_MODEL_ef6365260ca348ed903c1353490fc655", - "IPY_MODEL_cfbabccb48c14370b2a05af8b6c6a2ce" - ], - "layout": "IPY_MODEL_20fc3b9b9a4b48d881bc11004e753c5e", - "tabbable": null, - "tooltip": null - } - }, - "bfc4b95406a34e3ca4e93013d10e4852": { + "96640dfd8c924e69aea47a8e395b564a": { "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_23fe73f9bf064ef5bd44fb6b0e295456", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9aee6db9bd984fa5a023e92a81ba822d", + "layout": "IPY_MODEL_54fdb625585a48d3a536a808c7c52093", + "placeholder": "​", + "style": "IPY_MODEL_2cfc559f2c3f4eac9c1f78da68485085", "tabbable": null, "tooltip": null, - "value": 10000.0 + "value": "100%" } }, - "c0e6653485214e6295a6c309d77804a3": { + "96cfabb5484149d1adc2aa86092bc96d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6800,7 +6283,7 @@ "width": null } }, - "c3c495d6cade4b65834b43700b7d4655": { + "9ead80d683a6491081632b4f3b9c4a32": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6853,60 +6336,118 @@ "width": null } }, - "c61855779cd74bc097076c41da0eed54": { - "model_module": "@jupyter-widgets/base", + "a100d982151c42ccbe4279f192b139bb": { + "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": "" + } + }, + "a1f4f066adc94df989e214551e96e664": { + "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_095395d6aa1b452fb58afcbbc125c095", + "placeholder": "​", + "style": "IPY_MODEL_af48e3d34e0b412c8ae2a4ca8e9215c2", + "tabbable": null, + "tooltip": null, + "value": " 4/4 [00:00<00:00, 1251.75it/s]" + } + }, + "a27c3d6d92f64500b703f8534a213527": { + "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_baa7b6d1e7a243a095f0c691e7f0b5e4", + "placeholder": "​", + "style": "IPY_MODEL_1a5455d51be94b3eaded7bfd47cc0457", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 63.05it/s]" + } + }, + "a2a2ec56076b4b6c9ee933df0360e05b": { + "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_acb03260aac345648a1ec54ab5a858f9", + "placeholder": "​", + "style": "IPY_MODEL_b1d1d60578f74d3582ef5ae972a07297", + "tabbable": null, + "tooltip": null, + "value": " 5.15k/5.15k [00:00<00:00, 887kB/s]" + } + }, + "a2c2386f0ec849418856d89ef28a3475": { + "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_0df705c672fb43bbb9e3eda00edf190d", + "max": 5148.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b1b4a133d95946b4ad1d95acf055e6d9", + "tabbable": null, + "tooltip": null, + "value": 5148.0 } }, - "c639afb167db44978154a2d4054f1d40": { + "a890ab18730f4331a1b512e514f1475a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6959,7 +6500,7 @@ "width": null } }, - "c867ebcf347146abb7ec6115797953c1": { + "a89ae7c0705f458f97f4e50751463686": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7012,31 +6553,25 @@ "width": null } }, - "c8ad57476e81431f9ef31378a786d5e9": { + "ab47dc6735ee4fb097e7a3681e03b017": { "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_96a4611a123247198b0f710c5bf75bd7", - "IPY_MODEL_cf26e95faaa94e23b57c2179d5e9c64e", - "IPY_MODEL_d6650de0045c4384af30511ccab8a0f7" - ], - "layout": "IPY_MODEL_d0b1f8cfd74c4375af09960b50139328", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c932cb59d82141628dd6ec40fc18f737": { + "acb03260aac345648a1ec54ab5a858f9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7089,7 +6624,84 @@ "width": null } }, - "ca783c0a89cb4d7aabe9391975acb8ff": { + "acf5ba82150c4bf9b8924a6d1e39bfb6": { + "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_2c0b9d7bd4f0471997bca535de0865ad", + "placeholder": "​", + "style": "IPY_MODEL_afcb83b39c2e4478ba9ade81cf3b2fb6", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 69.50it/s]" + } + }, + "af48e3d34e0b412c8ae2a4ca8e9215c2": { + "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 + } + }, + "afcb83b39c2e4478ba9ade81cf3b2fb6": { + "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 + } + }, + "b0214bcbe8044efda63d35e4fa778d43": { + "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 + } + }, + "b1526e2e5168453cbfbd347cf38ac67d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7142,7 +6754,103 @@ "width": null } }, - "cabee28dffcd4272a0c22c2d5c770596": { + "b1b4a133d95946b4ad1d95acf055e6d9": { + "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": "" + } + }, + "b1d1d60578f74d3582ef5ae972a07297": { + "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 + } + }, + "b270b4b9160b4aa4bbad325928110497": { + "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": "" + } + }, + "b54fa885be544149b2d737f701302ac2": { + "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_0eae775299c340c282f40d2c11b33d8f", + "placeholder": "​", + "style": "IPY_MODEL_df797002b4914bef8001c5e05b7be0af", + "tabbable": null, + "tooltip": null, + "value": " 4.83k/4.83k [00:00<00:00, 629kB/s]" + } + }, + "b6247fabd3c9465980d7afeb4bbd7321": { + "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_5a1e3853e4354222b9159b4b5c75171e", + "placeholder": "​", + "style": "IPY_MODEL_146f9e51cdfd42749198b0e0c23d202d", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "b838c277255740a2b7434474c516e95e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7195,7 +6903,30 @@ "width": null } }, - "cac4ab6d168f46baa1c90ed6d428997f": { + "b9f07a6e2a8343d4978b34e4580a3d43": { + "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_8c42c7196455418d88da9ac9a371e461", + "placeholder": "​", + "style": "IPY_MODEL_f92dff6949004ae1808eebd99ba98566", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "baa7b6d1e7a243a095f0c691e7f0b5e4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7248,69 +6979,56 @@ "width": null } }, - "cb9288f8e40c422ea8a86e41ea2ba6df": { + "bbb269d13d924b65a58b0cf5db1b2407": { "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_1d882b744195442f85a86d91cabda0fb", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_eab0669b062645d4ac17d3d2f88de120", + "layout": "IPY_MODEL_fe362277556a4cf0beffd4b12f3e9ffd", + "placeholder": "​", + "style": "IPY_MODEL_20f24cbda083496a82c777048b6e35d9", "tabbable": null, "tooltip": null, - "value": 40.0 - } - }, - "cbfecf1072ed4e2caaa31da07e0da756": { - "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": "100%" } }, - "cc12a064eb004d6390ea2006fbb725ed": { + "be9b65965fab4cf7aa90e105e3881e8c": { "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_5afff76b270f4649b9c0d802eb9618fb", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f1266a3dc99447f193096776b9b427f1", + "tabbable": null, + "tooltip": null, + "value": 60000.0 } }, - "cc7be77366654c01991ea476f2293f63": { + "bfe066abcd864943a83e96669cadf170": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7363,7 +7081,57 @@ "width": null } }, - "cca5b38ac94f4b60ab4f617db8198ff8": { + "c05105f4332440beb07521cc771bd4ab": { + "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_a890ab18730f4331a1b512e514f1475a", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a100d982151c42ccbe4279f192b139bb", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "c1182fd93339476e85a73f0d08e7897c": { + "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_f36e7b0209f84d828549adece0524582", + "IPY_MODEL_dc304d6b46fb4dc592080af62f1c4444", + "IPY_MODEL_4b0eead14d324e12a33b2892275ebfac" + ], + "layout": "IPY_MODEL_52facea6cf664b71b4519a25366ea06d", + "tabbable": null, + "tooltip": null + } + }, + "c1b8508edf42452ba2d2219aaffc4440": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7416,88 +7184,7 @@ "width": null } }, - "ccea9ba4e771468a95236d2db5bf0264": { - "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": "" - } - }, - "cd71b5032d334106871d93c7901358eb": { - "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": "" - } - }, - "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": { + "c31ca76550e249ca985f248853c71722": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7550,7 +7237,7 @@ "width": null } }, - "d0b1f8cfd74c4375af09960b50139328": { + "c4b8fffb19594f58b1a2ba4aa8ec7d11": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7603,25 +7290,30 @@ "width": null } }, - "d2b86bd7871548f5b1e0d2d312d2ef24": { + "c4cd8eb28a8c41aeb4f5021bc0c08d3b": { "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_6b978c7a0d62417fa0d91f45e66413c4", + "placeholder": "​", + "style": "IPY_MODEL_e20bd95703544b4aad1922863bff7b0f", + "tabbable": null, + "tooltip": null, + "value": "Map (num_proc=4): 100%" } }, - "d4d042ad47854553b9660b36249c7347": { + "c6bf48673c474f28b05932dfba8d075f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7674,53 +7366,89 @@ "width": null } }, - "d6650de0045c4384af30511ccab8a0f7": { + "c992da0389a7424a9e7d4fb9bcf57de1": { "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_cabee28dffcd4272a0c22c2d5c770596", - "placeholder": "​", - "style": "IPY_MODEL_34eceefcb9cd4c0390182a3da1170a7b", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_50853ac2e6cb46bda181bddbe208896e", + "IPY_MODEL_2b7d5844fe304275909217722c7a1a24", + "IPY_MODEL_acf5ba82150c4bf9b8924a6d1e39bfb6" + ], + "layout": "IPY_MODEL_409e1ed6db8f49b18a518d7fdfad5fb6", "tabbable": null, - "tooltip": null, - "value": " 5.15k/5.15k [00:00<00:00, 871kB/s]" + "tooltip": null } }, - "d73ad391537b4ea7a7841fe7944270c0": { + "c9a8ae89a73f4e29a42513776b40e081": { "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_1a4cf1e028c44677995965a00cb4aa35", - "placeholder": "​", - "style": "IPY_MODEL_a64c2baabe354ca8a0f8f64ae6bc4ede", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3aa133a9e18a4c55834c6b51537682de", + "IPY_MODEL_1f0336efc4e541d2afd7b3eba8bb28f1", + "IPY_MODEL_2dcfa01772b042d1a18560ea0ffe622b" + ], + "layout": "IPY_MODEL_bfe066abcd864943a83e96669cadf170", "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 7362.49 examples/s]" + "tooltip": null + } + }, + "cb63a23d1fae4acc8bb3b9a248ed90dd": { + "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": "" + } + }, + "cd957436866649e6a210ee9e7fcb8650": { + "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 } }, - "d9e695fa0c8741669e44cf4502f36d47": { + "cdad743f4a2e4284a382dfde60cd6ce3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7773,7 +7501,7 @@ "width": null } }, - "da2c01112d1f4e749b0ca2c79b09927f": { + "cdc60c956874436f9c4e96ec9e00a78b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -7788,34 +7516,78 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_fc1d8341e6624186a80fabb78e1c9a60", - "IPY_MODEL_1bf57cd82d684206a95f2c1e179d96ce", - "IPY_MODEL_53049123e6a9407b8314d7f6b14f99af" + "IPY_MODEL_bbb269d13d924b65a58b0cf5db1b2407", + "IPY_MODEL_8d585cf69b3f4724a623579d5335bfd9", + "IPY_MODEL_1e6a5b63ab11465f9bbf613c7e547b87" ], - "layout": "IPY_MODEL_2b356c8f0be148beb637a84faef5510a", + "layout": "IPY_MODEL_23558bd264264033b2d9782455420348", "tabbable": null, "tooltip": null } }, - "dc0ef01ff0b14b05bd75d62108c813b6": { + "d0384f03015b4b29a2fb529874891acb": { "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": "" + } + }, + "d3dd1e811fee428d9c87b01b54f9622a": { + "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_5a58bb79eb564c4899ea598ea50d0627", + "placeholder": "​", + "style": "IPY_MODEL_f685e18fb6ca48bd8670c03342805ecf", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 60.34it/s]" + } + }, + "d414eae569544ed99ff808903e53ca34": { + "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_96cfabb5484149d1adc2aa86092bc96d", + "placeholder": "​", + "style": "IPY_MODEL_b0214bcbe8044efda63d35e4fa778d43", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:35<00:00, 1727.59it/s]" } }, - "dce15d5cb91b42a780b136a398da089e": { + "d4229c1699a84c50b8786f96f822af38": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7868,25 +7640,7 @@ "width": null } }, - "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": { + "d52ca08d27924dd2b2bbe5ac7a54db61": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7939,7 +7693,7 @@ "width": null } }, - "ddee9f52a482431d83538c9f941bfbe9": { + "d7867621beb2472ebd0b9a1429d5f921": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7992,7 +7746,7 @@ "width": null } }, - "df9aa39a8a3a44d8bb0fd0ea66fb7093": { + "d78de8b0f7b447dea71448f103aaeb26": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8007,31 +7761,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2f6cf6dc5a4845588c6a21a1c21879bc", + "layout": "IPY_MODEL_c31ca76550e249ca985f248853c71722", "placeholder": "​", - "style": "IPY_MODEL_dd1800dbb9064a41a02af5ec0f1f5b67", + "style": "IPY_MODEL_36e037e60520443395cc77903fbf80e2", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.93it/s]" - } - }, - "e03718c7114049e881b5ec8c92d14511": { - "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": "" + "value": "Downloading data: 100%" } }, - "e09e931dcf844151835a19cfddd0f459": { + "d94f3c1fdf5c401e96d6819b4179d2ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8084,7 +7822,123 @@ "width": null } }, - "e1a517549d364c30bc66ec975d022bdc": { + "daf2ab05255e474d8859a35c484b2160": { + "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 + } + }, + "dc09a5e9d9a34e4888bb48a8d57e87a7": { + "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 + } + }, + "dc304d6b46fb4dc592080af62f1c4444": { + "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_c4b8fffb19594f58b1a2ba4aa8ec7d11", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b270b4b9160b4aa4bbad325928110497", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "dc5e03ce2bc744b4b89300005f223fa6": { + "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 + } + }, + "dda13c05045f458785bc68041fee393c": { + "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 + } + }, + "df797002b4914bef8001c5e05b7be0af": { + "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 + } + }, + "dfb04d3a63764eebb3d552ee4ed183f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8137,30 +7991,25 @@ "width": null } }, - "e452cfce19364c6fb6b50db4144baec6": { + "e20bd95703544b4aad1922863bff7b0f": { "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_cca5b38ac94f4b60ab4f617db8198ff8", - "placeholder": "​", - "style": "IPY_MODEL_8555aae383d74d6c851e5296a2f76824", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e5a63f7446074676b31de8da1ee38a2b": { + "e2e98ad255df41788a76c3224436f2f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8175,31 +8024,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cac4ab6d168f46baa1c90ed6d428997f", + "layout": "IPY_MODEL_e8b64de668f34ba6bedabb72cec29004", "placeholder": "​", - "style": "IPY_MODEL_af859482bc704e13a4993c698c9ba181", + "style": "IPY_MODEL_8f6be0928cea469a9271f1c136a90fec", "tabbable": null, "tooltip": null, - "value": " 4.83k/4.83k [00:00<00:00, 612kB/s]" - } - }, - "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": "" + "value": "Downloading data: 100%" } }, - "e6fa033915fa4a5987c2c8fa374502e4": { + "e46b79664a5b499091764978698553f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8214,15 +8047,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3c7b41e10bf74f948457cd90e200afe1", + "layout": "IPY_MODEL_b838c277255740a2b7434474c516e95e", "placeholder": "​", - "style": "IPY_MODEL_9dfddac443ca480cac3f02058d42e8a5", + "style": "IPY_MODEL_fad6c2d0a0214e7780bca6c7a90edb1a", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": " 10000/10000 [00:01<00:00, 8796.13 examples/s]" } }, - "ea88c13811944930a76ece93362f7e4c": { + "e7654577166b46889495c83a9b0ff4fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -8237,32 +8070,69 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2e0923cec6894e0bb926973c7f50f3d3", - "IPY_MODEL_483cd57673244db39d3524fef0835d46", - "IPY_MODEL_d73ad391537b4ea7a7841fe7944270c0" + "IPY_MODEL_e2e98ad255df41788a76c3224436f2f7", + "IPY_MODEL_a2c2386f0ec849418856d89ef28a3475", + "IPY_MODEL_a2a2ec56076b4b6c9ee933df0360e05b" ], - "layout": "IPY_MODEL_7cb2c22fa5f843e79a58ef3c50f8eab3", + "layout": "IPY_MODEL_1a33cdc766a747aba50b7d78c6640621", "tabbable": null, "tooltip": null } }, - "eab0669b062645d4ac17d3d2f88de120": { - "model_module": "@jupyter-widgets/controls", + "e8b64de668f34ba6bedabb72cec29004": { + "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", + "_model_name": "LayoutModel", "_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 } }, - "ec1afef96b264d888ab90ae59248acd5": { + "e8bde9b4ba7b4598a05bb9a50e01884d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -8273,63 +8143,30 @@ "_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 - } - }, - "ec5400e36f274359b296a5d6e0e33a39": { - "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_944cd1ea980e485caca5409c07bca11c", - "max": 29515.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_270f6bf2c3e643ea8dd1a756817909fe", - "tabbable": null, - "tooltip": null, - "value": 29515.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ef2ba57d7cd44e3cb5973e6264f86666": { + "e8c7dbc5d3214dcb86aaab520f395f74": { "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_244159f1aa444aedbd2e3e9382b4326b", - "placeholder": "​", - "style": "IPY_MODEL_cc12a064eb004d6390ea2006fbb725ed", - "tabbable": null, - "tooltip": null, - "value": "Generating test split: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "ef6365260ca348ed903c1353490fc655": { + "ea09ecc214a24d27ac4f9a9984295435": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -8345,40 +8182,33 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f6079e4e10e545668732bdada9c65e44", - "max": 40.0, + "layout": "IPY_MODEL_7f7f3f87082943e4a0df02dcd27f337a", + "max": 29515.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e03718c7114049e881b5ec8c92d14511", + "style": "IPY_MODEL_39309087b22b46c9b493bf4992a41da3", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 29515.0 } }, - "efabf4edc4f14504b06bad7e71659998": { + "eb77dac7bc62412fbb597ff597b6c989": { "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_5bfde149dd324016b5d45c6bddf7fad2", - "placeholder": "​", - "style": "IPY_MODEL_dc0ef01ff0b14b05bd75d62108c813b6", - "tabbable": null, - "tooltip": null, - "value": "Downloading builder script: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f039d6ee9460446c9cbfff2777f2bb15": { + "ec1c6ef5a124498093928eb1908e2659": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8431,7 +8261,41 @@ "width": null } }, - "f34228361c9a43dc9eb3dc33677084e0": { + "ed1c314562f64740a4bdcaef37ccc36b": { + "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": "" + } + }, + "ef9027846b204180af89c953b7afc317": { + "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 + } + }, + "f0a39fcda0744e718b1f43880054db22": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8484,7 +8348,126 @@ "width": null } }, - "f5501a6381964ea3b5684ee5ac2a2990": { + "f1266a3dc99447f193096776b9b427f1": { + "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": "" + } + }, + "f1ced5ef687547f290b64d20124f82b3": { + "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_310f996b72454ddeb46aa59742828a71", + "placeholder": "​", + "style": "IPY_MODEL_9517d2ba00ed4eb094644c417a85d507", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "f36e7b0209f84d828549adece0524582": { + "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_1d5d7a8cecbf484c972e0942d6a03995", + "placeholder": "​", + "style": "IPY_MODEL_cd957436866649e6a210ee9e7fcb8650", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "f457d8bcb4404971bf2ad18e7efff0e1": { + "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": "" + } + }, + "f685e18fb6ca48bd8670c03342805ecf": { + "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 + } + }, + "f87fe7987a7548d391653382cc0cac40": { + "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_06215a9cafbd487ab2aabd7e76896c2d", + "placeholder": "​", + "style": "IPY_MODEL_daf2ab05255e474d8859a35c484b2160", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" + } + }, + "f92dff6949004ae1808eebd99ba98566": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -8502,7 +8485,7 @@ "text_color": null } }, - "f6079e4e10e545668732bdada9c65e44": { + "f9a76dcea2324e3a9d9de0674e2e43fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8555,33 +8538,73 @@ "width": null } }, - "f7496bea7da04557b5e943e71814b3ad": { + "fad6c2d0a0214e7780bca6c7a90edb1a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "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 + } + }, + "fb1361d5c5c24b5c861d4294a90ba506": { + "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": "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_67307a5269b545deb5492d878a0da28f", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e6d9862771cc40a5bc3f7cb24e629eba", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7aa6115a89d841d1b7d7edc93ac4abb6", + "IPY_MODEL_2d67f6cc14674d09b09b18b656234458", + "IPY_MODEL_8672ba9a0b95494e85eb4e493ebff06a" + ], + "layout": "IPY_MODEL_3222da55bbd24fc9a61751756a156f00", "tabbable": null, - "tooltip": null, - "value": 40.0 + "tooltip": null + } + }, + "fbbd08fb2b71411b97e8aee71a57b844": { + "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_2ad38f3365374d2ebdcb347fecc7f7d9", + "IPY_MODEL_1a0b4e1286164abcb03053f6e5bbc01f", + "IPY_MODEL_8f5c14e14fcf4f388e2f257aa70609f8" + ], + "layout": "IPY_MODEL_a89ae7c0705f458f97f4e50751463686", + "tabbable": null, + "tooltip": null } }, - "f7d5ee1b172841528a12c8d5a15409f3": { + "fe362277556a4cf0beffd4b12f3e9ffd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8634,30 +8657,7 @@ "width": null } }, - "fc1d8341e6624186a80fabb78e1c9a60": { - "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_603bc1c8b13349f78643f74115dd3fc5", - "placeholder": "​", - "style": "IPY_MODEL_1361429788e54a748edb03027a9cab6a", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "ffc2140e49b04844ba200898835e603c": { + "fedc3692795249689353eccb515a9530": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 079f8c422..dc39d6bb4 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": "2024-07-02T15:13:52.731591Z", - "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" + "iopub.execute_input": "2024-07-02T15:28:49.899605Z", + "iopub.status.busy": "2024-07-02T15:28:49.899202Z", + "iopub.status.idle": "2024-07-02T15:28:50.991494Z", + "shell.execute_reply": "2024-07-02T15:28:50.990945Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:13:53.829437Z", - "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" + "iopub.execute_input": "2024-07-02T15:28:50.994031Z", + "iopub.status.busy": "2024-07-02T15:28:50.993599Z", + "iopub.status.idle": "2024-07-02T15:28:51.011326Z", + "shell.execute_reply": "2024-07-02T15:28:51.010872Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.013324Z", + "iopub.status.busy": "2024-07-02T15:28:51.012973Z", + "iopub.status.idle": "2024-07-02T15:28:51.039303Z", + "shell.execute_reply": "2024-07-02T15:28:51.038773Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.041327Z", + "iopub.status.busy": "2024-07-02T15:28:51.040919Z", + "iopub.status.idle": "2024-07-02T15:28:51.044291Z", + "shell.execute_reply": "2024-07-02T15:28:51.043776Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.046448Z", + "iopub.status.busy": "2024-07-02T15:28:51.046144Z", + "iopub.status.idle": "2024-07-02T15:28:51.053455Z", + "shell.execute_reply": "2024-07-02T15:28:51.052943Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.055596Z", + "iopub.status.busy": "2024-07-02T15:28:51.055273Z", + "iopub.status.idle": "2024-07-02T15:28:51.057820Z", + "shell.execute_reply": "2024-07-02T15:28:51.057306Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.059818Z", + "iopub.status.busy": "2024-07-02T15:28:51.059505Z", + "iopub.status.idle": "2024-07-02T15:28:54.001735Z", + "shell.execute_reply": "2024-07-02T15:28:54.001210Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:54.004479Z", + "iopub.status.busy": "2024-07-02T15:28:54.004049Z", + "iopub.status.idle": "2024-07-02T15:28:54.014544Z", + "shell.execute_reply": "2024-07-02T15:28:54.014111Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:54.016447Z", + "iopub.status.busy": "2024-07-02T15:28:54.016274Z", + "iopub.status.idle": "2024-07-02T15:28:55.858279Z", + "shell.execute_reply": "2024-07-02T15:28:55.857718Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.860474Z", + "iopub.status.busy": "2024-07-02T15:28:55.860182Z", + "iopub.status.idle": "2024-07-02T15:28:55.878763Z", + "shell.execute_reply": "2024-07-02T15:28:55.878229Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.880697Z", + "iopub.status.busy": "2024-07-02T15:28:55.880394Z", + "iopub.status.idle": "2024-07-02T15:28:55.888083Z", + "shell.execute_reply": "2024-07-02T15:28:55.887563Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.890072Z", + "iopub.status.busy": "2024-07-02T15:28:55.889768Z", + "iopub.status.idle": "2024-07-02T15:28:55.898858Z", + "shell.execute_reply": "2024-07-02T15:28:55.898441Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.900726Z", + "iopub.status.busy": "2024-07-02T15:28:55.900551Z", + "iopub.status.idle": "2024-07-02T15:28:55.908414Z", + "shell.execute_reply": "2024-07-02T15:28:55.907969Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.910406Z", + "iopub.status.busy": "2024-07-02T15:28:55.910090Z", + "iopub.status.idle": "2024-07-02T15:28:55.918338Z", + "shell.execute_reply": "2024-07-02T15:28:55.917916Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.920407Z", + "iopub.status.busy": "2024-07-02T15:28:55.920098Z", + "iopub.status.idle": "2024-07-02T15:28:55.927249Z", + "shell.execute_reply": "2024-07-02T15:28:55.926766Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.929330Z", + "iopub.status.busy": "2024-07-02T15:28:55.929026Z", + "iopub.status.idle": "2024-07-02T15:28:55.936059Z", + "shell.execute_reply": "2024-07-02T15:28:55.935601Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.938151Z", + "iopub.status.busy": "2024-07-02T15:28:55.937828Z", + "iopub.status.idle": "2024-07-02T15:28:55.946138Z", + "shell.execute_reply": "2024-07-02T15:28:55.945575Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 5204560ef..551ba82cd 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": "2024-07-02T15:14:01.500489Z", - "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" + "iopub.execute_input": "2024-07-02T15:28:58.631257Z", + "iopub.status.busy": "2024-07-02T15:28:58.631091Z", + "iopub.status.idle": "2024-07-02T15:29:01.231065Z", + "shell.execute_reply": "2024-07-02T15:29:01.230522Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:14:04.115579Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:01.233809Z", + "iopub.status.busy": "2024-07-02T15:29:01.233237Z", + "iopub.status.idle": "2024-07-02T15:29:01.236539Z", + "shell.execute_reply": "2024-07-02T15:29:01.236030Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.238509Z", + "iopub.status.busy": "2024-07-02T15:29:01.238205Z", + "iopub.status.idle": "2024-07-02T15:29:01.241273Z", + "shell.execute_reply": "2024-07-02T15:29:01.240730Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.243270Z", + "iopub.status.busy": "2024-07-02T15:29:01.242968Z", + "iopub.status.idle": "2024-07-02T15:29:01.265333Z", + "shell.execute_reply": "2024-07-02T15:29:01.264818Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.267342Z", + "iopub.status.busy": "2024-07-02T15:29:01.267008Z", + "iopub.status.idle": "2024-07-02T15:29:01.270863Z", + "shell.execute_reply": "2024-07-02T15:29:01.270395Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\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" + "Classes: {'supported_cards_and_currencies', 'beneficiary_not_allowed', 'cancel_transfer', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'card_about_to_expire', 'change_pin', 'apple_pay_or_google_pay', 'getting_spare_card', 'visa_or_mastercard'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.272802Z", + "iopub.status.busy": "2024-07-02T15:29:01.272475Z", + "iopub.status.idle": "2024-07-02T15:29:01.275495Z", + "shell.execute_reply": "2024-07-02T15:29:01.274962Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.277490Z", + "iopub.status.busy": "2024-07-02T15:29:01.277167Z", + "iopub.status.idle": "2024-07-02T15:29:05.263507Z", + "shell.execute_reply": "2024-07-02T15:29:05.262875Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:05.266231Z", + "iopub.status.busy": "2024-07-02T15:29:05.265792Z", + "iopub.status.idle": "2024-07-02T15:29:06.185238Z", + "shell.execute_reply": "2024-07-02T15:29:06.184672Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:06.187923Z", + "iopub.status.busy": "2024-07-02T15:29:06.187416Z", + "iopub.status.idle": "2024-07-02T15:29:06.190514Z", + "shell.execute_reply": "2024-07-02T15:29:06.190036Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:06.192745Z", + "iopub.status.busy": "2024-07-02T15:29:06.192366Z", + "iopub.status.idle": "2024-07-02T15:29:08.085828Z", + "shell.execute_reply": "2024-07-02T15:29:08.085168Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.090050Z", + "iopub.status.busy": "2024-07-02T15:29:08.088906Z", + "iopub.status.idle": "2024-07-02T15:29:08.114316Z", + "shell.execute_reply": "2024-07-02T15:29:08.113817Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.117749Z", + "iopub.status.busy": "2024-07-02T15:29:08.116842Z", + "iopub.status.idle": "2024-07-02T15:29:08.126972Z", + "shell.execute_reply": "2024-07-02T15:29:08.126538Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.129068Z", + "iopub.status.busy": "2024-07-02T15:29:08.128673Z", + "iopub.status.idle": "2024-07-02T15:29:08.133020Z", + "shell.execute_reply": "2024-07-02T15:29:08.132584Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.135018Z", + "iopub.status.busy": "2024-07-02T15:29:08.134691Z", + "iopub.status.idle": "2024-07-02T15:29:08.140889Z", + "shell.execute_reply": "2024-07-02T15:29:08.140420Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.142906Z", + "iopub.status.busy": "2024-07-02T15:29:08.142588Z", + "iopub.status.idle": "2024-07-02T15:29:08.148868Z", + "shell.execute_reply": "2024-07-02T15:29:08.148318Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.150927Z", + "iopub.status.busy": "2024-07-02T15:29:08.150568Z", + "iopub.status.idle": "2024-07-02T15:29:08.156157Z", + "shell.execute_reply": "2024-07-02T15:29:08.155694Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.158158Z", + "iopub.status.busy": "2024-07-02T15:29:08.157833Z", + "iopub.status.idle": "2024-07-02T15:29:08.165960Z", + "shell.execute_reply": "2024-07-02T15:29:08.165500Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.168102Z", + "iopub.status.busy": "2024-07-02T15:29:08.167668Z", + "iopub.status.idle": "2024-07-02T15:29:08.173302Z", + "shell.execute_reply": "2024-07-02T15:29:08.172857Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.175232Z", + "iopub.status.busy": "2024-07-02T15:29:08.174914Z", + "iopub.status.idle": "2024-07-02T15:29:08.180090Z", + "shell.execute_reply": "2024-07-02T15:29:08.179632Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.182081Z", + "iopub.status.busy": "2024-07-02T15:29:08.181762Z", + "iopub.status.idle": "2024-07-02T15:29:08.185358Z", + "shell.execute_reply": "2024-07-02T15:29:08.184918Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.187396Z", + "iopub.status.busy": "2024-07-02T15:29:08.187074Z", + "iopub.status.idle": "2024-07-02T15:29:08.192050Z", + "shell.execute_reply": "2024-07-02T15:29:08.191599Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index 9f74b4e12..0eaee2f6c 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": "2024-07-02T15:14:14.103983Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:11.148814Z", + "iopub.status.busy": "2024-07-02T15:29:11.148637Z", + "iopub.status.idle": "2024-07-02T15:29:11.562068Z", + "shell.execute_reply": "2024-07-02T15:29:11.561444Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:11.564831Z", + "iopub.status.busy": "2024-07-02T15:29:11.564466Z", + "iopub.status.idle": "2024-07-02T15:29:11.689877Z", + "shell.execute_reply": "2024-07-02T15:29:11.689330Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:11.692161Z", + "iopub.status.busy": "2024-07-02T15:29:11.691698Z", + "iopub.status.idle": "2024-07-02T15:29:11.710885Z", + "shell.execute_reply": "2024-07-02T15:29:11.710323Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:11.713082Z", + "iopub.status.busy": "2024-07-02T15:29:11.712899Z", + "iopub.status.idle": "2024-07-02T15:29:14.335786Z", + "shell.execute_reply": "2024-07-02T15:29:14.335126Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:14.338194Z", + "iopub.status.busy": "2024-07-02T15:29:14.337838Z", + "iopub.status.idle": "2024-07-02T15:29:22.391410Z", + "shell.execute_reply": "2024-07-02T15:29:22.390817Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:22.393854Z", + "iopub.status.busy": "2024-07-02T15:29:22.393389Z", + "iopub.status.idle": "2024-07-02T15:29:22.535692Z", + "shell.execute_reply": "2024-07-02T15:29:22.535054Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:22.538266Z", + "iopub.status.busy": "2024-07-02T15:29:22.538089Z", + "iopub.status.idle": "2024-07-02T15:29:23.850373Z", + "shell.execute_reply": "2024-07-02T15:29:23.849867Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:23.852547Z", + "iopub.status.busy": "2024-07-02T15:29:23.852210Z", + "iopub.status.idle": "2024-07-02T15:29:24.256489Z", + "shell.execute_reply": "2024-07-02T15:29:24.255943Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.259129Z", + "iopub.status.busy": "2024-07-02T15:29:24.258483Z", + "iopub.status.idle": "2024-07-02T15:29:24.266937Z", + "shell.execute_reply": "2024-07-02T15:29:24.266488Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.268896Z", + "iopub.status.busy": "2024-07-02T15:29:24.268579Z", + "iopub.status.idle": "2024-07-02T15:29:24.287826Z", + "shell.execute_reply": "2024-07-02T15:29:24.287420Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.289629Z", + "iopub.status.busy": "2024-07-02T15:29:24.289459Z", + "iopub.status.idle": "2024-07-02T15:29:24.509373Z", + "shell.execute_reply": "2024-07-02T15:29:24.508832Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.511635Z", + "iopub.status.busy": "2024-07-02T15:29:24.511276Z", + "iopub.status.idle": "2024-07-02T15:29:24.530413Z", + "shell.execute_reply": "2024-07-02T15:29:24.529881Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.532504Z", + "iopub.status.busy": "2024-07-02T15:29:24.532105Z", + "iopub.status.idle": "2024-07-02T15:29:24.697579Z", + "shell.execute_reply": "2024-07-02T15:29:24.697155Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.699519Z", + "iopub.status.busy": "2024-07-02T15:29:24.699362Z", + "iopub.status.idle": "2024-07-02T15:29:24.710273Z", + "shell.execute_reply": "2024-07-02T15:29:24.709863Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.712113Z", + "iopub.status.busy": "2024-07-02T15:29:24.711959Z", + "iopub.status.idle": "2024-07-02T15:29:24.721478Z", + "shell.execute_reply": "2024-07-02T15:29:24.721032Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.723461Z", + "iopub.status.busy": "2024-07-02T15:29:24.723141Z", + "iopub.status.idle": "2024-07-02T15:29:24.749053Z", + "shell.execute_reply": "2024-07-02T15:29:24.748634Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.751055Z", + "iopub.status.busy": "2024-07-02T15:29:24.750756Z", + "iopub.status.idle": "2024-07-02T15:29:24.753280Z", + "shell.execute_reply": "2024-07-02T15:29:24.752846Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.755266Z", + "iopub.status.busy": "2024-07-02T15:29:24.754962Z", + "iopub.status.idle": "2024-07-02T15:29:24.773504Z", + "shell.execute_reply": "2024-07-02T15:29:24.773077Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.775512Z", + "iopub.status.busy": "2024-07-02T15:29:24.775219Z", + "iopub.status.idle": "2024-07-02T15:29:24.779215Z", + "shell.execute_reply": "2024-07-02T15:29:24.778781Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.781323Z", + "iopub.status.busy": "2024-07-02T15:29:24.780892Z", + "iopub.status.idle": "2024-07-02T15:29:24.814268Z", + "shell.execute_reply": "2024-07-02T15:29:24.813734Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.816285Z", + "iopub.status.busy": "2024-07-02T15:29:24.815990Z", + "iopub.status.idle": "2024-07-02T15:29:25.179432Z", + "shell.execute_reply": "2024-07-02T15:29:25.178873Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.181578Z", + "iopub.status.busy": "2024-07-02T15:29:25.181257Z", + "iopub.status.idle": "2024-07-02T15:29:25.184323Z", + "shell.execute_reply": "2024-07-02T15:29:25.183802Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.186452Z", + "iopub.status.busy": "2024-07-02T15:29:25.186047Z", + "iopub.status.idle": "2024-07-02T15:29:25.198843Z", + "shell.execute_reply": "2024-07-02T15:29:25.198320Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.200782Z", + "iopub.status.busy": "2024-07-02T15:29:25.200486Z", + "iopub.status.idle": "2024-07-02T15:29:25.213647Z", + "shell.execute_reply": "2024-07-02T15:29:25.213118Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.215595Z", + "iopub.status.busy": "2024-07-02T15:29:25.215421Z", + "iopub.status.idle": "2024-07-02T15:29:25.224854Z", + "shell.execute_reply": "2024-07-02T15:29:25.224443Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.226672Z", + "iopub.status.busy": "2024-07-02T15:29:25.226501Z", + "iopub.status.idle": "2024-07-02T15:29:25.235816Z", + "shell.execute_reply": "2024-07-02T15:29:25.235341Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.237869Z", + "iopub.status.busy": "2024-07-02T15:29:25.237457Z", + "iopub.status.idle": "2024-07-02T15:29:25.241007Z", + "shell.execute_reply": "2024-07-02T15:29:25.240556Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.242990Z", + "iopub.status.busy": "2024-07-02T15:29:25.242667Z", + "iopub.status.idle": "2024-07-02T15:29:25.292667Z", + "shell.execute_reply": "2024-07-02T15:29:25.292199Z" } }, "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": "2024-07-02T15:14:28.212907Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:25.294886Z", + "iopub.status.busy": "2024-07-02T15:29:25.294483Z", + "iopub.status.idle": "2024-07-02T15:29:25.299817Z", + "shell.execute_reply": "2024-07-02T15:29:25.299395Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.301975Z", + "iopub.status.busy": "2024-07-02T15:29:25.301501Z", + "iopub.status.idle": "2024-07-02T15:29:25.311839Z", + "shell.execute_reply": "2024-07-02T15:29:25.311419Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.313796Z", + "iopub.status.busy": "2024-07-02T15:29:25.313590Z", + "iopub.status.idle": "2024-07-02T15:29:25.524327Z", + "shell.execute_reply": "2024-07-02T15:29:25.523840Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.526204Z", + "iopub.status.busy": "2024-07-02T15:29:25.526033Z", + "iopub.status.idle": "2024-07-02T15:29:25.533387Z", + "shell.execute_reply": "2024-07-02T15:29:25.532865Z" }, "nbsphinx": "hidden" }, @@ -3760,10 +3760,10 @@ "execution_count": 33, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.535412Z", + "iopub.status.busy": "2024-07-02T15:29:25.535090Z", + "iopub.status.idle": "2024-07-02T15:29:31.824715Z", + "shell.execute_reply": "2024-07-02T15:29:31.824156Z" } }, "outputs": [ @@ -3787,7 +3787,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 458752/170498071 [00:00<00:37, 4495236.08it/s]" + " 1%| | 1769472/170498071 [00:00<00:09, 17569738.43it/s]" ] }, { @@ -3795,7 +3795,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 4227072/170498071 [00:00<00:07, 23242348.53it/s]" + " 6%|▌ | 9994240/170498071 [00:00<00:02, 55470141.49it/s]" ] }, { @@ -3803,7 +3803,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 9306112/170498071 [00:00<00:04, 35527365.93it/s]" + " 11%|█ | 18186240/170498071 [00:00<00:02, 67404805.75it/s]" ] }, { @@ -3811,7 +3811,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 13926400/170498071 [00:00<00:03, 39660501.21it/s]" + " 16%|█▌ | 26476544/170498071 [00:00<00:01, 73445517.40it/s]" ] }, { @@ -3819,7 +3819,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 18644992/170498071 [00:00<00:03, 42142752.96it/s]" + " 20%|██ | 34865152/170498071 [00:00<00:01, 77108513.90it/s]" ] }, { @@ -3827,7 +3827,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 23166976/170498071 [00:00<00:03, 43171320.39it/s]" + " 25%|██▌ | 43057152/170498071 [00:00<00:01, 78697943.81it/s]" ] }, { @@ -3835,7 +3835,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 27688960/170498071 [00:00<00:03, 43778497.63it/s]" + " 30%|███ | 51380224/170498071 [00:00<00:01, 79985067.48it/s]" ] }, { @@ -3843,7 +3843,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32276480/170498071 [00:00<00:03, 44429066.54it/s]" + " 35%|███▌ | 59703296/170498071 [00:00<00:01, 80909156.17it/s]" ] }, { @@ -3851,7 +3851,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 36732928/170498071 [00:00<00:03, 44053184.08it/s]" + " 40%|███▉ | 67928064/170498071 [00:00<00:01, 81246537.89it/s]" ] }, { @@ -3859,7 +3859,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 41156608/170498071 [00:01<00:03, 43037571.10it/s]" + " 45%|████▍ | 76251136/170498071 [00:01<00:01, 81748989.29it/s]" ] }, { @@ -3867,7 +3867,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 45481984/170498071 [00:01<00:02, 41764924.25it/s]" + " 50%|████▉ | 84574208/170498071 [00:01<00:01, 82134723.99it/s]" ] }, { @@ -3875,7 +3875,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 49741824/170498071 [00:01<00:02, 41917563.82it/s]" + " 55%|█████▍ | 92995584/170498071 [00:01<00:00, 82736426.96it/s]" ] }, { @@ -3883,7 +3883,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 54296576/170498071 [00:01<00:02, 42876516.22it/s]" + " 60%|█████▉ | 101580800/170498071 [00:01<00:00, 83638413.28it/s]" ] }, { @@ -3891,7 +3891,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 58884096/170498071 [00:01<00:02, 43617372.00it/s]" + " 65%|██████▍ | 110067712/170498071 [00:01<00:00, 83992383.40it/s]" ] }, { @@ -3899,7 +3899,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 63504384/170498071 [00:01<00:02, 44261340.15it/s]" + " 69%|██████▉ | 118489088/170498071 [00:01<00:00, 81517971.25it/s]" ] }, { @@ -3907,7 +3907,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 68124672/170498071 [00:01<00:02, 44684329.80it/s]" + " 74%|███████▍ | 126681088/170498071 [00:01<00:00, 66165934.08it/s]" ] }, { @@ -3915,7 +3915,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 72810496/170498071 [00:01<00:02, 45260580.12it/s]" + " 78%|███████▊ | 133791744/170498071 [00:01<00:00, 57474892.92it/s]" ] }, { @@ -3923,7 +3923,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 77430784/170498071 [00:01<00:02, 45420696.51it/s]" + " 82%|████████▏ | 140017664/170498071 [00:02<00:00, 52870786.58it/s]" ] }, { @@ -3931,7 +3931,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 82018304/170498071 [00:01<00:01, 45522327.94it/s]" + " 85%|████████▌ | 145686528/170498071 [00:02<00:00, 49446779.71it/s]" ] }, { @@ -3939,7 +3939,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 87097344/170498071 [00:02<00:01, 47025132.26it/s]" + " 89%|████████▊ | 150896640/170498071 [00:02<00:00, 47089743.42it/s]" ] }, { @@ -3947,7 +3947,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 95223808/170498071 [00:02<00:01, 57144332.93it/s]" + " 91%|█████████▏| 155779072/170498071 [00:02<00:00, 45996856.71it/s]" ] }, { @@ -3955,7 +3955,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 103972864/170498071 [00:02<00:01, 66190969.00it/s]" + " 94%|█████████▍| 160497664/170498071 [00:02<00:00, 45255686.94it/s]" ] }, { @@ -3963,7 +3963,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 112721920/170498071 [00:02<00:00, 72211785.85it/s]" + " 97%|█████████▋| 165085184/170498071 [00:02<00:00, 44462626.18it/s]" ] }, { @@ -3971,55 +3971,7 @@ "output_type": "stream", "text": [ "\r", - " 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]" + "100%|██████████| 170498071/170498071 [00:02<00:00, 63370425.61it/s]" ] }, { @@ -4093,10 +4045,10 @@ "execution_count": 34, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:31.827345Z", + "iopub.status.busy": "2024-07-02T15:29:31.826817Z", + "iopub.status.idle": "2024-07-02T15:29:31.894192Z", + "shell.execute_reply": "2024-07-02T15:29:31.893694Z" } }, "outputs": [], @@ -4118,10 +4070,10 @@ "execution_count": 35, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:31.896307Z", + "iopub.status.busy": "2024-07-02T15:29:31.895985Z", + "iopub.status.idle": "2024-07-02T15:29:31.935822Z", + "shell.execute_reply": "2024-07-02T15:29:31.935309Z" } }, "outputs": [], @@ -4155,10 +4107,10 @@ "execution_count": 36, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:31.938027Z", + "iopub.status.busy": "2024-07-02T15:29:31.937597Z", + "iopub.status.idle": "2024-07-02T15:29:33.356771Z", + "shell.execute_reply": "2024-07-02T15:29:33.356182Z" } }, "outputs": [ @@ -4232,10 +4184,10 @@ "execution_count": 37, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:33.359053Z", + "iopub.status.busy": "2024-07-02T15:29:33.358661Z", + "iopub.status.idle": "2024-07-02T15:29:34.163786Z", + "shell.execute_reply": "2024-07-02T15:29:34.163155Z" } }, "outputs": [ @@ -4250,7 +4202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "edeb0eb92f8e493694db63fbedcce068", + "model_id": "f24c55aaa3f2486eb049ea9140702ad3", "version_major": 2, "version_minor": 0 }, @@ -4274,7 +4226,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a53db32e879b421b9c5a2cb90345b461", + "model_id": "29d1b664fe1d4701876841f59c318a5a", "version_major": 2, "version_minor": 0 }, @@ -4524,7 +4476,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0ba1b4f02e98442dbf83a9d402d61603": { + "140f94fffe9d41998f052b476411cdc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4542,23 +4494,54 @@ "text_color": null } }, - "13f79e5c34544a20b6e43544e002e0d6": { + "16441724cc5d442083bc8dc57220bdb5": { "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_f1f35c30be0c46cea8172b99479542a3", + "placeholder": "​", + "style": "IPY_MODEL_5a67d709e70348c5a0788df8f06b7d00", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "29d1b664fe1d4701876841f59c318a5a": { + "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_16441724cc5d442083bc8dc57220bdb5", + "IPY_MODEL_a06fb116c7f54de8ace8c44aa9c3252a", + "IPY_MODEL_683dfd5472954d23960cee4af27b4942" + ], + "layout": "IPY_MODEL_c42e40a052b345028b0e318e573b39c4", + "tabbable": null, + "tooltip": null } }, - "21919d64401941e69bdd1e7fdeb39391": { + "3fe4d5f95a4b421ea7191cd652353f63": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4611,7 +4594,41 @@ "width": null } }, - "22dce5e6cbbd456899db36ca71231b83": { + "509a070ad7814351956da09fa31f6d3e": { + "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": "" + } + }, + "5a67d709e70348c5a0788df8f06b7d00": { + "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 + } + }, + "683dfd5472954d23960cee4af27b4942": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4626,15 +4643,56 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_246224e7d6e14ee996208e5a901506a3", + "layout": "IPY_MODEL_c65f07113a9243528835037ef624a06c", "placeholder": "​", - "style": "IPY_MODEL_669c9d816b274632945703bb33ea88b1", + "style": "IPY_MODEL_140f94fffe9d41998f052b476411cdc5", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 200/200 [00:00<00:00, 725.42it/s]" + } + }, + "74d7c5cc1042451a8756f13a29013665": { + "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_cba826c457dc4039b375f27857c765ca", + "placeholder": "​", + "style": "IPY_MODEL_fbabf37f132843a38191f8b243f94ccd", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 780.24it/s]" } }, - "246224e7d6e14ee996208e5a901506a3": { + "8356f3ec51974cf696a3e9692878bd4e": { + "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 + } + }, + "950c8017832643e59a584d9e5c14d158": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4687,7 +4745,33 @@ "width": null } }, - "49bd46daef6e4afaae2104d6fddc5eff": { + "975ecec9b0d240eebc17772cc26037e6": { + "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_3fe4d5f95a4b421ea7191cd652353f63", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_509a070ad7814351956da09fa31f6d3e", + "tabbable": null, + "tooltip": null, + "value": 200.0 + } + }, + "9e76293fc8c84697bab5d4b70a4497ed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4740,23 +4824,33 @@ "width": null } }, - "5f4143d1143347bf8d67acbd62e4c7a9": { + "a06fb116c7f54de8ace8c44aa9c3252a": { "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_f737276f279444d0a64c6ae2d3596e26", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f2a441d85bb54e8e87b2a9bd9ec7a1b7", + "tabbable": null, + "tooltip": null, + "value": 200.0 } }, - "5fe4a241c042442299e152f3905b32e6": { + "c42e40a052b345028b0e318e573b39c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4809,48 +4903,7 @@ "width": null } }, - "669c9d816b274632945703bb33ea88b1": { - "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 - } - }, - "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": { + "c65f07113a9243528835037ef624a06c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4903,7 +4956,7 @@ "width": null } }, - "945bafb17f6c4017b07c073239844118": { + "cba826c457dc4039b375f27857c765ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4956,31 +5009,30 @@ "width": null } }, - "a53db32e879b421b9c5a2cb90345b461": { + "e8e5474f252f462bb6b93f09309db91a": { "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_ae7e7acb2667481d93c3d5d070d947f1", - "IPY_MODEL_d03e8f0da10e418392f2df6f61dea5ed", - "IPY_MODEL_752199f5b1064d09a833e6e140acf999" - ], - "layout": "IPY_MODEL_5fe4a241c042442299e152f3905b32e6", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_950c8017832643e59a584d9e5c14d158", + "placeholder": "​", + "style": "IPY_MODEL_8356f3ec51974cf696a3e9692878bd4e", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "a6d4bb6587dc4b0ab299cde66d887195": { + "f1f35c30be0c46cea8172b99479542a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5033,123 +5085,47 @@ "width": null } }, - "ae7e7acb2667481d93c3d5d070d947f1": { + "f24c55aaa3f2486eb049ea9140702ad3": { "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_945bafb17f6c4017b07c073239844118", - "placeholder": "​", - "style": "IPY_MODEL_0ba1b4f02e98442dbf83a9d402d61603", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e8e5474f252f462bb6b93f09309db91a", + "IPY_MODEL_975ecec9b0d240eebc17772cc26037e6", + "IPY_MODEL_74d7c5cc1042451a8756f13a29013665" + ], + "layout": "IPY_MODEL_9e76293fc8c84697bab5d4b70a4497ed", "tabbable": null, - "tooltip": null, - "value": "100%" + "tooltip": null } }, - "b2c4bbe171d649419f7ff67ac4f27ab6": { + "f2a441d85bb54e8e87b2a9bd9ec7a1b7": { "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 - } - }, - "b7a191fc264f425c94ccbd4b2e6ff5bf": { - "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_d1fa249a3b3741948f9b90a3eba494cd", - "placeholder": "​", - "style": "IPY_MODEL_df530a10186c40c8b9ba0ace062c0018", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 806.22it/s]" - } - }, - "ba0f29fa569646e89dd03db3974a4a00": { - "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_7f367a2cdd5445f58aecb1320024dca9", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5f4143d1143347bf8d67acbd62e4c7a9", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "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 + "bar_color": null, + "description_width": "" } }, - "d1fa249a3b3741948f9b90a3eba494cd": { + "f737276f279444d0a64c6ae2d3596e26": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5202,7 +5178,7 @@ "width": null } }, - "df530a10186c40c8b9ba0ace062c0018": { + "fbabf37f132843a38191f8b243f94ccd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5219,30 +5195,6 @@ "font_size": null, "text_color": null } - }, - "edeb0eb92f8e493694db63fbedcce068": { - "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_22dce5e6cbbd456899db36ca71231b83", - "IPY_MODEL_ba0f29fa569646e89dd03db3974a4a00", - "IPY_MODEL_b7a191fc264f425c94ccbd4b2e6ff5bf" - ], - "layout": "IPY_MODEL_a6d4bb6587dc4b0ab299cde66d887195", - "tabbable": null, - "tooltip": null - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 46444aea9..c598eb866 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": "2024-07-02T15:14:41.637741Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:38.938026Z", + "iopub.status.busy": "2024-07-02T15:29:38.937857Z", + "iopub.status.idle": "2024-07-02T15:29:40.022180Z", + "shell.execute_reply": "2024-07-02T15:29:40.021619Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:14:42.750701Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:40.024706Z", + "iopub.status.busy": "2024-07-02T15:29:40.024284Z", + "iopub.status.idle": "2024-07-02T15:29:40.027131Z", + "shell.execute_reply": "2024-07-02T15:29:40.026613Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:40.029106Z", + "iopub.status.busy": "2024-07-02T15:29:40.028929Z", + "iopub.status.idle": "2024-07-02T15:29:40.040121Z", + "shell.execute_reply": "2024-07-02T15:29:40.039667Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:40.042199Z", + "iopub.status.busy": "2024-07-02T15:29:40.041874Z", + "iopub.status.idle": "2024-07-02T15:29:44.739408Z", + "shell.execute_reply": "2024-07-02T15:29:44.738930Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index d639cd18b..fd037f7ef 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": "2024-07-02T15:14:50.408143Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:46.799774Z", + "iopub.status.busy": "2024-07-02T15:29:46.799611Z", + "iopub.status.idle": "2024-07-02T15:29:47.868215Z", + "shell.execute_reply": "2024-07-02T15:29:47.867607Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:47.871043Z", + "iopub.status.busy": "2024-07-02T15:29:47.870781Z", + "iopub.status.idle": "2024-07-02T15:29:47.874130Z", + "shell.execute_reply": "2024-07-02T15:29:47.873594Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:47.876125Z", + "iopub.status.busy": "2024-07-02T15:29:47.875827Z", + "iopub.status.idle": "2024-07-02T15:29:50.933600Z", + "shell.execute_reply": "2024-07-02T15:29:50.932999Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:50.936675Z", + "iopub.status.busy": "2024-07-02T15:29:50.936022Z", + "iopub.status.idle": "2024-07-02T15:29:50.967828Z", + "shell.execute_reply": "2024-07-02T15:29:50.967284Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:50.970284Z", + "iopub.status.busy": "2024-07-02T15:29:50.969984Z", + "iopub.status.idle": "2024-07-02T15:29:50.996616Z", + "shell.execute_reply": "2024-07-02T15:29:50.996062Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:50.999234Z", + "iopub.status.busy": "2024-07-02T15:29:50.998784Z", + "iopub.status.idle": "2024-07-02T15:29:51.001903Z", + "shell.execute_reply": "2024-07-02T15:29:51.001337Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.004009Z", + "iopub.status.busy": "2024-07-02T15:29:51.003583Z", + "iopub.status.idle": "2024-07-02T15:29:51.006181Z", + "shell.execute_reply": "2024-07-02T15:29:51.005730Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.008311Z", + "iopub.status.busy": "2024-07-02T15:29:51.007960Z", + "iopub.status.idle": "2024-07-02T15:29:51.034036Z", + "shell.execute_reply": "2024-07-02T15:29:51.033489Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0af6d2097bac4b69850c70d9d5904db8", + "model_id": "b61f3b61abad4a309bf86a1aa8f2b9c5", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5e3107780da94917a2e6e00a57affa5f", + "model_id": "026a70ed918d454b979daad116575cd1", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.037426Z", + "iopub.status.busy": "2024-07-02T15:29:51.037087Z", + "iopub.status.idle": "2024-07-02T15:29:51.043755Z", + "shell.execute_reply": "2024-07-02T15:29:51.043334Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.045741Z", + "iopub.status.busy": "2024-07-02T15:29:51.045399Z", + "iopub.status.idle": "2024-07-02T15:29:51.048655Z", + "shell.execute_reply": "2024-07-02T15:29:51.048231Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.050733Z", + "iopub.status.busy": "2024-07-02T15:29:51.050424Z", + "iopub.status.idle": "2024-07-02T15:29:51.056495Z", + "shell.execute_reply": "2024-07-02T15:29:51.056070Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.058351Z", + "iopub.status.busy": "2024-07-02T15:29:51.058061Z", + "iopub.status.idle": "2024-07-02T15:29:51.087891Z", + "shell.execute_reply": "2024-07-02T15:29:51.087337Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.090312Z", + "iopub.status.busy": "2024-07-02T15:29:51.090023Z", + "iopub.status.idle": "2024-07-02T15:29:51.114800Z", + "shell.execute_reply": "2024-07-02T15:29:51.114219Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.117401Z", + "iopub.status.busy": "2024-07-02T15:29:51.117043Z", + "iopub.status.idle": "2024-07-02T15:29:51.231817Z", + "shell.execute_reply": "2024-07-02T15:29:51.231206Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.234379Z", + "iopub.status.busy": "2024-07-02T15:29:51.233839Z", + "iopub.status.idle": "2024-07-02T15:29:54.239820Z", + "shell.execute_reply": "2024-07-02T15:29:54.239255Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.242175Z", + "iopub.status.busy": "2024-07-02T15:29:54.241796Z", + "iopub.status.idle": "2024-07-02T15:29:54.298692Z", + "shell.execute_reply": "2024-07-02T15:29:54.298201Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.300662Z", + "iopub.status.busy": "2024-07-02T15:29:54.300332Z", + "iopub.status.idle": "2024-07-02T15:29:54.341979Z", + "shell.execute_reply": "2024-07-02T15:29:54.341413Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "50482bad", + "id": "4848cf7c", "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": "07405bb8", + "id": "eaad044a", "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": "f375f11d", + "id": "0b523bbb", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "ada84c58", + "id": "84320802", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.344243Z", + "iopub.status.busy": "2024-07-02T15:29:54.344071Z", + "iopub.status.idle": "2024-07-02T15:29:54.351389Z", + "shell.execute_reply": "2024-07-02T15:29:54.350841Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "13fb70ab", + "id": "1925ba8e", "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": "692524aa", + "id": "0829c387", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.353489Z", + "iopub.status.busy": "2024-07-02T15:29:54.353071Z", + "iopub.status.idle": "2024-07-02T15:29:54.371123Z", + "shell.execute_reply": "2024-07-02T15:29:54.370577Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "c63f4c73", + "id": "b4df50e4", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.373055Z", + "iopub.status.busy": "2024-07-02T15:29:54.372731Z", + "iopub.status.idle": "2024-07-02T15:29:54.375873Z", + "shell.execute_reply": "2024-07-02T15:29:54.375357Z" } }, "outputs": [ @@ -1622,7 +1622,170 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "03bdac28b0744e63957425aef141438c": { + "026a70ed918d454b979daad116575cd1": { + "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_7c008af7a8e14f158b908bdea72be0c3", + "IPY_MODEL_5d93b4262fd54a69b71094aebc5ee106", + "IPY_MODEL_2534d227f7c84093a890aa8da483c8d4" + ], + "layout": "IPY_MODEL_8dff42b5c3474095ada1b975ed329e2d", + "tabbable": null, + "tooltip": null + } + }, + "02875877ae314d399c4c41bf46506420": { + "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 + } + }, + "0341ad6e27fb45b990863bbbb6424cf0": { + "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_2d25e2c546e744e498c5589683f7a800", + "placeholder": "​", + "style": "IPY_MODEL_145cb1b61bd14b5a94afbfc522dce648", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1111132.78it/s]" + } + }, + "0626a4c0233f4806987eae80b809ca9c": { + "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_ac08271c7a40448db2e9845998b12b94", + "placeholder": "​", + "style": "IPY_MODEL_5a9f1856ee4548d29ac2fa74efba69da", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: " + } + }, + "12ba7ec803a94e239e6ab2e3faeb7c96": { + "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 + } + }, + "145cb1b61bd14b5a94afbfc522dce648": { + "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 + } + }, + "2534d227f7c84093a890aa8da483c8d4": { + "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_ce653676309345038faf46f6f089becc", + "placeholder": "​", + "style": "IPY_MODEL_12ba7ec803a94e239e6ab2e3faeb7c96", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1115595.39it/s]" + } + }, + "25d058621d424fcdb7e9277a77685c68": { + "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": "" + } + }, + "2d25e2c546e744e498c5589683f7a800": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1675,31 +1838,51 @@ "width": null } }, - "0af6d2097bac4b69850c70d9d5904db8": { + "5a9f1856ee4548d29ac2fa74efba69da": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "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 + } + }, + "5d93b4262fd54a69b71094aebc5ee106": { + "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_b05e5e24c03d4b8393b6fbc900c09846", - "IPY_MODEL_1a648d3a4fa24399916eb3b310f94eb1", - "IPY_MODEL_1d20270beb494280ba9716e526d53f5b" - ], - "layout": "IPY_MODEL_162ace65984b48448d278f7f058df9b9", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6437b738ec874299a07051ee9e8445c0", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_25d058621d424fcdb7e9277a77685c68", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 50.0 } }, - "162ace65984b48448d278f7f058df9b9": { + "6437b738ec874299a07051ee9e8445c0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1752,56 +1935,7 @@ "width": null } }, - "1a648d3a4fa24399916eb3b310f94eb1": { - "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_2fc72a84067d46678a572aa49ebcbfee", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_d390dd0db893478398a63bc195614ea3", - "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "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": { + "6b2574bdfa604c3fab7148564af0b00e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1854,23 +1988,7 @@ "width": null } }, - "28fb5636d77344d0af4d56b62998af86": { - "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": "" - } - }, - "2fc72a84067d46678a572aa49ebcbfee": { + "6ca71fb821a542d6aa63710c2222debc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1923,72 +2041,49 @@ "width": null } }, - "46803bc4f974489e87287e18bb02597e": { + "71c281f097914f28957bc6a7866a0b4b": { "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_ee2eaee4046a40398bf58b1b4a9e3ebc", - "placeholder": "​", - "style": "IPY_MODEL_65acdb25c5c948c9848681db00fdc46b", + "layout": "IPY_MODEL_6ca71fb821a542d6aa63710c2222debc", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7a2c46b59b8f4e74a0844c5242d93052", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: " - } - }, - "5e3107780da94917a2e6e00a57affa5f": { - "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_46803bc4f974489e87287e18bb02597e", - "IPY_MODEL_d31a1289a39a46899da8b556759347e9", - "IPY_MODEL_6e15813423a14f6c9174690b8907c81c" - ], - "layout": "IPY_MODEL_1edeca25122644478494bfcfeda4647f", - "tabbable": null, - "tooltip": null + "value": 50.0 } }, - "65acdb25c5c948c9848681db00fdc46b": { + "7a2c46b59b8f4e74a0844c5242d93052": { "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": "" } }, - "6e15813423a14f6c9174690b8907c81c": { + "7c008af7a8e14f158b908bdea72be0c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2003,15 +2098,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ac3767a4657d48b4a0218f3167e710d9", + "layout": "IPY_MODEL_6b2574bdfa604c3fab7148564af0b00e", "placeholder": "​", - "style": "IPY_MODEL_f0f4c38d78db4623a4f8710c6f263222", + "style": "IPY_MODEL_02875877ae314d399c4c41bf46506420", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1603327.22it/s]" + "value": "number of examples processed for checking labels: " } }, - "a9d39a3ea28d44c682f45f076bc8bd67": { + "8dff42b5c3474095ada1b975ed329e2d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2064,7 +2159,7 @@ "width": null } }, - "ac3767a4657d48b4a0218f3167e710d9": { + "ac08271c7a40448db2e9845998b12b94": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2117,72 +2212,31 @@ "width": null } }, - "b05e5e24c03d4b8393b6fbc900c09846": { + "b61f3b61abad4a309bf86a1aa8f2b9c5": { "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_d697f27b641d4fa4a8cece8123222bff", - "placeholder": "​", - "style": "IPY_MODEL_eddf7a5ddfe14a858eb2826e37c0b231", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: " - } - }, - "d31a1289a39a46899da8b556759347e9": { - "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_a9d39a3ea28d44c682f45f076bc8bd67", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_28fb5636d77344d0af4d56b62998af86", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0626a4c0233f4806987eae80b809ca9c", + "IPY_MODEL_71c281f097914f28957bc6a7866a0b4b", + "IPY_MODEL_0341ad6e27fb45b990863bbbb6424cf0" + ], + "layout": "IPY_MODEL_e819615fa807467fa92420d45a5f8e70", "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "d390dd0db893478398a63bc195614ea3": { - "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": "" + "tooltip": null } }, - "d697f27b641d4fa4a8cece8123222bff": { + "ce653676309345038faf46f6f089becc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2235,43 +2289,7 @@ "width": null } }, - "d92d13f5147b4721964e817fcd0e6a7b": { - "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 - } - }, - "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": { + "e819615fa807467fa92420d45a5f8e70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2323,24 +2341,6 @@ "visibility": null, "width": null } - }, - "f0f4c38d78db4623a4f8710c6f263222": { - "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/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 6e9b55b48..5f593443c 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": "2024-07-02T15:15:01.547795Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:57.626742Z", + "iopub.status.busy": "2024-07-02T15:29:57.626416Z", + "iopub.status.idle": "2024-07-02T15:29:58.762096Z", + "shell.execute_reply": "2024-07-02T15:29:58.761534Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:02.727054Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:58.764661Z", + "iopub.status.busy": "2024-07-02T15:29:58.764264Z", + "iopub.status.idle": "2024-07-02T15:29:58.939017Z", + "shell.execute_reply": "2024-07-02T15:29:58.938508Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:58.941285Z", + "iopub.status.busy": "2024-07-02T15:29:58.940947Z", + "iopub.status.idle": "2024-07-02T15:29:58.951883Z", + "shell.execute_reply": "2024-07-02T15:29:58.951455Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:58.953883Z", + "iopub.status.busy": "2024-07-02T15:29:58.953561Z", + "iopub.status.idle": "2024-07-02T15:29:59.157566Z", + "shell.execute_reply": "2024-07-02T15:29:59.157003Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:59.159969Z", + "iopub.status.busy": "2024-07-02T15:29:59.159619Z", + "iopub.status.idle": "2024-07-02T15:29:59.185196Z", + "shell.execute_reply": "2024-07-02T15:29:59.184736Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:59.187290Z", + "iopub.status.busy": "2024-07-02T15:29:59.186969Z", + "iopub.status.idle": "2024-07-02T15:30:01.138208Z", + "shell.execute_reply": "2024-07-02T15:30:01.137568Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:01.140791Z", + "iopub.status.busy": "2024-07-02T15:30:01.140323Z", + "iopub.status.idle": "2024-07-02T15:30:01.158153Z", + "shell.execute_reply": "2024-07-02T15:30:01.157659Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:01.160172Z", + "iopub.status.busy": "2024-07-02T15:30:01.159839Z", + "iopub.status.idle": "2024-07-02T15:30:02.572670Z", + "shell.execute_reply": "2024-07-02T15:30:02.572048Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.575334Z", + "iopub.status.busy": "2024-07-02T15:30:02.574705Z", + "iopub.status.idle": "2024-07-02T15:30:02.588246Z", + "shell.execute_reply": "2024-07-02T15:30:02.587734Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.590333Z", + "iopub.status.busy": "2024-07-02T15:30:02.589893Z", + "iopub.status.idle": "2024-07-02T15:30:02.660087Z", + "shell.execute_reply": "2024-07-02T15:30:02.659495Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.662433Z", + "iopub.status.busy": "2024-07-02T15:30:02.662252Z", + "iopub.status.idle": "2024-07-02T15:30:02.869194Z", + "shell.execute_reply": "2024-07-02T15:30:02.868731Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.871113Z", + "iopub.status.busy": "2024-07-02T15:30:02.870940Z", + "iopub.status.idle": "2024-07-02T15:30:02.887264Z", + "shell.execute_reply": "2024-07-02T15:30:02.886836Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.889231Z", + "iopub.status.busy": "2024-07-02T15:30:02.888911Z", + "iopub.status.idle": "2024-07-02T15:30:02.898236Z", + "shell.execute_reply": "2024-07-02T15:30:02.897802Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.900052Z", + "iopub.status.busy": "2024-07-02T15:30:02.899882Z", + "iopub.status.idle": "2024-07-02T15:30:02.982013Z", + "shell.execute_reply": "2024-07-02T15:30:02.981469Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.984381Z", + "iopub.status.busy": "2024-07-02T15:30:02.984039Z", + "iopub.status.idle": "2024-07-02T15:30:03.089218Z", + "shell.execute_reply": "2024-07-02T15:30:03.088619Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.091652Z", + "iopub.status.busy": "2024-07-02T15:30:03.091361Z", + "iopub.status.idle": "2024-07-02T15:30:03.094989Z", + "shell.execute_reply": "2024-07-02T15:30:03.094462Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.096993Z", + "iopub.status.busy": "2024-07-02T15:30:03.096623Z", + "iopub.status.idle": "2024-07-02T15:30:03.100533Z", + "shell.execute_reply": "2024-07-02T15:30:03.100094Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.102469Z", + "iopub.status.busy": "2024-07-02T15:30:03.102207Z", + "iopub.status.idle": "2024-07-02T15:30:03.138746Z", + "shell.execute_reply": "2024-07-02T15:30:03.138321Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.140829Z", + "iopub.status.busy": "2024-07-02T15:30:03.140499Z", + "iopub.status.idle": "2024-07-02T15:30:03.180614Z", + "shell.execute_reply": "2024-07-02T15:30:03.180176Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.182585Z", + "iopub.status.busy": "2024-07-02T15:30:03.182265Z", + "iopub.status.idle": "2024-07-02T15:30:03.268381Z", + "shell.execute_reply": "2024-07-02T15:30:03.267837Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.271234Z", + "iopub.status.busy": "2024-07-02T15:30:03.270873Z", + "iopub.status.idle": "2024-07-02T15:30:03.344389Z", + "shell.execute_reply": "2024-07-02T15:30:03.343870Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.346546Z", + "iopub.status.busy": "2024-07-02T15:30:03.346316Z", + "iopub.status.idle": "2024-07-02T15:30:03.553893Z", + "shell.execute_reply": "2024-07-02T15:30:03.553327Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.555908Z", + "iopub.status.busy": "2024-07-02T15:30:03.555726Z", + "iopub.status.idle": "2024-07-02T15:30:03.721372Z", + "shell.execute_reply": "2024-07-02T15:30:03.720791Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.723778Z", + "iopub.status.busy": "2024-07-02T15:30:03.723338Z", + "iopub.status.idle": "2024-07-02T15:30:03.729507Z", + "shell.execute_reply": "2024-07-02T15:30:03.729058Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.731405Z", + "iopub.status.busy": "2024-07-02T15:30:03.731109Z", + "iopub.status.idle": "2024-07-02T15:30:03.945067Z", + "shell.execute_reply": "2024-07-02T15:30:03.944605Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.947155Z", + "iopub.status.busy": "2024-07-02T15:30:03.946841Z", + "iopub.status.idle": "2024-07-02T15:30:05.010426Z", + "shell.execute_reply": "2024-07-02T15:30:05.009870Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index e3e8817fd..00df8da7f 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": "2024-07-02T15:15:12.510036Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:08.416538Z", + "iopub.status.busy": "2024-07-02T15:30:08.416373Z", + "iopub.status.idle": "2024-07-02T15:30:09.491929Z", + "shell.execute_reply": "2024-07-02T15:30:09.491393Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:13.634301Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:09.494528Z", + "iopub.status.busy": "2024-07-02T15:30:09.494125Z", + "iopub.status.idle": "2024-07-02T15:30:09.497173Z", + "shell.execute_reply": "2024-07-02T15:30:09.496630Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.499370Z", + "iopub.status.busy": "2024-07-02T15:30:09.498949Z", + "iopub.status.idle": "2024-07-02T15:30:09.506499Z", + "shell.execute_reply": "2024-07-02T15:30:09.505969Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.508570Z", + "iopub.status.busy": "2024-07-02T15:30:09.508265Z", + "iopub.status.idle": "2024-07-02T15:30:09.554643Z", + "shell.execute_reply": "2024-07-02T15:30:09.554193Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.556758Z", + "iopub.status.busy": "2024-07-02T15:30:09.556425Z", + "iopub.status.idle": "2024-07-02T15:30:09.573109Z", + "shell.execute_reply": "2024-07-02T15:30:09.572615Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.575083Z", + "iopub.status.busy": "2024-07-02T15:30:09.574778Z", + "iopub.status.idle": "2024-07-02T15:30:09.578384Z", + "shell.execute_reply": "2024-07-02T15:30:09.577927Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.580410Z", + "iopub.status.busy": "2024-07-02T15:30:09.580081Z", + "iopub.status.idle": "2024-07-02T15:30:09.593572Z", + "shell.execute_reply": "2024-07-02T15:30:09.593159Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.595521Z", + "iopub.status.busy": "2024-07-02T15:30:09.595213Z", + "iopub.status.idle": "2024-07-02T15:30:09.620528Z", + "shell.execute_reply": "2024-07-02T15:30:09.620111Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.622444Z", + "iopub.status.busy": "2024-07-02T15:30:09.622151Z", + "iopub.status.idle": "2024-07-02T15:30:11.445778Z", + "shell.execute_reply": "2024-07-02T15:30:11.445137Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.448367Z", + "iopub.status.busy": "2024-07-02T15:30:11.448093Z", + "iopub.status.idle": "2024-07-02T15:30:11.454787Z", + "shell.execute_reply": "2024-07-02T15:30:11.454316Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.456765Z", + "iopub.status.busy": "2024-07-02T15:30:11.456466Z", + "iopub.status.idle": "2024-07-02T15:30:11.468719Z", + "shell.execute_reply": "2024-07-02T15:30:11.468282Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.470607Z", + "iopub.status.busy": "2024-07-02T15:30:11.470346Z", + "iopub.status.idle": "2024-07-02T15:30:11.476456Z", + "shell.execute_reply": "2024-07-02T15:30:11.476041Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.478572Z", + "iopub.status.busy": "2024-07-02T15:30:11.478242Z", + "iopub.status.idle": "2024-07-02T15:30:11.480735Z", + "shell.execute_reply": "2024-07-02T15:30:11.480310Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.482783Z", + "iopub.status.busy": "2024-07-02T15:30:11.482474Z", + "iopub.status.idle": "2024-07-02T15:30:11.485723Z", + "shell.execute_reply": "2024-07-02T15:30:11.485194Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.487566Z", + "iopub.status.busy": "2024-07-02T15:30:11.487397Z", + "iopub.status.idle": "2024-07-02T15:30:11.489827Z", + "shell.execute_reply": "2024-07-02T15:30:11.489391Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.491611Z", + "iopub.status.busy": "2024-07-02T15:30:11.491445Z", + "iopub.status.idle": "2024-07-02T15:30:11.495334Z", + "shell.execute_reply": "2024-07-02T15:30:11.494831Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.497182Z", + "iopub.status.busy": "2024-07-02T15:30:11.497014Z", + "iopub.status.idle": "2024-07-02T15:30:11.524806Z", + "shell.execute_reply": "2024-07-02T15:30:11.524260Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.527068Z", + "iopub.status.busy": "2024-07-02T15:30:11.526763Z", + "iopub.status.idle": "2024-07-02T15:30:11.531196Z", + "shell.execute_reply": "2024-07-02T15:30:11.530662Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index cd94e39a4..b8ac96c40 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": "2024-07-02T15:15:18.624231Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:14.064593Z", + "iopub.status.busy": "2024-07-02T15:30:14.064421Z", + "iopub.status.idle": "2024-07-02T15:30:15.205978Z", + "shell.execute_reply": "2024-07-02T15:30:15.205327Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:19.810010Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:15.208418Z", + "iopub.status.busy": "2024-07-02T15:30:15.208143Z", + "iopub.status.idle": "2024-07-02T15:30:15.398147Z", + "shell.execute_reply": "2024-07-02T15:30:15.397569Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:15.400536Z", + "iopub.status.busy": "2024-07-02T15:30:15.400274Z", + "iopub.status.idle": "2024-07-02T15:30:15.413403Z", + "shell.execute_reply": "2024-07-02T15:30:15.412862Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:15.415691Z", + "iopub.status.busy": "2024-07-02T15:30:15.415248Z", + "iopub.status.idle": "2024-07-02T15:30:18.012829Z", + "shell.execute_reply": "2024-07-02T15:30:18.012269Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:18.015230Z", + "iopub.status.busy": "2024-07-02T15:30:18.014790Z", + "iopub.status.idle": "2024-07-02T15:30:19.361340Z", + "shell.execute_reply": "2024-07-02T15:30:19.360749Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:19.363622Z", + "iopub.status.busy": "2024-07-02T15:30:19.363439Z", + "iopub.status.idle": "2024-07-02T15:30:19.367038Z", + "shell.execute_reply": "2024-07-02T15:30:19.366492Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:19.368905Z", + "iopub.status.busy": "2024-07-02T15:30:19.368736Z", + "iopub.status.idle": "2024-07-02T15:30:21.276657Z", + "shell.execute_reply": "2024-07-02T15:30:21.276026Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:21.278971Z", + "iopub.status.busy": "2024-07-02T15:30:21.278630Z", + "iopub.status.idle": "2024-07-02T15:30:21.286413Z", + "shell.execute_reply": "2024-07-02T15:30:21.285940Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:21.288416Z", + "iopub.status.busy": "2024-07-02T15:30:21.288115Z", + "iopub.status.idle": "2024-07-02T15:30:23.806595Z", + "shell.execute_reply": "2024-07-02T15:30:23.806033Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:23.808820Z", + "iopub.status.busy": "2024-07-02T15:30:23.808415Z", + "iopub.status.idle": "2024-07-02T15:30:23.812033Z", + "shell.execute_reply": "2024-07-02T15:30:23.811486Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:23.814126Z", + "iopub.status.busy": "2024-07-02T15:30:23.813830Z", + "iopub.status.idle": "2024-07-02T15:30:23.817286Z", + "shell.execute_reply": "2024-07-02T15:30:23.816829Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:23.819098Z", + "iopub.status.busy": "2024-07-02T15:30:23.818930Z", + "iopub.status.idle": "2024-07-02T15:30:23.822031Z", + "shell.execute_reply": "2024-07-02T15:30:23.821569Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index a35b0cd70..2a7fb3ea1 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": "2024-07-02T15:15:30.956908Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:26.104900Z", + "iopub.status.busy": "2024-07-02T15:30:26.104494Z", + "iopub.status.idle": "2024-07-02T15:30:27.233572Z", + "shell.execute_reply": "2024-07-02T15:30:27.233026Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:32.097678Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:27.236094Z", + "iopub.status.busy": "2024-07-02T15:30:27.235693Z", + "iopub.status.idle": "2024-07-02T15:30:28.702868Z", + "shell.execute_reply": "2024-07-02T15:30:28.702121Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:28.705594Z", + "iopub.status.busy": "2024-07-02T15:30:28.705186Z", + "iopub.status.idle": "2024-07-02T15:30:28.708303Z", + "shell.execute_reply": "2024-07-02T15:30:28.707887Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:28.710362Z", + "iopub.status.busy": "2024-07-02T15:30:28.710043Z", + "iopub.status.idle": "2024-07-02T15:30:28.715820Z", + "shell.execute_reply": "2024-07-02T15:30:28.715418Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:28.717761Z", + "iopub.status.busy": "2024-07-02T15:30:28.717422Z", + "iopub.status.idle": "2024-07-02T15:30:29.197115Z", + "shell.execute_reply": "2024-07-02T15:30:29.196591Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:29.199674Z", + "iopub.status.busy": "2024-07-02T15:30:29.199317Z", + "iopub.status.idle": "2024-07-02T15:30:29.204590Z", + "shell.execute_reply": "2024-07-02T15:30:29.204051Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:29.206585Z", + "iopub.status.busy": "2024-07-02T15:30:29.206292Z", + "iopub.status.idle": "2024-07-02T15:30:29.210100Z", + "shell.execute_reply": "2024-07-02T15:30:29.209552Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:29.212071Z", + "iopub.status.busy": "2024-07-02T15:30:29.211769Z", + "iopub.status.idle": "2024-07-02T15:30:30.046952Z", + "shell.execute_reply": "2024-07-02T15:30:30.046327Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:30.049474Z", + "iopub.status.busy": "2024-07-02T15:30:30.049003Z", + "iopub.status.idle": "2024-07-02T15:30:30.269472Z", + "shell.execute_reply": "2024-07-02T15:30:30.269024Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:30.271620Z", + "iopub.status.busy": "2024-07-02T15:30:30.271296Z", + "iopub.status.idle": "2024-07-02T15:30:30.275317Z", + "shell.execute_reply": "2024-07-02T15:30:30.274889Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:30.277283Z", + "iopub.status.busy": "2024-07-02T15:30:30.276960Z", + "iopub.status.idle": "2024-07-02T15:30:30.718015Z", + "shell.execute_reply": "2024-07-02T15:30:30.717439Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:30.721098Z", + "iopub.status.busy": "2024-07-02T15:30:30.720763Z", + "iopub.status.idle": "2024-07-02T15:30:31.050736Z", + "shell.execute_reply": "2024-07-02T15:30:31.050209Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:31.053203Z", + "iopub.status.busy": "2024-07-02T15:30:31.052800Z", + "iopub.status.idle": "2024-07-02T15:30:31.411935Z", + "shell.execute_reply": "2024-07-02T15:30:31.411382Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:31.414916Z", + "iopub.status.busy": "2024-07-02T15:30:31.414688Z", + "iopub.status.idle": "2024-07-02T15:30:31.848848Z", + "shell.execute_reply": "2024-07-02T15:30:31.848291Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:31.852793Z", + "iopub.status.busy": "2024-07-02T15:30:31.852402Z", + "iopub.status.idle": "2024-07-02T15:30:32.294906Z", + "shell.execute_reply": "2024-07-02T15:30:32.294313Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:32.297619Z", + "iopub.status.busy": "2024-07-02T15:30:32.297290Z", + "iopub.status.idle": "2024-07-02T15:30:32.486044Z", + "shell.execute_reply": "2024-07-02T15:30:32.485463Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:32.488589Z", + "iopub.status.busy": "2024-07-02T15:30:32.488111Z", + "iopub.status.idle": "2024-07-02T15:30:32.667785Z", + "shell.execute_reply": "2024-07-02T15:30:32.667290Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:32.670272Z", + "iopub.status.busy": "2024-07-02T15:30:32.669957Z", + "iopub.status.idle": "2024-07-02T15:30:32.672881Z", + "shell.execute_reply": "2024-07-02T15:30:32.672341Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:32.674835Z", + "iopub.status.busy": "2024-07-02T15:30:32.674531Z", + "iopub.status.idle": "2024-07-02T15:30:33.648630Z", + "shell.execute_reply": "2024-07-02T15:30:33.648115Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:33.651075Z", + "iopub.status.busy": "2024-07-02T15:30:33.650749Z", + "iopub.status.idle": "2024-07-02T15:30:33.832380Z", + "shell.execute_reply": "2024-07-02T15:30:33.831930Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:33.834350Z", + "iopub.status.busy": "2024-07-02T15:30:33.834178Z", + "iopub.status.idle": "2024-07-02T15:30:33.965756Z", + "shell.execute_reply": "2024-07-02T15:30:33.965334Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:33.967745Z", + "iopub.status.busy": "2024-07-02T15:30:33.967436Z", + "iopub.status.idle": "2024-07-02T15:30:34.624537Z", + "shell.execute_reply": "2024-07-02T15:30:34.623949Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:34.626893Z", + "iopub.status.busy": "2024-07-02T15:30:34.626704Z", + "iopub.status.idle": "2024-07-02T15:30:34.630276Z", + "shell.execute_reply": "2024-07-02T15:30:34.629829Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index e7ee45271..a01751703 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": "2024-07-02T15:15:41.499853Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:36.707833Z", + "iopub.status.busy": "2024-07-02T15:30:36.707432Z", + "iopub.status.idle": "2024-07-02T15:30:39.352386Z", + "shell.execute_reply": "2024-07-02T15:30:39.351837Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:44.233719Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:39.355103Z", + "iopub.status.busy": "2024-07-02T15:30:39.354574Z", + "iopub.status.idle": "2024-07-02T15:30:39.661584Z", + "shell.execute_reply": "2024-07-02T15:30:39.660980Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:39.664187Z", + "iopub.status.busy": "2024-07-02T15:30:39.663844Z", + "iopub.status.idle": "2024-07-02T15:30:39.668417Z", + "shell.execute_reply": "2024-07-02T15:30:39.667891Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:39.670626Z", + "iopub.status.busy": "2024-07-02T15:30:39.670320Z", + "iopub.status.idle": "2024-07-02T15:30:44.460714Z", + "shell.execute_reply": "2024-07-02T15:30:44.460162Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 917504/170498071 [00:00<00:20, 8200886.72it/s]" + " 1%| | 1933312/170498071 [00:00<00:08, 19261178.24it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10780672/170498071 [00:00<00:02, 58894029.31it/s]" + " 6%|▌ | 9666560/170498071 [00:00<00:03, 53300272.89it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22380544/170498071 [00:00<00:01, 84273722.65it/s]" + " 11%|█ | 18022400/170498071 [00:00<00:02, 66766772.02it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 33783808/170498071 [00:00<00:01, 95827715.47it/s]" + " 15%|█▌ | 26017792/170498071 [00:00<00:02, 71799904.98it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 45383680/170498071 [00:00<00:01, 102972274.05it/s]" + " 20%|██ | 34111488/170498071 [00:00<00:01, 74851602.40it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 56721408/170498071 [00:00<00:01, 106415655.53it/s]" + " 24%|██▍ | 41615360/170498071 [00:00<00:01, 74050157.55it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 68288512/170498071 [00:00<00:00, 109377801.86it/s]" + " 29%|██▉ | 49414144/170498071 [00:00<00:01, 75240501.10it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 79790080/170498071 [00:00<00:00, 111060852.43it/s]" + " 34%|███▍ | 57802752/170498071 [00:00<00:01, 77927286.11it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 91291648/170498071 [00:00<00:00, 112242317.21it/s]" + " 39%|███▊ | 65929216/170498071 [00:00<00:01, 78770128.23it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 102727680/170498071 [00:01<00:00, 112875530.06it/s]" + " 43%|████▎ | 73826304/170498071 [00:01<00:01, 77447245.96it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 114262016/170498071 [00:01<00:00, 113610104.30it/s]" + " 48%|████▊ | 81887232/170498071 [00:01<00:01, 78343847.05it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 125665280/170498071 [00:01<00:00, 112903553.22it/s]" + " 53%|█████▎ | 89751552/170498071 [00:01<00:01, 76411811.17it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 137396224/170498071 [00:01<00:00, 114077850.23it/s]" + " 57%|█████▋ | 97452032/170498071 [00:01<00:00, 76559764.63it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 148897792/170498071 [00:01<00:00, 114231113.69it/s]" + " 62%|██████▏ | 105119744/170498071 [00:01<00:00, 76377178.30it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 160399360/170498071 [00:01<00:00, 114421071.55it/s]" + " 66%|██████▌ | 112787456/170498071 [00:01<00:00, 76223255.83it/s]" ] }, { @@ -372,7 +372,63 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:01<00:00, 106209257.98it/s]" + " 71%|███████ | 121405440/170498071 [00:01<00:00, 79156566.53it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 76%|███████▌ | 129335296/170498071 [00:01<00:00, 78370632.15it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 80%|████████ | 137232384/170498071 [00:01<00:00, 78523013.51it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 85%|████████▌ | 145096704/170498071 [00:01<00:00, 76246718.61it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 90%|████████▉ | 153026560/170498071 [00:02<00:00, 76949957.68it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 94%|█████████▍| 160825344/170498071 [00:02<00:00, 77200889.35it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 168558592/170498071 [00:02<00:00, 76174010.79it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:02<00:00, 75087729.26it/s]" ] }, { @@ -490,10 +546,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:44.462980Z", + "iopub.status.busy": "2024-07-02T15:30:44.462612Z", + "iopub.status.idle": "2024-07-02T15:30:44.467530Z", + "shell.execute_reply": "2024-07-02T15:30:44.467072Z" }, "nbsphinx": "hidden" }, @@ -544,10 +600,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:44.469606Z", + "iopub.status.busy": "2024-07-02T15:30:44.469261Z", + "iopub.status.idle": "2024-07-02T15:30:45.008289Z", + "shell.execute_reply": "2024-07-02T15:30:45.007683Z" } }, "outputs": [ @@ -580,10 +636,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:45.010647Z", + "iopub.status.busy": "2024-07-02T15:30:45.010334Z", + "iopub.status.idle": "2024-07-02T15:30:45.519231Z", + "shell.execute_reply": "2024-07-02T15:30:45.518726Z" } }, "outputs": [ @@ -621,10 +677,10 @@ "id": "1cf25354", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:45.521432Z", + "iopub.status.busy": "2024-07-02T15:30:45.521094Z", + "iopub.status.idle": "2024-07-02T15:30:45.524584Z", + "shell.execute_reply": "2024-07-02T15:30:45.524036Z" } }, "outputs": [], @@ -647,17 +703,17 @@ "id": "85a58d41", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:45.526495Z", + "iopub.status.busy": "2024-07-02T15:30:45.526191Z", + "iopub.status.idle": "2024-07-02T15:30:58.240257Z", + "shell.execute_reply": "2024-07-02T15:30:58.239689Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7134c3b9c85247698385a933e9c6f4c1", + "model_id": "3e5259ff69044f2d9040e07e24baf5d7", "version_major": 2, "version_minor": 0 }, @@ -716,10 +772,10 @@ "id": "feb0f519", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:58.242629Z", + "iopub.status.busy": "2024-07-02T15:30:58.242434Z", + "iopub.status.idle": "2024-07-02T15:31:00.298294Z", + "shell.execute_reply": "2024-07-02T15:31:00.297716Z" } }, "outputs": [ @@ -763,10 +819,10 @@ "id": "089d5860", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:00.300955Z", + "iopub.status.busy": "2024-07-02T15:31:00.300659Z", + "iopub.status.idle": "2024-07-02T15:31:00.552803Z", + "shell.execute_reply": "2024-07-02T15:31:00.552236Z" } }, "outputs": [ @@ -802,10 +858,10 @@ "id": "78b1951c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:00.555642Z", + "iopub.status.busy": "2024-07-02T15:31:00.555135Z", + "iopub.status.idle": "2024-07-02T15:31:01.217327Z", + "shell.execute_reply": "2024-07-02T15:31:01.216752Z" } }, "outputs": [ @@ -855,10 +911,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:01.220258Z", + "iopub.status.busy": "2024-07-02T15:31:01.219756Z", + "iopub.status.idle": "2024-07-02T15:31:01.555237Z", + "shell.execute_reply": "2024-07-02T15:31:01.554707Z" } }, "outputs": [ @@ -906,10 +962,10 @@ "id": "616769f8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:01.557486Z", + "iopub.status.busy": "2024-07-02T15:31:01.557127Z", + "iopub.status.idle": "2024-07-02T15:31:01.796360Z", + "shell.execute_reply": "2024-07-02T15:31:01.795785Z" } }, "outputs": [ @@ -965,10 +1021,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:01.798780Z", + "iopub.status.busy": "2024-07-02T15:31:01.798246Z", + "iopub.status.idle": "2024-07-02T15:31:01.878845Z", + "shell.execute_reply": "2024-07-02T15:31:01.878199Z" } }, "outputs": [], @@ -989,10 +1045,10 @@ "id": "89f9db72", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:01.881383Z", + "iopub.status.busy": "2024-07-02T15:31:01.881198Z", + "iopub.status.idle": "2024-07-02T15:31:12.064664Z", + "shell.execute_reply": "2024-07-02T15:31:12.064061Z" } }, "outputs": [ @@ -1029,10 +1085,10 @@ "id": "874c885a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:12.067005Z", + "iopub.status.busy": "2024-07-02T15:31:12.066698Z", + "iopub.status.idle": "2024-07-02T15:31:14.192509Z", + "shell.execute_reply": "2024-07-02T15:31:14.192014Z" } }, "outputs": [ @@ -1063,10 +1119,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:14.195306Z", + "iopub.status.busy": "2024-07-02T15:31:14.194774Z", + "iopub.status.idle": "2024-07-02T15:31:14.398366Z", + "shell.execute_reply": "2024-07-02T15:31:14.397868Z" } }, "outputs": [], @@ -1080,10 +1136,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:14.400634Z", + "iopub.status.busy": "2024-07-02T15:31:14.400446Z", + "iopub.status.idle": "2024-07-02T15:31:14.403545Z", + "shell.execute_reply": "2024-07-02T15:31:14.403109Z" } }, "outputs": [], @@ -1105,10 +1161,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:14.405375Z", + "iopub.status.busy": "2024-07-02T15:31:14.405202Z", + "iopub.status.idle": "2024-07-02T15:31:14.413303Z", + "shell.execute_reply": "2024-07-02T15:31:14.412850Z" }, "nbsphinx": "hidden" }, @@ -1153,7 +1209,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0ec7acb06a8d4e7c8cee6f0af1617289": { + "0a031667bcbf4ad0b0f0c1a2382f5f2e": { + "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 + } + }, + "2f6020cef60b4bf8bac0b8389a1b6833": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1168,15 +1242,83 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_252fe402519d4210ba4ba3fef0912a86", + "layout": "IPY_MODEL_fbf5a8d22db64f0eb01aa7fd53c1b68b", "placeholder": "​", - "style": "IPY_MODEL_b8360c36dca94afc98ec4fb786a3c57f", + "style": "IPY_MODEL_0a031667bcbf4ad0b0f0c1a2382f5f2e", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 152MB/s]" + } + }, + "3cd1e403a7754c11922ba99ec3494a8e": { + "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_e052a3ce162f4f6c9ff6c38cfe463797", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_aa0fb39e0a6040b5b93e4814da7189e3", "tabbable": null, "tooltip": null, - "value": " 102M/102M [00:00<00:00, 304MB/s]" + "value": 102469840.0 + } + }, + "3e5259ff69044f2d9040e07e24baf5d7": { + "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_f83bdbe3f66c44e882ec5a547dc9c059", + "IPY_MODEL_3cd1e403a7754c11922ba99ec3494a8e", + "IPY_MODEL_2f6020cef60b4bf8bac0b8389a1b6833" + ], + "layout": "IPY_MODEL_8343e5c5e9e14df4a633a441184012d4", + "tabbable": null, + "tooltip": null } }, - "252fe402519d4210ba4ba3fef0912a86": { + "599e155eace340f792bf1521c812754d": { + "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 + } + }, + "8343e5c5e9e14df4a633a441184012d4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1229,7 +1371,7 @@ "width": null } }, - "4cb10e135c4d4df6a0102b8fa2c4e435": { + "aa0fb39e0a6040b5b93e4814da7189e3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1245,49 +1387,7 @@ "description_width": "" } }, - "6b6164dfe4394da88a0985c0358adabf": { - "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 - } - }, - "7134c3b9c85247698385a933e9c6f4c1": { - "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_f75ac16d283e42748e30f48710f7c779", - "IPY_MODEL_ebbc5fc8b0754655bb152b6178ceae67", - "IPY_MODEL_0ec7acb06a8d4e7c8cee6f0af1617289" - ], - "layout": "IPY_MODEL_76e78968a920473d8821422c81a0fcdd", - "tabbable": null, - "tooltip": null - } - }, - "76e78968a920473d8821422c81a0fcdd": { + "c8f58a32013f4bc7a0fcf152344559fe": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1340,25 +1440,7 @@ "width": null } }, - "b8360c36dca94afc98ec4fb786a3c57f": { - "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 - } - }, - "bc50c73a865f4e2e8076a042331398c7": { + "e052a3ce162f4f6c9ff6c38cfe463797": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1411,7 +1493,30 @@ "width": null } }, - "be9da5a89136408299b9df5aa61bf8ca": { + "f83bdbe3f66c44e882ec5a547dc9c059": { + "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_c8f58a32013f4bc7a0fcf152344559fe", + "placeholder": "​", + "style": "IPY_MODEL_599e155eace340f792bf1521c812754d", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "fbf5a8d22db64f0eb01aa7fd53c1b68b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1463,55 +1568,6 @@ "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 d7791c942..d85d0978d 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": "2024-07-02T15:16:22.773416Z", - "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" + "iopub.execute_input": "2024-07-02T15:31:18.468176Z", + "iopub.status.busy": "2024-07-02T15:31:18.467999Z", + "iopub.status.idle": "2024-07-02T15:31:19.595981Z", + "shell.execute_reply": "2024-07-02T15:31:19.595445Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:16:23.927425Z", - "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" + "iopub.execute_input": "2024-07-02T15:31:19.598444Z", + "iopub.status.busy": "2024-07-02T15:31:19.598201Z", + "iopub.status.idle": "2024-07-02T15:31:19.614971Z", + "shell.execute_reply": "2024-07-02T15:31:19.614441Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:19.617151Z", + "iopub.status.busy": "2024-07-02T15:31:19.616775Z", + "iopub.status.idle": "2024-07-02T15:31:19.619597Z", + "shell.execute_reply": "2024-07-02T15:31:19.619180Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:19.621728Z", + "iopub.status.busy": "2024-07-02T15:31:19.621281Z", + "iopub.status.idle": "2024-07-02T15:31:19.720173Z", + "shell.execute_reply": "2024-07-02T15:31:19.719650Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:19.722194Z", + "iopub.status.busy": "2024-07-02T15:31:19.721889Z", + "iopub.status.idle": "2024-07-02T15:31:19.898619Z", + "shell.execute_reply": "2024-07-02T15:31:19.898073Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:19.900830Z", + "iopub.status.busy": "2024-07-02T15:31:19.900644Z", + "iopub.status.idle": "2024-07-02T15:31:20.105363Z", + "shell.execute_reply": "2024-07-02T15:31:20.104893Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:20.107396Z", + "iopub.status.busy": "2024-07-02T15:31:20.107065Z", + "iopub.status.idle": "2024-07-02T15:31:20.111228Z", + "shell.execute_reply": "2024-07-02T15:31:20.110779Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:20.113042Z", + "iopub.status.busy": "2024-07-02T15:31:20.112776Z", + "iopub.status.idle": "2024-07-02T15:31:20.118755Z", + "shell.execute_reply": "2024-07-02T15:31:20.118338Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:20.120896Z", + "iopub.status.busy": "2024-07-02T15:31:20.120567Z", + "iopub.status.idle": "2024-07-02T15:31:20.123220Z", + "shell.execute_reply": "2024-07-02T15:31:20.122769Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:20.124968Z", + "iopub.status.busy": "2024-07-02T15:31:20.124802Z", + "iopub.status.idle": "2024-07-02T15:31:28.571097Z", + "shell.execute_reply": "2024-07-02T15:31:28.570451Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.573996Z", + "iopub.status.busy": "2024-07-02T15:31:28.573598Z", + "iopub.status.idle": "2024-07-02T15:31:28.580859Z", + "shell.execute_reply": "2024-07-02T15:31:28.580404Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.582717Z", + "iopub.status.busy": "2024-07-02T15:31:28.582538Z", + "iopub.status.idle": "2024-07-02T15:31:28.586218Z", + "shell.execute_reply": "2024-07-02T15:31:28.585630Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.588135Z", + "iopub.status.busy": "2024-07-02T15:31:28.587823Z", + "iopub.status.idle": "2024-07-02T15:31:28.590968Z", + "shell.execute_reply": "2024-07-02T15:31:28.590464Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.592960Z", + "iopub.status.busy": "2024-07-02T15:31:28.592655Z", + "iopub.status.idle": "2024-07-02T15:31:28.595547Z", + "shell.execute_reply": "2024-07-02T15:31:28.595114Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.597477Z", + "iopub.status.busy": "2024-07-02T15:31:28.597173Z", + "iopub.status.idle": "2024-07-02T15:31:28.605193Z", + "shell.execute_reply": "2024-07-02T15:31:28.604644Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.607149Z", + "iopub.status.busy": "2024-07-02T15:31:28.606833Z", + "iopub.status.idle": "2024-07-02T15:31:28.609520Z", + "shell.execute_reply": "2024-07-02T15:31:28.608976Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.611593Z", + "iopub.status.busy": "2024-07-02T15:31:28.611160Z", + "iopub.status.idle": "2024-07-02T15:31:28.728605Z", + "shell.execute_reply": "2024-07-02T15:31:28.728116Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.730718Z", + "iopub.status.busy": "2024-07-02T15:31:28.730363Z", + "iopub.status.idle": "2024-07-02T15:31:28.833994Z", + "shell.execute_reply": "2024-07-02T15:31:28.833479Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.836173Z", + "iopub.status.busy": "2024-07-02T15:31:28.835822Z", + "iopub.status.idle": "2024-07-02T15:31:29.318753Z", + "shell.execute_reply": "2024-07-02T15:31:29.318190Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:29.320805Z", + "iopub.status.busy": "2024-07-02T15:31:29.320632Z", + "iopub.status.idle": "2024-07-02T15:31:29.390828Z", + "shell.execute_reply": "2024-07-02T15:31:29.390329Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:29.392859Z", + "iopub.status.busy": "2024-07-02T15:31:29.392685Z", + "iopub.status.idle": "2024-07-02T15:31:29.401028Z", + "shell.execute_reply": "2024-07-02T15:31:29.400481Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:29.403035Z", + "iopub.status.busy": "2024-07-02T15:31:29.402726Z", + "iopub.status.idle": "2024-07-02T15:31:29.405418Z", + "shell.execute_reply": "2024-07-02T15:31:29.404957Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:29.407290Z", + "iopub.status.busy": "2024-07-02T15:31:29.407118Z", + "iopub.status.idle": "2024-07-02T15:31:34.727159Z", + "shell.execute_reply": "2024-07-02T15:31:34.726599Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:34.729487Z", + "iopub.status.busy": "2024-07-02T15:31:34.729099Z", + "iopub.status.idle": "2024-07-02T15:31:34.738133Z", + "shell.execute_reply": "2024-07-02T15:31:34.737687Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:34.740153Z", + "iopub.status.busy": "2024-07-02T15:31:34.739979Z", + "iopub.status.idle": "2024-07-02T15:31:34.810722Z", + "shell.execute_reply": "2024-07-02T15:31:34.810162Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index f4716d029..4beae91e7 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": "2024-07-02T15:16:42.561018Z", - "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" + "iopub.execute_input": "2024-07-02T15:31:37.724618Z", + "iopub.status.busy": "2024-07-02T15:31:37.724448Z", + "iopub.status.idle": "2024-07-02T15:31:39.632373Z", + "shell.execute_reply": "2024-07-02T15:31:39.631704Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:39.634734Z", + "iopub.status.busy": "2024-07-02T15:31:39.634549Z", + "iopub.status.idle": "2024-07-02T15:33:03.062922Z", + "shell.execute_reply": "2024-07-02T15:33:03.062276Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:03.065400Z", + "iopub.status.busy": "2024-07-02T15:33:03.065026Z", + "iopub.status.idle": "2024-07-02T15:33:04.151127Z", + "shell.execute_reply": "2024-07-02T15:33:04.150507Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:17:45.857557Z", - "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" + "iopub.execute_input": "2024-07-02T15:33:04.153507Z", + "iopub.status.busy": "2024-07-02T15:33:04.153220Z", + "iopub.status.idle": "2024-07-02T15:33:04.156468Z", + "shell.execute_reply": "2024-07-02T15:33:04.156013Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:04.158554Z", + "iopub.status.busy": "2024-07-02T15:33:04.158229Z", + "iopub.status.idle": "2024-07-02T15:33:04.162013Z", + "shell.execute_reply": "2024-07-02T15:33:04.161530Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:04.164308Z", + "iopub.status.busy": "2024-07-02T15:33:04.163807Z", + "iopub.status.idle": "2024-07-02T15:33:04.167471Z", + "shell.execute_reply": "2024-07-02T15:33:04.166951Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:04.169469Z", + "iopub.status.busy": "2024-07-02T15:33:04.169160Z", + "iopub.status.idle": "2024-07-02T15:33:04.171836Z", + "shell.execute_reply": "2024-07-02T15:33:04.171413Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:04.173818Z", + "iopub.status.busy": "2024-07-02T15:33:04.173482Z", + "iopub.status.idle": "2024-07-02T15:33:36.513602Z", + "shell.execute_reply": "2024-07-02T15:33:36.512999Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d37081f0d674141ab48e998533cdac5", + "model_id": "bad327a6dcab414c8e0a515458d75ccb", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6181f2ac640b4d5694a1537900a59156", + "model_id": "7e0001601cd8466bb4509138ab4ebda8", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:36.516246Z", + "iopub.status.busy": "2024-07-02T15:33:36.515935Z", + "iopub.status.idle": "2024-07-02T15:33:37.144437Z", + "shell.execute_reply": "2024-07-02T15:33:37.143962Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:37.146787Z", + "iopub.status.busy": "2024-07-02T15:33:37.146361Z", + "iopub.status.idle": "2024-07-02T15:33:39.872510Z", + "shell.execute_reply": "2024-07-02T15:33:39.871932Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:39.874693Z", + "iopub.status.busy": "2024-07-02T15:33:39.874352Z", + "iopub.status.idle": "2024-07-02T15:34:12.280506Z", + "shell.execute_reply": "2024-07-02T15:34:12.280037Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee454cb23f344e94bf0306f6bd70e6ef", + "model_id": "88f1333cf1c544a5912e80ec16eb4ca6", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:12.282629Z", + "iopub.status.busy": "2024-07-02T15:34:12.282304Z", + "iopub.status.idle": "2024-07-02T15:34:26.864434Z", + "shell.execute_reply": "2024-07-02T15:34:26.863831Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:26.866952Z", + "iopub.status.busy": "2024-07-02T15:34:26.866651Z", + "iopub.status.idle": "2024-07-02T15:34:30.542698Z", + "shell.execute_reply": "2024-07-02T15:34:30.542163Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:30.544890Z", + "iopub.status.busy": "2024-07-02T15:34:30.544550Z", + "iopub.status.idle": "2024-07-02T15:34:31.919730Z", + "shell.execute_reply": "2024-07-02T15:34:31.919163Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "22d36bebc1d941d19f0aa385e194e320", + "model_id": "7eccc0839691439e89d779fba47b562f", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:31.922089Z", + "iopub.status.busy": "2024-07-02T15:34:31.921633Z", + "iopub.status.idle": "2024-07-02T15:34:31.951822Z", + "shell.execute_reply": "2024-07-02T15:34:31.951305Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:31.954212Z", + "iopub.status.busy": "2024-07-02T15:34:31.954007Z", + "iopub.status.idle": "2024-07-02T15:34:37.993152Z", + "shell.execute_reply": "2024-07-02T15:34:37.992712Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:37.995340Z", + "iopub.status.busy": "2024-07-02T15:34:37.995001Z", + "iopub.status.idle": "2024-07-02T15:34:38.050757Z", + "shell.execute_reply": "2024-07-02T15:34:38.050200Z" }, "nbsphinx": "hidden" }, @@ -1038,7 +1038,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "03565a80c7d34c6293339e9385fd0f09": { + "0fd140538c574ca89150cf75b6f35521": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1056,30 +1056,83 @@ "text_color": null } }, - "0aae918c08c84bed9ae02fe6e0c0d703": { + "1cda877c22e24a249a3adea1e5a3eaa6": { "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": "" + } + }, + "2daaa990afa74831ac228ef5af8c878a": { + "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": "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_80bc86fa44ab4ddb9d7fbfcd03b7da23", - "placeholder": "​", - "style": "IPY_MODEL_5a6ca2db026040538be2877748a39874", + "layout": "IPY_MODEL_72ec7912eda94737b355f53c83796387", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_480f3b3335064465bea3b972128d431b", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: 100%" + "value": 30.0 } }, - "0b8a0362dd6d4e8faa60de3132d8f2b3": { + "38ac20ffb13048faad531b95689a6281": { + "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": "" + } + }, + "3f14050e5552461e9f897fe3737afc39": { + "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 + } + }, + "40ce6ec00dea4ff68934b9dba6caed1f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1132,25 +1185,7 @@ "width": null } }, - "0c57b519892e41bcb28985872ce030c7": { - "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 - } - }, - "0d5c01051776423480b74fabbe29251b": { + "44d19e8829934708a262462fd0c1bf75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1165,15 +1200,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a5072a4bea8140e2808839dad337d950", + "layout": "IPY_MODEL_451ab150dbda4bfbb629498f0e760dac", "placeholder": "​", - "style": "IPY_MODEL_a2f21f1a521e4c718452c19f8256841e", + "style": "IPY_MODEL_0fd140538c574ca89150cf75b6f35521", "tabbable": null, "tooltip": null, - "value": " 4997683/4997683 [00:33<00:00, 149090.50it/s]" + "value": " 4997683/4997683 [00:32<00:00, 154921.47it/s]" + } + }, + "4517d65ed66644b08c13686bca4ada61": { + "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 } }, - "0ea451f86f7b4b05957d9adf3ae7f735": { + "451ab150dbda4bfbb629498f0e760dac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1226,7 +1279,7 @@ "width": null } }, - "16f22dbcf3de44d7b4fd798a1f0b853a": { + "480f3b3335064465bea3b972128d431b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1242,57 +1295,83 @@ "description_width": "" } }, - "22d36bebc1d941d19f0aa385e194e320": { + "4a0a519ca6e5455ca09357a81c0d379e": { "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_ac2a26cb4281477983b7746968ff3e52", - "IPY_MODEL_3401bd6344664b4f85cbb90cf38682a3", - "IPY_MODEL_4102b17c3b13403aa9b3d80fb7df75ac" - ], - "layout": "IPY_MODEL_ecae615274e34bf59e8e58c649c294bc", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_81e58e1b155346b3b6e2d9d89fa60aac", + "placeholder": "​", + "style": "IPY_MODEL_65bd5ddda279485393f9c86d24ce9772", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "images processed using softmin: 100%" } }, - "3401bd6344664b4f85cbb90cf38682a3": { - "model_module": "@jupyter-widgets/controls", + "50a714bd30054853846491d6d3f2c650": { + "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_0b8a0362dd6d4e8faa60de3132d8f2b3", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f3ecd743295c4f619abad363dcb05fab", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "_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 } }, - "4102b17c3b13403aa9b3d80fb7df75ac": { + "57447469206d4415aafdd42c03b73e58": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1307,41 +1386,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b2f361ba3c35411587bfb94055421b84", + "layout": "IPY_MODEL_b39e7c191a2443ccae358aec72bf7220", "placeholder": "​", - "style": "IPY_MODEL_97090cf694824c77b08cfdb17f057e3e", + "style": "IPY_MODEL_848cf4dd8cda496e896c4d1cc4c769e4", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 21.42it/s]" - } - }, - "544233229d944fd690c9e89d8792bbe8": { - "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_f14aada5b0eb4e5ba3b431cb7b518da6", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cbbd654031b747468c432f9278836125", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "value": "number of examples processed for checking labels: 100%" } }, - "5a6ca2db026040538be2877748a39874": { + "65bd5ddda279485393f9c86d24ce9772": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1359,55 +1412,7 @@ "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 - } - }, - "6d37081f0d674141ab48e998533cdac5": { - "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_b4d580e3f19a47c6a1dde0776a4e5606", - "IPY_MODEL_544233229d944fd690c9e89d8792bbe8", - "IPY_MODEL_b8c4edcee9764b17abf836ac1f23e926" - ], - "layout": "IPY_MODEL_98b4559124704d61a177e8a88a9d3c8a", - "tabbable": null, - "tooltip": null - } - }, - "7014f38f051242f19c5f77d4e2e3dd69": { + "72ec7912eda94737b355f53c83796387": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1460,7 +1465,7 @@ "width": null } }, - "72b5647ddfd149559d4a948d2411a55b": { + "7631dd07857745e3b56461586f545318": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1476,17 +1481,63 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f3a91f9853db4fac966be7c379809f9c", - "max": 4997683.0, + "layout": "IPY_MODEL_c7cbbf84083044978ab0df9b9c7126a9", + "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_ce421d89a8e8429486414e8e5f2fb0b9", + "style": "IPY_MODEL_38ac20ffb13048faad531b95689a6281", "tabbable": null, "tooltip": null, - "value": 4997683.0 + "value": 30.0 } }, - "80bc86fa44ab4ddb9d7fbfcd03b7da23": { + "786fd79b6e614e9b814448b582543029": { + "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_801025927d904866871b27bae4f3e30c", + "placeholder": "​", + "style": "IPY_MODEL_fa0bbd1ddd3b47ed8606049facfac47d", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:20<00:00,  1.42it/s]" + } + }, + "79753d45ae124059908bf2ba3678d798": { + "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_f50ec117ed814ac3820ed475371f1510", + "placeholder": "​", + "style": "IPY_MODEL_4517d65ed66644b08c13686bca4ada61", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 739.81it/s]" + } + }, + "7a8940a833324ba29bb1e6f53e89dae1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1539,43 +1590,55 @@ "width": null } }, - "9479cd74f7ae4ab98e7dd6164deed30a": { + "7e0001601cd8466bb4509138ab4ebda8": { "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_57447469206d4415aafdd42c03b73e58", + "IPY_MODEL_2daaa990afa74831ac228ef5af8c878a", + "IPY_MODEL_786fd79b6e614e9b814448b582543029" + ], + "layout": "IPY_MODEL_c83b606f3fdb4d6a8afcb5f78c107ac8", + "tabbable": null, + "tooltip": null } }, - "97090cf694824c77b08cfdb17f057e3e": { + "7eccc0839691439e89d779fba47b562f": { "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_4a0a519ca6e5455ca09357a81c0d379e", + "IPY_MODEL_ce881d19b69a46be8feb73a7b5a68d09", + "IPY_MODEL_aaa77b4b69364ba2ad48741d8b4141e7" + ], + "layout": "IPY_MODEL_84554b6d7bf44226bf53416e4d0a2b71", + "tabbable": null, + "tooltip": null } }, - "98b4559124704d61a177e8a88a9d3c8a": { + "801025927d904866871b27bae4f3e30c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1628,7 +1691,7 @@ "width": null } }, - "9ba76afb638e410e891187e79a458dac": { + "81e58e1b155346b3b6e2d9d89fa60aac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1681,7 +1744,7 @@ "width": null } }, - "9f4790501bef48328e2bd3eee0152689": { + "84554b6d7bf44226bf53416e4d0a2b71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1734,7 +1797,7 @@ "width": null } }, - "a2f21f1a521e4c718452c19f8256841e": { + "848cf4dd8cda496e896c4d1cc4c769e4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1752,83 +1815,98 @@ "text_color": null } }, - "a5072a4bea8140e2808839dad337d950": { - "model_module": "@jupyter-widgets/base", + "88f1333cf1c544a5912e80ec16eb4ca6": { + "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_f3b9945cd0f94d4aaf5d332693944660", + "IPY_MODEL_abfa384596354665b60a79f482b668f3", + "IPY_MODEL_44d19e8829934708a262462fd0c1bf75" + ], + "layout": "IPY_MODEL_50a714bd30054853846491d6d3f2c650", + "tabbable": null, + "tooltip": null + } + }, + "aa85802912774b7988f19c20712f63ab": { + "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": "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 + } + }, + "aaa77b4b69364ba2ad48741d8b4141e7": { + "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_7a8940a833324ba29bb1e6f53e89dae1", + "placeholder": "​", + "style": "IPY_MODEL_3f14050e5552461e9f897fe3737afc39", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 22.02it/s]" } }, - "ac2a26cb4281477983b7746968ff3e52": { + "abfa384596354665b60a79f482b668f3": { "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_0ea451f86f7b4b05957d9adf3ae7f735", - "placeholder": "​", - "style": "IPY_MODEL_cf74a277aa3141788d774f9065d2366d", + "layout": "IPY_MODEL_40ce6ec00dea4ff68934b9dba6caed1f", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b57ba400aba2414cb1bb173653025ebb", "tabbable": null, "tooltip": null, - "value": "images processed using softmin: 100%" + "value": 4997683.0 } }, - "afdd70b438584e8381748e28bdc60eb0": { + "b0d9136bfec24979b7f2bdb45cc8a04b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1881,7 +1959,7 @@ "width": null } }, - "b27b9980b1934cddbaa61f473c83ad55": { + "b39e7c191a2443ccae358aec72bf7220": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1934,25 +2012,47 @@ "width": null } }, - "b2ec5e9d337d409f8f6128aa41a5d79f": { + "b57ba400aba2414cb1bb173653025ebb": { "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": "" + } + }, + "bad327a6dcab414c8e0a515458d75ccb": { + "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_fa4275e75f7d47ca9abfdd2ff25071a4", + "IPY_MODEL_7631dd07857745e3b56461586f545318", + "IPY_MODEL_79753d45ae124059908bf2ba3678d798" + ], + "layout": "IPY_MODEL_c7762dfaefbe498d9f7c6387900b93a0", + "tabbable": null, + "tooltip": null } }, - "b2f361ba3c35411587bfb94055421b84": { + "c7762dfaefbe498d9f7c6387900b93a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2005,126 +2105,7 @@ "width": null } }, - "b4d580e3f19a47c6a1dde0776a4e5606": { - "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_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", - "bar_color": null, - "description_width": "" - } - }, - "cd8af1b2cbd542f4869d02c1d973e524": { - "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_b27b9980b1934cddbaa61f473c83ad55", - "placeholder": "​", - "style": "IPY_MODEL_03565a80c7d34c6293339e9385fd0f09", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:20<00:00,  1.41it/s]" - } - }, - "ce421d89a8e8429486414e8e5f2fb0b9": { - "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": "" - } - }, - "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": { + "c7cbbf84083044978ab0df9b9c7126a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2177,7 +2158,7 @@ "width": null } }, - "e5f3cab627be4d4a8d76b371ca9346f3": { + "c83b606f3fdb4d6a8afcb5f78c107ac8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2230,7 +2211,7 @@ "width": null } }, - "eaaf30bb31f7495d8fbee7118acc211f": { + "ce881d19b69a46be8feb73a7b5a68d09": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2246,17 +2227,58 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e5f3cab627be4d4a8d76b371ca9346f3", + "layout": "IPY_MODEL_b0d9136bfec24979b7f2bdb45cc8a04b", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_16f22dbcf3de44d7b4fd798a1f0b853a", + "style": "IPY_MODEL_1cda877c22e24a249a3adea1e5a3eaa6", "tabbable": null, "tooltip": null, "value": 30.0 } }, - "ecae615274e34bf59e8e58c649c294bc": { + "dcc356830b094ef8b7bd62ea9d5f4d3c": { + "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 + } + }, + "f3b9945cd0f94d4aaf5d332693944660": { + "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_fdf4e3973a9c42018c8d1b704c592c6e", + "placeholder": "​", + "style": "IPY_MODEL_aa85802912774b7988f19c20712f63ab", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "f50ec117ed814ac3820ed475371f1510": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2309,31 +2331,48 @@ "width": null } }, - "ee454cb23f344e94bf0306f6bd70e6ef": { + "fa0bbd1ddd3b47ed8606049facfac47d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "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 + } + }, + "fa4275e75f7d47ca9abfdd2ff25071a4": { + "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": "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_f58763e1734248fda5001bdfeb216209", - "IPY_MODEL_72b5647ddfd149559d4a948d2411a55b", - "IPY_MODEL_0d5c01051776423480b74fabbe29251b" - ], - "layout": "IPY_MODEL_d4139e678fc14fc5b5f22a2a0dd84027", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fa73892e099842d1b9af380a12979792", + "placeholder": "​", + "style": "IPY_MODEL_dcc356830b094ef8b7bd62ea9d5f4d3c", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" } }, - "f14aada5b0eb4e5ba3b431cb7b518da6": { + "fa73892e099842d1b9af380a12979792": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2386,7 +2425,7 @@ "width": null } }, - "f3a91f9853db4fac966be7c379809f9c": { + "fdf4e3973a9c42018c8d1b704c592c6e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2438,45 +2477,6 @@ "visibility": null, "width": null } - }, - "f3ecd743295c4f619abad363dcb05fab": { - "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": "" - } - }, - "f58763e1734248fda5001bdfeb216209": { - "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_9ba76afb638e410e891187e79a458dac", - "placeholder": "​", - "style": "IPY_MODEL_0c57b519892e41bcb28985872ce030c7", - "tabbable": null, - "tooltip": null, - "value": "100%" - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 42ddeaa94..4c5c0ea36 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": "2024-07-02T15:19:23.685217Z", - "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" + "iopub.execute_input": "2024-07-02T15:34:40.409044Z", + "iopub.status.busy": "2024-07-02T15:34:40.408876Z", + "iopub.status.idle": "2024-07-02T15:34:41.504750Z", + "shell.execute_reply": "2024-07-02T15:34:41.504249Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-02 15:19:23-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-02 15:34:40-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,22 +94,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HTTP request sent, awaiting response... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200 OK\r\n", + "169.150.236.98, 2400:52e0:1a00::941:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... connected.\r\n", + "HTTP request sent, awaiting response... 200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -122,9 +109,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.95MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.01s \r\n", "\r\n", - "2024-07-02 15:19:24 (5.95 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-02 15:34:40 (83.3 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -144,9 +131,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--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", + "--2024-07-02 15:34:40-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.173.201, 3.5.25.44, 3.5.8.134, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.173.201|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -167,9 +154,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 92.7MB/s in 0.2s \r\n", + "pred_probs.npz 96%[==================> ] 15.71M 64.7MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 66.3MB/s in 0.2s \r\n", "\r\n", - "2024-07-02 15:19:24 (92.7 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-02 15:34:41 (66.3 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -186,10 +174,10 @@ "id": "439b0305", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:41.507000Z", + "iopub.status.busy": "2024-07-02T15:34:41.506807Z", + "iopub.status.idle": "2024-07-02T15:34:42.679990Z", + "shell.execute_reply": "2024-07-02T15:34:42.679463Z" }, "nbsphinx": "hidden" }, @@ -200,7 +188,7 @@ "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -226,10 +214,10 @@ "id": "a1349304", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:42.682422Z", + "iopub.status.busy": "2024-07-02T15:34:42.682072Z", + "iopub.status.idle": "2024-07-02T15:34:42.685351Z", + "shell.execute_reply": "2024-07-02T15:34:42.684925Z" } }, "outputs": [], @@ -279,10 +267,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:42.687222Z", + "iopub.status.busy": "2024-07-02T15:34:42.687046Z", + "iopub.status.idle": "2024-07-02T15:34:42.690100Z", + "shell.execute_reply": "2024-07-02T15:34:42.689584Z" }, "nbsphinx": "hidden" }, @@ -300,10 +288,10 @@ "id": "519cb80c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:42.692165Z", + "iopub.status.busy": "2024-07-02T15:34:42.691989Z", + "iopub.status.idle": "2024-07-02T15:34:51.762665Z", + "shell.execute_reply": "2024-07-02T15:34:51.762051Z" } }, "outputs": [], @@ -377,10 +365,10 @@ "id": "202f1526", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:51.765158Z", + "iopub.status.busy": "2024-07-02T15:34:51.764935Z", + "iopub.status.idle": "2024-07-02T15:34:51.770723Z", + "shell.execute_reply": "2024-07-02T15:34:51.770154Z" }, "nbsphinx": "hidden" }, @@ -420,10 +408,10 @@ "id": "a4381f03", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:51.772692Z", + "iopub.status.busy": "2024-07-02T15:34:51.772305Z", + "iopub.status.idle": "2024-07-02T15:34:52.114037Z", + "shell.execute_reply": "2024-07-02T15:34:52.113540Z" } }, "outputs": [], @@ -460,10 +448,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:52.116302Z", + "iopub.status.busy": "2024-07-02T15:34:52.116116Z", + "iopub.status.idle": "2024-07-02T15:34:52.120622Z", + "shell.execute_reply": "2024-07-02T15:34:52.120168Z" } }, "outputs": [ @@ -535,10 +523,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:52.122595Z", + "iopub.status.busy": "2024-07-02T15:34:52.122423Z", + "iopub.status.idle": "2024-07-02T15:34:54.590782Z", + "shell.execute_reply": "2024-07-02T15:34:54.590025Z" } }, "outputs": [], @@ -560,10 +548,10 @@ "id": "95dc7268", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.593625Z", + "iopub.status.busy": "2024-07-02T15:34:54.593066Z", + "iopub.status.idle": "2024-07-02T15:34:54.597171Z", + "shell.execute_reply": "2024-07-02T15:34:54.596636Z" } }, "outputs": [ @@ -599,10 +587,10 @@ "id": "e13de188", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.599260Z", + "iopub.status.busy": "2024-07-02T15:34:54.598873Z", + "iopub.status.idle": "2024-07-02T15:34:54.604418Z", + "shell.execute_reply": "2024-07-02T15:34:54.603888Z" } }, "outputs": [ @@ -780,10 +768,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.606602Z", + "iopub.status.busy": "2024-07-02T15:34:54.606277Z", + "iopub.status.idle": "2024-07-02T15:34:54.632296Z", + "shell.execute_reply": "2024-07-02T15:34:54.631839Z" } }, "outputs": [ @@ -885,10 +873,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.634344Z", + "iopub.status.busy": "2024-07-02T15:34:54.634025Z", + "iopub.status.idle": "2024-07-02T15:34:54.638206Z", + "shell.execute_reply": "2024-07-02T15:34:54.637727Z" } }, "outputs": [ @@ -962,10 +950,10 @@ "id": "db0b5179", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.640053Z", + "iopub.status.busy": "2024-07-02T15:34:54.639878Z", + "iopub.status.idle": "2024-07-02T15:34:56.027864Z", + "shell.execute_reply": "2024-07-02T15:34:56.027377Z" } }, "outputs": [ @@ -1137,10 +1125,10 @@ "id": "a18795eb", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:56.030025Z", + "iopub.status.busy": "2024-07-02T15:34:56.029651Z", + "iopub.status.idle": "2024-07-02T15:34:56.033503Z", + "shell.execute_reply": "2024-07-02T15:34:56.033077Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index 02c17e98369c58e0252f31e571be6a624d73a094..e4147df35a4e26186176d90e054c8e8ef30b2d00 100644 GIT binary patch delta 61 zcmX>tep-A(9-~2~aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF QahgeTs$ufxd5pST083pGK>z>% delta 61 zcmX>tep-A(9-~3BWlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{d5pST08f1prvLx| diff --git a/master/.doctrees/tutorials/clean_learning/tabular.doctree b/master/.doctrees/tutorials/clean_learning/tabular.doctree index 04d9eb002612f96d1f7905958b4af269cbb80f3a..2d6b0ce870dde5ba4fce73d8ae164f767f99ee49 100644 GIT binary patch delta 63 zcmcb6i}~&?<_%{#4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F TEt8DXOp;R#lQ;k6On3|c&5jkz delta 63 zcmcb6i}~&?<_%{#4U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u TOp=X_%?!*9%r^h!On3|c(XJIS diff --git a/master/.doctrees/tutorials/clean_learning/text.doctree b/master/.doctrees/tutorials/clean_learning/text.doctree index 12d81867237edecf4369d74a6c74dce83e96de77..195579ead5a312a9e93568a0cf363de403c1b38f 100644 GIT binary patch delta 13591 zcmeHNS*%@E8Fue`5sI|h(3U}Z_qJA$w$e2Z5EC(K0+9k5jUuJ2QL40*7L7ykRv;wC z7{w#(AYrIRd5~za(c_EI8WVzvlFEw@5{SkGBT*YcUvT*LIhS(ptvM?WkB0yyba$

P1`SZF}*CCqr!0h1r^UdIo^JmxB-a5GJ`SS)}JaEnb zZN1gMZw6=FcKhN3K70FFgMD{wSpCVJ``nJ3_U_o__T7^92Df*x@{V@!{oCo_-FKWl zc>1>Ux}%p>YXc;{HmTG%W!HI7oSX1?AjonoJa48X{D-X1rYCqr>FsGfrzcF_c=(jCNMgfQ!RgG%(?)^HGcsTKuSw z?95-k`Ls6U>74dHgy2OW$wwl&i()NY#+LVn`dYMLMCuV;K+4;!372|wlcRTP4RTm& zt)!P!a2;&G@2luY5AhJgzx_0>GA5>qc<_ixNHel1suKMtfE3SOkyWhK`kf?U)*M+OeF~5Pu8+5O-LdI0-!O&qZik^XV;r3iL*VDPy?9}3~p94E_l$24ez?LK3c7h zDBGNXA=+DEY<7;sz=815%G9lXpgwn8ch~=;exPa!`z~bZq;0`P}zxDnF3A&juJLSsxM7 z$~X=*FpLGJI$~zlj?tw=_rujM)c05O6CW*exwM|xBNF0D6# zCj^t0=p-^UQ=pFO1U|5qOTkHel>l2w1j~dO)DWoLT3nDf(R;25M3C;V|$+;n`3 z(w<|k`_xO-#w}bgsx@u2652p(0Vo0>C1tL?lTvyfwH#g_>pingK`amNS~c@PH9z@L z$kM`zg=Ad%b?L59W^MQ4T)l2?@_D*TU#i~H&GVVp&N&#%NEFNoUea2_1voxgkrT+m zjuhlrYZ}39GTb{&fx1*$;A1jYMwy`-d3XHfleH$rT0THu1`j(Ms3n$g7qey1fm{sT zV?EL7$uaouvQz5!w}t&(|Fm1XKb%s(zHy97!C;jWiQrcxWpBE;(iS$!ry)YUX|}Zr zicAD29c+N%;07rn+<3@vYmS&cKOe8=z$rx1pdh^k#J%0rZbV@4kO#L)%xEaVh3#aE%m{ra5G;mjc8_0_j0x&+ME@Tr0pPM1@A;W)_rT zawOU;5epiVDw>m`5ta|yLni~e%NdR|L_CrlDVyczX$k>!$6u^iVLbjtgf&RcP^ajRxxzzf#P(UtZt*DJufGOY^K|pwa z{2H|ti-wpuV~Zc5)QGw7Cc3v)lRBdS#2mgL}u`8SQ~&gMoCUOe+qZ76A=I z7wem5*V7OoykJrVM?1m5u%l9WLSrK7G<@{T8s0BJ7U4KC^@a(42|`^d7%^BYxO=kP z`!$8F`wXT{p18B@prvWIgVs$;;mHk`3>wpob?F zw6CDj`(zb5WR{^%!2;n6DjGbE#)(5`l;Y^fV5u>gqmwHL z1_RRdP9Kt|KV9THX#@WcE0j)jDjuoU1EUzm2vV%^Nnq{F>2UH=MftYd5U^&~y|r9Yst>5z|q`bQG~{0x}&%Oh*yZQN(lxf9Yst>5%|Hk=_ul)rc9~byQnGCPDc^Thal5Y#B>xfc?vKcMf}@I_H-0+ u@)%?~ig;zXG95)!(^15Up9n29*{7q3(NAd?Zs|WCMd;x}pPKpf>Hh-6dtDy@ delta 13345 zcmeHNTZ~<06?UH)(hE>&IxVEYoI@!V%cTA97nDSdJXD2(L;{ss|0}3a5R9U>45b7k z1hpP;2Z@zS3{gp_sd(@~Y#)q~m>B4bCMX7Df{{d<(D;JUZ=XFQGo9xA(fDLeCut`$ z`|R~!)^}NJ^|AX`J$nDDL%&!-f{mY-H>s^WXfI7xA?swUquNEH6C>Fe;wTY;b44^! zhyT9f_0N2BM|F1p8+$hQPkiaz{wsSn_OIV_VSRL4HPt_~XQqGYp6UKsdp55OH@n?8 zrCqmwa(91f?}z^5&pdqhvA*A@`)}Se)&J4HZvTpX=l4H)&p8t_y-oG1k7EIc_D%Mm zojs@j!0bh<+E_O>!8YBvw}0UE^ZF0ov*W-1{OUh<`)Ay{f4Jt$?pwbmxZNS$vg@;V zxZQW=bjPM<4To;3&Y2v3_r!J8+3k13Ga&oV>z~-au77O*hJ|OaXTSPizVGmN?mN7> z7p&l!@|aB^B5Cbh5`zV`~q;`_S=aJ)#7WUUCt25H@;aGmp%A z$)cm#km2i!4>hkl{`{-UdXx&4eaOj%ENFCwa?4`2LNdaX48zwIA8KCr^ijOdN9r79 z&S1;JIOT}&np+X05;FUCv&Dy+*X?|9_3|D!f@Dh#5s`{0o#P})D?Cj!>g?Lrl^!}w zj-9c*iw&_5nN}hP!K63bNtujb$^{`)|F1{(7oRzBblu@KuUxpiXPstVW*w8zB5UbA z*EA8SGs)CaF+8>6L(MkN{L=;JR)?P3TAlew_1*fiN2+aIo?`(e?4S@aW?&cYa%SM~4>mKfYaU?~aZvYeG9)>u(*bR`v)t#?qh-a~^pn zJQ71;InMhWWJm+|<<9QOX&;EDL0OT#7Fs#OC?AnAoW8}SKY25OqytsLP(ZmqzQ(Z z6lwB~>+AV zVaKTRNn4$@!#Z7iBSk){vC65z*Pg4sQq2}F#%w^?ZCBs0>&opPxZ=aR3d!gfKV3Sl z4LfJ*H>W%6rbq9qK7Pr>yEYCFz!MsCCO}JO;9=^t6`;?gBq%VEdf$=G)Q^8yeQdG^ zT9rzx$b}R_6X}=_K5;IBpe*Gus5W*ERz1P31iC=P-U}*-iAowCgk}U(z+-b%YX;;{ z1FzAsb$#oRYI`wmi?7p7``5@bZM z(t+}@J*T)dGB-;+yFSvLST&e^rh2sMN=~6n5{%N2qh(He!MF`l@<3P^?Ay_K0Xr{5 zZ(&vp4!R)qKYv-R@7YYe^C|}13&_tq96od@cq*gOI?ruK3#vj&PAFWAU<4ntG>as$ zGnaf?vYkX+1VQqQlM;k8%Q@u=k9ydsJV$^nb(EIS?y0x}AWzlHk5w1X5V`<;kbFjz zw}C(pb0S`73(jl64;ml?FfpjTpx*U0>nElLUwy86pqedQlv<3YQmQU~x^QZK9(%pQ zu6&cQ8!sB7FN66RSDbMJa;MJHn6ychd*g`XbAr%Q3JRr@=3YeM;d-rsvN0(j3LMyL zq1O%6_ypt2w1vDtC%v{QQJFXsT;gRfRBu$hQq1~yuT@tSL$;{<;vcJ(U6ry9AW#_t zhd^s@k$Vx;0O2Bu52bfVAzf!}!v!jn#6(oqXt=)Ul;y!1o?7L7pX&Soki*J@#0FTi z5tmG)ilyK@iLg7NUj0UA?cfv3Ccf85$jJDz&Cx>a;jNJR);Fs2x~LogAB}&46A8v3 zcqNqP+8FAL93bJ4mfWM>&7+zK!@^Gwy{uv)~K#tsP=sma)P zpw>9=qR4RozJbOS!q%^n4(%19nnvVQebJRbR#-SLK`FJ4!I&7Iy$M?J`uJOdR|GQdD1yE7Ai4A~n)Fev^1i?#Cg6hZ{87Zy@me__lm2*&6cw6J)69v)AXbvE=#rGLI9SoYcT)|DN7bVT{t{455Qh& zUkk)e6TyV#3bM^<>==qRBm%VGup@) z4$_(oM+#~SsEu>BuzwA&4o%#eL5sms1m)GM~RCIP3CRM>yaoUz=_|XxF&Gd*ci{qB@(F4bcxsOr3@2Z?6kELmJ0vt!pzwx9!DzIo z_H-~XYhD|n-4Z$(LN!6HH9i&5roab@gzQttjTZB=SBO58z3I`xn|fyF7OJ+81&Bkf z9FjucfKya9C4itf{$1H?JPqMOm7QfN@2EP%d3 z1qh0CAdsS3rOLF0!uiQ+xI<;6K)qxvb^?`GXzN%F+Bo|2>S#61&{mO3qUjX{$S8Vg zQT?ND;x+iRKCo(HU27{#G3%G^h8-IjG6YsXer@MEkbr~H(s1HA2{;ae{O33|oTCJ? z=erE3FVPJNj&LZUq})*B;P)u!u1Vx22E&t;W6jlC#hd0M7o0Wq8~l<5=$fXA z8lV$*+hi|$hjf%agVjZr1RRTz1y6t^M@G-JPr5-Sd`beWa0&n;ihjkk)Dl@r2ul8@ zhU*qW*3aM4`A9Kht7kXd3O(a+uYj}T)a~oiP1Aa8802Dq`fEJp4LHf-%I?wg3z2nMk{CH!8YMlvKeB}|j}c5wVo^i4{^+q$P1d)PMF{<+Tj z9)wkCCMh$hM}*7;J_0aIeBpTuW%vLHYP(^2Ik=Q(U)y1BeY=+diAKYTYK2n zqB^73$);wAgF3%KzOd8GSW&F~$m6c@oA7=Vj+H)+_AF49#PG=>Cp5 zYx7(082-6=+0KV=UVZ&|HZYzIjAsLjCJE!&z|xsSD{NJ`V=~NUM2Dv8RFxx2j3a^| zVFVY0W+Bn&C5Yli6eDAV;9CSWuA{gL(S@RJG|pyru1x%H-`A7ZP3T&=dkx+2n)~jl zQ|Ej?=ltQusXyL0_56!l;OIZ7&ekzy$#C=s=YHhRx4BwdM%ozw^NS^!3@lh8w%x!@V`kEJT$`2{KwmBfQi?P}am+EuX%x z`$D%M$0i##o`fd~Itg!OOim~AArlY3e0hG)a_4&Y)U3~nrC^c0B2P)?kc|pil^Sv^ zt`6~1cWU{>bKRe2i$-uYJbtBnv>p8KFU~*M2a6y$dNeGEBPUx~N9{^hnN1yj_c4Th zwrU#m)9yJTDmX?XgT`KjgKE_Tn&dXq%Ke^sSOB&0}!Bvr+@ zWX)MlkG}Mk?q{<_;{bU$d222fjo4*~c>u+ zRoPghlVU35^H`wZ#u91r%Gl}3_Yzu&!cofXQ{YHSaMIGLpt3YpxE$RQmHhQ=(Fi^z z)3@TD-|fy0Uo+j^!-=nU_kV!A{@jOmzI1u}$tNFNY>QAs)Y(g$3c2ijG1BIuijo06 zJzRM9cEP+2m5D6l3ut=novXzsW3=^7rQyuCyL*3i{Ma{U*BbRI+X(#dW1rl8dzl9$!}^b&55sU`Gve_A)`s@6zzF6sU@-^maDWe0mHxx`ppE(?731ERtJgBNvN;;09ViUkC z5>E>mRAfiX3$pvothZTJPgzpIgF%NV*$SJaLo+C01~Aj9#UeK{NO^qEAbdalb4{X=gx<+6$DWdS#QdQU!JrN3Z*dXrH38H8@*H zXvPJloD9;)+IXHS^85=U-88~Zd4Tkf3_Ip=`J7ef+#_$It%__a17ZoJ5G!)B#I6_! zMRlB4r2)lB(5O*he@X^-5LGTnDocUh49hS6v-`p9TB9`TAZ{8Rc?f0p=-W!SQP#+) ztf7oDf#02v5|YG_OjZ&g8^zNJE_QkTYWH||S{H8#TIfv#bX1N>3$ohcsw%7D7cbB6 zU1^-vI$2|4sq;aj--~Bu!?&!$IjJfO#$7kI;`UuL4(CJJi zP{tX(4?$T2rY{`KRLH&*ucT2*`J_o#lQIN8`BD^0J09@1#4VuvIsmYh3KIi@-j)m) zOD8o4iYrBE#9d*m5qFZe;q|TV{vJm|ho!7xBcN{~S?G3RVXUns*yV?=j?P19Q?iH< z+~i5a*?3zsg9)JCj!Il%Ogk@fLMKkFtY3LP9Sl%i5L2vj3i9ruN;|9AK4?sQg;t+y_R zNRVHM8Q^F=%4M~x!LF!%{_DCF4Rh0V3h>JY{QA zv7vxHy;gu#U7`nnJL`pspy8m3fm`#z2aE33K$oTh8Jtp{WUUo<>e5D7yb+Faxc296 z(HmVdRFwtXD>3YH($>J87wwCreG!1%>HsE#;Z?+3d=Vme300T6;J#Oo6IJ2BFsXt) z*(gCV!buafLieK}d$pTn?dY&8>S%=>^Kiqr{sALtXS#H!{S^LpCaONUs`wdB|}#E^STS8d<%mZarog z%mm;~nG0qU9}B}58W`yahNN{P9b!W3%7{N8+Y(~Th7!0+1~{_SqkDK;I2aaB0rP~c zOPD-KgU2?94naR45uA+0rXM|bsU0( z$4gfF){46aw!Rg1f8x1w`0ppW?FFVauzN&85i^d-DT29>Q%zQ(K5lt_Iof-NDg^XOC9d6OuER+Wk0_Hhza?!#nVPAl84jy+^9}TWFm4bu4B8T;acQQfSz!s!so#^_F ztz+LD-Q>{)I;d@}zC8GJ>&{l%_U^us)daV?+xZheUA}Ga=gJ+IpSg1K(q@mc*`sXs zC`YdR4k_klkMh<_oXs9(vqw3w;yN^^ZT2Wf?Qk}Gl+7Mxvq#zNQ8s&&#`6>SUi8z_5pHqKAK-35UV`j)EAv26sb*eHT8pVaH zVFE5@5jWz@fGg7ACK3(uD+*E z)%Tt6JLh+A?fvGhy&t`OLgc_-Gq2e5I=f_iwYj?DLzFJEa7Bsgy@*m!*4hdu?APy{ zxN+yi0>AGa@0?iPdVH}QzxA=@V>cFm8-94Dd$y-2rDS0vTcTQZa$Ga}8e-5%xjLSD zy8BYsTakd=j=bLedzrMZvc+XPWn7t92Gu0>qr>YDo8;ZgC$aNAcZ%tH% zinO&*_BAG1uzSijv((t#$?ikr?O!f$EqZT?OS))VTqY+~C@MJRSXE25syZT*pD&t$ zhf{Z#qHo0I;rCzZF5+1>QPSCj6e)0sM&lBEGI*PdGV@J%?8XI`tSOSnSC5Orriv}b z%UI3$tDkhgSo8wd=xXp@I!8ejZ&Z;{7KIa-A9PNj5p#%lmv-3u?efeyBh(i@ zdH$=H_pe<$+wVusDb-v^ktRXfE1|NAR1IlK><%x+XqifmwdP0^d~hx#$=YRWlojLM zOWk)D*IRe8kATl!c&wu)v&t9<&EN-uE9oPQlv%}tpyASrQj3z)narz*6!$3|| zg_SP2(3B*uR;#G9ViLB@$r~wCj6h(wc1gvz~9+~{6k^mtGeF{Z-6 zxi#9CAY#fM=L@K2vB|Gi^x^M+UcS&bg7cKl`Qi1yEFT7YaYN(*u`w8!W61Jgl7(wj0%yIB3p|0Y5HQ!Qc0DfoZ;Ds zDc108#hl;Se6fTu4GjX)Q zgAIi8haGV&!<+Yadp~<;`Es|4?1aPNCKH&vH>ps?1!fm0M{%(7(c$)M-9zIC9=Pw? z;yQ%p*!$ULPYajZt6Rr*#I;6x@qlYj9B{3&5Lp;!3OhpGhz2ytCJV1rU_Hclm(xjH z>K-FzWsO&+K%%I4>thl@C2es3@?hbbZ7WxW2-Rm&PE*C%0`nz?kgHKb%Ms7~=b~>! zR^R?2WQ>lFs@61naM3-u0&PI?EV6}EX;DLPUIJQPD{@v37v+4Xy@%8p><_h7lk>0! zS;*ugVL3s1XOwbPc-d%Okc5u|)PB--!?kiob(L>GFjmKh4YLq5Lav$i?-Sjbl}p0J z5?!jmre{V~lY(=Z3_{4^0Gs25e|5ML8NH1jJBAjg$y*e7j2uJ~(^jXIhq7#?4|uEx zX^El$lCx(kP?B(1IqR8mjo5LW3?Cmiu2t>PN4k%m6&sqCAh|x&kTa}M!ZQruAxerx z80=&&>%Dcp<`4>_DiG>TirHzIVXS10F4XazOWk)C*IT!$ zfFmJ|^bSIX0t%@v5O!sdM$ zh6>i`6KPg4Yv@>@Xy?k|R1J^~pMYfimqRkbO$LCZ2el7Hx)pStKr80NHaFZf-I=GH z-O#v<$^pZT)6-(|jZiLF;u3U8QsjJ?5?T@K2wup-_@scpmW$9uPEJa*o#`eTcd|{O z7B06}w~p(GZmpCy=oU?i&WCK!dZ6Ojh-&Hu2c=*pJxbJC*Iw@W1nAmaO619m6d6!Q zC1)6GD!4B@KE8s9UYUw7i;|T$6D=7Ju~QFO1%xuwnw9Ggtr1%dSD!)K(u%v5w)cIe zdq0?#955#{v8Iwx5<{&~QDGx>5gT>|_0kc7;xRc!o7WH%m} z<~zIb$TUpZjYsY|o7s&=cH@zIj<2@mYBwJFpM#m*cw{#o*^Nhb;ebRt^VC}`v32OMnt z4QaxuY%DEIVk3B~gn)%MlF}m1_4-QVNn>~W#q9O%wHLFyHodzxy}EgrugLrMuDP>F zpd6t_56*D!QsNf4liVq1OwiWLj3J5er0Ta-zqAjH28`1$p z^I)rb+^K3$ZC6X5YNa$tgrLwkv?iGZ)|1JJNMVS!Ils+Lw8!_@&8eDr7FKX8$#^Vw zeG~}|SgtJzQhBVMf5%=_wRGIsY$7I6v`97y%2JnUWCg}b2%>%FXM@FJ-k$}<{DJklVD5~3qxvg0zeWo@skZVz8SGG2F2AF_Kjk0b!f zqOFzaopV8=vgn0%D5Ij^Jh!Ii$Wug*#zMlNq@$!n5-2Db31jqqIOY1v{ecl6BH~D8 zr8P0iC~|aC|HSFEFv6L0|DN8Q8#2+n+F|pD0PGp1n)MwvTZfpEl)99Fig5~7mntZb zko+jZKX~O3I~!28f3n?}?mep>btN@es0Wv>-B`Y|bm8LV<*u{cukKpg;PyhZ`kBof j0B*=mH~lZ{X?G7Mg`5;}QpjKb$p0>+ZdZ==o*(%R(`7gG delta 1976 zcmeH|J!=&~7=_unLGXG}K}iHbmSC4)-kEvls}TcQ2x=@9l`vng*b9Wa~UPK7q*s$Xf6mFiNSxfPPl>+p1sP`czv5kz7N_MVyD`ZyX?+XoxBWQkVsIp4x;s1 zNzJVi9*rUKOFQ?0y{c+30Yx;{aBk5Xd$Q%iSRG5?UUUCvLyC_ltgRyp| zx%h(}Z&!Q0*Ht}^Ok~28DI$RNNWo&z!bV60wk-$V7N{-{-#;^`yKf(|hqdJeAYv5G zTGZSH9|LL0G4l`{KO$YA%h~nS(T$fa@sJ*gJaX zOPvak(}*6WPU$yIr-MD3a^zo^n`Uvypn0>+=8hx}4 zN^?PK mE1Nz9+>o7b`rp{|?j1}9IT_?+kbnLo|9g<8eQ=`p^4L%D*Gggl diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree index f228ed7f37bf05663342a6a2aef9c34a0a2d5016..4761eb080c8c90f51f96c30d570c8cfd5eb44192 100644 GIT binary patch delta 71 zcmaELjpNldjty5i4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F bEt8DXOp;R#lbcz&+F7|6x3h9Fr78jdcL^10 delta 71 zcmaELjpNldjty5i4U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u bOp=X_%?!*9%$ixb+F7|6x3h9Fr78jdc3BkA diff --git a/master/.doctrees/tutorials/datalab/image.doctree b/master/.doctrees/tutorials/datalab/image.doctree index d3ee2c7ce167270230aa9f485d801049aaa600e2..86d2ef333dd404eaa889a258393c8368fac7c234 100644 GIT binary patch delta 31750 zcmeI5?T=)~b>7pQnI1BvvXV?vln8b=X}yr*a#>Zk>Q)t{AY>waupmW@WkgU!N3N<{ z6)09LF|y*o08Pi zPuy!%8;rI(s^CNnISa2uXhdj(bIJPH6rHk6m;d;>U%6+cLb1Zw&KI9`<5YCM^`QvU z1}kDCFFyNypIJ^kbNPd=yLMHgY+BiwWRlX+B*$O0@;Z3l!ibhn`pL>2QIw)fy!gbEpIJ^kbNQ8yPf9+xkev^fgU!kZt&D7}<#RN@ z>n{HMOMk?-9{ZNtKD@a22jBX|SAAvCKY#!1x-WeD7fyZ59sNsp&)#(L)|c`|!5e_w-M^W_C;e6X#~vU!%-+lkVzoJ~z8>^pVZ3 z{qIwcym|P5x^cOG#|^XlzuR|*P3&KJ&FmLWJ+k{6!+_$w=AnOcc>X=t{H2TEy8oX3mB)_WxM+foI;AE%-cuy)V%IjUZ%heM zm9>2Q;1h?-78fJ*ccd{Elf7@SE?PX=r<6ngm8TAFTz~oBAAD}MY=u#=BiC3LG!HnV zWMi@?K$AU6|J+X>@#yR4-<+++O&i&N?z2blUyR%CSC{6pzw2{H*DplMqSdb3gmKZv z=!pKNbR??I#PzrDpKn&ur083gmq?6J`{Fv|c&=;PtV930-#)x${plNze|ff0gjv_- zHW7h!m!t~~KG5-Z(?r)l`^|$lu2+Bm;Fo60am&8{p<52+s!gMoOMmH>L($*+`$w*u8eIS|CT63x)E+Qk8w+@mjZh)2AAaxAGqdHm)zSyvay4OfyO!7fy~BC`7vFzy z=XvFS_^;pl$sfGqkw?z2?yy!_X}WBHRJ?}{LAF}DuJPW3o%)C0IlpoJ#NRpi@ZoX- zTMhSa_2|)OXOE7bR&sY>`THLH&_~|){&&6my&svlrQ!6`6FaBw_`&o2TYlv5=GDZ~ zeE-1Z!?!PTm+&xUy-~y&hp#m`rD&uHB=`Q(XK|)s{W)6Og=AxDm6nR=ZM-do-(s7* z8@6iWcMH|EwzMfp-CErVP>_$(#n!Z{(Yk-&GxM9aZdbziJ1+h8SB%?EAbij(z#DJhWrKAs`lnNV%Wj&n^9y(mOqFiuEOM;?rgmc6jX$29; zXIa+Y{umc=VMAyoTjG4@v;?-=&hqAjo}@$UpMU&l>wdtCQOja;XP0)|YX8FPkL2Kf zU;nkEH!Ks#)M@}>AO$L?1JJ%JT?Tp(0`mIX_5kr|H9)(H}wxbdvL@0+@-@u zW{-{^S!_>x)%cAyXFPUd`$F}>cVC#_yqZ{_s6KetA}5*JTuhKkYwrY^Ot~DT=LV~G zJ^S=A$-?2JtuHuC_FB}C>AOzn(pX7a;Hl^5UnWmD94$0bMM^b5S8+`Njz*b^bD@9i zf7aZ%NqFN%jqiWny?x-p4Z#oJ36=IiltDqoXCWd6udJ<>5AKul~%1^_dva* zO`Anyxw(c*s=?qUC2PHCN+e(8p1{A&dCShaE;}kH8#Oftc#i<>iWCh9D5`qmw+u;d zHDOx~H+90cx{~$Yx}?@sKFQWoj!_#5^0xzPw#6y(K+`|>&6BvekfxXh808BIBLR} z#_8Vmhevmwmu|=FjLw}Ytt2b}GXUjebnvBUTjNa(yGd`NEbpKDU&NFl=}jEh8HbbO zH#MJj&xxJ8>TnZF+a$fa766UX6zB*lVcoC?qANmmsZ)Ht>7V`Sqnox#54u9&hBhit z4QY-OZDP?N>OdX{Yx&8;FIAi(JXupo0<`8s$O^>=5CloY=%#=8&#&Ojanm;RZ+!Xa z!R3V6njb{}>n|VOuq1OS{L8o6m(uQ2pbo~qe==i3!`1ONdnXM+Q$|1{5SncJ% zJ>FzF+|BFKBw4y*62XlUx@(NesB0w|JZ}{8mroBz8>LjzjF|#%-q^hx_zz9Ncw}-A!{|6lekh zLbl}aBs}X9*%~N8B_5&eZJof9?cf zPAzMb-Imq|j<8EqvO>p}18b-}@gO)mA?*5v|9bfG!&Qd5>tZrpgBNwR*a;y-l0+dz zK*z0T7Y@G&aDma}t|UGt@)&_LQD+&lM?*a;b`#u$-LC(+8|U9YX}2ag(cgE|{58u) z3SPU7l$}O*9f*l{1pb1^fRMJj+9wVdz?TkeB}67W1AH|Faz&THna+^#uY#~56LB~f z%2SxgqA01U?UVookY?gq{=^|5Y{IJSpZvMw=C(1LTLeZm_ts@&HJJ*}Aj{}ZCLl8V z&EBVEh#3`0vW5(*glEPJd9k=zafZm0T2X6@ow{^M7Hgu(IkQUbYUGhFS7yz#XyAc}m^5;(S$Bga>tyc1jMzS^SPEvjkde_Y{GWe z-)iUYUrpE^3b_93c78n$S-l|R;BfIVw2_s-ny8c$9wPKQe2sXqD13t}>R@~{5(nT| za%yN62oxpnrh=>>d zD?)%aG4Me_W`qXf8qvV@mwxx)1ZPBw$_`LWVMxW?7^iAIkiAtwSOsjk8a9HW3QVRI zRTHSCcg~R60GOslAu#<5ALLV?e(>O{vsD7RQbVtwpQ0_+vsJS@X_cf;JgFD0@0fP2ABlIc;LoA~|i<|LL8FH<5%# z7u4T=X})zkayKZ2dbNsMEyWi6qpatF+@}0?=_?0kSTc`waGNlI9IE7a+gQ-`a^57i zFU=Ovkp!+bLV4=?Ap*9ILZV?mh4s50H(^%x{fFl7oitnXnq7Ky4^@=GkVQ-rp)##) z({&AeSS!|CJAAF}8WBz5)et$jZ3KKWVMrhtB0~S=r?_wf>}n|M9B`}!Xlm*j2VV(B zGXbCj&DN9aZ(Kk7RVawXxaIzZ-#L`aal^Tuh3vmK&F@+Ycn%M53V+8g69x`{DK zWk_Yi{tY}nYY-vmds6;5e ze)1a!{~F45!uSIpJd&#kv)kfxQ?b3{WAoeZHS&TrAN|oQOeLhaB2s%cR8brLz$ZEVvjEfd@)=pi3rf`~I&!H-B(7VO9)u z$$x)tegh>LufuABYLw1M#hvfa40u~OzGkaU3^%sp1_xw|4unQI<{Ym_Et%C;nslyYUV9NfJP>`z$%iGlO<)4~M$p0wFT%pAh zK_bScGlZ}V*B~LGw)9VkFgYLaHL{pdS{xKD`l4}D4zf(hX6g6v=X{QQ<> zEF4rDV(J`$PB)ax1cbJQ^a614Iaf*Z2|0hbVOwV$J-Y4;jGVZ-^0ezt>}5@VLrFKW zw1Kc(!h;Ju%Gn0g-838xS4?FikpQ(dgRZuXws2vccSKf%Oog%lfo(mol*SE8wn|&# zh!piwP}Dd0f)Z9z1uqY9BM2WF-#2fC$}O^_A@WF^}*M~`j=wN$e8dPH0$Iq-eR`8u=OGOL&QcLk+bsM-<+} z72e?IDck)wo}SCqgz0dFNxApPw-;p7zx4U}8h*N7H-1>+v?op6)Oh;oiM^92emA+iD@kx+(69>7Us-&QgQyHh=Y)a^X{B2~ z@unlhf+7_Giew2{0<}QfkMM16B7#zb^1irUMLB?a5rO2*&cjv!ZiE%ALoA?F)e~8 z5wLI^ji~Ki)%iv1s4uCCwwVtOqD5#H^ob$%(>le%5l@qb;p}W!173(6vJ+iy(7#;Z zYzZE~8xjm51}u}qHgZf@RFxA_i&1-iaI@$C%lw{W?v1?Ay1~%^R4K4sL}^w8m8Bvg zJ-RRDN*7NE<%xc<0g?=PEsdYb8IKvijxL?{uZisv)xZAq59T*7Cswsn)g=@hJPh>> znxp`sB{4K`?OcYTl|ukJ_nO0 zc?OCql$IvQszp-({`mZl5nIRY5|V%O@O`TZyLO-}pPe6H%T+SmD+m%A^hi(i5>Zfk zkW@=AkUv!t!IhCLfMI|b?enJhh&m6px)QPGB&z|HaX2ODQz7#yhjbjeOs5OI1BND$ z%Jof}ec`u{6@34gJ!sr>$M3ui3fLRon3wM$fp7|PnMRz?L3RVi*wi}<)N8aGSRyia zM5FOUF+31G8#r%=V7HY8XrMxNgJG_qTPkXO!Uf^PqQR9g&_+XjN+Zsg`3jGhWdBji z#5&tQ`zMEYT#y%}-YMqrYRYF4Zehdek)vj%J3*O-#-C92_sWScC%O=K#kVqkHtMw7 zOkK&-Pvh%jo0d+J*lJ=`-Jl1c?^IN!Amz|{VuKbX5k^Q;jzTQ0krXSxM>}ojVnX`G zpV%RA7llN&8RrxO=P)#R)t)Juf7?wfbZrTv$poRb196#HPI%3Y<~?CLZG3DwZhFHy zi2j-R@pVhcZz>TQqqv*oPJnAETtS*+Csn00)|K!t53}PMG!8$?jWHBBHtxE?db$8!Nc{qGSP#6;;9_(m02JGX`hfNyQ)oLz zYNiK(5}V%7opnsv9$MAlWXDW*SK#7&@HBtHsU1(_F0hduU32`R+NaSB)Iq#5T8T#Q z3i;!`yz5IBsZ<`FIHofs<;30eryXr#>+BUrn^+W^TwA*kZ9SSJ1Tw6hrT+?W1*j81 z5Ds_?OHw^5)pW81bnry4cx*UX8aFZm%9gSFp|2kO)7fogIzlFc0F(karhq-F%_ZD~ zMCA^wJ}J@4xMlUTPagfv<+xqqd)3{xi3}bv;miDpMz|)-n+UUzOD=R%ZpC5W)P#g? zM(_ty;a!C!OQ@miOk@<_tBoZ@k_L65p(vw-139BNB5M>ReB}8}#5fU$A?3$x$6lVJ z8DreE8a%Dhbk2tyLT&0#bj6`;7k+Pmd~IUES6WJzpal33n68*OJKT=GyjrS4x2mRu zL9Smq#Bz!^itYqW$;swQ)UAg>`sTsOGA3-h4cwhFTlIR|zxHc~x8G~!1=H?$ z8AUk_qDde_Rb{@=7zm|C5l;vJRt!;OvMYQoUzvY#_UQQOob?%c*ha2pIqhK+Tjubv zRX_jg@lBQD7+X=@i!CcjBQ8Ceh;6KtQ5?a=tHl)k?=*0?a+eJ0=$wX72K`e?NA5u- zs!4aMT`J#dHb{3_{|Osgp>0Ef~e-T{2|CHpEE_hL9xLf^TUytc)fiw+nE81I4I&TiUW`viR2}l>Q>~NZ2X>|go4BrL>br^EyPD%BR)%g%>m;H!19C9tkXv;B z($o%nM<`E(!@vdGA~0kfjY5!Uz@bZk4nB|?C=qByyMo+AN^;j`WPobdQu)B56{Za6 zZKjKWB9GQ*jl2%h*)oy8Po*61J5QD+6m3u^yLZ2X=C)ODu(Jcf` z)6~fllT$?!C){HPuOgW(sU2Q?yYq=wJR%}5?m^zeGN#OKw8Yhf)!p`zO~ocIG`kfW zl6FhcLVN&?Q%N)51WZDDqtKwwZA%MK2aJvF)Rxs#+~dSCooW4_xSMA@ZDQ|EdM4kn z7+={{YGlDFQj1HP?Fy=gV5kNCo6dOpwV4In4z<9zfzMh2a-t2`6t07#$rtR#&}wu; z$K(Q9nnZ!1m?1}~zR5JIN9EaWzEpSv$F;LKeYT-eTI3fNVNtVLJ3{*hL2@; zvSGsLP<1j~JZ2QUTruRv!Tbdyb|9ptg;vr6#%wSBwY7^Kq|l*R1Esh{1B6_R&$11) zz(6|^3rEhRJtL=$-$$iS!^rVXi|t9Fwwzeh6%;vNfH-RZ2Q$Mka1!j;Hef(b3Wk`z zyrFp>DV0ulI*BUusOkj(|;hP0KW`E|oPfsZMqDx7)*$ekbYzM%2*+ z$x-+}OZH%wOe1%@28R8xN~$5`!6ZtWK#&A!Fhsgce#_A<;6s^pL)N4ZnQ092Fs&uv z$`TOOwms9Ebu4H|AYW(GLhB=nqhQfZfvDAFa0^KNU2M*VdSt?J%<$l7Q-+^^{Sjk< zpP%3KHyU$R%VrwGUNC;g`5@51j+D(bGC_JX^FS9XQ6dnWA*yy@KRpvQPk+}5(BU`+ z7o0e`Ga5Ccf@Amg%+uq$+2|7&Tum&EdC~uL{NesL9y@yNei(H>jJh92Ma&r)nLdL^ z_rs{KnuFaBqwa@MUsdPrhf(*#sNmwwAnbk^bw7-{nX}ywqwa@M_rs`Xho#Sq5$uOi zcZOgGv0y)px*tZpIwrgyM!ouvE9{3+&kn-whf(*#sQY2m{V?j)N!a}`>US9AJy8qx z!>IdV)cr6j|AOWC9Pxe_b=#b>A4WYj{<IdV X)bBV4yB|jV|7;j_%`ocEzvlk|2$z8B delta 31671 zcmeI5YpiY8Ro~e;`|NX#W1NS5<0SIg`#Omo+t=4~uK9NOVAJqOAd2%SkHit3bFR67 zHY9OP2&x(!gAgJ}jgyPK#4E(&Q3!q5T!|eXv13|j)e4AJQKe8(4E@wL3HAphY9aBP zd+l@YImgLbU-_`5$k9D_oi)~cj4}S>KmNo1-QQR~@f*v>e&;HYL;E4~J@!tOT{6D2 zxpT#bC|zuYD~;&97f}kGm9oMKd+GmP_31lS&T8$fP$synWidV*p;J`Wwx#WSy7;*_ z{mA4)m%hWdXI4(Pt`V3^R_PdGu-Xf&a;r<~glUIYO+0kz!}q-A%&O7G=%{tHR%jEO zpn@+>X6cg(UDIt|HTBS?^yXKbS(zp_DG1iwIn6&pYl>$(Y%$3t?dq$R6AxXKpE!Hz z-gjJeW+h9GMm0W`PU|9bls@~co2IkcDL1@r;-Ssk#C^Oi``8-C{ueK`l3ggBlFIq$ zyyY{)W|>nDU3$JfJhS2@GOM=jWVBippV}y7a;7QlV!JuVA6-s7bLr2%Kb~2(T#=@2 znnpLe6Fv!>tP#Fxly|-ir|2ghy7YHH_`s`{C3>Hw@=^w^gA$R2<`TGn4R=#teDX~{ zI{8d|^Vv(^{E6G%J+l|(m)xF~`r^aSoxAvlU(7G~$(jCRSI@4x`14=-EB@-CFZ=Do zhZq0!%fI}BpB(moarf-1PdxieCthXQqNL!TS zW_B^SrfWNsOcVjX5}Tu%dg#(C|K`GrSBbk)UsJjwb=zX>r1w* z^ly0a?C!7fm&5M$&tEh9r4vsa`-InQwsQ0v&aZ#-+S$*sw+%}tU?`)4c8>U=b81`b zO=yZ$X4u<_ht}7(J+yv)x!GLn>*ksl z8FhJ2&z!G&`hve0?q2`V*U$dy=x=ZDfA-ed)x+-&8+ymBbwh{WJ^i=a9sS~Mbw^LU zezTG9JoUr#>t8)zx2~~VAkn&z8{)Y3K?_+@i2N@X*KPyl#6#yJwmPDjGX?a5ux=UT*4>=8Sa^j&HCx85& z56;&2eDFoz{Co2|-u$Akx%lO~@96*kL;J5>W>Ks)N_*2tU2GFfv{{MJI_HIs{nM{r zT(^GsU3>4IuZUu*OHK;e6)o}Ek~4v*V_m7H$F+Q5{tL4kQ%K%-M%0k0iuP8v+(NIj ziBa}%`^o+5*AMse|1n#RTUPx?Kf3?cNu&MjT?=umSL%ZJ@Q2T@ZZfHB2?wEVRl^zD zWLj;q#SJ>w#ddveb@2ZAvSryF>*z$1Iti0nXIjFNAPY&A`bYmGUwQBSd!L@I3hToI z+RXOrn$wBnv2|ooYeEQq{qd*wK08}Y*lqe}{%|f1$L;o?fApZ~?>t<*w7=u0_Fucq zG3%IYD>;3Q)hKfirM0bYdI-|@vsuOtC)d77*$MKg;?@O~ZSa|hAbtOMJHMg- z$T#hM-TL0|-TUd;vT)3bL{26rn>(A7Y>RKBEJgX)tndHNizjBQi4W-dd%k18IUKbt zd-+Fm_sBc;ZaFWTfA$S`KJcEKHs_?H>RQz{t#zK9+Y&Nom zee=U{WVMcSZry_ur*son<4yN|*LT0=t#ACsZ~N{vdPjpj`SiqIrJLS!e$~J3ee>&A z6N}3ILw~opb=h?pUlXOqRP8k=u@S-t@0BSa$YJR(`CoIE?ounf>{4zru&Bea@G4ar zyk7;`KXn1;{L~-p{p4&3TyZ(;5QUSPSnsv%q$sLsM8oADUV8WctbR9cKKIxE+x@pL z!-VPkZ#WRQd9TgZ?=-;Ig=h@R(rt+WXp&3goivRXeuyK(c{WXGTGd6ankKjf9jtYN zU$@2>(Lesk!SSnl$VL^z%`e&!QMvgwpyCKMW_fP*1B>g|;xpA%C%!>#d~oH)?U#CC z#o&dX`OW=TES&*L;(y8k#0h5JbxM&Ii*K@T?E2Z~_lYPfHXX;9@k?@tDFnn2fRBxF zZ4>PJ@sI93$tgJ!L7{XYA!y@F%8Ft^5mqEIx9NH6hV|;7?)}PaHDNYw7BgkGf9l-a z^-nyxf7^NamMhK|auy+XZ7^1989jfNvTKuYDv{gG`tis2-Zx)P6e7J`A6&m)J-Gi9 zvj@iy$%T_1HE~(Flb~UxE!Tpl6(RLNjE2XnnKI1MTnpTm=j9NeQ z#`y>4hpm&z2+o;@q_EuMmVoMlkoZ>V2|7ofmr9e^Lf#cS-Pw{V^3t)uH0!zwJti<^)t)FoV zZYCd`U3Iw9DoyMOA_$zbi`0LOOY7O6?>{-a(G??_mMSWUNV(~~OTh`;%{ImKtB)UnZU^hG*F{YUE33fo=av~vJhbUCl(kL-VdY~3VJ z26r`>XrM)sMUXiZ;FL(%^iO?g|9He$CPmS1yCj38Xe-v|Qrbu!)lp>c*F+eS+qh{X zhiEZjTi0k|`+xM8`>*fcd&}aQm1#o*_;YMb;VNsYutS9 zZ+LG1-c>VhdK3Teyd5Ack8)dOf(X-6BoI>v%p!E>7AQu*k6l?m>-OG1Uxl`7Tij2p zTs?@zt4=h+c;H&`(X5}kxc3Ce%XU#HvbX7?jwX0Va`zoq26Wk!HJ6#oIbm{pIj2ls zPGvVQC9IT6@H1^7i)J>u#ytnx^T8FlCQ8*c7!J@ERSRgXWr!U z%t~iM7q$wtDQaERiWnY=5ya)w|M(%+_DRs;;j$YFRod7?jj_Y?QkL>31M4Q z%q4{}nJChqb9ngxinHe0MWPI@*Z={^-hvvI3t;ikb^Nh8NK43?F4!VSMp%%<+Dj0w zanf1Jj5YNIZZ=`n4LKl;TMU=o?3P1gTvSp)?EN;Hw!iDC9pJA=Tg(xneR$F!(#8kIlrY@B=(HFEd&gRy`JIZDg=W-hQjbF&- zq(e?^*yU{2wxf#*n{0LnjxQM$)WX_o0zP~0o6gwe8xX}helOUBYQnYrq_lX6wl+j4 zADJ6&VkikPhv-Lpl zyv<1MkLoiSVQNUXj;2EJWYJ6(h+INYZiz$uU)I_L$O5?mJcei>LtAn)JW0n@7F*vU z|56A^t}z^olL2nKgBBtPca${k`fb~{fAUB3gZ^`GTD<-aCy%iqEs?VGRAf%`8P3EGmX$kXC^rHT zZ2ioi%-=sh9B*^ZqO(s3qkXF2)w#Gbh;ZZ@&1UVg*DZ8uW2Q-T%(gVTxT zNX_3+i%pHX-TKk@5nonTQeXi9Gwub5r^v2w@{?KpdSbHLzi6b+%%49L<($=7C9oN!pE60Qm8 zH#d$r)U^X!`PjXCzc!otfWhe|?3e!8wD_*WR`V_3MeV$yji?edji6v?bjS`aWxo=+ zl^7=1)>dgPsi7u~AYX_=)kSe;i6s7HefBf6L%1#qIgwzifG#>gK+>cH&_gaKsH#8T zKO#ZKzX6z2S=H5q{Y{PL`n%q{SlwZp3(kGt4_u}p+W-zCEK?UcEs5XUuiRB6-YMIk zyZ7Miu*O5cGbDVZ;-ur&lBA7+N1|la;>r@3ceq}><()GCZV_PSVTyHtL zu2%Qw-gEG}{>jfQ_LrOi0X5Jo0!?#fJ|%7=oVcRbVA}zBZO<8qO&@{|8*!h=4d0Nt z#r1N~lb|Mf;}I!xXsGM5XQx6D6cW)Vs&H5_5x5au|Lnhmv>R%+ar3T!!&8fUmtw;5 zhEVwHe(&o3PoG-6c$vA6#W|YqmV!pe08xTV5GT>^$w(WoMlQ`a#?b-&ooh&TG7H85vC0U`KKy^Y9*Cw(6U#m9!-etBN zz8U<+niI$i0uwH!HbLO*l50#^!z@$7!R@68e-8FWBtqp%kAb+-&@nXRB#% z(Ed;jO^^@_btskdss0#9dbgxz@ETA>(+NDBwRuk1s|gi9L( zbsKO^4T)snPP9j%Wgr*Gjk-l%)hdJVhc+{amW;b;0}QUm1sSv5Y;xXe!nWF!c-wsE ztt%;aifT+-QkW~50u3_X1*7snP%Z%~C>abrUeOW^IV9b|TYGf!5nn=XB08ae-=3k=$b~)2cWEoV-m^ zjw({X6jr$T`A^ON_3Uu`18VMTmgCkpx!cyXcYXi2|84QI13*Y~L5IQJ9KfHVl^D+E zQ;Os19;q5qLGDBBKmTe_5%J`{`HiEBuSebvEFJo8&G<>Flj&>Xj-GPQiH*AqHdo`z zN@_@URJUF08@k!FZYlLpz(h~vfs`YYnXSh)G(yqb5#TA|Jb)!q^8?7ajI|ALyz#~% z!uA4TY6;bS1O`z-1gApJ{&jOf8YjmCjb80gRfn}Ck7}fs^iuY29YFGsUqAdWit@} zIQmx z{%LB}QPV?hx16wA8{EEs$-kMeE|_pZ*<+<0__6jcRg?s4N@n1$S1d0yhz&Ot+x6?0 z9L(P{zj0KNSU3NORhc-aQ?0m(`+17$oY**f1TB~2>pEWAb%=Y&?=-FoXc9FXxe4ED zBa|aLjctFp2t|>eokWvDQRAyK7R0!q7*1TS@Bi%L7pe@(Dr?|1GsqIU6a58}RD@$e zbiB><@n7t{d_4$E#?A7Dc-v~itQ>j+Pb?13EDLu|){3iDL|@`Vw&>0Rk}UylfiS3A zp`kax4{2K!0)DMX6V*BDIaFX21;OcUimSS=CEaEpL8pr11JW>&4K3xL!a=1Jpz1GN zmSRlU&l?1`gp?Y!y-9R~(-~A|0l>!xYDz)0BC_I+79>cg<+9V=H(zE+gRu)1@&Wgx z?STY47WAC_9&lsZkAp~}zf78>gHo%*1h|Gnr09m?ZRp2s8s1^t?8f2bl*NiVzVm{) zAjL6PLljSsA|RHmk(lq42|)?azBu3qXts892cSHm$E~sR$lE4P=oB?FbwN*N!igQb z()gZOTGbWA`{Yf7}*U8OXjD8j3_ovEcWjOfNT-dF*W zp}Ts|58-M{JTY=sKp=Uvq~JkAVYVRy1qNIo-cr>u(StZE84n{yZ6xVmZOXSp?NVh-_;`j7(E8DR zph!ZAp@ao)zU^PmUs<(I;I46_RZ=3sk)t+;+U?q(KDhd@j%?VL{j808`h(R6*irf| z9{KVRE$;ruRbB@tD?|&tr4)^@RHBI;u}=^)Ori#GwUgLVkg+a+CS5w55EXCNQm?`^ zfBaP&`5;2P zhGkNcLaWvU3rZG^BUK}7IMQ8Eh0=fpmswbNiQoB3j3V}8f+j?J3@0up>~46x6>P?+ z*@1|xIcpWUx!m_$$22$)a3T^C1OjkV%yaOC5B+cM;USk29S@k#b|}*LcKEy?alA_I z>l)opwBKqV473po2Le0>pfSnePzFhKHX$BQ!c!kG&>)qk8To)s(fplv+6(GL`T{1v zRwD*SIqp+o?#jhVwL!_zVv4o!WNqUU5%pV-GS|d8oyLjdck@*DZDPy1Bb>NuCRVj^ zv_mcw)EuG@32mt1B@FSrC5nTMq8Qq53>Tq#x5$+%_Xe3ko#Lu^n^8E#_F(DD#Qzeq zjEGM;OX0&f7wL=lF)jeV2MBBHE?u`xUgM^_G`zi_&O zOi(4?6(6Enpj0DX(wj%gGj#76LPREns-yyB-O=wMxzXqYzknMFH`{@R3A0-gz8yBk40J4$Zd<{$5cg0O=#n@Hpjhf;&4u}8WR`v zR1Tcltjpduv8q8*vVBr38N%A%6R%mYTjEES#7?L-I2zm7!M#xbm!H63oS zAF{TDrj!|-a#>eGDxo-l)xuc;JG5a@f59wCdQ?dfKx=Pcz={*Ly8$cGX&AM83@b9< zND0q3QSDdMPttXW*HoJ#T5{fZMY2VINBKIONw*Mu3;_ZUr}AceF&)WDEK(AB!IgES zBJ1EvxD})ugf)z^sDTrI?A;J`%W=y`vaNBeTW!fEyD`~BJ%%WVc^J4y^v9{|(3?6` zyfg&OdiIezHdNWv+Kp3O)P_%!5V=)p-~)HrZmY2sBZ&A>m1w~qAv5k+ULd&_E16#BUdd*emIj_e*z&}B;rG^efXar z(59%+#XEY@us5_i7~g)i%8?w#ib!Sg{AcJRPyLd&I zJhiF{C~xk6|GyrX{;_FY|IwSNk<8to z>2A<;H)witzI8Wfx|zzoO!)5xO?QK)+y6ko)a>ML(Dc}F&D3TcIoNK{bT??a8#MjO RbHV?R22Ixtntte-zXuG5UV;Ds diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree index 53c0d5503ce9c59079bb47639bc51226087df8f6..4913419361dd189649286b25bb3be8aaab8b36da 100644 GIT binary patch delta 61 zcmZ23wOndL5~D$;aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF QahgeTs$ufxNsLpu0Xg6jPyhe` delta 61 zcmZ23wOndL5~D$~WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{NsLpu0X_f`wg3PC diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree index d9266e104e2b93bec80241c9a780254d7676f762..778f3f11d6bb4bac7df8ae661d2cc02539eb07b7 100644 GIT binary patch delta 67 zcmcb(j{V{~_6_?v4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F XEt8DXOp;R#lbhdjZhz0om~<8Z2@4jp delta 67 zcmcb(j{V{~_6_?v4U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u XOp=X_%?!*9%$nbGZhz0om~<8Z3d0r| diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree index c1b34d420539c2920e808f30174d6036c0b4d12d..02cd8169deefb5ce848160ed12c1d585d1543b5d 100644 GIT binary patch delta 479 zcmZ2EnRDf2&J8;`4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F zEt8DXOp;R#lQ+NQTpBq!CB=6#TZ;7LN69LaT~ZV#A4*o7+@CBuSusU<^2QYL$#awC zCZ9-gBTAh|+PUfWI~Wxw|H|T@9#hK5H@P@nV!A;oqx9ru>GG2wz7Q8y*HKVU&PdEl zPmPH$$jk$AC-=XUp4^xzIaw)Pf+?nU`b9M+naNfeQPeR?YncSK!$qRN5+&PLsUmwU~QAtlNDap)Bk1sArEJ}?}PAp0R kxwb5`I59rIC_Xo_xFoe`vU;`^EzE89%GvIf!x(=T0Ls>}y#N3J delta 491 zcmZ2EnRDf2&J8;`4U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u zOp=X_%?!*9%r?K|TpG!hoROH9o*G|}nKyZ9it=RR6phK>la(jmPL`fr0OU)hC`?Yw zmYcjLS%3266!FO?Qrt+@;*oZa8)8cg*qX*n$;pbD^3x4V8Kov)%8;0RAzglQce?a+ z`yGsmlVh^@r^l2s@=bo7F3A*AJN=>>lgwnRj3~;C5Sw05$;df*cV@E!!o%u13hHH< z#fkCxMe(_b#U-gl$%#cNlOJZwPTrI)Eu5T~mzwN8vPb{` diff --git a/master/.doctrees/tutorials/datalab/workflows.doctree b/master/.doctrees/tutorials/datalab/workflows.doctree index e4ecb07405e9aa8ad6e0efdfc85ac593b1bf6084..06b48f9c2f24a6baff8430313e202cc26fb34ca2 100644 GIT binary patch delta 10536 zcmeHMU2s*^6`tTenHmvfjNniqH`v-%1KGc4pMyyxknkT!K#&NSoU_k4~1g3l13ZyeFZ3on()6VVGQs{L0&>0o_<~Y-;?L+&ZRHiT0Dt6yV51( z??Yxb>wJ5C-&$+0{lh)>?%l`Uy?gZcx3+9%yWO46K(nB>C%EK3^&ISbVTfIdJ%&Bo za~vgIpG91SL^TI#^+fX%>D@b3cxuNjvz}_Kd$zHz#{5k%f45{B>(H;J537(#uWx61 z5cjF{Hr$lD_u;-`8!2O>Wo)dBjhC^BLfde9kdz^gZAHNJAT5K7Z6!EX1{d2(a6F5< ztsr8(cRiY8#lhrq!pZk$5hiELTMR)UKqmf&Jr2`-jcgfqE`jU~WXcM&i>7~4v4 zA{$=`F1D56>@v96He3RfWq`4*1m~2&#kLZhTLu@~65R0k(j@WPED0rNEOANVg;R=* zjitzPbsdY;^Q$2pY{_SI^Em?~-eQ0qq zT+xS?CBw6Qs4E$M(1#vQhD-X<`egXCe)LE(Oh14YCc|zItxASdn$Tb}Jk|t&?LKNv ztfTqihS^{}n;*VohIRSfLbtC*Q}ys1Wa&2*no;;=bTId&q0mV9IJ`GNGZWJN0a}v` z=QV?>#t`^lZ$|U-UWk5S37V$oV03RX>%DM1k$ME9`HA&F1P(4oXl`P?8KH-g;Y9+x zFMbnvduU;VRM)J_N@ga)C7aO9TOoG6W2I3Me>l1cH6%VqH=)kFRH_fP=VpH``QE-6 zM7_^IR1*hL0~|#4Z$XR3rEY;~*SEkjAzNYE+X7N>c`Kw~z%Go1>9{7{(!{c98=9W@ zzG0(>3c?zD&V*$!;|V)1LB)>8LB$LQRP;OOp?t_(-v~{}-L4^dbUXTLLUP?f^9xAG z+AN8SX!3bR8u&J(A*hiw_=s2H7#TJx^HyUuue`$5C@(*dZ$YXjipjva*s5xIgG z{CGirxC5GMV+)9Qu>~Bw(*h!{?L-Unk=lA}i-~lk6@5L4;`LUTHKnaEDRwR?18wO3 z1bVIwinM-!)j=Q)jvgb~^e-^6-Tx#KiR?EzLB=ajLWaDBXkG$n*$*`D zF9h<}^VXq7Kz?lzkWXG*NbcsyuN*Msw+;dMON(LOJ~4zkZ-dQ(;A|46>4Af0!pB1> zm56B^Mjb^c(>w|4=rH6fwG?$E6FwbAE0f`+rKlwt-du{>i`sBx1M#)h;NjwPK>VfWf%wY9Ks@q15cjNsX|umw zM9g!HHP zyQde32YP{ctQR~SJC0WAC6A)}N-od^S8aH3R38_)hga$Ku*}uC+%2};CAR1en_OW( zdxNb$F2-;7)yGBB*wx2{r>l>Pi`B;kTrjGSi_q+ny85`NJ}#<{i?|i4kBi*@kN>Y8 z7a8-oxICdQ@*?Wn#Nk4(J&8IB|NWzr=(UOY@3>8SFHe}Ezjq3)E;dj3`-BEP?KB!H zoPF*zA_f1>ymcD=BAQ0;+qUaLjOSwWgs~Zum{T#wCi=(!LA}#I=i}yG&3bI{ggf;E zAET@Fw581S(NEBq_2|}HYIXA`=<(0R{JQ*?p_^!V*)N&D+(h+lsfcnVgy(t8j2t`k zF?U1HB^*mky@2VyF6(e*RLu(OuFM}>tv!=d)D4Jl+dK+Mz$GS*v>i?&;VMQPPk1?x zl~5Vu@qB9oi$WfV$Q2&`dcZRvM)Qc0Hrl3#3Ch6T}sE;9)oN z7>VqFg}Ubl)}8w5zpbg6p%d2bng&;jP=(6%aX>i^LthaoB~^;rn8{F&{oJb0T=~#? zvnEwR0n;;2TPqtX3D|MoYSyoww(1&Oft7=abQrZc3x(?mhX-yb6yX68nf6RJB8Le( zVxcmzNUkE=wLKQPwo4u9_yLb2nx;p8V@=W~qKsboee18O&T7S;ADyup_2@6HiJ5^rYrAWPE7fh55wWDZx36nuN9Xds z3TxLcM#~BboVBq@pG39O8Y%>-O>VxTKYhuv8Y0OgmA0v{%Q+9sPs&``UPuEbTsx$> z=9%;pYrQEsH8~=IBYaEk-cq()h=bmSF z53{VjctX;d6?E9y&RkJh0<`ALh(t^%hW!vLZmZC9h@`&ja_I_AGpV<%0W?#1whSaT zivlz5C}B1p#|a`Q!nQ91J>{g?2vaq+7iv<(HnB^yzbdm_IK>Y0l@ll-DaC%^kFVV? zX7yNZ?S3&W#@YIex1%j0EQ#q7*YuX4j@eAIyIN#ZZt|otuUI5uq;MN#MmoVczD zKv17cn-jCaDpA^v@eX4lv$H$IHa6IR0b`rL-~h4rHv|aR=Fgyj z4WwyE3WX5VQCof-sim!e#Fkp!swsi0Pn80=Z&9Qc(mo^)fd=)ZNfYSYz3X#-xZyrl zmF|PB@!4B{2G``LiUcd1?uhm`CF|2^Qf$3YqCZ_FS)2BGF zENm0ZTwUkb#JtvvyE|)l#&_><{Wtr@&3(D5{MD-R67^H6e#RxL%F*v;4VuIwe)o=e zKkjzp+i-R4@ipDqHj>9i^Vsw}HkQX`WZDMf{Ui@@Xv+e|`)M9rXv@Lrd2pdE2gj1Q z2^kT~y&F)C76vabY(Q-}xv?^JN3zX4wj5k2F$Wjga&V!_;W=<$6V~$G_E}UFs zXv{_Cs|(30%(kdA$b+R(<4p@wKJmUN?~ z(QsuqS{@D0cB8gv_*pkv6AfE?(1vLE>mKw>G>kum7DvMl8?B0lQ>syaG(1)ffb9;d zkF3M#;l_DjJ)0ihQp57}ZXugiqv>+625IsG7tPLmGTfhfQkiKaYz*Gzp*a!heh;mU zhK;qLs>%ocYqh8`?S;rUTG0$ygVE#BtWSN^5)FqiS{Mxv1mNIGfaXWmdjVPy4KEVl zJ^eiJcGAoUsVrHal&q@=TQ{RQ;~{q0*RE8A4~}j|m66Zk&8Q_UmC8d+soCF&o;SS! zqHgOTs+xhQUIwCiwxA_rQn$dgJ6m9xkgYK7rU5CqvK3O$%QIuqWmuCAQ^m4*8=4jQ zzRS^qjIc&tQ(-C0Si+8*prY?ZP%+yA6+ISOkPey2yS@s!LnxAGx1(=IBzG*dFoT4w zOOgmgqsO!3z_&3DK@G(r7hl9d#qfMopLX@*^~%%Drxn|;dZ7yM_kxc{`!Wk+qza<& zV+DDu51MLI9f&wx2M%u4fr!x^XmL7HE=RVgNQdgtccUn-)x)eQ4Vg*dsigEapeG~f zxdtfG{#~Hp`YurL$Zqh}xf>K**bVEnrx7iRoHgu$VjS9o7N#YdDPFu#rRS49P>lUe z*`TnAL(sl{G%s>`p&!l9^c1#k2rAzTiLTiT(vIu}Y4wZHqV!ZN)j~syfWK!S#5SoJ z*6p2rkdkxFkdnL2kP_UInNv%SkYxH7so0);2^o>>>n$MTotGd(_F~i+0qXVx&F#fN zel2YsSOVmuOMrax(oAxZBENb-k&hn$@=Hr$-##&bS|-3|L2xn&Gi2{UHQ~zv6pO@E z4Wi~Olxm&`b#xH&61 z$G{5Il&%|*pZRE}96F>V?>`J;9&H0L|2zz0F13M}J8jushRK+hPhSQtCsu-%zpex= zm%k5X+SU#<%U^-sFX;g5tyh5jYzL6v>_AOXQr`UmybTX^qSnZIqZ8QPc@-k>Sq0PT zj-VwOxztlT#-!EI<$pN>#K%_y@#t#saPc)D{>JM-e02>F54{e=ooiv*ydP!}vlQ|D zwLo0G4u}t~1L8lf0}p480`ccZfq3K@Ab#T*OndA{S;WyEd1pQPUX-)Px`4R1 z3y4R$z{9cQXq9Yz7Cn)3fX+B-!-b=Gxkz2Sa*v03j>h>;vH1?MS!dYj2>aj(ws^T1 zJKYyA7g1vuFBiTnUM?;cFBfpYC|)j-|ALXl%SG{WQM_D)tx&vNgtu7vzq(u`)aBwz zX}KQ+R5yKt2)XVgYR>%o=O@wo6VuOegS6i+oh^UzCR&|sp7L>NrJQjJ4P?$fcM6e= zzY`yxLca-S=ud3h^)$xwvAV(-oHA+gG+Tp^el9k%D#GmW6J(FX$p?i)_eajRMqr?%eVN=%;SZ9XAI7xXdgi0un z=j&r;pmS4A_dJ6!BQPx6cO9Zrj4AV6TO{m%Y1>iE2pD0yN7OH!nNDDNYL@GGochc) zZ6P~O-v}q4Zu*1v70T zNZ?Y=0!9s2Fjv)=In?JtU^qcS<>PWm?BU$5mkOo;uO(G5UBtq?hEmm`J2%ZeaG*Li z*XD}Q;f7^;E_1N$P$68;=iIX_lL#ZV7%L03R2Y?liIp31^FKJqg2~`5_*?z!% zW(T(IQ}t=teeCfEx>NO{Zw1&>OT%Zx z($(%ny?_Utc)o4;US@X|(va-Vm9c^n#@chj9JNc?fkRC7?VMp^I@olvsZ(88j1iJh zYch^1v11d51xkO?Og**b=(g|}rXD98hn*I!T-!JOXdEtmG&V!gg_Nae@}41K&p)bt!yn(NUnbpsp=-+h-M7@? zaqt7@6?L3}j9>>wK%|mPjf5^nN?MwXixPkROTD}@<=8YiU#oPGiiw;}29RH-w?xN5RP zFKxz(k+O3XJ=qpSZm^)yy{leYnQC-7Y0*_G8k2w^x71`TrsV3ezwwdUKYV>!eKqO8 zqUPeO2`!;ZF#>s;|0{b%*wWMgD#c}=jNJ@VqQ!_Pnc z%yY*^w3>F_9&6&-(S!Zv>+0cdl$^}_vB&3q@MuE{mTIPeCPsz;PS+Z%ILzg(KAiYi zWV`ALIPyjpnOVyvg&Iu=9D*m8{cPO(79Q-J*fOEx=_H00b%>>0ts4A7MO!8u zt6*+IbARypxT*8WI9ZIPu?Nhyu>hkuO@s*vpe}IXr_P@0o@u|zHqnlfNH>+r!K4b=&R+aixTENE;lc>;Ju^PpzkN|1n2MF0^});9 zw&=i8V=1!v1{71x1K(1wtaP$tILHveiVdr=v3iyjBrT{ZfdBos bwoJfT1}_tEnSk>D^?yykFzotUU4Gyn`j1Qd delta 3877 zcmeHJzpI@^5alkfNFc$&7wx>?duV0Io86tAoyEdN3&BUU6b)g2ij|6D5DY2+jLj)mch(N1vwC@LC^kSTAl(Lwo^9YrHf0?gX{^zX z&z@5E&+dMr&Q7K}5^}K-TQq1x2%2iLNjh%}@2cn5rnQvjqm$pNrO@6Rf8F9!6eLWqLy+1$PgIEk8Y{;ar*~#aCY=bc(2?H926oZ!`IYY(CztjB52WKC?uP#mc@C!aY;3c2yH`SvE^YyVu9KZDX)@#Q$ zPo6wHY-%zwS8^g~LPl919vHgZWYiBovXCJL@maJr8ZL;L*fewvJf+O3 zkYCF1%EuzZL)?-hOT~MOSR`YxI7(!KShMkBEth^2`{<~RWR<*)#37W($vN_F?&Z28 z?$VIm4i<(q{|!#_Y4cF4sj*fBq=+Dqk(%SA6UI=jq|1`c!jJ>Gm`gG)yBv_S>;V(& zk^oxCNIQ!=mG;FB8Cwu9aH#AJ`>Hu5FfRB}!fKBhyDco1w+H^086O+px}-LSn5`!- zS~`hL7CvA!H>=54uO--b6Kl}qByjOu5|WvxB1=)GW@sKvv!x@cYRB556lj#=BH4}< zK;jmBX1&nro+G>P3sODcU5+HM*7zgF>~>@=HAmyLF@z|QFG-PFZnX$+^t;ovdzI#T z+_Pk7f_1OSdaJv)x+mh_mh6$6TXOvMTXkl29#`jabskseap!w=bsqoaHm}a(|Mfij L*}gy2m4kl*u^e2p diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree index ee217aec4f1536cf23b56143a888de9f2292a1e7..44fa44b70cd78a71ead2dc6f18486572eca37637 100644 GIT binary patch delta 75 zcmZ3wlXvM(-VFyi4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F eEt8DXOp;R#lbb(swtwVg1Y)M`A32$~1OWiM2pM4j delta 75 zcmZ3wlXvM(-VFyi4U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u eOp=X_%?!*9%$h%PwtwVg1Y)M`A32$~1OWiI@EFGc diff --git a/master/.doctrees/tutorials/index.doctree b/master/.doctrees/tutorials/index.doctree index 6128b05bd7783b8439254f326d9d6021e4b93e0d..15688f158b29ce36a3da8541640dd122efdc850d 100644 GIT binary patch delta 61 zcmX>saadwQ5Tik+aj9jIMS;Glky)atiMdIVQKE%GQetYVfu)f_qOn1uS)zqSvbklF QahgeTs$ufxCdPwY079!0j{pDw delta 61 zcmX>saadwQ5Tik|WlFhex|zOlvU!rJMVeW1nt7^)Wul>ZVv2!zs*ypOg@r+)p@B)V Qv9Xzfxq;c{CdPwY07lCZ^#A|> diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree index a67074e6129e5b9efebb0ce4a8222d8c71ccc653..d6ee0383bf1fa852a9966b368c8bd18abfb4ff20 100644 GIT binary patch delta 71 zcmeyif#cf-jtzS`4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F bEt8DXOp;R#lbhdiw!h_M-2Rr6sgn-?dgT}7 delta 71 zcmeyif#cf-jtzS`4U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u bOp=X_%?!*9%$nbFw!h_M-2Rr6sgn-?dNmhH diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree index a456524f4b2abc2556b2e1891232cea362f5a7e0..6b67f1427a0d63d1d9b116614168a28d14e28229 100644 GIT binary patch delta 79 zcmaFyo%zLg<_(uQZ8D8ZEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F jEt8DXOp;R#ll4>blZ*9>^Gl18QzsWPYi+jVTK^FMWU(8N delta 79 zcmaFyo%zLg<_(uQZIUfh%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u jOp=X_%?!*9%=A<8lZ*9>^Gl18QzsWPYi+jVTK^FMUojf? diff --git a/master/.doctrees/tutorials/object_detection.doctree b/master/.doctrees/tutorials/object_detection.doctree index 6e1c4214dd939ea1040a63c0b693b7b547fe25d3..bde74a93e796c7a396f20f8352e29d13cba67a83 100644 GIT binary patch delta 71 zcmbPwoMY;7jtvJn4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F bEt8DXOp;R#lbb(swtwVg-2Rc1X^A8NR5BNa delta 71 zcmbPwoMY;7jtvJn4U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u bOp=X_%?!*9%$h%PwtwVg-2Rc1X^A8NQ-T)k diff --git a/master/.doctrees/tutorials/outliers.doctree b/master/.doctrees/tutorials/outliers.doctree index 6df76bd38e2256e1e649dd344838f993821ce72e..61e44daff38b86a39dfc19c89777990cedea9fea 100644 GIT binary patch delta 2187 zcmeHHO-ofl7;fOE87-1b7cDwoB8V>DcfQWdq*#P40xzW)5~MOSXGVpRiyD1^B(+rN z<6b+o>jNSXg}TJu^%q(e?P}GcMSmb@?!C5%v{QS}W;pQ7`#kUaKF{3SZ2Pg%mRsJt z*8V!T(E+pcx|)~eA}6utOfW;V1f!h;ML<)~v6j*bWvFM?VO#s$acLY)S6=C%0y%zl zu^-?RKp_$wq!2{)k$85oxG=GQpje)FgVmY#iRw(}YV$(R4i_ppa}zzObgmaF!}fF+ z8Thki<*7ZAJ~)BevvIf4cx6)$=C>=;l=e$g6Sgf|?S1lMAfk>FuDp*GFy=`T4sakG zVMcqNh{~g9cQZjX_WZ*{_v$`(tvsU_W*4fJ>e`pbdz#ymy5IpZ>Vx%BSDAZMp4HkW{DP9Do4|iQFN$YJcCwHL~Vi{1FD7K z%wntvFpMLdd>>t=msF5)(5I&H&b0_=Ho7O)Slpe$YU191CNOXF34`9xT>` zO5(XEuBAUBJ_Z8Tg~Kr8rO_6Qq*CcdG1^>PPknF>y+oPJtuC^N8(lzp{}wvg7v@BG2^`_-9wBGoA zbLa1?Qu(0a{Kd;tr3*vnhNrST`a3#njQNQ|l-4{t9A$Oc7|*fv+cFYqG>Jws{l6Q( W-T1YFzZ<_DU$Os;Up>UHr~4A8iDUA@?N(4uVjjMg<`yHwhAB&0`A_U>4nVB=wHlZ4o_(-%qVj;Sa z+;T!}5IZE&aCanjV#CVL$6w$tAWp9~sVHY<*RwspnR%Xh&oAfu)5dGh8XGPzxIOQA z!_y}4COs$nR44b^NsP)z9ne^+Z3;Rth=^b*2+z3!4Qk?R)8QkezpCx-Ep*e#v)cf+ z0+bR8ih*HLcNiQv+c{iW+|@aBCUlSYFRYCBFCVL~SoOCR)%or`xtBd(OG?!qzoU(I z&AM&%)8k}DtYy|%^}fGIj#jtZ?hUiPuhUIq)pN7&Gx0Wk)Rkz;b9ZMvN+z^2$WdCu zc`#l@;c0d3!PUahM90IAmDbze?#!z{V~fJ6p?*6&Fg#uzf3cdBwTC1pw=xw%skgTY^FO%a9iSUj>pj>*9iZm(<6qv(knHNED`72xU#0f^@*tQN7&t_MnnKiG>sm5MUgcs%2tQ5}ND2?17VAiuWH}=+Nlhvk1!sk`JcbB{ zvfOm$#2n`3@m!_e8eyY17`%v1qq5vf{ihQIudR2WET$r8pHC~O2sYAubPT7Q3K8l~ zg@;c&HD@<2kj)(&M0sak;Py+GN=ds`L8DP<4FjQhipZ#7%p{bg+WqcYN~;ykwK!dL84irg+;Qt SWs-54Nph-T^5$$tVUboQTzH{Ltc zxgvy+R>+h+SsNo65v2C`eul-hjgr$dSKa~61t>&L6 zy5v%f)e}A`h=N0zBG(W@auSUXvv;0rzP;9={G>#!QR0}d%-LuegRwraRbI{}KW%z$P|qmk(I{ViiT+EdwlGUS@-?s>SQ!=KYge@Gn%)5?`-3{%1-;BFqy%V z%D5O;?FCB}9DdZIh-pqq>p)|^$X$_OWUcIlPot;uNK{_Srzq8mw@h8ni}S8r|a@3)?5zSvbW42X0kdr6$M zSEAq&Mmt%hkz%(cWTea1lx(dfioz$Ut+z2Tab_#~%da%Y7Ugt!1*0<=E>e*&AqXF< zima^zPJ*A|>>p6iNajU7qlxGBoIcmwCv-UJ<+&HOzx&kYg$rBVW^&G|T)EU(DEJ(z zOICXqgor`&UTLGC#&wVMZ~wWuWp?IN`-92F<=vu9q<-kpZ#;eGYhQiv;iuimlz!;+ z$kS~;wbk|ieyur*iC$jZ(C5$JGk*uC3YZgIkU5akE^1?{(aCEUhWFTOu@_ZG@P`p- zu{E)Q!k`YCMcH!*v(q+WK<4XM^zUs24Xle>mF&I zSOYPL7&BNd>qrAct+0YZ<%C~yv*#aa|2XL!C=a-bwpd9eL;#t{N8^qZXDMPTirU*_0R|v<+@6Oqnm*UG%q$27M6#8Y~G07h%XSc@;4}wSQ-= z#l*~oZh3B5`(NMNK0cbaKk zQbC3z=p9Q)fP6#ItJNI$D+Bl?1Tra$8YmM0XR_AH1|7gSR_rOmNa6+Lcr5Xt4EJqH zA?{h|0i=YZG$LcMqF7_JDnJ*iD9(!9UwXIM>x-yaLbT1&fha0bO>&v$Be;s#zonVls=8nx(n>zg%f(I+k-!=x*vC2<=@Ds(Zin!u>*g zNr}3)e+d)}O?^x=;UTIVZT3ViUKEJgrXQg4x4=m)dyFL#tH*v(cyX>Z%jJu zB+f-bN36?WO0i^Ml&y%Wi;^~6p!`)c%r?1Tb&g9br4K#aFgs}B~l8* zhlas}Q5LMC+bwU@T>WbTR{zuS_SMNn8fnu(AT1wH5e}Xnd%5chI9Apib^Uf~UH`+Y z&10jUYp=^Iv$ryPVrBL|>XUB>-pcH)%-+iE%}x11@miU^|7|o^W^ZNoR%UNy_ICd< ZzcPCtwwWul_Ys@?|CzlR%--*B{x42|olF1# delta 7670 zcmeHL&ySu}70#!lbOZ#UomM*U`@J10MwxWZ{e7=QY78z&XayD!gQUGbg~WhWkg%v# zSs2)XrY|dHuVy}@-{lYZ7H2ba=RNn_bDr}& z&+K1+asT>@Z~l3cqDwER@8~@dO;EPzSWLFgGvgDP%&AyQo{@|~XfmW<+TZ-y{^khr z3B)H68;DOKK8?5uaR@Ov^BKg=h+7c1B0h__4RJf-bBH?-cOniWjv%%WM-j&mpGVw< zI6mxeZr}g)PA6f=vO-B4$t3eZ+U%TFLd4{w>L0nh{nPrXS0CFxGJN#V_PdA1n&ETY zM=dGHq_mTsDPbIuSB|Ov;mg}Eb(KSUzz5$r-Q_HmjDd?Hq|8o+LV|SBAsNa#m)Y&A zr>3_%)Nh~oMaOv(I^+;pa*`$H3VAQA%FM|uIL*FTJvI5_Tf5D_PIQGy?~_-7a*$aC zFM<>-1~i@79k^xHKg3^jIg@D&AU; zI%5C+s68@tXPYNBJ_cl8ZVvZX{?XhrTtD7k99^n)YzOHdeE3`E&V2KMZ#;CaHmJ*| zYtOgyAO!(pCYRwq7RyzkY$qICZmHeEP5xTE@B3}G1CQT4zY-o zrA#IOR>oP6l^3w(rZd~`{jk{@Zu>&}{HV>|7eOHiR*ZBi*%ER#j9DKeXZ?krH+bPG z{%RMP;P32sb^2*RwzCwX)x}GoN~RQCG9{FlNCcBSpV!hUucJpX#5GA$!iLPPF-BT1 zoHg>`#A2;w$801UtqeIipNSVNO^Sr=DJB4Xd(^JZJA+>}?E-%1fhiNf9uydw5K)Qu znqjwyXoXKo4Hu|=cB2(aD{m>9gj1S3A%x2gYq%sT*dy1P#U6>t`oaQ_At^vvTI~|? z$@VrPD=z1Iq?UJ92(8ZRW_tv{+v?xjYLDN05P7~NJR5eqRaRyzEW@+o`9q2ttADt< zfx@c1HtEn3>S}FT#?k8GWM%nu?d@)$u2LIK-er!uBD`Z|q7M>~*Pr-e`_;w;4@xRQ3!5b)P%V&dvH@u+J3K?}_cz+8CS2~U<2VwaMl8y= zhQ|ys68l&G)*PF$#JJ$cT4ZK~k_&3DF|OK&tq+UT=Oi>V%xku7>c|kUX+j1q};53Sp%!_u?QVYk8+qv|XLH zr(ZYh^5e~BM;Yj21%wE;#*LgZps$40lT9%KQ#_N@AZX8xfqsW^fsjsM!s1A)#D&)B z#uWc<)J9B%jzMz3!eCc{S0Tckwgs0`{e`pbvElrWo8OG8$!EZXnOmKEI>~>0v^@<< zEKvCH1yLw$GmKOy!GXTPXDnDbuf`cnb}X<C`S1>!(=t{tdwfv1t^d9|9qmoV~?GZ`~duN^jX6s7}zKHGX=Ynm4*9r z5qm$(387u>sQN1#5Uzhe-o7%rbd4*zZYB6|KwWp zNY#ohCU$KC*Cudn0{@SPUVv|H0@o&RZ2}K^^<@%Uo50CGKi4L3Za~*25dWC0c%-!n eT${kP3H+b-b8Q0u+dVII=*KdF15DswZvGD{bhYyU diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree index f6678e867b289e9f283e22b6f88a3adcb23953a7..e6f247c188b40034375905de7038a4ad282ec9c9 100644 GIT binary patch delta 1527 zcmbRJglpjwt_>$S4Kj^OEsHD)^i7S-5=~9aO_GcfEew(pQ&SBrjSLcv4HC@~Ei97F zEt8DXOp;R#lQ;k5{B0_4&dR`GQ&5y@$EB-lWME{XYhbQxV5DGZYGrInWtc&XJA;&#f8Nx1q};hJ>$&>92pr|A$C9Xb)D?%ktlDjXJ~G$XJlZg zqhPFOs%K=XXJP_jTId-Xn@pbI9Lot;GdV75udrHriFK?3(9wEEX1)pvW@dWElQ;Uv zbA!w^RL}tP4ZR#D>$|d#=nz=V)WlBaxAP-}@Y8k-dgs zL~ry tdlGw$8hV*e?uq6YQ6ApBE5T$S4U#QW%1zVF^o^6vlT0nr%#zd0Q!OkL4b2l%49ruF4ALwt3=$0u zOp=X_%?!*9%r^hz{B0_4#>&88Q&5y@$EB-lWME{XYhbQxV5DGZYGr6?Wn?^gZ$PMu zo}Qk9p@pfQrLmr&o{@=(j)IYifq|8&QL2HJVIq)aWnn(~qN^!4lA6gE9FDO7wU|ye zh?eIDnPsA&0pf2?jAdkG1q)2(^H`>0re|nusAq1fqhM;JXJlxuXKA1Zv;oR7GB(pQ zGMzlZIhF&a5NNXZlBT+dEIfNf3B8te*LUq`q=7Wp5;*<>b*eMx;5yQ$?n-3)T%%9BnkBxI` W5i=tL2uxSource code for cleanlab.internal.util

     return noise_matrix
-
[docs]def clip_values(x, low=0.0, high=1.0, new_sum=None) -> np.ndarray: +
[docs]def clip_values(x, low=0.0, high=1.0, new_sum: Optional[float] = None) -> np.ndarray: """Clip all values in p to range [low,high]. Preserves sum of x. @@ -729,17 +729,14 @@

Source code for cleanlab.internal.util

     x : np.ndarray
         A list of clipped values, summing to the same sum as x."""
 
-    def clip_range(a, low=low, high=high):
-        """Clip a into range [low,high]"""
-        return min(max(a, low), high)
-
-    vectorized_clip = np.vectorize(
-        clip_range
-    )  # Vectorize clip_range for efficiency with np.ndarrays
-    prev_sum = sum(x) if new_sum is None else new_sum  # Store previous sum
-    x = vectorized_clip(x)  # Clip all values (efficiently)
+    if len(x.shape) > 1:
+        raise TypeError(
+            f"only size-1 arrays can be converted to Python scalars but 'x' had shape {x.shape}"
+        )
+    prev_sum = np.sum(x) if new_sum is None else new_sum  # Store previous sum
+    x = np.clip(x, low, high)  # Clip all values (efficiently)
     x = (
-        x * prev_sum / np.clip(float(sum(x)), a_min=TINY_VALUE, a_max=None)
+        x * prev_sum / np.clip(np.sum(x), a_min=TINY_VALUE, a_max=None)
     )  # Re-normalized values to sum to previous sum
     return x
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb index 46b37d704..828649547 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 ede05464f..f1f31cf72 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 8111dc9de..e823f64a1 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 59d67f1f0..4d748d34d 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 b77f310a5..a73e18182 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 9b208f9fe..42b41bfef 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 469ab488a..ee1f8403b 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 c59730731..d032d6e58 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 6ca13ceb0..3be3a80a0 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 8f5165de5..4c772eda2 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 5d2685b6c..40a6de3be 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 1f90e9ca9..4724bd561 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 97e4513a6..3e0188c7e 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 bb22c545c..0b98b27bd 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 d2b0bbb42..3f71203b8 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 d8a611d23..b7d1965d7 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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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 ed2af2ad8..c313406d3 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, 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 +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, 108], "746043": 10, "794894": 10, "98": [10, 97, 98, 106, 108], "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, 108], "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, 104, 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], "93": [87, 97, 103, 106], "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, "change_pin": [88, 95], "supported_cards_and_curr": [88, 95], "visa_or_mastercard": [88, 95], "beneficiary_not_allow": [88, 95], "apple_pay_or_google_pai": [88, 95], "getting_spare_card": [88, 95], "lost_or_stolen_phon": [88, 95], "card_payment_fee_charg": [88, 95], "card_about_to_expir": [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, 108], "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], "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, 108], "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], "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, "590": 92, "329": [92, 94, 103], "88": [92, 97, 99, 102, 103, 106], "195": 92, "391": 92, "493": 92, "060": 92, "769": 92, "330": [92, 103], "505": 92, "425": 92, "476": 92, "340": 92, "589": 92, "328": [92, 103], "310": 92, "496": 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, 108], "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], "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, 108], "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], "63370425": 96, "61it": 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], "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, 108], "174": 99, "163": 99, "150": [99, 101, 103, 108], "169": [99, 108], "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], "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, 108], "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, "75087729": 104, "26it": 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, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "kb": 108, "mb": 108, "directori": 108, "inflat": 108, "134": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "3mb": 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 diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb index aa00ed6e9..3c2c0a9bd 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": "2024-07-02T15:09:49.406100Z", - "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" + "iopub.execute_input": "2024-07-02T15:24:50.264127Z", + "iopub.status.busy": "2024-07-02T15:24:50.263741Z", + "iopub.status.idle": "2024-07-02T15:24:51.435602Z", + "shell.execute_reply": "2024-07-02T15:24:51.435065Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:09:50.628776Z", - "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" + "iopub.execute_input": "2024-07-02T15:24:51.438162Z", + "iopub.status.busy": "2024-07-02T15:24:51.437743Z", + "iopub.status.idle": "2024-07-02T15:24:51.455082Z", + "shell.execute_reply": "2024-07-02T15:24:51.454668Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.457364Z", + "iopub.status.busy": "2024-07-02T15:24:51.456857Z", + "iopub.status.idle": "2024-07-02T15:24:51.773985Z", + "shell.execute_reply": "2024-07-02T15:24:51.773375Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.803385Z", + "iopub.status.busy": "2024-07-02T15:24:51.802942Z", + "iopub.status.idle": "2024-07-02T15:24:51.806575Z", + "shell.execute_reply": "2024-07-02T15:24:51.806033Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.808623Z", + "iopub.status.busy": "2024-07-02T15:24:51.808291Z", + "iopub.status.idle": "2024-07-02T15:24:51.816291Z", + "shell.execute_reply": "2024-07-02T15:24:51.815728Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.818342Z", + "iopub.status.busy": "2024-07-02T15:24:51.818171Z", + "iopub.status.idle": "2024-07-02T15:24:51.820601Z", + "shell.execute_reply": "2024-07-02T15:24:51.820168Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:51.822635Z", + "iopub.status.busy": "2024-07-02T15:24:51.822329Z", + "iopub.status.idle": "2024-07-02T15:24:52.333801Z", + "shell.execute_reply": "2024-07-02T15:24:52.333291Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:52.336000Z", + "iopub.status.busy": "2024-07-02T15:24:52.335683Z", + "iopub.status.idle": "2024-07-02T15:24:54.133804Z", + "shell.execute_reply": "2024-07-02T15:24:54.133179Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.136490Z", + "iopub.status.busy": "2024-07-02T15:24:54.135807Z", + "iopub.status.idle": "2024-07-02T15:24:54.145523Z", + "shell.execute_reply": "2024-07-02T15:24:54.145014Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.147631Z", + "iopub.status.busy": "2024-07-02T15:24:54.147304Z", + "iopub.status.idle": "2024-07-02T15:24:54.151216Z", + "shell.execute_reply": "2024-07-02T15:24:54.150782Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.153210Z", + "iopub.status.busy": "2024-07-02T15:24:54.152897Z", + "iopub.status.idle": "2024-07-02T15:24:54.159804Z", + "shell.execute_reply": "2024-07-02T15:24:54.159399Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.161777Z", + "iopub.status.busy": "2024-07-02T15:24:54.161437Z", + "iopub.status.idle": "2024-07-02T15:24:54.271636Z", + "shell.execute_reply": "2024-07-02T15:24:54.271152Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.273415Z", + "iopub.status.busy": "2024-07-02T15:24:54.273245Z", + "iopub.status.idle": "2024-07-02T15:24:54.276027Z", + "shell.execute_reply": "2024-07-02T15:24:54.275583Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:54.277845Z", + "iopub.status.busy": "2024-07-02T15:24:54.277663Z", + "iopub.status.idle": "2024-07-02T15:24:56.177124Z", + "shell.execute_reply": "2024-07-02T15:24:56.176537Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:56.179941Z", + "iopub.status.busy": "2024-07-02T15:24:56.179406Z", + "iopub.status.idle": "2024-07-02T15:24:56.190491Z", + "shell.execute_reply": "2024-07-02T15:24:56.190044Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:24:56.192341Z", + "iopub.status.busy": "2024-07-02T15:24:56.192169Z", + "iopub.status.idle": "2024-07-02T15:24:56.274113Z", + "shell.execute_reply": "2024-07-02T15:24:56.273673Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index 9c18d0c40..2bf9854d9 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: {'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'}
+Classes: {'change_pin', 'cancel_transfer', 'supported_cards_and_currencies', 'visa_or_mastercard', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'getting_spare_card', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'card_about_to_expire'}

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": {"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} +{"state": {"499e72220e084b96a2fac6d7d24f3e36": {"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}}, "d398c27b16594cdabb17dd18d91923db": {"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": ""}}, "2eda31c15639409c917ce8f30f06fea9": {"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_499e72220e084b96a2fac6d7d24f3e36", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_d398c27b16594cdabb17dd18d91923db", "tabbable": null, "tooltip": null, "value": 391.0}}, "31829b18848e480eaf3d67d16a966abc": {"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}}, "b2d0af45a331440e91bb670c810aad25": {"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}}, "40fe431dbc09475d9cc4e82668dce66c": {"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_31829b18848e480eaf3d67d16a966abc", "placeholder": "\u200b", "style": "IPY_MODEL_b2d0af45a331440e91bb670c810aad25", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "16cb8599d6034cd1b810cbf99f703579": {"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}}, "8e1757765b51437c9c3576da0b2a84d2": {"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}}, "71c0454905f044549aa29c3961bc512b": {"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_16cb8599d6034cd1b810cbf99f703579", "placeholder": "\u200b", "style": "IPY_MODEL_8e1757765b51437c9c3576da0b2a84d2", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200769.1kB/s]"}}, "c4843a0ecf8947089a56431c777139d7": {"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}}, "98baa2df749f4718a19b8ed6f6b64516": {"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_40fe431dbc09475d9cc4e82668dce66c", "IPY_MODEL_2eda31c15639409c917ce8f30f06fea9", "IPY_MODEL_71c0454905f044549aa29c3961bc512b"], "layout": "IPY_MODEL_c4843a0ecf8947089a56431c777139d7", "tabbable": null, "tooltip": null}}, "b1a6862286e144a5b4c7e3b53955c909": {"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}}, "9588d449e7b24e6db9e90daa5078369e": {"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": ""}}, "20199ed9391b4fd3af9ffba1bd110646": {"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_b1a6862286e144a5b4c7e3b53955c909", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_9588d449e7b24e6db9e90daa5078369e", "tabbable": null, "tooltip": null, "value": 2211.0}}, "71581c894a0e453e992f58ce4ec00848": {"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}}, "8d50a9bb17184cec871c72343c762cc2": {"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}}, "dd67fec157b9489faa0dc3ddbd95ef35": {"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_71581c894a0e453e992f58ce4ec00848", "placeholder": "\u200b", "style": "IPY_MODEL_8d50a9bb17184cec871c72343c762cc2", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "4e0a66ed3e51494baf8c4960957f2abc": {"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}}, "2df28735813745f899e2ab60996be667": {"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}}, "41c9e037f3074a5d9b33b7014d85cdfe": {"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_4e0a66ed3e51494baf8c4960957f2abc", "placeholder": "\u200b", "style": "IPY_MODEL_2df28735813745f899e2ab60996be667", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007435kB/s]"}}, "b42bf80fe5274a5e908a3c39822c2629": {"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}}, "697435a323a84bea9cc7bfb42e0a5e62": {"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_dd67fec157b9489faa0dc3ddbd95ef35", "IPY_MODEL_20199ed9391b4fd3af9ffba1bd110646", "IPY_MODEL_41c9e037f3074a5d9b33b7014d85cdfe"], "layout": "IPY_MODEL_b42bf80fe5274a5e908a3c39822c2629", "tabbable": null, "tooltip": null}}, "31425907e4ff47c1a17ed0a9935430d6": {"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}}, "5042f028d3a24a99a3cb8dbb370a2d51": {"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": ""}}, "d62a7bbd2b814718bdb6e5c898023432": {"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_31425907e4ff47c1a17ed0a9935430d6", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5042f028d3a24a99a3cb8dbb370a2d51", "tabbable": null, "tooltip": null, "value": 665.0}}, "477e3bda05594962bd8ba0f1e3b7bb31": {"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}}, "dc42abfc89cd4333a4569bc9139f85a5": {"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}}, "bb8dedc70e1e42f985ca245b4c1626d0": {"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_477e3bda05594962bd8ba0f1e3b7bb31", "placeholder": "\u200b", "style": "IPY_MODEL_dc42abfc89cd4333a4569bc9139f85a5", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "ab29117829274449a8621cc4708da2b1": {"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}}, "e6758addea6d44238157a9fee26b6f01": {"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}}, "4d8b4c898e4d49c795640c857d8f9648": {"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_ab29117829274449a8621cc4708da2b1", "placeholder": "\u200b", "style": "IPY_MODEL_e6758addea6d44238157a9fee26b6f01", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u2007137kB/s]"}}, "787647896a374c4baef37ba555b3d758": {"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}}, "f7905c4a4ded402ca686067d0aacc292": {"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_bb8dedc70e1e42f985ca245b4c1626d0", "IPY_MODEL_d62a7bbd2b814718bdb6e5c898023432", "IPY_MODEL_4d8b4c898e4d49c795640c857d8f9648"], "layout": "IPY_MODEL_787647896a374c4baef37ba555b3d758", "tabbable": null, "tooltip": null}}, "7aa30d069f444999834643e94fe81c9b": {"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}}, "8429e83ba7114915933be895d5f6fe3a": {"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": ""}}, "1643bc7cecb74bc190903adf9229b34d": {"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_7aa30d069f444999834643e94fe81c9b", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_8429e83ba7114915933be895d5f6fe3a", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "a215428c7e0d4fdda219b836792e0bea": {"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}}, "967764ca5ac248399e557033cfdf7a08": {"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}}, "e7f2051c6f3e4cb4afa29879fc4e25d7": {"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_a215428c7e0d4fdda219b836792e0bea", "placeholder": "\u200b", "style": "IPY_MODEL_967764ca5ac248399e557033cfdf7a08", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "21cc8aedfd874b128773266b548130c0": {"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}}, "388e3769071b4755bf2c6825a5643904": {"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}}, "161fe61da4db4f7e8074ff8edcdcb759": {"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_21cc8aedfd874b128773266b548130c0", "placeholder": "\u200b", "style": "IPY_MODEL_388e3769071b4755bf2c6825a5643904", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:01<00:00,\u200727.7MB/s]"}}, "e1ad008614fb43fdb5e809f4d8511a82": {"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}}, "4745e1e3131d4034bf9a596bc4fbddbf": {"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_e7f2051c6f3e4cb4afa29879fc4e25d7", "IPY_MODEL_1643bc7cecb74bc190903adf9229b34d", "IPY_MODEL_161fe61da4db4f7e8074ff8edcdcb759"], "layout": "IPY_MODEL_e1ad008614fb43fdb5e809f4d8511a82", "tabbable": null, "tooltip": null}}, "5dfa8539a6c948378badd6686cc253dd": {"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}}, "c9d0db8230f34b468c783b24537b3a87": {"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": ""}}, "5f8acf7619d14a2494c2bd74f4c2d794": {"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_5dfa8539a6c948378badd6686cc253dd", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c9d0db8230f34b468c783b24537b3a87", "tabbable": null, "tooltip": null, "value": 466062.0}}, "fe6d4bd14f1d4e9c889f127632dfd090": {"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}}, "f1cd701a314345e8a946773e606e7e5e": {"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}}, "cfe55f8de045405f86e66d60c9c1ce0c": {"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_fe6d4bd14f1d4e9c889f127632dfd090", "placeholder": "\u200b", "style": "IPY_MODEL_f1cd701a314345e8a946773e606e7e5e", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "1a2f8352992a4e8d8667c62f94d7e0e0": {"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}}, "cc575b26e89c48eea0b3767999b7957a": {"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}}, "ef11cb19d76d48a098b132ab8531c488": {"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_1a2f8352992a4e8d8667c62f94d7e0e0", "placeholder": "\u200b", "style": "IPY_MODEL_cc575b26e89c48eea0b3767999b7957a", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u200735.2MB/s]"}}, "77ab3ba493da4bb68b8d39397523e2e4": {"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}}, "48016ee17d6b4133971e8d4b58046f98": {"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_cfe55f8de045405f86e66d60c9c1ce0c", "IPY_MODEL_5f8acf7619d14a2494c2bd74f4c2d794", "IPY_MODEL_ef11cb19d76d48a098b132ab8531c488"], "layout": "IPY_MODEL_77ab3ba493da4bb68b8d39397523e2e4", "tabbable": null, "tooltip": null}}, "5b6cedd62c3b4e2dba4fb6cf6ac81919": {"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}}, "51e4ce1aa08a4875b0078386841ef6c6": {"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": ""}}, "f717bfe4b6324a74be7f067e29025df4": {"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_5b6cedd62c3b4e2dba4fb6cf6ac81919", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_51e4ce1aa08a4875b0078386841ef6c6", "tabbable": null, "tooltip": null, "value": 48.0}}, "3090f81fd63e47748b48b354dff579e6": {"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}}, "229a059bf66a476caf93f93617098541": {"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}}, "c4ab79f47c1f4a5fa7a952d2f4243909": {"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_3090f81fd63e47748b48b354dff579e6", "placeholder": "\u200b", "style": "IPY_MODEL_229a059bf66a476caf93f93617098541", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "b876cea56c70415bbe96c6d929472c03": {"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}}, "987bc5a0a5174818895b0e59be32726d": {"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}}, "cfd42f9c1e5b4b6e957253d074ff0a96": {"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_b876cea56c70415bbe96c6d929472c03", "placeholder": "\u200b", "style": "IPY_MODEL_987bc5a0a5174818895b0e59be32726d", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20079.30kB/s]"}}, "2059ff2a4cfe4817941404aaf9b91a27": {"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}}, "3081197287a941cea971e1c24a1abd42": {"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_c4ab79f47c1f4a5fa7a952d2f4243909", "IPY_MODEL_f717bfe4b6324a74be7f067e29025df4", "IPY_MODEL_cfd42f9c1e5b4b6e957253d074ff0a96"], "layout": "IPY_MODEL_2059ff2a4cfe4817941404aaf9b91a27", "tabbable": null, "tooltip": null}}, "4f6ef66a05864b318215d64d3df902a8": {"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}}, "b71a8e9e851b4881bb5145531e49ccd0": {"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": ""}}, "66867813190846ab8aed312ab6908a43": {"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_4f6ef66a05864b318215d64d3df902a8", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b71a8e9e851b4881bb5145531e49ccd0", "tabbable": null, "tooltip": null, "value": 231508.0}}, "8c2f5adef8594b9d94b856fe69ecef0d": {"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}}, "40a195a274ab4351b4d37e7880f857e7": {"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}}, "93b428a1e9984e3985970ffd82b85429": {"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_8c2f5adef8594b9d94b856fe69ecef0d", "placeholder": "\u200b", "style": "IPY_MODEL_40a195a274ab4351b4d37e7880f857e7", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "790e13d2858c4cf4bdf4bce6bda99ddb": {"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}}, "29ab7c4b24694381897b57b84ded9af9": {"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}}, "ea06e6ec819142448a354fc1926e13af": {"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_790e13d2858c4cf4bdf4bce6bda99ddb", "placeholder": "\u200b", "style": "IPY_MODEL_29ab7c4b24694381897b57b84ded9af9", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u200738.1MB/s]"}}, "1f83772d100c412884fc22f69ab7e498": {"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}}, "f451a7bbcccb4c0ebd05353c6b2b00d8": {"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_93b428a1e9984e3985970ffd82b85429", "IPY_MODEL_66867813190846ab8aed312ab6908a43", "IPY_MODEL_ea06e6ec819142448a354fc1926e13af"], "layout": "IPY_MODEL_1f83772d100c412884fc22f69ab7e498", "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 cac09ab25..5c8935803 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": "2024-07-02T15:09:59.845378Z", - "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" + "iopub.execute_input": "2024-07-02T15:24:59.158944Z", + "iopub.status.busy": "2024-07-02T15:24:59.158774Z", + "iopub.status.idle": "2024-07-02T15:25:01.944133Z", + "shell.execute_reply": "2024-07-02T15:25:01.943585Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:10:02.562794Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:01.946518Z", + "iopub.status.busy": "2024-07-02T15:25:01.946245Z", + "iopub.status.idle": "2024-07-02T15:25:01.949591Z", + "shell.execute_reply": "2024-07-02T15:25:01.949140Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:01.951916Z", + "iopub.status.busy": "2024-07-02T15:25:01.951599Z", + "iopub.status.idle": "2024-07-02T15:25:01.954525Z", + "shell.execute_reply": "2024-07-02T15:25:01.954082Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:01.956541Z", + "iopub.status.busy": "2024-07-02T15:25:01.956225Z", + "iopub.status.idle": "2024-07-02T15:25:02.013993Z", + "shell.execute_reply": "2024-07-02T15:25:02.013542Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.015897Z", + "iopub.status.busy": "2024-07-02T15:25:02.015721Z", + "iopub.status.idle": "2024-07-02T15:25:02.019309Z", + "shell.execute_reply": "2024-07-02T15:25:02.018806Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.021419Z", + "iopub.status.busy": "2024-07-02T15:25:02.021114Z", + "iopub.status.idle": "2024-07-02T15:25:02.024406Z", + "shell.execute_reply": "2024-07-02T15:25:02.023893Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\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" + "Classes: {'change_pin', 'cancel_transfer', 'supported_cards_and_currencies', 'visa_or_mastercard', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'getting_spare_card', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'card_about_to_expire'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.026552Z", + "iopub.status.busy": "2024-07-02T15:25:02.026250Z", + "iopub.status.idle": "2024-07-02T15:25:02.029388Z", + "shell.execute_reply": "2024-07-02T15:25:02.028932Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.031230Z", + "iopub.status.busy": "2024-07-02T15:25:02.031049Z", + "iopub.status.idle": "2024-07-02T15:25:02.034449Z", + "shell.execute_reply": "2024-07-02T15:25:02.034001Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:02.036228Z", + "iopub.status.busy": "2024-07-02T15:25:02.036061Z", + "iopub.status.idle": "2024-07-02T15:25:08.385948Z", + "shell.execute_reply": "2024-07-02T15:25:08.385389Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c943f13df8c04e77aae4c7ca2cbbd613", + "model_id": "98baa2df749f4718a19b8ed6f6b64516", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06405b534d7c49db89f3d29b52da1f80", + "model_id": "697435a323a84bea9cc7bfb42e0a5e62", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1146fbcfe9cf41da81392df94520265c", + "model_id": "f7905c4a4ded402ca686067d0aacc292", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bd1aa12a83f148a6a04b7394dd645fb3", + "model_id": "4745e1e3131d4034bf9a596bc4fbddbf", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3840f91804d14d5aa30e594b1e1d7fa3", + "model_id": "48016ee17d6b4133971e8d4b58046f98", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4522cd2764fc425b83a55e8426ac45e2", + "model_id": "3081197287a941cea971e1c24a1abd42", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a72b5f7de474f75bb371e057f0f1914", + "model_id": "f451a7bbcccb4c0ebd05353c6b2b00d8", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:08.388716Z", + "iopub.status.busy": "2024-07-02T15:25:08.388336Z", + "iopub.status.idle": "2024-07-02T15:25:08.391310Z", + "shell.execute_reply": "2024-07-02T15:25:08.390833Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:08.393273Z", + "iopub.status.busy": "2024-07-02T15:25:08.392947Z", + "iopub.status.idle": "2024-07-02T15:25:08.395525Z", + "shell.execute_reply": "2024-07-02T15:25:08.395074Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:08.397396Z", + "iopub.status.busy": "2024-07-02T15:25:08.397223Z", + "iopub.status.idle": "2024-07-02T15:25:11.020595Z", + "shell.execute_reply": "2024-07-02T15:25:11.019993Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.023619Z", + "iopub.status.busy": "2024-07-02T15:25:11.022845Z", + "iopub.status.idle": "2024-07-02T15:25:11.030391Z", + "shell.execute_reply": "2024-07-02T15:25:11.029947Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.032276Z", + "iopub.status.busy": "2024-07-02T15:25:11.032103Z", + "iopub.status.idle": "2024-07-02T15:25:11.035851Z", + "shell.execute_reply": "2024-07-02T15:25:11.035397Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.037827Z", + "iopub.status.busy": "2024-07-02T15:25:11.037491Z", + "iopub.status.idle": "2024-07-02T15:25:11.040763Z", + "shell.execute_reply": "2024-07-02T15:25:11.040318Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.042952Z", + "iopub.status.busy": "2024-07-02T15:25:11.042502Z", + "iopub.status.idle": "2024-07-02T15:25:11.045617Z", + "shell.execute_reply": "2024-07-02T15:25:11.045190Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.047754Z", + "iopub.status.busy": "2024-07-02T15:25:11.047338Z", + "iopub.status.idle": "2024-07-02T15:25:11.054029Z", + "shell.execute_reply": "2024-07-02T15:25:11.053482Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.056131Z", + "iopub.status.busy": "2024-07-02T15:25:11.055786Z", + "iopub.status.idle": "2024-07-02T15:25:11.280111Z", + "shell.execute_reply": "2024-07-02T15:25:11.279635Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.282450Z", + "iopub.status.busy": "2024-07-02T15:25:11.282132Z", + "iopub.status.idle": "2024-07-02T15:25:11.457525Z", + "shell.execute_reply": "2024-07-02T15:25:11.457055Z" }, "scrolled": true }, @@ -1058,10 +1058,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:11.459928Z", + "iopub.status.busy": "2024-07-02T15:25:11.459576Z", + "iopub.status.idle": "2024-07-02T15:25:11.463148Z", + "shell.execute_reply": "2024-07-02T15:25:11.462676Z" }, "nbsphinx": "hidden" }, @@ -1105,7 +1105,56 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "038891c782ab46f4ba836914abbfc5ce": { + "161fe61da4db4f7e8074ff8edcdcb759": { + "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_21cc8aedfd874b128773266b548130c0", + "placeholder": "​", + "style": "IPY_MODEL_388e3769071b4755bf2c6825a5643904", + "tabbable": null, + "tooltip": null, + "value": " 54.2M/54.2M [00:01<00:00, 27.7MB/s]" + } + }, + "1643bc7cecb74bc190903adf9229b34d": { + "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_7aa30d069f444999834643e94fe81c9b", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8429e83ba7114915933be895d5f6fe3a", + "tabbable": null, + "tooltip": null, + "value": 54245363.0 + } + }, + "16cb8599d6034cd1b810cbf99f703579": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1158,31 +1207,60 @@ "width": null } }, - "06405b534d7c49db89f3d29b52da1f80": { - "model_module": "@jupyter-widgets/controls", + "1a2f8352992a4e8d8667c62f94d7e0e0": { + "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_5316135ae1ac4592a7b4239c553c02c6", - "IPY_MODEL_43953f2b89bb41408d6583c47201913d", - "IPY_MODEL_e0adbaa3ffa64032933136aebb9a1f7b" - ], - "layout": "IPY_MODEL_cbb206b9c0d64fe0b479fb7cc9df570b", - "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 } }, - "07d79636c68343278e1f779c27aef268": { + "1f83772d100c412884fc22f69ab7e498": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1235,25 +1313,33 @@ "width": null } }, - "0d5dda5aa9e840a7969a3facb0c17f1f": { + "20199ed9391b4fd3af9ffba1bd110646": { "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_b1a6862286e144a5b4c7e3b53955c909", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9588d449e7b24e6db9e90daa5078369e", + "tabbable": null, + "tooltip": null, + "value": 2211.0 } }, - "0da07d715efe402083873e0661e556ea": { + "2059ff2a4cfe4817941404aaf9b91a27": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1306,31 +1392,60 @@ "width": null } }, - "1146fbcfe9cf41da81392df94520265c": { - "model_module": "@jupyter-widgets/controls", + "21cc8aedfd874b128773266b548130c0": { + "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_59eec5b7926b48ae8d162ecab0db1ec2", - "IPY_MODEL_c18be04c279b46b48b79ee15e32435e6", - "IPY_MODEL_e32cd0a87f0f4ad18f72cc198abad4fd" - ], - "layout": "IPY_MODEL_505538aed9b54bebb4c3f0107daadf44", - "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 } }, - "131d5d7c7d344158b759f1061ef8d42d": { + "229a059bf66a476caf93f93617098541": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1348,7 +1463,7 @@ "text_color": null } }, - "1740b9557dc0484c95a45ed25a63585f": { + "29ab7c4b24694381897b57b84ded9af9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1366,30 +1481,7 @@ "text_color": null } }, - "2694565fc4d4493db595fec20b7b65bf": { - "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_f25b6a55df5d45bc80be73c91794168b", - "placeholder": "​", - "style": "IPY_MODEL_e970db685471489481e8d686d97b3cc2", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" - } - }, - "2df9eaceb2e14e8fa3a19b25c7d76f35": { + "2df28735813745f899e2ab60996be667": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1407,23 +1499,33 @@ "text_color": null } }, - "360120e496a841119a2a238bfb8c7631": { + "2eda31c15639409c917ce8f30f06fea9": { "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_499e72220e084b96a2fac6d7d24f3e36", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d398c27b16594cdabb17dd18d91923db", + "tabbable": null, + "tooltip": null, + "value": 391.0 } }, - "3840f91804d14d5aa30e594b1e1d7fa3": { + "3081197287a941cea971e1c24a1abd42": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1438,42 +1540,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_ef479a0478b644bbbd209856e893f0ed", - "IPY_MODEL_d9d22d6398f34538ac40fff96fa8abaf", - "IPY_MODEL_8b024eb1e7d34d94b81d05d691a3874d" + "IPY_MODEL_c4ab79f47c1f4a5fa7a952d2f4243909", + "IPY_MODEL_f717bfe4b6324a74be7f067e29025df4", + "IPY_MODEL_cfd42f9c1e5b4b6e957253d074ff0a96" ], - "layout": "IPY_MODEL_cefce6f533124c4e90d7c277319c65e9", + "layout": "IPY_MODEL_2059ff2a4cfe4817941404aaf9b91a27", "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_ea436305a9d143d09b28e8d32eb3020a", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_360120e496a841119a2a238bfb8c7631", - "tabbable": null, - "tooltip": null, - "value": 54245363.0 - } - }, - "3a43140a399d4c6b8942f19c238c99d0": { + "3090f81fd63e47748b48b354dff579e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1526,76 +1602,16 @@ "width": null } }, - "3ac7761e49b1406fb28de06a9a09ffc6": { - "model_module": "@jupyter-widgets/controls", + "31425907e4ff47c1a17ed0a9935430d6": { + "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_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 - } - }, - "3bd0776bd0b840e8b2a1d15afd1e27f8": { - "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 - } - }, - "3d25676d34544705a2bcbe334c412efc": { - "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": "" - } - }, - "3edc8ef534274f15b48e90d83188b494": { - "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, @@ -1639,33 +1655,7 @@ "width": null } }, - "43953f2b89bb41408d6583c47201913d": { - "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_b01dff0aaef24c71a79429be65e9ce07", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3d25676d34544705a2bcbe334c412efc", - "tabbable": null, - "tooltip": null, - "value": 2211.0 - } - }, - "44b0dfbf62fd4d088800332e97908228": { + "31829b18848e480eaf3d67d16a966abc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1718,7 +1708,89 @@ "width": null } }, - "4522cd2764fc425b83a55e8426ac45e2": { + "388e3769071b4755bf2c6825a5643904": { + "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 + } + }, + "40a195a274ab4351b4d37e7880f857e7": { + "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 + } + }, + "40fe431dbc09475d9cc4e82668dce66c": { + "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_31829b18848e480eaf3d67d16a966abc", + "placeholder": "​", + "style": "IPY_MODEL_b2d0af45a331440e91bb670c810aad25", + "tabbable": null, + "tooltip": null, + "value": ".gitattributes: 100%" + } + }, + "41c9e037f3074a5d9b33b7014d85cdfe": { + "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_4e0a66ed3e51494baf8c4960957f2abc", + "placeholder": "​", + "style": "IPY_MODEL_2df28735813745f899e2ab60996be667", + "tabbable": null, + "tooltip": null, + "value": " 2.21k/2.21k [00:00<00:00, 435kB/s]" + } + }, + "4745e1e3131d4034bf9a596bc4fbddbf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1733,16 +1805,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2694565fc4d4493db595fec20b7b65bf", - "IPY_MODEL_3ac7761e49b1406fb28de06a9a09ffc6", - "IPY_MODEL_a588c0dcd0974f8da33c7bdf3d4a35e8" + "IPY_MODEL_e7f2051c6f3e4cb4afa29879fc4e25d7", + "IPY_MODEL_1643bc7cecb74bc190903adf9229b34d", + "IPY_MODEL_161fe61da4db4f7e8074ff8edcdcb759" ], - "layout": "IPY_MODEL_ce7bece14db441b3b9a7a133c7e71a07", + "layout": "IPY_MODEL_e1ad008614fb43fdb5e809f4d8511a82", "tabbable": null, "tooltip": null } }, - "4cbe66fa6f75404e9a5e6814de5f2203": { + "477e3bda05594962bd8ba0f1e3b7bb31": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1795,25 +1867,31 @@ "width": null } }, - "502164ca582c4a0bbb3b20dc28ae0b2c": { + "48016ee17d6b4133971e8d4b58046f98": { "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_cfe55f8de045405f86e66d60c9c1ce0c", + "IPY_MODEL_5f8acf7619d14a2494c2bd74f4c2d794", + "IPY_MODEL_ef11cb19d76d48a098b132ab8531c488" + ], + "layout": "IPY_MODEL_77ab3ba493da4bb68b8d39397523e2e4", + "tabbable": null, + "tooltip": null } }, - "505538aed9b54bebb4c3f0107daadf44": { + "499e72220e084b96a2fac6d7d24f3e36": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1866,71 +1944,7 @@ "width": null } }, - "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", - "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": { + "4d8b4c898e4d49c795640c857d8f9648": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1945,31 +1959,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6ef7ce225dc34f17b94e5dbe3a820e35", + "layout": "IPY_MODEL_ab29117829274449a8621cc4708da2b1", "placeholder": "​", - "style": "IPY_MODEL_a99583c6f0e24d69872cbda693c983eb", + "style": "IPY_MODEL_e6758addea6d44238157a9fee26b6f01", "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": "" + "value": " 665/665 [00:00<00:00, 137kB/s]" } }, - "6833c949ea36409b943f5aa9a751fe85": { + "4e0a66ed3e51494baf8c4960957f2abc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2022,7 +2020,7 @@ "width": null } }, - "6c0bd2a44f6e49e7bb1931ece0eab850": { + "4f6ef66a05864b318215d64d3df902a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2075,33 +2073,42 @@ "width": null } }, - "6e6cda23dd024255af57333cc9d9bfea": { + "5042f028d3a24a99a3cb8dbb370a2d51": { "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_80258d9522ac4e858c828ed1eba37fb6", - "placeholder": "​", - "style": "IPY_MODEL_2df9eaceb2e14e8fa3a19b25c7d76f35", - "tabbable": null, - "tooltip": null, - "value": " 391/391 [00:00<00:00, 67.9kB/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "6ed70f51e8b34c1094db85d132e5ce9c": { - "model_module": "@jupyter-widgets/base", + "51e4ce1aa08a4875b0078386841ef6c6": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "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": "" + } + }, + "5b6cedd62c3b4e2dba4fb6cf6ac81919": { + "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", @@ -2151,7 +2158,7 @@ "width": null } }, - "6ef7ce225dc34f17b94e5dbe3a820e35": { + "5dfa8539a6c948378badd6686cc253dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2204,7 +2211,83 @@ "width": null } }, - "7042493643924b098a59e80ef3a6125e": { + "5f8acf7619d14a2494c2bd74f4c2d794": { + "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_5dfa8539a6c948378badd6686cc253dd", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c9d0db8230f34b468c783b24537b3a87", + "tabbable": null, + "tooltip": null, + "value": 466062.0 + } + }, + "66867813190846ab8aed312ab6908a43": { + "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_4f6ef66a05864b318215d64d3df902a8", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b71a8e9e851b4881bb5145531e49ccd0", + "tabbable": null, + "tooltip": null, + "value": 231508.0 + } + }, + "697435a323a84bea9cc7bfb42e0a5e62": { + "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_dd67fec157b9489faa0dc3ddbd95ef35", + "IPY_MODEL_20199ed9391b4fd3af9ffba1bd110646", + "IPY_MODEL_41c9e037f3074a5d9b33b7014d85cdfe" + ], + "layout": "IPY_MODEL_b42bf80fe5274a5e908a3c39822c2629", + "tabbable": null, + "tooltip": null + } + }, + "71581c894a0e453e992f58ce4ec00848": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2257,25 +2340,30 @@ "width": null } }, - "7416ceea465444209f091f4e54c9aebe": { + "71c0454905f044549aa29c3961bc512b": { "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_16cb8599d6034cd1b810cbf99f703579", + "placeholder": "​", + "style": "IPY_MODEL_8e1757765b51437c9c3576da0b2a84d2", + "tabbable": null, + "tooltip": null, + "value": " 391/391 [00:00<00:00, 69.1kB/s]" } }, - "769b1ec27342400b9cb6e41638eaa9d4": { + "77ab3ba493da4bb68b8d39397523e2e4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2328,23 +2416,7 @@ "width": null } }, - "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": { + "787647896a374c4baef37ba555b3d758": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2397,7 +2469,7 @@ "width": null } }, - "7f092e8ede4e4d63a5fde7a51f92e32c": { + "790e13d2858c4cf4bdf4bce6bda99ddb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2450,30 +2522,7 @@ "width": null } }, - "7fd953032b654b6080e9188c21e62fc9": { - "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_c176725128c3447ebe665fa5ac3e316f", - "placeholder": "​", - "style": "IPY_MODEL_59de812fe1e841889d9b5bfc883f3b40", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 38.4MB/s]" - } - }, - "80258d9522ac4e858c828ed1eba37fb6": { + "7aa30d069f444999834643e94fe81c9b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2526,103 +2575,23 @@ "width": null } }, - "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": { + "8429e83ba7114915933be895d5f6fe3a": { "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_54ed34f093ee42d68b280d3bd01e5599", - "IPY_MODEL_c5eaef5240de4848ba66275755fe00f6", - "IPY_MODEL_7fd953032b654b6080e9188c21e62fc9" - ], - "layout": "IPY_MODEL_44b0dfbf62fd4d088800332e97908228", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "9f0bab6dcc71427ba6d39cfcb15d877f": { + "8c2f5adef8594b9d94b856fe69ecef0d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2675,7 +2644,43 @@ "width": null } }, - "a588c0dcd0974f8da33c7bdf3d4a35e8": { + "8d50a9bb17184cec871c72343c762cc2": { + "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 + } + }, + "8e1757765b51437c9c3576da0b2a84d2": { + "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 + } + }, + "93b428a1e9984e3985970ffd82b85429": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2690,33 +2695,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0da07d715efe402083873e0661e556ea", + "layout": "IPY_MODEL_8c2f5adef8594b9d94b856fe69ecef0d", "placeholder": "​", - "style": "IPY_MODEL_ae1e6cca03b34513818a3bd133ae6f5a", + "style": "IPY_MODEL_40a195a274ab4351b4d37e7880f857e7", "tabbable": null, "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 9.25kB/s]" + "value": "vocab.txt: 100%" } }, - "a99583c6f0e24d69872cbda693c983eb": { + "9588d449e7b24e6db9e90daa5078369e": { "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": "" } }, - "acbecbc8b02440768730f4625e2f605e": { + "967764ca5ac248399e557033cfdf7a08": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2734,7 +2737,7 @@ "text_color": null } }, - "ae1e6cca03b34513818a3bd133ae6f5a": { + "987bc5a0a5174818895b0e59be32726d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2752,8 +2755,32 @@ "text_color": null } }, - "b01dff0aaef24c71a79429be65e9ce07": { - "model_module": "@jupyter-widgets/base", + "98baa2df749f4718a19b8ed6f6b64516": { + "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_40fe431dbc09475d9cc4e82668dce66c", + "IPY_MODEL_2eda31c15639409c917ce8f30f06fea9", + "IPY_MODEL_71c0454905f044549aa29c3961bc512b" + ], + "layout": "IPY_MODEL_c4843a0ecf8947089a56431c777139d7", + "tabbable": null, + "tooltip": null + } + }, + "a215428c7e0d4fdda219b836792e0bea": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { @@ -2805,90 +2832,7 @@ "width": null } }, - "b65073e7ff2e476bb34d75a0faf77c6f": { - "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_7b0e3596ec8d47089daa31da5c13b681", - "placeholder": "​", - "style": "IPY_MODEL_502164ca582c4a0bbb3b20dc28ae0b2c", - "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" - } - }, - "b6e52d34f81d421d9eba7c8cc9b7847a": { - "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 - } - }, - "bc071c694fb7476a8213ad06a4cef625": { - "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 - } - }, - "bd1aa12a83f148a6a04b7394dd645fb3": { - "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_b65073e7ff2e476bb34d75a0faf77c6f", - "IPY_MODEL_38b1bd4b5ec44d41981a433be46d45f0", - "IPY_MODEL_9a092b3142994e27b931efc3d8141660" - ], - "layout": "IPY_MODEL_7f092e8ede4e4d63a5fde7a51f92e32c", - "tabbable": null, - "tooltip": null - } - }, - "c176725128c3447ebe665fa5ac3e316f": { + "ab29117829274449a8621cc4708da2b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2941,33 +2885,7 @@ "width": null } }, - "c18be04c279b46b48b79ee15e32435e6": { - "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_fabb2363238a4c1a91de78e13b8a0a3e", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6680b7e73b4d4bb589857c254d0632ef", - "tabbable": null, - "tooltip": null, - "value": 665.0 - } - }, - "c33f4e7c33ca4ebd8cb646737881a850": { + "b1a6862286e144a5b4c7e3b53955c909": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3020,57 +2938,25 @@ "width": null } }, - "c5eaef5240de4848ba66275755fe00f6": { - "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_07d79636c68343278e1f779c27aef268", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_fba1e85bf6444f50b1917a3eb9c35bcc", - "tabbable": null, - "tooltip": null, - "value": 231508.0 - } - }, - "c943f13df8c04e77aae4c7ca2cbbd613": { + "b2d0af45a331440e91bb670c810aad25": { "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_fcaf1661c4314a27a12b7c20cbce3cc8", - "IPY_MODEL_914b3f22f6b446608a80b2bd86dde07b", - "IPY_MODEL_6e6cda23dd024255af57333cc9d9bfea" - ], - "layout": "IPY_MODEL_de51505fe22c4b5fa21abe797f7a7d7a", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "cbb206b9c0d64fe0b479fb7cc9df570b": { + "b42bf80fe5274a5e908a3c39822c2629": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3123,7 +3009,23 @@ "width": null } }, - "ce7bece14db441b3b9a7a133c7e71a07": { + "b71a8e9e851b4881bb5145531e49ccd0": { + "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": "" + } + }, + "b876cea56c70415bbe96c6d929472c03": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3176,7 +3078,30 @@ "width": null } }, - "cefce6f533124c4e90d7c277319c65e9": { + "bb8dedc70e1e42f985ca245b4c1626d0": { + "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_477e3bda05594962bd8ba0f1e3b7bb31", + "placeholder": "​", + "style": "IPY_MODEL_dc42abfc89cd4333a4569bc9139f85a5", + "tabbable": null, + "tooltip": null, + "value": "config.json: 100%" + } + }, + "c4843a0ecf8947089a56431c777139d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3229,7 +3154,30 @@ "width": null } }, - "d5eda741ae08481f90152525ac295029": { + "c4ab79f47c1f4a5fa7a952d2f4243909": { + "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_3090f81fd63e47748b48b354dff579e6", + "placeholder": "​", + "style": "IPY_MODEL_229a059bf66a476caf93f93617098541", + "tabbable": null, + "tooltip": null, + "value": "tokenizer_config.json: 100%" + } + }, + "c9d0db8230f34b468c783b24537b3a87": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3245,7 +3193,71 @@ "description_width": "" } }, - "d76fd6c68456446baaae1b57ccf46065": { + "cc575b26e89c48eea0b3767999b7957a": { + "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 + } + }, + "cfd42f9c1e5b4b6e957253d074ff0a96": { + "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_b876cea56c70415bbe96c6d929472c03", + "placeholder": "​", + "style": "IPY_MODEL_987bc5a0a5174818895b0e59be32726d", + "tabbable": null, + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 9.30kB/s]" + } + }, + "cfe55f8de045405f86e66d60c9c1ce0c": { + "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_fe6d4bd14f1d4e9c889f127632dfd090", + "placeholder": "​", + "style": "IPY_MODEL_f1cd701a314345e8a946773e606e7e5e", + "tabbable": null, + "tooltip": null, + "value": "tokenizer.json: 100%" + } + }, + "d398c27b16594cdabb17dd18d91923db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3261,7 +3273,7 @@ "description_width": "" } }, - "d9d22d6398f34538ac40fff96fa8abaf": { + "d62a7bbd2b814718bdb6e5c898023432": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3277,17 +3289,58 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7042493643924b098a59e80ef3a6125e", - "max": 466062.0, + "layout": "IPY_MODEL_31425907e4ff47c1a17ed0a9935430d6", + "max": 665.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_7a60c769a9a94e748e35f240733664b3", + "style": "IPY_MODEL_5042f028d3a24a99a3cb8dbb370a2d51", "tabbable": null, "tooltip": null, - "value": 466062.0 + "value": 665.0 + } + }, + "dc42abfc89cd4333a4569bc9139f85a5": { + "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 + } + }, + "dd67fec157b9489faa0dc3ddbd95ef35": { + "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_71581c894a0e453e992f58ce4ec00848", + "placeholder": "​", + "style": "IPY_MODEL_8d50a9bb17184cec871c72343c762cc2", + "tabbable": null, + "tooltip": null, + "value": "README.md: 100%" } }, - "de51505fe22c4b5fa21abe797f7a7d7a": { + "e1ad008614fb43fdb5e809f4d8511a82": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3340,7 +3393,48 @@ "width": null } }, - "e0adbaa3ffa64032933136aebb9a1f7b": { + "e6758addea6d44238157a9fee26b6f01": { + "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 + } + }, + "e7f2051c6f3e4cb4afa29879fc4e25d7": { + "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_a215428c7e0d4fdda219b836792e0bea", + "placeholder": "​", + "style": "IPY_MODEL_967764ca5ac248399e557033cfdf7a08", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" + } + }, + "ea06e6ec819142448a354fc1926e13af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3355,15 +3449,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9f0bab6dcc71427ba6d39cfcb15d877f", + "layout": "IPY_MODEL_790e13d2858c4cf4bdf4bce6bda99ddb", "placeholder": "​", - "style": "IPY_MODEL_7416ceea465444209f091f4e54c9aebe", + "style": "IPY_MODEL_29ab7c4b24694381897b57b84ded9af9", "tabbable": null, "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 401kB/s]" + "value": " 232k/232k [00:00<00:00, 38.1MB/s]" } }, - "e32cd0a87f0f4ad18f72cc198abad4fd": { + "ef11cb19d76d48a098b132ab8531c488": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3378,15 +3472,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c33f4e7c33ca4ebd8cb646737881a850", + "layout": "IPY_MODEL_1a2f8352992a4e8d8667c62f94d7e0e0", "placeholder": "​", - "style": "IPY_MODEL_3bd0776bd0b840e8b2a1d15afd1e27f8", + "style": "IPY_MODEL_cc575b26e89c48eea0b3767999b7957a", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 118kB/s]" + "value": " 466k/466k [00:00<00:00, 35.2MB/s]" } }, - "e970db685471489481e8d686d97b3cc2": { + "f1cd701a314345e8a946773e606e7e5e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3404,136 +3498,81 @@ "text_color": null } }, - "ea436305a9d143d09b28e8d32eb3020a": { - "model_module": "@jupyter-widgets/base", + "f451a7bbcccb4c0ebd05353c6b2b00d8": { + "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/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": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_93b428a1e9984e3985970ffd82b85429", + "IPY_MODEL_66867813190846ab8aed312ab6908a43", + "IPY_MODEL_ea06e6ec819142448a354fc1926e13af" + ], + "layout": "IPY_MODEL_1f83772d100c412884fc22f69ab7e498", + "tabbable": null, + "tooltip": null } }, - "ef479a0478b644bbbd209856e893f0ed": { + "f717bfe4b6324a74be7f067e29025df4": { "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_6833c949ea36409b943f5aa9a751fe85", - "placeholder": "​", - "style": "IPY_MODEL_0d5dda5aa9e840a7969a3facb0c17f1f", + "layout": "IPY_MODEL_5b6cedd62c3b4e2dba4fb6cf6ac81919", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_51e4ce1aa08a4875b0078386841ef6c6", "tabbable": null, "tooltip": null, - "value": "tokenizer.json: 100%" + "value": 48.0 } }, - "f25b6a55df5d45bc80be73c91794168b": { - "model_module": "@jupyter-widgets/base", + "f7905c4a4ded402ca686067d0aacc292": { + "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/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": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_bb8dedc70e1e42f985ca245b4c1626d0", + "IPY_MODEL_d62a7bbd2b814718bdb6e5c898023432", + "IPY_MODEL_4d8b4c898e4d49c795640c857d8f9648" + ], + "layout": "IPY_MODEL_787647896a374c4baef37ba555b3d758", + "tabbable": null, + "tooltip": null } }, - "fabb2363238a4c1a91de78e13b8a0a3e": { + "fe6d4bd14f1d4e9c889f127632dfd090": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3585,45 +3624,6 @@ "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 4aeb3965a..3e2253eec 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": {"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} +{"state": {"65ccc00f12204532a50813004d34c322": {"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}}, "47c5d5c584084493b0861ab73b489d4c": {"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": ""}}, "d2c10c0d82484b1d9d529d2ef9dae7fb": {"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_65ccc00f12204532a50813004d34c322", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_47c5d5c584084493b0861ab73b489d4c", "tabbable": null, "tooltip": null, "value": 2041.0}}, "969d2813a5c04cacb6fc3a37ed2dc5fd": {"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}}, "191733153ce746d1a8ff2b837b57e9be": {"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}}, "5b0eb572f4a44010b0414f2c88c3cd1e": {"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_969d2813a5c04cacb6fc3a37ed2dc5fd", "placeholder": "\u200b", "style": "IPY_MODEL_191733153ce746d1a8ff2b837b57e9be", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "eacb5b752481476099c10dfacf56c89d": {"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}}, "f2d5653b2c4e48d493b970861348256b": {"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}}, "0eca11d8b9ca420cb317292d15607da7": {"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_eacb5b752481476099c10dfacf56c89d", "placeholder": "\u200b", "style": "IPY_MODEL_f2d5653b2c4e48d493b970861348256b", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007483kB/s]"}}, "09f9a3d816be47d78e516de2e21980b0": {"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}}, "869fc446c9a34743877fe8b5536872b1": {"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_5b0eb572f4a44010b0414f2c88c3cd1e", "IPY_MODEL_d2c10c0d82484b1d9d529d2ef9dae7fb", "IPY_MODEL_0eca11d8b9ca420cb317292d15607da7"], "layout": "IPY_MODEL_09f9a3d816be47d78e516de2e21980b0", "tabbable": null, "tooltip": null}}, "b911745838c24922badcd8ce2b74705c": {"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}}, "41f5c12b350f4abbb8805e5d672190d7": {"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": ""}}, "1c477dd84702413bafd29643b995393b": {"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_b911745838c24922badcd8ce2b74705c", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_41f5c12b350f4abbb8805e5d672190d7", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "e617db8fadf64d708385495d20c7702e": {"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}}, "8920ef4b3a4a44c59c581d700192ac74": {"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}}, "6d2f84d14e3a4939a1c606b1769f2052": {"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_e617db8fadf64d708385495d20c7702e", "placeholder": "\u200b", "style": "IPY_MODEL_8920ef4b3a4a44c59c581d700192ac74", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "5ce0ad1c70004fae993ca54a47dea1ac": {"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}}, "4b23757c53804abf826b76129c71f84f": {"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}}, "638ba26f976e403cb8fbdb569fd358cb": {"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_5ce0ad1c70004fae993ca54a47dea1ac", "placeholder": "\u200b", "style": "IPY_MODEL_4b23757c53804abf826b76129c71f84f", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u200762.7MB/s]"}}, "f0d5fce51b134affae64c7e5b3ff7cde": {"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}}, "40119d252d344b50ada2deb76d102ba9": {"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_6d2f84d14e3a4939a1c606b1769f2052", "IPY_MODEL_1c477dd84702413bafd29643b995393b", "IPY_MODEL_638ba26f976e403cb8fbdb569fd358cb"], "layout": "IPY_MODEL_f0d5fce51b134affae64c7e5b3ff7cde", "tabbable": null, "tooltip": null}}, "4d42b878a1484c2a8c218ceb2a870ce6": {"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}}, "3e86400f85a2412dab304636e64fc255": {"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": ""}}, "6339c8e626034d7c9fa8cff1415bcad2": {"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_4d42b878a1484c2a8c218ceb2a870ce6", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3e86400f85a2412dab304636e64fc255", "tabbable": null, "tooltip": null, "value": 3201.0}}, "a687a32ad4e94ddab8899265482ec95e": {"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}}, "ee82152228b343f5bd122b8ee22d3ade": {"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}}, "20439df6f4ec4c92a86ed7db1713c94a": {"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_a687a32ad4e94ddab8899265482ec95e", "placeholder": "\u200b", "style": "IPY_MODEL_ee82152228b343f5bd122b8ee22d3ade", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "28bb50bd3b734769b604cbc0566fe965": {"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}}, "035ed0ac88b843c7986fdcc6053c74ea": {"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}}, "3780dbf7cf624b0fbcc7e46393e604bd": {"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_28bb50bd3b734769b604cbc0566fe965", "placeholder": "\u200b", "style": "IPY_MODEL_035ed0ac88b843c7986fdcc6053c74ea", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007796kB/s]"}}, "968ea4aa03974c94a22690152df3a0b0": {"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}}, "bd27c6867d9a407d8723ff685bd632f8": {"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_20439df6f4ec4c92a86ed7db1713c94a", "IPY_MODEL_6339c8e626034d7c9fa8cff1415bcad2", "IPY_MODEL_3780dbf7cf624b0fbcc7e46393e604bd"], "layout": "IPY_MODEL_968ea4aa03974c94a22690152df3a0b0", "tabbable": null, "tooltip": null}}, "d749fc0d9649484f8df326a2bdb58a4f": {"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}}, "1a33cb48eeb1453993c6eaa84fc502fb": {"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": ""}}, "f9493a2b41bb4830b72c769a6924862e": {"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_d749fc0d9649484f8df326a2bdb58a4f", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_1a33cb48eeb1453993c6eaa84fc502fb", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "2a8eef61906f4e02b64d2acfa416df0a": {"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}}, "79f5954a72a84d65be69edeb832c92fd": {"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}}, "05ace592e52c4898960ac3a6834e81db": {"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_2a8eef61906f4e02b64d2acfa416df0a", "placeholder": "\u200b", "style": "IPY_MODEL_79f5954a72a84d65be69edeb832c92fd", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "53edd2fc08c34cd28b365529e38e14de": {"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}}, "3a43941544024cce8e00981fad13ea69": {"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}}, "0f2e81acb0c846a1bd10715932ea462b": {"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_53edd2fc08c34cd28b365529e38e14de", "placeholder": "\u200b", "style": "IPY_MODEL_3a43941544024cce8e00981fad13ea69", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u2007164MB/s]"}}, "f65ea041bc1b48afa70ae6281b147089": {"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}}, "baafd2ed9b6541319c792073a72f5e7d": {"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_05ace592e52c4898960ac3a6834e81db", "IPY_MODEL_f9493a2b41bb4830b72c769a6924862e", "IPY_MODEL_0f2e81acb0c846a1bd10715932ea462b"], "layout": "IPY_MODEL_f65ea041bc1b48afa70ae6281b147089", "tabbable": null, "tooltip": null}}, "746cf50561de4f28aec72152e2a74e40": {"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}}, "9ab4d21a4d204ce9aad54b4aa19ef440": {"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": ""}}, "76f9849e481f47fb9422fb236d93ee5c": {"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_746cf50561de4f28aec72152e2a74e40", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_9ab4d21a4d204ce9aad54b4aa19ef440", "tabbable": null, "tooltip": null, "value": 128619.0}}, "3af2e6f965024e0faef7da0c7564e4bc": {"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}}, "a1ecc93b0e124f82a9d890e1b8dc92fb": {"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}}, "6de7c25d6d174a3e92ebb9f11d808ecc": {"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_3af2e6f965024e0faef7da0c7564e4bc", "placeholder": "\u200b", "style": "IPY_MODEL_a1ecc93b0e124f82a9d890e1b8dc92fb", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "fac867a2e6ae475dba8300fafb622e27": {"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}}, "d7d5493534b94ec28876947b06bbca4c": {"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}}, "c41ac92cd3a44fa7b86ae4c7444f9d57": {"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_fac867a2e6ae475dba8300fafb622e27", "placeholder": "\u200b", "style": "IPY_MODEL_d7d5493534b94ec28876947b06bbca4c", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u200717.4MB/s]"}}, "a66d028f4eea4d9cb7a62da4d3f9af97": {"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}}, "306f5f497ce647e09871bff6b0c431fe": {"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_6de7c25d6d174a3e92ebb9f11d808ecc", "IPY_MODEL_76f9849e481f47fb9422fb236d93ee5c", "IPY_MODEL_c41ac92cd3a44fa7b86ae4c7444f9d57"], "layout": "IPY_MODEL_a66d028f4eea4d9cb7a62da4d3f9af97", "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 a4fd4545f..b77ccf17e 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": "2024-07-02T15:10:13.381463Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:14.546953Z", + "iopub.status.busy": "2024-07-02T15:25:14.546784Z", + "iopub.status.idle": "2024-07-02T15:25:19.407981Z", + "shell.execute_reply": "2024-07-02T15:25:19.407476Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:10:18.676864Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:19.410926Z", + "iopub.status.busy": "2024-07-02T15:25:19.410231Z", + "iopub.status.idle": "2024-07-02T15:25:19.413722Z", + "shell.execute_reply": "2024-07-02T15:25:19.413173Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:19.415856Z", + "iopub.status.busy": "2024-07-02T15:25:19.415529Z", + "iopub.status.idle": "2024-07-02T15:25:19.419947Z", + "shell.execute_reply": "2024-07-02T15:25:19.419526Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:19.422117Z", + "iopub.status.busy": "2024-07-02T15:25:19.421696Z", + "iopub.status.idle": "2024-07-02T15:25:21.022232Z", + "shell.execute_reply": "2024-07-02T15:25:21.021605Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:21.024570Z", + "iopub.status.busy": "2024-07-02T15:25:21.024378Z", + "iopub.status.idle": "2024-07-02T15:25:21.034610Z", + "shell.execute_reply": "2024-07-02T15:25:21.034162Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:21.036772Z", + "iopub.status.busy": "2024-07-02T15:25:21.036453Z", + "iopub.status.idle": "2024-07-02T15:25:21.041969Z", + "shell.execute_reply": "2024-07-02T15:25:21.041413Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:21.044032Z", + "iopub.status.busy": "2024-07-02T15:25:21.043619Z", + "iopub.status.idle": "2024-07-02T15:25:21.448517Z", + "shell.execute_reply": "2024-07-02T15:25:21.447933Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:21.450756Z", + "iopub.status.busy": "2024-07-02T15:25:21.450428Z", + "iopub.status.idle": "2024-07-02T15:25:22.369181Z", + "shell.execute_reply": "2024-07-02T15:25:22.368692Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:22.371664Z", + "iopub.status.busy": "2024-07-02T15:25:22.371311Z", + "iopub.status.idle": "2024-07-02T15:25:22.389436Z", + "shell.execute_reply": "2024-07-02T15:25:22.388994Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:22.391511Z", + "iopub.status.busy": "2024-07-02T15:25:22.391097Z", + "iopub.status.idle": "2024-07-02T15:25:22.394237Z", + "shell.execute_reply": "2024-07-02T15:25:22.393731Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:22.396091Z", + "iopub.status.busy": "2024-07-02T15:25:22.395918Z", + "iopub.status.idle": "2024-07-02T15:25:36.049207Z", + "shell.execute_reply": "2024-07-02T15:25:36.048690Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.051828Z", + "iopub.status.busy": "2024-07-02T15:25:36.051442Z", + "iopub.status.idle": "2024-07-02T15:25:36.055485Z", + "shell.execute_reply": "2024-07-02T15:25:36.055009Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.057411Z", + "iopub.status.busy": "2024-07-02T15:25:36.057242Z", + "iopub.status.idle": "2024-07-02T15:25:36.769000Z", + "shell.execute_reply": "2024-07-02T15:25:36.768447Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.772680Z", + "iopub.status.busy": "2024-07-02T15:25:36.771734Z", + "iopub.status.idle": "2024-07-02T15:25:36.778368Z", + "shell.execute_reply": "2024-07-02T15:25:36.777878Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.781861Z", + "iopub.status.busy": "2024-07-02T15:25:36.780939Z", + "iopub.status.idle": "2024-07-02T15:25:36.879710Z", + "shell.execute_reply": "2024-07-02T15:25:36.879109Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.881988Z", + "iopub.status.busy": "2024-07-02T15:25:36.881620Z", + "iopub.status.idle": "2024-07-02T15:25:36.894208Z", + "shell.execute_reply": "2024-07-02T15:25:36.893742Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.896273Z", + "iopub.status.busy": "2024-07-02T15:25:36.895955Z", + "iopub.status.idle": "2024-07-02T15:25:36.903547Z", + "shell.execute_reply": "2024-07-02T15:25:36.903009Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.905350Z", + "iopub.status.busy": "2024-07-02T15:25:36.905182Z", + "iopub.status.idle": "2024-07-02T15:25:36.909462Z", + "shell.execute_reply": "2024-07-02T15:25:36.908923Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.911557Z", + "iopub.status.busy": "2024-07-02T15:25:36.911268Z", + "iopub.status.idle": "2024-07-02T15:25:36.916706Z", + "shell.execute_reply": "2024-07-02T15:25:36.916183Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:36.918893Z", + "iopub.status.busy": "2024-07-02T15:25:36.918579Z", + "iopub.status.idle": "2024-07-02T15:25:37.028414Z", + "shell.execute_reply": "2024-07-02T15:25:37.027859Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:37.030519Z", + "iopub.status.busy": "2024-07-02T15:25:37.030189Z", + "iopub.status.idle": "2024-07-02T15:25:37.129978Z", + "shell.execute_reply": "2024-07-02T15:25:37.129407Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:37.132132Z", + "iopub.status.busy": "2024-07-02T15:25:37.131812Z", + "iopub.status.idle": "2024-07-02T15:25:37.230271Z", + "shell.execute_reply": "2024-07-02T15:25:37.229796Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:37.232307Z", + "iopub.status.busy": "2024-07-02T15:25:37.231996Z", + "iopub.status.idle": "2024-07-02T15:25:37.334860Z", + "shell.execute_reply": "2024-07-02T15:25:37.334301Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:37.337103Z", + "iopub.status.busy": "2024-07-02T15:25:37.336778Z", + "iopub.status.idle": "2024-07-02T15:25:37.339748Z", + "shell.execute_reply": "2024-07-02T15:25:37.339340Z" }, "nbsphinx": "hidden" }, @@ -1392,53 +1392,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "026a8d3d502347a290f99b02f8529d28": { + "035ed0ac88b843c7986fdcc6053c74ea": { "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", + "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_86ca63a102954afa94795c3e61c20880", - "placeholder": "​", - "style": "IPY_MODEL_e9b3119fe459439e8146cddd6880a9f1", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "0dfc7f988177492e852e1a1e2e2e3c55": { + "05ace592e52c4898960ac3a6834e81db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1453,15 +1425,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d16b4b3c59ba49d9a539a98b5d9ace25", + "layout": "IPY_MODEL_2a8eef61906f4e02b64d2acfa416df0a", "placeholder": "​", - "style": "IPY_MODEL_8712797b65e1448f8a93f64e6e581af5", + "style": "IPY_MODEL_79f5954a72a84d65be69edeb832c92fd", "tabbable": null, "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 502kB/s]" + "value": "classifier.ckpt: 100%" } }, - "0fb76291c4d34ebdba9ffa24ed513e0a": { + "09f9a3d816be47d78e516de2e21980b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1514,113 +1486,87 @@ "width": null } }, - "0fca732af2064a0da3c0e7502ad49282": { - "model_module": "@jupyter-widgets/base", + "0eca11d8b9ca420cb317292d15607da7": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_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_eacb5b752481476099c10dfacf56c89d", + "placeholder": "​", + "style": "IPY_MODEL_f2d5653b2c4e48d493b970861348256b", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 483kB/s]" + } + }, + "0f2e81acb0c846a1bd10715932ea462b": { + "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_53edd2fc08c34cd28b365529e38e14de", + "placeholder": "​", + "style": "IPY_MODEL_3a43941544024cce8e00981fad13ea69", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 164MB/s]" + } + }, + "191733153ce746d1a8ff2b837b57e9be": { + "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": "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 } }, - "1b8db158701349408c5bdef740765f91": { - "model_module": "@jupyter-widgets/base", + "1a33cb48eeb1453993c6eaa84fc502fb": { + "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": "" } }, - "23ef0aabeddc46538e90abc8e42e36c0": { + "1c477dd84702413bafd29643b995393b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1636,17 +1582,40 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5d471f2ed23e49dbb159b32a3c2d406e", + "layout": "IPY_MODEL_b911745838c24922badcd8ce2b74705c", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_511baacd1c3e4816bbe0b13284bf3fdd", + "style": "IPY_MODEL_41f5c12b350f4abbb8805e5d672190d7", "tabbable": null, "tooltip": null, "value": 16887676.0 } }, - "299896a97d41456cac583393c5611a30": { + "20439df6f4ec4c92a86ed7db1713c94a": { + "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_a687a32ad4e94ddab8899265482ec95e", + "placeholder": "​", + "style": "IPY_MODEL_ee82152228b343f5bd122b8ee22d3ade", + "tabbable": null, + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "28bb50bd3b734769b604cbc0566fe965": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1699,7 +1668,7 @@ "width": null } }, - "3c2e1352b6dd4da68e969419d9ecdaea": { + "2a8eef61906f4e02b64d2acfa416df0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1752,49 +1721,72 @@ "width": null } }, - "44dd265825e9454e868ab092b351a312": { + "306f5f497ce647e09871bff6b0c431fe": { "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_6de7c25d6d174a3e92ebb9f11d808ecc", + "IPY_MODEL_76f9849e481f47fb9422fb236d93ee5c", + "IPY_MODEL_c41ac92cd3a44fa7b86ae4c7444f9d57" + ], + "layout": "IPY_MODEL_a66d028f4eea4d9cb7a62da4d3f9af97", + "tabbable": null, + "tooltip": null } }, - "4526bd738aca4e52beef9dbf333987c6": { + "3780dbf7cf624b0fbcc7e46393e604bd": { "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_6a601518732a456897e40425c7447332", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8063b86fd1644501bd7a35a8f8c91e8d", + "layout": "IPY_MODEL_28bb50bd3b734769b604cbc0566fe965", + "placeholder": "​", + "style": "IPY_MODEL_035ed0ac88b843c7986fdcc6053c74ea", "tabbable": null, "tooltip": null, - "value": 15856877.0 + "value": " 3.20k/3.20k [00:00<00:00, 796kB/s]" + } + }, + "3a43941544024cce8e00981fad13ea69": { + "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 } }, - "4a111d06ad644ff3b7dfcd8f9a93b27f": { + "3af2e6f965024e0faef7da0c7564e4bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1847,7 +1839,7 @@ "width": null } }, - "511baacd1c3e4816bbe0b13284bf3fdd": { + "3e86400f85a2412dab304636e64fc255": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1863,7 +1855,7 @@ "description_width": "" } }, - "562674bc865d4b9c866de03e113283d6": { + "40119d252d344b50ada2deb76d102ba9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1878,27 +1870,77 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_026a8d3d502347a290f99b02f8529d28", - "IPY_MODEL_4526bd738aca4e52beef9dbf333987c6", - "IPY_MODEL_72f97078b8fc4a6d9b507a102418aea1" + "IPY_MODEL_6d2f84d14e3a4939a1c606b1769f2052", + "IPY_MODEL_1c477dd84702413bafd29643b995393b", + "IPY_MODEL_638ba26f976e403cb8fbdb569fd358cb" ], - "layout": "IPY_MODEL_cdbef2f290304f54be4b5de93eb10cc1", + "layout": "IPY_MODEL_f0d5fce51b134affae64c7e5b3ff7cde", "tabbable": null, "tooltip": null } }, - "5940aac0c62e430f99781eb883247639": { - "model_module": "@jupyter-widgets/base", + "41f5c12b350f4abbb8805e5d672190d7": { + "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", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "47c5d5c584084493b0861ab73b489d4c": { + "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": "" + } + }, + "4b23757c53804abf826b76129c71f84f": { + "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 + } + }, + "4d42b878a1484c2a8c218ceb2a870ce6": { + "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, @@ -1940,7 +1982,7 @@ "width": null } }, - "5d471f2ed23e49dbb159b32a3c2d406e": { + "53edd2fc08c34cd28b365529e38e14de": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1993,49 +2035,7 @@ "width": null } }, - "5fd920ec2856445d98a6f7e8f0229c4d": { - "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_a5ba789c57d244a6afdd80d9221598a2", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ba0d6ceeadc14ee192ae1dc31b4e4186", - "tabbable": null, - "tooltip": null, - "value": 128619.0 - } - }, - "60b559ea05d04ab2aefd425629200a9b": { - "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": "" - } - }, - "60d05f62061f46e7b7f871ccf49bce6e": { + "5b0eb572f4a44010b0414f2c88c3cd1e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2050,88 +2050,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_795a99fe1a5a4fe28962d2835b6d0806", + "layout": "IPY_MODEL_969d2813a5c04cacb6fc3a37ed2dc5fd", "placeholder": "​", - "style": "IPY_MODEL_ef52679868914ac0a1bac86a98893d61", - "tabbable": null, - "tooltip": null, - "value": " 129k/129k [00:00<00:00, 11.1MB/s]" - } - }, - "618905a858344a0488c8d600292e4cc3": { - "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_7b05aea7bfdd48ddbe010dce6c119c9a", - "placeholder": "​", - "style": "IPY_MODEL_d1a86a88d50a4bb2aee04ac90b0d069a", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 790kB/s]" - } - }, - "678fc99edb84497dbac731cf9a1fa79f": { - "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_df22226a67914b8a9d0693b25dffdc60", - "IPY_MODEL_7f009f8c42734f8e9fa9ea99cf520312", - "IPY_MODEL_618905a858344a0488c8d600292e4cc3" - ], - "layout": "IPY_MODEL_bf8a89f9ccd04b1a857340ebae05002e", - "tabbable": null, - "tooltip": null - } - }, - "6816c11c8cd945968d5b0ce5adfb87f5": { - "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_4a111d06ad644ff3b7dfcd8f9a93b27f", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_60b559ea05d04ab2aefd425629200a9b", + "style": "IPY_MODEL_191733153ce746d1a8ff2b837b57e9be", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": "hyperparams.yaml: 100%" } }, - "6a601518732a456897e40425c7447332": { + "5ce0ad1c70004fae993ca54a47dea1ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2184,25 +2111,33 @@ "width": null } }, - "6b7cf1b25d364dbab4d57e7ee83bbf50": { + "6339c8e626034d7c9fa8cff1415bcad2": { "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_4d42b878a1484c2a8c218ceb2a870ce6", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3e86400f85a2412dab304636e64fc255", + "tabbable": null, + "tooltip": null, + "value": 3201.0 } }, - "72f97078b8fc4a6d9b507a102418aea1": { + "638ba26f976e403cb8fbdb569fd358cb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2217,15 +2152,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1b8db158701349408c5bdef740765f91", + "layout": "IPY_MODEL_5ce0ad1c70004fae993ca54a47dea1ac", "placeholder": "​", - "style": "IPY_MODEL_c0c1b2f629d145fa8b43d2ac3c1494a6", + "style": "IPY_MODEL_4b23757c53804abf826b76129c71f84f", "tabbable": null, "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 176MB/s]" + "value": " 16.9M/16.9M [00:00<00:00, 62.7MB/s]" } }, - "795a99fe1a5a4fe28962d2835b6d0806": { + "65ccc00f12204532a50813004d34c322": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2278,60 +2213,53 @@ "width": null } }, - "7ab9a8d4e3e64a0f90d15eb2e77ce38b": { - "model_module": "@jupyter-widgets/base", + "6d2f84d14e3a4939a1c606b1769f2052": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_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": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e617db8fadf64d708385495d20c7702e", + "placeholder": "​", + "style": "IPY_MODEL_8920ef4b3a4a44c59c581d700192ac74", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "6de7c25d6d174a3e92ebb9f11d808ecc": { + "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_3af2e6f965024e0faef7da0c7564e4bc", + "placeholder": "​", + "style": "IPY_MODEL_a1ecc93b0e124f82a9d890e1b8dc92fb", + "tabbable": null, + "tooltip": null, + "value": "label_encoder.txt: 100%" } }, - "7b05aea7bfdd48ddbe010dce6c119c9a": { + "746cf50561de4f28aec72152e2a74e40": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2384,7 +2312,7 @@ "width": null } }, - "7f009f8c42734f8e9fa9ea99cf520312": { + "76f9849e481f47fb9422fb236d93ee5c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2400,33 +2328,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_fd00c0575ed7437ca5aabc5c08229d32", - "max": 3201.0, + "layout": "IPY_MODEL_746cf50561de4f28aec72152e2a74e40", + "max": 128619.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_44dd265825e9454e868ab092b351a312", + "style": "IPY_MODEL_9ab4d21a4d204ce9aad54b4aa19ef440", "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": "" + "value": 128619.0 } }, - "84fa5f8c2433478c9dc01b8a41eb48d5": { + "79f5954a72a84d65be69edeb832c92fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2444,7 +2356,7 @@ "text_color": null } }, - "8531becaffca44989779b1d37c65260e": { + "869fc446c9a34743877fe8b5536872b1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2459,39 +2371,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_85e7b3e36e7c47129e29727d2f646f2f", - "IPY_MODEL_6816c11c8cd945968d5b0ce5adfb87f5", - "IPY_MODEL_0dfc7f988177492e852e1a1e2e2e3c55" + "IPY_MODEL_5b0eb572f4a44010b0414f2c88c3cd1e", + "IPY_MODEL_d2c10c0d82484b1d9d529d2ef9dae7fb", + "IPY_MODEL_0eca11d8b9ca420cb317292d15607da7" ], - "layout": "IPY_MODEL_a3dbbfce424340438202c2a4f54314d7", + "layout": "IPY_MODEL_09f9a3d816be47d78e516de2e21980b0", "tabbable": null, "tooltip": null } }, - "85e7b3e36e7c47129e29727d2f646f2f": { - "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_d116ac8503dc4fa19dbf254269e57e81", - "placeholder": "​", - "style": "IPY_MODEL_865a2e4e16fe4c8fab1e00a4c6cd135d", - "tabbable": null, - "tooltip": null, - "value": "hyperparams.yaml: 100%" - } - }, - "865a2e4e16fe4c8fab1e00a4c6cd135d": { + "8920ef4b3a4a44c59c581d700192ac74": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2509,7 +2398,60 @@ "text_color": null } }, - "86ca63a102954afa94795c3e61c20880": { + "968ea4aa03974c94a22690152df3a0b0": { + "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 + } + }, + "969d2813a5c04cacb6fc3a37ed2dc5fd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2562,25 +2504,23 @@ "width": null } }, - "8712797b65e1448f8a93f64e6e581af5": { + "9ab4d21a4d204ce9aad54b4aa19ef440": { "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": "" } }, - "97d74ea76f524faea7cd1c349d25ed2d": { + "a1ecc93b0e124f82a9d890e1b8dc92fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2598,7 +2538,7 @@ "text_color": null } }, - "a3dbbfce424340438202c2a4f54314d7": { + "a66d028f4eea4d9cb7a62da4d3f9af97": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2651,30 +2591,60 @@ "width": null } }, - "a408ccf89ec14eceb847510634138186": { - "model_module": "@jupyter-widgets/controls", + "a687a32ad4e94ddab8899265482ec95e": { + "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_299896a97d41456cac583393c5611a30", - "placeholder": "​", - "style": "IPY_MODEL_6b7cf1b25d364dbab4d57e7ee83bbf50", - "tabbable": null, - "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 190MB/s]" + "_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 } }, - "a5ba789c57d244a6afdd80d9221598a2": { + "b911745838c24922badcd8ce2b74705c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2727,7 +2697,7 @@ "width": null } }, - "ad707c12b59d4885be4f947c794ca0ea": { + "baafd2ed9b6541319c792073a72f5e7d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2742,32 +2712,40 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0c9f4529a42246ee88b9b31ac1212ba1", - "IPY_MODEL_23ef0aabeddc46538e90abc8e42e36c0", - "IPY_MODEL_a408ccf89ec14eceb847510634138186" + "IPY_MODEL_05ace592e52c4898960ac3a6834e81db", + "IPY_MODEL_f9493a2b41bb4830b72c769a6924862e", + "IPY_MODEL_0f2e81acb0c846a1bd10715932ea462b" ], - "layout": "IPY_MODEL_3c2e1352b6dd4da68e969419d9ecdaea", + "layout": "IPY_MODEL_f65ea041bc1b48afa70ae6281b147089", "tabbable": null, "tooltip": null } }, - "ba0d6ceeadc14ee192ae1dc31b4e4186": { + "bd27c6867d9a407d8723ff685bd632f8": { "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_20439df6f4ec4c92a86ed7db1713c94a", + "IPY_MODEL_6339c8e626034d7c9fa8cff1415bcad2", + "IPY_MODEL_3780dbf7cf624b0fbcc7e46393e604bd" + ], + "layout": "IPY_MODEL_968ea4aa03974c94a22690152df3a0b0", + "tabbable": null, + "tooltip": null } }, - "bc115d8e973a43f98d22e93fcdeb0d0a": { + "c41ac92cd3a44fa7b86ae4c7444f9d57": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2782,15 +2760,41 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0fb76291c4d34ebdba9ffa24ed513e0a", + "layout": "IPY_MODEL_fac867a2e6ae475dba8300fafb622e27", "placeholder": "​", - "style": "IPY_MODEL_97d74ea76f524faea7cd1c349d25ed2d", + "style": "IPY_MODEL_d7d5493534b94ec28876947b06bbca4c", "tabbable": null, "tooltip": null, - "value": "label_encoder.txt: 100%" + "value": " 129k/129k [00:00<00:00, 17.4MB/s]" + } + }, + "d2c10c0d82484b1d9d529d2ef9dae7fb": { + "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_65ccc00f12204532a50813004d34c322", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_47c5d5c584084493b0861ab73b489d4c", + "tabbable": null, + "tooltip": null, + "value": 2041.0 } }, - "bf8a89f9ccd04b1a857340ebae05002e": { + "d749fc0d9649484f8df326a2bdb58a4f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2843,31 +2847,7 @@ "width": null } }, - "c0a1f533272a4f6cb65d54cbace4208e": { - "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_bc115d8e973a43f98d22e93fcdeb0d0a", - "IPY_MODEL_5fd920ec2856445d98a6f7e8f0229c4d", - "IPY_MODEL_60d05f62061f46e7b7f871ccf49bce6e" - ], - "layout": "IPY_MODEL_7ab9a8d4e3e64a0f90d15eb2e77ce38b", - "tabbable": null, - "tooltip": null - } - }, - "c0c1b2f629d145fa8b43d2ac3c1494a6": { + "d7d5493534b94ec28876947b06bbca4c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2885,7 +2865,7 @@ "text_color": null } }, - "cdbef2f290304f54be4b5de93eb10cc1": { + "e617db8fadf64d708385495d20c7702e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2938,7 +2918,7 @@ "width": null } }, - "d116ac8503dc4fa19dbf254269e57e81": { + "eacb5b752481476099c10dfacf56c89d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2991,7 +2971,25 @@ "width": null } }, - "d16b4b3c59ba49d9a539a98b5d9ace25": { + "ee82152228b343f5bd122b8ee22d3ade": { + "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 + } + }, + "f0d5fce51b134affae64c7e5b3ff7cde": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3044,48 +3042,7 @@ "width": null } }, - "d1a86a88d50a4bb2aee04ac90b0d069a": { - "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 - } - }, - "df22226a67914b8a9d0693b25dffdc60": { - "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_5940aac0c62e430f99781eb883247639", - "placeholder": "​", - "style": "IPY_MODEL_84fa5f8c2433478c9dc01b8a41eb48d5", - "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "e9b3119fe459439e8146cddd6880a9f1": { + "f2d5653b2c4e48d493b970861348256b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3103,43 +3060,86 @@ "text_color": null } }, - "ef52679868914ac0a1bac86a98893d61": { - "model_module": "@jupyter-widgets/controls", + "f65ea041bc1b48afa70ae6281b147089": { + "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 } }, - "fb83ceda077c4e68aad24838206b09cc": { + "f9493a2b41bb4830b72c769a6924862e": { "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_d749fc0d9649484f8df326a2bdb58a4f", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1a33cb48eeb1453993c6eaa84fc502fb", + "tabbable": null, + "tooltip": null, + "value": 15856877.0 } }, - "fd00c0575ed7437ca5aabc5c08229d32": { + "fac867a2e6ae475dba8300fafb622e27": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html index a88328502..151566cfc 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": {"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} +{"state": {"6672342c648a498e8e29b58fe5564cd9": {"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}}, "50f19109358b42a8bd1e48290795156f": {"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": ""}}, "3a0a9f8d4e1b42459e15f37672591429": {"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_6672342c648a498e8e29b58fe5564cd9", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_50f19109358b42a8bd1e48290795156f", "tabbable": null, "tooltip": null, "value": 132.0}}, "d8843a6d5b2347648570fd72914f0670": {"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}}, "dc282718b936425ab24a89562150ba2f": {"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}}, "2b1e6fc670724ce38421474a588fa03c": {"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_d8843a6d5b2347648570fd72914f0670", "placeholder": "\u200b", "style": "IPY_MODEL_dc282718b936425ab24a89562150ba2f", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "0d51225159934baaac34694b19a43d65": {"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}}, "a5b5d4b4892e4c63a5e61e1415c83270": {"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}}, "4b2b2a28121d445c839398dd3d1fda37": {"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_0d51225159934baaac34694b19a43d65", "placeholder": "\u200b", "style": "IPY_MODEL_a5b5d4b4892e4c63a5e61e1415c83270", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200713229.66\u2007examples/s]"}}, "cdee337a805b485c97ee06232ebd5b25": {"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}}, "ffa60db8cf7d41c485a3bb13eb857c21": {"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_2b1e6fc670724ce38421474a588fa03c", "IPY_MODEL_3a0a9f8d4e1b42459e15f37672591429", "IPY_MODEL_4b2b2a28121d445c839398dd3d1fda37"], "layout": "IPY_MODEL_cdee337a805b485c97ee06232ebd5b25", "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 0a658abc0..0f238c16e 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": "2024-07-02T15:10:41.435250Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:40.329888Z", + "iopub.status.busy": "2024-07-02T15:25:40.329681Z", + "iopub.status.idle": "2024-07-02T15:25:41.468704Z", + "shell.execute_reply": "2024-07-02T15:25:41.468091Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:10:42.619570Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:41.471552Z", + "iopub.status.busy": "2024-07-02T15:25:41.471147Z", + "iopub.status.idle": "2024-07-02T15:25:41.474151Z", + "shell.execute_reply": "2024-07-02T15:25:41.473613Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:41.476320Z", + "iopub.status.busy": "2024-07-02T15:25:41.475903Z", + "iopub.status.idle": "2024-07-02T15:25:41.484450Z", + "shell.execute_reply": "2024-07-02T15:25:41.483895Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:41.486500Z", + "iopub.status.busy": "2024-07-02T15:25:41.486077Z", + "iopub.status.idle": "2024-07-02T15:25:41.490609Z", + "shell.execute_reply": "2024-07-02T15:25:41.490062Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:41.492643Z", + "iopub.status.busy": "2024-07-02T15:25:41.492320Z", + "iopub.status.idle": "2024-07-02T15:25:41.674964Z", + "shell.execute_reply": "2024-07-02T15:25:41.674459Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:41.677160Z", + "iopub.status.busy": "2024-07-02T15:25:41.676819Z", + "iopub.status.idle": "2024-07-02T15:25:42.040724Z", + "shell.execute_reply": "2024-07-02T15:25:42.040194Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:42.042978Z", + "iopub.status.busy": "2024-07-02T15:25:42.042640Z", + "iopub.status.idle": "2024-07-02T15:25:42.065518Z", + "shell.execute_reply": "2024-07-02T15:25:42.064981Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:42.067639Z", + "iopub.status.busy": "2024-07-02T15:25:42.067331Z", + "iopub.status.idle": "2024-07-02T15:25:42.078083Z", + "shell.execute_reply": "2024-07-02T15:25:42.077538Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:42.079921Z", + "iopub.status.busy": "2024-07-02T15:25:42.079754Z", + "iopub.status.idle": "2024-07-02T15:25:44.012829Z", + "shell.execute_reply": "2024-07-02T15:25:44.012212Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.015522Z", + "iopub.status.busy": "2024-07-02T15:25:44.014996Z", + "iopub.status.idle": "2024-07-02T15:25:44.035612Z", + "shell.execute_reply": "2024-07-02T15:25:44.035047Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.037621Z", + "iopub.status.busy": "2024-07-02T15:25:44.037319Z", + "iopub.status.idle": "2024-07-02T15:25:44.054645Z", + "shell.execute_reply": "2024-07-02T15:25:44.054093Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.056426Z", + "iopub.status.busy": "2024-07-02T15:25:44.056261Z", + "iopub.status.idle": "2024-07-02T15:25:44.070971Z", + "shell.execute_reply": "2024-07-02T15:25:44.070438Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.073135Z", + "iopub.status.busy": "2024-07-02T15:25:44.072826Z", + "iopub.status.idle": "2024-07-02T15:25:44.091424Z", + "shell.execute_reply": "2024-07-02T15:25:44.090905Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "850cf9c5dd514c5c8c5878e12d30f5ca", + "model_id": "ffa60db8cf7d41c485a3bb13eb857c21", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.093259Z", + "iopub.status.busy": "2024-07-02T15:25:44.093091Z", + "iopub.status.idle": "2024-07-02T15:25:44.107694Z", + "shell.execute_reply": "2024-07-02T15:25:44.107176Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.109743Z", + "iopub.status.busy": "2024-07-02T15:25:44.109410Z", + "iopub.status.idle": "2024-07-02T15:25:44.115034Z", + "shell.execute_reply": "2024-07-02T15:25:44.114606Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:44.117072Z", + "iopub.status.busy": "2024-07-02T15:25:44.116754Z", + "iopub.status.idle": "2024-07-02T15:25:44.134358Z", + "shell.execute_reply": "2024-07-02T15:25:44.133820Z" } }, "outputs": [ @@ -1447,33 +1447,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "07b42c7871184a77913db05041f70f6c": { - "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_e5651455523845919804bfd3f20d32fd", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_da3ba2f2d038490c8a65361852a477f2", - "tabbable": null, - "tooltip": null, - "value": 132.0 - } - }, - "81406c4c29884619bacbf6314e1bb90e": { + "0d51225159934baaac34694b19a43d65": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1526,49 +1500,56 @@ "width": null } }, - "850cf9c5dd514c5c8c5878e12d30f5ca": { + "2b1e6fc670724ce38421474a588fa03c": { "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_b58f2ef4defc4008a4fb84b628c0df08", - "IPY_MODEL_07b42c7871184a77913db05041f70f6c", - "IPY_MODEL_ef016c3dc0df4a9a878a4f9644a436dd" - ], - "layout": "IPY_MODEL_f8cacbb114a946fb8b37956128a62704", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d8843a6d5b2347648570fd72914f0670", + "placeholder": "​", + "style": "IPY_MODEL_dc282718b936425ab24a89562150ba2f", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" } }, - "a43777fd323b46498d1b65ddfdcb03d7": { + "3a0a9f8d4e1b42459e15f37672591429": { "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_6672342c648a498e8e29b58fe5564cd9", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_50f19109358b42a8bd1e48290795156f", + "tabbable": null, + "tooltip": null, + "value": 132.0 } }, - "b58f2ef4defc4008a4fb84b628c0df08": { + "4b2b2a28121d445c839398dd3d1fda37": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1583,33 +1564,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_eae1af9f890445fab406fb6b04a570ff", + "layout": "IPY_MODEL_0d51225159934baaac34694b19a43d65", "placeholder": "​", - "style": "IPY_MODEL_b7fb3ecf4b354ed5aa31f23e9c2d7f20", + "style": "IPY_MODEL_a5b5d4b4892e4c63a5e61e1415c83270", "tabbable": null, "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" - } - }, - "b7fb3ecf4b354ed5aa31f23e9c2d7f20": { - "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": " 132/132 [00:00<00:00, 13229.66 examples/s]" } }, - "da3ba2f2d038490c8a65361852a477f2": { + "50f19109358b42a8bd1e48290795156f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1625,7 +1588,7 @@ "description_width": "" } }, - "e5651455523845919804bfd3f20d32fd": { + "6672342c648a498e8e29b58fe5564cd9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1678,7 +1641,25 @@ "width": null } }, - "eae1af9f890445fab406fb6b04a570ff": { + "a5b5d4b4892e4c63a5e61e1415c83270": { + "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 + } + }, + "cdee337a805b485c97ee06232ebd5b25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1731,30 +1712,7 @@ "width": null } }, - "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": { + "d8843a6d5b2347648570fd72914f0670": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1806,6 +1764,48 @@ "visibility": null, "width": null } + }, + "dc282718b936425ab24a89562150ba2f": { + "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 + } + }, + "ffa60db8cf7d41c485a3bb13eb857c21": { + "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_2b1e6fc670724ce38421474a588fa03c", + "IPY_MODEL_3a0a9f8d4e1b42459e15f37672591429", + "IPY_MODEL_4b2b2a28121d445c839398dd3d1fda37" + ], + "layout": "IPY_MODEL_cdee337a805b485c97ee06232ebd5b25", + "tabbable": null, + "tooltip": null + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index cf7301700..f24727539 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": "2024-07-02T15:10:48.203913Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:46.607904Z", + "iopub.status.busy": "2024-07-02T15:25:46.607488Z", + "iopub.status.idle": "2024-07-02T15:25:47.726756Z", + "shell.execute_reply": "2024-07-02T15:25:47.726167Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:10:49.373236Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:47.729301Z", + "iopub.status.busy": "2024-07-02T15:25:47.728883Z", + "iopub.status.idle": "2024-07-02T15:25:47.731886Z", + "shell.execute_reply": "2024-07-02T15:25:47.731441Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:47.734139Z", + "iopub.status.busy": "2024-07-02T15:25:47.733736Z", + "iopub.status.idle": "2024-07-02T15:25:47.742361Z", + "shell.execute_reply": "2024-07-02T15:25:47.741931Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:47.744536Z", + "iopub.status.busy": "2024-07-02T15:25:47.744028Z", + "iopub.status.idle": "2024-07-02T15:25:47.748673Z", + "shell.execute_reply": "2024-07-02T15:25:47.748258Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:47.750739Z", + "iopub.status.busy": "2024-07-02T15:25:47.750428Z", + "iopub.status.idle": "2024-07-02T15:25:47.928460Z", + "shell.execute_reply": "2024-07-02T15:25:47.927961Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:47.930585Z", + "iopub.status.busy": "2024-07-02T15:25:47.930309Z", + "iopub.status.idle": "2024-07-02T15:25:48.243872Z", + "shell.execute_reply": "2024-07-02T15:25:48.243309Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:48.245977Z", + "iopub.status.busy": "2024-07-02T15:25:48.245615Z", + "iopub.status.idle": "2024-07-02T15:25:48.248189Z", + "shell.execute_reply": "2024-07-02T15:25:48.247774Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:48.250260Z", + "iopub.status.busy": "2024-07-02T15:25:48.249944Z", + "iopub.status.idle": "2024-07-02T15:25:48.283563Z", + "shell.execute_reply": "2024-07-02T15:25:48.283148Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:48.285612Z", + "iopub.status.busy": "2024-07-02T15:25:48.285304Z", + "iopub.status.idle": "2024-07-02T15:25:50.230973Z", + "shell.execute_reply": "2024-07-02T15:25:50.230329Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.233409Z", + "iopub.status.busy": "2024-07-02T15:25:50.233125Z", + "iopub.status.idle": "2024-07-02T15:25:50.251378Z", + "shell.execute_reply": "2024-07-02T15:25:50.250838Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.253418Z", + "iopub.status.busy": "2024-07-02T15:25:50.253081Z", + "iopub.status.idle": "2024-07-02T15:25:50.259217Z", + "shell.execute_reply": "2024-07-02T15:25:50.258792Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.261072Z", + "iopub.status.busy": "2024-07-02T15:25:50.260808Z", + "iopub.status.idle": "2024-07-02T15:25:50.266744Z", + "shell.execute_reply": "2024-07-02T15:25:50.266289Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.268802Z", + "iopub.status.busy": "2024-07-02T15:25:50.268478Z", + "iopub.status.idle": "2024-07-02T15:25:50.278692Z", + "shell.execute_reply": "2024-07-02T15:25:50.278234Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.280493Z", + "iopub.status.busy": "2024-07-02T15:25:50.280327Z", + "iopub.status.idle": "2024-07-02T15:25:50.289428Z", + "shell.execute_reply": "2024-07-02T15:25:50.288895Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.291436Z", + "iopub.status.busy": "2024-07-02T15:25:50.291131Z", + "iopub.status.idle": "2024-07-02T15:25:50.297777Z", + "shell.execute_reply": "2024-07-02T15:25:50.297230Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.299787Z", + "iopub.status.busy": "2024-07-02T15:25:50.299609Z", + "iopub.status.idle": "2024-07-02T15:25:50.308846Z", + "shell.execute_reply": "2024-07-02T15:25:50.308401Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:50.310782Z", + "iopub.status.busy": "2024-07-02T15:25:50.310611Z", + "iopub.status.idle": "2024-07-02T15:25:50.325913Z", + "shell.execute_reply": "2024-07-02T15:25:50.325466Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 25690c004..2e195bdec 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

-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
[3]:
@@ -824,7 +824,7 @@ 
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.

@@ -1073,8 +1073,8 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 1 ...
-epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.690
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.414
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.590
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.391
 Computing feature embeddings ...

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

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1096,7 +1096,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings

-
+
@@ -1105,8 +1105,8 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 2 ...
-epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.642
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.471
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.769
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.425
 Computing feature embeddings ...

@@ -1114,7 +1114,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1128,7 +1128,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings

-
+
@@ -1137,8 +1137,8 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 3 ...
-epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.668
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.531
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.589
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.496
 Computing feature embeddings ...

@@ -1146,7 +1146,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1160,7 +1160,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings

-
+
@@ -1222,7 +1222,7 @@

7. Use cleanlab to find issues

-
+
@@ -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 2852ac72e..7aa81809c 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": "2024-07-02T15:10:54.880751Z", - "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" + "iopub.execute_input": "2024-07-02T15:25:52.913028Z", + "iopub.status.busy": "2024-07-02T15:25:52.912856Z", + "iopub.status.idle": "2024-07-02T15:25:55.694684Z", + "shell.execute_reply": "2024-07-02T15:25:55.694130Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:55.697195Z", + "iopub.status.busy": "2024-07-02T15:25:55.696837Z", + "iopub.status.idle": "2024-07-02T15:25:55.700483Z", + "shell.execute_reply": "2024-07-02T15:25:55.700023Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:25:55.702500Z", + "iopub.status.busy": "2024-07-02T15:25:55.702164Z", + "iopub.status.idle": "2024-07-02T15:26:07.555107Z", + "shell.execute_reply": "2024-07-02T15:26:07.554548Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76447603597c41e58c504ba366dedf8b", + "model_id": "2af7057d9f9e4382b8969af056a70b31", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "74d7207adb634a9a9648063cd4ebf05d", + "model_id": "fb1361d5c5c24b5c861d4294a90ba506", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24554a44a66045a29398e71c18b39f2f", + "model_id": "4f579184d87043939e2f168b2dd1baec", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52a2b90360f7460f9d5e8e206e5b7b47", + "model_id": "1ae5db8cbc4445e2b177cbb75cf548c7", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1eca5328aef44e1ca18c8c422f647377", + "model_id": "954702277b7d4c54bdadf343528f7419", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8ad57476e81431f9ef31378a786d5e9", + "model_id": "e7654577166b46889495c83a9b0ff4fb", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4761c3ddf1a643e8bda01b752e44ad8b", + "model_id": "c9a8ae89a73f4e29a42513776b40e081", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8d04c2d222424f08b06b6508223878ed", + "model_id": "8b40f010ec8b45ae9bb41931dd82974f", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:07.557271Z", + "iopub.status.busy": "2024-07-02T15:26:07.556985Z", + "iopub.status.idle": "2024-07-02T15:26:07.560864Z", + "shell.execute_reply": "2024-07-02T15:26:07.560308Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:07.562941Z", + "iopub.status.busy": "2024-07-02T15:26:07.562622Z", + "iopub.status.idle": "2024-07-02T15:26:18.751429Z", + "shell.execute_reply": "2024-07-02T15:26:18.750809Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea88c13811944930a76ece93362f7e4c", + "model_id": "4b0baf8a26df47d59be9d019531cbf27", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:18.753921Z", + "iopub.status.busy": "2024-07-02T15:26:18.753662Z", + "iopub.status.idle": "2024-07-02T15:26:36.659431Z", + "shell.execute_reply": "2024-07-02T15:26:36.658833Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.662110Z", + "iopub.status.busy": "2024-07-02T15:26:36.661747Z", + "iopub.status.idle": "2024-07-02T15:26:36.667344Z", + "shell.execute_reply": "2024-07-02T15:26:36.666923Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.669291Z", + "iopub.status.busy": "2024-07-02T15:26:36.668978Z", + "iopub.status.idle": "2024-07-02T15:26:36.673079Z", + "shell.execute_reply": "2024-07-02T15:26:36.672555Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.675451Z", + "iopub.status.busy": "2024-07-02T15:26:36.675030Z", + "iopub.status.idle": "2024-07-02T15:26:36.683889Z", + "shell.execute_reply": "2024-07-02T15:26:36.683372Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.685830Z", + "iopub.status.busy": "2024-07-02T15:26:36.685513Z", + "iopub.status.idle": "2024-07-02T15:26:36.712057Z", + "shell.execute_reply": "2024-07-02T15:26:36.711539Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:26:36.714189Z", + "iopub.status.busy": "2024-07-02T15:26:36.713888Z", + "iopub.status.idle": "2024-07-02T15:27:08.085740Z", + "shell.execute_reply": "2024-07-02T15:27:08.085120Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.690\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.590\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.414\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.391\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "860c6216e3754afa972fdf5b5a0980a0", + "model_id": "c1182fd93339476e85a73f0d08e7897c", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf64e375efe14d25b7e951f059b16c23", + "model_id": "cdc60c956874436f9c4e96ec9e00a78b", "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: 4.642\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.769\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.471\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.425\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8259ba9a3539477db64cbdd68592e635", + "model_id": "fbbd08fb2b71411b97e8aee71a57b844", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "da2c01112d1f4e749b0ca2c79b09927f", + "model_id": "8a55c388532e4805923cfc19ea79d6f9", "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: 4.668\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.589\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.531\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.496\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "26d250d79c2447489401eb9ab9ace7df", + "model_id": "56a210c9781c496fb5787a5b0e41aae6", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a3115a3594ce4aa497f8a610abb0af9e", + "model_id": "c992da0389a7424a9e7d4fb9bcf57de1", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:27:08.088336Z", + "iopub.status.busy": "2024-07-02T15:27:08.087813Z", + "iopub.status.idle": "2024-07-02T15:27:08.102193Z", + "shell.execute_reply": "2024-07-02T15:27:08.101748Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:27:08.104234Z", + "iopub.status.busy": "2024-07-02T15:27:08.103814Z", + "iopub.status.idle": "2024-07-02T15:27:08.570021Z", + "shell.execute_reply": "2024-07-02T15:27:08.569491Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:27:08.572683Z", + "iopub.status.busy": "2024-07-02T15:27:08.572172Z", + "iopub.status.idle": "2024-07-02T15:28:43.786883Z", + "shell.execute_reply": "2024-07-02T15:28:43.786321Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b66bf1f268f64f16b0ab04fbfef16cb7", + "model_id": "1f428038b90b4831b764456ae8104f11", "version_major": 2, "version_minor": 0 }, @@ -1144,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:43.789396Z", + "iopub.status.busy": "2024-07-02T15:28:43.788846Z", + "iopub.status.idle": "2024-07-02T15:28:44.231298Z", + "shell.execute_reply": "2024-07-02T15:28:44.230774Z" } }, "outputs": [ @@ -1293,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.233896Z", + "iopub.status.busy": "2024-07-02T15:28:44.233519Z", + "iopub.status.idle": "2024-07-02T15:28:44.294651Z", + "shell.execute_reply": "2024-07-02T15:28:44.294159Z" } }, "outputs": [ @@ -1400,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.297029Z", + "iopub.status.busy": "2024-07-02T15:28:44.296671Z", + "iopub.status.idle": "2024-07-02T15:28:44.304958Z", + "shell.execute_reply": "2024-07-02T15:28:44.304515Z" } }, "outputs": [ @@ -1533,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.306975Z", + "iopub.status.busy": "2024-07-02T15:28:44.306659Z", + "iopub.status.idle": "2024-07-02T15:28:44.311193Z", + "shell.execute_reply": "2024-07-02T15:28:44.310773Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.313177Z", + "iopub.status.busy": "2024-07-02T15:28:44.312780Z", + "iopub.status.idle": "2024-07-02T15:28:44.809240Z", + "shell.execute_reply": "2024-07-02T15:28:44.808660Z" } }, "outputs": [ @@ -1620,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.811393Z", + "iopub.status.busy": "2024-07-02T15:28:44.811216Z", + "iopub.status.idle": "2024-07-02T15:28:44.819548Z", + "shell.execute_reply": "2024-07-02T15:28:44.819000Z" } }, "outputs": [ @@ -1790,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.821446Z", + "iopub.status.busy": "2024-07-02T15:28:44.821277Z", + "iopub.status.idle": "2024-07-02T15:28:44.828159Z", + "shell.execute_reply": "2024-07-02T15:28:44.827741Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:44.830046Z", + "iopub.status.busy": "2024-07-02T15:28:44.829875Z", + "iopub.status.idle": "2024-07-02T15:28:45.532924Z", + "shell.execute_reply": "2024-07-02T15:28:45.532367Z" } }, "outputs": [ @@ -1909,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.535268Z", + "iopub.status.busy": "2024-07-02T15:28:45.534928Z", + "iopub.status.idle": "2024-07-02T15:28:45.549825Z", + "shell.execute_reply": "2024-07-02T15:28:45.549359Z" } }, "outputs": [ @@ -2069,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.551906Z", + "iopub.status.busy": "2024-07-02T15:28:45.551577Z", + "iopub.status.idle": "2024-07-02T15:28:45.557082Z", + "shell.execute_reply": "2024-07-02T15:28:45.556637Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.558956Z", + "iopub.status.busy": "2024-07-02T15:28:45.558637Z", + "iopub.status.idle": "2024-07-02T15:28:45.945267Z", + "shell.execute_reply": "2024-07-02T15:28:45.944621Z" } }, "outputs": [ @@ -2202,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.947598Z", + "iopub.status.busy": "2024-07-02T15:28:45.947419Z", + "iopub.status.idle": "2024-07-02T15:28:45.956445Z", + "shell.execute_reply": "2024-07-02T15:28:45.955937Z" } }, "outputs": [ @@ -2333,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.958622Z", + "iopub.status.busy": "2024-07-02T15:28:45.958449Z", + "iopub.status.idle": "2024-07-02T15:28:45.963062Z", + "shell.execute_reply": "2024-07-02T15:28:45.962495Z" }, "nbsphinx": "hidden" }, @@ -2373,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:45.965091Z", + "iopub.status.busy": "2024-07-02T15:28:45.964919Z", + "iopub.status.idle": "2024-07-02T15:28:46.137736Z", + "shell.execute_reply": "2024-07-02T15:28:46.137194Z" } }, "outputs": [ @@ -2418,10 +2418,10 @@ "execution_count": 29, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:46.139960Z", + "iopub.status.busy": "2024-07-02T15:28:46.139791Z", + "iopub.status.idle": "2024-07-02T15:28:46.147323Z", + "shell.execute_reply": "2024-07-02T15:28:46.146797Z" } }, "outputs": [ @@ -2507,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:46.149214Z", + "iopub.status.busy": "2024-07-02T15:28:46.148915Z", + "iopub.status.idle": "2024-07-02T15:28:46.321937Z", + "shell.execute_reply": "2024-07-02T15:28:46.321507Z" } }, "outputs": [ @@ -2550,10 +2550,10 @@ "execution_count": 31, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:46.324150Z", + "iopub.status.busy": "2024-07-02T15:28:46.323864Z", + "iopub.status.idle": "2024-07-02T15:28:46.328076Z", + "shell.execute_reply": "2024-07-02T15:28:46.327547Z" }, "nbsphinx": "hidden" }, @@ -2590,71 +2590,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "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": { + "06215a9cafbd487ab2aabd7e76896c2d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2707,41 +2643,7 @@ "width": null } }, - "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": { + "095395d6aa1b452fb58afcbbc125c095": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2794,181 +2696,7 @@ "width": null } }, - "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": { + "0ce0648da73f406bb87655fdbb7d5b96": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3021,59 +2749,7 @@ "width": null } }, - "1a67cc49a0714635846f10e32202bddc": { - "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_ffc2140e49b04844ba200898835e603c", - "max": 8845.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_97bd081fbc2c473fb8852e3e2ef1391d", - "tabbable": null, - "tooltip": null, - "value": 8845.0 - } - }, - "1bf57cd82d684206a95f2c1e179d96ce": { - "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_c639afb167db44978154a2d4054f1d40", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ccea9ba4e771468a95236d2db5bf0264", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "1c413b52ad994cc6818c637e38897e16": { + "0df705c672fb43bbb9e3eda00edf190d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3126,7 +2802,7 @@ "width": null } }, - "1d882b744195442f85a86d91cabda0fb": { + "0eae775299c340c282f40d2c11b33d8f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3179,7 +2855,7 @@ "width": null } }, - "1e4c7bfe71e44d6dbf26a8dff0f90509": { + "12840034e5794d6f899a765d58ab3d47": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3197,54 +2873,85 @@ "text_color": null } }, - "1eca5328aef44e1ca18c8c422f647377": { + "128b18a312b94a7e8f83f6ecb1cf88ae": { "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_017871e5d3284cbbbede907271767d8c", - "IPY_MODEL_195a744ab07644a29d3e3e02108d0aa2", - "IPY_MODEL_5b344538fc3b43a98f559b15bee9a5fa" - ], - "layout": "IPY_MODEL_ddee9f52a482431d83538c9f941bfbe9", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ec1c6ef5a124498093928eb1908e2659", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_892769ffede940149b9dd4ef371e6c6b", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 26421880.0 } }, - "1f029c9a5f7842a19a17b9c9f75ecc48": { + "146f9e51cdfd42749198b0e0c23d202d": { "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_f039d6ee9460446c9cbfff2777f2bb15", - "placeholder": "​", - "style": "IPY_MODEL_5795395d630f42f09192fdb4e89f8465", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 66.57it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1fe8a37c7c4b481eb559efba20e116ac": { + "15c4c2eccdef4d22a0be623db1c181f8": { + "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": "" + } + }, + "15f53de19f414e4b8e01003cfcdb4910": { + "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 + } + }, + "1a0b4e1286164abcb03053f6e5bbc01f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3260,17 +2967,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cfdfd821fb03474ba31a8ca207281b52", + "layout": "IPY_MODEL_f0a39fcda0744e718b1f43880054db22", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2130bdaf86db48658ae28f45cec5400b", + "style": "IPY_MODEL_cb63a23d1fae4acc8bb3b9a248ed90dd", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "1ffe24ac54fe4cb49579cb75ed7db9b5": { + "1a33cdc766a747aba50b7d78c6640621": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3323,60 +3030,49 @@ "width": null } }, - "20fc3b9b9a4b48d881bc11004e753c5e": { - "model_module": "@jupyter-widgets/base", + "1a5455d51be94b3eaded7bfd47cc0457": { + "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 } }, - "2130bdaf86db48658ae28f45cec5400b": { + "1ae5db8cbc4445e2b177cbb75cf548c7": { + "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_b9f07a6e2a8343d4978b34e4580a3d43", + "IPY_MODEL_ea09ecc214a24d27ac4f9a9984295435", + "IPY_MODEL_492b6ce45b6d440ebaad497ad655ad65" + ], + "layout": "IPY_MODEL_90fc60fcde7f4ac094170e750a73f244", + "tabbable": null, + "tooltip": null + } + }, + "1bd4198065174e84ab1eca765b96c630": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3385,14 +3081,63 @@ "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "1c5296256b6443e3ba9e455d23cc69c1": { + "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_9ead80d683a6491081632b4f3b9c4a32", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ed1c314562f64740a4bdcaef37ccc36b", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "1d0de356a8a64954852f43aa35806ea0": { + "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": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_86be3dd24db14a8898fdbb06836f6a72", + "placeholder": "​", + "style": "IPY_MODEL_95465de05450430197a0f59c479dd7a9", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 6181.68 examples/s]" } }, - "23fe73f9bf064ef5bd44fb6b0e295456": { + "1d5d7a8cecbf484c972e0942d6a03995": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3445,7 +3190,7 @@ "width": null } }, - "244159f1aa444aedbd2e3e9382b4326b": { + "1d6d3c5bfb63408ebbd5b2eeefef7d5b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3498,31 +3243,30 @@ "width": null } }, - "24554a44a66045a29398e71c18b39f2f": { + "1e6a5b63ab11465f9bbf613c7e547b87": { "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_7e502dd5790a480887b970c2862e5170", - "IPY_MODEL_be2ba8934caf4f6eb77db4a243219f26", - "IPY_MODEL_b36cf6fb19144ae68ceeda6d14a0c88e" - ], - "layout": "IPY_MODEL_5d925a37f7f24e27bf2be497abcd3368", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_558fdfb2b93b4227a1cb1db2b5e4c0c6", + "placeholder": "​", + "style": "IPY_MODEL_e8bde9b4ba7b4598a05bb9a50e01884d", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 40/40 [00:00<00:00, 69.82it/s]" } }, - "24566f0819db4eecbcd0b9831228c09f": { + "1f0336efc4e541d2afd7b3eba8bb28f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3538,17 +3282,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_34591a8e01774b23835f7bec29c46ffb", - "max": 4.0, + "layout": "IPY_MODEL_2903dce2463449dd8f64d52f8dfc2e79", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_618936f2cae44a09978936d5100bc0a5", + "style": "IPY_MODEL_1bd4198065174e84ab1eca765b96c630", "tabbable": null, "tooltip": null, - "value": 4.0 + "value": 60000.0 } }, - "26d250d79c2447489401eb9ab9ace7df": { + "1f428038b90b4831b764456ae8104f11": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3563,32 +3307,57 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e452cfce19364c6fb6b50db4144baec6", - "IPY_MODEL_cb9288f8e40c422ea8a86e41ea2ba6df", - "IPY_MODEL_76f7038f04f14f28ba39042b9a0b29ca" + "IPY_MODEL_b6247fabd3c9465980d7afeb4bbd7321", + "IPY_MODEL_1c5296256b6443e3ba9e455d23cc69c1", + "IPY_MODEL_d414eae569544ed99ff808903e53ca34" ], - "layout": "IPY_MODEL_e09e931dcf844151835a19cfddd0f459", + "layout": "IPY_MODEL_85c28b4798464f5a8417d3b2a042048a", "tabbable": null, "tooltip": null } }, - "270f6bf2c3e643ea8dd1a756817909fe": { + "20f24cbda083496a82c777048b6e35d9": { "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 + } + }, + "21cb86e6ca264dc18a3fc1d6ca53b8c8": { + "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_0ce0648da73f406bb87655fdbb7d5b96", + "placeholder": "​", + "style": "IPY_MODEL_6c3cb9635fc043b49f8d58655d66e4af", + "tabbable": null, + "tooltip": null, + "value": "Downloading builder script: 100%" } }, - "2b356c8f0be148beb637a84faef5510a": { + "23558bd264264033b2d9782455420348": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3641,7 +3410,7 @@ "width": null } }, - "2b8b9a59fe67431c926345fe531d94ed": { + "27a10e327ddb4b9093bc5cd059dc47ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3694,30 +3463,7 @@ "width": null } }, - "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": { + "2903dce2463449dd8f64d52f8dfc2e79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3770,7 +3516,7 @@ "width": null } }, - "2ffdee00ac1f47dcaad4ccd7cac60390": { + "2ad38f3365374d2ebdcb347fecc7f7d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3785,33 +3531,91 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f7d5ee1b172841528a12c8d5a15409f3", + "layout": "IPY_MODEL_1d6d3c5bfb63408ebbd5b2eeefef7d5b", "placeholder": "​", - "style": "IPY_MODEL_b40cdc4a6ff640758611c963c4a3014d", + "style": "IPY_MODEL_93285482bf7a47948bfaf617e6560e62", "tabbable": null, "tooltip": null, "value": "100%" } }, - "3145d8f1a50346caa657e7b6ade92792": { + "2af7057d9f9e4382b8969af056a70b31": { "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_21cb86e6ca264dc18a3fc1d6ca53b8c8", + "IPY_MODEL_6052bd16878a4d93852135e9889c44fa", + "IPY_MODEL_b54fa885be544149b2d737f701302ac2" + ], + "layout": "IPY_MODEL_772715de5a304144aa16418d39998969", + "tabbable": null, + "tooltip": null + } + }, + "2b7d5844fe304275909217722c7a1a24": { + "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_50d4841ee3f9430bb8d68aa6756d0196", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_502bd5601d62495fb79895cb6330ab83", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "2bb7e9eb83fe4497b90616077d472f4b": { + "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_b1526e2e5168453cbfbd347cf38ac67d", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_15c4c2eccdef4d22a0be623db1c181f8", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "34591a8e01774b23835f7bec29c46ffb": { + "2c0b9d7bd4f0471997bca535de0865ad": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3864,7 +3668,7 @@ "width": null } }, - "34eceefcb9cd4c0390182a3da1170a7b": { + "2cfc559f2c3f4eac9c1f78da68485085": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3882,7 +3686,102 @@ "text_color": null } }, - "3557a3c3c9e84bdd905c60c08626cdc2": { + "2d67f6cc14674d09b09b18b656234458": { + "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_fedc3692795249689353eccb515a9530", + "max": 8845.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_eb77dac7bc62412fbb597ff597b6c989", + "tabbable": null, + "tooltip": null, + "value": 8845.0 + } + }, + "2dcfa01772b042d1a18560ea0ffe622b": { + "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_dfb04d3a63764eebb3d552ee4ed183f2", + "placeholder": "​", + "style": "IPY_MODEL_ab47dc6735ee4fb097e7a3681e03b017", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:07<00:00, 8758.15 examples/s]" + } + }, + "2fe3b5eee64448a9ac18e339494c4892": { + "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_374e196ced424d1c8ed2148c7cc59a33", + "placeholder": "​", + "style": "IPY_MODEL_4ed6c09296d547b6b724e0ff1423ae0f", + "tabbable": null, + "tooltip": null, + "value": " 4.42M/4.42M [00:00<00:00, 109MB/s]" + } + }, + "305281fe64904e5e8ca2bc65aa7315dd": { + "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_90d02f25013745e0a5fab63dd3a30607", + "placeholder": "​", + "style": "IPY_MODEL_6d30b5f8bd744ee9b5e0f9ace75f28e1", + "tabbable": null, + "tooltip": null, + "value": "Computing checksums: 100%" + } + }, + "310f996b72454ddeb46aa59742828a71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3935,25 +3834,7 @@ "width": null } }, - "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": { + "3222da55bbd24fc9a61751756a156f00": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4006,7 +3887,7 @@ "width": null } }, - "3c7b41e10bf74f948457cd90e200afe1": { + "34f47aeaa29a4cd099d2f0993d0d6cd8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4059,7 +3940,25 @@ "width": null } }, - "41acfac3e7634989b2ccc6a4e9503ea6": { + "36e037e60520443395cc77903fbf80e2": { + "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 + } + }, + "374e196ced424d1c8ed2148c7cc59a33": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4112,48 +4011,23 @@ "width": null } }, - "4398abd231514ecb8426aaa36111102f": { - "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_e1a517549d364c30bc66ec975d022bdc", - "placeholder": "​", - "style": "IPY_MODEL_9975419ead304f1bb35f1f16ba689746", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "43ad8b951bf64a8b94108e8d800b7f93": { + "39309087b22b46c9b493bf4992a41da3": { "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": "" } }, - "459779a55e494dea92f7ce2871528083": { + "3a4b4cc3e84240c48bd636935fab2f51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4206,31 +4080,7 @@ "width": null } }, - "4761c3ddf1a643e8bda01b752e44ad8b": { - "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_a685a21bed8147549eee85cbac9f5358", - "IPY_MODEL_53e6c8e4d9e0441a8b68c5ea09e71ff1", - "IPY_MODEL_726586d1a8d9496886b7c74c9ae9216a" - ], - "layout": "IPY_MODEL_86c15161ec474c7ea837b47a73b55834", - "tabbable": null, - "tooltip": null - } - }, - "47b85a2880604d3dac3aeaeda306130f": { + "3aa133a9e18a4c55834c6b51537682de": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4245,41 +4095,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c61855779cd74bc097076c41da0eed54", + "layout": "IPY_MODEL_f9a76dcea2324e3a9d9de0674e2e43fa", "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", + "style": "IPY_MODEL_dc09a5e9d9a34e4888bb48a8d57e87a7", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": "Generating train split: 100%" } }, - "492f8e571e174d8f92e7b18b8e13bc62": { + "3e820559a2b24284ba9afe2029142d45": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4332,7 +4156,7 @@ "width": null } }, - "4a3fd3d2d678476d980c97d8064b895c": { + "3fcb5400e6364ce08bf65fad0810c61a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4366,210 +4190,26 @@ "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 - } - }, - "4cb21215cfbb4594b1f5a243ec53d04e": { - "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 - } - }, - "4f03fb7101fe41f5becc6ecfabbba17a": { - "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": "" - } - }, - "52a2b90360f7460f9d5e8e206e5b7b47": { - "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_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_ad18fb7ca0984499bf68129daeec626a", - "placeholder": "​", - "style": "IPY_MODEL_b42a1a7b436f4dfcab7ea91187e0b743", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 66.30it/s]" - } - }, - "53e6c8e4d9e0441a8b68c5ea09e71ff1": { - "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_9a4fcd9b64a048d28bbf5ab053b0a6d7", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0fab8f6f053241e4a76c15c455304f10", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "5795395d630f42f09192fdb4e89f8465": { - "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 - } - }, - "5b1660aaad334a86aba0c255fa391c8d": { - "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 - } - }, - "5b344538fc3b43a98f559b15bee9a5fa": { - "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_3557a3c3c9e84bdd905c60c08626cdc2", - "placeholder": "​", - "style": "IPY_MODEL_a121eb4e9f9e48609d3a4b697bab56c8", - "tabbable": 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 + "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 } }, - "5bfde149dd324016b5d45c6bddf7fad2": { + "4017e76298ac40b38081de747f8fc4cc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4622,7 +4262,7 @@ "width": null } }, - "5d925a37f7f24e27bf2be497abcd3368": { + "409e1ed6db8f49b18a518d7fdfad5fb6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4675,7 +4315,30 @@ "width": null } }, - "603bc1c8b13349f78643f74115dd3fc5": { + "492b6ce45b6d440ebaad497ad655ad65": { + "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_d94f3c1fdf5c401e96d6819b4179d2ab", + "placeholder": "​", + "style": "IPY_MODEL_dda13c05045f458785bc68041fee393c", + "tabbable": null, + "tooltip": null, + "value": " 29.5k/29.5k [00:00<00:00, 4.76MB/s]" + } + }, + "49a533c6ecaf487b84a0ff78339cc685": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4728,7 +4391,96 @@ "width": null } }, - "618936f2cae44a09978936d5100bc0a5": { + "4b0baf8a26df47d59be9d019531cbf27": { + "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_c4cd8eb28a8c41aeb4f5021bc0c08d3b", + "IPY_MODEL_be9b65965fab4cf7aa90e105e3881e8c", + "IPY_MODEL_1d0de356a8a64954852f43aa35806ea0" + ], + "layout": "IPY_MODEL_d52ca08d27924dd2b2bbe5ac7a54db61", + "tabbable": null, + "tooltip": null + } + }, + "4b0eead14d324e12a33b2892275ebfac": { + "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_9586a95094484deda56ab744317ea1da", + "placeholder": "​", + "style": "IPY_MODEL_885edcdabbbd4bc9b9f7adbbe49d7b71", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 61.14it/s]" + } + }, + "4ed6c09296d547b6b724e0ff1423ae0f": { + "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 + } + }, + "4f579184d87043939e2f168b2dd1baec": { + "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_d78de8b0f7b447dea71448f103aaeb26", + "IPY_MODEL_128b18a312b94a7e8f83f6ecb1cf88ae", + "IPY_MODEL_65791f080fad42fead67f26e5eb9754a" + ], + "layout": "IPY_MODEL_54ed5f61afce4a71a0c2578066595ba9", + "tabbable": null, + "tooltip": null + } + }, + "502bd5601d62495fb79895cb6330ab83": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4744,7 +4496,7 @@ "description_width": "" } }, - "6719b46ec6814ef5869cf0982143c632": { + "50853ac2e6cb46bda181bddbe208896e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4759,15 +4511,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_04cb19cb27ec48d8bf07ddb3dbafdbf7", + "layout": "IPY_MODEL_49a533c6ecaf487b84a0ff78339cc685", "placeholder": "​", - "style": "IPY_MODEL_951638e8145c4431963a7a3c2f7e4b09", + "style": "IPY_MODEL_7b4c55cd9513498aa91be74b3b85459f", "tabbable": null, "tooltip": null, "value": "100%" } }, - "67307a5269b545deb5492d878a0da28f": { + "50d4841ee3f9430bb8d68aa6756d0196": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4820,7 +4572,7 @@ "width": null } }, - "691ecad63a504dffa766ac43d3b78fb4": { + "52facea6cf664b71b4519a25366ea06d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4873,105 +4625,139 @@ "width": null } }, - "6a1591c936ba4e709d74e0ec278cefd6": { + "544f80d6b8bd416c90752e013a500795": { "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_b30172a2a47d40f0a77a14540ecd18d6", - "placeholder": "​", - "style": "IPY_MODEL_4cb21215cfbb4594b1f5a243ec53d04e", + "layout": "IPY_MODEL_73fd68751918467e89cc16c9e0562921", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_86c4fe875d4b45fdae915826bfa54ee4", "tabbable": null, "tooltip": null, - "value": "Computing checksums: 100%" - } - }, - "6d8a49f522924aaaabb198187245001e": { - "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 } }, - "72292d3faee34bf1949667097765550e": { - "model_module": "@jupyter-widgets/controls", + "54ed5f61afce4a71a0c2578066595ba9": { + "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 - } - }, - "726586d1a8d9496886b7c74c9ae9216a": { - "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_c3c495d6cade4b65834b43700b7d4655", - "placeholder": "​", - "style": "IPY_MODEL_f5501a6381964ea3b5684ee5ac2a2990", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:06<00:00, 8666.45 examples/s]" + "_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 } }, - "727d6b8ed0214669ae4a1c260b9eb2ba": { - "model_module": "@jupyter-widgets/controls", + "54fdb625585a48d3a536a808c7c52093": { + "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", + "_model_name": "LayoutModel", "_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 } }, - "73b0c283a1d549d786b83bf092b5a247": { + "558fdfb2b93b4227a1cb1db2b5e4c0c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5024,7 +4810,7 @@ "width": null } }, - "74d7207adb634a9a9648063cd4ebf05d": { + "56a210c9781c496fb5787a5b0e41aae6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5039,16 +4825,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0e4e51c9e4794c289497fc9034858879", - "IPY_MODEL_1a67cc49a0714635846f10e32202bddc", - "IPY_MODEL_17a61e4c38a64575bc341e3504130922" + "IPY_MODEL_96640dfd8c924e69aea47a8e395b564a", + "IPY_MODEL_2bb7e9eb83fe4497b90616077d472f4b", + "IPY_MODEL_d3dd1e811fee428d9c87b01b54f9622a" ], - "layout": "IPY_MODEL_9cc09cfd956141a89cc11fdb073b27f5", + "layout": "IPY_MODEL_d7867621beb2472ebd0b9a1429d5f921", "tabbable": null, "tooltip": null } }, - "75137f463796461d87f332d1e82c826b": { + "5a1e3853e4354222b9159b4b5c75171e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5101,77 +4887,7 @@ "width": null } }, - "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": { + "5a58bb79eb564c4899ea598ea50d0627": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5224,30 +4940,7 @@ "width": null } }, - "7c31f8bf76884e7683d9da37b70c4cc1": { - "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_1c413b52ad994cc6818c637e38897e16", - "placeholder": "​", - "style": "IPY_MODEL_954cd8e3b16c4419af3905829990415a", - "tabbable": null, - "tooltip": null, - "value": " 29.5k/29.5k [00:00<00:00, 4.33MB/s]" - } - }, - "7cb2c22fa5f843e79a58ef3c50f8eab3": { + "5afff76b270f4649b9c0d802eb9618fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5300,30 +4993,7 @@ "width": null } }, - "7e502dd5790a480887b970c2862e5170": { - "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_691ecad63a504dffa766ac43d3b78fb4", - "placeholder": "​", - "style": "IPY_MODEL_135c72d3fed34ffc90902f7faf2f27b0", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "80bc3ab0f2e34cba9a1c31d7411ecbc5": { + "6052bd16878a4d93852135e9889c44fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5339,41 +5009,40 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c932cb59d82141628dd6ec40fc18f737", - "max": 60000.0, + "layout": "IPY_MODEL_3a4b4cc3e84240c48bd636935fab2f51", + "max": 4833.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_b4c1e3125e1f43ae99fc53aa3a9c0505", + "style": "IPY_MODEL_6c4e444daf724ae993ae806d939ccea1", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": 4833.0 } }, - "8259ba9a3539477db64cbdd68592e635": { + "65791f080fad42fead67f26e5eb9754a": { "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_47b85a2880604d3dac3aeaeda306130f", - "IPY_MODEL_1711a1f1944742ea8f38809143bc3cc8", - "IPY_MODEL_df9aa39a8a3a44d8bb0fd0ea66fb7093" - ], - "layout": "IPY_MODEL_3bbe5464e48448959dd872a56942a7f5", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d4229c1699a84c50b8786f96f822af38", + "placeholder": "​", + "style": "IPY_MODEL_15f53de19f414e4b8e01003cfcdb4910", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 26.4M/26.4M [00:00<00:00, 118MB/s]" } }, - "837e8c16ffdd41a4ae0f4631055cca57": { + "6b890c9f41c34c199f4422339b9a7115": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5426,49 +5095,7 @@ "width": null } }, - "8555aae383d74d6c851e5296a2f76824": { - "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 - } - }, - "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 - } - }, - "86c15161ec474c7ea837b47a73b55834": { + "6b978c7a0d62417fa0d91f45e66413c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5521,54 +5148,41 @@ "width": null } }, - "8d04c2d222424f08b06b6508223878ed": { + "6c3cb9635fc043b49f8d58655d66e4af": { "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_ef2ba57d7cd44e3cb5973e6264f86666", - "IPY_MODEL_bfc4b95406a34e3ca4e93013d10e4852", - "IPY_MODEL_039cadb1d8a94b7d86d6d048e6ff9a52" - ], - "layout": "IPY_MODEL_ae9e474ca22b45648e40de05673fbd79", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8f9cbba88a5d4fe29d7de8c5c017526e": { + "6c4e444daf724ae993ae806d939ccea1": { "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_ca783c0a89cb4d7aabe9391975acb8ff", - "placeholder": "​", - "style": "IPY_MODEL_43ad8b951bf64a8b94108e8d800b7f93", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "923f0259202b48ac847034f1d10cd3e9": { + "6d30b5f8bd744ee9b5e0f9ace75f28e1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5586,7 +5200,7 @@ "text_color": null } }, - "940ecb97d50847cda5f82f56b828ce1e": { + "73fd68751918467e89cc16c9e0562921": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5639,7 +5253,7 @@ "width": null } }, - "944cd1ea980e485caca5409c07bca11c": { + "772715de5a304144aa16418d39998969": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5692,23 +5306,7 @@ "width": null } }, - "94e97c6d2fc14e229a4c96a99d543e33": { - "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": "" - } - }, - "951638e8145c4431963a7a3c2f7e4b09": { + "78f28a8c417d4930886931708429e1f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5726,25 +5324,60 @@ "text_color": null } }, - "954cd8e3b16c4419af3905829990415a": { - "model_module": "@jupyter-widgets/controls", + "799cd6b93e0f4d0286080d1655cd9063": { + "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 } }, - "95c819d6d1bb48a4a1718e13d4b708d7": { + "79ad2adc81c04b66803bcf992b75007d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5797,23 +5430,7 @@ "width": null } }, - "964944a145304bd58d1fd62856054c5a": { - "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": "" - } - }, - "96a4611a123247198b0f710c5bf75bd7": { + "7aa6115a89d841d1b7d7edc93ac4abb6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5828,31 +5445,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_41acfac3e7634989b2ccc6a4e9503ea6", + "layout": "IPY_MODEL_c1b8508edf42452ba2d2219aaffc4440", "placeholder": "​", - "style": "IPY_MODEL_1e4c7bfe71e44d6dbf26a8dff0f90509", + "style": "IPY_MODEL_78f28a8c417d4930886931708429e1f3", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": "Downloading readme: 100%" } }, - "97bd081fbc2c473fb8852e3e2ef1391d": { + "7b4c55cd9513498aa91be74b3b85459f": { "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 } }, - "9882b87501b84979bb5ca2b3e35d53d1": { + "7e3f05f2d52c45518e7fa6ec6779cb4c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5867,33 +5486,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1ffe24ac54fe4cb49579cb75ed7db9b5", + "layout": "IPY_MODEL_27a10e327ddb4b9093bc5cd059dc47ea", "placeholder": "​", - "style": "IPY_MODEL_cbfecf1072ed4e2caaa31da07e0da756", + "style": "IPY_MODEL_12840034e5794d6f899a765d58ab3d47", "tabbable": null, "tooltip": null, - "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 + "value": "100%" } }, - "9a4fcd9b64a048d28bbf5ab053b0a6d7": { + "7f7f3f87082943e4a0df02dcd27f337a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5946,23 +5547,31 @@ "width": null } }, - "9aee6db9bd984fa5a023e92a81ba822d": { + "827d7da7658b40f6857044407fdda547": { "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_305281fe64904e5e8ca2bc65aa7315dd", + "IPY_MODEL_544f80d6b8bd416c90752e013a500795", + "IPY_MODEL_a1f4f066adc94df989e214551e96e664" + ], + "layout": "IPY_MODEL_3e820559a2b24284ba9afe2029142d45", + "tabbable": null, + "tooltip": null } }, - "9b19aa98c0d64d849bf08b178b9841e7": { + "85c28b4798464f5a8417d3b2a042048a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6015,25 +5624,30 @@ "width": null } }, - "9c4688aea9f9425883500665dfa60bf9": { + "8672ba9a0b95494e85eb4e493ebff06a": { "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_cdad743f4a2e4284a382dfde60cd6ce3", + "placeholder": "​", + "style": "IPY_MODEL_ef9027846b204180af89c953b7afc317", + "tabbable": null, + "tooltip": null, + "value": " 8.85k/8.85k [00:00<00:00, 1.56MB/s]" } }, - "9cc09cfd956141a89cc11fdb073b27f5": { + "86be3dd24db14a8898fdbb06836f6a72": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6086,25 +5700,23 @@ "width": null } }, - "9dfddac443ca480cac3f02058d42e8a5": { + "86c4fe875d4b45fdae915826bfa54ee4": { "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": "" } }, - "a121eb4e9f9e48609d3a4b697bab56c8": { + "885edcdabbbd4bc9b9f7adbbe49d7b71": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6122,201 +5734,123 @@ "text_color": null } }, - "a3115a3594ce4aa497f8a610abb0af9e": { + "88d02b42470e4501bb3cd1e3e6ebf7eb": { "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_6719b46ec6814ef5869cf0982143c632", - "IPY_MODEL_f7496bea7da04557b5e943e71814b3ad", - "IPY_MODEL_1f029c9a5f7842a19a17b9c9f75ecc48" - ], - "layout": "IPY_MODEL_f34228361c9a43dc9eb3dc33677084e0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3fcb5400e6364ce08bf65fad0810c61a", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d0384f03015b4b29a2fb529874891acb", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 40.0 } }, - "a64c2baabe354ca8a0f8f64ae6bc4ede": { + "892769ffede940149b9dd4ef371e6c6b": { "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": "" } }, - "a685a21bed8147549eee85cbac9f5358": { + "8a55c388532e4805923cfc19ea79d6f9": { "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_cc7be77366654c01991ea476f2293f63", - "placeholder": "​", - "style": "IPY_MODEL_6d8a49f522924aaaabb198187245001e", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7e3f05f2d52c45518e7fa6ec6779cb4c", + "IPY_MODEL_88d02b42470e4501bb3cd1e3e6ebf7eb", + "IPY_MODEL_a27c3d6d92f64500b703f8534a213527" + ], + "layout": "IPY_MODEL_c6bf48673c474f28b05932dfba8d075f", "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" + "tooltip": null } }, - "a8e1731ca82b4390af97a6c9c6efcc51": { - "model_module": "@jupyter-widgets/base", + "8b40f010ec8b45ae9bb41931dd82974f": { + "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/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": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f87fe7987a7548d391653382cc0cac40", + "IPY_MODEL_c05105f4332440beb07521cc771bd4ab", + "IPY_MODEL_e46b79664a5b499091764978698553f1" + ], + "layout": "IPY_MODEL_6b890c9f41c34c199f4422339b9a7115", + "tabbable": null, + "tooltip": null } }, - "a972ac7ecf4c4746bb430a3161226786": { + "8b8994fc668b4dceb9c06453fb39bc0e": { "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_9b19aa98c0d64d849bf08b178b9841e7", - "placeholder": "​", - "style": "IPY_MODEL_3145d8f1a50346caa657e7b6ade92792", + "layout": "IPY_MODEL_79ad2adc81c04b66803bcf992b75007d", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e8c7dbc5d3214dcb86aaab520f395f74", "tabbable": null, "tooltip": null, - "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 + "value": 4422102.0 } }, - "ae9e474ca22b45648e40de05673fbd79": { + "8c42c7196455418d88da9ac9a371e461": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6369,7 +5903,56 @@ "width": null } }, - "af859482bc704e13a4993c698c9ba181": { + "8d585cf69b3f4724a623579d5335bfd9": { + "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_4017e76298ac40b38081de747f8fc4cc", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f457d8bcb4404971bf2ad18e7efff0e1", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "8f5c14e14fcf4f388e2f257aa70609f8": { + "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_34f47aeaa29a4cd099d2f0993d0d6cd8", + "placeholder": "​", + "style": "IPY_MODEL_dc5e03ce2bc744b4b89300005f223fa6", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 71.56it/s]" + } + }, + "8f6be0928cea469a9271f1c136a90fec": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6387,7 +5970,7 @@ "text_color": null } }, - "b30172a2a47d40f0a77a14540ecd18d6": { + "90d02f25013745e0a5fab63dd3a30607": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6440,7 +6023,7 @@ "width": null } }, - "b31e5df3571f4ac28345bf70cd4e947c": { + "90fc60fcde7f4ac094170e750a73f244": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6493,30 +6076,7 @@ "width": null } }, - "b36cf6fb19144ae68ceeda6d14a0c88e": { - "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_a8e1731ca82b4390af97a6c9c6efcc51", - "placeholder": "​", - "style": "IPY_MODEL_d2b86bd7871548f5b1e0d2d312d2ef24", - "tabbable": null, - "tooltip": null, - "value": " 26.4M/26.4M [00:00<00:00, 109MB/s]" - } - }, - "b40cdc4a6ff640758611c963c4a3014d": { + "93285482bf7a47948bfaf617e6560e62": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6534,7 +6094,7 @@ "text_color": null } }, - "b42a1a7b436f4dfcab7ea91187e0b743": { + "9517d2ba00ed4eb094644c417a85d507": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6552,23 +6112,25 @@ "text_color": null } }, - "b4c1e3125e1f43ae99fc53aa3a9c0505": { + "95465de05450430197a0f59c479dd7a9": { "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 } }, - "b66bf1f268f64f16b0ab04fbfef16cb7": { + "954702277b7d4c54bdadf343528f7419": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6583,16 +6145,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_8f9cbba88a5d4fe29d7de8c5c017526e", - "IPY_MODEL_80bc3ab0f2e34cba9a1c31d7411ecbc5", - "IPY_MODEL_9882b87501b84979bb5ca2b3e35d53d1" + "IPY_MODEL_f1ced5ef687547f290b64d20124f82b3", + "IPY_MODEL_8b8994fc668b4dceb9c06453fb39bc0e", + "IPY_MODEL_2fe3b5eee64448a9ac18e339494c4892" ], - "layout": "IPY_MODEL_79afef11e9d74259a71e933ac999caf5", + "layout": "IPY_MODEL_799cd6b93e0f4d0286080d1655cd9063", "tabbable": null, "tooltip": null } }, - "b76a6f2e44334c838c6350a5d3bbb530": { + "9586a95094484deda56ab744317ea1da": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6645,109 +6207,30 @@ "width": null } }, - "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", - "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_4398abd231514ecb8426aaa36111102f", - "IPY_MODEL_ef6365260ca348ed903c1353490fc655", - "IPY_MODEL_cfbabccb48c14370b2a05af8b6c6a2ce" - ], - "layout": "IPY_MODEL_20fc3b9b9a4b48d881bc11004e753c5e", - "tabbable": null, - "tooltip": null - } - }, - "bfc4b95406a34e3ca4e93013d10e4852": { + "96640dfd8c924e69aea47a8e395b564a": { "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_23fe73f9bf064ef5bd44fb6b0e295456", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9aee6db9bd984fa5a023e92a81ba822d", + "layout": "IPY_MODEL_54fdb625585a48d3a536a808c7c52093", + "placeholder": "​", + "style": "IPY_MODEL_2cfc559f2c3f4eac9c1f78da68485085", "tabbable": null, "tooltip": null, - "value": 10000.0 + "value": "100%" } }, - "c0e6653485214e6295a6c309d77804a3": { + "96cfabb5484149d1adc2aa86092bc96d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6800,7 +6283,7 @@ "width": null } }, - "c3c495d6cade4b65834b43700b7d4655": { + "9ead80d683a6491081632b4f3b9c4a32": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6853,60 +6336,118 @@ "width": null } }, - "c61855779cd74bc097076c41da0eed54": { - "model_module": "@jupyter-widgets/base", + "a100d982151c42ccbe4279f192b139bb": { + "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": "" + } + }, + "a1f4f066adc94df989e214551e96e664": { + "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_095395d6aa1b452fb58afcbbc125c095", + "placeholder": "​", + "style": "IPY_MODEL_af48e3d34e0b412c8ae2a4ca8e9215c2", + "tabbable": null, + "tooltip": null, + "value": " 4/4 [00:00<00:00, 1251.75it/s]" + } + }, + "a27c3d6d92f64500b703f8534a213527": { + "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_baa7b6d1e7a243a095f0c691e7f0b5e4", + "placeholder": "​", + "style": "IPY_MODEL_1a5455d51be94b3eaded7bfd47cc0457", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 63.05it/s]" + } + }, + "a2a2ec56076b4b6c9ee933df0360e05b": { + "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_acb03260aac345648a1ec54ab5a858f9", + "placeholder": "​", + "style": "IPY_MODEL_b1d1d60578f74d3582ef5ae972a07297", + "tabbable": null, + "tooltip": null, + "value": " 5.15k/5.15k [00:00<00:00, 887kB/s]" + } + }, + "a2c2386f0ec849418856d89ef28a3475": { + "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_0df705c672fb43bbb9e3eda00edf190d", + "max": 5148.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b1b4a133d95946b4ad1d95acf055e6d9", + "tabbable": null, + "tooltip": null, + "value": 5148.0 } }, - "c639afb167db44978154a2d4054f1d40": { + "a890ab18730f4331a1b512e514f1475a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6959,7 +6500,7 @@ "width": null } }, - "c867ebcf347146abb7ec6115797953c1": { + "a89ae7c0705f458f97f4e50751463686": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7012,31 +6553,25 @@ "width": null } }, - "c8ad57476e81431f9ef31378a786d5e9": { + "ab47dc6735ee4fb097e7a3681e03b017": { "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_96a4611a123247198b0f710c5bf75bd7", - "IPY_MODEL_cf26e95faaa94e23b57c2179d5e9c64e", - "IPY_MODEL_d6650de0045c4384af30511ccab8a0f7" - ], - "layout": "IPY_MODEL_d0b1f8cfd74c4375af09960b50139328", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c932cb59d82141628dd6ec40fc18f737": { + "acb03260aac345648a1ec54ab5a858f9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7089,7 +6624,84 @@ "width": null } }, - "ca783c0a89cb4d7aabe9391975acb8ff": { + "acf5ba82150c4bf9b8924a6d1e39bfb6": { + "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_2c0b9d7bd4f0471997bca535de0865ad", + "placeholder": "​", + "style": "IPY_MODEL_afcb83b39c2e4478ba9ade81cf3b2fb6", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 69.50it/s]" + } + }, + "af48e3d34e0b412c8ae2a4ca8e9215c2": { + "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 + } + }, + "afcb83b39c2e4478ba9ade81cf3b2fb6": { + "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 + } + }, + "b0214bcbe8044efda63d35e4fa778d43": { + "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 + } + }, + "b1526e2e5168453cbfbd347cf38ac67d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7142,7 +6754,103 @@ "width": null } }, - "cabee28dffcd4272a0c22c2d5c770596": { + "b1b4a133d95946b4ad1d95acf055e6d9": { + "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": "" + } + }, + "b1d1d60578f74d3582ef5ae972a07297": { + "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 + } + }, + "b270b4b9160b4aa4bbad325928110497": { + "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": "" + } + }, + "b54fa885be544149b2d737f701302ac2": { + "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_0eae775299c340c282f40d2c11b33d8f", + "placeholder": "​", + "style": "IPY_MODEL_df797002b4914bef8001c5e05b7be0af", + "tabbable": null, + "tooltip": null, + "value": " 4.83k/4.83k [00:00<00:00, 629kB/s]" + } + }, + "b6247fabd3c9465980d7afeb4bbd7321": { + "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_5a1e3853e4354222b9159b4b5c75171e", + "placeholder": "​", + "style": "IPY_MODEL_146f9e51cdfd42749198b0e0c23d202d", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "b838c277255740a2b7434474c516e95e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7195,7 +6903,30 @@ "width": null } }, - "cac4ab6d168f46baa1c90ed6d428997f": { + "b9f07a6e2a8343d4978b34e4580a3d43": { + "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_8c42c7196455418d88da9ac9a371e461", + "placeholder": "​", + "style": "IPY_MODEL_f92dff6949004ae1808eebd99ba98566", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "baa7b6d1e7a243a095f0c691e7f0b5e4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7248,69 +6979,56 @@ "width": null } }, - "cb9288f8e40c422ea8a86e41ea2ba6df": { + "bbb269d13d924b65a58b0cf5db1b2407": { "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_1d882b744195442f85a86d91cabda0fb", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_eab0669b062645d4ac17d3d2f88de120", + "layout": "IPY_MODEL_fe362277556a4cf0beffd4b12f3e9ffd", + "placeholder": "​", + "style": "IPY_MODEL_20f24cbda083496a82c777048b6e35d9", "tabbable": null, "tooltip": null, - "value": 40.0 - } - }, - "cbfecf1072ed4e2caaa31da07e0da756": { - "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": "100%" } }, - "cc12a064eb004d6390ea2006fbb725ed": { + "be9b65965fab4cf7aa90e105e3881e8c": { "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_5afff76b270f4649b9c0d802eb9618fb", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f1266a3dc99447f193096776b9b427f1", + "tabbable": null, + "tooltip": null, + "value": 60000.0 } }, - "cc7be77366654c01991ea476f2293f63": { + "bfe066abcd864943a83e96669cadf170": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7363,7 +7081,57 @@ "width": null } }, - "cca5b38ac94f4b60ab4f617db8198ff8": { + "c05105f4332440beb07521cc771bd4ab": { + "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_a890ab18730f4331a1b512e514f1475a", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a100d982151c42ccbe4279f192b139bb", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "c1182fd93339476e85a73f0d08e7897c": { + "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_f36e7b0209f84d828549adece0524582", + "IPY_MODEL_dc304d6b46fb4dc592080af62f1c4444", + "IPY_MODEL_4b0eead14d324e12a33b2892275ebfac" + ], + "layout": "IPY_MODEL_52facea6cf664b71b4519a25366ea06d", + "tabbable": null, + "tooltip": null + } + }, + "c1b8508edf42452ba2d2219aaffc4440": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7416,88 +7184,7 @@ "width": null } }, - "ccea9ba4e771468a95236d2db5bf0264": { - "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": "" - } - }, - "cd71b5032d334106871d93c7901358eb": { - "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": "" - } - }, - "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": { + "c31ca76550e249ca985f248853c71722": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7550,7 +7237,7 @@ "width": null } }, - "d0b1f8cfd74c4375af09960b50139328": { + "c4b8fffb19594f58b1a2ba4aa8ec7d11": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7603,25 +7290,30 @@ "width": null } }, - "d2b86bd7871548f5b1e0d2d312d2ef24": { + "c4cd8eb28a8c41aeb4f5021bc0c08d3b": { "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_6b978c7a0d62417fa0d91f45e66413c4", + "placeholder": "​", + "style": "IPY_MODEL_e20bd95703544b4aad1922863bff7b0f", + "tabbable": null, + "tooltip": null, + "value": "Map (num_proc=4): 100%" } }, - "d4d042ad47854553b9660b36249c7347": { + "c6bf48673c474f28b05932dfba8d075f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7674,53 +7366,89 @@ "width": null } }, - "d6650de0045c4384af30511ccab8a0f7": { + "c992da0389a7424a9e7d4fb9bcf57de1": { "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_cabee28dffcd4272a0c22c2d5c770596", - "placeholder": "​", - "style": "IPY_MODEL_34eceefcb9cd4c0390182a3da1170a7b", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_50853ac2e6cb46bda181bddbe208896e", + "IPY_MODEL_2b7d5844fe304275909217722c7a1a24", + "IPY_MODEL_acf5ba82150c4bf9b8924a6d1e39bfb6" + ], + "layout": "IPY_MODEL_409e1ed6db8f49b18a518d7fdfad5fb6", "tabbable": null, - "tooltip": null, - "value": " 5.15k/5.15k [00:00<00:00, 871kB/s]" + "tooltip": null } }, - "d73ad391537b4ea7a7841fe7944270c0": { + "c9a8ae89a73f4e29a42513776b40e081": { "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_1a4cf1e028c44677995965a00cb4aa35", - "placeholder": "​", - "style": "IPY_MODEL_a64c2baabe354ca8a0f8f64ae6bc4ede", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3aa133a9e18a4c55834c6b51537682de", + "IPY_MODEL_1f0336efc4e541d2afd7b3eba8bb28f1", + "IPY_MODEL_2dcfa01772b042d1a18560ea0ffe622b" + ], + "layout": "IPY_MODEL_bfe066abcd864943a83e96669cadf170", "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 7362.49 examples/s]" + "tooltip": null + } + }, + "cb63a23d1fae4acc8bb3b9a248ed90dd": { + "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": "" + } + }, + "cd957436866649e6a210ee9e7fcb8650": { + "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 } }, - "d9e695fa0c8741669e44cf4502f36d47": { + "cdad743f4a2e4284a382dfde60cd6ce3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7773,7 +7501,7 @@ "width": null } }, - "da2c01112d1f4e749b0ca2c79b09927f": { + "cdc60c956874436f9c4e96ec9e00a78b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -7788,34 +7516,78 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_fc1d8341e6624186a80fabb78e1c9a60", - "IPY_MODEL_1bf57cd82d684206a95f2c1e179d96ce", - "IPY_MODEL_53049123e6a9407b8314d7f6b14f99af" + "IPY_MODEL_bbb269d13d924b65a58b0cf5db1b2407", + "IPY_MODEL_8d585cf69b3f4724a623579d5335bfd9", + "IPY_MODEL_1e6a5b63ab11465f9bbf613c7e547b87" ], - "layout": "IPY_MODEL_2b356c8f0be148beb637a84faef5510a", + "layout": "IPY_MODEL_23558bd264264033b2d9782455420348", "tabbable": null, "tooltip": null } }, - "dc0ef01ff0b14b05bd75d62108c813b6": { + "d0384f03015b4b29a2fb529874891acb": { "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": "" + } + }, + "d3dd1e811fee428d9c87b01b54f9622a": { + "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_5a58bb79eb564c4899ea598ea50d0627", + "placeholder": "​", + "style": "IPY_MODEL_f685e18fb6ca48bd8670c03342805ecf", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 60.34it/s]" + } + }, + "d414eae569544ed99ff808903e53ca34": { + "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_96cfabb5484149d1adc2aa86092bc96d", + "placeholder": "​", + "style": "IPY_MODEL_b0214bcbe8044efda63d35e4fa778d43", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:35<00:00, 1727.59it/s]" } }, - "dce15d5cb91b42a780b136a398da089e": { + "d4229c1699a84c50b8786f96f822af38": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7868,25 +7640,7 @@ "width": null } }, - "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": { + "d52ca08d27924dd2b2bbe5ac7a54db61": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7939,7 +7693,7 @@ "width": null } }, - "ddee9f52a482431d83538c9f941bfbe9": { + "d7867621beb2472ebd0b9a1429d5f921": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7992,7 +7746,7 @@ "width": null } }, - "df9aa39a8a3a44d8bb0fd0ea66fb7093": { + "d78de8b0f7b447dea71448f103aaeb26": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8007,31 +7761,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2f6cf6dc5a4845588c6a21a1c21879bc", + "layout": "IPY_MODEL_c31ca76550e249ca985f248853c71722", "placeholder": "​", - "style": "IPY_MODEL_dd1800dbb9064a41a02af5ec0f1f5b67", + "style": "IPY_MODEL_36e037e60520443395cc77903fbf80e2", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.93it/s]" - } - }, - "e03718c7114049e881b5ec8c92d14511": { - "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": "" + "value": "Downloading data: 100%" } }, - "e09e931dcf844151835a19cfddd0f459": { + "d94f3c1fdf5c401e96d6819b4179d2ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8084,7 +7822,123 @@ "width": null } }, - "e1a517549d364c30bc66ec975d022bdc": { + "daf2ab05255e474d8859a35c484b2160": { + "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 + } + }, + "dc09a5e9d9a34e4888bb48a8d57e87a7": { + "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 + } + }, + "dc304d6b46fb4dc592080af62f1c4444": { + "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_c4b8fffb19594f58b1a2ba4aa8ec7d11", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b270b4b9160b4aa4bbad325928110497", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "dc5e03ce2bc744b4b89300005f223fa6": { + "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 + } + }, + "dda13c05045f458785bc68041fee393c": { + "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 + } + }, + "df797002b4914bef8001c5e05b7be0af": { + "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 + } + }, + "dfb04d3a63764eebb3d552ee4ed183f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8137,30 +7991,25 @@ "width": null } }, - "e452cfce19364c6fb6b50db4144baec6": { + "e20bd95703544b4aad1922863bff7b0f": { "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_cca5b38ac94f4b60ab4f617db8198ff8", - "placeholder": "​", - "style": "IPY_MODEL_8555aae383d74d6c851e5296a2f76824", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e5a63f7446074676b31de8da1ee38a2b": { + "e2e98ad255df41788a76c3224436f2f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8175,31 +8024,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cac4ab6d168f46baa1c90ed6d428997f", + "layout": "IPY_MODEL_e8b64de668f34ba6bedabb72cec29004", "placeholder": "​", - "style": "IPY_MODEL_af859482bc704e13a4993c698c9ba181", + "style": "IPY_MODEL_8f6be0928cea469a9271f1c136a90fec", "tabbable": null, "tooltip": null, - "value": " 4.83k/4.83k [00:00<00:00, 612kB/s]" - } - }, - "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": "" + "value": "Downloading data: 100%" } }, - "e6fa033915fa4a5987c2c8fa374502e4": { + "e46b79664a5b499091764978698553f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8214,15 +8047,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3c7b41e10bf74f948457cd90e200afe1", + "layout": "IPY_MODEL_b838c277255740a2b7434474c516e95e", "placeholder": "​", - "style": "IPY_MODEL_9dfddac443ca480cac3f02058d42e8a5", + "style": "IPY_MODEL_fad6c2d0a0214e7780bca6c7a90edb1a", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": " 10000/10000 [00:01<00:00, 8796.13 examples/s]" } }, - "ea88c13811944930a76ece93362f7e4c": { + "e7654577166b46889495c83a9b0ff4fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -8237,32 +8070,69 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2e0923cec6894e0bb926973c7f50f3d3", - "IPY_MODEL_483cd57673244db39d3524fef0835d46", - "IPY_MODEL_d73ad391537b4ea7a7841fe7944270c0" + "IPY_MODEL_e2e98ad255df41788a76c3224436f2f7", + "IPY_MODEL_a2c2386f0ec849418856d89ef28a3475", + "IPY_MODEL_a2a2ec56076b4b6c9ee933df0360e05b" ], - "layout": "IPY_MODEL_7cb2c22fa5f843e79a58ef3c50f8eab3", + "layout": "IPY_MODEL_1a33cdc766a747aba50b7d78c6640621", "tabbable": null, "tooltip": null } }, - "eab0669b062645d4ac17d3d2f88de120": { - "model_module": "@jupyter-widgets/controls", + "e8b64de668f34ba6bedabb72cec29004": { + "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", + "_model_name": "LayoutModel", "_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 } }, - "ec1afef96b264d888ab90ae59248acd5": { + "e8bde9b4ba7b4598a05bb9a50e01884d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -8273,63 +8143,30 @@ "_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 - } - }, - "ec5400e36f274359b296a5d6e0e33a39": { - "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_944cd1ea980e485caca5409c07bca11c", - "max": 29515.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_270f6bf2c3e643ea8dd1a756817909fe", - "tabbable": null, - "tooltip": null, - "value": 29515.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ef2ba57d7cd44e3cb5973e6264f86666": { + "e8c7dbc5d3214dcb86aaab520f395f74": { "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_244159f1aa444aedbd2e3e9382b4326b", - "placeholder": "​", - "style": "IPY_MODEL_cc12a064eb004d6390ea2006fbb725ed", - "tabbable": null, - "tooltip": null, - "value": "Generating test split: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "ef6365260ca348ed903c1353490fc655": { + "ea09ecc214a24d27ac4f9a9984295435": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -8345,40 +8182,33 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f6079e4e10e545668732bdada9c65e44", - "max": 40.0, + "layout": "IPY_MODEL_7f7f3f87082943e4a0df02dcd27f337a", + "max": 29515.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e03718c7114049e881b5ec8c92d14511", + "style": "IPY_MODEL_39309087b22b46c9b493bf4992a41da3", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 29515.0 } }, - "efabf4edc4f14504b06bad7e71659998": { + "eb77dac7bc62412fbb597ff597b6c989": { "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_5bfde149dd324016b5d45c6bddf7fad2", - "placeholder": "​", - "style": "IPY_MODEL_dc0ef01ff0b14b05bd75d62108c813b6", - "tabbable": null, - "tooltip": null, - "value": "Downloading builder script: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f039d6ee9460446c9cbfff2777f2bb15": { + "ec1c6ef5a124498093928eb1908e2659": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8431,7 +8261,41 @@ "width": null } }, - "f34228361c9a43dc9eb3dc33677084e0": { + "ed1c314562f64740a4bdcaef37ccc36b": { + "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": "" + } + }, + "ef9027846b204180af89c953b7afc317": { + "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 + } + }, + "f0a39fcda0744e718b1f43880054db22": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8484,7 +8348,126 @@ "width": null } }, - "f5501a6381964ea3b5684ee5ac2a2990": { + "f1266a3dc99447f193096776b9b427f1": { + "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": "" + } + }, + "f1ced5ef687547f290b64d20124f82b3": { + "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_310f996b72454ddeb46aa59742828a71", + "placeholder": "​", + "style": "IPY_MODEL_9517d2ba00ed4eb094644c417a85d507", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "f36e7b0209f84d828549adece0524582": { + "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_1d5d7a8cecbf484c972e0942d6a03995", + "placeholder": "​", + "style": "IPY_MODEL_cd957436866649e6a210ee9e7fcb8650", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "f457d8bcb4404971bf2ad18e7efff0e1": { + "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": "" + } + }, + "f685e18fb6ca48bd8670c03342805ecf": { + "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 + } + }, + "f87fe7987a7548d391653382cc0cac40": { + "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_06215a9cafbd487ab2aabd7e76896c2d", + "placeholder": "​", + "style": "IPY_MODEL_daf2ab05255e474d8859a35c484b2160", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" + } + }, + "f92dff6949004ae1808eebd99ba98566": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -8502,7 +8485,7 @@ "text_color": null } }, - "f6079e4e10e545668732bdada9c65e44": { + "f9a76dcea2324e3a9d9de0674e2e43fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8555,33 +8538,73 @@ "width": null } }, - "f7496bea7da04557b5e943e71814b3ad": { + "fad6c2d0a0214e7780bca6c7a90edb1a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "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 + } + }, + "fb1361d5c5c24b5c861d4294a90ba506": { + "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": "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_67307a5269b545deb5492d878a0da28f", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e6d9862771cc40a5bc3f7cb24e629eba", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7aa6115a89d841d1b7d7edc93ac4abb6", + "IPY_MODEL_2d67f6cc14674d09b09b18b656234458", + "IPY_MODEL_8672ba9a0b95494e85eb4e493ebff06a" + ], + "layout": "IPY_MODEL_3222da55bbd24fc9a61751756a156f00", "tabbable": null, - "tooltip": null, - "value": 40.0 + "tooltip": null + } + }, + "fbbd08fb2b71411b97e8aee71a57b844": { + "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_2ad38f3365374d2ebdcb347fecc7f7d9", + "IPY_MODEL_1a0b4e1286164abcb03053f6e5bbc01f", + "IPY_MODEL_8f5c14e14fcf4f388e2f257aa70609f8" + ], + "layout": "IPY_MODEL_a89ae7c0705f458f97f4e50751463686", + "tabbable": null, + "tooltip": null } }, - "f7d5ee1b172841528a12c8d5a15409f3": { + "fe362277556a4cf0beffd4b12f3e9ffd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8634,30 +8657,7 @@ "width": null } }, - "fc1d8341e6624186a80fabb78e1c9a60": { - "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_603bc1c8b13349f78643f74115dd3fc5", - "placeholder": "​", - "style": "IPY_MODEL_1361429788e54a748edb03027a9cab6a", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "ffc2140e49b04844ba200898835e603c": { + "fedc3692795249689353eccb515a9530": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 079f8c422..dc39d6bb4 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": "2024-07-02T15:13:52.731591Z", - "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" + "iopub.execute_input": "2024-07-02T15:28:49.899605Z", + "iopub.status.busy": "2024-07-02T15:28:49.899202Z", + "iopub.status.idle": "2024-07-02T15:28:50.991494Z", + "shell.execute_reply": "2024-07-02T15:28:50.990945Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:13:53.829437Z", - "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" + "iopub.execute_input": "2024-07-02T15:28:50.994031Z", + "iopub.status.busy": "2024-07-02T15:28:50.993599Z", + "iopub.status.idle": "2024-07-02T15:28:51.011326Z", + "shell.execute_reply": "2024-07-02T15:28:51.010872Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.013324Z", + "iopub.status.busy": "2024-07-02T15:28:51.012973Z", + "iopub.status.idle": "2024-07-02T15:28:51.039303Z", + "shell.execute_reply": "2024-07-02T15:28:51.038773Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.041327Z", + "iopub.status.busy": "2024-07-02T15:28:51.040919Z", + "iopub.status.idle": "2024-07-02T15:28:51.044291Z", + "shell.execute_reply": "2024-07-02T15:28:51.043776Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.046448Z", + "iopub.status.busy": "2024-07-02T15:28:51.046144Z", + "iopub.status.idle": "2024-07-02T15:28:51.053455Z", + "shell.execute_reply": "2024-07-02T15:28:51.052943Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.055596Z", + "iopub.status.busy": "2024-07-02T15:28:51.055273Z", + "iopub.status.idle": "2024-07-02T15:28:51.057820Z", + "shell.execute_reply": "2024-07-02T15:28:51.057306Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:51.059818Z", + "iopub.status.busy": "2024-07-02T15:28:51.059505Z", + "iopub.status.idle": "2024-07-02T15:28:54.001735Z", + "shell.execute_reply": "2024-07-02T15:28:54.001210Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:54.004479Z", + "iopub.status.busy": "2024-07-02T15:28:54.004049Z", + "iopub.status.idle": "2024-07-02T15:28:54.014544Z", + "shell.execute_reply": "2024-07-02T15:28:54.014111Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:54.016447Z", + "iopub.status.busy": "2024-07-02T15:28:54.016274Z", + "iopub.status.idle": "2024-07-02T15:28:55.858279Z", + "shell.execute_reply": "2024-07-02T15:28:55.857718Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.860474Z", + "iopub.status.busy": "2024-07-02T15:28:55.860182Z", + "iopub.status.idle": "2024-07-02T15:28:55.878763Z", + "shell.execute_reply": "2024-07-02T15:28:55.878229Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.880697Z", + "iopub.status.busy": "2024-07-02T15:28:55.880394Z", + "iopub.status.idle": "2024-07-02T15:28:55.888083Z", + "shell.execute_reply": "2024-07-02T15:28:55.887563Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.890072Z", + "iopub.status.busy": "2024-07-02T15:28:55.889768Z", + "iopub.status.idle": "2024-07-02T15:28:55.898858Z", + "shell.execute_reply": "2024-07-02T15:28:55.898441Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.900726Z", + "iopub.status.busy": "2024-07-02T15:28:55.900551Z", + "iopub.status.idle": "2024-07-02T15:28:55.908414Z", + "shell.execute_reply": "2024-07-02T15:28:55.907969Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.910406Z", + "iopub.status.busy": "2024-07-02T15:28:55.910090Z", + "iopub.status.idle": "2024-07-02T15:28:55.918338Z", + "shell.execute_reply": "2024-07-02T15:28:55.917916Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.920407Z", + "iopub.status.busy": "2024-07-02T15:28:55.920098Z", + "iopub.status.idle": "2024-07-02T15:28:55.927249Z", + "shell.execute_reply": "2024-07-02T15:28:55.926766Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.929330Z", + "iopub.status.busy": "2024-07-02T15:28:55.929026Z", + "iopub.status.idle": "2024-07-02T15:28:55.936059Z", + "shell.execute_reply": "2024-07-02T15:28:55.935601Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:28:55.938151Z", + "iopub.status.busy": "2024-07-02T15:28:55.937828Z", + "iopub.status.idle": "2024-07-02T15:28:55.946138Z", + "shell.execute_reply": "2024-07-02T15:28:55.945575Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 1d126d098..054fd75a3 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: {'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'}
+Classes: {'supported_cards_and_currencies', 'beneficiary_not_allowed', 'cancel_transfer', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'card_about_to_expire', 'change_pin', 'apple_pay_or_google_pay', 'getting_spare_card', 'visa_or_mastercard'}

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 5204560ef..551ba82cd 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": "2024-07-02T15:14:01.500489Z", - "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" + "iopub.execute_input": "2024-07-02T15:28:58.631257Z", + "iopub.status.busy": "2024-07-02T15:28:58.631091Z", + "iopub.status.idle": "2024-07-02T15:29:01.231065Z", + "shell.execute_reply": "2024-07-02T15:29:01.230522Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:14:04.115579Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:01.233809Z", + "iopub.status.busy": "2024-07-02T15:29:01.233237Z", + "iopub.status.idle": "2024-07-02T15:29:01.236539Z", + "shell.execute_reply": "2024-07-02T15:29:01.236030Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.238509Z", + "iopub.status.busy": "2024-07-02T15:29:01.238205Z", + "iopub.status.idle": "2024-07-02T15:29:01.241273Z", + "shell.execute_reply": "2024-07-02T15:29:01.240730Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.243270Z", + "iopub.status.busy": "2024-07-02T15:29:01.242968Z", + "iopub.status.idle": "2024-07-02T15:29:01.265333Z", + "shell.execute_reply": "2024-07-02T15:29:01.264818Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.267342Z", + "iopub.status.busy": "2024-07-02T15:29:01.267008Z", + "iopub.status.idle": "2024-07-02T15:29:01.270863Z", + "shell.execute_reply": "2024-07-02T15:29:01.270395Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\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" + "Classes: {'supported_cards_and_currencies', 'beneficiary_not_allowed', 'cancel_transfer', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'card_about_to_expire', 'change_pin', 'apple_pay_or_google_pay', 'getting_spare_card', 'visa_or_mastercard'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.272802Z", + "iopub.status.busy": "2024-07-02T15:29:01.272475Z", + "iopub.status.idle": "2024-07-02T15:29:01.275495Z", + "shell.execute_reply": "2024-07-02T15:29:01.274962Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:01.277490Z", + "iopub.status.busy": "2024-07-02T15:29:01.277167Z", + "iopub.status.idle": "2024-07-02T15:29:05.263507Z", + "shell.execute_reply": "2024-07-02T15:29:05.262875Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:05.266231Z", + "iopub.status.busy": "2024-07-02T15:29:05.265792Z", + "iopub.status.idle": "2024-07-02T15:29:06.185238Z", + "shell.execute_reply": "2024-07-02T15:29:06.184672Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:06.187923Z", + "iopub.status.busy": "2024-07-02T15:29:06.187416Z", + "iopub.status.idle": "2024-07-02T15:29:06.190514Z", + "shell.execute_reply": "2024-07-02T15:29:06.190036Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:06.192745Z", + "iopub.status.busy": "2024-07-02T15:29:06.192366Z", + "iopub.status.idle": "2024-07-02T15:29:08.085828Z", + "shell.execute_reply": "2024-07-02T15:29:08.085168Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.090050Z", + "iopub.status.busy": "2024-07-02T15:29:08.088906Z", + "iopub.status.idle": "2024-07-02T15:29:08.114316Z", + "shell.execute_reply": "2024-07-02T15:29:08.113817Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.117749Z", + "iopub.status.busy": "2024-07-02T15:29:08.116842Z", + "iopub.status.idle": "2024-07-02T15:29:08.126972Z", + "shell.execute_reply": "2024-07-02T15:29:08.126538Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.129068Z", + "iopub.status.busy": "2024-07-02T15:29:08.128673Z", + "iopub.status.idle": "2024-07-02T15:29:08.133020Z", + "shell.execute_reply": "2024-07-02T15:29:08.132584Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.135018Z", + "iopub.status.busy": "2024-07-02T15:29:08.134691Z", + "iopub.status.idle": "2024-07-02T15:29:08.140889Z", + "shell.execute_reply": "2024-07-02T15:29:08.140420Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.142906Z", + "iopub.status.busy": "2024-07-02T15:29:08.142588Z", + "iopub.status.idle": "2024-07-02T15:29:08.148868Z", + "shell.execute_reply": "2024-07-02T15:29:08.148318Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.150927Z", + "iopub.status.busy": "2024-07-02T15:29:08.150568Z", + "iopub.status.idle": "2024-07-02T15:29:08.156157Z", + "shell.execute_reply": "2024-07-02T15:29:08.155694Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.158158Z", + "iopub.status.busy": "2024-07-02T15:29:08.157833Z", + "iopub.status.idle": "2024-07-02T15:29:08.165960Z", + "shell.execute_reply": "2024-07-02T15:29:08.165500Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.168102Z", + "iopub.status.busy": "2024-07-02T15:29:08.167668Z", + "iopub.status.idle": "2024-07-02T15:29:08.173302Z", + "shell.execute_reply": "2024-07-02T15:29:08.172857Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.175232Z", + "iopub.status.busy": "2024-07-02T15:29:08.174914Z", + "iopub.status.idle": "2024-07-02T15:29:08.180090Z", + "shell.execute_reply": "2024-07-02T15:29:08.179632Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.182081Z", + "iopub.status.busy": "2024-07-02T15:29:08.181762Z", + "iopub.status.idle": "2024-07-02T15:29:08.185358Z", + "shell.execute_reply": "2024-07-02T15:29:08.184918Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:08.187396Z", + "iopub.status.busy": "2024-07-02T15:29:08.187074Z", + "iopub.status.idle": "2024-07-02T15:29:08.192050Z", + "shell.execute_reply": "2024-07-02T15:29:08.191599Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index dee8c6eb4..18f04bcdc 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -3140,224 +3140,224 @@

6. (Optional) Visualize the Results - +
- - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 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 [00:03<00:00, 56242831.52it/s]
+100%|██████████| 170498071/170498071 [00:02<00:00, 63370425.61it/s]
@@ -3716,7 +3716,7 @@

5. Finding image-specific property scores

-
+
@@ -3733,7 +3733,7 @@

5. Finding image-specific property scores

-
+
@@ -3896,7 +3896,7 @@

Image-specific property scores in the transformed dataset -{"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} +{"state": {"3fe4d5f95a4b421ea7191cd652353f63": {"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}}, "509a070ad7814351956da09fa31f6d3e": {"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": ""}}, "975ecec9b0d240eebc17772cc26037e6": {"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_3fe4d5f95a4b421ea7191cd652353f63", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_509a070ad7814351956da09fa31f6d3e", "tabbable": null, "tooltip": null, "value": 200.0}}, "950c8017832643e59a584d9e5c14d158": {"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}}, "8356f3ec51974cf696a3e9692878bd4e": {"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}}, "e8e5474f252f462bb6b93f09309db91a": {"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_950c8017832643e59a584d9e5c14d158", "placeholder": "\u200b", "style": "IPY_MODEL_8356f3ec51974cf696a3e9692878bd4e", "tabbable": null, "tooltip": null, "value": "100%"}}, "cba826c457dc4039b375f27857c765ca": {"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}}, "fbabf37f132843a38191f8b243f94ccd": {"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}}, "74d7c5cc1042451a8756f13a29013665": {"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_cba826c457dc4039b375f27857c765ca", "placeholder": "\u200b", "style": "IPY_MODEL_fbabf37f132843a38191f8b243f94ccd", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007780.24it/s]"}}, "9e76293fc8c84697bab5d4b70a4497ed": {"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}}, "f24c55aaa3f2486eb049ea9140702ad3": {"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_e8e5474f252f462bb6b93f09309db91a", "IPY_MODEL_975ecec9b0d240eebc17772cc26037e6", "IPY_MODEL_74d7c5cc1042451a8756f13a29013665"], "layout": "IPY_MODEL_9e76293fc8c84697bab5d4b70a4497ed", "tabbable": null, "tooltip": null}}, "f737276f279444d0a64c6ae2d3596e26": {"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}}, "f2a441d85bb54e8e87b2a9bd9ec7a1b7": {"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": ""}}, "a06fb116c7f54de8ace8c44aa9c3252a": {"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_f737276f279444d0a64c6ae2d3596e26", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f2a441d85bb54e8e87b2a9bd9ec7a1b7", "tabbable": null, "tooltip": null, "value": 200.0}}, "f1f35c30be0c46cea8172b99479542a3": {"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}}, "5a67d709e70348c5a0788df8f06b7d00": {"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}}, "16441724cc5d442083bc8dc57220bdb5": {"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_f1f35c30be0c46cea8172b99479542a3", "placeholder": "\u200b", "style": "IPY_MODEL_5a67d709e70348c5a0788df8f06b7d00", "tabbable": null, "tooltip": null, "value": "100%"}}, "c65f07113a9243528835037ef624a06c": {"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}}, "140f94fffe9d41998f052b476411cdc5": {"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}}, "683dfd5472954d23960cee4af27b4942": {"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_c65f07113a9243528835037ef624a06c", "placeholder": "\u200b", "style": "IPY_MODEL_140f94fffe9d41998f052b476411cdc5", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007725.42it/s]"}}, "c42e40a052b345028b0e318e573b39c4": {"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}}, "29d1b664fe1d4701876841f59c318a5a": {"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_16441724cc5d442083bc8dc57220bdb5", "IPY_MODEL_a06fb116c7f54de8ace8c44aa9c3252a", "IPY_MODEL_683dfd5472954d23960cee4af27b4942"], "layout": "IPY_MODEL_c42e40a052b345028b0e318e573b39c4", "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 9f74b4e12..0eaee2f6c 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": "2024-07-02T15:14:14.103983Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:11.148814Z", + "iopub.status.busy": "2024-07-02T15:29:11.148637Z", + "iopub.status.idle": "2024-07-02T15:29:11.562068Z", + "shell.execute_reply": "2024-07-02T15:29:11.561444Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:11.564831Z", + "iopub.status.busy": "2024-07-02T15:29:11.564466Z", + "iopub.status.idle": "2024-07-02T15:29:11.689877Z", + "shell.execute_reply": "2024-07-02T15:29:11.689330Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:11.692161Z", + "iopub.status.busy": "2024-07-02T15:29:11.691698Z", + "iopub.status.idle": "2024-07-02T15:29:11.710885Z", + "shell.execute_reply": "2024-07-02T15:29:11.710323Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:11.713082Z", + "iopub.status.busy": "2024-07-02T15:29:11.712899Z", + "iopub.status.idle": "2024-07-02T15:29:14.335786Z", + "shell.execute_reply": "2024-07-02T15:29:14.335126Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:14.338194Z", + "iopub.status.busy": "2024-07-02T15:29:14.337838Z", + "iopub.status.idle": "2024-07-02T15:29:22.391410Z", + "shell.execute_reply": "2024-07-02T15:29:22.390817Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:22.393854Z", + "iopub.status.busy": "2024-07-02T15:29:22.393389Z", + "iopub.status.idle": "2024-07-02T15:29:22.535692Z", + "shell.execute_reply": "2024-07-02T15:29:22.535054Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:22.538266Z", + "iopub.status.busy": "2024-07-02T15:29:22.538089Z", + "iopub.status.idle": "2024-07-02T15:29:23.850373Z", + "shell.execute_reply": "2024-07-02T15:29:23.849867Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:23.852547Z", + "iopub.status.busy": "2024-07-02T15:29:23.852210Z", + "iopub.status.idle": "2024-07-02T15:29:24.256489Z", + "shell.execute_reply": "2024-07-02T15:29:24.255943Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.259129Z", + "iopub.status.busy": "2024-07-02T15:29:24.258483Z", + "iopub.status.idle": "2024-07-02T15:29:24.266937Z", + "shell.execute_reply": "2024-07-02T15:29:24.266488Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.268896Z", + "iopub.status.busy": "2024-07-02T15:29:24.268579Z", + "iopub.status.idle": "2024-07-02T15:29:24.287826Z", + "shell.execute_reply": "2024-07-02T15:29:24.287420Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.289629Z", + "iopub.status.busy": "2024-07-02T15:29:24.289459Z", + "iopub.status.idle": "2024-07-02T15:29:24.509373Z", + "shell.execute_reply": "2024-07-02T15:29:24.508832Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.511635Z", + "iopub.status.busy": "2024-07-02T15:29:24.511276Z", + "iopub.status.idle": "2024-07-02T15:29:24.530413Z", + "shell.execute_reply": "2024-07-02T15:29:24.529881Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.532504Z", + "iopub.status.busy": "2024-07-02T15:29:24.532105Z", + "iopub.status.idle": "2024-07-02T15:29:24.697579Z", + "shell.execute_reply": "2024-07-02T15:29:24.697155Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.699519Z", + "iopub.status.busy": "2024-07-02T15:29:24.699362Z", + "iopub.status.idle": "2024-07-02T15:29:24.710273Z", + "shell.execute_reply": "2024-07-02T15:29:24.709863Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.712113Z", + "iopub.status.busy": "2024-07-02T15:29:24.711959Z", + "iopub.status.idle": "2024-07-02T15:29:24.721478Z", + "shell.execute_reply": "2024-07-02T15:29:24.721032Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.723461Z", + "iopub.status.busy": "2024-07-02T15:29:24.723141Z", + "iopub.status.idle": "2024-07-02T15:29:24.749053Z", + "shell.execute_reply": "2024-07-02T15:29:24.748634Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.751055Z", + "iopub.status.busy": "2024-07-02T15:29:24.750756Z", + "iopub.status.idle": "2024-07-02T15:29:24.753280Z", + "shell.execute_reply": "2024-07-02T15:29:24.752846Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.755266Z", + "iopub.status.busy": "2024-07-02T15:29:24.754962Z", + "iopub.status.idle": "2024-07-02T15:29:24.773504Z", + "shell.execute_reply": "2024-07-02T15:29:24.773077Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.775512Z", + "iopub.status.busy": "2024-07-02T15:29:24.775219Z", + "iopub.status.idle": "2024-07-02T15:29:24.779215Z", + "shell.execute_reply": "2024-07-02T15:29:24.778781Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.781323Z", + "iopub.status.busy": "2024-07-02T15:29:24.780892Z", + "iopub.status.idle": "2024-07-02T15:29:24.814268Z", + "shell.execute_reply": "2024-07-02T15:29:24.813734Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:24.816285Z", + "iopub.status.busy": "2024-07-02T15:29:24.815990Z", + "iopub.status.idle": "2024-07-02T15:29:25.179432Z", + "shell.execute_reply": "2024-07-02T15:29:25.178873Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.181578Z", + "iopub.status.busy": "2024-07-02T15:29:25.181257Z", + "iopub.status.idle": "2024-07-02T15:29:25.184323Z", + "shell.execute_reply": "2024-07-02T15:29:25.183802Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.186452Z", + "iopub.status.busy": "2024-07-02T15:29:25.186047Z", + "iopub.status.idle": "2024-07-02T15:29:25.198843Z", + "shell.execute_reply": "2024-07-02T15:29:25.198320Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.200782Z", + "iopub.status.busy": "2024-07-02T15:29:25.200486Z", + "iopub.status.idle": "2024-07-02T15:29:25.213647Z", + "shell.execute_reply": "2024-07-02T15:29:25.213118Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.215595Z", + "iopub.status.busy": "2024-07-02T15:29:25.215421Z", + "iopub.status.idle": "2024-07-02T15:29:25.224854Z", + "shell.execute_reply": "2024-07-02T15:29:25.224443Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.226672Z", + "iopub.status.busy": "2024-07-02T15:29:25.226501Z", + "iopub.status.idle": "2024-07-02T15:29:25.235816Z", + "shell.execute_reply": "2024-07-02T15:29:25.235341Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.237869Z", + "iopub.status.busy": "2024-07-02T15:29:25.237457Z", + "iopub.status.idle": "2024-07-02T15:29:25.241007Z", + "shell.execute_reply": "2024-07-02T15:29:25.240556Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.242990Z", + "iopub.status.busy": "2024-07-02T15:29:25.242667Z", + "iopub.status.idle": "2024-07-02T15:29:25.292667Z", + "shell.execute_reply": "2024-07-02T15:29:25.292199Z" } }, "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": "2024-07-02T15:14:28.212907Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:25.294886Z", + "iopub.status.busy": "2024-07-02T15:29:25.294483Z", + "iopub.status.idle": "2024-07-02T15:29:25.299817Z", + "shell.execute_reply": "2024-07-02T15:29:25.299395Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.301975Z", + "iopub.status.busy": "2024-07-02T15:29:25.301501Z", + "iopub.status.idle": "2024-07-02T15:29:25.311839Z", + "shell.execute_reply": "2024-07-02T15:29:25.311419Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.313796Z", + "iopub.status.busy": "2024-07-02T15:29:25.313590Z", + "iopub.status.idle": "2024-07-02T15:29:25.524327Z", + "shell.execute_reply": "2024-07-02T15:29:25.523840Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.526204Z", + "iopub.status.busy": "2024-07-02T15:29:25.526033Z", + "iopub.status.idle": "2024-07-02T15:29:25.533387Z", + "shell.execute_reply": "2024-07-02T15:29:25.532865Z" }, "nbsphinx": "hidden" }, @@ -3760,10 +3760,10 @@ "execution_count": 33, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:25.535412Z", + "iopub.status.busy": "2024-07-02T15:29:25.535090Z", + "iopub.status.idle": "2024-07-02T15:29:31.824715Z", + "shell.execute_reply": "2024-07-02T15:29:31.824156Z" } }, "outputs": [ @@ -3787,7 +3787,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 458752/170498071 [00:00<00:37, 4495236.08it/s]" + " 1%| | 1769472/170498071 [00:00<00:09, 17569738.43it/s]" ] }, { @@ -3795,7 +3795,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 4227072/170498071 [00:00<00:07, 23242348.53it/s]" + " 6%|▌ | 9994240/170498071 [00:00<00:02, 55470141.49it/s]" ] }, { @@ -3803,7 +3803,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 9306112/170498071 [00:00<00:04, 35527365.93it/s]" + " 11%|█ | 18186240/170498071 [00:00<00:02, 67404805.75it/s]" ] }, { @@ -3811,7 +3811,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 13926400/170498071 [00:00<00:03, 39660501.21it/s]" + " 16%|█▌ | 26476544/170498071 [00:00<00:01, 73445517.40it/s]" ] }, { @@ -3819,7 +3819,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 18644992/170498071 [00:00<00:03, 42142752.96it/s]" + " 20%|██ | 34865152/170498071 [00:00<00:01, 77108513.90it/s]" ] }, { @@ -3827,7 +3827,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 23166976/170498071 [00:00<00:03, 43171320.39it/s]" + " 25%|██▌ | 43057152/170498071 [00:00<00:01, 78697943.81it/s]" ] }, { @@ -3835,7 +3835,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 27688960/170498071 [00:00<00:03, 43778497.63it/s]" + " 30%|███ | 51380224/170498071 [00:00<00:01, 79985067.48it/s]" ] }, { @@ -3843,7 +3843,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32276480/170498071 [00:00<00:03, 44429066.54it/s]" + " 35%|███▌ | 59703296/170498071 [00:00<00:01, 80909156.17it/s]" ] }, { @@ -3851,7 +3851,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 36732928/170498071 [00:00<00:03, 44053184.08it/s]" + " 40%|███▉ | 67928064/170498071 [00:00<00:01, 81246537.89it/s]" ] }, { @@ -3859,7 +3859,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 41156608/170498071 [00:01<00:03, 43037571.10it/s]" + " 45%|████▍ | 76251136/170498071 [00:01<00:01, 81748989.29it/s]" ] }, { @@ -3867,7 +3867,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 45481984/170498071 [00:01<00:02, 41764924.25it/s]" + " 50%|████▉ | 84574208/170498071 [00:01<00:01, 82134723.99it/s]" ] }, { @@ -3875,7 +3875,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 49741824/170498071 [00:01<00:02, 41917563.82it/s]" + " 55%|█████▍ | 92995584/170498071 [00:01<00:00, 82736426.96it/s]" ] }, { @@ -3883,7 +3883,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 54296576/170498071 [00:01<00:02, 42876516.22it/s]" + " 60%|█████▉ | 101580800/170498071 [00:01<00:00, 83638413.28it/s]" ] }, { @@ -3891,7 +3891,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 58884096/170498071 [00:01<00:02, 43617372.00it/s]" + " 65%|██████▍ | 110067712/170498071 [00:01<00:00, 83992383.40it/s]" ] }, { @@ -3899,7 +3899,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 63504384/170498071 [00:01<00:02, 44261340.15it/s]" + " 69%|██████▉ | 118489088/170498071 [00:01<00:00, 81517971.25it/s]" ] }, { @@ -3907,7 +3907,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 68124672/170498071 [00:01<00:02, 44684329.80it/s]" + " 74%|███████▍ | 126681088/170498071 [00:01<00:00, 66165934.08it/s]" ] }, { @@ -3915,7 +3915,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 72810496/170498071 [00:01<00:02, 45260580.12it/s]" + " 78%|███████▊ | 133791744/170498071 [00:01<00:00, 57474892.92it/s]" ] }, { @@ -3923,7 +3923,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 77430784/170498071 [00:01<00:02, 45420696.51it/s]" + " 82%|████████▏ | 140017664/170498071 [00:02<00:00, 52870786.58it/s]" ] }, { @@ -3931,7 +3931,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 82018304/170498071 [00:01<00:01, 45522327.94it/s]" + " 85%|████████▌ | 145686528/170498071 [00:02<00:00, 49446779.71it/s]" ] }, { @@ -3939,7 +3939,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 87097344/170498071 [00:02<00:01, 47025132.26it/s]" + " 89%|████████▊ | 150896640/170498071 [00:02<00:00, 47089743.42it/s]" ] }, { @@ -3947,7 +3947,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 95223808/170498071 [00:02<00:01, 57144332.93it/s]" + " 91%|█████████▏| 155779072/170498071 [00:02<00:00, 45996856.71it/s]" ] }, { @@ -3955,7 +3955,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 103972864/170498071 [00:02<00:01, 66190969.00it/s]" + " 94%|█████████▍| 160497664/170498071 [00:02<00:00, 45255686.94it/s]" ] }, { @@ -3963,7 +3963,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 112721920/170498071 [00:02<00:00, 72211785.85it/s]" + " 97%|█████████▋| 165085184/170498071 [00:02<00:00, 44462626.18it/s]" ] }, { @@ -3971,55 +3971,7 @@ "output_type": "stream", "text": [ "\r", - " 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]" + "100%|██████████| 170498071/170498071 [00:02<00:00, 63370425.61it/s]" ] }, { @@ -4093,10 +4045,10 @@ "execution_count": 34, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:31.827345Z", + "iopub.status.busy": "2024-07-02T15:29:31.826817Z", + "iopub.status.idle": "2024-07-02T15:29:31.894192Z", + "shell.execute_reply": "2024-07-02T15:29:31.893694Z" } }, "outputs": [], @@ -4118,10 +4070,10 @@ "execution_count": 35, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:31.896307Z", + "iopub.status.busy": "2024-07-02T15:29:31.895985Z", + "iopub.status.idle": "2024-07-02T15:29:31.935822Z", + "shell.execute_reply": "2024-07-02T15:29:31.935309Z" } }, "outputs": [], @@ -4155,10 +4107,10 @@ "execution_count": 36, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:31.938027Z", + "iopub.status.busy": "2024-07-02T15:29:31.937597Z", + "iopub.status.idle": "2024-07-02T15:29:33.356771Z", + "shell.execute_reply": "2024-07-02T15:29:33.356182Z" } }, "outputs": [ @@ -4232,10 +4184,10 @@ "execution_count": 37, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:33.359053Z", + "iopub.status.busy": "2024-07-02T15:29:33.358661Z", + "iopub.status.idle": "2024-07-02T15:29:34.163786Z", + "shell.execute_reply": "2024-07-02T15:29:34.163155Z" } }, "outputs": [ @@ -4250,7 +4202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "edeb0eb92f8e493694db63fbedcce068", + "model_id": "f24c55aaa3f2486eb049ea9140702ad3", "version_major": 2, "version_minor": 0 }, @@ -4274,7 +4226,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a53db32e879b421b9c5a2cb90345b461", + "model_id": "29d1b664fe1d4701876841f59c318a5a", "version_major": 2, "version_minor": 0 }, @@ -4524,7 +4476,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0ba1b4f02e98442dbf83a9d402d61603": { + "140f94fffe9d41998f052b476411cdc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4542,23 +4494,54 @@ "text_color": null } }, - "13f79e5c34544a20b6e43544e002e0d6": { + "16441724cc5d442083bc8dc57220bdb5": { "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_f1f35c30be0c46cea8172b99479542a3", + "placeholder": "​", + "style": "IPY_MODEL_5a67d709e70348c5a0788df8f06b7d00", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "29d1b664fe1d4701876841f59c318a5a": { + "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_16441724cc5d442083bc8dc57220bdb5", + "IPY_MODEL_a06fb116c7f54de8ace8c44aa9c3252a", + "IPY_MODEL_683dfd5472954d23960cee4af27b4942" + ], + "layout": "IPY_MODEL_c42e40a052b345028b0e318e573b39c4", + "tabbable": null, + "tooltip": null } }, - "21919d64401941e69bdd1e7fdeb39391": { + "3fe4d5f95a4b421ea7191cd652353f63": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4611,7 +4594,41 @@ "width": null } }, - "22dce5e6cbbd456899db36ca71231b83": { + "509a070ad7814351956da09fa31f6d3e": { + "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": "" + } + }, + "5a67d709e70348c5a0788df8f06b7d00": { + "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 + } + }, + "683dfd5472954d23960cee4af27b4942": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4626,15 +4643,56 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_246224e7d6e14ee996208e5a901506a3", + "layout": "IPY_MODEL_c65f07113a9243528835037ef624a06c", "placeholder": "​", - "style": "IPY_MODEL_669c9d816b274632945703bb33ea88b1", + "style": "IPY_MODEL_140f94fffe9d41998f052b476411cdc5", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 200/200 [00:00<00:00, 725.42it/s]" + } + }, + "74d7c5cc1042451a8756f13a29013665": { + "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_cba826c457dc4039b375f27857c765ca", + "placeholder": "​", + "style": "IPY_MODEL_fbabf37f132843a38191f8b243f94ccd", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 780.24it/s]" } }, - "246224e7d6e14ee996208e5a901506a3": { + "8356f3ec51974cf696a3e9692878bd4e": { + "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 + } + }, + "950c8017832643e59a584d9e5c14d158": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4687,7 +4745,33 @@ "width": null } }, - "49bd46daef6e4afaae2104d6fddc5eff": { + "975ecec9b0d240eebc17772cc26037e6": { + "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_3fe4d5f95a4b421ea7191cd652353f63", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_509a070ad7814351956da09fa31f6d3e", + "tabbable": null, + "tooltip": null, + "value": 200.0 + } + }, + "9e76293fc8c84697bab5d4b70a4497ed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4740,23 +4824,33 @@ "width": null } }, - "5f4143d1143347bf8d67acbd62e4c7a9": { + "a06fb116c7f54de8ace8c44aa9c3252a": { "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_f737276f279444d0a64c6ae2d3596e26", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f2a441d85bb54e8e87b2a9bd9ec7a1b7", + "tabbable": null, + "tooltip": null, + "value": 200.0 } }, - "5fe4a241c042442299e152f3905b32e6": { + "c42e40a052b345028b0e318e573b39c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4809,48 +4903,7 @@ "width": null } }, - "669c9d816b274632945703bb33ea88b1": { - "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 - } - }, - "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": { + "c65f07113a9243528835037ef624a06c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4903,7 +4956,7 @@ "width": null } }, - "945bafb17f6c4017b07c073239844118": { + "cba826c457dc4039b375f27857c765ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4956,31 +5009,30 @@ "width": null } }, - "a53db32e879b421b9c5a2cb90345b461": { + "e8e5474f252f462bb6b93f09309db91a": { "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_ae7e7acb2667481d93c3d5d070d947f1", - "IPY_MODEL_d03e8f0da10e418392f2df6f61dea5ed", - "IPY_MODEL_752199f5b1064d09a833e6e140acf999" - ], - "layout": "IPY_MODEL_5fe4a241c042442299e152f3905b32e6", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_950c8017832643e59a584d9e5c14d158", + "placeholder": "​", + "style": "IPY_MODEL_8356f3ec51974cf696a3e9692878bd4e", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "a6d4bb6587dc4b0ab299cde66d887195": { + "f1f35c30be0c46cea8172b99479542a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5033,123 +5085,47 @@ "width": null } }, - "ae7e7acb2667481d93c3d5d070d947f1": { + "f24c55aaa3f2486eb049ea9140702ad3": { "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_945bafb17f6c4017b07c073239844118", - "placeholder": "​", - "style": "IPY_MODEL_0ba1b4f02e98442dbf83a9d402d61603", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e8e5474f252f462bb6b93f09309db91a", + "IPY_MODEL_975ecec9b0d240eebc17772cc26037e6", + "IPY_MODEL_74d7c5cc1042451a8756f13a29013665" + ], + "layout": "IPY_MODEL_9e76293fc8c84697bab5d4b70a4497ed", "tabbable": null, - "tooltip": null, - "value": "100%" + "tooltip": null } }, - "b2c4bbe171d649419f7ff67ac4f27ab6": { + "f2a441d85bb54e8e87b2a9bd9ec7a1b7": { "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 - } - }, - "b7a191fc264f425c94ccbd4b2e6ff5bf": { - "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_d1fa249a3b3741948f9b90a3eba494cd", - "placeholder": "​", - "style": "IPY_MODEL_df530a10186c40c8b9ba0ace062c0018", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 806.22it/s]" - } - }, - "ba0f29fa569646e89dd03db3974a4a00": { - "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_7f367a2cdd5445f58aecb1320024dca9", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5f4143d1143347bf8d67acbd62e4c7a9", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "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 + "bar_color": null, + "description_width": "" } }, - "d1fa249a3b3741948f9b90a3eba494cd": { + "f737276f279444d0a64c6ae2d3596e26": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5202,7 +5178,7 @@ "width": null } }, - "df530a10186c40c8b9ba0ace062c0018": { + "fbabf37f132843a38191f8b243f94ccd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5219,30 +5195,6 @@ "font_size": null, "text_color": null } - }, - "edeb0eb92f8e493694db63fbedcce068": { - "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_22dce5e6cbbd456899db36ca71231b83", - "IPY_MODEL_ba0f29fa569646e89dd03db3974a4a00", - "IPY_MODEL_b7a191fc264f425c94ccbd4b2e6ff5bf" - ], - "layout": "IPY_MODEL_a6d4bb6587dc4b0ab299cde66d887195", - "tabbable": null, - "tooltip": null - } } }, "version_major": 2, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 46444aea9..c598eb866 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": "2024-07-02T15:14:41.637741Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:38.938026Z", + "iopub.status.busy": "2024-07-02T15:29:38.937857Z", + "iopub.status.idle": "2024-07-02T15:29:40.022180Z", + "shell.execute_reply": "2024-07-02T15:29:40.021619Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:14:42.750701Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:40.024706Z", + "iopub.status.busy": "2024-07-02T15:29:40.024284Z", + "iopub.status.idle": "2024-07-02T15:29:40.027131Z", + "shell.execute_reply": "2024-07-02T15:29:40.026613Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:40.029106Z", + "iopub.status.busy": "2024-07-02T15:29:40.028929Z", + "iopub.status.idle": "2024-07-02T15:29:40.040121Z", + "shell.execute_reply": "2024-07-02T15:29:40.039667Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:40.042199Z", + "iopub.status.busy": "2024-07-02T15:29:40.041874Z", + "iopub.status.idle": "2024-07-02T15:29:44.739408Z", + "shell.execute_reply": "2024-07-02T15:29:44.738930Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index b910d04a5..9e03f53f4 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 d639cd18b..fd037f7ef 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:46.799774Z", + "iopub.status.busy": "2024-07-02T15:29:46.799611Z", + "iopub.status.idle": "2024-07-02T15:29:47.868215Z", + "shell.execute_reply": "2024-07-02T15:29:47.867607Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:47.871043Z", + "iopub.status.busy": "2024-07-02T15:29:47.870781Z", + "iopub.status.idle": "2024-07-02T15:29:47.874130Z", + "shell.execute_reply": "2024-07-02T15:29:47.873594Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:47.876125Z", + "iopub.status.busy": "2024-07-02T15:29:47.875827Z", + "iopub.status.idle": "2024-07-02T15:29:50.933600Z", + "shell.execute_reply": "2024-07-02T15:29:50.932999Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:50.936675Z", + "iopub.status.busy": "2024-07-02T15:29:50.936022Z", + "iopub.status.idle": "2024-07-02T15:29:50.967828Z", + "shell.execute_reply": "2024-07-02T15:29:50.967284Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:50.970284Z", + "iopub.status.busy": "2024-07-02T15:29:50.969984Z", + "iopub.status.idle": "2024-07-02T15:29:50.996616Z", + "shell.execute_reply": "2024-07-02T15:29:50.996062Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:50.999234Z", + "iopub.status.busy": "2024-07-02T15:29:50.998784Z", + "iopub.status.idle": "2024-07-02T15:29:51.001903Z", + "shell.execute_reply": "2024-07-02T15:29:51.001337Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.004009Z", + "iopub.status.busy": "2024-07-02T15:29:51.003583Z", + "iopub.status.idle": "2024-07-02T15:29:51.006181Z", + "shell.execute_reply": "2024-07-02T15:29:51.005730Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.008311Z", + "iopub.status.busy": "2024-07-02T15:29:51.007960Z", + "iopub.status.idle": "2024-07-02T15:29:51.034036Z", + "shell.execute_reply": "2024-07-02T15:29:51.033489Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0af6d2097bac4b69850c70d9d5904db8", + "model_id": "b61f3b61abad4a309bf86a1aa8f2b9c5", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5e3107780da94917a2e6e00a57affa5f", + "model_id": "026a70ed918d454b979daad116575cd1", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.037426Z", + "iopub.status.busy": "2024-07-02T15:29:51.037087Z", + "iopub.status.idle": "2024-07-02T15:29:51.043755Z", + "shell.execute_reply": "2024-07-02T15:29:51.043334Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.045741Z", + "iopub.status.busy": "2024-07-02T15:29:51.045399Z", + "iopub.status.idle": "2024-07-02T15:29:51.048655Z", + "shell.execute_reply": "2024-07-02T15:29:51.048231Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.050733Z", + "iopub.status.busy": "2024-07-02T15:29:51.050424Z", + "iopub.status.idle": "2024-07-02T15:29:51.056495Z", + "shell.execute_reply": "2024-07-02T15:29:51.056070Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.058351Z", + "iopub.status.busy": "2024-07-02T15:29:51.058061Z", + "iopub.status.idle": "2024-07-02T15:29:51.087891Z", + "shell.execute_reply": "2024-07-02T15:29:51.087337Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.090312Z", + "iopub.status.busy": "2024-07-02T15:29:51.090023Z", + "iopub.status.idle": "2024-07-02T15:29:51.114800Z", + "shell.execute_reply": "2024-07-02T15:29:51.114219Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.117401Z", + "iopub.status.busy": "2024-07-02T15:29:51.117043Z", + "iopub.status.idle": "2024-07-02T15:29:51.231817Z", + "shell.execute_reply": "2024-07-02T15:29:51.231206Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:51.234379Z", + "iopub.status.busy": "2024-07-02T15:29:51.233839Z", + "iopub.status.idle": "2024-07-02T15:29:54.239820Z", + "shell.execute_reply": "2024-07-02T15:29:54.239255Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.242175Z", + "iopub.status.busy": "2024-07-02T15:29:54.241796Z", + "iopub.status.idle": "2024-07-02T15:29:54.298692Z", + "shell.execute_reply": "2024-07-02T15:29:54.298201Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.300662Z", + "iopub.status.busy": "2024-07-02T15:29:54.300332Z", + "iopub.status.idle": "2024-07-02T15:29:54.341979Z", + "shell.execute_reply": "2024-07-02T15:29:54.341413Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "50482bad", + "id": "4848cf7c", "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": "07405bb8", + "id": "eaad044a", "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": "f375f11d", + "id": "0b523bbb", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "ada84c58", + "id": "84320802", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.344243Z", + "iopub.status.busy": "2024-07-02T15:29:54.344071Z", + "iopub.status.idle": "2024-07-02T15:29:54.351389Z", + "shell.execute_reply": "2024-07-02T15:29:54.350841Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "13fb70ab", + "id": "1925ba8e", "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": "692524aa", + "id": "0829c387", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.353489Z", + "iopub.status.busy": "2024-07-02T15:29:54.353071Z", + "iopub.status.idle": "2024-07-02T15:29:54.371123Z", + "shell.execute_reply": "2024-07-02T15:29:54.370577Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "c63f4c73", + "id": "b4df50e4", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:54.373055Z", + "iopub.status.busy": "2024-07-02T15:29:54.372731Z", + "iopub.status.idle": "2024-07-02T15:29:54.375873Z", + "shell.execute_reply": "2024-07-02T15:29:54.375357Z" } }, "outputs": [ @@ -1622,7 +1622,170 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "03bdac28b0744e63957425aef141438c": { + "026a70ed918d454b979daad116575cd1": { + "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_7c008af7a8e14f158b908bdea72be0c3", + "IPY_MODEL_5d93b4262fd54a69b71094aebc5ee106", + "IPY_MODEL_2534d227f7c84093a890aa8da483c8d4" + ], + "layout": "IPY_MODEL_8dff42b5c3474095ada1b975ed329e2d", + "tabbable": null, + "tooltip": null + } + }, + "02875877ae314d399c4c41bf46506420": { + "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 + } + }, + "0341ad6e27fb45b990863bbbb6424cf0": { + "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_2d25e2c546e744e498c5589683f7a800", + "placeholder": "​", + "style": "IPY_MODEL_145cb1b61bd14b5a94afbfc522dce648", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1111132.78it/s]" + } + }, + "0626a4c0233f4806987eae80b809ca9c": { + "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_ac08271c7a40448db2e9845998b12b94", + "placeholder": "​", + "style": "IPY_MODEL_5a9f1856ee4548d29ac2fa74efba69da", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: " + } + }, + "12ba7ec803a94e239e6ab2e3faeb7c96": { + "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 + } + }, + "145cb1b61bd14b5a94afbfc522dce648": { + "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 + } + }, + "2534d227f7c84093a890aa8da483c8d4": { + "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_ce653676309345038faf46f6f089becc", + "placeholder": "​", + "style": "IPY_MODEL_12ba7ec803a94e239e6ab2e3faeb7c96", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1115595.39it/s]" + } + }, + "25d058621d424fcdb7e9277a77685c68": { + "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": "" + } + }, + "2d25e2c546e744e498c5589683f7a800": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1675,31 +1838,51 @@ "width": null } }, - "0af6d2097bac4b69850c70d9d5904db8": { + "5a9f1856ee4548d29ac2fa74efba69da": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "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 + } + }, + "5d93b4262fd54a69b71094aebc5ee106": { + "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_b05e5e24c03d4b8393b6fbc900c09846", - "IPY_MODEL_1a648d3a4fa24399916eb3b310f94eb1", - "IPY_MODEL_1d20270beb494280ba9716e526d53f5b" - ], - "layout": "IPY_MODEL_162ace65984b48448d278f7f058df9b9", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6437b738ec874299a07051ee9e8445c0", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_25d058621d424fcdb7e9277a77685c68", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 50.0 } }, - "162ace65984b48448d278f7f058df9b9": { + "6437b738ec874299a07051ee9e8445c0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1752,56 +1935,7 @@ "width": null } }, - "1a648d3a4fa24399916eb3b310f94eb1": { - "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_2fc72a84067d46678a572aa49ebcbfee", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_d390dd0db893478398a63bc195614ea3", - "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "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": { + "6b2574bdfa604c3fab7148564af0b00e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1854,23 +1988,7 @@ "width": null } }, - "28fb5636d77344d0af4d56b62998af86": { - "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": "" - } - }, - "2fc72a84067d46678a572aa49ebcbfee": { + "6ca71fb821a542d6aa63710c2222debc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1923,72 +2041,49 @@ "width": null } }, - "46803bc4f974489e87287e18bb02597e": { + "71c281f097914f28957bc6a7866a0b4b": { "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_ee2eaee4046a40398bf58b1b4a9e3ebc", - "placeholder": "​", - "style": "IPY_MODEL_65acdb25c5c948c9848681db00fdc46b", + "layout": "IPY_MODEL_6ca71fb821a542d6aa63710c2222debc", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7a2c46b59b8f4e74a0844c5242d93052", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: " - } - }, - "5e3107780da94917a2e6e00a57affa5f": { - "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_46803bc4f974489e87287e18bb02597e", - "IPY_MODEL_d31a1289a39a46899da8b556759347e9", - "IPY_MODEL_6e15813423a14f6c9174690b8907c81c" - ], - "layout": "IPY_MODEL_1edeca25122644478494bfcfeda4647f", - "tabbable": null, - "tooltip": null + "value": 50.0 } }, - "65acdb25c5c948c9848681db00fdc46b": { + "7a2c46b59b8f4e74a0844c5242d93052": { "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": "" } }, - "6e15813423a14f6c9174690b8907c81c": { + "7c008af7a8e14f158b908bdea72be0c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2003,15 +2098,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ac3767a4657d48b4a0218f3167e710d9", + "layout": "IPY_MODEL_6b2574bdfa604c3fab7148564af0b00e", "placeholder": "​", - "style": "IPY_MODEL_f0f4c38d78db4623a4f8710c6f263222", + "style": "IPY_MODEL_02875877ae314d399c4c41bf46506420", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1603327.22it/s]" + "value": "number of examples processed for checking labels: " } }, - "a9d39a3ea28d44c682f45f076bc8bd67": { + "8dff42b5c3474095ada1b975ed329e2d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2064,7 +2159,7 @@ "width": null } }, - "ac3767a4657d48b4a0218f3167e710d9": { + "ac08271c7a40448db2e9845998b12b94": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2117,72 +2212,31 @@ "width": null } }, - "b05e5e24c03d4b8393b6fbc900c09846": { + "b61f3b61abad4a309bf86a1aa8f2b9c5": { "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_d697f27b641d4fa4a8cece8123222bff", - "placeholder": "​", - "style": "IPY_MODEL_eddf7a5ddfe14a858eb2826e37c0b231", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: " - } - }, - "d31a1289a39a46899da8b556759347e9": { - "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_a9d39a3ea28d44c682f45f076bc8bd67", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_28fb5636d77344d0af4d56b62998af86", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0626a4c0233f4806987eae80b809ca9c", + "IPY_MODEL_71c281f097914f28957bc6a7866a0b4b", + "IPY_MODEL_0341ad6e27fb45b990863bbbb6424cf0" + ], + "layout": "IPY_MODEL_e819615fa807467fa92420d45a5f8e70", "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "d390dd0db893478398a63bc195614ea3": { - "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": "" + "tooltip": null } }, - "d697f27b641d4fa4a8cece8123222bff": { + "ce653676309345038faf46f6f089becc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2235,43 +2289,7 @@ "width": null } }, - "d92d13f5147b4721964e817fcd0e6a7b": { - "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 - } - }, - "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": { + "e819615fa807467fa92420d45a5f8e70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2323,24 +2341,6 @@ "visibility": null, "width": null } - }, - "f0f4c38d78db4623a4f8710c6f263222": { - "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/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index 6e9b55b48..5f593443c 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": "2024-07-02T15:15:01.547795Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:57.626742Z", + "iopub.status.busy": "2024-07-02T15:29:57.626416Z", + "iopub.status.idle": "2024-07-02T15:29:58.762096Z", + "shell.execute_reply": "2024-07-02T15:29:58.761534Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:02.727054Z", - "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" + "iopub.execute_input": "2024-07-02T15:29:58.764661Z", + "iopub.status.busy": "2024-07-02T15:29:58.764264Z", + "iopub.status.idle": "2024-07-02T15:29:58.939017Z", + "shell.execute_reply": "2024-07-02T15:29:58.938508Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:58.941285Z", + "iopub.status.busy": "2024-07-02T15:29:58.940947Z", + "iopub.status.idle": "2024-07-02T15:29:58.951883Z", + "shell.execute_reply": "2024-07-02T15:29:58.951455Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:58.953883Z", + "iopub.status.busy": "2024-07-02T15:29:58.953561Z", + "iopub.status.idle": "2024-07-02T15:29:59.157566Z", + "shell.execute_reply": "2024-07-02T15:29:59.157003Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:59.159969Z", + "iopub.status.busy": "2024-07-02T15:29:59.159619Z", + "iopub.status.idle": "2024-07-02T15:29:59.185196Z", + "shell.execute_reply": "2024-07-02T15:29:59.184736Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:29:59.187290Z", + "iopub.status.busy": "2024-07-02T15:29:59.186969Z", + "iopub.status.idle": "2024-07-02T15:30:01.138208Z", + "shell.execute_reply": "2024-07-02T15:30:01.137568Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:01.140791Z", + "iopub.status.busy": "2024-07-02T15:30:01.140323Z", + "iopub.status.idle": "2024-07-02T15:30:01.158153Z", + "shell.execute_reply": "2024-07-02T15:30:01.157659Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:01.160172Z", + "iopub.status.busy": "2024-07-02T15:30:01.159839Z", + "iopub.status.idle": "2024-07-02T15:30:02.572670Z", + "shell.execute_reply": "2024-07-02T15:30:02.572048Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.575334Z", + "iopub.status.busy": "2024-07-02T15:30:02.574705Z", + "iopub.status.idle": "2024-07-02T15:30:02.588246Z", + "shell.execute_reply": "2024-07-02T15:30:02.587734Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.590333Z", + "iopub.status.busy": "2024-07-02T15:30:02.589893Z", + "iopub.status.idle": "2024-07-02T15:30:02.660087Z", + "shell.execute_reply": "2024-07-02T15:30:02.659495Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.662433Z", + "iopub.status.busy": "2024-07-02T15:30:02.662252Z", + "iopub.status.idle": "2024-07-02T15:30:02.869194Z", + "shell.execute_reply": "2024-07-02T15:30:02.868731Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.871113Z", + "iopub.status.busy": "2024-07-02T15:30:02.870940Z", + "iopub.status.idle": "2024-07-02T15:30:02.887264Z", + "shell.execute_reply": "2024-07-02T15:30:02.886836Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.889231Z", + "iopub.status.busy": "2024-07-02T15:30:02.888911Z", + "iopub.status.idle": "2024-07-02T15:30:02.898236Z", + "shell.execute_reply": "2024-07-02T15:30:02.897802Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.900052Z", + "iopub.status.busy": "2024-07-02T15:30:02.899882Z", + "iopub.status.idle": "2024-07-02T15:30:02.982013Z", + "shell.execute_reply": "2024-07-02T15:30:02.981469Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:02.984381Z", + "iopub.status.busy": "2024-07-02T15:30:02.984039Z", + "iopub.status.idle": "2024-07-02T15:30:03.089218Z", + "shell.execute_reply": "2024-07-02T15:30:03.088619Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.091652Z", + "iopub.status.busy": "2024-07-02T15:30:03.091361Z", + "iopub.status.idle": "2024-07-02T15:30:03.094989Z", + "shell.execute_reply": "2024-07-02T15:30:03.094462Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.096993Z", + "iopub.status.busy": "2024-07-02T15:30:03.096623Z", + "iopub.status.idle": "2024-07-02T15:30:03.100533Z", + "shell.execute_reply": "2024-07-02T15:30:03.100094Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.102469Z", + "iopub.status.busy": "2024-07-02T15:30:03.102207Z", + "iopub.status.idle": "2024-07-02T15:30:03.138746Z", + "shell.execute_reply": "2024-07-02T15:30:03.138321Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.140829Z", + "iopub.status.busy": "2024-07-02T15:30:03.140499Z", + "iopub.status.idle": "2024-07-02T15:30:03.180614Z", + "shell.execute_reply": "2024-07-02T15:30:03.180176Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.182585Z", + "iopub.status.busy": "2024-07-02T15:30:03.182265Z", + "iopub.status.idle": "2024-07-02T15:30:03.268381Z", + "shell.execute_reply": "2024-07-02T15:30:03.267837Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.271234Z", + "iopub.status.busy": "2024-07-02T15:30:03.270873Z", + "iopub.status.idle": "2024-07-02T15:30:03.344389Z", + "shell.execute_reply": "2024-07-02T15:30:03.343870Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.346546Z", + "iopub.status.busy": "2024-07-02T15:30:03.346316Z", + "iopub.status.idle": "2024-07-02T15:30:03.553893Z", + "shell.execute_reply": "2024-07-02T15:30:03.553327Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.555908Z", + "iopub.status.busy": "2024-07-02T15:30:03.555726Z", + "iopub.status.idle": "2024-07-02T15:30:03.721372Z", + "shell.execute_reply": "2024-07-02T15:30:03.720791Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.723778Z", + "iopub.status.busy": "2024-07-02T15:30:03.723338Z", + "iopub.status.idle": "2024-07-02T15:30:03.729507Z", + "shell.execute_reply": "2024-07-02T15:30:03.729058Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.731405Z", + "iopub.status.busy": "2024-07-02T15:30:03.731109Z", + "iopub.status.idle": "2024-07-02T15:30:03.945067Z", + "shell.execute_reply": "2024-07-02T15:30:03.944605Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:03.947155Z", + "iopub.status.busy": "2024-07-02T15:30:03.946841Z", + "iopub.status.idle": "2024-07-02T15:30:05.010426Z", + "shell.execute_reply": "2024-07-02T15:30:05.009870Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index e3e8817fd..00df8da7f 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:08.416538Z", + "iopub.status.busy": "2024-07-02T15:30:08.416373Z", + "iopub.status.idle": "2024-07-02T15:30:09.491929Z", + "shell.execute_reply": "2024-07-02T15:30:09.491393Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:13.634301Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:09.494528Z", + "iopub.status.busy": "2024-07-02T15:30:09.494125Z", + "iopub.status.idle": "2024-07-02T15:30:09.497173Z", + "shell.execute_reply": "2024-07-02T15:30:09.496630Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.499370Z", + "iopub.status.busy": "2024-07-02T15:30:09.498949Z", + "iopub.status.idle": "2024-07-02T15:30:09.506499Z", + "shell.execute_reply": "2024-07-02T15:30:09.505969Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.508570Z", + "iopub.status.busy": "2024-07-02T15:30:09.508265Z", + "iopub.status.idle": "2024-07-02T15:30:09.554643Z", + "shell.execute_reply": "2024-07-02T15:30:09.554193Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.556758Z", + "iopub.status.busy": "2024-07-02T15:30:09.556425Z", + "iopub.status.idle": "2024-07-02T15:30:09.573109Z", + "shell.execute_reply": "2024-07-02T15:30:09.572615Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.575083Z", + "iopub.status.busy": "2024-07-02T15:30:09.574778Z", + "iopub.status.idle": "2024-07-02T15:30:09.578384Z", + "shell.execute_reply": "2024-07-02T15:30:09.577927Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.580410Z", + "iopub.status.busy": "2024-07-02T15:30:09.580081Z", + "iopub.status.idle": "2024-07-02T15:30:09.593572Z", + "shell.execute_reply": "2024-07-02T15:30:09.593159Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.595521Z", + "iopub.status.busy": "2024-07-02T15:30:09.595213Z", + "iopub.status.idle": "2024-07-02T15:30:09.620528Z", + "shell.execute_reply": "2024-07-02T15:30:09.620111Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:09.622444Z", + "iopub.status.busy": "2024-07-02T15:30:09.622151Z", + "iopub.status.idle": "2024-07-02T15:30:11.445778Z", + "shell.execute_reply": "2024-07-02T15:30:11.445137Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.448367Z", + "iopub.status.busy": "2024-07-02T15:30:11.448093Z", + "iopub.status.idle": "2024-07-02T15:30:11.454787Z", + "shell.execute_reply": "2024-07-02T15:30:11.454316Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.456765Z", + "iopub.status.busy": "2024-07-02T15:30:11.456466Z", + "iopub.status.idle": "2024-07-02T15:30:11.468719Z", + "shell.execute_reply": "2024-07-02T15:30:11.468282Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.470607Z", + "iopub.status.busy": "2024-07-02T15:30:11.470346Z", + "iopub.status.idle": "2024-07-02T15:30:11.476456Z", + "shell.execute_reply": "2024-07-02T15:30:11.476041Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.478572Z", + "iopub.status.busy": "2024-07-02T15:30:11.478242Z", + "iopub.status.idle": "2024-07-02T15:30:11.480735Z", + "shell.execute_reply": "2024-07-02T15:30:11.480310Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.482783Z", + "iopub.status.busy": "2024-07-02T15:30:11.482474Z", + "iopub.status.idle": "2024-07-02T15:30:11.485723Z", + "shell.execute_reply": "2024-07-02T15:30:11.485194Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.487566Z", + "iopub.status.busy": "2024-07-02T15:30:11.487397Z", + "iopub.status.idle": "2024-07-02T15:30:11.489827Z", + "shell.execute_reply": "2024-07-02T15:30:11.489391Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.491611Z", + "iopub.status.busy": "2024-07-02T15:30:11.491445Z", + "iopub.status.idle": "2024-07-02T15:30:11.495334Z", + "shell.execute_reply": "2024-07-02T15:30:11.494831Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.497182Z", + "iopub.status.busy": "2024-07-02T15:30:11.497014Z", + "iopub.status.idle": "2024-07-02T15:30:11.524806Z", + "shell.execute_reply": "2024-07-02T15:30:11.524260Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:11.527068Z", + "iopub.status.busy": "2024-07-02T15:30:11.526763Z", + "iopub.status.idle": "2024-07-02T15:30:11.531196Z", + "shell.execute_reply": "2024-07-02T15:30:11.530662Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index cd94e39a4..b8ac96c40 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": "2024-07-02T15:15:18.624231Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:14.064593Z", + "iopub.status.busy": "2024-07-02T15:30:14.064421Z", + "iopub.status.idle": "2024-07-02T15:30:15.205978Z", + "shell.execute_reply": "2024-07-02T15:30:15.205327Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:19.810010Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:15.208418Z", + "iopub.status.busy": "2024-07-02T15:30:15.208143Z", + "iopub.status.idle": "2024-07-02T15:30:15.398147Z", + "shell.execute_reply": "2024-07-02T15:30:15.397569Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:15.400536Z", + "iopub.status.busy": "2024-07-02T15:30:15.400274Z", + "iopub.status.idle": "2024-07-02T15:30:15.413403Z", + "shell.execute_reply": "2024-07-02T15:30:15.412862Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:15.415691Z", + "iopub.status.busy": "2024-07-02T15:30:15.415248Z", + "iopub.status.idle": "2024-07-02T15:30:18.012829Z", + "shell.execute_reply": "2024-07-02T15:30:18.012269Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:18.015230Z", + "iopub.status.busy": "2024-07-02T15:30:18.014790Z", + "iopub.status.idle": "2024-07-02T15:30:19.361340Z", + "shell.execute_reply": "2024-07-02T15:30:19.360749Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:19.363622Z", + "iopub.status.busy": "2024-07-02T15:30:19.363439Z", + "iopub.status.idle": "2024-07-02T15:30:19.367038Z", + "shell.execute_reply": "2024-07-02T15:30:19.366492Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:19.368905Z", + "iopub.status.busy": "2024-07-02T15:30:19.368736Z", + "iopub.status.idle": "2024-07-02T15:30:21.276657Z", + "shell.execute_reply": "2024-07-02T15:30:21.276026Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:21.278971Z", + "iopub.status.busy": "2024-07-02T15:30:21.278630Z", + "iopub.status.idle": "2024-07-02T15:30:21.286413Z", + "shell.execute_reply": "2024-07-02T15:30:21.285940Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:21.288416Z", + "iopub.status.busy": "2024-07-02T15:30:21.288115Z", + "iopub.status.idle": "2024-07-02T15:30:23.806595Z", + "shell.execute_reply": "2024-07-02T15:30:23.806033Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:23.808820Z", + "iopub.status.busy": "2024-07-02T15:30:23.808415Z", + "iopub.status.idle": "2024-07-02T15:30:23.812033Z", + "shell.execute_reply": "2024-07-02T15:30:23.811486Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:23.814126Z", + "iopub.status.busy": "2024-07-02T15:30:23.813830Z", + "iopub.status.idle": "2024-07-02T15:30:23.817286Z", + "shell.execute_reply": "2024-07-02T15:30:23.816829Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:23.819098Z", + "iopub.status.busy": "2024-07-02T15:30:23.818930Z", + "iopub.status.idle": "2024-07-02T15:30:23.822031Z", + "shell.execute_reply": "2024-07-02T15:30:23.821569Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index a35b0cd70..2a7fb3ea1 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T15:15:30.956908Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:26.104900Z", + "iopub.status.busy": "2024-07-02T15:30:26.104494Z", + "iopub.status.idle": "2024-07-02T15:30:27.233572Z", + "shell.execute_reply": "2024-07-02T15:30:27.233026Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:32.097678Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:27.236094Z", + "iopub.status.busy": "2024-07-02T15:30:27.235693Z", + "iopub.status.idle": "2024-07-02T15:30:28.702868Z", + "shell.execute_reply": "2024-07-02T15:30:28.702121Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:28.705594Z", + "iopub.status.busy": "2024-07-02T15:30:28.705186Z", + "iopub.status.idle": "2024-07-02T15:30:28.708303Z", + "shell.execute_reply": "2024-07-02T15:30:28.707887Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:28.710362Z", + "iopub.status.busy": "2024-07-02T15:30:28.710043Z", + "iopub.status.idle": "2024-07-02T15:30:28.715820Z", + "shell.execute_reply": "2024-07-02T15:30:28.715418Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:28.717761Z", + "iopub.status.busy": "2024-07-02T15:30:28.717422Z", + "iopub.status.idle": "2024-07-02T15:30:29.197115Z", + "shell.execute_reply": "2024-07-02T15:30:29.196591Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:29.199674Z", + "iopub.status.busy": "2024-07-02T15:30:29.199317Z", + "iopub.status.idle": "2024-07-02T15:30:29.204590Z", + "shell.execute_reply": "2024-07-02T15:30:29.204051Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:29.206585Z", + "iopub.status.busy": "2024-07-02T15:30:29.206292Z", + "iopub.status.idle": "2024-07-02T15:30:29.210100Z", + "shell.execute_reply": "2024-07-02T15:30:29.209552Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:29.212071Z", + "iopub.status.busy": "2024-07-02T15:30:29.211769Z", + "iopub.status.idle": "2024-07-02T15:30:30.046952Z", + "shell.execute_reply": "2024-07-02T15:30:30.046327Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:30.049474Z", + "iopub.status.busy": "2024-07-02T15:30:30.049003Z", + "iopub.status.idle": "2024-07-02T15:30:30.269472Z", + "shell.execute_reply": "2024-07-02T15:30:30.269024Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:30.271620Z", + "iopub.status.busy": "2024-07-02T15:30:30.271296Z", + "iopub.status.idle": "2024-07-02T15:30:30.275317Z", + "shell.execute_reply": "2024-07-02T15:30:30.274889Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:30.277283Z", + "iopub.status.busy": "2024-07-02T15:30:30.276960Z", + "iopub.status.idle": "2024-07-02T15:30:30.718015Z", + "shell.execute_reply": "2024-07-02T15:30:30.717439Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:30.721098Z", + "iopub.status.busy": "2024-07-02T15:30:30.720763Z", + "iopub.status.idle": "2024-07-02T15:30:31.050736Z", + "shell.execute_reply": "2024-07-02T15:30:31.050209Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:31.053203Z", + "iopub.status.busy": "2024-07-02T15:30:31.052800Z", + "iopub.status.idle": "2024-07-02T15:30:31.411935Z", + "shell.execute_reply": "2024-07-02T15:30:31.411382Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:31.414916Z", + "iopub.status.busy": "2024-07-02T15:30:31.414688Z", + "iopub.status.idle": "2024-07-02T15:30:31.848848Z", + "shell.execute_reply": "2024-07-02T15:30:31.848291Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:31.852793Z", + "iopub.status.busy": "2024-07-02T15:30:31.852402Z", + "iopub.status.idle": "2024-07-02T15:30:32.294906Z", + "shell.execute_reply": "2024-07-02T15:30:32.294313Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:32.297619Z", + "iopub.status.busy": "2024-07-02T15:30:32.297290Z", + "iopub.status.idle": "2024-07-02T15:30:32.486044Z", + "shell.execute_reply": "2024-07-02T15:30:32.485463Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:32.488589Z", + "iopub.status.busy": "2024-07-02T15:30:32.488111Z", + "iopub.status.idle": "2024-07-02T15:30:32.667785Z", + "shell.execute_reply": "2024-07-02T15:30:32.667290Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:32.670272Z", + "iopub.status.busy": "2024-07-02T15:30:32.669957Z", + "iopub.status.idle": "2024-07-02T15:30:32.672881Z", + "shell.execute_reply": "2024-07-02T15:30:32.672341Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:32.674835Z", + "iopub.status.busy": "2024-07-02T15:30:32.674531Z", + "iopub.status.idle": "2024-07-02T15:30:33.648630Z", + "shell.execute_reply": "2024-07-02T15:30:33.648115Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:33.651075Z", + "iopub.status.busy": "2024-07-02T15:30:33.650749Z", + "iopub.status.idle": "2024-07-02T15:30:33.832380Z", + "shell.execute_reply": "2024-07-02T15:30:33.831930Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:33.834350Z", + "iopub.status.busy": "2024-07-02T15:30:33.834178Z", + "iopub.status.idle": "2024-07-02T15:30:33.965756Z", + "shell.execute_reply": "2024-07-02T15:30:33.965334Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:33.967745Z", + "iopub.status.busy": "2024-07-02T15:30:33.967436Z", + "iopub.status.idle": "2024-07-02T15:30:34.624537Z", + "shell.execute_reply": "2024-07-02T15:30:34.623949Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:34.626893Z", + "iopub.status.busy": "2024-07-02T15:30:34.626704Z", + "iopub.status.idle": "2024-07-02T15:30:34.630276Z", + "shell.execute_reply": "2024-07-02T15:30:34.629829Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 78064e277..a9863801c 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -780,7 +780,7 @@

2. Pre-process the Cifar10 dataset 
-100%|██████████| 170498071/170498071 [00:01<00:00, 106209257.98it/s]
+100%|██████████| 170498071/170498071 [00:02<00:00, 75087729.26it/s]

@@ -910,7 +910,7 @@

3. Use cleanlab and feature embeddings to find outliers in the data

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

4. Use cleanlab and here.

diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index e7ee45271..a01751703 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:36.707833Z", + "iopub.status.busy": "2024-07-02T15:30:36.707432Z", + "iopub.status.idle": "2024-07-02T15:30:39.352386Z", + "shell.execute_reply": "2024-07-02T15:30:39.351837Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:15:44.233719Z", - "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" + "iopub.execute_input": "2024-07-02T15:30:39.355103Z", + "iopub.status.busy": "2024-07-02T15:30:39.354574Z", + "iopub.status.idle": "2024-07-02T15:30:39.661584Z", + "shell.execute_reply": "2024-07-02T15:30:39.660980Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:39.664187Z", + "iopub.status.busy": "2024-07-02T15:30:39.663844Z", + "iopub.status.idle": "2024-07-02T15:30:39.668417Z", + "shell.execute_reply": "2024-07-02T15:30:39.667891Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:39.670626Z", + "iopub.status.busy": "2024-07-02T15:30:39.670320Z", + "iopub.status.idle": "2024-07-02T15:30:44.460714Z", + "shell.execute_reply": "2024-07-02T15:30:44.460162Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 917504/170498071 [00:00<00:20, 8200886.72it/s]" + " 1%| | 1933312/170498071 [00:00<00:08, 19261178.24it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10780672/170498071 [00:00<00:02, 58894029.31it/s]" + " 6%|▌ | 9666560/170498071 [00:00<00:03, 53300272.89it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22380544/170498071 [00:00<00:01, 84273722.65it/s]" + " 11%|█ | 18022400/170498071 [00:00<00:02, 66766772.02it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 33783808/170498071 [00:00<00:01, 95827715.47it/s]" + " 15%|█▌ | 26017792/170498071 [00:00<00:02, 71799904.98it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 45383680/170498071 [00:00<00:01, 102972274.05it/s]" + " 20%|██ | 34111488/170498071 [00:00<00:01, 74851602.40it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 56721408/170498071 [00:00<00:01, 106415655.53it/s]" + " 24%|██▍ | 41615360/170498071 [00:00<00:01, 74050157.55it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 68288512/170498071 [00:00<00:00, 109377801.86it/s]" + " 29%|██▉ | 49414144/170498071 [00:00<00:01, 75240501.10it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 79790080/170498071 [00:00<00:00, 111060852.43it/s]" + " 34%|███▍ | 57802752/170498071 [00:00<00:01, 77927286.11it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 91291648/170498071 [00:00<00:00, 112242317.21it/s]" + " 39%|███▊ | 65929216/170498071 [00:00<00:01, 78770128.23it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 102727680/170498071 [00:01<00:00, 112875530.06it/s]" + " 43%|████▎ | 73826304/170498071 [00:01<00:01, 77447245.96it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 114262016/170498071 [00:01<00:00, 113610104.30it/s]" + " 48%|████▊ | 81887232/170498071 [00:01<00:01, 78343847.05it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 125665280/170498071 [00:01<00:00, 112903553.22it/s]" + " 53%|█████▎ | 89751552/170498071 [00:01<00:01, 76411811.17it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 137396224/170498071 [00:01<00:00, 114077850.23it/s]" + " 57%|█████▋ | 97452032/170498071 [00:01<00:00, 76559764.63it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 148897792/170498071 [00:01<00:00, 114231113.69it/s]" + " 62%|██████▏ | 105119744/170498071 [00:01<00:00, 76377178.30it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 160399360/170498071 [00:01<00:00, 114421071.55it/s]" + " 66%|██████▌ | 112787456/170498071 [00:01<00:00, 76223255.83it/s]" ] }, { @@ -372,7 +372,63 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:01<00:00, 106209257.98it/s]" + " 71%|███████ | 121405440/170498071 [00:01<00:00, 79156566.53it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 76%|███████▌ | 129335296/170498071 [00:01<00:00, 78370632.15it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 80%|████████ | 137232384/170498071 [00:01<00:00, 78523013.51it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 85%|████████▌ | 145096704/170498071 [00:01<00:00, 76246718.61it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 90%|████████▉ | 153026560/170498071 [00:02<00:00, 76949957.68it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 94%|█████████▍| 160825344/170498071 [00:02<00:00, 77200889.35it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 168558592/170498071 [00:02<00:00, 76174010.79it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:02<00:00, 75087729.26it/s]" ] }, { @@ -490,10 +546,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:44.462980Z", + "iopub.status.busy": "2024-07-02T15:30:44.462612Z", + "iopub.status.idle": "2024-07-02T15:30:44.467530Z", + "shell.execute_reply": "2024-07-02T15:30:44.467072Z" }, "nbsphinx": "hidden" }, @@ -544,10 +600,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:44.469606Z", + "iopub.status.busy": "2024-07-02T15:30:44.469261Z", + "iopub.status.idle": "2024-07-02T15:30:45.008289Z", + "shell.execute_reply": "2024-07-02T15:30:45.007683Z" } }, "outputs": [ @@ -580,10 +636,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:45.010647Z", + "iopub.status.busy": "2024-07-02T15:30:45.010334Z", + "iopub.status.idle": "2024-07-02T15:30:45.519231Z", + "shell.execute_reply": "2024-07-02T15:30:45.518726Z" } }, "outputs": [ @@ -621,10 +677,10 @@ "id": "1cf25354", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:45.521432Z", + "iopub.status.busy": "2024-07-02T15:30:45.521094Z", + "iopub.status.idle": "2024-07-02T15:30:45.524584Z", + "shell.execute_reply": "2024-07-02T15:30:45.524036Z" } }, "outputs": [], @@ -647,17 +703,17 @@ "id": "85a58d41", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:45.526495Z", + "iopub.status.busy": "2024-07-02T15:30:45.526191Z", + "iopub.status.idle": "2024-07-02T15:30:58.240257Z", + "shell.execute_reply": "2024-07-02T15:30:58.239689Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7134c3b9c85247698385a933e9c6f4c1", + "model_id": "3e5259ff69044f2d9040e07e24baf5d7", "version_major": 2, "version_minor": 0 }, @@ -716,10 +772,10 @@ "id": "feb0f519", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:30:58.242629Z", + "iopub.status.busy": "2024-07-02T15:30:58.242434Z", + "iopub.status.idle": "2024-07-02T15:31:00.298294Z", + "shell.execute_reply": "2024-07-02T15:31:00.297716Z" } }, "outputs": [ @@ -763,10 +819,10 @@ "id": "089d5860", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:00.300955Z", + "iopub.status.busy": "2024-07-02T15:31:00.300659Z", + "iopub.status.idle": "2024-07-02T15:31:00.552803Z", + "shell.execute_reply": "2024-07-02T15:31:00.552236Z" } }, "outputs": [ @@ -802,10 +858,10 @@ "id": "78b1951c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:00.555642Z", + "iopub.status.busy": "2024-07-02T15:31:00.555135Z", + "iopub.status.idle": "2024-07-02T15:31:01.217327Z", + "shell.execute_reply": "2024-07-02T15:31:01.216752Z" } }, "outputs": [ @@ -855,10 +911,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:01.220258Z", + "iopub.status.busy": "2024-07-02T15:31:01.219756Z", + "iopub.status.idle": "2024-07-02T15:31:01.555237Z", + "shell.execute_reply": "2024-07-02T15:31:01.554707Z" } }, "outputs": [ @@ -906,10 +962,10 @@ "id": "616769f8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:01.557486Z", + "iopub.status.busy": "2024-07-02T15:31:01.557127Z", + "iopub.status.idle": "2024-07-02T15:31:01.796360Z", + "shell.execute_reply": "2024-07-02T15:31:01.795785Z" } }, "outputs": [ @@ -965,10 +1021,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:01.798780Z", + "iopub.status.busy": "2024-07-02T15:31:01.798246Z", + "iopub.status.idle": "2024-07-02T15:31:01.878845Z", + "shell.execute_reply": "2024-07-02T15:31:01.878199Z" } }, "outputs": [], @@ -989,10 +1045,10 @@ "id": "89f9db72", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:01.881383Z", + "iopub.status.busy": "2024-07-02T15:31:01.881198Z", + "iopub.status.idle": "2024-07-02T15:31:12.064664Z", + "shell.execute_reply": "2024-07-02T15:31:12.064061Z" } }, "outputs": [ @@ -1029,10 +1085,10 @@ "id": "874c885a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:12.067005Z", + "iopub.status.busy": "2024-07-02T15:31:12.066698Z", + "iopub.status.idle": "2024-07-02T15:31:14.192509Z", + "shell.execute_reply": "2024-07-02T15:31:14.192014Z" } }, "outputs": [ @@ -1063,10 +1119,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:14.195306Z", + "iopub.status.busy": "2024-07-02T15:31:14.194774Z", + "iopub.status.idle": "2024-07-02T15:31:14.398366Z", + "shell.execute_reply": "2024-07-02T15:31:14.397868Z" } }, "outputs": [], @@ -1080,10 +1136,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:14.400634Z", + "iopub.status.busy": "2024-07-02T15:31:14.400446Z", + "iopub.status.idle": "2024-07-02T15:31:14.403545Z", + "shell.execute_reply": "2024-07-02T15:31:14.403109Z" } }, "outputs": [], @@ -1105,10 +1161,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:14.405375Z", + "iopub.status.busy": "2024-07-02T15:31:14.405202Z", + "iopub.status.idle": "2024-07-02T15:31:14.413303Z", + "shell.execute_reply": "2024-07-02T15:31:14.412850Z" }, "nbsphinx": "hidden" }, @@ -1153,7 +1209,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0ec7acb06a8d4e7c8cee6f0af1617289": { + "0a031667bcbf4ad0b0f0c1a2382f5f2e": { + "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 + } + }, + "2f6020cef60b4bf8bac0b8389a1b6833": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1168,15 +1242,83 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_252fe402519d4210ba4ba3fef0912a86", + "layout": "IPY_MODEL_fbf5a8d22db64f0eb01aa7fd53c1b68b", "placeholder": "​", - "style": "IPY_MODEL_b8360c36dca94afc98ec4fb786a3c57f", + "style": "IPY_MODEL_0a031667bcbf4ad0b0f0c1a2382f5f2e", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 152MB/s]" + } + }, + "3cd1e403a7754c11922ba99ec3494a8e": { + "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_e052a3ce162f4f6c9ff6c38cfe463797", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_aa0fb39e0a6040b5b93e4814da7189e3", "tabbable": null, "tooltip": null, - "value": " 102M/102M [00:00<00:00, 304MB/s]" + "value": 102469840.0 + } + }, + "3e5259ff69044f2d9040e07e24baf5d7": { + "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_f83bdbe3f66c44e882ec5a547dc9c059", + "IPY_MODEL_3cd1e403a7754c11922ba99ec3494a8e", + "IPY_MODEL_2f6020cef60b4bf8bac0b8389a1b6833" + ], + "layout": "IPY_MODEL_8343e5c5e9e14df4a633a441184012d4", + "tabbable": null, + "tooltip": null } }, - "252fe402519d4210ba4ba3fef0912a86": { + "599e155eace340f792bf1521c812754d": { + "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 + } + }, + "8343e5c5e9e14df4a633a441184012d4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1229,7 +1371,7 @@ "width": null } }, - "4cb10e135c4d4df6a0102b8fa2c4e435": { + "aa0fb39e0a6040b5b93e4814da7189e3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1245,49 +1387,7 @@ "description_width": "" } }, - "6b6164dfe4394da88a0985c0358adabf": { - "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 - } - }, - "7134c3b9c85247698385a933e9c6f4c1": { - "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_f75ac16d283e42748e30f48710f7c779", - "IPY_MODEL_ebbc5fc8b0754655bb152b6178ceae67", - "IPY_MODEL_0ec7acb06a8d4e7c8cee6f0af1617289" - ], - "layout": "IPY_MODEL_76e78968a920473d8821422c81a0fcdd", - "tabbable": null, - "tooltip": null - } - }, - "76e78968a920473d8821422c81a0fcdd": { + "c8f58a32013f4bc7a0fcf152344559fe": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1340,25 +1440,7 @@ "width": null } }, - "b8360c36dca94afc98ec4fb786a3c57f": { - "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 - } - }, - "bc50c73a865f4e2e8076a042331398c7": { + "e052a3ce162f4f6c9ff6c38cfe463797": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1411,7 +1493,30 @@ "width": null } }, - "be9da5a89136408299b9df5aa61bf8ca": { + "f83bdbe3f66c44e882ec5a547dc9c059": { + "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_c8f58a32013f4bc7a0fcf152344559fe", + "placeholder": "​", + "style": "IPY_MODEL_599e155eace340f792bf1521c812754d", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "fbf5a8d22db64f0eb01aa7fd53c1b68b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1463,55 +1568,6 @@ "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 d7791c942..d85d0978d 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:18.468176Z", + "iopub.status.busy": "2024-07-02T15:31:18.467999Z", + "iopub.status.idle": "2024-07-02T15:31:19.595981Z", + "shell.execute_reply": "2024-07-02T15:31:19.595445Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:16:23.927425Z", - "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" + "iopub.execute_input": "2024-07-02T15:31:19.598444Z", + "iopub.status.busy": "2024-07-02T15:31:19.598201Z", + "iopub.status.idle": "2024-07-02T15:31:19.614971Z", + "shell.execute_reply": "2024-07-02T15:31:19.614441Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:19.617151Z", + "iopub.status.busy": "2024-07-02T15:31:19.616775Z", + "iopub.status.idle": "2024-07-02T15:31:19.619597Z", + "shell.execute_reply": "2024-07-02T15:31:19.619180Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:19.621728Z", + "iopub.status.busy": "2024-07-02T15:31:19.621281Z", + "iopub.status.idle": "2024-07-02T15:31:19.720173Z", + "shell.execute_reply": "2024-07-02T15:31:19.719650Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:19.722194Z", + "iopub.status.busy": "2024-07-02T15:31:19.721889Z", + "iopub.status.idle": "2024-07-02T15:31:19.898619Z", + "shell.execute_reply": "2024-07-02T15:31:19.898073Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:19.900830Z", + "iopub.status.busy": "2024-07-02T15:31:19.900644Z", + "iopub.status.idle": "2024-07-02T15:31:20.105363Z", + "shell.execute_reply": "2024-07-02T15:31:20.104893Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:20.107396Z", + "iopub.status.busy": "2024-07-02T15:31:20.107065Z", + "iopub.status.idle": "2024-07-02T15:31:20.111228Z", + "shell.execute_reply": "2024-07-02T15:31:20.110779Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:20.113042Z", + "iopub.status.busy": "2024-07-02T15:31:20.112776Z", + "iopub.status.idle": "2024-07-02T15:31:20.118755Z", + "shell.execute_reply": "2024-07-02T15:31:20.118338Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:20.120896Z", + "iopub.status.busy": "2024-07-02T15:31:20.120567Z", + "iopub.status.idle": "2024-07-02T15:31:20.123220Z", + "shell.execute_reply": "2024-07-02T15:31:20.122769Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:20.124968Z", + "iopub.status.busy": "2024-07-02T15:31:20.124802Z", + "iopub.status.idle": "2024-07-02T15:31:28.571097Z", + "shell.execute_reply": "2024-07-02T15:31:28.570451Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.573996Z", + "iopub.status.busy": "2024-07-02T15:31:28.573598Z", + "iopub.status.idle": "2024-07-02T15:31:28.580859Z", + "shell.execute_reply": "2024-07-02T15:31:28.580404Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.582717Z", + "iopub.status.busy": "2024-07-02T15:31:28.582538Z", + "iopub.status.idle": "2024-07-02T15:31:28.586218Z", + "shell.execute_reply": "2024-07-02T15:31:28.585630Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.588135Z", + "iopub.status.busy": "2024-07-02T15:31:28.587823Z", + "iopub.status.idle": "2024-07-02T15:31:28.590968Z", + "shell.execute_reply": "2024-07-02T15:31:28.590464Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.592960Z", + "iopub.status.busy": "2024-07-02T15:31:28.592655Z", + "iopub.status.idle": "2024-07-02T15:31:28.595547Z", + "shell.execute_reply": "2024-07-02T15:31:28.595114Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.597477Z", + "iopub.status.busy": "2024-07-02T15:31:28.597173Z", + "iopub.status.idle": "2024-07-02T15:31:28.605193Z", + "shell.execute_reply": "2024-07-02T15:31:28.604644Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.607149Z", + "iopub.status.busy": "2024-07-02T15:31:28.606833Z", + "iopub.status.idle": "2024-07-02T15:31:28.609520Z", + "shell.execute_reply": "2024-07-02T15:31:28.608976Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.611593Z", + "iopub.status.busy": "2024-07-02T15:31:28.611160Z", + "iopub.status.idle": "2024-07-02T15:31:28.728605Z", + "shell.execute_reply": "2024-07-02T15:31:28.728116Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.730718Z", + "iopub.status.busy": "2024-07-02T15:31:28.730363Z", + "iopub.status.idle": "2024-07-02T15:31:28.833994Z", + "shell.execute_reply": "2024-07-02T15:31:28.833479Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:28.836173Z", + "iopub.status.busy": "2024-07-02T15:31:28.835822Z", + "iopub.status.idle": "2024-07-02T15:31:29.318753Z", + "shell.execute_reply": "2024-07-02T15:31:29.318190Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:29.320805Z", + "iopub.status.busy": "2024-07-02T15:31:29.320632Z", + "iopub.status.idle": "2024-07-02T15:31:29.390828Z", + "shell.execute_reply": "2024-07-02T15:31:29.390329Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:29.392859Z", + "iopub.status.busy": "2024-07-02T15:31:29.392685Z", + "iopub.status.idle": "2024-07-02T15:31:29.401028Z", + "shell.execute_reply": "2024-07-02T15:31:29.400481Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:29.403035Z", + "iopub.status.busy": "2024-07-02T15:31:29.402726Z", + "iopub.status.idle": "2024-07-02T15:31:29.405418Z", + "shell.execute_reply": "2024-07-02T15:31:29.404957Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:29.407290Z", + "iopub.status.busy": "2024-07-02T15:31:29.407118Z", + "iopub.status.idle": "2024-07-02T15:31:34.727159Z", + "shell.execute_reply": "2024-07-02T15:31:34.726599Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:34.729487Z", + "iopub.status.busy": "2024-07-02T15:31:34.729099Z", + "iopub.status.idle": "2024-07-02T15:31:34.738133Z", + "shell.execute_reply": "2024-07-02T15:31:34.737687Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:34.740153Z", + "iopub.status.busy": "2024-07-02T15:31:34.739979Z", + "iopub.status.idle": "2024-07-02T15:31:34.810722Z", + "shell.execute_reply": "2024-07-02T15:31:34.810162Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index 6c0dcf672..9d29e7511 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -800,13 +800,13 @@

3. Use cleanlab to find label issues

-
+
-
+
@@ -891,7 +891,7 @@

Classes which are commonly mislabeled overall

-
+
@@ -1140,7 +1140,7 @@

Get label quality scores

-
+

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": {"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} +{"state": {"c7cbbf84083044978ab0df9b9c7126a9": {"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}}, "38ac20ffb13048faad531b95689a6281": {"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": ""}}, "7631dd07857745e3b56461586f545318": {"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_c7cbbf84083044978ab0df9b9c7126a9", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_38ac20ffb13048faad531b95689a6281", "tabbable": null, "tooltip": null, "value": 30.0}}, "fa73892e099842d1b9af380a12979792": {"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}}, "dcc356830b094ef8b7bd62ea9d5f4d3c": {"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}}, "fa4275e75f7d47ca9abfdd2ff25071a4": {"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_fa73892e099842d1b9af380a12979792", "placeholder": "\u200b", "style": "IPY_MODEL_dcc356830b094ef8b7bd62ea9d5f4d3c", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "f50ec117ed814ac3820ed475371f1510": {"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}}, "4517d65ed66644b08c13686bca4ada61": {"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}}, "79753d45ae124059908bf2ba3678d798": {"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_f50ec117ed814ac3820ed475371f1510", "placeholder": "\u200b", "style": "IPY_MODEL_4517d65ed66644b08c13686bca4ada61", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007739.81it/s]"}}, "c7762dfaefbe498d9f7c6387900b93a0": {"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}}, "bad327a6dcab414c8e0a515458d75ccb": {"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_fa4275e75f7d47ca9abfdd2ff25071a4", "IPY_MODEL_7631dd07857745e3b56461586f545318", "IPY_MODEL_79753d45ae124059908bf2ba3678d798"], "layout": "IPY_MODEL_c7762dfaefbe498d9f7c6387900b93a0", "tabbable": null, "tooltip": null}}, "72ec7912eda94737b355f53c83796387": {"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}}, "480f3b3335064465bea3b972128d431b": {"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": ""}}, "2daaa990afa74831ac228ef5af8c878a": {"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_72ec7912eda94737b355f53c83796387", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_480f3b3335064465bea3b972128d431b", "tabbable": null, "tooltip": null, "value": 30.0}}, "b39e7c191a2443ccae358aec72bf7220": {"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}}, "848cf4dd8cda496e896c4d1cc4c769e4": {"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}}, "57447469206d4415aafdd42c03b73e58": {"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_b39e7c191a2443ccae358aec72bf7220", "placeholder": "\u200b", "style": "IPY_MODEL_848cf4dd8cda496e896c4d1cc4c769e4", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "801025927d904866871b27bae4f3e30c": {"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}}, "fa0bbd1ddd3b47ed8606049facfac47d": {"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}}, "786fd79b6e614e9b814448b582543029": {"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_801025927d904866871b27bae4f3e30c", "placeholder": "\u200b", "style": "IPY_MODEL_fa0bbd1ddd3b47ed8606049facfac47d", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:20<00:00,\u2007\u20071.42it/s]"}}, "c83b606f3fdb4d6a8afcb5f78c107ac8": {"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}}, "7e0001601cd8466bb4509138ab4ebda8": {"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_57447469206d4415aafdd42c03b73e58", "IPY_MODEL_2daaa990afa74831ac228ef5af8c878a", "IPY_MODEL_786fd79b6e614e9b814448b582543029"], "layout": "IPY_MODEL_c83b606f3fdb4d6a8afcb5f78c107ac8", "tabbable": null, "tooltip": null}}, "40ce6ec00dea4ff68934b9dba6caed1f": {"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}}, "b57ba400aba2414cb1bb173653025ebb": {"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": ""}}, "abfa384596354665b60a79f482b668f3": {"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_40ce6ec00dea4ff68934b9dba6caed1f", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b57ba400aba2414cb1bb173653025ebb", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "fdf4e3973a9c42018c8d1b704c592c6e": {"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}}, "aa85802912774b7988f19c20712f63ab": {"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}}, "f3b9945cd0f94d4aaf5d332693944660": {"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_fdf4e3973a9c42018c8d1b704c592c6e", "placeholder": "\u200b", "style": "IPY_MODEL_aa85802912774b7988f19c20712f63ab", "tabbable": null, "tooltip": null, "value": "100%"}}, "451ab150dbda4bfbb629498f0e760dac": {"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}}, "0fd140538c574ca89150cf75b6f35521": {"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}}, "44d19e8829934708a262462fd0c1bf75": {"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_451ab150dbda4bfbb629498f0e760dac", "placeholder": "\u200b", "style": "IPY_MODEL_0fd140538c574ca89150cf75b6f35521", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:32<00:00,\u2007154921.47it/s]"}}, "50a714bd30054853846491d6d3f2c650": {"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}}, "88f1333cf1c544a5912e80ec16eb4ca6": {"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_f3b9945cd0f94d4aaf5d332693944660", "IPY_MODEL_abfa384596354665b60a79f482b668f3", "IPY_MODEL_44d19e8829934708a262462fd0c1bf75"], "layout": "IPY_MODEL_50a714bd30054853846491d6d3f2c650", "tabbable": null, "tooltip": null}}, "b0d9136bfec24979b7f2bdb45cc8a04b": {"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}}, "1cda877c22e24a249a3adea1e5a3eaa6": {"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": ""}}, "ce881d19b69a46be8feb73a7b5a68d09": {"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_b0d9136bfec24979b7f2bdb45cc8a04b", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_1cda877c22e24a249a3adea1e5a3eaa6", "tabbable": null, "tooltip": null, "value": 30.0}}, "81e58e1b155346b3b6e2d9d89fa60aac": {"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}}, "65bd5ddda279485393f9c86d24ce9772": {"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}}, "4a0a519ca6e5455ca09357a81c0d379e": {"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_81e58e1b155346b3b6e2d9d89fa60aac", "placeholder": "\u200b", "style": "IPY_MODEL_65bd5ddda279485393f9c86d24ce9772", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "7a8940a833324ba29bb1e6f53e89dae1": {"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}}, "3f14050e5552461e9f897fe3737afc39": {"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}}, "aaa77b4b69364ba2ad48741d8b4141e7": {"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_7a8940a833324ba29bb1e6f53e89dae1", "placeholder": "\u200b", "style": "IPY_MODEL_3f14050e5552461e9f897fe3737afc39", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200722.02it/s]"}}, "84554b6d7bf44226bf53416e4d0a2b71": {"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}}, "7eccc0839691439e89d779fba47b562f": {"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_4a0a519ca6e5455ca09357a81c0d379e", "IPY_MODEL_ce881d19b69a46be8feb73a7b5a68d09", "IPY_MODEL_aaa77b4b69364ba2ad48741d8b4141e7"], "layout": "IPY_MODEL_84554b6d7bf44226bf53416e4d0a2b71", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index f4716d029..4beae91e7 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:37.724618Z", + "iopub.status.busy": "2024-07-02T15:31:37.724448Z", + "iopub.status.idle": "2024-07-02T15:31:39.632373Z", + "shell.execute_reply": "2024-07-02T15:31:39.631704Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:31:39.634734Z", + "iopub.status.busy": "2024-07-02T15:31:39.634549Z", + "iopub.status.idle": "2024-07-02T15:33:03.062922Z", + "shell.execute_reply": "2024-07-02T15:33:03.062276Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:03.065400Z", + "iopub.status.busy": "2024-07-02T15:33:03.065026Z", + "iopub.status.idle": "2024-07-02T15:33:04.151127Z", + "shell.execute_reply": "2024-07-02T15:33:04.150507Z" }, "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\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": "2024-07-02T15:17:45.857557Z", - "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" + "iopub.execute_input": "2024-07-02T15:33:04.153507Z", + "iopub.status.busy": "2024-07-02T15:33:04.153220Z", + "iopub.status.idle": "2024-07-02T15:33:04.156468Z", + "shell.execute_reply": "2024-07-02T15:33:04.156013Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:04.158554Z", + "iopub.status.busy": "2024-07-02T15:33:04.158229Z", + "iopub.status.idle": "2024-07-02T15:33:04.162013Z", + "shell.execute_reply": "2024-07-02T15:33:04.161530Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:04.164308Z", + "iopub.status.busy": "2024-07-02T15:33:04.163807Z", + "iopub.status.idle": "2024-07-02T15:33:04.167471Z", + "shell.execute_reply": "2024-07-02T15:33:04.166951Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:04.169469Z", + "iopub.status.busy": "2024-07-02T15:33:04.169160Z", + "iopub.status.idle": "2024-07-02T15:33:04.171836Z", + "shell.execute_reply": "2024-07-02T15:33:04.171413Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:04.173818Z", + "iopub.status.busy": "2024-07-02T15:33:04.173482Z", + "iopub.status.idle": "2024-07-02T15:33:36.513602Z", + "shell.execute_reply": "2024-07-02T15:33:36.512999Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d37081f0d674141ab48e998533cdac5", + "model_id": "bad327a6dcab414c8e0a515458d75ccb", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6181f2ac640b4d5694a1537900a59156", + "model_id": "7e0001601cd8466bb4509138ab4ebda8", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:36.516246Z", + "iopub.status.busy": "2024-07-02T15:33:36.515935Z", + "iopub.status.idle": "2024-07-02T15:33:37.144437Z", + "shell.execute_reply": "2024-07-02T15:33:37.143962Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:37.146787Z", + "iopub.status.busy": "2024-07-02T15:33:37.146361Z", + "iopub.status.idle": "2024-07-02T15:33:39.872510Z", + "shell.execute_reply": "2024-07-02T15:33:39.871932Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:33:39.874693Z", + "iopub.status.busy": "2024-07-02T15:33:39.874352Z", + "iopub.status.idle": "2024-07-02T15:34:12.280506Z", + "shell.execute_reply": "2024-07-02T15:34:12.280037Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee454cb23f344e94bf0306f6bd70e6ef", + "model_id": "88f1333cf1c544a5912e80ec16eb4ca6", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:12.282629Z", + "iopub.status.busy": "2024-07-02T15:34:12.282304Z", + "iopub.status.idle": "2024-07-02T15:34:26.864434Z", + "shell.execute_reply": "2024-07-02T15:34:26.863831Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:26.866952Z", + "iopub.status.busy": "2024-07-02T15:34:26.866651Z", + "iopub.status.idle": "2024-07-02T15:34:30.542698Z", + "shell.execute_reply": "2024-07-02T15:34:30.542163Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:30.544890Z", + "iopub.status.busy": "2024-07-02T15:34:30.544550Z", + "iopub.status.idle": "2024-07-02T15:34:31.919730Z", + "shell.execute_reply": "2024-07-02T15:34:31.919163Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "22d36bebc1d941d19f0aa385e194e320", + "model_id": "7eccc0839691439e89d779fba47b562f", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:31.922089Z", + "iopub.status.busy": "2024-07-02T15:34:31.921633Z", + "iopub.status.idle": "2024-07-02T15:34:31.951822Z", + "shell.execute_reply": "2024-07-02T15:34:31.951305Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:31.954212Z", + "iopub.status.busy": "2024-07-02T15:34:31.954007Z", + "iopub.status.idle": "2024-07-02T15:34:37.993152Z", + "shell.execute_reply": "2024-07-02T15:34:37.992712Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:37.995340Z", + "iopub.status.busy": "2024-07-02T15:34:37.995001Z", + "iopub.status.idle": "2024-07-02T15:34:38.050757Z", + "shell.execute_reply": "2024-07-02T15:34:38.050200Z" }, "nbsphinx": "hidden" }, @@ -1038,7 +1038,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "03565a80c7d34c6293339e9385fd0f09": { + "0fd140538c574ca89150cf75b6f35521": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1056,30 +1056,83 @@ "text_color": null } }, - "0aae918c08c84bed9ae02fe6e0c0d703": { + "1cda877c22e24a249a3adea1e5a3eaa6": { "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": "" + } + }, + "2daaa990afa74831ac228ef5af8c878a": { + "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": "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_80bc86fa44ab4ddb9d7fbfcd03b7da23", - "placeholder": "​", - "style": "IPY_MODEL_5a6ca2db026040538be2877748a39874", + "layout": "IPY_MODEL_72ec7912eda94737b355f53c83796387", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_480f3b3335064465bea3b972128d431b", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: 100%" + "value": 30.0 } }, - "0b8a0362dd6d4e8faa60de3132d8f2b3": { + "38ac20ffb13048faad531b95689a6281": { + "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": "" + } + }, + "3f14050e5552461e9f897fe3737afc39": { + "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 + } + }, + "40ce6ec00dea4ff68934b9dba6caed1f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1132,25 +1185,7 @@ "width": null } }, - "0c57b519892e41bcb28985872ce030c7": { - "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 - } - }, - "0d5c01051776423480b74fabbe29251b": { + "44d19e8829934708a262462fd0c1bf75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1165,15 +1200,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a5072a4bea8140e2808839dad337d950", + "layout": "IPY_MODEL_451ab150dbda4bfbb629498f0e760dac", "placeholder": "​", - "style": "IPY_MODEL_a2f21f1a521e4c718452c19f8256841e", + "style": "IPY_MODEL_0fd140538c574ca89150cf75b6f35521", "tabbable": null, "tooltip": null, - "value": " 4997683/4997683 [00:33<00:00, 149090.50it/s]" + "value": " 4997683/4997683 [00:32<00:00, 154921.47it/s]" + } + }, + "4517d65ed66644b08c13686bca4ada61": { + "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 } }, - "0ea451f86f7b4b05957d9adf3ae7f735": { + "451ab150dbda4bfbb629498f0e760dac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1226,7 +1279,7 @@ "width": null } }, - "16f22dbcf3de44d7b4fd798a1f0b853a": { + "480f3b3335064465bea3b972128d431b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1242,57 +1295,83 @@ "description_width": "" } }, - "22d36bebc1d941d19f0aa385e194e320": { + "4a0a519ca6e5455ca09357a81c0d379e": { "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_ac2a26cb4281477983b7746968ff3e52", - "IPY_MODEL_3401bd6344664b4f85cbb90cf38682a3", - "IPY_MODEL_4102b17c3b13403aa9b3d80fb7df75ac" - ], - "layout": "IPY_MODEL_ecae615274e34bf59e8e58c649c294bc", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_81e58e1b155346b3b6e2d9d89fa60aac", + "placeholder": "​", + "style": "IPY_MODEL_65bd5ddda279485393f9c86d24ce9772", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "images processed using softmin: 100%" } }, - "3401bd6344664b4f85cbb90cf38682a3": { - "model_module": "@jupyter-widgets/controls", + "50a714bd30054853846491d6d3f2c650": { + "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_0b8a0362dd6d4e8faa60de3132d8f2b3", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f3ecd743295c4f619abad363dcb05fab", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "_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 } }, - "4102b17c3b13403aa9b3d80fb7df75ac": { + "57447469206d4415aafdd42c03b73e58": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1307,41 +1386,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b2f361ba3c35411587bfb94055421b84", + "layout": "IPY_MODEL_b39e7c191a2443ccae358aec72bf7220", "placeholder": "​", - "style": "IPY_MODEL_97090cf694824c77b08cfdb17f057e3e", + "style": "IPY_MODEL_848cf4dd8cda496e896c4d1cc4c769e4", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 21.42it/s]" - } - }, - "544233229d944fd690c9e89d8792bbe8": { - "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_f14aada5b0eb4e5ba3b431cb7b518da6", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cbbd654031b747468c432f9278836125", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "value": "number of examples processed for checking labels: 100%" } }, - "5a6ca2db026040538be2877748a39874": { + "65bd5ddda279485393f9c86d24ce9772": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1359,55 +1412,7 @@ "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 - } - }, - "6d37081f0d674141ab48e998533cdac5": { - "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_b4d580e3f19a47c6a1dde0776a4e5606", - "IPY_MODEL_544233229d944fd690c9e89d8792bbe8", - "IPY_MODEL_b8c4edcee9764b17abf836ac1f23e926" - ], - "layout": "IPY_MODEL_98b4559124704d61a177e8a88a9d3c8a", - "tabbable": null, - "tooltip": null - } - }, - "7014f38f051242f19c5f77d4e2e3dd69": { + "72ec7912eda94737b355f53c83796387": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1460,7 +1465,7 @@ "width": null } }, - "72b5647ddfd149559d4a948d2411a55b": { + "7631dd07857745e3b56461586f545318": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1476,17 +1481,63 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f3a91f9853db4fac966be7c379809f9c", - "max": 4997683.0, + "layout": "IPY_MODEL_c7cbbf84083044978ab0df9b9c7126a9", + "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_ce421d89a8e8429486414e8e5f2fb0b9", + "style": "IPY_MODEL_38ac20ffb13048faad531b95689a6281", "tabbable": null, "tooltip": null, - "value": 4997683.0 + "value": 30.0 } }, - "80bc86fa44ab4ddb9d7fbfcd03b7da23": { + "786fd79b6e614e9b814448b582543029": { + "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_801025927d904866871b27bae4f3e30c", + "placeholder": "​", + "style": "IPY_MODEL_fa0bbd1ddd3b47ed8606049facfac47d", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:20<00:00,  1.42it/s]" + } + }, + "79753d45ae124059908bf2ba3678d798": { + "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_f50ec117ed814ac3820ed475371f1510", + "placeholder": "​", + "style": "IPY_MODEL_4517d65ed66644b08c13686bca4ada61", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 739.81it/s]" + } + }, + "7a8940a833324ba29bb1e6f53e89dae1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1539,43 +1590,55 @@ "width": null } }, - "9479cd74f7ae4ab98e7dd6164deed30a": { + "7e0001601cd8466bb4509138ab4ebda8": { "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_57447469206d4415aafdd42c03b73e58", + "IPY_MODEL_2daaa990afa74831ac228ef5af8c878a", + "IPY_MODEL_786fd79b6e614e9b814448b582543029" + ], + "layout": "IPY_MODEL_c83b606f3fdb4d6a8afcb5f78c107ac8", + "tabbable": null, + "tooltip": null } }, - "97090cf694824c77b08cfdb17f057e3e": { + "7eccc0839691439e89d779fba47b562f": { "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_4a0a519ca6e5455ca09357a81c0d379e", + "IPY_MODEL_ce881d19b69a46be8feb73a7b5a68d09", + "IPY_MODEL_aaa77b4b69364ba2ad48741d8b4141e7" + ], + "layout": "IPY_MODEL_84554b6d7bf44226bf53416e4d0a2b71", + "tabbable": null, + "tooltip": null } }, - "98b4559124704d61a177e8a88a9d3c8a": { + "801025927d904866871b27bae4f3e30c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1628,7 +1691,7 @@ "width": null } }, - "9ba76afb638e410e891187e79a458dac": { + "81e58e1b155346b3b6e2d9d89fa60aac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1681,7 +1744,7 @@ "width": null } }, - "9f4790501bef48328e2bd3eee0152689": { + "84554b6d7bf44226bf53416e4d0a2b71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1734,7 +1797,7 @@ "width": null } }, - "a2f21f1a521e4c718452c19f8256841e": { + "848cf4dd8cda496e896c4d1cc4c769e4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1752,83 +1815,98 @@ "text_color": null } }, - "a5072a4bea8140e2808839dad337d950": { - "model_module": "@jupyter-widgets/base", + "88f1333cf1c544a5912e80ec16eb4ca6": { + "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_f3b9945cd0f94d4aaf5d332693944660", + "IPY_MODEL_abfa384596354665b60a79f482b668f3", + "IPY_MODEL_44d19e8829934708a262462fd0c1bf75" + ], + "layout": "IPY_MODEL_50a714bd30054853846491d6d3f2c650", + "tabbable": null, + "tooltip": null + } + }, + "aa85802912774b7988f19c20712f63ab": { + "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": "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 + } + }, + "aaa77b4b69364ba2ad48741d8b4141e7": { + "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_7a8940a833324ba29bb1e6f53e89dae1", + "placeholder": "​", + "style": "IPY_MODEL_3f14050e5552461e9f897fe3737afc39", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 22.02it/s]" } }, - "ac2a26cb4281477983b7746968ff3e52": { + "abfa384596354665b60a79f482b668f3": { "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_0ea451f86f7b4b05957d9adf3ae7f735", - "placeholder": "​", - "style": "IPY_MODEL_cf74a277aa3141788d774f9065d2366d", + "layout": "IPY_MODEL_40ce6ec00dea4ff68934b9dba6caed1f", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b57ba400aba2414cb1bb173653025ebb", "tabbable": null, "tooltip": null, - "value": "images processed using softmin: 100%" + "value": 4997683.0 } }, - "afdd70b438584e8381748e28bdc60eb0": { + "b0d9136bfec24979b7f2bdb45cc8a04b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1881,7 +1959,7 @@ "width": null } }, - "b27b9980b1934cddbaa61f473c83ad55": { + "b39e7c191a2443ccae358aec72bf7220": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1934,25 +2012,47 @@ "width": null } }, - "b2ec5e9d337d409f8f6128aa41a5d79f": { + "b57ba400aba2414cb1bb173653025ebb": { "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": "" + } + }, + "bad327a6dcab414c8e0a515458d75ccb": { + "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_fa4275e75f7d47ca9abfdd2ff25071a4", + "IPY_MODEL_7631dd07857745e3b56461586f545318", + "IPY_MODEL_79753d45ae124059908bf2ba3678d798" + ], + "layout": "IPY_MODEL_c7762dfaefbe498d9f7c6387900b93a0", + "tabbable": null, + "tooltip": null } }, - "b2f361ba3c35411587bfb94055421b84": { + "c7762dfaefbe498d9f7c6387900b93a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2005,126 +2105,7 @@ "width": null } }, - "b4d580e3f19a47c6a1dde0776a4e5606": { - "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_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", - "bar_color": null, - "description_width": "" - } - }, - "cd8af1b2cbd542f4869d02c1d973e524": { - "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_b27b9980b1934cddbaa61f473c83ad55", - "placeholder": "​", - "style": "IPY_MODEL_03565a80c7d34c6293339e9385fd0f09", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:20<00:00,  1.41it/s]" - } - }, - "ce421d89a8e8429486414e8e5f2fb0b9": { - "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": "" - } - }, - "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": { + "c7cbbf84083044978ab0df9b9c7126a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2177,7 +2158,7 @@ "width": null } }, - "e5f3cab627be4d4a8d76b371ca9346f3": { + "c83b606f3fdb4d6a8afcb5f78c107ac8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2230,7 +2211,7 @@ "width": null } }, - "eaaf30bb31f7495d8fbee7118acc211f": { + "ce881d19b69a46be8feb73a7b5a68d09": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2246,17 +2227,58 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e5f3cab627be4d4a8d76b371ca9346f3", + "layout": "IPY_MODEL_b0d9136bfec24979b7f2bdb45cc8a04b", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_16f22dbcf3de44d7b4fd798a1f0b853a", + "style": "IPY_MODEL_1cda877c22e24a249a3adea1e5a3eaa6", "tabbable": null, "tooltip": null, "value": 30.0 } }, - "ecae615274e34bf59e8e58c649c294bc": { + "dcc356830b094ef8b7bd62ea9d5f4d3c": { + "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 + } + }, + "f3b9945cd0f94d4aaf5d332693944660": { + "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_fdf4e3973a9c42018c8d1b704c592c6e", + "placeholder": "​", + "style": "IPY_MODEL_aa85802912774b7988f19c20712f63ab", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "f50ec117ed814ac3820ed475371f1510": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2309,31 +2331,48 @@ "width": null } }, - "ee454cb23f344e94bf0306f6bd70e6ef": { + "fa0bbd1ddd3b47ed8606049facfac47d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "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 + } + }, + "fa4275e75f7d47ca9abfdd2ff25071a4": { + "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": "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_f58763e1734248fda5001bdfeb216209", - "IPY_MODEL_72b5647ddfd149559d4a948d2411a55b", - "IPY_MODEL_0d5c01051776423480b74fabbe29251b" - ], - "layout": "IPY_MODEL_d4139e678fc14fc5b5f22a2a0dd84027", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fa73892e099842d1b9af380a12979792", + "placeholder": "​", + "style": "IPY_MODEL_dcc356830b094ef8b7bd62ea9d5f4d3c", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" } }, - "f14aada5b0eb4e5ba3b431cb7b518da6": { + "fa73892e099842d1b9af380a12979792": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2386,7 +2425,7 @@ "width": null } }, - "f3a91f9853db4fac966be7c379809f9c": { + "fdf4e3973a9c42018c8d1b704c592c6e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2438,45 +2477,6 @@ "visibility": null, "width": null } - }, - "f3ecd743295c4f619abad363dcb05fab": { - "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": "" - } - }, - "f58763e1734248fda5001bdfeb216209": { - "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_9ba76afb638e410e891187e79a458dac", - "placeholder": "​", - "style": "IPY_MODEL_0c57b519892e41bcb28985872ce030c7", - "tabbable": null, - "tooltip": null, - "value": "100%" - } } }, "version_major": 2, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index ee2b80072..0453d122a 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -710,16 +710,16 @@

1. Install required dependencies and download data 
---2024-07-02 15:19:23--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 185.93.1.244, 2400:52e0:1a00::871:1
-Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... connected.
+--2024-07-02 15:34:40--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 169.150.236.98, 2400:52e0:1a00::941:1
+Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
-conll2003.zip       100%[===================>] 959.94K  5.95MB/s    in 0.2s
+conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.01s
 
-2024-07-02 15:19:24 (5.95 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-07-02 15:34:40 (83.3 MB/s) - ‘conll2003.zip’ saved [982975/982975]
 
 mkdir: cannot create directory ‘data’: File exists
 Archive:  conll2003.zip
@@ -727,16 +727,16 @@ 
1. Install required dependencies and download data

diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index 42ddeaa94..4c5c0ea36 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T15:19:23.685217Z", - "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" + "iopub.execute_input": "2024-07-02T15:34:40.409044Z", + "iopub.status.busy": "2024-07-02T15:34:40.408876Z", + "iopub.status.idle": "2024-07-02T15:34:41.504750Z", + "shell.execute_reply": "2024-07-02T15:34:41.504249Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-02 15:19:23-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-02 15:34:40-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,22 +94,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HTTP request sent, awaiting response... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200 OK\r\n", + "169.150.236.98, 2400:52e0:1a00::941:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... connected.\r\n", + "HTTP request sent, awaiting response... 200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -122,9 +109,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.95MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.01s \r\n", "\r\n", - "2024-07-02 15:19:24 (5.95 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-02 15:34:40 (83.3 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -144,9 +131,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--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", + "--2024-07-02 15:34:40-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.173.201, 3.5.25.44, 3.5.8.134, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.173.201|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -167,9 +154,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 92.7MB/s in 0.2s \r\n", + "pred_probs.npz 96%[==================> ] 15.71M 64.7MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 66.3MB/s in 0.2s \r\n", "\r\n", - "2024-07-02 15:19:24 (92.7 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-02 15:34:41 (66.3 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -186,10 +174,10 @@ "id": "439b0305", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:41.507000Z", + "iopub.status.busy": "2024-07-02T15:34:41.506807Z", + "iopub.status.idle": "2024-07-02T15:34:42.679990Z", + "shell.execute_reply": "2024-07-02T15:34:42.679463Z" }, "nbsphinx": "hidden" }, @@ -200,7 +188,7 @@ "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@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -226,10 +214,10 @@ "id": "a1349304", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:42.682422Z", + "iopub.status.busy": "2024-07-02T15:34:42.682072Z", + "iopub.status.idle": "2024-07-02T15:34:42.685351Z", + "shell.execute_reply": "2024-07-02T15:34:42.684925Z" } }, "outputs": [], @@ -279,10 +267,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:42.687222Z", + "iopub.status.busy": "2024-07-02T15:34:42.687046Z", + "iopub.status.idle": "2024-07-02T15:34:42.690100Z", + "shell.execute_reply": "2024-07-02T15:34:42.689584Z" }, "nbsphinx": "hidden" }, @@ -300,10 +288,10 @@ "id": "519cb80c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:42.692165Z", + "iopub.status.busy": "2024-07-02T15:34:42.691989Z", + "iopub.status.idle": "2024-07-02T15:34:51.762665Z", + "shell.execute_reply": "2024-07-02T15:34:51.762051Z" } }, "outputs": [], @@ -377,10 +365,10 @@ "id": "202f1526", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:51.765158Z", + "iopub.status.busy": "2024-07-02T15:34:51.764935Z", + "iopub.status.idle": "2024-07-02T15:34:51.770723Z", + "shell.execute_reply": "2024-07-02T15:34:51.770154Z" }, "nbsphinx": "hidden" }, @@ -420,10 +408,10 @@ "id": "a4381f03", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:51.772692Z", + "iopub.status.busy": "2024-07-02T15:34:51.772305Z", + "iopub.status.idle": "2024-07-02T15:34:52.114037Z", + "shell.execute_reply": "2024-07-02T15:34:52.113540Z" } }, "outputs": [], @@ -460,10 +448,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:52.116302Z", + "iopub.status.busy": "2024-07-02T15:34:52.116116Z", + "iopub.status.idle": "2024-07-02T15:34:52.120622Z", + "shell.execute_reply": "2024-07-02T15:34:52.120168Z" } }, "outputs": [ @@ -535,10 +523,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:52.122595Z", + "iopub.status.busy": "2024-07-02T15:34:52.122423Z", + "iopub.status.idle": "2024-07-02T15:34:54.590782Z", + "shell.execute_reply": "2024-07-02T15:34:54.590025Z" } }, "outputs": [], @@ -560,10 +548,10 @@ "id": "95dc7268", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.593625Z", + "iopub.status.busy": "2024-07-02T15:34:54.593066Z", + "iopub.status.idle": "2024-07-02T15:34:54.597171Z", + "shell.execute_reply": "2024-07-02T15:34:54.596636Z" } }, "outputs": [ @@ -599,10 +587,10 @@ "id": "e13de188", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.599260Z", + "iopub.status.busy": "2024-07-02T15:34:54.598873Z", + "iopub.status.idle": "2024-07-02T15:34:54.604418Z", + "shell.execute_reply": "2024-07-02T15:34:54.603888Z" } }, "outputs": [ @@ -780,10 +768,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.606602Z", + "iopub.status.busy": "2024-07-02T15:34:54.606277Z", + "iopub.status.idle": "2024-07-02T15:34:54.632296Z", + "shell.execute_reply": "2024-07-02T15:34:54.631839Z" } }, "outputs": [ @@ -885,10 +873,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.634344Z", + "iopub.status.busy": "2024-07-02T15:34:54.634025Z", + "iopub.status.idle": "2024-07-02T15:34:54.638206Z", + "shell.execute_reply": "2024-07-02T15:34:54.637727Z" } }, "outputs": [ @@ -962,10 +950,10 @@ "id": "db0b5179", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:54.640053Z", + "iopub.status.busy": "2024-07-02T15:34:54.639878Z", + "iopub.status.idle": "2024-07-02T15:34:56.027864Z", + "shell.execute_reply": "2024-07-02T15:34:56.027377Z" } }, "outputs": [ @@ -1137,10 +1125,10 @@ "id": "a18795eb", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-07-02T15:34:56.030025Z", + "iopub.status.busy": "2024-07-02T15:34:56.029651Z", + "iopub.status.idle": "2024-07-02T15:34:56.033503Z", + "shell.execute_reply": "2024-07-02T15:34:56.033077Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index c750b6915..38a21b122 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "e67c4aeedd6310b5ad112e4c90674400bc877e0e", + commit_hash: "c915f776420f13284807e915043326eda337d0c4", }; \ No newline at end of file