From f83f91325306116b0234877a21fb997a11cc3bb5 Mon Sep 17 00:00:00 2001 From: elisno Date: Tue, 5 Mar 2024 17:49:25 +0000 Subject: [PATCH] deploy: cleanlab/cleanlab@90ac7962130e2a11bea5dbdc98b03c02744b63ab --- 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 .../cleanlab/datalab/datalab.doctree | Bin 174024 -> 174024 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 5977 -> 5977 bytes .../guide/issue_type_description.doctree | Bin 121635 -> 121635 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 68053 -> 68053 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 88321 -> 88321 bytes .../issue_manager/multilabel/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/multilabel/label.doctree | Bin 78419 -> 78419 bytes .../internal/issue_manager/noniid.doctree | Bin 90559 -> 90559 bytes .../internal/issue_manager/null.doctree | Bin 68181 -> 68181 bytes .../internal/issue_manager/outlier.doctree | Bin 75294 -> 75294 bytes .../issue_manager/regression/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/regression/label.doctree | Bin 108542 -> 108542 bytes .../underperforming_group.doctree | Bin 120431 -> 120431 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 4492 -> 4492 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/outlier.doctree | Bin 17168 -> 17168 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 103926 -> 103926 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 164172 -> 164172 bytes master/.doctrees/cleanlab/outlier.doctree | Bin 91933 -> 91933 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 52029 -> 52029 bytes .../cleanlab/segmentation/summary.doctree | Bin 68105 -> 68105 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 79104 -> 79104 bytes master/.doctrees/environment.pickle | Bin 15154967 -> 15116475 bytes master/.doctrees/index.doctree | Bin 42561 -> 42561 bytes master/.doctrees/migrating/migrate_v2.doctree | Bin 28116 -> 28116 bytes .../.doctrees/nbsphinx/tutorials/audio.ipynb | 1282 +++---- .../tutorials/datalab/datalab_advanced.ipynb | 368 +- .../datalab/datalab_quickstart.ipynb | 130 +- .../nbsphinx/tutorials/datalab/tabular.ipynb | 138 +- .../nbsphinx/tutorials/datalab/text.ipynb | 1772 ++++----- .../nbsphinx/tutorials/dataset_health.ipynb | 34 +- master/.doctrees/nbsphinx/tutorials/faq.ipynb | 676 ++-- .../.doctrees/nbsphinx/tutorials/image.ipynb | 3294 +++++++---------- .../nbsphinx/tutorials/indepth_overview.ipynb | 219 +- .../nbsphinx/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 98 +- .../nbsphinx/tutorials/object_detection.ipynb | 186 +- .../nbsphinx/tutorials/outliers.ipynb | 668 ++-- .../nbsphinx/tutorials/regression.ipynb | 244 +- .../nbsphinx/tutorials/segmentation.ipynb | 1046 +++--- .../nbsphinx/tutorials/tabular.ipynb | 130 +- .../.doctrees/nbsphinx/tutorials/text.ipynb | 164 +- .../tutorials/token_classification.ipynb | 172 +- master/.doctrees/tutorials/audio.doctree | Bin 336272 -> 336268 bytes .../datalab/datalab_advanced.doctree | Bin 199380 -> 199380 bytes .../datalab/datalab_quickstart.doctree | Bin 146255 -> 146255 bytes .../.doctrees/tutorials/datalab/index.doctree | Bin 3205 -> 3205 bytes .../tutorials/datalab/tabular.doctree | Bin 116412 -> 116412 bytes .../.doctrees/tutorials/datalab/text.doctree | Bin 290836 -> 290836 bytes .../tutorials/dataset_health.doctree | Bin 324921 -> 324921 bytes master/.doctrees/tutorials/faq.doctree | Bin 196491 -> 196491 bytes master/.doctrees/tutorials/image.doctree | Bin 432934 -> 395098 bytes .../tutorials/indepth_overview.doctree | Bin 219071 -> 219071 bytes master/.doctrees/tutorials/index.doctree | Bin 3232 -> 3232 bytes .../tutorials/multiannotator.doctree | Bin 137376 -> 137376 bytes .../multilabel_classification.doctree | Bin 64488 -> 64488 bytes .../tutorials/object_detection.doctree | Bin 140179 -> 140179 bytes master/.doctrees/tutorials/outliers.doctree | Bin 104221 -> 104221 bytes .../tutorials/pred_probs_cross_val.doctree | Bin 17310 -> 17310 bytes master/.doctrees/tutorials/regression.doctree | Bin 106904 -> 106980 bytes .../.doctrees/tutorials/segmentation.doctree | Bin 1994473 -> 1994473 bytes master/.doctrees/tutorials/tabular.doctree | Bin 59790 -> 59790 bytes master/.doctrees/tutorials/text.doctree | Bin 92656 -> 92656 bytes .../tutorials/token_classification.doctree | Bin 176703 -> 176709 bytes .../_modules/cleanlab/regression/learn.html | 8 +- master/_sources/tutorials/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 +- master/_sources/tutorials/tabular.ipynb | 2 +- master/_sources/tutorials/text.ipynb | 2 +- .../tutorials/token_classification.ipynb | 2 +- master/searchindex.js | 2 +- master/tutorials/audio.html | 2 +- master/tutorials/audio.ipynb | 1282 +++---- .../tutorials/datalab/datalab_advanced.html | 4 +- .../tutorials/datalab/datalab_advanced.ipynb | 368 +- .../datalab/datalab_quickstart.ipynb | 130 +- master/tutorials/datalab/tabular.ipynb | 138 +- master/tutorials/datalab/text.html | 18 +- master/tutorials/datalab/text.ipynb | 1772 ++++----- master/tutorials/dataset_health.ipynb | 34 +- master/tutorials/faq.html | 8 +- master/tutorials/faq.ipynb | 676 ++-- master/tutorials/image.html | 71 +- master/tutorials/image.ipynb | 3294 +++++++---------- master/tutorials/indepth_overview.ipynb | 219 +- 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 | 668 ++-- master/tutorials/regression.html | 20 +- master/tutorials/regression.ipynb | 244 +- master/tutorials/segmentation.html | 10 +- master/tutorials/segmentation.ipynb | 1046 +++--- master/tutorials/tabular.ipynb | 130 +- master/tutorials/text.html | 2 +- master/tutorials/text.ipynb | 164 +- master/tutorials/token_classification.html | 20 +- master/tutorials/token_classification.ipynb | 172 +- versioning.js | 2 +- 168 files changed, 10431 insertions(+), 11310 deletions(-) diff --git a/master/.buildinfo b/master/.buildinfo index b748f8a89..327a30468 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: ed55d71239a5c5c31aeb20e01291a591 +config: 4935ead0c867be398fcbe262b0f0318c tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index 511fc2795ab568df7ca73c1777a2911844582d66..53d786315cff0ea71c17030fffb46787ab38a882 100644 GIT binary patch delta 117 zcmdlWxj}M6IHRF?UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr YnVGS%k*TrC<_^XHPBOG{axZ5D0F(e9PXGV_ delta 117 zcmdlWxj}M6IHO@!wn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm YMM`3lk(o)-<_^XHPBOG{axZ5D093vsDF6Tf diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree index ec4ddab355a1978ac368d7c26cf22fa713829388..69838e3b39ed2cf296a6232324421a3581df7ebd 100644 GIT binary patch delta 1464 zcmX^3o8{ndmJRWYhUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMF5FtU=NZL*_z+-4PKKNiw$1ZkbFYs1JtS%9OEEbZ-^EjjOjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= OOjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= O*t z$enIsE?+e2k9s@$%bguQPlwMJcDgFtU7ekkPPez+=c>5qcY7-Ro(i|u>vkXXxGRhi z_5qOTJ3k!}<4vlj`?W2`vN9G-9sSRPg>FFdI%6f2R4}Q&63Wvj7A;0!i;@9ZG`?t0 zI3Q)n2=}#NU2ii&x92UXQ^l-Rb?F1c5knoU2GD=?x;CtVcw=C==WOa)IVUrzt3M=L;5BWQVI%2s9C?+PIq23E{yhuKKnvG+?{-Wa6w?J<6WvQZpVl96 z%1g^8n&eq@>M<2G^U7mtGmBn{fK0rBR6 zEEEZcl1FbWUKiKY!no72d$Fq4S+y=o1O`;_q(u$iVqEhThCxrsS6l5D|#AU(+d&(jJnWN?j2 aiq&&Ataca4^d}FbE;fJCN&e`MI(`F|AB;%= delta 4197 zcmbuC-Afcv7{)ngX8mfn(Zn@Hh(hU0a#z={Oh_<_K6F=W(ag|she8*#G|0M0)F?tQ z7~ymwBrMW}1=b{Q7u|RjU3O7WZi`?k6fOmVBwlo;q`KW_|A6QDz0W)EdC!@dSk_D| zYqpJfyL)^_UuYm)Ql?i{Rh1bbk8XG>^=ejH`+BR!dnFu6~{^!Ah1CYGIS_{S%OlqzLv(2exhc(!yq(BzU&-Iua zQU)z!umkIQhZVfF=t!I@=AEk792yN<>ToT9{;M~&Q4PdfL!*7?64%N(nM+(H0{P6a z!VCSWn6}%xx%7ae+UeyYU@q;NyDj6ZALja3qp!1IkJOI!Q@yDtAWWc za#@!UE?_s?#xwVKLgUQu<*)&WZJiyw)HPA$@9`+>< z+w)B{f07N?0OGCNxQ8&HJ3P zld|cJ^E5jBh>st~Y#POogJn^8gb0XTus(RNEbJ4rYo16Nm!9A=m@eI|4&p*^La`G@>Qfuy?C-u(S(t%W>Tb3 znLr+iwhc*}Gsv>AlZeIrY8_2hDwAzsGi`@@ikXUX9dl?V*@A;WdXfR2B^6xA;2ISc ZsOMc+?Ou}UPaR5JZ2zQ_{LvqG{{m8o4PO8N diff --git a/master/.doctrees/cleanlab/count.doctree b/master/.doctrees/cleanlab/count.doctree index 6164240e4006e897cfa5806d9c19e09b6d7dcf3f..c6b8f232119414f804ac5bcad3f2061f8181f390 100644 GIT binary patch delta 3571 zcmX@QLh$Gc!3~~_hUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMGq74c{!F*1<%ktqPjlEM#iktQ$M42)VYd zv0g!L2;Xt|!c2PPY-V)*Lat+LJ$94p*aN<8WH=U_e$NHYA=lPVVdu%U)iZhvxmsJ} zzmS`f7?T>vjU3KYTXMCYOHU!!ue-8ekrz4kn?(w>cu7ycz$9V6d0pMSAhNXTPxe!n zoE{s+D7yL9#oL_Zo3UB%j+H&>+NTFTX0(`YAjQbO{f0E-EixQ{WP*|!;}K)h-2ebn8myXJ%@`$MV)j5 KwgbCfElL0;O5vmc delta 3571 zcmX@QLh$Gc!3~~_hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|yC74c{!F*1<%ktqPjlEM#iktQ$M42)VYd zv0g!L2;Xt|!c2PPY-V)*Lat+LJ$94p*aN<8WH=U_e$NHYA=lPVVdu%U)iZhvxmsJ} zzmS`f7?T>vjU3KYTXMCYOHU!!ue-8ekrz4kn?(w>cu7ycz$9V6d0pMSAhNXTPxe!n zoE{s+D7yL9#oL_Zo3UB%j+H&>+NTFTX0(`YAjQbO{f0E-EixQ{WP*|!;}K)h-2ebn8myXJ%@`$MV)j5 KwgbCfElL15@lSXF diff --git a/master/.doctrees/cleanlab/datalab/datalab.doctree b/master/.doctrees/cleanlab/datalab/datalab.doctree index 7b92ec8f2cb76967e48ce4690d33e046b047a605..8b14519cfc3a840c436c1ef4f6f112c9f6722966 100644 GIT binary patch delta 4371 zcmbuD-%C?r7{@v9wsKpp4NDR#6A{XwwvPKnW)?-U>5@tj6iufi(bhqUoK$lc{UBqG z4&xxf(1ws$B);m)m~>IHl)&SzGNf(-F^Is6?t9OXyg$$T2YjB-_dU;Zp7Uc_HZ9Ah znR?FQ4u`#AS8(XGJ?M4@-R^F?!_(;qg*+J}eDNAxgMtQ|ar?N*Ngh_AI7@Ycb~LNH!{ljS*R z__eko{Ofq16Krn*lEda=yx2Yf&8=MYk6q2MIrFJKdl%|P!0?KFvM0`B&-M1Q*q6c{ z7W=4B&tktjP{(2q+`A97$Hyyw#Clv)sE4v)+LVj(CzT- zC+y5!_|`}icHSLxBpw2q)!>mtgvI_soMy4-lW7*aWlYC{Prtgug3qR|u$)~SUt+Ou zry&pi-GN=7{J>(jPsuF!*xOdXO}ZO?bf$>KUX2@B@RJLd0pamP<~3woCNekia<a>s$kAk~v7LB^5rA=N16XR^S%W`;}`1w3$%2 zun2bCI&tQKoF49<4d5MF+ATn`1{5n{h+rXK zN|6!LyZ;$1Bv^sw55O`sOPl@3D*Y%$0x3I--UGHHp+Pcq-)i8S7X^wPB; zswJr#s1LT@)k=g`DhAxWN)oB{HabjbiUeRDlmdhnFaVd9l!_oDp`ECPeV+RXEih-* kJ_2fR`8nzb44`_|C|f{s2~-A{KUzTfudtu=Orm!54~;k2g#Z8m delta 4371 zcmbuD-%C?r7{@v9wsKor8!JhumWWUWHFeWPW-E$fQ~HKj8CxzVCUS^PC?`Io(oD zmulv!Zv^cDfxpv#yw+Fmbk+uVn=fFi_nojid|rR8-{z>Pw)25HyVuEkeKmD95N`>@UA)By3-ZVletTV(*_B{)`* zg@#}4w%}if`yF6=Cy*S`m*Rzgd$5Vc-g5UI(2iY0vn=MP_s;;$%E)v+e5@*dWUx7WYG?5n-D1yp*x( zLCeV_HZB{m@cA;tYz}*Hn#<|3<;yHqY?w+EJ?L)sWXCh3P238efadJou+ga-Kpm;?GqXcDUuWzs}DQcIWo z$VK8;P(N(FE0qYXR0_Cz4j?X=-9@ RW@c<`WNK`(xtyh&2LLZQ5a0j+ delta 62 zcmZotYEs(Z#$uS2ZIW7$Qj()@oMdicVVs&`kd$g*o@8lanv|GqoNQofWN4C-YG#sX Rk&>8XWM-1Ixtyh&2LM`|63qYr diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree index f5a21a577bd6478ef4b2182f853818a04cbf18e6..4a604460425e7c3473fb7a919e8df27d8071b907 100644 GIT binary patch delta 64 zcmccfmGRD3#ti|ChUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wME^8KWx!#IX~H delta 64 zcmccfmGRD3#ti|ChFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A TCW#g)iAhFgCP|wc8KWx!@Dmku diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index 3b14f50d608c3674ddd2705643decfb23cf23c05..124b706d82d2ec19df52a0911f12f1b981bcdf40 100644 GIT binary patch delta 62 zcmZ2yxXy4xHlv|=UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr RnVGS%k*TrC<~fW);s8=|5i9@z delta 62 zcmZ2yxXy4xHltxywn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-<~fW);sAZr6Bz&i diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index 0b6c6a0b77a915210dd654ac664b867f62aeeff6..1df134018be6cbba2ddb0067170a126bb8dde286 100644 GIT binary patch delta 62 zcmcbqcT;aeG^3$;UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr RnVGS%k*TrC<{rkgq5xwo5)}Xd delta 62 zcmcbqcT;aeG^1fwwn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-<{rkgq5zJL6aoMM diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree index 195d7a4fb74b40a5b9a4e6f11454bf26812a9dcc..a3a691e00a7c022469cc9fc1569f7165e4ee57a2 100644 GIT binary patch delta 68 zcmZ3yjeYSp_6@0whUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wN|v7`IPjWbC~O0N|w*egFUf delta 68 zcmZ3yjeYSp_6@0whFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A YCW#g)iAhFgCP~fH7`IPjWbC~O029gh<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm YMM`3lk(o)-<~qjhf@Ek*6TZ&~0E-DEZ2$lO diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index 88dbd10c2c0d0e5b023541eabdd28fb849416434..27898636a671eafaafa8340d38748129fd86a2ba 100644 GIT binary patch delta 5702 zcmbtYZD?C%6y|ihX}6@eX-a8>bLmF9POBzany;;+oo!ji>Ka-W#=52LN<-BybDJ$) z*Q^uWM44;dtGqHD>yP~rrdAH_{o%xc{t|=;4pBh`1!b&Ol!Ev%d~fcM{(dgpzt6`x z=Q;Nt?&1foMg8oiNFtw4<>MpMq3}o|Hj+q;h9k)%kz6hrj>eB9A}zzIXe^nEwM66b zXmndF+9IZC2|!gh@8h4!77#%VmA7A~D&aGZW6Hu^Tf?=kYinV69rwBqfabM&9q+6> zPzI~l^CR9tT)mz*`T*YrYMtaOn-8I?3&F#v z>b0g;piNxe{1%qyEVqU?xS*M4#l>U=FUP%Dn=JURpl~|nzVz%C(YpP8oIqU8?BU{$ zgJ9o8ds{i*YM;UeofYXWpZGl6DDHK9g4Mw3t}hG-+AqBOJ>o`gjQu^gfV7F~<`M-z znf76`x8d%-=dkWs@MXC4dVVugz@}~qn<8}3;P(b*aPO7k?64_5%ie`+uo(us_VU8e zc{Bw-_&j_gH{t~T_IMeZ084#G{$U5|Vs3JsxOeg!u#4J2iQ$dRUcNaILldECE}eGq zi_--(5SEd^to1s0RQx&X7aKb}Mfl7hUKR&tm%zUA{JF2oLFM^#2LJxb8Z;0ZE{flo zd|~c7I19e{T2BZw&lf)Z(2qfdZ9D^_y>~qFqYm1{Uw!`&CpCib!-D=uz{{3$#0}X z4xA|MLaNS&Xv~QdjrXcTgbJvMV&i(HH>J%1 zUOuslR=|X6c?(`4zkHnf9dIn}p@7mU??Yo@8POy`RosmS+-jyKnM1Svzlo(DB8QcD zpEmRNE+Z zI>1YfUoU+}={LA9hRyM6p0JyvR}>o!`SA&=R)u3Uis!{9v96cW#h~U-I$42LY-(>_4*$SIUZ9hX>@vSJ4xYW{l8pjvHCXt$?O6spb Uo7(mg1wBwA_bL59_S?1p0okG|LI3~& delta 5702 zcmbtYe@K*f80WFx)4KEZJP|$CI*T*g+;*p)dX}M0B_o%I(h*swcY5N=?5ASoWn-(c z(bDzZct_iqfAr6wZH|Za{bP*<^;ZzwU@^AA3N|Pr=hzU&e#E}-y)pdz^M&`{&yVMM zKF|9;yz^hU=GC)}YW5$9qz?}r9}U%|8l%y=^iVjJ4mYNnBK4`{VBKK2zP2VZlx~P5 zqeID5Z9^m_CTRgc6<6=%U-IS&!tgTgcJBtwEA=wo zQn))0Rxju0y#2U(Ij{Ha2i4hKDpo+ok8rD>G3W$edio$%?*xAu=)`KB;1AdDMOEj5 z`%u-(Rn0(~xVZWQEYAsU4zF=RGfjvK(E?s<@M3MU;lF~yu_pJ$;~PZtrq6K#aVfr? zvzzyTeG{!M`Fy=~5*u_v#5#Q9cA`=|Zo7fiz_E^R3<%mQyt_Q&>hK8ryRHLi6UEgz z3Vu1}!)EWm-94{h-Lv6~aOvgzemsLs-4-@QXph04^-ba43&okFDQ+bm!8JGx{T(}a zZr~i6f_pv>UmYIQfqy)jhbF*Q-#qrLhPs%USS21G`vL5tGLU0bU$7e2c{RmzVhtZ@AEUd6OIz`QI)|belCPx z>W&wO=Y{(N;2ebap~)4GUd8T;USTeoT>r-|Fi31JO=A)676TQA^;#2+@X3G6S3v`H zILh(~yKQoC`DOu)n;P;8Ut(Eh)TzPIy^aD_wv8Uh1AgkmlIb)NNTMED0@*Sk#;Jz3R$J=9s-5lK_TWMHdAEjcMIYenZFAj-)z2q(iIeScJ1(vbN znQ=OdFT*k>`y!H`OzLb!j?HKN6I3xAozmF~he>WaO&js8$dS0@!Z{kn7s4U26iJb# Wz5*?>_6-VpphWJJ>VNEyEB^zp8_~@G diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree index ddfa4422c0880d7eb7bffa62d1b387fec9444ae0..29b96d62f657d2f5306f40faa6a7ee2e5b27cb83 100644 GIT binary patch delta 2705 zcmex*o8{|mmJOkdhUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMHF7@5e>HrY`zX0s@BKNsmXg0xOHP=X_z|&4K&}naR_>dA=|+ z3t3vzCp*XpZ}ydlCeP;d%?>gb$gp{{qhb;}Sq{$G?4apMUexAnR@3fNAkWsxYTA60 z<9$5H(hUye$-WZ2o8x_tvyyFu#O7ez*4z!NE|ncciQiRnWy#EBz{r!>{ITE!CAMc4|0mP$o63vGw^(Ge zTxAR;NnWdd85yCrnX!2{8QLZ@HaBlBXjdYmL<4Fq*lgTW&rL@1W^B%wyr7MH@`+pO zn`>v7krUcbBQ}T6nnX_Jwee8l=I*7Z$?-Qx>yK3(;-vd~^TtCP$VnO-57liJILRYO zx{Z?s{}oJrf0JkO$y@))*S)#)4wC>`+G{8Cec;(__|}=6fEN4*(XRJt6B&tYv*5qy zoMbo}n6ReH@Gy#QU&g_Bmz)v=Xux)$0c2M9vD58^7)2*X%o5&iBEqHrY`zX0s@BKNsmXg0xOHP=X_z|&4K&}naR_>dA=|+ z3t3vzCp*XpZ}ydlCeP;d%?>gb$gp{{qhb;}Sq{$G?4apMUexAnR@3fNAkWsxYTA60 z<9$5H(hUye$-WZ2o8x_tvyyFu#O7ez*4z!NE|ncciQiRnWy#EBz{r!>{ITE!CAMc4|0mP$o63vGw^(Ge zTxAR;NnWdd85yCrnX!2{8QLZ@HaBlBXjdYmL<4Fq*lgTW&rL@1W^B%wyr7MH@`+pO zn`>v7krUcbBQ}T6nnX_Jwee8l=I*7Z$?-Qx>yK3(;-vd~^TtCP$VnO-57liJILRYO zx{Z?s{}oJrf0JkO$y@))*S)#)4wC>`+G{8Cec;(__|}=6fEN4*(XRJt6B&tYv*5qy zoMbo}n6ReH@Gy#QU&g_Bmz)v=Xux)$0c2M9vD58^7)2*X%o5&iBEqPgb#wMHV7+aV~*Eac~VZr1D;sTpDv-k*+t-YB{omR5Ko3Be8VkS@fW-D1+ z7P7SVY&KCcBv0$a&FboIWW*0JpeJtD*Stwy98KJON+*z9Tkq;!Cd1av4-L1llNGv^ zo9(T7GRV?exH)&R3mbV_C+9Ba-JG!MlQJ3F1s{}c{&`M`oFoC%I{D{0$<4Pfv+|H` i^Jc*Ze_2V_Hd*jN-{!N=428+i3d!;Oo7erSW&{9FOG=~w delta 1137 zcmZ4agL&l-<_*4#hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|y?7+aV~*Eac~VZr1D;sTpDv-k*+t-YB{omR5Ko3Be8VkS@fW-D1+ z7P7SVY&KCcBv0$a&FboIWW*0JpeJtD*Stwy98KJON+*z9Tkq;!Cd1av4-L1llNGv^ zo9(T7GRV?exH)&R3mbV_C+9Ba-JG!MlQJ3F1s{}c{&`M`oFoC%I{D{0$<4Pfv+|H` i^Jc*Ze_2V_Hd*jN-{!N=428+i3d!;Oo7erSW&{AnQ*S!} diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree index 81d8ffa228fffc1ec03981b9c62a73840ad5a7c5..7fa3acdb119169fa27290e6e52f40b728d0b8279 100644 GIT binary patch delta 122 zcmcbsd{=pcKck^}UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr fnVGS%k*TrC<_5;O%tj<@)1Ul+U21bCYY7hkc?Kcs delta 122 zcmcbsd{=pcKcit*wn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm fMM`3lk(o)-<_5;O%tj<@)1Ul+U21bCYY7hkO$H^z diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree index 08119dd59ae445ba5d9d3097950824f3c07857b9..dde829b92e67de5d80b2cb94ae1c2e7c32e1a507 100644 GIT binary patch delta 1125 zcmeBu&(!;#X+t=pp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9Xb`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8}~RMm;Y8 delta 1125 zcmeBu&(!;#X+t=pVOF+DYC%d#j=ph{xrK#sYKlQps)2curHN@$VzP0vfvJ(9NlL1j zNuotcVv>=WNz&#HMoD(kwN2hAUa;ATvz>`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8~0*D__|F 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 5af0880c82bd3942039aae52566d9e359346803b..6af8f6cd6f94d14196b853e62d3932e7316751c7 100644 GIT binary patch delta 62 zcmew$^+9TbE0dvlUY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr RnVGS%k*TrC<}#*VTmWDW5)%Lb delta 62 zcmew$^+9TbE0bYXwn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-<}#*VTmXx36aWAK 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 15fb20d8749877d1aa46d554f52187724650272c..7e84e05ade5401b7f45038eca4403a69810d5ad4 100644 GIT binary patch delta 2628 zcmccdp5?}SmJQL2hUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wL?{81IsyOVOxqvjlSk=EeSN$Vg3-Ax21Swh8Sd$ImeBo5INoZJ_qe_0e^d z*!(K?6&WE8_xHg>JxXl9ll+Jh+gGPAB_q-{H|CtgL%khskx0fV046iwPC{q&sKx!}-B%q-z64{LIb&mX&dk zsnu=+6B&NpT)0b=oC>CJ*Q(8lhtfF7aIB(H!Q_TF(vu%t5ZpZF%mi|Z7pM`NA6(c@ zUfm3Gf!gM{`;9#0d0=zH8zoILv;yOJ`Z^Itw#mvZ!rNQL7+;VX+mjcVv2M3fU`!=5 Mp>5ArXUr7<0C{`!=>Px# delta 2628 zcmccdp5?}SmJQL2hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|Zf81IsyOVOxqvjlSk=EeSN$Vg3-Ax21Swh8Sd$ImeBo5INoZJ_qe_0e^d z*!(K?6&WE8_xHg>JxXl9ll+Jh+gGPAB_q-{H|CtgL%khskx0fV046iwPC{q&sKx!}-B%q-z64{LIb&mX&dk zsnu=+6B&NpT)0b=oC>CJ*Q(8lhtfF7aIB(H!Q_TF(vu%t5ZpZF%mi|Z7pM`NA6(c@ zUfm3Gf!gM{`;9#0d0=zH8zoILv;yOJ`Z^Itw#mvZ!rNQL7+;VX+mjcVv2M3fU`!=5 Mp>5ArXUr7<0BF)i#{d8T diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index d720f888095a3aed77cf6aeee96d840cf776b192..7696e33a0aa3c6b4f1bd97746b78b68754ca7fa7 100644 GIT binary patch delta 2632 zcmaERn&s_jmJNZ7w&rPgb#wPkH`N_rl#rdU0$*Gek+8b{^$T*kLkYrlB_WVQ+7B zUB4u^0EEejzP`gh!hj(*WA1fOL+BK%8kZbz~FW$|O^QV$)J4pNPWoG2s?w`gs z`NKM%$tJNu)2E9ws&2O0c!<0NkUcs6y!2!pf1b_z_e>z8r~#%SxDk{0?_u36esB$W z^>6m(`16Z+$cjyn3)D6rzVk_gEbaZ%<9Qfaw^y+-zU3g#0AMa+-L50R7)EA#nBFVJ V$g$l^oKcFr04UiGY`Z;S1OV=d_on~= delta 2632 zcmaERn&s_jmJNZ7wprOGsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCQ14!`N_rl#rdU0$*Gek+8b{^$T*kLkYrlB_WVQ+7B zUB4u^0EEejzP`gh!hj(*WA1fOL+BK%8kZbz~FW$|O^QV$)J4pNPWoG2s?w`gs z`NKM%$tJNu)2E9ws&2O0c!<0NkUcs6y!2!pf1b_z_e>z8r~#%SxDk{0?_u36esB$W z^>6m(`16Z+$cjyn3)D6rzVk_gEbaZ%<9Qfaw^y+-zU3g#0AMa+-L50R7)EA#nBFVJ V$g$l^oKcFr04UiGY`Z;S1OP<*Mymh- diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index d90e2dd2030f2da84ad5bf57873b9c79782f4dfe..07ba9a832c36f29528e2015f3e428dad7c5b6977 100644 GIT binary patch delta 2466 zcmcaQndRzamJNZ7w&rPgb#wPkH`N_rl#rdU0$*GeSHHlAtOB5z{TF`hJHzI z0SJ>ES(_C#s>utkMUy`Sux>8VYaq|x5bb9SPB4+>cZl{JlO9TJzhL%;65FR(t)Rqq z5!+{!*nY`j88aC{SLm~DbGYjqa&lLp&*sh7y)w!0^X5XI1>`w)^X9Js>15bA8RS@p z;CtkRFi0y?xE>j{ZcdEoB-fwkV&;(-DYG|QB-OH$l@umS=FjAr{J2lAtOB5z{TF`hJHzI z0SJ>ES(_C#s>utkMUy`Sux>8VYaq|x5bb9SPB4+>cZl{JlO9TJzhL%;65FR(t)Rqq z5!+{!*nY`j88aC{SLm~DbGYjqa&lLp&*sh7y)w!0^X5XI1>`w)^X9Js>15bA8RS@p z;CtkRFi0y?xE>j{ZcdEoB-fwkV&;(-DYG|QB-OH$l@umS=FjAr{J2h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-<}OAFVE}L%63+kt 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 65bd8c8a71bb680912df0bc6e7d5827c5830e2d5..5d6c9ec556288557f686c2eec56618a04b70e99b 100644 GIT binary patch delta 2506 zcmbRCjb++5mJN}NhUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMG)7+;W~ZL;Hvn#uAk4WwHL)Y&)yo*bO zl{_OhcL;1?CsXSu2_5o6taI}XDK<6oY@IyAF`7J`o7Xy>qb#6VH~V@tkm+S$RD73y=}Yn?+L!$P0(A%?@cF$@4$NX8x=cGHnJsJTT_~CAPoM*Q3Pt--SCUv3+7` z4Y`q~;KsfAa@8qLvXV{rWIbQ5&B?7CJVvJW&Fc<)B(Hkwn!N6Sz~-#u+T{7W zYjeOwQSxfXuFVQJZOOECvR*FFW|ha1b=>{x}+?!**Ns_65J5c|2 SJ+kZuwWRpBFE(Z@W(NQd=heXg delta 2506 zcmbRCjb++5mJN}NhFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|yS7+;W~ZL;Hvn#uAk4WwHL)Y&)yo*bO zl{_OhcL;1?CsXSu2_5o6taI}XDK<6oY@IyAF`7J`o7Xy>qb#6VH~V@tkm+S$RD73y=}Yn?+L!$P0(A%?@cF$@4$NX8x=cGHnJsJTT_~CAPoM*Q3Pt--SCUv3+7` z4Y`q~;KsfAa@8qLvXV{rWIbQ5&B?7CJVvJW&Fc<)B(Hkwn!N6Sz~-#u+T{7W zYjeOwQSxfXuFVQJZOOECvR*FFW|ha1b=>{x}+?!**Ns_65J5c|2 SJ+kZuwWRpBFE(Z@W(NRwBO=%U diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree index 964a0c89b6d5e89bc1689e02f3d93a5f58cc9a4e..50043e9eb14f865d21b9f7bca1a51c77167b71bf 100644 GIT binary patch delta 3023 zcmZqN#M-!tb%Q6Pp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9Xbj=&(VuEZ!C@`*Vg-`C&;z6sxp&Y zTenqjC)ZYodOdP&b!=QuhOL_wTV%D+|<5KJbQZvfTvs%?~CTbC79- zz)a!I*Jd3fQ~PG0B~j%0yMFSnZ0^m8s~X7lJ4pMSO}g@A`uqO{q0QZwPLo%9)K3O! zm$-hAT)*o);N3j!ZXg?(jt6O9@2fUHaw? zzc=!d<#3SA(%VJ27_)gO&~GHpxJQF5{q>WgShg>6V7y9R!4Jx=tlM?m7;lrQe>%Sx ZBj@%5-i+J0$Z|iZInBRaDV*^>BLF9ZYv%v} delta 3023 zcmZqN#M-!tb%Q6PVOF+DYC%d#j=ph{xrK#sYKlQps)2curHN@$VzP0vfvJ(9NlL1j zNuotcVv>=WNz&#j#tURj=&(VuEZ!C@`*Vg-`C&;z6sxp&Y zTenqjC)ZYodOdP&b!=QuhOL_wTV%D+|<5KJbQZvfTvs%?~CTbC79- zz)a!I*Jd3fQ~PG0B~j%0yMFSnZ0^m8s~X7lJ4pMSO}g@A`uqO{q0QZwPLo%9)K3O! zm$-hAT)*o);N3j!ZXg?(jt6O9@2fUHaw? zzc=!d<#3SA(%VJ27_)gO&~GHpxJQF5{q>WgShg>6V7y9R!4Jx=tlM?m7;lrQe>%Sx ZBj@%5-i+J0$Z|iZInBRaDV*^>BLG%E%0vJF diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree index 2558a738a6967e95af208003784c087000ab839b..dfbf866453be70c5039217c038736353818f28c4 100644 GIT binary patch delta 62 zcmaDV^HgR-Fr%S)UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr RnVGS%k*TrC<`%|>JOEy+5(EGM delta 62 zcmaDV^HgR-Fr#5swn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-<`%|>JOGLf6Y&55 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 ce17507749a94f611769794ea3efda5c03c0e5df..293bf9049dafcd953121ffeb92b045de770baceb 100644 GIT binary patch delta 2704 zcmccogyr%RmJObahUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMGq7(bGsZSukRrpau_z>E9d7XQ~TNm zq0LgwIs9a5&z{b`IA$_=1^%qbK;4^-X3iHR)AIK^Y?EWHxi+s^txul!i#FG7-%4Io z7fr6)uCtkG&q7|Z91qeiJ$e0g?#(%;T6xGfV)OdzOqyhApS8V)k?{w44TV|T?YS8f z*+{o{`$a)UGj`JT0kh?-$>&mex8Ij!oJWSeaQ&f*jD>t;*$-+D@^9}jU_8wT01p%s AD*ylh delta 2704 zcmccogyr%RmJObahFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|yC7(bGsZSukRrpau_z>E9d7XQ~TNm zq0LgwIs9a5&z{b`IA$_=1^%qbK;4^-X3iHR)AIK^Y?EWHxi+s^txul!i#FG7-%4Io z7fr6)uCtkG&q7|Z91qeiJ$e0g?#(%;T6xGfV)OdzOqyhApS8V)k?{w44TV|T?YS8f z*+{o{`$a)UGj`JT0kh?-$>&mex8Ij!oJWSeaQ&f*jD>t;*$-+D@^9}jU_8wT01$d- A=Kufz diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree index 715a032aa07ee85f4aabf70d1a85593241dcab78..29baca17ce1aa8a08c6c27cda0d37d62dbdca3af 100644 GIT binary patch delta 3004 zcmdmgn05bQ)(zf_hUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMF<7_XC|ZSuwidDCylGxASnWGN)wR*?3|3%GAy50}1zfY3 z$GF|aNOl5PSq%%7O+A9<-RmjrbIQf4e z>*fr%SOK!Nb8M~;XCg1@fNU3 zUqu6%Uf=woYCjX%4wu+mS=U3R&0vS0Z#Yef?I|rQD6#!o+fhnvPwncU#P;ny&6L>A zGQpp`5(AQCWT%8uqTOc39r7H%V6uEM-{$YLJIP4zn>Q|yAWqlU&>Dm+)*3c%n{$_Q?THST|e0W#=R> z=peyg-^sn1^?MO{&dAs*z_^0E@+4!b0HenCN*2aeUb37I(l5PzlMrJ*4+Z+K$}=9+ zAWMJKc27^nhva1%aLVV{{?w200l5a831Z}&{Aj!I^dEkVPTQ}AFs5;l<&C`U!1nVK FMgZ$@iUR-u delta 3004 zcmdmgn05bQ)(zf_hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|xX7_XC|ZSuwidDCylGxASnWGN)wR*?3|3%GAy50}1zfY3 z$GF|aNOl5PSq%%7O+A9<-RmjrbIQf4e z>*fr%SOK!Nb8M~;XCg1@fNU3 zUqu6%Uf=woYCjX%4wu+mS=U3R&0vS0Z#Yef?I|rQD6#!o+fhnvPwncU#P;ny&6L>A zGQpp`5(AQCWT%8uqTOc39r7H%V6uEM-{$YLJIP4zn>Q|yAWqlU&>Dm+)*3c%n{$_Q?THST|e0W#=R> z=peyg-^sn1^?MO{&dAs*z_^0E@+4!b0HenCN*2aeUb37I(l5PzlMrJ*4+Z+K$}=9+ zAWMJKc27^nhva1%aLVV{{?w200l5a831Z}&{Aj!I^dEkVPTQ}AFs5;l<&C`U!1nVK FMgV{E=5qi5 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree index e22171d104b49bc171bdb5319e36014c13e77f54..8b13734fd6f8101c28045b6d64b88dc20f3e98fb 100644 GIT binary patch delta 2688 zcmbuA-77<39LF0wb1XBFT+p0CbE9p}&N-VhWHl?r4U)WVS)rCPk;zL^F4i;;xgak$ zC>J(Ai7P2LZd{>=v}58d;})x5 zk^G9%XSF$ewm`sP6=k1dv-dhh$>EgjqAZJIr6k%dtS;ToHq9IzXxcg3zj>Cd8el7# zxpbW2*1-5QL`LaGpQVLRM%%_BtZtaq3LFjRdwIyL;yewRO0m-lQ?2lg2hU?dQw^&& zXF}Pd#Fby52fkP_ja6oJV_8ju0yC1t2x^^^PEjlG+y$(ZtZGaLd%n2l0psuXGODf{ zMSG69>(Rhd4PF$DG~OeD&k_^N)nX#CxeK|&zc{&CqR1)#nM-J`?F0{ALS-Gjsn}aP zp)FE-ulFpZV*d0cQt3SzFoOeQ>-`4m9^6BoJ9TJ6$Ux^KM|xl>Rs2!TA!Wv51aaeX z78S>yk=I_etN8iKJMvAb_~u47g|=o=cp(i0&X0gB`yjf;{=Nj>sa(k xPoB#sWZtodMS8y-1l}}?J?IQzXyJt;?^6PuanCDH`)>t0&>sKdDg1Gl{R0Ws7%>0< delta 2688 zcmbuA-77<39LF0wbGFPxa$#`_&5brYXD^f?ik0F9NnUnrin&lGYKuuL7i*e_T#%O= zlna}m#FdmAH?B}b+BG-AmAsw&1-|fby|)r_ze zZ5|zGHK|}c43ROq(PgV4l+m`qh}8|UdV!Cf>bYjjJS#m^uQM@!&qfTH|Et0C@>?N7)7lS+bL@0t9Jn_rD|F;!JaR!dBFI)y^Lxb zCefZ#O^s;anHD#SMqBTZz-P%2b9EGx*xrZS;a{9w9WmsT|IDSd)^&mhFQxJxPdfI_ zUTBMy-s=Mk>6ky|WIDa4hfLtW*!r-Z`u%&zbEgbV2F)ksut@K>e&9``_=83dh8A8paz7=}84tYjwEtG90quz|o`N5DIX|vlZ0!I5 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree index ce143823c09e6a9ede34cabd7a31eee1f5acab50..eac8aa3cb62cf17eeb891eaea433d696b7331177 100644 GIT binary patch delta 3004 zcmbPthGpIvmJPm)hUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMHV7_X6`ZL*sT&mLr{tx`4YUMpiOm~T z8_4xFNc%tawM^tWeshBM5=v~osC$u2n}JDHVspOX3QBB$Vtkhp+xyIID6xII;$yJ(-T5?690~v+7Dca$Nw@F1}HmOxq!G|KEyh^XFaF-K5{Lahe9LXJif0%mJzMn8+jRD gl9zp3w<`!Uejr2dc1Hh($ delta 3004 zcmbPthGpIvmJPm)hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|y?7_X6`ZL*sT&mLr{tx`4YUMpiOm~T z8_4xFNc%tawM^tWeshBM5=v~osC$u2n}JDHVspOX3QBB$Vtkhp+xyIID6xII;$yJ(-T5?690~v+7Dca$Nw@F1}HmOxq!G|KEyh^XFaF-K5{Lahe9LXJif0%mJzMn8+jRD gl9zp3w<`!Uejr2dc1HJOEy+5(EGM delta 62 zcmaDV^HgR-Fr#5swn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-<`%|>JOGLf6Y&55 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 42ab5dca7da3a547c1dd38248e8a57aee09a314b..ec060fedd912e779fc17cd61a7a5485b80996988 100644 GIT binary patch delta 3483 zcmbuC(MwZd7{>W(XIgU;Tj|6SB#4O2GtYLmT@+po6v|;DmPF-hEk+9$?!v-`E|5|S z4`UbASgk@R*y_c75Rn%}SCYF4Zx)T9i*8~gsEcBBK5z1N&;Eel`@GNdzVCN9)pcWa z-I(iVc2_v;39J689@+15`dzMzvfVvy4+Pw@qK>=lj`JSH>Gn7simEC~uTyc*6srK3 z#^f2T(-5m&P>@R|D_xMj8!-BnX<<7+R)R0J^njUeG>;*|m0+IU?w&@n66pT6KiiC3C`1BG~UK>UY=;>>HpUiY%gJTg!C{}up?SDjzl@62j zTh0u{C-9C$k(p@z=TZ}1Vx`E+2O&Jg?;m2_=;R8a2A+~}e)Vc2f2^|OMyU5$E!(n} zuO4MzvBTZulR7W)sFyu&!J9}{M%gfq^F628$6Xko{4&Wp`N|a*1w-I^fhXAd&Ic{w;gh@VU5Y>8RJ<;~SG7A8m9DWe zfN4yg)Y=Wv>IDTkZ?ckK`fkAJm!{c`09g($YN>uR-Dn&|gv-Gkz1cc}WF^p@Eq^vZ zP3K5zyC(rClhbszd8O>Z7GpzXeH2%qH%M zB6bD(cd&?#_3iKss_Hyuz%C$19VZr0)$GX%>NW10MpX@`UjwRC4jw|Uy!qlBn(m!& z0ccj={G`@yFAd^^c z!ZW?Uh7R_F4$cKzH@*rIg;-}J9`uwzl;WM8qvUE>ovFY$<*J#WIBNR&s|5RUV0$JvK17@znu&f59X1r`BA;Cg{4*!oTh d@z#VVhMHgF`PeNs-U7K7zebIG;UOE|{0}RPQabYX>Ml*85x?fC?oq2*2=9!A=jH^Xo>}<(J03g z9W_M{!7x%WZ|WeCGiYDLKp}_(33~ltOznHtj@BQrt3Tkk-+f)@ex7IZ1IGCQ<8U=e zve%pMwR`G!Sv<1MBg^M4lEW=kRyr(JyIYp>D)Oy1N4_o3YPVahyKUAy(M59r<#+l- zt-;J?u^vRaf;U6rasC>=%>bcqn35F?9TcS&12?4ira@*MPZ-HIVP)xbx+ustv0;0S z5gwo%BFDK~`i7Mj;_HrPEOLm$dzNCLfkXVMz=vAT?cW*;T5lBfqt?ulV!+CNr;JuK zcH|OZ6n=@T z(%x^NpgDT!^3nv`)3n>U#MqrZBy>K~HE!*7}QSBng~f=Znke8n`k_pPN#( zr!FGo2mH=D5zflgyj3&?TvL%hZYzBF0?j1@1^nY5HP92Jz`wi_;gdA;9m>c7hKQ`b svgJ7$v?QjZzz*wcpp?EO2xc|$k2>M8QF)htetJ3rDY`tA34B8hla1ca-1igMRruIE+N9zyR)gSQN@4l{cKhLxILH+!oex#Z# z=gwO_b?*9_U3rqtZqM_$&63A#lXhG4C8sOTWzNsFSlylitJCgwO1TAAN%YVhK=~YA zQLi&{d8`YOuHdbZc!IyiZ_`2O8>VC#Lx)6}S;tN3kJ2Esh9`_>8L+Z6I$acI8CbWy zRu2zQ0g)5jYx#zi7UXO8RxEOm!+Vxupn-$@snCmB-TSx3g4XNB1E@8#v;?rS&tXF= z8b4A87=_OfWYh7pXwJqH8&RwGWCv9t*`!w46FF3M6V@< zV`}^20kh%{u~OfTG_0XJ8f-vKLPK3mcQET){?RPZtT)=)Fl&etI*QP&hrL&@ZyVy* z_h75;eifSa;sZ09Rq45mTG#fiL#;a%3u<*fUJY1T@pmJSqv9W9)6i$+F<1PI9*Z2>VX^5@20JIS6G8mDdqe!;9(JIPUs>5RI^}F@>iV`_TdyZW{UYuVUnp(E5+ys)(VS)aXBe8fkC=*jTOk zp!$9T1&q;4mzO5cnWo;#A;#|PVWIKa`6PhQ%l?+`WObl~Mw7t#HNK>ru7WEg_e+$j zKCL7|e!%Bw5MfiMdZCKOfom%A$8CiV*U(%tP{2R_VG}(;3jE8<5k5&Z-lmLfV2H@- s%Uhn4PEBGu3hc1926EX;(l6hgodGIR58 ztt<7`OSSYT2co7?MAXX)#Iy^RP?kuPK}cn1J0kShw;uNRUH<=k|91|X9MMdUXzuP5 zta4RVc9q>#Yqq%LESD^oS*(sytK03c*zBdUHFHn4Ez6Oem1(ovZML;pwoGmkL;yx@ zn|5=`tbMcoT`>ZsWeLY2WCeK*5^onIaph@RCJW zVo|@f_(Uw~?~?tosHZM3LLb<|R5>2oI*+E0p)+gj5}8-)*q<=~s*}dP3-cX~xKSRA zW#QLyUSsfjHg6~duA196^OF$4W$2G$MX-I1v1NFPdAd+u&0^xj=TaC71K3T-JD2M zUZv4C^mETqih+iwy!q&}Q(c&N!HLr`I8X*LacP4|1LmN*S;s4zN>RAQ{}B|n_-FHz zbH(W47y@D-KUv6V3ETkR(Q$oZQ*XJYv%D!M-v@Jc*3%3m)TVx9fq- zu`lQVVB`xn(oomNocnlD_=1PX+$qhJ#qai3B=IVZi3>Yqq%;&bn>!~E<-Vt0+S;HN zMpw@zoWjIQA3*O&)q#nZJVbhy6gWE{+kq?RzNcdVOi?^cSHmKegolAEnMU*Y^S#KG zMBuw0C6Me)ql}jwpaF#;Ft9|(<2QqAkd{zfFc0gHmI!;?R4{31`J(0W(FtS)(+urj z-XKRmqc75#ubsgD{vy!RuWws$68`5AYKpPRfIn=5VpbAmep%R>r7r-wLcSv!Ni`2oicoL;GZ3}%h2#Y`xLLn%vz6-dXZ8J9xiDZ-E*@S*?;JMwUN-k$*1c26 zt#2^coi%QUF5hM{oAd28dYfHuvaK@|*s7}YtMvtWxrQ3M(NJZssj}r64K{8Q%$Wr@ciq=P&v4R4os#$#~ysH(AX9qd_nJq}Y9O{elR&2vyW0fUD-7O+(nSTY2?nCuX*lcN9$mX2l9>;&;UsER&5iRIa4;zAXM+ zX2ND}ce1VsIc1qGel)RtDz4*{%|bojeK10n-%; z*|)8N8?+I|>w=v!0jdyj9TLI01Ofcws11q^ijX>pildl4xmcYGG+= zVr*t+Y;0s|Y_hqA@je^r+9q%044wRtV?OCRH*e(pMTR@U78>%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*JevKZed}ZnqrWYYG9sZX=0j`m~5PEU}|J&l9Fm> zl4y~Vm}F#TlC-&o@je^r+9q%044wRtV?OCRH*e(pMTR@U78>%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*Je)%8a z?REd&Q`Gux_%>=?e*jxoM@Ntl)>1F281eDz$Du~&iQ-zS4)n^d@e^dawN!yuCU*cM z3G{4g61Dy^I{=jBXC2s53S6I2&_4H{4*zrCHW0C{X87brO)X&6!h<{==xBn_A?gSnJ11ES|EP10 z*-IUxRjq4Xt_NT--n{%99d9u1N8+oi#cNQp8UB3V3m8dcVd%jV)SBpqt*1S+sP#`m zL3`bMIEq@o_uoaWYmZ^;%D^Kegq73`DRiIaPJ^zVJx00IU<>4K` zNCG_`8Ah#VdNY7>#B9cvGRM`IQMAv!SKUCdmm?}p^dT{bWDSq=^w`EEmD{~U*#O6L zgnTsi5&U%iBT%%k^TOgJHsvHOEH?1`n_B^}@6LpuV{b=LyTI?f%VPhXMCI+9+_GgM zb}cM%dHWFAw~gB7Mh+FAsbArqoiq}ae>fG2_0+n1n}V-B?E4~tGq`M|P`p6y_f;j; yuXWtI`{I8pu#W(yj+6p;^GQ?_FFHKK!HmKjbaO&+XvsdBufV$n*IVyQO@81 diff --git a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree index 77a88ae1bfbab101126d3cce2c01bc816433fb3e..70c28d6d518fb0c5cf816bb2408cce4c976a5e79 100644 GIT binary patch delta 62 zcmew@^;>E~3zMOFUY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr RnVGS%k*TrC=B-S}xBzqK5_13m delta 62 zcmew@^;>E~3zK11wn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-=B-S}xB#C?6kq@V diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index 6e9a872a74cdca0333444ac9a15974d721433315..f2b06041651277143969f34d2af624b7d6c094ce 100644 GIT binary patch delta 1253 zcmdlng>APgb#wL^N7!Q-KYkGhzqtWIiOb^+})I0eFX9Ia!jVFH)FxdQuYXU2o`Zs?N z2xcNv@8k_)rkn2w7m%xe^9HfqWJD4uxQ)Sfvq;vHYd2WGjLa^I?9Z2TrO19?#pf*K zhW!Tt+09$k1cfQl?{0O_icI~}*K0FMZf-5L7oo@i$1cZu@(c*t&dS9oMTYmc1Cvji rBzcw^Z?`gMJWhtC;50iOs9AV>g*W3#8}h6-0u~hf+c$SGwlV?$!~;}e delta 1253 zcmdlng>Ag*W3#8}h6-0u~hf+c$SGwlV?$LYR8x diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree index 7c0a82006299578c8e44540b8989a22a26b9c865..9d45a30aeabbd78c2fc67f8d4ded723397f7506b 100644 GIT binary patch delta 13101 zcmbuG`*VzE6vyYhm#~q|=IVZFLzuW_Wo<56l8B~82|?fHERsQEJ4D=D^=!L97u%wfK;d<>wZ)OQ?ee++QEJ%Q z0Vvh5-hxuE6i-5_KbOR!)I*zMP-@X9k5OvS)@dlU>t`lZ`qPdjsPxvZ8ECE#b}vAw zv@Z~q+P{RU_UiA#6PP(!crHX5JQEHc(87MWlZa7Ny{%s#h zO}Yu6be7v&P-@n_Xf)UO2ggzB_P<7>)S~+HDD}x>_!;r{f2&Yx@^iSGVjG`;RIVf% z3znG6Rr(v{Cbb|xRI7Q?YE;|PNPST4KPI}5YM1#_I9lh#K&n8w$J$aFa(g6S+PSO~ ztwOoguh2R)Uqe^QN4Y(^(`Hng5<+W{i#kWGR`#K>D0iWm4x`#1EVLV~vniZn0WRl9 z(pi*QH-KgWOvfb>-x)}squib`G!*5U;>e0}Zw#S0lzaM38iQ)T8b-yawq^vOuLN8< zlF;WVzB-!b0Q*nGzD+qSFL8qbF-tM>p}+xsmy0JsP?vb(R5Ah)yTry!dL0y}aMcWo zhQ&K)liITRH|377lepIcdJQ;^ME-hplt^Aef#P`{T?Ob41C|CQ9-vs{6p~38tF=t7 z`;fel@;PIWHVayzF=X24M^MXOm(Y$jpjb2F&9H3FacRlAavh>P@ zd*B{Z4VVNEFQZ8TfY{l&<_faHy_0oWmT&(MHJ+m?sT4R%T$PhHrO4KE?w-aQ+p%_tVqHl#hYX1E345MoP}j+vnUW9aL$?zFH(eCF$n44%?oVG>27Es zr%ot_CflS%nG%Wi8%wVquz31eWiMI`-f&JadxIt5xXVg3dQP%htFRd)iy}YOQ`}5a zgGH9Fx(jq+=@J9{494}4Fshr{4xNiaihZ~tSUu1YjNRejA^OItx7{(|Plu?7ecW=m zo}~J?Qw9$q9Xxs2c(t~*JAsIg=BP&Tm?>Da$x-jSW5v>HLTAh&)fIv$Qp=iez;fmj zT!k*jH3NI?QLh2_5w4Tp;UJ`g9~bOXtI@F_oHk33ss{0>Ol@p_8D0j%(j}|AI}V~Y zL~ALQ-%&MQTBqJbrqmy!f+D7DHZ-OhJpm(%~`su;E(VoZH$mcS>Lo(!rZo+}9f6 zw-m>aVI%3i;KRo`rldmvS1I~Q1GpgE+NgJd*Wz1WeIxvG(AZ1HE?*9|!FcIV0*}Pq zk-Be_2jDELeiS`WH3) z7EMU8k^N8U=h4SMo_tX+gr|TMdvb22z6l)wyy})70yloxefXP}= zGrbYM(CXvk;mv8D9>dWUXX(%$_^W~QgMvuNkT-Hw9X$#?o8PUDf2gqho5^+G{{Up8 BRcHVJ delta 13101 zcmbuG`BRl;6vyYh3vz+WWp!T)MF$bNvWb?0LgW}Cs3@i=BA6ly?ukoMDrza>MIUpt z)J#Xsk-XIrcSnb8EYdMB#|5NCam&Ib(ZQa3Z!_{M-}?uAzn^o?bDs0O@AKY!w4v?M zhPGuXG$3bsM0S4G+}zOc%)Zgl;n`W{%xrVt%zhD(nUf}mPc}z}^@+&Jj*6HRoi!;l zEGi;1G%IiNoX|OW^JY)ZwiNb$-Exr5^l7n+R3=*+6c-(niW@y#OqSKIvoN`Jkzv1z zNiB;EzLxw9&3eLkRe>y0ETQdNSswcNio2u9(;Da94GYEYV}0m%T*Jb!hPL0>3X+bs zCIu)SEtgsgdoQP!%Ve*Nw~Qa`Zrx@M^M>Uk0VSosXImtrbyQr152Vkv^5`TaXG+ht z?o7)@TFfThHLe#xwNmV=&v*BE;=B1+4?-xu&b3Id#k4i5rDMF=>mZhM%)_oX{QqQayi&D#r5>V>aHP29L z*xLRm)v!JsrCuqSfKq=c9fVR3ZHh&y#h*SwsR3KFP-@rDO{nze9g9)v?OoH*Tp#S7 zk5Xx$KPt6;1yd^y!qoQR8w+(28Yj_}9rXxq-*;ICH8%n)%asw)DcjhcAEvSG` zy4-VXP^#tp6?9zVG?xWhJz7&PlImMqUQSPy}l!4qH$(MF6 z??kIm?v2-I9h$G9D;1*L9^Gj(svRFhYmtjOTdh`x&?uCFXhFhXAm zxN10|&r^JL1my$!PYimO@>pKt1_EN1V&p@C4LX-gCO}Y!cw8nKfruSq;}m)m6p!cX zX%qvCcg`TywdFVEwy;w)8I7=LrlQw0@*0b-Prkms@ zzO1ERfeP3=R~jT4L9sADCf8;S)ZrnOqCLRUD;Vy+YmIaO-Mm7I4ea}pF2Hpt@KxM7 z�_{6Oax=5wWd{g(-tPcyv3(18rsgt&4IIZs5oOWjpYd)iM4n`zoepEwn#U*$D?C zz3#vz=oh1`Lsr1Pi-x?VB%oq(VXC5uPQzsjb$&-FfZNz+!_wvxWE$FsiFEK2wcYjq z&x7Sz%6T*zmR{H46VYCgq=kwn$Ieq$qcPYE%hG0{KRV#-HBBs5gi|pH>EOxpEz0R` zXdtIfD28U+q*b{RjrJQ$uO6^?>RDwkS`6NBPBD9eCE&QrN(_2VvRbRK86=BhAJt9V zN>u|zj<>oCbYbZd3;Ybm@sN<*O>KwHMIpsr+!&}H=m^Gcb8r#8a3A3~`E3qDI{5JNed-N#EC{>JlB23YJT6z8T3&`%!LW46>F$h! zs14Fw#j-o9#!KqdTj;cM>Lnho)zW!onr22{8|Dj1&;ZSkWn4R)3Q5S29EKcb(Q<>p z;8=F|ZS=Uc)usm@JgxoE0mO^H>SQtG^~fZ&7%Uy0pbs04#m%eNmO4`cgOCoMyyCvr z1iz)&h721?4~7pP`epXr}{G_XQ@l$ zOV9Nt_(Chh%f*v3+*}gT6=&(t9{8(){eyx?%9b~B)g4`m+*;nPwtuLw{F}*j@BaYJ Ch3Vk{ diff --git a/master/.doctrees/cleanlab/experimental/coteaching.doctree b/master/.doctrees/cleanlab/experimental/coteaching.doctree index 04ca673977382766f6116825ec4fe13c1f719cf4..0f6c819c5abaf198a507f68c6398fe88fd11051c 100644 GIT binary patch delta 1676 zcmeDE&D8swX+toht$ALaWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9Xb{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$sZ)tH%}AZ$V7qmPhwSM+P?XN#A!-wPLpvW z(`K;0gXPvyV*47!UP^3#q8v_%?a$O4DY5;MMlvO~KhU;hAv4KL5ai#y#$Y7}1=?R& zGE0)F-62$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm90NI@G1poj5 delta 1676 zcmeDE&D8swX+tohZC18PYC%d#j=ph{xrK#sYKlQps)2curHN@$VzP0vfvJ(9NlL1j zNuotcVv>=WNs@j_esZyXaeir0a_Z!RjY^X@F&;G}*%YA8pv}LSI@rk5I{E!>{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$sZ)tH%}AZ$V7qmPhwSM+P?XN#A!-wPLpvW z(`K;0gXPvyV*47!UP^3#q8v_%?a$O4DY5;MMlvO~KhU;hAv4KL5ai#y#$Y7}1=?R& zGE0)F-62$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm90R2276951J diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree index 014357d92aab740f7dd4825cd8dc7380b7d62eb5..6eed041826dbeca7efeb508cd2b7fd3389c2382a 100644 GIT binary patch delta 117 zcmeyW`Big6IHRF?UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr YnVGS%k*TrC<_<(^b delta 117 zcmeyW`Big6IHO@!wn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm YMM`3lk(o)-<_<9YcP3cG6L_;z4Ga|wEAl=O-yIG;oq6ngg78MJE(!?Ynnij+y7Nlt) zf*{d4=%@unf);E+?FfTl+WHX`q)IQvYY&3pNg+|egR{*c=Y97Nc%I*HXXc&R!dgdR zt>bwHR3n=;vwG6%SCU37X&5O*jVIJ}I<9DX!ce1QrWT8vv8bl&nih#^QFa$LBQP>l zO|!w4U0u!-H_)0~Y{8*qJ`^CmVVOk&!>wpVkf}kyHwd!z?NMYpn-4AEk~3tyFT#4m z&yYA9@4N0nEt#FXn=SOyHXP2@^{^L-J~;RpmzrlgkLo?B)I4iVoft)-Q>1h83X(LJ zUgJnMyLM6o$=PVbWthGDm?81-8Qpm$+N{~a`P)))2ThNUa@Gp1Z z5*FU5-!lVyT?qmY;i@aaL>aca5}cWZH?9QrIWQ5=TXPHBUO)vg+7 z407&D{ALAK5RZ;szeEM>;d`pkj`%sU4Hi|tf{)JC!A~DoAi+9YcP3cG6L_;z4Ga|wEAl+oQX(AL_6hZXRqGCZ%vfXWn$%55E{FnnMvdFc(M^ZHkB_J$N>4bX8D>O_M~%21j%Yf+2U`i4 zHBcJm{SABCoF{IgWjWVCL#_FM&*}`yJnS26BrE(p-s(a@|l`C;05Up`PeMh)!yc;9)TxNx%k`pQkiIm)JUa0M3+ z!k~u=zv_eEgrTb@4;=xypP0;CIXLT_Me)gMvvbZa{McFwxNs&9DZ&j^6MVl7?NkAN zxeJ%5@Misv8Q5Q!!1oBQ)g?$4V0&GHbF=WaE3O0X}^F4cx?)OrtPNw$k=7%`$($jZZ6Zlp0?(6#l`Pc3OuNySPINuscQY;@UQ; zOx%VP3eCwa&O6}G1JXEU{REfCU8+>@I}a5PW`ZpbNiv!zr1yk-z!snoJR=-D!H!yG L*@NEm(yzAv2mBYY diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree index 1e628e920ebd1b827bcb00160be259d984f28078..ee37e343fc567c606e7bc3a916764eb6845a1946 100644 GIT binary patch delta 15985 zcmbuG`&U%g701`PckaC~2#mu+e39a#N^9aE3~qeZGc$F)VRgM>M>AN2t*9urC_1NXvL(l6&9T{*St4SVM&#wiSR$jB+9IN|V?^LAsC+bM*NB z5UG|k?`r?E5*&DlLk=jE-IqIRg1Z5ky(V~_-LzV9OdT1E)AM|^;`7g= zM8^tf^E&f9UUU!$*6x(5rMajyX!RhPYr~p+l-gQ06Q!Pe9nQ72JPoC`ZY)7_z550n z@Z&ANM5)ua%|UaW_KTNMYTOQJFmm>A!p?M*I&;?}G}nLf*(mi|RU}Gn*t-g)eo-Bd zQvXsj7p0#4%~vQjx9&BRy1CvTmBt;aM5RAFnvdqX{rHrXD{DQ%g_7 zJ176l1GK{0xfHZa(s}r7QX7w;)FsUosPyfYOQ_V`3g2|*&-bF#jJ7N2x_a^9)QZjmaw&s^|#AV?7q9R0jxfnR`s4jsjLsa>h65? zJCr)3KMu|HnSmCRdSEaUq|#x=z67xptuS;wIFWN1g#of^WB*BLQm(sC1< zhn6|d%nT?uD43biQhgf2_MqC3Q7jDAc7?J*R9iWQMWb~t9>+RSZvOsWEtP;(4(-dY$xo50wJF5LUhE=2eJbfxlM7h@I*vF{0K7rK&b*7EavkNHmQ6ftR zn9j?O29g=_0Htfky~OsR+|m@b0L^zzDzl>8`DrW-<<3uMFQD3_Otufz@O+}la@Z#0njzB1rRoy z;pPA40yFYoHM4dw2@(dc1p;Dcgi^~fz#5}`$FtXyKl2b1crdC@RM>OvcVgv0Z23nrVSv*9#BT*^SidfRrCioOY*ELq{UZL>-@fnmtC;b+81ZvbDlx_bWVi;_Pr zoFzSm@xtM2IPryP(mP1q^&KO`M%t7qy^J`_DU;G^X|}WnX;fWYu}rE0jye+JO~}3! zx>u;MT)K?hZ+gTRwn>N3bh!}w(a;^z&x73GoV@p-l*frP6JM7trBOw#^dle;5;oTH z0N&Xy^xGEcZKPB5UK3((`uKAx26dnH1F97$KICb_>gcO6sHc*|y`6_e7$ElOQ7rsV}~$`CG|ZB112sq!jN zKxgnIT&@7~o1S>j9mU%f!QZ=;O+XXq5eS!Ez)5i8n7RY#c!A8ICPjS+@AMZ;D*o|k zTcGNLPERy)teOj~v+Mmti2cYMuKEFQbu`DSZa|(+KIu~z(xPY8m|*vtoa^GTsVa2J z>*BU~YVS&r%!N3B=YOasVg_aQ!C8J(a8_+XmP~|SPXB0BZO9F$i<_I&mB^&8i*Nl^ zO@PVm7ggk!%g*&Kr|Ye115BQKNnHr%yy%LW43pE_)txZ8szYss$)m5S$j>xf)n4AE zBHwMh#ItXx8{nK@?^a8J8^cBZ^&Ry(T(F3HD)QS07y0l5^*%r*(c2T@bPqmK_XAfQ zpCfB4spqkZ45lQ+tAVS+S*EPry{fYNf~wFWllBpq_O~Xd1GP(NBs6A>CIbtmr$LUf zS~WtB&(w1HO^bF7xH_C#%9^768<@z&-v_PyXs#B?u66MI$z|EKD5A>pzU$p1TG z1n}k*ZMf^};~qeZGc&b4VYNMBN7C7(f@P7pD{ZeVpEx-?IwodvuFaC2Yl+UD5^2rO%9)&Fu|`aa zwB<%cX2saDvLm7*v+Y}0KcK1(PILIn76T^bb-7;t_DfnZCbw7l2RJ&tmV$I4AL8it z{V7r{XWmu*XCyfA5QiL4D7!CrR0nqfGJAFKTDxhL;+Q&o3YM=+9zJp&ma|K4`B5vj zSOt!~W4EBvkAKqV<$k3_$ub?uk?j~Y)1DkT+;M5jpAd3fezx6`pg1PP7bA3+{1%Y1 zOX+2NhTR%fL^>Q#<`S@+D4wA8U@aSGDdarvc=%BHBDfwX?}+nWS^@*`zicMyX4h%2DZC%@&UEKhchb>`i9P4hCqS(E6W&>D#G_CA8iBefFeAV6l z`u8YxMqezN>r?&BD0Tlp21untjC}=SDN14Jm`D*CyNPoDVq|DX)t(&0kk=V8W71L+ zn}?P;&&&)cHz=5y(NcXD!gizDkP$2l)pmxm0aRNtibbJyE*ir+P;TEiwgh|4DJA7e zR*7ZJ7O?=jvXn)8;t)a&%UB}nKq7`;pkikNA$0g<_Jn5+8&jvk_<-EUzEe38UUOh^h#}{vBLHuC_yXt<>x&aA;vnndy z&lyK*t%oke{?xLY75RX8{=@<1#hYtcBWOUvz%c|A^Eb~iU!IpL&7`{1Yyr?Ut_2V_ zo8aaD_B=E4cbZrmm;?!fR|5gDvvK*K3&jcsUt`H|19f+?p}=YB>U|;hrCqmJ6|(d6 z7W`u`y9p%!SZn0RIWzNVo0U|0`vJQ*2=!WTl)Pw&B6XsTK*Hf!xCN6;(iwCc(~dx? z-PirZNfhxu81Z5n6E3AAV!dtKOGMvim9~}Xb`RxqZhp$^8cY+2a433#F zv5g`N+EVQgD_}74mF*A zWQxI?>QBk{;IUJ8i2X?JKsPwo9S(jf`yvbQ|17w0@aD_1Bs1l-$k9JW*F!`zm@Iv= z(c2@33$h`@3ObO8VZH{6o#hOrx;R7XlO8x2siMfxfZX$s7sSYgLooHfVkn*HNgz%e zUv4^S@SWed590Es*v}2U=#8lNJQoh3)X{2a91aFTmR>^{ToEA- zq=Y`hU34gLWl=X^Fdo04*gdO-aPgsjgVFOQ=_6jfOq+E_z5n?|whpT?TTOG}@s_T)blTUlqg|z5tH73~oCg-|%bcza{ z^18Tnp4zhT+b#*Tpyg zrpCkMwhJoq%VpVP|zk6G~1{ZA7T^0H5gNuCVzIqQJlj!a7aJmN`s{4Se zj?a;`<<$L1MFvw6;#I&^;Ve_;t{zp{bzW6ykxBa)O#6G2Q-Rt=G!hy$N|S*F)6*cw zXsrq%$7N_Xe#4?&1+EUKmNFx>{{R!Y`1_!hAF*j+{LV94II*WS@PDc<7ZNTzfc$?D zMgVV0)`q&iE-rmR`v6#sANrB-n_+anvLq_&4NQGbI*FpL9p^stapZ~g_ib9#blf1F ur-XRzq`V7S0`LI%eHfY7wdhG6gh3*V4?T+D#8X~*QBVYFB>uYnt@(ec>8bGm diff --git a/master/.doctrees/cleanlab/experimental/span_classification.doctree b/master/.doctrees/cleanlab/experimental/span_classification.doctree index 07069d5afbdd8e140cbe7aa793adb7f9a63ee069..56abcfd9305ef6dca3d456b0d31f01508d912f4d 100644 GIT binary patch delta 976 zcmX>#f$7u)rVXi#hUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMGmG4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb7N%Iwr delta 976 zcmX>#f$7u)rVXi#hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|y8G4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb7TXHmA diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree index 4df795899344aa251113f978db2cd1d760e58be3..a9ffd4ec19b936ba5525ca1df05fb0c6b982f42e 100644 GIT binary patch delta 1139 zcmeBrz}oeIb%Qsfp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9XbLXJt*yfkAB4h>`SbLY^H!^~4^G5XroMidec(R>=_~tW) z^U2e0IQ>F6qu}Oy){K&5Sr6486q>S)eEqDG8zh9c8}KlCkeANEjsP1n9cT#a_U|H$ r2c*e&j^On7HjK*KeGM7)xhXJZJJ1w0L$VAonB2+Dzr8h&v6T@3$<0C$ delta 1139 zcmeBrz}oeIb%QsfVOF+DYC%d#j=ph{xrK#sYKlQps)2curHN@$VzP0vfvJ(9NlL1j zNuotcVv>=WNz&vR#sg&N+Nf@@c`j2o2bo$sxeJ)d((1A~kH3VKJgt+@Nl9$Z7u`!v zC~Z`C0c+nWzJQ55+c%$+>LXJt*yfkAB4h>`SbLY^H!^~4^G5XroMidec(R>=_~tW) z^U2e0IQ>F6qu}Oy){K&5Sr6486q>S)eEqDG8zh9c8}KlCkeANEjsP1n9cT#a_U|H$ r2c*e&j^On7HjK*KeGM7)xhXJZJJ1w0L$VAonB2+Dzr8h&v6T@3m=4j?X=-9@ XW@c<`WNK`(xq-2pnG9_8XWM-1Ixq-2pnG9_Pgb#wMGmF*1>%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?Pgb#wL?{7(bJt>mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi5M5#eG0 delta 1690 zcmaDdi}lGY)(z2&hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|Zf7(bJt>mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi7>)fZg= diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index a0822d5994128a90c2adfdaa7ea199c7481c1758..4bd2899f2fe91cbdfdb247e9df37cae22dfe816f 100644 GIT binary patch delta 1932 zcmbRDmTBHwrVZ(ghUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMF*FtU=NZSqH!#LfE56Uozhk92oVUZ^`|^At8A4zje)*qqOAL5^D& z>dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$L~ delta 1932 zcmbRDmTBHwrVZ(ghFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|xTFtU=NZSqH!#LfE56Uozhk92oVUZ^`|^At8A4zje)*qqOAL5^D& z>dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$r2y79L70EXUplVU_nNb5}(Dk37HM3f4mtX@ks%m&-4AB@40YZL<}z? zhRZ&(&|OpGsd4$6^Q?Zi)9-c%thW40+onzVR=caxZFBfMc4xlF>9D(8cKZUS-I2Gk z*58oVP}{iGA7Ei`F1te>*kV?ZWI4}B_(CvLZNE=V?5wUH!}+PCOF9f?edY}QE~OC+ z9>s@^WfXJgedZzD*}|(Q9S5Cda0_qGn41U%Z(-I_6K|b*0PDKKuTS5O)vfS1Gt04> z75-_~GSqcwb~osHcJ(}vVXpzeI*KbuxHyFiBAG}YG-JRDD-a%aJ2TQnkoj5Lb zM^-;r#BCp%G1nrV^YIjNC~B^bFHnaybjr6Sz%Gc^`uuwhTI=;6zW`UYH-R*%uT`2W z+9*i|%!O1qYz5Xf>|Mh;a>t2R4)RtX2fwm_oJqrYbZFEdU2tg zKw~3&F`6Y!^i+_|rZ}^#je{4R)nozk7#S@?3QT05&9bevo-s+bt(f3?VhI~7V zoA=07U^4!evWPj4Ng^=o|H{03LeBpOndQ$2^6xU1M#%o;09$WNLF=$ZTzg5XQ5hi@ zY9=^i(N#8xLj&aL9Bjsds>w{pccs!cV6Tp}K@&KcN_8S{A}!8}J%*6u`UGsi5m8{H zB{Kli-c__RId)E!T$8x(rJ2)WhqZH7Y}rH8foh{{YAel|9J{X;dOcg@$`u1D^#JB$ S0uLj!2zv>Y3;8=-Kz{@OIFnfb delta 5860 zcmb`L>r0bS9LG6FXUpl^G7tqqoF5{Wo3%AOdX_wjMv9dcc zPV_J$DWVr$!0Ld$b5gm*&mHTU2v8q+xzUDSCTRiMG0hqiVRKCnJ zgrY;f8^}m1p(FC3GV^`@3}h^oP?)a^_97kD(Vv@kA@%BLNpl2sjcnBbSFVH>0vOZ7 z`gTH>kdAh$chNc*?A?Y24()fMuEB$isOz~Si-B#q#KZ+G~yG ziZ)7;0dpZ04%vaV4ZByd_S`Yzm5aPJ#=$QyA!pJsUfaEaBx~J%Vh~5miJBCricwss zCeYZ(UW{Z(6Wujry(P{ptK;BBM}XubkCBlwq`-Lg*(%#wPhKIn$seUGVQwVH;)s%^ zNrB-gHCspzutVg#F1j{ZqPv9{%y?T(n!AY@wYGjTQEWL(l8}?>PnKaRF)2*81A(+F zb7`HJdzx5)t1}8~?RSY$^j#vyfdgYiAx&g=kTIB$&hbn zaq}Lz3QWe|QZ_N`F-Zhw{a=}PPssWIAhYxtLH=Dv(+Jt09AN8>D(D@yiEA%O0F@DP zp{9c~7F}hNIM`2~&cbHQuUV{gY-cKM1NQ208+3sasnj6y#?$hw*kcGeu1~-Q92O-G zS}_eU?O8!R$+2^q(PF@S SRN!HRmSHcUY9W7zOXzQNbx3Xi diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree index fbb0356a6494bb47ba07551be8f1806e0ae4b87f..bc1612ab689ea8426ee2c580ee6fdaea36f2276e 100644 GIT binary patch delta 1199 zcmey>$@Hs}X+u1tp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9Xb1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGm`GE2|^ delta 1199 zcmey>$@Hs}X+u1tVOF+DYC%d#j=ph{xrK#sYKlQps)2curHN@$VzP0vfvJ(9NlL1j zNuotcVv>=WNz&vAj6ccHRmc;zS%7&RITix7PA+KXpB%zkNS4J_n+w=3u#&EIb0N=b z^4wd$nTLNq6X`Zio+wqnIa;Wh99uzJ`$YW7wY69LG8tMoPn5buhBmN2h2>1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGo1PI6QL diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index 7ff7a6724270a71b7a9eaeab33a604a35bd459d3..0a86fdf14085631b7dc432f1ddab40eb8880c7df 100644 GIT binary patch delta 491 zcmbQx#yFvkaf2_Tp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9Xb=WNz&vx#(QMwN^o@Fe1&NyITj{322cLTQb?AiE|c$b$W7Mb7T(Ok*3Uw| i5u3HR&ofh?y@x-BJgw&rPgb#wPkH`N_rl#rdU0$*GeArSvA>W4vofvME5FS)0R{7ci2ib^25uM*hhQ zSPRM0-nID*n;sikTKhKdr*V^+tfWxB*2Ua*aC^Oh|NWZJ&DVA~S|@@(Gx;kh}Pwod-=TyXMiH=)hXUWzD@ NWqa0kU=cr$5dd(a-S+?h delta 1705 zcmdlyhh_5|mJP*>wprOGsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCQ14!`N_rl#rdU0$*GeArSvA>W4vofvME5FS)0R{7ci2ib^25uM*hhQ zSPRM0-nID*n;sikTKhKdr*V^+tfWxB*2Ua*aC^Oh|NWZJ&DVA~S|@@(Gx;kh}Pwod-=TyXMiH=)hXUWzD@ NWqa0kU=cr$5dfnU6Y>B6 diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree index 15685759e1fef158be408471ae416d45c7326049..66286b667dd825cb6ee0f5d32a6ddd8706840f5b 100644 GIT binary patch delta 7878 zcmbuE{ZHFf6vlIJ*A{7|E${DJaMpyND->E-W?PpzY?`ql8JRdhUN;bmY>QzinG+>j zvXH@7yx9xRY-1sNVRM(|41>`)S>_T5>d*wI0w%^EL`a;oBtrbQ%>4mR|A6QDJm=hd z&iAIBST;^98?P0zY^UE};CIxtZ?M%k^J<*VI$L&rb#{GyzRm8ac4p^R71;Cg3-WU9 z4u{>IlV{IW+SnAJnzg#*DA#dJDgq9TN%3e~A$f!J=?X27v{~8`w;^4cwKmI?CG2>sPycX{b-%TTX&$;bKVYsDvNz{DDm4OJ3tI| z_xz51DD_4O^sNZa>8YK_EeZwxX4iJ0R#{v&gVs4&UI`FGVJ9l9Q0hc=4@zCB-HlS0 z_H?1t^u`>3Di1f^Lx~Uk9)K7+nD1JPQR=Pz8&GP`n+H(p`L+g>dikv(l)BW>jZ&?L zKLx1L(m9M0YrFOUMD;o}%K9T-E%X-j%cAa=P-^z^`)fez<-R_Q>X$R33+=G>)DlYV z?=MHGCqBN3QWwuQqSU2#2837GlsjM7(SC2`R$}z2M9*>WO54(URaFc2fl>i-pTNy7j)1@n{73=@1{j{&zJ zwM6o#!MiF{Z33m3`8m))xmstU=i_-1xO_wU$TZ%D4(l5S^rFSiwX~AU^U)$6n_S<> zPeftYN(q{LR>Tc7?&Sx;y%jVe)g&bn4q>2@SNJiUZ=kXY-izud4@Q%}hDU&-p#=4T zT7EPctUv2es(5aI$5F;RdHBeS(r79NDg{r8a;V z?(T){2Tc9RMD*abBp;9Bkci1^pjZn)f z8)eb(sR(H3DrC*byLwEj)Qo6l!)QVrBzGupOvOm;OdYl@mgV!mO_)Hsb&WwfgSw}1 zx#A3J=?-I637CgC>taSao5X&_&dw1w5UPQeG@0y+)hmhN*u;8Q6hk#gp#v@!uLEP8 zxWiW|88#CcUS+x1zHo^DO~l?w9!C@9%oGj9af;8~ES~n&vUKbl`q&_(5Bm zk51>@-%Kaweqb>oYL1!c!9CV%#7Yq%D^jiW@hdP;ekn6j%PfmVXCss%WFmG2)vKK` z#|+rwcZBLm6Roq%B7S_t{=sWXqv{~5SV0Qi%`ColCH?5h;!lga_gI`z`=O^l7>@tvXTiHFRBa-qn)rFpK)L3NrWX@%aV;&`c>!9)6O$Vo z_{k{jS}7ru&q}$0Moai1aBqc7NHs}~ghLpp{55_Y=NqW9ns=l6@k22bsO1shXec3l z(94gdfc0k|P7^Qm@p#I7hfkqJs3z3;J})w0A!uV4KZYJ)=X)l4`3(1>%OJUr`$4TZ zDtWZ%h~y@U8Q?l_m`HWG1ZtI{Q;9S*#2Lz(Cwch0o;Q5DL zXzzXA2E-8V8D2s4ll&{72c(L}0jT<`&GHLY{5j8Kg-SQm*T3^mfCx{lNVTd5vnag- z>c_E!C(!I8ehvM!s3w%W#MhvY&@8hiMcj_ofbR@a^&f%R#bUZ9d3hAwNt$f1jikC9 PPlA?M5vl(Nf3N=!NY&Dl diff --git a/master/.doctrees/cleanlab/internal/validation.doctree b/master/.doctrees/cleanlab/internal/validation.doctree index 3eeb2306e998f0378e1e4cc0a76970a96e38a0d5..1125e30a881a396d6c5b3ad064c2dbf82d77fbf8 100644 GIT binary patch delta 1783 zcmcb6gz4@PrVYW2w&rPgb#wPkH`N_rl#rdU0$*GeKQ)MTwW!z^-vMFGl3BtOYpE0#DlCOR8{ssJ# zzp)gOZ9>RqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_VRqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_Vh<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-=6XgY4gg}h5{du- diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree index 82913eb2d69b48b02ace8a2f766f4a35f239a9c5..8580e354c0696b192cc83625b333125523361896 100644 GIT binary patch delta 117 zcmbQJK2d#xH>06>UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr YnVGS%k*TrC<{HMuY-DH);Be#y0FIm=O#lD@ delta 117 zcmbQJK2d#xH=|)zwn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm YMM`3lk(o)-<{HMuY-DH);Be#y08d&YCjbBd diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree index eeaeee47f37b3922c5c5e300108a5ffc786102a7..71652fc999fbbe6f6f88d8214c3666975900a948 100644 GIT binary patch delta 4019 zcmbuC%}bO~6vjFE@s49=A4Z~Ph>0b^%=FGDnJywrt0~ajHR8-PNaWz4iCNGvB0)tT zT<9v9RD=`;7MHx}BF%_eh?}V0)S__F=SU!}qWe0aZRdIafZutZd(S=R;VzHoE|2Fv zZ6t>$8udlpp?;e^Op3`=RH|U5&yms9k^f;XDKHcT@xtzM&t?PSTx>NL1Ury#2 zdv>N)v}tcDFqE}v7JlLuY3#x79;k$$&DyqzM>WIQ7%paQLs=NAl#9hL{o6Vdc+8V$ zf$(PWxBgxpgtm&Rrp?%yTg8iGb7hd~bBOY@@>n^a-bkB7(b=nLkIQ3Sz(}aKZOB&& zdv_1oV*_vKJ!D4R*4Jo8z2rir8TC&Wxf%81_=p+x)BvXhyx`%28~} zj8~V>>aKr>M<$=Yc2cLPxH*UIbU*tsyq-mGe+3;Vn$Oo#L^KXfgAPPuG=-u|4?BxM z=ka+93tZB8yo0huYD!?++0Xu~qY3XTzZLdl6@?4v=2JGTqVnB&EGYc!RMNs9CukQt zlAHz)ph}2qA1AcTaejX*HH(?W_dpL;ST?hmBetgw15>!h|NLEF3VJv6`D7slF+L_$ z?o9%jC)LH>0T!Qe=wA7G8w~(!FAW!z5)wDEy^I!M(GNSw2OoE`g2rIwtSUnCEqzxF z)pQP44%QHoZE5AsJrskL2W{j4Cn~@X>a>JSLqMuX`vW8be>p%Wd9seID$uHV&pvt! zI9M$rRj$?L0u|J7wa6O>s0f9*f-TbCL}4J@jK;U{{X1#HO0==mRJR;$qvPOiO_!1? zCBs0nt(wtfS2v9SD{idB`bI$lO8~1xb#|&#}zc3deB*`*SV|pK!>ZtSzT4>Hi9+ofX4{vRW)v1oTL7X z)G_v~yI!R#E#hzE-7E-g6J^btu`{=c7e{A{Al2s(HuFFTT zDKlSPHfy-{9Uhr%{@P8wqU6Rbw$pv=$MAY~`qo#_fui|bJ#`B2&=lxE#77e-y7Zto z4|E=zvoZfgjmNquT_h$2ww-K%6W7z zy#*Ys7Lh8~8Zv1-wg2sg9wt$g23+OQIBEHN38qwRDI+^xw{ zQl+E;B-^SPO@<;g0<5^T3S3plkazki285jXHvIf?oW7*qP^s90oYXn<%WVF8rTX`d GrT+tutN0B7 diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree index 242f7bab921e94d4c3cef12967484751dd519f60..45a90e41d92d0c175ab1e1bbe2d268e7405036e2 100644 GIT binary patch delta 1709 zcmX@x#dWreYeO`np?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9Xbn(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$xjHnj3&CexoPb`Qw)=Q@Y!WH=Ta zr|+HbkZWtH+ahvpJ?QzEoJ8oToCr$#tedTa=82IJ%uxO8n~O4k>?BYBWCsJT>D_M` zMYc<+Fy0U(-;nKnW{gfrr0d_#IFpf^i*$X{f%+1*&t1$I;Ypt6=*jW6T-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y*H-+bRG6 delta 1709 zcmX@x#dWreYeO`nVOF+DYC%d#j=ph{xrK#sYKlQps)2curHN@$VzP0vfvJ(9NlL1j zNuotcVv>=WNz&vV#v5ela#VKMe3i*vo;n(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$xjHnj3&CexoPb`Qw)=Q@Y!WH=Ta zr|+HbkZWtH+ahvpJ?QzEoJ8oToCr$#tedTa=82IJ%uxO8n~O4k>?BYBWCsJT>D_M` zMYc<+Fy0U(-;nKnW{gfrr0d_#IFpf^i*$X{f%+1*&t1$I;Ypt6=*jW6T-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y-%M5hVZs diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree index 7ad499eeeef97d96ed874995060fef3138f4ddf0..1a059d3b2055ff6f58ac73d748cd7a2fa27ff552 100644 GIT binary patch delta 1200 zcmX@z%W}GxWrHuHp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9XbWbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgUV-O{@R_ delta 1200 zcmX@z%W}GxWrHuHVOF+DYC%d#j=ph{xrK#sYKlQps)2curHN@$VzP0vfvJ(9NlL1j zNuotcVv>=WNz&#zMnN*PO*WKE+pNaCgqd_3L0Ts#vNw>WbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgYZ-bT9w_ diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree index 045b3477656b2f1d6b6af7032594c12ba70842ad..9b1fdbd61dcc58bd4f1b594d6795b4e9d07105fd 100644 GIT binary patch delta 751 zcmeBL#@e-vb%Qsfp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9Xb=WNz&#TMm{pMO*aT+Oy8`;JfE3#D?xgTC#SL(lBYFgasZ#|<_R3ytYqrn z9Kd&kOfQ4%*4lho;0Y6%b_4ZmZY~v(WFuFh<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm uMM`3lk(o)7eoB6Fv3_xWX;E^j{^UXqjm_5?MOckUHwUO!d9wri7j6I<<|}0Y diff --git a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree index 178e91291ed3b8abf976a512d5394d025e066a62..cd694895fcbd8da6263cd225001150c289fe961d 100644 GIT binary patch delta 760 zcmaF+p6Ts-rVZ|lhUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wMF97&*w%wvp)MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~vwcOKg delta 760 zcmaF+p6Ts-rVZ|lhFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|ws7&*w%wvp)MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~vRS@cm diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree index 26c1e9c0c9d4fe25f2598b3c441a337699b0f6c2..bf220867501799e5e6fb0f900f768d2b69bcf23e 100644 GIT binary patch delta 474 zcmbQRl4-(9rVZYVhUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wL?%7&nrk%Yi3o@&=|w-=E&u=k diff --git a/master/.doctrees/cleanlab/object_detection/index.doctree b/master/.doctrees/cleanlab/object_detection/index.doctree index c011c614fe09e0e868034124893f9ef017dfae17..b2ac3232220209c747ff6ee160c76f3b068fc028 100644 GIT binary patch delta 117 zcmeB?>yg_K&uD0#muH!0mRzN8kZf+6Y;K-vU}%wKn3iT?U~HCTZfKHdX>4j?X=-9@ XW@c<`WNK`(c>-fEIokY~_i+ONia#H; delta 117 zcmeB?>yg_K&uEyHZIW7$Qj()@oMdicVVs&`kd$g*o@8lanv|GqoNQofWN4C-YG#sX Xk&>8XWM-1Ic>-fEIokY~_i+ONO8p~_ diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index aa329c7997f7f662c03dd4489dcaaf2c8948d39f..2d305e8c812720b327d5f063072ac5e90b07ce07 100644 GIT binary patch delta 1704 zcmdlyiF5NL&JFI2hUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wL?17~hhiYoVau<|j;cOyp^uKGmIxfAR*_Lb9}1Z9c>Hk)159^_z3~ znaObU=7oZP$l?61IWQWvMb58^@)NKxmvd>&m~tY zqk0OtT1B+hk*l>(cRefViEwkEu@Nun+9n4Y=WJGRH1H%{>*mI;iR9###;&x@DA z8E2B|i0K6iOyb*tfl)0$o&z8T)U9On$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^00%AE)&Kwi delta 1704 zcmdlyiF5NL&JFI2hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|Yk7~hhiYoVau<|j;cOyp^uKGmIxfAR*_Lb9}1Z9c>Hk)159^_z3~ znaObU=7oZP$l?61IWQWvMb58^@)NKxmvd>&m~tY zqk0OtT1B+hk*l>(cRefViEwkEu@Nun+9n4Y=WJGRH1H%{>*mI;iR9###;&x@DA z8E2B|i0K6iOyb*tfl)0$o&z8T)U9On$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^0NK_L(EtDd diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree index 53a3c7e243980b955223a760200c87b32d81227b..99c29e34bb9850519eb4e63582e308109f0e1497 100644 GIT binary patch delta 2442 zcmbu=-771N4v*u(5!#1YrE@9Sy(Rg?f zk_(iP{FwsLWjZx)A-6Z?UZ!u$C3e&>g>v7pYM89~`(QN^Dg-6jice6gmp-6Jttr1o zs|S^HX!YK<2Ua7R1WJ0X8$+p4F{1QBdj%-P+-(Wa=N6{hrZb?(W9~3@w{|B&)d`Zj zLMD)Q4(#?r)Jx;hNE$}@c(jC$jy<&)r;Vq_RE+YCrvkFPZ&qPk@3k_kXe(sH*rW3P z4YZfCzFh1I_*O-MP#f4@@<0512V2d7eX4#q$kO>*G+T%vCK>-ZBvu0KISU)nch*_D zKJ~&fqvmO5#zsVZq}q`JD8^Xs5t{hBjF`T zE>cqLXBWGG_6J&4q znLygsx7PzvH;o1Z2^i(0!F)PC>8-#xZHAxXG0Hc?*_8IaRf=)F%fc+8Ifo5mkIMZw z&{DvVPgb#wL^N7!Q-7YvX^T%}bb8v5=>AdS@yl|KtPgg=A@W*}RN{jXaxOCNJX< z-fY6l%SxWjlO3d3H}4h_U?)%eW(TPVa^h&?f0xM)3xzi;$gFN5&t{+QCCYDk97n>YU7p+}aDuG{bHG3IfQr*}F~FW2_7 S=8TcLWEo(z9auz7VFUo*AdS@yl|KtPgg=A@W*}RN{jXaxOCNJX< z-fY6l%SxWjlO3d3H}4h_U?)%eW(TPVa^h&?f0xM)3xzi;$gFN5&t{+QCCYDk97n>YU7p+}aDuG{bHG3IfQr*}F~FW2_7 S=8TcLWEo(z9auz7VFUoO*2Jd( diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree index c44db5ab7dad4979df898669d79d9b488acd30d9..d8b95e356e2c7420af8cb9663f79709793227a1e 100644 GIT binary patch delta 2066 zcmZ4ggKhl}whiu#hUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wL?17P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY D76&nG delta 2066 zcmZ4ggKhl}whiu#hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|Yk7P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY D?3Hf} diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree index 1737b0a4c2c408610b09282841fce3746d285a5a..4620c97b295a968b3f71f3e1dd1ad9ca54928b89 100644 GIT binary patch delta 121 zcmbOwJ4<#$Fr%S)UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr cnVGS%k*TrC<`%|wGPF&ez$m&oklB+P01ccVcK`qY delta 121 zcmbOwJ4<#$Fr#5swn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm cMM`3lk(o)-<`%|wGPF&ez$m&oklB+P0Nt}BQ2+n{ diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree index 3b800f636774dfdaa53bccb33331e6c6bba59eb3..c7c4152c1868a6290369716d3fe3439b2a6eb28e 100644 GIT binary patch delta 4106 zcmbuC+e=hY6vjFGj80AJI8zaae5fo8tek0_IZFAI23lZ^w=Sfk)>w(h8a28+C}Iz0 zpmn5OOiTq?kVTiBE(k0NMG}&jmu?2rTM zyUoFJhuFPCOSkUMkB8V!{S0=Wgt{u-JsB7Pg64W&` zIgGlR({a?*Hq(i^u6R|3y8f7j$97pf8{5sbV(!hj8(J2%Li^g%W*w!6dz3h`kR{hZ zOG8Q|%epC7YFRb!X;acEyInZ}Y}Wr2Tql*p8tXC0%Ui=MbCql!v$JIsjVeLsLR1bo znVrs!D&6}bl`7LSV&l7<%*6+yGN?oy3(#jfO8{phQ%lK_lv|ephokX<61JT3a@bou z2#v1hvY-YAacorMo^33RhDw-<-Ki>5ijWk!4qPmfjk*<>Aj$R&(QF-y0KHA}t^3_( zy<*8Xau?adtQ9!(B;3F!x|l{QJD3|7xG4pa3at(x_UBpZcDmNX#()*hlQ5UCliv5S z`b@z7&-Y?IeZ0teJqu9CC;KGpuduE_GK*d?`x<)bRu3YR_;2D|Chnw9tJM%%Ka$_G RoK4mfm*c9GA>HCI?X%eNXAkjIsbp%o&J3Et;k2BBfmJ~+-ml+Jgc z=*+MWJE^dkxzK{T&W#d^uDd*mqCL?NfC^jeCxFRD3X8ipEGSxc^8)jqj}=6Pjm2kP4OEP}cYyeLCm zz0;$pt3De?T}^Y{sOy?n6{zcvd3bDB#Iv#8TrU>hOxmGkQ7^Qwt?kB9dU!yIBMVt_ zO|;UhM6!&Va;2Wt^1gN@ow7TWlfY*EPr-IdNo+74gS@` zfHh_U_J6(?o9W|4*6&z?LOz+N*7OS>9CCh_0IxlG(jpVq5hw0Pgb#wL^d7;lrI>w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7Pgb#wL?%7*`sRtV@4#Ah*oq9%h5dk!<{vH!<~)X~gCpW`8C!^+K($C11Pl qn2-Pf delta 525 zcmdmUpK;H9#tq(#hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|ZP7*`sRtV@4#Ah*oq9%h5dk!<{vH!<~)X~gCpW`8C!^+K($C11Pl qjdq#FcIHRF?UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr fnVGS%k*TrC<_^YMMkA87=}(@(BDUF=xt<#UU9=%T delta 122 zcmX>jdq#FcIHO@!wn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm fMM`3lk(o)-<_^YMMkA87=}(@(BDUF=xt<#UF|;Ka diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree index db7ee9524d21d5197c22c8bd698bee9c3f3a40c1..0e4309e7190062c476be2a1e40f31624b77f0345 100644 GIT binary patch delta 747 zcmdlxjd|}h<_+$QhUR&BmU(8$Rr&_W=BCN!=BWmT7DPgb#wL?17$1_MtMR1g=F3ddOk`;dom|3NNS02w$qt;7ljQ`3H+Qi8W1+|h z@y)tC-sA*W<4Kq+#5c?H<&zf#U?Vom3Ho!A>4|{lQk(xt&ETUzySi3eFqzuxIwdyS X)Sc!cPy6P&&P(>BYu(&4|{lQk(xt&ETUzySi3eFqzuxIwdyS X)Sc!cPy6P&&P(>BYu(&=WNz&vx##dzMT3F(<`6-hN6M0&vcXBZDPu|K}NS1cr$pKt^lRpRwZ$8SV z$V!nB;+s2pHjxu*3rk?G5Z`={uZ_HL02{ISgW!D1Tp+R8Sfqji7XX7&Vza;aZ5A?v zvO!8<^A4GIJ_@v-)n4pDy7tWrOSbTmu5I$dlGM$wo2tCY(&{_8K$L&;r*j_c6lgzw Q&DD@B?LOOqS*L*!0Htg}Pgb#wL?%82^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5daSIiJ<@h delta 483 zcmX?gh4IuC#tq(#hFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A zCW#g)iAhFgCP|ZP82^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dd;XnF#;@ diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index 3260618b3dcbb2df436abf831a81a276a8e8e69f..a79fd3e473560924ef4c34e639c8c28d70af70b8 100644 GIT binary patch delta 122 zcmca7cTa9ZI-{X^UY=#1S#p)WL9)4NvblMxfuTi`VOpAnfw5VVxuHp-rLn1nrKyRr fnVGS%k*TrC<{6CZ8I4HRrayTfi`?ev%*VL_ifkf( delta 122 zcmca7cTa9ZI-_A$wn=J1N=c5sagw=(g>h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm fMM`3lk(o)-<{6CZ8I4HRrayTfi`?ev%*VL_UTh{= diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index 6f304727169024da8268fa03e6c0c6a932f12b12..a607e52a4410e588d35693196fe726fe0ccb261d 100644 GIT binary patch delta 699 zcmZp_#@v35d4oHnp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9Xb=WNz&vB#-C*9y2u{6`8U&27P7QnBSV)Wf79kD&S|WqYu&8KKbwhkZIc!G z+cp;qEhJa#X;BRhvTV)YJX!7pIWeiopTGIHf@m)3wr+OpTS1PEj(s_s+a|O`kZ$AT Ni|nbJ7rabo1OP5l+3^4X diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree index 24e5d7d492e3eef3d75447abe6ec46789af620fc..5d571f3d5fb743db98d71afdfaddce38c6d7b16d 100644 GIT binary patch delta 1013 zcmZp8#nSMKWrHuHp?O}OWu94bmA*l;xoNVwd8&b-MUr7!nuUR}S(3S-Nus5(sfDGf ziLsfPv9XbT`?v5<<`os*EXf|jN|6!b zn>Bb3kl}5h`(dsS-#nXtE_p!!GeTnX8=+PTT(Ox~bT*|fh>@saCO;@Qm&nYb!~?73 z`jg4D{Z5Pc=DSTrWZJ&@PK#g>nKtkJCbYTdyCRu3PX=lC`MKPZEZaf3hi7}gF=HVk E0B>a_E&u=k delta 1013 zcmZp8#nSMKWrHuHVOF+DYC%d#j=ph{xrK#sYKlQps)2curHN@$VzP0vfvJ(9NlL1j zNuotcVv>=WNz&#zMkaE!ZDd-*M7nL0H!{Ufe#=@*md=>T`?v5<<`os*EXf|jN|6!b zn>Bb3kl}5h`(dsS-#nXtE_p!!GeTnX8=+PTT(Ox~bT*|fh>@saCO;@Qm&nYb!~?73 z`jg4D{Z5Pc=DSTrWZJ&@PK#g>nKtkJCbYTdyCRu3PX=lC`MKPZEZaf3hi7}gF=HVk E09@Te{r~^~ diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle index 94ad32a3927dd343d3c612039eba37d38391ed2b..5a69628919d17fb9e0140ce4208a243ae02a8515 100644 GIT binary patch delta 556855 zcmb@v2V4|K_&5H$e#ddg-EoEM4Ny=46>Bu2Cbn3xcLfzI3bt6Hf*lnDy6D)&ZmcNE z*u{b+Dt4nrBgT@%ikcWTQQw)_xg*K%|9;+ohU8<|d3I;N^VFGnc4qg$hh?(;7njJg zH&m@PY*?LPHT#Z_j_F&gM&DYsl47dX?o+jY|JpItYxb#CH8!zM^%}M7)QGKKvu5?` z)oN6aop-&T+;>y{XMzrPdG(Y&qXyI-&_6%dF+`YFy-G|CSJhf|tJbMot+uOD4Eir8 zQ`RSOWKzFDBXTn9M2{MpivRav{Qqt^CsQ#pW%P)?JkIC;7aY(3TLNy2dtl3d-F$Sv zl)fXQc@A7k5D-$N}>Pv@i=ZqrlyCZCyERgxWGk89T!3{bCdh&<%5Rx zOB$P#83x);ZV|@ihJM0jdzD;j9~sxaUloA~-2Hgfgetp$;Il-0my>f>uz->!W29WA z@Os=lPb02+LLqM8KFA-*jgBPhS!;xfKuinZvit^5{;1U=)ZQo$d z6(-?I!lJn0VU@WHJuIM6h@(0e-KR2Fy;3-*iSP;{fHt661h+jRlv@+&=5njJ1YU5r zdFwFlUUwa?9s#mX+7yBuZpXz4&RHQ089)b81umODCsXaWLEn_o!$##~MuLX#>qH{{ z+^BU6$f|i&hKMk*W964&+{FkBm)s*nUuL;}iKCF?qWhen>WHdNRJA5Ic}m3mi7GC> zn_l1n!)vw;<=Q3K1mWQ0x7u)I;y|wM8V9Oa9}Hg`(SYme4d5(&O(6bZh)jqYIudEB z$fbqK2$$)xj*x{~hnUOty0j`kuOkwN4M7?t#!v4p6D5rv$sKf=gCwI;`X!Fb$qa0j zIKmZEBR@MTKaxvG)CcQEjUJUUVo+l8NT08bfEZ(7;advSmF!3 z7js|tt?DyDN?24`lnHbASke72Pq2W7{a0BKBfN8>f&)z6&0*6~m-9J$<0uaaNnxfD zRK0%ENaC0eBQB~>6&xUfzqu2QiprrnZb~;D?i2*n!wyvm1U9c;!(ED~irrZdj@-~i zox=h0V#c_*9T6UG=f#S`%nDr9H5ItK-SuT&&V3AbbM3n6{y#dGEYD{GGm7nDR(}8> z3+sW;$Nc>b@xcnjX-R*fq5Xw~9?QA|VJ7e30-r0&ksPE`*_K#Az;{{9P-`3ZAlJ?iYQ~e^gvun;3tcgsmgxV@}KD(+IU^_C!LhBhUbHlbv%cnT0|s9_$tHl0xCo2)0)Rzgjv zyn#^13H2!P8$#t1N|wn}R|zG3!BZueN|VKc&7|sPaO7&EblFL$TtW#BICB8eN#=drqP`}R!OAEr}T|$1SJd03}P}>^u)KWqXUS!qanmY)k z$roxdb%Rj#k1QtCGD7L|I_mJX+k{%#p>Zgtt`lmiK^TUqlZ0v-BMeK!BuB{BHxkIj zTZB5(AUqsj+eD}}TYn@}I-&Ag9gM)&wh*dd98WDF)X^_qMdE8K3DsiM*5R1qyE*U4 z&qrV?i(G5M3_ErMX}O=Sr={6>pMr?M8W|(NR(8A80sU7#Wb0 zJs9`NSveKCwxhgU!KliD0i58QmfWyeQtsdu6E}KW4X$W`l{-Fs5Z8G4K<>(xirgO~ zyxi+7v!Fe$sl>@aRO&c{^bOZ?WCxJGda{|@wK)`naVHj7{&uTzn;B$F+XjnK^#fd9 zs);KcujT$6ZQ#PEg>qxQtSro|%Iz5M;!cf5bJdA%5cjEG#!XHCj2pd9gX@C$;3yfs zdmeY|%Nl|ytOvm*7!3-43sZ8NG8=Q;glLf3 z>}mj~n^2#dz5_!z7v2uVGt-7hlqi5EQ^AO+MF`CYkAf?n&=_I#7x34!&s8A$IKmY# z=XoD`KSUXbWW?hRYCf|IQuOCsYIN(ZU~xcscf+@v{;xb$(KqGs^Wh8fyEz7aNt zU?6cY*K`MQB;$lS#3YzS$qXy1kB2*4KXqm-*ZJ#)1nJJ=D5@>2O=N=Bi))~&cr5&< ztk}MPkyr2+y-&;NLFK^1CYGZyn!z>TKF*eMg>$+Pb&VZgoAA?ibz2!4P6@o~Ya6#Y zvk_9|K5{L3ZWz~YVifTKta3cEVm7ijha*4MfYP_wN>Hj#7IW>sZi4y$i=}aWJoXX^ zv)7Y0jq?uRfch)jq#a)8cx=YJe#T86SCcz2KZu(?sXBI0QTE{Net*Yt!R;DFd|^Hp zfUZsZ`?daDV?j3Ya~zba96NV7r#&|@r;!4W)3|{MZr%bLNY~U+ar${p!JS>>q?~Sn z0YS|sP`L|K-1r4*86LYa`?$y8`-fO=`QnDi9(WwbS6s82@VCDK>B7v)1UG(FeN7qe z#2h1ca$y0&-pUE`YH=ltk~qUQEq88*0i;h%kYP39k>A&jYKhg~nczMMTNyb8QCpkE zv8Zi4Gr+a0F0+8>{w608c-B!p)eZ@n7REZHeT3ojLbx zSyf~Z>;QOzg6q52b>MDosLoAGZ;U$INbdF8%B1m2q>oG9pasiH6N!nwBkUYq8;j~i zAnqp<+Ow`bxxf2{D(KuPWJWhPbyg4V+EyFxrspqLa0yFIoMKtTUxP;Q7hT8C6(f6v zA;aR?4i+;C8-m$!U*_%!f4IhU26%%$!c4L(tRr{G!@B!lOIg$iy9Oy>T`AIYnZ<#;M+@NBJ|`|MaMxL`$RV$qo& zG^p^|u~KBkCEUuQDKb2^QiQx8AVaKVH~x=MQhiY`0G2Hr&f) zyR{b=|Dtlf@WBsKkp4Vb%e{Dfg&in1KjY~MArI8Mv=1Xte*B9x9wfiy)g=)RckK6N zjBrT6*nH31=aetcpZ&`L1RMfEV1DQKtptRv{M{e!^QGOJ1duPm5kh~lu2^uN7XU=d z1q@3kIAOvjiiL|6g3}VxC>R+eNGgX6aH&SHmU0!Z7wjw#RFOe2f>$hP79vP24^*;Q z(2i>GyhZSqGI+xw*j66Y7B0a7o}Yo;M+RG3^1#@ zU>;?*YApe)(-YLXwFR9iv#aY0zM#bq^#q%E%fr2m1c$}M3E-@zf@^%UF!c+;O+I{K z_jZE&v_gDm!8fcjf~?+4)Lh@UPpA$S5gZRfOI@s!xU?w&CgDZlk zGF}(=ZVBcp_~PV;f`|0%*k^)j6nFniK^{Fj;ho?l_3;jW3f@t6dJBaI>Dek`;crS- zZAJ^PM!`E8p(TL#2(?w1D=H5dKwwfh+$n5H`7aI^s;N{@BZVz!v01e6BNe4f4dEK< zE`jxgKhO$)#0gI_8?)QSvk`E7Q(<3TDzL7Jkot(Top2qqd<1qvCt*BwkK0{^`)F}v zPoar&ej!m9O-0iT5Zq40Nl_W5Fe1>rK`oeKPIFOLZqQn1CBgbEpd zHvfk31Vxzrz3?n84!A8;i}(sNN`x6K*7ixYgh9jT-qvu@1L5~P4`A9a!s)Cq8IOd; zEK^|2189{ck^@c8qJxZHL-?a! zvnE(nky`AQQS>#7#da)_4h5xlQ4DZ7RL#MYiOF8D)+)NlAf6v8Zb-_Az5_i`|CdcB z|L;8}07F5<)gM&4Ki#5B)Q#`Nh>A75mmX>;>P{;}elD6M=FjeJE~?2F3tEe;G&(BB zi+a%Oi#v$6&^YMSLo`9c-*qoh)P-WTOcH&hA<<)i=ol@=3=-X- zU|WOCeWE~UgrZNV%lFwM+Dwa%eWLYL)T;+YOaDEiaMdBy0ss5^2xsut5z!nf;Mn6L z#+XQj`9-2k#*RA`mYfs~r9AZdR+Pgk3U@{n$gGFBV*e=i_yUU=cy(5|B-+T#L+Vx0 zN~#Cfb{}4j?!=%yo#rp}k)>m-sj>Mtj8QaR!;{&j}T4=~QHECGlxmVP+NaW;!EiTwUB% z#n;^$C%!A^iz}Op_wzi$A+5wG>Dk7e#MLO+jGp346l_p$aSPho@kDVIS`_vZqb!*4 za3M)Np5;-7Z?+c=PLYR>vUQZz#3>ROfvo&wJIGf6He7blqohp4jTRfez8aiM6 zLdtUzzSyUNf~DecDpjpDVmoCzX`T29E%wOSXt#Qx<;OCO$*Y9@`;K zqS~snTdbvLyF;;qwmbWvxGrVv>JhPm7M~Z174$a6Y4JrW#N%SInVvOV67Qs7*RP1L z(6a}xi{DWmPTdsOSMZ`4?~7kk1p6bgZ%DlHSUiT_miSz}Hh|WBC%#J&UE=4bx; zNQt4`z%n>xoMZ@<<--KOEY~ObW$8avqN1`)%aGVv0vt`~+JJ;NLsekqlcAI0_AE)H z#AkzNIg%>n!3>7mmP!^;2|BOzOHg%{qye?Yn`@E|jH4)p%f2B=p}c8+kj$bXu&G3{ot_>1Kyr@ui_LzO ze5&Nb1$>muqCr$OQ2LCXjR}zswld5J+SZc}0CBzS!JuSp<+fm9tn?(65SP)zyq+Qp zTv(1=tuN%|FU-xBK*(qB0OoG4e>^}aw z)%*GD{+1-mrFGXQ`|BnR^Ve0U`s-d8F55!u&K>Ko+j6|W?r&fD>%xh$JX&|`G=JS% zGyHW+X8P-{p5@oWkZgb5$oc-dmpOmk1q)<5DQ=f#{<@|W{ar?Kk@C z>Nff7?%(XMJ8`S*HXUw;?3T$G+!i2r{|rRwQ#0Yn{jvghiOW%9c=P ziajNB(_;T~vN&g`Z!yfuW;L$Js5V-$X_J)SQQ<1znWwok8?hAp^ z0J$#&76;0GA<$hd?@ZNx%pkAMu+W1aP4J5dJ$JK(j7M3+vUBYs7r9XPi8O7QtuoQ?!`cT;7*uNfI{%P(x18 z@rJn<*xpfYXD}>6=?KtMUjrP_NxnFMFbcGZ^0a@C1rgS0mQe}a*ApJ^C(q?o21XB% zXZ-u-0fCt_P(GL?I>^mahsnRBJOm7vPh{o6FhU;6690sUlcVK}DG$TP%4e|hFnyf7 zCyOl-^W?qY@CnF}yes@ZNzO845>Z6y^6L^F=E-z<3aco;%#fowHV!5*XpWp^qZi=@ zWXgpi9%fUPybmikOS0v&1q24xm@ofS$QQz2L)do z8msu7B8;xB=*Bn-M8oJ$6;EWmqGFmThOpuQgyV*`mWs{%s0y!lQv9spS-m(w;ZxV* zK?MDd7v#+4Haw3ZjV^0H2v}=mUIa-fs$^3W0a60X~iFa|CRq zw~Y-A_*Kn=O{^a96;&BBTq5hzJsc4X)WMxU2lQZg0Kvy$_N!$T;mXp0*}NO>{#4nQxL)OAhNwl;}w} zVN|bBUJ2xjZw*Qf?NKZ?rO(w~Ig|wqsSRuxrTR$-k9d`6(TB7&HcWY!I_lt9Wmph@ zHn*|T=d+cYD8Hvp64FNL^UkO7N}qm@wNv`^JEeoa*sznbBDLH2?n(tqc0|;ZI#r3j zb@ID17*-sh9BbsmUNKNf!~VyK$}pDR9b-d;U6*BFu8%P{jLlXyp|L$@zS1`i_RUeI z(vI)`a^(@K>4KF?w40SE{m43{hU)akt;%x@zC#Upa<|`*lc2IAHRSywdQciUqC)+8>pZG`7t5TLS=~@ z80a(J$do{z@yw$F!>MiJ#|8SrH0-NDUzq+eAY~a`e(Uz$@n2XMBJ2J} z4I8`1Z&=-tK=kS^36QF%{6^?;-fx6;*ZfBK;`_jsl)a620+&*4c6bnYo^cRt&FgUY z^T4IF>ythYq+Q>9sj4-@_zzL<^`3I(3|3vGV~fP18u?#E$VP|C7a=Jwl`leCMW|RK z1g&Tg1Kg>m^2Nr*hN}IP_2tb}zN|vgTIGw4b{$o|*m%=b7S~5TMN=qBQ5}%-zJ6u2Du5x?1q7$lSXDhL+?q)$-~4k(y2>~IY&BKo zn}3d+sq#$~6+l|C>Pv>n9(rKw0NFuTm9Wium4ZcS^WLZ$fyy^kP7n`*H1OR;)nOLd zd@xMg4Bx0)fYkPCBZ!O9>*0{^Rhc4?kX=#*TaA2OgIoQ*k}nFXs2@rA z;-A&j6XhVUxP}hS`9#g?4m`Kui<_!mAZ?gh4-Vc`v2^7=BN}1pXX-7yykKi{^_+4! z#KjisI}Awxb_nWH&0DE^QCHa0M$NPh*^gBcuQo6SZwrVb2korKwgg;lZ|s$+6VV+4tP09 z9Y?td9jKmFE+aJ=r0&Gng|qArDg&%HN&S@7m;6d|z|-k!^vZMT!&n_?HC6pJs~yYu`f8fG z+JB(0Rx{NLsJ>p!QZFn&UlDWEv;PCW+JCJcNclROt!8?o2KSO>`g%K0UHl)|aX^lG z8|5n;s9Dy^N$*g`SHDH-EB^ss*O#cjp?u9>u3lb#JDy*mMz1)MKzg=Homw71h`Nnu z6zk4<_D-x!Y4VNgTmONRP1~&QK$Z5(*8hSp-FEdRmTcSS6LOGUpq7BWMd~QXpolU>F#9O1+&iLudi&JwB@fql1jq z;g=WGxy+1U7`Wn+`aR3DQLK@L=<~@yofw98-=K}7u>KwOb0t5+Z}?cvvd&5-hCWmk zsP#&%1+CwzG;nsZ*(3z#UaD;%cVq8b@K&i>ts?OV|9G!HL#OB8eN-z={MlE=pq5E| zF@8eObvnVXGdbv_nLn#t8dNciFSaNQI>xkX2=}SaZUzN`567zn!%e4yj;V?GVE1=H zXdmrMwFPd!9>n&Z55k5ByEf&#)}5eeeue?ZJP0~K1^(=(Ahe$mcS|t;Nzg)Cq1&sV zJG9v1UC>5a6bUr@C|I#XGmzy`3yFg4KebW_6`Bjoen$}GftrQP0e}$Hu!3H5tvo!a zLC*>%B}kuCQ3FzXnqsTY*Kh`p{6V4tkyC=>*5s}6Fpmb zjRw8YL;8o;Yc=SRC@CIXukm#&(#@Jt%t=t`x5(XkM_e(2=f^dJ!B;ypXLxVxzEk65 zHqYNsrO>onGoGK7z|~L_fp)d#SJ%PYdo_EgH0$?k(92Lnu&BeDO68Hp4bC*dE*4j{ z9q3l1In8(GU5k8fmvlmtMQ3i3Z#As5K!_wnU-%*%pFHO`(7f}0In0+e*O@0yCF{_f zsoj3P6=q)7OcwJpbiU=2q0Md00Or0QW5b$vH3zA}#Sb(G=$LZx7tIqIFvX>so~&K_ zg2iY`@pda(wmI=egLe2lZHS&1T0dw+tp2onu@=xCs-aT*faQG-H0<#Yci|qRHd_GJ z1UVcau7N-ThlFS=iUg}bj#WG1-*?&&J$G)2Ziinxwf7~0d0^h#bO(q!u}%Y4g=u&F zdqDiLnNV;?k3Oz-G+c{z45tTqwD4Y}wm6ZBB@(CSKAbQtq0%MjMM&L70lxIT@Tx?Eo77){gQ|gxJ*~YaCx2< z3I(%Z$9(N_2|B*oqdhG^$AkN{u}Z-dz9p1a$ial9vvSb{l&t%5Ci;ncyI_KG1$C zM#uTTXpaifvEvhMPkhXOrk#P$*L|gxU^?zMZEbuU_d9?6ySLg!Qo+I9sk%ZFI$bqH zS13TomuB60d^~E^-NeV`cHKpMOmXTs6*`Kl=#&^HC|dVBJ};}PJA;pxtLd)d<2SLo zBltL^rmiZkx4gD)yc!)tn(9W0&~a`H-EKKLHg2ywjO&Ry>Wc93bSGUBVh2X|$b`gp3Yx&|HJr1SOmPSHt`Qeojt-6f2}&EnhdI7fF9)AqT# z{t9%Qwoo?_-!ELE>!w7z-nM6L#|Vdv@vOV0v4D?ghrl-J|P) z)%pH_PJ_>@59?my^MWI~+Xi&JbB*V((@mWKo2AQLT?1Ti(LLQhT(A8D-7mQP`oHK# zq=xNG?;Fs90qo)S{hU*QPA3O=m^Jro4lpu5*x<0r8w(r)wU=QZw z!tUS)SiXW|!SNVJb}qO=Fgiw;1Q+4qz|rhQ39cqHhhm zRo2&L8DsIyz*foV1ToS2uNYegF}3wZh}JE=Vx*CNv3iy+l5m269B_XP{cD!#HBkgY zSSmzZoUN;0Sq^a=P+m{}gp~`d5C@$1seU$Q#n!N#taNFlZ_nVc04byw_@O;V0SnkES$AJ$9LS76D?kSOiZE`5C<_>ifWL*yul*e&p<4Z)@i zeK088f+SoqOWy{cel~~ITlhvGYGgE{v}J6n!ToH#78I5kG_YEh{v4z337`sQ&(}wm zhb0w22x@?QsXsiNqaVN+q&zp^Lggz`cyNi{%96$VjOm6GSLmZyg+gzD=nt~`5=`Bw zuZV{d@^Q?qTliikayz@Di0tCwRY;`QSS#ccI&@n)~zP$iW+9`)kiS% zf)M~hQ1K3PXn#2z24MvQz6Z2Cv9v`0`i&`s&wultLbYL*D3bdeO0o&2R@(dHH>?y+pb5%P-Z#b!uVKAP*{PMYB4I>6X80e=) zpQ4vxni^JOu$wImtIG`*+0xL2Vc`HGvHoOPNkR8@042>00T8WMGp*G55dd1MZUwLR zGMr@Dci8Yt6pxh$8A=p19znoh1D!Lp9%=Xt&l$MU28P$MMA>@1IfH*0K+;)Rv?lti&3qFi$Hi`xq)es)u)L- zfDojxPOjky%W43IfoYo!CCs`+HwS}LPU9D_(KZ9qiy4XUk)Nc-T#>=$dkjsbG_>H` zL#P8s#D*gVmL23}sOOFv#~{)DWo3)BLyfJn!`_T z7&2KRg0PfVY|w!V4-Hb#)ozT1OYa+2F!P16Ai|1d0SWX*aHA z<_5#SqfX=ZEZ&c@s@4!aj^8d86!S|90llllf~GM>#c2#y7^@*w?bQ7z-F# zfe47{R|kl>>^qETrM(#ZmT!cN4Dj*SXSeY#v$=>a-a?}ocLg8z8BdgpmGK9RZI~YQ z_&bxd!^X#0D?^SNkCcm*ACDPdv1$b(C*FhoHT;zEIv(*po;B_+7dL~8jZ7bFLY^`Z z6jr|?2X$}wMvvSp#^%^skG^9KA|4VV$XkqK2tfz{OS3gAVCNE}4)a60@JaWKS8;FI z;U^>77F7%#zwnX&;F0ljmb`>0p!AwC0G@kpWZIWqhS21tQN{9>20_rQH3op%z3f(i z_CHUD3k4>eFGK!z5yP zVCQ39v2e3d4$;o%{eGsaR5G0{57Pt^Z>E+5wAWb*cSM=gc*<2+&2$!vFgMmz1G9dt zt|=6cn?E-&ea`4@sle656acS(ZfedL(iGurQ`0CsE;VdnlE~4qX**M8rs?oV(?qbm zyGf1qmzgGG#2vj&#~HQBBLRdY1wjoRQdqyQiOe*<9B8_NHTuO+(*g#~L1g?8+7J$EjKKa4kh#sG$A!~;REnpbonXgXpvDoa8?>0WEH>5{Jz#}rc&MlJB*5P@&O zY#zuku}&&`g4`Nc4WPKG34Ofl*Z>VYmu32h#}upw@axwm^eYZMjTWfpOgk9esjLYp z9Js*L4*U21C8kx_T0K{q&<@06=v`$xijNOhn;_%#xCcZjNSHuSfK#q!V9{pNDwa{` zSx5ztRTF)NV%Zkc1{U)qmY7n(jN2hj&?!Qvfx&sEi9$gEoVU|779Rt4o3f?oxOBg1 z9HtG2Ol|RTK%uEngpL)Dn>LlNS@~tth`M{((=RLl+;+;e9kBy?oi%M_P$N+S!r+&2 ziq4r1v4jyq$>Lw`?tI16T#VRRb)A(R0JS$w=U7}AaRK1^Tc!)hOkm6%)82ot4~Q^Q z7_15f_e_1T8Tvgm?fLi30>b+JpG;`VQ2>8`WMcZ=4+Js}7QZmPb|R1>ZAdL-2>9L* zg0_+tzzLR+miTzu5t4zA?Ytr1<6~6CkPY~Ft#Swo^a41xYRD`lItuHDpslpt=vFGFc@$XpqsCtV8} zz%l?Kc+%^i5gg$k`7Qn-gk`A^?S!rfCf2@+ezE*c$W(?TfV(@|3WwegNfLl>J13~Q zMoCr>H%l)mco_0TDmctD7yC41%fIhVAY$%?2)OnnXkqZnkZMA{*y-003q#)q?$)_l z5lpIJt_eTB4mrkyfy-}0SbiSVj|C8x8VddhL2q3VM!An6ohhS#2+T9++4)j4(>Edh z){YJwRG7=dZ6nZ1W2TQegO%n#dEEk6keTHpFFxKHL4>-NrE~NlwBYygq||5?0E#PB zL5uXw%qn<3#N0&&;F|;ktmiV1r3NeVn3Mm#EBTEd4{v(SU$8s^#LPnEJ_&)mp`TYb zyQpyHWk(%CIRZDyEI|^Yif;ogEbq!96y9FL)fN>? zXXXb;(pnDjR*BVH4wuJuZW=7+a@298 zS@yGZV1$JOEi4y!7M8cN>}S=&7i}!vS<+R+!dj444=vD?T(ORV0i98#;jRouceU&j zkkY$umQPuFSOmQoI3|RL@z6{A`JfB?`=I9xv{YbONg&X}9HEgsv}F{628H_tIvDq* zWiQWmz*tKIX6#b0MGIoScSyjnc6I~&bgJbtuNKb?%W)q6_n8*Rmk!VJ$uoP7?_9gN z77=Uf(7DJmlx5f^YRTJd3Fd!0wvE)50mZ8BJaU*8f43Jb$+@a;Th}uf4yQpdwF`n|xcNvE+YnH? z$f^LfyIUDj8Q=q`0}9XB%&@SB^(PS$Xk&u)Q90OYh(1VSeZ>+DK7PDl?kqRWZt4xT zYE+0Rvd}s6OZ47aiPC0)Uyrd~6Qa{ej^neuni-xlH=UOe@pJ8GZaTgv(}IcVUaQoooHJT&%qL z+WKvIwA>J`oM*j+Ss9UIy-+SzoC~c?=?2ycM3Jk;;r4N<^?bRw*u30YouyMFwn<-R z4FwGv+La)tnk5omUSr*Z9VT-lyTg=0mNvCGVB99_8)kd`1IxYDdLLU(nrA)Bs)oI-WRVt-+Gj_*^;?T=?BB1?j)L#h^jOiUx@CrwrV{ zpzrtwVsBU@STX?8K>S^6jk5d$p#0-7ls&MbUHbE(?I$aGLwG)X@v{|e6P^$EKH?jk z_JpTfKC`Ns8zu_7@Y4_;K4R_*F08jkgX}J0Yv6ZM z+g;oZkR|DJN?SMP`3b>Ho$XQ=1lb-+5vM;HZ1b5pBnmh@++G1D*lcTY?@2f`yKHk2 z0XKMU`tk(}!q%e~vu${MfPE_3?qKCMinLW?$v=GF7z=V|+AVxGe5RU>_(@5ujd=Rz znzl)d{;&kx;q1*}{YJL-47m+N+)0p);mH=ZbNINRjqM3m;*|Eb35+2<0~D&=YLd$o zbhoW#No5dM1Vs;1l5OZM$4R=1&Pqm>9`19fS(`?o8 z`JhZ&8m^y}Z99mMtrpm#BpAWncG0l9RW&zu4n_<_rr$5yd5?#ZsSV6ZB}_OLqW;sR}>(xnRN!-vX95S zaKN^j*`s|ahZEK;vPlJ?-;*_VP&lMzB{1!TZ9T)lLKcJK+g~-nebLg7Rb79<1-$BGM)&KDz^u~F*j^8 zrHCn#`_#E(3kDZ1qTDC0#D;R8B4D~_W66DRa}YH*h^43W@u~)MOm;0uSZoi0=(nM+ zhy+FO_#+$o&SVih^V~L`p+f?H!=76AI{V&8gtX3|096&jeQ#_KYiz_HHkN54g4+sh z?`+OJ@_mn!TxS-rZw($19Q5T*53+s|VL zYHIB)nHIsgud_3yH|XXp@M%MPAjxb%+<*i*G??vcaG3A4+Zi2!LXydEiqp>a@kBpo z1g4G0egi8hBFuh{%}Re|swCY0D|2LHbwQLC{fzmURr$Z*Coam)wER+LK9UDLjIp1@ zOl_-fKUsby^{!#>z}!_MCFw!hi0~x%q@MjSOPYdZTTp6OF02uTmUgbY+QEBG?7LW| zfkaCmWT-2Sx$Urmlw?<5_^_RQ1~b;7M);dr8Ifk7S%X$tln2b6;J#jVlswUKsB40q zjziND?a_=~Qx+iYXw1$)IJci2B?d)s)DZh@mU#|QL0+>fK_KCn%M79;>d4^J;r0=@ z(>F4z1S}x}s0eafR*}NKhwLazD1ujx+EJEJ1e=_( zqb#8aetOxCvVTF&5p8!A}IR7j{@E?#_WX&0mV{~dOpko;>Nn#!-uc!UPcw+?m>!^fuXS82YVzd3b>;xR={Y9V;5sL zMP}7butDwk6Xf9+cF@nin83#*lN)$74#p29Q~@QOu4?gT;H}WBb+nfX&cZP!he?8t zSvJQxi6DbtRVi!imWR19nCEpc^;2y(IwpWBJ<-d6_!Ty=B;1k7F!CUjS+Jynq!=J#A=E6ExrUD= zH8?omp#|^TI5eX$R*C>aCR>k!WVz+?!YqdZ%(-gH z0Ut*>WN^hO$1<$1PsTWova*Rx=zv$pI#?13qH#bFhJWQ)jaku8avUiaD_heXOiL+5 zTZEN1Qylv-E6VAP6Xjy%n;DK%%&tUOX+FzQh*=59bg;}2%Vhdyu7l|z4no+>XPtF& z9Bajhl^+(d`bs{Ww#4zCQ6c#-X}RM#)mB8*`yQ%lnJn#~Th(wG6dY;zoB75?pZM>WRIcEMkEY^6g7dfJ--7wkoWhK>pQ z9nV;1h)Ay4z;)E2A*&huljPOMd2D!s$DVS^(S{j2ZxR~e;-e~|hlO~jS>#0?yTN51 zJK(CLJ1aJH-FAGyN^q7q(9%c-{PnH_Jv7aL2k!CUGavADo1YwCGxpuH(aYO^+gB3z z(t#e(X23ST^4O-=4m1tSfIs~1VESkUHo(F^(J-L=;6T4_k+HkL*@0!6NDN6fl5~(c z(c|3=I9B1T!RTqTL4&qhC0?;n0%wN~T9~DACin+JXylew)W+zI|GN8!i zOk$Qedxe8+C$oZFN9)0GSGW`Xd_@Lm`?j3{K8QN4*8snj47Q4K zqRcD*33R zPLytCz`jGBDBa3{S4TThx|IRXO?0AkD+7WwCyBFOlbtBt%7F7ToG9JOfV#O(lx}6f z3-g>P-O7L;7dlb8l>vWV;+)Q67{t*(=H^;hXN@yiE;z%d4kd%!6@g)klQ~7XkZ@HC z-)(V98Da=l-R_jK4BqqJIwyhLWp4Vmp=O73i3l{PW7LBEyPV%L3}!w+2W(g1Jjdc$ z2w?{QqRB4@ovA8tcD~5~hn;bL6!MHsDt2yUV=NQiJMXN+sHWv0Yql!{ptXmdu=gcr z6JAZQ;HvX`wB(O823Oy7LJ40ST!OrkFE;$qiC*?16@IlAbNn!#13|wGdu-?_AFF zvtW{oE~eLU+IDcKExhe^^NHQcDy}Dd=Kz*fV~Q|}7Xd?K zT}AFI#wyTbd<%55IsK%eTer5?TKY;jC*AdFgwK!KEgQfgXy&Jih7S;R! zQJ=f=C@;^OmXDXQ&0S1uSAJdsTe`MUUe2^;iUvPa>v&gHmhl?{fcQGafgrt}JsEE3 z>`G>N7EEADdbvmjIsn#9bai7{Vk5LIgXD%G&gxt2;A$ zac5n6aQKcf7>+yU8YLo}f$2wG=v@K1%L1cMx?0ldVdl3kF>6N$Q8N!IyS2`{&QpV# zF1ntx`rg9SiU!a-s?q>h?S^YVOCX>`V*vk()BF3bS}e4i5TfA~wL6t^)-l+fCCmDyU-Bcc7T4VRS!F+xW5x2A*+mZ z|HdF60)jALwEH59`=FZ_gP?a#`aV_R1oy8J#0uFNv1yVU^^w&)7WjIK`?e6BzB1j- zvZv73h#K~r>F&+a!}<}x`FUbesF~zh?j~^2}_swD-gs=j1 z-@_Qn{qApBF%BMZqsOK>V11#RVKXVuN7tinrc4+Ku^6~cRp9qi!rxE2PvVaI@LASi zplDDH`50q{~=q26896#l>Xj-p{eWl-At3hGN#JDaxv?P8?BkM}>}qO5KfF zGFE~e)7zygdku_V+|<0|8wmT8Z=m$O+s!ifCJhuyJtp!Q3jQ^)6AF(VGk;&{L2I2k zaIeaP);e?GomSbt+fNhlfC z6FeTK_bISwq#3e9vt5`6eH}Ij{u<#yFPr7S6P5VmqADKrIp`eNJ=%jdf#g6_HP3xq z@6Vc^*9=oseyDbhs91^EN!U;TCZF*1g!?}A?8eQc#_>A*q>*O?qwYq)_*S0NVz%yg z1R!bB?+G3?{MgRZ8P7^Ock-b1yBrwS-GkQea-goaXENg~Z3H0MQSbhqJ1p4|G8sXL zhMKV+v_^+~Zh{A`(dEEb(>!R6E(bo&^q@7m9Ju>y4_c$kffKSlXpJrh<^c~{qsxIY z%RFd}E(bo#^`JGn9B5zf!GjHyZu0bCHfLTFw+o_AJpd9FJ9+QlvWw?nWPxWrJF>(@ zc1Y6KrA8d|p!M_|xbUziiy1Suw4#@P`zq}O54Yx&=QJ~1$ytvY#DqI7u-AFd7Cdm1 zt#{7Lp0BZDw_Wovt$34;hhWFWI8|`Nbq`wX&Vi%8_n^h@9N6KuXCq^WIR&Ep$0`sk z=kqb$&mN{9bHZP>(EQ?YFif?8cDi>MIQQ7Y^ePv+7fPRcqQ#)JvRVt8yzty+h;_mO zg5f`lUh>j2g2e+6N(yKr@_NA74<5Rp`{`TH2@%hm;k{=S8*e-+M5w1(x*8u}UMQA& zzY~K`ax?~58sM$Xl3kU7Jyv^1^J<5~gS`g@L`vAe;4i97-mUcPnGo+R^f&{V2qroF z6{@@ZMS;g(_psNCo?()@wg~U{y!N2Hink_9FNye_3`^^K!*xXN4VB`()E@QPdpEM# zV=_R1a!~ltCWUJ|c$v~)%vTu>Wb|zu3*2Pt~y?!Fzxzv`-&z zAJiFQgZFnJ(XWr`G+J{W%DN1O5cMHTrn%S6&%BJl?xR zNH_+ACwh0VX?`VqJju(HP9vUH!nDcWT*^btG;iL&=K&&^L5!W;O4uU9dx$n~p5tX% zz#~$u%=9wl)8yv0S>C;rhhg)*EFWSeFt0c-(;76ndCo%b6v~5Rv3JwIHv=9sR>DO~ zyeDZar>^jBVTF0J(tDP|oL=K)`qeqiO_OzArYswYG8yEZ@zN~&>?ZFHI+%UEoi$7- zAjxU1JG^2>kNqpmANgMNEqF4>o-gq3WK;HE0ekN8zGQg-P^K>;UZwrsU6ilVgXQB3 zCLH$OC?88P@h&Gc9`){^EL}g&>X(02Rl*5xO~!$rz^Fm)d8?FvgYn2Y?^xy`!gb4A zmRt{jeZTV#WXa2j37u1lb^dRvjD>;syrY@15~iWmZnP`b3cEe?o|l2jkJacak*~eY znH`@X;_a~D+IuuID>`~LAYnmdX2Cme9e&*1-6FIP<1BG9D17ie|MnuBAq@?Z@(gbZ z44ps+{aTt(re#Z_SprVpSCs2Q&+?uKALv8T0w$R-6d6NbGrQA;Y@~^p5olfNkt6gZ zqs0dDE0{+kLqB0j&5@MoC+F}-8qY+BCQ+3&tR9LMFNuIbwL{V3B`N;-Nhs6jibxYA zJr8OedYWxrRg@sbn|P6mmuJ6YAE6N(F+oe6E|YE_4~oFor*5 z?;3wq0|&1U4G@FEly+J;eN$*CV_y>qTz5m`z=`dlPO$qLdii?q*3c1*qfi+ZV=_$u z2JmZ`HFtz=5fSmgt6iZdSyVOMO8`^gw1QC9)RiE#1@Wgs>E}|it?dG!<51{63Ft5f zzfN#0^aJ|bFfs%9^F%0PO2@YXQ7cUEH=^#Mj88Qsyp-VR+vxiOc7 zGS2QJAquWK<%!G`=)E6WKpA=TFqCoTONHwHb0}--MiG)8hoZ?P@%9SOLK)L&inaQA zC~Fc&5n^A4j;Gul{xx(xV;=zve+!M`ds6W6ZMi}?1rGls^f(DN9ccQod|12phOwq} zks!B1SWo^Fn{}jN)2Vdl1Hzc&iU5+Fr(Iwe(+4(5FI5KP3JOE16LF;n+Ax;=`u_kN z6dd-9dE_eNtBNrUy+=U!x)~D2+@Y5NcCm!L{SWx6U=Q0u`MU4?FZ4Cg9d?^>UKF_j ziATqj9g$)C1VmqO!vA6IJHwkgf_6XS-n&gV&9Ws+vL(66vgO__O9BD}2$;ywb*-6_oxeD-7L$=CQ6Qw9UWr@$0?~{mp^L5v2Baq5j0Os!nA{ZWbDZ zoil(oV0D&s=hr^80NcOJf7B(-Q|Z%e_B@FU?n+hnb9J3B8PYX05W02^tx-XWe-?KS z?ISEf(JK_YY~&70G{_$^z3v@K*N)r<&h!nXYe!z(&_A?p`9xYbCe#_W4lSRjs7n|O zss4{F1Llngtu2LuWbANT7ut^v^%K1yn;c5Nj>y@EPY>NHBK8+Qh0--5|MkY4(0-!W zenIHU@{ON$hI1Z3v-&I)x*NW#)PzlILaQSe%zU~{lvd6Cw}$GFt7ZYim(Klmh0>)n zSHQEqp>*lYi@Oe%s%7QT&;WE-Q(sb#+kgP#mF0>Noc-&v~|A|N>3K&R2dDQ=%Q0}pyom7Ojo-Swr0#BJ}*KSVuBpD zW?V4*ygxZS$l8K@S{)okivEzf!p@hW-7z!Miz>42Ke_w%^hWC~0!4^yNd?;W$(pkLU*9(27_Do|P$yD(WjmQ{KO=A;Ty_LY zpt(YZT841~?Nl;)`l5tHN6CJ}5;(1q9Yq@DBIpt=+r=s6 zBpmPzqo-<1AjU6@o~kW@<(0#jvBCUWVf0jO33R9vMo-n2K$I+uo~kW@)~Yais60^5_rQjrEVsx;eGXQ-VO_5lrJNn#}9eMD19;GjyvTro=cjs%E}`34tcxh56W#GgaE>kj_4xwd8)Yus#T9!apgn zZru6m#N51xE8J=xmgivi1EFsl#z0Z)Fd8h3FMZ`OoTPQYH*#ydc<+$V_YO7WR&f|T zgV!;vA7;{AKRRqUIoySQLc_aD7-mtx0c!LL6UR;ab`ASrPonBJ@gpOP!yX~@$N+%? zj=I>|38#jJxnq1fuY6=g*y{=shZ8@9As*ZS zh53wevb5g3vR5z(eFalaiq3@jl8U=Syok#n+kgLg8}0@2DPc*TBqy_JAPkrn=4LN3 zp1Cj#v-53h*$b*H4r_%pNzUC1nk)^Y2jf^DLi~!bR*q6}^;+uoq~eHmVKlW8zae%* z7~Rj|#ZMcb%nF~woV^`}THo@@WA25O2o9+a!Z0aCIn19A z!~PPZ`O%Xw%rce3%y=61Qo#K5G7R%Udk&NNDh##SrIqLX>n+fS@543=$G-M6cB}|M z_AqmEr*KrFuQFQvk!YNK_*KzmwVlEb;!O8nwBWXL_(Qb&w7oRzOA`EB2Ej46@GFAc z7SI15hm(s%h!Y>J4zJcLL1O2zsjs}IdK|6V_G`>piQ$73B3A6@}# zX{-@AWeT6@g|+z7H`Yk=E1^E5<2JP~v5YCsg+1Sezp#_Sl7r2|e-dQ7whBi*VdOe3 zdxNcMn{fJYk%tTV_Tlv5A}?0z7#@!ew}8GzIv&|q#nPJg(BSYQ%$kX(`QOTaT)zBA8d%}&|;iz0X5(0g36lg9T)J79S0Q#D!4C#?Z z%p4q6723WJkK#TcaPunpJLHl^g6ioYzaaG5*hM}_DE^^`{JtpO@Re^7owT%)+`}H{ z8hB%5ST%Y5a@2>V)#W?HK<`mgJ|hs1OgAL^YtZ)G4l6VtjNFR)gfe@ye?)YV{E`YRsOx7-4$uy`05h5 zx4p#b<8nD>OMpvAF~tk3lUB&vVZx37!jQyIavCViQ22oTYWX3IZ~WhY!wnPO;8*or zceE!xog{T7?UG}1CUIK46P@?SQS13Kgv0yfH1Ovpwc&sq^R2%!tO1AQsFi&gLh4aE z^GIy+ewQP%DUmX)N+;x~HGUaF-f4M|Fzg=Z0ku8Yvs zS%Jz!ONm^Bac&CRa9Z!FKo0JUFu_}a+Hj{FPLW+d2L-@tKgAO}?qy&_fTFIWyFr-L9KPG#XF5A1- zt=1PNwNms)W0}v&gWz*F#db78NS|{duw|g)DdtSd?Q@ECWb#IOTr;IvHGfz!MiJ;L z0%pBj!md*Zi#o3Qst{NO98M7VU3a zrtpyFHMp=+aTbm2y@?8Y92CxEVB0KzqTWwWw4g-y=N*yfY_r?+;D`iaw-c*G*cxqAErMm&!gP zg6<&m;^~|Sx`WJ%y9**_A!ncOdA>nu2f0oCh=Ukor7Ghvkzm(0g6<%5PQyD!upMN& zT!Gic5p)Nc|N5kVsUd6`SZWA^21n2xWPZc?Q4w?pnHM$VBIph>FFuL-b4(-_SQHDr58B8i%3Q5i6VrM=5v)k;Qukg$I))X<~quwn1RI2 zkfLE;n+>V>NDVJmBFv@4NboTG0w{jq(QsA##hodqH zb_OUhd#^xQa5e0xq{Ms%O{G6IQ~0O0(gC4M(1a^d zN7-q;0W)i=9Lbj>dS6m>OsRs-5z0MwVjw|^N?8*rSr_=CRbI!0a8xZ_2(9c1qhpj` zO1aFAQ>rkJ4N@B^dy(DwN*5yUSjC?dK9A@MZ&H+tn8opv_G>bf^pgb}$e3*93WWLD z@V($(juN%U%@BYx49958)_e#_`8}c;+15nqPpT9uF{@z;H4bhSDhruvBI_zKuTWBq zsj#x1ay`Rn+E9u3jv`QuD2&x{Rdng9bcK*+$}Nn*g%(&LdJzUhy!}+wp>ZpeZy}|8 zB$&r-l;fD4^=_{OteusSde=dDqC84+BP%PaTEK;r?QDTHz^JbbVcdLWjdCFniV#e?FH{adj3LhB$JNRpLfzaE z=)PDQksp^Tx0eI0I`m(z+|2w<)G8$=OE2#`N^2)#?kBYJ_oV0mweWS@s9^kaQZ%Bn zvx7Ptm6KSvZ`zC%uMD_4Bm&M0mhuD2kFQt0+o{}!Q}@65+}ou*hw)sD4^VyGLR1D? zxddsxqqvKUIj+1YJO`m=F+dAXD61mqNPYioyV99FJ+E|vkaOt4^*v!QKw;8o#04~& z!e*ow`kw3^cFu_`*Q#ie)|--3t8rU-0Ru;UWLpyDtMY=`50w$jv)#`7CJ?SYQC?<- zz2zCopOvDL$A8nMo!<+k4zsi?g#ikKjol>i7!eV`4RurnkgA1B%=4a7M&!3bb1`m@LCq{U z8O&n62s?7aS>+6aoK+*4z!lt7$k>%b5(?=H{oGYoFhku&ObYr!yi`2sTj`@h27Qv0 zG!T7)@eqPdMWE5%aV!8Eg5IQSh>E77?W0T}%|ldwAso1^_sgK1w$xM^N>_N6S}JUe z$E_-u_$cUFmd1F|q@(#{CDL@LGLu{gS4}7f*(uZR1*HzKvQ(U9(zfnCv3`d}VoqX+nk@h){bf(5wl%{eeDJ4;Uu&0YE zL+UUR&|TH}>*>P!?y3U<;fJ28?q82UC_)FA+)G8jFv8`$*H1N17}2zWs$fyvF<5oi zPP*Zjp{i%L;&4@-gY>K0XqAW1RMTVxeC6w~69Rz8w+j4$13Pbt?#ig=HI6zR2Osg>#0yEvm)BFg|aik%MF|t9PrO zV)oNH*QYB~9)uSkL0aa0{u$>R9b_;7Y99PX3RR27!qK{_z*tm3F zb;q4F_(v^H)rS(0Bt=R2zJD%g#LruS8z2whL}omPvii%=`c(7#k6 z#Q7m6Zut+06KT|`2c0zM6gxtbN2+Crj`|NC;^Bv_ezr7Tk33a%w6_~cbT3tiO@AAh z8AkrC!n`761JIBzA!@NLo!lX?3bE7)bc9zQR8jtv9N(MPd#U@-&Lr!6)I+|0ApfT! zFWBd+PDezv|3L{MpZ`?(0Qjr5h@%hxfht!&^1ODNYp8=AsCJImQ7^-5#|(io_55-u z#taR@)vZ}K?NF!};fMeoa(|SIL%dqzFgFUzfem{z+}5Z`xrkJbR*z=NSQM*9JWsKi zh#CHhQ|~Jm5le!)CnItoQN0w`E0RXsjp}XXBGNWRB620Id_-zysOuntMy}Gy9n}q_ za}*Ww)HF}gNYb-Vjrew>4Xha)FsmzICm7168GK(?-H-Lpi~8y%$o}CtSq;?2auJF7 zR$a`9lr&K zW8TG;KmnRF1NFJ9ev2hR%{^vjpOfz7%b#jT znEwdPhS-pRB84zoAurT%?siMbl=nlc&+P5!LqalqFA&pbbyYGn-ocl6x2mZn+dru> zCp%NC`c@}C$9%&e^ox4D6M1paH;3G;T*;9<^T`Y&b(|tGTZt5vetux{d1vy(De~yo zPa9<6#3(IPbcwudZ#NlUx<_VJpjW+DWD3F?aWHbtClayRV0Cq5a>YoU6gbuNi|p)Z zHx^&O)+aG8y3SkVyaXQCV6maxtTl8yY#g zTvSHLB2ll{*gD7n9psTT^qNd=Mnq037Zp!cOASSMsehZT3Dg?HjAtXg)<`01=-lf zt&ViTJdxv_!Qcpmb0Lv3s}meu7wN5`6ffV7JZwj=3-3m5Vw4-ah+OYMuP&b>i{0pT zfJ@XmCwf(SN9|zwF6wp_oQk!~@rIDF7g1a6W=StGguaYgh5%dtIKB?4@pshUh`K#i zYAi7QRn!uMx(TaHwtbAkOdq_i-cBP9<$V4p%F&(_Rn_~>2+$xFyqpVAyaO0>q-3nb z=L=0LWN^1JTIZzcDlsCFZkiD|-AbrHQ45d?76?9mnm6{ON>hCxR0+}?MG9G;c&RmF zQ+2M7rWxF+qDk|RVD;ge#T9rDkiQg~kvMsI176^u)QrLmINA^Ofl_QGDISJvG^0eZ zVvJ_GDDH~a3=>6jqGq0>#J@tC<}hX+VQrGaEDh3#J4)dX$=2A@$Boo}$*RU0%q&ZJ z6vOo^l<`B}LieXW)rn%tY*Bjp#nSC_pFkvXy!_I!1HiCn8Al@ch z57MBLigFHDhLq|dZ#Z5LgGOlXpvOgpA2eNr9)^y=>tVxK4b9xbhxm{QnqsV3P5)6t z-@4^6Jtk|$V`1h`(cD05q{ej3H-f_tGc}kKK)iD|6Ak9EjN~bdKbLY?GDkBJtB3P* zH7C#`Bx|82U+7`cV!R$sFVSGOk|jMvFV}Ps9M-PDcub&ufm;3ZKCHh+V-d}7{sr$W z@2}GwLv#b&-l(}Pnm@Y*BfX?Vm#vz6h~}lrlXht6nGilAYwgw$tR*(xqj`fKk+1h@ z1`7@&4`^m!IqW>Bd5qRW?$J^XmyY2%_#7_>hqdRgDjtxq`!q zfAD&6`m7m)=Ag0D(yy^{XL;9Ni#b5Y)mhz9+ZWA2@1pG`IP7-QA{H4i%w1a_-F#m! z?F!Mn)>n(z?FKtPEq=zn7oc4rnx7G@MNd~JD{6m7Pglk&+FgQ!M|JI7>}V(m(cVOJ zI8jqeKQzKEaakShLM(^Jp;~+(-6>pqN^mHM&@RSun5xuXM(e>VQcFKH!u4RU)nX1} z^ATy%X>X%BoQ~1n7aR`9Ygc0R5RjmKfE<=RVSbW!qTpam!Hq;+Q~7XhBf(+nNG*Efa{nmpezYFK#+3HOgmGH**3gde+9g7i z?i00Luw!NDB<%$O({8F3y;(M6nijuNReh$mx8ShkC+!rhD3@kw^AJa9*zl@Pnyt`Q z=g0F&v#rKkux)|1o1mm$tVQk?a8w!eTcX`6+)-Ky);ZP(%J8IsF|NUSH9=|6TI~#h zReJ+=EDEe<8?{%^Ps^xKE^u(GR#kyNV_NRKLyH{ToQ(^ab=l|zi*{*GIdE44Blc@w zVcEeyhqY5N7BwtWX94dM+IWnUV3i52ToG=a)E+||86+HG&uimk*m+qSjFH}k;72Ak zO>u(GH?$KZa{<>o+9<@U7ZNASGp#!r=a#G{fB&Vuh8X8uu@vR1lhnJ3Z?ww0lfNz)W0zcy6fp=Y1?g&FLOhP}OrLBZ zMKRHi{61FU2e#S^+!*AZt)O~EqVZ+24gWtc9@7lF%N=h_mjFPzZpdLeDKhzu^G z>W)j-RYK?(sD^aH1`g)vt_g`Y*}5%^kPF951s4l z=r@d6EDDPo>)s2ZYnthPm8KUM)xZdG7sn?y#_I|5om-jBKxF_i76qP4>j z1K^kGx^#?o_+W`#t@VVFb97BGgGIBC8fQ`o)oi77z`9X)QD{!TRd*P{8fcrRkz8BCj03tf(f-3Dy6Z@rvXIfQlRe4q zzmi*%lH)pmGVh8m1RkBx(VW0s;*Mv_VHFhHpL~B&=S6l+*EN8C7j)OKu72iSogd4t zJqX&|&{f7tyXCIVPB~32z&(|seI;%A21d)V+TvnTD4U;t6B6Ars2nOIgPW%K!qeF3W#wVS zP=X;UG5V(}l8Ih!7@dgiNs`|?+Ji?mQ0GPESx?(ly*Zr5GPl#U^ zUWq=3RU4^dmueuIThXX5Mlm5NfG5nq9c>k%w%vnhTg3JBVRWE3V%Y49i)lcfS4uU& z!FSQ9?0hx^PqNmK90(IXL^t%5^!CO%=CIJ)3D+3<^>?o0VeT>XoAA8Y$TKF|4@>%3 z%@{Az?p~mpEEqsz^ZGSno+F`vLS+cASR=fY^E-Jd=T#9g^!ovv^LSN^BjT+(plVu+ zS&-6tt;mTAse$BKlhnyjk`S}4DpF4JSHDyv$Qs4Skwh9W1Rv6WL`)!|{Y+qLs<;jvN)=bNQ_Lx$xKEv9;sX%O`S9*IBF2Y&>X*vb1dinWFjQwVD4h7B|WFbt`a>FIWyKafUEr! zs};W%{T#basG;VZ*g)agvlqn%3ypMJ7fZj?!`*qkO|hRuwH6S2UyQbfyJEYSM-5)& z`JwY}q(*XVB}o^r4#oab9$l0HIzj&NSd-wn;!LbKh7Fa@$NnjV4!l?@wBdTJS_s|$ z&)BO%=(f*dJwncvUXyeQP45j}72-NzLL}bp6ebM9o#W<6FB`!5 z%5l|QrJi!A6X%ZZlR1|32@tA^J0f`_@>vsy2{k!KisdEk%)0C19tq=djExINcW^?R zbiR`EhpUFTp=jk%m|*xPC2pMPTappCM)>4!tZ|dXudnOJJwP^53_}{nSrIb{P{j?x zYzotQb;-Qx=>cT##PlVwtz#T2VW zQ|xM#T=K|>C1W4NVV7v|_+gxPgha$GApW5+HT$6WAmJz!72{uuUsaXkUx}i&YJ45x z=h}tFzZSOGMG=2egsLW8ya#&!;Y6PdA9$P-Z^Z5v5Q-24do1zd4H1}7FJ2~Q=3g7f zkHLgxy!*W;X8Z`>wvX4M4TvImLZvS8uY`W9caOL2sXprwzgyVo?%wg!d=RlCCuv%f zF$G$Uj*k`tx@3Gj=83-z%L5ioh~FbRzIbB%1N0NUANOTUgD$h<3(?x62=0)*Al|l> zP!5#3KA`I1wH2Qb}1fAw%4Q59+x6;MiwXiYQf0URgXEz@?X4q zZu*l*o8fx$B{a2p5M1-nZx*x`dFwH!7XM4@k&hmeE{YpdS@&)7*LM}HbV2$b%41br z`5lAxC(5I&AmVeRvnxEVq^Ixva%1mNRX?^ov`CF}8dq3dO+N{z9e5Iaj|{6ut8G?=drzwXVK9lvwnAF>=w$zqv}PiE65k!?aITQ;$4z z)&~%uR(dDC#|*j_`b-2JX5kVRP^{5T60y}C^s#7SPd)X4WO8>(Z0pt-HT=?5pMxgG zuz+Gsk%*Bi-SjkBuR&iKT79pdhqTdpBxHcz3C0i7XL(BU{xMFE$>u33kArD`)Grq% z`e?HLM^PL-UH`>qxIgKWgo)mtqYn^8w}qv@Hd?B;7r%C1p`R*>-fK(E`Oz=><-%|m zuGfzfHzaN;y&-68slC70R%-7hJ4)@n-7bB9QSIeEeILwrEjJg->~r)K$Ps;pqoh9b z34K3gUzE-Dl}N{P`n`7Jq*N@$=k==*j*NyZwMpkw=UrjI1^s$V{6V9q;A#D5xWkZ} z`nq=B;~f6ye>!?Q|Ihz?H$A*?f`X)e(ksC2w!Vdv9g&!Be4yWnWmV-o^1Y9?GjBQ%c3=O$EilZw}tCv0>QH>^!)DvF+)6RJ5%zZ$kD&~!U| z2>-GxAy%RbZ}uf*h+p#$Cd?7{zWXh~(?z;B=xjn0jL)Tt8cyn5P8j(0!w6{IB+OLK z+Jt>q6KHyaBz$IMTz&H+K>+@13!BX)d}12WP3L zw>(Y=7It;xbwV{^6&~*suG&dAy!()_OB4_NlR!UM!j0+B=LDI!_miE0o*Lo5dO8^H ziDCsO1C5mVuh*OnbQQ>pv)l}H4~-YwdK&0+EMBbTW7sWKDVWg44h8CiUg_RAT z#SQDK89oWz-64iZ@$2WBhMwZy;h_c^&U4)Ha>Fm;hJ2-AoGAXGHUx?rJhY|Mt45bn z_l-3i6HWXTXLu~CJxnkh6~+BYhKHiKBgJrD6c?r&wuoZ;ECW5m$T>{SHFOq5ZLx{NHSOdcg(eUv`23rSgYix)YH&ke5uoDOYMTXX};>@d*18K(Lz`wTw`hq&Ufp`B1+&0~fHVPKt48gj(1 z2hSMJV&algV&v^;Lon&;pU{l#xMaY5ScgLOCwre56jI8ueU}YWr6EdM-!^Q)bpQv{ z!rMEBwUVG@%O8db<-!ViXwVC+4u2XjQ8W`2sGzq5%=+hskw{8#>xH4WfNA^Munq~s z)UJiU-x&4?tY1GEFmI#Tx=;%d|CDN}_ZI_Zhp-fDi(MjJNK0wKYL7${=45pl!UgX{`dT?>^SfW-`f_z%SPAGK?gvTb zU4umQNad}j)VFxSsD_E|LZpR_6Y2Ru|JVTe8^McwzYracENbW;6zWsc;Se|gBZrN6Z(+u^UqZSItnJ1n$7Va6FUgT z*;BD*F9DNAeLX#3@w7x+ba$I1?hx_InpuhTL=RWF`~1ZDn2jSYz&c-QG4l%|U7+)_ z#2{yz-pQK8^+JkoH(;%$53!+p!Rn2PG6@jwLShp^-MlODgZQ=a{zSD1uV)=i3>P_UHVzmVwVV3SRKB@y%98e4s|1ByvMdC2v?>1N^q(HmoL zC+)<|j$CuB1q`d@_=@AZU^G1Cv^kg8E4!yY=%KCN~(ImsF2+A=4nKT#)2X zZZ$Vn27gmhfA;f4a}s6)*4C02{Ax)`#SA_hLI}ClA%zArKL*Ljle$UA5zfE{h4DqS zZeM9Bx%iu;I{s86N7^SLzDmb*KNMX7I6ZtcPG&=#*QLK_o95^!hK2kn77S%+j9>ljbLrp zJc1W~@X;hxHfm15|9H|&MquU1B+P~v$M86vgi1xtD{nlTG?fv^x{&k>b^t89n1sng z4g0Pn`LW>g+;x20(Kk_TOTO#m9cl1I-p3EVAO1+fB%hWDe0e0dX% z&ywPp_FezRn_j0^Nw=BxnBFE~UZCI>IOttcCB}n%#7u_JxK3cv6z_9&5i>ig6dReYlfa4)g1rVSL93w8=K^!xFfdV??Es<~`6M-}s&p zXjN!DfF*FrY((Xf<^;OeGt!TDjv@gKF!nFa~p-U4ZpOI6V88=}S zv9!4nl|q^;qEbs~EWB=Q+>IsBx~&nFKbjNh*hT6IukQHiuv-r!DtR;~VCZF>&ARbo zAG|5(`x@^sjH&~Sm`AI0gJ_}ytzI)20t$z z9BV`+isnMBm_XwiDumx8;}Wb8?Is&hS)w_CPg9K(7=dv!jOcyJ`!kKG6w#c(p;^Yx zjDT*A5wT4uIi~S*ji~(4oWSS#QWvyaWJK@J>|ShK!h~qG%sA-lyAgbs8#}V)!N8S9 z^d8ljRYv?4QHQn0v5f!U>x`?up1;%y<_$(vI%wJnElJT~qm&LBXgcUtOc(oiV1spz zCIiJTBmHjjDDrkM*3Ai^7W<967*NO|tg~tfRKAl8BkCA-moo6sW6GHF8Jiy=_Xl0QLy3$AE1Zp|3 z?6a{x=CyGS#}YvUO$660Tg+v zBdy;nDme^efV6&Vh|(n|Af84OTKOAzu1{7Xb@C0Nl|2Ye@H`O`6O%)nsUS_$ldss( zYg}e>cT8?E?hF4~e9n)Y)azQp+J?ynwEcB0N?wdqYJEa0`@phR$!F=T0R7u0KVed} zYo9E`9N6OqbgQ1u6KIZS)ae!*!UU*SoSeu^ZgSsbSLShk9w>3_IwUzCJ=$uFPj)Bj zwz}4^W@PdqX4$>Qpe?dKP`J8iJ-PMQc@Ub;c?^u7mfQ|45{2=Du|FlRX41sYNiOli zy2G-p&gWMp?`ArmzOEd~p>RDg%Hcz!#zCVeq1l{U!{!~yHL*j36fMX)c!r$g{fP4GFi^94bCJ#VEQs%On!mpdg^YnAIZB;Q>?Kg zqM*2)TocP0C{}$&DD+{nGiI9R!;ocm2AbKqD)Zb|UzWoZC{h=UDKHu|iLf8g#La84 zBZLA}g!2_r9x*ZYxTMU+=dNpYs?B`s^Gj6{w$o*S9wZKg*#N5Kyf zDWkD=!+=OxzUDeHDaqx~6o(21`-Bv4=6wBAQYHkILqeXhnb3^QgJ4U;@(}}?w)vMT zl#{w|N?U9fmZfWku@h4^upyE-Ed?8r)Ms#h@L@(uBHFqKMNfmmc`2>YeZ~?tr!7v| z!2HnbWht)S$nIikmiIzBq>Oi^mVI(>3TEez`wgD2`SHG#9?VrX{WWDRbDi#oQmV3R z_>q*}jOx5&DHquPd!I=0@u1fquB3Rg|IfUZ5?+m7_qnyS|f3A|+g&szR z&hpfYOk)`lsTK{y>2XiIH zbMqy}7E|g?#yHBHD#yH!Z)5CF#vZG$hLFao_!yM3-3mjRrrxcJmG{)QF+o7{8h?lG zXo|2O77k2xVBNcHtfZ{L<0WNjeoVc^l;t-mwP^sBV_ELyIv{OxS29oB&ZW!Vhf!?bcB8T%9^ zHWu3C$2?3ywBhArUz%VU zXoBV1$Zd}Lyk~)8nW^r#Y)<17!lf-~JD94cZ%gBYa`=w4H1_|4yVCYDA%EDD7RrLX z)d$nUn6T&0rrl-2ZM~T0$*za4q`hYDdgraQpUW*|S+--5K6&)xG}LQdrJY5nxMiqL z@a}nbeud;xLe|j}`RRpEKXVvOdO)saE zEMGFve95TK>1rC1!R74qnDVH9283JF+2$>AZjg=-?vykQF8_92 zc*FEZ%#8dRr`JWVF}e3B&5=xKj^vIov?yJP4f&)2Kmpl{9&oLF`Yz@?f9{<=pZ!0f zU%GqwsJf3x44sqL|^`!KTXq{1n zAlNWH{UIABvuCEiWE0H9+36qIb=-pVnTTK)tn1R((eXqoY)K!95RF#vM0DHJ{Yc!@ zI0JNp^dx3%Yqq6(V_Z9h`-bc|obFF(rsUSJ?bq}t6pk!Al#Y3%%+P?M@y(z6zol0} z7!_zvrEkIn4zylt=zk{tkb>$X^;5bZqIbxYt{DyBibn=2ODV5Bz$>Fu1ZD6*pYe{_ z%`HQQjP;PSF{1*zx}{_|u&Z}kMl^F@8TlEG%)jPYGH$XEQSog?EMgfCwDGN&V2##m z4LAB^>}RAF_REN6(aEKu85=MIkAi&zJ`*!g9}HrZ39ak_eWqpbeZ@{QGx)w@;!hbT zn4fZ=ld%hv42sh#T$6Eyd3pJU48$X9W*OfQis24%8#DH>o_)J1!xy6@=IMZDAx32o z=WddxFb+7J!8bP!|CVt9y|?|}{KY`?7uTdA4;gkV1M`MGCr2TJq2zYP2WC)HeihW#i`7uH0y5Wzh5P6_A#Rp-8Yk-08a?Y z+{Fz-BM`uoF*ZBC%(d@d{khz&%`y^*hWU6;e%Y5uhug~jcu47%Wuu0}QcJ0$D za}K-ucgoz)t`~|kx3lY%{+UbJHEwvO3v)y%<0PDu6Ec4bu<2>-!p!Sz_TrDt7c9@z zF>B#3V~$&$DPG15T$|~InR9J=z`XcK>kcAAzqAMh&rO-0j8v8FnV8co6lfHDza#Si zvL{Eui(Q#RsQyWhy?6r0_hp_!6R39}vzQS$bSQH^&Xz|(l_Qy`LeGh z+~{WJWY+VKZf9a%`sElocQZdSAu=9hVopMFjHM4Vw;~iscxC@5nf$Q${im6mu{~&c zo_QEe;P~H}8<`e|y~+IL>j^-Mx0&I{IjS8oJ7&3%lz{I3r1_UjKPdc?*_L$%ceh6? zWIaU;38vL{K!L|oB)IC5HHiubo!qm&XJO}EpR6!Ml+HlEBT;{4I>OwbEPk?^b6iy^ z>lKot1V<>GCt0`0*@^s6Gs~Avi9Qffse3CsQnhAQ)oFWQ`jUU*Ev}^dKIdqntDbd^ z3gHrxwUP<5u}+qTUGK}Ye#h*KGNB+@ot2LkfmU`U<78a|AvP*&4SQn5)$&fCWyXX* zT+7~lvz*~tN)~EaU54;;TGkt;oGCe3t{5&f??lWNb5;_z?o3U$dhq6(tj8>NzTGgZ z3*G)CJvuuDkXJ2eN7KJ}!SI$@8JLb^+-91yqM@j5)0P0WC(B3{CD{Kqdub`$OfCS*v5d z-chk6FC3;W4BnpAi8+VIyRzOhk?-xvdKOHt=PqV7VkV=yp0%6({^Ukh3A@g?m9>vu z8{Wy)4SN3&`!fo;u09Di5YUR?9QJ2j%v@$lEYiLP)c5+23**!~k z1EwKE14)Dr4J8p4e=CX5rb+frrh!^5rN7^9CH;L|8|m)_?XsV-pgXZ!HY!I-sf^4N zc0r3i*+JzKG;+Em=xibhI%8J0HxqQ}`fLjm^vxDYkj+~qL7Hus1gWtzdnFU}*na8n z;|@rF*Bz4n{`zqCT_)(|li4#cvm+IhXJ`t6pi9}gL3V4TBvZ?uW^YGyEKlnG{Gao| zy!A1oI#d2&uLae14K`EZa8s*p_D zc8=zbywP0__1n9{Ph2k>N>}qvGx*jo@>sG|BuK^+;Tbz-RUopps33C3!l>@qxkoxs~NMu;o6zE$y=P!FYG~Ax^ zK*wo`uZ`C6_Bj(pe|7F$E=y=lPB{lV!Nx8*?gLIo{;sJN*XIeL;>hcrVClf{9=$s)o)bIdUm| zm+O+8EJsq~m&(4RXhqIMoQxWvfW0vu+Qvo$jn?EWs2~w>U6(TkNrXwJfjaARGRvbI z4g9kyhkm-4tHo_=4rX_&OtQpnIjFs4h5(j*Ir}h2LwNJIwx6p66OQN1!8|>n2x(+; zcvts-`Q0~Ha=KP0$~Tn)q2K$QSY(fLSQp8uMg5a=&xtDDOOf za=&qrigMpvu#<`}{BpD1rDAUN+*+PeabQI5DRJ+2(Yd4jrC(oX=PnaqtMhXQ$fRH6 zi*wINh7VQx=6(4#{)C1QisbJu!A}M?VwRIL{0A*3P3J zO=Dvcbm4jSf?u{O&t^c0(Rs7!hr!tm4~=;Pge~<-&D$(`Hz6l)yy&J2mQo3=4f2*@ z&UH|F+&`Zu!v|V($gCR`)nsu|9(Kxsq854M5o(&Yuctpmw$1y-&Tb=&YL`b%xy9z5 zIplEHJj{xP&wiAO>$IZT0W+Rma3qhr<-I_-zB8u9D)hJef{&AVPcp6@Tv$0hUFb~ zBqJ{O3?yWD9`!2&@+!m0(RrvuEdLjF+PJ)No?K?QPV(pnSD5&)YF6GQN2xe{e%_!8 zQnA*eyhY-M^NaJEi)xwc^Jda{lJmd7~r>_uuA?|9T2QQBWJ^y!Cls^2SQ7$5+Uo z@b#@rmN?QrzZPblL&rf6vLGnm1uS0qbcU~z;jg&b+juR{le&v`CSEz>fd+y)yrxR5Gda*GeUisQjNXi|iMP`F#i##UF}d z^P_C?#MtCHlaP;jx&{hr=7y+o}nvmx9 z7wFrNUip>aWaIpi&b(Kkean0qW|);Cg0fBiL8(J3bjaT&Df(2${FjnsXS?O+N%E+_ z&rf&Z(heVz-vjZ=J7I0koRwb_7W|lhRPY}>BmWJS|CU+#HznrJ=j8t&u^hc5|1Njo zFkn@_UgCRjWBy7<&V1;${5zPXz_4riZOE}v{*ObV*|DC@_>V&JW@_&&^v)|+|l1hc|@|Q^^#}D~>PcD?%xnQ&OTcB&f z8%ZHmJqto4z60I~MGx)%6h27wI*b8L#%C9tlz zpegWT4u>J*@l=T#5J{%ZOZ~?QW%qw>sTyT&s3>k-%VFmLLUBY434lf8n>To{H z9aV5ra2PbEVDZ;;0LtMP#8MG76AQZ2-2f*2hA9P0zP@F`rI$@BXoHr%$;^WNLVESA zf`wQP^M5YLM040Qx0J)^1qGPEm+Rs0g#`!D97ZoK*edj3DZz8txuReTX8B147)A!R z{>}xI>kEGHBz-@24-9EOqg(y!4L`)L@bwsT%m8V z$pJGo{{?fprYpqsHuV(>+Wox=v0=m%B=O=$KqD7uhtl|nA$C}*iCEGeN!Gw8< zpj2DZsix?OCTC1Az+pNxGY^9XGfgi3WYzA5fn<3JolW-I7gmM^D@=t#e_vLax?%N~ zKh5j~L2FDsF<~XA$Ph@!HKv**WnY+_4EV)#syt*ElrOmzY;Hy>wJ8iBgF2cpj{t0) z5CIt*Oe=*m|FXqYg5}1X1vlL0TTR~OvNMh%+hN)#y6@dC6DIYrO+txP<2|O}aN6+k8ChmT4)jw}enmcTC3esm0^I zX}VC$`UfV=E(RCs`9ssj@)7Cu#57D0`Si4${nPfj3H8~nGJAXavNU=d@Y=MXT%C0H zjmZufG<76VEzG_!`IG648=u{vr%T~>%<7nnc5A1}7cP4iz7=4%D;7RQgH@|qSQUP# zUP#Xlv6vff$P2q+o-I*keMyI7YBkhR7kVHjG@uA`9Hp7oKeABFw8=4rZcY-j#)iUi zm`!u8vl@AYt}rH}@BxAu3k*9FhCt%Rt8<`9cHyN864Br27ow$*pm$#3GRaMX(o$$A zipFmW-KF^t8h=}O4RbO;)1xqxG~257ft_s%QCZe)2u+}8`@$a)Dg=rUhw%%vUVZqh zXJH#m40d}^p;fY|3Ihw*BKi#|!dc7=nEFGZg||$WjG?1r$jHJenC2nZXK#^OFjT-MFnn)1M2Y-R$PUlqH1=T%;kDwx)2CuvF)bg*)w}U(anS zoFs1OwX3kUxS?c!;dv=Ghlz&@6A)%3(fZ}!Iag?Mvam?dI(5155?V$MX1G>3TQGZY zvv83ZZ$IBF{7DqkAC*e+{nNrD7-O80YiR6IPQHtIo9N0wH}hUm{KMUxjqyMKZVPh|f@|(s zMRO0au;zRA#%=0i)9z|(QYJ{6*Bqb^!0Ccs@s|Bs+GV^R)FSZ5> zxD+$cIh&$dr+uMQq?vAj@(H+;)=ZD|@}e%fwD%lh&HKf#7vs#UMRB^`JOwilX~Vt9 z$@OM`xSnRdXD`iV?=#I}F0;=ze}{1Q%PFiFaf?jm)uPzEuGvKt`!+B)#0m(Na(#xWA3SbmZt5ZVv?iU*wqba?iy1QvZP;G$x~usGW-u}YpzNjyDQopH(_|;S z1N#m(KM`Hkd#L$OQS33?JkMFWp~g5f-K*yJ_L*olU`*O30R8+qTelyXW=0(eoogN= zt@k!eH$Rprku|f-3lU?2GMWtDv&~ZwHUzYCiWri$7Md4fn;*5v+#k_A;oSEwHGdS& z{r3_x=CgSmCS#@fGO|aDVec>I)<|nCCeKxcPH=p^`H{0UOpa|c4-^*HXrKAIu(+&4 z<_+Rk*AwR5!a6UVG#9x_CYgKL>@4nWaozkJGkZ`)bR-KFn%&^dBl9;75}l%_=6T4@ z5nzj7m~F1c{*C#KnEl;8n5znb;_NK;f_lEAWvw^PjyjTO%gprKK=mz7q+p^|4Sxh$ zP`Put8aNhx!OWeU$nx=l;EEOxCrKgk)h+Z*R_+GAuVbM~vqd1FTJf9`{PY zxE@-$H<^6Xf_XE1{UZxakTuyp0FK|Xs2%Ojl7H@7enm(?foOL62NrjP1VAgN*weOO zKC!I8wrzcCL7hI~#Dia0dQ_kU#{O-YizRUFmE|&KcZw6}^xo2&5xD&kPayh}r3NO1 zr3Bi+69;P%W+3y*_nfRr_T*3P&+cSZB?mW%b+cZgwglcj)^iT@dN07L!MGg?-jLk- z(-K4yb~UI1C#qRTI+is<-c_~w)9h|6>rWLsUzlFo`Wh*MXeIY%122e?S%0Hlu|;k@ zXGgi8S6C0R>l~%EE5^%n8EU+=xWOE))!U(rKJ1OPwkQ`Op@=T9BhgAfI(8O{Q>?!- zqOob#jqLh3-CB()-zCR-ja}~-SaIJWWSwxmvDFWb*0t9BcQ=3e#@YZAL^72CMNDP1 zLYhnZ{iDdblhIz>()t~<9lQ3{lkESeJ6PAU>(EZtH1_+i-L3RH4QF9WvD80pds%-& zEO3E(&6;*(%H<9@#QR04lDrvc#l8zj*b8{XC-{OVIXcLC`Rg4Bdnu6BcOhSXw93ie zVf08=wINnD!k7MUD8#v!|A1t}U&F1T4rKC^4!)%JXzSW?%9l+jp0rgme2i6t7y+g9 zfU0XFV#t>QFnycIprtJXp=h$Tr6X~j*(ne%&aehL*_|YGH`1^?e2&$L%MSWQ*1sL4 zVp55>sCBbm#AIH5$^;q>Ayw2e|IL&W7N*D z?U;2qx)B&)^&$(Z)$^5JBh|mG8<4O!vR9Yx>|b2huKKRAt@=kswTg>r6&KegN)z8w z)3$AVlrFYqoF=-3UKbOukBQdB#_Dw17+o|_;{Nm?=8+^{a`2oL6WLINANiyC!V6Z^ zqjUy9hTaQv1?6Sy>I!72D$|cVzG}tn>6Mbnzivf^JEZ_MZ&~NksR1(riLJ1_B&;+DxFBAy~f+7&INp?5=-ralC zHoYMv5=26WAZ0+2Dky>y6d07=gCG!46cH(c(xj*~rToslcXk2a_f0+y-`{^eA9-eW zGWX1BbLPyl^#?}#n4l83}lS8CpzB+a-$y&6r zKaskFd&h1kr7io$E+W}|FU1NMW(dU3h>l%Cvi%0c`jF2O2gmLp+<9?mtQXCiipZUt z393*aCtx&&5WN?A&Y@)tp11IvQ@{ z$Dw(#s|l&~`LRaHR^v8hu`&pn7kJTqbZiXGK~<5|BbP^dBlbe52FFi`)l$|J1>~TWXuvso`2%a7L2SK{!d{^n|1+m~kh@|ve;wtr678^|Jc3BzQhh}yp z68Px)NM97OF4hx$Qulff9KAMHPfeu&3j?h75|t>i)+x~O8)D&lE|G?rTVml#0x_Gi z&6Ry|hfB||?u=bWDtxj#me??yyf5~90%6eCE-v^Ui9HB+fCys#^`r~fhEuNW_|q=9 zFQ0M2^*HB(yZ-0c31B?V4s4ExUW)alR+N`+kM!U8M{FTA^8^6%{)qLQ+anfkdA%JQ zMpT8nAKQ`;KK*g*+wSPBZE$nk-reL*N)0}yc0?0${7utnR<%GAvXRqqs+WB&Ig4LJ zm}r7}pR8ydmBWnW6S?VI3U4zi)$|g2)ZXNY;S$98G$WxC0*z||-#SqAy}_aQwT`Bp zf#_`W-a)9X&a{k|HBm(H@ zYs#Qm>=Qp8o?u!`aCl@fO`+vO_e9ff3LG%t0Jo)yV(7`3rqVW_#+s;RAR-GwIMaH9 z!^sTOJG3|iW|^p7qZM)ZF~0Mw$HFq~`mLp;yGb3KLZ@tSE)!Z^G~(;ZQNoG?`Kg_~W-s-%uN-_7hAS2o9Gg znrP0Ph@{9To2cf&;>Sy-y7=(w40=A4&oo`5=7aY;CWv4c)p5xjdK?bVHAPUUBNF*A zYrbhh6I3(4Pjl4yJ?dVGV;7o&sd4aGY)U757_-zgj#ej^E;Ic>EeqocQx3sl!-uA~ zX>s`JBhydRI54YC$pnYPpU^lBc-*I8V`*`JZ=Go-X}gN29}^th zH=AbDlG103X)rYoowu9v2o4|aq#q@Y?=n$^m_xVBMO)u(5rQ3iO>oV({f7ra=U+e)Xm|2wn%irSG~oPMD|;4T>gg<4My;L|*wn znx<3o1}{BrI!vuIA?Hlh1c%i>o2qGXIB~&5b#I5rhaJC~Y6uR@725t=ifgW#sP3*6 zamcyu!lBtMS{zDnuis5tdRg2y*$E#e-=#O@pWQPB(rmYie8_lcdPl-xu1_4zS#c3c zUs)V|w{UlldyB9EdB#!AJ4TsGah+FO0?jfH5RiaZG>@xvM?;N$LeZ!ead2;EnJEn0 zTgJUhF_9Adl?3FuI?W$>spI@nc$0)SC}z>fR22$!F!mmQxXa!WL5|6BbuOL9*{4MAbdfHmnX)Tx zEa7+9{zc>GPR$j z%+@J>BO$13*Z2bjN%wB?V+a*B3Rh{iDt@QGL`9$e@j*0Hh*mRNej*4Tc_luG412oQ zoC0b$G=4P=Jx{=n7#`1hOE~UIi2sZbF(E0wzo%6C9TOi;q~ehgzl4Se(YQrs*N5OP zx$$oYNZoLf!%4)AiuiYEdbbNK(mOKzaPzV8xkNHGZ^c7+zNkJQRL3_b+5J=EeMt8F zjCduxXX2PlVtv7RJX@lOjNnE`VLVC?29DMHgqnawnS#KZxHz zX<+&xMKw4{wI9W2P-7=RKmfrL4fh`!inn|m4`(|?6#rV~MDerQ_^r=_q9>}2JmHUk zi$>gSLwwukK~{o=4{eNpU&0f=xh396qrPHi@t+OhOeqK8WsEB`6v3!;S9}Q0+!b#i zUGm+XP9B`!7k{4yZ&xcAS|NV^#zXN`4-yJJ1HWG%&wEM|vVP~pZqWDfXKAn#`x-!O ziP!uPzl!u1{!IL4VgOa=;^Ao_vB&*=K7IwsuDcKqF%V)Y`-`iz)uni+19Sdz{8yyH z)~oSHNcR2TT%QfU;rh(|mg}>_zYCw$B=%{JKmIfRbsC|H1dY8H?~8t(p$NlM|B8pR z7NRe{vmq0%ji!&)T#MSPazumswzzL$T z?Ojy#?U+FCXs4|)`QnFN6CM(*19~Pjr5W@@kqf{O0Q1K0DHGt9DiP_9nuN2|c5BIJ zrsjB}At8oZRYVBZUI_+bP`@-L5C`mdzl15Iv*is;IMj-64X7VV-)suU`%@Bv2n&Bp zPY9$YSOhl3o^X;#za%r^A(8%D*$F2|Hp`I!2dzZ*78WGD{yeR<#jlnmkZostsx09+ zX=3}Rg#Hv4NMYq$39k}H%$$%APq4f_DWL^T_X21yqaE!}1f#l?gw}ZVj0Ct>RJ0^l zYMgRBF)N`vX(nWD!aW)%1ICAuc*eU45L_n!$5R$1tfc4#7~p(G)V|LL2|5~z#g4T7 z2U81lc71~P#*Y(*kP$XzZNftu+FSrJ0CWnv2q(NsgB?uzUdBDQCY&epy^K1i6if#a zLTL0*fQSHMur#6l^H2guIGBATp*;Sv!^yU0GGxTa%~WTEfDZ#uWP5XSEbnNR5wK-1IKi@=&BZi= zcY#F!)(bP;%!o`Zo2t#u<&85MGaRxOdH##R+>0<;`I6b`5e7w@zoj`B2aFa&*yOlm zh`Fn`Y#*9+*)RnC8e{&DqK`cZtBdRm)0Ar?&F3hfoJ%pkhVt83;M}sw+={}4BPRs; zyaY4NiBb5z1V@_9-@-}UBPBt|GsXPzzb6C(n0gv(58qYbG@E%HT!J7pAH|#J{rlzx zE>vK9hWRBLwG=-dkz<}saQGwFOcTK*f>Gt0*V6Q1@#7hV=2ZlTAtm%UOe{5zr{)8G z!z^5LBFLg|g_&j}SR|#a(o7Y}C4T%`mH9)0LzD6J`Y`BiGtITxST@x>*B`z2cS$I` z(q>MgQELFf&#*$=n+&^*{%L<(^$!4NMgHaEpJYt4Nr zB~8HEJ~tN-94~AzccZ`&_up*Jc9Yn(W1E?3Z5}{m;!ZnV>{_zhOf^LWcEzHe#b$q8 zcEJ3-m&C5RI>IjLSaD6gc`408{}b4|elSDqt z?mf6@?ngNU5MVnmnLmOHEdp?jfhDpHi zB?ByBc>H8buO<@v4o|f>=wvw@e=*I{laPCVriCWxL_iS%7=Iiz%W~dR(l7VGwE=(hT2TG1PNM5xChZ!r@b90x2jDKtsM zVfj~~^49Jhe$Cd#762@6dqtB6C^cNVIn zP~yiAPFfrUhc|wt$6@_x%Q6}_8*s4Vv*#@uNejE45azfY&WP=%wE*=l{L3VgLmWkboCne#_E{#%BXI zjli$owdg%1ICCFbs18?%a4tW#oF@bHk8al8WZ16tuzp6e#a`AhVpbRVSs^MyP*FT8 zzzXL=#B5Y^>qLsBdckyzKvkdBLulTpSHf^iTWg$`M9>%Qt+#2!^bABV{Bmb&5g~2g zi!RdM?d~EirKfcO%_tzY6Rov+qiubyUbu(edWwiNqPLZ5*~bMD1H?B7(nI>YkZ$|3 z3+Xcht#X>N9274GDTY~_q3$7pJ^Ni zEO{1qYPvOpSROZ^;>^QyVsRldlU74SZRfz zZBggvR9SZt2ZOJ+Qn?b)&~Fq1fX_^*cBwT=av;iQTI+}d5i`e1b7zSYU>7`pu5}HS z{cP(mZ_iL`Z`_veNk&F1EB>ZH4pBqL7w-Vx>7_`vjX`Kef_%8!l|duCtaAY&_RL zCpJSjSSM2X9RfBnf1mY3^Y>U)c%1 z+IKK0PV1B#jQbw3MiBp`;9KjT)ch2{VgP%_>7<-;4T`m=U4vrrnI}%l#V1b6uU0sr zCK~KkSFH1>oD@+}ch(1pP6`4iWfVUCr}Z2m?ZDsG$5fum(_p|9dLwXDO7OP}VpG`K?`; zJKDG~?-`!hg~nSEo9NjAkY3y|(Nl6+T6IYLn|8z$Kw$v&3sLoVJ12IbSbsyX0MG}+ zO4s}D=(8>%Lr|zLk?P^ic9xdNPoH>+f}W)l6QeP2NQ5P)sw*KuSl1^J9$XW%b7K;3 zH=_h8yk9IwVX28UD;j`Px#WC1QhC@rg8}3&0wSZ)GI%G*fBuW3QYTAE0+0V$H=C`(*NjlzWuaBo^-c&XC@kG zEE>RJq&LDH^AbzLQ1axEP<*F0aU2C%$O+6Jw_KmtzNttc9=bI#N$PqS?@5FJ4ACY` z-?3=czrOh9(s)uTZ;>?ELnnK1GY-jiB@IM*=aZuFS67lE zh>Y%EOBzQ$ocg=#L;3Bb{iO8nUrBJ)yU5npnj|}!9q69yWcG27yo$7__mE>yy>OKP%< z|7pol&7@MLBY6YSt-A%u5W^%=zPLR3EGez2NbXBYe|jq!Lde7lpTF$_Hn}>vmh`dL zW+iVVtOq5{8QEv=FDu7GL`W3MS02!QhKgaiqi`8?vcX! z(%Gl}NHICFEq<|AN|~F~Q8xBX`IKe`0!k(UL$7x2>%Vb8ijnk&*M_IGBsdqvr;H&& zdX$*rN2$xv!l-vf_LHyyUyP1$DP!;FeCRc##jgES?^u(PP zr$l(le!_!Sq@=n*y7Hrx3U^4`f0EK%NOkK{&I@VljVTU50#|QKxi9>#+MRMpNQc#> zbZrXhsQQ$8`E#%ipS_i`G61L?_s{}o!)2+(&qXCbdlA>Wr~c^!xa|u_ZQ=*%fwrmf z9i9V6l&(F|4h2jAww>;uI$Ho;_?$v z(2+M%+o3bF!-EmLz1a-+ryGkEddh|P&*VD260Y`&r=N)z3B-UF)pe5#?%{4 z0H^RTQYlRLKY%U#GIa{g%BVWG1 ze3TZ3FP=?3O5(qc5c1cT!85L0Z`)t=-5?$7m!<(h z;q^gj@LbGFoD-UMt|_F$)@Nh=e|FLqCRAe5h1q|Fq5 z7irU`3BMof(^?DlTK7&1_k>j6KW(NDr2B`aJ@yvTxU}vW`@S}uiE!T>m0VCb&3 zX>$bzS-wb%5C|*ZopwV&JfkkHn~=^ql!gU@UO$>vFVu_pL1N_0pCm@MJ0~%+--WbE zLjAf+Y5qd}@2;k03csUoN{sya_q5*y2JN_=RxOkd{wr;-0ME}o{jr$(r^7=;Ab^1M zlR~;BD1DQVmIkM96;gF*dcBZ(v`k+wq+hm5-zcPoVbb4wwoShw;4gn6y=^BtMvLYK zBki;aBqkNi^gaquqK|!gM0ym3T)?V#Koz=@cIfP)l-4NbV|aX|re=sIUYL;HLnd2~ z51P~SJ!QKk<9zaDRwwjPYC7#qK(x^4tPw82ew03vXiYAM{% zKDHnjcV^OCQ`qchK*7BrT>3P6eBx2UqDApLd|KxtZWI)&+W0mFdOLOO4!0|J31;XY&1Z_v#IJ756N*B^)w%sf6e zHGN@I*>0RaHyzy8-T3hQbZ}dD5HOEJ`lxP1tG_eJHmyN2RDNlZ9}F>#Uj znlyoZn_+e}7fHnC5(bW=2-M;i${09~B4$sNGqCF9c<$XhCXc#_#cvWL$z z`97#WS2P4o_>G}hS3v!t3!t;_mV2PTN2JT~hU?6G6xI`uyUA3^0*<-=&;Mixc>T}+ zq|E6&d|Ctw>dr>s9=|hwp0e+r8s1mkqwcM!_AkZ*RorK2w#Q{H+2QEJu?1oH;a|)a zn*Jn0m|`#TM}z-j{88K^=1q#u4Nxj^)ng_Do?VOU5rpn}vY*p|nSdE@_76%*CSadt z?6M{j4qN?Mn(NbnlnK~9keyD6!zkPtx0(+7AstzoLr;0={hdQ!1DJ3ug7jfqla6lV=!Wt2?K5S~`|$APb~vgRUZaXSkOMq6Mq{@u!g z(H65ylUOj?V%DC*KJt*V#Wofm%N9%HGg+|TVzz5Gd)HISstVa-Zc;Y1lm$yIR#;We zGTu_QO%=O~e0F`DtF`yXv$do&bprc`uT+zko0v~HZxk%f^C7qvTb4blSAyh0Er08kL(y4Reu6e*#E_Eo?-h2NH`w4?h@GI zo9stKN|xVUvh(`Wr72(CW*-wOfB2h)eM*7K4R>r#i63ZU`-A|P=x)15cvItRyF(a) z0&Q>$n8+J@bK9Gw!XK?%r8~lGQ<_Q@k~+BH_UL5$lzjHv3$|R9R3RtP=G4&WWEKL;*VbUZE!nnPdw>GtaPrcu%EGML6v>-9^w3 zGi**y&#tjK`{aOkY|cLEF~{cQbm+S_!fC8r?1JFF)Wzyu%UrBJxXQ)qW1qU1y13Ss zOIRI_U8Pqx*(MNHSM74aHSKY+y7@jAt84DLSk3)q8%M1*?&$j0G3I3Bvun z?bhZJ(?1EbXHhcUj6`5-BkaM1)yKQrojSR)r`^fwG^O3iYJaue$?AHI-O1{6z3fg_ zKkVm%aJ0YOsns)HwmY>Ny=Hf6^`a4Wr&i~Tw8M&tsED`BuF`E*`)$&1_N3XJR@1`T zqlo}k<+xg_$hA9ly?4Idsq0@XwL5iv#&UZW5rFqc_Sb0yAeywrpW4Il^;-J|VjnkQ zdwU6WbZx7h=H#>sECR4zNIsnN#ER{9xVlVaNXSlm5g9PhZu?J!P1RqyC`&qIPnVoR zeCW7~c?-U^ZzIghJY{b|NS}GeZlqWr5C-ev=!p+&yKLVr8z*?@Lr~0u4lkm2uh=Q) z(ntQi6N~#_vv;SMa3J_D5#7uv3c-HY?SIh>2mqlNt-fWa*+yAs;NFsgT7Uc9KC&qq z_Qq>LXv72irhm@?1hA&yT@UTuX^0R$-taMZvWbMk95;?;YfD7ohC4^I%Prymf;YFB z;P8hJM{`kth{KR(9Mw!h{J187J3w&AZq8B6Bm`0_5ll#0;ID$Yd|G+nKU;7=5Jb+j z<`&cHgs}}r^Ej9!kVoO%mjs6cksQsrWs#@h?Vdv*Tt{v)!Qt@>9L(~s{)8rw98-%m(*O8m_Jsw-<4%EwZ3DlnFZz4dW;TmrvYQ^Szro zMSf^J-+_V}4B))IBtm*kcM($d4xihc!kz-ObW|(nn&GMy{MViYIW&JYPYlz{H9SpZ z7IFbb0N7hn&rjC!Fvk{U&}X9y^--JoIGQk9p%Gnyq|%@+8BT-sQ`Zc*D?|KjurlKhVw#5P zGn`FC7&DyR_js=ixGhzzyRc72YoaFmUUrqv9F)II+j#tTqg2-cWMUAU`6Wkr)RuCnDXJAj7M%#>BjjPu-zk8NM7V( zRnH|ZW?xw9V(TZ%Gi(xsCf{q&t%{5S^wRo_W_a=HjQ*sNTWegnE&0^N`+ z`w5p2G~c`E`1zzu2rEyygmCekO9*FvaS37lC6^Gc-Eaxv?>{p3kwO00Lst{a9=nOlb0vlGo1=O!86mz%bwnu#|gf{0h!yI$?BwucHpg!FQQl4W>QYHTO#-H z%;OZcH>hi-3RQK0aXUI9^E6HWazdb);=#9xC|VH$FQ#_r!H~JdW~8PLK8U0GpjZLRJU!5te#CFeO;b)(Hl}#=d726v`~?SfF-!8 zI_o6#L;SNL3y!nc+7Zd}&W)Xa|0+D9qB82p1dYpXvXHlNRW zQ6OYbe%3}{8y+(zOYEDor%8moI5X=S&2$40AB58SWO*QTEw>H+WM0;NPeKRY{C*bA zaU`KXNiaXOEX(o(WUXtn?g?Cv-IV1g5Rtw!E5!@aZFO1i1<)B5aTt-KQ{QK8L?eG{ zH3UK6CXI57x2tdYA?qLo8YA0gcf%J>Wx2V_HsMQWvfP_M`rEmzgF^bng{<$qAf0tB zOC|h$&&@1GDDU(~)<7EF78*nQZ)edM6!FO!vvAd0<&Y;8Wl1KsSJ~-9NjS zPH${*VkbLZ2Kvx8DEpBBt4(Nj4K1vGEwZWZIueQ5)+&3az>umo*)+Zoz@U1qQEb~J z&lWlhlDErVMoWBr`|Mz9ZFtZz`%eLZTb;Al&?2Dhn*Az`=>-l9!Vq{;=mQAM>NPYJ z9Z+UBM@u}i!|?Ln*}EwF*dQtXnPVNHfn(FM+ zLiv=M?2#Uj{x~<=D%dIAyV)&)BY612>=r`GEy~WKanymjerW94nm|1KgY2(Dfke^Q z?fzx9f>K%%I0WFl#65m|WlMI5KcM*dtL$N5)bOzS>~i7v-ZR-(TSB_-ZuTsJgqQ!y z-YAgx*JJ7Tv?e*f3cpLdb1n!ZTm5ptKiY&Z2ITx8lr~YRBXWqUE=kP! zlu}itCh+#;obCeBzcD!z1Y(Laa}Ef<`{w09#QG-eTaY8lZ&zu~9AQW`SLQSkR6B7( zPKr=()0CX?lpRosYTE=y;LWo@!cG;9n3oeysiIDB^K%M>zSV76&PoBn;~(be1;Ttk zl@u%G^PJffQo&JQf{a5S_`#Z5+PnP}&P2cyy3sHC4|Da)i#_@v$#T zO-OHv&el78Fba0)WU{09aERl&hwLj^MlHPfnct z@ySxh?=-yxAiRjG`V|aBH^bc&C^EvYCH}0^(T&LeMwR0ynWPFoz2zu?3nc}uNSo() z-Al@DdEfD(reb8I6J9)9MSLt}beR3e}nF#4vfSkao1JdC6zc>}btg}cT&lV46UOFx;pFL~}A8s|miuJ7M&ZSmqxxfcT^mu!JPw~ofYa{_CMZ}!XGDLI>X*T7tO z0!=U)_`O$hPfFPjCk)B;kqYO<=0f;{_;)rgcL&L;6LPnbtjv-N;WuK1v({X=T3E~u zWplqLr5bzgcO=`E&s|Eghcj{^!bhx|o|ijVVl>`Vko&T)RNAZ}_aLczsWNvisXOEC zT&J^@Qk@${oVZDob6+R)T%7KzuzqH4B5~s6$W?lMer|9xsfp$*a+i||`_|-k4UkG@ zTXNya7!k9#w&kXf?9CmygD4JQ3%0|A(i1%bgnOcc@u|bPc^;A&Q@_b=PdPM;5JPd; z_qjQQ1M^Ph!XqLg(gmk;#}j6_pU=HOviE<<4W)5SCH(K@`r*aba^Yr7aZ8R@1&lU$r?nd0KqrBoNE zHm18cHJ8nsNqC~ocER;`D)Ys!VsA@F+UGNki_fqVzj`bb9dXO|#*s(!z94<;`nP$lDAY+ZG|zzb$Lmh!-6J%7c{=YdL44QGd0HYe z?<;vuBYWX$9vq_=1v&Rt9y}2#W;fr-8%s^(j@OTcp`&j3UI^$5#=k$v8{{S$kPWZr z|3c|1n2-P!15^*9O22sJhY)gn{qqNsYMMoy~WI;g_TG*VC+{3(FewM!d2F|Isy{YPnMYuu+-c zw+SrCoio1@f@9VBRv-9L=H|w8asF@-LI_ z*J^P7bRzbRL-XB<%{(|PpX!-b(u_Ac(+VyrsxsyOMx!RrKtTYdIjY_6<%u6y@+(Q_ zo06PAmf#wemhUtmlkNFDjZ-KzD6D9`j%J1oZjM*wa8n{qvjrbtJp- zME+6IC(&tF-Gno)x{uGg>h3)6s=Ms6t1ffZRoCY?SKUL`^Y;+AOK-dCrr&kdZF=8T z_uyaoTS(pcvH~Z*X14-@??#UTC%U^l3r>j_`FOBVcS|K{(=#YZm&!bitl=C+t zjni!k-X(mW6jcyKvR`&8m_>`HOOJxl$H%=U;?gHJ<@B0K@HRzXN7y`8x$mpO^Rn_~`6{02-Zr2B{(F z^%*03;Kd6H^faTh3l;`>$h?6zF$AvHLf8d3I=+Ae^x2jd9QOf<=szu}@`QBL`hu2% z#Exz%2o%cC?J5Wc{ld5R7u;?3yo?dG;uG07L|cXXx!yccP(}eZDy^{n>?*jQYxs8s zG>f*%?+PN2zpA7)8v8Tws~(DbxQhKs4jb|Jj6e_wtv9A{$gRS7o<^Y>Gu^);XqIL-LrRLQ2?aNUMnp1fV5Ri;RQbw^kdKvG%&T0B2o#1 zDxaX~-#}SQ+>a?tk;$gx_u0ZIPuZ7J#}aSK?3PtX+p$ngUZDr79eKiIUUuOrI`wZU z0MQEfV}@WyPT@jI%@@HOphcnTy+R0V7VgD7mS33bj$%5c1)-Rd!qv|OP`n#+RcRsB z1CN5#kcgKECVEsMtWb-1{W`jkChYMa0NYg*PIVIj;%#FK-;!YCk#7}F@^}uSW6%hD z34@PKDohDP%g?5U;(-easov)kwHD~FY9=8W^ikmmNieu@O<^U;KCUf<1q6XDIAlX% zBB?ueXW>Sr6-*s^FzQ;C#ykwx(8k4RN}I4lTh3X48`4uCA4P*fE2Jk+sxY-!P|CK5KrQAIQ%1)>fB zr&wI_MiI@Gw4xSafbf7Ia{A38Y6B`F5>iz(%=0-II}pu$t27=Dm{{~n0NQ+Vcqs0R zibg&UqeY0$$1>h6g3Hwe1;EXh6nT3~*%v=58Yg^A~j zCJ?ycmxRxb=8kBNZ(S~WpHx_UwWyd>Xn(8dP3n;dq@=_0yG7xFB74!>cZz~h3<8t- zrdKgloV`;EbqK(=kuG)h_9?zbi1cVt>|}IAM6r|6qa%wSNnH_7?pO@Nm&k?7U5Yyr zBIP}c&yms~O>wG^RH5pnVkb*Y(Zx=dHW^U7v#C_!o1v}>9}aU>NElI^OyKU0D|YgH zT0-$v(!`{s;x0shyO`nwq;w%)O!dV06OKfWEeb>{o5uln^NN!^C5^sbQhe!oG+Kv5 zuwJXv{BYr@;*Z@V(z{g@ClMm&k8|;S-DDTf{iYQ^B$Tz8<>L96xy9fzi0p6rZZWtF zVm4w?F(TQ0%Zg!1Nh~e=sQ5FQqy0{Xh$F*J*}SJFug5nO(+pN(m}kw#;y?3*U z15LW_x!>O6$28ME5md|;csK&0^U`Y}8(gd~o>*9yLK(07J zoqw`8-BUIX-#=5l-viQ}7mJ(vKzi^-@%>Op_j;6!7RsZ2OL}-gn%}(Shz!zxAthKy z?}nCa5z_T7OL_}%Zibh9>ka9?P9?W!d@R8FiM$ufW=LwQSC?#|kc_xO2pSuTM`%h; zQ!-JG;jM>UnQR8WqAwZgiTeNg4taY*oUpEXsf4yOApy0go_4sG$-CTikEFmMTQGT!}r$@kubcwGB;Nr6C0l&thCf#hpG zrRxQeE%h(;7Eu2Qe|YR$btt^r9r}P|L&!Wy&(NmQ@URuxlC7jNJw8cl=czI ztNN9G360?1LrUMIiH!i#pY*a-X!-hm#Cr5gD_usRm@b<(k1G|Yjdrvuz4RRlNayUO ztaRh#d8YI*O^*WzcPJWW@=M1_v%~lK($C#xo6*Re(*1ORCgWp{(o_oMF<{Z(4ccB_ zRJw|`ZO@X@<`iusXnS_rBgSgJ=$=J@;I(DKs6CV)-B8>KYnN&twDj4u6&#wx+b zIl>&!6OX7W{eY%p3J@?r7!m{|h`=x4#KR zf!jRXF?eQ}l2K2=f>GpEFyv8f$_xS<6Wf=G?CRRF zOk~%|PGut5^E#I`6aKzjQ6{oit}YYV`?p@If6yq^AKkm`dx1Tsm&-)G6|1$qsj&e@b8W- zGYf1UQC-$b_? z`&c0DaIHkzqR%AKhOL)K^VwJiXDv750b9x@2pU|st!$#8!7;m~`jLC3`bYMa?Gs3z zcUUUtk4ojC$0Rt%j+gBgzMJ=bSt`vK3dVK}T5_punXDeox?dH6-Wk)REBa|K-xOV) zeyj~zd%29_y%ap>O4%RqlG(0;AUxl%{E|${&I&01)ghm{}jl(HkE%EOy9f#(En zt@R8>A9gJ7j%(YOpO-m{FcjT(lj?rbrTn_P>kqX(%Hy7V3BS7ISCr-5pDjX<`SM=) zhPvE?wCB+aU`XKH`;`Zg(oTcQ6MUSIQTgz47*MBX2MS;N;e#W}vzkbs42mruLb8kE zpn0j(&0M~QptvKkyuG&*5WdHj?{#zjLK`>&76VvoQ)fBOEhvAzova>z`A&JP3{t;2 z5=rn!YIy^o7)ZraaIC($Wj%pq3MHpKIlN+#r?R zEcX`b`}|oxR7ex z38`=EQCDP;-U%C}3zK~zsw5t&9VMsPQH^pN*Ak6MKjAxZ>r12H-y866G~W8sn6F=w zUBXi<$G9Dq{fcG|t$0<=>Q#(_Rk1v0H)@nBj?vo;3Ra_J7`0JnP$8y%ZwRj1P?h8! z&S;c&ozh^_YwT)`f>j$;TAoug3WHY3E78oydt2bZ;)I`b5Mx|pkI!49n zR7NGou{yQd26+kQjyv;C%H$2Vu0|u`Dn`f~Y*?*C=c4B;&~I@SzH+tF$SL$jwVvZu z8jW7dXmvKVL8DRV0byQ+XFcAV?;gQu6h?#IW>?sFwMMB`GkTR;t5d)ytWl#zOJ78- zaZT9#k7N-ByIpHnF-ASF*YIl2s8ew&txap!>eM=|8U@W?5rQKRReUUKtYySpnx_Op z8futF{dJW}bkV0WO0ESy8Cbhgudp*34RBd$(6X#T&l?nKt%k$Om-gkmhii-qjmmE4 zIE~JrQ5cL2{7I)&Fm{eNFnC=+u=Kp+~{X4LCgB?wN-@OGP0X~aPj zE4IoSYY^SiR;nTzYEElJJ7tv$RCA@u3*8Q`?A%Rj%o)(LIHg@>Ww*$7D#oDIaz=%Y z)oC?GJI5&0I<-w>SFoJU$QtpuSqt*rnbv6!Iug3lV zn7>FiR@tyv4DF!3S7WSc!$u4o^mnm(mTD)Ks*HY=j^MidsFdO9ZFY zEA0lOTB))rG-|yPG(ZLN(b%*$MyJJ5%PR`pBQ=ad!Kqmr&#)jsy~3cdX&9qQqvEw7 zD>X1@Zp9){F_qeAFzEF<#-IR|wzC@6u23mi1FL6P#*Ri#n*=efcjkRBi)^@AJA=Mz zUulvz(k@ai+^0Z}4wV6s2D_e9+CUUGMxilq3In6k+jXo$1zHMS$B5VE%!iJp&>J*r zyP5;F)#zC;0ZOO{NZa8Xn;kE$duO{$4jsp)1N@bGp4X@pI)(#oFnU&DGjc3goT$_a z(y1D47Rg#oR>~W0)rQukR47n-RYgZR&oOF-XOtSSZyF=d8GzqPwN0-8oa}l%0tAV% zi(u_QnL(ujy#tx?N+80{>Nz8?1p~vPs67ip;l1!lt7Q=im0iW@4PZ*NyoQ0#^g5LS znz7lHDvbf%daE*2vJsJuG;c!;U@C98Wwl@=QeRYQmBXHNE{n!P~&BXc(Sll{!w#K`#SyslrwAeM@9x8xf7_*{;%v zKJw4jZkSC5mC&&~&Y>3a28pq&P<`@(u5y;w0Kav5;DcVHQn4@=ft9lxbT%Q+5_4kY z4N8UH#u?N$PGc|_S(RO(<5X(9f&-(dmj)tf%+j(j;4uo0Rl&faRoPf2t1z6oEpnkws{Y2Ix^*9j8!g?0SQN z)7gx6n^DQLyiLy|7z3UCtC6M^QvYhWWrNhe&W@<;9AQ*3YOqe6N)JAQf&-0&Ud}0b zEhwTwiD9?|8?VvW8G~KPX~3$(z|1kc63ht4F*Y5HudIZh*tO(ZgH~gMvDv293uBj( zwE<&bB-4R{+cY|~^U|bN_~z!Sl`^@e;kMDp$`#7+2Af7_qURgY9Se+HYV}him)>Xx zZPO`~g4tCt21aiOdNf)CP^PkRcx3O%KowvQTvi(Ote%HjU|+y~*m<>%*RX1^edi|b z6GpCvWutwz%QT{s%s*;3%q)e_!S)=R-!h_MdXRF!`EBXL2E(d!0ZUlmP*Mf87*T(a1l-@t}0kDO%|@>!DuRM zj9$ZN^ePT~5VcYdLn4$IcvSBT6{Ufz;dV6)I+$5$YOLXN+fXNTbXkQj%FU__kn_;7 zZEBs4GYB4@68t0^3>ivRtrx}sJB9%V1PEHjFiN#X#o58BVl@VZQV^e3VT3|0iwpP9 z-wp#S3|Qb`3Vxze!zi_k3V?z$saC7>YNLX9Q<04{sY3lj=c~dSY!LOou>gjxy%pdL z+6+d$(Ppp%b2U7O4}1f)PRaA&NPvV00SwTiRNHMjgBHA6182~%3N5eZ6*jF=!9c$X zs%(X$yHtJ%L$D5*pn+k^0NqHT0V^Z;9E^qo_;$M;gD0^~7THL%Ds*ltQbsh=>bgtw z^r+x)#S7iQLO(TR*$oCYtG7eP=564ED;baxV+7V4l`5kh7mi&};4TN&8nreaoEuiJ zVHpjhHR!;)GisJG7**i-C(IAU1-&aL%f>b)m}mQnp+SY@|1oqm!Y*FuS+~*9AQxE7)E0NiwKrd2NdfB zpIP+w6-IEidx8H^^FzfnSp*O63#&8gl{U~w1&kt`S^@4p%i3TF(@O4?)VUgIS%Y4i zS~)VZ;g(sYVDEdPD^n}|!@(T_Csl0|<|I51a~6$V!>VkIN~^VjH}#^>|9hZh_Xssi z6+y&0n4V~~dOHh>#)G-it9X#R8iO3cF;qc+)N>kPwr117ti_-;YGEp4gGm9$^E?Ix z2Cam#v4KW;R&t^Y4Y!EKEnUzN9sRYUhg`=RcmpW0Ud@4ZGTIm=%ds#Q*4m&C@jA5h zEtum;RB$kFQQLt`odPB&HkLJTMy-lb@tjVfvkCsUvx6}z@GlI|$<#ol0ep0Y5xSvX z2V;p+2Q#H}y^%Nuua?OhZeB;oX{2fA%*z{Hzgf|tr@Dtx|5RU7LqF1Tg4?2IH9Qz> z)~4g3E7|m5DPYXPOZQgf!N3f?O{dd?fz&ENC$$U%oYpJBMjDmi=}Rt9!KQbn$i_Az zD*dy)4Q9^`spvo4%0_sn5qewc;i{Gq4O1n|UOJ*9hpY5%b2UCFsBmiVe`_B9t$F;n z<^kT^e`_B9ueFx{);#`O^LS=C>K}vCe`_B9x8;`q);#`O^Z0MgjfrqWB>`oa+@&cO4 zp75V|c0(ondlR4CFm|R#q)ovaIXf)i!p^RSfz?@sRuBK_IM_;r|Km{Y!0JizMrxt{ zP3N@6Besk^dqd8tH8ulm;cyDrNl++-pa9tVSEykv$_A@((nekFjRmljs50td<&D#7 zg{4AXZ(xjCo`nw}a0M2}G3?VVmdTY2Y`R16hg}Uzbp{>$St+b<+tfx_0nr(s*(z_i zX*F88zG^ruU^dh&3LLag+HvTtRI7R@#E1vGP75(6uoJ?lH5xVS2=H1v58IQl0nO<- z%p}c+6>f+F&}m`GR9MG{%|8e=fv_9c{TFtRV3S;0D3mtCU?Cqi*I>JD`VXC~MA~gtgXFOL!6>vUHNN)@YC!^)qAvvbn^q+cS9AZTf>_E^bSNL^7+T!ULzX%j~xi$ zOG7*p_x8l7ltw7ip{RQmNl4LdpC7uoy6Uz6I^KUB@4t@s^w9ACZ^wgCxfzcBy(-2_ zb`HbPoJKJ!PlV`B`1cl$avNV!A;VJm>&3C-H~#zZR|K%+D0)u02fA4`zEFlSJ8rzY zCmL|MOpeljeJ2plojAS?4U*?-#^ZA}Z^!*DTPOJ-$@f3>b3@xcuk=R!zT5ACjuf5n zz^7(U=muBu)lB$`=AZ?%5s9L|+wU#_^FoPk7qK;J%^&;q?~t?PigJ)F=U zWt;0F(A7s1x*{J-T}w3L@dPh~EOn8ni?yyZx?!$sgGN7|5Q19VS=s`nKANDT1R$$K zBkRY7pr0R4xaMoiWb!h&H)WJn2$0z!s zzhu?c|6kkbh$`KxKlhPH5h>Z`ULA>Eaj$NJ=BCxPK&rI5cIaD=>J}(24TzeX3QYYg ztu8n~!uko);U3j}{ukijS3IlP7b0QD+|H;~tX=`3l6npz0_+?_F9OY6znMcTJ6y{bx^(Y@`x{M1UT> zs;BzMVgFhI&ZZq=4Al_+2)n`H=h^KLOs3Q*bv!C8*)ut^kxIM-uwW1XjD#>OUa8h= zAuJnWHy~~jA`v;IT?xDWM&1a~i-ObLcr6jdQ!&+BeIm7bhT%9K!Zn3p5vAP-;gKA~ z&l$kAgO-RJ-$*UVjUSa>*k~;g z#lJJEW8B;|h8~J}pMTI9mFHAz#9yeY$Ltr-iJa;i($94-RjbhhM|HC6XD76z=Y(cx zMsD>6scbP@9f3mgs(VO3Q=+TG@%p^#7T~o)ya|MCLW~x~X+dzZQK^R+jY6eXKQ^K!R(s$VOV!qm~tuhBwxt7;w}@ZB#nvFA&-UQH+d+hdGoIqK+ZV5SXOas#G?h zjYRYD41{JFAf5wu)PP`x5ds-)HXX#HfMczM;8P)NDgr|A)jS8`VnS#$1O`Iv9E1sS z8rERo^+r@UeI7)!^xn5d7T#c+VicdektU_Usg)BZMap#$dks;!Iw6h(rY^#q(g3q1 zwGuc9G4+z4FHI{Uya*x^4(-C^R4FXMpAc(k! za5vVdhtOcPp4aliG?m07MmEy4PKW@5fW(GcMLXLdr4&!UVaB8m-IP6aPlY8y@T&s; zQz^Beu{;aYGKfUe@erjAhRlwG&Ol_jyb(Jo1Ux$fs2UcL@gE@_jp(K3*#HoTDrtys zY9XXUn7%|bOcaW~v7i$gkzVN)&I{qMN``|;G6dJK5d6!+83~9egy1{{Ip$#yqzTcb zIt7IDz*Lik$+eb+&}^eoq2`csLPaP!u%c4I7-G}wAT$P!Bxqor)?k1bCa7l<03+y& z&d7R3-C)yNG;z_S5pZs#!KS0(oJSXxO6lrqdYC-IZ=Dc$2(dW^P&thbB6t})#GvD# zcSTu3d>7bxgNnDY5JC&bZ$MX#dNtSwyFtriP(J8s5cjQyxL~l}EZ7Q|So08Q2T}QO zwg=9Mpj$OSw6sXjV4E7LtLYkU6G0TS61DzlQkYx=Cy3ybgHZ{QU>cnbq9!>Aj52~5 z)k0Xc4cEUetP=1Lunn5l*#m8i+twD_9ubo{mw3DtZ_$Z8|l?VcQ{o zSFQFAMuVa$!R&7pI9DwGk8G!21@2GoAtq)y!-j`OL0X4KeuyH>A- zGeS0v7S=Ri6z1R%h5}5Z5>8S`2c*O(w?coLkaJaf=bG4vtf3ZLk;$vC=T`!{Qx;>#<4$ z4@X9LtQ?LYaJZ2M?I>#1US&kX?UAU(WfjfP#IGi`4d-2WuWYnw2#M{8s*X?6byGfbT+GNpAgfNWKj451 zN7oDr!KnZv&cL|*^huqDeP}7TJo3~#Q{i|^LvqsmBb>fr4b}al(>202q0>d8QZT-e zs)i}EN$19RPNUG-;S`5juh#GoQmeDWu^kp-*ufK2kkK8Eh-i%veGbRuAh;7eG_}D1 z^B|*L&j>yws%PLhlXScV;xFMqkOIzn!lElU<>2Zop~u0RF2o|M^=Mr(1f)t)!VNdA zvm@KjlT6@*HQY9wQc@s~3zGsOV6wt1VC@@@9_qkqz}h_o#|i^C2M5Q%8$K?qgLA^s zMY~-E4l<0uFl@uP2QDMH<#6u64vX7Q1+OzIyU`9_yKs&Rjyo}Sa5Q;1PoOcV1$*$+ z`Id&8)_}v70s})MO~b&TL=Ue`Y8R=}=)fa@Ed}@=#0x9oRE?6;D!>jJz%z$KjD`J$ zQ*hvGf|LxfEDf$Cpb5eCMmP`)^9doi{prreDPdc}2%QU-&7msHWA)&BuyD-82#0la zQdRMMX`^jRal*pU!iL%wd)wL770r?Vok>o z1GpuyOiseNg_FSYMy8^8@Np4W+sbxajQ0(V1yWSHisL>95_L+f$?iV~t`ub{gaa5vo) zK7oSb!n*-b2OWy{&upiQx62k>-gay8z+1d5%eCR*sl)1qF9Es^FSqfK%4`EaTU~e! zj`ta%ZIhtD+Gt%)yw|b7l>y~}eIee6Tda6tpt0L^u29*1^P%JtAk>H#Bk}REGfXX= z(`bcSZGl$CLsJ75naN;f@2`o$D?cC<0}WPHxF zYonK1{E;*8&D(_qPvBiy+)nX#WojrZJUD{A4~rU$!j6Y>c%fv$13RY$6DD?UYLiZ9 zg0}-Vm8~!&;U$3Y0go2&l!84RbSM-Xo*Zkymv}>jT{zyA>+w9#&JIrb-;>S5)#0)TSw-b;wQb>2N;1=V|Kzm)NAlu!hx4&I!vq6 zrUMm#Ro zo18|x`*fK@MT^(HC#mBj6nnSa1PC?a-R24HiVkYLC({}naMu~2va}dEw+@>ehu-LN zXz^@IJV{dP@Zt>T@=VpS-a$~}2_NiU2cA4)hbfMD`+VTX5wFvRL16wS zW2Vhkv&M$@crOxQ<@RH40;CpN)O>|G6*JL|O$U`8nBO|zl5fx|c5rxniV zUa`GGryPqBu|0Gy7W4sc(Tx_YBrReGV2|Z?!k8bm>!Yd{T6ow5gnzQ&}p#P;-TfP zyw32~VT%*qxS3eQ5`+xEGkfg&(IqR$fH%(s@qibB_k7$`-i%t|6hf2>@TwJ&KyVvG zhn$^~ zV-O}_%^3G({1MZ+*ebg>h+qT>dK9^E<#(+ZRqOe$_54e-n$s{3mcI*LwcroI2Ec{-aJaYCZpxlWRTywVwZ|GpSn7 zf34@g*7F~GYq{3*k9)`o+^e;oe{Gvu&;S2FJ^!Jt_pN**g+9}}Ya6=$zT(EAW=AR- zB!yJ&m3<_Nr%eq)*X%1l{hz#V!sDz~G{e0zgZ}>8(+!tt$E?KvR^flE@xRCLzsEz` zF>BszOSL2OWZW%Qtx5VHJlA6Hz?#w1nYPJ-8(|I{bRz_$*&;vINd(1S zg)Cd#wKTN2YuR_-6OBG}w#nMv65ahlFpJ)vzP5GblTo$y1p6zu!P?%0{TS6~E#*H@ zWsgMv-DGWB8M=FKwZ-kgsXW5k;`KUiiR*FwACVwU?A9TBYZ1b=Ron%|wFTr0u;5}R zf@b3^(1r+}7A@X9>v7)_alRv$Th%U&4l$%~J>O)~8mzb{hbt=xb%`sXS}U$Ii+!rN z`jY^+mTnlfwlFsVZaE}pui> zD?c8Q+y>Xd5fdCog;v~n*W#!ich7N46?b?LG@K$bJ&uO^vG>{t534ug9W)Ctio4J} zn*d&@AY!QzPq#xj^u@)6xM&?#nt8HVn-dqMlV;wIh>gXDk4Lu`IRo(Y!j1^Rh)Rk3 zjTmfPbJS`~7(-ld)tcoq(j43*VV?nki)sjbkB1Bn11>2b;-%JTr@Nk-pB>tI3OO2b z5N_73V{uotZq{X|1yN+#>wCKn7iqN=F=xY(+7ndX+H&HC2j0Zs>W>@ew_03qbzz+-GjFI9tTXw(9g7woXQE<`Koc-& z%J`6K&M(~Q@WuxBzg>vxi;F1NNQrX;s9 zIh>F~NaSHOBi=hAx#N~IVmU)tAZ|a!wX>$7v#%5*#I)OpJ7Mg&85g_E7DP(7B31%C zMiz}#=cGGrc<92HoH_wsx_;Q&!3ofAm5`@{EjP8^;lM>fq;J3#aj+eZi&?@D9K<8m z1MZc}Z@q9q)b7Tc6FZpKfE$)zJY1)?;r;@m`dV$`o@bvSE3nwKR-4s@oDT?m3n_zZ z#Ymfh$QFn=&eBLI?js~ZO)JYk)~XX>7T2#0AKyXM%hu+pStJhx@WAayqbteoNO5sMT||dahQ_#TsL^dM=K7t<`h2dag!Wpy9@A0Y3s3oJZOZ?2FC2RR?Am2!n!KUJLr#53r~ZBR0CM6>y5Zx6{iILn zxvrk(bj_%Z?c3`$`PRa2{NLQ+8clbNro)8kJ@}67Dw z|9d^W-h#%cG;3*#|Jyj$*nUst-Q{bMz{9%WJxd64P88MN>2+C*JH-9N~!90&zIyK!Q$xyw~BV(6kHw}e}_ja zkuSr{@o`isZQIZD5;-6b+Lu*j7_8;qKsw9oiCtq4$Yk}5b!v(g8?DPZS)Nw-Xy z?Vx>b_T)&r#Q-*=wp-An(j^;5%x_{6O?L6bjS39Wo zwS!J=@41I;m#Lm~zh@sg8z%FrXyCLUbN>ULn6zg&nbMOHO4s*%CQaHw&kdj3h9iA!PGQyRSt_-pMX5`(XrG5XZqj=voqA<$AZQt4j0*pH&(mbP7?JdzF@;^TX0}QdgLF^~Oxmqs5JrSpt|WxEx6bKKo|XwL7iC)~ zR_GKX)1Ov8^yCDRxl44p;V#kT`ny<{yN23~^8^X$v`h54<1W$XCMacnu1_mRv$eD# z*hTbt1f6PL`U)9}R+yr`2k=ZvS9+v+UPg?MJMUPi4Mx!jH5<>Cy zHD#ArU&U@uIvwSL#+lI~$t-ARf-bd`M4wDx9^ha|qN$akT-XUnVBe$>>IoI%!Fpo@N|^O=coy0O`jGi&79?V zgM5#H0i&kP4S#2(e)OqB`Vx;xB9B}Lw>|(MGQ> z(a^4oJWtSRo{ia|{fj)kB(iZgEqcW_lx#p7gFUUKS3+e=Jewr)kC-+aGl)F5cVZZl z-u#GX06kmy)D2{Z+~I$t4(sq|yu)2Xi&uC$)Fa2`9v_o?yi4vepjeMtd$tce^TX{* z`)Fp#rdvs)JrV9oKP>kLusNH2YFhk2NjIWaLi`YcU?W3cnlvbb4)J(kzIi-DNw+;T z@ABGKwA2Io%zMNX4q1|x&Pt|EoK?W(Px|plE5Os1jyyF#pA1yuIT?X?FWaLI)qTn{ zPZj!egJ-p>i7&anREPav*vc5bhgQy6>85AT`_j{YiPS{fL&$h#v%`oGBQb+a?1YxB z$!tQ$zT#O+&v$&L8|}2k^C(%fN6gx45YJ|944s;{A&X`N*C&U1{=@TuL@IcPZR8o= z*P-n1cV5G465m@dpu8=g4~=7l#r+hy36bV@6!ml&UM zGMT{3c_EB{3bh#)Pa>6j1m_<_lZ+Ae?Gdu;(>FbHQ^-YBV)FWrJ%aU*qLi^dpg1F- zG0q71Z_y-A8%geZ=7*j)(7`Pac=PG+A9`Mty3oqkJ$*fg_hpisz7qXkBl_QGX-0CW z!zZ4j5`AY!o+_00iRU*->dxV*CcS>9=tY^_3)JSl2)9D%wi9y((A<3++et%dOQ>^o}6uOx626^~0D}#4YW~w++(2M4v~^ z-wY|5>+6!fKL*W5H+q}YOz75RZ!(>HlQ%amNQU%P6`GrQZ_<7WqhaGJ3-FgPf=qx@ zG1h~ho7)9r4X$WK7ry`a$nb!ZvxHlkB;{){;>lkNEoe7tSQ=X>L1bmNuyak93qiwl zI2OUXN}Ev*%9jX=Yr)*F6>{oNnbyMm1UTByRLBq1%_UyB*m9QT|ia{7k!tN1z5L@hh1`G|x$_ zZjSSl#2n}E<#Q}`rnV!VP_7nvo)PW~{%yQAr2RnBjuG!qn+ZH=OhIBRB4iF?G^4a)t23&JuKz>3bCuTe??FY=I2? z*j~lNZU7{k*uIfMKCxT&3KQqf@OMUnY;vvy7PdmB3$mtivSz1uj}W7rQ`#)_$vSde zrtBDyGr80?*~{gU$;{+Z6FP}_o71b;Jn4`d=4c5ul8Ki1aLtJHjp*bsnTV@$C%JrT zPP|+`DH^#UjAsRnS1RNaY5a{CC1*_QTrB+uU5N;&{|q>TwEaem|Ku=6#rR*6$Nv}~ z|7wNPY?*QI{YEhEU9|W-7#EVrfG{KQ8sOQv4YEmZGUMzNX{3>N7`bePY)S~lWz(c@ z6r5WulR5qy!MS6;5uAG$&HEjkJNTPe&Yde)2y^a}f>2V9k!JfQE|Nm>q^^>IU;9SE zxu42&QWh!Xocpaja_hq183{7yZUGj?x#fbab)2l(>5C)8C^;84e3dkk8qv!7-ho8* zt)PzyBk4w8fp=Q^M!9zTZv}Ns6iHUPE7{vaD6{7y08AW7{aKkieM{ss-;lRu7<6>V zlY#aOO^rh74KcUUBZ8ti-^zLi*$gxqgq(5Xq#G~Ytw=YIEC|S8j?Auz;3DgtL{MyQ zu}1)fJ!!7j+bQimnas5eo$Mo8BS_ua{qa@=_!+5W=C`qP{7<>2Jjc>Ug5VG2A!Ji5 z9ppp!nGCs49zv#bq%)BrOxqC#pl2I;=SkPZ2<{Dk{Z_a%@Smy?1Qt%#`<$%V=^sRh ziJIemkvS%&@5E}+(=Cm?{b}iQt5WIvS3QmCnwx~Q(i)kx%A$vF^5!KW;9j(#Lhg=4 zaLvfz?}VT_BGWVIJHa&&R13Hb6+zLgA)-**#=9mVPD;Lv?UVliMiyG5z7s<0R178v zt${JEC^{1sRrkjQ$$*)9wkeqSk4WQOtMnkke zov@pLmUBqOcd?91rxM8vd@Ja0;+6={byj>sPJb=`By~=Q2T`d@qEP%MGVBuIV9? zC;ri!SSU9nSVv?l{DDYBvf@bu#V7aZ_ln8AL#Ffa_hNDnd@mHwC!9v|ukT~$wvSvP zJhx5uiJ{Bbr%*gy_KC?Y3Ip#`Ol~hgGLva&q>xW;;XX0B4~4(eZ9SkdIsgG1wG8%W zYs&T$q`^K$*6j2FVbHI*2fNA)b|~g{u}olzOyIo!UhK4G0>{b?1Y`n75Abdyf0qfI z98>u6K<~}uJ(2bR6n;mfD4O%HMG+yjT<$TA(SqJlE`>QZN8VW@f<*RzB{L#G=xgGd$GS> zJm+>&h_=}de87I;MSJKz@6*y)8GvoT-TTD`{4M}6cLD&JI{`zZ+&$`Qw>PCmvTbSj_?jAB!s`%;BI|; zzJ|On4+1{EJ<{7Vx^P#d@a0k7@zI4xBZa$d-dp9ueA+v`v7Y=EDcocCPOhps+2J)v zDbzO7dq4Yj)!_|Pl`(px7FA_KTwbSKrkDv@I?$U+ScjNbFFXV|GQu?YH$B>p^f@5B zIlT^u8R&jM%)q^4ytmPQW4sTN2M!32&V2{?6iBzwEknIIaJcLo>b;XS$^ALure6|# zIenZriNLQ4%3IN5OL8XNI(&geS}w>fK0wVAy=775jCZ8~FHb5yAXqmFAh$tdlk{R$ z0}oC1PKheNG}&vB-m5Bqe2OA=Y`9|?y~x63Z_wx@jnHtPkE=b9Z?+=gPe9y4064LVvvu{ z@U9{44~p?^dr+`=LliO=2hh|Nu;*e&c$-2d4F(MJG7UONcRe?!U+A7WUL+fNW{kH@ zm80lWnLgJ+-?`$Zp$m_A4<(T~2ZiX|8(lc81epVxw*cGGd0M<(h`bch{CAl{hW;mBkgJpq*1 zrGI@;p>i(D^zA+z{zZxN#TOabEOU-EJL|oY5j+&WrH>Mp56|D98B(g^C zLK_+1YSD$zSg$uEk@YgE?PE$e`@MRJydu+@A1+nUx6i?<(N&oM-s63=IbzsZi3~*R1IUA^9OAgfVB?nUZAj>2(9r~-6l7GrX zzkZk&WIbl4noa-8puJ!8&7tXMy|>W6Ui5V&+Yi%gm!Z$T01S@p2wgbXd@UBX32CDOqp3{OjDr0)oH9V`&bnQpmtJ;Fy}kj^Q#|cGde(l@#kK^KH*-UK9C8rQq9_D+S*^Mhm7afz@>l zJWaOZV>IKgk^2R3escF7@ePp3cKmgDZ9ZLJu}n|)@=8P5$B&bJ@5qG;jh$YmXoM-0 zFm4=N?EM>Tr@I%mBUdVgLb-_6nBDXT29_O}pEN8hBiToULTPeDDC}!=+mqfL2E*AN z?Qyo3+a(+7(Q`jOY8MUPaYQtH>k-j#pCfFH`-V2u_uZ66>_-&h_j#Glkw@sZpXS|9 zMjR0b;Qfx!%tz;DNk_zR_X0Ih=fdevM><$A|53bAU_J87h&?jm(j#L19c$sE68-*z zpepo33*SbZSpp3^!#^KxnZfC#y6RGKq@-|EIlgZ?jxwm1{O_J%Ya={ zXkMewmXZ%;lv`z#?;aJrS7GoCP9ukoDtON*_hA1~!F%5w6?6E}QM#*eZWDs4d=B3N zDbdWAAL%=1KHk|TKZwp|{2)5p;0Mlpw0GI&Mxl&beUD1C#n43!=>E0h@5d$5?FYez z3*`2?{J_|ds(hP`pJXrgZ4}WHgdl1RYE)$ zf#B!si-!0Qwzjj1ZG~e20jV$f^2Ek-Cvn^{vX*UyeZVQ}sc2`!Z9=&L0%V z=dVUU`0@E+W&Q1uLT(m>`?{AFEeYn+dq()ClgmE{f&CXqV}{0ln%T;jMeljg7a-}! z#2Rh&kgo%2d`zrSd<;J1>r+*jHWG#HB84kQVi-kDq<>^_f#%)Whm^4YTOm{=x-0AN!LK)y`SB$w0Srcbk`WlQgI zz%VOi)v*{ANHgkxlSYqksIbxnv;7^(#$!V3ZvY_E`l!J92aR$=BHZ}zbk_(xCv7#x zx3Ws%V2jPZ%{#n!P*^)kkI|yFi}Gn!<>Kz-$T2b2hd>M)>&twsL*{Y57bS8QAETAy zRe8t&&Ue?7Cis>~@O9*?SgaK$yJX~~UMk&C zP|=^Rnc-_Eb){SEY^%}a%?Q2cf z?_SbNTFrjrtZw#?LS~@}J~b16jFnkU6n%F2ouZE)E2#KTreZ6afZH*2dx?LEHvjmZl? zDMVi&0>VY#+sgX;BZbNilOH(OPbA;}#1$A0Un{5idUEUdD@++P2|sw&S5D6VL^Jyr zk71>^ll>@R%nk_7>=-@H?D8m*)0mtMC4SQ8xDYZek4Focx9K3auOrbP7otUjVkTMu z#^zo!i$UXeG~_1f$L{4#>8`)~+~M_#q1`rgWfmQAWZ94^;c{6B@|0J6{m9hgLbyx< z5fCoaItDFhk_RP+4!z2zM{J1A18!BT3uBr?4?yQxwBBt1_t;z zxf3|Kv(raKh~)N?JW%AxG~umSeAL&D&N}MrEuEl4Uh&;eegxr6xoxE9>Xz8Uf0F0% zz|`Qsh15Bz7`vC{#!)9CWipfyuSOuiqkl=cbjvm919o9ix{>{6*M@rG^R7yKj!2kL zq+UeA^i;O|W(0yum{uoz6Vu6{lL`s*ZUlr&m=VhQM!i4*MQo*1-gB9(_ypw{L zW}g(iG~=X@Gtd6+d!2Ys3jQoRDfn|f3Na1R4ACKWbD0ex=g}~R^9%ze@1Z?r(!70A zDQzzLj*zcT3PH0ACHbX=qA7lSgC@8Y6K?9VXo~zSnz|O#)Fbr1tG=7*ptIhNQgfPj z$v1*D{h13dc96|RyxLal(xTsdvf*`y-1M!e7xA=Q^gTk~um4Rw`lsZ#(y?j&j?xIx z{sW?Y0(kD#h^4rpqV1J(+sm$N`|-MdH~shzUqhOI$v2o($c+N9x<=`c-+ZzQXFHm# zyI`QCZ1L}@{4Qzt?!&z1P*!rPsei5vkx%{Eemz-SRTyg5%x?(SSN6}Sz{AA6Odma# zgd~ex$e(WGZ%1A@CDzchXpsr|XHE$r|7=TtSBd-!WHBMX?UYzVFQJslGC*-5k4E_d zk{c&?qzmKpR2zSLazSqToZR#;r-Z0)qV|uF=!#r_Gdkp#N9)nax&ADADEMeT-Ja`j zN_VzE;_zd+{tUYM`DHC=+r02MYaYH`9`#rYIx`P7INa*0Z=dD)n}}}**|&EV&TTp6$O`GA$#Zd@;PjgIu z*p-gt*odC9VZwqY4K?{X#N5&8(v2FOS;N1n*f)dGVo66zUOIK=;->ZSh218yq$4YL zW%0sn27>g&7E?MxeNfw%-lnPe!iF4aoqFL)@ZWw~pVA>${NMi)9V{z68HJ%`T7I=W za1Wm5u99~<`^{~~P8-1oXoPZvM*KI1=-g*>ZlK?G`Cp}>X!-26Wa?>Q=g_L*;XAL6 zAN}u6-Ob(n+5cVkZ5C!l6?A>&i|nFPgX(Re!h<7%dEZE=+yEGI|={!7R@mDZzVgF z0_1(UZNNl~y+cZf4p?q~rV_eLd)}%0HXYUPZo!xM~Mldp)D+ZyZqe!=Gy7RoRQ~H+?FlG

laVs6sKX=iE3;Py(9;F}cG3U_FMC<~{vzbwZNCU>@Zv50HIhRHV25y{ei6!S z5CGV80gx**G|ZJ*7{MiB5NVcuWRn2GEW(w)MB7?(X>Zjxsr1wxu(kGH_I4ys{~`>; z^#Ei$QB>fp@VXdHz7q|%pBy^e&wnQlWzcx^{xPk-uuTA|>@sOoU)(9_G0;CftkA(A z++aB}&_AHcLV*#&Pdw)g!pA><;TDSY8=b26w;@gI({1aX&I%1F^#3f;eJ20l$b(4Q zpunF>e)~n3CLa&>ACXAb8L?yiy2#&~v_8XV9m+mVx(gOkxUk9H{y`+asxb7{-TwaJ zs!FS*h1@fS0#=Dk6Md?{KagG>27Bb*Gjv0`&kpwE=E&eP!W_AKsNW-zNg#!pBjeAA z861jIHiLj-Glu<0>t)BcUV~S+pl2Tn z8!ag#VT_!*>(LhUOMCd+aQ5xWd$>tDfble{CVK$=ek$lDMy8O$@FY=)OED!o|w16!)ebMdDq<2sDH>Zat z!V1iogptYJjnQ8}YH<@py$vyeuiMHuuCr=VK2yHaUpZ#ALC2owoJpTVFnz(V&>Z~wM=2s0L-8Rt= zHTGZTqh?X@B<}BzD;V57qD7PaE&rPg-6+a9>3migCo3Yu!;O<&pRuG z@@Nz?p$wpEgmOiF;n95-rA(tfb5^NQ**%d%Ex$>pCpJ`AsFvW^=)JQ-qrMG5rcqIW z(+>^894=vcH{nG(^9&Tu^}hy(*}17Oydp0!Ux;*4p?1g?S)Vq1#6PM^cS1j&o4w>g zQvaOT*r%Qo8~YIKnq~Eygl5h6k548o&MBM^jf$5{AbEhRaz5NVdHLN8CU=p4cr~!5 zYG6aTB}<-_qJeMLt{p`NDuK^v*WQ7vmRn{pbEjicd*SwfQ|=#Vby7Pr+AI(P3AQ<8I#4i*tGk%tnw z3=tj?&-2|TJZW?4b!Nm;rQxLJtEC)7I>kN_Q9h9goHdIo7R1s(;$mFbH!5RNC%|l` ztNiN*sS)lqE^40B{Lo&1s@jgAoh*+ga$h2KsttLa9UAOPk+|GtLw;pB?4tv*EmyUhhJ}Z=?y;*bN96YSHN&RtHk`Vl=c?WY4^> zD(`E8Nisx-a@59W&)9IR@kh+f0zdvXay|mA#_ttNncAK@t9F>+DsOeW-2bt1>T&WL9ac%)rqU(qzg6Pl|X zh+mAE#EU%^VpXHJ+Yo6Oxwi4<)Q$kHH1DBD!r5P432@VE|6Dgj9UrxU^7Z!Qo9i0n zAhNgx352aWBQgpj)hrT`BfGN$>)&eB8bu;=tRbDvsxf0lB9XBbfq*p@1dYXm4kyyo zT67}Aa*oTSM<7tdL$;aCh(2w`yIH#n*<_J2+Ga7)TpuFJ@`aY`j*nGC^Pc-itB#LU z&a#|;Y+WXOkkDMULCeDYI89bFx}ryVb@nU{X^8DM3&fp6PDYGy`xtA7)r1a03P2`6 zx@Zw;)uh1`YC>R2L|Anq?=B!AV>C#~YjxY~$dztGGIuLPj@7CN7Mq&@t&7?od_=>d z{KrEpvXB+;!5Q5sDUe0$KQO;>nhv>h&H8+VwVwaWx@HKI?QmQ5E_4wu{WN-zZ$iF1 zi^-xfYjk#VsO%bBbO|CuDWk2%pD$NTvhoBxs*7Ht2?j8d1~x&jD1=aZ%A*=16)$5= z%oef_(t>tU{9$ zip^+p=!Ad}3IbZt2@Qv=+-5ZI(pvOJJr*|>gPz4Z778La0d5oL+S~+a5r-SRO|b+u zY8^r|n^}Bg1U@%l*;&E7P6slbLlUs`>Ov(TpFD;c!NFliAQ-jN3Z>|_S)4`_0wL@4 zBGkL+hZWE3k>A*6LUee8O^3k1M*M--Wd<`C-zIdT5GM(6<0=%$%uRq>%fcM@q^f+H zm!^Yq)tU44Ccg4=TrRfAFgXzQY{u*^C&m@rgSU!KH$8V4daO8~LXXD}J0?dHPcTF7 zn3WWtOEuBZ2}X@aIli#qiDv4!T{4(x-V6SlkVM>!d9vF~TAKlZ)O83Y&Lq0U<-h{N zsR8HE+_ir698t9qM_yxwNX3(69b)9$nYG9QLSdT4Z1H860}h9+V1g1sbaI2nX+>Ob zWE@A7HWsLjN(&GkUk>3PAH7NFI$Z+PkRTcgU8hnz5xfDxIUq9;m>+o)>?UX_NDu^y zLuPM%NTee$xiw5OT6Go|f<8E$Ht@0*LHc1>Bf+^r!K1kbhs}yO;985>g&69{0uP2q z`T`v~&(x1Kl)3DYcO{(3#Ko%>{Fwl+mKOD;_o*>-1~&wm1KQev=;#*q*3#nCIdvK% zhLYNLeIyHwTrGm#8QgA!=EtT0u}LgC2u3^XdJ6&zYBVrYo_yqeDF@GGFcxMv0+$$& z#@))`5pi7S&>{4=gXQQ6=V?!XTTi9FRM#dhQkK?ROG}5)*3Gq%c%CC>AVoV^&CKG9 z3vFl7TP;?NfmsSV*!YO<4g#$XWQo95$KeQve~%xBOsVY>%~kX|iqE8m7;Og&D&816 zd2I1V=QqoV-znUNe4>+D3&E+yp2ls3*^IZeFd@wj3$_s$4V}#r((Zi%dR_~w#A$H2 zu(z>+OCgiAHVcBpJFpLe;8iR*wGKJ#k=b7FM2u?el)%o&PXVI=`~Zu_skAN=;I`9w zqiA75+n$VXjm~UBt`M!oVMp|T#;4fRU>9LVjuQ-q5h?*jHP)|Fk9{t}TiBguG1nsMeO#2x|} zot+@ck>E5DzdykgS)$ii0=|q{WBPcb2EoPF*v1c^XqFSdQ(_k<3`&PlZ-F?4Ji$K8 zgv1t5ZHO1D)w?VXY|ywUJ^S0b2b0^_A$_p1b2*V?!D_VVoG@EWI-3?&ncY_9I9VmB zkx)a=@@c?AKpGnd3^lzONqMlhgFw@|6gH(=n*jY(*AsWDSyqR5sQE^YFEucy5iSkv zuc6N@P&t@OZhZ|P`VEM`_!Hlq} zkjHixva4XvV>4N`@V{W^VS}R3gy1N8Ps)jpkmkMfgif6RUu2QxVBBoThykN%|oRv*FB4* zB@lKNlbMMMgHvyW_epElJCPd+`#mkK{5zHRq6rWqGT1s@LcBa3%`hL$>t@7$&8S#n z%s4SPk-|q0uNHDWp+j!B&4|<Nv3E;7N0wzn!4n+?LXv!k%fbAyM55e1j80tW(j&FTq7eZX}vbc8^@Fc z6c6I8z*aUn^e(eqj|e+Di!lVp8)T>5WP^@#AYmN*oH`qJEy&jeDTFX~aFjTejuChi z>?Sy$%`k8bS{;_I4t6Ni6>JSmxZoDC+{|%NyR`ChOMpA#7~yLU$=!5_3#7H-@|gkA zgyBDh(Q3r5TyL`@uTV)Dvw*>muofX*j4W3Y)}#gg8yL$_4#*m37j}u@REHS>vs`AK zQ43cf#4*%{*=Ev1DeDYYEfOX%rUs|NzaAg8hB2zPO1vl5;}{ z!_#Zj!>wX)!!Ll`b|w?%$%&H)bCmN6XE^b~l`%UjyzUZAlr4HCCE!y>G<1Ri47((o zspI#IyGAFt%lOVplF^HrD{l3ih7`M>H;+0AZ@EI7LHl zGLy??fEPk&w>DOsLxBa1PL>DI0#`fsbvAf7b+FzHw73vy_A(w`_nm~ze>|ivzSwf& zBc&R)0T&h{I1#cMPJG1hhAo8*iUvqcc<$KJqUSn3fediJt%FX$Isqf=-B?D5pl@PZ zA%k7(Fu_Zp#Xdo1P!r4;xK~UX4Gd76&RO82fN|+WhA^{3FZO4$I*5zeB&!3g)_7A`Mu!%e>ab--!aE2IcyhFe(2Aht7=5P`c?=yIn3u>N z2R3%PVPMER%##y8l{$Z!v4&$HgP37cjyc=pqe6(VJmH?|s?9blLS$9Jh zomH{G>=0=%Fp$g>@)r(b2wJ$dH8`p;BD9uKII9u{NefN=c6o)g&=G%bVm4-R`4NXh z(d#O~fC-ml%lXaJ@jIjyE=LEF;~61@;SB;;;$#<|L?p#C!H~xYScUfkC)v!cKE(OgI(Bi3UXu%dq8` z0JH4-h>zJJGHr4nKUZZ~?RsPa#XidnF=^3S-Fmp^kwO%XL#tjdYXFzl>}KcQIJ|}( z3C4r>(PTuhG^{n9%`Oh5a$WE^;D{3IlN-}8;v80lx`A8T4n61&dm9s=9G^HiFpUBDjvx8nT`BPfpIk7Fq*I2*(H%88()f zIla+^^onLAC)5e+LO!6tX&eko6P&A%6IhSzmIvgf9+;4D>IUQlKfgN(ZfObl=G0_Y~FC3iV#bHfd z%rQ2!e!O7|tFLUh`{MRW?9R*vY-_+r@SI_pX|acftIZAZ>4ql`HivwIZ?(IaW8SW1 zCuHnI7YhgWKK7Mnoz*QwI@TF(j9}iiCMY&5;{tX>0HYE6T(cWm!Y&Jc*<_55+R427 zMe6vNE$rl}oAByG&O6~bb{H^?1{~DDy@B57;g!d=1h-9Ci`Zf#i4IQHG_V4h*^71K zK#E}a?XBn<-PRd9NZDA#h6o-5Y&9JwgBiXdqXvIMO(L=?-%MoA#|A9{R*kT+;v&^k zeph7FYFZyoU*tTtG6y4!c;x=E8J%!5!Iguv7wn-!MLkwPgQ1t003>(T0EiHQ>5gyz@dz= zC5&0932wL5-qotTt5thf>-xKOwRg2@?`qZF)nd0KYwv2+-qotTtF=kWt-Y%yUns1- zs}=X2SnXY{+PhlSU8jrjiPYZJihEV8_O4d#T`iswu=cK2?Om;yD`K^GwQBEb)!x;r zy{lDwS1Zn)!k9ZHwRg4H1+m(@TD5nzuDeGUvoWi^t5wY{vD&*@wRg3uOFmkASF84} z*7Y~UYVT^*-qotQtMzMYAe)Ror-+;QaU=-WoV0+eDxeqwhn20#Y(g{B1CNt+A}ws_ zwe)~Gseb9Xlsc>P$4<13M!=CTD-itp(Y@I;tzkezp4?4)Gz{p;iam5n!@v!s!&lVT zFkmFU{q)}r0|V+UXH{F#t<(3m>gFByVkQ2nKXa^WR@aAJQzkz=-8*joF?>=@u-RP` zy7HLM)4b!pV&6I=Zsp#tt;Zk7?}n3SOrJ2;^>7u0gMDp5w=Ub;s$RcS_}!FFz2VEO zG*s@x{%lJpE#KR&<*&cscYPK^x+|w1#V6UP+?n)o>z>@CadpqpJ2L{UN%1+VeQ;&- zg{-G5#?Ahc{rlu@_V0>4?B5Pwv44I0*}tqyD|lB{aOziZdRL5Nl&%=ZC|fa((X@h- zvSJ(~Vg<*$0{GbPV)pNhaYyUbsWabuo@c)f9Kt^@uwO4@tjE2`e*N_W{&|W0I)s58 z_jmTo{1g6pnf;msg2%nWe*JjjM*O>l{TvAT$Nhu-di^y1d6oV84gDDR8vBK~ZMMy` z?xlU&XLe4i|3wPDs=YstuD`FiacETIKq!@b${B80kJavO(5;Jl1iG<&sqes>AqHhh7CSqh!0}K z#^D#u{Qc=w*-@X+Iac`XIl9KbWL!;Ujpz~+Eo>8bR`si*JzC&dRM_l{;tO!e&81Ln z-Dg|Ug*kzI=|A#a|3;%E9Fg_b!Tga z=P-yJD6d2%V!EMXQ8zj*FR+T-_^ZEdV2!Givh~a>Gw;l-s^37w-|ay2$^u%tevc|M z`vwJcq!S1cJgXI*cthZBa`UhBgBt=vRRsz_8r^I|(QL2(u-QM_1@0vG{Yv|`58S1? zzvgBeiDvKl51XCUAux?h{FR>S5SXQ!TywJ-qS^8PS+k*n8w0B))zX^V%M|TB8fmXt z^@9q|_;B)&Hs8ihN7QsyZYgORYSKASl%!g(#L2m*EI%+s^{n!CyZ^CUtZR z+(y>_N}uZ%c)FUZb2Nbks&|!Dx15`K8||wN6tP;;Gt*l|*V?8H+^^cLtWaQ|bq5`< z3l!7gUq4WvUVOP;dIKemDRfsVX-YqN`Cm<=o8q)P^?{kHpOx)#0y-H2Ll^-pokeu5 zXx48cMmcJ%rjD7}l;#-&|5l}+S1hzG3uYCPdgp0BQ=o&Yp0Xsm$?O`M4E<#a+%Bm) zC~ILz6RVx+EaS0J2xSJqYi4ajmF?(lTTa+D|cyr*G zMAn^WnRL3-QEiNwibbNYrfQjMkuyhu?Rw&S(G62e`q48bD{mmLpXXs-RsUc$!a|=% zX?^)T3;5bKlyrN*ErtHLEwBJ#%g^(;uc|MU&^**^2+%A7ENbl<>MhJx_D0ci(d9*B z6O7tmVL3gtWaT63>ZdF+2^+FE6SM2H(H=2cV9=qxN5+`V_Bi;cLqLaMjRJ-q9#QTCsk8{wNH1t16nfeIs{)<1J71n zf@%0#u;QpUH{3whUJ$IfhV?#*6<1%Nwx1SvqZh^oy!6MhfxnX%E(k_^Rtd`)(R)EK zBI>YE#M#O@Qnbq$v8J*<69RuDUtge^`igg2p{ArN*N*pv=;-9W&2DuVBG%fI{s-Ln+F44c5j%7DQ z`I$@fvKfJaq~CA!;Ece7(X|$iUzA73%nbBaxuR+@jf4u)tU%}J3eohPsfIo-*OkMp znBi8l1M6#&`MYKZK2-UxqoV9LdS*`GHdUFjB(GI*q55&fmk190;@aSn5GHYf3ME>U zx-=_v^SnSyqIyHo9#;o#!iG;_JxO#_*&0t@nXUX1A%gZ_5ST&s{3aI9PS$YL;`#D7 zA;&i@42()5r+yO)=Y$eYd-H-G2&&Blxb!70C=9fw#haGuYC_1@PW8lu{<%1iDyf?M z4l2UZSPGToQB6*9PgN`B=gdE56p|*t3*#oL0-N{9c-&CkcyLRw)3N|fQt6eAa$H;A zp082$P=4mP^uN@a@ z@^_xrpw|nFHKD^FF6}GP2L3=Uo$^vyQ;~@Jl@FKBihRr?mB0H+ibp6~8W`15=*&k; z)f`?;M-A=JAk_VnrMdFo#a&b>wD{Ad^~gDl!?SaTD_R*|)r!c+_&NG9dFg$uA1r{p zN;w`}BFA6Yh2!{|rT)*CZdT>|5zEtkTFlK?bx?lhe3tVE-LXE97hNHmcP7`wJ2fr2 z!y5uKq(WNY$I{#ySbT?S07DyQn=Ln#4wU{7T+{pMfKhS_$d@S-1o}dn-tK6L`cg`$XB#(?f~*MXks%CnyR1innrm1 zwZOYIs0&~3@-I~N{!~mqv&oXkpD8q@@1EOe#s!k1!e0@Q$8<*aH3erVCQD7q$JbQ0 zo|MlZUH=r4p%ZH{JjHyVJN_v|`IV&d)k)-zKZO{$RSCx@xEtWu1OuLnff^`*7`UBY zN-ghN6FI%p${!~)Wpbt|(QtC6{3-J3Y)>!WmPXe8$*p9?+u453Gk}cdNw{{IP{P-LPJzEbp(V`Cn*`c|u%ZDbD^M5k2q=3>zL*1WO{#g=9`YTrM z^bUjlDY$UI+|y6xi2yO28{bv-wDO6$!>a8mT&V|``wcy1`Xsmd=?jlb|0I$3>8+0C zi&fqK!soE!W&7A{v*L?_pI*8VTv$U{{!QS*p??W3yoc3~;=-c81Q)LARIaH@reH9T zpGh@Q3CE}T0l+bfqKKBYFGrSHWM8cbBU4D~>Zu8L(@jg>Tq>tQ4g4hp5B-ZL9H$k! z@^j?Xzhbr11bG}^`Aclh7Eb(E*U+7Yat)GP{uQHtrp3V4HI;uuz7i9y;D>o(FxIDm zEgGRccCqNlsCM-fn0iTkMbK;tzsJ>(nb1hkMdttN|byP5HYrv$^BE3ER0X( zmq;h*TfNFd>i7&gn3l|0ltnl8E`Oy40ZYf-T>h2nZY9B7!VbQeLKh6&Gl;fnU6G=~ zUsWSTxr800_@ba`L;-lahOz?%<+I4#i-NajvHDTGJ^dorqM=^5mgh;tb5ZbisS=L! z_9K8}5*F}W!q$L|8(KA#y<5-w0G)DIxtV+@le1NchD+FYFA518>|0J#$)Ss}687)% zbnU+=Bv)7RmP>*v1)Kh zNZ9A^F7K8^x?PH0et*c#b-Bd#{)D0B8!*NyX&=n^t7h3;~e z-z|}MF%(SRzNLiYJh@5kI^a1^R*za3|2373E?-Z6LVae|{eT&*N;3WacTY|8l>Rf* zv;NIAdVE8Ll@1zP-cfZ$Nd+0ZL3l94fy=hAVV+TzL~ob>n5le6f?1 zh-~U5Pr0%_a>LDR8mgF3o+PPE%Bp-LION-pd#ZXXKQjZp0Rs#R(iXIGLU{|id{TMC zY8vSC%9t*yA+Ze*(`7nfa(NftP?)(=XrGH<9J1*0|Coa&%_uJ=yJezwC=qa;`uAln(B7C;Zknim zd*wv=hlKRL5-ZA*>Ios$<4Wk_CS?I2@<=vCUwYwthrPiWs>%Ctv%mQ2@l zB|a`yrd|zYrUrYci03MoD2fF)L>_`tdhR?H-1^;1dP$G5-#y5jt3s$uy()ytB;`QX zC{+4ajVU*hIAx#6l>O^E%C-Rq+l7AEENHDqj$VxwEPt0bMz`pURQ zUHv?QOZeLTnQj?!a~7SJA3UkDT&s~O&sH_60O1D>C%Oe6R^3r^&0njlsa;T-|AscW zS5l2s)?`ws!Ek&DEBYdurwiUiAJ+#PsMz-^smio)lPZC7LB=uk8qMoFyA3Tqv@;`Q zGz8a5ss+kg{0OA;b1j=wl__|KYN@ge=ODf4*0GJ#nk{Dpj62OX^PSq^AHJ<$aE+hk zMR5Ku!@VT1pzl&R6+GQU7Wm$jTe*(8|q!v#HwGNv#?my6xh! zfl?i+y}Kem^w2HAmr_YV9lGWH;AmBEB^Vc#o$AmHo1SbVwWV9kSk0yPEbp9M148a3 zslE(k@g=CHd1HdYNg|D>mED6{+NY1FB^gtP+TMDqU+8pUutFk>>(G+p>y>`|_9=oj zOX|?Pk?Wg=+6)QSC&UN1B~K~6^j%}%UcM{1NFvYFp}QV zabpK*jQGw?9A%G$PhR;YGT`Ap_NJMwj9H;u?hAe_sdlpg4ckZ2%I{X024!oMpBs7a z59SlqVP#d$Z)b)Dr>l-DKXV2eC`)NeipYuzh}wO+<>MA=1; zOJcFl9vhTY6>;KjkCHSu`ACBdVrLZaYc&g1<_ zDxE)c&qMUU4=E`s{1wCDcPPF@FolLM3*ITwF0NpD62JJ$?pW+y9_*=_sDKxDEL?<6 zToG)~fyqb6u^e?M{j%J5B_3MPtK6BRXefd>JZ-PDX&XRy-o27Rh8ul9<&;m0)~UCnbqlbpwLUY7NM^ zf4Qczr-JLHm#L;SIFP)UBt*lqr-QE(@+syhdQwwUg{t@nXdxQ>Q!0`}u4jWKk~qD$ zJsT{O$amLGZR6-_p*hb7%LzGg-PE>;!11a501!-4fGjR4lsyn8fbvUZaPf;vzm9a< z(3M%V-OIt{>gu^54EX~hS_iY}p_hXP<6kB$e2Eq<3Fg!EEkUoUYqElSn7ko3CDXsQ z1aDE@q%7gOw1eVHgi`waKZ57TfMi`SuE5&j6B6c9YA&_3n9GE*iS`Iycc|n?tCwJh@43lOZJqq!j>)&X~|;6q8?wd zHDKi{wuZ8TuFLz-($aaONQ=5c7-!ez!k9kz>C-86-3P&qL{~RICSQd zU|mVItZvM5N?KGml@>g-#~wCO@pp^@FIIesG)Qm!Jou*SZ;CR?FK}&Ee&I&@-|AAy z@N7;rEVuGC?O*R!zdHD7{|??(4g3%P4)#`kQC$UYoJJ87Z6u79hf&!V{v;*TYkM$N zBB$!ozB_^j)i2jIeiGE@er~22=~BAF;uDC-6rxJ4rx?8GWjax= zkX(;T7b4YTd{wLP{>aKf(j}X;>sOnL|WvX1^4w>p(<4_%jMdQ^|O*?!aJVmC|qgnfc1*(Umuri`2 zRZsLZnTByP(YpOXhoo8^RXvL68FGc?GSN%o5FLg^<24Z7_)zedvQ?GAN=dans(KXJbL0x2%Vd8NhwLyc8n2e@+|bw`f+2}qs7KrW z7`$2adlXtm^{>@aJx?a$mwF-ab!Ae6jFbpV%U7Y*KL^{Ynk&8wGd&}P27eBAt**xJ ztVXBU8l73&VW(j7k>ZMc`L)T7!UR&H=0<_6IwGEywmFyt@Q zjZ}>)EWEs<xv=7Br9K1~sZ?xD-Uk#)%rDY44(d3m%dft&4t zcae%6b;)mli&m5$#=vzgOW;@8d(W3Ta!yNZe?r2;!%Eyv`+^&shC9ur;2$S#2Sv8xBjUi zY&)@j$E1oWb16ailoh4&DU#YASu)vFBcKQ3!uUTNCQPDZWGoBs}1aX~9fQ7Ja34 z#e=H%6p+FTy`%ps6`lL}!4~xFmQ9W5E8|zD(}u@VThQWPQX8l~R>E`3OyOV#-ATS z={r=+P~D?M$Zf{2JNzH^-aEdEqI(>Ec0vhc0tqF7gfw~!DTEYS2pvM`y{15fC;}={ zr1wadGJ@QjTj?l8yn-m8R8fj3SWv`972#2l2NmBlGke*yo4rB(e%}9n{BhZwJ$=qO zQ+9UNl1+zVu<&5ta%@FCOLIMezNy2^np!M1V&TY_`|DdK!QKtKza@h0oHwNdoyEoK z5_NhxSzyB&T1J&oHmJDvDVcN~#uXVmznmxg5)xZEa3Ysa^m*MM=h%ijo|SAzEvAD?|>& zSJlW^b}HR+i6)mvnZ$e4sHO-~ocIZLxAg4X#~_VmhdWr{$xXjSmD!C9OKqi(uu*RI z@5%Y=LLjTWn4NllYip^hC{IaTvE6?R#uXvZue=Napb;TpuC456ecXGqgT+SHmY0*# zRpnXmDSIczw}i3mPL`>3JC-SSfo&}11PFxJF>~1wVn}hv&1AhQuC2o6 zbg}$5#JqhxFLt$*R-(;23vc69QXB75rezDgftpIzckAFIo#}ruSmgGN@@#CUO*Ksj zEDKg7(NX~@2snWsaAsxClUjNTSR?8lg_sw&5;8O-Sb``w%L7=#$0s^@y5?Ha%adIJEGKj` z9+jO_J@sk;i`nUlVQ25oNg>++jn@JO?yGUKG65zcDwiNPioLN>W?QV@S6PC>EN0`p zT(~D*m=IQwb0S@s078TJVU48i%3L<>dh2VSon;;ch=t2~hMS&_<6? zM6G0Bemt+NCx41%g5v2t#RAK*HK}nhnxc`2>i4^9@l~}!E~^$_!WR47{83%ycC|cH z&J^WEnKzX;$=b3juUV?F-07Ade2m>Z!)I7pDs&V@i6aJYInzf-$@uO@{N<-dFp%&8#@~|hwI41Mzi6FLJM!NzMadnn1unA-VyDhd)5--nYq#eZ$Cu=8iXuDo2p0av&M3Ykhnl&n9ld21u_w){(&+ve^KoO zZA4;XTVyAVA;n$39Qvm{@Yt-|ddm(vA`pdv^MUjesG12y>; zk13qq+j_l5Pt8=o0q~g4)5T&*P{^7<*?uo!G~Ry747PHyB~p2w?Obf>>`N4{hEe8f zcth63LrQ%E=3t=giw^{9eKBm$4&z(1v*{;T2hksY1g-F>KL#Sb{WUm@AH7dxSuR20S z?8Lb|pKr3fN}5$*FK)KXp(zMa_Vy3TPNuBG7E3&BjqiyrKcxaYyTy{Gr_xV|-cU7Z z`glz|*S1>P5}J+0X!UF~1`Fxg736eGN1&JPQKb)+(nHPtP;9#6bsug8IRwBExqiJpmXGM`*a^HZ2;2z}@8VqA+6@<)vvsdq z0x1VS+UCQnd(;jD(KLrEu~79MoA8Py_c2H(SuZ%rLHUB^Y_R0gPdP(xdW)*~Nd-C0 zy7z`9BLL2U05q7A@oCmwj4NvNmlb4<2AZ6Yin2n?)sr>a{GR9fdzSYU8eS1uD#9hO zS{Hw|_GWevi$4U9$bLO)Nu{+gmWZD4ifqv_%X2354USs|(q?7^myTOr(i7+Z1_pbXTBOd>wU`?z77(bFm-MQ`Sh-5_9-}U11^{{ZSM6ld7$%>4lMG; z3!Tb5fWust)MiDKF4Xl~49j(6(rk^$AHT8e)0P&b%L8UPZD~g`9BOtf)LnHR9H|!{I-LQAcY0BK;gmyu67}4 z%YQe(3Qb|bYVrFW4Ef%;Q&LnQ%V8603;|_?4IqF638Y2Dyk|iScP6zY@cQ7x|Uw z&ldA5QR$ZOD;~QO3l^9C5Bu$M>|i#dPk2Li=Ypjtweq`YFJWhPD6Cc)r6Rlgp1Z1N z=S52+g>0|LX8-njM|Nl4l+ppOZ~|E}*sh6dBUbIaRF!4Nh4{1ON8MFZn)xp&^X#~Z z&*Y6-4JYY6+6c^MdN%@_7g4tFGfS*LpMyXlJkEBNj_bsleGVPg2aohSR*_BEXwT|? z5@h53Pe0DjQz5T?CuLb4G= zW5{+LI#v*Beh(J;U!EWCIeyzRoY1L=oF&|~^rCYx*7MU94`SPyiIYo~=;ahuF z1gNgIX%#uK#Uh#V@N^UQ>(7?&NKp{`;ulK{wPWTv8P*uq;8#l_P0v zua*QofpX6>g6_w-LO;NL>z>7>$K~o(*3?svwX+1tf#iC`1bHoI!O1XB%lnp82qsnI zO9+z*jgS1#@-+PxgN4SIf;>wfSjs9?M`2JSRC?*t$Ep#|p7{g(<`>Kc{%L7UO9z9N z-arys?E2HvnNkeqS_E=L?>K5BQ`!yAWLqCv#^@Pvr7CH@Q4h|8y{W%g)mN!}`zPFU z_bVD^4l>&tXGLSzQMk&^G?zsa{vIqP2H3TBygH^$SAnD5FrY%#2F zdFw2?1A`|Fvt^Nu!7MHSnombyXrw5qh~w`m;+aXUJqSI6MHwuMmJCllo}r=DRafg; zeOF`XtE&WA-y!rzEK|hb^&Gbrhj3uO5pjfiglWzr{E4jF;i3O zuT$ghyVk1gW{C9&4XuPy7Ak8DyA*0|LZdKPl&{cAY*aOCCq02s?z3vv6*K`82<3o4 zJipUPFz8mtL^r;g=c~W?Fw8od_QRrhVNK{9q&)K&PAGz7}actH(7MnrxyMyvnkftf?echmi@XCa7YF*Ej}DAnr;m zVWqtz3+g$$CZcL-LItZR5v<~#`SF3c@%`Dsp_99iw~7hwC|O`!xwMKQT7iHoF-+>t zVk)n)(~CO4@IL=z1kuRgC=zRduY19Dt?TG*M8`xUoe4NmpWf4fQytxvN^EK5=7D;A zgWpF;o3JW!yxgk3^%I3usBG%Y91|)LmY5GRsZpnmHCU5I){V4YWdM77KtpUk>w{~Q zIajZLr=a6d+gFy0eHmPgIx1tzOUE;n%5t$Ux{397g$%1K7yAZdH1TwyV`aGz(7Li* z2rw7T?%ubJ@zq8jCRjK6%Fn_?>sGoP`+$%BqLv_trnuL@W%78!l95)kUL)Z=(DWp0 ztU@;;ek|Cvu{PV#%o;;q#bDWCfSDPNa1#QLyGsl`WJVzNWfqqZn*tI(G9wUGPDfw} z!gaFYv4Mk5V5z)TG>^NIwfOLNWa9CivV-;t<*l zY!UQ2qi7cl5gkrO6&BOhnt_VqP7{_~1#t@PuwokQHbLLD^JG;|-*(o8mB`pCrp4k7 z6Dnb+IEr?Y-o)pxRVi!P-B@d! z=fObhTMF4*MJE1kj3yG_T16&4Jg^okfLJX|>(3Oi0O;a1?W ziwv#t4-NfvI2igr)zEv`0%0he;0%VctWB zAocG}ZNw&|Ea zqr8MvU0zI8_r-?MRnBnSmarN*B^u!d5}7H$S3FqTtU6#d$bol^3IDDKe=)Dn;DEuAX10I% z$aePFB9#@x?6-5Ou%8^(CDbnj`PUG#byXE{$O!g6a`Q=-b)pi@CfIl|^3aIj%yE8_ zun^HRvy*OXJta*=<*W3`7*)h=%@E-TC5-KwZB z0a%WR*6a}Z4qcTk)-{SUhhy>((AFWcTB?44EiNmdpK7tq`cP3;aYPvb2(_ag_23Se zk0?%#Iuv2`dV~e0MY6Mn&L%8-wzUNrt=gQ;ZSGF7N$kpBwsAhP*ORczdQ+hv)kt zH^Mk#{LyZowT41lVz_~Jf*GwCfZW_~T|{ViOe>7-8tOUywlzbcUGXig1+p<%v;ZKo zUZivlJ;k}}j@qo!A!`HrjFEzL4P|Q&S=Z|cL<uC#`*95d$$4OF}55SE`0$c5*_AA;r~T?C|v| zomjkMeH~K1njH6(t)}70tp%m3$-4T|uaL41Ubz z%UO^4JOQ79iMk}^8nXBV%vDc)l?vPjN zkcq0Rp26medOuFO%0s@eE>y@e)lG{ro^aD7)lGo^Z{5`5D{H9YBP!YG%hoUGA?yY| zJB)rCL9|x(ns`JF%963(7ahE(=M}yhb`dd@i{bzK#@d%&#$YjHxLA#C{ubsEIs!ww zo>7|y%a^mab>CSB!=YN3t05%3X9DYJ#XN4gVVl>g1A=PEf1G;ET9}>kt!pFVS<=heM`y1BXv@d7xjeAi5 zkKDA*qeC%R6u`dKne&#lpPs=J{jWU-UmNhCy}?*CVP`h(2W82s!i7T zSL+b+vD#$kFxrIr_7;lTVt_5?Ek0!N7JE$2{Kt>Q^nPUlDHSGzNC`7)^su^|upGT_ z-R4iC!{ioKZ4OXQJ#jQ549!@=0F7@^EfXeZEWktz*ndYZWhKv8h{_ji<1>~#d5CM@ zFqUw!C^w*(n%%?XWaVn9qNb(D_%OLqRa3LK%5H3!oT}_7SM**fG8^#bJNmN|6^bgN zNEoR{exqU$oN=%+Oz!{FcuvyL#5AQqwYopZ;L{Y)Nm!l9we{u%SZzK{$$Vt6==yvF zkmrS}MavX=7PA!j^g$RK8CvuN{Xlm|AK}YX5htKo;Ek2gqX<`PcP+xh*w@vH!hLAM zf~yxj8D$=Nz)TD}SM#&3<*~5vTKNQ}NS*vB;hH$vNk4>^;75i0H>_x^5}~5<$xj$Y zKAP`Z%P_ZB_*B8^0h0Cb_t7XxT)s%f4*Dyuz zukKBFBV5M)%>G4d!r&_e6Dkx9V29>UE`!2;sR8$u2}S3tDW7w$#ir;-HCz$Kf9#JMridK$eSYctJYwrmKQywRdsjtp|7YXiw4(_ zp={osEI3!s`?5d`cje4D!=rlB6#(nhODVwxF}tf?^r_y`;V)N^2jLo zgTH=&u|)CgUPG1Pdkc>O@t<0ULX1o`qBGRT*)qAvrk_!OOTFI;vEfFg!%8P z@mJNh0rf2b0)%MKozMh|1K zaOA!SHu!W=yq>_|m;0)1SfH$67=qNBdZuWSa!p2>SOxr^%hlC}Zz5!E`21{2@Gn-6$AYgE{Yl=9G>#IRX=uEJD6eounzOSNUWg#`BW3x(ip#}J4AIQ=DBl&G zSLjbAv{HtzR*Eub3Cu+pbdslOU-_YEs;?ez!quW5X>b%!cq2rlVg&@zRAnDtgH-f! zulx0)BeXuEL#Y^#!6FswN6A#w5g1Z&plUW2D2ptHXzF>*9?lM5DC)`%WzMfn21LmY zyD!&37puLZWK#D35t4FzluXL87)>PQpeUJ?fG(2KT(n$0GQa0J_*2nq@R}X=fgugY znprA}@q@cXgiv=0wByWZ#e~{i9d_#%Na1%?zZ}E{ivh%eD4E0E?iGzKMZQoS^C`v? zjya+_2Jl4^`;f+yIMn=}Kl|U^)kUOqw45%GXnCI0(*s>)nd8r*`y?{jn8!<0W5c6m z9>@Mw^n^m<08gLCtMqs*T(Nym(xZ)e{ECJqh62?9gP$b@s)!)~)?4Io6ozOK<&8Xt zth+&_Y@hnkr=kJu%_>8Kdk?S1>}1`=5KRF4)ZcdfF%z>aneD%nv+a*6annxL5w>g4 z>L%>>^0patE!I-x{wf4P8e9voJsDzt&ohSFaum7)5gM0+-%&k{8AyXM1c}>U!8VJ$ zA1$-@IOnX(-Xjn+{2Z5BLAIlWd>bv(_iKzNqV$7knZAH8($|MH(bf34x4w#Pm#;<5 zoT|3zv}z1!=WS@=!b%9DY3|7iu`TkWwa`s=rMhh@ZG<7c-m$f3+rn&}NUa$5OPH;0 zF@hLRr*PYJg|;b1RwcsrKTakdcj)98O6yl@b6`c8Sgzh1X}d<~5X>63G}mGu#MoNW zJPZ~a1Bb-0yqdOwdIB++y;jrqBAti{MBxX54{O;b=?TCKF`FjTw!v|1m_WGVa2?xQ z1gL~fI=u`wUGZsC?RqvC2RSfnE|KkPV9TdlF<3|(*U*+p95L)bLt8676+Fo5K@VaA zVH4oK*2p%-1oxa7?&-$1?s{BRFm~ggeIXr__BC-GuvICFC=6Pda(`Z66Be1)d?q?!Vr(08~oxm(xxW+I@Q*{ zOd&j3T~ePNwp?gP&W5tlX|~2?_QPual5BR)dLfsrh-MGdY#mDnK*zU)O}AaBOBO+| z(A?I$Y(?oNZyj z3aCwm1)N=B!6I?Z`4j>@|4rpo+M7;&xKl+D@5`^mM!kOgN<1R(&#%O@p#l6#OgRVg zD;N&e`%l3jekuBq!Td^;wITdUWcyHlbvMRyr-dy>p-=O_ytNAgpfj7*St(7PtjYW2 z1eSdzHmVUH9;+V;(B2O+pXaixt!y9AMa7t&yb>~3%y=b zeH+HtP4PC&FI{cR=xNMQbPlHwL}Tc=n`!&rk6uDI`MWML%&(`d3H=U(Md7_vlP&3K zi!~wmp{K1cy=No{dLsJR z0$}eK79a|KzrMCq8ipxEaRf!hE=(PPn58%EXG^1veGpV(L3w4%_yHBmNNZPx-S1~> zNL&9+Wd9J6=6=jnH;MnH-T#IQQyYUp^7b)1G8=LDXR;$(3VP# zs+M%BjtWNO>SlJGXXR%0Tg3z?`e>I>9W_ek-(PsK8GCvZjEDVeqmbwM8d@4bw6^qT zJG)m3hqnedPp2UW1MOLl#9)yNA+_bEbsd4hzip-NSgPE#zHqedCxs-}Hg#Hi6Dr=V zMqxfkdo_T2qV47VYRkcPZ?3jl=H$$*2gu}u?ekCB4ifToZ8_L}661-%_JG=QunqWP zux&0|%#|ObSDrWk#gje3)`HMA*pFi1w!FW60JUH+3_M;WYihF@1MIbm6L?M!utR6J z+d$Etr4O(+YhyS4bR^I}^MDk!{u`T+CV)&Cx z#PW3kc4dm~WqJ?WBlbl<#1OH|;a+XIFIvm57fg6Q+814^j#1_5Cg>uGCr`7z6+vp& zF^$LBCREzK=)_`D?wjC0K7O;{2-^8as(6An*y8Apl6UIJ zqg8eeItp+6jJK(u0mg#l+9ZY`TW@W#?ebYP@6Ly!`J>xDnf``7F9tckAc)4#b9S38 zyA&;57eHdr3BwBU7TK_!ws>}9s;xW$2#;Goz+n@j`_|AS2k)}AqfwY#j7mz^Wv}hB zozmlqJ}v>{iW&^Kse5c$dR(Ez?maLsYljI$1_8m4*KP570_C2&7Tf>2Z6oc63B>$o z=U&@l0#vQuuyxi`36%;kf#|ZiKfGP`be2)7;xzWY$5yczZ44{(Ca|1lWB|fETi&#l zQ}h(Pghgs8&_GQA+%$5|-+I5T7on>$TcPO&3=z&-U6*COZF^3StCi$eF@Y$_Kp;x8 zjyWgr#2&OYR_J>MiuP>FA=^0uWTGVN$PD&)_rL4x5j6utwBUm)kw;D0mEqQ>lpmS$ zicQ_SaD!{%&2N!iSL@0I|L#2-eDtnNJ(=Ks^$?y&@Sp0+E)VcUmuD_oc6sLaylQ32 zQufRTwmyU=a6#S-5uT5&$D+?b@W<>);@M|mtlJM0h!Bsh z=lTAuEltr=h!}VRBYQQu^pVY?$2GJ~PhYXN_5)JNQ|@CMe6ntCG0rpVv3sA`TF{yJ zPR2#BdJTpM8v*x=3%0=~xSPyyFJH7>*W-e9G3?h*!A<)xfiMx6i}2GaVlc6fnTeur zuJO6;AU%hf3QvB9AwtZ#dh$B}I$SZVziEa$_7Y^C4p-Q4=&kAT^iMMa`v^yvp1@#3 zKc6Vb9_?d}qgKCq@G&y}41VYxSo^$+8(8$)-ysNY zqPw<_>96P>6Jh!%hKP{L;w1dZc*XJ8njdDQFqk3>JVcdp$9pE1O34iN)~WDP&kh+e~B5ei>7U|;&# zcj^hmbB6<^>@Dd7Ou);3Am{8IGPPyFSy@$B6=IL8K7aM>)zeqkm$M0-ctc=a^c7ek z=H@YL8T&*M(@=&|WJAqx*_?1tli2Kd#NYlF^Z468Ce7if5dK}w6pSmvsa8W-!+=IS zI`tt@%n8izd7XVj6ftEa3w{92m(x%dv9Z)XMIqBcBmN~Vt+nr{HcxFRpPo*sV7~#y z4)FBB<9)Bsi+3Z)K0+bZhQ=bMsJp8HPi%vj#MMY-J+1KW2IwLR8P;D;H84c;qNimQ z`$q~r#hv9H_=xVUMTk9>p2uJjF{c``(;@bydICcmS5WoC66N|FhG;sl%xcia57ajP z4Y!*Z6#gRH*t4a&9m+-JMzVc_8X*p%eg9C~7w{j~zM|T z=@}hkZ{SClqnq-1uNG=eRM%q$()Adkv1GIB*e8+gjbwUlLDoqRY-}Ve^_9BzaD^OI zUH>k|6Rv-?kxVVX7q0gqO{A9jJ!7r%k0|P*bb-0SLc{`)1pSu%|P%`6MK}RGgs)f^EfpJWkycieL6xb(jET7;fMsC;Wo3==E;pJA|ARyr3emz9bUs4M-q4HW81 zKaiCjY`5y=7&6lP;wn~Kgn2ZE6gPya6?OLz`$%6NYCP1wF2sC(#=aP8KOE}od9v8d z@On*kGXr!Xax+7{CM-7F-kRL4!^URYo02Q_S@j#vCVo*(U=#j}^Xrl!joI(n_Aqj- zF$>KB#Nn}QK#siwc{`R_bL@>sXcKlS$6jw{y(WAU!d;kS78YEANnK$9uz=qoP!9?V zu8H5_;M2l_x5aNLzpHImXbyiC2)kPFE3uf|l3$5wY%6{xN>*!rB|4^bekDp=8@Q@g zAo4VW14YcW&$41s%M2AK~Y7UyC5x+!)5GOJV=61%kF>PeqK+-(*g+$lv665K(X3YZL<9cJ%|~LmBw;Y>}Tn53>GVm2b;*p zQJN(|J{Ws8O|>T}^b5>FSmHm;K8Jpb!J;&NKFvOme9?p@O}D%BR0c~b`4GrD$7~E? zQ)bwkk^jW8oiprNl*FM}5c)rCqHSwJ-ce5$)piL&d5gCdiI3F~eRh?^}LDJ*cnz>*_211nG zQN?-o1++5;3$Yo+#Hwe3w)%TwJ#Wmn!&m+CF=Juoj|<=-(bE{*YY!aZR!p3F%4hE5 z#qrY?ql&o)`yW0}d3Njk5p*RME4Eu`Wa_z<%P@)v>Z67B1GET(#d_B!3=u9Xic>%3 zith-UGm{*tbRPzDfeLilF3!ctr?r|zHDz$? zP{q^QHp}ck5ppGtKdsG2kz3b<0A>{GZVR15`yw%l&NGy6|HdyU-_UA}8EF1E-`G;{m)!su6dI!hLMeoz= z?E!vdO1vEPK9BLls5duW&T0W)JZ17BP3&U!ac^-EXtGh&#EB({r44xe8hrLtqUV;) z-h+^XswM|8p3r2ostMr#TTOm+*?Y2*%cexJU{#XnsZzG82 zQMlr>pUi12gUqYqxx3NcQK5e!RJmkbHUYEa=gR&SFH=~DtEF&tGlElF>}&PRMW+{S zS;~4B3?eHOq zm6)k;(b5Dq_H`%-OYt3HVvW7_-PDP}qPvfL!~PNhs@-qcJL##kmUt6WdBwc#vFGUt z47$%T(fw}b#*tM~ENNlKD(-{Cx||?W{1TU_OYzSVm790!P{Shq622UsHhSIeza2j8|2_oGpX0QM%2q4QG@ro`Oj(I%S@+n<6* z!I)I*#hYO;j|V?MWcJEqqxBR9r>aL;>V<(*BTb#^wZ$&Cr`gKxtwn|=N*52}3g}$i zKT*1P_A&c!3YnNFtLQTrkNX%-=CdELM-o66OM_S!#PV;rcso<WjdmrJu4qABD#^P&B|Lchj-7nyktLqukN zohU1r4p*yW56lRLoU`}TGZ$I+={fsIN}GZj-sllox8x(}9;BF1c4vKZ89$K4a+j)0wR>|K(Ra*gLpqZ%0R0*6ZqHMlarz)f~&KlHE!2@u7ydT3uxXJn>9pagscR zVv?{kf`B^W*#n0U%nwN+YH5(m*8fyBU(=&&ZhopNw*kU-HgDK;uxkUkSg_AQI=U|j%r04nNfY6 z>Zq%y5@#hY+m_RSwlWgS>_zt)g#XRITWNQljYTglI+YY%wZc^tdUh?HsXs zF~WJ_3q~c;?=h7(@M79Kdg}?a=K9TyAgY6-wVptP(9#Z$2DDtVNoWUmbTrlz2$c#t zItJ2kGXk-BPG>nUrjc6ePgc)F(xtX2-0yXAOruRPQyDuvQCnbe@V1TJm1J4Bb@&ER z(@aJA9?z$Bbxb6=$+BLL;1YH9dT6q&*DTX9guIX}OZ8-oCdTt4ljV3G(En|*wq}7M zn^i^JSb`|lj$~P^LwY#E{m2nei*NSOT>O%1_hHb4SL-9a9drH2CxEA~))oWa>HZGI zk9?nOtkzpJJW;LRNtV@mt7<9eV_vPJa3gE(ge&p1@^zLm$X$hfGsN*8t(1bIPmIjM zFhoRrr4%{H)!~X55i4$u%?Q$lL&WO{#3a!*+~J_DF@cyQf&{U1Qb%Bjs(t@3sRzoavJb@s^-FB3_tuni8-BjB1a;{^hLMJ19xz;fYLxl4ttIpHm zYR-Gfj9}X+$96sQ+2b8us0-r?zX9{IPdMJx;~M;ST=g3^PQFx}Iok0&J%E{t9j97A zpEM&Ci|qMh98ZxmDeU7hj&byykp;?$Q!0xO!3}U&Uda0hS938FOXzE!baZ7;J?W@M zen?>po^<5XYY1CBk3pP2P&t41c+Q?vPdWTyX{rQnAFAAbFPR%(1V=^HYn{%wVhR;L z&H;y&R!>FQ#oLg*HNsJzz+*1aeAQD~oe7Q`dNf1xoiS|#{>>gN6uI)RXP}66PG!BW zxpD)zd|feZn<{7S!=7_|sgRshSxtvyJW)-%rpjsx_@bKn5SEa1svr`%RTBqSqS##F zhZm}!DPEi^r|*kW<<6cOQykY7;sTZQh4h+gvmKP+h4f3iV*nw00Z(5@Z$HBGlak$HQ`7nitQ33-6#wxZq5U4PA4W#0KT6pyR2Wa_e4+e zUuhuQI@d9lR7o@1r>H0O1*gfard#JZJ|T4gO>bUV4ejoH$7s?l&1hW(4MS`PgbxAF zb~N`ZcmUQ7bw(BVPXhwQ^EOHbnG&c=E-bD!UMs)a#j)t@VgT)&GxoOPy zqGPh2K-9K7V{(SmX*z;KFnCB~4;MS$*5e!Ey}D`=)=(~zbz17EtnqrU%RjPOosR))N@?4mZfp(R>Y8B0u0$ z35`!RVJ{3h+MRx_lgy8j_!2=hTK3fNO%a~FRgMo8dK+QL*Vl)waWtX#FjzF!?KJko z8t6re6AWGJs89Wyqek}adYn%|$&^rgO<%JhGgvYi>;UwyL3Po1=23Y-O|NJBFbSt@9%^ zwVLg3Zp?S@5udDLo|oMl?cARZ1(GpPVxloA63WRz~183 zOt~t|V?S+iyiLlrFj_Z6Rl7_Jxd}6Uo1>0G!T?WS&2sd3?B;fdlQe8$G;y?sCQS5e zAqKt5PqN9bHkTz0+a*gHhG>?uxcrk59{X#Kxe6W70^r5#+6W95kw2gXyRgggKu;hl ziYO2hFoCEjK)^qRgo687)eNka++~O%NYBZyJ0_9UEo5-70ZY+ zh3se{BYP{x6aDtu7P8+4{C^wTar;1%&s9w>VhN&e`?!S%4*r|2kbA{K;YHQxpVd&9 zaKK?vNZFRgP{o@?w<5e(57 zqPY^TMA$n^$*s7Db{re&YZ%q)UB^0K(}!E{I=0XOErFdkn#A;>FM<>ox`o;6ovPxA zIl@D66hf8H)f+T+rqZz(JbS{4?i2vtrJm+&))7Z@w&j?kf(iZLJx5aMOe4M0l1(}e zMZdVbACEiY=}Hs)$tN6HdVE98-=?+-HdpRS#gO6-*u{&Dx-0d~mNL-yas_mOzPqJ- zKwtHgBeoPd*HQ-hX^bZVeSb?C=zuQ*-CVZZlIr8$?XytBe+PNI$lYhz`=N%{{>V{U z;a{>k=n$LDweISD)$HI_vd$ei?--}RTj_wQex^yoJ*&rMyFYQ)oXra*8F)hrNYO~T;9Chdc4CW)gK#u6miaShkWy40o%Z^LzR9beRo{J%LZ<%Re zZ?h@H6aK9Ojs*P}i!x|`+l-bkpn0Z#@2IKJZ;Z6sW~r<@Cba%*t420qZLd0((4R3W zFC`&&5JdC1r`0vbB84igk&j^^F^iVN5YZzkt>v1H4p($xE&D%Ro}In{1Nv|?sy;Vi z$%y%D(Z2vE$$o$rF4mzi`n1ixs!<><8e{ozV*4D<6jrV}< zS6a(yT3`)l2e#xlN3XTzogwV&0gD3JF$AhfUIP~TK~FXHY-2Br(vi+;Agvb*3UF3n z)4YGJxZnsR2V0B&4;6?gFEzJYEf6mQ7Jp%I4u^H7*i=~`Fa-I!`vWlkC)M~{tsfPL z`D}QFi~cO=4@Vk{_yEqdD4niRsilIXbD3(XxFwYlr2@+;QTp$HIu6r#&e=z8NK{SL zaTJpQ4AC@W+y8P*g0BxKcOf9eA<$is-m;{3VzwV0y*+V?vw|Odpbbz*(>#nNf~!Zm z46aV>+9RN3>+c+^ zDEp;7+Ij0*m(ov9z_yfkUWYFb2>Ss)9fi$MFDkbvlQ>1|^;eck1AA&CEbB z>w#+4Z|Nv((kePL*hhV#a|vprwTl`tfo)L4{Kf^V@3!LBEHPpl2Rpx@EjVW%5i`zL z#6)2kNHev&n#h93E_|8{Y$FR&Kcu{DyxwgX6r^6BsZ}6u$b>e$Dh*&)LY>v=(->Jq z&tO32Unv11F$^)6U1C{kRUKUXN*gKsMpe&VD$m?y4HED@z^{zHDZlET?i;+hG zPhWUm(&I5GJU^08pj8GrhM+m;It@*XuwNDJ)}MeHVtEXkQ(|*njs_!2XGkMR28xq) zH6c!Ro`EGN2cm@+(zAjmmtsP5f? z`}GH%FyTiG(X3~0B{(OM^BFQf&vKc%0mFw7W&9+l@TSfbKXNNWjvB6GJf51$CoH3y zGm-$h$V?x?#3GN6dk<3}s6*Pypsw6j4j86)b4Iej7S0}Q{0L`Rw*DhWAbW^ml}TJ% zW7=L+9p1PtQ?i|dJwRVqA#DIppSE9Wc;f7?{VknWNzb;%fx&lPG!~QMR0jqP0E-U{ zDsxx_>$zoWBZ}>k>5C!AvCGoI(pjpd)8Y00Q?L^|eXVo;+KMnZ=$YY6V-1%=3a@Re z5viqqm5{3r4yH?m#NYsnDoMDfMLXxS3O&qO`$*&8%;L4A5VCfF5c;aE452TO(y}c- z1xtAdJ?!YT5b|4F7W4Co(E%7u6!)*&%Avuh+^WtY*dWafqP9nw-vcbM;82b!|6R>U zOgkw)vYjmQ5K4+dnzu7XOOSeGZ%R86Eo^yDXLmLt$2;hp(E`SyURX_%3H16X3eo7j zQB;i+^kCz1opt!t5Hh5lF{mQER4ll)Gm|6%0*|Li{$~v)h7H(C(WH;D8%`wVb}v@# zUerztEVi;Q^b29%K~UM-Y2H+W$|{7bLA8i$ppGB@sRr;;de3zDK6QWRBKiTB<0Hc2 zRpWL1ONy}S10lk$x04a}1G0cOai#(m^8lMN$XSml@Sfs8NXec2OI&|pR1sx2+sP>V zRytY?K(QK{bL23>$GvXDAcY&Zml4*WJt|hV;m&WB4qOMrAhe5WbldhGs7kXGrN30v zZ2Z~d5w4sJL1n7OqH~>RU}P<<O$oh^Zi*+}Xf63E)s>im?5&rLb#_;1nGQ&lVb$Vk zGg=YXn46{!`(T`Nl2VJ=hwy;qpV*G(+;tsrL}!0jj=N zJnTj7_{gWvlGPoJQ=$_Z8ZTVRSZR|uVh7avXEwzx2AhsZn+{_)-dqB1?D7Yct0fRru1U=2o z^%XVc*FYjZBdt`%v5Yy+Gb||RVj1?qTId+2R)j!Z)X5mAWWErmb`0Y>$&LX#^zUTJ zeKEH1eEhQWjzafvA>Mj#D8hfL#_9y{A{>e#SjoTIZ0N<7Ok4+B~P%!`IpIJU;Yw-btc?p2ePzMl=tK)xaCoyS?B@f{ z=h)TfCj|1V+4N!wUKsZ;y)aRFA$!3Kb;vKm3%LQ<42ss%uzvpeFWn4XVM_Ni38PiE!WO*ER!nsZ9$(0#{ z#SqpJ**Ze1Vw?-;w753U>4RSQ?u zb-7)6!>O~^ZY=nsvopmpSyFXkE5877t5k6-x;~1_DAw{z=!Jv6bml7Cxy5`khekr5 zsrGJRF{faqK*VT^FP*j7^7o;m7f-8JeC2#md0Y4t5njfGaa>AGcImRSzw)8xSA_n< zBXqD!U3=On4DPS2_=mPeeFL`ssoMHLbL~QA3%^*Fh0b-32&|l0%)ddI$iEM`OgK-8 zKf3* zT@uG8Y`Ks?MyAMvf_k-74+`qil8xJQETU||cQEx^QjbmAcA)_opTQ2_aJC|s+ONIo zj3DhYS=3Eu{h68IgTeyNuCQPvY_}~e05^YxbfwRS7L4M#J z7Cj(b{ZP=VTh2}XWNM~*OwjQE(=kC!=_D>njH@PP%EN&$1n!Bh^~Fr}a3Ehd#o<7s ze{;S}U6`SmnA;IVV<-*>@+`jZq=fE981j^?e#AAL?#Ezw5vc%@~c4xA~zdLX1 zsraIJ1gI%T3f|L%uxNJW^s(XOVkXNtb1aj7hIxy9uAgMegK!S*dp4lPBSz{eK{`g_ zwI1t3XAZrExrl}Q#(z0G(qAxGEaczHl&1t~8dj8}I2~iY_kZso*BOOY?1q{}yksRj z5{ki(JQB#Uy&EHSbwI!nI#K~Nwk@fsh?4wrGovK>oc50{RWLAT0Mx1oPix0Ld(E(%?$Q{s_3*I=-4 z=hAMhHFYKE2@D!kl^PWOO%1XFU9g7l!JC4A6-kEKi^~fxQE$Ulz&> zVe0SQ*wa;9O&_6gD-mz?`%^mJJk2~Es=Br)v?k^!@=U~ALk#xD+dxt?i`A~?ny;tg z0xN4_uYOG`5%o2GSoTq)ebD=apt6#*@X(i5u+FQ%DM)7Q}<$6^id$Q!|P#RgXi3C`-R!@i0`G_SX z(rDJNuImUriLDWVcn(8EAfC)(yF%WN3cxqS8-L$oh!=HReb+)gTZ1dQNJX(me0H>% z{9eoznI@>h6_*>j*7=cu?nWo{Fd-1@N|%RDZa~7j8+|>zMEpj@@N-S@`Fczgwg$zZ z+G!WagZyhGxbkVI?l{r;p1t-6qS3Ou39fatAG(Lm(He%q!t?#Q%kP!x2n?QoMrx1c zG2zaLBv&0mCJJ|UWI;V=*F;(JoC%e%8S~NfhAY0LkvW@PX;QW_8=m4CPnThJd2vPT z7x-ZB%PnuGx}GE3y2~|^&7841kdyN)HyFX!NWz-CTEV-v-Q^m|VT>o126l9pi(7y% z9x|94BbV}h+$)#vdigO8KVCNckLj*ldJj86j30kS5X}zHJ8fJu2rbnEK;H4IDBxq- zxt^vJgT*`S_1e4oky1U_jP|Z%J(cK4ue5i)N@FmUh?i15JU2SH`YR?BV>`J<)21dA z@=Gf^#S9U)RW>FzOm6YS5Y1FJAz*JhYxCXQX0U$Wt04^U#FL`|T(+)N`}UBny0)w9 zxAMxfDlUI4Iv(SSmL1wdwk*(yWlJBTVdbl!s%ZnTR^OgXXDe2`kWDNgmp`ak-$PEP z;e+RYDQ|G`ViK)Y-%q9CdwR&F%fAM>7AnedjwttdqrUGe9V(+kUEeDgIc7_C_S}?c zg!%m=OtBELvxi*P0cFHO1U8JX42<_-&X>boMWk#`X-TP`(vnKqt`4Cob)R~!MSOho z`1r=riA|d(G)->WtVsf#8%I}%u|bCn^!}qIna9m`0R8OYgI|h&-dV z!8q9lo`FxeIw)kZ^kJ1p;0ARAiM|- zPXy9@VM>1jdWZ+x#R4hH+zNHEx~{@9M%kLMcbQ$dCBDJv*UL!I!i-?o1lML-xffDX)WrqQxKe3224Csq z>aO&X+uw`hk9-yef3Ze<(hJ6!AC;=E7XYKhQ8XRn@_{1jJjw;jnDzYXARE)oROUmL zwH0S1kUqss%rL_jop|)~u5x8bUN12wLJ~TdQHVj&+5GhhYRKv28*6&NiWuOmMct8!21!T^TdCu!raXn2w?j?r>=P=rY`t}yk{9;!wV2i%Phr#@*NvQcfe-@Rpq6pTh?~ZgO zu6<%kb@F>J&*Ay5(hA$R(p4|8N^eawb=5bxx3($?*fJ+Ece(6~AsP=hd6DZ=n$Ed; z2Me0>J7KVJetK_K;YHVz#R${|kwInz%@(^F>In?3_0m6WtsYBU6O~zP{CRymIab$2KQgC}O#GP`MFi24ePj^j^^x-dAA)%1hxoX6!wE{f zrYf-=OOTs=`p7YCXSXY=3^}1{aunkUO?Ii8Yyk}ZlDQ9Q;>oS~JuqK{^Y?5*%7UHb zR}jnxvH$JEGM+f{MpROKPRhu{`1q9M+>uGSxyiYCX*sF!`6+p$@{^j5OkxQcs|uqZ zgOtO9V^&ln75a)U7>zZ4RGTiKFN@g;%Rv5pQE>we#IWfnZnU>tU9nr+GTZsNp;2ND zUTrw94Og{LV7q#QHLd$fYg&M4zIWiNsUQ2dvtlNPYIwnK zyS^gV!4kg7Mzb|a!w|N`^atQl+;qX!(zeBF(gogA(-V)=n z9(CO9X(R@VQWMgTm3z-s%Y;BYkcc-S@H}|W)l$(@i1oX?qpo<`7E_4zI}maEsB4Fw zz)+FAAIZ{LF2+O{0ycT;&W26ao@`d{SL(se!(Fd=`?-8B+>0WTGOwSk3O7%<8W8?+ zp1|cD-As%tYg9kAM)iAaW$;xs4t?N)?S~*!Y(MNLQ|R7@t|HiZ2%^P4Uo8&0sB!Ra zKRK5u7B~Ly8hFv;QBOp%a4CGAeqb5Wp~p7()Fp=;h7BSJwUp?2gVbwZ!ti+9`OII z>zCew%$_ztHu;nRG6`DW2H)5D1bqJzlBo__mk&T)Gf-kwM$Q65Vqe;n0Pq#{sS22 z*Tqc5u2HQLA21{3_2V&)dzXLx?P=0(pp51=T!b!~TMd+b)h~a#3d@k817$Q1!gxHI z`70wgU6BOP|7|cgDC4$~1t5-hQ*#H(VE(+UdraU4Bu$Oubpufx1G0G>`zRvpbUF7S zdK|k`#K~Lb-M#4<3>Hmyd>|Vg;O?a-Ff`o{X8sp((t)~R3&d5-RSU|WFjxq?I?!{r zf;-zlVUTvsOj=33MRc%x5(yb38@cizV`I*6T&aF3+hF$?ifW_qN1HnV|Y?CgPO zD@AyB-DFiTV<=+dxhVHCg}#e9iP(sX0Riu0u!xO!2eI2R?z(ycapqn|O?P8@2@{C3 zses@_P4`eefta)Rs^#8CZ({;s!r_E z2_=Q>!@BN^zLq_1)^l&6aaa#<+W?w^AzU**z+r#xJ>P`wT;nRMrvUxhv7lO&%11Ru zWLjpxm6%Z1ka1JFp}QXJZ$PN>%`2@0N2(TLb{=Tv6@(7MWJ1+Z7$TfFY%u#P&JCX- z!goY8ERBbd)5ZhYL^)(Z3p7$Qre{}7`hrP<*WP$VGTy~B^x9wHTq!DvE} zz#&qR(tyDiZhQz63peKXym1DvJUlx4wEGaz3o5WTFvT6k#&>Yl0`#|^Zy|lnwbCR00w`_CUc0aw^(~QL&FfweO`NV zAlJMxMDvX&b&xwwp*HR=Z!j48-qpU^>oG-oJ$F-A_I2*m z+T`t_GNT{Au0LQjyK_;!|=`a#B;1 z@{&e2&CShA7@5~JDPdGp&!Ky3KUJbimY&F3Gk3qJrjL=l>)P6Q_Stjppvav3w5FqS zlTy-B(!h#@#N51`QK@MO$tejbX{l+fS8gE~Ll_v7pOloF*DNxO_N5ZC8p-*r{*=w2fwGLz#UJ_ zmq*8yiYW1s2#fE|LOtVKOfOVsKBi%kYl6rwJqL}*>e0SKw-F`BWPZFJyJUAI<(bSBITBJ*z#NvCQ4||7F|%%eMcQZO#6F&$bjc%RQaK`{tsDhewPs z1UWP_8f|?!WZGHob{{Nxt7%pZHhaokcujY=`_)iiFEfeP&R)z^Ups^8s(9_J%o6C| zm$YERcU(xJ&y5*7c6GrW*Z{U9h9$gup%w}5!>n((>yUDN*wHuK$z>bB3~Wi8i7dLF zyN!RrJWiWB5%wmZufm3V+)bX~+zJJU!h&FU&|6qgPW*DZLV=IoFe~Ci3$dZW#;?Rq zTRXoJ+aVnMN^GNW@++|l#{HVJsyS3jY&oYg@f4V@3;?=kl}3hZ?AV`cjiqgO-pkEAHHdG$W0sl z16|mpL+*t%!CZwVstQjZcE3l`u?kH}P~p>e|Dg&M-vbo}o2xKjxaaPBZVEdDRPk-T zvy}<(DLv*N+5W{*cdQ@eBpVyqCx$Jr6I_Y4KIWd?ea<6Y#1(h-lF|SEwZ|C0*`aLl z-c?mu+Hv=1bT#76#Lmo>2;yzp6~jHVPq?R+q7HOZEN4Zm;1C|snd&hVnBFttL-%Wh z?!;7L#*lT^J&nGJ!D11q`Z;$V**Tmkze8p%-8m_MSxwm zw(GRJ4x96lJ5SGFOe-|4{ADs?t>oFrFP?W7&~LE{-2Bk55kxcJpJja$9Ojwvv3mlc zzakX*shzvSJzX!l%Mp4P-}?X9d++$Dimq|odlOo?p%Vlp2~DZV*6c1#5D<_Wco3v- zb`v19(4?0D0YXh^Lni^U8=8U=SFr$^WOor2#f}Qt#e&#SY`-&S?&i+DxtsX-e&6@^ zkI&~5o^|f&bI#N=GgK1JKDC%6JOlP3^s*ZxBYhveTX0O2>W%}R*gh>E%ScC$^L_b# z!30r?=09mj*U=$O7odHYuN1r|NNspjI)Ub-s;7f0JPzjX_EEvoMk=$(aSpa6OE1;6 zlA0eEjFs-^X`=(($CW4+^u6(6;`)+<(P#htRdGAr2mfvEyUNxkr_W0w304yp9!Q<-Am3k5;}>mj-%@`CSuq)v~k1- zU=?9K+&!QBQ$dsZO7O%o&i--a`JW1G63>YUKf^lR4?J-U;WeMlUoQdzhVV1D3SNqc z7+;;7vqm(grH6hg7^)_N%|Xjw3*M6&j^}(IX$`zxa1yY5K&yce(TbyE{vGmwRN`oC zCQGie8E(mle-u0~b>UfQ0wP||+9!eyef|C{xVNg2o%z-ct~B#**afo@+}f*qE&d=$w^4zuFyu+nSL>3KJ>eRQk{B zl;ErMpU$*`9rT~^^urkXPc;1}ivE*G|7j2Z%t>F!kz12aNo6CMHAo^G&|%P$jks$N zZ;f(;0CGn|A$Rpt~?vWwx>GyzZ+`Q6ZA2}A~=_BTjC(iT>Hu7D3 z^(b*8)K|_PBN>0KLmagUnO@wk5gGSgt6JoSafB2BMkg5yMfrMxA8LAzRu_)}fkuTR z$?XQ7D5GuKG{>|g*MdZY%Vu@iY^esLoh}2|4d$d2o6+KQn5}k))nZOcGMi0SvxQ(6 z+rObFRys^2#OD0)cy_%2UjpB7;Pfm=pR+w{QloGm@p^$*Vm#AG+>ZrOBGAHy_7X>3 zp+!!fElqEv{|(yL9a(qzdf(|ut|EQIp|idm1d;pl2F#~uoG&KWLi2Pc-Bc1c!nDFqZqMs}2(6ZOBK{Rh5iK(|y)X9)lpcd1ah~f@7p9dZD4xce+|Bc@ zRsyt#H1n(+ zPX>RoqLtuaV&I9yB)3u#Y{%D3^8e$BBNsQ#Z%gtTdZT^8NuIW%@3Bdq!J+_}{)0T2 zjbA4EfGYCYks*+JPG7g(JjK(v3f!U{(iP4=4qwo`fr8LTgJ7EJnHniPQigCJ2isPG z#gvahZf28Cz>L0AVs$#SKUhu(rye{z)tu#N zS5+LDsx(T`8Hn+4FhyM;uQ%FZxO< zmlqAdm@3Qb$x^rY5=B4wg_{|0B)0mh2C%K~St&@-X;2&remd0V>cSJwv=w(W1CEj0 zqQy@rd7viGa>j&`e$WQvh01mJc_s-$HWO?W4~2rw zlm$z#T(B5@!ASdjPp;= zgkI8RJ=l|nJcHD*n&#~_J-N!}01~h6-|@=J>^O9y-9NRGN#b^P15aD8=SeAg0%tf} zXNlrUICmRV=$Ra&|IK%?&~rhMT5^a4=O*p>60~8<31s|HPpS@VS3TJ5V_-uSU7XSl z)`MMn+>@z>)!6%M+1|VoY?&wbQO{LDn#fZ{`7`(ulz-v`Vm;}ZuZG1Q`tnInPieUx z!h**=g*pg>o`BAIs}>;wq#(&+mAiE?l1_R0OCBwT2r_+e$}>ZapwazXJ>9X9=dHn2 zch@JOk$-stYvj*Uo~m}oKLNv=?D2W_i=RzkZT!bP7!7aXRdRB-CsqV#Y<9x%!ZhSC z-@bX;GgquXk;zeKqE?RRiA;{f?(3=&@(iq~t$YT?itQ&7=Uw|J*5u)_y=gd+HH3io zPbKQyAitT<`y3so4X5-&tq%If(1||h8Bc^D4dp+f(@x?`WMER~oTs@OR#S1c%@$y9GU+?f>Se?akbpkk!A@o^e zzZS3mEhJW)NN#?%JcYzxKGagIIf?mO^+{T}s!U>Ys^?0)%LI~GcXvOM^IqVgs%E>@ zXfTp^r>7OEddV|1BFSMV&0btrpG?2xnNZbeaF}goGA6|nOKxBCjEu0@jYMd^p*}e~ z#?z22eZw=fN|MEDvXOCPJ@=45-td^WXU~uJbvysR%;rAv-bw7b0%f4uT6VdXleqa{ zGXSIW!RSeBJ_v@uv6Dzm6bgZ|`Cy#R1ZY4r#g*xVaJ4u}HuH#bW*#<)+;h;oZCcheVo*Yd}_-!mh}=NF(dstDZ6c zoJ}lg@{woq{{X+X>V~l$lh_FhMcg%txH#ROA-midaJg;!W8u;O^td4!j~l7C_%I;7 z%-L|(8dq`jZ(%2Pjh}fo3(^Iifo4R%ql_%)_J5<*+efZ_4wo!_DSP~98jtS>%HeRi z{&mj)c*YK%1*T(ueB+D<#Sr`oT#pXsKL-1}zJEZNK^Ys&SFHA7Jl!fJQg$g)`*dw< z*Cw6yu0Q=8@R2{qtd@xNhNoEQ!-?06N6HoaaNQV{E*Ac;vKG0LIw@ioeauW}GrQ^O zcrk0Id^b_cN0Mz(+ z)rkUoL4if*%t~j+e?TnsO5V_Hw9c0(hJ|0)4ZeZbfB2T)_H+}acc~FWji|=s-u;Ij zcZKz{8PDdl6n>^ErAR;Ub%oRA?^p%<>iy;UjlK}!{0gRz(qu(k`MeI+X;TrvEAzN; z-Uugbhmy1XvRc9$XTU|C&^mZlC{fa6dMDXlSnNYthVTIB{aq(B?+3uj94wqH9jkhC zge*>s47Pn6>-wFpRBe^1NmYc>pn1r zB(?FTs}VHg*IZc-o+En*uzg$aQ-YMnQ`N-e7Cqd*(F^Qk`N%ihdoRT6?_Pj{*gRYl z*c?47!Mi|O!;3=a=V;MFH}#**jM}KZT9H{S-#!=*8f)^p&osE#5Z;@#86M zT5y$z!#3gqoQ0=7B*0@E!As83#Cd0!ZyoL4JTZDI>wBW6vP*$79NyK!J>)gBSJU;B z%6hh#id_#`+tGVNuu(wlNXqR(Tu{Sr@9do_z$#Pi=9^OX)HYU^!Hv7#Dr-qJlgSa7#@wLVR%4)pV`HGH^E18!*~sD$?AUt z?$}iQ%pJiUF^vVxuxTt{#w2^Uh%-PedIz1-qWmuFJ#88bmkh_&4&=lTZ>hK(VAYct zF_P_@0ql(N{uuJ-P;Z8~c^W&1tB|Y_0;4*`k~$++br2^56g`YPjFt_Qcr_{t6`%7V zZ+;}%)Z2^6XPUOZfy_unjn-M5Zj%?qf0;%SoZh`Uu=Vs{H>G$J z)v%c0`c;becc~?hfC;WPx3`dTxaRh@S7Wg~PMVhG;IS}D0ih(-o1#Y01X6psQh5DY z7xhURT-o_{nzyTz%v07>YJwgr`LV ze2g-m)fMxh*=F2ERY*FO{cu3Tmnd?=FN}Q~$vk!7L3~X31#&YP8k4iTRz4t|p#rLU z?q{d7rm1MU7q){gGgy3~@Esl&o2TOGtoH^SoNeffz!D?Ee7iUk8p`S!tf8!up_N9= zVE6eLW_w+t*eruJl(9SO^dA zLGpgj8_oW6e8S2Yl9!kziF1IQx?N4xkSqQpuN}o&DQ0D8$NbY2SR90m1qgcg1?Yo= z5MFmS`Q=L#>xbM1Zf!VqKz`cuDY=%4clvXz?5;MPqW0T>l8Vg{QK0ueOXFO+(`$&EC5I%X#OLP!^o4(a9jnqTvbkf^ajiBl3 zt5sp+LEcz@$4_dJx>Hy8CCARfJJ6XkS&T2C5~%vAg)>>tQ1pcN*9dXfOx80H9uC8K z#Y{GYUPxZs6BK#Tt?WIqe=Wg_}+uqL2WF17#Y41zo2Q#%%+fLT~y_sx< z^V?^4X@YV zY2AA=OJ7+J4>ucUF-s@R;w*x#d%IbBt-JW(EN1fFJOY~BJd1V4fcQ@v_;$t6z%P(R zn#*%Qk!H`r242YWbwBTIDoWM_o5N_dC#7~Un#hOiysgN(3*I3$lM*bj+Xq*WbTE=; z)7Ld189{FyQvGG`5XqQef-^A&Luv_+%$zU-6Eh`+onwO5hf^&(d~nBVwcS2_uFYY&p6jlH$W#ckfI>WZrS zzSkmgbz~sdUWXD@)x=-&TB?U?h2-z_HX+e(dSlhKvG0=COy1x8f5Yh>GWt!gBz^`_ zp`I(}%O?65+@EeZLQg%1bbH6!@PAM1II{3vZ$mQp(&}hZz4taNF}&riM_zejSHk}V z<7VP5FoL>4JEk!L3`2VUU(|mr$-C^WMPA$Wf5ood2i|D0+HCFc>M2Ek99~_OL+^sz zCPG|b=$+Gqp8f!9S6S3mIPTbW#d}4RI?d*ySs6?8;EQqa(P=g@e(c?;gMhvLFg=8| zpLjd!AY8QP_m?KV4rIE`lPH zzksC|mFAjJNTAGwoOR$b%f`NSUwZe6(h;6CCIowCla*h4CrTdvi(=1b`C_yuARPL} z+ggo)1HV`FU{`(XeNhdI!?>$@u+iUn-&MmZf_$%sus8pYSp6Too^S^`Uj4zFANH^)Uf=TUc=`GkSPx{chR4q#!YyxtxERhr-14@F zs0LYeA7p36ip!^vxwpK7s$Pc0u=VZ8lA^adhfQaI#cA6xTAmdK2}}qKZka_Bgyn_S^xMX?$^Gz>XDbk!dg;aqX8+6?PU^&LMwB70ys&Q7>x>5;AWIJ@n19W-SUs?hpi1$nV!k%haRHg1C zU@07?(JG~jzW|HpSygm3RUn_hRap#ksY%1aBT^flrpDIt`^G9vIG0c;+8)8-CRZ)Z z*c49%N>5b!zHL-kQxtnY!cJ2vj?qd70XjtaG^NTr$eGde`if)9@ia3MJS>}5@+FFI zg26{EsAWywHxE9aOh!qenOxjv9Hn;oK%gNQ2am9s-FrL~P9RF*_!Zr*eI6=> zCNzcNg?mx@-$J!9f}PO2k-4^o7&VtkRc)?Ts>r!)>iKc2!cWB(bD3E2JQ#{4&1GT% zcx7UR6L~>vFwbCFkO4dgoNzukm#sFu-KKDoC{B_kN#o&Ak|DAr0AHCTaTObA2J3EX zB^2&x6vnYh*M4)Y#CtvHmUdG~%9;pzigE{6qL?*g{<);t>YDX^*AffUBc#I|D8Uxz zBwvCqe0VNXI~P8vgFreL z8YGhzAp-12gmI&qs4+Bc!F(3md=;`S_CS}yI->L-PZf{aFHIiPMjFBwW2HSf&$qr? z;lqL&L&I#54zo@76*XL)aepEF9frRn@OKpcj=|q?_Qwj zN<1OXorf1G#qh!_{>S!$!hC!1hf^;~%Xux(8Q1b9R4-xqc1vNL8WJmXj}}r~K9B7G z>C^*iSd9VJ>KFj~u#BHi)fR+Pq#4yf}E3 z(|NvxJ!~+KT>JHuK@E$_ywAg8b-plo=zcYIbf<^(U>k}>_o-o#?nEst z3ehDcvV|HL#g5;*s!)9&gjJ)5v-8=e@v-VfjRo<_eAZFF z&%x%$dr)5o-ogF9X5 z>xd+}$S;}!3YM%msOQvBpVTVqCH7drmf#gy#br>qu5&j4(2Z4)0M~PPO<2kbU&7hz zuDV56rHln!_@P(M<4e$kGvFq{dPS4fuo#e6ulDaMt<^*LsD4prJ%sF3$dVaX+@Yo( z5(cnJB8{`W`d>QBm4-!&q;otg420(vkSAk`?vbA3zi3#!_g}JlsS)_)7YmqQeo6_e z{PH8HY`QT$rf1b^B(ZVPXQHr>`R1QIEc)j41*O z;LQ9LErtlxr?xIKtEp4JQ%pFZrmV(6YC9u5=qlBX$>S(-j)jRoOWPE66{H_I2sSbQ zZXuh*f5(5p0I#};Ga_mNFyz@=BjY4B@F7o2a$|k}X40Kn3=yb**d8=dA!sy_WKF)V zToX(!QmQg!165VxiowGPMN_11yf_%pofnb##G)2bXZ{NnCsrX^OV2+jQQxbHMUo(m z;Bhs+RYwmO=eWFxT-Cf}ESe+ES;Sg{nUu1sHJHALP2$>^i&}`{#zm|pSjWR*OE7m4 zYY6}zCvjo8;dXJDZ+|8gkr@4H8#!t#+9RFi^`^^oanErjisbMMmtR}6r>-yAi&~1( zD;y|s?hE#k-r`Fzre0Y@4t9c3fC?7x2>qo~QG4kN9sx&;fUvZ4Q9CsP98qma-s@b{ zOuEe@gg89Z1qL=M4w~u@UChEO$I|`!cP+Xg)mseug{G~V+HWt{K99N@CMNLrB>ef{ z?x9Ps(#~XTWW4m|r3{h8MRd zi+9wkRgOwCvTtN@A8C}9ln89G*H($BF}+d8ery)``KaPGqBM)A3hK2a`^OfylNR#D zsN<}~q}8}$hZ=zmMCiC;Py=wM4O*-SAWxjX4UN}98-LW&R@$${5P{E^9n*@R7NsR@AX{Szd)++6m}IgWY!*GgyjY(ZSjM$@tQB8xd9J~aZBhJwiChkYe52h3B2kOE+mz7H{5fO ztpxzsCiPS*gS~D(Ox9)yus(gD59!r1X9&43qof_lm{Pn*oVJ8*nJW?vlPOFFiC~28 zTROFPj36!rIJ&E>z=f{)k>Ng+{;Z+$|N|Grp&24h0 z!1FArN#sP&RoQWs0pVNqAC;@k>9T}Zj8teoMYr}3mRjo~&{`c{3t zrOY>LFJ-lvX8@yPZ8kEpUy$AT%N9v&w^UoN3uIcYm$I2xya`fJpuXypLFSwoa&`Ec zXcFYhnv0!*h`Q33DMWG?7Jnr6U#c1S4p<$6AlD`p-%aWytTNr%E|q?56Sz-FzQt&TY@21WZNy~|%zMh&VY5tkC$ocZU}kYMK|Eeg)g21l zwdKX9#OKPXx<3TLsk&WObr~x=J9V!~;w5MEP`(6gA;++9O;+(lLHd?A4-V?CFD3m} z7spH2`7hAgwvgU~)35bEh6oX#$`s1ay8AEyNIk+KR= z*s<&Jx2`Q7B6Uy^rK6{|T#2G3{KA&%IGfJQTvyxx@+3G=c7jgf`Z zBG$juIi1-;90Bz~AJiU3`KUUWA?XoR!JkDueP=xz*H6=2qAC6<-$DXEL|S=E2ad zpgM5O0^pUoRVCr{39H*DN%B^7ttJ9UKt1cmOR{I>C70YAeSs4}_N*X986ZB98OEu? zS)gV?ah`BH#ILbW|PN5GBiELY6Jn2FKLx$zBP==*UvmX8(|=ZRgHvp%rX za;D(AKn*hGNU?+bF#r_&PkElnJWs&V_u)qR6UyZS4#I1zxIcOJ znc}P%{eGVef42BBX#md+S05haN)$@)3;WLo?ADdd&lPtNq){9wOMCeG`QrYPi!Vm+ z9JPYYQdI~Vv&6`{@%-5sj4x3nB74t5Tex-wYYSIXo~pKR7KLjG4bLG)>6C-N>3<$GA_yGG8ioI8l=-{ zz}NZU!A^IQ(FIR6T2qZFK)9b~J0wt~&%K3>7KbRYq?bm#dB zsdy%LuTl$a?B94L(j48i#H>3c*$RzIc8uvACSdD;0XE@?KeGkONibg=fPezJ!a4$zn;4czY#_ms`|O zs(ASk^rJ`R{)i~KR$quAH?I}ny(Q;TBLZ*0wc%#f1+qN#v$%5wb>M0idamI2mF!#r z5X1iNrlvkBn04+es}wv}uv&N!MPR2>duM6I?U}`@Vc1cm4fpn;U zcPe+K{@6*T_TY+&8a!WyqhziiJ;RgK^bgz12ytxjrJq@yN;X!qGS&btBi))v26ZlZ zSbBqJg5APvT!~T_@C&mg67~hH`9L#e5vVvBwO<3{(H~zlFT!6n729 z#Z_{z2|~aGf()qtwmgGayB-GEpK)TygZhtG@m(DlXGU>viD$@KL%xKw{NX_*d18ar zB(vGUeh}x#b1CIH_tk?A(lZNqwq32j%RKL0&GD?e`oHAae<<)Av6?A5OlB$!E4iPH zI|vh(Y32D%=J^4}f2ZS(he{5Mdt`n)Wq!v;0KXo?K)=V!^Lvcv2N>9h@fNJmruidF zHpJ_XHbYguQ@$u;;i!^<(ic>KQ0qurnV3(x62OeU26}tm74IyxGM$-b(|%0)Cf3#z^$3~JOXWd zB7ida>-!bW)EJt!By^pON^Z=Fig1(U_pooiS- zG?r3UwL_!Uuy*LH2_=t-3)Zl9Xf6+i@$Xv0;vc{<{=+cC5wrd`V2r)S4-ss{>|Mh` z;?No#F~?ie(wqjX+nNfmm08?YIJ)U_rB|))9wswphDIY4Ynk_Gl zFI(QhyKzQ|AT|M5_3$`K1KV&GM0VS3?eMs+0*1qq2!No&V~_;3x~|bme|Y?7nL?~r z)2~Ed(??3~5~Z=(Tx4N5;HF+0UyR`Zfb5cN6$VXO-QJ-_8)=>vK^&V+o|#|LT!#Y& z+*&;Z+zVAP(8Sx_vW0k^*d%~2Q6eMcs?G7%6uj6^JekcR=us+xDuRwe#nCLIMvF_% zi09!3{WE(eNPZp+Bj|~27C``xX$AVM*|Kh!Z)=u9DBqF=`H|;d%)raS%NcbeV}y>fqcFOw0);M=QnxIXZ|7Q!3t-gD#=|9?=?h_W$Q{_ zBHwN*xsQlm(E9Fmij|n9g~k-rM9y~Hc$XNlE{xLQD)-&ol02A)A+z3$+0_Pb!>#RG zz|I|VK-=-AzV1e^Bg&& z$UV>0NMf`vtxtxKl76vCMvFVeX-`W_b6G6*B!|UrH=9#aOs*7*-C`xq*Gd}rPTbfz zIx;rZVlt&9r8!&%?Wpu;Z6Z}py`?~MgJUueboMyM0(wt_K%W1NtCZ(iU%?`6M z)nsuy-7bTLT!2e;E|F}Yplq_i}+#~o_sPTzOWEb|EQm9S1C&M}J&an_32lKjO5M)KW=#nnho zL1}IBU6;~&iAHmR#X4zr;;b>v7cXuf-`thrw57UiX=zFJREyh`;&6cDS&R;+H7&{L zaQN!(D1hp!B+pS@2aRUbR?A3o9467<~RD&A265|<~k7-Y4 z=PZheuS6lZV*lv&HnCC`L`H`R;wddH#hH@oayguk+F^oM2GV@V zS9U@*noMrDDb)fJffw7L>99Er=2WA}Voh>_>^_hZ{4~wwu-I${v)i3wwwO~u7njQj zZk^&Zr?^~B=BGJ3wtI!xin)^9Zaa+el`w@z;trRjlT8;&TE&7)CW|X6HOZA~v)J6w zD!Aa)V0#h-T$;<^%lvhFMr5qnV78^0&2F0oR84{gFvUXuXG^o1jcy|`4kV3z-P$bn z3UL-Yv?f+ts?}|DT5Kuy6t~fwWH7p|U@Ci(%a^=z#~Gn%g*-{!7j~E`W0@PV;~o<4 z-qC>Mj4!Pvn8^O|rFXV7^I>Llo2@3J(d=+oQ!ECT(`j?rEe?a*VKNv~oaE-!9Swas zYfGUisW=ITHaZP`8J!kq3rj1hCv5;#?4d4oxK(007Y(sgy9zFw7T7C zj-)hus>5bUvRR#GgUe)dJ1xdETZ+vow=xd!T4$2Mnwn&>S*$kjwiKHSdIhV^1vM=< zxH<1FS}!y;f*af14o7Ml^a=3Xw8;gnnA>PEr=_Gh$hnR?8_IoLC7kVK{--5FAU~=? z&Pn9tm_>=C$!8_C;w(<5(P~SBK!TU7Q%ojk%^ha9A<67^q!?0+vMSVjtu|;>QlR^E z*d0z=lHKS|wL9G=2q#Daq@5P)-<&pQio>0n2EG8@J@nL8N2P?EZlN;X zBpDj~xXM^xgP@R|!|v{(U!-)F(B6DQVo(e9x5GoeYMK21EPNbD)*B8LbAZ)s$c{bA6q~ z08Iql^Rhy_0?MYCU3N2gCRCiw>2&)tbD@!JTH#h03}ko4_%fc_yjECRNyTa6&sL$! zrWn=nSBW`OY5Wb_Lt^49(aFH#&tPKa(vb#RbDPJ5;nChGbT!(_u@4 zhTG*ZClO=TqEP%*!r98kn3Zuhex~m2OfU$B3$Tn>_gvhNg@4>+9mojwHL=4i}2iX#rH9J;}4TixkAM^$gd#*I6#bC6gm|@@n zgBnMQIR##{fuS^X*>L9=fq8*ELau}>_IB}=FonVA^<_!qYM0VFWPd?vy#!-IlAVh( zmm|%XmX>NarJ3EJj@w}{!f3)~PqM*a!esR|8@mX6t&%l1I_nAba~0MOv`Om7aaQP> zwiGpVC8l%II4c+qaaM^|CerMeMWcwJdFhCM=X3wg=l-3~m0Nhwbcg@W=Ty_kf9G@m z&gcG}&;2`}Q_Us+ozML{pVQ1y|DDhMJD>Y^KBp`-{4dYvmK7|HByq<}>xu`~VIBu} zp};KNy}!o`8j`ceOWonJ-a9^8x-;B){4XCZ^)ZrO6?i^d62y5wY8O76!$l$RTT1K2%2N#E~&AO**Z%Dvi--LYg)LVB~ zeFd@_*LNVvPrsE|?IXx&S|6K6&WpaUrr&!fWzubN$#)95`3+mE`1=9JeHFcO@9uF6o0~c8QE$5n8evr zGw7df+BC%*`jVKu0~Sh>lFX3cW44fH5t)t1 zuEuZICbe@{NYeL|Ki0ta>)0izd!LWM7crWoc;D?$Xv_67uLyeBn|uix`sEGetItYZYFIGzaOrbBSm}$>3^lCA3?rDR zyi7DpvQYf1oEb*yfa0xA+)J zF0YLe5aiEqO1Db=HgaK%eN-Q=go74tKpz$6Tjq_@ol**i$6ZF8&KI9M>lr9)WOos( z;5F5-nyJFG!@G!m$8VNi62#?b+XqOU!I{lDKg-s^LId!0IF*+DSUOwG-^j8m_fa|J z9-d_t?glPH;R)FfekxrYA)emIvKf6m9A+~f+{n@@0UpyT!*IhB5@Ehczm?{M8vsmk z9!MeyLp%>jU-PEKy84nU;lw}wJJeN}Zyo+9EtMiR0d%PG*0g@lGb{4wn5o)`FHx9P zC)2NO8cgC#SF{k~N$yvr@_XG)DGQZ7n`|no>#r#YunUe*^vU`p9ty45d=s-~lT9o) zRG%wLQVsJhAo@25lQ>z@crKWz>e|Fkoehul7ev7W-yjjBOxc#UM;trfG z-NX)@P2R)~oBmjZv}Ul}zo+jV$+Bzy82)Fin3T zz0zWeXvkp!+95~lP;GNq+Vjlj{?Em}IV?UO;K9(NOgYS>0372}pKC=P<*V7szgLtp zC}}=wVilu~EmMty!QqHLyO5G@a&7FsDdGlM$+f)n>~vHPYlVJp<2Of$9$CwSJREAd zN!D`pKh~05PVnbS7b{RQQ&#e29tMw)|B~qcQTmWCrmaexVxJW{sMSfGb8{2OW~2YG zbc4smfzK_z1Uvg1Ipn6vKTZt`Jx33+?6tXdW4`7PI9Dk(`@fND=W^A8G$q3WWOg!@|IObGVrGr&r4%e!qan4Be@cV3u)ZZKRL`d->#0( zxJt7*M0O@hsp@>Ws(Iq92jNQ;cGby)OXnw(aT`1>#N1pK>>DY4RqwGbmj(NruKr=- zkz5w+ULFj?U`s9wb^ym<*JsIsUH_YJQ8)i2QF@Kn5ay)Aq<8reoQ}Q*%zF5Ds9}|> zn7`r?aM}n6fAsXvRU^=`57&}Bsb@ZVH0#L+{O?6b5u3p>p+*S3v)Y6Ho>EP|7~MHy zGx0p=H>(jeyq?nIg+nHN+Yq?cXO3vv%ql67@>Eq)`^{KMzIzAwdkbQ}&8(LC@Nig5 zmd&h|0RA7=5{Vz||0&#%sa>*vue4ZY5qd?_0~3 zX?XsMmdxYBeCzsT=vuKKH?y(tO{f@phV0uh{&iyX7IubA3Gv&qu2Eaq88Ud4J%Mx_ z=Pwas0hXR2Q(*6q57+}Nyvn{xOxVJXk0}t<6bKw-R09Zld<=xZ!38fhTT>y_@QPRm|PO zynQ1Ng*CKb3-k7w0I_aw?{NNrCl`6i6mIYfeYBZKgg3IB1h4rzCyVtw&k$YbnJsK| z`^hB#-WuWuTi8)V#o%paGrR}Ycj8bpa`EPJoA@?>(Yq90+`@*oKm@J-6mQS6p>3Sb z&~`uz#k_26TkCXYE1@pA>Gr2cwYDnyFej_I6^FIkGxkQ4(*v>+N&H>js=lj>{GA2g z?0NnILFhnHHACI`GLyDj*(-<3w#>JZ8%?~1GW2ADJxJJhoA%2X0WPNol1<6@#q??D z;R~8`o*KIHB1HLvH&(`zvqDx3Qhsu&|7B4aScZNdhnb_uG(*hmM{dsYvr8<-!yWiy zeUOyi&H(zNMXOUwggg{1sy|qsS!T?NJk*!C5)36R=SgauKCg@r$Cfto@fACpM`SO+ zTwCQ|6kp*eW5AmNd)w&9YX1W15HAazrpD)UC5jWkFLdf-BAKy%o-?jusK@aJAY#n+ zKP5fG(bG1=K3Q9y8XJA^C7cu_U)(*2EE}@>Uf~_)sc%y9p*Y8YdVMSF*BtBp_X@&I z<^%L$ap_wg7USnVxwZiZ`!;>H*dAA+@&%@H;wJy^(w*BtrjXBQD)_ZmV#ml(0(K#BEI3g`54e1aNo&ldqxe$HStswr$L{0jDx$!wHkO&Hscnrvml9 z_;1yVuUjybTzqbROJOt9aT6~$R_J)@GWJnY2p5o75-`)_O8C-z5WP6_=^dt*Kk#lPfp7S zWZ-^(Cfq;j+`t+-zNL~BQ0!~J*v9%=5DMESUI8`};Y$=(fnONB_2>;O4SZMf{gI;7 zd3$&#Z6q`1$zfbp5(G`@WgQQ0_bhKIJj^u8Xj1Y(P0A_cwdb0^q15fnY0VygQ$c{Y zu{Zc9O4E5Ltjkf`nZFL*Ua7xUB3Em?dWO{AlUK#p;;_GcgtW5)?K_0gJ|%uzwL1j* z@Y@~fyk$7;`W%N~eUhnuV&(i^(i6N^=*=gX@}%%l|2W}QruzjR3Uz-<)*Yb#VXXR| zKH-OFg?_F;(a;4*yfS~Mn)_fi<3V?r*d}{vlS7q-*Rqw0MrET3xp^j#NZvT*UqMPw z`S%L#Df~pK4G)SM!;34}@dC;}jojKkh}?$=K_4S5aC<%FKQ2tCOf`Y~uw2Vi2pbk( zymYVw>2lhi%q|?RbK1XFfZI%|a(ZDrPK6#rnm^;;Eaa5I^I@E(fMOU=A}oxDl7AM) zQs$BzF*LQP~-1B?ulcs)ug7_J%-P6Zhhv%}#TqEXgu1@~U&iYTp zzO8a{#Jdy;pLK1&bWZxhYIoeRcx&r)N-8`3Ggw#8P6uT87ck*pK!blN4wv}wBm8GN zj*Nbyzi@H?XZj0QZ*I|FIFJ5?{=&i5uk;s7g;Mf_}snU?~u!&_n zObYtX)9KwCX zGzZfg5O+47AZ|-oxDtgROLGYG4bvQwIXsr;0AjF?CY0tdkO#puhplhVjR zc{xzLJ$tgIOFQ{u6z{|p|6nn9hflofe^5}P;TGtlJO-}wN>0+t6-`29Ai1R;g`R`IQnb zXM>Cr_!4A%58ORBsQko75u21{?DZ;e6f;JFq|v7W4%4A2aENlBCZLS9$}2+d+%p|v>?5~6Gb-f z^Cf7YH+GVvzxb!AVL{)P zkyABPwTc}W(hA}#zTvo@ja%OPqAShWK zIpr8ktq?x19Q+I&c-&XywJF~e%qO|0verq@s_aF#e4ge?6f}|@6-W>F4W324n~ZN- zxLCTxA#*t%m-%9>!ArYXl7|XGQ!Dbbr@TNc$-^2Q_)8FfKvT;}9*WzoXZBEGp*{E# zMKSn=P2&#kx#xPj*?Mm6-3lC=noGOcW@~QUz%5a1x0|i#w&vk*V$onXn^*umPNnpD zvo-fH-#W$wa$@xBkX(%k?2^V%u4R24?$&W7oaA3Nf~p&-|83Hpf!X2$S>L(59yphp zy_+pyKTw?Ys<=f~H-`s9br;F%0yxg2!Z5=XkTBo6-yPT>z93gHwn&+84yYz?i?rXvvM`og z1Jk3#ZU9HOL=?EjYB&N~N5Zya;9L!H@E*21q5y|dJAf!o_1f%VYBvNXbX7Te4^ume ziW~A5TvgW5c)%Sp>ycnI7*K@soBjO*bHuf>Ay$_a;JHa>iR3<&HL#DjZSoaFIv zST*Zq837*SB#d)Zg+{VAn*L1l|+jE%4$qbj0jXCQ6mD4$e_Qns*$h$%xX&({FN0?M*Wo) zN8109)q=EnIM9+@9ua6rt`85yll0*L9P@L23>o|9|B#CJ;Xp&N{a&p@j+Se?&0e-w zGH7JrVUj#La94vzCe3n=9yfjF=y6k>vu3BxbUA0bNo`?O8&WS~RTJ{7xT-PvPF&r1 zyRa&f6psdJtBwlPA_s(3caa98Km*g5z@2QH<+iZu{~^(qVi!D{aEuvMAW9sd_Y<+Foh(p!L>k|XN zh(DC`=#8@G-|Z#BczVPCrf`wzVK~n;siVgN@>u;|D zhV}Q2Tz?>#Zv9oF-XVPSi6rK}RE6}N9`H)t_i@!lt29oj(!)Ac3OAS5sFy&tX9Si< z{AZv1gzd#Sf$F5ojDTHwm?wwBu7~y!d%ET3k`4i8@#nk84C1JAE`<~M>$3vgU>SphV9(TUkOkeqe?dV( zy>8M$z8EzFgvpNt21zOejbO)Q!LlnB4CdN4lF?~TyGp9>C?k(%&1K&!6`C<*~Tj5ZB9ajXB1Zgafin4Ktz0EyxhpQX03dA zG7GaH_U38Hh-3GYTB`zk!tq_JhcIPzprsB%S-v4T{YW56gmqf<8sGq_hTSJ7VhaQHM36|g`wOFvug>8BJgogJHB>0 z&10RG7NI3+do*B{-qIpO03aEcyQ;sMg~l7=Wwm&Z$L2GhFH!0oe&L4pJ@P)(Q=3=a zL+r-`W5P|+j~);FD2e%;lDPNr7bmVloz!|PFgZs5n{U%&fnbExm?xzPKbx!!Pk`;J z@+AtpkTP9})3S1!n3T_a+)P=hIt4>M8}bzv1*VFF@|lbG=fSW~u;*i+AQ*_TFc3@f z{*!44qYTSysaUGsEU)DgU*N3t2(L9R2QJ}D&_N!_Clj9z^jE{8N8yRb96f|(rvqKo z2pa$BuV+%+Q0n_kpo<_KD@U~utoST+N`P~y09#w3E^i4v}JRg`VUXttQBBiXV zpO^D}u-QFG5Wkk|=1U#~>*h_lZeIR>Qa9ttwa*VtgX_Dyp9{!a@G%FpQq@1eyk>wSyXycONB}erB*It#C;wr-Jyr_#ewb2LH6&Q=PQDoZ>EnO`07!8+bm*tkB(l1`QJ>{{6vQ3ts-ylMbO z*YJKgz}E1-I=~jQzToX$wuTp{x1zN|F*04mb5SYDAH~p2VFF3lgIdFNI!HU%VdU&P zo`-y|ei4Wld^JA`923Mb2ep0eC7JffgKYg|%JAJA843x09*7}^hj;H*{=2z24JfF4 z;SV(w_Fnajp%_nz;*x{fuK2nFjPuXo2TAfLd7T8{PAA+TI4*G7sY;=M2)ojEq0BC9 z)}UX#zAwLlB|PbIo}{Mpxm`wxWBWI}_zseM+&@ix^&snfE|jZ?AXtmeNa|nr3=qUG z4zk|oQyvbT<26}-fd8ir$!kACGu!YGlce4utxMEC#2U1peh&D^pJP@_#CpS1EG8Uc zBDLcYP^3nOSd{=`C1F)bs5esHq7p`s%nx9hDktkuqBsl)Lz19x*X@8y5NCjV>QP`* zIR$6hA(kVU^E*sH#pM8}_P7MClB*T*y^5eieFtjwit0PC6GEJSDwc0csanS{3TwaLFDl%k1LN z6)nZ^YZ7$$i`%M5wTl<9tthEOs=u{b$e8@{G zZtX)ka!~Ww+7q-{P?ob)E2r=xwOJouMr?3&Rq@jTZ5SPv9qA*m7dvIqMK$0+nX6aW zjPJ7sJ%ZHH!x<7s$ot7^yL3NaOh+%#6fm^rae#DhQd-3G#>v=xu7r!; zCS8M*WAwlIc6SZJ`Nl0AB0FKA3}iy@RPh8@-S8zGyF0rFf0K?>K(U5=r4*0Prgwaa zLJ@x9II%W+9qMYzf|i1x`67;aiYYJEVDX%XHEFJ%!BL`cmBLPxKH$MHFoPb}Yydc} zW$3eK``Tf?rS}QuiBVp*mQl^iR`m1w2D8K#UTyTXkoAuDvV2*?7h5`z(fxv5*ouB) zzu+UH1+dhy*j9tpzJG9n*wd@+(M$>$hGA2Hpqnuu6E-@${4CR*FHsB_GPZ*>>mF=K z4h;lPo8g7hP=kfD*71OKbCS; zU)1x@Zh8dI4Gq2}NXL0Jl(~p6L79(x$*6~dE;X!@wdB`BnA+RhT#bMY4`waBtA{XR zMDUOr0j2p$5BBWC!AI1v8Vla9qwwDe9mqF7b>PA6#x*U)dWEdRtzD>8I&)vCkPT2* zjSjvki0um5K%g}bhhfv8kcADvR~9yL6+785<+UwwSRs>QNFfWNF3#WvaTm@Rh9g;AXrPPei%5T<*FZMx#N5Ze8W!XuETQfc({+4JO29hOP$EEMZsZ`m8XjR zwuviIsFIug^6vJnUmW}_QtH7$5ZrLszdX1@>c zvd*+$O}5{|vnDjNnK-h7266IXGC3<~mZtIys2kT;DAZyUCLLz`qG=o`rv&Va_7RsK zCP!BVjnYaE2FDp3x!R1}vP#L_MNY2{n#G-m$&J-Po3y(EnspdW@;7KkW(RwSCk~Uo z*}>k@;}y`X%V<9OH)u{<8~jEJRzRyBqg7HdEzFiKXLnW2;r)G|9%k9?pYt3syZvKi zAm87oP-?)~t0!+*@*W-ni^Ua zB}i8|aJF&$zO3o{{1?>pCSQV@0<5qvAe>?sBI5j+yo~i?SqYzq2l@w@)ho+owP0nwJQ4e&OvJ~%5Id4>h);;8DNp7s~ z-%QHYl9r$#G*O2~Jg1&-K%P4kj3tH@8MV|zfZuTGpcWsrzHl_?E(h9-j5!AL{URP7 z>a@SKt$JPgJgWI9rWq3Py#A zrx!jxipx!08zLX^;X!?Hdf|vD{&18XTu>T4i%xq3gIRX4D_Yfxku#&`^%ZLz)3Upt zk>Fw3q=PR}LKS{t68eMemV{+n@|R1kj`7h%)4Q8Ot`($vZWahqciT9oOVsx)# zEbm?guNmkJVZ!t5Fz=pZ#1-}yhaMxDw@&rrZ2OQ7Jg(aC6cm9&%8q6k_g}>qwh%Lp zu_@M6Dz7|7Br_(!v2EZ+w*Uq|6Fe%2*~i!nYZVWMGpw1%*bFNjAm|k;`mEXJv;Mb` zSQChlpSfLp;uxD;oj8Ws7I9sw+;K|&IRvknlvjaN{3r~YhzM^gXFyv{56^v!$E{Vu&}w&U`sIC+Q-8? zcP0R|S`w_G|FR|6?Fk_6(iel{svsOO1cR@1FeaD=+@Clr9{x9JTD&2_uE*GHh;MBI zNb*5(aKOJ7fr{rX(v3_$AC$xk;IS|P=UPSiL{AW06qb<1tyPHgm*olM!;8V{s@g** z8rEUq=qc{$vu7=iH>$nl@4Hi-3&E@Z$aQ*=KQ09){T=^V`$o`RmH8O~cEd5G2 zZhoBg*iDYJ&CxJSY350s{o!M2k4C|@D4?I?m85B(bK zrbfUySn039M5%!u!r%e{B*IVb)89>3o|OyJ@47)X#4bc4KVz$$gJmcn~<)$ z^!E+Y^Ks46c^TDcorfo0H$SVs4|FK?GDwZ)ihOoEO?z}A%JTu(!wBFGae>OT8<}ebuTu#ZrS~*mmK1keu(!FqSF?3&y`Wa}+5~cNA1Ds%$cQ?Lf54zc6zqJE(uPqN8Tw@e5m# zb89cwP#J7qd9qK@rMz`s;>|@Z%#`GA@k{yxbyit3qvu2Uq z-~le-XQKxop(eexs+CtIJ8GdkJo3*pro4MXg?78Sc2Q2H&;n_< zUBWS^R=Yr3HEI^n3*kje%{4uK@ieW5QbcfpFs%S3t=6=9MHKZnnNyV%!n8UR&MyJFT)uTP*iiVm$P;<{4nvH4fWNyXK&X$H>l9QswaF^0*r*J7AY2kwgm@|#iK5AFg#*?e5?~DFFDss70 zq}TdbGG(VwAupW zTiLHD-kbcnQ*4}Yr~AR3j*rT$NWn&B$Oj*lshJm}GBw6+S%v=_TvUsgV|FPP!ArS; z8pABY6hmX$6-9X_<;K%qo0P=tqR+il)YCJq#vrQ5ZQp9XD8K5f`>>*ytC$Dtr~`M= zixZ0)ng^=C@K!rpjp62tw0t$jBdvQiX137-d^yJB0S;ebl$Ni@c% zDD@9q>e8c;`8}ft5@%s_;Q0VsWIWs9vl#PMqX(+z7B%vGfS2qSMrp6)7R~ZZtMMIS z(s!uuPfZ=y506xEZiP7Y9>vZ*b*Jpsc=F?J(Uz36Cp;SE3%kWAaMzroY2?9f(Hh@Z zlkukHr`@6{L2}+2t9en7{P8{=z=+R4^lfDV@*ddsiOA`HGEH+8^3-EN!=sf ztRn+^yji;=4Z+-N53_MU5K~J96V`=WocDw?IDY1};2Vw6OsxM>5D;RunHZ5BJCwtTJk-5~F{%9)Him zr7MfWFu|!L)!Jx+%(#jZHmb(x1tihHvGe$64#?6%VWNwe7!MO&P$<0T-|rP~G?C4P z!cIR{({VdpRw(Qg>A9U6ixv~$#=kjr){bSXHO_*C zhAery&{U~`Uy%uBvHxr-F7qW1kj~R$$7<5`+grTLhnV+iE%pOnQKUslj7rQ2XyN^9#cM*z&3#&3>!wpK!xiBa-Mp0XvewGE%|Lu2dRm1}Jz@r;x{P$?GcMW{k5nodK z@>$DdbLM`CQ7tj5w-V#d0z$l%`;Z2Pw+x|&`dcoW2P-MX=?Lop%k$=uYBXQ@T@v$3 zBbwc1Vr?>bzwpW)k7~?qt<;*D5#ij_7=6}0rX??~4xv|`w=DWU6o3o&Sjvq9EuYk^ z==2Ex2t@c6c}xf01Aa`$i{j-LwWdw8)`!xohfjx6@4*&-uMt?ZUett6*mAll8NHik z4z|Qq>bW0BXBN(_M+O(twSz5a;eEC}`T!#->m@q{c(f~kbA%ChV?;Yj09OI_M< zm8CYZ?WaRWTT*EI*_Jrk^FvDnIVWRwFSEqbGt)^8Z(;Pm%J>&%AwH$Z5=`#Om_4r{ zX2@%nDzwv&mH-o`L55m7(OP2=n?A}CMy(Ak&E73P=SwS%wbY|)ysSy4IwDPn`AGBo za7&!2fr$9>WlQYJ$5wAT`0GHw{1B^`sg?L4ZZc9He7%-8Eq`)$F!dX0@iILre%kW7 zhxkkpdx@cMj5hAv~3hDPnMEG=P3NN~B1a#hQ z7$Vk=wS?e$F&?pQ8Y0?JTM+xoHcPSlEkqnnEeXX}@_0lUD^6>h(lApy5s^Cs5nOC{ z5z%%yBFz8VVom)-#LNYV@IBrrlrGK2YYY{!H)dMm@}IQ_o3Kd2BpsQ6RO2V00H%wG zgTpNC@v{8KJ|plWOeWN{QbZ)NLhN`uIZ!%_4I-K{Bl7*z9%kAhB3>DX2-|mULumQy zko|>-En-5H4dLlF@%7ZHai;}IXaejHxQ@e(2et~lzLYKn*sOrw6UK3#`int~j~iP&{-AhyR; zOO&aph&Ztr5wEr?iNv=AIjC1IB6vyk5)q$HM8v>v`_!d3ZachiuLn<7kHx0H0X2>g z5w+e%#Qqf}VfbDZPqB71B2uPXYM63F#Hi(n@LSu~i+(xX;%8bUVnbg=4R107p!Y?D zWh9cl@@+{S6V7k5R~xVb5xh_^2{sPa%{6ma{5a(u{Fu0KkeNo#ghs6*b_G+I!#mq@t zMZ_m89VZovh%aU%!kRg<8vSi?X?@c%5u3x_`HOj$R5)o4YA^{AL*^smqKFufjfnih z(hjEIM8qbR$6hB&2bk`Oi1+6qA~V-og=Q_pyH%mNxyX?hi@mVWGSCzxB7SE2S@n*k zqbW*6oMPm5i!2@RO)UP%JFg<*!|_vm<-4^N(J!#F?Y$W3x`_y1<|?i)w)8di6%mbC zeJY<^R@0OvBL2tTV$km&J|{_IMf5u6M`tc7>t=dGMEuT7Z_K#$ZE4oKs6_KbY&^5< zt=r0anO2C1MyysH{O04{G`fD7mkHOrSZECky~jeIve0rC+Q&l2S?D+mSy-r)h32!+ zB^D}Xq3bNPh=uO6&VY%tDDQ^fn8%W1)9gs5=XtWubm7 zw2Os?vd|6|8plF&SZF#6eau4hStyT%RZS?CfAeZoQ)S!g#4eZoSAS!fRn zSy|{S7COs98(HWV7Aj?-KUwHI7RoVwiC``Z`mxZrEELW{`&cM}g&Zu@l7)&`s51+F z#zGk^bdrT$WTC|@G>U}|u+U@{+Q33@u~0q><+0Eb7W$Bd3M%IyxRnJzXOV?0RKh~X zSZE##m9Wr$7P`nnr&;JX7P`zr_gJWqg{mAx=mQoCVxgTZ6vaYkSg0`zt!AONEOdc| zx*?P^`g<1a%ObyFp)3}9mxac%(0LYmgN3%T&^#8J#X>7sXd4TyXQ5Rrw4H_4ve4%& z#BJ+a7Fx>UPO;Eo7Wx69NsB5^96#)JFI?E3FnSYR*3aIWUU=W~vgr|X4ozwF5lapF z@2A#4diX>1f&V?ob|JMk-)E2u=){ly>mUvMWpyMChz@y@rmiXVL4SMb2bSF=Qy4BK>KJ}p`l5{&n`}}C>Ml(B<#*rrvX(gm0 z=8=s*Ghv-2gRWm=i6gHZq7!mK9dU@R&#>}m1L>^wmWJ=%yobX>Yb`nd`|4J$_`&`y4&E=~Oe+^ah7&lPY7Z?d!|bq>>}cWj;M z&Q;D*hWZ1cIb^ouX3GE*hS}_=!{LrdUw3f_)1e<(Ix`LKd>-mY+3>>c2#vDBt+%ht zS-5@33x`FyChsqcL;nuAHcDYUi(gM#dFnptB^h+>H%mjP<5PCA1GBn)9(nNyGx54$WS_pm*a*6vgqMK(zquBmgqDyId!T4JWtcUdCxQ%gf> z{q>gS^l+&qq7q(~7Ol77Xjz##(DVV#TxW@rrjkM5-vQ%0DUl^wWOjmsBjf0T0!t9> zvr%O}-e|~7r<-?L8WG%1$qX9xPLs{U4egxSfiC-4#r6C|k6ZSvHG>}CZK+F=B(4!P z6`!NhK}btS9AuZ5c!h+KmgLnT@=>=(@j9E-y&ta|$y(PAZWG zY^WSa^M;oOnVx08H{;4wEaw>(D?ae1D7B=gErX8gUYbbeOZr@eerqD^oD~thRliCg zO|In#G|e`o=NQr_gq8H6Yo9BvPP5Xzs?jC~EdJy(DQA}=XWbZ~;|Q7=G2WLB`RmoH z^ub1p4~Ahzud>Zh7*|n!QC_lC;;bi3bJfBIhkQhzc^;zz3a?esPZb=+5$6w@#* zHA_#GB^XcgCF@<4<<4f*^-4kX!{^cdm(y|XxwGLM;?c{3l_*9Y~Zro!n-i;8r zT-LCfd}-RZ9`4cmE0-@r8jx1B(l`9aRwuZ`sLbv>8j)HB5Ae-X4_Ew zuJPUVS!`z#2jQ~yOS^~mbiUP+Kn6+jKrK1*GmsmkREwtT5_(ppIX2)27~=cs@I2%B zcC@q$C4Qb3pPp3eOJ8(Y>R}R!zmq1hW1_P1f*NZ^{$`%G zJVktuX}c%h$9R=h3y8xm{_X2SFMel9##{;~ws#XF=z*Qy{xth7-@3HUIdlWs=&&s- zz*aenq3sw!e8o)+qS*_rz5=_$_f(v~j?iIWssP*WyrsG6J%L@RVzY+Al6Y>D53a6F zQ!b#7wp@o@s>hU(}(G@_@UH(h(*;X~(~l^qAAVV}tJ&3j8^0T8@}1^l$m}x)oLc+N$5!h>R18`!|R99j=!PL z@XbYiW^Kk2&~fTZ%R2;z@t%$eSEiL)I%CG}@)f6QUF`f`LBZV1(z0^37d7;<>_zF6 zP-Agm7)`yH>xIct&P`IdT+27l`d6jT-^3(gBleT7@038-{{m0q)-Er(S+~$|3>L^2 zJdy3u-!=k0s|8W#n^&e=9tVcd8`W)IrgsE$zQW8J4$Er)2eg`X2a|1c4e7IWbZ)cr z{=^elBw#AVrYDz5Q|?;YnsynIcIZhB&{3TG7iOk^5~Lp#608Z+mAQUCH2ZH%&R#NP zp4Tza{7oT2--tey|2M)-^NIvt6--md1=OYE7y4DD*>_O=&EIGpcC~MW!*-Vo8&9G` z{|~IbljL?#x!G4?q1pdhnvu3TS}Q$m=mSd*c~yc&si3Gsa8KOS*FD7OVWf^WOi$x^ z`~DHesqaf8|Yl-F)(_RNp!l|n zrL+}qxpTyy@0}4?iw-ekRdI_J($Uu%k981(4B_5?ysOC2x^G?HoC7RB}_x>f(o2zHY?2>dE3oju&PnOrW?vven`FwEJ5lN#qI9 zz@iv~X!ihXoGF|J`dHg2$`9ia`5UYFFa$@URGW6RcNW69$xPo?+P2bY=Y$noy`>f} zYY%GQE&J}YO;u}Kg__B!G~lc8zI1A!wG+Mlftcn%-dg3GtmsY1)n1Y-^O(8DDot%B ze^@Eh=_i<(wXU4t%ED9jz?$^t$x<(}&i!~0T@z}JHDP}PUQu~`)d}tN+-&+<4Qqj^ zP~gnz z=mFf|U!52fN&RE3UgUyG85e79K+ftY-{~nl87|TYiN*Pwq+K@ySYfCVK$LQ5Ly1q4*bW;I8*9YK9 z9U!#=!0!oo?NKUVd;^%qNF88U1%Me1tzl%53i!RDHHs|I0p8XFxO2PK$Qna-sDR;( zk*94sz!o=v*9w>>uO+>TJZd8Sd{S@>^=kpmUR0?QlB`MOoR0cEV&sfDFBkLN;7IZG zBlT%V6WIN~x~K1nJels&W$}1xr|0`V*;tW8X5T11K=;zo01;AQpcu`Q5+;o7H%8a_Rxr8R*pRG+@r60+xMpPpm* zbmi987UX~G)5}_0+vDm|&C6r2k-^LRZ^QPs?QPH~IaEY^TWdq?EYKi|3=xgnSrf?} z_32&htSzvQL-X|ShEKn9-)R6ftV#iQf>9QXj8lzxa&jr7NwA%T8#l}`sxb3Y$Q2c%l#$YdSlb&*w5 zHjQa%jTDV!%uDEA3`|3pahiLg18$33m}$(s`QhKkFGL!aB$%bH52 z2#BJOi9DZaWCGs6Ic31ccy)TicW#xmO)4#W7sfo6hL|tO>K_t9v!{f3(SUSoAo)lK zU$2Mrm4+r6s7BvO_(>I>`o67>;NF@M5?wb!BRj)a(LR@;zjG_!q(H~k>4x(~KoA(0sS zD|)JGchwV&SNqcAeNhwI2`-)RCLZ8xv>cti&Z?r9E)Vgc4|`byO)cDLr5u~mK0T~$ zc>g!a?kWY$2uvU^%Zv|KGQPIIwFkDpxiYDc1{;zv&7MKO%tDX%!2p=Z3dvliFb_Xx zea5uJkok@WlY1WMg4YKFU3=5*12ImtN*P6p45z}eE}Z7n^*3&jims1obiMdR==yJo z{8K?X67bJW09L#&}B z(xIJs2zRhbo<1=!9q!&r_%lA z*?lSZ8?Se_2a!2C5xEfIUc^tQZAPGW?2)LQDvFoZtS+IUbn&YYw_S(&ROWWG!)3DE zt95x9UhT3(UR058uQqFNsF^0cg1UW9$NgS#@kPOc=TV=LV?!l8=o3<%mXE@yx;D)n zhMrDMx@;sy%E_4ykXVN%>|#oXGn*v_O)iAy)!8EqSDjfT_W`Nf7+&-|`^i2S1P zYA_LAG;f?W66*pyk!CgK6&Ib^2^Kh_M|pa_+{xB{v`4lz2wfvC3|mS(g*j)yyx|N( z{AlX8p+1!SGA#&`!7ho4uGYJC6}S&=O|923$~`IAC0cgs+g2=7oeB+8)MRsG6*U#J z*v{lzx0)`$Zhf6#kFs2Z*W~)pJ~TZKCR#h0{x#&?bMd%h)*C5iBxxt`q;;uM*EI?$ zco>RlxGAvE7Ny!+-?Y?aI9wTlm1@)!v*E)fd8kU}>IkS*BZ?ag8LZ>I03P!q&*9XP zY=WVNesofu8o|_-AL>QZCt}F4Ov0C_aHmGXX+acH?{L5_4^FrGkau)q=DWpk_k!18 zHOqv_Hr9xy#troqO=tqT0<4=e=B(1qVSCugdE}5Sm}dX%?B<-|P<1ELX|5ePQvyTG zp#IYm17-K@k$i`Lo$v57dONNPmOrCL4LanFL~lCoRH+ZSr+bCFoGmu1u=T<|xo}?z zWx{pGm&xK}OlOxTG{5=DF#6X%EUwfo(-ve@nNWP4GMD0q&V_Z4cndQw&r0kwDi-cL z8pIf7J~~5(eOiy@o^jq>c*fZho1F zwlp7lye6=}sMxHYSb&(m(At0wX>~GyUZn7WS9I9RZY*~usF}Q(zU}s5Fx3qD*8A4F zrdXQ094&M-O{1*$jr!>}RLAKW=n%F%6+&mOLJv8DrO_?C)n>7BHXIVTMEDslu?!NK zxAI>k_Pp`o@Agk?`qAvw=pDSoWXoH3?J&hwiM#|W14CR|hiq>P|{-VHPPlOYf{~AmsE5Q~?%YTMqZWV-u(sLW2?(YpH zoHCZcSL*-Sh#`dUY2k9cPm6lZb*B~ zJuhVPx&HWHOWM08yx6%I((|-vc)YHb#F6eCp|&|)PHSfm&R#9?r-_>}sLwhrW)F~t z+e;cy%wGEaR^py}X2-3_aw#O@I)p6M30Z`n)ja^VTD5{@v$jX-W2e*y2-pVO9lH>- z?pwTR^fqgAvR$W&Pa#5f#`)fgHvhxS+C_;vr=ku$XN{nTd(?`fj~c^ye6K^Dl3FcA z9-X6jwgftGhqWa(HBI;wPT2c9QCK{4Yi8MEX_IYM%rz8P8B{LlQBEN8azbXg z`Zh~-LL2<=1yY^se{@8;m-NoAwm6mBiniZv4L1#8nUM7`bFD?xL-Hb*q88=z-?_A$ zw#Pcjlq)hnQ)V8Iq86su8s`5M+<;!3;a`ADw~3bZ=Z`CzZLIcgMF#b%Xe zxnpZc*JamkK(qVTHq+#NXf!|8;WkKptVRB$hI845z^nVLO;L?F_LPbpJ3?Ta(0~K5 z;8GpYt|#)Q2oAprZSy(SKB|DrHlq@ndqB}$5RDin*llU~PjJtVb?gTfu;+h?HLzw9 z+eC{UbPx*1X&fd@GvsK%VmlM%?Ux<0&LczcF`Nvn(`oYe*3Ks?e;tUwblOpC zOR`92Y=M@?i>Um=+AZnh#5QbUy^Ybg{LOdA6RAK+dHE0*B!^9 zMcmnBpn-x4@#uSZ!L#L+ZT{qEDd@+F1x1(Du1+tVj1l`8@PSh!X%~#8H9KKV#wp7q zU!p7m&WQTVt3<(f)<)FPpmqXH{<*dn?U-KMn`Yfa&#L+vZO!(<_Et9BP|A|`tfm&D zN02Jm9aVO!#X5?#({WnsIec=j(2B0qSiu>sa(EsyJDqGo%l|+fds#;ut|#*8lz;44 zYhNXZR3)x`_U(qI8}9Bm(I9d7uzf6*EKAf7VJSMq$*us2Ewe3B#>0bnMHO~ z0wnB|J!Kt2aEeiLLgEqFs_!a44c~<$oi0d{3V~k7kVZO4!dX$y@n=0fR)aI}Sc4VV z3o5KQ8?I&6>hOlbZ9S*MKC8#_8a(ctwGFl`x$`(*#qt`Qc{#i#oiz+)KUasHT>#?!7Zba8-^c^OkbIoHl z-6d}p+lCfA)yEfSOg!>td5Hc^+{oaJ_+M}ua<`FtUbqoB0RHz&uw>>&UYIJj(qp$$COLMI zUY=?nO^$Gg>NgG|#&c`f;vidn@*{s>0IeQuOCsgEr!HL74CS%Y@p`arH~HtH$PE(k z+?>7WTIl7wYu*e+3B_X1@l^W&TB(Mu1&PMNFV9o55tsDvUuyu~?vj8bffLSEfp_44 ze}0j_I=e`upS?Ako8T`$^^$O(i&o({;v}88*YLB`bF$I+_Lpmh@D3qabxBwfy(BCN zfIylRZW};WAP1huWtV{~{Ux8BUR}o)PmW2z5fy+_qm5|ls>pizGh@B!;h7j0AJpMK z=h;jn`;a-e&&;5UzJ-`N{{P@Is^=;AQW69f~D7&zRO~p{tFd6B^jENvY)7wY)bQ+J>P1X!3WW z0ucZqQ;kOu#kF`my)Mkd)3NaM@G7#h$caVS>4V#|#o z;49cKH-@}5k#aCzPbAW`udyt)vueXme0BOLHvuQHlF>hTUJB}OD5&7why-OE-q%o2 zrcMx_)?V1mR+qdj@o#DI&SGpw5Bp)5pORj?Dj$rSW*N$O( z9*+vk-0#jeUHh0@@+*@_$z|b&k}-o$`TVwxA=M4Y#YiGu5$w7j&1{XP;Lc^f*Pmx9 ze-<_-B>#+g;^`^aQsp>{iICsj*;UqpuU&TeBi{0KY>OHg@q;MS+B7iH)+~RHwLTl$ zHl(-A(P1>REl#tuc{ME3<7c&MhtTy+P>_K?xSSDp8n3m7)95Lg-QCQY4bhfyX8GV~ zUz&LqbJ$(9%rqU7=d<$o=r~%Ci`kKN=dheQMbam_=@=vo$MU=;i{o@S-Z%WA6M9G= zNyvH^1l=u}!`_plYtzqLBZq6W$PaW#p2Mb{(S(&qq|Jp?G+7=|*DU71inYvdjhQcZ zK}+~ZGXHfmF=UbX?AQ+ZWXol^(MR*@cXdn*gND*nOEe$Wl14v@Qcs|Nbg(t$Cd9k{ z>$=-onc{h*b|SGZi}fgQJ`}pr1HFOgB<@)^ZpTx$VWwvcNf|m4GB<v&_rY$Bi=DA6tw*j4_ z>(N`G(N*ZW<+flGu5jyx2ASQspzO=2M4R^TEF``_(Xww$ZF-|GJZoDi12YIN8BOXr zE#J{kPR5y<8{(5)c<%7vP?&=={lc9V-_1whuXxm;UEEO8N;&Sq?xrIaDR1k>k~o~T z(TMsQze*p=3pf4wFnpa7^M7oHCEvISk8Y0Fex>rYoaUF+wA z(5VmmiI*q+`Wahu(R1RYS4Y_Tx;-n6?#qhN^Og{Q8q*iWXvQ_P&Ls=B6|hPc>RMMx zz1`1)YR|Hb$9xi=)0uDw9gRD!UN6~NlGg;yG+sQ(n{=MbI>T&}FpXry8>Qp%1(mhK zZB5Bq$y=@E;jObVXJo|7*YSA%u8xEQv`8LKyRbKN=WqYZwuzW8GUDM7u||8G*KU-p zIk_)+e`$HecTm20AIBunywVsmy|oq1=p94+Eo1z=F}6fgJ(~NHtuxlmhrWy&%oUmW zs%;Eb&=H}ltr09%c8`^^<~^blV{HlSo-~HbRB(BI$Y<6-crN#RSZ_mmPh&dQMWt+8 zBXnvw{Y{0A%=T5B%o~Q_DaK$vjd!V)y$Wr2tgdi3rvC{MxZKXAk6AVh9Jr$0-Ue45 z8K~zYV>!;k#F=2(oM9fGzsYxs7~grv!Omu_`up(@ig!e z+|zuec$(rL#9}GmF~4ps{7n2W%&(0$)unyLdw8>`UtA7?SC&!JP+7YFA{>4fx^ooF z<?fV!q%eGE(*76MNSZ6<-w2GCnBO83C3%8E=KDj! zr=6;YS`-{aFTEV*&3zee;B?7QT-q2V$87)36?<$HJ^UuxkTnwifdc1yS_Hfp>3E+J zmlzo2eWl3za%jpuicP2A<)AZKEXg=b=u!y!09owf0t3`h9g5E-Ri6##drzY7Dk$D{ zN?jI@M3en|E&eF!Su!>YXph< zRXZb4=T|YAq`W-uM9q5}j{Oe~(@2>P?$E=bf($xlDJDj|u8KNViRR9B)-iXd>Tq~m1N!3i2EJlp;R%}d zmQB3{=%F6XJ5(!{+3JzzS82&=TwCJ?yE8p&88XdQOL1{7V=8^LhU@|9^%X_&2FoYkxZQ&^BWTuCcKVSW*mnHn6AMI!bf~;eQ+jSqt|0s zUG9(%8z-;Au<>vCI1UOpA9ucYG@aEfK9pwFiEemI{or!|yHRSXf)ZRW4pRUF8x{-W0t_Ho8 z7B8JnFGG9}J)SwCR0pDsd)lkGFoe1tM&ThdO?;&Y& zaQLYdvsojC@1{Bl6*=_=EJ7P+ifjr!Tfl2;-%D3 zhFvFvyXOs?&;_ePCu~HY3D=5m@F-^Pp?ke=YeAzop&Z!$8`EP$`U4%^siZ_xGeK{n zrE}FBj)ZD>&#|%iU1L8DSgeQr9Qo8Xf$gwjIet;g#KzeE*o?#9LqqDXBXN({e7h|U z7dUg=0xiy|NX?q^qZj5jRE*y73sPWtd9TD{u z+Xb;%OU7+*#raY)vXpNjor|x^|aNT*X(?}P*igQ56s&>YHbb8*D*f&%xpRmoWTQ7-@KY-PMDLUjt10>tl zHt%zEEVoMJCKbs?4gquPC5oLKAL)?m^+;~`MPFhd_MJqYRFN1iG@{659Xc!Sd=R~J z5Uogwj%zdIX3)(y{;5v$;?1 zb`(Cjsl+B}u_(0RrbaAQZI}~Sglw3VlHxkZJ%;YoV99$y%Nw79Y^J_gFInscc}`1y zwgU3kwjA<~gwEGOu>{hH&T3h&QGQR1Mdxanvnyb-^(xZ|4fRy=lZ5`Dg<{2`5lyz(THy8|jb6_i^SF0;%?j@lMF032e$oGiyPoDR z5d|ON?wt|y4Q(~8c0=^1)zV^%A#3cm`j`yi)D8-j@?64TWB4Q*X{#f)(i3?X!d8Oe z_^X0AN+qs7h2d;@-}-T46Wd4~ahRLP9U)u`@0-=WE>!Ke++zFA0wGGWGV zugCY0zIHZed%dpK5Iw%vI{J7M-V%{h%VQtJ&N2?@C5?Ko4()+6qJqx1mzCmbFt zQXDqf3nUM8qV6NcwL;AGvvxxLx?;+VS}|pYhcT38>(^PO^&9g=37Ti~m@*5y>FHv> zK8NY3UN?m!=s}m+oQ{CA_{Q11pXw*NXU?!5h(u}y?#v=&1@43)oI1X&brDW%s=(9R zrOvrC4!?HM_BvJrHMH^nK^t9eYeznonZ?H^JTrSK(%z8r%&r?9ExUYcwfNQlf&b&Z&FyuCF0J z(-_a^Wa?c<)A_c$o;am^HNcDV9m+==_b)nYSGxk z%$-PJ{>=Jy=*_>7du(-P;x&Rg3PB2(J0|~o82i00>DgL3pAmwKV)Q^7ecx7_ZGbkQ zk9N_QJ&V4&^rU}DPuj#wS=nXZz9GB*5S>;>HRsCi4n|NHIJO~)97l8081(?AaZ={A zM9pb6ECe-wXnW0s)nRAel$pBXKRi9@f=6&pk0j_{Ehy`;Z6+2BT_9y<;x3ErOl~7% z(si~qvMk6x zl%)x#rvmJOq`3~BTrvDfGEyRkX^?YCmJT$i0?>#MJ8n&spga}SCDcBLEY^V*RscFx z!`_h`mY^?H(2kn+9CAPhDy#s6qDUdXOVBkHRHL?iHu*&dx>5n?<8b7t_Mddw4y-1m z1={5j7Oy@zb?iB~;(*6$oLL}?^{9!wIDI3rhpqdc!V7oN^3d&=h6Mv7UWzeqM6|s* znJ9S^w7ixv_9<8#Fyf6d=IyU*Z%Nim-Wn|r`5Q`;Z#VFxkN08I!Uu-r_l(JRK5f)c ztYF(DzF3RD5^sOQ^sOQExC_b~AvjFl<;EH{T(Ve6hBxMMTSOdRNk0Fr=!WMe*fUA3 zyTYS)EOj2CuW?s97ZHpYSHD=4SVMa)(*CZf$!)MLgY6Ut0%zgmZ9E-F%e&g@+>CSh zK|D^m-pC$FMo7v_9u#(CO=e8vD4PEaD(?^->P4Q(H1a&MFP%DH<2EJPW65%fT&f}+ zkFkZpk_0!qNJm?sr}1r7QOWkYDDZtcs@O@aRha= zfV-{x7cV55+0QPo1meZy+|Ut7fo(_+wX_G}bA)~Rp(bnUY%LbcZv(=&Kb9qmi%6Y| zqK!y~;G3Qn5A&65y@a~7JgYG-i}t}?3cb_qKBlJ(Wpy)>#ZATB*4~KBkg}#}W#LeK z9eS&+y}fC&A@_A7E+443ZEue!TO{`rEtg-*KKL0sj`QT44529k?hXL}-P zE4i(VxEZ9mj+U&a@p5d}1+p%7c*M++TASk+ULJp&feL>yg-dH0?Ru zmV8esezyQebYdgA&L^=duY)Awp4J9x-xD^V2;ikQJOj3sc~96@ZxzmK9?$sUUFW@N z?RK!Oo;q%KJ(t(Mk6IVr*^?Qm;(IF?gk-Q|KaA!&KztLv#zXQ-f zuvwvFe~4KjwyA-App$%iPc(c>Z{u|RaZYQk3>uEo+@AJGI_r`noNcei1q_)qy}vz# zT$4HdMdlRGV;dFwXb>*Òo6a8tx+C+SL@t2^d7 zG=w4dX~`xG!tRT*<6L~mBqD$18C*QL6{Y6)JZ#PHzP8kS?~79NzVE8J+|eWrMC+Ay zUzA#kie1y+DzG=UBnH!*#$`UFy$;*PjpZ7>jJIHQ<>Eat?{-xFeF%zV8m=-G`U+T$ ziwmGHeguMj_muAKBO4yvUvW92G?v~+gl6saZRA{Cdu{wzO|xtdx6Ay0D)J9V%ui)l zxu0jRhFd`r>zdtnUToIA!pC?8SqphPyD`fiO}>}JQ!ZkOtptrZyBQ&`Nm4L%yqM%i zR}6)&OLauMREou|Yuu&NkM4iTUK49{eFmcP5gPM6duJRfKz2*sut(GJL+v$bR)ZuT z`r$BeAM3aeD&QU+4sJ8SZBhYu^$2?%9FvDob=I%}HNAwswl<^1BLN&DfG>Igk4z}7 zL&prYhtpf>NdYt|u*3(;bQ)@Z9hG;xtgqN(u|mgRe}M=xfXpvhlT-+!6En(sM{1kn~s)kIm+Ix(o5s* zwN2HqF&`I2rvD9V&F-5NN+U)mRj231pikA# z5RcD#d*a2%2z+0MD#i-_Xb*niIMfDi2RB}~N10wWqzg~MQfQ&V4iTXAXsnJU))bb7FuuyS5G8-f`v z zCb|@(CHrNS&A;f@ZttmBaa+=G9dW3R$X~thEHpz@5?83i>bdq7WSNe$#KqyI1XZ$9 z!4Y<*V^J=v?})>Byg^`}ww8YVG&IGoZtMNPE_UURvIp96rvowSa`mt|_L}6L%->y^ zKLCO#|16JrEu3t?sm_3r)S1%bK2Y zOW@Oljo*Y-^Un-ENlV@oBQCd)8B)kJt&pD`` ze)*$gk-Z(=Gs~`g0kK`K4#(Y9*b;ji224PZ>aocOT zP9Kwu^Y9v4OD&DxsMd2GXqZxDv{$q=d_b=rjazT8fwS-$+Dlp*uM*ui;GHpz$Y?9I zv_~84DLC=20WCEI@w%1%k-ZkSA9LDKEe#i7;zZ@PW@fsnt0M@f-Zk7Wv|RqW{XfC$ z-WEtKhj|tXf8FFwcwOuh@GOd}TAJGfJhvGhAnFNv`Jg=w(^ES?v3J*2{I&|-lUrr_oN|?YxAXT$mcYPo2=mY z6~o*~r4}{~qM83gH|=#TI9m(mqqkSKWAyfkWNuWLyix*JndU4#X{Kurq6WQl3E*{F z@P}G3ul=(M>~%<)1UnS4)8~g{XEx~er6+yp<71#VnU2bD)zXU$>3O?Ae*&LKV@39` zVtPa#=!5&=%dP)t!I&NKEH`dmtM{PsPn6(>w9gigN)P+$V5-kz)mk3MBD7vw^nc&q z3*1178=%EG>v}kSz0e+N>Zb+wF$D8+%io7`nCE`nQ4yd zwBX;S`1bRkO}%NsAq>U5SxU8dtFQ=N@#0O!!+QhXfhHcY`@>ypBxv5@uYeS(tCm^` z%?u^*A@9|r81lX%-eH7JLVtWKGwVs`J4{;+CsqCxoYYHN2}2AexLshS<8ag~g_z}9 zF>d#A={Wj8OSSMthHyT5sP>J$f$6B=9@28{-`LYlUubFj4Qbry8=bJ%#b>*Cfnq16 zXMw`zo6zy^Vv9+15mazh%l+Aq%eOgnDz+!U@&9qy-r?QszrEn&mz|Jp0Kc-;-vSH5 zvcw5kkmk&2FpJe02UqN1t344b5H4yrHmX+aYB#mqPQ-V;_kt>*8GdAcwsNzEEP6_pydlp7&iP_U2fFV0ovr+P$n& zF&FDg7nj{{7XQ6Msfx0})A3fm?8O_4E zMUXx^a=MnxoZXw}?J;DIB$Y@!A7p~`1JW2iDh@?KJTe3+6q6Gh{@5C%U)%X?AF6I~15!#wmW zdYH>4b*W0tzH0A?xl&Eu7U)>K*Yo)`bW;vV%oi$#_o)5nLJ^M^VoHGzbM|Ygdv#P^ z#`~^=dRuJH-ULl#fXyFg_1ZQfTgii9k8PSk|)vS#5X7irf2wsA*Oox-Uhwm7VY^LrlbC+%kx&5=S^NT?J=qgvcu05jsDwS5A)8PctIuN zwr^j$?kU!*$Y>i&t)y*LI{qH?aaPCvPS57OmU;KlYpG6`{f&b1sVWMJ=QcIY5r#$j zw$U`_A9$duRkcM{xhgL*qU083hE3`J+T$?w%!#d4;*baSo}`72gYg8b)f!GZ-TM&s z_Ojp%S2?^^V+`fTl<7mGrnmK?Yir=u!xv$?bf`|iV7-8$kL@Ed(aiI>Ol6}k@-p(F znID|2wldEVNS5e;@3?_6UpvwWEFh**NgA2&MRQ{uDRl5|#g=T}wGF|A3KlO@8Ivas zD%0UKCxnoL@m3EhV6M zRS9dbYlEBft+G?K>7dn^4+{0ch;X=8%+LzNuzAe3?c0UPc`Pc$prlnXYb|x?!7NAh zmD%k=>Hk*ab2&+7NLi+pvcy=5bH8mBIx8(Nf{w55@FK^gq$3`ZOn#10rh{7S=bqTW z#4`5vMBelaehoQV;t${-5_rP{*mM;%gg$Qd{E!Z@^wj_q!EYR|xYA$or1VF;NM8zc zv?k%zM3IKk+-i>QD9yk>N`q>*K)bm1?e1 z<@hbZu$Uea-_?n~wHU@SGsMxygmc3NuueJ{-+p}|)R9akN!V*n*s{BLYnDx#eWrad z&HS^yAI(d|Mws!2>{oScepD(f%#lPkNcLJM+iBH(O{)!I@9SXvtN<|5 z9T%FpBomI9S4LqeRgh`4QxGgN{k8HESDX$P~tG5fJ3*%q)F*RWc zbav2~#maFMu6(ZSkl(ip-M&mHFZq1)FKnHxXCU{5N*s(!%3R>beelHU#*QfLvNd9+RKR3| zo9YXYE* z49dVVX?2>}3GL!(9kNW1k_I$yrpLBF2$vRr1p60BCJ~aJsr|R^DkFZH^nB-=u zT-!JHTBsWtMiP zeR?_FyEBXNH2E1vW$O2!lNY_;8{RFDCDJ*1KNhQZ^t`iLE5ngMy7-A^KTVP0th)_p zvJG`1)q$=*Upm6gO9nF9S0ICZOoDp}bL-RqnpXhRm>{K%Q>39?$;A4n5&4z>i@fG} zkk{=3d5t80ppc{bf&88!8TT+5>BVVsk)&AizEya(F@4bC^uJZ72xG2aN>rQl-zMRO9`Hx2Wr{cnfE- zqD{Qhu_m8QBT@cZmr=)G=Q7-`^Y{yJozF;kh6-n{b7f{)5G{BSX8yDe`;;E*Y)0yl z97&v^5_x~)RzQjuy)+2JVf$d{ZkmoeSvh=B!84w$N^rZBCL7ay z>5wQ)-RHGHdy=RVgVTB1SKw79`xT7PvLt+v3U~BEFDb&0O^c7gB!T~NWF?>;BwQMc zX_DY)1^atRr5m zfS5GF(GuPH^{-$A7Z|8m2<~kjO#A}1CgB^XD=U63ea!2QHt520PKwIGh3PMm_Bu`* zJ%_*H_DPNo=&^Io7?pz?HuwuB(ZiD+jmRrH;s`y_S;h(IrE}tYD$!lAYtx^sN*_&y zi&?3|F4trExrc~19F5Q`=h)*a_Ej@>YGTReDRNW?JEVv4x9&E>(F(nA4!f&V0a~BphR^ME{tLTKq(?qI`^Ln$dt2@Qjrfg7{EJ z{6|l8c0he-;yW0mCJQXiYPieP-3di*YvFUFXuxsUSu@D1AFR(UyGnGsFkh4f7nKWR4D59N!R2a&k@wu|383aBQE&81UDo>zB$Ne*RjlHDqUW4Wy^@p`e=V z7tyU(2IV0k&gn32xK`>2$CM36b#bHEw>&Wyw6<$yy5l`Z9p?0%EM=WLg~iGtLVFy@ zmTOFWUGdEOe9DG{Cn#XP#~pKF*aODsg{vH4tSK;{k1?imrSeU2mp^dS!VC@^hd0l zF%#+7fT3*NP;GUN3Ki8^DNep7{`W>lsOecjd`2bOmk3RS&{H2dLP&;=__UsgQ9)n& z@5ivK*@Bp(5}nm8mCVq=rs-kM7p_7BH=`(a2;w%C=zQxoWQ&gYiJr*qH+U=T_q>3e zQDGVb{$r~njGWeC%k)^D(e%BTnW|PpG|yf&l%m0z3TaY*ZiC;dR6|=dPt?#A4POSU zx!qBlw35i?D$@DhQPh8Gx2p8Kf6|=4L?I^6zY(BDnRnWyC#vpsK_e|?P27K4s=-$aupf;bDv#s;30Iv zHs=)0g?;db=^9b>jdgyHgCo?OCh=IIJX- zMt|X`$5vHXKF29w-j%%Zg`+EGUyL&QiZRn=R`@^&XMUhCA0EWJzh{VBX^eAjQSzo= z6v7NY=wE8aG&=`>tAL&UB#Nf~+!gyU2b2bp<27lMBaQ(|TXxh~1~=9xk2(@Dv(CwP z74ndt7zOaQ4AvAtU)AlSo>1Nb!8jFZI=9om{!?;Vp387_2P#XNGKJT?- zZU7U@e|D`&vyX!uC4SeOajC;%aj$s@~A(z##Y++Qd-O?v6Qwf z+}euvo@9w8*TMb=)!YFLrGlG z*oFY@iSud2yTp+}oC|*ncV|vG>MY*zHs=*r33R>PL)r1QT*^kB$)F#Wpby!bJ=@vT zlO>iG0z?qS88Tlw=A@$<9aoB4-d+5zu@Lm_AcEfdyu_ECV~{;H?mgC)!F#+R;8V2l ztdoxRSb5_jHAwX843Nw?Oq&GlBP}g;G8S?FeRQgpu#NR@E*#?ln~^HegHyQN6i|fR6maCv?&w7Oq$+ntUjM{egB`v7&RLETY52TKM`|k7 ztzNgoI<4p4o!#`>1qZGUE@cje-3E*#TvBWPyr|tvrYN=h+PI$hti)}H7ry4_migjT zRP(t;GG}v{{G)$-WZw9)Bi^)CsAdy;pgYCGxSx?thc2yEQjMmXPB)^7KcKpQ#D3R# zzV#w&{KVlJ5$0Kkr2mJn>kf$G`2OCi6h#p1jiRV1Dn(K3V6O=FF4$drkKd?>igHmS zD)Q9C7LBpR8cQr#Vv7QDw|my8sEM(cB&Hhkd-Ha0ZtfO;|J>Z{?0n|UoA=(lsY|3M z^JjR2&iC1Lkujd!y@;4AQ-F2DlbkX;1yh-h>uq>Dew0dgQz z`c{J^jb3)&uF(Q7H}^%?qS$L3e>~UWo``U#U+2Y0j|_A@bkIc#hONwG2TI-06rni$ zn4}3kZCdjg*QFBNo0;t zfeUS^~cOzd+b3^n!PzfoaS9mXQS;7u=m(X?`e0WXaRv@r4LU0y8- zrznVd?*#<O3xe(LfW?y|nz6@_IapO$ zO?3yWeTKzu$D*LaN7(_=x+zZ5&`qJNWP|$tAi~1 z<>W*=9{A`F-TxKQ|Ni$w=!c41nqXh)Kx^)ThTkwO4#h()c&o^FUF*PIFXYaQ{{-;W z6)d_}Ay7`!+3!nOLeSH7V9PGpbXF(V8~2y3Hd!=g2;RhSd|Mwl6@csuft}-uLyM9Y zc1?w&4ER79bs(@)oF`Pd)zw|Q8YKf?6m80i0*fOtY3paHiysSd6mrg`5K=p0Ql7PI zY4~phmZXfuBX4J13dt(45LyhGQrZ$~`b{Y0jY}bPj)e+IEMuu>dg%iFtUzdcLzIHO zWi4f-pa9Kg_`@47C!w)7MLqT&e$lcV>L>OZUQR43&mI_?1ebMZNO3xHXIEsjASC<# zFg`8L1Jtp2gsg)ra$9F)ruDcdKd`W+m9)>H4`lmBSF+TS7VD)gbSW(})KUYUdtbv7 zK9w!Lcrw$a26KgS1Ym0AWmPQYq;GWK{Vu>@?}Z!SflL?BUCyBPX=Iw`S{{I)_jS;_ zuAp@OFD}s12v1)!P{G37z&yHzp*EC*EFn_yK&|<3%W!E6e&2zaDs-|+L-+-?9@Oug z?u&OArBrrmG>N^dH`;#z+_uD4vrLwvb-XA6uUoLCGakKhK?!q4A+wAmGPBHD$X;h+ zWW5?_RVVAv69j0xRmGY9$=y(aYhn|PbuDQ$%gmR-Edbb+W`H_74o?=B(ZgLAZIHdNb*5-y&#eR32&~Wr6dvoQnAzR z;%h+OzC`!Ta=3sJ66ER~R}a#>Zb9s6$)|6GrLr_YhwUrE(lOh*6VUMt9NN_n0+Zvt zp=~6(-n|Uq-HqV1q&>Voj?25JhO2INz%28ME1KzmO$x= zPUjjfKu*3+&Yd7GPc)dbHMUfj%2w6Xv=n~s7S)vD&`{&!goKKa z|17>N%zF{9T^EEGPT@NS->L-ck3`!ipc9#Inp!GKt#$Mm2Rh&}t>Q3e5jNH9U*oI6 zy=J%tx_N=fO$tW-y*VOJ(vio12-z3z^iL=bcUqvD$qsmOclSlu{Q#tz1R0r3_8}`s zJNL4urgm?w;~P4At3pJGRW|KrM2XX3JF2pAIgOF2;w4&H&}G!g!^sE6*6=!bT`gLx z(h!)QPy_xdi@q+-Kor1D{B9p|lYOlZOVRGt#RABs+@?LFdv#9*R#F&#Uwn36U%L?Ci2jF! zO3+eC@PhHS1Uw#}fHIHQg}MxAYf%}oQjKa{$Y^1COb3fUo@ONIff_V()JrqiNDZu? z0j#$XjP!qgCrf3VMkFw^4i<0-f%r2I4 zICDtYhjnawC!k|reUg9|5U@jm2Mw_MjIcPrKL$@V65_*ZkdT@;3{DS1xhIake~Hq$ z?WN);Q5s12-K%NBU#+MhTyg))_LI5?(|9G*TNf!)H-d4QZRvuP+5G^vZ}Ub^OATq7 zPVQtP7ZLPhx#&uG!ldRsD#MjtNbQ6IsU7Q}7VkfZjknO?nRxNEH=4oCI`{?$IGrl( zJj3D*r~6o{;zBRy0;&G3PP*y~csL6&M#>F_pqF{A96-wgti?eC>Sw8f3%kxhPr4X@ zNSB`Vw^Wk;2?opas{$C!%RMk~y*l-! z#6~)zi2@ZXRjp3yNEIxWV4w?VdBkUkrK;3R$LTKM+n2I(5PVIkbVM zVv?nrG+&2F7GUgNrzeDu?ok##PwG(|e1@V9&Ne{LGC)(4));09mG zslH}_Rtko0H3|jMvxcUWcuX_N(w>^fA&6Jo^zwz}M?1J9XRx!w?`S3&6|)d~YLEbG zxejBntO;(`DF||LfsjP%^odC7NgeW-1JX`vB<|EH z2=bc`L(ZLykWY2UpN){-P-0aNTuxbyayD@aHrbB_mT|WNAxZVga3y<8m{*`mw-#X1(ZM{G3R4!zbiU8F7P^$Z^Fr;edzQ>xqY;5t8Zt8&71VFpCdBdbv zJ&NGGWN$CH_z!OVRSfuf27D5>b7o7B$qg(EEUnOV`}F2Dub~iXM$$LoNuck1H6RV6ZK>)G#g6z&>#Sv(FEC!lrFK+~7r}KIK-= z?dcAA>1aO}7g)$b*Fwm_c)Y?=&2&sF+9DBM7yE67r957Tj;*W@pE{@5*NA>+P~wR1*IL}qrT)2i zCic&jYHLbezP2{of%)lP4sCFfc^%gRUuR;cP^PxSR}1>YJ`VM%&4*1il~j2RP9=5F zA=`?Or;eh-@_db@rPNA5s=c%TB(e>0pIh*f+I5yH(sUheiU>!p6&pSgg@K=;d73ET z*8BAT!3~za&>=T|2r>vxPwV3jo!6ty`CP*llwxgdZJtM{J*l)$S+}AvdV{5cbX6}P zTP%RATFj+B#UXw(u1>Vyi1U`;8^A9Z!HKowHX&=@>9~K1xWw9Kn~}A@8_<4rppihK z43I#*CgITbcliIB(~v%rK@!#Cr8f>#1 zG|lGVzEWbSR)snX0O=KoP3~*42qV$s5frA3wz_gbqM^X z1&1KP4gD57p}YrueWA;7GzGu8g1ynI#&#P>FGIPC*^&KV)62faV9wVT9|^y)NlTQ$ zkhBwhtMr)Mn&2oS=4{`RFlZ0D5;sk6A-p3N>3g5Y4csgygWWS}Jl%m7W72snQ;hG@9Pr8X8li}MkO&`0$e^f9$ zY0B3JQB(Hl@Vf-~z2k9|Fy|ZWJ+`~zefj_6`9WCMe(vzYA>{aN9r;E9$N}s<4>)!h zTh-=ThSj+Y(#MOMFiYn``2;`qCR5m+%3V0CcjkNhSXFT1Rc1Li(6*t2+b^%>lxLQJH2w`FH6t)){2uoFfwT6(^F78fVcU1ngfL>g(L4dt~O z>^o!efPg*N_H^v#xdHph2e99t!)e!mdYTefhNN%pO6zBG6bVc7+AUX+LbD$i11DuruzY?BRs+Tt?y705&U%G)Ps%Su`-ZE!}@_kvTa;f*g>(B*Yh zo4nMaR-AT>(UI_b7CKTD2v^WzHE{*Ok5)K?Q1389#eQeJ4uT)8;N*}vA@6)3+}yu1 zq|HNd8s&7QYB6-#o|zKlFC38u;{@>8{-vgjb1RlhDLtO0L~i6hSZG@U+Ao z?50A(wWh^6IGf$aCQ9el#>GeP*OrPfY1QBm`iunbo}rgqMmV_4Y-47~K{ItzZSG+= z)k}x(?g~$zq_Wc=1Ys$-)uCH~@Lf1OsXH>7JUqMr6(pgtEr zY*@nH)Y&lnnecJ-x}Oa40{^*qCtTGCNO^137BboP^t(%y0)G`xHlbHl!mt&YXg2`RV|AxG#A^=#a;ikqvQ%NQQPnNA1zX><2de8`Ln?liw^ zm%Q}ZIJ%>wyLVq$I-*sjBo}#Gz@D~}=0$K;({{k9SC$CrTOAa4l{kz%TF5__z(cm3ajl>Mn!c&|zScnPi`uvdj))Gx$YT7e`{s~AZYmdZtHP7h|q zMHgn3ihX=#oEGm$?LI(+Ch}ehH0w!hRbQfrjc zll3vP)SGa`CC46!=)8T|3h94X4oVaCqVNS)loXE}I@nc*{)2?Cxlr40){9w=`*O6^ z@(qS!KEt|;=8+`eJDnk?b%r1SEywB%IUPCF-?1Ixq=6~Nbfz4M=U^F*dz4PgtVc#BX;Wa^~sq19kw z)^OS}f#-$j9T#!MqaJT9^z}?hpp;Bx1(P*|9T#8)v=<5>=YoVH@6>RA`0GFH%-R$P zj@wHM0=H!MftbQpe-j=TVBpgPa2ie%I8t8%CjE#v(f|D)I`oqZEMY>yCH#pUNROgO zJ{}=pC2ST-aFCBpSQIwy8IDJ(-{Fm(ON!x8YD0m=eCAXP=^)AslRTL90)0m6nG^5j zkz0p7b0^b7ROLtrgg3*NR(PNhPBkO$jB-VFy(Lr*E9iXt5|)Y6xavd22&< z+JnI~7h%FGSO-c?3^0ufhFMe58p^(>#$aR-Cb5z=K^kR%8Lq)df7REzbd*#w>bIEW zHG{>o1UEGIU~XyL)2d^XTfZr<%1G94>3btlUuR3k6J)sSCl@ z8m8_VW;YSD+aV1PvXGwQtG8&+8B;PUuu)R$m}ath+g| zI&H@LwxO#mAJ(Zo*pRc9<}s#+TEp=RI|TSd1f;XZUF)Ee@W=r6&ZSpDjuDwk^HN?u7k?8NSP7RaN{d0Ch>2=DhMu+}12r*LbO z6l1_?Zp0yPZ&`#j0>7g}IFm#iI-lR91`b&VD>%D7-he0@5y=Ydu5WFCAJ8Gh^&(eCtJqIt8)%3Z4T$F)h$Mkwa!oj_mJ@9)Z}Nis_3fg!^tOI{LGp}dtsu&uTePdcmj_Z4>p`h1CNL?-7pSPh z(OOYgGKH(H2hY#;$4}unidw!=pMI7S3`NCJQFdDthlApc7nkE9tX+6B_6&y6N}3w2 zi4(%;v(IBYS<&f>?(3oL=$eG{^3FDVd^A@eJb4)p6shW+o0aP%&8E9{MV&uF5*4j|= ziz2>|j>n;JYMjzt+)~`XKhPQtfjyPtIMlOm6DE&lOndZbdg|y2uIS|5wdi1t#Yypk zfIAlmOe(gtBX%|^IxudecP1OBzk1Z#vGlmx_;?c;aWDoQd!!$ghFR#-?v*@C}=p*g+#0HqIP8_ewaAL>z3FuF^vTM7{%p%sSdKvbC~qd(mQ2yt8h5 zAK}=JZ%V&6Kf8+5ps6;bKmI$eU_~@_weIzrGV88wdbWEPQtzf<3B^OxO*O&6toXHZ zc55vfN8AgzZcmg;o$sU&!0 z*A`?~lz!KVnO8-h_LqJ&Q1qH862HE%40eseF43!*Rt&yC#k|mqId@-Z7McQS(M)TW zKw;zk9et5`m7nOsA1^~_+b}34=@?MzsClOe$pv= z;Ha`?igmJ7skz3x^364jdsES5m1*wEF+V)9&zw*pJGr%9OK@zle)?1o=>G~gD0R@$ z+UjW3H+SxVcLv2IPbo(;vQjGpcuNO3aSJQUxCL&*t@?CDU=hAeprf=Hmps@4R9zoM zpCwyJ0HSl2(wwU=>BbJ7ux-sX5y8BEK=U_a`!ee6bJEfl19h8p>eeG9Qx$}!ww&8s zYs)2<6f!M%6uXb~Sf_2%RXc4z8EAVzw24~sDU6tdTJl~m0bigJ?&&2|4Ae?+43EMg zVY=N>iK(R*h1;f5KC+7QrnOrbwFL)un70)!5$vC3Z4a1RUx6YcZYJoQLU1#bjg&ufgu^V-tp^WUYYNLIJc~Z!e%6*BsNV0t=YoQ~-$t z)`UbNb*fz?=3V21`cC+r5C=bY-$Q=H&s0jga^tAL4Z9BE3LbIejz%oz)z2Fvv30Z? zSK?da@=ZZ}xb7fRb?|{{6)xnRnTH-hv6h;&F<3fpa>9&H-CMe9SCN*Qb_pVRp8YUYmn5yfEeLGB>rLD8UIX&h-PiA=kOqF75qe6TqWKy=ERTmglEgF zHL+o^P<{MeH_<-nJ%dn1J)5eRGpQxtl|WLO^>&6IOjvGp!}AGFNPdvJ-0Cl_F(9pO z$z}CSCk4NukZ$!ae7q%oF{N>9+#8sV6QpMuRC72zTJbdytUpTq&%x%35D~Y=HXd15nK}tZ|Yzp4%p)l!&|He$22Y;I}w^ zkwUgAF1V5&^blyjjw?x1JI@S*Cn{bM79oOtm1%8ms#g#g?gU{xa0N>`f_teJ4W1dL zAA;{GqV-v0ZD~p<2raH)G~!mqw3+tL56u+OeqM|2^t6J|@Y59{ZT2&%g!c3=YdLA1 zh|~KsYZdlk9i3rz`S>nk-QLbF`EDG3zF4cZ}9uZ~b0~cHUsUAVf!Q zbV^;gNsE#~ATBAy0VYTKS6EiF|E=&dlJtmfi!s%8^i~`J4t3=5g_m2c4WuE4j05ROtkf@-3o5&>Ds!7PP)Zf^&)Q}U zk>(rnCmZt<0dv2yR*<%f0I$D7$S(~5Uwi=I#dd2I>52$2YzL~^B?G|u4*+CU{U`#K zZ?(EfFGZA+&^GMT^1rvQVN}D55}kU)z*f9@QvO3ViQ9uyRc(1TKlovfwJCmgiDeVi7OfjT zpf0ajE8afD-{1D);t+n3Trb!U#_huy*%7V?LqCi#e!sP?X^DW~b->yg?=NzpA+hj`y)_P~ zE--CB1QUD$_d`q&0WO}fcEeq6E&!uH1h6Z1ZdIL*6(Y>^ENeZ}r>-zd3V^|0F&37d zveuK1w1zj`l&;b@t)cj7Ya|Qjowc?DWUe3Jr+b^5_PYY^VGkNXw=>q#xN*VGA%eVc z1|fgeA%AQQZO>X8OBgyJZ=AKZG2M5Cy!#;^+B<7nX+Pv#6>>a+&k!75TPtHKpG4B-WMQmaLuWW0N3rJ zHCBo>z_rleq=44iA`!|PiEAOP9%NGfjS|MAoxewi-rPnL+zHY4!Pz1M@`5&8a^K8G zJz7#VB~p{z?T*}jP;53HjQ`S*`wN|}jcuF_%j9h2$`uiyM2@wCbjbj4{sRE&@{}0J z3!Cev%iT*6>qf4%yY$R}_4or=?s;gZecN(g_`zp+))@Tk_=<~8dRUwukfFr-*n55Q zNRCnY~DsG!ayxE3A9i#BzJ{Z|DW5V~(Q zI?8C*q?sa2H=DHPZ&(DpKlwB17Z63OTR?HS4F)MeMD`3k$YUZ|a`M*Zu;+VB^sN>v>h{W4E04Ws}=zRL6@R;tj> zSwvslvDTK_3i)Bf86{k5WdLaT0f4#ptclWS5gI#L>802pcnp#FRIeRQUlhycYN zpqQl@08$+QTq>RVJZX=J!a6-^mjPvaJ7?MSu8ki^olsXPCFP2_eSSg@>xv=wC7p)z zPHb!l%g3xPqsiwR5v6~GQdN3sKzUXmN)%LixLA{Nw5|K^qisb=z)e6*$bDh+Z6`JL zrU#^^&cs9kbI$SGqz;4uf1_{Sz!f3F34zoCnL}!UQbKW*HWJ>y1>yDlOE9qOmSi`_ zWeO^4*}uAdHGM?Lf~HJw@62>2wXpBLwF6)#5?N^b0Q;=JvS8i=YiDUidoHDCUMP(r z=cTnS^m?ej4?SODNBHwYYh%HEIO3>{keifi^lNJ(&Jx&(^n>(6$&nNUH^$JWLIYpM`ZtW>cBuQC!X zNEB3hV$aXS306MQ7?tw`8ReWyl%O#J$xMQzkp_~764EPfT4zIA_RlfVDHB2%a z@-O;8ev&`D*_`KR@+a*!z86iKqLP2J#*jV=naDjGV^Sid0eGyBd3=34!ZVZ7iiPB! z5e17SB_8kZV^D%f<1`=AVDf~`g^|EXAA1&)uqMQ!ryaC@>vnHpL3;hfnZG6x+wKS)8oE2=bqlLeOC%NIK|X3S}SXuEa|B#O!V! zN(a1--X;6Zg0qKuD&67jJ8TW47QY7Jsq}-Sf1O$bzf#Ux!?j?q;z~QIdMB+(DD$r3 zN>@|W)hUX|#{d?mmT?6bZ%Xf=bF701GrNS+%hc8drqu^w_ITqm(^wIpg^$w0G};AV z#0LTTl~jgFpNarwd==TW#04Pjg8&2llzGyA5g@9Rve>l81z^_)0d~DjZ?1Dfb%H6Q zbTV07U~)eQGq9{ON%}(ss9sK)Y5L6t;LQgC-ko0&tJ6`jv(T^=DkyPMxz4Uuw=_M$ zoi#2Z*`}g0MrtksxK%>Rniv2YeE{H-hASKBbR>!}k1C_A4>G{?{{T$IRx87Gm<1xt zDu0~%o@aoWqru>|Y*P93wo{6;f35Ii?M#<66Zq$KP3Or3#x2A9hmY&flit`jpd2+o zAL`6?-erVy%aL0j;aoT5RyD#TOJ{BmN%j87Gzl?=tj&#CiT*C< zR&~?okVgtA-D@cWq+te>LG8rrOjSpe}3RU)ND29O1KhQog7f@St! zmr+V5VTX`gu7lr2-)6|Yr3*y7#7?0LADK}-xVK@-7BI+({oa6ap$lJGBT?8}Pl?7| zat{8-hT$;xiI$JLwtZCwvC(F05o_NLm@ zRH==ZUIx{7;<>~gw34v3ogm233l(zHKm+fqcFtX$=O(uf6@UmrTS9TPOcX!RS@UVW z{z@BQ;Jj!We&e2KOZ>(@wirj~f-^HFj_VV5?r=KJo=tP_*yOA;QE3`csd3I#`lJ=M z@_r$A#nwtQHYe+xdzT?M^YkQm3$bmK5Yu%AKnq9kx~EdMu!tjt~~4_jOQWaTW*R+5_e4+)-(d3sx?czrc{u4&%;IIZoFjZTF2 z`dcHkUd(5ny6_HjAvdub%;~K3kT7(RTf2+WA8&MG1qe;tzninAXw+31f_Hp4=zZN) z8NoiW=3L9g8oj8ZoL)pCS#za#_rDsw`Q4DQr(7sK(G6namF^OT4oaWLEB#QLT_`>5 zLg}9g%5dBi<)HL%cV#qQxaUIYL!uNmx5cq8`T2-rr_-)+Bmh-Su)HS<7+xRU4IQHL z@!-)*>5I12(%Ig2$9gG)q%!fYou?l?iIrSxj++QR?Wt6R%6+inoi)@B@i4m&GPHw$ z+O#h^RBbg>L6C4IrGp?yYu9`psk=MV ztO%?fpoHRAI#`tpZ;khazxRLM1mXvx>Do#pW*)~Ij}8n}I!c=j6mDP?`m;syirU=| z#7A>c$Qq%wS~<>Y;;=g@-7!B)p}b?HLwoe8suOVTsB@>AhZ8uOwxO z^pByge`-*|mdjdjW*KVo_ zZ~CLHN8$v@i-3PeDa{I-6ST$rqVjLWJPJX}SvdKap1`&rHKR3sdSOEQukZsNqZK#d zb+_X9`sVUO{B`NrEM$_*39xXCQVkD3I_hbXmGROdLp>MJlUPQblaP$UFC7n3D#7fv zc#qWFu}TTaqBAi!0e%>(j4zzaNuT9~v?PuxQx5j1Gx5txZcXr8yJd3Rqz58VnD5m% z)U;fIsLA7%-O?2UQI{~E=+S+#Zhbs4U-Ig%DZ#vnsFR-EU0cnKp2SLpyK`Ps*Btld z`gWO<6*nnN&p%``8p=9`{IwkUSx?IDf7F|fI@S#ijS`wNRyyp>j7SCF=q65kgvaA~ z5fr-;a(BmiPvgwbuQhQx|0H)8j9|_*rLQ!~K-X}cF0?g72zJrTnwdtz{f0_2m~tQ| z2ugmQQ;|=9=g&~eNqecX?eJ#!`&F#fq}>{IX&n*bUecq`bom7?V#!6^@#h}hjJ9>xe3E?pU=E|!_Yp1*4Sun{k1of5Xf(sTTl#vn^qYa~ zH;in$EyiWwRknvVX2Q(czzdIW1x<45s7ta3t)vn?xPTzW1gWu@^V>N}OH)JFoOpi9 zB`1lNZLTs|8YpJpl#B!Pey-Vj^Xzs#qwH_zDG5@tQ}z_44_?{B=oWhHSv|N)-~?_% z`1%T7zqGgi1~+M=n7QEsrIG1#*Rt1YWoJ4nO=WjlsPvc4irL>UR1&ebcH!9xo}Gjb zNujP(;32^^DZVLMBk7HZ*>ADZ#`Mw^^O=SzV9;k#{Oe8($to}r=j8S zh5ZYh+R~dnaB54U;~z6R@;F=1bDKX=n&51`OYR7sn`-q&7toiwfDZdbxDR*kQl%Yw z&IB3rmwhM2ID!-!X$&4-5$)F3BakCFn0JGADM|;xoPIF-Q>6|3k)nuG?P(e*sXe(+ zQbqP`#QD(uyi`AEy$RjQJ+2km#dF&w3*Gin*c02y#hdarcO*3t?*zV~~ike2vl=ue@`?=uP-dl*WZ@o8#;9ApjFKg}_14;o zGBeSV3io#HPz%wMSOy>pmsHD{xI`wu0ksw3}ZjHDG!sDgu?* zZ;Mg~sskEwT%<|_9B0M!*7}NSXe-&Wg-|Hz!@%#ph|~srA32KmgO9&dT1e9kG)!SM zP%lB5$8S^GN^8V?USDCEs|{6NsaJV&S>ZO821fwlHLDN>5^?XuLwQE>5%A zSK<9*w$>&CC=C};V)x4uirLTZ$Jx*nL-x5k5g#KAn^HqK z5+fwaiQ{7qVwZbR1T6iH(ns270Q_196aa9n69H`wDK!gU>!W$@MdZeK!Bj*YKfBeq zgG{u$@6xqC@a~Wj#X=hCBKJ{I52K}248`R+iX(}^)*$*T^wC_VDn0l#PD40#@yjmw zff6)z0ey8fo?zdNQ@O8e!&d0a8xjN|RJN~Htv_~TJB}eOU76ibTC3v@KA?Z+ zPs%jlu5zgnZQD5N$f#}GIAq9oK%+<6>rx@wws9Llv#wTxzGsvMfTjCsdpB+vP;4JS z*>x5@s`mmFFyuQWQToRK@Mi%4zpxt<&Y^w<_0w9ED6l<7`|aP)wM8q@lUPGN8Vvd> z7EYyOhtftw`Q-u*e`5_OEi@GTYBsgbDrz0r6c|(-+z;4o$>3;VE6?ATu%DQ%L)UGL zn_g!bP-pbx(#HEKU5`I=3Xf*cBWY?sO?5N23OiT6UzHsv?a|8`@+BTs+hr(bdw(t& z8jIPsm5$ly5?}`WcJL`B3T|a9;&zUUI%Ve|DM#^lcoR%v;c1RN>!iE5>wk8xQ`V;+ z$jWwD;m9!dIu0+W+(!A;UcLwx!WDin!cGER`TWkmX-%9!tZ{cUx zBn77mDtY|AQXj7(BiPrexUcS00uvm^B?F9XH|^s&G{&3ok}+on?9>?W6-3<8`gFc^ z$fO9FQ5D^w-z#gRDbE!l#}xsG`>Ra93z`3Yg!7`my5@WBnh!S(>Bh0bKusaBroNy) zR$80Nf+^pr;Y!g1+Mn>%0L>9c4v-_BMkQ%J((akUR*#aav)fAvI(E!rec2Zep)+iO zm7NE2R=V1@Nsio_Gmh7_xKJi@%J3SqgdaIqM@p;2%v-XRMy7Pv%*$Ld`)kv$WZw>p z*=e+Qklq$3-{+v5bkF+@`T*C&e4oF;akAoCY@Vao98NPKWMy1wq1*MpM4W_QaRugg zSDarzh*R&}m0%sG@*u5su?28r*l)OTvce!&Ygv|_#Ijoyp4jyRug!acy6Rvm5xD$! zrHRy8gsk?55+ijmK(^H(M+uO)yw4zU?-NP@lNdMH^QTfS`74|w>ui$GvcOGVzycq6 z2MfI96D;tRH?yFSyn+SpvY7=Yc|HpY%O_b-R({BWB61cBD#*Dk@Rkp=ps0L?1t$3v z3yR3!v!IZ?h6Ud8%497(&w>(iHVX>NX|?Wf2YN^ut1Wn+QaKCaFy(e)0_#l#|))Z0A-cc@+zb$v?2b zQ{Jfs^H|^}?`A<^d7T#9$N~?UWX4Ti&SFL7(=h)ZrFCN_$AdCtU!1#5BHQ1+-?ZG6<8{dB~i5%L*${|~O@XG7RKrDY{1Ylu?rtqD<>RAJ<=1PLRb zfcYKHCmn=c@09S;*m9T@A{Xgcw2Y7h4O z`;XiT$ye}aguJW>_zs&~wj$#=ORDz|CWW(~Nc$YNk7ZSv>~0;z!7`!otDPzSGxS8Q|ysb3^gU0 zV3_Kplg=<0Eyk3`5o5~3V$8UulNLE*skvGjGj8efVyBqdDaQ1SGBFa#&nQ$AYINdJ_6&Tb;d{Q#Yf2+{wDF6_GpXo(Kh3w9mYqyjgR&k zA05yhC6h7Mt8~ct+EL@9L-`RembwvK@Krrq(i5|7VbAA^9;A z@pt%dRYt9pj5(++cW@arPA-JB#mT0@(Aq<-A5@rP!-lXFwoDo)7r`fHxhVc(&*?rG zz(cL;t@q9E@r@+;ELc6%8d%rAJk%bh9njTNZG-mvfx4}0?Vb736UMK4=2TsKv|Ho4%@x^je zk4*7ZSDE~opUP_5pd+?rU}yYEi^HGn_#y~?4yu(>YXmU^kNGR06;C1`mSxz2F?sqV z_F6v3rPNS7QJh&y4adu8FP2h2#vxSe(rQrYt2%xe^y1{7@n?j*2|g-~ujjzl(&~28 z1?XBv4K2fbTP%b12^jp|p(qyh0G5?OZrp?W_=f2&lqidJ+Xi7})pG4v2Z1lD*e4{B zo6BhGc@7e*g+)8*I{W{L{5RDWz{CQ&*4{A zz)>huP7U{Fodp(f9Q%$WRD>So)V>XvKg{spG^)azGtAp!zA<~0e;yxQyP!E}lyfHb z9!c^sxK~as*PA)Z?6nbh@wF$oOF(}42!C>3vJA|HW{Kus@zIjs*dx}-VCjL_tt83& z^z=;_lUx6bKW=XB_|kFcQeLf0y#T(uSj)n^Y>Y848w2KLW58?;2F%OGV80eHFB@ab z%fSCSAkr+e zF{GKCE;EfF&GLGB!p4tgnGGM!CBa%`|W{)40)0!$vcX8reH+ z%xIRAnM#^z$Y`b!qnQSbW*RS=$)hrprkQE5Xr{5EnTCpKG*UFvKrxNRiDnumnrW11 zmYE`%Nsr7lLNwC=(JZs^p_ztXeWg1Ck8VRMD27YNWlgKm@$TX72G?|HG z+H|>=sU(g8ao^5*Qz4YX5E-TNOeM8_Ad@{A^$W*8URb4Rc-5gi+ykRWQCvf?4+^QQ zc6YZ&QpCuu%IYAV(xQqwnn%8_q7JX2=VAEI1MnxKnjJqlnBy;iM*eD3J>vqH<*&Bn zkyHL^Yaa0oP&@KSzW_CsM>YqjU3uhffZByeIs~ficw}{;@Y<6=HJ+!$1PQM#3sO7t zlzU=IovH%BysBz>{k0viv8o!yBe$!nEqElbnp&Sn`d3q%@yPmWY6Bj*QBCd5BT>O> zHy&9MtS0ct!(g=ok3?1%(56>co9hhU!5Dtgia$fH*%|)Z4bJcraHG1~P)9idWooF+ zd8A(r&ejvKxrW+`r#!_-DXl?IF#Oh!@#m9Y3PDf}wHCa>-Cz9GnKcE(<2BVbJjE@9 zxAY0<5F(V79>Obj0`7%y88`veYw?CU0kdnV-TVY9DoW^}gcpW@+G_0*QdWr&I7dpl2X;GkXKtRt5+clUex9? zkOi@!TqLt#MX1_?zxFItZObE#>Zox%vb2uciAV0#QG4=8bX_6O!n#79n{|aewd)Ca zQtPRWIl%dP0)THAXM7e63{#u%*Vcz|Y0rYEVQPDx5*^MNp9OQnxgKOeUO3mPET|SC zyf!96cR=v8idMsPO=Rx1+!LK^LDm*B?^=_!Q*bz%i~T7m+L$xq6eKoQhw3P& z7|L+;!sVjLCfz_M&EqI*;BjMhq^=cgu2??I61!%CrGtw1VNI%73nulhSVAXo4dgaa zC-X?Vrs@nH`L3xtlSg8ksUPvk>1OH_9%Uf^AxrI8GM@qF6QWm#V zC-9VaEjhziLZuirNl#e`3u1VlmGDQ5I*zBvv7DTh@Gw@L&Qpf867ndmcsVPfLu+2` zm2kSXkP_8~i^)pZ+D0A8(R|wqDf8N@V|dEzww&to;L}bWs;8WXkJ@qQ^YEe_C+9pQ zwdZ_054YP3uf=yzCvt!b9eCx=L(7ipM7?d9$0kobif!wC3}U;gkr3XXN~nh}Oy{9` zCv}wg>hM$eYSt<3)x1t>9o>EtPA*FfIc0qvZ7DpJ1g-= za|(u>#i%B^X|SrZ+E>R*WAA>$5-;B{!Q>%`7qia4hNs;Yr@^2uT=ml6Vi(TSG-%kB z^E3^%b>;3v8WfA;3YG?w;?zW)KWT71P94r8t-7frcw~?7=1B3cTpSX}bdbda6V9 z)?tpe-1|8)?9B^U(M#627@|9J(f_fq?F@)q?{2lB|1UYtQ!plfelr3@I?n-`S< zH+pkP&w%(o>LeW?11|PaXXve#!H78VFB0+hdlLjr##WoxmwPc8aH22Qstkzk$3-#& z_V?qOm;u54Rhh%D?k}Wx4B(=l0ka3FQzA9lp@JV5!@+zNFG&}zH~3E%EjH$m7qB?% zfFn8~UGW3eP`!-xFn=Jo-0LBKAQ!Cl&}oo5RIeO?gj7V3?<-1p9EpkKifR=haDJ|@ zU18Qk)xkpUrGvRtt%pAbtD|^{BZdemc|+8}dNWcX?*g%q$QqC^Ppt;0D+Skru;sx; zdEPaNyk}Ss|0b$Kbem3j%htralki8=uXQ09)p)cO<|lD)Vk`WR#6@{4v>M7Gx58IL zIc;0PZI`eI)OKx5I&vTrjtT|0quFb}*0PlCvHD9>pbRJB%OA#e6$hMhgJV z$8eS04m-zi+p!%g%0kLwnOAB%yp%b=wnLAxLdvnRYLZ?lHoBGnjmKK_!6huMQpdXU zN-crdah%#Ea9|woG?zfwc&;c*VApu{W4-a0K-~$#Yda_KzI6$NOjQ5JU)wQJNU1rA z>+TZRIEfpbB~WIvkg{wtSI8ycHidVzOW>m^>S(=EOIW4$j>lSbo@}qwhAF&Kx4>JlE=JzZVNBW-600NFFt`8=i9Om(rYG;Gp9 zewTto99{s4Gu6>hGN`s6?<;P>doSehm&I3?pt`HeNmn+Kw z$Q2`D$y^%_!1821%sT+jlevl?fOhk^oj(9O#E4glIzXrT08B{X8hQY7Qq%;Utp^xe z_isVAcHIsu=BsTXuTtG&JlmrA0(n2r7bt4KK<%xI3t@c!6=Jm9ZWotjZ2DERNqgbD z1>Eo73!V$PL%$b#FH}3}D0>+t&v2SnUUk5Za^)aLSqK*v@*&GYa7*P5@IvU7%7-iq zVUrknmdb~s3!%XxK1Nswix#QvbfdqJm0aO87WL(62`u$+=oT}&9$tIizn&)-u!Rt? zSil;+m=8i1!oJ0PEVK~bi;*sATv*a!Y#Jvs9S)~)o}|OOG>(=IZOptkO^11A4zLzB zn)v{EEj%=OIM19Hme_73xX z{!W5~gP8Ai`qc{dj&3b&04_ zwv;n&Eu35`yjFCXP@#^?`1HqG_;i^X!|AszQ`_nCN+cKdEL=!l?FmV1)VdJ<)zzX7 zDn@lNM5em2RmzF)pCJn_r*w9y4pen z3NDL4KINd~GPSPPm`T&^?(14Onl5m`V}(%h_!R>Bh80|v*TM@irRhpOeqReqR|-|T zzLL*wtc7~3gmUJt;!_>Otd(_M^DZ>iG7u@5dsBW8O3iSDSRw01kY{eGPyLpYu)yV8Z9TQvta6Ik(NYOtqd*MF7lQ z&qo#jFV=H62B7-}KFk0(zCkFb>PGGh0Ay_Bo*uyaja&!;x^3e2C>17b;s!MpPH*C~ z5UEgUGjEnu7_nJ+?Z{@{`=x@<7it%sv#E?L#}6YL5{|&DZE7P(pVqt>H@~THjYGgUd=PG~X$ttllZK)a#u>J=*T#B9#i;b~(z)+|7+cDkSdaZgwi1 z+s(%gsZjfCE;q=OueoQP3Z^{{Fc`3hn~hXBx`#_)Dg^8mi2i7=kjJ`LXo*_;1fu8e zz&*$G=K2}d{!|Ph8$44=%#=~@=a-6itStmw~;MFQ!uGp zTVJ6mem@{!wmrxVTPma<6r||>LEf3ALdZ7)xzoSlGq0(5pD?$7sZi{YK-Azv!fQJZ z3FW*!#CsKN<-@{j=EH(y+&(N2TKk9)nRSF4j#RjML|}i&QGtTlNBQVE6>c3>6Le!n zJUSSIJQ~}+h;C=|*y^}uXTOGoZ@J`s4LiQ&?e;ai|CYO?U&Ekd+^(F1*~hr2cMeV* z+W@uC^8S7UEIZ2;cLV%=mMh@~==q%x+4CKzU;}ua<0`!Y z#+(yg`|cd4Z39$3&rQz;n0cOaYXh7*&!u_;6uZD#zX3*G;BvSDj$II{Q2e4$&WMWw z^y!Oyh`#~CF9{TwFLBM^0M{>ZdxdwDe9xOmfyD2*=}=(b_q@RsaJ$UAHU;7_qRW%Q zTCxfbQ{@x513>dQR@~w8bQQk6%*XCHb7nTF3E!Z@>_&VkK}0DVv4Vvz&2RDptVssMdJjQptzQ=1L%2!yC)Br0QBvFjrU^@7;=Lx6us>0rDHsRUv6;1A3&3vybF2&OKgmjBf+;gc@JRxEv_XG;EWh4bDP)v0Svv(_22<~dzsHRO_7X!T)~XI&%-M{=n_oJ*az!GxHwIzQeWY9;kPCJ?=r^U0(7%n0%M>?=Ga@ z<(B&{yuQor%w6bzk2^ef;mkd?zivR-o)dY)mq^~A9d>FXcXDdCL2N!JV;ii^=P27U z?{i8123p?dRD1`6?{l;K9UQ-}ChBE<$I5zw+rH%KxZ6wPOfTH>rE_Kr#6IBl-va3m z)Udi5&#Wxtakk~&F?hLrn`dNOV&ty3i^A#4YFTJfZfHI3EnSDk54rkYhs6)MQN9lO z4|zLWho~R9fL@2RABB`VKk}-jz`Gy0QBHyGKk<5|z}}y@Y@~qOBVJAl40*)+l@vJs zNC5Etnagbo^!u3)nNlF@XSIjUHMZ49p3xAuloe{`rgN=%bBt=dxde?K^M<$tGahrX zz67TpbBk~Zq$g?%9g^)ElBXsh#<*T~$anZphdc@+pYV)FVaF3Ldq?5z6TU;_D1<%b zTQiQrxTjoGj>5N3c}pGzk7t}4MakQcn!F2kG`T(K_0Z86gLC8rJ1UJ3wrUvfe(L!(#1Yl~iS192H{ zi7B;S3joQld3_GShSyy34#9IriVDTwaI33A?>Bsq&h~1_!*NrhAm>xB@^(1}U%lad z#4-5e4QB+du>QhJ{sIzz;X?2Q?D&O~@dfU0gW;pMJN=|A{S7pdbcd9If`B%eVQ?V3Ejj(tMt zIMjH{Id~jqzg0We)+C$q91BE@Wz}`a>GIdKD=|bqgrR(RFuZxImL9-2g^g}Bx{+pA zXir%Ck+dg_m7R^pha9QF!T3l^UBFVG<6bXE>ehKYbq6H=u9i#IUZt%>Rd8>Tukj5|`pl6dOj{w(>AL^0XoD46ftaAc-! z8yYn2$LNULI2@_89Yaf91Mz>T<=nN%N|=BVy|U+7nektX`DjU9dzDtv-{3hI2Wq)n zoM2iq-f>e*y@97>^vcqbs*G_*URqJrkk>b31+yhpM>4H7YRR;oR}t499jUaOr=@O( zm_K=SmcU?)=+&W>9DiI+a^y?Qz{)L}T*(NSsC^9a2H2f;Mx z(NM3lyrppv>Iof#N+q#pZ&r{+*~n&SgU@|R7~)6k$VFz67BJ5tMz;Jnxa zCI8{oc>wh=qF3i0%Qpy}Ku5mkYd9+TU=uO!NF^6cD{~vmdkCE~N9sgJncuL~Gq}ax zk($T0*lT&eqw4g)2aaU&xU}RgEb|5Q#T=>04rpuUJ6@aX;Exf#Hsq0ViX?ZV`E=A)5M{+LP=dUFn zVwp2B?noVoyZ!Z4m8It35dcSOY3v^K)MeoPFR$M*_UiB04Le@F&khA>#jtY%@^Fkh zQls!dfS$?@3dm`AO2CmC<|u}p8IVih839LXmv=nxK7W?H(J7hwP_5wOaQ0s{Si+sq zkN;Jxv(V?gT3Nb266(HJNBG>rV(>(d{EC76FcLPuR|CsF!iS?9$*yVF-y_hok?`+( zbr9aKI^aLGxhLyH@zqzD^FQ1$be6$nyTj|G>@utUZ4o}~0jB?kh>J6pj)vI(Ng*)T zWD8yO>d!KTGvu?Yzqfsi-}!87vMs_O+hkjT!C=X@41?#AEv^XbYGHo`kFwCSu&t^m ziy~m*QIK5N)(OuIT`p|vhe2==+aL_)7qJb);879V01R3cwT;4HXHnZo41C>ei5N_A zqldTLY%<2$y3@mL?zRya^YWnBcn^BT=3#4wN0R(JZMA&Zj$0Ui9W1 zFIybOvb}8ac=D-A30sus39Yo2qu@m;TX{(LvDNX;praCU$D7FFjyEB|$JW+!T++}H z=FJ&9au z4NBX_V6dUIZ9E2EWo%>JDO<1?SjyT0VM7^P2My?$mkBn__bm@@Wo_;8-MF&0YFf%b z550)>Wo;gAl)V)Ama$cEmqWbJDOdql%G##j9k4CR+4^~!H4;)r!KQMyXw!VSS@8NypnB%_CiVp6TGULQxT3-vbDpM_mxmmPr``Gwh0)h zm2J~7=vW2$c@hp*u?^L7y{l$|gg&LpLzKU*yY?sssa-L>R2A6mkFt0Y-uWYUPQt_h z+YIf6A&1$y##hC+(jIkk;A*?aV>hfBp-CA5$btpI$Xn*+BE&=FZQ_2EkMvMm2$ZOfO_T)z)sYif z@M(1v$SnA)Ix-{+Ce%PKWr3}RZ6XG3Yog3%!Ty@I=@`@up|np!DD8a+rOCA@Ex#7h zodr#6+eVgPg~}_MGEr)363Sg(+g23*scrMZ7d=9;x>+zhlxWQkwT;GDR2|ghEXb^5 z8;3#Rx+vsXFuATR(TgAtw?)XqZDDgATO5Sevz5Zsmi6eRuj^qYa-n>ft(_0^-LcN) zN0T`j0KQYp)r5hOwgzYubKy!D(whsh;kMZrWQE(NVh|Z&n~A~B2-_?Sf+H!|5NVr- zv4Hxv6bv@kx20nc*}%30gRBPFl)2C%it-(fvQ5G>ku@9IT6iATN^dk8CN#vl9foTS zQCto~$40gV800jv&Bq`<8k_12tcbQX_hG{Yq_>SmZ#3L*Y^wvFjcwj|p=$ZYwhlgQ z#DFh!)Lt0OUKosZIR$x*ZFBIYxF$%`DY(@H?b8~FZffiA$u@FhmY$>Gv!=ETd>Y=& zwhMzFn%Q<@u(-KxF9wZT*!E%YxP@&$2J2eVEA3)zyS->5c+;iG@1{#3A;z`}#wQ~| zYhY9C|8aHR0aYAb8xL407VM2~?_%!-ySZL_Z-9z{6#-E!u^=k;*!74VHEN9A80;mM z7<;>m6af{~H!&Jh-dJM%o^ytsH~H=#_ssm}Ox>B;-PxV9j-3!)+BjXiXJprZ`s203cd$6`K(JeZ=q2ULT0UsXS@Cf)9jS$Nt2hbJs;&;ZAD*~Fr~Hk_-D#R6(K z7^Pc48G~_17SN0#I0y^Kcc^0x#L=M+g0K&Btb=$u3@yBX<_t$Y@Og(3BF>Hwm4QKy z&#}w*ZV-Al-}L&B3@>xkdpY7o`-YD_)i+1dW??bexd_5l1(I%=M zKXO*|7}1wo}kBo9M?TJxfmb2DRnLq-A&;MqT)0UHQY_>=b^9e zrY7^ncWb_5Wf4x)c=9P441aVx3{Bf^j-xjn80shh)!!DNh`VXtLev5;1TR7_nMacr zp_lMRb5wtFxEdzyzHpQ#&&7^Fe9C0_Vn<&Oeo+?T)5GP21G9I_94pa2i>codOssgp z_6zie#gzPo<6DTxr2_qyIS#{jZ<*sL#KPs`YqbI$Z88041!n2Rw0foZ0#~7nET&hh zFa<8AwW~2<1&1+ zH#^Qlbll>&2;sI>eAl)*PQo`o$#EPaaGUr(Y;&A~@5FXoY#gQg8eTKxbXx> zSbGvziHQ_(QrMl7l1SrIxLZo3HK%a7mq`CTg~}x2eF{fdVOf5Y7d!L0(biLro-U!2 zLL#^rmCi^Wi_SwG6k(RaAMOeE(E;)}R|Lz76U z7oGv2aiPZlJ21O#>ydpU_|3SjO`~gQik&0fGoMW%b@w;kvBHywQJ zXjejw1PL5+5rIQ4Qou9EaPoSDJE{b#@knw`d?e*4`dG4D@R-ZE_KB2q$`i@z*%QY& zmB;-%Nap?>3j7JloX)}6!OLGcQo6ugiE=y_oiKqKJQd%br_u~no=Fz#pJ5WlOP8AR5hwoy`sznqnPzA0l!IKIQ<)ELN$NKfJ~sZzhgiq(8u2$6I5nbeUTadDEK@&Iu2YD zD)y%%zx(Kri6Ik0aW+YytUn~#Pyg%qOvPE}k2v`Icbp zJNmfu(+Bkav18q6(>o;e%U_Q6wCwo(l2Ewv7ka=x>hJ;QQTRSM+Q3+?%ioS#Zv0dN zu_leBmVaa5nQ*arx8hPaJ zUEJBY2O~!2L@0k(+6;rJRYt_>GG-sATlB1q*~Eh%93h3@R0;!-LP;uC)+|J}ISzN4 zRUBWK@>|}!8q^cYqKaFw&S(zv;0H>G^-jeaOG!6-<0i_}tWRz4^)8H-+)63s%y_5| zE^o#`q?Si@w^H8wem$&Ij z!6W*XMT$45mX|r8FzXp*3*u={*@EO#(QHoV2Hz>{jNtoTsMQUs=8d+$LCd_&!4UuP zHitlT^f5yqcKVq8Am00!10eeNippMJa}az5{Lsm7&=^1QomHZozu6x!(%xq579;bafHetE1kRscsDkTVKO$?zD#P*T8XKqtXge?d?zYii0Y+ zR~i>JS{CH@Q|p>$eUJT0X|z&mO5?{2Doeq2&HS{krs?DKn38K^x_GR#!XxOHn&zjV zEo-4EAJax9KGZ@pKc<1T%~231Yom6LseBy~lk1?8kLhk5OaPCmSzQsE>zX6r`%o91 z^)Zd9ht_;dKR}e=oUn+8M(01A^jfY;+FG6l1elfc^W#Pe3?5#FRtK0B-Pnt^S`0X8qvTEf;iIvLn@0ZHN;@aq;(C=E*_aG{svX`39Akz z)uwZeG0z=bHM|0pMmI8-L;TUm`~qT5V{<-4p(bVwEP>5zVs>)lN0=zU?g+ZBXxpZk zOm@=srsfb=d4l=tg#uJ;=5=qfH8UG}FoOOemElliSede$WB5I6X%<_%b3_H|^)|SG zvvGlbYGwv{Tu|!YD)n)edN=gI3-on!bCJ^pYSO}N>$IIFwJ=+`ZCCndBj{uc9G> z)za+hbeBR~nw>oEs=&)Cv2Ns4cBCJrwlr(u+nxnlnaw;>l)|;V!YPG_R%UA`>}iGL zltLw2V|v5eX036`NTEMlW6DaQ&^F?8v@ru=&a7-(vyB@+NJTv}B4|WgM2x5J+hPig zr}pj8`0}vLKx=OXXp_g8z zMcpu9uhNTd=$BWi`={uoSLxWNIFDSVN;WjcRU#z{bw{7QO3~d#C8N6;37^d_@Sxoc zhp$2p@x}EJm7jWG20BWudYY|0_^~mvZ>g%>lX@4AC{4eVipWpNt<9ozpr=_As%bsV zmiSavnO^7t_f?GcDh56QnG)^4wyEh(`}>+jDEjF5@-WCu2Yca|d_k{!A&)OqjBYAM zAjc?6y&}gKpzPk5L5BA>D>_6$Z_;a z(Lw#u6iex={-)iHA5SANd>no3kBRCa4IY5ua!}F8akO~=TJIpa55#HvAWa*HLvfOx z4m8`lom4uL$5H)3$oeED4noH~NyP`_IGv<~!Dc(xJJ2!uRCS^3FYZgsZIMv?9nH>~UNrazq`9 zHWVFfVpC?YSpy0!g3)u2)B0cx{Nv;vV(x%A5Mr)?2poyY(xh7>%`i7prSbhZYBvf8 z*`y7lP+618jy9LNa-dPVzYDFXH?u9h7;S#4P@4fR^!oH&9~u^lLuJypq2_7`+ZZzu zB6p0r7GnBm<`Jhy6cA=6xII$2LKs`BILbrfccE-_{)nz=BcFvX8E z>pE?~va~s)5MJfs#?BXshl{-@;Dp^P(yWKTS&;>r^rJ14&8kZI#&(o!`l9Oe$7Iv)lu8|@;2@^bgDIHfH&UCaW_7oXDuv#W z6gAcC6dDo;bXHqw-7=4Obp)6Hc{ zVZ&J$@|&={4y8}WAl^vbXP7@jw3un$fN+g7Pb4;*+7v_FL2E6tApx6 z*B6?F>D+v?08Ln6`YMgq(CGbSO;tL#z^twke)W=GEimmW{Mkzva%z6BGKDR)vT=Tm zmTrkJET3LzR#j1Eyrv%)n$uh#aGM2txY8Ey_1);(ML6*?LdzXf9i=nzw5oG zwB{SrgWOi27dkC9+bYdXAE?Vx^P!kj)qTHrQ^mL6mTPZzq$Cnn(r^=J+PQ%E|RS^c^Q@%?E55)7HO@nFYI`f>; z*-|ulT%uXTN%@?MQv7=Jh$;|nx#{DMP3>vs1~Wp15AvYb8&Jvb`EV2~Mxh(c>K^RD zf%vEnL^N$pLR+2MX!dbk#cHi9y3$`SHuWQ86NWbka`7_$K#SB;I3qMzSm9@DG4cTgTbCHZ) zeQD2DbG-^rK)BQMExl-Ck~u)3!v3ywb{@V5R(G4(LPhz>pQ5&zp(?yF3f{Bs)|yms zyBS_6gX>bcE*h~J-5HqFh@x#<3(%46xP{>$)Twe5z1VJ!QkpNTKr?{iS0z=UxE-e9 zBJne;(=R(PHpSyrm(K4oYtx=D&GxQB2Gw?@Lw?w8Rd}Zvqfjo;?gmMn=-N(mLSfMw zS_etD?@HNSlG@XfU8s)W6LnnaeAlEJRC2f3O`%eCU1?F@Brlq^8zX%W*PyG7vUi)q zJ=lXX2dd-HkJ^n%@~3fMnT=gV?Rfy&W>r!X%KpmiuVRKY<+3-X@q5f}^zl2hfOC&l zT!C;E+_@Fi|Jsa_kbI9ix|08eZFR}~+6+=Djtg|9cF(r8q$ztbiX@ZNj&x=(E+E9y z&gM#O_uTWQcKd*4aak7*N8R_{3#6y}aPh$iB_0t)Mfc-8B^p2Xa3y>F9o1>>ezU!c zprU>0#(s0M@)Yb#wgcumrCb$X^DDk)M;B^v5L2Bf#}B8~2h9U6dpWz%V7hVGv=bdN zt9md&ZZWETy3wqocr5hdkXcLVHV?w%A9JGe3=gKn!)Eou;lcQfge0|W1ShrHbv>u+ z9qUSdXK?OWbHwy_V}yj`qUh`qv!l}Ze3Yv?-PZfYY@<;A&rsOEF8lHH6X_!9#6@7Y zv6-a8A4IxRz1BZ8pxmS8P=!h(pPrZRwWfq`&4GosaMl+WqpLLJ{#u%f{cz8hJd@2z z9vp-stWtIBMURqAyc)H`tU}9^O%GRzxpHz|2Y8r_dk{g%(_QJ8y1T1U-(zN17eS{M zt1f)=nAuX1j&rEkaWh)QxR&5bOHN&^Psfj=i%X2G`K}as_g+P+a>8t-qI|QMdyXG1 zIAIQRjpJfxk}JJFd$}V8oHTukF+y?osp2-TN6X8nl%-*jW+kQlWNBXOT|8;FRH)yI zywC@&!*JYgbLws32;QseDXyh5w(~S|b zJ|9K%&fv(1M!yYt+5K|HY^%`Qt**3nf?1P7&SE%-)`XpO=d8I!g*%X_9IQR(Fu9!L zd{!TKrQ1K^VD&q1*79J4eD10$4x$4AlYQydm!^w8EJaJKiH4y`{)xq3W8}WP161!j zv!_b)vm<#2Xy12c9~a4{%W-vp8eFiHh92zFph`Y?2tkuPq?Kzp?K-4(9DA2>!1HB>0Wxi!(f7%oySCEMgq-5b&145me!-&RsX_A)0p1$i8Y#e6 z1Lvs%{57D@1yo|dulyQgW(lZj0cnov?2D7|zvfGLO)K1JuuMR04ZPL}sH?%;^#TGk zxVc?G0}aZ5C7_W8n-2uzXOO{Z4A_8&tIZ4ScB7l3mB@wn@^m$Aj36a!^!|b8nnwNAVh;1 z1q6&@u!0*5FFtdWF#^<_3~!hY|J6;x$7=8$-c^Q1qy{hD1&r6AveKBS0p5w`D3dj4 zsWR;Xp-O>wqgf;FSEQv#NtLR4GnxzflLE6i5q>$^*jZHK%&~wR>IC-9=HSctC>D}j zU;znUWciF)D)UY6!4JMsYLpLj1JyR^z}ZvlHWDDNX+p9bsv1sv4iKt%yZG{D={oXxiy z;N5Bl$2FK$g+ZKgN|PKR##s&Ux;4l7PJ<^3T+*ORHPN`D!6$gp8d0ukFj|3|8t|*w z(72-jkH2QBX!n`O>(*@b8!9Olki(E_`S@*Y4$jb^QcVF_8uVA-F@rOFVA)VOqU2gY zQhWwqoC<$lN5cPOg&X_o3iwF_mwEzz)}V_5uQWKIz-tYH10>3C8k|tz4-MW0z)&>G z^`|By>kD|R!MX+l-fQry0v|MJ&`>l!YOr1bC%o&aYR7MgBbR&{3~VK!Kp3C@q=DR9 z3vtmZiR}cqX)rxdKp_q4wHHuC1HX;}JT%CM*T|7?aSiyra)6Q=?279mq_ifBx(X=E zfZs2dy`hLx!2)vVyeyybtxfcOG}vJm;HN=g4*?Z5$W)**gE-0KkG`T(RjbtMFQB>x zZ3YOasloa|0%~jU#Sj5?HMloaK!67Ih6`w*L8%c=a{gZ4xj;gJGKm z#A&c+i-33yrf(H6M}yZ%DoTPTO|}b}uK~Z^kAf`J!2D9cVhy~$7Vw1zFBMp(!Nk3y zv4X)RKK8x#30R#6;#6;2tHVp~m+*BO@HPOHV!Z}071*f3mTyF3vj(k?3fQUvZwf#f z+cfwtS-_64IICO}V6Po2JGF|>2?4t`U^DH|*rNfPW(U}-0o!2**w5fPS9TA!i31$6 zfb{btmd}Vjug3qknqcz)M>x(vb_eiAbAVG8kQB~XKC$MG;By+p{2=<@Y0&JdfQuUZ zp}=JYc>Eu}CMs97$|pAjT-D&N0@pPdbW=2LYLKbGZ4H8Ni^g3I&fgJmUxTUl05JYc zCcHnv;C=I`O{xW?vgwx3h`=^`1V7YZ_7eeF8syIr@JNHB3Ov!EZ?0(MS|H9it;jQr z7<2z4D*w?S_(uUhY4GJw0)EzDzzYGdG;n$;;I#%_eiQJU28R@g`$H4Y-$mt521~d( zD*Ykgodu*h{$ii(iC{YdXpWB-kYFdg{j7rP{8xhWY2fsifC3tP55U*IE}HN*3jj9_ z+&>B^q(NmTXVxg9L3w8Z9vWQCC!n|n?rs7~YLKBoX$Empm65PAk5tQQ6*fB$P@cgi zZVR@G0N`Z-sjQFXGu{@FU_T9by9V?tYOu4sfXW)M)p@@Dt*S|?5Tm*VlRYI)O%43L z1=QAHtB-)X8ua!R5TF6uoku|$Xwbf@fJO@N_}i;U6RpDb<`JhE1KtsW-F*UDT0m;w z+VUB%U=#xSZ8f-1S3sZ!3+oB!pux-l0iEJBxn5sL7Yza%3h1UmWFrAK4Lq6(uxpUN znSh=e9B3|}w+8Vo1oX8)oLHzw;rla@jU&IS$_}!CRCb8vGm>G#9{R&H*wkLY2n~EY z2ng2TwgMwHi0mjDqr>_7S28W$Nyr$jGEIRn4XShz4MT(ZT?IsF(5#z)NDX)k42l}9 zK@AwL2Z+&NRh%LdHDTNKP?^Mlx5F3(`Use60cnnD?Bm@qMt2oF(*hDa%kmkX{Ur8m z4gMM`U~V2j{bvpnGEb}28!ljh1~(K~q`?!Wd;!NbNLnc1qy~K!2{^66hs6TUYS3YYfb$lJGxjNR!6HUk)r6NA$etax z4ui5@v4FJcRm*3bTO+~OH8{IYz)cN0B?`E$!4(DW%J`E!2Co;D`&y;eCIO}f+ZA}A zf%|6BNYh}v0vQ?<*diL48l)r%$kw1|+%_SPHThP791Xm;i^fw8*m^&jz%Su^{ds^p?ykc^eq$8|(#U^UKBM3s3I0=qP58~s0K$BD5e0P|N5U7l@eOzngXRX2>ebo%4pzwK|nbMyaUT%GegM6 z(*jaiZ_8)Ay)41L8iZUI;2)<+s}v!XG$@!ZpbCRT&V!8=ArE|!Q31)LrsXrXJeJ_v z8t@kapkG&m3Qq+DXmI(RnkX7*vh=;EG}3@S7l1^XXs}m-W*YEE0-(`CgWQh-T4|8| zNj}zSqd}6hfOZC-|DHUU|ul+gEZhxXoxaI1KxcGFiZpAvI0gJ zJpQBsc9atmtW~;t3mB=vDIWo&HK^q;V2lPkD+&nHU|MAXh6bgo35d`jj(4)5Xpx#s ztSKN`1OA`@G-5PZ-$1}b4VE?$FiC?ZEd)%_Af~l|&o!vfPQY~O|Iz?e0)@=fDpfiN zn599!jsj+DP`k5$xf=B9B4C~dt-A_XpuruRfJGL-`cH2mODtmS?<-)b1~>W(SgwKp zKmjW?;2n2pqSYGkmk0pXGLVgRY)T9u(E>7dH%R}N@QOhazR3!gPZA))77h3-1OQ1I zj2bOqy9SLz1$?Q&NkhOc28(zGX$UK2NMnx$!kY5^*IxGW20XE125`UvQp!V?&zKh_ z(T`~09xdRg23^MsNY>!G0>?FIKUXwP<^hcV^Ir%#tyM-Y6L408;wuE4*I@8U0T(m~ zS}ov`29?(e_+EoF)%*)POfCqVT_Hz?%~Re$}AXWdXly@QVWf)!?V^ zMdOVH;tbcTLf%=#Xn9S*UmA?LA>eNf_-hHs_Y>^phTs6r~7viqL(1!vFYY>nnpr{5vJQ7e$gLO{?l+d6?u7FY+ba^hIOddf0=be)% zd^xSs;kAGY8vOLT051)yy%FG}!L4@!{4_{=FQDT8zy|@9H8}P+1FS!2^5~QNT#)J- z6mS+$Q-fa>sI9@gf}&AZ1AAcs0UGd@OeESsgW1IdG*W=Czs%x7nrM}wB?UCofIrHB zL|SMNURppa4IX(4Xrn>2mwI|^bx@8@5XyY`e~IB6-8x$1`nzV7^K0p>H>yn;9XO| zFb!JP5->so-ZYAQgEfe*lV8@~MryLKuBeRGz*bMd7!7`EE+9+;{uTsMH5lxZb4)h@ z<18T4YLw+Od~FguJ`bS(&+j2*f>ycKOF%3G-fSu+Fp zYH)pkXw1^!(=h^OkLBwx$>W=FA#+*f2xl4?C1AbSU`f8T0Z&E z1cH|{$Y%YNiK4&C0;0c0@|W<5QzV?MaHGOB0f`#$2391wL4%F61Z>ivBksGPu|G5DHGfi0~Jc32=Rj-R#dw7l|V3V_`jT*Di%0DCmZz=A2jUJc%^6|i4}VtB>| zje{C^;Q9{Wum)4`8X~|q0CC1 zOvk%nNb$S|i?PlIa6yAxc=d?K|0PX+K^sBkdkvQC74U-wwb8|)aZQ753f$13>VDC< zr9mLp7ZBx+22GC&xF~e@WS$g|s=$rXKP4gUC#fcy;NBuS5-g%s2(4_^sz)gb710qzXYY#{03|iZ@5&&~D6PqWLZVVu1ODy?;*{5*b}<2-8XPJvz*~bOB?b6uz~9|K z8vYs_Dkq?l0(|~$=qaR%R>|}cP)&nPz5;4!Fw9RtEe$;V1=P`?LPY`fH26?iKz$8< zsS1Gc*H9DQ$c(Z!)?h>p0ZlbnUQ0l84Ib1H&{Bf~bp^E6U|u}|Z8b;;5D+Lp#^1U6 zLON)bQ4Ixj(jcypfG!$rXe^+c2FXnY*fa=iCcw@BLs1zoM(ui8KzdAEAIoc0Y9kT) zX;8bJfB_mT3luO&gV^>0hG@{Sqkv%=l{Z-%nIxwMyB60w!zl z=O6)7HJCC)z%&g?4izv%gUTTS;xss?K)eR_k*pDC%+ch@Xo-`c!G$pb=4-(Fy-}Nm z46?X6*swIf5(`LkEVX=wF;0S)Yp`d!fR!4=-Iyn2wI-vN2w1DZi4_9YX|QmOfb|SA zxfE=`8l~7|0V%~6%V%_7FTqI~#BUL>9UxAnuxPuGFSQDPmjp@fVt`YtGJ_4U#{!ba zUdv}>vs( zaKi!;e9Q6~FCR*JcQkmLBjBC}pFb1e(BR~20Vx{%^*&BWswQs>7UWFR8N_ob3c{Q> zK&Azx6xo)~7+X|=A8YWRQUY=`7+68TQw`es3V1F+=D)A~h5V>hI#m|%LW3?<1-#Ut zSb%_EG~myqpq#%lxWWZzJLCX=SU?K?r{yzN;yWq`j(e-g*PVsD*Pust0UtEr@1`Km zM+UfLQr64?oUsd5faH)50o66=xt&3rQB#v)J4L0o28VYGsH;KwuLJ~WFk_E^1{$pW zT0kQWYVQ-!M1y0;1vFEDuRrW3g|uLTYhz_e9u3sm0@6NhEuZnrdC?DKaG&+>-4M{x z0;1p9@)?fH65N%+7p%Vq2Ii5z4Ioa4M6h!>F1(cidW83~fCTrkd`9?fiQZ3xoeu;I zV6cr7U>o&FV6X)wfuWYqc%CJ}!vW${grA=Z3DPRpUkV6efEz4j*d9rawtyr##_}19 zUkW7;; zpE0n71fS5LY8wHkHFy{(;G72MI=IOC&jn3JsXQ(-z?~ev%^M0~zI`P4 zrUvc$3Am#H%qMWw?`r_#2@F#50P4SckdQR35-?Q2Lk%7c7m%$%H&v`B3~)Dy?h`8- zPc0zjd~W%SGovK@`D;Q21{{&fM1NVtpTGbT-u=2#C|DSF(jY-)x>kd)9*IVx2C2CMHfnH3K%B8fld$KavP}b+ ztKfossR682FxagDj8ic9T7y?F1?<-V<|$a?kOnYL!QdMOc>K*!B$>$rZbI0nV3iXV zkS083`3zX3;NY_wz#avI?=*P(r+`Zubbcq`iUzPs!BMWsag$6peGqa}tH2xutK89` zq>~iuz6OO9NYS93vuLDg;Fn*(Lk(b@f)mZQK%B9wsE{WXF%n^s0xC~6U=tMp|Iy%) zyMPxOq$==A12$5DD8Fj3Mdk8`2F?n^z0m|_Dmc!24F;*h@Hd0+`M4A-CK^uIT`fS4 zOFs5tm0S!|Z~_G_Ai=Jd&wxn^4lbkt3{uGWE2;_XQLsvJ4PcFeK`9MfeFc=&0QM+Y zqk;yoM!~>agP#=e(*X7;SfdhyxC|~io1*}!%7lTGyt?Hx*aiiHYiW>KT|iw8yi@}9 zHGl;Qj?zd2*q>m~R0Fm?!S^36G=bp>R%xvP%uX<9$6ybav#U)&2Mb6!J6S#h#wJ+5 zs|F!W1=uv`(MmuM4dOa0(pwXloZv+IX#j&0339Ls0G+yn>D(*VXM7%bEvE<(T(4Pb79 zHI`Wb>))|LRx;VbnX;`3sI0MoWJ;FLfI$fkUatYnNif)?!6}u%Rt-AN5smE{z@7w0 z*%`&x-;ycJNig|Jt9+vp*~>sq(y%4L8V4*O#X4m93>c5#;BPd5=?DhN8cbX%;DiPn zR;%%US`$;{agG6=)QJ@dPUM0GB-2Zl&wvRD4!)uR3`j7zron`r0&Z#m6B4X(hXI}; zq5iNS!Q_4(QG?&Kd~6?rgHtttbqIji>PCh}T~rcT8o)RNM|!LQOhYip)c}Se7!-c4 z$&%yrTfvk@ad>d|$#9&_l$fIgp4Bltr9_evyqz3yp%Sx|IIYAJCC(^uQ;BbtIIqN6 zCGc2`Q(33PN<$z-uyiA3HJ32(ac;`uH6NQnd`_9=k}ejLURt0C|tjzzW-{IC~3ev}HqkMbb! zxQzur@q*x|SP=YZ0)n3nK;RKT97|s7hsaWb7n$MXbzlfy&4u7KS_ocxh2Rxc2wpga zh*yHw0pa7NItX4GgWv@(2wv!c;Po2_UY3F2)t5L(UPOW5g%1c`&w$`{3kY62fZzoK z2)=oS;5%dpzGsEt8#)NS1%u#QEeO7Ef#ACU2)=TM;0sg;z5s&Yll<`Z*nh9Qd?p0t zGY<~QFLh$qmu|P$LLbtdLMdhFgnLS1w`u=ktKJ`RW6fFrqExI9G_jia&)6*srIdo* zH>6a)L_po>$j??I-i&|hlrK@=;-CME)7e5P#Z}yjZj=5MRHBg;W%56HS!z=_rI1_P zKUo>ds*ob3os)+j(2T+nfhJh~FI1}G{}RB`?EmtsND-{P#tdr93@kw*KF& zP;s!$|72C%w*1puVO8ymuH5Jw{za+n%N?$?xi}83q+X#^(;(dgH~w4owr7efy@RTl zq*}hjdzI5=A5Ic0%+gw0D4=AD_o|OSNbRsz{SQiY=%U~+|A~E8{c=r8wd>!s*cG8z zhyGs@KIByrhXL!`|6?`W4*b*W_rFoH|KGfUb$AZ`i_^hDmkM$fc4&OAREpoK$m>F} zQl6KW(3w|-V!8Yul%|)$5ymq7KiPg}d;iJm#BH01({~U4`EVf7AGHIsPgcG3@Nke?$1shQH+_m$;0M7!w{5if?&#o|s&;OiCB0f_!GcN0Y_} zo26aV31UfgdOg~#4j-QgK zGo?n!Jj`{s5`_hSoYyes-K;FRHLOrI_)8dh5xG_J()003sZu5n^EnoU;Bzd*IU4Mh z;_b^7Qg=|?Hfqh9>#8p54<)bhO3Cb0s4H5=7HK;TJFs-;%?$q{xJP7k&OXv<{%WdU z+_`Q(5PCQ=rEyyhv7dLc|B0YjE=OA!2vcUhZoo2_a%I6Jm~y#$S+0V9>`LxEDy8uq zcS%3jUKTcsDl-}9hRRqeo&j`l*SLLvfSGE zK{J+VjUO~-S-i=EI$ijB*obI_KXJ{3nK0$r1p8ph^%jP~lq-9n<$4BFV2T;A_+`wQ zpU~t%azc~TiyoY^t#fL1A1U%hTMk;OXi!Y(=-5%ALE#f7#PV>0&Wg^dRao~}=hOx) zUvy6OV(H!`wR&M`w4F}&o7|quCc9RU3J>j~RF`#0^=8%mT~gb!eAOjYDpsRwY76#< zc1^9%a$Q%I#o4Z@{wm3~1?&q7Aj$abmY$NNd$-gEaq18ius=kQ9HKG{P$#}cFuKEV zm~u6N#W3aC3{zps^&B?Bl&c|(gDKZBSOrtAXU8qqJ(vMgYz*vwDc2(y{t{Pg^2u(g zVKJ?xy`pUk&|bwl;+rq{jBwo`G+cB{P*7e6TWni`P>=tGPMDY%NWrJD-5YhDek#?6 z<;GK~zAR7Y$;?x!p6q{rN;Px&)2Y&V+MHJLhMZ3IllZ4ot5uW+DdudipB9i8nxS--GVya^$b@Vy)f>+NhG^{=pqsF2Z;&WJOq z)rOwprXKzu{E6#7zgeyWuqmckkq?&3@1y0a>I7R`5~C~3hAG!c*b7sxQU%$SoP8#> zzVwcgf2!Wn@6XhBYCQF|^+#Xm-Htzm`)6u-uFmy8Q#~8Yk=SP2Z&e0|AtE$VABBUq zLkKCv)kH|>cojfiZ&J&LNz{|JQ&zjx9TgEWVM0)3$T-#QF-x4WokOS`#JXW26N0SI zMN8E0Y?lyNQl*U#n1zoHnHU;`zmU8x@V)Ii6ujv5o78fyqr*qR-Z~%UH>uUR_|M*? z)?``oZK_NOZQiP*KJ=|R%9G!!Ua{`2>IL8C`BUDiqyP48YGsb^`%d+aj_=fnoAOSj zzwMnGX&HI`g6~snwdQ+bV?V4#Dbq_KF!`ihw;Ninb}+}JSSYM4Dc1`aPcqIVN4`&e z(Sh?E);3MbzMySdSN7j+o7RVA)plt;SWaq}7Rd5=yEN(9Y37;y zs9yQh_6ognMQ&NoS~7TSzuL=NrP?zVseH?&dK+oE+Q0yk@ojSP=(O;joYM5OX)^mA zfRtI!wobZoZ90^kRy)7+7q4?^GTPgmOOsh()H&4_OV6drspIsyv_?gxO%^!W*CO{N zRp|V&@>ONdtbRVNR_U``h%%oeYjGWiO(f%Na@hH_97(W8t|~-)Zdyk!)c3h*GRwcu zO>58owolXA*N`6n!uB?A)_nmpNy?y7&Bc~05hjd`b;*04rhOxwBe-~aUZpzNK1Z<{ z(4+O9_)>TA^a`r3(`_4p@hxP=`?8>j*_v-Lv(s?qu4 z-kx-JymwJ`R0r6G(^ANWb(dD&%Y15rmY- zx3DYA^gpy;x(uG_{nBgI;8iGN;w|JXt~8jqQLajNEmzikb|v5Lm%gof5oxK8wmwL) zVRs5%onC<+E=Vt@y42Ab=`wYs&q$YHSa4=~0GGDe%yb!UQ8Ux!2yU2}E*<{%%=G%c zyZ~lQdx|{7brq&+lM}m*Q01SEoxq>a{xE#{NyK)9bRlu{vE&Lw~PM zZ|KVlXvQR%cu^K(Iv2thTbacgS7AJ6A{|P&?@if_%auxwUz1)-2Fw1ObeSYla#Rl~ zl$%~dwcKu77V7Ck{x2)}shU5syYon>PmglaOVNQQ8NOVo&qBhP{`QJsOHMt4QV2UO^K^Ma8J8UB2!nqbZj?&IDwr{ zRZBiVLcY8@Xe@>`7-dJMv6tmK?#-^`8qd!&5Q4Rmz?!oO_Q}J5OG78g&rWu7fr$Ws#nn+x`k2(sptX;V>G^$xf$@1dZ^GJV9&!?oIGtrMr>=9S5MmrR0VT!#hR69`H`xH zxE8fHX0&jXJXYBb(t(W`RmkI;nk8kn{c2-|T;%?^QMGN&O&Q%e&Xi3VEmS3)q!ha7#w-5}fAXLm4uSe11q3>cpXpYHFSv#GQi9HMmuVoDOHmh_7>4^|L{T zGrFoqh_%I|oSqa|t&W#e^4j5yfH+>xHkQMbhBDJp5+*d1YngW4h0zSfn!`ATa<$xI zxn{!Fg<`qd^$gZ66srjv70R{3a>W`=*E)YP5uI>UL}>EoM>3j7tq$MKkQ=77yBRWR z{&`o`w%k26P+H&1kXx70kfnL`-FO1)70T{KgG1~}-h40PsfV{5()zY$sBDpbArr>d z9TO8ZE(jOxauQB@l9AW%huI>aQi2Yp)h$^_o%ADZGtrK{f391G+q*zchJPVm;5TN# zLWMCmxqePY=5P)v-u|KV=BDi*%0w32{-NAP&20bBx1G!>e>m9-Bf}@= zUHfkC^RO(JCb`c;ztV}Eei)2JC`$yLVI{&yOn%YlVU3EcQ#SUYOgF7!AIiyjMC?O< zxdp>5^#r6|U}8*2cx2w??qu5oAXol^X)LaU^Js?czj+mEg?;X7NBoA7mTNC|wktLh zTiTVYC-$-{mjj#F#T9G3Lu4HMnxi6;|BijwYcZFums4gXxxu%0bFweM?e-zxR<)#$ z?sdwPX*1O+Q>IaO=S;a)YVMrbObx(4o$RIfE~g?T6>n8hO^IJRXUgd>!#Pt<;vV@j z{gw71XZv;5_Mut7KlD-B!TB;PsB6)e&i22pqxKY=-qnWsDcJ3Ot}r;)he@gE9oAmY}u$#G3g#SsmXndKDpT*+g>8H zD2MW`WOPVO$T+nY;L$p>LL=#{pE%hI@*L*ET4TcH?gr~8u1@wIj0-W2#$tkX<1@T< zrVQDctureP<>y&O^DU^HxRzr-y>gwyMtbGiQW_tkl+cU*maAGt%e6?mT2`~zt?KMb z{;PGSXR*ps%i^{+T#wP=6QUzRrv77112=bpwgD9VGE3TLXpc-kb&VTpGxGXt3ma17 z2X01E56hL?i(Sb(dSu$Z;`V+0XQs@X-fuGHrmoAIOc^+1-(*%*U1y22{cG+zm1+FV z%*v|keE%l1bwg>x>(2H9`4Cz-Xkt`QWOPtSOiajBHFE4l^4UvsSOFQmG7U9-s}ASD zx0&All7KCreH<%!1VzWhMurBBoEkJLDmGHxYOZ;!N_YNkW~tKr%+MG;2IUvm&2V-l z|M)g@yG#emif2_UC2iK%mLM%)ERBYWBl%MCtYyvB8KaV|F=o2pN1yu0FpINgRaTv{ zuWdF8;ZOdrKK1v?8~F*gGhptcqQ*tX;>I{y>P0VXS>?J((MvgD+~&3Q(kaL)n@iYe zn&sLt!*YF#1w|D)0u!rpJ;d`-BRkpNJ*#h9RR(`qaVbl8u68f4$THhil)#IUo^+R^ z{CaVgTt#^;$&v}U(~>Ni-i#$#GBGcQzps=n*vTGcm8~XjFw_>>r&tYDuK(g%Rk=#y z0j+Y?TxYpzU|LmdB^Eo4uagUYk!2qtQ@pLVtq#gmJZNA{NOW{)4CdxhArTRJ!J?im z;Qs~xZ&is_{gfrkBR77^QdiPHW#!$7PPDDSfw|MqUXt3q$f{aa4&-WED$;V95H)7v zI5kVtj2Brl1~{T#@ZtdK&tQ_02KWE9EON*bgQ!UM1&r>S&GD|KhZ7;Jr@bFmvGE2_N`(9?ru(XKp;&s(k&=<13k-~q34 zRVaGZa(;^k;R;U119D??a?-1;S{_{Rn}27?srkjCHBMr035L<#NV4yK*(f#hh_3dHlyLm%?him9ur3zj^`| zww>^>-ME#!q)>Jb>6RIFvpwYuV{6BWFDVio6E!LnOH>o1#)evF-3Z$%KFH(Ho5JzH zS6z{;Rm)!ELSsI|z>A3*DRVbU`Hk%nw2O|4iinB{nh+W}F*I_NoT+I7bfwk{mLi*r(X7s@!Yb@ zlCq^`_a$YQRTnCs*%qNph551}R83y$Op~3IEhlY{ZQ0Ui1GZ)RscGoG?f3bs*SzX4 zc-ppX>0|4*Wy>Hxwk=yaZaUOC!w=iCWooXrUHQA^$+6qBy_-vgcc{hdCGHbqNAjs~ zP>?kR-*K`#xT_b*3;EyrAD!QxEvt|jdCC2~JzLHMHFv0De7YlB4r0`fY&ktGF z%z+)*awfjHLlx&2NM9+&QD^&kK2sDK6BRQqWa2*-8>!xx*>V*-;LGfaYWzHNw!gK; zPYO01%R3!L{eqUu2V0C4i^kSs<+_4B#mdzMn~9a{T5-#D1KWocdyaj>d7Yce(kD_3Le23D>{*aoazWvf^&_iC1FDmMJ8(5cwzt6Y1q#aFq0 z#ok`!`T?7Im1`?@^TO2$opB5H@hX^B-*U~s#$Ck{v13=cj$*5>a^1xqUF90uj9tkm zcV@qp6tyS-DTi+GnUSGbAqRL?jEscN%<7ntZ^%fY26-+QGL@Lr9ipgg(seRfrK zhD@=&Kt7epY5!m!%6y;g$|L6a`|O%-QmMb~E>86FeYPhBR374@N)q^2c9XcrJSCjM zeqOaTxEuEIDp$q+mg_dQ@+vlJu;sde&AWLquxnSj7GQU-a+SumT;+PHT^mC!R_`;* zl^B+vo%7%Qm7VL(pS6G5wq;iw8fJTY#>?Dz#a`k7W)z5d2uyu(E<5H|1Q)g{b%)Lmtb@*`$!i zGP*a0JeJAka>(O)>MAGH#lF@huV>7~o?x|k_#!p~E7vn@0#>e>*!`2XzgD1vKq`v7YiAA@bcYPatO z?Eh7+9oYA)T*t8C*T_t6vF-6gIct}H`nYy2IsSX?ZLK8UVVAGk4*Uk&d&L!NgzhWA zGkpCe!8McDKYeV-t$4vAPo$0O6nT<&b=XlBZ7Ndn7G`BNq!!!0%WIQFZ0?m8%8dNi z*_FJt$dh-nYO-<26PeIY4S7;kEiQd;%fRuh5JdG4jaH8t3lDuFCxJRcpH!|WvuFV) zdkd6#NwFZj4lpq^My`nY!H!%&PZ;_{j^Ey)PvrPNK>RWiZ;g}vOI26n;vE!ZdvcLs zPmZ2djog7REosGMcz3TRQPNs`*IZ?8apZocS90>##@y+(>q5R!`&yR z6?d|(J~^@kKix-#Z}iD4yH8G456Q2Vv%M(~t?EJ4`^;!>^7hSMA; z@y(G7?W4XqGEAQN=1A8l>Zf>9znr{b;+o$cnm?~gxMrgL`E^30WUl2}jSba`IbK_? zGJjdFk}d@intZ@7XHhu5q@nXSKpX(GgKNy@!4l8abK|1a6u)IonUEu+cFglmB_@@3A|lyjo_s z=WPEOISZsiK8YPo6#Sgz&R!fnJS z-`t&Jj#mvBWt)bwdSYojtTY{beqYWEw<_fNt24tlcv*nrH-&q;Nd;_A?RbLXnOm;( zzn2S8HE$=oFU|7ItwK?rxsu9M&)i0ZI^lsZ{}7#ydFD#D$@R>Y`x{@c+|Fte%W$%{ zv+8^W+s4)Y@=IMTSD4*$RqDg8;;{-JyrPsiuVVSOH-TSxA2YzORd7z314X>3WU zq8+1xZ)C{tQMh_bW8myu>81;3=gI&$Jv+CRx*u%dY#(TqqY<{2t3Bv1v8PVacqGs}!^Kwu2^NR_c z5I#CKBqAtev=KXDV(4hU)_x%)CsemMElJO<-nWvU3igW_Kf!P0RKJN~p?>4A+7c4! zR~=W7e&g`S!Y?d#Tu5XcbjHykBO^ln=AwLo53lwf*{a z>oGWJKw$U&opjt9etwnwCWed+RXK-D@tYXshxMLGxT~-V(%LV89u3U(s}&htH*`vL zb(G})SFPUvs+(7cTYVnXPIjdK-<~=-K$6-7?=6s(G+dCRw$0axary?68{k%djLb7| zYrkIR86=v0s~CMnncAltnzAYJLo$~*ldn1`bE!}Ncs@@P(m2p$@(qTFZTOR?3AV^L zl*u=5dZ6qxc?h?F$+v!brR*~e2)B{R_u2GSvd^GHf-isufCgUFr>`-+p;Dh%1nqnI zhBEt>gCuyJb5axYd{PsO@<1K4j3Dnsumm{&W&qdyLQVuo>V&bFflEsvVw%8(rx2DG zaD66(^%A&=AHsSBToDOj?E)?rgs_Ak0@pKwnWb6Jfh&H2L!}ba75%SCZeJnyY%;i< zv$cK(Zo=eRKhuVEJiEMifs2zY?n6M$Teq*VekQE~k&*=s9f10z?~ehcu57;@|Ex=k z3sh8j%Xs%e>q1aAYwvod0jbhYfjVVi&oy5F>H+r4fs6QnyW_KVZ9mub>@T?5J$vXG zxORMZ=o#2eJcpktfLj{Ac8tE+z`O|7&-4JOYY|94`*RrU8E{j4)}rmO8DRBQgc)fUW*0Qf4uTSUyQ=+LNFKm#{`>{kHpd>X3NLitL4;;U4OtJWv3SA{B-dd<4l zN8!6E?@Lj8n<6=`@{q&|{f6Y_*awMIE0n8H)frQ%2K-;SqO(j4{5K~(pjX_m_&x)M z&PlHxoiaFy{PSV_|896rdf>3c;X`}#I1T}e&^U-}0dk>4|Ihd2K z0BT6w;P_#4y3HxYZFQTFA+5k5nUh|JYg4Wix3ss3yYF_1N&w|VtsjR6aQX3dxvX+Q z+@>B5u3v;6W#(R%amFYoa#c(5ayi{&xD_SMT<177ms~Pj6oC%L8X~y6aKo%VdX!at zNPws;QqOD~#oa4w!&hmWf24)OHys^5{inxbmfp=zNV8z1Nu2KB z>aI3(7y1Nny&}VAN6NX;ZYx)zw?pL0cE!86o}Fu`_5MZ&C-#X?7{(QLt}im8Cnrgmvvn0G8IIn2tTM*r4*TWPI_p{@RY=%1L6{fMGqL#Cw>$h@uT$} zkdP8TlsoT@K(qTb4d<5kjX-e+hIzTVJt~RZD7i`-FN*Q#T%hyvA`dEGDaFGbhzvnb zo3yl|Fptj6C6%oKy0@U%={mV63W?G^QQYPTtH=emFe7zNK>*xPN>l=UI;APWW%Vp8 za-i}<%R9JPWuigvTbGWA;9TBXA}djkmD^gv!%0e7xV$b}ksWEu^|EudJx*?ZuSk&- ziN1_;f+o$Va6qydDv?MN)Ho6$o+Ln^npyi61_U+vhZe z%2E>h#EqVl9?~$rm$O3EysW6a(uRNPmDdp0GqMU*>0g8Z#(Y97D{18<9 z^+F#hqDSt2HOIIH|@ z(Y|rXT&~+p<3Tis+j=pQyWsT-t^tTL?p|l=aKT7rv&)gjqqA`3dpp6;t=zaSLEPy` ztKVtidJnqSq-6wmFh1570HwQE;ym6j{t{eb!Iu?+J5e`CbfpB0r7*K2q}9CV3-`+<*g_2VPBL%m81ZVQN?d~Cc&JurWI_m$&W`k399PQomrjQC z6x@m)kzlS$bBC0ak3pz*E5wK_=w-FbmON{}s*==c z(*ON$MnL1q9bFy5AB;SS`x-nM=14Ea4J2QCQT*MS9+30$!vOZ@8T(xT1Kg#xAN3Y zN;Ta%jh?$tsf04=lv+fo9W{As7NtZjzN6>zDfRx>EJ~fC)H~4}O3je|FBO^(kDp7) z)%4`p;XHMlQuC7L({n2*weIjDN?oDU$7f3@b)8bHPA;X?RZ4lvuc6d8lzLH*r_NBS z_xEe*xxGN8&IqVkZ3iXi)04e=@1)cNN+~Dt)LBX?ChelFTS=+)^RH8CBc)^yK5v`}ES8lp4G3 z5v49rD&+cOO6{hUt;G{c9i&uT0#6;FRAA$u=(#PFl7~K})DB81O7hfJO7%{C|B9Ym zM@e1e8%j-~RGpK*Q|dgW&OLiasil;f9rB)1hbYy(^G8Zuq|}{fJhg#R0rNi5b89J8 zxP_-SQR-lJ>i^s%?th@<(lTO7U8dBzr95?*Qk^kR9ivprb_u;QlTyuk$tbmuQbR8A z6sFW%t(=~lPN~zwws=T;`zaOvb4aR}klQGE;=6cCeM_mQIXrcaQuPi!rRTO&>Q;{f z5#+vfN^P6hs05+rQtILDpDC3~son{hRmr(al=Ac_SB+3pDHWblu39P~Pf+sioQd@0 zPD;(lsa&0$n@y>Y-Y1kgN~wvPCf6kACR3{NWuDqhDf7dPwaB^eDD~mHq$Y&gL#ZYU zf2GtNN{xNFt7!)noLo=I2JbI7>%d(}Y$BS*Ig=`g=5ZGn8oBf&7niosEy)_VfJRCdCu?9KcYb|)|J{boCQiLX$E}(_klyXP^RIWo_(Ym)fyOVN z7)~G-1D8fFYqXwQIxd>iLjzf(HQXP=wcPN{d_85jp1U%$9?TRcjy9sA{;D8u!|*5& z7+mK5Ff1I`~A&#fF+n%qn!*gCp47cr_2+7Z=bH3FQPW~c2w{m_fZuS%lcX?)g!I9k1 z3K`x=D+d--7XMY}M7w=vtGNpc%qVSq&j3!h*+RXl@v3@o8yN@xBDLr%Xt@CwJ45$3 z`SYjO5OhGeB%>wavc8Z68o5t19bJd(J-d?VOK$6=Dq(%% zQsR0i#0?u3KP)O~G`P-nE`JV*B@N1IQZA5NKCTR~cvXl84LB>;pQ|f6L!F-lr*e)> zw3jP0XHEbav`GU8Q7;LgtGdY0M zA6W0ny&l?;E01D8v)bTBU>7%bURBNjL2cb?!`ys+?%KAvrKCW=Hk@i5*eVYao<|c9_7Pb?eq=Nf;CdhC2 zzU~(Eebu>JYfEvn*F}(H&?K1=kS0ZQ&0NS<+MQANnzgQkIK3=4YiRh~<}FAbK9F=% z+@{5Lae6JHZf+?T6i8+ea5sc3TuAQ=T;v89BXbFI+)a+7%!zG+2y-i`rgv_wDo*c= z+H4u!fP1*a0IJwXkCRSy;zAeDODIhQOyz3jloeg$2IfQtlF0+XbaDq5=uqy|&LP~( zT#G20HW9O~xsg*W@)GTXRAg#Fq#}w;*{mVa!{sas0d@0Ta^qF?x$yKh+}+IzuKw0~ z+_U9XLRM@FNY zL&M?xQaB$IK&Cnby2pmT+)o?FfLnOD-xr&>=*>E=>&AFG<`L7B8rp)(8D<7%$aLth z%p|8fHt)G=Qqa2wWzaC$H~iKxUNe19x+i#L;3;#WKsl}>EpRs zdGTD2S##-_oboq zdT{BPJtNVbQ6NV1{i9H=QE>VUz+M1YP{L8J<)L5DxnEW&xQ$1OxJpNoxSEGYp^Pzb z*_Ve$!4;dirAH?PkO>!2kn{L3?)OvUQR+CjApArMmvCkrmwsX#mvVY6y8Y{sK<@47 zVaPcanp2z_h8%w$4&>5LzT(18jNud~$8y~9RL*>S3|Dko!4;jci57FQXUmIj(7##q zZvpum=o_4TPbkAV`#FQ0%kFA|}nmrRYh&NsKSfDn%U7*OEiM%?<1<^D0^KHKiRld|19 zo?G)EhwawxTO%_=Wh-yT$A^4v$9fUwYj~*GU#%e( zJ>&&I(Fzg6%MVm{jF_0H5}qF@Iw_-#qG6$;_+rR_7pg@o1+H4_MB9o3byP1J$}1K% z4j0802P(lNYAa~)yjk>4VDP$Kw7EE_O`W2>JcHQi5v}nx;1z9ivDHLv1df;25H%C3A8U%%^XA99>WL0WsVCs+jYL=Y zwsBH3(G5O^ahJBDhr$J|zY?uu6%-Zjoo~QPx`_rzc}cCkLkIhH9eVF0zrlqit8TY?rBy%LW#11rl+QXNiHN)~{Ekwq=ZV&scJ zt8g`qWFMm=Q04?jSfQ1aB8EC+kSt*_)Yb*^!Klb4i9t@gvI%-NKEZ=lS|k@4Wb;GC z4oV!_yT3c?|FR_H|9eFUB2XB(`h$q}%q6)`eD!>cN6$#+sH*ENwe zBxO+xNg3k%*4B~)LQiim*-L!8VOL4I49ZJ!lK$krn(>lQa$WO&l1s$bodYD#NU0no z`JI%q!ICcI{gpY$O+4;D>N}?xxyjUw?$uX!ts;-x8B^W<#V&MY=235tIt&*jzs;H7D z*+W$EY6lAoh=_sGcS}OB0ZYChj@sc}Kj!M!QyV4|Fcw91=FwpXpWF;#D+-XS&vj$?%@gvI33oP2_wOH~8 z$y#Oxk}gY@5)G)YNmz188Xi5aOAaxc;LuIUaKb>fJM1PHe^+vZnSuNFBx?u*GagFT zvHC#4BS{{!2}=DW8A%w(`I%h96R(y zFvOkCWCS<*VM( zv*dVwymSW3Qvl!WDH^wS1t*K~v_$D@f)|}EeL!YE+B9hn;dA@9(vxI%GGT@^oA9HX zEftZ{FrSyFXo0i};kV=pX*t4IhgH%)N!fO-G=`Lu*Grp|^1?=`oRn8LOK*}gZ>w}T zkvDRO)J5oKSQ;gVa?CzyQ_|kigHjVI?;e$!$aU{dNbeDTZl04y5?c0y^boo4M1k}n zp*LNV1`+!E8`2hmP|6=l+$X+ur zi~;i`;pt{K8TI2GJ!Ba~0rmUH48$&-2gq78^4bv@Mm2QtyzWhu1^9WrHcX~34yFoE z8ZD!9_g}{GT%VZ0bKP;W%tp9QnIxCGndX+hj%L_*g6xGfL40LxMcrIqCq8I*(pyc#ja zj5_k4N#u{HD<4RVaIc}f49g25>N@JL3PO;>o;V+sZ!S+~5ZRA{o3xOZ0lv_z#trp& zMoYOkhye~pgQh6AhrBGxDV^93Pw6Dz!V-7{1_R6&V0CowtA@Irz2r%xw@prvk7ww0 zNMcU;(xRSy8>ZnuljIdyJo9fbR%CCGD93k)$sZ7V9v&`VOq?@rj9f>Yb;LM%QwG`b z%Zc)6miyXFm0u_RRc59UziH#!^qh`NoU*^8riv z^SMj;^Fx;fTp;J`ujS8+*YoE$Y~ari-W2ehbn9Jq1Q;1S34sTGiK28#>3GKcdw44w~p&s|^sF)u_8AX~nMe4uD!hkiL zrTd5by5S>z6gj-g(C~hWY5%@?L}6z3R}5r{0($f0A&Lz>1oXO-Sh-n{rI;b2Fu3w;#T^0BJ6AEE6|osoL<{oG zT&U>F3X{4>(L-SD#bU)1fw3pc6bo4qH7gY31;&=HVb@sZTE%YyH`yB%vst;>u}LwO z!J{#F^zVW*56EK=sPWqs^8}a@SdqXgh4XvGTR|C-dlggvJq*6LSCPq*C@njzsG>vz zvYYAgvkQt+EFBa;G(r~wjB+%xMxYAk6e#ZTvf$a*6tJL+4tSk!C@%7f!ZmLzS_`Ax z;(H2>aP5JIideo|!0M+8Sb0Hjd-AgaUhvaulNX9@O1>KMK{3d{SEp$My9l@a8XhPK zrEALhy&{2Lqo9 z{!nl%aIIjhj8lQ11gyoE0(%GYvTAMzHVfdZgC7PSQ!$7Q-F`H3CI%Yu`qzPzL#gb@ z@H$Y7vX&^!I8hQ5%n(Rqo*}@#3=Fy`2ysanR8H_tjW)<<-0Qj^pKxOI<;%i;^IlXuy;@gpNI$YpTdDCLi0e9#lTTF}d=h~RsIj}EL991+U9Yfk-O zpSzZA7<^OkkMLH(KF56CI@qV=!)=3oN}kl-U#;6QxRhYLFxW2MZB@e|&HROLtDNF<{qVSzPI3%!Mmu1veDJdgkn4-@w;XY z=RI5;d{EHz&ZWVy5Rndt2Ui8F1)bjC6nvJ!cLYNo-{Cjp1RUH!FyzCd!P|tE)|~bm zvf{bm6d_u^zZ_gmsETd}XA5z$=3ek}q1ySe-_p@f{FatI4X!NUHhU4gl{tp?8bZoo zuxSSqKX|=Yhjd~#B7kKGeQ0NQNPR)9&5BJg#+5MKm7Y7-JDMBt~MA!b3A()~kx)*6-=;zWt*ZP zGoGIrGET$WwDhu&`V8vfJ6RuM0DikFL@fAZ@Or;>du;tLtc$_AzX^t|^u6D(VFyE0 zg67Mg@Ef7)dA|`>UGW>C+0BsV0()!khAb4c+5S<;dB#z%CHf*quf;oFge(;LI{Awb zp|6`QSGHsr>oIg!9|X=ETIFS7Fp-&+!~UxXS!-ANA|%nN^hHRENF{58WC%*VTS4iI zjf-`adj!@OH&*)chQO9eUu?APp!CJY+fGVfPqv}E(iahP)BCZ+eul^LIvKaWIdY9^EFkcmwQ$3aO)jun!#w+;h zj4xFAj84FFi<;d~c1Nj0R64ZphLWWhCq99TzEo}GMMayMsAjUd!NsPky9}X$fd|-! zNEC^_Z>7p)ZN9LzO3%`#Q5XKMovOFMgSexLWi360Df5-;0D}q;y?JscRa1e7J6+h# zAnvBR#LUBx9;(3t56|LMEDuYmD8}9@53?;U#j9ck9=!ck(^>tY;Q&=f#=f8_O3Q<3 zRpD}#fxMj3;l_!oH*(%ZTa93H59nb&quO!mNYy|V1^okN`WV$nK~r55l~f!9VCXh}5wPyAX6nEGp)|!>)vf=)$-dd3YA-15r%nF_ zUtwER>siufpLhzCwNoWSyN;@&(6c~wARfD2HM=<8p}+vRnB`eLWvNw5RWK^sRbL7J zcvQ8NnGphmLr$o+FwX6oqNL1ADl{V0Pyvs)pvqxpguvjXKdAm>d7w!&GS{h5B2`>O z09`(bYu#172fLQ-e5av$x`YqMaZT0jSVss7crw7KL{kT< zRoS~Wqw&)g>N5NYhD)?j=L&xKV_Wr!AU>G#yQ!B2@YSL5>RzmI1x*K`9>djYRJ#Ri zM*xAo#iB%YFfSEqlB^a2tmRO3dNJe+!q7-5Aw=elRr?|Pm*-rpRgEAwma(s5Kx|6_J)kS{RrAyRC;b_?v>Rp0~-mFx^ zvrYO;V&7`DuUnCCP^U0wLPe+4aunP5j2^4Es;h{2f9tYM?O?SB`U~O?^+bL~f|p}; zq!{IQ_JrZvyVTzcLag4S9w|6t)B$yw;t1hFryCM?iY?z3bv~*-$@k%%j{00I{+K#b zn6=4Hsaa=$7=#lZX5iyz{dSpkp2!hX(Hn-%m(*98CryQ`V7}CLkIsVAuc;?Wc^SUC z<&&Y+ZFN89E}vlID)-d;1cgfuV22GYow2C}2hL4Wd5y|kNa1DIT zU=^BU(Tw}|y)}j>$Ian({GCJdP=;2Fi?l&NYmWVUB=Y?ZFZo@DaP)2TswiAkLUSLC zR1m4b4@ztLLIja&=W?3upbS!VRMWs$(P_1OJB?Skpl=6_OQ_EKO4AN(ORoL1i{=bW zvPkv%0L`}oZsK6gYvF=nLp5(99LWXaQZ!`+gk2*v6EytM#?v&bgxfyO)Z7!UEy~a| zf)0w@)|k`Wv4{}AGJ41sO+Kp%;8!%PcxkR?hZq%xeWAhed72CvUzP0AKqp9#p53kS z2BTbAbYqfLC{V_@GYZt?n1*HPCeb{ueO$9INR);Dyr@Z$LYaI;1DhW+czHVA((GoH zCueej8Be;cnHM0+z?Mgv5mG2;|D=Jfiy64XGfg*A=KZ3XLXOvXt&tHr_BTy6QjY$e zKY#U|W}aM>u_Gxg-w21xhlk~hpnPQtJ5S0(marS7Tx<)wNXkS<7^j3%QZ6i*z=TGJ z!FMJya6tL6)1)BLD+6`UAsqNKauup{S-EwoX;!b<*V>(*s3s~ugSGkZwnOK zAadQRVC`XY-vFid5NWq;n3l@(t5(~D*rB#ddxD$~@My1+cJ4-Kjimg&q;?ZIzA91+ zJ7Y8O#xh#i;S1MAX&;dC=NN4fIe)8ymfCq^CGBXUzY;aH-;j1U)Y3L3`gqeo3*QyV zz^58%7ZbXFYwa*{{#HA!lj!q9S8WY~SFMMZBlbNrSWETWG+A4MoKGI3rFxn)PWy`3 zb@*iMZ{)f`v$Ye5JWuCpCxt>ec(t|;vG1l_t()+1VTbk+k#FZ=ZEJ!Pa8_GV3uSbn z_9$upw+C7&x&Hlg?GwV+gE!jKc2U7!Uf8(}I+ho9nqR`5C}(Ct04_4<1_X)d{qWm`8BUN7~Ts(q9`<(nn%s&h4QsBO%w}+k6^U20(7k8WjeyX!wJu4}&FQwfZ;Hju zpY3!%6_1j z$M)5IBSDQDbk)N&Pd84Gv~PlrCDZYF6m39-hYi*ZE{^hpG4NTkSP1COP+cfmGFumg zdL`?g7sHo=F+eeWYVxyIl1A#}Xzv1@8TTHo`^eZc(H+X^0#ggdz z3~8M^URQC=h9$bCpIOT|-oEw~poO4%CW& zLzeN5wSP}B9E$=L9`N;Cmk;X1#S?qW3Fwk9OaQ>~OLSGx>Jz$fbb5)dJ+dDwrr18@ z1ia(8j_E5;pu#RFa-cI~3##jnrn!~wh?23_^fN@iZyF)TtO^Q|~M{RXoO4(0o- z`L}dV)?NbFy07cbPebs=$GYnx-j(M*(LEKav!3eq2-R-SbjyXR`WM|&p_>0f2b%$C z1k8S^+bC39z4l)#{>^{wwl_L>VoR?b`&M_#!&f_6^{|?i9xY?nFJ|q3adVfxo*>K8 z2z|JbhdfbJuV8tO3f5tn9xQ9C?|~=O(+}i(1@x|go@1D05Euk71}^Qn0@rAyUn<}| zZK`KkZS{A&(#`b^8QxrAK>3s99S+>r9u+px2Vri_nb;wlxU>1k=wZk%!z`gBJ?mWGF=O=wh5sJyw2jKJH>3a$qi=CyP z^Y1l=e5j@IUf)E{51Q}x z>o*BLgAeLwF!;=8*wcsf<5=YLBOpLfp}R-)ED=FaFo<_R=$G?P>RG<5;A5sF?dO%J z*}~xOC@atE#|TP}zMyB>0q}Rc@fY=pEHU9j(Vz>9VXf58EBY11u@{Arqv|>OCb;%> zeL72EP?mDf>BG>4$9g&HWHVI43m>w@svipjR%u3V;&A#)eO*2}(7;!|(J1t--uI9x z=RIqj^uve#&<|l$`D?b=?1b-p)Wgpnkl7qsCpIv~L?;9=ZWQgm*tJD?MGw9-WHE}ov!UTIOIiuM^h1lY z43YSo=7tdhAHl7edIE}ddy~PAJGU`(VAOO!+~{jVc^T?-+M&l8-3+i1g6c-!)4-Co zQ=SH88eqv`oPlXT^C39U_z{NsC|{`%_?V##zPP|pN(j`T#Rk}PO11aH5(BJvqt*F2hF5&}qD89=EI(`TncbJG4NSXD zJ|kRTXMja2gfX;qqhU2$toFm8Jf<({6GpIYtKq7^gKWED4I>YT8nD+6!#!pLQiONd zAQgHI)gHsnVsSHOub~yo24#&R6s8#@8%wdVE3z&fQ>8U|5d7M_|8P8RMDauulQW`hEpU z9Ilq*tx-n!`8g`Vg$hPkOzq`Bht^m1V*WY92rLPgu(t(;K!q%=}PCOEx?hagT1sHhe3n zV^<@5^uL?Ij9>LIPG(d+Z3IIj4FpwA>2EwDsJ+Wzg< z%Sq_e75)01&nPO+xRuePx*UNR+F)GHGFJT(UJ|Ku;8VwoHX7Hk7$mOHn1rU>4tJoAkzs1A%{7h} zizeY&+l-?~8MMQgC5Lk19^+_2>-R$(K-n+fm@k2{)Dh$Q;x((dWE@&!7khd~1i+h5 z7`FgBsQVe?S_U;zB>)EhLh0yP<9?Qi0hCOBr~6k0#wJo=XW2DYb`aLwFrH=cW8ebe z)whfnz)Wc5UE{8QuMZ3ui40c7q6fxa#0-5N8^8be%_7SB!yk=d#It^XYGnG^5C9pC z&%HFhaRA6sO?Xu>1pc58hjq4-@HlgLb5fqPhfgDATTl2+Qbv^uUqi|(Wy6<|a#Z>7 z>A_HnYlXu)=SjFn-SA1Iyx)MoKe}l+{6^0te5hr3S8|=BbNE+;9@(A0?tL%*x<>u@ z>uwARXZlD92$PJQIhTTP!<6s^GHBu6=_<*c(goay6Aesm1zbBTOY^TI2Wa^H?{SX(~{PsZVsiF^Tj z!dZq{;-$$5_&1{$N7R9+c8*?+QV*+J;w=aN!vFLm;q{>vboO{S!?u^dHzLQuC&TST z_odJBG8CN)&j!;S(9s z1I{(R1WLVI9d;tO(5dm#hv5}P%J1PvA_j&A{O^C==9Fs5-E&(}>~x)M=i~5aa?u)o zv3RBD;T!)wzZm#^DG{x~l2_sN#85W;HM}%KH^(iT3cpqnWU7Tfy$L@IK_2D4UefA!^1;A3I?9-Az;b76-_b3qUvgmm~ZvqE_upZhr2fYG~?3texD{w2j@`O$cIX{G^#_U@;<0Kryy5-6GcfwT+2k z*PCBR3@l9|@&7|d(`beXo*xLcN-*j8uQ@2YnM7b(-s?OAOtXq3Ee7103Sclj#grEa zW$XmgO%asVG?OtH%1Sd$W5CDpmf5D~wxo%>3GlAjW`%>8E z*+heWy<>XFAUhyzrv3SWLKDj`Xc5`xp;jNDFaK1@uEp=3m?|pKdwWwodi$eEJ2%8) z#zWqiZjvpkz-8xmCYH3$*N6&Ve{b5vsO5PW*p#y@?DIA6K!q{pp=gWT%(5yEP!8ht z0p?J$M;CN)D#Xmt2@-|#7E4x{l?m_;&{&uz1dVOUT%b&;q467 z%OQ*T2qQ13rz;{553!kbtjwVN$t45vK&RQ~u^m0;qm10pxDrR0E3+~~0qmvB_XN$T z%b1T9hnIb2%}W?Y3*@S1-p$V`I#)0s6*`=lsx0n{0qYuzKH$DX_}LfcLO~a|Ynu-; z=z_w`iZwI6qM|pCtZ)87;GuIvb{KW#x9QTWZq4_JJ3Xf-*-0GX6bAJd_GF7dRB(=3oLW+!ajcR z{Jwthnf=WrS>6i#1^nQp5OiT}L;j+)`hBVyUfk2_f$9EsXUy~;ZJS}1u(E(1^UQ-8QW6sD^HJ^wvzGsj z*j94uG%Q_azAdGLI)1atT*TOg%*OzmB;h44zs1~u??~XWjya8C63=sbzLptw6y=w& z;&;2uqa^&y-iGMk2J}+4E z+;Q^|7PZ5uo`iBa+bG=Og86F}4O0YleMe=Hm1-;D{_&GXy^nu;7NvJOKSRnT3(F>(4*95V5m9?~CcC@HWFMRW7 zNf{Grp6i~D$5phbgpgiX+2V`i-c|iPmacAD%G?-qe*q}?qkSrN#acWZ4myiwB9B5H1fhm#*qp2fc ze_dg))r`LzX}KbX!|`J*EN@r-){+W89c$^t(zE>oMm@n|EuA#);a4!n1(<>PZ@FyYR-M z!%^=nJ4tho{;tgjnJx)^-fp4hHUIU}Jo|sr4)=vs+o85hYG)XC22F(o;~r%B7;I&CV{?l`NSJa79sc zI5EMRMEEL9vL-8_Y&+WedjOP8Ct6PutLP?M;e}2bzWI%{A~_zKZcQcEXJ%RVk+Ma$ zHA)8MxCPcHM6UQH)~|}=co8UP(~j6=gk)7>skbT0NwVf;{W=;$V8wr@TvnD7Ud?3f{Pz$GouDx}4dqeR+oiS2=2x zi$vLc9`wyI>uQFfh2%jfXLo@fFFp=RWkL94O+l?sSy}QR%_r+B3@MOU3St~SZ~d9A z{Y^OiqV+aQ=l}zmDD$C}&v_8!Jdr3#VGF_|uUn_efhn5(RKIK0q6-%x`-v^ILiUr5 zj1R0V*$-(BLvsUICKf(kRcNNs1|MvlZxepss`-!BkrGihKJwHG-`&i{r(amdF!W34 zZw*YYagBXBBp|Kw=RxJgc=uZ?CTbh{hm~da2ymOQ^@Eiq5yEhDZ_o}JF1E2`Vzh0$ z)W($g(3_9RZ3RU5g@Lvktp43D*v6FaPzL&{Z089BRWvr1tczki46`w%I&gC)%ISGF zgl0J?wqH*L)|+fANWky1*%-Y+fTR=QM2C&(6O*4gqcE-9w(CSmkrB4DY*zX!Q-vjL zzcR--Q5S|3=?URydfESipV%lH(;J)5N}@T@;~3jLs8hGbQ^qMVr_(pH-5-8n)X}iOzN` zvO#8$jVCX&L1vJRlUCVM86zcOZu>Iv_~l018kVF0v_$|)p`7OBkpTA;*#2_0>?${tP05R^{ATh|sb$_%$VgTYi zvq55zjXM?DI* zv<#0l+Kn{5S~yi zJHyw@5ETtzGg&4UKAu!)-)y@EeQ0G@<0^ISZ=)EML*%1alaZ~Yon;KB9D$`G(V&Tz zLnuLEzBD(}9*AaMHqJqxQtSbENs4`upt5Eo?F{RHsmc(T(T*>Vva@6le?dqdYhNyK z}^u8TdgisBgdEjpdJ@Oyz z@uPhLV}Cybg=WIqxTN#&q9gW|9TuU`c6$8k2QT@?4hv=I(O-VIcVU?v68p^k6NV31 zg90l?2-c2cjxSlE(2$1_nYev`1D3K-zNQ5_A{d==2CCCW6HH$81S3cL_8Oe2cJvhH z2U3IMOU8H;V9C24Y&AI^!&)EW>sqIy1GCgwOYC%Qng!+9TMoqAOE};+LFh#0S!oCP zAve-ON|b|XT^bD`Ukm)YG~6P_aYoQ^>52|ml15tys_baM;^52bIhOD*Y5LW2JQh>V z(Un>bEmAMHn{j5WgXJ4I7+?eWkp=H*=z!H}l*#GM9I!f#R!6jQv|x4)|0^8$Kt=;AqHe4ez|{xGAM9;^j9SSjJZe7CMfJ_-ftz z4)_W>(J=n?0r6s5-Sf!tnwJb8c;eV8AWV7gzpdK~|844*{@bp;a=Z|3yZW1BI>Q!B zVzzf`g64a z=jLK@lc#WAV)hs+PrG2}Zh;%S%DKH*+$;@sGQD*9tea(FPWW~l)lEOWlO+}SjP;|z z$&{siMi^&uP7t^WwmO+(o9a2u<~%I)V(ned^~KWjX}9x)fVHxOb3?JPtfibx+k`+% zsdI0;Tzr@&2X3Cb>RW~DCaI_-4xo~CGde%C+tq3Go58qolH9|c@5dz z@Fp(6D900KI75Uf$ihtLX<@Q4a+Y(wP_4i@;n!E_6hxZsgim(S>hZbG4ZO$T$@87? zRXcig*HS-1_A)<0>lJH1s*-N<2@0nfM;~`K z7iNd)r<^P~DTWqcyA`!U)$>lsKWWu?(fNYa7*F1hTOT1-yl)1EEiyUkO5veC~w&l?vJ5l@sz;TCMQL*_=gQ+78)Wg<*Hq zIhHq#K7?popmIT$O4;e4bwQR&tH&)a$Wm$bfX4+{Dy>eBaQWunh0{FwUJW`Z&vsFEn)3Vo@B8yCS_4zC%S` zLPocAy=349w`uRvu{@LmtCDF(V&Ut)3>#+uOY8y$Ucr$H{JfjW7MYZBV>*bN^+m<8Xd z`DwHZzUF?PeSI6?ZQ;M<-XYO-Rf3%DG#XTEnClUP<^T)hpy95IEbaq0&qtpV`TTLN zUuB|M{8Ehd6I{?eR&bl~n@O(QVxGx|-?~^P{=RlpxX)Bq50*Kk9|4`8<{%U3=(a znmc~x>+@$hrayDRD_dery!jUwyt1X$(M7I`EJ-bGA*P2@`FUR&|5jD~-rr2+KYh*Y z{OHoK%+_fu`Es|BemH~QoOUeGZ4>19HP{XBSg9N*m2P;)N~^O%-9G8MYWzpNTK7wq z=^QWURrrlNx{NfmUe{o>;G8Q6TkLL$)HgABYlY;MP7Hdw-AsEvKDR@ck+zL+FXOue zEG_MZY?q35rHsG2wwxOhUwU*Os%S}j?@T_D^RQq}#KVctvnM+l9frOHryitCEd z#AEJmcz10#d?uSVk{0V%aI<>uUW|qvid(mEpJd6vfWx7PW?;YfbgS^Ewr42*IL2+>$Q$_*J9WoOnnH)LS6D*4t8 z85phpk?w{Jj8>0+=Y|Z7R@1ZGkb%)^K5|0_Mys)l+>n9M>c<>6WMH&fZnYaSFj_UN zcQ}YHz9M8&a&lRFKy0f?_$Wx6a7yJk2 zGnFR=FmasLa~AvyN9+0WgTb?p(0jr?^JP$Wu=B?~PQHBU;?HmKc)F7FA(5V+L1Fl9 zSx-Zj&eg|O8(dV&QzA@|a!r}m9%6>F?K~_=h949Gr~>6bw#xC!_8zAE{QrQ3-nRE> zkn;zRkf}HM$^-ih^YOvXp2I{Z-}dyJAv$c^%QI9C<}DQp!?3_JqL>srHT0G2_B|gpRk6Z#|b4K;QYqJ zlCt>3OT@X~dYH2P&j?MYdG-^91kUsvV^j!^N%yQQPVbwDS7v&45iYvV_8ev8f^_4F z_%-KYdSCuoAv5QCCJ|Ot^F1uN`)8ne3p~e(k$W!j>@Ai|&z5@55TIQvJo}0T)NqxD z=~)1kDJR!6j1Q+X>pfdZk3C@va|~fXV|hkH2W*^{=c!E;a$u+D5UWCffw8#T_nucQ z4h|vyJ%di6iW;MitWYn5PQ!m<|Zt`1jBU z&w55Nj|BkJ7A3trEC2gPW%}XXS3Uh%GC7}~+M?7#kAdXrqj1Or&+y`5eV12)|KcR> z{EO#&0BE1iU~0edG-2)wsptS~LRUV(ARO4iqeeZmOQ-LA@2L)B?T#8=_6Z@u#y#ZT zDgeZ@x<14^4jdSlRC}5BYJS!$!0Px7n~E?mY}m}lmvvs)u$hl{7`$?p<)ELj!4uTv zgDwg9soi^=*=8_0RO8>O9xCnqf+eS?EKwNx&C1i!Uf8vnkIPr|!miDH{HdxJc5UXv z7k|BR%ppYKX+9rX-+Qt+Q_gDSu|PD=Y43&2oB3GN$qSn|^YP?3FKph-$72V0Ve@7_ zZZOCTn>S&mJJ<`GH}i39iWfF-=HvWPUf8^uk0*@x!sg9t7X8ZjH#@yd=_de;MXgVGg^#~vS=t6+ z`+o0k89G&ch#o&b?1lX?M9=8YW8SrF9FE1%0Mm|5A9pHrS{k82TjC>xAEI`g_I@eh z?Voba`+y}${u}n>8b{dkD?nO_TXL>eVtnw5cfO#nLD#)3o4x>yx9I&FUZ#}F50G@* zJ5S)nROsEvW;>pj%zIv@#LCZ$=b;z&;!!_(`q;~|jg>&n?NU1s_j}^4#2D|qF!4Wo zw+g(J{Kd=i=*KVC@)usF{K^kd>6Lekz{`POy{n63qv$u@1AIuJPw!YmmDf=6AKoJZ zRKri+b;aOn$F2yb919dQ7S+*4xcMBbx;)}rp`$z>6u}r)6tFaI8xq0vMNl70rLi+K z0(N#%XMUuKU|H=?urP=h&gjU3DCGr8_l=1%q*IA3R_uex*JKN_>j<=Sz zDbZTi@Z^vkl&wr&R_WdPa$ueR*-|~7Z7t?iZU}Cva*>Yahv1efPfjne7T&VR`fF%$ z$ObZxk4G(As{ES*v0sJ!X3vVAclT%Yz7Fward9Q8Kw8U?0J2H*0{C8P&DJ4!fs+g3 z($w0QN&*tT!9pBLr`F^#}>J<>X;eX-GdYl8EdRLfRSn zS6R;Rq;Cj*V1%TR;0g1ovb)|Lb!?28v{-`5`MS({y|Q!wxMFDjK!QYN>`b zA%T<`ElSxzp3Dx~8WKyH%yI-ij~%!x1dl*-iJ$BV!M$mo+;u?G!_%hSePGp*5MOFN zvORy-cVHtH*IEp|r$X@RHwV|8u{O|mXG3r!ng6=?d`M$*Iq0AMce~bfEd;k~IqaD0 zAv!W_&)Xp`3_n8fEqu6RMO5DnndM?rE=}z6nb)(B1%%-OznHq`**RZ!XsEe0`>?ud z3@iCF)CG1v4=JVeO3!MBma^k7LI|@mhIxOG54PsM3fXUmqPe{bxk-=;ffmB<_aPT4 z3m}+z3@i2uwM@*)@C&a`15qWj>_X$5k(BzP!hhLKw@``TEss#j-kDSeU>UuLD&_<| zSS!C!Z?<(~s25A$8y3Ni`GlULY3nl{lnsOkqm zFyBIm3<_Ori)Ejz6S|M4g@w?rUMOk0#R=GihVGLH><=l@sWw48=vy zauyd8`WuzNX?5rkqERk{?s1{JBmz;1p_DZhF2sSPP}0nfXD-o(ej^dMs|%&g>=md(6H#Ka_HSn^QnMIqgL!VMi^D~0FBy_7p z?2^qK5?V$XQ*yvzx9d%U!f~P3$OtWG)$oSL(?k1KA$UU0q2%;g2MAmcx|*t87_mGQ zH#y4XU9O4iLW`(vh#``=@wsecC=sP>h(~;x9ho(`cBOHNG zN3Pxr#cj26$aolv+iK*AK%TwQ`6F4Z|I^a%iUv!yUD9xT6lk9kp^; zm=K0LYUOZR8-_b-<*+?1EQ4rJW0~a}Z{46?ZrD3Aj44y{BOlNm{cU@kn z6H_?^c7vXc!xR*+hHB=sm>_8Wbr@bxTq<8r?C#Y~&HiW^M!ieO=64Ijc3cb3A$Rre z<_R}jg%#K%I9^wbhmv+-IB=+t_vvnSt`p8OJ6EElH|J_G3^vr7;k89IyIB_I4d=Ur z4Wx9S>%R>f#SV4HPkeZG52H*FI6!cpFme8KK##C@b|`7@vaojq?<}DKqK&558D`!j z&oSo=3L7cuJ8*c|0UC>)aB4)D8^x(3i=A+Ibl6K1p=myFARluW-VPVpH{t)&A zD;pekhQ(3*ij_OVlJQt-_pp|1e9v&dl)LoZOT^stKLPW3tb#o_6?T?NVC9)G(hQmtXnj5mzw=hXaxaBlrV^-fC5$wK<^)s~ zVK*cK9yh}-Q3;&A6-JuCasuP-ge{i{sPBdyrxN(KcAUVzJbn&Z3WJIBCMlMF{r^za$Z`@ed~$6d;C@ z(otc)mGU{xeu3flDZ@BKn8)J@0e%fIf-UK-Uc6qdW-igVRhu7VO7oh^*C>5HHqc zo64J+#+5aIU%n21CiSW7TZONd$hK@7PMIq*ODQ#=Mf-4Eu&jWHPT@FftN@oT;rT>M znfE#1&D^qZ4@zC5(<+mvj|Yc^mr$mU9Dz?CKaLK^>0<>3tO&>HV+Cj?hU4_H0)nQ5 zPa%#!W-Ur(7na)SIorglX*P9G~^^_p;;K2|{BmT;UtRzSe+aGX9?z|CL6 z`2cF!-f)~gR>0N6;W&M)fab@;ar#&R)z5_E^sxf+FNWjvu>#ar!@E+(Mbn5AlFHtH6)`=4rZRa}4#r*)jon!N)YkdzuOShHO$#P(13R3r z!@`F}Y$K2vsfe5iKMju{9q^JeGt3;s3?Cv|vQwiXYSSpJVL=cP0WU^JgxIs_Bdxtz zqIz&I_UzmLr#d5QJv;s{SWnbrGc5zDEp5K~;XoV+5U zBW184QDasZmii$AJNTKAUSPL6;vi+1^?w0JX(zs-PmH3BWjQRUc}Zbe|+ zZ*Dh@??jxbloi+gGlI0R@L8Te??qs2FV1G;UlE5Y#irk*2pi#K$32TULc{{Bv-$$2 z-RlU-v4zj#MZS$#KpD0p!by3-;l{^^CBo^q1-jn(wx@2 zM-m(NMVROrNm`ml4yV|z9|HVfwNKXUo*x^xb%^&>MWLuJmPad@Pk;4m;M^5TED4?vh>WEFf76X_(1?;aaT^d=V} zFFum$WG_N-QsiUdLkwCvhcI2_ZlY_w2#?YtF?tv`g$J3D$NqaYFkYLxvm=L5W{H@2 zo4hbJy(lt-a;_T@*0S{9BmU2+36G|cew4Kf4j5Dt>CL|cE8i`i+%|F$8OwM!A^<-2 zjNDE}csw#P2(}D~d`wv|&WdVaTecJDY2V7VfdBV;z+TXS`vWncV!G)EPXUWK(n^4%}5b4B* zbjbB(s(pA6B+{m;mz-+h6_pNA8zOCNEy{`9D3k*|Hbr(PYey;;uvj12-N4iBkr-Kx zD{9|PYjVc!$Ti~EhI=AkQI6Vh^?B8a^nv6Pkr)z1OqTAQiabp8(rX!qTX0@<7d~+C zQsf>%lU=cr?|jWlKJ0pA9%VUA{U-7doA4nG1A{+_{OBN)_lNIZM)oFX9uRJV(D(88 zkr?BQ>zjRu+$54MK1Gi5qO&Yi0wQ<|_1R@d;k5-W*6I8xytcrTyNjY`Q#u(0p2j|Zl&gK? zr~?!ewZQm+O@}CqtH(Kw>Jo)<^?33{Sro?APd=Fxh38p#GH^x|#?|9Vr|+Zskey!ef+$D8uPc+HQcE5Myp~2?r)ZtK zqnndXh%5N%Yoj^_*j$j$*4HyofSLD!TqEYACg~%Iy^%;P@sggRCb+2x6_~D!jn= zeUz7j&4tbN6-OuyiJKv!Q7$)GOjw+O`^!OrXQ?i*m(GgSRLyWZZsDp}M(DVtCe@t< zR#nuHPs5$`RA3OE3#`ajv5v-vqt%6-ehSiBtHcM8&m75Kc2rkTzAR^vsV+p;R4k-) zKOA9!MmY@b)lv8&75Jx~!k(aCKqS(6Z_GD%Rvo1S`*avTAzgA*p@eQxieGF{EBvH( zx>8YxEZO`&&$=+T5ef~Y#3~k2Ms-|45)6!vSJ+8)#aaEBs8CY24l=$}^kKUT70xW8 zOHE%^{4}Zuyh>LrluVHWugO;6;g<_+T%KYD!7eTM9&jgLL0adQ5C9p5Q?z9%K8U3s zi)zWXHCOnun#Bsrj2NNH;byU-SW-=NLj~moCSpv3m5mhZC5#qL6@)Jo0%F8a?2oIW zdk=*R1hrIbkqBI9O%5HWM_RlDJWl%|No)A>8vdm04iAvD{ILa~5XNqZN!YsNu|>HdQ#X zr5fcxcu+2P1|KIXE>Ymr+Z6p^=nMr$W|mdPwoONE@-fom?-gq#%bW8f$-_y|456Lj z#T><5iuyhyl+RNHR|s}%tZ-P~a)T_m^2_e&JpD}fdWgO({a zOa3Nim0}AK2$0rJ!YF{DOf#(mEE^lBzfmz+s_adhsY1<#R3LdgKrT^|9=_hGpd9hQ z>~rDHF2y;DN5ag**@|k)nlO65ViysiKU=%%gp2Ice!daz#-fiZ9?)u3Kyk+we_U}9 z)xxJPm=?{l_=KVsndgl)xPtKHyuty3&XJo`e0V&W}@%u50kDdrlQ?J z@GZp!3JChc4lKr7=>b39Q$$H#>{bEZ2)Xu9aal6wEl)_k%Zkb#{Ed@5pJ$3#$|TPU z0|-MMr^(_;M8sfigoDzL)hbp{_IIp|*l)#(>u~P10}fwm3TqXld_-`HpBY)89Jka_YOV85Q(Yx>jK>vLm3c+ttQN<3acn@i zlH#{56srP4rCFG7CuNjkm% zh4M9_4FaNd^+qy${BXD%+G0tj(uJijkMV(Dx+}A>o|t{9a>##gld!&2xnIiOueWl< zf6vY^duN!^N15e-;+-3)Tq~K(=po85>A7kc`c!<*8=?HfpQDt`?D6k+-zaNI>Zn?- z>?-{|dXjRJls{>PGMdsAN+ej1UP^DN##^$|pOloty}ZiiuT<8S2h#=flq)3zc)3uy zn4lO7S_l*@Rz9ZmET0kXFID1q8ZNVLE0mNoy%G?hV5Hmb78XwItI;8DkhWggP@=*Y z&X#UedJ|h{bB2VRpkRw~kz@&vw<*_1mM~|x@*`!{oU>FtI_u81jR?`O_P;7AyHQpY zc(Y&G$|`2JL(1Kfc}5*q{w$HadRmDWZ7##%^UCLLHW%fcyPy(fC=0($cfL=nop*+h zx0PSpvMJ5mctf{8lpiQ&Gj*h@n2mU(3}R0ANUqBQ;K;u0+6#NVIb{ye{62OHgjQg| zqU>g1-bUy;WI68)(E3A&7naOX&sRtI|-8E?C5^ zTEvs^*ck!V#K;tJ;Y=IvS~(Rw*{0qExJMY?4$~ zHfv%k$>x^2vTSO^slFAu*^r=`P1Fsbo^Mo6Ev6JzZ$ac-s%j>Yh~z;6!A-B)Ryj5u z(yeSBWLA<*oorQo!Z46wdu+^3wNTZUPm^G`xDP6gR zk^=fSR1Fl>W%Gq;pIEsCrQLb~)d)m8z<6f0^nul`kNILGYXVv+58Cz7V ziF9HBVb)O21fVuz*oof7K`?l)>LRsx3853z+^-@XBIF!U05ACZsOlP-gM`2kp$ZG{ zRO}B&kE`lZYXyqWsvgjZoK@A7IUG5!iY9Vs3`4J~ZW1{(W|-NRyKu z*t+U|(QNw%)k}g?&Zy-b$h?kvhe60E)dWYj$TKjX-KbIBfj#lc31jsgqbaL{h+2cK zseRswJ#>sdLZG!^Qj7+wIY(c%vpELO-J)}C@tN!qolfcHB`|i)E1EJb!Mw3-O10=% zc?ea-n=T1Hoc z;+fIs0xU!mS4TTjw%j;T363Eg9#s!DJHnB5(Vl9gc==ZJAsc)y_&s`)M7hbc==D|b z>HIOe%oU%5onzKH;#1)nvr~HSzE?~YH+;UT8BsAE8SFEuG9(HWn8 z(qn$|p^E-113(;w;U}V^&c5K!j_+diM2lgh4A~ z9@a!+9S_D_mQmMZ5XTg8R zP)>~XTB$a{j2AJB3F>B2X148p45jDbd6R9_Vmb2TKQRvY)ewb;_e?)EWrKhVf_VEW z=7^ci%=o*?yYYa%St*m$$ zrB;s>$!hWH86vqWNj*{|jj8IN9GK!S4}Y-DR3D;rAyS#FI9E+H;x6*Yhvlj5d}Zye zYNn=iu*jnfDw?aW5*1el&0DH*kw$7m?0SisvVg@oc(+lL7Wa7O?(NjLT+cbY=%A)7 z1#_5)PUJ?x8+NG^jFISf*|zIQaCX*F{c0^*PE2lq+%P zKy`b;K{JGIlkJA8N%1&2hbzOadMFr0*Tc}!>f2;{u>D5eUFc!NIJzD-j8|hQ7H-IT=JuF;A*Td<>YSQvCS7O{Ub!Wk0?Fx#=1cV_?{CV+>*QiaR z_{~4lo#h|v)JF-`0Jk=(Z;9g1ZlOpo4MNMnd8_&kp?Ep-ytqtv&ULR=!ZEI@unxpq-K3l2>|55BHQ|n$a&UrM)Z9PRVq(TN-lKP( zf84J`2j1nOdYbL5p&mZW{J{>=*$B{XxZ8W%p%SSK2+iAY1`pQ5D%>XioWM@qu!C|+nhA_u~ zk#3sC39vE#o%7yP?BaPm4jc3<}R^W_J(;n%_PA=pH6QV12Qy21dKzr zhTLsd&C%48*Qi)wzJ|EAR0iW3XnrNv%Hu+73prS=zjCJS{g z`AS3XH?KF-)FfI)8O-@wgGcbVb;gy@Eu%+k&20hGx}ByE)yN05*Sr&!F}kycJet|w zMKfKnY1v&foXTcI53Bd++l$hd%0rWtWg5~wK<>#$^u^v-uAe{pYs#sj_zcwiL2fHQ z48{S8%%R>e4SDUf$#6{!*-j%yX}%O3rj5~%*Dd#r)$AkdA#9wrE+&?1$SXrTCTJE5 zQA#Iix>Kuk#AM9{0n>4shP+lbbGnAUP8B#y(^qg<{Jmx>Rg_D!H3gJYGuT2Jvz99~ zf&3Z`Yq?c_6SmFQ^c0km7iox#1spXL1}@fY749f=In_Bf24r|r!06Xdy_%pjbggEl zz^b=_+7<;?%Z-{VjPp3c zljJllD~XSDVDD$5?>rKGr{_|&>liqM5Ikpec zR!i8h5>$6$R%~^GjsZ0kD{SCEe(W_N(T0LpTfz}*hG=GXxK~;g7F3FMTG}x72|+Iw zlnKzSaV&nrR~jC}qGqvg1<^GvV}F(V7#P_)whpn0npxVIv>t41`&eIC(jj)P0B_WV zY^)s76<&3VC4Hfei|sZj)(zJ6iM>pf3J_tJkmd5w*kK;D3NW$sjcFdN*$%&UQ1D&s za?zCSrjfP75&huj8L?Ru)8dUKdbP$K#>|OrPH8Oay=a`e<*46oOJa?L#=;~7K!i`i z`V6aLF%&d6&%iaY_z@MJ3|kjlT~vU1W9&ttx#X>}hX||z%REiw+6HFskIfY2-#Z*z zL9{6o`}Sp;JKOyztsPr_Jl2=}bR{+j?w^RoJ4syPE@vxY6^QN2`d^IoV7q3-Hi3Z` zVy{tM{jA?(eWVE5L!tfk*cw!6xBMP!Bb<)U{n$?a#0G?9?Ah;u>HVSKr`Tt*L9hXK zag^CPx(1U4&lkqn$JvQq=zF)g4g{-`9JLJiOZ7N!S!Le-aS>FNN!$RTEYA8>P+XgU z%IIxai*#>zoDjFPGK?6aRE3z-xbJJqc6zyKTq?CXSz+I}Dtu_e-gJxeh3Y-x#tZY` zSQbYf-vGkKQT(A)gIk8i-6AV!Xq+2#9~HMjxT=_M;%-p;3Yao8F5Z>vm|d6`M_HMa z#9;_i6)NV(^%73C)xx-+gj4lc5?4heqn5?B7o-=gj5{d*x@^E2tRC)-#&3)JBmidb zh^tmx{&nA_xD(>n1y|zEQPsw3+GJ>%`eq#ITTqga2;dI$ZpE3!c-!V~oMqVc_+Fg9 zC!yKwO^k2Co>tG$!htt&q?mga1b4Ppo8}Lb-o-U_m-Y6_DgKbq+X)#Pi#%E<-h*|#`C*CDX=^xep@Y~ob1uS3_XN?86QC;(nLb=VuMD< z`!lR(2Gg3wKNd}AYDcT$I(N1zu2$FhQ$lecy2U5?5t#Gl-DPyV7yB?UgHH?`*xQk$ z%9fx2q4r~hDrsVnHZi_Juv1QoUrw2b*|bRf3R=yN-$Xf6DP?Au*&jA6ju&fRpjj4Q zQ}hlkSH)itO+9CQd#JkuewQ?uftYCM(V3P<%DvLlb|CA1J8q zJrRFFSiyN%^vi6@ei_75M&b z{C!dB)85Cg5Tl_YyM#@0Hw4}u2|GPxS+CbhI4xw|6P)lw^dfueB^Z6=7lIQK+EqrX zYOLqM1Yg#oP3Ab*o0i~232bDMFAz!_BwQAvH7iWGBZM$EOBgOZ@{NuOmxVk>x+YlW zBA0t4tP+(wb5O#UWO>%D$*jSi?91?FpO$8pLBN=VA1RA?Tpq*j|v+M4c4D6NbdJlMKl6VS;#MYF{Uj-$3*hHChX<-zPPCj>*_v&;Y+`1V8se(#05 zu@z?$#Hnnkem>zZA-exXtLWN_1eFkd&|e8xh3FlgB)E&@nCA)ih3LcHR6_J8ky&+E z_mHf<;9;BCnKDq~m5(rC5bl&XN4|Ri=W8Sey2!HH*H3gK*UTJK)w(@7M#M`1VUGA6omq?x;oY;*rrsg9Sk7#4W<$s!vU% z&fx(8#Thl&Gb8a2N+aOJwjav!gOr~VQ)DAxV;3Y+_Sr31HDKPtL@!}bdzK`=rWh0l z>vAT`3-VVb77(n8uSzgP*a!#LB)Uh+!;e2MCO)E0OxdL>*$Hg?-9+ka4IbP}^o)|d zmaAXVJz;8g0Z9SETSQh%dMBQlU_-?g^lj+n>5{vIu9|d$({;rzfFo04SM;6B+4-WE-+$MfrS&3 zei0R4HYw>Yd57=2z1h>D`;SS*WbGk>8|2MTvaIU3E>2oaZafpWCwa5C+nsz_>6Ps2 ztksq5Qn<4*=|_2x!KQ3WqQqR4)CLl?sLHjwlJIgX9|JwvpY)K@^GbUIP;xY>BgLO$ zW`0vR2s)liitx9Y&8FT@dh*|G3-GRU4T{%Wph|8C#SfE$DeR@p3^O~j*DsPNC$$>S?Siv^!kyTIz&$&)E=3w7Rwy?e}03#`9>xg^<6{Mv0r@-&h3Tx+%A`#&cy6NbBBeR8>YA$5!Og@CQe zc!G!9`?GCUdtbi8YVRF)B@YtWp6^ZWM_K>j=JL2kGJa3#aB{ALtUlw3QY~-t-ui1N6U;yqPlmz`;}1>4Rpv!SEZ&4Q<$0!G3PG z;lbvKz3&zbWqUFXk&QqDWDihIfL3BsPf zoYDb$K1jZd9r%Mr-tg6<7_(bKG046y>ht47KRN}IO81jDR4Dd#CuaE>rP6;1hCSc(fnm>Lij znUX}A(MnkGJ|zVx%@;F|;HJE+b9PECR?8m0!ZfTT#Uv{HMmwtU$f!`QLyDuX+}ffC zrabqQlbZ1a+xDH|Qd3u{wah@|`Gl-dsRuiEV?7>9?q z;-7b=B*=8()!vkB@oV9MlsV$vH@~I0JIn6|oK0y?alELeQLO&u6v|gOFt3i%S4w5V z-m57XH>yk5xb~&(`gDtqZIhUk6x+UCOq=#GiEWbO;@h-t8{0M}UZaj{*FGVxbyB<5 zZ8QmSZ6p^p3f5Glkd9De=8=ruNFm%_l?ooouHH;JOd~y#eQ`U5awAPD7lYKL*gLUO zr=~st4^jezU0r{fQd?L>mA5HZaj+{n|2OYac8TP{e^Q=Uk{?q-?PjGTz(*VHNAaV( zz4nes+B#~b(Xs?^%}FcG1f}F`S1qoi@ni>gEpBV^WL+=qZjpTEqg4tUjjpO~E!Zrm zq5UXcSXW#7LE!EV(ngD4Ki1Lq7VnM<(c+Mv<4%as{w!W7RA|dZ@=ul4-y)?(i|1eX zyS3u1)V&k5N5!v?615LRwtFesBO=v>C4C&T zjy|<7ly=tc_2%x3HR!6vZ|JvRwx+Gqo)zw}-wrLV&Pa;ivRC`P@P;c6X*&u9);X$85$4tH zq&8ptdf<%qEM;6`CB|NVtF6j<_@=aEJ1%J{-`PQ^L2S=MEw1EGvGaqym$lQJxE!qA zE$tRs1#mzOyuPhnD+|iD{HdK-DXgG-+GK&%`7bSH94!e7DCkWAv;L`e43QGte5UOy zU^=|it|P)oYS+Nuue84itY6=0DVNkNRj7fef2>;S`$%ORE6 zdW0eLc1*>|AIA!COQn2@)Jh9hS4lNcmc2eBT<}cA+vc3jF`v}+m8!gEeyJXm(Isay z|Ay9&g_oz+WSwfI`m;eR{d}Nvt<<4*?6-P2$a^1@>M68VvwkYM`ADTehmcfDzc3{{ zH9+_;P^Lx_*-vEYEo=LJCeDz!3BnUn@!C3PUy_{qUf54JT`Hla0qSU)i~##HQfG@~ zkDOH8HkN7+x)h|2r3@9h@=Y(acv2&#iyJ$U>*d964p5kxW5ZNmw!BTM2Rqg(WfJ?< zoT`$A!T+cwsv@>YD!GmFGSeHIJYa0oR5u~ef@Z0B%#;gcSCYDlBG9dFsgrnpz=#g1 z3LBa8-j1nX3eJ(;C=D18_Mp4WZ0n%Z?Sk**Dni}9F>zu7gT`y$#Y6I0y`Y{W(2dv(h8Y%hRCK)V1z5^W`AtNLk>#~@UlN)QpeWzivBnb``opm>9VsL#LX#g-TtB zNM4H3bs|(7^h(ez5Y)m_bO$LzJcJl5Q=6x=QhShYr52p6TS}nD)(_FOVMUE~BJ%aq znFd!FR-kKS%QeYr6zP1~%~tvv;A_wg5>@<@QAZf_Sc~$2Uro9UN^`W34PrMtr{lQh zyMR#ku%Yfa!QL!T6bFTQ2gozZMPKOZ`^qliQYRf{O&GC8K;14n(uZhx=8fHS-|&j@ zVTC>E9A=j4NTIDchfifXTnOUs`iH){Ikfs10eAc9NTIDy57v)_-UD?Q(wTF(KUhZy z{st&YE@j1nt6=#^9ezWQ7q1wjBbwC+=s#9RifYX{{4-96AI9SxzL`Kbg;NuCq^Q=M zL*vO-JzSbf*F(MOG#Ab}Y?-MWDfE#4JzWpeX6tZWm8-MW933U9HD@zwuI{FQ={;XZ zIUK`bwk^<+qVe&}`HOWJNtuhXZ>f$FYmCFVEZ32u1@X-DS6VsLSfityrs6Pd*Xl@7 zt$F6#>ved`f$O2+Cb}MGZ`P5bT5}GrTXh&Om2>!hJKb6Su|r2Xjl?;$+pWXT(sB-$ z_mG_Bg1x#z%5^nf{M-Y&(V}?uVY&*29nq0OTXPP+$91y=hwUeI3#ffomD4&>Xlu@4 z<5}G_!J)|o9qD9(G=5lmQAY`F4STQXe8l|WP6fUAxErM6r2*jxICH-*ijt`a*Y07}M9Ub(A|vyh4V)(cy9` zcMZ$lQ#w!R^^dL%Wsbv(AM;7KR7jz<)svgV1Uo$`qBZBR&{02CaL9Ji&!@VEnXYG8y2|hF@`U%hJjvmJsk1YlOkGkJ=pr|@mMeCaJZ^|Ayp4GtLsS-tvQG6S}1_b zp(0REo?1i(>q&8hIfqSk^%&Ec>tR%gemPYSS3~vm9=lnDev{x3q|g((D|n{R7m=Ig z<7oY9QT!>5ei>B-;c9j}fK0C5%beuOU)^ z+ZmOx#%|eq4E4l0^v%;#_V~CqFXrn>QLTCA&V~B7f%)PJl29r+z*2hO*|V3P6xEt@(Du>)D5~^sKe}Zk571*&Yi?kQ!TLV` zy-EQWrY70T2&E(Rlq~};&o3kOJq3}pF?z~HTMpB6tkrQ07)S3)4~*B70$OtpD<b zgw;y9sZN-yCk3?T96rvo)q~EnzP(Orn(qDoBjB34$vcbYRU}h1}y83%P#)gut2ey3F zXH%}bb3`eiHDEw%FUnq|1z`}(u97ywQKl2=n}!jsIkQ^TVMm=|<}cuBa+-pupD!3@uEH>A^(075O$&08)zK|04ez~i z&E(~zl~4kvac{Y2!AWn1k*nLn+NNn*vQhRZNn1o|?c4w`vllFFn|4<2I-tBm8U|D3 zvi0wj7CBriggE!VNE)@V?me*jmpweg=x+okoH!X=E@;y7{8j695y^nOKy?D z6Vlw6szYo$STiQ=pa35;j%=xo0TCOLb>=!KwhF`0)#G5o^t6s-sSw5o#($r-S_qUo zC+&NW|K56~*3@tlm9|@S35(ZNLPZ!ByB9@8yvYO~(7Q8?U)>Hi??|gdZ8EH6 zNo*+`-JfPdZa0Wf1-?F%hEZ0y9k%%`4Wq2^WbCmtvAhAPC)31IG;}zVhLNoK3&j`H zZjq^<`aR8u6;xn&Iq6g^MBYlPLlpuLt1;)~0}1!iUXd+Mif#=U-MW@=_J2LEgnb}b zcZz*T=89pIeE{QEuc3;90M+2UZ92w*<5hLiIeh{p%8(#q4;a6Cm|X3wtX?`{3eKD1 z=g`%o>ZfDqYR+v|RC<4^8F9eRp{wi1r>9jyW%dlAs)Aigx~Fi~_0rRa1Q2}==O)Fk zW*EPEC~Rq3Ic|VZKi8C1rXP^rgj$`S<5$D@N$DFzTP~WOP94`YmJog5-OTh+B9V1!Gx!$MWqv{SY5Op2m$g(kyV$Cp$bq9#E%dipdCf7N`GWv>j&Cqxf zTM!Og;~fFxS+^$BlMtkc*dm_w$C?@4-DQ;xiO9ehq+ENAqcSj_HBUMzGw?bnPo7t0 zW{bk*(8S=-pWA;tWa{b1phU{RGCP8L7@PuQ_WoFgh-$ zSiUY}A7!@2aZOh#0*Nk+^$)SJ2_(9&G46!H}Urd~7qtum=`6X3n7wQ`i(mq6?GaUQ1D{x5IZ{nf=MDCI7;z0!wRUVgzhXe12di zM!@FDW}%s@T;*hdIuj#c^Iv~Y%ftxSJb551^NHwv4&-KH1Z@6m$XA)eDlMcO1{*L8 z_7X}2Y;InXkbnw%Ekt8IGBE-+=Qwd_CPu*K$yyUKFAI4bCud>=Z2oJ&v`mbE&68*5 zW|mb>9%~qEz%bbLsAkL!01(S4>c?wyrlt3}wux3jQ1r8!))`7lLe)SDE5yC>(r~i2<;=2>aeyB;R~5D^Um@sV z-qV65!eN`+XJI&O&hufXEDVRulPmjXVK{7_EEt&ezH%kHjn49CgPSF4pvLel(pNS< zYl36-Y>_w^T93@yUxhlp_|!RZJG7gewUI0tA_TyO8Ce*?n5${ktSk(c&67XF&w?O@}2d zVVqy_n(QmG_F3|VY{H2F$%ejQ2;&Bc8?!O?HRt5CIopP!Gs#^7My@7Bo#(!mhrRYY zlxt$%RXt|}q2rPY04!hv z#n*9m>o(*BvGf^vo-nIc&Tip3+K1*05pCdkL=Fac;3}M|%;_hR)zmqb5yHl}oH63p z{G^=zB6&)ivqU7nNy`}~>cc-X=ZZH6+tN5^zNne?&2#pPUet~nb-^3SrI zl_I%dP|i$|%o~;SMkGz;))H<^%-QZIYj^E}oC^HfnRHir{<54{N1kNmt8>ImxNejq5qDY{lvN-3(t1t43}$$jod@$ zaC~phDKdvf`*X?!hYJUDCezx$Sg3h8hji|nE3x)bD~I{VbEZ={+&__Xk)Q`a4j6Pt zYGN$pT+Uo-@dM81EGKO9f%#HSxG28-Y7XTh2v_3GYdNG-dOY)&H*%&3D|mY=hjPc9 z!{q;-gE5P^CL7<)p`35yFiY;`Y$a%t@yxytb1at#UOvuQLaj~H)0{(O4#)q_*(lUG z^;Hhxei5{OofA$O?V$=fGNVJTGfVd?^<}L-<@iAHr<@KVGQ<(v+{c6_B5AfGAnXLu zr~KudD?AqrcFXN3RyY3k%B@P6EJ)~Ivpuy>xv>w`N}V`!*cFhAF^qZdv8sCR3nJ;y zu38_>eu?Y zbwu)IMD9+?s?q^WWX$zot;7giqH-5(NSa@V-Xig}ZqYov^+xV}q*&N;!g^jy;9 z|1-i5nYpjzUY^~{$Q51tnf%UNr9dZ{?Jc9**AwVq_nA6h{c6HAEUCb-i zcg_7yOcsr$x%RU6fOciMwJD0U)Q=|w4#<5cMq$cfxukEtNyOQRw9+Cr*)Pu*YK+NU z9Zztp7MO-GY-MN^XFPfaLA{Rf+z{kJ;T+jVpB$wRG zT`!UyZ|ANP%f{jNt?%A_V10MWL+iT@ALp(W?|Q$qzI*DG_1%GQtnX^yi^tJg~-pwXMWWA>=8;179cDk14-NIAqCe9Q}=xc>i3F@T-=!NUcn z+vNXji#)UJ^OyfW=e)ZMF@27&KYWaG~i0fa~#Q|MdP~*m4EIO=5It-|h|47opg6j5uFeos8 zu_JO13d#S{0iOdR^WB{AIYylyCq1_%<>#Rxf{iYJxAfkgto)iT_`Ft>|B$kQEKvh& zgUh`w432lopCr*~(XCQ--Hx4ds_FAu`7iyheY@Ai2T)+vLTMoW5LFZ%5Njt&-AhR=WOuV>D&Cn(o;P?e;;KeX_0~Z zIqsXbF?GS*+S8-2Mls{!mXU17)ck$~@)@^n*pYH^o^YfciM#PA!#;zbgLXK1CvN%82!Dc!$t;nZb zLY2?}(fU#xtc8jizFd<(-xgWC{W+f!{pmAM{q^}dmC=eC{@IlOL89^w@_(k)I9IP4 zQn%%Apo|}6mF~^oOF56jH8JmKel?hIJbx}_%L5TI*_7}eZvW#vUtP)X5r{hB&U4_~ z`~+fcb70&qmwZ;tKlyhYZKi|2U4sPFHxxNE=wOG>jm{0)JL9vucY{MV_`K!Qps5=^ zlLH%Q-0|5hs=-a^{|#{sm@hv6$ZN1g$~UL5!GutJhL<(CjRp;F0~)wG;WJ=(gV}!g zTsOTzH!pnFUfdu@%HMW%gFY_!ytKK&R}zlqNQ2kX?~!L3m?_&=sJ8?blyS-rUfpi6 z3rm)@b@)L8szD04agQ79l>{t!E-S?6MT4o5fQ#QY*d{$&ern()3EI=KV373p1Gj=d z33dl4z*|=fZO@FbwA%<5B+3_W@kBoSJ($bXz%ax7a{aH=Zw>RW){hV)v}-kY@?Snx|_ z;r0$HXh|462x($xg`BGn2Syegc3?;RI`}g-s=${84=$(yC%-MA1nBt}a8`N2cy}%< zRIma()Gdh(t7aEma*&fV<`rQ0KmKdog#~y3oF~sODrhCL<*YBLE8ZQjrQm>gp?GV- zQ9@rKxHV)4_7{9_b40ewB?k&9-}SJXBs_p! zgHWdT*@9P&Ea7BzZ&vL}!LI*~z!tnIm<%(o76een9XxZ_8wJ1F${ccU7f=FOahQd_ z7uXV-vMfNmKMVE<4)OO3D0@kq!=wiVzGM!q9~Imc9KL#5u#j38JDwGg0_Ac6j4unu z3J!m~E|~D&IRL_tmcn`QKYc2|SQFeFlWYqo{`cbL+8kq7SeG*8z}C=<%?~Ja29rl2 z_S{GKAcl>1FT{_vUPV9n!l%%cV&@VtATVw;O2WbF8ify?|em{kkr0#o2VMw&V|@Fadn^SQutg5d$wm`fsjzuzc9;Leqq$`!d`^<1-$fG zvkU9M{O<~nNV4#m#ju%$uc)$YnO%582=H`H;WvWnw~GsZm%Td-UR9VZSRdF}xY9uu zV8ph<+mzA%$ZLh|+0l^&__dIuh2OYYBv^U@k^g(61%zuXDF69;;cY?r@`J)Ug0kD+ zg?N^YSIX5Fg+GhrkFN@Gl)!&2ep86w#pX$ecZJFBa??^5(iD5#cuQIII2 z$hYW}C|Fsu$V+IYIJl^YGVDGbSJZ*6Y}wHlvLlLyI#}Gt-H0OLKJ224D9r#6xQ(Eh zomy1jDoZynx9FPC&qQNUsfYZld&{CY%EZ?)im1+h?^3kK=9)Yp`m1Zv&i`Ja?DK#q zW<`-dtJ1xQ5@E$WzNjTD?p5T-R$eptG1J)TUa+f25viBAU{MZWVO|F7%8EKTp<1dA zDykr81Tp5p{=r2TC~LbsbC+R72W*i*%*Y~206UJcc2rRi#l`T3j ze|`~V`I!^=dqL5DGJ$WG6m69>kiMKwVCRaWErhu#L-r%tkak}?gJONrH}1$n>MYrt zUFcNPYZ-7%w!7#&u~Bhg4;a6IkA5xkC0Xdi=eu z>eRWSC4{b&*{BR9%=B$hO}6XzB4-w~tf?=gUM;F2wIDvq=z6_qDM853z<|JiN$Ohh zXVD!Sd``NDUJak^9~5nJ#Al6XMT6||IrC-Fal%R*Ggn0sf~FPK2Ct7r_Z=+6J?stB zNfvB@V+iNOqC?G&(89^UYkZH3VLC<2NO2H+^fdg4a>{C5>t`q~5R=@149pPN8II`<&JHv% zEFyF@|1;_-J71NZEHFGGptO!I{t|MEXljx&vt~vEWthYB_AD_3$XjE}OokWsvo@4@ zv(;Z1HWLM5h!+UXO%0@F5euV0*!PuTh%F~$Y+;yGDKcZeHjskw^LjyX4$D09+ZqT9 zZtSy2quUwmD5i@DoguiZ;i&`HN1a{<%Fd;P!Vo2ZnJx#oz<@G?J*8Rx56tO0E)d_> zFhEw%?*0bC>XD=#nb!0XkDS3~pn(*d{@*Cm23zW^>o5aldGX(HzYI5gS1DC-(d%X?$!#<7MeG>aBBcpsLkWR(S!1Zf()Wf%u)#kYPF04C1m(?cRyDR{)!P^Qv7ucIlpO(!!A3#$2E$6( ziGSK+pq$pSK*?vxT5UCWR?5h@?T79#?3F9;%`U@As=g%W#o?O$VjzXevWhmVx!wgL z_Zgncq?88?l!@kNxKuwE_~D>oCS?GHDEQsc3PXt83ELev7-c`-_KaZxO;sFE3CGSF zx)AG5Abh-F_*PcYtjm=o^XZBqlu8CrJ)#qyebca{Qc7{WZO~JBNY$i-Dt{Pe$Vyp% z*RYsK2Apm1K5)go_?3)xt6K46GFHpJr)t4BfyH@l zoY(b;;+~XkCFC`Lbv~+6L48$m6+$Nlh%m>2UG9UOt8a9%=v>p{i(MT#v1Zz0Qn(gb zVFkr5FfP0JE`gXd5bVN&5rHL6P~}7Oyy8o?GSA=eBjxfSpl?C(Qtlim=reOg3ug#cPjplu#*6YkVtU{n6EKB>RsHP zGUU27wAd_LlkJe=wS-CoL^w<7^fJCFHt~|#;&H{bVECBgsg&Xo<`mIYe8rw9&(#9> zu6O{UWXW0u!uiR?Wv+a*3N3ypj&rqH2vZjq&v)j{DRWKnCfQ7#HWu%+;lqD8zo~eV z{NH_c71xvhd-=ZN^L!KzlMWWA5G;r@f`%uHI}0+WE*D>-N(P#1#fxP+cW)Fgl$+NN zcZ$E4Q(5;dwEI6UK1{K^sSS*6;q0qoJa{CH^I_dT#eL@b^94}WbZg&nDL=pM}0>a z|B_R^Mj3x{;>|I*+?XTh=r_qoirjCZ9wg7d4^KBzF7YACIC<)}VTSPmXT;XbHZCBv zZ-klxob_=UYpq~x*jCt|G#utm>|!mrr9GQN_# zcGq{tTCymKHYPincA ze5(*FQyTBV@my0)F}nI-GLf#=^Km2M1hZ2=_Hu;c#-?Mkvy1xL)JZ10xQ%JMEI`k8 zCJ+_W&GcHXw6s#wB00z315Ar3V{a}>@Ng3;s4wS_nSG(=NYi^;xf6*RV{(^kq(Qmq zrL1V0mMJhILcBun2pKHpeG;c05V&OSCKHfaM)O5~^ zx7amcx+BlY7yfE$EXL|bOnWIY%dix_7Z(JjmrNx#Hn-U6%cl1PYqRbvPWSrBn<=lF zo)SP>4>q>|JV%vI@f?`BFPn11M7ip`{=NyrS52|=gX1?%DhE`qTtIDxKjO?@Q- zx8BnU#C8)noqza)|uMKrk4!~^0(&A(wK zY>6=At>0U4KGJ+pdd^Xpdr+J=7a;h#$ra{k%$}$USwDLc%&jX0#}LjLcBGo|Zs9E` zOE>>2u}sJ`ZCWc0(sM*tbEfqBuchYW(%)0d%?g{8A>)63nZ1!ODYd7*{7krY@Fq3wkB>*?RZv}-sYXX@A(LF3X8FYEB+8`p*l?d9Yu&xZHxL1E2?4T$#86%b~F1D63_*KPPjP=6h6rM@e&VVL;!<^RXtdxuAH zJAc5x!`;T+rWjo49j+HTgiZkCvwe5&yLWqTXZzd+=2qlz+KmsH&BJ^GY zp_7CVLP>)_=mb)LcXqXRK)%mwzmeZR^M{|+$=cD(M>En$8fm-592EsRDU11BOfFW$ z!1*OXOrt91d$H``9x-shNyu&7D`vTvJls15i^+L?V^)gEKK*07#n%!B$Ltl+=`=jX zgJvm1K<8e3j355a5HpSz7{pUR4?YSF!_5+6W)O%_cax)?e@cz9i#%Jom zFjaNl7e#G}aTOLyaNkWa8fqzdS{P`xAX1`)&5j10QWNu+C`0AW7&tL0=x*|EXR^~? zr=H*17X!Br3I#qo5F_?sIO%ZAHzI|BUpi6nITdrs>qV#?|+HdQ_n0#vRcmmZAW84>Yh1#c7o=4Sip=g_P@fhJ@jhvRfcvXQuhpGW9v zTny*>6H1$U!5eLg)Ssj9CtIZGd!y&A^zIlg;af~G7eNjRG_DD}YeRjX4i3e`+UWNM zpqa^Q15tCe{yjQLim+LuzeS@Oz(^5(sMC*aKzNwYQ$L-ShYx${sY3b$9zO4@&lh>< z@rHgfEf4Gf{SVYQ{5nWKN#tStQ2i8I9@Y%ge?zHIMYzRCJ$$5G&pBngv;bevHV%_9FV8;pbw#Hw6#4#TY$& zwM{UFKI7;WF?YP4DuPtdgmV*McpyBCpQN8j*$Vi>$@=ru9aF1m`Wlgko7464X?cj8 zsi&F;3plKt?Zjc|JbD~TEA_XjaqwKApDyCC@@;w^PA<|%P^cq6;uqohi}i3Pji8h5 zmg*~L+ahL}KA4&Z?-lwqk%w`s^pj|LSoNO%A$42m*6OoG9%|m#Po?GI`w#TrQ}e)V z&?kvJocxH!X~2^{hCY^-_oZ9(m16ZhKBHI1%o=?UY95Ae(|;)P;JQP zCrb!eipDoj=?98@^*N)5Zwd?D$FU3arhE4*J=OU-p$pr3QU8J1UU}c?=Tc&WS6$Yh zq}G{`Yx)Y2hYdf_ccWkZsHeKgLcn3~FM9Z7kI)v(ueA1CjH_%RB%>JYE|VTjmN2B*Lcum`1U1<7J6H3|ktPQfk@bP85A*eTfHp|O$TYo#M&TT)B2IW0B_H;;|=@peelGbPrA z#xezIfZPnRnTA?oeMRWgGh(B}HkNrtZ?kbM`T1>7T8#|Cj8ql0RC~s?Q zL$rBLtS8PbkG<*X5Xm+(_9%@=PMR2K`ipR%o*#Qn)UN#tW4{)2KU@+ETNHx&6)la8 z@gm8u*Tin|A;~41W39AuVUX*Fhf|u}u@fn6OF*atxx{7G{@95k=3z%-f2N>_WyfQ` zr16E}JtNYs3=PH0zKqTH5d}l$ZyepnsEe@|X;$!|!TO@Tn)9Lf{1JOxEa3JR zwMGlI2cWC2ao#l-;s!f<9Q3IqPV8t?T;p;nRr?j<*x(VjT;$x{CvLmQ`I&}sj_!57 zUtE?bWy9dOAQ6UcP2ymWPJqEPEUpc$exuDNPy6GIk#P}@p#)!P73WSVS06&o(l)L} z6scp!xML!luASq?iE>m)ow-@^xP5*^j-LJE0%_zB#)i`a;sWr=L2*GK2L{!g^Nw$mL9i~hHs&dlUBgKLbIH>1^y0OoMCfl z*xm7Q3uuhoNf-g)P~lp495gX5M{JX-$#E{CKx--hFe1>AS#jQC^7_0unV4+7FitKe z(^1?>N}sI{;>ARf?6QWaYIz)difvik==w><)51UtzI7tHK+{$`aLZo<`bRfa5jYLQ zn?Hz4uMdA=2q?Itk$%HN@y-w9J_9Ck{^q#n_2J4L?cN#}gaA4f?pzbsygvN7W1zLc z0Ue&OGfqdN!GgW4n0Wy{eR&MT{dJx+7!~i23&9!tdgzsYuf`8@~?*OUX zBOZv{U>w zrx^a-;=O2WP8cDeDcq%^u$i?EL4Gd!73Et>tfG#QW_*c7`JF|_>88}osuqqjih+F%7if;T0mMRxTO=+TV(928S&ru4)DA(vL>u0xu&|e5 zccZE<_@UdQ4c92BTuU|#LwU`NaJpD;XhPAABL{`Z>Uaaq0Z@3q2uB$V7vS4X`Eb3V zd$Qrfe=i6IGG}P|dw6d=PBk00z_+FN>RVV1)&IVF9)M%~4-KZ!>%+jQhS?NH60l^pVUZu&adBiQd;ramNTb$3!iwKrZpb8J;Kr*A z*)+ofz+psHR)a5Y^1flPr@$2A)yA8QPqDHz55 zb{Mi;2xfbC8>p7;fkXywyU&T)$^! z0554D_|}QpfXjw33e5OUYdBgl-VlI)y>5UIPeJ4U{>jjXavTte9l2q64?bH6y(u1h z+W-sbLQ;Rnu*_9RZhdI@mZb2lCx+hCobMTHfU^L90sDKN83IY;qs7l(!#PM|q&iwC zK;x_cK?NRY-rj6?eB8y@OBAlDhjEK2$0Z*l?AZ!(Ztyk20UaTk;BO2iqZ%#`F&0sD z1o+kF8%3eKcwa{& zE0IjZtvegPcawZWco-%((%ewa3xuRLa9nySjIKPjep(}iAY*d0^xW!mw z7>$ttp^={uk3FO!&7D&yElOB-LsA3=|CHaKc8dl#>;uL(nxcz zl4lPA4L=+>-+0{}1bu{zPe5hy?RSm80nhl`b;el!*=>_?7|qPWSp#Y|*BHoa8jUA@ zW|uLKT*WeIoAFa#)BfCPT=(xm!$4>_%@6|bmEwbYjpO(R>wAEvZY0o{chE@lL0a;% z>k*@YXCUdAamBwEp3qqRh4DigFI?dM@d@Kno`LzNj5L?g2sEyrHnyT}f{)G_?K}e! zUl~`^B9QsDk?H`H!2RDBjW(Wv%d03x&)0O3|DEA)`T!IJ2L_oC#qv9Eq@z$@mB|CG3^Tc-zK=``@zLg{@1PEHH^=9# zOpj@r=zj@&;y2ox3V5Nmb|ON}>OzDX(ajV?)7uMmM4PXAp*_7!?zpSQbjjJ25j{*) zt2j=Y7-){+xz+X~+%|lJaC>TisTAka9T zVY=W4$ge6j4d<2pw=z>TujEt9O=oy-A-=*ywp(8GHs;lBt(8X;QPoljc>5hck0Q?sih{mVcN+`+vO&PXJy7p(;7-t zFkpPMet-;GV_M5|aeJMK=Hs7^mhONDe_*0>8=QbN-e4Nbv$EhL(+8A*@JiPK|MIbk z&Vg{UGI5KkglFaVPwUCb@EX%}Dpx{e#RpX%G%4|-?IySSw6hPIrO%szx9l_pIQo0^ z$8O#c;3qmRdrhv?OiGVI|9t1RQGrhRCNx3eZO-_h-wv9t(Y7E@52qY9-Qe*Ty6ePa zran|Y29M#rH_p4GJ72>{J*RC>Fz$8A6yY4H?H5dss8Qt!V<1eNj!UA`aanXau82;@ zPomTDi)jVOgFSyWt)p@}cu^`lo$bWrB5*Q_@CE;bVu$A8)ZhfFqa8wLBcK3;$MT-WjHZO; zq-KQW$nXRmji(_{K5e<`jXHz`$WfEl34hV*uRujR!c>l1v`w%$mH3_Z2^}aFzJxU< z3~Nm9x}vig?S`OGb;4!})?m%6G4jQQyn}C8SwvvP`)I5CoR>B35hfl{R z(0H=~56{aI(y4g}o#e#f*lJFKRW!GE zDigFceHw7WkndaDsuPOCkac;xQ2hJmgh>=)AqO!(-1xJER$hXDc=)b_L}GdvA54HC z2%%4ydpH3$SA^u_(+PjLlH`jE3D-q|9$ZWqBfd87S^}J?5XuhvAt4;XviT0C=vjil zFG;@bod}_hLT*RjM7Z%?NX`yUoFe8%HcA}qMsiD=Idgp?5;uvt$6GlmFl`gx@+4(% zb#<1VC`%k7()~u0XcC$1-y?CG$huG8M7TdrsQ663#81TJ+ixT;^CP+aViKo}pi?vk zo!XWdh`xwy6pY8NObiPp&9uHI@om~>!a#ATh;N6ziH>G@u`hABC}!f(L|+k|x4uk# zBuYHtj1#Dk^NCx;0$*Kp0+sb$;zw>s`b)bY+~Im6oUarh+4*B)H7VQR{3ImVpEv;J zUQcX+zxXvVLM-yf?ZinUx!Dh#u zDDdf2C$X6oNt;De8#X^_tB6qG(xfpoPNowU^n7fB7miq)bVbA_Xj9Ux`eDOUL_pCK zUEP-Gj{AI))Kav>g6&RmhU`qL7G=15C@Ec3j`ZV6{Y2&X{A|)B5s={*lcZvD&J`y% z%rz%5%@0Xq#RB_pCUq25@!72;ACYd;`_A0hhe-({=6jwbEu+?{6@%b>-Oqm~_3{*S z3T=Ou1YalrH6aXhZppo<5fsRF@l4(!^7u!?WQRJP@=M+%f>YTjdA5jYr^w{BV(zuJ z$&SvTN7rPQ;sRj4@9&4CGZUKQPTiABT!^`B?Unp7&D6t53=Qqr%WrG{WSwXY!$u}I z7I`j+OCBdS(({C5UrJpT9Ly`Ib%1RMx)RpZ4c!eG*bbfIlBq7u2LBn)wI;WqnJ=z- z_d;jnpPSqrHSz2ghEp?=TT-c*lT6RmDUnE%lT76~0~HL+__|9@qwu2SOEd$Q$VsNK z2Ya|Und-s<2P}lju`$Un8URbj#wG8nH%qZ)$y9fsyv|Z)dGbA;rGzQT+w0BJ^{L5a zR6W3JSa+F`d=^-$o}EndCE?e_n>;7En#!>hIntx>up!Ox4-1pSJOQQA?!KWocv-Tc zK2i!aJ<(~8#_qWNiey-;IgJOeO-^xv=eiG)$GgI_)kn#2iu5#AZ%Mw+Kbvk%wgCgU zVt4W%{O|Gu$tU>dh@;6Jz2G_KO!Aq?dbD2;v|?;>OSJsco}TEgqFXSIc#xdo4-&(k z6`a$Sq!iXi;+NF@@Yk*>cf5h)!~Q7^eBpVlc}iUCdU3vT(3vRYH?6S;zT7WmAx}I1 z&6G|wt7#71>46+HeK8Z0@|PRXOg5&B;h$e6r&QKQqk5pzZ>6+AR~Ci`qf;Z(8sc7D z%DF%w99Wt{6_V*>8Ux{6AE4A{TFNnAsl4W-ENfH`XuBimgpW(MlSrgdYf>npp^&gD z1EWe)9KAM$YJqpshp85HdPJH#zPBz#Ni(WAC`6)fHl)xDC_n%{7FPhGaA!0L>^R+rCHXac=nr?+f-$}F07 zQGrVIPbnej^ulB~gVQdzFK&MzWn4IDk+9A0wLc{o-Fu!IhJU)6a+=1w;;X<=#e6qG zUi{$Klu#d%Z1Q`Gr6EbqZIHUpgCtYEQzKnSveGxTHEBJ3FfetUm^&^s^^6xOkQ|ws zCKfo^Di!8`JfxUuo7zw;JE21=oFx%*A9qQ;Ef#nnO@-Sugxtxh)bV1jzc$rNe62%| zRA1Og=ZOvOmpaj#B)=Y>de>7-#-@ggNw?(GGLgc<)YLCU3i;;L5n>gm(o^3M%eJzo zo)4i@!nr8wPFgTNIxY30vvt0mnku%=535siD7rZetJ?Fr(xa)(#pIe3sqc$|CY?^*C%)G8TW4#m_}zT33l=A3knK z%X63PA>+oyi|5(`WNm7b_?O0hfoXretB zw`bCt(u^ms(eguAxip%C7!E3NX!AUK2izw;4et1`4{I8T8raef)kDNMbgw8a0$1jw zQH83%dyY%HOEW*^DPU;+UVf-uRMSv= zc6Qn_IG_P&6YM5?_*g>uB0no+NqpOd8zsFO*$*&Y8@;kOmj*2)SLqPJ>(ih2*_&(%_bV zA-UpG+TUVr8?U8}qFE#m!J^HP|W?Bf4d2ho1LmJkZ9ig?^~-g{JLM`QR>r=*3+o0NF>v zKaTZc`u%&J7tUYXj3*qAHuh#dr+>c}0_QjwOZqZ2TU!n;&d@WUL3{sXgHXWt_KtYo z00zFSvb-GdNgcva?BzQ6c}bLfv26cP<{gR=>m|8>;pan8z-~7$6d4;EK>X7;M>A$` zG(M+GC_b3Tgj4iS|Da!W(XAn#$}szb1PrjDh=JoTJRG=P2?NJpgydJH3@rT%$t7l7ihVR0nbB7K6#fJ?@p3a*D&3}N%G)62DX!g0+k0C`2LTO?0bxX-6A1*{y4Kp z%pH4%Dexq@&n_~1#pKEBOrE!-hX0`I5LEFS^Xb2Dbkq;*Vc}_u8|t&>f;$?On%@-H z++mhd^vrnDJ*HgZU*r0J|CiH~-1Fq49q(G$8_##_UM+{zQ8ie)cD`lcqZrD9o}H_@TjnGJYubIWvI* zdZ1E<%U>|*9+EBShC6$Jj>a_1c(M;E#h8Y@8?x^;00uttV^`5K;2OZrrDR|X?jFp- z9Ufay>qhLxf6o906sb0e;k{{C8^-S8>wemtrHJCgXTn(p&CFM*dwUDEhOgV+nqBwr zJx| z63}%1BkO)RU=S+_l5D|2Mi#zsx&>c1vG9e{EqHz+3vpswaBMQ`=MGP^nZ4!;&w&{% ze1moi4$ERazz%WCeD=BvJl%@fg#Zp-Qp)Cg!qcamJjLcK znE9mq&kNZ#Zt#2d5_Tp}KXf^Jnqr{i(H(_0tS<=0@IlQ3z%c7k{zMK(v~l z%|iQq-oU(zFJ9_u_T<~U+{f(BOFlioe1gx94KmN;U;Cj6`Mo*J{Eip=9^RU~AJNvl zn=f~{ow-a7&%^|?pg+NcRX2CEXFEk5yxxHiN@xm=ukn)dK62Lxr zj{vq~JppY0#{{6t&E_&5Fkei5f4j}RhzHERpS=IZK>}EV!vwJMrv$LLKh3jvz^*?6r?d!wo#}270Q;>EDZjU$MbNMbZ&(Bkn?B4UXxO|_7D2-jM_Z=xfL$|?-yfSS zZv5}hQY}J{GlaE7^8hZ%CiHAM7D3}W|X{ z3Bz|bTWWv<0^l}mX~qBeZkOdFx?yP);;AB_3K!#z;wru2b7P5jkngi)Tr3``F-3WgX z%;nLx27c6t`xYXV*7^jYt4%qY%MAo^bj>)Lk8}}rdLGVgCs-Vd;+D`VWq8YaGzZs) zn@L!B(T=0}dV_$%zz!UhV<)`)c4zJpVPQ!(j$$H1u>4!fji4GjdAoL1aVH52KWMmj z|9wMX5M(gTMkTyw!;AWIv%FEWL0+LKYZw=d(pqw1m>JGBrBrSkZak7xbmr|GCywP> zLuFbBS~I^OWaYRTgR7v2 z82YV-Yl_c$Sf4Z`7=0gNeV-O1LUGqot|68+u}&i>9Eq@sXt}huifFMNt#iHkM&7Ej ziW+cQZ51J!rnPP*V!qkk`Z-B8>go9J@m|&yByU_FYiCl$wt?0o5|lQ^GYHQgY!xxG z47DC01+~MiF*K7$L45*Ztaevo6YhGeXcPP6twvgq@Jf?)65(5(=71)bv(6%UUN)=P z+*fj}Cy4;N^Bj3^=UY3{SVD^m>sXZgNBdwreyp{J7isDfQ>?8iq`*MT(?c-P?sFXi zlq|631pWK|3IVM&v^f$qrhKjSOLqeF;tdXquHBCVWc5s-xl*>5x4@kg7i69~pV zw*uS3kc;2iVb#+F$nv^@PE9=HjgyX8;cM_hr}@Vv2jBO;v!0=VmLEYxvZJqBm$?cc zKmXBML-gR_&(<9zRdvI*ObkV9Jbm^j*p6i~zjgVQ^ zGrcL%hr@3;@+t?WHzau*-*mh?ab&v4R@dls_$qs}u3-?qJvu#(R2O4RUqi9kKrjoi zrFTWqe#3+Du9S40sNg;s=~Ul_A%<^Df9@xM6FDwj?4<6FcSx{&Lb?bnQ=ZS2;lZXmz?-c(=hf zN_2mGx*hfYEWIIKu_3)5LH_Gf`QVRb845RGKv@6&U7f);fFCAZ2F(>60@WQyIX`Udp79aQVuOKYs*=l;7EY~Xv=)YytCcKjF*I`sin^BJj`m*5Xhaq% zZe7Z#0fy14t2E8|k|YL_Q)#+|*O-dH*M7`^yBR8w@s|u5J^cr@${QIEXj;`tX6vnt z**rUQe#@X)IB?S85s$!!?qqbP^6B`7OPlheCu(2m))dcvl5x(5Z?mSZA?W={^2glfp@%{Np%XyN%MW< zdzrq#tZ7r`Qy%r0ZJEBj5NZ1|lReJAXodgWj-!+ToK)gVPbIXdP1k#L~cxDxeJiP0jRYr@$$39u`{T>0%Q+`=yS~7?I zv*JWm>KvGLN2J&(G^?DJVxLA?R5uO@$nI{EwNC`6s##Xmzvlr18LCem3f1c)vyO|^ zcWaqNb8&<~CazUhFtvXC-6rdi$b+Oq7EM2Fvmy(Q zqYGubC-`8?9RIpAE5wfo;eR{}&aw-lC7;Pk7jrLM$-2~-ejO z3h4jLT1RBB@y>Q+E^CnewFt;m&+PLeAep||rPM$?U-$#@!OoV@kM#|6%n9g zy6lCbBsqPun~TYJ24r^3cTU7g% z3E3Z0sy(Ry?@7w;A~ODr$%aD(Ld#Cc$o^c+9g&+2Vc$Y-Gkdn!-uP^B_Ed2^PcO@U zASSb>WseYFJ31@7kkZ6uXmhjR2)tu{_DEOKY7y1h;gq`WAhtN$E?R8Q_p;$43_+kD z-p_6)O5E&Yrz+-rnmvW0)o_c?vsa4@U)+`5iH2byVZv|;{YZbp;BTj#yuN+G$?J(r zPF^FfWv`;(6@fm>P|wY9#C*gr*%p!O!?&`B(s2Ef*t>VK`ExXUC;Hv*PS&UV?PPsN z1DnI*eLZbQC^baz_##oXzwJEQ&%7VLu-m0h$QEGx;NOqQNbr4MG_(1j%FP$Na9*Ho zE0skB+CmrZ{m=)c#HTezSI^kIF$%V+C6X2RWQgsK+rpqrLs7+=Gl8g4GuwNVd}82} zDum$7DBA$Exzrws_eR*3x-7hUD-hdS+S+ucV8lra{rosH1({4XSj$*Sy_SK2UUyfX zBh)hwfxZb7fE&9V%tsLefEj%ZJOT4DIEgo0*M~A zZ<`Bxw1QfXP=2r6IMnx|OJg#nOWkgZ^WXG&k@$}Twy`wP0WT>uIcj^0 zDARji*cMV4-Ahv69=B0l!2Oa!`bk?s1GIOCBnaI)ZKIgSIH8WjQD<%Y>j%yBuWVZh zG$SwCD557`VW#R^o3eh)WPWd}B+PhSv(Zd%UZOngy6qgzVEU56{-1z?puy{I*w)rd zY3|*$QC<4`l9_F{ZAS?+^Y7M^nM?O<*J%30m&`B^ZTWYGzdkm(|{I3G+YAb^Q0U%A5q^okb!?-krrc!3_nqeR?<$O*n%0XA47>dKb$;l>i zS6|9GM)0v;&Y40GaJ`=MBT4=7b51CYw@G+^n&XRC+|GfUF$MRYd5|-XmKaWZnxiE= zt^XU83+>I51mP*qMa8JG?Pf~8myxaqX zXTyTrW?n*rO<9_2CT~1hk*f}+8!Gf@Wl#4e0^aEAn9ie7m#w+S2hKYQeb5Se`Z z@&=GpT1cKhk)*t7o<^`jx zdA2b8MvJ`9XqLzM#fIupgI3~)9rL!(v=dKYt1Pcq1Id1L%`hkg$0+hl-tZ&e<{pCg z_R7nl(LIqZB}%gnD#yM0=TY5%`6`v>IAUPlO=4|L2ItKsExvVlo-64HkB`Ws`r4^j zhbOwy1n%@H*XR94qa&|TK|m!4ZQkSIj{i31m65(;R#M(X!f9A)p4eB+wB%W7JU6~J zeue2xR2ecj2(Qo1>)M~9$8?a1LFtF{0#Vc;TL6xJd$5-TZ`zdCk1#9w!~yzWpXOa@ zEcDg4j^|YolbUlT?_<&=!_Va{CaFi~^R|%G*01tT6C*^I9p&P$ILf`a>L|DGx})5C zHy!1eTTqS+?>@ifZ6*Xy+{rsg2(EhUD46!dLD1_D2f^ci=Itcq7EA0Rg9aBnVQ{OP zUF2`SyZs^=kv96-eQ1pbtWQv$Hn?wyy+?hN2ZIX!Mqt0(%>E96ct#6*3zFL2);^z> zNvF01qYuw{%*2&l?2Bl=xXP;x2F=?=lzQYFcF`n<47A@R+A$>At`N0@b$~zIB7%<} zaSrsirrQk!{fIn!IAz~A0K?k4!437lSwi+AMwQt=p)iG)dlC0S;0~wF_<}1+oM695 zL25MYM<9Vp)KYr~lzPDq!SoaD+g(w)e`z2dGu58nfF$o#*gte3$qlpYHkzUEe=Cbb z>*v~O4qd)Rsy~KX#U2pm(5DOR^Xm&le|&nO-JeE%U*l^CdUM|Bu6V@~yM|^UcGAMo zHWE*?v@7iFZfNT2jk+(h2cy$dV5`#JOcIFAtL^8!Nz)Ae*lu?x$)lgyJ%nZq!q>Lh zUwDz+`}^%KaJwAexNb-7ztDW`#mN_P=*&lGb@rP>5L}zu5l#5YP7^NOA~nAi^8Xyd zr8jLYggY+zTc@UdZRaSOQuehy0{O{{nxct608Tf*vqz$>!M1obBE}Geo>>cg(cY_o z(u;5G-Rn=j)3(AeeDb1wC5=V=FRL^GYOhi0L87+1e{YBDKkrFL1fsm_cA6beCshnE zcB466?xessL>pAqKG)}!yQr#vvtRc`f4GbY#dH6%Bbu@3e=8t&P1o$nU*{_bk4yaW zg91o$Nko3M4@v&nA%B~MBoB1TPxc^5nZlX-r!rsZPICM7$Y=dY@{?itoEu5DkIp~u zEBS!j`#2yapXOp7(D|=uItCECG48|UCrczB;CI>l7VeUdUb%p>b7nrZ1xDxQyP>}_ z^RLnwMq>m7RQ!#TA=s9kzl_o*1Tr*dCU|5}`O^F$ zGei7;_dLvpBbR(?(c}CoQcnGMexguY&3gp{XqJy17)jCQ;6mWVvmnY-zz7}lC!m4x^MAa! z*R%!aJcZPueg&>PXo2|KAq666?+q(}qsn|<-kSv(GU|b*>AeF;AdJ(0Rk$t^0Pv?RXx4nE9ySUK_-$!BJ9mB z2(Jf62_*htp{=0G6`*RJUx4dDjm6Un3Zm;H8jB|u7p!jptaKPtu&Q3H6unhIbq6c2 zKNwK{o9E)o2?aDeO@a;r4Hk!#7mT1;uW(QpfGQ^!$Kn3d3x4(oeuUF!y->mE`hYCZ zbbJrvodP&ca~3yPS4#w9@a(X;;A;syC7%?0!~gyk7ktM*@h*~I zxu@WQJN)Jj7fkhm=dWi9;5MeSc-OfCxP|E~wp}Rb$LD{1wcsfKyYza&BL02f8>HOT zo1|Rjt%50he(?Q*dDJ5YAg;!dPYS{VM5xfz-wT3KG=jcxf=3}$G`*uC)CdSSBOej* z^e(*33-vg(Pyo#@qEG-WE2_|s7vilpg{fdp_+p1b76046Tj72Fw?tW(>kUtP??M5j zH~JO|AU)_;c*YCz4-O~!Xavb0G^)_XzyE1$p#a|a_`<(wFohTS-*c>m zRA1tLDO=x(1p#PXP%Pk-TbKy6iKg8tD!fr2O;aO*u*Zf}Uz|Ut@Ix11w)yzNY+k4t zlL&aLW)kq8%`Wui^L^$M@Uj;b&gF&sV@Y8M|MXj4_&)#K`d;COd^zTW!u>P{>m5Q1 zo$)@&1>&;`v&M5Zg*4*>zY>D0wiX6ZxY76whPI^W=-|CHv}Z$#CyJIAiD79W#|j}V zt%g1<4TvV7v~EMhYlVN`Q+UV)_1ZQp5S=(sNOPCeYXr%a!hH@EzMz?A38bQT!uJv& z&fE>{aV_bJ{f`$;qyQbgMvKI(ldFj-6x*<@B z$K^wcy1AhMt055W9Z|I7-wT0wGKeQ51QhE=6@5h0SHDhWO-#|p4G34}xS~b%=4xwv z(H0kh9v+iW^d2FNJ0%xUeH>okweO@MVfZvt^firAIw_!gB||dsW=oOTA8pue2t~8Z zMc^t7EDpnQMMZgDgy#!oMVH0of$2p914wSu#YJ!>INx;m$0bEK+mPh# zik^$FwK!D-_lpZpenp&)Fh4m?I=NT`WowlMz>o=F*(5<%O!=dm$7JnKxRF z-?~?%r-eqK^43~55g&L^^o^&04Bq@#kzEu_CMn)73ih{m@fxvZxA+x56$@++DK7CL zg7u3kcA(U-WwAuejp$Nb=|KwYRu*Fsj|z1$Tn{7Qq1P6777JAMDgMxthmRuHjohd#^W41uycM1`h zyy8h@&iAgh_)}K^WOR1%5jsLM@mX7O3I+2Rxaj8sb+0ZcUQb)Mdr@%^McoMMp4lMX z7p>A2Yv9WSp58dLv^b#waASX~cr7hAr^Xh4Pvg}9H`)9Y&mE5{FJ41qwmbz46ov$X z2!h{odttGwr=$|!Ut0XF2Ru)#E*=N+;UVjaFF}jr)*Fi9&h1M4Xk#%NuE>;D@@1HJ`_i8SY_biveXd%DFmEyDf`;UGuhG|VD zK5~QnzvS2AGv4rf$m3$p9iAWmRh&h$X$cZ0qth-WA%f?J!SlnEK)oa*I(;La6b!&6 z9wnI|p8y^*xt9c^s9P`$v}jhMo+kOB zX9=CN=SmLn?^S;u*fBPbKi2}n_HD$ zmpHO8WIc9)g70=HeeUYa-Q2Y__T_8vs|y|^dzBKOg>tx1i|zH5s;mb9A zUV4lF|5M4B27LZsZe#uyetV61#Q*-yZ_HEv_s>CNTD!y3r|FnqCGh+`Y>YZgGLx67 zIg<94c;IN&m@u0C&}&O78>3rCF8EB})q4#5y#;?`@vh$EzU(de70)gk=W0Bj>C-t5VHcb#kRz!Rh2`DW@~5Iefore4cBBS*KH*wHD4IGs~57l}x8l zYvfu5tCceq^Nz4EYNf^uGRQcY(kzoPa;RURV|fstXiqm%4Ir@ zLdtP6pu@rYRxCMGzFQJmdwmSLAFxE(rtF>}! zE5lmkO07bxP~cSsWqGdQYT({%ky$t;l(K5%Iu+oo;k0JChUM@k|FV6ONZ?f|ld=kn z)hq-4HLM!mVl`H?T&h+{&3JUm5@5fM`p~ufGFeou)v0t=)Z$XL8a)gyYu{NX%kJN; zFu7$}S?8#htd&zKR2Cgbz$tZVxmIaaNUeaoOrw=C3fv+ER4P(o)~FeomE*K(XaQaV zg+-}m1iTH&g3SK<@R@bs(i_ncO)Fsb(Dt zjjTf;z8%|C4?`Z|tV$}?SwVB)sx-4kr_n0RIwiv~GP&HW#{2FdXhgG$;j|XDSt+$z z0UZS+wX#-~)ocahGowjU7c{~Fh2v*SB4tV`t5GnF7EDs91U2TQaP68@D%I#Xxkigx ztah3cqpP)M1v=WQOdnZm%{m3TwjW7RR_ikV2(wxzW2{oCnT1Q=SgS&3R)C?XWY9h; zjT&9eM4?;18lUJIDN}1TI;GZXW#KwFt3{`iLIYcYLA6{B)`eFMFWV!DU{yfKs*-6` zI*Zb*GHZ2GP7P)X0;^SOB)eH1imQGa|DhzR_KGcL#5}8(Mb=)a7Hvr$FGXqPpeJ(STViWlBb==2g+|8qTvKW#!Plu}Ud)5f%+=0iHR~PPrB3 z=N<~h_cqShAc>M$E0>yeIN;$S z=IB$XWbL(T5d#(!RY$F&on0PP);@w!b1*V-3=FDD8L(iH zt3eX2+$=TAIE&cht3d~~a7P&%)=6?+Q?Ojp^{3$GQhSfrc z1va9D!Hm&rVRUB{I%po9Ovblz6Py-z=mSZ3tu+g#7Fk!tlfWL4U0vpm9vaKqchaa{ z8O&rBi%z2fTxD?SB@6^Qi<}>;tul*(lQS5!DBm?4^hL^;b-;>T$!U~2ixoNumeaCI zt6YX;v5OnQ`M;SnB@^ptBJ=C!Q-@yuqjVjZS$Shq&N|d6vUZUK?c|3u_i%**Tp$JS zL4Z@D0o~)EwZOYFt8|P7NjEJJM*;9hz+fyIxtvib8I?}Uu)M=0)ylOx&gbWSk1%)G<1h!lF=W)f!IDVd>54JCZ1-)_Ua{bTp$(A5}-C&=Vw&?}oB6 z%lspyI!5Cie=QWMy*j}+jHI=$b)mq^$RwZsqe52b9_p%)_lh(q@8tM) zUDc|t@>d2gEexFu49ONN@W5zU1|}5n4LFOO1(lFn@wI6)KpyBNz~s%)2g9fZ{hwB; z&`4RW8j5QYSdzF zS=$!iuY$v*k%LCbVKxEXpn{dbjER%6Fmx-V78Ix~BW4xF$e`VHU~N`VFqIreZ5YWo zaOtHAnF=OZ2)vD8Ts6IH4!BqvjajKtT0vQrFg7wWFi4rktdYqzGOY^FH!ip>iK@M3 ztsD)ST{b$R_G+tFEs&v~XP5bfgJ;MpIk{N|BaYmx0#8Gwws1O~&LZP9SW?N)xWK<< z;omSBfG*$6f-kFOp@i8YwMxO?$NfTP?t$LkY}N7;1(;#7O0(R|ds7-t2Dc3>S+iDw z0-K{Ic(ZKrI!TnZ)>_pTqMCJ7i&i}bcjENhWnH77$C84Jq_aq2R>jJ6tU}7j7yuD$ zh=EqY`Drloim;lcQnii)8Tk%`)xvei8jBTVQK&c-gGOIo6C%2Z7O+Sdq0Ca9Ov`%} zEUN`alLO~O!{45)6kWtR>P4d;fu9Srf?BJ+zkG(2_=tDjEoZSWkEm7xgchB} zEQfXiKb2Ln;I4z!YgC|m#8bjmHk9NVu4U9#sa$1IspU$GT&e@Pzz5Qp6)gAwd`IsX zzv^hIe>jVp+O<;kx?$FZ96x>)Z(q$=K+ z>l{+^f9o9ot#kah&hbyb@4t19T9;Y=Tj%(1o#VfCj{nvH7r9bC#S@jExX@_&cQs}3 zHvxZFGqKVwQlWrdPN`Z6!4wLm4)*MIj8v(%Dq&Sp1G~g1u;JS?qv|LGd&`a2G{M7n zPP}>-)&y0o4t6DB!wbR#V6{!5f<1pTqvAK0r5X}(5^05i09ZnUP!uJsXu=A(id9)) z%}~qlgz1!c5+8CBp@X$G9jq9#u(1p!SPMjez|uG{$w{@a2b)*Fl^Rd?60DWg!wp zt=7UjQfLhpwn*ToxByybo#H}n?R6r_?s8%0&I4JGl(%+=rRA=$by9Vtym6!gcA%`V zN5@(;7T9TG7_}54F5ugBR*hLJQ;|K%h}snwLEKO%zWR7PY%XK%0cQUBWB-oQN{ zlt+6=J|V%I!f3qXk8)Z4M&r|emp4I2+8*{phOtxprrTX6!QW(TcbU>*ykr`QeU0sV zHWABf1KL2;$8132zoL@7!G4$6)Gjfy6UXp zaVTX9txT&?%PcC?PggOc?ot9pN%xA`-cb;jq=4}nK4lBhbPy~E!8{Q9#|XhJeDo%v zSZ67LqHQ2gQ3FvUe0+#X29d^`86tThvJ9fJI5-1iWRjl@ovasNbDwIlytAa2e6~qNX;IR^# z0b=E}R;8Ixth1Cr@kx3`w2P};*Hw!4rB<{@!Pyl`=g($nWo$)5bT+%SXtelFP267$hms>Fso_Kz4g~~Nbp@u*FWwB@Vdh3zrTo7u~vxXVZfD#9dbFeH3SG+fikCr zDUKE*!ugQhNTo$(k*OG%SU@ZT`1cSk!^t4LS80VHAge{3W(aW@QmcZMD=g65GKd6Y zb*u*BpBYxc#}RV6S7LU%)KRs9_a}3hF)h1CDInTq)yf6^!)s*&-Cqxp8Jow?@Q#F_ zD;A=OH4uo*Lf{Bg4=g|!pjrc=)mo_-U?YX-P?&^(05S+LW;j-Av6vy!7lgE$b#f9g z6bX?!QjHa24?zr=t$}TD7C0LqM5aK113u%w7M%V%YE%>NqK+C#JpAfuGa@5(5Cj4< z2RZMqE2T<^t+J>%h?It?3n@%G#5tmd-=(o|S~&~z7A4I6U^1zOpeP7ofVh2_knqvR zk*r)}(P}hWodUvsH88z_ASnp_vZ`QiB9n3?mQaW;sjFIatqjS)wri~w0<-zB_$~8h zwCYNhoJ3BGeqKPlnf(NK)|4dg>Z2tz)u37gedt4INhP(XW|g*tA)@+ zIRxpzNdpKnw{Q@@f@ISm%6GH-Ay@&Zy=pa@vV6v<$U3U#C!Eqwa+uwAm6Ed#QV4*D zD0#V72D5e0MVLcFNSE3Q2V*!`AHkBWB@kh*lFK37O|DXzA&Sim(T>oSs989zqJy|M z5(O2d(n7SGR&CaR@+#$g#1zC9!Au*DMu1g1gUi|4Yb0}j3sY;2&H$)}DD;CFVGwAf z*6{p7>@ExlDl3>e^oC}s3_@?!G96|T7v#A{DdFS-oCD%4TB%kEXL@uH@yV$f3mjok zLFAcCTmXPjDmXr)u|QnB7S0NB5UFjJK}aMACx)O&9YOMS)QPTjE|W#pQ6*XhVNxV= zH)6|-cAb@S#VhtE)mao8R;lFR$OQD~@F{yfYztLsbF6(T^DBjghU9GuU#e`e}nqbYLzgmMbyp%k`Snaj=fZN;OhgS%P?1;jD=o0_`B2P$ir%BccBm6%2ly)e6UMASx2Ne^_t>Ta(FT za70B#LU|#qmV?N9vy>k>K-*;utTsxOtWqwusyVfWjBIsQI)6V53ANYf2b3Au+^cP{|HO01F}~FqUw@Lu(oJ8mJ$X1AEVT=;NEjqSZ#z5Qngf}fzu6u zf>tGmRnIHqbNTa9RHGj3*J28ddFsW+8A%*0Uutr8ZM)IV_>nvCvT|eRdBXWW-QlpA z4LIu?t#~PksRs6XEm#z9CMoD;HT4rpZsKpgRKcT>Ep>7Zr||y$ABka2DQQHf{eng6$p(tb9^(- z)Ht;|EjDXB2f|F@Am%jTMG@ZQn4pW`>MXcE(+>o(z2WN4g4fMDJ^awkm=i0GrtF!O z2@k1+-#lJm#sn)Kh^EJesL&c`)SK5dTbHf3C>9wg42gk9Hd+IogW@rh2FeBA0^o>( z2?;%Bi(`*rr^?VGi!(t{%O(z6Mmfz};*c$5$xe?wDrP)*Ks>Mn>tO%ZYq2qDAlNll zJFHC5=&=Xkg_A?<|~2Jb$w zK(%g zFx;@Sh#oI3)h4qG_ndep1AfyPjkaKB3p|}o!y_s%EleJ$hL_7`C#)lO4U9QZlr4%E z!MIk&i$*nGtT20@&cLpJaeTwO2|b#dT=L?J4O7n-Uphd=0?Ho*|D>7sAdFVHb2ZwH zc*F~etD(_aK}0BET0B5>xa8+-b|;KZ5UqHU4g(5!85*P!&yK7vyj-y;1&GF|V~==T zMkpbAvjN+`R)@_K$_WN!)KfgtkvHjhpqa~;HX|lr2obisRbms;3Nt9Ro|m3&$ks<3 z-0&hE5gBw^Bc8iKaGBM(LB{b5TQ^iHTlkG{MjY#85K;)bWSmKgT7YqQH7e#n9OCIh zyIdxF<{x8FF8)4@oeE)8#1mSawPD$X?xluwx7)RFgs|f=j?)sXacvXA2;i|gUi`tt zpv8*P;L#!+1?+Y_cg7*cL^sa&$zcRC@f1~K#1e5a%PAhOLS0}h56pl=5;#A&F@>JT z!>ZU&4Rm`~>~k@pntkjxen_`@RWfiM!~0%XAq@s6PC4+;fB_Qw1x`Cgy!;J9&xKu8 zXU9u>I71*B1$GkLznSo+8LFhiXag5wW2naLcQ^sTS44~FiFgKQGiVW40v0299vDDU zgINUR0C{6Vw9(?CO`43@0J(pMnmV4D*8osqfW#gRk?+#u9L8SFn+y;eM!dPU>p2ew z+c#eVO&HcMgBovN)sP{0-D<_5%?Q&g4AW4MO+i@e@bFTLV>2xHc;U@#KnAtdXo8u_ z?y#Dim|nW;73gF<20~oOSEFUBDhR$C{g{wGxjNBW)tFDW+vBOSV78 z855O?7dI7N5q~-64JbGImmB@r@(t~2aqb$*jsA+ul5(Rz4{#LQ1Yd6SFE{#^b{SP} z^v^6e`j;F1OY^WPH~N0({}$Zx-2AsIQBD6W`I+>9u1l*V1mCyLul?Ui55}HK z%}E(JAOBx~|G$d=zlQ(69!wdy@X-C#`r$|!Ozi~=6aJeIn=*rgJPQvmz}+40yby{H z9@AO`d}on?*_#p;NgZwxTBmsQ5Vsb8qR}*~U{Xp3o-W`53sKhLriX~t@Z!b2DxA`A zg|5@%T8qD(K|uD{wfHNFX)Oza(^@Y2@gUJzg8h>hACzc=Z>OqghvdbLk5j zyEO1GF(HUHVm^z2sRk>asq;wBuE_@xDK!>!6J7ek;+(jUc|`R~lNTptSnP<`uSPUR zxN7Qg^9q+v_;@?u^MiLJxYR|=Q#dabd5Y#qtk(LtKlAoGz-j!P=r&D1Jx)lMPozN(kn%q7SJhgFRqwj);i5j zglc83#c=9&+299<+hxp@){dKH`6z0E^8?uFi4X)tL1_Pp%!qL#KhO0PFK9PY81aJ;V z1a90r!}Sgk{ndEIfoQ@m8=f{9f*ocpSq@i8I8Vd1%Ymo?U|}ReaN6x z#Re;fH;Dt4`&Bo0EPjMm+pwu2&EB@SA_7|&K{$l&Xa0$Hh&1>`00WlO1c_K!*l~F8 zGw)lxvQWb}0x>8M85wUzEIKs;t~d}@gy%D1j&*R-(}UyHNZkN>upsjWIFsNJiq?*Z zuL^m=9*)I;iWf?F{}d0Zi2KYInobMBZPH|0bjpQ>&=PhR3q{MK2SaFC;P|0a8(na# zwOg#>Wmjgr9VocSJ$ET`7sQ^VC>=izZ${OQLpEE;u_r?vdqm79S$H2Lc$@H;v@e$L zZRLAg$orstZwu$|Dc{=^J8JpfR=&5zP(Y$~tN+ElEr@tSUnEKE6fr#ppIKb#zj^en z8~pV0;yEVzSi!`1$)#;nnl-*F`E?uZ@Z{`|GVw^tj2GN4c)98k(B5oTyO4MQNgMEp z8mB+4IKIZM#g4C1WvXfIZbJ+7?q*3#@{Uxz@4mbW+v$~-1zic*PCK-k-Cg>Xp2%=F zqEEgxp#?b$6jD9iY4x-2W~AFqk0AAA2sNS`y19ELtr7_J*iJQR`L(5~H2=W~E$Gn}=(q94smNg+ z?wkixxX@^I%hHQkNCnBpx^7L^^_p-$U8i>^C%qNXe~q?}4IMjV@KCE`(2$oh9oA9E zUuSZsawv&x2sAr=b!PQ=l?L>hoi2@Fpj=7oN&uW#GNCK!ww+oJOnHugP&NLX*okOC z;Z)C9(yfT@?ChQ=eMmbLOk7LemFf8wirIW4NgtA*;njc!fjQtG3};z9Eo2X^k<`l$ zKWA>Ph+B)#_a?=Be=e~5N}lg8#e5Th<=DN+u?x+0P|Wpp8Mmut&9xz0V=im1OUlGt zld3zYX0~T|a-RytmHb$v2@2hEz!5IZRln+kfc6+H=%u%Zbda>VTT z9bV0c#SH%g*uUfNCSh<`%=8tLdmH&aG*K5shg-x%1-F{r8i^cMlKWaj4Laiy_j}~m z5TS0C6mmjsk_oj|#^+s*57xuaf>3t_p&r;N2-SF}SPy0w9+fOl$tjhI-!i3|6G;-38Y=LT z2Gfntxx3J|e{+9N=I&%HJtDcL+~F+z21^GEA9J^oh#%cE#+=sMot#`RvI>3j3HLkX zt(}agoXHvCLQbmbI|WY-mJu4n5gJEQBu1*aB&P{2ZZf3{Jvx4KV{!^W&yBZ|<2&j4 zg`Q`mBLcxKUEPPsT^YgVGJwBi1P$HY4@**Ynw1w4ksL{VWHov^$6YLCvRba>cHuIP zu+32EfWPFnoh_#Ars8Fwu@?V@)e&3wq6MshmQ6Yu4x`YOIsfiBB+ zTS?9?D)q#_-6fi-fxt~`;+{fso)QBZuxL$VDUSh6qw8jlf1JFuiw}x8cF`+i#%I%< zr`>*OhFqa|d^a*}7w!3s`!O;Y6@Z51peOpf&(SxUOnIF0-qtC4dr$7|?OlT1lY6;a zOIuk5>_MBoy^`(nAivwiCz2lMYVRT17(tGyi!i^LyT$yb?G~hHu$wO0z9>65wy!%th-Eju za$&|mX}2K5{V4plARRx4&zsnZ^}7_=br+=Pl2=%ajd;a{ zHy?PWqQ#m*2oc1LP$L!*3n>-17GGFXce91%N|HxC2{aD7cr-nc(h5FZU>JEImRu&J3}xX^)sM{T{)(7l*ocNKeZZ80+Tl z5p&iC71#kIKrY=DE^Rg1 zeOcO1^RuU<2K8gy?IiNgJ+yeGw-;H5e`mNGft}fc4Bi>*UM!J~au3Jk9#CmfP4_D$ zl~3d0=&HfeQN_3p$>TbJs*DehPINb|DBVyD>^FH}$K`>cCL0**RFmnR9O|4@CUpIp z36K^A<64pQy+SxwAz~$=@~(Vu50Wi^uMz$Z3Is^Iy;PIzZb%D$^gctL*vqCRhvv>o zN@gX)Um4qhZg|3zoLoEH%v$WepAN9(XOkD?KI?`HrE2c<;MO_rNvZ^I+Z3KgDfq0% zJyn%F*&`6j+e`De`&~3W$D5qoFx(Q|=8_3=k8{G`Bj~u0UfEudO21kOq936(E^p4J z->h=$>CIMeRAGO0Aj|ghX_5j|`qP~zd8wuw?lcyyc5jx*yL;)z%D!w`VC|(NA7J2r z^Yp6n?%qoGjke_-fENF1d%AJ8yFDGS#$A|g25;sGf+RK`27m(PC)i=}kq+3{U{NAjDH%jlMEGZ*#>wmyfNZ2Iqi z*FC-xN!mxVZjOJ6RNNhW4GJ#qzSlh?Mo%@y?7CasQIcexD-c<4r@x}My!3Ld7H>UvbsoWDb{_2vmdPkKSd&p%(!ZopudwFfDlCE8p10t;LT( zt?2MG&|aF#IJWpfY)uaX6g%$L!tRM_<;UIX_!d(8k~8E6z_mlC+3QJv8OOdqh%GAD z;F%|lW1Y*YgIaDd`Uf$QIZZtmu=S#Xd`B|+tbU<$$%iD%3FvPl% z5kK%fO6se?vp2_c9L+g}*#v&;hmEs`XexE28@o<7(P1sWu}U(^+hmkKZ05;FYPc}U zU1V54%P0?->S;-bwEzS1?RDx2_ju`M^a!4eYBcqv`z4aRpR+>JbD=_k{V5syZ3^t8 znV?SsJ<)siI_z28?D5Ec!2@_d%kM?;vJ@AnE+b-a;XOK%Y9q%d!t9uZdJ?x5S5KbX zFSZuDjNQ}w1ry}%rZhr0Z3@!C69`#!KR&T5JXF!7%t>`iGRP)O8|>48)+KDYkcxcVU!|f ze43c?f`W+y$PF3I-}aYCsICwY$V<+IQ!V=FRrmb~2%ae<)TJw2bd_2vi>c}d_z;uk z$iS=6o)tZHgp?Y5jY}zI^Lq5yZxB^7geXzfp8nTbG$UCD!nC99B~N8ZhRbDEEVO08 z6}t+TUYY|!dKHC6hTqI=lYBr>A0#YQXuTl|Eyn@Du+{^DVfAX}eBrVq{2Jc77&TVh z6|=}|2%)29f#GDkO}tv1VJ9CD4Evsp+XT>qDb!;Diz(EA$vLARZJ!$A*fnAH-~#JC z8NatNt`d&jET)q#tqPicE#{JQ>wX2rlfICaWP*9Mtf0K-vQ!%!Me z*H-r2E1Uiu=2R)2?#l9LeJ_o#Pa6IxFl6$Hn0^#%YL^U0kKb%fl_Hx+=O4ptlDt2v z0hde9qid%69}O4sElR_nx1sG{9^a6GFLk0%iHVjLgfX4_qkMM4)j3|f!6N{X)R3L2-BC!Lp3EN0*u(P5vjRI|Tw2!L|@W&wD~_Hs1d5`XwZaEgsD|{mw9OGW`IL z0yb$5?U+FsAYbgfeBH6ue0Dp;(36bMM8 zZsDepXmdf-yX;afgQm*lZ4&O7llOT>BQ2daaYa)`sM6R|YDx3cJ?jvCos)Ad%#6%> ztTVvD3UMV^CS-)|LLf?;7GXqKAQVK-u!lkh$E(FT^ZSE>GYv9!TNo%|%#bz-46Ao= zHza_~F?<&|lrU&3xlxEgZwWmAz=%tvQR*Q+b@Z+d!lDL#lx&_&4=Kq0gxpoA2Iow} zA;FnB;X+Qjs#LOgdJ(|kI9=yBjZ3zK(TQNnrvy_DX05bz=Py$lNa&bh$feONfnjoQ zx%qS%$%wy5u){3|K9dMXBwQQg)#9^#^^lnDkuq|Z7&v*he`SBSp)00LtU&;q zQ+5wHfoFbIdrbnC{kx#Y&wLs*K+yGv58MV#a4i)!;MU7kx2Vz93x4 zs7DNk#fA-73^Az@-SxDm6D=|0B##X>h*Fgc_}mk>jBA06>)zI$-sD{wSD)PWEg9Dh zZ9E&v=L%fuu;;-U2z3O zyXRA$7F4~cnpPq-e}L!tgqw#e(e?FeriIEb=e9D1ODKs~@B0+I%%~JTPjK%|~eJQ=T>vMaN5uDnIRcIil!lNm27>Jbw!n zwN9>SB#B44IfnJv;#tqoP}!DeJ$h0nT-NJ3PhqHxAwnJsm%Y`?;}B&^;(r$&3nzYP z>^o_G*2=Clx3{N>^d!|x^vI_2?nlKebU7+!;l&p`#q^UQC|+AUzB_Rq74y&&#cUo> zjn4zR6~IlcU9$3(iJ&lEkOGTJC9=WU(5;&ULH|qMjB+-LQRX6VmXQVC#Cv1VL*b69jED z(6f-}j|sNc924YBLm?w4Dlv;Ym3s`1Z}o@<0GIVL1x&2T9fG{eyk zyLkpME)mx-{%=Hc-_8Pcx~<1^sFq7aL0VDV~K;fi`Z!xY*52l2&fejA}M}YSR_p!ophJ=`X1zt!RCLZo)ApO=!%kzJgb<{2#YU`4Hc#M3KQ5-PH*f?x43 zyDAt&*Pk1ME4tS_TgYu-j(fZ0mt`QR!^Kjl?r^$m^h%TO#aXrJmsKV-ks8q9XG5Z? z?g_DyQcsALG+_H|gQSxyu&WUD39*_Qp#ob?sK{3nI_Ik?1Y`mOLdlCK1UI~JLae6l zZcj(p?a6(@ZXa0gc|t`mJinl_G=>3@StMV^d?Y<_)>At;^IcDy`qCU$;iaT~k!4S8 z_DruQQC21lRxZ)n0#QsC!Zn6_VN&-lWC1giz)e5M)aE#G=B`tax2=b zu1enYPcE;{2mCK1C)gjz`u-FKcTz4b5eu7u7Z!r z4q(KX^)q=ysKl?$>R$JBmB^8kVr$yrnW-bEQH1Lh_A%pz=QH-N*ebpVx8;l;0`v`= zTE^%mqB+{N4N}cE+Zs#7=SlDZ$%SQOI~3l{jxRoY-wxsNd~$_+Oqm z1~-nPM#8bl_^}xrRb+J;%j8;04ztmD2#Ymldm6Yd{P*_ zL!|3SYcw>}3ihw(EtW`~pM=h`Ln)fj2{1fev?sbw}xfOm(8Q$LPJJ*poJX^T1L8jNr3PqP)!d*si(~~s+ z*=a&-?riF9Lgt|ZcFi{XC&5xPev-Ah$12vKE4rMnO1lkTp`&Syy#ccFC&6LMe-a$F z0CgFMp-pKVMtVav>pROvIiTG#pdB)xtv?Aq`~3m$lS$+vx@LT4lhHo^li;&oFdpW_ zu=gjyXK2OGGT(tXC8xnY;jx0h=92qQ^D#*o^vcuTXGx>eg0nu;d0Qutb|_?=^^V-A z^=ZLb^G)9O63COM6`b`^WEDE?=68v7UB@*Qg3q`0PLzTj+j^Hur2lEbX<(Avm(;7|?>~gUbH+K` z(YuBG6C*6Sss8jdlT5g&9@#YosDv-74~CnDFRFLQ{T>b%@{8)#k9%uYBFCV$mfT1m z4_Dzg(wF3>x5D2e7!TG>bx}5buZMS(ltLE`_jYn0KU9NM{#l6AB&xB_twrB`(i@QK zv*Ly{_p!tpbX2bQ0V#{ur^A+z8l?Wu^m?wheMBW&PgK%{D^;ogbNH^h8NK|Zw>dpj z?5`4P6h3jLR2KUz(BRn0=#Uzfu}%uQ(1Mn8n@BIvT~4oj>gn~fSSQc?EY``GXS{A{ zB&#rx4EtHEi@qphQ;s@(U7%Agc|)Bys7yO_pX!sA(ksKfumXMdMg!^R=&kG>wv`Qj z-fN*xEyu3?&!5>=1`l8~d4y)({<;<^1nHO`No7GUwPjWR5IIR>d}OEFQ7?Gsha^5p zl0S|;KC^qtCih82p^#NSG4m1W3a25Tn2YknJj^F1xUjGHJxRJNXk7hNy&B@g-dv_1 zYV!l|mVVyZ5~*@Vh^UDJyzNMB{9q!n%9&uQ)tkgig^X$$Zp?`KP%Kst)qjuOt<-8m{C{GOo+c2*DIMBLveNn5MA-(oQphDf6u&lg=na zQ~U4`A)4&8zuntZ+NS`zTL!x03_oZDmyPr$5zGUC$``#eLtUH^g6fem-pPcJUlf9B zVYmv{#M+JbUXn=7UxZ)^giE<#IxnNXUnUelbIG)oBR(#9a;QF7on7_Hgp=MBD|qu{ zZ?4pZu2|`Po;>l3SiSFx)oYvT{g8e)_Q@*LKGnOC^!i0;MeoZUqBfsrxfS`KvO?4i z`9+AjkHeL@sGA~x|2+Jii@LSbz1zv_zbHhVkAst)a!?YMbsNKN!?Nyex#O?HgiVlYH`vLe_m7uEJ&AzvZS$NkV!=4NR&*izgO!^*y+%2{{W4+5Yhu@ ze`eRBnZ@3hLVQ3wb(ve6ep2ks4apGcVZj-%w4TwKOhZpN(@B$Q*6-dlRy0Oki?&Ma zULn|OmA5+~ub&lM(Of3pWc-Hhj7}OdJ}FrJP46cXDLP9#JP!Y(n`^vX$m+8~e&FM> zzk7qMkn2Cw-|}`PAB77aeA|1970R+D+&gC%-#gy<^x=2CtI2j5eyeZ|#+4z2k>lu- zq9NowI%f0s%ULmRr_TzxleX5oOCpKq#KcMG#Kc`dDVsRdViPC&67ENW4y0p}kUG`h zhtj1#Cpy=h6P;(D6B4Jz2VSiHTy)NKp>8sQIp>7P>3U9x9OF4Ia-?j!@d`MA3!UcY z6hh~*@W{B(slLJ6l+2W2=F2drpckfZn>Tu2u0U3xlxamCx#`k#!K`n+4JwlL=M-}0 z&5}|fioV+B?OB0rJSUEGABRc>Ufc<_P{w&Sx&eWtDGHn?a-7E{zZOQ9$s2hh$oENN zV|sU=*GB$0Cr)42@h@}b*_A|xzdFuJLcjMXDMU%N^MZhf<&IH_i;@ia`|37HQh8CT9ZFBoU$c_B@tv)&KMD)hxH;497x zrhFBJj44rxOB3|TrHR}($xP;I2-$|gvKK)A2A)cz!6aJig4ab(o#%(>nZIGn#d?-FkaVq_=lFuOJ^9FRo{ZcX+e}O(;**8J1L#Js*rqWB5 zeLBe6*S&IxH|fiGI)i>OOKoHhr*`uXbg1wpNXoS6VuB$@FykY3)}V&`GsWV|6WGO*($b z^i72kH&^3|d@O1?F$i*JonQKbv}k6h2!k`}R_J?zUW(y2?PSEZjWpP3yzoch0r zLBnt7v?Li9g(RLNPf??bVk@hEQEX)uE{bD4XvU8+@cv``UE;ByJUTqLd@Cz#;A={r zmMNYqQ#=Qk9=JlEy1UJ=5zcnWnc{yA>xh^8kF*aR;mph&G_pVcIcVg_(avO@9%*1$ zrO+J6E!aQLj6emXnlP!^FIE9h6?kR#8~@`K5Z%Pcf1v`>f?0Tl&isp915tIvPgcW( z)KPLF`-gX7R$KJ^8!G7V_)Y{I`zAbD>~J}ClT5k)`1%rXJBsD=Kbo!abPKRG6NA3-=J##2Z`^8_w_HLavE_-OM+v5@~ix zp^4uNSK){HN93k;Q-mrGjQP1xjE+9j+1HIep@%Bb`;yr3pSvVB{HHGoCH!fFZy_0h zUYI5^^pa4*`=F31;i$wN8)i{Jsp6lXt>RI1?GkywE^**{Uzoj59mfOp3j zn7X&TGv>&F<~S%xR+{DVJxlss7TVssU3_kdOuo$LNSeS3+t8f{W+o9Ay8moZx^!7+ zeCU*Ge4);JrwEOYcmAH-`P-uNHJ63LH@OE>x8^?$TLxG74nK*%xWd;X*H@d4%?&AR zfg!MNw|oYQ-S;KmT9292fKFrI);trc#}&Y?J)sodiu|^WecRkKRF5lr6`%TFg3cAa z??6(1ej`{J*|FZe3c>AmrD#Q^-=F#tdR_VH_Vs5&quk)G&>Z~Y4b z6^KzyrEjL!<)14uEl_Kq9bWL&A}06&fA(5EQ9tVi)X!yI?)fOx$G)K}Q|aIrK=A)K zXr?&f-oiS=FWZBc%haj^BQd;p=u3Tlrn-ZNj)lH7#5HupE7nnih7OUnDcZIlWafW} zNN>zqSx4wWmHYj_2c`R^W{h~DM^Tl$DM?WT+?i68SV>lD{`2!Y$?B{BABS2sLTh-0 z5lFlTOa3TK9ClfN2Wvg|_*;{^5TI-u!!qRS_cy+N$0aWbFI}!Rgyba>9^(G2GhO2J zHI+uO{^k3#;a9|_Ipm7iG~aajywYoO1$K=y|BBc+$Djg(h>H9S9xajoEw)W5NPDg= z>_$Gg61i)RrxRxUkW827!RqtI^VmAKU13|N^lvsE`2r1X_+S{)%#;;EeoqROhM7|7 zn!&z*l0UA9UF<-3q}<97s(e)5I$s*%8&G06!A59(Y3_@p!Bw$A*2TYUgZwFlUa2%8 zHMnc2FE5cibX90N-0^Vgkhz12`YHn^J07lUw)kmQXZ&cm>)c!S@lxsr3x>}9yA)Zu zwPx`E@`AGRdClS{Se@aogkCNGEDsKN^1(5DpM&)t`}wZ?S_v&SNOV!RNu4r`2;^!( z=ujQPaBJpP&J!j?LZ_# zmYUYWBg@K3*llJL;*sj@S|^gQn~WxeGDYlJHF9s-Ee4xYWPw~)anp|y(xbr>yYr$#FN)%+MO|xfm`#Wx$2g36FG;i*@4978Z~0WB1gH!gh-0WK5KGV5s4L< zZ9{3m5mr=ZK!963ze0djrv=W7TD%=KAi}KPXmhc!xS?Q|7LCOLs{)>vA_*-r##&tn zv4o7H2-#_h$^aV&wur?V4>aQ9Y93dF6Y4t}zWNxzDC;BQPmNmL^nRYNS7(92!-gju z2&$@gu|UXh#6%ux8&cg`%ubOFH65WkT}X$EFqg2w$w_ORqV$PQ;d@{vUR3xwIbQH#_rPKm7E!QTs3;Djzt$WAXhSY!luD{70zHhjW*JJ z$BcbNk!CcMa5~O(>LT-O#^G^SWNmFM0_tq0P*}{&I3pAJZW|-Njl*hp+L0Gp=W^nu zv(;o(+noquV?nrCryiMJgEfv#8j_ffc+E&1jv&o={HizFUC6a+a;Y(gc;KP8QmIx+ zVs|8T#B3snt;T^A@kk$RFt`vt6>9*&TwQ8KvT0O^4V#T!tXhKxD?uX`ZANTd3@l@P zQ@YbLIVD?eCNEr;uhs_P;b2K+jYbU%>xvgECL{2Gba4bXzBq4aV!8{t#}UUBoQ(X= z7>E{`)$yLrz#jhMS=gfYG1D9Tli!vy)J~lpIjZf5XpNw^Y9q$%LbhZ)L$zwq3KDX$ z9B48#9&B3tDw3$g0i6%&_+MPOdW6_!kzNs^)q!_`m{yA!(TWghT(7p-?ZK>mlaK-! z;jNL6*kZIHmMxM!n-Sg(p{!l-ueKoxuw5k2&A?)3aadePy^B2LY~dShNMmhBbX>@J zmx$Y&xpmT8QhIE#6tP@%8L?rC+_@<{s#YrzTdVlFCFw1)m1%U129pbsst{cmdB4?W zC_^+Gqx0LFW4d+*e0=OU~#g45qVgIjJ6{Vw37vJHyd4g#DcbJ zu%%dx7R2mxL0+>RC{^BQ4Gt5$>~$KK#*R?#E(ZdPBkZ=xY=IcG+AP%CXE9F@|D)6_ zHfU{l$!hryn-LES9;978(^okYk>N3)b{!Ir8xV5=v5B#$v<55NA&`zdB#97_8DaA^ z8k<>%eCY^bYzCVncf7_3`DEhBk%O1to%;@=VVlhw=m|&~jfHE(W^6$CY(%azS&$xD zqfvxjPLBX2b@pr&pHERnyGZe9h8rkdO|Y!D=w*01v7O9%1XPh}Mn=*#W&*s+1y>H5#7X)+D50g647T714twb6jv2Sw;;BrVri5o}(IXMG5O z=7Ji7$HvHiZo=e%&%{QNfo%jK+c5?^qU1wIvYN3hEjGIW*{vN`OK|xrEraq#n>kSqR$n3%!M<)yVZ?G7Jyvan`aRU;@ zy9^LQ2ng=5n)EJY88>1<*%9AeLu<^LR7+lQPmPL(-nou4ccAMRdT;>+1 zVwcQ}1KdE{o%iKt!~xE-aI5K&^FCFE3zKNDx~wiFDMB_#lUb*ROxGHrk24)lP9p$m zfKh;XkyBl7L^=m-5y)JD*q>^iw=lEtLOJz_Rg zi-K>b)y*(jArCZmDA~w5qPDSv5r}FuAv1!}X-B@CYLiog?VB%w^svMZ+_-T-iB4*c zJx`{{6%>a#qc%8QZ1Rc3A&WZieih#3ry+SS5 zU?+3xwK_9m>g&x$=xr{fP5=k8xN&%6&QG1y|264VFd?L|njPJMhD+x%gH?4_tUv<< z6qFdg8}7`Sd_c;K1KvefZ^43#2O4<=)Xi~{QmbY4(+I@O4m^K{&WW@JR-1!`*Tf!< zjSeePR>@4*so058ZLmOm;4Fm%3T9kNTYx&4I5_+bxleoSu+(!_JN`R*eRl znaSkRA%}oMBZF2E6Drj}r_784)L63kYS1pbeHj@>Fr^L|aI`uc1PpLiu(>n_7m~o} z9f;v)5<|&EC<>@$8mktINbj^btTq!CEmFuB%uc3228#CYq}xKSFx! zQBn1X$q#U)GSg<)>Kq!4Q3nM?ue0C;0LiMhK-1D7*NqXJH()Y`Z?qz)1C%cZVj)48 z;t0jEUKo+L#Eu<5c;!3fA<0D82Lyl6A`cLDHzV?2n2io3E&KQYK$AYF=87lEHjxl7;c$`R=Ac zE}>XJ`9QF{HL9n6VDPBcYV{*(bqirvGU^T5Y?xyBKBv{$Em&#@R|De!Y=$MouY3^s&-HoEK(2e4?FapcxRJ$K-628(k3 zrD=6!%MjESSZ>WY7~w2{Tviqb^h=l!AuF9mH8(7B%g`ETGKmFQ&7nsgF^As3(wxb=b-W;kEa?wUZn38* zYEO&9k%q_>7KdmoxmG4M%->WyBhH9e5+q?NNe3$-AdOlJ1rm$~YaZ4Xwje!t3X6?O z-C+a5twi6%G-Ps9+jLN=Ft;{4wm+7)2@%kt+n97zngVk@&tjJjDT!q1IwXZd!aF@o z3{bAY5qbnH*6K`D^URXkL7_;-25!^vwU%j*2^nsni`3MdVQ|1=3DY_J53#+m#c#mY z4pS?{x*e8Lb}SI4YAcNNY9s{%GduLqnyos6PKVW`!OYvtt{{#Dko7tTBm*u&KnfQQ zb~qDhbPjCeNJgedvOk4!Bra&aEyRJ!rbI*8T1$7}IHOf-aKXU@KMWoYXt`K`SSt>- zkuG|0Q*AL2e0SMkbOfTfTY#xn>#!JIkcCKVW-?fU9a87v@CHptZ_~m0gL5?O(a-=i zI8Ga3fMz-AXzm{D4!H$O-iFc~3$B$e!Va7k6DD1Lojyp9H}UJUb=d~JVu`Vw7-q%< zU>RI)!BSvni2;#jOQS)!P;seM@H;d4#EvE@+ehNePLzElHb+{cmRW3qX`IYH;!X0` z&4@iF*oxkm`~X|QFff{M9Kb=Fg~m7QY-$({UC0k&(_$mC$p!_aHiJ%rd2}H$8FQVr z;nV{2vB7M@Y0oGu9^$A5GquBLv+H0E)FZ1LuKi$mSL5n|r6-8UViyOrPV8p!KqE(( znzG$2!@*2zb}$e4QV$VtbHWY>n*!vwQ)AGHc@**j$%0^2#f1@&)ETjskX8~(E;93h zwZ)y+hL4y09Rl9rbU;62so(T295|3T0LD9$219{iM3_k80$%YdERZol;=*g=2TLBw z#BGZWTMq7Cn0*KG%i*+JY&t9?Twpkmi%-h)LyvdDaA(x$aP_9s<8s4{pj|KtS@`{r z*(R31Frz~0gks_}K?TLej)R;@3xlgm?Zjq|>p&)XLg}>Pz!saCBMxY8N6kItt3==F zNbhfD%GO&HE6s^32Jjj*ScbEW>E=*>S^7j=DQcaL;P8g*m=ZgD*a zmS>5~j04rk@5Y6)h*Yc^#riVfwo?lusnNmo0_2)C!_dPtdhDh!nep?6ygkPbD#~s@ zLza+u^Ao8D#^y^)^p$vQ(rMXtHoEQPIvMds1|_;F9k7R1Ofxe52{IFV80^cqVS=XR zG@5YI20^G)afz&;AVDxPuv)bcq)-VBAP*u!L-HWqE{q>GihDTuhOLpt4DCj*vm4+# z0p(K%lad7rDZ87p)8i{>U0G#BS21Ag^+IKg16Zhx>3ZC}BT)_3HoL6RGCRJ;;6P?t zgBmN1otwot(y{WKFt_4%%#QU4OCrv40D&zHS}?A$1JGy(?3nv5_!K5cW>B=V{Y9sN zi42OH1BqOrV}n6tdq`}+=&;3PO+26*S1x&gUE-^_(wL#ebtJS4T-!mV&>9>F#E#6= z*syT$vg$%219?PXi-ge!X@e0e47xj%5EHu|MXqO)E@*wnhs9>KLulHumBAhfBbE&o zEi1eJz|N0NMQQI(j}2Bxj5t7922xXKshJFlr3UfqfG%Wqu$ZEEY?ut>&oRFAmX4to-S*N9Aj`N!T0Hp&)A!GSp;6GIdxh zbXFv`#!UyZ7l45WKmCD&@{_-eF* z&k>y;iKOlDK!aGu3o6{b>S0S>bUbu6%ZLqLN58K^d%)`_CTMm;D7Lgr*sZL%(bL(X z^J1Ao3+m=CaEgl!m1h{o2`MI2u5VrbU`Yy0=kS8T?H6K6 z>tOQ6Whb(5Tg{jeofdhHgUD?@G!Yr<;3ouAkKKgb8qOCMObbqD*r;$)hKeW~b2M5z zELO-OX2ZJG+i@+3TMxC%f$ZWMgI+Cet#L;X6QqsVanv|6#sW!uKDIjMh~q~FRsX@CE>O-4L8qlTY~w^in|=# zLNPl6{2ZaM;I@^*9?4B0xJ$z!8-_?G|6%h3w;PN&(Bno68wXm^6SL-WcM;sB#er_; zCjNA&7_lIq@XmdJ4m(Wirkk+SxZt5?*5ba|3>3lz|N>Bn>J(@5g0xob(sqoSlXaL&Uv_*;_?Yjold<@9L_RhLxs(>xM-6m zJr2~8WtOf}!&eDcXiS9RwvXMu!-|MI23-9+;7bSnLfE7lfh^E$alOZE90+}Bc0zqH zm~eq+cLdiyGkGH<0JdMqMqJribg(=iNxA`cZI{-C4by2b$jdA?NT?Ou{VWzZs52dE zEHTl~|K+RwH=QBcw6B51H`}B%iNF+Mhcy~95WX!MSnS|_2745S4KA^pgITMXdK@Q! zC!T`L<4Po)v|>+H)ON*kAH-nMD37iy*1jm%B#8 zZ?@buy4*EdzBzzxq1-jP+%>vXk8F07EO(8LbSy79QVL(dDku_vW-Oca0WypK{mevK_L^U8Bogqsv{R%Uz?(U8C=H;E%lHD0hukIAoW* zMwh!rm*%}*?iyY08eOVGcDZYGYOvfjy4*Fo+%-C*+%*~w<>ju?<*w1?uF)aa^~jS} zxodP;ZrSCo(dDku2-&AaJP#Y3?@d-G-oGDNof?YpRqh&H?i#Ih$S!w{#>>q(Uc}|D z(KIz!?iyY08m&n$ca1LHzx@BwHF_ACa8;xOxE78p-D-Dz%RFXFdGsNtC3F8RQ(>a)fi(&=I3NdFPQ8_*KWJ=$#V;h^yHoTnZ7gDuSuJ57G+h3jvh65kaI*y=?M0<{utULu)BUjUat#u$NX6h zD?WJvU+K?vcb*EW8<2K@v#50(aggL*nb)R^Ek@$fF%2yp*Sy(p)?Puu>Ued z=H)RI<^d}EFM~NRZz22d=)6-E@4IiZ=b^lR;AfuUd;DkWKiQuLx8gq^u)ih(k-QJt zU+aKO-bd`O01(OxvcJqgD(_$HuQfm{?_>7YugCG9PuO2y0KvRZ*Ig!8^+e_cQK5dOP?{rLhg&)dlU`sO13^A-Cm5#z}Fn*D|FB2sQ3>sPQ1ngrI8%n-wp0@!XZY14r;pgrZ=tw-7bXzpyT?I@8}owU@y>3S#B$ zy-JT?Ui@ku)k_w3BGs``7$Ei1QjPc+LNm>ZKTcRug^N z{|kL?c-=peOu9zr44pq#H6=!$)kU8Z;`bSxU*LaLQh8%^Rzq~QD0XKXE4X_q=1-=6 z#Qd$-PpKX3>-P6dP_0$M;|z1q=O3mDDnE0EDWv}As8HT&?@88PqtY9r8b-F-=p5UMS_=ISt4>63FW?`ex*65yV{~MZKbK*`@?J)^idq7-1l=P@ zIDFuk+B9#ef2*qgZ;GY5e(8i9Qu{XqGxk5As;w-E>a$K6eFi%&_dhPF49cdQ{`*(> zyEoV1bBO+lnfFb~l+$lKBzj}TCGv+4eQ2?NysE46Gw;m25|g0$jV@T}Z&ZP`8c8n~ zI8*6{mHyGH{>thdR{0BAO_t(8!BzE2L7(OA$WRg~yt?rh7bfW<=TnPG^IfXxN-$v& zH&yXD+yVXa4S!ypGRhu()Bl6YuV}}mg#z1F0$XKsI{fyS+QAiT{I?{s<~N$PcE%%g zKrdqrMJrt?t=JgLLSxF?{!!$c-*~>Ll#L7>A?|7ZV!wtSuRE~;`2w7=eNlGs&^!K9 zgd71}r1(lX7**G|*PBc>|0dFwpo#U(**Rqa9lmSopt8z_t@AG_Ys*Mq?_aH|b{$BD z_N(v|s1%<|Mu8L~|G*TKQMUdc{%+)f>vVm=xIPLJ4iF?%HbT@SjPW{pARP|7P;cM@S>(Q(_u#`U6&!7JUvE2hcuav687tF66{Nf}3 z#=2z0b)NN2F}@+CG{|4#eS%ECr|)g0ROQ^HB#wgGZFhp3keh&IA4^%r#u?2`QUIy3 z{MkYOF8^Z^`Q*A_reIW6&P=PW3uX#{I!G&C)+};nDg&Q+d;QDFPuE3ixf7s7Nou*z z_xXQS-3)_;%6`S$liav2a?7DEOOB&b_DePIjT@Sw{8BQ$62;>eo_j8sd%!GA&x>Y^%^^*Hr>`9JKOfm(*Fk?b)uW}=9eW6JI->I^Ienp& zY2zIE&|&`;RWD_qT;Qf1@xP+#ul&pf?!hBiAH9C34<7Zu7}*MZoJH3k^|w=vifF|) z5;W*~%-=k+K@@_QN{>M}+lqEn) zR#BHKK8Gijo<9lUQF>YM1i~IVxJ* zWJ(u0b>W&j411RSfAE#kBa9`t|724`w@Q+7*8jx*>f<)yS@{ z7-ux01=e0VRp02YNXHv=QSt1iwEuOelo1F-A>XuArwkQj3j7g>Hb2HkD#PFMM z2r>Ef4Ql#DyY8g?=6vIn9#ek2BwBNyp2qO!)StJe8EO{{lgQ; zWni{9zptWRx2SsOH_gtF=+p{@nRMDa4OPMZiG|%I={~BNiIHS}@p}94K`f_Fs4W|3 zH}WGu9hmZ*^fmjLP7CTzP^*;W=0dtGLM*FP_>HRN9|{K6FrI2(e?qp(5b>EYaLYgF zrSE5FMK*}yfuaAmJn(RG;b^I^80p%X<2xlYuu3+VmR;OX`a^KRfbFvl(rCGI@%Zj3 zBcf_^PUwf)j1!)t#ls3amo@fSOfaxY;R>?+5889{)NX*yQcVVzRV|#_NctB8K1eZ@ zGvxt)_=lMCb}SGE_V(p@hVu85=I-x3=B=K~A9s@i`l*n?|l zHU6aU4Jz!es-Y|i?;TYYpTkJdfq8{5*DbxQ5*>PN^ON-Q@WQ5~&7VRXSXeJgK7i`o zQ2JBI_fCTg7bK8p{}kfjsi>-)3EKTB0`qSTV|AA0Z8yYXa~(- zzaSSD%R&R0C8Ib!3a&UEO#M^jf2}*RaAOiF{*!CdiuHRyp4eryXCYSiPQ$ET~ksK8rag}S5f}V)KIlXr}w?_%uluYg}x!bB3@9X33R9$aoW3o@i zba$zk?f@Xh**Vh+PbH8me@08Q2NMJfUPMX11@(j9&MbU7k&v6w(o7q!m;c=Bb%HDB z6>do&HIb_I?-Tkc*uP!49#1BDULNp%d8&X9AMjS?fTbgIUMO|InR0I!FxS9KpFg^F zVd1B$E;kiCTyjLpd_wWLgxg9e`eXvN-U!&zV^(^W_B>SFg$|uLzA-(sh|!!HBO4E%XxPoNX(L5q6t^@OUF~_CS{bZ^FxBYbyMtMZ{DOQ zit{_up9>4GkgxC~a@QIxQ}RpT&yIE5nijMS(xO7OME2Y>*(0MW_gGfApPag9t|x}8 zvAHIn-Q=Q+02MyRO0>lmrTkJtzpFHGcuHE(P+a(^q^f>PF^l~8P`t8Gt*WQ|%#RP% zZ_yU33LlSbVAbb0rYlwzKA>t=T7#_Bg;ygRMD1*I$xf6Ku_KeuI{B&d2>0NzC@|#HW=#@_jZ7H)^ulMl!j9VhX_Uv~G zvm|LHtBQoL$mg1}JgPqD_XViV_#MqSzn6{#=l3|ncq4(X?>o!JG2AUM+z|y)Na?M& zgp@wLwvbkqE(#1+Y?+XZ`#wk-_>GY>g~~_1;@TNTVOl&)+GiTegs1 zBlp(l7DZ}n2SbzG|Cf*&8Gn^V$wq&PMAvCuw%F;H(+Zo=e%}=SMXP>USUEW5^TO2< zY4=z3dcGk;Y5f;hN$9ceTV7YReZV`uFQuW0dJHC&G`dGlX%c-E}e5;|m)i;-|j)o0@jotMg#?w7-q;wQw?St7)}NQhlH zCz~$#u5g3Or|iE(*4J?>K9>;sD*fc&g)f&?HuYgod)j{6Y!msXJV)zFoul_K!ZG8s zgMqDuPfKJg2*V`%cTsgYr++9D15G%mmw^@6P0J`N+yTk{`(IqLOR&HnD>Ty;ZB|#O zJ9ZXcP^oS!2FEQpNwUVH_t4z2%N)hg zwXHn;%HD#z_7>KYRF5m0@gwrIg#{K>j`A}@lB^rpLe;;t2JZcZ zEg~CmW6ZDn3!ABiD;sd#6%d9WDC`p1AWG6dTFOX-j-eb|m{RoZ9}CBlCAY;o_A;3F zNW{gr1&<^jDwHI${~gHoGL+oD&6QS#7<^tP$<5nB4Bogc#NeshLJXokU)%?j1TRxp?O8%# zE(Yn&&&M~F?g;W_-zklJ&F=))_Y9;YkeoZw(k`K*5Kmq2hy&P@&j#Q!(C<#P7^@al z@A~tBd4vqP6D`IXh3j!K*69u#YHQ%fXQlZaG17+^o}r~srWpIHfu@t7p%tq{6Q-rX~77oqs4kmyrA_ z-D(XiBR|}sgKdGARRT)0D08FFBD;LD06;XYctvPfyS`zoql`s;y=ilo+kH2+RO zdD7hAuS0)gHA)UoPoL zv1T}DYnKE@sGf~(nh~~aUM;0*CE7EoW_GZvKd?_y4N*4cQYi1f%5-8NFi3?z!YHx5 zRF2o({(ksRtruk3M`gXuPK}HYniso0{v88gJ}O=Lwu8d@^1BRg9xeVbpz(eBPz`A||C0{bt*=O@ZK_sVfnBIJ zu#-9#bx_%ruzPeY$_frz5f~z=zEOVXgWrI`>sAI9sPISR;1$UHAtUoy2{Lt*5K5N) zRk~|kfvqfweREadhU&Z$OjzsvMe#Y@U2wn~fu|+aeUbt-u4Ns66Y{xo#Ag_OVTfLI zG=Kx#g9%L)*x#11hh8a@vd*8Sf};aIE>a5EP(vkhe0!{`{1QgD61|hKswe$&`_!hy zB+(j^imWMmhU7g$Q6o|5Qc-qr&D(+ZDwAA^T6->gDJ3VWE*FXxiE5fJO_MZiY@O?r z4q3>=(#w1w23ANDsP%k6>w9T@eKJ;}!;9bS95j9uSSk@WVDJ7`c{k9slHkb#DcJL0 zflNYHOVQ$`Wl0r^Dz$@ud=i);kq;y;UKEr!hUzigxe2kA^cusYmo!#FNei@+&Kbs}QkBbEEV$q36F^%E2D`{DdN;wX(vXx&H1Yh!N;8a=M zzvx2Xl&Wt+H1|KBKzCdWxKz(8OG>yu<5|V$Fd+QJx&J-R*TIdK0vbs*N>P<^du_&* zKpWL0ox)Nv_*&s?Ntd;4agfGri$}eGz==WC{uO-O5#$evdYYDXHmZjOj z=D!6bLOz4NpKTW(N7WTvmLRr^rF3|mBKdZ?48V-j;*jYpn*Z!HN`3}*oaJDAWm`wp zKXIZVcY#MFw`~dW1n_11!p)n3uO(7FF`CPXQWYC%7}E=tDyG19T)0RIV)4hrOuJ zBu>3*MU4~4FZb|3&8T``)hr4S@`vC7g=(7?uE*ufuEg;CGi11&Q6etnjPgsE+BCOr z(HnJ2XAYWDujp1;GN@7gqVH90DkxZmYtQBimFfC#c0WQVHz=y4!mkJwTKFhs8Wf+y zsDviIWo5ciN@_`)H7xps^sYdEZdmk%s*j=x=P-p*)k-EvFDkVv;wOVT&nT*gAy8)0 z2+l|=`kSO0p{T)}8MElM^rCFl1m$P0Q;euUCuI~hi)_F|T*8=Z@zO}ZbM>gOG*2{;#%T{eEt-)vdqQ^>U(9%%U zPIa)f2K-nOfl`zPGDxPXa!C9UO>ma6C`lr>D$)W|kzRE>x_5Z3a9*i?s$LC+1b$iW z?>eTr#Pm^YE~-RSbt@@|8D-RbMQ&EB5?9qpb^fn9ybu*~AyoIeuVYb1)m0^8+h)5k$QRC?cp}p(=KTX9q=4QBlGF%*@{G z+0EXd{_p!0_bC0|=l13|6!(qwAR;n2TWWLJ(E)ttP`o68IdriNReLgf2(-4z_ zW0MLS(5ASVPYV1wpyvc@5VQ9$31Z&|%&%cWAZ<`d1Da|iKq9NLV}nY<^$0wtQLHUg zr46ellc}!wFG?m8SkSDzwY>I)o-Ef@4YB5Wd`QVLIuVP_GaaqDE>eZVs8zSriHLDW z4=eFi=sb+84YB8-W>j*w4KJA#?B!k9h!R-7VgUo4wPCwsn5V z95SZ593+k8gv&W?SamtS{e3}6u|nY6J2AhVWq=i9qp{WH*odPnD#`Q~C}M7lCDa1U z<2B0GABw5%?zgxkT8Ywyu1CVM6Lw1=MAin@X3nCLdy(ej4-eJxEIqa)hTT|F@&9*Hsz);n6oRVDArWRPzqVCYlhDFnl3Hb#?)R*Qis(Ee! zC0?3GSxdV5kjj3>2~OWKI5)suCZvv^u`CZQ17TuvbAyKk9Gm_~RsEu`tjt(FIm5v% znlap;?kcGbXO#R<0%@IT7u2lbum^WTXX@qMmEBO7bN!^r9_0Ago#G49QGT*8civM{ zMhyzzUD%`$>-iis(+gFcp(lFYlE7u+Fpt1mc~j!;hUvcO2Qc?r`A7s3X-` zv%gNJvcH}wX-bc3?A4F|4*SU-FY4~e+8j~zc$kT_D{j$jV@-DI(6x zJLOEtI^t5ru^X7k<07TN<&%>=+#B93>Fi5hq3r6n7JP)_f@-*zC@cDP=P+`F@>pya zvJpn79SGrZ!B#s4R8&wlvMX2asI~omaM^vVW+RZP@Md# zy5)wd>vgQF*Rvy`DmBnG`w3f~7iYTHd{yG3c*#KbuCGfvz;2E|vaJ|(P4j1+zAbr( zPQ!1+5Xx4^GL|maHS^IE0J2r@U5xf5n+{P+wkZfvi*a|WYbA#WEk!Sln*@DSWibJn zHn>G2$$}D$J6F8r)*3c^1QzrjubSnMBW9@^X;Jv1~8E`FM%7!ltw82GG)B5 z<0%tm{tko^bDoLNasRp=mOxwWYNWtkc?%1iyI4B6yWc$nTbV_F`pYR%|6fbKB1$-i zuh07bwG_{Br>Rz{S3?Hq={qHvJ_?M^IPC!c`JK_+Xr2MKu=Z(*ARwJcORTc&GH?s>$7o{tiat~W1)A)MlQ;O)L9GbmppRuz z)Fjh)9+PfYO}ea-rIE4egW9W-?EL?t69z=aE(z^2x``-d#rxQSMI+&Uc)kiw64j%u3;HN z+aMH?yeOQWSHr1gP1#d6L$DfovH8Lp23Yo!ZZ+AZ0Lv2E0|7EPT|Sy-;ubM3=uwk} z*0QAPVGWa^7gPbd++P59Ty0AiLZ|BZ@VTQXd~vXYRUK3DX{JJEXTd=>P-u?T%;$l6-0k=ye6~M zw+x`i@f)q|A4APZ;4$HrNx@#;eI9O^L#~0rc>Vr{_UuXmL67gqchrAhS6?j2#sSc8}tY~n$T*}A8IsVVdWMaZqdx_5&r@87QRz)mSa&B zUIUbQQicgT6mNMs#QcghSShW)qci#DC-;GQk7X!6u-D1fhwu2d$_!9yb$9mSPw(|4 z)o-(YZ7nUxwqM!&ww6}J?+)AD*3zB~`;&d$*3y;q|C6;$wj`5h?y>x2O9t6>j~z_5 z3?YpPtDXWgnsCAfr&uz{03YV!|BbKAeonCrB`qqmzNwZrWSh)Klq@v zxNsPJB34}3U;KRr&cGKJ?h=0+!>Oj?!f^3-0GvH3E{uoW%i_Wo;_r*Vu()s!tRfT_ zI>q0gL0-5DQ2Z@~Q9*IxK>@=>D=yrt8PM+XT;vDiC{v^)jt>sV6$RhZ=jz5ay0PFda zI5e<61v7ES>KZ&{G7XBorl#D-#8OB!rB*qL+@+Z-+ZR1a($Zh9O zJgjFIuB!R~``}I62v#%AGLP=#FFe@?i|f7E;$BK+m3p7t#k`b;d}M!c`5z?^{`l}emHD5l{7-fM$B+N<=YMMQKY{#D z?Z9g6+LFhT*x2xhz*^sNd_IQFP`~cE#%|5AhBI4uM9W|kjS9bmg52fxdhEF#mY%`y zacto#@g(dI0D(O%={`W&ol~z`H-$g33-(6ev}nNjN5Y?(h4ZWOYb^_TOYuzgl1uTb zkTadh(l3QB^Z}ymY}kZod12((K9)n(NUuOX=N!SN4z&3G*9#-xu5vkuz0u_L8nio? zNGQ-9=r(zMbuey$cS5CMfo%OCOQE-SfrBl-&>5Ig;1Ek@$W+v#QG%_vjgL+A`Z9Xc zV`1)qp_VbkzZm1Qt8GXW+dkY9OV^`zW*>1TlN1NKn~bo`QYc1Y+2^g9;lntv@NbZu z@M5W0(FJ=OxQ*5FMWy`!$EH8KD3Tt)0%l^(RX4AeowTAGCFy?lFF`V>pb7H4JO;N6IhWF&iPrsfm^uv^#DV+&k7{>kFaq>JjwZL!~`ZE9MTBAsdC9>QG(CMc+O> zhD@r(if*6Ipi|1pG7*djXBQBWFmG{tZi;0bSyqd+eaJGAuE3PCN9imVE-5^w%DxzZ z>VqnbEu3oEL`rJ0+S4psX(<9^2AUC)=C*%8vv|7Yd2*;0>pTOJ_jye7gaxA1GQ+Z% z9>dMk!%8#B^R?I`Gr?Ut1fJ3;BqF5{2k$S0`}8bJKSDplYz!JjNn#&WOw8b6)wFsK zziO@H9{q^rWkUbL{6wt%UW+|E-*Suoj^Bvj+_V4+M~&JjcT@y->xGsDiU9)*fnubA zlwBuA(OO3xmbl1r%D)LBDymci)^sr>Y7AiE152FSnf6!y0;;wkC4jTJL;{FUxF( z)ho}14M>#~6O`%eEOp$oi$N9TIf)qieQ3m%{zWkev4aU>V9e{8{yOV!Iq+C-ONm8O+cPro5(*@E|^Do`9^EQyx|-jlEF-;|t^*?QCJl!!ZJnHXvQ4)}}YTPaUCT#4HF_)RnByuACqu|3Sayo4w4^9Y;AMR)oVZjs#t7^D;`kP)^K+goahrsvEM#Hq~k2oGBU z;i?&eIFX=3FvQ%G((PC!#_O+}*(~c6dIu9VM9g**R8hp9by#i_TD1OG*eM;$irq7btDqEXoiI+o@SZV z#AX*Ateb3OQ9rR)G@+Sh2ppj~%RD8X++FktlNs z7%u`|-nkE)vJ}DW0K0$(EmF9?H-){BWYKR}W|L~cQpL)_W-5|kuHs`PI#?PE^$|3>Le* z%6QhfZfX0Ffk3R>CNIzu`y9PECr=_naqPg>U9*Hoi0QUE$@YXr>oV)|o!%U__}XKQ ziCvYr)Kuc_!E#4!(D#-ze&i)EAwS8YN&KZs@(57jCs_{sZfR4EoDG)Sy&Bx_WpM6n zrF247@)@YeZwb&q{M9S_fRxxka|`8(Q}ql;BJ|x_PT&6aQxT^6eyZu4&3AjfI5o_# zbT_RPg3M@$$|kC082_K7VJmjxySY7C=TFu)CT&7wjJD!@%B5lR5RrzgR*ll8q*sUx z)b0jQ5vXw?GEf2Iet~+QB9Vloo2^PW!@yF8?bHzQL5F*EVCiOstOxP*LC`_9&l=Ez ze~`DVcIjP(I6{m;(8B;%J-BqfLY@sVMnbj*C-(eTg~&bsIjZg;j|c^wKkTnNr-tB` zGCRY%+z&_QhPk(dmY!4S7ueAUt@bP>guN10dYFEWUqqj>HeyfJFKtP0<7N&|LaMQw z0m=3B2r!Ij#16zvj;2-XqDWF1+zv0@S`I#%9f>IIPeV=M#lS!(5FZ$zLOIr3NO}wM zsZpitX-CY;5ad%;itR9r7;CJKF0HT74BT$Oooar=VOCQrC7??e5#*ELxC=PVSL;pU> zzF1y%o7<-Jegp3{a^Rg3Tlyva3QH{p-v2`_T7t1vaix=j&EL6ceCc#pu|SAo%?AbZ zdR0wKK-AX|+@i6o#>O7o*pE%CzO=Ct$}Ws9RS$&(hnB%;^Bht;RMytF+LrE6;yL&P zzcvO)QCh=7WohLIe1v1pT#Rrc&EElgG5)f%xD5E+&9v0gUX|!@&dbw58xm)yndTyK zYIiEVM(FqoaCe*Gihj;qIQBz&>1<^wvR9G83bNWiD(&UY=vul$Q7qD6+YF$>V9Qm5 zag6_;!Csc(Y41_`N3fT*frC9uKc?rfJw!mhjatgQAISdf1p#UP&b^>_>5~fm8X>ai zjMCZk25u2^gReu`t&GxfdRRk_o>Sd`naMfY|N54WfE&5$d3V3(mSP>Qa3O9%Y8|-q zI$U5E=x9tD)|2CjC{DiIcp|)>jQPj=m%c|j)RW_hR0F8+VdHu-=s5=Wp%=ztSnuUs z;K0&zG!LsO>Yo3g(xr3~ZWeVfuO9n;Q0ZVjg2AR=o7uDiySU?ql)|bgE5~eOJ$Iks zr8N|~5x?Tx(7^MS%55!}6psCetVXb9#)Uyd&Wj~&+3^=+TSl520$@e<-(u4Xx8>dk z=r=z45_iXvH&s`@!TFYR(|!3&Px2rKnfACH$OUh9T!Zz2}s|H3_yb<8XG4 z1}7#qb9E-EO2&aj;qD~@5(+bnC}#$*Gn@>~3_Lxe%-!k{xY&oSe<(JLJyYxL0Qa4R z;2ruo<|Xp;<1n^wQR#U4F@D1{2Kji6%JzM!Nz}uQnuNTI;LGC1lQlJqN|U^mmJvl> zOEvqrsB{jgU0;pI`ek%c_wHX`mb&(jl};h8>dR8s+yE-_qfUKkXTZ2$e%!~97PymS z$*JXJlq%V913M;ur2*LS$^KW%OTVv29;t7f?C(^KIJ>?)(mkWTJkmV|h?Pr`A@yZ! z4CI{tS&CS-*nkvSN%pG3?luZ5zQNd8U#3T|9i>0iC8xoT`Z?YZ^%%ozK!s2APrqEc zxgPnjzHyHCgaPi$4@)oACEtOL`q}<#8l0Hpy(+aQ`&B1_JR%vK5`2&RPAlOsS>PS347a0dFJniz@OhQ;~0k%XDcOXl+@I1Vk9q zWtmFSKSHLxdIe$-NyUu6SWs$MbEFxBcN0g|yurV0zcGncwIrCSpfGDTIl(10e?9TF+jRkGNHJZrCDFYLtzsrfq)Qj961_^ro` zFp!adp13JeuDm|o-5R2h10aIFS8k$OU|*zMc^%Z#x=0}>B8_FHwH}U*=xx1AE<}nd zgT{499x$$kyh)Qh9my+=ym3$2BwvX8G_fLL3CfGw?mm63mlS%h0!OC*TSxl$vodev z$s_%&KhUTs6gDD-!%>SC5+1u}!ks#+AIXWV7-}Fp*gq|ZJ(q3WMUxRdl)`DaS)_1s zluThAf+2;6sRqF8WhCPkO>CAn2qL)vqJ<}H9_MLHSTZ(>O&@IiiY$tfdAh&=#RFHF zz^=}>MsQd$`}e|4%>K>axvLGcKBLf`SZza^PBBwX?9_G}Y5m2WJkkmi^yexdFx89z zuj2P{*KE%f;>qNKYO1paBgmwGD@slJG1e)S$Tihq-xy$p!7fRI0frbJnHxpcH81b> z=UZ(gC|V|XV6;eZHul0BRoV4%*566%Xk(JER@HA2?amo*T>!sv5^c=yjb$M660Q7+ z;P|X)<0xXg1|~)kO{2BkCLoF!LSV6FUgMUsNDFj#m~6f6L+3|Bla~VRxvCozs4%Rf zr(3`Cme)};tnYhU9Q2=QeV*>Xl8D7Y2Wrs-XK}NvlOoOE0hFl2FN#cepPOavrqE-E zjB)MyW!0pZoKz9FAm%=Etc%Hq;C`ODmpSEfnR_W(hU4T%tgjLB69feR((*?Gtmt?? zj+Xr_poj$a!c_D;=I=ZjUuL&H+Yq#6p>>ltBR{>+x{x+%00Gei8_RA7$PZHmNb$ra)q&${;-DB2; z3hh@8ZS*qh9gZe;adgZK_4YH>Ygl$B*4^(dw|-0f3lVAc{B`Zet)1u$)Gkjp7B*me zAGZ#p7(<>li(6%VoG!%(qB{VD;MLYSdIWBZT}M`1;c7XIAZ+2g#(IYn?#oK};GruAndC7QyWB25Sa6*nm~uXzi#+fW5hX?5#sqUjirkg(d*@`$p@; z@?i7|rt5(X*=)_y!y4rO(oBB1TPld%+-w~kWxg-q3Exku$bGAATdalDr=c-OA3!bT zgY+Aj?dzEJtU?13s4?5WQ$@t }|Y_M~+miEhZA+y-86h)D>$&fIR@MIsxDGuUY7 z>$({|(X(L=Pi!Jqcq?2KR?pvkS3F)}r%anM_2F?-#yvD^=BC1)4cUWE zYvQ7QV6fuCt#F*JxbSn>DJ(8L1Di3$g`7(uU zQIf}>#7V?_{v_(l4dhWr`S>N>Wzm7rpM>Gvs)?sTa`ZH>mMf`e0_TFJ@>+%>;tj&wo zL9}uskBdPY@!1VveD4ZSjxj{d1RmlD zst!Rc7A$zlno8SyK?q_2c~z?rz_uN?)@9vZwl<}i6~g)e2bP}NHHiK8vNfJQSRt@V zIE_v>f(bJA)UZ&4iu|nTNv<>x!3;ln1QPpCjF%#D3-YLnR!bh+-;9~^c?P1ae$cW3 zfw-Yh|3#rr)Wmp4M)%+rVQpt4_SPxuVm+)ltpAc3Z1vZz*Y&WxH$&2>UvOY)7(c8} z-fJY*Hxa1%xrq)`93%C4(>hq$60ni7dA;WNy6KzNVT$q{CXZz&{z)70Y2N$mF0z*m|kO5GP+JvN`PLg9Q4-BJp~??^ljtT!fft&9 zigZ2krS)hyiECnvy&@BYvQytAy&QwzP2j!9s=GraTc!^kfz=c%FlE7?Sy?>@tFy6p zifnE*+iWtYiH!bPoUktXr#F$&|9*AbT|(A1kD8jx}zt1zNYOgtG&Y3l%B^tm9CC#@=g=Eq#-V80|c?8ff>uwa=1kk z1)jt(2^LOb9t~mxYS}hTeo;VTl@UQ$#&aF=O%@npyTxuq+x+ze#Qajrg63)#U?0gz|B42-Y(kxwix6}V zZjqyerff@N+blh-Sji9t`6Y~?6=Y{qQIK^EIFkF}rnXiJy;u(Gr)IVb#0h@lKMtis zGw9?w(y!A=Y;fTgEfU$e7LeM1HI=D-hcnit_ODH4YPW7>gP;7X(@dszt!4;Lr1m|C zP=00w;KjO=7j7c8&EL686K&H7?aWDgQb?G%Lo+rp+1Ay+1A2ple*kKigK@wZl45H@ zF^0^qRr$}gp!pa<1ayaH?wC|ts-njbE`JyUb9sauo={uL;+~xr%6hlA&DG-?+|u7$ zsBL+hRB{jKV1pm@+hF8G)-+>joowy>*Pu75=Hgax7it%p3rLS_>8z-OEuBqEv(@lQ z;RPcF&a+u(+jkW6l*6-5>0r{+7(uuP5JXh!5De-MG*e&n(c`+0q&Js|*@?68BxX@PR|cG;6F=p5pf{v18PQzkh$HJ2NZRr}daD8vGM`39s`Q>Ux?ZUcCJ zENEo5Esl`=&E*D#2DdWd{^z1%zVMKS11(!P% zxCL2m-%#5gZyoqU!)zA%D|V&mz<)+9h|;{_ws&cz7SQ6URU#HCZV_=?sfCPN9ju7) z2s79#BW(}q=^A2jyJ`?Dm)u{#ElARuzdJj#vDKF*k?t*IFm>UKJ;5Zrn$|)b2XdE; zu}xRVm=-dcMq)S-O+8!4XaaaKfb+sqRCzD&Ud*xCl$9)cmMw!U1KYq4d$AvLZD)ub zxawo+MOD>OkdA*wl#y?P-|Kp|g)x>+=;4@uf$bUcY71j1y{UomP*S#YA>!HM`L@Pf zl3VXNv;2^=c_2-zgBmoWxu2O}I|0Ayh#d&-Z^UeqY%S?;xLL%&k1be}$+iRj7(uP$ zK2vO+X!VvTj>PZ{FiNJ_M(SA@BH?{+kq~Kadf*{S%ctx_Nzr5y+fqhDYtCF34J}&A zXy`u8wpJm%TgqtYf#F0n#KYAI51)=uTK?x?SUeL-%S;f2mzHTQWoda~mTj&=Rs&mo zINVhAEe7#;IK29>4ZgLvv^0i;=hH$DT*6%2g9_Q#(ijhlddwT(L}^*x5|tJZ=3fK^ zJJ)z;w0q|Q+d+kXgqUz@kxvR{m(CM^1ejWO)6m9`#q9cSPv*`mNdfm$@Clu?y9;$t>tP=c>apk?x@YSSqf>^+L(iV^}y~hW{XrvN^4^d z4$|O64hFVHIS3Mo?j1`nyLa4z4BUE~ZEmo6Vz9M4PA9Xk{<_$S#cj9c(rK*?aS%BL zwP-ZqNfgZ-`2|;GiETcik0MYe#+A!;Eqs>1VLT)873@OBt_kdHi7k>1&2L;oPe9a& zwS9Lb(XB=*yJZ1)4;;;?Y!-~0UgwwWw zp2Y}a?t0B>8_GrM>awlVV;Q=pJk_n(rm}3|mhx6El%IWF*8A6nS-`^KC}qEl+(elxA!Wb#kg7YYDF>3{k*M(9zg_EjTdac((M>Q z1l4Cf)*T^$6>!)VRvybe?6A#8p$E#L?RnAmDo5i#V2?V?RjTi>GW_?bg4q9#*cRPq zWK=9Wiv34n)$1bWDC~0%wIKUUI%exk=oR!5Ey#b1o3-_yD=}jIryP>HAb+HdGH6z!p{3$ZddsMSc6*4vRhnk$U6OYk@^(gY|gHqw#AT` zHZ1Usx0+Tdgj8Q0#HQD{987*|&BmUwbs|$@nC*y8Ce*nr-xI9FYNo?=V<}JKS_G}zus&e&q^+&c}3EhKHMFD!|Q`<^<2sew2 zT=AK0oAGnU=?8ukYQW05n2=>DeN<8d?ZO<-zytBSooyZKdMliC^+^}5TZvGRt_MhH!dD}H%t7#nll z_9)4UWuILK1^OdE*@^A0`Uqv)p2};^I2xTD5>oUxl|sF_wD0|;aR2>- z4StYtHYO|vaaVS3r|b{EaiQ#)miB>kAqEhfXPa_XZz!E!F=u{*WMny`Vb8#guHx=P z8|G`sGWGsV3kDJKUE?R)F}fc&ixsdJaEllb?2lD{o)*6mgWhK@?M$MlakB?u%rCY_ z^~4Pkt6rk16HA=O2Y%G4De#>$qP3z zb~b+poJEh4&#q>inM9sfMS2#q5+XgLiWFDfK9G>Jsz`5PI3bc-6$#+~pCUahFGw6S z;LO%w98;RHw$!lirw_zKgQxEnp>vlDrM76m?(mv+_+{T37+j=nFm4f9SR-DhtqxX8 z+h#@te-7vgw1w=z)?Izr#9H=rJu|VbJT=nZfwsd0L~jBd*G_(}(14)tZXcB~hP4x& zJ_}g&a3%NnAo~o34#1Q}|0woiN8n~{EIuHf-43=lGQsK*VqZz8mcxpuYoA8|Yg=9W zW<8d$7sog|SNF*$egUOK1sxBa|H%Zhw|O zi<^bA<0I@F2w?pgVehHO(tPrU4vSwv4hU7E?0E(Ry-${y$S$_Hu$QzLgeQ@0E1A-F z)g*EwUKW7soVTt3d>b#9CZBI$U#5_%39Eb zB?Wk~zUzgXSW@)zZc4n}O7?>!d_{jxf?VHy>AfdsvT1W1f$WKgFIQu&6YZ&NL%yRa zJ%vRO%e^NO&~h)}iv>W;OD+K77R?Usx^3;hEA&TfaFLV0;TDmTKPJfPri0b0TlGW~ z3LXSks=dFSxX8EYcJ_Q4h7m-*z1Pm(k4QEl?B=$%Rro);x3@2(vE^WEb+8ZOuP1b{ zpVD(M1jZ4QR1=@6xI5Z^CbS=>EX2-8WR=tHnKT2xkqd~QjZgB!tseLqUF_}j@WO^O z&0z0!vFDV7ZN%1gwYQ|p%n<5yvyay!$dqBE65H3!-igTO z4557wdoMi#tbsOSFXxYIO^=!)^tAUiK{zlD5Z*CENb6;f(<6vru=KLeVEybet3@0< z*xNpmfE&coZJiDmcH7^izhL45;cg%MCLaJ~%7Y{8=~)<};h0G@h_QKUU;7+dGYQ48 zu(EYaULp;_&BEt3lUUY4Fue}J!0NaOtBNX;H47?IrzBaC+H;n=(w>qeDiX8q995Og zDRBgCdCp#goozo_{)=_)Z}%rdlVrsogwaKz>YOAC6_EL_h3ZZ=2)I-gun2PyrR&io zd7s0yf%fXb#0HYUW+a+|z9;Qr0RsG(gI}S&K`=R(B=Q?*4O7thLm#0S)zP^#fnp*6+^ag$- zf}``!yiT-ITN8wSrS=39gmuk)ThL%LghZ=7N{?{xy))fuGZWY?Hv4Ejs1WA(?woG4 zoe|y#q-IC!jBjj$G0o;*i7mFnFsrXATF##X4Ke|vM(q5Oyl6Vch(W+i=NVtju zrnIgZ)`Et^xdh<@!EitwLhCRk5DK{6z6Sm@JF;gKm4654i+#@slK) z12(8eIS&+g_4Yq%f6Rw`nQW}y76V+!al7I}en~b~@7)?4uiospi}rZ&HTH|D;vmq! zD&MTeDf<-~oq~c`jKP}Y77+u{DRLaEgVn~d?adIDybgJwN8rEW^uz0R2hGF?e4^=%TF-5&#-$p)5Vc{3;Q|T2WHp+;%RP0YuvWW=#3-+u=6WW;%{M4TSq*akFq=vsCubhxTg*1ikytnfZVh+hEKd z*|XQj_Swo%mh(OAaftI-gE(JZiOo)xbN8K}*gsX2X(}u~pEU);iaI+yRn}R+5p~uJ zS22J0@~+JlyNzrE6Z82yOO;20p04WnPEihV-V>2WzE#ydkm?TfbxbD8DJk8}a~snU z*o`tUd2a9c*Yg6?^5$tUZR*Zx9rK0mdAP(&0!w-1xp4XCM`Q@4bZb4R@ zf6YFdRBb1_uuARF8?Gb#hKbJ%6#kq(?9eF+x?XbW-zasC(UQ zdn1MJ!c;{rIoq+Czrl>&iQkBkju7KT+$_WZgs9(PfL0!%|4(as(YMSHLhjf%=@ATa zhndM;QMPXX!@i9Eh;WOWn7~#+dv-N#H6v?5AYR*6w@0;&JO%vqW5h11@;lng;znLNC>A;-W2aAOmF5yOcddq)RZ`2qfZ z;T`Nnl1HLJlJ8VWzQ!CxD16aD#`#YT97zhP+|d{dkE@!KjxrXGG;&xJ65P=k3mXh@ zpEYrWDWpk95e(YGSmt&Q9J6 zbedL@JEi=TT2zHoiOko0vIu61FW(X%@9hrIrL-)2vbp%#N zo&>}#nu6?J90bxE9a-Ss)46_}r7ogRbdls7^AND|c{A zRY+ndV=$lB!?8a*IG*E*rX4YFv38*3qITO@ALZ0XVK6RFoMRj=ut z-0&fUzrv!uxR!5m~g#Vr~M_u_7j_X&NTQ}qP8p{KuQ z7AsdsxQPK{UWVgc_(dj6 z)*$H(uec{O9SszE%ZRI;fXcjSf?I7%oroB=s-I&uC27b&yb?tIjfiRXgeMW3KQsGH zdu_L)zhkmO12D1}%C;ZiXhrMdW>Fjh(&U0oc?4MQOr$N$5K0CDDIJ0#*KezuVS!oE zL#OlWm}&&*m(a_y~h@*xNDNK_IKOO^%gddnDyD&f! zT^N=D5f;gA{?21iKYsr(Bbz`jK0;d$mf~Lr9$Nza$UCDP!`Y`(9saEIWAdNl$Ucxs zKWOt@-2HSKOJC|3!LbIgAC@}Cmp#>C2hJ2x@^YH7B-B+eO$Jo{bs5WA?x@SLvf1#N zjvDN>vVWeP>F_6K(~PwuQo|RuVi$-|_Ms}MqSXp)1X(L^i)J#9SsF>R+*8f+XByvB zf173JJJQ+A+fXlpI%`O>UIccQ%tgHb1ku}KF8_pYYk5EQp#n#DHhfp4KZBtR! zQbyuc8|XOkFpMw_Lyv+fhI^++b(VoS9P7nHLK(uQFK`TSKQfVr`TrY|y`=4}AQ9(Z!udxs+v52x+!Llcd=zpRgwV%eKh+4&cb0w7y=jgq3OUo+ z7;uC2aO|fUj_=5aosDrdN&^#d^{ndJ^EKv&lakIA#Z@r&0}7GTvmG{4JzZv5m2??S zZRWtqjK}9VQd!pD5L1!q2to8g_0v&I0oH$tsX`f38|FG*rhPaIFOf9WTO@fYXr1Rn zK^qS$!6m9RAImOENlv;Pyg)&V^C64VdEg9XEfzaM=|T)FqGw{dj2=Mxe-^Z5GJ2j? zO}#5!j$WXkRaMAIkU}3ht5v06Nq0l_TCR|H(~Uv1p$w#~O8r2-gqt4tk7;PrnI|p>LQ~ju#1eI|2+=HXzO(doEBY-fzz}L3LHTAPl2;b1djWs z)sDJ^4(2?(M9yB5$Z^BqqM<_bDnQ$Bf+l*<$O=o>bCu<+ufE(zZI@(<2xLA)cS4&@mIsMYu&%6`sU6>XcUgkNeMSME(b{#nbq6 zE`@Gf@<|t&#`)VF$v))IF0%grj^RWGf7wN5Fu;opHs>!Fjm+P9WIHdyU4dKMo^xc7 z23=(WFSI)pxOKa$F@fJ#&E3AM%+3c-L)W=>ha-#q@PcDP*;B9_gY@fa%p*e<$@`EA?L8m8IW< zA;Za$O?w-#$EQD0)CZo)!8`4-r*^gailmYzxqAVC& z0?}D_2zuh!ZnBoP?k2~(5M2?>`T}_UnjM@eo+ddQwMlOfMc-otsMiJe=%xjn8xWc( zWJEV(bOjrLA-cXHh24xX6|RAazN2e5G>`=uD+nrYYDOIeHMgm1GN8wU>7!#1Oeg0+ zmvI1_R*W_FsXWE_6%fp$$s46WEEz(5ojLO^Fi74u0c+-z2rq&7d{9Yjn9`+mS!I$v6U> z`^C|H${|;4uD83@nK0gbQ7SjdF)=_MH5ow22<|5Jn!93FL!#7{vsr}=5M{bn) z8jL4`j;P~>r57A4D?ad$xtrx|TM@&CUv%`Rcevc1U>2i?Ur~z|Q0}^y90`P0?Sa4z z3n-IS<6{DHNbvr9@Ow?PP6fCRnc<4Ei>Ybiz>~o1rVNj$(#*yj z#8hvFl;-@-ngQ-LpE=t2koBrGYcQ;kW}Yg|j2<$By>Jx!?q1$a`~phkQILjDGY@m# zcToX9ieR9H;&q@?$?e| z@>LIGXJFM3#BxW+ZyYnp?HCfpQh=CHnec+=6^<`3^K~+*4}W zfOWp-ILFc+xm<-kcpWC6d3_;G+xJB9B280zO1?Zzl?I!mo%LD2-Sb*e%tMYaaEnF- zoBiS$ajWrV^~ooulABP`@>%Mt&;b#X-!r%crk;^ssdx386>i|NMiR0U*|Il zY2VuzM|G;ov-&B$<#?uqIztHQ+uIl~(FVA7HJzIkGOD*RKVv*_tSHf$p}-dau}(SX zQs4*F5+P^Th0KdB@3i|U1t*lkl4`p@tK~fBLyLQR_hFjKAA77qA6i1dhdX;qA38Z@ z-0u=wZ#h=ITi5wxb#ff+$NhHc3TRUlUN!r;k+dfj&kB@2U#=H*>lxllpz6f}t2rC|J3VRPZiWP~E!3Mn-fi>dL3J zaoR`^kcMw!bnPR1n1Jq(CSh^T7VO7ZXD)dVgwXfkn^mPp^^sG#tRBuD9HAl0jCbxO z)A|_0b-Mv!ae{L_S=`4Mtqu|;^s&U5TYvNMe}Tm=>Fb!=79b)Qt3iLQH}7iRa#jRPX#$uFu=Z)rx2VEx)VTXLhe zgu$?icT3vYercV?;%IaS@=U$>%-E*ZiwAF3VFMnIX~CKF)(K+ zYt!A?fSa=ki|G!AeW(}oVV{&U?AIEqFzoZ1VF^g5%^7jR{*w{mbs1W;ugur#eHqyH zENj#kB0B6Pi0B4=4fZ9GeYL?T0R;0t`aU833$uT}J&46;ISage*w6N7IS&P!-?JmG zQJURGy+#S{jQ~+s$FcOl%L(KUxT+xR^&~PgMc$q?77lj6?MY-Nh7>1r9^y~pDu${2 zNqmVijX#MaKGXS=IP5foKjlU8GbS_nqu9@y#h=7F&20YkE6my9!lY{4PH_9cYv?D8 zJ{jcPRgDz)vd9O&}jF~TkIPIRqL!cSfe zvJ`rICoW0qUguI8yZNAVI6Z`E^4WdJ0o0<=RDaCq(pWyqJApvtg=3|K&N1{1ZWdo< z^_}F*Bq#bZ-+bo{Jr-ZT4}X#)@VSxaJ|)bXMc+O>hJ4+Z72Q6aLBG|}=C=cU*;n5G zqY+WBL(&lupQL_M;LKsy7C3zg7*1?8%pMOt+$gn0>@xh>SKfrAk*g_(ZhCTXosio( z!3jUj7Lkd)!GAwc7@-Mn){GF5$+k~&w$)?7Rm_c;-?z>L+8$#Gc>rPUWM?Nmf{^`{ zS!XJ-dy}29G}8>Lc#5;V9*f($wx(C0)T>^m{FWgE5z+iDKDTf?9&$dR(5VPQx_i_# zaQ7VCEHwRuxZoGC7dsPKpXts3GBuNpn(pkO$2SO5TM9Gj9|*H}hO@UqOE6c>fG*rD z3|NxMR?Kq7=@AUv>qzcf{{i)`Z{J~@WxsEj*H;Wp*^>fqybw(-lCT7d0T*%d-QorvjCdNNoLAj{Lhc51?&v&k*zF8>Z#Wmu! zaEq|IZx(YefPmJ)aupE+^|!Hf42pPStzGDZ-%yXkM8zkdBHU7Ovo72+Nn942ve>yy zk7_Wi`WslSvt-z}K;(>Jf4<1shtM$yLVR9?46FW1)`*IU84RmRpwpYLV0taNPn=yi zo-Q?tfh-v#{ab`VM8$J2cOIr2aI^S4tYihG<2KwZPR?z}VjWjHXBZIlPKz~R#V@^5 zo@MqO&TwWsy*zSr=S5LSI`!wgbkd2-uSPxY?CPVuDiLG94~<~wxB0e0m~Bm%iCeI+ z?W(cp3;m*4=^E!L`T^ESgyt38B0}?nEP47(2P-~p{K*XVmvzn+db)-%?k$DHBFP_B z^X(@u=F&=Th6$*!M6->~Cw)l0e)1YH4WXYI0zVoSb>Ok)q(wOfV=9DiR}Mbc44!{1 zhP6Ry(dJ2K2_4c8<-gbo7>HUBbh~ZNCuu%&(}v>4RDO`=Da zO(IA7$;AOT=inKli3$31{p9LLp2OJ@z7_5#7YEK`II%czte;%H0(f!DkU3jre;)wx1ZE^|=1?TSD@(}A+Vr<`9YUvtO~?EHnfQ3&#@GDw&< zXDI!(g&zWPY13w`k(@T;7R{s{9j~!$n}aFYQpMWYQpJ&{o!vtcn@pb7J~p;pY;3Ej zgp|0{xWt6mnAjB7`Rd7f>_SBl(T&%IDs9;G>*bB}lhmVCwp_#c;EZ!}P-2JjTs9xv z6ec$b7Qg8oq^Z)Ga|vew{_{H{YpFl9=i##NEoYi37nu2Ns?BP@?M!QGCd9;9ZDx+% zv}{w+riGiN+zs|wKxsC`jT!Z8bH)NBlpE!yL^4UA>%<{uE1Aca8YOojO&Rups zPp@Z#y1K&nEo#wBp@wQE{G>wcqq$KP1H~(4zeyRZ3=m_72+cIfsuGxn3`Fspl(ots zv^PT#qr#dWIXBV<1CR>hmVt{OIg@A$+$^eXg8_29q(czpdEdt{jO<`U@Bu)UzVVTI ze)x?TciKMO+>K`8W-;yr4&qxlP1!Uv*~L#^=9bel-3(TYswRHstX`GO7$8Pf$duj9 zFti`PTvCq31TzG2=m2}8yxXU-tNV(ASif&zV6h#mtxZ^+xLJ&Y0FVXTX&b7?;GGpx zVfKHNXukYSuQvzCvB(>oyRN4?H9&q6t6XOO!QQ550E_-fQth|=5~;q zFkar({{gCm|3F#2eFw7Xy<8{RJ2#wN82JM>;2RD^qKQ@fsDWtB6A;9jCnhe75^g~x zPTX{UK(jbePu^-y7=oKc&}R)~Lw|Bk(jyoq`V-8s9{t(bLXTze+y-x+3-%)T)?b{n zm9^|(ZI?Q{SdA?s29U)ArPF@B?VO@K#X%>~9T?`atKrnY%q`BtcDsf=qX?dgng+HB}ycOm7e>pctn$KBO zDj8474=zXDb-HMiK`0Ew45lG!fp=<3xToH8zC&mtdMT$c6BSoH?SPv_)FcjKzbdY# zdIYg_(TBLY&@7DLS-MChiG$p=d|ZzxdNf1WJgvH)S}mrfoenW|cS2Fr1AoiLZ16bzO-lUh&f`K7YF!D29>EMi4X=e1(1>$`4{ zIfLb9qy~LYWuiheCBntX3ebgr`=NnVR<9BSFfrg7t12i4Tp*C9V9&uu1^e4iMVKmh zOH+_{y}@35dMnChBQLAUA61oa+Q9V%dB2?U0jiwuO6A>_hVZMFa7EWMLU zJw;*8zQXcQJqbeTC`iLkQ5=R3|M;h%X>DCr@-DFEXD76f@2&d&!Vo!io}TOqRLECD zj2YBV567yfx|WgKL*$VOjoc6q7}r2Ofp>O@HgksanL;&j0uifLQAn}*?Lnc&L#09u zhVmiTDb~89E1jJg0^L^op~&E(|4tc-Dia|5y9ka-b3Lul0#4Oa!$buC*4fpWK7^Zj z1p9M9&o;??_DY&-0SoQo^4AkEbc+*JJLyayM&`IhQwyF%xJ?z|#uo2f+K6lhgYcte z8?gGq=WB<`D>NSN=E_mXez+xzA4}SU;Y7T#p|Xnv`1^H5krm4|m!+SW%g6_+kKP;V z1|L<1qk4L@b9u@{xDY?g}#l=ZJ21hi{A-XygiH+jBvf7 z$1=F$>3``8clan5+?sG3GcyQs7r$FS(w3zRWB(iNO4cJ8n}yPKhO^5#u7i361G6J$%*3Kc?L5~Qa5?E^ zY+DgHm1epI(9dx5!R?7;s{o2xhtO@#U#W;`Ss<{Rk=rq zqdsUEtcDpq5hpLLaII131x!f9$s@%;|3lm?Lgc~-mi)M@i9beAe;)hs$6c-Hb&Mf? z9vd(MSGh*%S%|s*wpFed>0OKfDs*SA)vhNAV5L0)^e`W}q#!!oS|gF|xL`g2$R<cC+fN1oZL} z*GmLM>TYql^jHSpMwzKD>Jm1FW=-~CDU7Jzh3^pgnDzo^tm~J~j+BKd)#j>RiTp59 zj;O9-I9`;L%k1DDSS-DOSiRgIG!s zyKu@C;~u!nwcm#%jFM8tVlYuE>yMJ-yr5BXoM+Bj&g#6p+qV}cL8Czs{+Zhd&Q{*0 z!oJw&T1y@V)-bcvcE~cbR0qst*)O?*S>OQP(p+9oJWQ7Hz`tIGOYbkd0o^AVjnoo;`J4Ntjl0WX zm%pM!a42=~6ox@XNK_pyL*noM9uj{Zfsp7mT6UP-N6U%+W5-VMz`mz{my za)&r{EJ{DQ=-NmIjFBf&bO?qFJM?e8V!z}X9~qmJ5F3{gmlvBCpPCpSo06NGoLi8R zoH8yiDL*-(z@0O4!_-PqiE)WJ1>=(965HnICdQ@a<;3NVOU=s%jHI}@wki26aK(b! z?tsG!Rx9B-DM>jA$#De*ZDAQaDK52bK~7S9PHJ31Y(Yvc3;crBai5P~_+wR{1le6d_!DB#ax;FHfzkzZ(2xM z*w?NGk@3KETy8?!gapupOPE*CHlZLVxov8GZc1J(%f7TA$UXLzEu$($B!PYN6JwL& za>ga*rKG0Bj!TS7PDpMW3pP(pa$mi$;kXhJTacQPoL4X|F*zTU&dbk9NJvbHZCj97 zkONAwtJ}B2#n)V$`*L3YDvOIMsF-P67X9&-6c+QNt6}T7q~y#laI&Pq!iBA(8Wg1D z#TT?q%1bCHNKQG-n>)#S`uVQ)`@^?Zc;mUdpS{mM{ftT< zcwevnqtYsL%xs$)x?-a_oh?SRC)PzOK?UR1`bXwynQ>qzz6` z(Iai*j*(<;yt{{Zdmwow-rc%tA}q?TO(5OlE+&b0K1^ySxKpd9!T4!ychcp~i#^3n zBgy&%_x;&Bpf8Y{a~rnt=H^hG+?+evzZ5n%XA}K8J?CSX4xtCzo}wKGwu9j){l&KL z9;3h5HnijP7uzNAH2uYmm(8vsy-IPyLq)Knft>B&j<-E6tgSkA z;`Aw5uFW~L-#b0$TRMY77Q#Q}=5i2)aV^~|rR(M8=p$4SzGjk;tYr7s;_q0HjK>SA z*3y@wo%q+}Xnz;T5jU!wJwOhz;VAZ`G-=t|eL`$CN^6mZ6sw#?NNyXq)b4*V#mP1x zLU(-;y2>K>?sk`m1ImfeRaXStGk?(-kGBI!#^_6uElV=6z56F|W;sc+^(1L){U7WA z#$*!$HKWM2H)2{w!7c_eX|9+=Hngf&pRDZYUOXVbOf>e3+J5cmlxgE9(Z8Ir7S|-_ zv~elsOj}x-+2u53x(v2VyTzL6$TDXctR|zy=}NUY(wvS=Q>rDCT-&xGj*L%nUzheF z!i4Vli7P}U;cDJ9sycb4b-kv(&kXL7(b91Q!e*qJ0{~Z|pL~f0rotzr&RX5uMd<`a zW#fQ?H1|BxZ6u5X@<$Oi4#?_M|4uT;?(P=_(8Rc0Y)IA}n^}vDz5HGxsn*FoLwt7> zIiKNfBAysUc64%YQd4Dv1x29hJQ}e;{9@?~o!vRocUT^_Ed21O3@im z#M%!kEInpgsT#bIHEsKJNDgDNTh+PjV>6s;`5}6340s z+>2sWHk4a{J)=l`Klf&7Fs8~3IRKR)LnifiUl7`lgO5|?CiD3Y)#N5K0!rcl_m!xa z378&#N+V&_L_k4EIAiw8lH!?@P($Xpv0!afQ~XEcb2EqR^V>bl<0@r3n>T1(oe+sE~dx z_bXC=%#O|sA*(0qSeh1SF4-P0n4)|K&Ym$-Wr={3Qf zf3Hpw$WiD==loDpfG$(cEGqS8XvL=H{KQLk(wtTe!qsK_hq?D)8f3C4clK#_=`cEJF&qVrR9Q~&m{iiYg zryKpJGyF3>Cwx-@CATVvlFDUdRxuK}fDY5GxeRv|!&{|v0vUJK`Y`Ui9irUdqyI4O ztZil7S!FWr4Z_@Wtl$%`6^f zZRt5GG)pnh0dabZ0WN`WtmAdY!EZ5$@a@_H_kQV9Oo`?8f1?tGQgu?aY3Z1_NFS3n zFKz0}TjU;GMfx4#^TdCKmeAfyzlPkuF%{knjaxwzf6Yv!7Kz9 z(!KYrCI!7`H5Hp=^1BY2VNxs!-)bxEUnc23Wf$B$fB^Y2=B@Q^r}Qu` zW=jz1ndI08_YyS%ZKRM8as%J@zYwBszI&xOEt71^cdwCVU~U@4sPA+!>M=Bd~e{1ok4YinvC5xrxrLSWJ{=bbVc1OshEx0Qu>96^6#0r*h4(WNd=OS9sjrKor}+7Feh_JAcB`gUF2XWf?sF~iB1?M7fY zwrtne$=7}3dMtxADtvcq}X=>w6l*F@2B2mxlY}1t;JA5P=!nl%f9(#AWW~CHa%L zJ4i;px25>AEY=U09TV$2Fz*K?z2ORR_u86bLl>KXMH%}Tul{;2dYI*Lv9^n@Qg@2` z6$r^=-g#ZaV_u-eR;_PPF5!M3n3ErAY0f%$vJZrXJjls!aFWUQcpT)O2RF46`@qgL z>NCAu$ns3so-R@}-}8v?&9m+~(W2AEcj%Y3N$FMO^CJ$RKwn(H{I&OX?cF)N zp#m4Dft&ljdwjIG9CW0ITokyr3LIObAIY^BA8>J>1bLXk_fSby3ezJlx(feUrZ7w5 z`c>%bd(AyckeJi7 z6!D7k;X}7rM1C9%mnc=_OZv<`N07e9%vg`9dzZ(OrQdNe>oI)~=I#(^qCrs0&|j_q z%&{UF$lx#C{l(_6KKjM3VMv$9bm`DUz)n_oD2Fgi4}o>$yT>e;Cw0w66|S6n?uCn) zl3la;xo5?!qx2}VbI(J{N#W3g<>#Kib+@Z3j>%@NHB!{2hoPK%o?VW`GChO}T$4CH zFX>JUA8fFSTeEqbZlPMM+V+jvylpT3(cMcBk7x6?{V5E`+Pdx8JdOaKZRy9=jA&Ng z*rjhJlVi3M@iK@+xAeaUhUo#11Hb>P`*Z0Q=F9fMUe8wc!BSWj#F#nXYlBM^aeV#% za2E+uvnQ|%z*q~_g6DrV>W8=8(?$Ce+@7hFvdW(3C%8R33!XZn_}~-Vo&zx)vuEcg z$hD8$@dAL;RH-jNPn8kA{T}U^E-sV>nTHuLyUvyc8CumdMi95kf^5NXOpwJ-@QS7I zl}M7HFA11{tt13Uk1vC(q*p16a0^QZG|1X#Wow_nFf3L#*7UqBox{b@D9YoL_i!<^ zq5~u-2%k-ANlw=Ev?SdnPi-B1HeUHkiw{=+Rq_l}!)w~q^LnDv4!(q~P?7hM-L*Zr zkvcP7>v+DAnvF)LV%^Zjs6=tCu!XO&iz{OFfqaeYd0rPJGonD)D5EnjVM)k5njCE4 zap}OO>%qR&5NxHQ%j$;Nc9b5%8;v}R)d(8nej^(fYr^Mk-jDTM6{MM%DpO)1E@4W{ z98DH9_RLnpvP?S45V}vw*Fz|2;yIv$u(heDr?gLt5Cu|@!D5wWIv5L^dHPGwXfZ^P z>8|FU$!Y|R?$`BnXU#aahEn+?{dAyvLA||J@q^L4)wx9Zs#=|kqxrtNPZK`Gg7M?S8j(epm&TA|1O7g#B$4E32d(oIkO!*tq#*?fEJY)YB zf$RM|F`^gT31KTjPl=~KY24E3)LU9}fS-V}$VPZq^J4ZRcV|PR7`CX3U zcNMfYz7YdFM+NCJrmN{LZY?8(e7*vbENW<2Q}(NDgYUQvD$A9T64tOsJTu|IA?LJgc5kUO=ERz`w6R>O7a#S& z{eEx{K$%nzV^}6t_pv-p0?uDS9_Dcg^cuJ&o-wGEdQq+b z0wTf?+}CV`=M8oP92N2A*-bqVY>5VT(kM?qL3oj?llaQ|?g;jE1(wa$Y#xhFRDeuu zl!p@p?`Pu@rM`SmWqL-4(ihZN;f7J;NSFRsNBYeLj#OnF&qCrjg&8;7ZMn*ZJ>mIH z5aY-3UQJUB$EvH^I9^>8{;&0F2pnvEh_Gi+`^n<;!EBfZ^%}?fSfj^zCW=m38V3er z(xl7M0C;82Q%TqFC$jkXnn494mN(#{ZI(sL$2^&6>wzbov}`!ZGqR?5Y@9X^-XZTA zeR3SngNMeE9*KK9#{!{pX%J{J$5=zTgloFu6!=S7#nTk%WZxM<#IYlY<|N>D=F7@pj_fHxaS>3y!4|NVfwJHY!-nXzYq!WwX`%+Xp-*LGqTPx^Dtmd&u*#v=4vLP`#>0m7Tf& z?Xc&HK>C$;Ys zYUIi1J!{29z?)uRrL-r%%bL#z8R^=`goT?s6zuafASa*qR42!t@zf;oFM5i^{E6B) z5u%T9oQs2*!N!Zq^M!pHgwAw&+n2MBe1=yi(dB} z5P?J6H$3gtSZoheAB+%|ruLhjOf{?~nA*$LfQ`VD^Y0Wj0~6KMU`Jh4y0I<#oM*5& zbrMOeTH05dg-I~ErcWZjo%8gTrsFSEF68P4nfT;Me7oBO1glgffBGizEstN2b|VPF z_-vm5?q4h+;P6ZEVZr18tn8aHhibIDE5!kV*vr3|o1RtE zDR{x}ZtqXRRT+v_(1#d`RZyv1L8tytDrhlT9KU%GNj$W(0l68sFIlWRg=f;3DM)xG zUQKu#n-20m`Nq?}hS+|Jc96GPw%FZM_#p2txC)YPO{ou*%8l}X7|TG_D4TFfsd$vP zRHpweWY&8;S>)X3o>pRpEbfSM;trj{XFFdqZ>dB2|G1QmJvdA#(JR}YI;8vq>nHz%c!5@lH+@d1_)Dp zX))e58d#Pk*VXpEDE)#FSeD#c$2&v(2-E6Ji&K&4Y#Gsz zU&kaR;bNL=q5zOAs@k~`N$FFkni`E|XR{VVY&_N1ypcCrPy;hF^wI*yP&vYD+oMQP zcSjtl7Uz9bjmzX)9p~*MJ*Fi9+(?Hzy+n<`M)n)}d#%zKJ%nYAy=^rJ>XCi5D!f;T zn=syo!zHL)*xkfCQ_O=loNfeTi9+0^?>lz4C|?|*wR zENj*_PUQ~1dMfXKN8tEh7D_j{Mfjdt8i}rj6k)3K;8pe>PRg8{)aCJbf(dP~+d zBqx9H)FTtD-odO?Of`_Asem!e=pD%6Qi)Mo+mK|Wdb7x+C)QOJjTUk?)%$pODR0sL zTRu+FJx!bJ9+&HQFVsHWhq-4OPj+j-9_5l9n1nUji>R^wyJR<5rXNmrAGPeq0hyjtbCgx!DsE*44+CH6%^$^|qtGYKRsK8GNtT!v6zoov-{y zDmgFzNA)N(*tp((K*QVxXsmg8`~NlFo{jf36n_A>RmWnHtoV1JtGp0sCZle98WO|9 zb2^Z3yZwEQ+IRQ1q^pAe59r2{W2K8F0gnCWc+2%jk0`3af?+dq-`NHKXZ6%RdbR+^ zczc{(ko135^$9WfDWh1b67MCWVcm0zO0227!NWkjEH^sfm(26emGk_F-IeW%=U0@^ zQ~G#65~T;GqljXO`9W0)Jv#KjbQ0+2-KK_BQl(Q5AtBw{NsYjg@jbnk^p|GnA)G8K zOjaW>X;$gM))?q@t6^COD1KkzJZU>dU>%^Z_7y$`Ius1{=Bbf1BmYpD-H=IG@2uH_ z-o2vqyoxeCU;fN=()bbYXz3aJg^7JeRYHvju=Yp2ZPc(z2kJvTgr<*qOVkLgH*`Y} zcJJffOKMmqh%f^w!zKYh5c5#2&>B;m(KE$hx=MnxE>I5lR@MyjmL%#QB>|}L>{9-Q zdJV|p;ofBtj!m+wOwKsut4v@zfW6M00xGO+LypH^Y#X%}PK4YTwz`EFJDPkw%9|kO zk0(-wccA#yL^2}7YZP10AbA(L3+jJ3;SqI`ck zy?d*O!)DOjH>ZvDz3rgP%T)@QuBpy~TLJ zN~(?b4wRN-nskmZZV4(;Xv%MBUmfo~7%go=ApA7>vr}LwzXKPu(0h2Qw`&ZA4tLez z(((GA#gV%vd3%zdrg~%4q^K7*A4{oFj|o4ljRj(z=>%MYM3m9$ljBu4jgnr)($Ikz zqAVXBJ%K=c=cjvzh|)P!MA!qX@8J@NpaH@Ra&?yXgc_7Z^6Rs`nbKEUcyR_C(NCM& zS_k3E9B()2cP#=im^s(`xEh{alUaKvs2lbGS(bJ4y!WX=nb?UspzL&lN<)ps<&&q_ zE`08@{Q~bHDGigpC z5ZD<)l^+xwTiRC~SB{&RBV$lLBEuz$6On_JIoH zIp1hzEIIM!o&{0?0!I(X?#9Jzm<)jYh?@#SQ!V#$9k2}i4xo`qrT2@^&MdnFs2(!& z{W?gj3CCm2$p=ecVlXyXdS@md zEKzVe9~*($EuSZ+5i$ni)^r`n(Rdb@BX$=0!sKW$i_0-glTg29*^O=odA zDEMEKqfS1^F-n$WIA*})7&41b36I<89V3dfWI?85I3|cg76jlc6C_^OAYcI28cycV z-;R8+bZcw`-yKZ6+oeNPgm4={eK*#NN)&VYhHmw)h?1T|A9;g!5|^+p!*jFvYlTL z7Yn_wO4Vm0E3jPjMUmGlf+qQUy))HVj6-vb5SAve&)ZcE%X@75VIC+&58;{p-UrkO z&o9YHLat=sIb*TG`j7dB-S+l83(NEG?5es+h^B~;EYN;*HA%*ZS@ zNEm}Kb-u>1Om%>5I<&N1MCyNF4E_?pY;tpGX)85#cJ6PDv$(BPXATY@I@SRebzQ|% z)CkPq22HCOP0qzlHb_YrD=Zl6bf}cj*m;wV#n`axAP zbO?XZ)Jb6kh6xcE$~nqIl750$LvQp0=M+929%PGqiB%x`6`qvKngk%;H|+gLBzrHiGh(LD2~w^24%X zGg&mZuoaboP3>UW`9wA@K^8nVyZEZKa4w2W=CCVq33Kj+b4l;H#bed5ELN{x?cYt> zriUd(|U(#ZL41n!jxx_tiDaiyspo0{--=2gM6-acu|9 zE$oHkGEtMXZXpv5L5)90ae7ZBrra#!CMxy9!NsfZdX z+#O}>p=F|{*O1TnVRMkh=ZiY}My@S>KoE=P@f|bIV;aoEpPfertS|mRdKQ0S9?r(# zXK*n^hyp-92rrMp2H_uSF=8lSxFg$yoND-46Y}|l;@WDK)bkoD3e}PodK#*Z-br;7 zQAPeDTrO?uYq+tvn;=EaN9JLw-JVC-kp4Epv{*hE7Q5PSQ4ZsRJim6wSrq$50{O8v52TjGPV~_7njZFZNOqmS=9zCn9oOhuk0vp zDT=%1^G0AhhGUJuiut?|0C+aqi@>e&Mj*Wfe?a=!p5i%?hI`!$i+4(Au@%^G?{!q7 z=;7;ERNO<9K13hMwc6k)pW+hcfgjE%Y5R-Y#9&ytuexr3aeL_}j6r*oQ2uEl&1G??ilJRO# z-XH&NLmw$e2cBhYl@ywYGwNB5b;alTOIC@}5=;~{NG6423)@MnaWV6tB@4(4!4kU~ zLFrHI)FMOyW#ahlp+q$XQzzrFrLFXo7DEIU3rb5Cs1Y>!nDz8wt$==Qk#AlrnJ&Jw zK<>R#o~l;h>;m2sc;NMtOXBqfa_1F;@y_dlvd-(361W!a{K9sz5g5{jAkw$9Zn*yFq%r;vyvrK8O)GT-u#9)TUP2lMhtJ9E`X2^y|IIN_HeZ{KEq3BEB zuzTSAwUTB8j@l${>Ao-#<*^53!x`aa?XnlYJ1*qCfy6&b8i*hdT#yR;4iC<0CpB7# z!W-Hk`NeOx0wEg^0BGMP{l2m);V*tWW&Iorc_(Mp+aEn_ELO+Vx zoUo^>lv-b;!R-7=$s6$29|L7iS1F(?6;M{|mPM%J3z90DH(jK4^@z1TRnfenT8Hb0 z3dN1GRW`_0Azzd{FC5l2$VQp$0ofqazA9P9!vKEe2`}l|Vygye&DSM+;3+IUTkKIl znJwJd79bxR1mNb3k6Cev(r)^iUoROgNPl3fu@2xbi^$a*C5h56_zPq@I97AB#4OcW zjLghZ3Lw04v*dyff~{JfSxVAF82DX@MU6lcM50pfhsvr*VORYQGPuWrj->5>N(M_^ zFj>~SvZE5D=U4wJF~V*N^pT%^Q+OSfd0_&4B?^})BuTBGAQ?Tem?tAArL0Ou4!Dpo zv@3<={9N*|xM(p?M)NTkO-8~PBGmB5ivZ0=r4e|sJQU&E$l^eEen{;Q=~3I?PxLM( z#`^vsN%*~_Cz*H4-}+0qNCgqJC(iPY_}sVRk50thqVo`Xw}k}-dl6bjcl$FF6M ziE}U3AZKou*oou!lGal65(Llg!GfphKx??Tg61f|V$O;gFqR3pL?HpcSdKbJu06RJ zcFPsK7ERt2{5zx+$|pRAW!*6wE@A$evV=Hx?`o=sWxc$_!2eg7HqOfCOCf5hvS|6%PM%MA$$*VsD+k~aaAk4p;QE=J60jC20uEhR7 zvU}cybk6>Pr9NO^Lm;FMu+1vRHUqJJU}*&Rj@5T>-!~2YaBOQmC8~1f0$jqJdHquE z%qm#s%m?&fg=l|*ny%u_FX$l{8~eMc5g75;^usgW(3 zJe}xImRjgx8I%05oQc0^MmbgWL}a(cEo|Zcx7106s-7mPu7}DxD_CFT`-7AH%Oj0f zzDf2Um4;((EMX2oB?={9&Aa>$2vQFE$oEMsZtd?cO~J*?d^yYb-I^){jUi%XZ7@f^ zVF;HfGQcm^-ZvwszMTC4S>#!kENmnBWBr*@0Tn>i0&ZHyTfmKN{g;HNDU^IB?I;Fi zt>BJjycGl#)(Yx#=L;A5-@=l$q8CUU6F-6jTF@Md9{^!G2+VS2r^16lsdighCcKtu zN^_bVCP${hY=QSOt*%TyCa4sOjR}6{sz8T0K{E*KAe-|CFn!q|@VjLw!Gb(W!*>bS zS;d3E)@2pN2Z3#t^X9%4DO&39L(ZD~o5dc>`Nn3Y1C=IYbzAPcX7)>h_~3GWzCZ!# zRt6$ZhFkp;#mwdWe1QViO95kD+|J8UcfZwgq*ROLP!-|Ek|`CjyKhvKViBZYv!qKJ z4Te}j7B)8#ZCER#G6;Pf_)JNx;6t1Xt4Q#*WVzYKL9&H3TGx z31(9A>NtOB1v?1f`)aAbq`EkLh1Lsu%16$pfYP*1y07z_YltgWXkBr*2JY7a|H0~F z0jNjMt0{`lRNz>hOyr7-!@zX(yc)>EbRASq*NC3jStdCInk;%thJMw7;;i+{CyR@h zHT1RE>mMpeuVF5lIn{zPvdHQX{|!mC{r(M+vci%5{(R|UOqOMZtEfb&0^bq0|E3__ zLLYfnc=4dWjr0pHW?A9Z3ex|8zoQO<_kh2p*mAS9Rw9GdGPbUWl_lGgXzR z{5QmjD|ugi90p@q>hYD_!6-Q0A*aupXDR(}VX+QF7dALS+$l@64YOi-t3Z~h>eK!& z#b;!Rp2lEIqTRAY0M7EJK3Be3D8jcL#n8Hb1cI=13H0jwLuBx?{vM=L1L#oyfO)dv z!*?rjK3j>Je$0V4fw%-2z1wrZv*jwD!V<}~3;r~6@)`f8O%qx%oFq#4_E(L zevQq!4YQ+3=!D-T9+u@ffaK|Q3Wji(z6DWEl@sL^EXsjZyuHIb`Jf$_D5eZc@gB+F zA8JS*D)CQ>)D&GR@h_KtqU=%5NRRy{WHZ$xxA$sPbsTf zs+?TjQY|{?KPu+t@|J1^24n5Qlw8u|uF`lRmp2O$c(Ez02;VNh<$qp$7UZDK!oPB9 zr^J`wbXch)aI zc`gM-%4VlP43^lBVS`uPZ~mdo{=DCOTh{3JTt1f$w?b4SRWJDmlk{sPG354pey4aR zoFS$M1eG2~w0!cr(Q4ndOa7jMc=u{;=cQ&D2=7N9y6itF+E;7)F7*^J+IJD^6X835 z7t(#~?4}~f#MJwxoO%)aEq^NNv3`qwWr7p2WEe+^ZWcB4oxS4!Q;^25Mq#Qf8&1c? zEJgs3u*<5;V_e-B(_UJs#Sq7@CWHR%Z=u72h2b_m1h&$u;-HDbX0qk5imV3^p*14? zKY>U(xtd4P%ao@ol3rNNBk9Pe{uAOQsB$_#cM*fJNP2ZO&-oOb=6uW^k*p|`ntuU; zR9(XbiCTlOSa993h6^(PONh9HHC&Kp7>o&0eGL}`z*(xVK#&CeZ@`$jft6p7(ibG6 z!QC4O)4Azm^I)d9@LR}#))ye@jKUJI8C)Quft-k1IVCniDShicM7>ckNQ#iqQHqeRB%me&i;5gw| z;5dYHd{pMx@^^pFEz!VmqKe_-{SP8`0L6+!~4*7Crs;@ zohJBXNP~3cG8!{2uq13uBlTKrZRD#vb>12wA=7HIWw|Wy#wkg|a z$a0#@CgK>jv7zr+)zg=RgbEoOY-GWt)9DEnbGDMnW40Mc%I4EG5>jnhMwh|t%uaI| zK*nrGwjs-EPj$J>hAdl_nH;Y(zu~hN=M9TaaHiT|729Yro6Js2rp0P^nO*P(cBaFX z>N2I8eLWiHo)F@#Cac|=m1auKv{)=zCRb*bEfv7v4syG}l<5;bT699FlxfnKZN|#@ zk)j_+D{^l0e2KJkZX8DD?K)jE!D+IADUDe!V`jD`+iA8t>~@RY}+4s$axc^6Drwb;xo1) zyM=`nSChfo0{HbEG-Ooz!25djA61Ea+SSmNSS_{1i1x&?`gAOH<%$#{Lbubc$e=2u zN*lR0XEcFE#TC4 zv&~>I0=UU(vuCD}!5{8#$b&B4k)35SrP(du#95YXo6})%K#00Bjj7;22C_JBVi8#%QR&M*n*PNyx^YE3oUOb&|+oZf5% zYdK8jESt?>H6m9{sEj?0zIbcJoNw-4Wh7TuY(N-3}^PvMg3;A#5&-#gXO!*RmOWxAJH}R!)i4PA*zm8h&lE1ElaR3@gTLE5qyQ>|tzqy?+RVKio&>{(8T zA@FLe(^v1Kc@v`JUGRK`2^wGn4ALwniz_Y723w-67K_aZ`H(PJ@-OCbb8gelVe8&IV#!Kn&#QfYY@TAQl~|X^;n9 zCU7*T3tSfxoDr0wS=smpwDpW^h6)3~BJB5D(R# zndWk3!!|!lrp;!Dd}_0TFI%#*T-G$7aC*@zLSiLMvxsBFJVS!BVumOWPWqJ8OmIOX zVKbQw)-0#XVzk(y(*dDmaAcdoWM+r2Xv%zWN=T3flPN0=D$ilbgx*M6n%(Sj!saw5 z*umzj*Ni}(w%Sb)b0!z8P?>4|gIu3w&2(gE(!A(0@WwRn(|L!WTT(Gwa&7W_V`3#- zO(f}ZK`Pmkak|O9CT#>7t!A^)+RBEz%TZGF!osVY8vH-hWO*O+!3F>uj@|v$8CZI*pDjSanRx zwr4|UGs~6gTikR09_W!7vu)ICAbcUzvNF?*X;}ud3j)<{v1Vm*Bb`f{|DKRg30pFM z(&+(}aV>M&TD%vU?QlY`3#3akLYK^718>btHJWVMS&;MKDkkGm=>K+aw-qv?J=2t$ zZOJreLMO=PFr|T`Lst)yUTU@--YVThSVlY|G*30VQZt>#Oz5ydGi`8ylh{q!mNc6S zf}Ce}a9v2974x?7C^S^aHG+* zS}CdvG$q4n)fgf7JB>~k?K-5UrCOmH%ra^DT0kWiKra7z@Xx%IjJVtxAkq;Ssc|X$5SjF>izWk?XU(`}zO_j@I6?iC z5!Of>l3H!|x+3jpIvX3<7U_0?1#tmigbPC1J*#79$oH(mg`w=8)r+vgxzV;yQTrFSPEzL180)OH`kJS!85I^JqkO$*EX3!CMEluRL}Vkc4e;ZkVS6V zFLa2?xwVR1X&Oit18d2P5AJKeaC*+%YIoc*e{-80Ns|(R2zkk3;68Fq%+j;GXlxgE9(Z8Ir z7S|-_v~elsOgMl`XE`%n23safY-KvKVDSeQ3@uJqD$LtE9Wc3M$t2gdZHOZ;+!bgl z3G09_TR0Zh@mpJQ34CK4IU26xx3)(3#%^u3w+ZZ)I%0BkD{WjmRHBfpPKq`y9TThn zk;HckE|U5nM0Q%Sc|%%4w^j7JliwH93xlw0%+9wBbP%LRaXGtnuU-4VCTS!tX1DIu z?hu$PKDv${-cVuD9WjVu6CH;1l1~Y1!1rO|5=CL(jgEo4qhnSf5#;@Kaa)&Kq&!s4 z_SZ!Lq|du`X|h26Fa&y$y4#o5*2C_%eW^j(tHoxJ;p<^OCfBwvh0FI-1BDXi&n~n; zI^NB7#PXA*A3`I=m%u!hhe^?Z{EzUdL~zIgdaUJMt%_R&)up6{MR8f^R(>WeD9T{fvv*wkiN5MGeL$sxfF4 zBlZT){+s`T{oH#3T?MJhdJrodI_&D}7Py4%=x(x}%nqfo-f4sE zEE`KzS!}N(E4&>mS`x^OrJP;Tt3IPglB3oy4Mnfh;vBJAL@04rtM!z$7)O#tx5j8L>o!-H}{- z1HU6#iPWLxL4C#v!FlkC@&h#YUI z5;yE>BhALetf2xxe&4bRLsL;JxyD#Ve)DI~p@9p6n7@JE{5cwVAM{@!Y38U9Xj>77 zOOTqR-Q-z=NZq&hv=onR;9Du5q@sqq0&FYep$+`bsVO4^^P|Ml4SX+U0K?I}lyDf) zkrRj)0i5mW)Mv?0O+@%sloiOUq&A^O+Mnr27B~ZsNWWpLvfBI^l_+Y!FXjpVVzn6; z0f;>H(T1kviYstPs-Fia;r3qBdcMLs3hfUn$ zQFwPfk8f7JyJYEyVxK&2;9e>yFtBLMb{pj_H^K ztLiDhfj;@uJ-8I!LEN0jngf(M-{s2Z=kddF>+|T|)lPA9fKa)^aX^c0cAQALgpFCY zue>?yV9G`S09HZg!nURF!Z}D`-%YK=kSy%!a>Blv$4|%&d1vX4DDe|88{J=C)*hvY z@7}rw6zB=L6_=LAM2kP<@lECmoK3#5;98#V_|iZ@lvo9hqS3=~3dp@>ApAnYD_n8$ zHMvGW6sGHma=Nlj<~q8jw~|Tz4B-M18XKX)4(0}?i}&Pn$LW%fs+m=oJ)e6{wKajy z#mDox=RAVJ*f!@L`P_3TINfg@fmsEf<11Vj*d7}jZ8 zw>9v+^erx?W=T-YvQ!7PI;neVW=GO`d*Gn-C&p#np{g5^0a;(`&wR3CM_`m17Lvui zWYPJV^AS*%yG9=9eNwe%p>x`$cl? zW8ysEaS38~#0^1FxRD3^E=oC^DOgZ!-$(;Kh1Bu}9u}Y9$OHaa490TtUNYDZxtK!J zc#pt~#k>ADU%i8YF=EVBq?$a6^&Spbq%Tl8KP7f`BS}3H*eYQRqTDzAD@LFr$|wLN z1B7KWG#K>S3&niT>Y0z-1i0m?!23~B{Y_w?aC^i&c4W0>_eo7~G3##uAo;V}vX*4j zzXEmCXtdLXSU#u6k~Pu#mLhO3(R8uLCSGIRDCcmEEpD>EpRkFw(7pvVm)$RjLpSjn zdkllIR_xwQtceyn6XS;gc&7H_`G$(BEkR!xV9o;WEWfog!@DAyY>ip=cBJ{2lg|fs zNo%pSSSaM85~R=TFF+{hfAgik7#JKa?L&y#P`D}^78Bs1fJ+d&^(VowCuPIFj4j4O z;RV^U-<%3OBZ!w|%U;BA%(Ab_mVJR*R=&eK0!J1Q5x%9L4it(}oB8pcJ2vx^Zc77! zHDa^P+OEg1vZ{?Y^AQkz51t(RwX`m|8VEEczXSt2$W#9gG$-tWK*infkZ%V7Ja`R0 zmNY93OcJ|p)^WI!!k6Gef26C+d{S$HN;B;{-dyNBaQ zlh^CZ+^}6Lthc&vK)tO5OVP(fmr=^$fME5u7>q*iv7Yc|;H+2(wxegnc4079ZMk3r zdhq~&v!hv|~@^s6ZESZ$g#F<2PwMlXh_`O27Kh;pRdKxUw$bcv(tERVHtfS66F*r zCWhRA^$h)K1ycQH;JEZJ%#Ajg3CB>0QgvaOkC4eZ+cRVJfylvIfp?_W5CLt|>5~<} z_&fx0iNcml_)lP___^#$pHRv;1chtVmr{IhejjKph<6n55c~s!u@L-n3lG6-TX;7j z0y7$dmFWLEuQUHof#0Rp1t3n?F*G%G{(n{U*e{^yeFZ#ddQi$(QL$SA&%Skk3rrQn z5wfO3F&xvhcLCRw!qX1ZU)S_wuIYn+1`4EQ6=?d!|ElSj+4~PI5`Yn;p-ldCW=N5=$bilYhlR~My> z{Z+^$`H8^ECOkS9U|3d*IfVOcCi%NQ+lC|+ZExr+t`{^!Ne3!W z7Zz681fy2nO!9K0V9iKT^I;><`LwL_t5^?~*iI-qldf^WQQ{R@<;xh9sT`D5K2f>K z5rpv#XdJW)QkAWd>ok0`l2*yA2qejqNA`Cw7_qzoIT14_S!}VDS8f7jfh$*Rwv{(V z?=}l231a81ykgTZ9Mdv+E3a69uPi*{u#gJB>g4i`O?w0vIlnyEPt4rPqw@X4;FID4 zU<*;HOzNc%kt-G64Ve^-B?Efz>p&9Dgc_5K7QwUPhOI0*5&S4M{Ie~Co5Vd^wQ-pp zhT6)e%!FAae}1qNVO>-ZNIxtiSlHL)u3+D2>GcXKZE^(9%&H_YfmrmqmW}O#d65!+ zTKnMnSpD^Iwt=DHI(Y*FtevwB3^zf}wcTNI5(qz?66_fD15Ebcps&SdZ)C5FHx@zd;X1^J zcjZL<)+M-G>WwMUE?L~Ys6?S8Z%EMp$TuWpAVj_)p*=03sUf@}VFU(Y8xromCukF- z3AmhXNLbfBxLTTli(x}TFEYAEFh`uQjc-U$VX;LZg`vD5LB)`5NWdEKnK4|VC`@2O zLNnjUdxO74NrhMrCf?Sa%O^?)a50FtSuEV5!?=~=2%$V&qC}$>>g$L7-5-vdoW4jH;WBI7v zn=1r+@E$`B24NHDdxiu*m*(PPWqp4sE@leP-Og_UE|0)=8*I@-V7m=e2yFh_s|U+= z8>nCvX`a_ZI6oq2Rg2H6>Wm&NyM{+aToWAkMlh0Z;;3MZAbpI9GB#i15@w)}x0CLU z-~=@+=nEIwAN918{?J2kW(F;4gnHiwlgXq{CfcMrJCOT_ISkDTwoxN!6p=ShsA`*S zEoi-iZ!J(9Lw*-n1wcG?M1lIK75~Nh=tl%^;X(#XP$L~a6 z*jTvfPICO|eJQ@ybAmsLVyB(Fw{OF6the8CC-3b8Jezumz>UpwM)-DiK`<`?dz>N* z{9@e}cr}IYf;qe}=#<7$8N!*3RofU;qTrGRi=f&feEV||XrC)m)P z_6sc!)DW-l=wN=a{QH`iJW_HSq(9H zmpmlh#dU5>g;nXyCUV}ljNE#3BXt8=#e6>?kmAUvBhpa)0VKC`3C8IBA7~+|FOI+dE<}eiR86E zm$xKiPQoz$+dr4blbln*rsUf{mbW2?|6HCx3T^|)&J)2n^6bfA|G$9d98=Ut?7W-L z>nWZyT(06Y5RNW8eDZ2=2zgpu(Tbe%g8;{KS2nKGx_x5ntkI4MPq>DUnlxqtdFphq zG1*uGvV2++Oe7gmE1QraVMPLYNLbN`-0Kf6_}dijB*#h@OJe`s+K9+f%Uck=qCGiQ zWkn0pJ+QK2G?kP%5?996R%B)(?+yjy>Qglw6S7?6MopXIm@qZlHHC}_fdbAdE1Ht; zqF3BWo(%*WkhQ^J=l^4xb?0whzIpNH`I~toGJLl-fX6Cov8hn&ZZs7N4#Qe2?B;wb z6qhJL0l(N(=oJ14&4t&33#8oLDC*hFz2XBi%fcMP&>n<7ebVD~2rLhH3|%GMM=6Jg z`Yf;tcl##40i9p*1h^YrA$$pgu$Vfqn>R{@yZ>@bwLJ$h^`#tBpY7)JWEtm!w?%P} z*2~w+8s6T`dtReLdpr0NE(E&?VuL+e7u%u%{^af8r((-J+A77ajcO>mD1(!r+?Ws}qu@YE1+S6oh%DFEisgdgvyGg1-q0>movgVU zgzZDRdB-PR3w|L;A7Sy>04(|ApiTN57a!kKM6Lhq$H5deJe4|LsbHT@1^+i`_hoWw z=i+fG8x0Mn51FAG&gU{iVnaqY+fPIe8qKX6fV}FUW|n5JNtDo74GoGxKJO% zr0KYXI#62*6Fzklm6(z!x6EyL)2yPvUjAut9cBwwYRSZ4GD}JRjzDkn$Tz{Rq|>fI zZ5=tbX$gqq3yE+un5>3np0DiOJFLYL#?#7yXJcANwL-r0o}OLUOe3x&KGTRxkgIh2 zF8HPNCg!KH{pWiA$@X*o>p#JkqVxgAW%H9!KL(dcpWtFPefr*yL8tgZA?fxL93WF+ zG3~$pDfqbb3&vvSC<@8$pM%rY@R|tvQcuGFqT_mlcU&hfo83}OEaG9*ya?Hbg;A3t z9!8Gef|CW&Uc|#F6~nPGYEi`ZQWX8=%(mc9$ZT042wj)WDB>fD{qtA66D`gIwsc`u zX*NW8VRkma)1id4bVYg2Yw(ll!K*wFVnKDu^-$M+IP!}BT-TtM33(=QGCEA>w|f6eQ=2)1^KX9Xr|a}FL(8x zl(MP=cMt4Ar859s<3r=ck$btP55r*0)9>HQ6UIHn(J?e2HUcj;LW}Ticw*>7X*t%6 zP4%tACCm+$?#89H)Rz8_H?xt<#tAT1{VJZA$_}x)WO-j>} z^w%>eTPnKJG6bgzU)1sweS67_R-x;X4}Z~69iT^*wNLuy2|LTf%Hv|jHKNi+9m!b*WAw-met%Ra?O2EY0XvQq8a4c?Z5<~4Ou)W1QuR4yO-2z zpH{LK`?x!O-ad4gH0cwHC&!9r_apQBgldYgJjf(!yN?eDC@dZB>sy3BC8Pf>Y^@yL z%s5g)=cUn9mT)tpxXUD5%=~NgKJup_^pqMwQ+*Tls*feWG*jr8C}|a@%9z#3N-{}# zxR^0pwU2zA8XBxd&@h{<$Ba#B>T6Gq9xi-{3>vaIS@5CCD5lI+ZgPAdIdAYx7S72~ zP!rM{7>ZTL^Kx|nG@J8?z_g;+p4&N;C)`19WrrQ(56p$-wqJn@ozj!KhPGE1;0?Xe zp)TwNIi+b^E!&~-em<=iyPr?%iTib?^?)ABOg~|hLu)KQCayQdwY1F6bY9QNEjS2C zoeqrD7JOho&&bXDhEk%0aTJz6IjyW5reBcBjvL2I7;?kg%S4i!%W%S8sdZddGvs2%mDXOp~HU93=naS z?{&oh6}Xpg!K0yVf^-Vg)$|X4EhB_@{}-*>9sEVAlW#6e7Oo(}UoL0(_xAH1*zgBE z>4NZ+3`-sOdko7Q_^NCXiu3o{lUiIpx{@AHXFd_?D+tgPrLgqnbL7Px!?0AJe&QUr zGIb?5X<0nUi<@JRa2vv>uW*TCO!&q6NN}AgeO2yUPUu%bdXt(l?9m#}3qMwe$&nu8 zL#5IO6<~Fh#a3Ze5CY`a389(7FWdwy)BT96mNjVqar1L;LnnpWiDG>Z?>W`+D9Z6{ z_nYhj|Ka9gCq}Rw2%fE<=zj|v3i<~REO~&OYqcd=yvM^kbzLw|CY#;EvoM(+8Y4a~ zi}nZxW8T@r!7E(50|ERdh4{w62y_Yxn%x>?=;=WT(+_yZuxv#o1G_0!l zJs3_s06QrA(Jhdqk0;jm!@5wzYGM_yb^!LG0>=hwd0Rp~s)~)g+5y;`3LKkPyw24Z z-hgcPLko`g)OF*?h>2JdafxjEiZfCJF#a z%GQ-HXfV_RuTRSaK~!14`f5?=IYAnO$!enfb3NRO;&s*j(5*;=whivkacK?aMh9)N zt569@816V7^@PSo_~tWsLvW68FGAFg&cbg}Vd8vrhD#8;Cl7{xlb)}DVh#DGCyZYq z#jS^7ym!&dn}!RNa#%GMk>|X;+4}iNXqYJ8@bYHs8w|(VhWEU@Z2)-IHemjUChHYO z_;&jkWUa;rc-D$Nz-R5PPluKZCUPrKK54a-RZTg-XYSMg75Y}Vmx7kdS#33-KNN>r zlP`)x-GzrZye4m%!tkWW8<4l!A3%94mX^tR3v0rs&~ORTyQwd9R$6obO2Q2h8_ll5 zC2Rn&=m6<<0HR0*s~G@v)2m|^Dz^`KOoFt(9M!@DWbGl($5J8wqM_Pdk189(jD0C| zN|0W{xJ-IKE@9HYbb#D^C6uLxRkkZ#)I-?)%+3~S1ZBI@7kUWao(%0*BQR-x)q{QH z)zAzztVZjedRnulBSKBSSOW2qbdbkO{6SR5EMA%(E55%Nad z-sZk*Z-&yMr8Cr8;W*JW_2HZBRCFt`6$n?rGPb0}6WK*GPu&TsJ$Sb%N)iK4&Q2BB#Bi%mjSp}$z7u1bHc z^732f=ol8}wGN>T-ZAfoPE;409HRXw*x;S>H#d0C`8d=@ilaPP?Zg3Hy0;sbz&DmZ z+8*M2yCZyKH_lc4G?XXZhsn`Q8`lGsDCDY>>y6@@_!2)0eI6}6h(HKiP_TR*+A0mj z#q5H(X5WD4K6r@aeiJHEW6^e?d0UFWc2d>iYdn}Yp?i?Y*>6KB;+#VT*I`O|9;Qzn zv%W&0HYWfl4}K5zjGc}k6&={+XQbYZPzN#p5b1s+WRx}`K-N1zBhF zld*U-Y>?IO;zCToAZ)?6GfX(2AwV9KkqYD|dOEzza#iW?7;#Hyw70Q!h{NY;d(q7U*TtY!5Cpyt|LIcmEvnz;ji%* zrtlp{kUulm0oa0Sr4yv`u=Vv|k5w-~B!rc0$Mm56mcfcD9^6o^M!s zT=HvR`%7EIdcqy_e>q?vT0>;Mmf>!kwH73>LNQdvYQeg;dZi)`~BEPO?0SLXY-*5{#%s ziE#MEx+sItjf6cBB-&K}04}Ef9tD7WQ@sj9)A@OjOOM&JP4#4CQtAEl^s;QbR^+DD zjC1CrI*6<@j!7w+kX0>8rwI!=JvvWXYaSJtcNl3>hO3clZEh+&$;VKiwk$m?2wN%m z=vrGekOgmVsVOdgl8>ThKgmZ?5!kX>hX~)gv@YEvzWOAebvW@Po6t+>Qss_2x=fnl z>Q=Ku2Y7SL(!pTrV6r6|ES-$0o!~joyf&pnTUg8;O%_W>tD&RGXlS1Z&$Jpk!CQ2O zPDY2p*r^jd{Yhf({{Pr}^YEIk_W%Fc8%ZQ5qeKXq3^7N{Gl?2XOQK4P*3jx;j#dX0 zQPpazM3qG;p|cjDt+9w|wI?}4B8nO+s1C-Uv{d=M*S*#`XPv!wp6~DTxvt+|-`{gx zkFKn<_v>E6z3#Qvy@qXX9qRWS%f82cA9MG#k7ogm?aA?No(&GRkdV}{MTgXu?EX&n zo1y-qGrHS9sn?->YMXXVliIX|=(hNOtJKyV@Mr5(d<{0WWlKc0?!ZFsv5%O1ZY@8_>z~IY*#Q*l>eOqSmysw=+~AbY~DN>tGN5Un)>$M|;`-N^N>F*4aMh z|DEo<)XqM-3TYXGyg{r}d;81(L*>csYR_aj$L56oUtbKvvd-tKiBJ93|Wir#z!c`iemZt(shI^MF_>2Z3a4Z_>srk{OSCH>wF zRpP~!hx0bD z!iVjPZR#70hS+P>XK6NpXKM|$H>r^RrTWI2q4s_{-Y6exukTN%3l-lS{bgg@e)R=9 zE{_+!A7+2l{{>3U5%pP5a!x;DzwZAmcaLqYJlr0j+0LnNkbi9G2>aKSc~04yIZws1 z_=oH?ymYW7e~4F%#EYjsrNswdeT1lIDkF+q`jUka-&jzp7dTB zWly(wYxraKmbSW9FPwkO-r4^J!(jFg(PmWL5KEhCj9FXC*`|-DRbx51FE-bEY@~%} zWs-2+3W}W)tbF#8=K6!C=Wkj(Pi~EcuVq$cTc5B$Ya6ZR&QMh2)!(S7MsH-tpR_0X zzeReJ_tTOdwwdY+q&Ikh&*PLl{A-b>$qVeg^tVmPziahq?t3v?Z#`|lsp)Gs(kbo) zuc7K!N$#&!%a{RPgB7fLF;(9zBq%S~tX|+#heQ=&s-}L?%1OeKjl!0>c;?kf1lh8p zjbc+n8{oF=j%6>CEN~WxIqcCOs4Zb1BItAingY|D)Q?u>Knk^>ZsNs?MY~5u0 zH?~Oi7p|~MQPj063i3QgJ!@a5x6D^uS?yE~DMssj_Sh774_d3gkUAAivA4GMQKLOC zWIksfwi)U#l!P4~>9I z0I>P9r_}d<*pRgpSu<1rd)Bz$L#a^1u`+kNUU22o9Tfoy(bV6p+eH11l8SUID0MjdO$q33^(^`CdM~tp9HbxGWbAQzS-s)w zaeg(4c+%>H|5msgmfN4xt85m1jK0}h?88Hj}!|J(N*b%>HX-GH1>e=P* z+uQqd4efmX|Fj_Yet?2}RTktNL*X#$taCPRz4D=bAkOE?GF)kRjLLAHEW@15!rfOv zs~~q@1;3SlYR}W{NDDtewSTkNznAivJtIgz4bgo6p08{BQ0DX`p64gvN6w#HdF!1o z?4Jec*EWkye7=YG`9HihZI``%kY4Evv1`Kj+-cu)G^snjSxoBk|1M0x*H&mwrzT#^ z>C_mnCa&9h@Avkax-IPsMGJV%ve1+EU>)o8CASLuYxqJ+wI34vpCVo9tG*{6y;f-N z>;I^s`m4yz+-i#WQhDmu(?#~Ds@Nu|P_BU581~``EW?daf1qx8mKsC4ItInXmuLK* zr9RTPJhRB^En1%Ody6EzZ}luK&-gt{5@>nmE2|f1dB*PrTAumd>e&H@uukOnEa3H6 z*&%zf?I+_4mHC6L#f|2%?774CHWj^h-uRx5avFRDeZ8L`YSaH3pAF^Dc*aT@EXifh zU?&&$3-f>1(EEArB7cHql-avie@uZY+J~jbc->a_OGW#r78Ac@Q%~3%+Y;4Zs6N`L zF`j2Lf3{Dsc(%9IvrnHiKFd?Cv{}z###&#B;iz@enW5A6-n_CG7x-co*Kv|}!BTwW8~5;dC2>#Z-@ncwWqEnYa;wY!BF!rwxp^t zz7k=V&!fM%iu%JH5+RqlOw;7lMhRzN3QQUc78SVQa|+czRsEZmEoTQvatpEqp8Q1uau1pRb4AZ%DrA z9s7JG7Q{ZhYG1EuPb=Ad!tgw0cfeO-sDamLZ(#-5`EOahbKRb&z0FD@3h&bwekFRz z)_>Wz>)6$R!WIi3N~`N7bNzC&sj&$vkmWH1x|DD zXCF}>d&r06{iOCGPfbgfGd-_9PZIT$YR*JIsm6FUVcpgpdSSM1yQ$V4HG!d33X^Tx z7Ntt5k=^`CbeYB%l38*K2{R+;KD}MZ?&-vvwGotaAAt!1%F?~ z=>MsPYqr7{^q04Ywmgf=G_~cIwurWTEwpehdq^(|We6?OS8NfZ>1BqONX1{-B2*mj z{%=)0Eut__{|+hRBf)RBa2@ZnHKJ?LD=}Norxbo1qyLNq_>Tvt$=n~qTl_$M{Vs*? zH_|WTLz8P)s;XO6o=5OJm-$rTl@$H%7UNKGx$ij|3Le>_`1g?{YaLIt)^QXqosDY9 z)|9V_-kOzL__%Isx>Zqa()crPL*rYk(WGNdw+f9nzp#EWUg%}@0$qUV_kzLlH+Z`oc{9uOxk ze53D9GD#{#bVh27g6+Eu3cO^iP~f*Y*`&Y=w+i#TZh7H#P2aFp=)866 zu$S(>_J8o5f*0*jGiutACc~Z>0ZNi}9H8QByNfAEq3Vu6PxhPLR zg+y_s{CS&D%GiLS^C9{zi1t^?bXf?0;d!o1z4_Q++?WB}RYK*ray{71gDq?kVQrJSw(xWnEo(uWgJPP0BxNJDb|NsIJ8e_SQv% zZPP7Y*gCCEQH19IhM~C>%aT)5ESifN~^7ory*YU>2;cDcer@d zxb+X2^MCM;w?}91IEq@b?4yNEmEq`%0m7tso}@lqOJs|Ri(0UPJs&hto(&50K1-VT z_jW}Sku1_gH4CJPiE50uJiVHT@2Ol^5zWfKE*hU?ZK?1M848!w*@e4`UaDx0n!LMc zy=}w}yrSxC^+%K#UlLfCJwtp%*uhV<4k6p#% zeP@8S6TjN4J}Y$eiurAyTKn0v>#12OSaO9zmPE zsG+ukwXqj%XDtsF)n(u5jyUZE*W+$1;{YNS6c#0D=R`#1e-IH~R8$*xD$o;OpX5&z z6;;zJV^aiPi9Ljf_s-W0XFoTc9iT;u*hh{QbEDO(P5G!Rcj(5_8vz>!8Aub zt-Xjid=wGkE=cJqBKluI!~$1Qvi5*{;yfaJQbvf_+Gi1aX-AArs?c~5ozJs%*IiUo zn<^qMI1#b_v)FL?+^Zt`*FO+Fe`MbX8I3OrB1amIQxzB0VXLE@aT@Nu63s25E zT2upDQz_!!pApgcV9o0CReXhzKbLnC(e*kzW3}%^#FjINc(k-ARy!ynhB$acR6-29 zv)UPePj=CJ_G5mrmqhGGzZBJD&Brvbv9g0j0op$zcEEK!du~*66my?&1>j1)*=3M? zkaJVVGzb;X#)#N?|01^Ip`vK5j)*vP4iP`zbw+8;MMMu?7YVPH#AuyGM73WLvFb3g z+DAlu$L0CRgGKD?zae%ZYF2w(L`=Jah>-7^#K;oK7SWskMD(SW@nJH0hKQd1JF+&Z zZcK=benUk6_7k3_a>f^-c(%SQMa1s2MKKyKdEpVKiV(5kyen4QBqB0-(Oo!(^zIfB zmtBYm8|RMGibTZz(}+ktIWCYj56F#KdICjxMEuf;{}T3B=NLAV$DS3jhszObztp=H z%S|f|z-b{8{0>(ESKpFo?XHLz$K@#6V`JJyB=E-Xh~CiQhd@?7v2V1@UQO}a{u2Cl z@+9;;MMMnx8xh0Hit1>sc*JAu^YO)r8a^_^Nj0zH$H3La30gl9v4<-i)zwfDv4FR| zh3ktOYEOxX{j0$-pYr7?Z_lkJA#ndJkBH{YXK1S_wS|s- zE@C@zYeAgtBH}8q5CZKN5reKFV#bY{={=t@J~^OR;a`_;%fF*x6=OFVI_72@6rD0{CzW#*I$&M?r>Yt|LhML zLc$gAPXV)@n;$%W(!{5xjF~e2iDz+@bi=XpD&r{63EuTDasdFU-3dVq%x^3Vu`W{o|`gX4K*5f4q}p_h5+ zRUX>LLyLLnV;;)mq3?NUJr8}$LpyjVhljrBp(Q+YkcZar&?z1|#zU8Q=rj-g!$T{1 zC}`H#2rlBm7#=F%p*lQN$V1I}=nWp~%tIgXP#+#T!9#<2=zSh~oQDqaP&NnXL)El4_)P< z#XNMEhj#IhZ5Kkbd8j52*?B01hmP}5D;`?QL+Lzpiii5~(5x?ba3~Lcz(Y^*(C0if zg@;!0&}<%hhldvM&@3KW!9z!Q=o21#lZU?Ip<*8ThKF|Wkei40^3ZV}+QUPC@X$sc zy0L2(f?x4qrQHZ^;-Ls0`jm&_dFWLhYRp4!9%{!!t9hsg4}HT!19+%}hlcaeXFN2H zhd$w<7kOws56$JF13dIL5B-SHtg)+ka37ESoQL-F&~_eL$V2;iXax_I^3YZuD&wKo zdFUb!?dG9dJhYjI0{0*^mxrQx$i+j+JoF_GrSi~x9_qwHOQ|sL<-vR&If#c!dFU}7 zdX0xB@z4)E^b!wk;i1=g=xrWa%0u7r(EB_zhllccXaf)J;-LjRRLDa+dFU_?{metZ z>rGTxO=@_TYW49|gzR>E@?<{+I)nFYzY28<5;2<4;hozS}1KE?c zobC0h1sD!F+lX1iAVp4k9gmy`jB+vySmYNDdN;DRqd)5u=%}x~z~AkzP2msAqyiDe zvZj`VEWUNS73&n_h+-eKaI|E(KRXjbe94~3U+J!mW979Ri9A6B9aThos@sgWyW@ww|w@QwGh zYHXp+5yi)!lokKf{$L{WBG?e>=mZ}HrS^eZe&i8vI61i?>lUWhVaLK8k=oyu|g)!ylq^p8B`~c50_9Lq~m4<=@W*$ja}Z zQtGRGK10;U?q#QRM=#c?L1{fcAO&S{*|9)Hzeexn`h`0_qa1PGdP?p4eN2$hzxjLF z?5d6obkLN}oy|(4SXhjsgZ3(qz}jDsBVNJH+3QuK*)57QS<~u{Bz=oyeBom(9qkBV zZ!D|SkkyWL1f$#b!W1RK$v;oF*X?CjYB>_TDctpGNqzl{dAT+3GS$XbMCrASmSs{YYoF;3tVeUoVKszQI_9vVl+#;zr?1G zmEfloaAtUM2wPR(ft#1sR*q!ZiBSL1mLf)3ipWTEyreS;en$ascppVk_-&;&Ebo3- zFs>xDO7;RvX34MaOm^q%k}y_K$5B)NR?_z>^wpuDUuuM8XT?tnVIyvg4`lgup(DF3 zf`7LJ&wi*RlU+!0w9pgwk&@`O_OZRq z(8NefQeP;cqQOPn69Xbx`j?Y}*qsKBV7&Up9GBig`}W!{xV80Z_;U zwlQo%7^_iG5~V+5k~PUJ%ai;tmUy93Fw5Fo60U!^k7e|6b=On@o-D*K9dfWT%ik3k z$U;*}BK6fKAuG*7P_r}i12Wxi73--`j*Vj3X^u$UVItanL^7kWS7b(W$S!c(DEd~I zMT#Hgi>Lmz^Zms>6U%*Wdi=M-#D@1PuBXMZ0r&cK-?86%?ct_>p!+GU(H(aCR@j%$ z{At;*;`K=O$saBoyV%;%Q0pLn^6#hG^4RWrt8cx=Q0tERt=rboP#^y-E6H?rHzLo) zyKSsd*Q!Y@uVLRnme2_;=}AlY<0kmt!H!IJ!dYAuXH6Z^`f{0Ud{f*%+2`9kTH-{h zi1kl#t_hm_H@byK$37m!K7BqYoON#N2-o*Z@;)Q^Vn=kr1(xJpCNd2%+H^(%Uz5CY zBd@%(qZKCMRw+Ga%}eX*$k3C%6Ky^IJJHsuvSyt@$#$9oT?@SwoWd@@85GDy)-4Uz zrWQL=a-rGd$VWus*g_=6>$jWBS)!h-M zPZL~rD$_G+p%ky~P(tKZZ0kK}9aBu?$!0PY>W@8;kdI|zKTs2Uc@z?x-_y~F^>{#z zTNE3&%FLtczuC)?q92rrygtw`k*M-0mL3{fgPrY-M7m6g#M)_O{K^cq6_Dkr+{e*C z*Y;CQF@3NfbWap={_fkIQ&{3F?qK~N6ZCH%lt`#)A>gF9*dwt$|`hL;& z3r>5+)m_-Ap?(IS<9>_r)x_XP;hWbV6Y14Pu*nZ$r~yzE`>`Je1CQ_s)9`A@ey=&v zGGR_4TyP6r?~legUxHp&Km}3gjqZEeQIn-D2&=-}nHT}Q>fT(tu({@STqL69O^3Nzo4VKkEESNRT3JYRo17KRPui2Aq1rM)`0Z)U&Jn8*5+2(00FJzYY(7@<_wR3ev;h6l31fzC*+qb0eWiR`P&xFM*@ z36RkimfVnRpM*<8F!+4R5{|nE4axSDN)W3(3`6{Pzh|T7nBhOq!(j2}VUBj%JC@u< zR^0xq&Z9tnFU#ax`6i#yih_>8Ab6TW1)BU}Gd zlCK!a9{o#Zd**~y730uLmgMs$GIg1$Ssw?oLC-}*v7f$hhQo8JJqI1hd#Oqk8#UQa zTgpc{dSPROi1kl(4Ib;KBc4_h#O{CG5v%nV*z9Gv%I7`<7&D+Q+|x?Z{JA|%+~fz* zY2NpQBTbtwkhvF{n^H=HPc-*2F!4@uQb-7ITO%iWDx z&`3S`ewYgEI zXbi2h=L$^jZ&GLQJqeyPWjOUlA@BX$DYf0l9U04JwZ-bjM(^8Etiw!4UF~z@+n*K+ zPhu_$o#n{KK#9|ZUgIay>yOypw;c%ze?<RJL2|f*iW|2m*b5P|egDhN>JOL@nI^>EkuW{4FT(uyMeobE7CF-O=S(j@V|sb= zBks1W#$rdTzQX(Ri;Er2^<}1)mzZ9rdQTjIHQebBRf}bL@1P)ddoh>aK|Sm=VYUg3 zJ`EMdy|_?9Y79fUI;jnB{!*0TMKA95Qb$+)oC)_EVx-fal5>ec8A6?&`W&{*(Mb<> z8tXHVVlwo~PVZ=+EO=;JS0J0d9A4HmFXHraM;E=B3DMY$pak6cq*@)e@e_=`2YL|$ zIv>Ct9xEJ8^(+&opP55R8NaPsqToF5<@~)8NqN?U$TlM=DLwallCs>3prkBag`_Ms zaTc37qpm`^@>XN+`?VKQJI`^SzTNcp7W3PbiUogEix(L==|xZ@*|!E2d|YrUVcmrF zUGGSzK3%C6LVUm0(M;D}o=PogjV6}$K0J_jO{utDK`MrS0KKU1MHGDiy{O|d>O~^O zWMCIr^5>qO0UAcud#F@c7+(6jlQ1!&FMGLAG2gz~@fabH^(%r6e^Q83hB1$`h%eAc zJVT(DK5@kB^L(#Fva6pU)pJeBd|4)Q7AyG%iwolYw(RM|=ul}#K2s^9*1^VoVxoNL z620L;*6cHMS>K2UM3;r%s8#w&Xv21X4$^s*-F&^Hj{dueeb&p?H#qV!G(ELjth)$2 zex@><1r#>|kITPD$}~`y_)eNqrhGr0%v-vf6oD=1P$EYgUA%b3W_NTe9i;XUTm>sz zMOSCVn_#6LbBl@b2>xPkPKshQ|C_lP^S?)Utl9BEJnAjU+4AleP~Z6y^tu8fFYNTi z2dlG@t)j!(f-fDl;B)XImHMBZ?6gsWKqc7_t0lg%EswizU93uUe zxFVM>DNSUL?1FRb9uqDdIPM(lQtT~JasW)<4NZMsVxLv9<(~l?`U7T-BRfP_W#jgs zJIpq5Cz!bz1&$f|`x2R_B8TmDJfOd4V!Z2PP$?qo6_nz-Pot|zeLpS*{A4IgOmcT3 zxp^7UL2SZKtgsw1$tyJ!ZN2YMv;ieT`75y&KYQl+{>97k_D09D-0cAKLdHk&#FLn# zD-G;lnVDOLg6>&>X6E?HVbfblNoht&o)YhZ*@zddG>pbPw!5y7QPhLk2s@hGNC|z^ z2t}DU|Bx3?wSZDG*8+Kc|5+aEu{z78*ri6XUB89jsM!yg zvSiNw$mSnS!QqV;!bV0u$3%sn-fBTh7weG*%sU_Ff`jz#p2v9Uca5O-M;vB8&@ z)7BENs*|BR>2}oC%O&}uLiRIsaV%#z@XfNq0)^-DoTZH4yfUb;a!V>ItceF4)$~RO zgmR^@jNkl}E1}fU9(y-LtiK{99q<|eD)o*BF|QaXL0Lu+t)01k$F7N_Z(;)AswoX+ z-3nvyk<=gIeeLIypy*Ry9^0PA7QGL6%Zgl8_a1_<94TyuQP{}qSegz$?C6N;jyLHF z_ysRm>&>*Ym^CNy{|Hfjwvlpn;s{*5+odoZtMn@(xWlTk?Cnt9^dDk^*{+{Z?O*t$ zDlISHS76DT(6MEH>#V}IAH}fsjAWM?*o>AmXB~5N#jXIWj2z{$BH4$1B}9oXJnpEk zN0bVK9$G34I%yIugHun36o;_a&%}hXjm4;nZiiuxf=i8-KCqNbGxiozQDMtN#YHvt zB#Nq!l-f&?suWWh)chWklypduQvzS<^g4yoc~;`G6`Z$ZYO&(T>QU_TW9U^Tn4n`t z()FiGz3M}S^UxU-PM+kxr*eITlU=)d4W(S(HNlw=PHhZ1@E9ce8$%iXH~ao;M@w3u zrSVbYRmw&uey~74Un)jM>7P2q$VmU4$n6x}IZLG@Jt&lgu-{SAp$A2~#fFYMd=+bp zvjOw8v<`d=?j1*_LaBVvTgJpV{|8!8JITO)4KITRL!T`>h+EE=R1aZC&%tlsf@n%B z!v5Slavap;ptpQCop(&pM@uF)Xn2`0^hx;4cSJR|VMS>eoAd?@So00=tYd35lBs}) zS%S$%*0}`bStRM$s^O)3S_*Daily;zQMuff_#rv8Gqh$Z-tOKiOaXS9%@&yaZ!|h8LXI0V`cIHlrIVTUQ;|ryZ&u z%w`^`9>lV)!7QFNia1@7i2m%UKjHDJ!7_AbcP)mM1Us=e>r*Vs`AJA(i62#$%MMZe zA=4(Ha2_iLw@Rfhs)pSouik(Qt*4OCU6IiLFUMf5nC*N#uAPd7zLlCRax${q|u(OiFX~1@sUrW-B4)%Y%U2|lJ4xQU9%*X`w_{? zpiPt2!J!~!qgf_P(4BSkmWPCWXwI%gIz1a9xmI`-I)<%Y7#q#{T*G*=seH(P_|u5T ziY^8sNF zi;HF2bCt9M63LQw@vULIBNABX;Tl1#TOe%6Tb5~m!hQqZo7 z1XX8Ad8JiZ^Pge^S-+}~w!>1|R!eC#IeH@4*-*bI<(xChxt!~4%*uX^4P)i`HDcIH zPopw^vlQ`*r3jkXF0nZq=y8WdeaEtrBu{-)nYr4!NJJwxBLo?UmcRQ~btI4NuA``^ zno&2rnsG-AG@u*nldDYZ`B^1dd_xY43NCs_PVCupot)~L(LlDQwlk4MBt_rDc2zkL z49mGA!kLd_77!;L`2#Vy;Igtr9QBmxy3i=CANOWOIcw@`NYq$u6+NZ&7%R94a>-Cz zb=9otnTG9?B~rj&ku9J>C1?>gfJC#b^|9fs`C4oZJsyp7Y`f$`{>8F`!xLE6yZ6=N zP8-??QX1n-(JxB&IU{@MUTA1R&zkkvo?urL>Qcz|$Ns`&yX&V8`zz-4SZ5nO4u{J4 z=G9oXHw`wLb{kWwBl4Yfd4ih>Es5s8t6gU#j}Yb?V5-I)vS_z*d9t`y$hc)9j*8oM z@<0O1Nl0oT+Vk}G%7!t1I#E{l#3Q1*FL%YhFWUM&j$|6P=RSSJfBOiPTIRW$=z*L? z0eY^?AU-|r%^-+T>{7gQwDtyNOED9#9q~64Z4;fX^c|A1)yUY;#5s^<50103?9jMC zHtA_>_WY8Nipp%_5u&6v9Pw5GZIgT@+1Wz>P11ic(iaXwQ_9PZYtG6ab5_TxOG6d> zYzY>6S=ZT4kN!#2Z6wQRiB6hwy{!lq0L%_ct0x7r-Nk64Vf<%P_l)bQTAEW$@VY4& zSY}9gR|Vd6ph$2dR$ey_8(*FPy^|%qeZ}-?4bi?PO8Pj3p4G@Xi(eyUkyu>#R#9S) zH-?t4kho?`VSKPodV8GgHq5VZic4*jirb^ z|5Zd={kjBS^#@PJQlnLhFIeOFz6DncXLG&IQK1Tn>`EU`Gbeqh-42P(KMUneTZ#QR zwfXOAGq1@aGhjOu#ibEiwbsridgf7fsvO(%4akByaF=x*;Y#Fs)|fSKFQjwqE&jcUR2);$CMP?N7nIqHw7!^ZPi{HEg%?r=72u_HSQj zTkNq~^C+r0ft3vLG*YVT;`^YdwRo&rAJ|*H;a!JGwtV$U~H-~g1K?}b%2v^sv;(? z%Ja`QCQ^;r$cJh-5w>Zqk%>)J1}532ln0$nu$79K2aL>152Dw18%fwZWgtZkcD|(R zCxq3!Yot)hP>hY1uC7~^ExcM1j2%;k>cM6y0~0>#Ot$J_XR6*@f}65aPkI{kq->8q z-u9PV(ljZu+b-)7eaKS>{O^Z4p^B6Qh>qUW8(pnwELQ z%$Wr-eZ0>IG5FadhKdatq9Q8vCYhx@;`SYf@DDK`M&nc12e-$vCn7GP#Z$fL9kCWEg zElcoU6@!<(jsbl$32w|b?S`wR{1JBpRy+-7jdISz{gXX7vKGsqnGnDtI@J#L&2t-w zrwzxj>+x9ObyPAzuG7)S5)xSYXqc3tQs6_1K(ybctl)NyIF@uGA(EBPM|K{x6fwY3 z1ocZ{3!I7iYm)woLLYeq+3B*&lb!H|7(UOoq|daZ)9H~XZzn{H+0>U(#3n@qoPoo& z4VKK$EScDNlF5d>?M&8BN!-s0F0VDWJG|_NFLu_%7H6s(Lunte2J4K(pn`2iJmX$w6R02a$0n#N-dH{^zt%iD4RH}VJKTY%N3?~0{Lwm zw^Rj$e+O-+r01?murWSu*V~u`w5(V_L;Y!)@Fz^%{KXj5Y)o@T>W`U_Bg{ysMJB7i z0xtC>g!E5od9Jf3D+o&#({+8Ji8$X!B-4ZzPp0WTYEm>h%r9|-;KUF`&-6u;VZS#* zalLx4&(h0Ii!owZ1L=9yPmmmS;qd5b|B)i;_k=y+HHFR6&xo46D+>syD0Zo)jX?)IdvmmNlKvYr1^|r^~C7K1ZR$>qzv5-m#&UV+o&O z4d=Hmyz~h?WM4>lz5;)3jDXi*3qNtju?<+}#no?C1+&hQPD3B(It+cvBpt(OwVb=N zviLeSRuJrp)Q(z$;bX3-#NXML)Nm<|@9Lqy2vrD%$CD45sTOHUMaABwV^SQ=BK5#u zjE%hVFD#OM*j@8gCb^Ave!*9o97)nE*HH>+rwZsY9EFqLEeY3k@%G)?#BO0`^9kFm zO_;DfC5fX|BAE?Ja(PTrEX}C(Q6}tgGnQ|*Lz0{6??~(-6-x;nd2dovQ3wl6#5YYu znw4Gq3I=qqB<@y;6FtB=v5EZ&uC%*!?(L!dFl7yXrCQW5p8pAEmX?VKLJpN6vXvYPHp?nEOHc&!Z34^2=?-z#osYyFsn9x+1Ug?tvy zDg)|(H7enRvj)pOkPHi)=?vF{eiM~a1z%F*(*RznmrghzphuSWhVOxPyiqaTwv?56gnv%DT!YuOjc zOE1?eSyX1U9^i%Z9NJmB?XL0Cn5xi|S(M7S=lSgC=Z=8B3n4 zPtG~h^uRwvH(B`)rI2Y%1Y{hWRaRF_F?C!#YPcrnj+qPZbrw5!0ZqBJ4@|v?68xc@XjR*|#o6T4e-jesZk>(9Of!*~lBbN6&s9)c^PXzK;G;`kTsCQBwYljty4ivF8qVm{+#E{p{;e_nmL zLjt>S{Fzumuqauwz+EGd&gct586w%51}-^tCXFB~oPU-pSy?@!voef7t!hOG+tk@5 z{FF)sdWs{G&8mi6)37i@3hZhWh|(F3{Wu12drL5sIg@=B?W&_sl(=z59C@56{U?9J zZSQn<$H1=I^&2C6j~|>&5t=tQMOrag7BOvuQK7g^B93NltH-*1=j8cS-g-5xCGus} zY%GH45f_aEtzR6y8wXvbY9z+a+>~io`0`82t+qDBlbsU3wzL)mr~mg3hR* z^9PZ(kqunwdYOrKq5|6bhOYY9Y%{7oy3>eDN)7nD^2jZgTS`hRv+a#sae62|e&Amr z!I#7kN-0IMwohzIse_~NSU^n|0=lRIsD_e0LJe`FsCP7B+nKR6jEZmOYK|Q^l;p80 z@nSskpZ-Wn8rzfO3f7-85l5SeG%Ov~+?9q6I7DPB5epya5!$3QQO0HOn6Qh?SW4)7 zEs#*$u*A#uTNO(QEgqH9&^IjGYohM9pmLY!(N?a;*o;HeD=HPM5+n3WCeC>?hl~gg z_}6DIc6YtW+ifJnX68rPjC)+Fv#Z8sQ^nF~s6%^K1HJ!c(Qfa%tY{6=(3@p-PYGb@ z&%samQxBKo>B{Tr5}qzaQK-Kk)6vyXpDuYXs=U=}Q2}KG8`R^TF8#U7qmwG|&Gm(TTs$D`Bz_|?yJRGZ`D&hUrO~3X*S&>3sI!N7hP}fVc-(0r)@9JkX#i8~GguijB*Sjk5t^$YPWESew+a5p6udfl$Id zq~d7LZ0^|x&3ryC{Xr9R03|j(mJa(U}9rh9v=Vz5e*&KPltFL~{1UpgzY|I1b zl6;Q$uq$G$gU!QFdd50bBiI@I5KG6b0$+Z?PwK#?3Xc#YQ)AJEWw|=&ovzS=ossxU zmg_~WjS<$eVi=OzUVmC95F3U~34Bg_%!nIdiKDLT+D8rRi~S}`h}qppgu8H(w$MnN zZ%L#@4BJDlX8JcWf!H7HpFqC0v-DxtJ^b^u{soB7bXgT3Uxpem#MNBCERonF?2kkW zA2N8!{xHISwS-d#@$N8JnqK>=FnBeuD(w$js~a=XU>;1S!IobAs<91LyDID`DxFLf z+9a8adcO8U!;W#oG3*#5#IT+dU^~hCOlsxXYCK%eH1Y43e1%7Lb=^qV)t6OePk~h_dqNKtKQBtc=tB@1a5kr*;D*wiizW1syppA#&c8}e}P;KYV ztNuekQi6{bH%ex^(`p2Wao`!5&N7)!pdy*`2^fwOrVJm21UUpGgVDBc<7Q$vUFbE@ zEQ8sVS?G!ga_cGAES%d!glW~ZGLIE45~ssFlTb1quZ}^B>?m>V6x^_}u9x?kTb zak~}V=E<%__%fhXX17`5=pEXB4gByg z8{?w}FCz8#N^nmFJYhOgk4JDmeJYlQyIYFr_FqNZqdzCX&-jC1#qlkx6pybEw{!*u z1ZyR2wL<%OhHI|2(vr5kLK@4SWSgX&H-}iHD*14gKgUJfKaHWaoBx2tiLK}MXx1=u_ zER2i!7aDLkP$V1qOOu)`svV9e_mB_y_jnn^rvWngOy|Ahir1$|?qnm^bMH#g|T^o5rzK4VzYFD;TWH3QHNw zOfpa`nQX^0xUgJOMv=daXja?|H@D+Zd=iUj(lmtS{aT9s+Hp;BFm5?c^8H{b=sOaG zgJ{|&m%L6hgNM88T`7N_U8nrp9*?eYCFy~GiR!4#CT~RJL{0bv*JJtjH4SGEuSC8y zmYnZWPw(9ERRUFUzxUu&#G}R7^=sgIdsFh}DLfppe@L5aq`YjV z@Xe?P-v?!zqxLB z5V-sY;5!$FD)=3xEyq+;a{Iq&n#jhzhfPei{`T~*8CyiHMlr0_q_dIVLQ#jULWRaK z{A4n|Q9M={-|)Y^_2QWU1heKvO=H>e)ev#76w#BvL5f2nhN6G90(Y|nlZW`w=Ws+n zCvndxIJiZJpntW(jkm;+t0Q0|TpepAZnc6VmvHgdP3y7Xd{oj(OYm|pSPN&HzLkwg zM^Tqd#RWO0Ml|u)CUMNY$rYd_tFEfe0_ z)Ubo)TeQQjij~ACnqVdI@oCIMTE1Y2bu*j=N{An?!iDEC=$wV z`fJY40#~A5_m z0L0$%8UV6R_HSUFvZM$Mro19N7ow4fIs)9+2=46%rojmEz{ef@tD0r5&P&JY0Xhnf zqFg+x8~b3CGHsn$*L#cNid&)zCOn2?VYn?DNj99tSaQpv2AbZ8wXs>>qp|Li+1Men z0Zt_A@@+8|ihgi4#ZoOV6{WPlyyY!zS__#~iVuQsnUostH>>$gLb1D%mkmMbeuc7u zskk@ik<>8Oybu>5z`x~R=r{}HDfDB$LZ_R4GK*Zb_2_>@y+$(as0SS3ay4Kh8>I%b z_6{(^_)jKt9>QZqZCZeAY*~$#o1wQ`tBnQ zshq!HNOix2_c6kA&$_y@n5(>3_MchZV+9ml;jb9yOcg-Ju6f+1lvYxp^}o5`ue)Yu zW?M077I^u0%mP1>%nz^oG4Wxp*7)$eA;a%^S$Z{;JHN{pWy|YL|5KE@7zjLhpR((j ze<)o)i^uu;dHx_MB;EdkGbkuLLnn0dUvEuOw(nfPyMcE^B~@lEG&lC&48q1(`Zmn2 zi&)dtc&y2e-|?mtUr|8S$g9Q)I^lMY5IHw*EzvC$G~MvI;%X@_2Dyx(QL2xx^lwe> z_$Ty-tFBu5a7i1c(D31w6gJ}1mVqpBSc@Q*y4W3ziI`QA@!e9*ExMiu?x+Ka{*35v zO8PvNehv0&t|ji}ig6$O36r`_;=WRFxc{;z=3-W<+h~cyXHN#Qtv7fDOB@dV$V!T5 znW4p@ENOa+FgE`h?!g+y+>toVYehe9NhdAc{x`HV>Mp5(9)4HYWYPh;!g)lhyB6zr zxMc`ie##xG+wL0ean-xR9vjQ_n_I4$dN;}KtZ*@;3}NH0yK1qdKT8AH+!erfFk#!8 zu~Zn}-$r4K#}xzkG#%y|bRq_+^%X`q%XzLvxUF$Ld@Pqfc>AP@{kWM;iu=PIDA{tE zaWwwE~FN&0@t*{5>KlifH~qPdgx0uyYP8Adriqq}4EYm!#( zqsc56=e7uEyDPbC>K9GOb7mxE^q0!+T6)rd*r>Yh42-z5B0bHXT2tPm}x?6#n^m6zHC+?&|s!6L7LM@N}>{PXAc4K2TW8L)=~UH73j|6NcJN zz=~Fp?9ugZn|@HTN)#3*IX!fjiB)7`QGFnfHDG8*)^&4VjITNc52e@NOx_$p+@zq*xmYgy1F~VRKb5oLBWRM z@pp^6Bc)!VuQ4C21{D}&0zO~{QW9Np#X(GqamV08)RfM-3RT=5Pw7nG+9Har+yaex z*~Fe@W}`T>SmjuEGCnm#*v%@InjeUj*@aklyuQ&y{M<~Wad>P^cM{IP67i%;ghfwb zM7&cIZR)s*_)`VM2DM-#!gbLEZ7ed)(*%9Qu-figIJ1wROxs#x-m*gVXFcko;P**Xfr27qmh=bA!XR6 z>uhqaySoCXYRanIGJxe=hJ^Xca4jr0H(S)zZzc(JGJHq_C}}(!&>n3*j%9Q}C6i-~ zy56!+-T0W`KP}*}`xB#@`F3{{?i|EJ>hL(4N056V+%{<_kQJ6lvas(rcQ?jI-6^>)Ba*&zMJLlsAO#HHehBvRxqE33 z7;%}FI9%G2#p6;~Kqe6nqR^~XnWy=Oj zd!SK&ZsdMiF?V)ncU%3WXUU_6;Ux&*`|S>ilG`$ zx>FiKLuM_2|75b@mDrjG{Vn+HboUg!gXFbUc)0q!2FnR=8Ou(jyQ{Lo3>bq}me4d4 z6q`u1*!k}6RQ*Xweq15LRV~S#Gh5c80v%}ye$)ge8&;zytoULH#`hS!ML_loNseH5 zdb$&E3$RrYykUY;@4csCaUg5DpdBvm?+w#iAo05tyeQ!jEyG#<$d*B@U>h`Hhoy|I zCK=?_>DmX*go~1VP9bC5mco+yBF(>9@_sS#$YR}pABGcg0i+9hY=Cckq>N;Ko`hC< zVKUO&^?nRzq63U$uSmRSHTD|R55wEu65d0FU+M@G5cZTij77{qVWjnUN9h?RZdWrG z6N|C>6p5VdLwcrBS+F@1O`vgR5H;9A128yTBWbIAG`@Nu8*D*-%POqx14!Qr6Lwhz zSQ^a?9f;A4OR|d;Hlx9D+Jh*YA56&aDnJe#gaOSzlJ>Vs^NnbV-$qf!?L`$`H*v3; zxo~cd(;MPSe?FJ0&(@6gn1O|#V?~~NmF5p)AXkU~WHJMZJl15NsZ<{tf_cgy34Xu} z_EgcFmep8?q3C!rEy4HufytEwp9;B>{*kUFID^O~c--qL65ge5=f#&KvA4OOo)^dl zOvLmd@1o}`lJ61O;8=P$kR=ZiOCyx{zaB*c`$i^yk4QWawE9z%72j@Olbsz7JNC6@ z;M$dk44Pm}!^$-QS}zw5_NzFnN5LmdXpDbCOs6*}Fc zL*C{iA1POdhmUjD#H0$vqH)|QN8O+Rylvw`E?A7UKE%bxK3 zL#;ZVAhAFB;z{hboywirsVB$b0~Z0gF-uS2!t2IWg?^_Mc&^QE!1liCR*yy2tLoKl z8aTZ^2?MA5tBMxihd(XT4@MNjv9S=g=d3$)Np{B=HVf&~ddX*u&S<>ZT~Dv-Rd7<8 zU6V1LnJVee8|hrj37psY$13c2gN{{M{xeX^XDvl!^H{S8*RyDxA4w7K8%5AgqRYR@ z4E)ovV-&Mbfrvay5$~Bq(5Ur|pxipr2Mo_7*~Vg{AnYCpVMTM?by$+tu_n9rJnVtf zQb?g!$eO=OXcYh*SSB0(Oh?Zqu>asoE8GD8YZOAGWC%%Pfm6Z1<>M<}kH361Om*<@ zRChbQu|zgt1K#wjVVpA$VaKPrhv9w?@soeWCi7Ua8zCXsTe+k zEx~ji3)lMuuoX{t#ulpkvA(lOir8Qj;cW#hC-7h!*8INQNbNIAVV_tEqb=FzUUqlT zf0iPS8AV_>`xyQuXsR6l8u-zYNe2?J91-$}u6PX1hAHVJrJ?FGR~(ZJFMcqEwc%oVBni_zDBH zpB4+;jr9YPiSJADLMA4@H59_$b7DyW;L0pJqzkSLT!_+eSkmpHG?s@jPrE1l!?_s6 z-Ie&;3Z8~s=Rd_gnw=IQ$=DX-sYs=<-!PQIj#Y5=bbcG-$VRvmfEy9)3tkNcAa{(X zDEjct!K?jxer#%4EqnfWo41`)3VXieuCEW4IP44d$I+*Mkj(n*v(sI}*wUU@2Ez_v ze>(QFSkTw3bq`?^mm>djBpthh{ppx4rm+jb2jzkgHVgZMr&)sG)EUCM-s~F6`VYD% zfEE9ZJt_H81a=IoBH~%jGw#r(pLfCSI#ap?vt_4H*6WO7uyxoX{Zu-;u3?Abs(0Pd z`UxrFsJ{f-ds~AfjC`p}D4V|mDuOM={u#k;VhagWLK{}1gn~mwbz%g$&SSu-I;n(4 zHtL2QvhSf|t1N%_w;>vj#Z|?=nx!@aZAsGGNpfo=8EqzwrFQ5R#5S%5xrHS;)kLO| ztt$^B+tHFd%19=sOxQu_%*?%AV_94~jKqdp;)j{=D3WY_kt8oLl2ICc@kuUGM*d|m z&xG=nK`@K>5G`i6gzhv#sUY?&>k`gByNM2Ln9 zFKggb_QBfhQeGGA9Qy?NflIrnn*FnYTOkeAD3%|tLq^)Cm|NBe6Iv3(v@ISp1@}cS zEs95&CT!tM+v=|ta44YVeCCePdrDMy1qI80FFwL$h3afVQ6tP+k1pgHiJGLK$W8gr zXRykJ+q>D=mwWOe@dYj`@)#4@m+A2vk?GYEzf!>?(`l@HGt6>UJ{t6LOY%|^8EJk- zcSxSy%TtWR$j$EB_!5^@Lid?)J_GU27cdYvCHRIHENlZSKHn{YP5%;&?3yJRXU+`u zM1_0qZnsbt9D}J|3R|=v6HTPPVSaRm^O)S7G z>*P&%gzyR|3gfe$BfoW5$B8}4z;L4kGM)*yVLWN!3fo=$;n?3|0rx4(ih3Clv~Gn% zd?evbqXeJUlt;ilI4SBN)KbS<4XtBg2aLZ!?0~Tlh@DtN*yW52T(!{vdb3l=+GdpH znNQYdB&#evqbhsLj)se!G5%t(GsZ%UFJFy^W`waBjoe{4^+)-_ZH)ePL40?A7?dA^^ zW|F1Hcdf&QCw2`Ii)EcH@f}Qfx+20>;%=@_lKAodcslM9!rnaNPGyND?obw<0vQ|9 z&?3fI%6QTw!(-$c>MNy;29~;RYB}wHE2a2V!4~#O_m}Jlqk!`2lP4vT(e9%b7D%OKo+chND zWkOL2pZW!hfR%Au4X;3r?fuiQ0zGbvfmqHp@cuFJVE>-iTS!!@5#=#^J#o$qQq;{L{2&vaE^B%6Jm#ctO7c7- znFg0MC*4@%U?^+W7v#A{^2;VN8Mp-(Vc@n&@>l+3kAb`3uF1;hb&VAL7t;?VfZ^RldG-*3Y#8OY?=jBWqUPvft0SDlnWEvM7^7Y!iamJSg@p&)?_?apBnr>GiP zUvSqOxtPOZliTW^mDwXziWA`KF>pV&S969Xm~SFQp1g0^Z*uI znn%1HR-B|Q65ItwE?V1taOxOR39D~Gql)@C0vX>eXgiHGPsS73Yms|bR*t%WL7V{cVMqrt`N{#6FsUy+1A67WI|cB&!tA%-nV_R|OK z0i^PcVhdx6W8l-l@2V<<^9bwqwyAn?Eqqp;xZRChUx|!NJQ&E{jYYHUW`uXPgj3}m zPeJ89BjA&aaPquIBpwW5*=0R(wN3HEm>1N-37PRm31ciJh#n&jEgDXHUMwq(5@389 zv#cL`2D7hgA?3@B+@%$BkJknl?t0F(TXGBHi@CqYP}2J>k)(T6gK*Y!uyJddzeh4<UhsUcBcWnz|)MfUa*wqQ#*X)gv-K5P5fn{q${)c8lY@GHX=W;ME*Z~U1wkvH`C@8 zV;gKRHoY2C4F+S=tHJbYV|oV@>V{rJH6(;!Ai>n3V@g7a2_^}l_ulVzH$bQcUPvRf zbO`W0((dVWh40Vlv|2qHjYiUFG}5B+IuP8oBG}hW7$iPt;tq*n2p`6J)I5X=A9aNJ z8HICZTQ<|bw&%GlhfjFEV|urey|NNe#MIMZsc-7mob2Xv;=GUft$7hjdD=XXN*zT{ zf%U$rTkGP)kB`>nwuPW7_VP*%s|c9v-HYHR1!ob#^z0AtJt!KT24;NsBBE$fLnwuq zx4E7HWx%Ot@Hlqg_+H)=zAb0E+7u}uPIT9#b5B|pqqG>izmihK+{?>bP%DeWhk%v0 z@g^4$S_T?*l(NAheVs{qheBEf;#3zq3S(zl+gAB0anSc+)IXqJfVrQ%+LQDZCh5zH z2$h46A=95aN+_2t_zMy|cedfk_8J(-&U)gV^1|by-tRp}1?826huaAlr#ZQ}2_c9s z)xw;G3uWB7`hr8@=xlN`yLExcWcI%aJYX1OOh%TwFBn;iuC#IFv*ClhP3)Fq#*ts0 zcO=#>%B;BZ;l&z7Wo{-?oqDKHZm1}4ZRX*|poaqI~S{h$a;ADLxW8J)C52D~k z_DV{o7t9VX7~Txc8e_zHU}9y-Sa7KeqF-9)2&V9OdpYH{7vK#qKt6ycHL*9s=ae^e zMyuqe=iMZ}hf$TL_D;~(%%|>_8(gT?4Q%d!CSYDeXbRqAVvAC;dL@otd?X!nT+qKw zUQ0cP$6IG-5u?+bK11yonA}fV7vugyaQy$ch5Z}bAf9fXhD8$Zi!ID&+(~G_Xz6JG zS9Q+@(6f@m1kx3L8JqBm|mwJ}As2D|Tx7TKS;1L_`o#;YqjEit&15fB#HwZev zAtG+FqXea|z&$#p#<$5wnQiP9ZQn2`NUA$;@TDs72G)31E0byz!Kud8Z0s4QI&ZN{ zu20zTYeMhdy-*yUQG#B~()hGw8T2fcOWFv30g5f3J$|2xEx}#|M-h^e(Ikb6bIC{E zv~=8;^Q-!OZOWYpS^HF$$ogF~n zxXlD^==)ke>TnJY&72*LueT{o;Ys$gO1xJIab6{GF;40TF&@uxX-t2M!=N}~lfB&X zpW0NQ4xQ{_%1AG`_r2gEI@?q51x`-kIzi#CT6SLZQp;DiElS_ET^y>c_5xes1;%?s zdRHt`E}MuK1mdC}ajo{u^Jslu-H*E2^C@S&AW!**+|(WV=qt0je+_n3cUKpDBQoSy zC+H_V?D>^Hy+B`>ph}+NW?w_&b6I*S$

e#2{Le`et1yt<)<)$$x4pRz$I?-SaSU zjS@nBxNFV1-u76fH*zr3S`U_^C0%~SF4AIV(WyyEc__1wy%|=-Nb)kNbmB#@uUN5R zbet(ke12{;s2dE|{p>}QR118L1ipHSy$H=b+b)vMsaWiNi-^%n^!VX_0a1Ey8dzdpGSS4ghC2;>YUP9Kwc2%g= z?@%gk`vkw?3!KZ@%t09V^Ood1r>5iV)^(qp?OZwH`27dl8(;`dn(EDVIBHHcwO)zs z9D3UjOabuE;mui(y=FBh*GbW7^H7KA_T_2&P&gbtoWKOqeFVb?blw4db{L>l3wnr8 z=u%tSm7}vNSTc~Mdx=Z309QId4l#XT!>+?e7%{P7XUVELLjHM88rL(1L$J1$G}WtK zHyqYs`7bz{hrj8jl1#p>5ASk)s1UU;WO+4Su&6(4QV%Fh>m%*0Y^Pl0N`vHaj>T_? z!&sX{Pe(!Q{L1oMM07g%9=3nb(Vo1p&Yk5kjGqth5GQr)y@ zXr8IwWpZb|r1AE;N}^R#JI|8%z5*|4bd*torc6M!T6?0m@QKcCdX9G6=~ zE|H4FoKc>h&bC*^NmF0d_|#Q~CuZavdm-h7g?UuMgry0$MY6g5*&KTmlulp3``v)u zoEr1|lMrT&Eze4sr%i!iwJL06O>n$aKuROakEO&c%$xFbmBiw1mp~Pb;8LO#f?qOQ z%y2Q`pOS^>6{BbK>{XQf5nfsy-e&GDc_Lgx3$yP>EwGnXT3V3JWJs*_gJ}AfV8G01 zIA#_7)Lu-9_ri^H;qtZv`Oda;vV#w`qXvkfcBWO@bh$K)wb)J=lNd?|7D6qZ;#JV3 z2zS0L-k(^UE~Q~O+-~7*k?|nj94{}$biJF4)=dKFW-rhU7WM1gxROA|Vo2IG3)hfw zA!;2JhZoT0cL2Ryij|ZfjoF3;0lQodhKzd|_}~AIBa%jqh91Qm4HeHzPrkr#o7vS7 zro8dOf91mGYz8-+Y@THurx7c1TSKY5dQy4Lp4lAwAE&}=g2&T3bo6qsAoxW)arxHL z?<-*gR9(3AU<*bIUJGn41yQjn-^*h?a}F=q zY%i*Gw9peh(Ycd#!sqr{wst;2Tl)rG^aVguEYNB10xe1-wm@*;%fdYgb(NRDd&`IgJD;n!ZO2@lWfDj$_Cj1aN0B*{ z5<+kn8pgK9>|2JM+3t+I#woKIybN=(4tP%8Zu1#mtPg%G-LAE8hdojW#}N`7uUl62 za#^J~{=3_zij);H#ka$sf5pbsvgUXmhu1NlBP+f$_Us}QKD0PBzLivr8tF;FG~)ZD zKzh6rm#Eb*>p8mD;YG#cix9gWN(QTow;h2Lvq4qJTS#UxI~RchvP{Z>*n z6MU!__#iKEJ|vX*#$H@mZQ))!D&tm}1YE3CEb^aq?U;uWA0!1((fS*5Da*V{SnO2- zm)qFgp!}?p^5b#|M@HHUn7q02b5aQX_a8hYk9*-B@r_$=57_>v#r79?$I13mhrssN zdtkbH>V@^#3yaTd?e^JAD5cBsCZxYk%QUX9>&At+Ke1r`-%hN6ij(8AhZ>FtT+KoY zmiiA}7?Q%r_ky63na+-t5??Z{Nf%3Y3bs^>1Kxh%{(hBmuxVczh>H15M_(7zVEPx~LY7skGY z2e9E2nD@asCt!xjMG;q_kp)n^9kT+jKdv>ajhXwN=GIa+_J*ktOSOi*DUm)pg(}rH zfoe!V9ZuWZVG-*A6zvlTRZXPrXY8dhjdF^TC7@wv?Qz&q_5kYT69|4hiM0Eiy$t3} z4)mD>lZLUu=ZU8)f0X_(aO|%0N+OuTX`qQ&JaGT`NPLb5M4L={?fxFu$ zF7H9NFJS``S5LDB_(|YG_6l6Yk(bjjOJrV#3ICxdsrP+Casj+^1#@*_ioXTO0u*}J zO|YHv7ee-CUGmbXrkGB{*h})}A>5bmdbcxgo(B#z4=$x<;J87?9UN^`gkt7&@~2`W zy5ylN8o(QQf;aF9e(ajP3;ndY3%rCo+Jmu~%u5*MQ35~4%elf!2_LW~n1T~As0{Zk zWth)WcxU?Sdo(3(u4YwO<52){ea=}zQF?Y2;43}Bm-_@q6-Hw(nd6@G!1W+J`r9tK z>E(5>=d>sG37^>ff{G$HV1)nIECAQTyYb#tz_T0B4}SNA#n)9~^#4*wtlj*Xt0*jV<3 z*2X6qpLI)SL7kduqQN0Zpq13o3puIJvQQbg2Kk_Jl22St<()4&1yb4j*eu>*g2O9F zfMa;Ag-v8%kehu%nzxDkkMAUNuEHV6iRNmj^shUG*xJ zcOks>qJ4r#v8TReqscGWQN-CWMmZ*KK{?!rg4iBFO7Jtw{R5~?OjHclLJszVgt|9DS!0p8(nTh_ z$RW*d7M#b*^JHs^xpv7*S_2na)~!2(=9%FX)mLkv#9J8=tiYezsN%+%5*D0 zWm+x{wVm+9#kQ)&R=)UyhRT;$kZenRHLd*(hnuTF{$a*`5Ab2ez$jDY9X;7P7_H>M z6ZeId5+(Fqh*lzf@dj3|N>S!Qt3niwnT;c2@GXQ#)?1jl%SL%Fs3JIIyizhssJ6@+ zw``9iQt4*lbhdEdxf+B6%HJ2G4&QXkM{5Un!;vlQEGbD|unFFaFn82p{%vA;FYW$Ti=0~r=Y(-We;!^QSMlv zH!V=-5S5d%aykmo+eU^z?VN_HKMio?Qm%VZ46_I~KzeYL0&+ScZ9z1qxT6Waj+j(} z(KZ@K?44ZAPe-QUhK2N3-EhJ5N5J242MT5m_NLrR6*?APUMt%{aO(oaf``fk z3FcBW=Xz``&m*yVRAtkRA%(8waeRX@nw4eR7?8xN#>c(bGj3zpxJBN?t=2ua<-q`h zv>-ZbhtrIO&kZ1_RN9`(tS4BTUR>4|sNIWL71g_!*%qv}}u%jHi(UM{I_r&Dm)rBxeNqRdV3kMjB zV7=SNCwfmWbbj)wd{IXZI=vvzb85zSA(f)IB3Vafz+r3Xy4A|M|qcCop-h zgP3rugN70}nv{oE zO3`RBqAx1#NP*(a(b~vpe8=KWIS0OSzN|+svM=osxV#Vsqn2K1&5(`xxWwa|$>s$# z03A#v$~lTab>_&Ed?7bh#(QCn@xtN~7gfPg2|6>!+AL%7Y3FqXM_pxu7uGu8SWPQB zYQPxDu^b$$G~KD_s6w2H{HPCSV%`@$icl$gcihnl_hRL;7ybn=eC{bUIm%H*u~jvv z$2T&uI4TfC36&iUl~-P1|M&)5Sp{Ix7FbkOskJ%S$Y_8?R`qP(a(I#LPsO91neQ0d z+bK#sj&Y0+jo(T`u*8u-nc0#KZnNkweP9f#a$QA>v0ZdsK5& zRaRT1uCPd*-sN%_J3bCYxw<1>S>{FRVvE#;RmI@NTYN?hwCPzBjrKHgsze-*Zw>dW z>1d-I_rg2!4!rMbIVvfCT6ljnbHn2U?U!{O6{+Muy#pyvZJ33hdLci42eQ46qqK%QIS;F;(!7cf$txgqg&Jx6&Z(L|!It=(wE(9{%8 z0Uls2e1h?HT$i{$;S|%*5v)wJXoF{{pp8ops*HOPQ+vWgD$WrG&j1hfac<~ba?t2p zl177*%#`|Xy5v8?K8c|mh%u$+>;^aj?&Sy`t|Pl+gy8UPwv%i6;5#tv#-!U8r8i7U zxi1EAaE5y0pQR=`~ysb?N@^qOL$Un8A_{DnmCs-VLf0D5IRJFA^rmET!b`2RD-)ax087&;0 zlvppsXm3P5QQd6`1#X~(m@FgS*?TFNcD8b~SNeJ(_A(K1)-%3?r2=8qi2qISQ3WP) zeDjWLK}Q^-(JX)9O{~P0)aWXB%|4K!q;s^|lGe7CN_9b(WEvmywml`$c-*29*>NXV zT}NKJvM0Tfa>R?qgC>p2J~)zFZN!I;S_zJ-*y0myMDcx{!p|}J$y0C=2DCkWD^Nlr zwsjwSq5tec=d!vMadr*Ws#sH23x`M>OgkQ3sG!7FGiCK*2=vrwx;Mz=lBBCeRWq;E z+n4uJ2+i*3_#Eo2^EGN*HaW-!2zHrYr1UuJ66TCBsjw%HFN7(Rt-_Y)$0uI+S4bv` zJW`3{s=4R6Y zQ#otGqA`)NC2>p_uVeP5m!Pcfj^g-kk}u#RZoua7U~ywKPjX3I)FCmsm=dQ1R5w{a z_Xu|AI5+&rHq1@ihQ8`_8s@J26|0+LY|`_H`gG$ zDXKr#AT!Kj_??SL;^p)+pYh)Jwv)65;gXSwiY9B_Og8NBgx`)-Yr9E!B`BK33)~;Z zc)>@?r@gUg;A&g+Ku2SC49T4hxPJz0P)=7DLcnb=(*`*rl(!ZhE(~+WGY#02QThI# z4=79{2SX*sX(V@KTtMT2%r}ic9pb30RIXu)?spTMqRZ)o`$TaXeQ`h;8g+OeE-AvC z%Z`jrc5678k0)Dmr9?F!D8C&B<3SG#`|2b&Y_4w{9_c8!Tz#)71=NQ93@1$8pA}D7Xgd1k0LnrPey?H zZ!GG|PV-59JdDR4h%h5dh`h%^I=F;wkt9s5CLIlke&Q&gynwqYQ|0V6oCgA)aww0P z`Ed=GsEu(Xz7^S);} z(C`!RB|Ec5?qm=01m>z}>IYCAS6gr^JmDb79We6xf?Mnf$9u(BQ_w5UT5zX4;kfS^ z=R!=PQIhseg^Yc)kO_!8sYg7qx#(V>1`YC03;Ts9wzJi+sTKha-L^X|OC3$9(*lR(Qq6^eulzabS5h3snCAPLTW^Nu`NLkx2#^w3Fa3@ zaDu~UJE}1K*Buu6uM0MQq0_L0Uc$Q^u2M#CbWL&-8;VJ;CNUoy823>n%NEu=?lRYh zf#}iecGqI_TrIyyW=`Y$82*VP9NzAXAMmN+RL8{OTPhXiI%+fJ7i3BrgMs|k1i|av zG!^n1P}}Ufe)N33L_#R>j|uMoacBuDb#ExHk)Ov81e!0oi`W}cedSdxF=ucByUmAX zw1!ngtY;Cw)q_%vBFa(mLf9CIw$k0vquA3XGBTIoiciao<6E>QuBUure!Nxkk>V3`Ms0K0?Ej_RQc||D0%Vim z7jRPWnw>9O^k0zZ2Syn2-IrBA^e$&kcxQc9;*?p5)u}aMiN}-K_*J^pHp{ z^qW|VTXygAv8H{UZ~UykhK-+MbxqMusqeI{7pA10ZlYbFuBq3qr?nfQ*J3q=bknwu zBwj|C`HXj;2{|OKq@-`0 z;s`A3i0Jw+95MKG3X(1(e|l#HXxjThZ9tIFPj7MceJf z__V36=!IM|4sFB8cgw>2!4r>n#V-#yaNK=E_7JL48kf0T^+ZOSc_WWea@I3z>rc-Q zxYZW>!SS~2?5W(_vatuI9Z90mcEZtzGV6`ZZyHo;S(w#5F}W(h3C+9FFO)`yUkt+@ zfJYHkd=>#!ZkqC5Jqk5&c+-Q_7-s1MY#ACo=V5O1X6rtluswaka_;u|){&1s`(b1O zO8go+Tv|PvwG&^K@+bxNzb>%$msd3SI%W`Fh`zhnU_`&mfQ%)pB!B%#KIp_i>8S`r#=CwpA} zVyx24LXQ{dC0KJd$A#R!;PAE&I45T+C}S+Rkpj-z)dD~2+n|~IAaSTqOs+X6Z1l@R zU*#DQ5*k@?>BmcFEuFs9ypm|0RSu5s3#QyX;%JMHjr%HMna?7eWapvq$JukiyZ`~b zU;*O{zW^2uy1WN2M9n-D5J0)hW32*J-bZzg`z#BMI$in40{>G27flK9Fd7{LzXYi9 zK5(D9;qrkWLmwZMC-Xtp9H=qA@@GBGXMCW}^B1(~onaS?35W2bryV8mk&So_5*72- zGIS`78!)S9JH`<7GA)RnoyD#|KRD^R zpNsCzvE!8<4JiJg7Ow!A(W%sHVA|^5^c1?7Wun|{l6N!NQpJ=FUgWKFk;iS6r%lRE zSd<~F^^wOZTgO)6N{n*Ui?Tx&W&1740Lm#_ZBh1bF;mO8wbyD<-YeM5&(YAFO4Ca_ zO<4+@;JnoQw++ncbSt&6V>-q8a_b$lrI5_a`OhlHKSpq%Chls50kOSRPG&8qHrhsD zNXvni4aE#78c7+|&>S{~vm7l65a@}_qpMQ2vH0dnW`D~BGmNEs4IA);0j=DG_+CRX zAG)kZYb^AY5<2%{>U|XpyX8JH;R+*RasdYATv(g&B-g@fR$s{~$L$$6Emm~xhfRj- z;&q7&-@CZL9b@@z9j`4qaoCS>0lBU9(D^xI%hKH%SX=(hlDru32QPv%;JJ$dn6eV- z)8`I&!^Cm+Diz|)PQa1*N6>TqJ+>lcelXU&Ttq1w=h+h?kcI8lx#K1fjvIt4-7MVB zGH!>fu#u$Saa2^2ywDO{Xq=^B181owcw`zn-?S9cWf^3bBPTNvO={!pU`sfb-2cXi=yW!8K1qeoml<|eF`_CzjUgoNa6TTKqK3!dibBHvDKd%!O@J> zShZRBHgrAYm!zm}BOiS!xslnI`m(pJ)jcry)<+Pvc{;u*W%V3i%3R|0vR-mujdkY* zE#H(Q7h4tmXWGbI{pmBSg!!H&aQBCjkFj)^>l625fva=@>1^*8Rp1g(iziW5Yu$Bv zi}$5VjVL}*x2(_S`CAeP&bT<>>PJs4`W{MEu!Gcdz^- zUIwrH15tTbUn<_%WN#62ymqpegY&s@%9sf;wDW_p)*`f^^^$uD`Pfsw62(m}PWEyI z6TK>kcKzRRUJMq7lo{w=ufNuz%1X?XMv4Hfyx7m?_eC~ z`$=9~v)B1I)$l>-Rc&1}@ASVMG0I$IZ>p)dQk0P`scRqd2Q>3HHOSUjc!BUm+>t*g zhFZr~wl~H$pVpm*{o^PJCkcijHQ?sPLWG~6&|>Ls2|Wij_!slm`Np)!q2VIve{u1U zgR`Rn1#4O;O9AaM<{7UyN!V6s@Dc;<8Bh97@&~LXm1B*?yudvO?&O$M$)fM2Mc8V*7Chl*ATn+P7cXNug; z!@ZnyGqY=jupD*f&QHH**NWM`;JKxC-4xH-HJrYF=xA?SO-KE-Zp112WkKc* z^NKZ0&8B9{rwfHCg?*gj%%BWuuDCBuACRBNY@7tGRkz+5S&by5z=08@)= zJ(c}l0DIm6aJa2jP05l08kW@RD!07=Znyw=X8}ujt5j}kEn{{{HnF=eFXo`MJ=0^Y zDi&^LHqWy&PV-m+dR|kL2k$Vb!cJ>e)}@;CX@piwsqM-aMkPvPpsK<1NfQT5@&Ka7 z$1euinu$iTmC<@BJ>}frmC-8WDhc+ol)JOf+`RK1DXXgcV z^M{N1sYXSuJ}y)C06xSX)TUo5YWbCD7k4;vawQ=DWg;uTH>0ddT3vR*CWAyfh0(Yu ztr>2`_kjHE-L$4xM&-)WqI#Hn^R5~hOlgO-(ClTJ!|OLQC5Adw!5C7eIpw{rwJ=M_ ztfDnxx92x-Ym*ZEfoF?ynr}vaJ`Zf%rH{g#*j$hy zcubJl0S+!m%%$ainz{1b6{{_PWd${YwP2ZZ&AW_)NE6iJ=I*jPa~@a;Z(1uGl*Q_p zQ{hPKUeR6r0oN(MYA!^V3twJDndW;Ab+L+Pc`~Y zFZ5)W=do2>=40n58bV__x{ad2E!`RTv>7^itSes_t&Y@dV4srZlg2?D&gclmGP0$+ z7`{IIQCYHA)cuvtGDdJqEe0Q>^uS2)jd5qg>7LXmdX2v_NyfO_OdpI}`#dnlcwu0^ zR_Wg^S}RuVF#5cWR-ZoUs@0WB<6;-o*+k{G zOyTzSS}7Xb)2*m$W>JO*pHfz8#cep7FbKVblUX8BD~;agQM;GEa~JQRm7y)YorE$G zrHDS5g#(XL?><^v#j`l3H&fBR&J1}wVy(5jubIKo*UZ3$7WLyojCSR5hzA$5oSZUq zrFH^7ltPk6cGeoQ^9HOU8Qb{?d5aj7c$Ni2T&Z??bL9)KOq=+VcqP4+ z5U|i+p@`i{IaA3TV=g)oLeE~#M_1bGHI;K-FsHp?csIYfWR59gxFgLXWJAYG`RGUw zZJ_el3+87RjGM%KymgK_RMGvx7;e9%q`-nZVzdZCPWz5RUcN`YNkM=Ap@2V`YU`zGYtdIJ24bDThVow@Dac4tQnWYi3p| zf>Uff1;@B3LIQXkV#MRKTABLe#3Pay>E&VbYEjr&ErFUJy;PRYk9Fh7e=g>8f-(nd z)oi$lX%d7M?}t*zcJzgkZ9Lnc7=IG46mBDgkgo=M%$XNWGly#aOo0?2cZXs{5bp&N z=MBPD6F*gp#Z5=ZFDpMo+T^6T8uRPZSDi434rya5D4fWM_q8Ntpcj6!3!n1|mF0Z; z7p_2T^;0=Z>aJBrXrZrOVFO>1`Lir}GkA<>ULdl%0smdxZ6nY2B-&3@i(E43lI~v+*b1E&I=@ zMe!CwMGN8ESz3Zp-V343I}q-Es0~xvS$Q{nq>WZud*yBMj=WE1!wNCV$~)_0?9mPP z$~){Gc?&jK979*;U}dw|0*L+uR-=Vp01I3IykDbV@_xOkjoGg!|0wnApZ>hiKsk>b zOx=6*vvX76j2G(3cJ95Ktvr;UT^Y@#hw{kGm_6_)&dIIN8ZWQ;UJ$PBa-K}!9lPoR z2yk$FQDzvOS%3v#PP(_zO;z;g4}2l~^eJY7sxrdp&$Jr0%AN=neIamL&Z$jH>eA>3 z_>6T&8Kzj8)&d?=9)z^>1;eWnw+KCYf{gInBCQwu&W3wcM#%_HP2l-rEm>J1XU>gS!iw`B&r<&srDCrgm`xNQaWRyL;D}r?sgxi|16OG) zkmGYVE|kSBC^98arZ0=IxX?z<-FCHB1?v})lV3M;kAx#2&R7bf+$4-(z`8`t{Q zy5|j}yxX-pwpE^l;+jZS*QJT>b)CO!WsIr#9reW7{UwZzI4kd7;r(7XO!rg1_>0zm zqrsd1-pZWxCIfYP#PAZbKS<0;b?>iiKq9(Hg0YwTK(lfeL>MD>XtFai_l3 zdQz(sZfgB756XT$x2lIVl&q2IoBy3QNLlGc-*St-B^G^v^GV-%hHs9QI=oU2EnbanPBCn?EY#9kc3F|gTqO)zEl zap)SeyfV(P2%MT^iWq>Le31$Ei^~z1jSdxAQ^uRZog^KeLa(G9o3IDkE1w@|U?<|GIxUioWDZmp&4$w`|pXw{WQ9Zf-K&=L0a*=xGc zjEfj}8c@h>EU$P+zH?C+s>%w603APLgC4zpnLG3ge0UnyCPP60d}Ha-X~?_u3W~o8n}cS3h6`tM3I<#|6VD zXVBwpJ03@WaP~E+E#tGF3b6)?b)StCd~DcbOFc`S@ynKN zLBODq+Ig(d}&tBl_iv(R>xKf&Xypbq!Wkp2h@1qnjl+e_a<+$dTn*CuPCi#oKBB`)7%rM z$-8kz|EP^o-j^Zj{iJ<>lY$<$@b#cx_NzOUm!_XRf~ zE_5Jim-!w0xJ^AXH+IX+D+KZ46?)ytRIWeulkBHO{=|ZLh6($t zzw;3ORE{D|1s`;A7txfzz>KvbTNoAk8y0|7p41S3z}smleEOVMP_SM5k@?bV(u_de zA66;OQ-1{MaJk4zHoY$H=<_HNp2?uW zvq(5P)S{~g(!;60f+fa68Rw{?SGFzi#7Px6*bNTNuQ$Qug~0S7Txve~!1ZJTXO=li z@2)S&Sd|=buhgybW;SsOnVrK+LUyUE5Hi?!CMYjlS;8o-y50hNHXc=d%pZ{bqwzu5 z?SF@W;Tx|7ba+2Bu>?#B>*hIihVUox%FkVe3dK9U5HDLD34{rlQqv2hS~sCDab~}K zxgnfpzJjjRUCLalp?Q2*5git+7IpdZ8q$@ukfgSYp}*M>idhqdZ?8NH-=R+)>8=2;fcFG9N9xOD`wAHl(WI-RV>w@y(@bd z#g@UC+r}$dFW&0M!M+r55z?UQn(FJr9;t#DtX3 zW5^zhL3yi%`ZBkptn#@R;6_)4xR`ODjWe!1p*Y}(-SCrM8ISQN@ycP3s<^y1e(Av$ zTgB-2ih2z~@u3vd65G>1dO`iq8wz^YLM1o+X_ywy^Zpy9Co8$|#JxcR_!Dn3ZgkKa zC^0g?s3g6qQpF3P(mMdApZWq`zHH;PlZ>*Us-B>9@Iq&s zEJt5mh*#!Ys8J8mE_1yAKkg|6jZa+LU9X4I!s>cUWs6sy&HPEcvZ1GtF7D^BpSIuw z$mm&AF&>%$N(`kZ-86stcKx>z{ELOOC*Ex+IO9TkhiY`Vi4*BgExj(aYU-B%7Z>$h zsxH*lBbB_p%;x0fx2vO9rMb=BRHIz*uh*L>0lftIJahNDdVQt3ocVEG^ywJ(wyYLa z@yd?flv)&AAM;69IeS`ty*h5H@yHHym`CaEGp;gO#t3hyvzshDFvbZCE(Kr%mjW~h zkE2m}{6jR*Z&Gjlv=M~rkOkiX#~;iqiuQAONt@ZzO9+Llv)*#$vmC5`@FeT1NtW)x zSXWnlD`Rj6tN%PPaDhEzk0*x9!K!p`vw1;(7;y)yaEj>c)N}egl;=j#_7=t5;MpXXMh_HZz8amH4%JiSkszn z!0bk5=Y_&NR7rQjHpaSpEeDWVXjN|?Ep)_{#T-s;2R$j-=c44h-a`D{y_A&uG7dU$ z9ot<`oLfF|xUhU*@N9Q$s>;#FYO2N%wH1l_AVt9w46KqR;O)DS_}CgLWAL%HoQWZE zqcndK&psL57tCaLf?z?1|4s1iEY^f3e-HbKTZwB@!o#!i=FHW<55?xP4ooS1yuf?* z5v=ATWD0-1JJ;%!XT) z+$--97u8&W*xm#4{6nkc+CBAp%6+dqcdhbnxk%?Nn$SycrUdq7TZntwx9%3K)K+S=3=Ebv!Xu^>dvVnMLFmIa~e=Pbyk-e5t1 zN-VIcX)MUDe#(M^s=)$3^)3sFs25m}NBximIn=!@u&G;E;HREpK{oXy3-YKlQ_OG~ z3v#O)Sdd-4X~yoeAVgim0-L(Xe0Y=vdDQb}Y#s|j)Gt_IQ*WEGO)SW#US&ak^#>OC zsn=MLTb<2LnKBR?}IKsP1EdpSpwv{^}tX*wk4p zP}FlQ2vBdaAW+@Nffq z^;<$ZZsN1Q;K)q=MljA;1)le9EleuvASsJO153yxazsAoX)u34e^+L*CKZ^db za8-Qz`}{z?Q86Z4$W`c18*(uzLge2h1(63-c90$cQ&x*XdQ5jFrVP-pb&hPRKYj-B zhGCEHpTML4v7bly8N}O$J->AWkM6LaEc^`O4Z@zE`@lb&dI3L!)UAFL`rep=MHtIj zQuHe%#ju}h_!*?Grq_e?ui-?VIarVA!1%@9`kPs>|7Aa~*w1VB^OpVCVDDD5v!5L7 zCxHFrWIsXdCpY^EVLzelCtoN<4AIN*{-4GfwGh~>SM!!k<7{(2N>d9}vSnj2*3nfa z4QXmi=~0^6+ATI=tob_Y`)TSVx7cJkmZDCTqv~|GSc+S0mRsy2w^-+6W`S%3NmIMJ z#k#x2db-6@db>sYy2bjt#Rj;=2D-(Ty2X~e#h7q0)-hR2uj>1N#G_|5zEXplb#VuBK6a<@5V`hwXORVk|tLYYF{eb0T z{UVKb2v>}C2o_@_R~qjbt{Cegt{A#V3Xih!ERA;-mT1OUuW`j#uVFDZtflep(yy$y8O(1w8{I?7v7Jyc|XeVezep3(bwjq6yBMwD(&`uZLjyE1Ky7e=~0?` z)h%}2Eq22#b}KqX=JFl4#4NYieYe>E++vU1Vn4aXesPOEc8fiU4lw1{TvYx@X^Tthu14zZjuM z2S@bFnJrS?JRM`~DylqEJQ-2bFPoZ9gI!PFHsnvzT~E$5<4A zJj|aYjM8gaWfdNWT(kD1jnXUH%H=}w>QAVX8iapKmR4k!5k>NC(gZaGj}p|p{pikU zy?R6#kGZdV!Gc4+=jMe+ZG9457n2w2n>6i%EY=nU6mxL)iC%C+WlSVU#g`*zwoLysuW6jKKH-8Sep_qOBb?sqPQNHs9TC#y&3=?uM4#7^to zAMAMTONUE8QZHk8PgZwPg%lufqfRM$J)GYAECp=3LYXOgOddAWgP;Q#`I8~WPg3;m zDNK)LWz$gz^7CU%Nn$!Jdz5twkFK6J^(vlo62|^ym1(&w{cprn)sCMn*V#)<>t)Xe z-NB=u;Txbnd4!)!Pgo+;g;`?i?|3x-5B7-78M#1DDNGZ|>S?+%6RW3%)_W^tN;3kcG$UY2GXkbGBVbB10;V)0 zFqJ^AGb7fPDa{C((u{y9%?Lg+1Ew@1#*}6R2h4ye&4_L1!GsA56m=Z$WEO8fm`4x^gOCC#Ldc;#|H;Oy!HjRK7+`<=r=x_uf?A zc~g1cP32uTm9Gv{`Qk8@uMJcA(lC{;3{&~SFg1lQ3{%yays%~bm-W(Az9vjn*^)4o zYf`CvL71wt^^WNk zrm88dPp0w}U@Bh#rt_DkhUzf`{Ro68q|seIixmoNKL`Km9K zFZxnd*1u2y|78olRKDIzRqgyg>r|=gF*ErHkFdUz%9Wv1m96wr`9d$1uk%v*GB1^@ zL#cd`m&(_8sk}R+Vr9YJXQH3V1wEBZbSf9(RF$pmQu)#@l?!Pqm(f&+D3-%>xrol? z5;~U)XeyV_xm-Nwa_OAQg>x>K&AD7O=W@wR<$^hvujS@)shrD&axRz2RKAXz%O!Fy z7s$CPlgGJS9Oq6@E86m6Ne7>uwYF@K!blZT*?YF$vI3Jm^)++f`6n2pkH;-}>cBum z&p=!wxlpPX1G3TOvPMD5nyuFiuo9^F$9hK*neegRM?`-3SRX1Pt>)+h%S&L7!vVgu zjI*q=Wkp%5NSmYAw=%Ax{GaHJM5N~@dQ%bE{E6OLME>|hj~9^!bM^Kjk~UXwCn68# z>McYhCRKWEcBF6TSc5A87V!@iVG-9z3rM0wEjyzwim$%3;sN@p8uobyW4VMT4@6y~QLPqY= zttCR@?o#EYdMA-*&QiU(h-gdo1QDsQOm8D1A1u>5Th+bGs{1kq=96Zzv}h@|j~XAy zpHo!#E`=|bP{uEpP);q^I}6BCq}LUZ{v?r+K~g>bCcU0`tzDZS>mN_ws9D!rX0WW0v+TA_yFt!-A9c!+;igI=Jss{|Pr zsK9E$=nFJ#wW#3*I=NclU!a0(MC)Fl;cJB8<25-_e68Nys?-IVx>oNYA~)BHQRxD; zS*O1zQodcMce5IY*;m#4eL?Hhz7(+rjkA7Xp#oODc;X){aeaShy{aaQdVNTrtQRf! zA>EQAvFUn0tGo}1()GR~@?W}M)xu{=w%Qpgwlo&c9w+d(P|^m`5w=kJ2EnQ=^lF1X zpqkm8wlI`|GmtGuij&IZnF8e`OTLE`RtSyRug5I^D<8bI&CDn1*+zY=Mchdmwn-l& zBKJ1w6Gf!&W_^-~+}f;vAR@g!mjH~<^>HF4;R`8p_6vQ4NNKP|e_uqt+Mr7p+J;>s$(~;G~$<=Nx;b(Q;>X+mfj6O!{uCo@`;yq>|h85&6uj zGDS$;um*2-TWg~Yb{(f!))d3w^LK>5ct6K>QJI-EW4k^2zwouaKCIk1l1xtE^FciQHBZ%V_RSLGUvAZ>MOAWi&BU(723#&D2MUJjuI6^_S6& zT~bPiuk~T#wWD9_LqsI{8!2VoH@YfPLcSG)?M;gQR&>;x^y#Carnvjm=aAl8)KhpE--af>M6h+xkm z8h1p9`y%@Fh(6lFUql0riUDI0-90M3*6o-+!6JVVBfqRofk9nSD0Dl>Py0L~lG3z@ zU@<8!qISpi;R5)`aUoTUDCUHevgw2n@$c4ly*`G-%;9mQV8r(Dsf8hWTn5uf6sz0yI6dlrd$-Vah`5o6cs#A%`XYrJx>{zM8`c(L6@aG!!PT-EJmKE%a;Y| z^XRwBVj`YL?XL*wnMeDs2x{k1S-WW0d9=(f+I1cUIz*42N5dTwz;%b{O7p0>CPsmI zv|G~$TXkp4SG7)S)Ln1G>K;q>q2)^p@tQ}ey5QhEdZLT!&Z91d=#ca1kRd8{n{F9` z+S}Crdm-z$>HPO%qPR^huIf`QuH2@xSEbjQUlViaZ8~vHpJGu9OHwvrKHe&96BWOu&lHhOxAYVdsd!ue zSVVT;*5`{z%RBmF5y`qE0SvjTe<)I(-PJ!4kr`Q1B>0{_N2IL0r_U0Ri2M3%5y`l( z&$9T+OcrX#d8oy2^C|v+`cSgJ7v(RcU=w}!K%Xo!*7~1lw@tM7fBH0wH5{eTVxWw{ zk2yN_Un*F0m3lrDi-oJS{h?3^uhQFxV#$1!x<3*+>{ZH?BY{7PPIZ-r$&vFvihgvJ zD*YtZg;(j5pTyvBmG1o{MwhG9>}M&m#!-^XH|@B?jrEY$rU==o#4jm1f3Hd24h1nZW3?d0TzZv-c=QSxtM ziF1uM{3ev&YxL|lvBbGX^`A(QIZwpO^cr1xB32OBDB`Ig;~I^8Di%!FX#Z2ai8UQD zX9l&)F%WU!n6iAKUWBqbMHirk&-4IGGOkhEXJX-WjaDL3#q1S4Z}cc!ERO6(AEAn7Vd0eP!d8gKWk&>PZeqAJbE>_wX>F#sAro}G~;C~ZT|6S03kUIY^_;rxh{w_Fqkbe4I&~%WZ|Bzmr^oN9Y z9y{EO0T8=Dbe=7KZP0OAXWTJZ*1}QAdUV@G}1vFjnd<+c44MO_4AzB zv#EoFvQtt?eCsUzNcAF44j!cTf9s9pck2h^-SWAuche5WhS8P3#Q=Gb?))v09sQ5Q zk!f<|{67+2n+ywzLCobS*Qq*rNWao#?vT45LM%fgKYay1=o z@$TFtx?(e;scMHSfucc|eQ7w&e+QR6H=I_pgH+3AIE_*VX^PEo+652NNt@v`N*$!! zis7{Etfb0{(ZUk3l{8r~oK~8ZbXhT+HolcqD!bvd7_Ovo*$t;bZY6z}T~M=<{>yGe zS!fR^+E0{oiQ4-a&gI1=`qodBbBS{1Fr3!XOEe&d(bMACB_`Jw$AAmfW@o2TrHqox z^EW6X7Lr@%Z#XRnm*_Wt!?_^1L|p@Hkzbd;mT2)$us5zLWQ_h@%HR;qVr{Od`rPFsg1=rJ;=Q7$_Et<~i(tI}{ zs>4$^v>aK{P)Jugwa9I>w3>jEop=;)Mjp#f zj&P$gWu-R^Dl}nWvQy>auhcn)S9ajrwl6t}B}F5pQ;j@E2T{-Yc?_o^Hl1$d5kino zRYMG?;X0jCLZlQUM5;w}UL#4M&5vBp=KBBOQ%=)4d)g>I`u1HIPG2ObgF=mK=7cT z;WT@s)0%=3+Fu2Yu5#nucm;l+e@)2&7y=tNii@DE_u}#ub!(Fwukp^-H>Uhj#K0C6 zC@Uc@nBEjJ^2p7*7q6&}Y<`p#f{*;|{HkeQmu!tFEH&QA!cxtP6cNJ)Tm*_pBDk-J z)RH-iO0CkpsMIQ76g8at5$W`@s07fym>9*D0S~(M0BB{cvng@5hPgqDB>JyuP^~UHrJYRR4eF%*{(mrKgoN zoO>&fmXbzunWgok0Q+AQKEoW09+NlR2%^zh&0XqrI!z2WTFHc5FOMAWw)KW%AIp63i$TvCKtU%Tu%>(JniuPbv=0XvMa{-@D6CxyWzZ4+` z=Y90pm9meTl@{&1k5-m8x?4IH^GQ`NbU=X}I{I0o;t!n=HAlrIRH%&MG`laMZe>I( zE}_+BjM`SWT|z&U5mVL@Dp}TWT7{O-l(I$ztFS|?Fn{K3;M#l(hueWw*ep6zR&>`{ zRH~e)%sFaQPC%Zc)#b!%=je}ef|ci}O?ks<4nIfR%8TYaN6*U}O)N6bF)~hIjjU#1 zOWV|2XJP-{BI6eIs33atEjm&`3>3GhXrvecZqbxTF;Lv1n~|bXZc*Keg1}p}SdRQ! z(TK35Y9k}#2)6dQWjeVk8;2LS$5$&%)^DVNl?47qI#tPVnzc4kWR&4t>T{H7FoLU7 zaTLBPMBc8oFX{q$8YPCDjnulbAaWyZsVv4H4zeHvAjNkGkbIO_#c-~$H&Wj!qD~uW zPZguHT#fQ@5l~~l_M`OLMp0^fC83xl85^l*wCG_Q>GNpOj2p>TRg4xJsY_MS^Ec8L zRb50cj}iReNWEeVrx|u5U5Jq=DjO@tp^Y>)R*b+K=~k?eqkHr!Rt#kKsBJYNI`?Qp zH8IKGqhArRq~#uKrV?-`QV-s8O3NbH3#?}PhB{X_oSUP}Lq>hX5=$bRDOtbcpY`r$ zI#XSgxS2}S5cF@Ri8aKiyqPZ55X{(2C2NYVyP3w-G?FaBHnYO|{Q>lce>n@=|F2H1ka$T zI)-x_JA+2lk;>UsM*{e#j?gJHD6Xz(t_)gGS4z?3Na1=yDb1i>^~8XaK`ZMS&V9ZN zx=~N6Pm%f(TEF_Dy)$TMeWRf@&_dR;sn@H(muP)7U1?+#qtOjJ<)G0YcJ-sQ1zp3O z9VmkuG>}T2(m)7F2Aymm5f{)^;{J@Iuw&-i*dEXn!2 zKBDGeu#S-%Sw9C|S)~`Gn{&HGItg4ydYqVN*HQUKVh-YY7RCc(P!o!6#72f3ju2PA zla0jkVIAdeEP?cIY?QOaBZU?;7TxDMec4#F?sa2SHOd zQF0qh45OFy6)kS3-mS#MV5dW^1mb?W)yhb+K$stpn#G*zE`Mi&JloT^f?&vgTHV^H zV+|$8*t^HzZYNF5_LG7kN9fts~4@~o%Iokc?)r?#ENaB!R!b{4ETPS-jM3XW5mE@IR?P9wSqb{?k_ zT|^Tfr@~!D=Q>Uk5wRrsIIDZ_FHqTyTWMx@qZ;*3@XwXcsya{odMi?MZlf#Rp=z_Q zygG-u<21RO(60Hb)3@W*IbSlMSn9CZ&ySuRas;^EkLWIh^EgfDE|_whPIfmEt>)lJ z?GFGc0RE%q2y&TulA2QIpt?N_rVX97pW2yOWZ+Mql?|#XWFP*|?jhUJB z(l4WhneP@~1YL>`T9^%8l$r&qm1Km48&dK=X(j+|fwZj!b zOqNbi4|0doNcAK_^%Q;<1hpSEaKi9G&2h)qxYL-Kx|XGufD@T3wRe9MX{H)1bs;>Df#FK+cU)A5`v*M786{ULcMmZ0-Y?0ZxBVa|^L=$Q zc@fR(F9<$L>s^uabO;fPdcK$WM}0*qV}D0%0+=AswX>=15O-0)_wCG7zIRn}tencX zuFO>C%&R_FBBy@3O(6fkseiLoPUgEcW@dcUBUw;?kruil2WbZ)7WK>-Snbt2yF}mP z_k^%KV5x83ms1bJhu^9jw^%Mk+*LPjurvwaHoCOca;m*Gw0|>o9~Y(Sc;L#+*Y#%d z5?1h5toU82`3DKI4pGVgQMXyN77?pDeCd2`yqu4(oJ~}$kFu$L=$tOp25SUK8(At_ z@48aogD<*8Fkfap(&SXW$}*9^;f;|Wt?kOp7glESW=eWb5S&4y5U~j6N-z{>DPOt4 zqM|8`qz5>l;6h!4^Jo@HTumH@xGR-wiDsEjEm2B+yh7x)bN-LQ16Ss1Oc^sXbLDI| zjyZC!NaV2y0!%V)y3f7>GxKe*~k+q&;2Z`z&r$kr8K_d{c z>gJpXC6F>0s4b3(`Oj4ZpZ-h&oYS9__Z~ZqVWvW=Fn1#E%3BjBF|5?@xopkF`3zTb zoU7my6fsy(f0`P(A`7X{V56)8*Yb}B8|7HIeXvnnc{GI14mJjb{0t17ds3gW(tjO7 z4Tcz{3jT?QLu;#^X=8@~=;aVvF~sPDZ`b}c#Auk4xvJyUH^@2ENX8AO>xLTba;-3* zW*?f9Mh`Q(FM0Y;f$U3hAZp++V=CUs8fMHvFhn&LA^2A{K1J}!`$mEvbKIs~MFI=b z)Ax8;LTX0If7>6kUxtu#sMvha*sC#AQ(L!gl5tC z@%+ug6O07JhDE4pm%B4JR7i@zU~%M(^BwHb3x) zb(}}pXx1j95cQvkwes~&7lReNn=}c0fJ^lxo+a=D9wdKY^uxnr9~fN`6rODKMlfMA zShbdZm~8YkQ4hTVDt_qRYZEn^e*P+N5r&#R11a+qyBM@wvjvX+TM=cvn!&8h>RCk8aDv&?f zA8e!Ki5Cmgh8adny!(Gtop(SM$J57yjUoybEZDH2VnM|k#oiIr*efD7P6RAiqYw6m z6=m!Kmc(8ow!{)+>@CkdN(YTb-^AF`Of-4FGt2ES`R8W$v$OSf_wM#?=bq2CSBEHO zvj;%5x7lkzjJ4VE&tOhqPjM$&d$@!*{b{qeaAkxxTr-|p#o1dpt)Z!L_I9po6x}eM zzKpZC1pPe@=VA@@nP-o3T1gk?*$23-R60rMsEsO_W$D6vdsP}VAGxihIrHrcAedeX(9U9%EU#IVDuvU)S{fAv9YStNAVTt=rgkdxh_Um zSxF&_(H&OO(Z%*jE~3+}4r&lvsuFcwf-bd^Xo-C?gxga4G>Cah{GmkjGW$Xm>!aFE zbofpwKdQOh9-`1_p!nyd%G3JgIPELx#d7;hh;b|IL!9h%X@xz&m2daZKHrb0zu|R} zyT7-{Kd)@*Qq(ivUK@(B@%DBQC*x7E9ICt$&C2(Ii1ubYg|0+mIdoNDW16(l zUXzkm+w0QEb#`w=n7z(EM5$~JfXe0oy0yk0Obrt3#St2qU>{RV5*XMTp*YaRH|R!j zEA6#iBEv?FiCmAJlK&(~6~?W%k4K!l>oFX2sL=+T#T?qc0X;E?+!F;(Oca=&D6ku$ zS~uu8*}J&iP*txL;YwldrVqX9UB;c_$X^6wBZ4$4~c6$tVcV60V zZ{x-{(rEoID8if__EClLbYRSh3NAXdt+4bGn$ZMpMxmR zQ&7~;D0U|%nalLuPWuoyzFS8E;V9fc*YTA=?)&l{bYZu>AL3o68@ufLp*elGeIG=% zJ@&&8_xIS3KrHyoezc_A?AKY2bI1ow?HkD{y?hH!p+Bwq*Rje_+)W*>|)#ZllPdoq-A z4%w3+;tnH$II4BTz8B)&5eb`e)Sd!gnPc|d5a*7GFZ8&5D}3*d+c!hd3Gp@g!oJO2 zo=N00@TmtV?||K()_hyB7?nSXE5!!tf70IDl^;w{n&b#NbP{d2fj&+}q8lhH6&Y?I z+bNWP1Jyb$@cL={PWYnEplBP&>#X=rpS5p;Z{Rum7I%5f8GRnDHu^lZ{USfZ#B=r# z*N7=oV%Zop42FMc-;M|kzq0p*)sKZ=*?SjFR;Bm~XPfRWwwFop3@?{hqOuElowrw% zUBO1oMJKA~Rk;!kIFCsrnGT;v$uel*1^XZuettqFuF<5o_J#>QYl_pzukBsXs2SAe zBAO$EGA`P;Kuo`cw#}e&U!!svbmnXO28iC5?F8}cvV9}Oyl*5z`I<^)!xnZk;?OF1buZ?Xv=H3S9nA>u3_*zqG8wVi4bqE+jl@D+>o%EH<867 z`s${A9Ymj7$o>)ia7%o%ZrkJGbG~C=4zccz_^RB+jmIN8co(+~TgmmFeUJwp1h}rw zhK!y$7#JVZzI(X5uccS_?0sGN!)S!N8`SeY(qBuD?xTCIrPbe|$FHSUcKcC?7j}W0 zP4xJ+6y&fUgm~vbldh%Y2k2dEsp~`Wy?ki@9KMZdqS7TDbIAqzI^EvG?Sjgwj)5!F zO?2cnS@w^~mVqnN1=^KiUkXt_)4m?!Ql>otVnCLC6U3`5fs3+HybDwz$G*{>lQNPj z@@Z^ttUtMeRZ<~p5tZl8;7=K=p`@9DwM)W~&=Iu|qObp>C1BK+Fk z71=H)*B{YomQ(VN=n2cI#ZTy0%gOu+oqst^eSsX7Q=y+lW$VxA7t5*XOLWrZbnm73 zj8`bja{BNJm0eD&UW>2l8x$VjD8E66*h3}WqC@cBcNAiAeuto=OKN>g$KIkxWYHgQ z?S0+&*&Yfj&^8Aj@Oipbs{}>AvkyWkv*_h7=n(j#_gD0UEc)a(`$-7b-vyri-F^(d zs6XsqK-73Az6bBnb+TyTpO`wc$p0_#W&VZkkwwejV`j*rMjtRUWRdv+SK6J_{%`wu z4_-n*Ar7cgCeyrner@S|0do`zv6F`VW53|Eb3-NbJdRoBH;A><$-Ds(UBJ8#(V?IT z1u9s`yaaKjkohgds=^`$6fv*BSIODD2$AD#UWVA^BEGS%=2`fvxtXUSGTp?tv8Z_t zzW(lJ6pRF1b~k%?@EQh+XR6}iKMTdoktmsi;)c#7ZpAW^?zS>!Ti7_rDr2_x;4foQi;pJA z+|Y|k469#_?v^nNQAk;{F0^KqH6tLtFKbSO_@tcK3U(1zl`~to@<*tMT66;4DQETq ztx_JU4`^9=a}2~wCHi}t;qaaIHX|W?E12UTR#h;^Li}1m!Uk6~N5OZlqB$O-W+f4; zDw*w^Zql7fW_=I-92Gg$R)y?DJ1UuF=w@ZJ1SJJ5s9XT189u3Oegf^?mCb$-e^xfT zL3FHQj)397lqzN$k8>)CrYZ^iORbvOh2n}dDuT0cj@tN`L!r6Z2jw|OfBT@4_{Oy= zs&kHZRW(OLxK=}v@zrQGGt8Y6GfMo8b}#Wa9j@V@^ec(SDNARX;PLm=y6`Wvpo7KvjpD73r0q*`jFp_=w2SlgC7Ht+&;c zB%SNw)SRQK^(4EDdgf&KLh4JdXX=}i;H%fbj8(PX+!#r2ZcGh=%u1YMV|wLp;%3g@ zEKa$5IuwDjx4$_XBGKQR3*psJ5}MZ#Bj+5wYKXCMjv^YFhDxtsC!|-f6ZtemD>i6s zmOyB`#?mNz8%u#a1LQ152biN(l#P85Wn&*|Sc{`HF`W^mSrcgwY9j6NdlU3Pv}#i; zO6wtr(s~HBZ(>%ah6P)?QA~4m@pnzlIxyr>y_wn0jh742$M#IX|8r`iZkAR}I$ynI z325DDhNjp=fz45oO?05S84Xdf1v1=3pSHl++(bDo&}Sc$PfIhXQt<4P(T?5S(5Z?uwt0BVMm@6S(w=p+DEN^SBfvC{VobJw981nID_f2H0 zhno3xGfUE~c4j>%d@0t>?BbTBN|AyKb)B)y4nvjbQ1OIFF9Q8FJT=9NL5Y` zkQ^Ee#8^wBEd!+qiVu<+%^f7AelbW&J#?@+1uAz2o0E%4`@SxXrCJ;)#t)-7ZkX9p zPR^bo63c6-X+S-0s5IQ`p%^nsG-#M)^UW}fy(DTh9OEg8HV>DCi;oaEYlOsmG6EBF z5(S3}JRK@cRd=K$y;%v@FkCK@U};jsvM@6izJJ1`k~2mLJT*#^t{09A7CyfXm$t1o z+MHbUH1`l*4396BP}Q$A9UpBrab<|HF)NZ@L8qALR0%>YaiBiEP)pB&-AdDrF=iVW z(a9czYuITD9}8asrHnP}dhjAT5?Bc(Vn}0qz`cHg)|{rLjJe^2TEj&!hGcW{BH7MK?vd(ui4RxrC8hN>jHe zTyA`(_9&HD(_S8mE(Ru>YZPIaor+&m`bnxJwDw95U%ETN^rMDPdz8U>x<)l7puw)u z<_S3E*Qi(|M)oyY7Kuy4HTpRc6V)~9HxZ{RlP*p)dwXQ61dpl&W2w)wp0%l0v{{!f z48L2wfRRZXqtMEkR3_S-53wm4-7}La#h~kE()Ji6kB{XhiAbD;-jG5cCYe24`5ge7 z>~bXao{UUW=;UO?NTKFakW31lnSu_TLS3h#)27fxC0fNIrxZFJi|(C5&8A7%k!d*d zDO6`VMotRtnvRA|A@>>RO(`^e2J%gzw=>WkQfS;v^vM)@I8)+%GD{-dnuQ4~g@(_T zR2;L>=ToTDrx;Z!bo*1ZN*)EwF}r#2TM3l+SJe(f>FD#`HR#YBvn3Rs&B0*Gqv3PW zuk+}N68<*a0vxCLHnW-QaTU41M7n6h)#Et%#38#~v?ptK3ZfKMJ_K}VOG0W@+%-?Hcg2k6Q|a{x3eEi(H+L@zQwDaH|u0Y&*GLD9l= zWV%_7a#vtgCi-N*>d^69Yz~H4xY!&5k+T@L1P7@765QV$pu{DT+KVNk(sijh2ry|W zdgTFfS|+|BN*rHi_6Kxbj!|%chAcPxLL6GorAS;Mii20+(sh8&t&k*3#p6Z@1&kN{ z3-RVK_$sZGM5e5i2sf3exk}QSxyl?4l?SWLA?{Km`I4%A3!1wUZP#m!=|PFHc2DxW zjvv#tyE@Pf&9R+UtTtP_@(UuIhv5_H?rKzWJJnr-NoYIK8g!}c^p6tb)}rRy>DpQe zYq|~{U_C{z!%1GR;zv!SL+fxIx6#9OW+M-qqEpm)XijSruwLmr$MmGlV+Iw+zga&? zFdMq^3nt_}V#d+un=mo2AivE>WCg91xJ zT0w)iVAQOjueM-N+@*l6W>*jXj0s8YQ%Oys!ql+Oy-M^n$qevdgc4j(i43K%+z~Y>ZqCTU6qkaVpc%VN&%`|=ys7uQ zk%bGu(%9l;v%Twi73mxFh-6Hf=c!AIxg6qN3a&DTsl-k^u<(00Bz#*XJSbu9?h5pG zhcIWFyAyN!VM^VJd;c#IcbT1C`K26G&50DU3qAfQCGEmv3%`s5O`k}4yKs7sQs><` zK}YG(ZcGOF4r&js$wz739!yL}6F(DA%x7kM*f%@&nc2o|uZsTTL|5vy&kRWn8B-oX zqdv#vwwJO#$2r(bpX|joe=l9zi-~V9b=-%9_fqOUbdJ4LX+P%My_B>c*T}t8?f~u> z7t`PaW_?%w&ET*dm&5vPSt>7WEmIuH5Lky27 znR8l_&tbDF?MNGj7aeyf_7Gy@?0w_lpQUA~5z(dMrr)cfZfZ#IF}?(?{~U!gJ=a8g&( zybERr*VQV{;wZX#0X0}nO)jF7uciYRajI97&n2|<77DwBGlg#vxDacj=(9^^CD3m! znav>HUot}=g1*Lm#1{JYYus~hq2SBrR2Pn6d{@tT!w~%M^smbjsr)x)Bi8-~4V6lV zzcGh-q^g7usdBfZSKr_=6mZ3?=E5OHlK^LODt5m*#a%H26x!X?nW{d+KO^s7LG@GV z?<;0!h)&;1qMv_@+pA?%@2WZ2b(u>0QWUMaYIbp9uQBjrXYwC0qb@mLGwVaK={3w4 z%V_sCa~DL9>*iXB%6K4v2IHw*(+%&r>)UKa1 zt$s46BAGYQ&A+1dx6Cyvq|E?l`n3Jr%Jlm!T%^9DKDW*F5KnKLn;_!un28W&@0!P* zj?s;~<|2<{D#y2~Nd{8h4O|gDO{|`*xM$XP=3wKiaBB1uZdl&jL#>a|fcxfQ7YPU% z?@YH>&+9M+ijBeU-6MKwWnz%(-M5|0Jy0B$L zoaJKCGC$#FtnIg@Dd{2l`g;@S`8HOO%{i{Ol~&1Uy63<>`)#W50JFetTK>RHf~fn@ z+z;{Kp*hEeRgK0=oat4?g|%s68lI$Y)2TG%f13)YW4^mhL(?%I+@=HR<~WxVto}CH zncC8#01C-4t9vj)ks7Nawc#R_qq&)85!#+%R>My$E@qgMRm4IG&a~#@qFOX6)10P| z3FI^we~4?Ag`3S2v@8ox#wX})7Rr5s0<#6K$;PaAf?j3g2I&NK%E9oBr-M1>2+w$x z`6nv#PX2VU*YwKNG8c18Jg$(q%Z{h)T%68$nv#dBMIxo;nYBFRS|B++!Asp* zkIdRo=>5o?=fVocsuRxC{`1w%$@4MtPo$W~W(q{9@6GK>VcI!oii*A8i9UR9c2a1| zd1tCPWlepW_5`Cjk$!k$zEB})7oF+w-8IwblOHglNCpiqJ5$)NYg*FdA8-?#NMX;+ ziz?*6RcAW%&Dua3_Z%0#M9O|{9)yVhKXZmsxP9H3a@w!+qh>#vLC#0GxLe=Q(jU!U zuI#}HSshKeKjKd82-W?`T;L)q`@VCg`tkRh(Et5p1}e0{j!I5Xs6l;Sn7tHgY&zrP zfrMt1`@$TpP;!?uunX%1GN+^2Nor8!i2ra!0pugvbs6aAd#yfS+$Prnz`*2K!n>sMxLRf3)` zoGHWH;7dbaTe4I-Z+i5bd0B<; z`WuD(;(mWB_=h=Bg;y&;)BiB9sBmxm0^5Jrmfp1CojFN`_bN!m{xokWkAGpx`O`e0 zlxr1s*^s-n0bTsd3{}A=J*d`u^P2K}?wb1hd(*{9DJQwQP?bSjXVJXB&H1WwuZrTw zu|w$f{HI4Q+rs8M0Z4CkvH)U!GrDeRb4lsyP56@&McUlwuHput5P^<6||LunVf za1TqV7m7GS6v`~;LhZI}^QX?vj%5m6Depp2KinTjfi8{+mG;)EbkN1IR)v@KaiNO= zJ8IEHSI1<9)>n0*m%VoMrsi&r0SZ;9=0fwP>}W#Q+#DY(^e0fxoE_z;dQnGDg*sJt zp<9VN{3y97@;b@&2=J#8?v9Bb>_I&OP!HT#45f?39CfLepQ9$V`Tah&N{aFaU;JFn zk>Dbp7j;~yVX34>^kp$ef0bBWfBdixU9xj=M+Zg5)u&a(9V?XQm-^)6;W({4242K_ zIJT*@t~GGku;sftbjZ`uT?JS7r@uWN(_GeZAqVzECwX_HDIG20sO8ECMeQ9!c_kdf zoJHgJM%3QN@iEOU>G(v&iD{8vJl9f=o=RzWpeo+dQjS2S)IZRL?*5rvk)D^b;=RJ| zP@`^21DaIY5vsI4?chRpdZo0YQeKX>3O$3?_H`*0DAvo-UZGwnr0sr6CCcz}40mCB zzsAN9ICt-Ew5NGx9929Rq1+Qy6StvzgPhe z%YEldtIIliJ4=#Y{pe{~$3_+26n_gm@L^|Hx>e3GRE4h@LH^|(hg}wM{!_zTu=#Lz zck=gk)Ny5m{O83`n75;k(&#qQh3>W5?ML5xJGv_riAeK;cGvJ+vfh{xhTl<0QrkoG zlR8$x(ZdCs6LI4{&4qf}cGshZ6&+Pw86ovmF*Ky2qm$BDHrjN@=B zm5-x~A~)hy--xK{Xr+?xyAX~4_Up=Yy((Hu(nw$ILRD*CZbbd6;S>q_d8rE?I4@VD z3)LL075bXX#huEhx+ApcSuW~}jhJ!Tky9XUl(tuQ1bHw;Y0s(Bwxq+pSIUuR4M%ks z(SNf#zu`h_p!|Zu*KxzGQ{f8|Tqra7$^g7?a&%N^-v$@*>4NT4$k!39(36emeqFz7 zOe=gH?LD%&AV2LzL2C9JGh-^P{n7NISH76vI0S{c8$(rVIoi00V))kl60E4@XzMJ< zC57(Qa;#V3^FPDv67pR?x>y_iLBhY^n}60i*Kv$--pASPJVQt8I3hgQgBZ|XU^sVE`%Wn6v7Oy${ve;n6$bhbU^Z&3L#zWHK&MasTcmXALpHPpAH2>X+L zd=SkK>|4t9VLQhN{so{;UNBFj-5O1wgC`3R- z4XXDRP+5bqp9t{L;LH93s%da~n1C7@v>GA6SA!EaMQUr}Gg4IiG-w$npq>VIMhR%3 zL4lb98fw6XlTrJ|405?Sc$W!4QwvCQG-sbp!rw2E@RnA%(Rj6hRvJ{?ARthKg_{Mm z)!_C{0qr#?zgIv<4YnT<&{>09r|~~2l&h;Ir@t1J?i%#GE+9w)HnWTMPmGK(zx4npCXz<5JP7ES7@Gc-AN`rO<1;l7D zr?7y@4CG7Qj>QDT3Q$YPNOHOkf8#3QvoyHmE?|xZKf4Kt)1ZdZSfByE2Ig`u)}WQj zv@3)v#V{Aqu)RTAij-2R)<#qXepFyJCqmqf@zF4+y3PV}s@Jn`4F_MrkFyX=7Ledg zmd}V&!CN)>3c!X4JD9BG6uMzY6+nswB!yj;&nQ$wfS> zt|kMUaYK{ug&4Oru=z@ydm21ez^*~pTB7kl1AO1i*`#ZLubUZUX>e76Tm^XiwNOQS z%*4*8fj71x&QlA>X?SM&41D>_!9QwH-A};J8uVA-HG>>ZkvGF3$}bj>6n}%yrox}q zm+(KVaN}SD0e@=X>@VQG23-~STZ6+2e1xwwRiYzdjvSd5(BO;$g*5oHAxuU?rHCe@ z8VPXGU?aY?hK8F4{OL7-y9SN%p*28p4fxAz08b6pwh~ZMgMs+^8XBb|`1&Ue+`f4G458o(sFT>m<1!gkP6+0G2+bM1K}CO|g}NbP%A zK4Zpc3GS)Enh63zG{}h(&|8BVF#Z3y} zAi={dpYeFA#2%r+`xyd8YQSdGk>V%@^5J-2>;nZD1E7AOB1w+Z;V0uHJVJxIiv>*3 z;I#r1HF&y2G@>=gSSnzW2K=EqGM%D9G6P(HV>M|NFDlbDh+8ROrUrFa3z)6Jr1b*k zXux*Wk&R6QwxSL&k3k%_C+~s=v+DgNpkFY}Mdr1-5H2^?+z3 zF_6#Sd6zHBzB3=#RBzj@!%H2K@Xs`eJT73b20tsXUxOVdMB|_afnNwXtiha<0*-2M zDOJGn2%A-|DW`;dp;aoM5s<0@f4Gm5pVr{E0%tX7aX~b`WRT3Kdf!C>7c3zC{F3G4 zkM?E!f1^o?ig1-d2B*N=o{{Md3rGsLET3@#yQC3(M}x`N1l-er?Yu+dI}LtSz*K<8 z|FIjQ@<6M6bW1>*2KN=n&|uJQ(a6#uTY(%6M&1>TJPj`16Yy9Aw)oED?+KGfTv^`4 zjj}$ofK>MXET0hxoA3z!NrU;11^lc*q3;E}(%=gP-e}PGiD>*{0h{rqBEMP0SomC2 z{?K6L{|We01GWo~g1pyYz)u4H*1+j!0k9U4U$w4p1r*TWr~W& z8n8`yM5(Aj`2qqeYjC-s03Qw9Tm@9qAX9-F3~W-BQEsB*t5u#UP@BO8uIvJL(Wq+y zsce1AXS^#W!TuWbFCn0j27Ag22+*LmHv^l|RFgCz20oX{&vbePiPKVps+9z^(ttN* zpg96H=u<^NTMgd(2xzZC2VVgl72xrAK#|T`rA#eR>B`_b*M2Pw6Cj%&7LeNaw0y?P z+7cY1!PgB0^wwaRzkp9PnA=c5Kbt0O#2$GJ(4c*QfI%8WH4!jG1CQnchG|f!g@6$n z9BwIKqz3ao7BI>JHp5xfZ8VeRTv@ilfP##(fK)cZ@)@bEC3u1c+d2rCs6myE0-`mz ztH2};qB@Dj6oc=7CDRg}g~V!=*$Pb8pk`Omn5n^%ZUSa&z^3p~@HrYh?jbg*MQCNqk1!%AHshco_bp;71J#?+}d_8azlA@KS?bwiF?+H94ukTMc;65z6|j21gb6U4sRCMB|+X ze<|>n2BSU`jSm`JQ{W$i??2PHkv}^oDh04`!;Qgg&l-Houc>xtP zD1SjfB@Mn%po#)~{o@}4prBQ?$_)joYta6ZXw=kz{|A65wHVyw%Hj_I4E!u0m91y_ zjCWTgxPb;?Hw84bY0~*D5Q^88B$0z+epu;?Dxecc=#Zs{w%F8ceel z6%wim|6u?sVHz|mCLmmcz8(U`Xt3B*z&H(#mJ|@7L5(s3CTLKltbmCVc>GBN>?$WD zTB~%cBw&&Tyu%D7o1#J8ssds)*i%iwbPZ~AuF{?&5iU9y2paK7+02&)K2<#?+G~izoKx2~yY}nlqfCR2+z&}9%xT-;ig#xbU1C0NRD}~(DDx+2lxUE5nwF2&HFnFDS`x=C< z7hu<*Mxp?R2D~i~1$oFIiQ9sWit+U)!y;1IEX!wnzfB_KXi#N`fIJO`Bnfz|0skcd zc|6geBev@S{Gb8v-vfBAfsKEW0P>?IS566dp~2=e0$ysc;jDny8VvYSz*`OY4+%*0 zR}DPQ3;10F{xbqJ-pTlr_TV2Ofc&LZzPu#hg9fL*7VwV-LoN$&!oDHZrgg6fD5$|J z1qy5M@5MsG)TKEz(WK3Jpm;&82p`pQW|`CAizt5 zNofLXWi`po5K>-)p;-bdXwWc6KqU>Xw!N$h|s%lW@iGb=FbbThEWH2x*F8_LqL5E?))jhUjzQl0?uZm|A7wz0yN;CC&>DPCV3wf;yBGU zC|p25OAX#A&`N{FMMNV|gJ5?7Z8ZolE}*>z^F0N0RDkcl93_Nw)+$3w3FxW;Z_7l< zx@!>OB_K$Hyb1z>HHfJwpqB>!D9}fPRh6KD^}oKFRH`g-`fG4Nfq@$EUl&l2!5Umw zV5kObd_-fo2F0oh2-RSt0$~Dp{oVLWk#MatqMF1RqXGY90r`&8V0LW*5gJtT6EHyo z{*wcuOw_==o`7f#_zw&`|4-6nSp!j-qCt?qfLINFY$;&62FKe8n91O@Tw}Tm_|yV2 ztqyhi_0hu0Q zkjbTBvy}kHEim53@7lhwyoM(}+yqF~;0788;Isyr>jj+E;9a7CFE#MQXIF@FUIP!@ z;Q(CJU=tlSgNazHLISrn;GabR z+|^*k=K}6)aAz+7#=l*YSNlc9p#lFa0*O4-pdL0P0iO*~lJs~X#7nDWy%bPZ1O5XA;*@8APVtcu_`86L7LZa@wtU8y1q-vj zj|OFn2&kq(Ar}TVqlPBDK^bv;H8@mUKy3}`c?$5;;AjZ}^)xtMN4 z0vap8*WZ8&LYiom?8*X~X|Sz|fEF4I^AYf|1{JCbXsv;FH34lj_)tSYI}Kj>0$}`g z&?L0BsC3d`L>&QLG+0wtKsOEe=N4$Q9vU2OAfTrPi~R+JXt1cEfZhUR{9R}y(TbSiFs<)Bwa1H9U z6EH>t{>=qa7^gvO2LTZpbm}Bvf(Bk)1WaUrnH%f%umg-VV)8-$`|ru@m9{7eOTY+^ zwSbgzI{O}SbT$->;8_-s;7=`|u{c5(eVOyYj9?*fX_5own4yN z26wp>Y=9Z1IA8%O#UaaQ^xQ1LM>OD{Zb1JSfK8>ae5a5TT4nog0Vf&YV)T*0W}Fe{ zv;`!OvzAYO!GYi}HOM|G`sW#3tp_TU=fLs#=fH*&PJq> zUZw>kINS0WKW9mBt_JVE7w|}fSw9H)UW2o*1w7T@{a-dA&otqGd?3^RV}Sb;{79^| zfEN~!QoOW$#)RS${91$Or3JjzV4$~vUo~i3MZoU@WX$icD&(D3>0Cp=UmA4v74Sg= z&xQj2(SUyhf`U6?=c}=p3(jV=0SZ|_3SPwW8SC(;5Cq#?G}+%ph?@o>Jq5UHz<&lo zoZ<{{r=pB;1C+3Uai4JNG<&`5*C z^#TGkxUo?{Qw`203uvxEubm8RMoUfj=Obv%RvPgCMgRgeD8E-gTMhU(BhYBCLE?S^ z9W`Kc0?_EJ!Ku>%x+=i;AN+?AsB~v?j~gfv{}=)2X#r`U5X&cj89{Jw2DoF#rlVV; z-`4`7-{0~X<`oGZ$N&!k_z(;~7(oI<0Bk}e!Y~fU?Y#V9gu_EEAi-gl&xp7y(Ze;^ z^H9JT2Do8XR@jlicne4ZhUGJ!<%oVHfK5es@l;5ZR=N4JfEWh2$;QqWSZ+s>Q!F4! z##%mO`7aVYox#_v50eC}Kg$B5|0(8bn{9LE$3K3>Gn1 z%_+=v7qHX;BtM0jsFS+PY_U00~ldojUpNtQv|qba8YTv zGg!pM`godXcvwJ+Rf2uDC3uOme1BQmA`)K4@)^@+Nc8d=oR}q`q6SZA3#g*OqE7`> z(*PD6xYRW@@S7)~wgNo=Cd?O7SF4;~ETDk~ekzeh3{G&#Vd#NVZDIi_cr(jqOj;(v zEj56B2i9+`0jxVPXlv8tN0np;4eo9dmChP;-zuP+29=Wp1Zgl{r4gdRV+Hy!i06Wr zJSI{4S->VGA7FWnTRSCuum+=d2^gkc=H`oCYu+ z!BsZ|$o-f7q>zbPB{5Y%j0UhL!EvT&0BaHqrfG2Vf`FMCz^Vjme5wI#N-&TXmx6m< zQR9EUR)KK|R#~LMm}>%-YVcHn6&k?Y1V>rL;0QOzR#<-kSZe`kjs(kR#NLwN#C(A9 z57QGIVUt#Y;Ryy?HGtU(20Ju>(Fq1A8nDR;fZZCLdL-a;4W2y}uwR2*0XE~1Cexpb z%25rX6*!?mzZasBssU_IaK2|W`1_@RFExPm3D&rv0c=k&_*wxTfANZ3Ve%a}A^P-@GmUjCkDEMk1_D&SWQV2*+{ z{?LGpQ2_j<0qjsP_*(-uLjmA~z0Img*#ZSXAq`3>U~|@lEl@zkO@ndjG!$cyZ+b#B zJS`ySr6l{V@@eZ?U4p$VAi-rVp8?YooW8dPFgzjG-%6Um?gXp&Xt1@afa)5+@&s%6 zY5=WfB44SrFeiv~sm*034fHG#niPPL~7)m5s!7_8%R!sY~Pd}0A9XFtnl z{N6}{2WkL&6RbZ(gW-V!hHGGhwFy=ksR?XNFbLNGmL?dC)c|%T7({3QD-#SN8Hhm$ z*qC4tZ2>9gB+F;Osssm51+b|IFe<@hx>oU0V3r0jE5RCbG+3kZh-0vT8wZ9ZSYv?& zB-2Hf&wy144qmDOY)Z)e_kZk3u*ynS!D|e$Cc$8h1tiIJmd}7G2@c+%L8<2YNS-0Vdo{Q+N5BCMZm0wfYXGYe zoZ>MJU^9Zj7cqSQEt$e(1d~%*1qLG+oMnKQFUnj5z*iQKVqLI&2CPG{{?{6WuM==Z z1K5XPjcXdfI)se>o0`Bl1gqR(kjjbPZq)LxuW>{0A~OW914Hm?F2s2y zc=2FL=Sn3q26LegnbFG7!A_VuR#G6bN4UfZ+8E z2wt~<;Kc_BUNC^*hi3?WLWbaHRtSEegWyLn2!7Op;O7AX|fiTne&tyk) z*Ez6KvKl{UJwolLI?8wjGz@LjASPA}|%Cvv8vJ}`d(ZwCb zjr%t)Lj_|UrNvCwn)qt8DWU5qa+;%_D;7BOy~)!Y^<7u}+gpv^O>-1=UGZP$KOHRo zKdf|%|F!yR9nxopqZ(~tZTb%@9q>QxmH!E$oZBhV7K#7HHK^?jG+V;IS$)^#|Mu1( zkAG5%a4T-m81<=2*)z}}YyZoBYQNK!RoDC%_m~OQ&H3yb3Z3bw>ALOTUhjm~UrXt> z{F|1idV#wny{-SjUX2@v>YlUXKS9)RmZP+jWKkX_+L7({|AxnN_#sN31q)zU36>MI zitf#F)N#d1@&CL&3EQtotq*A$_Nk+C=|=y{8Een^`W%uFe^qMm*?%bX8(SR~%gX=L z_n{wWqvd!1hb2Valsu38hf=Rkp}Fhdtd8qp?M2xu(UDJ4RV-xxuL6}4{<$V?hZXUE zQOf$|vfu;%#c_Wqj3xGeYtNYD@U-2J#g#XY57B&h%ZG42Oy)x*ALjDmC?8Jn;UOQs z=fiJ&xWxv%7_2s!%aS%;lqAD9O6SnEH8NNVCMxsr10SfJ|yzt zJs)22VF({)@L>%fGWf8S53~8Oi4Vj1kiZ9%4c zcCO^ZJ3cJOVa=#`=h2a4BO>`1Wc_1P_s(^6b%HV1*EUB*%%jEQ9R8R`+r&8H9szFJqd2v6_vKJ>cdHgAe~ z3p0mTsr@`h%|blEQbLTm#7U4-Ad0Dp0iXcOt z@Z-zU5#KB2i}0);Uz*yt`nHVIJ_?)f=ql!F=iqWgN9H?vy7A>`4yOLb)2PS-M}U8m zX|5lI#L9IEN?a2NzAi!VbqRt?ucE+&;p?f63Z;$wSX>t!(kIqYsYcVL`J>2E;ma4& z;M!@;M$;VrIt{*}>5z*v96sJnWms7W@TE?Nubt`eA`EobGFC8O`>f#jN(oL^D(jTF zsjMJ!`qWW=SU}^@CjUvCn+~PpR)fGTC`4?$XVj=EG29;e4StzDaGk$dTQ-z`$wbdBahUWt$t07A$Ana5QZ@5bYd%(JAPr~~y9FCx%9R7-UdrWG(Q*~>VOQ!;NaUUyrxNrexUAC>npeJ%mxm$AmUjvs zS^!2Fy(sUqf?kDSuVNaF-}azRaS5GSAUJ_TOHrTxb}uf_$!!nn7nQJ61%j^@TtbbS z7OCd0(z;$SI1?l!q)5eroKCCl52O;Y+aEOLxCgdBsNa?EgN?}6IEZU5%!4V{HrNGI zu6Hm9rd+uLE!Ph)0j8J%OJBz3B|bYIr26c5c-ezfetPI(ZI+&gA9}N_clcq=Vp8GP z!OhWX@ii&z!GoIBxO!;IkDY>tFfBf2%G8L7VN=J18qpC^Q>F5|538i^APG+Dx5Ez` zv-CUiuoBDeM;^9ix%|k(7VeVTf`Y-#U@)6rC%O1@y%Uc*-!e;4MSfB+dcuB~ay5hDFy-0~i($(3 z45q@AD*!gal~3X6`yh)Y$0!)AoN(!9Q_W zNjloaQ*tirn^uc+Zs(g;ndL~|G=G+BebXASyy%O)=#W|Y@*VVt^0SvQ{5cNddj8gO z9flz>#fp8fTt5F;E?+0u(~=n7U@J_y&caBTa+NN^uGHDJ(i%yhKNOlK-Q;CxT01U5 zqmgM%T5=78)(0h_4jx>;2BDGBVWUGw!>OtmloE8vs(gdc(8w`Sp`p?%w~tIK+e)Gz z4>}EvVk&y*_^>JCl?qZm8*~mqo)Q!u88&4~XjIrlz7iq-S0mL~EEcBDRfDiJ88L(M z<@~TTKQ8B?u(UcX)56j!43{%f$|<;voROaVUs;rD%p@zv;BrpEl{u`06^0ovV(OTQ zTEWLDxGF0Yw-iQ2M@Qz@sn4jiDryAzItAB*7Mvc%rB_G%bh3%ZY*m7@0Fmnr3*fBx)2 z=92Y^ugb_^-?%qT2K(8)s!gBnO{>dEl-j3yZR>r?AF(g(W9GZ}rO7;*xlidk?@w#a zyxsn^(nI;l-x$;ng%wxw0L%3O29y-D!*Y^xl^D@2VjW--Nx6!L=DT8z&OMzz znu?z|m&Px?4wfyG`e=XJo4Trci-OjndX=f_<+RfDs!?W4zFs+7+>!3Hm<#C4P>a}kIvgZ{H<6V^HY6D|R#{AR+57Q!g zxkx|nAM`2puAN?m4*r$?Q|dZr`1lEZt{2Sbm_Zw>ZI3a zKB!K56PBy$qzAD4x=wmsA6_gm%FIG`;yMkRO5%z&K8rZZCu&xD zCs#>(d(hz}bhk=HKe_Hco0Z;y!|TmXZ=%Zleb8$;FKZ9t_t=YOr~9ju@{5dYIVlbp zOHyW)3MX5xUMcKK&7Pfpy%?vrZ&|tw!|Y}00dA7-fS}B-CC+^p?>8&}TfOF|pRlyR2HlkpaY3;J~($uO~7k8^4B?FhE zdB=UdRSkCr?Oj4g{Hj!v8s0gVUQ=m)5p)BZrD)QCDsq2P_RI9TbuzdZQ|};iaiznA zj&fDMZ@F^p>`I;RWqOLtgC0-QWxnqHG`)t(F(PR05(-=5Q&n#hx7n1Ue7b8!QzavZ_}kHemc1}HSa_E*j`-L?!g(gR8@?id8mpntzA*Q z6s-=<@KWh72-<`6Cq296RXlXcn5nTbp%bF;^G8Iz7n~tI@k4Ni^uWN73>j&WAsK<3 z^!|_x=^;5G8B!e2UP`v_m7!WM_z$OGcid6Z$nNF5XkD+2QhvPbXsm#l8D*fRX+_I* zx>CL?)`%=#fQRO!5jFi&|L&EMEVmoU(HU~;ZbYk0-bZK1^jaq-qmHWI(x6qSUs+0u zt0@!m%$SV&b$ET#=-(J=i)#t2vnW^LrunW|Bfbvu%bym>w@hk*Ng01iMgQKe@~FE* zHNb=&8FF$!MYimj`V{;F{?OTy`a+wyNn4zV2Y+9hvc6 zg*GxY9|k*F6K)^aE>T8KR*bM*--TMPORxo^La)IHh>?|A=G%-|Ih!jBWwubAdSuXA zB#+KMuSvez)u0@p##Hr%Ut^E_ z$ucjv2#OcqmR=XhtVY>8GRjiS`1B&4Dmd81DcGCC>(eCL?fPl}^e&Pa#BI2(NM;w7 zPl{v)O6`MfPQmferaraGRi{4AnWX}Gq1vb$hx&*s7FI%(tHXTD<-X8z^#gTu4hX#dng>)e93`75?K*w8U%JubOt4w*!`UldBUnm%lVUR0xyHuj%cP!}Rg3c52xAV+X&4VuZ}!a09?qHv_GQ*k z&x13AYjVHxrmB4!R*+Nm*S<`7&MCh?vr1F0SMaZDj*{zQsc(@Iztb>g!_tLv4Stkq9V=fc4`) z3980E$>=n!UMLF+<6-VXxw^p4g>f!*$=S?0)zmnz9W)5B%WinxvaA{u53(}l9_U?G zW>tBR42lYx0+qs3Cx=DIq>XVpJ?J5j%b00lk+FE%%`Zu2&~N#9x5lP@wY`4}cIzwG z0c_J(uDRHsuUx&bF<-e%?8p~atnnuzqtEz-N2YGd&g{K{+xy3eEIGTLMwZ;sb~Uop zU1e~RQ}9`pinODJw->jrY7iciRD z$ceX`pc0)vAxrKm_D#r=+w5BtvSjZ4YeJUyI609&7YP0v7aM8cr`W)+j04QT&VA*o zvC49NgRT0CwZ$HNGyIF4Su*3--IXOb z5|O*IT5)Oj?#dd}Ub;jpIrAm^Mof%}91}8TDjuaGq9UeR&q_g)gJ$3?mH1!yzd8#E&Z@?^UYvbmTOn5S{A!gn_a22C#!;IWvOq8pf;#a z@zD`eVj{z4hW_u-pYEQ_lIOeMPGZQwoC{`!m?#mr9oJBV|8UXR3P{o-)?xOc5AHemFvrKN%T1}&0w@DB_`nU^|pR`%+atn&JJwk&~opU9RrZFeGD?o^^rWS6NZ zQ7;CU;3=JS{O~$mx$11PTdTzn*!xp^sVLcE`Hr|p2TZ?6+9iU$c>cL)`N2D%Jnc}Tuw!G*IJYB zqp0dny~~eQ_l_S2bw-oBVcE5Dbkvyf(bmdBx1fQ{6>FToDvN8_kf4bCf#i$V>1tQ+ zX51qwR{^}}SFYyi>`HYXpX2PVhE6%Y6i>k#y%W~zjpM1crsjlHA1~*DtDKj;_MV$9b-af1)uLxCjOGaDEicLdH$c}@_S*q z{&;D*X8pph)R7x=u1@04;pdiHv4Yg5L(ph0KzL+COekOK^Sfx6Tdq8#&UVX{nPHz> zuJr61Zn<*n@WL&(mJ}@L_h2`kDgL)C9upfnwo(4nSS)`j&oewaYHaM3i0G(LES*n| zkjVshO*Nc?yYom7o7K0BOtsUBs`NJ&&8^)~DvtZ~rJO;@n8`S?YSI0_iL7u6UMCt< z^+Nwrt)*duH#h|+GxU&q8m?L>E-C5?hm3dSFB#+Kx})&88agNx|JOysv~|ywQ8Uav zSFVB6-Bo!ux##*-lkzk#5Ih8VS^FP1A4&g){i)zrBCp+N9EYurA- z7HhQ^yHOF#RT-PA6^r4&J@Yp%##QX1R<5qtKCN6gN?5L2*f6cwGwhVk55*Q~5%H4|H$Rp`t*mTMn2H7oW8 zyP1{i8n!VjR}%Iw!_^rz*@2zQ3Z^%*TywBhS+PynqpVzCU~{r^-N&wE1@4~LsZcTE#$P0!wqjF{DJ_D()gyBJB(NnzWb-8TWIwxE?17M@#%dWYw_^3WX`o?BDZVM|al>TsmhVEn=f`W$#8Jh#4zcP;2H z-3!mHK-)Un-8rbv=v=wa=`%Vve^Kh^p!diXZ;lTQRu7u{`;L|CXYJZL#$x{CEZ3&- z9{ElzH-{9Kb)3V)yqBCAlbd>GOx}esuF>M~xEK^JIWGzn&+jIqZ6#zna6xI0q*> z=Xaxp*d495QD4TkXyy6=+o6?fF7`nymwm0}+KHXd#){Om>v`qcs%}#)XcEd$jq+X( zuc63PbxW1_!75a++au{1v)mr}{(o0jA5&Es#yJ<}ZuT+lQ#KJ7(k2;16nV=(A3N&ObWq3ZW3kqkk zRn#9n@AI6qRs3^)yyyKs&-*?f@B0ICRF_EXRoEC&k<qeP79n>tD zEtM<9cE58!IwbQ!ZA}iYucPu6t503PbtY1D(zgPskGE=zFNp75KCGcRxOG^=THCGX z$HN-xrqN*yCw|__mp-DQm$h+3D=NpmyUo%R8EC0DgZic;ui9oyUxC)>q0uNK-}7c_6xkixrn60$iOB~P!h9k)B<#NO4|lJc0yNyzfLFs#C1Zo zt87%X)<*A^!JiCOdew#u;}+V0l_3K^m=dC%9}5||>SVHH$%o!F|pGj z8gEwED9x@&kP^fX&0n#^D(w6&+yAY%f(eJi2KrG0VdFX4fNo1{gFVPmAi@)4{^~{> z?P4~{eMum7eZ=tBi9XcK8dhMmQ$7zf=;wzWir)$b$$hv=`03o;nIiMptl=X$?3UQH z_>3$0M~l-dt(Y^rD=`VW!N)T=z?&yj z3WRw=db5pQ1ByJQiA$u)O@40cCy-K%mtU`)|wQ+CE zETH^vI@k=~6tf%Epm-vG3-0F7>*_?-!uw+8@<59ny4VFbc8T)8z0*dCa6OdL3gHSU zp(?n|N$4kF=@Zi6?jWH7xEe|5F5Es;uBfXz%&%~ty?e$)rJT`Y7L(6*L>|DkqoiGd ztmEqD9&<$=z48iQUAS}iufeNN)%I`9Be+kbEtt5g7cZCvMW~6c%L~>D42?lvaVuLH z0**VPpfW$cL|7viocnz0sReTaHB3uMJjaKn&oZ|VHQPJn)heNkwzf zeb{nOkM8%_D*AV}(r)c_ptchU-nYaFNbPEg_m-g~$zJ&ctcP{ZR!Pgb#wMG$GWIM10I@j}#Q*>R delta 64 zcmX?jhUwrLrVTBOhFRGrsRbz|Ir_#)<`x#lsVN3YsRrgrmL{f2iOI&v2Bt=aCMl_A UCW#g)iAhFgCP|yOGWIM10Nk4uvH$=8 diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index c6b76318282eccb140592ff5297b2b37757ae2a5..54fc61a72817cb136a8bef07f0c2ab44903e678b 100644 GIT binary patch delta 63 zcmV~$%MpMe3y8e\n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 34848\n", - " 0.203922\n", " True\n", + " 0.203922\n", " \n", " \n", " 50270\n", - " 0.204588\n", " True\n", + " 0.204588\n", " \n", " \n", " 3936\n", - " 0.213098\n", " True\n", + " 0.213098\n", " \n", " \n", " 733\n", - " 0.217686\n", " True\n", + " 0.217686\n", " \n", " \n", " 8094\n", - " 0.230118\n", " True\n", + " 0.230118\n", " \n", " \n", "\n", "" ], "text/plain": [ - " dark_score is_dark_issue\n", - "34848 0.203922 True\n", - "50270 0.204588 True\n", - "3936 0.213098 True\n", - "733 0.217686 True\n", - "8094 0.230118 True" + " is_dark_issue dark_score\n", + "34848 True 0.203922\n", + "50270 True 0.204588\n", + "3936 True 0.213098\n", + "733 True 0.217686\n", + "8094 True 0.230118" ] }, "execution_count": 26, @@ -2279,10 +2337,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.560278Z", - "iopub.status.busy": "2024-02-27T04:11:10.560104Z", - "iopub.status.idle": "2024-02-27T04:11:10.564723Z", - "shell.execute_reply": "2024-02-27T04:11:10.564082Z" + "iopub.execute_input": "2024-03-05T17:26:21.208808Z", + "iopub.status.busy": "2024-03-05T17:26:21.208568Z", + "iopub.status.idle": "2024-03-05T17:26:21.214798Z", + "shell.execute_reply": "2024-03-05T17:26:21.214202Z" }, "nbsphinx": "hidden" }, @@ -2319,10 +2377,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.566856Z", - "iopub.status.busy": "2024-02-27T04:11:10.566684Z", - "iopub.status.idle": "2024-02-27T04:11:10.741038Z", - "shell.execute_reply": "2024-02-27T04:11:10.740486Z" + "iopub.execute_input": "2024-03-05T17:26:21.217468Z", + "iopub.status.busy": "2024-03-05T17:26:21.217250Z", + "iopub.status.idle": "2024-03-05T17:26:21.432838Z", + "shell.execute_reply": "2024-03-05T17:26:21.432261Z" } }, "outputs": [ @@ -2364,10 +2422,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.743278Z", - "iopub.status.busy": "2024-02-27T04:11:10.742977Z", - "iopub.status.idle": "2024-02-27T04:11:10.750387Z", - "shell.execute_reply": "2024-02-27T04:11:10.749877Z" + "iopub.execute_input": "2024-03-05T17:26:21.435255Z", + "iopub.status.busy": "2024-03-05T17:26:21.434873Z", + "iopub.status.idle": "2024-03-05T17:26:21.443496Z", + "shell.execute_reply": "2024-03-05T17:26:21.443041Z" } }, "outputs": [ @@ -2392,47 +2450,47 @@ " \n", " \n", " \n", - " low_information_score\n", " is_low_information_issue\n", + " low_information_score\n", " \n", " \n", " \n", " \n", " 53050\n", - " 0.067975\n", " True\n", + " 0.067975\n", " \n", " \n", " 40875\n", - " 0.089929\n", " True\n", + " 0.089929\n", " \n", " \n", " 9594\n", - " 0.092601\n", " True\n", + " 0.092601\n", " \n", " \n", " 34825\n", - " 0.107744\n", " True\n", + " 0.107744\n", " \n", " \n", " 37530\n", - " 0.108516\n", " True\n", + " 0.108516\n", " \n", " \n", "\n", "" ], "text/plain": [ - " low_information_score is_low_information_issue\n", - "53050 0.067975 True\n", - "40875 0.089929 True\n", - "9594 0.092601 True\n", - "34825 0.107744 True\n", - "37530 0.108516 True" + " is_low_information_issue low_information_score\n", + "53050 True 0.067975\n", + "40875 True 0.089929\n", + "9594 True 0.092601\n", + "34825 True 0.107744\n", + "37530 True 0.108516" ] }, "execution_count": 29, @@ -2453,10 +2511,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.752334Z", - "iopub.status.busy": "2024-02-27T04:11:10.751944Z", - "iopub.status.idle": "2024-02-27T04:11:10.924530Z", - "shell.execute_reply": "2024-02-27T04:11:10.923931Z" + "iopub.execute_input": "2024-03-05T17:26:21.445632Z", + "iopub.status.busy": "2024-03-05T17:26:21.445274Z", + "iopub.status.idle": "2024-03-05T17:26:21.651921Z", + "shell.execute_reply": "2024-03-05T17:26:21.651295Z" } }, "outputs": [ @@ -2496,10 +2554,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.926763Z", - "iopub.status.busy": "2024-02-27T04:11:10.926334Z", - "iopub.status.idle": "2024-02-27T04:11:10.930611Z", - "shell.execute_reply": "2024-02-27T04:11:10.930192Z" + "iopub.execute_input": "2024-03-05T17:26:21.654676Z", + "iopub.status.busy": "2024-03-05T17:26:21.654097Z", + "iopub.status.idle": "2024-03-05T17:26:21.658861Z", + "shell.execute_reply": "2024-03-05T17:26:21.658395Z" }, "nbsphinx": "hidden" }, @@ -2536,101 +2594,59 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "027e45bb8c5a4d319b044fd806e409b4": { + "00117567805f440d9d5d764e0ce0c942": { "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_455fc43143fa462da4615d0a05091c09", - "placeholder": "​", - "style": "IPY_MODEL_659e01794dc54b11953523e915b610dd", + "layout": "IPY_MODEL_837534ba61a440e29b8bec88ecafa936", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2adee810714f4132b5db9e250308fd95", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "093a2599e270466faea60621fd1691da": { - "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_811410c9f7a8407c9afab9994a295677", - "IPY_MODEL_14b865ceefa7490384e4edcbc80229e0", - "IPY_MODEL_3763c15b0203445aaea40c64099b052c" - ], - "layout": "IPY_MODEL_7a4771c243a240b2b91afa67fe46ecff", - "tabbable": null, - "tooltip": null - } - }, - "098bbaf5d84a421588aef46a8eac6143": { - "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_1a3831876daa4f518f667abecbbfe437", - "IPY_MODEL_37b7a9f319b54f61a56201aab953a926", - "IPY_MODEL_3fd8ad114b9342f1b1541ef2fdf7e55a" - ], - "layout": "IPY_MODEL_449cf6286eb141d596a5d4eb92953c88", - "tabbable": null, - "tooltip": null + "value": 60000.0 } }, - "0e3ca28ce7344406851f80d680a12506": { + "03271711edad450897e85e06eb6cb127": { "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_fb58bed5d89f4661babe6b776dac767d", - "placeholder": "​", - "style": "IPY_MODEL_cf5c2930549d4fbe8250517f5696e479", + "layout": "IPY_MODEL_c466561275334474bb5df6065049149b", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f03f41af5b5447f9bcca654369f6a2ca", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": 40.0 } }, - "11b49017a0b64344bec8ee657afc5c4f": { + "0386e13f4a2941548963bd69cdaa9593": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2683,95 +2699,25 @@ "width": null } }, - "12cb341fdb1044cab97410fd09a1540a": { + "05f112c96d29414fa42131bc8603da0e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8c67daeac0434fb382558a823e32902b", - "placeholder": "​", - "style": "IPY_MODEL_da48a4f7c7cb408096c49e6124bf70e4", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.93it/s]" - } - }, - "14578e5a36ee46cf94f7f04c06ba4a53": { - "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": "" - } - }, - "14b865ceefa7490384e4edcbc80229e0": { - "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_b5ce17ab53824591a98f449e8b025965", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6eb5308f57c4436da74f9cc6dfdc56c1", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "14ded9a461884f059d39711716f1f6e7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d1c62c6e888d44e4be1c96009d1e1537", - "placeholder": "​", - "style": "IPY_MODEL_cf534b6635784368915fadd5cbb13ef7", - "tabbable": null, - "tooltip": null, - "value": "100%" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "17cfc02493654970909b80a5b0796ef7": { + "1157e6644cf94a369b0bc0c79b48c0c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2789,79 +2735,139 @@ "text_color": null } }, - "188195b2866e46598594d40c79b6011b": { - "model_module": "@jupyter-widgets/controls", + "120d1d74f90b477aa1f7c85cb04f2a8b": { + "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_e7e978c332654878b944425a2cce7431", - "placeholder": "​", - "style": "IPY_MODEL_79471511b83c4838b8cf566b168e574b", - "tabbable": null, - "tooltip": null, - "value": "Computing checksums: 100%" - } - }, - "18e7d43c53be48b3b4f95442866c0a87": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "125e89a524574e23a5d582ea6a6193ef": { + "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": "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_7610d89b3fb84ed8b8f85e6777c2be44", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_23e6efa276194e59af80b0da3f3d22b2", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "1a3831876daa4f518f667abecbbfe437": { + "12ed270f15a14c4487328aa39e253957": { "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_706ef0ce82a848d5a2b5a1cfeff3b66d", - "placeholder": "​", - "style": "IPY_MODEL_3db1ad2d9bd3486d81c82e7d825fefab", + "layout": "IPY_MODEL_7ce21c448ae34e09a0b1b077bdf8f9c7", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9f08aebc732446de872f662bd327955b", "tabbable": null, "tooltip": null, - "value": "100%" + "value": 2.0 } }, - "1cf137b879214a3ab635c3fa84b1bdbf": { + "13320e008c6b4b55b02b9da6ede7d759": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2914,25 +2920,31 @@ "width": null } }, - "1dde6247d89e422fae510063a9694bde": { + "13bdb84d4d494962babbcc3d785b981b": { "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_87a35075be054c99a6917be1ada6b70b", + "IPY_MODEL_bf535745edea45cfac37c688118391e0", + "IPY_MODEL_1f63f86750404d358ae1b3d0fc4211c1" + ], + "layout": "IPY_MODEL_2ade4dc2e32040a8bb5ffa50bc1ea807", + "tabbable": null, + "tooltip": null } }, - "1f39f77cae9a4b8289093a19d2b053ce": { + "13dd0f8d07254c1e8c1c8fce7499453f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2950,46 +2962,7 @@ "text_color": null } }, - "1fcfe42385e9486293f75bb99e009cbc": { - "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": "" - } - }, - "20d5d6d7592245cb8e6ae6637e124962": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d29be32abfeb45e38ab62bf213c3c810", - "placeholder": "​", - "style": "IPY_MODEL_564b4d4343944d9aa89b5c29b117db36", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.91it/s]" - } - }, - "23c9db1ca6ee4834bd0db0e311214d5f": { + "1754f6bec411400e8a0248d2d205d9d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3042,23 +3015,7 @@ "width": null } }, - "23e6efa276194e59af80b0da3f3d22b2": { - "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": "" - } - }, - "25fc4b8a75354f75a538e45656f2b4dd": { + "1868860131654264bd9a733aac14b217": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3111,53 +3068,7 @@ "width": null } }, - "26fe5f0671964c01a572cce50f2bf3b7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_39502ba2184b4f83a2158243bead98e5", - "placeholder": "​", - "style": "IPY_MODEL_17cfc02493654970909b80a5b0796ef7", - "tabbable": null, - "tooltip": null, - "value": " 30.9M/30.9M [00:00<00:00, 37.0MB/s]" - } - }, - "273c33e7bc0e454e972f56f0ef99215f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5fbb4d5a4d0044699dec9b3b12696dc1", - "placeholder": "​", - "style": "IPY_MODEL_def8761350f347f58474faf97a0ce8bb", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 57.49it/s]" - } - }, - "2b36aa46f93e41ce9638a118aa74e2f5": { + "1b1616f669014ec88ea42a507fc66b38": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3173,17 +3084,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c932e3e7a73e4533acbe1af723e40e3b", - "max": 40.0, + "layout": "IPY_MODEL_743fd21aabd3441080cbdf88ef4750cd", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2cc4ddad126c41428a99dd2b3bf5f5a4", + "style": "IPY_MODEL_69be4e0cb1f94577b806e6dcbf5729f8", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 60000.0 } }, - "2bfb9fa1816c4ed1b4fa5a535ee7600f": { + "1c538754e2c94b6c8cc8835413e8c193": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3236,7 +3147,7 @@ "width": null } }, - "2cc4ddad126c41428a99dd2b3bf5f5a4": { + "1d9fd4ce611a49c1be1a8cd6c17a0dc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3252,31 +3163,25 @@ "description_width": "" } }, - "2ce8708f33844bb880fc5b942a72dda2": { + "1dbf4ffd3fe34a0e82fbab2708457009": { "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_919d270fbb284450aff333e907475643", - "IPY_MODEL_b87e8a4c2ee14767a3f43e0ed5c95bef", - "IPY_MODEL_26fe5f0671964c01a572cce50f2bf3b7" - ], - "layout": "IPY_MODEL_1cf137b879214a3ab635c3fa84b1bdbf", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2dd5b78c91654388ac491464dc21b064": { + "1f63f86750404d358ae1b3d0fc4211c1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3291,15 +3196,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9e0d2440a9254b05bc81e16591cb45ca", + "layout": "IPY_MODEL_f53af9199dd543cc90ef5fbb55d8ec83", "placeholder": "​", - "style": "IPY_MODEL_92681c8858d448d49fca40635dc49cac", + "style": "IPY_MODEL_7ae1a926cc1f4379a9d86080778be517", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 53.35it/s]" } }, - "2df710401168425db8bcf3af9d77a3ca": { + "221e7e9db86c4eda9720dcec34776e7d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3352,25 +3257,33 @@ "width": null } }, - "2ef3598161a949e9aef2bb7578a46bfe": { + "286acff3d63f4b4a97ee007082ffc9d2": { "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_120d1d74f90b477aa1f7c85cb04f2a8b", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1d9fd4ce611a49c1be1a8cd6c17a0dc5", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "302ea6b92b3b4650ad94b51b2022da44": { + "287287177d414d93adcc6f20d070dc87": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3423,114 +3336,7 @@ "width": null } }, - "3229250625af4d5a9210d0ea9b5fa16d": { - "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": "" - } - }, - "3584d39ca68a4c4ba99d6ca4346077bb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "35ddd41ea21749febdb2e58bd227aecb": { - "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_027e45bb8c5a4d319b044fd806e409b4", - "IPY_MODEL_18e7d43c53be48b3b4f95442866c0a87", - "IPY_MODEL_acd9fc454b7a441bad4a264362ca582c" - ], - "layout": "IPY_MODEL_6ded2348d0c14c1eb3186fc9fb33ca4a", - "tabbable": null, - "tooltip": null - } - }, - "3763c15b0203445aaea40c64099b052c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2bfb9fa1816c4ed1b4fa5a535ee7600f", - "placeholder": "​", - "style": "IPY_MODEL_4906b500667e44f6b41c7438c7ed301d", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:00<00:00, 972588.47 examples/s]" - } - }, - "37b7a9f319b54f61a56201aab953a926": { - "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_f259d9b670d14c18adbf4e4088c33b4c", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3229250625af4d5a9210d0ea9b5fa16d", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "39502ba2184b4f83a2158243bead98e5": { + "2ade4dc2e32040a8bb5ffa50bc1ea807": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3583,48 +3389,47 @@ "width": null } }, - "3d289127d48f42a38c13d633650495ab": { + "2adee810714f4132b5db9e250308fd95": { "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_5d661f0602724b848c5f85cf262fc17f", - "placeholder": "​", - "style": "IPY_MODEL_aa210d8e134d4e65a35337751bebb8fa", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "3db1ad2d9bd3486d81c82e7d825fefab": { + "322b3bcbb5394c948154c32f4eb5e609": { "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_c613b29260064809a8c4178ed6147c38", + "IPY_MODEL_968457e5d1c1482a94cea2642422460c", + "IPY_MODEL_98cdb184470b45209302dfb69e4de525" + ], + "layout": "IPY_MODEL_b09dbf3fce1a4e5491433501bc5e7751", + "tabbable": null, + "tooltip": null } }, - "3fd8ad114b9342f1b1541ef2fdf7e55a": { + "3868e1a7559d457d96e83bd991cf34fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3639,65 +3444,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_302ea6b92b3b4650ad94b51b2022da44", + "layout": "IPY_MODEL_c10bd78b3a524ca098f9b1d758f653b7", "placeholder": "​", - "style": "IPY_MODEL_c229dd5792804b6a88e3be57058cd2b5", + "style": "IPY_MODEL_a3fb24f690c840348a883369a4610bd2", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:35<00:00, 1565.74it/s]" - } - }, - "40daa0fc37b74d61ab0f77a5b7507dee": { - "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_14ded9a461884f059d39711716f1f6e7", - "IPY_MODEL_8522d6bc397849cea3fe47036e466f1a", - "IPY_MODEL_273c33e7bc0e454e972f56f0ef99215f" - ], - "layout": "IPY_MODEL_e4ff32be7a5847b891bae956c99642ed", - "tabbable": null, - "tooltip": null + "value": " 40/40 [00:00<00:00, 59.66it/s]" } }, - "41eddd2244a74684986dd50abd365d34": { + "40b7f76f7c5040b99bb6d3b1b4aabd78": { "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_bc23d1163ae54391b6d1b095111b7b64", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_56ad411b8db441c7abeb093633bccdc9", + "layout": "IPY_MODEL_dc0af5e6d82348d69e1094d9659e5f15", + "placeholder": "​", + "style": "IPY_MODEL_aaa247560a9f4de5ba6276b9318cbdd9", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": "100%" } }, - "449cf6286eb141d596a5d4eb92953c88": { + "40cf93e1ad9c40329463ec8680702b75": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3750,7 +3528,7 @@ "width": null } }, - "44f264b134e14625bbdfc087820b1311": { + "41c91ad69758433799e5eda8ba8446ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3803,7 +3581,53 @@ "width": null } }, - "455fc43143fa462da4615d0a05091c09": { + "4c192cd329c54205a7c62f4df4cbb056": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f73fd148bfd14a059bf19d9a0008bf30", + "placeholder": "​", + "style": "IPY_MODEL_9fc4e85f7c89462f8e41f451f28c165b", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "4ca98e8187e745858cecba425db799e9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dabb292c869d416bb677733a64a4bc31", + "placeholder": "​", + "style": "IPY_MODEL_6f0e630928ed49cdaa9710f934e1bb41", + "tabbable": null, + "tooltip": null, + "value": "Map (num_proc=4): 100%" + } + }, + "4dd4b6127ee54c23b831b0010934f8d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3856,31 +3680,46 @@ "width": null } }, - "4739241d01104e08a7e32ab4bd340c07": { + "4eac37c8dfde423abb9aa3bf66ee4869": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "4fd94f96dc754593acfa96a01d4e99a1": { + "model_module": "@jupyter-widgets/controls", + "model_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_0e3ca28ce7344406851f80d680a12506", - "IPY_MODEL_92cf2be851da4d64855fbee49ea41f00", - "IPY_MODEL_bea8e00f12f049ad985bf7dcd77d49fc" - ], - "layout": "IPY_MODEL_657f66f6978d40808cce2ea1ad7c4e35", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a09459f714804969ae04c7c5e268af7c", + "placeholder": "​", + "style": "IPY_MODEL_cb06124a899d43a0a1959a132eeb5c5a", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 40/40 [00:00<00:00, 58.17it/s]" } }, - "4906b500667e44f6b41c7438c7ed301d": { + "518259e3f72a4db68b3a4b1c66321dd3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3898,60 +3737,47 @@ "text_color": null } }, - "4ff17cf5ce1d4837a0986a522b40bcca": { - "model_module": "@jupyter-widgets/base", + "56b4f5069edb439582d40c62a1b11bab": { + "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": "" + } + }, + "633e645b240b406a822c9ceed34219ee": { + "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_deb611609efa4c2db0570414b2d76844", + "IPY_MODEL_12ed270f15a14c4487328aa39e253957", + "IPY_MODEL_ea3ebf4b767442e281dfd166c4d944af" + ], + "layout": "IPY_MODEL_a343830a4e5b4d87a216175fbee9c91d", + "tabbable": null, + "tooltip": null } }, - "5049b4cd31d447ca8d4c523ad88cd284": { + "66123941a1764558997a9752673ab190": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4004,25 +3830,23 @@ "width": null } }, - "547ab24770b14064ac8dae5e0841c9e7": { + "69be4e0cb1f94577b806e6dcbf5729f8": { "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": "" } }, - "55052c746d8f42be983228e459b49afe": { + "6a18c90eae4440eb893c9f025767dddd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4075,41 +3899,7 @@ "width": null } }, - "564b4d4343944d9aa89b5c29b117db36": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "56ad411b8db441c7abeb093633bccdc9": { - "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": "" - } - }, - "56de5e02bd01436b8edc20836384169f": { + "6c2b6b6278f64121a4277ac53a3f4f9c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4125,63 +3915,53 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_efdb227f3798423092002d9ff1033b04", + "layout": "IPY_MODEL_7914f506574d4f4196c3e7b33bc285e1", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_9c3c72650c7d4960aeb4524159bb68ea", + "style": "IPY_MODEL_b6ab6ccea1a342ae815e4c1eb98b45d4", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "5804b31687494c07b710386e859b431e": { + "6c94014e9b16448d874b35ec257ea024": { "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_dafa2013e9ee488dad70944f3e7d06ae", - "placeholder": "​", - "style": "IPY_MODEL_d70fb12f289249e6b2d96f5899ec7593", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.54it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "580af8c56b8e460ab0acfbec8fb3e2fb": { + "6f0e630928ed49cdaa9710f934e1bb41": { "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_6b91322e23f945039bf15f4d04da6d7a", - "placeholder": "​", - "style": "IPY_MODEL_8cace21a20524e9dab7a18b2058ebf7d", - "tabbable": null, - "tooltip": null, - "value": " 10000/10000 [00:00<00:00, 636059.57 examples/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5d661f0602724b848c5f85cf262fc17f": { + "743fd21aabd3441080cbdf88ef4750cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4234,86 +4014,48 @@ "width": null } }, - "5d784a31f1c34dfd9151df789b1bab99": { + "7455f99c8036436092de8d7492d73268": { "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_ee24cb9ae816475d87eaff5a37eafbe2", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ced1e34cf86f4b69a1e90969c5bfcfce", + "layout": "IPY_MODEL_125e89a524574e23a5d582ea6a6193ef", + "placeholder": "​", + "style": "IPY_MODEL_76c06e0e535b4eb2a0d04e8168ce3990", "tabbable": null, "tooltip": null, - "value": 2.0 + "value": " 60000/60000 [00:50<00:00, 1187.52it/s]" } }, - "5fbb4d5a4d0044699dec9b3b12696dc1": { - "model_module": "@jupyter-widgets/base", + "76c06e0e535b4eb2a0d04e8168ce3990": { + "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 } }, - "61c89909398349d4947e964970ec402c": { + "7914f506574d4f4196c3e7b33bc285e1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4366,7 +4108,7 @@ "width": null } }, - "63039be8332146a99b88b787eadd7730": { + "79b1180eabc3436fac65786d11656c22": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4384,30 +4126,25 @@ "text_color": null } }, - "643564843e7c4d559d15bce8b09e60bf": { + "7ae1a926cc1f4379a9d86080778be517": { "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_44f264b134e14625bbdfc087820b1311", - "placeholder": "​", - "style": "IPY_MODEL_c35b5ab6fc2c4e4985a34b5cd00d779e", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 60.47it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "657f66f6978d40808cce2ea1ad7c4e35": { + "7ce21c448ae34e09a0b1b077bdf8f9c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4460,25 +4197,31 @@ "width": null } }, - "659e01794dc54b11953523e915b610dd": { + "8287864aaa354e0c91aae0254dbfbe57": { "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_a7ed57099d7d4be183532589e53557cf", + "IPY_MODEL_de26db0302f94535950aced09b5e1d00", + "IPY_MODEL_9981abca60404a29a95ca6ef747463a5" + ], + "layout": "IPY_MODEL_ee680731bdcb4b61bede18e265e6df46", + "tabbable": null, + "tooltip": null } }, - "65bb8dd226b741aabd691de81b94e05a": { + "837534ba61a440e29b8bec88ecafa936": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4531,7 +4274,7 @@ "width": null } }, - "6b91322e23f945039bf15f4d04da6d7a": { + "8398904f0c42454194f5d9c89dce2cc7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4584,7 +4327,7 @@ "width": null } }, - "6c7e3e6b760e4744982009dc6ba085cd": { + "872d173a4763445394490e4d03082558": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4599,39 +4342,85 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_d39b3c56fc0643fa90446413b6ae1c01", - "IPY_MODEL_2b36aa46f93e41ce9638a118aa74e2f5", - "IPY_MODEL_12cb341fdb1044cab97410fd09a1540a" + "IPY_MODEL_4ca98e8187e745858cecba425db799e9", + "IPY_MODEL_00117567805f440d9d5d764e0ce0c942", + "IPY_MODEL_8e7816b7f5d24fdc9d3fef632c20c5cd" ], - "layout": "IPY_MODEL_5049b4cd31d447ca8d4c523ad88cd284", + "layout": "IPY_MODEL_8398904f0c42454194f5d9c89dce2cc7", "tabbable": null, "tooltip": null } }, - "6ded2348d0c14c1eb3186fc9fb33ca4a": { - "model_module": "@jupyter-widgets/base", + "87a35075be054c99a6917be1ada6b70b": { + "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, + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a5252d7ceea648dab3c92b42299622ca", + "placeholder": "​", + "style": "IPY_MODEL_a8ebead3bd874a8d8eef78eacf045d09", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "8e7816b7f5d24fdc9d3fef632c20c5cd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1754f6bec411400e8a0248d2d205d9d3", + "placeholder": "​", + "style": "IPY_MODEL_1dbf4ffd3fe34a0e82fbab2708457009", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 5376.58 examples/s]" + } + }, + "9259e5716ecc44169ffc9bfcb2347eee": { + "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, @@ -4661,23 +4450,105 @@ "width": null } }, - "6eb5308f57c4436da74f9cc6dfdc56c1": { + "968457e5d1c1482a94cea2642422460c": { "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_d1705d9417414e228d2b64d20c5d00ee", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4eac37c8dfde423abb9aa3bf66ee4869", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "98cdb184470b45209302dfb69e4de525": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c010c260788e44cca6942cd5c1a20a87", + "placeholder": "​", + "style": "IPY_MODEL_daa10eea47ee4dd185c055d03ea53de6", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 61.29it/s]" + } + }, + "98efed1f73804d8b998a51e41a7b91fd": { + "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_a957df93ee9243a1a41c86fcf625f3b0", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b3b164c849464da7870310f7bea566b9", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "9981abca60404a29a95ca6ef747463a5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_221e7e9db86c4eda9720dcec34776e7d", + "placeholder": "​", + "style": "IPY_MODEL_c0f595a8925341fd91b6fa8d56f70231", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:00<00:00, 267427.36 examples/s]" } }, - "706ef0ce82a848d5a2b5a1cfeff3b66d": { + "9b3fbd16257049c7a45c921d39d3eaf5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4730,7 +4601,23 @@ "width": null } }, - "7465a94905974f7097b588489de27e46": { + "9f08aebc732446de872f662bd327955b": { + "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": "" + } + }, + "9fc4e85f7c89462f8e41f451f28c165b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4748,7 +4635,7 @@ "text_color": null } }, - "7521a6e0209d4bcfa4688c2f87743e34": { + "a09459f714804969ae04c7c5e268af7c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4801,7 +4688,23 @@ "width": null } }, - "7610d89b3fb84ed8b8f85e6777c2be44": { + "a2d8a1f3b9974266876dc362568d194b": { + "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": "" + } + }, + "a343830a4e5b4d87a216175fbee9c91d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4854,25 +4757,7 @@ "width": null } }, - "79471511b83c4838b8cf566b168e574b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "7a4771c243a240b2b91afa67fe46ecff": { + "a346f15cb5134231b6e007dcf577d223": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4925,7 +4810,7 @@ "width": null } }, - "801f7cce1d194f9b9c33149aef36026d": { + "a3b36ce8ee7f4f819f3ed5fb5a98933e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4978,56 +4863,25 @@ "width": null } }, - "811410c9f7a8407c9afab9994a295677": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f6e05afaf00440a7830ad926432b4f83", - "placeholder": "​", - "style": "IPY_MODEL_a31c4401cfda463da2542a23380c4208", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" - } - }, - "8522d6bc397849cea3fe47036e466f1a": { + "a3fb24f690c840348a883369a4610bd2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f867132b50104b83b0babfb88b2684b3", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c3853c49ef2340458b92a49f587b4960", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8970c3588e8b4775ae0c5198bb726b6c": { + "a4463a06d49648eea5841f1b1c26666e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5042,15 +4896,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_25fc4b8a75354f75a538e45656f2b4dd", + "layout": "IPY_MODEL_13320e008c6b4b55b02b9da6ede7d759", "placeholder": "​", - "style": "IPY_MODEL_af3de8894f9941dfaa69005524af544d", + "style": "IPY_MODEL_6c94014e9b16448d874b35ec257ea024", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 62.59it/s]" } }, - "8c67daeac0434fb382558a823e32902b": { + "a5252d7ceea648dab3c92b42299622ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5103,7 +4957,30 @@ "width": null } }, - "8cace21a20524e9dab7a18b2058ebf7d": { + "a7ed57099d7d4be183532589e53557cf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4dd4b6127ee54c23b831b0010934f8d6", + "placeholder": "​", + "style": "IPY_MODEL_79b1180eabc3436fac65786d11656c22", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" + } + }, + "a8ebead3bd874a8d8eef78eacf045d09": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5121,7 +4998,7 @@ "text_color": null } }, - "8de2e10eaab64976bbf8d7da16e4e743": { + "a957df93ee9243a1a41c86fcf625f3b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5174,446 +5051,14 @@ "width": null } }, - "8e80e402c8e544be8f3665d48bb331b1": { - "model_module": "@jupyter-widgets/base", + "aaa247560a9f4de5ba6276b9318cbdd9": { + "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8f5530de2e7a4f3f89af3a5ce58a19fe": { - "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_11b49017a0b64344bec8ee657afc5c4f", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1fcfe42385e9486293f75bb99e009cbc", - "tabbable": null, - "tooltip": null, - "value": 10000.0 - } - }, - "8f5abaf104bc43d39f6a5747eb17b340": { - "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_9a0a4a25729b4c8792a119ad77f36c3c", - "IPY_MODEL_8f5530de2e7a4f3f89af3a5ce58a19fe", - "IPY_MODEL_580af8c56b8e460ab0acfbec8fb3e2fb" - ], - "layout": "IPY_MODEL_f85f0334db114aa59b010e361bb4fbbd", - "tabbable": null, - "tooltip": null - } - }, - "919d270fbb284450aff333e907475643": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8de2e10eaab64976bbf8d7da16e4e743", - "placeholder": "​", - "style": "IPY_MODEL_2ef3598161a949e9aef2bb7578a46bfe", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "92681c8858d448d49fca40635dc49cac": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "92cf2be851da4d64855fbee49ea41f00": { - "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_dacec0f83fe048a7a0cef5a9a98e4b81", - "max": 5175617.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_14578e5a36ee46cf94f7f04c06ba4a53", - "tabbable": null, - "tooltip": null, - "value": 5175617.0 - } - }, - "9a0a4a25729b4c8792a119ad77f36c3c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_65bb8dd226b741aabd691de81b94e05a", - "placeholder": "​", - "style": "IPY_MODEL_7465a94905974f7097b588489de27e46", - "tabbable": null, - "tooltip": null, - "value": "Generating test split: 100%" - } - }, - "9c3c72650c7d4960aeb4524159bb68ea": { - "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": "" - } - }, - "9cb82e8aadeb44f696eeb05cba80bf38": { - "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_3d289127d48f42a38c13d633650495ab", - "IPY_MODEL_ea94ee3f1fa044819811fc2417e237a5", - "IPY_MODEL_5804b31687494c07b710386e859b431e" - ], - "layout": "IPY_MODEL_f3388fc2449142fab5c93b916b11ec17", - "tabbable": null, - "tooltip": null - } - }, - "9e0d2440a9254b05bc81e16591cb45ca": { - "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 - } - }, - "a31c4401cfda463da2542a23380c4208": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a4cd2589ffb34e0a9fe14c70c848ca2e": { - "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": "" - } - }, - "a57b7f6524be4d1487e83f44670fbd99": { - "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 - } - }, - "aa210d8e134d4e65a35337751bebb8fa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "aad5e096b2544d48870ed3fc61b6b5c2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "acd9fc454b7a441bad4a264362ca582c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2df710401168425db8bcf3af9d77a3ca", - "placeholder": "​", - "style": "IPY_MODEL_63039be8332146a99b88b787eadd7730", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.75it/s]" - } - }, - "af3de8894f9941dfaa69005524af544d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -5624,7 +5069,7 @@ "text_color": null } }, - "b46cd35a553d401c8d614aaa77b758c5": { + "abfc1d43df3449a99634921972eb546c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5677,7 +5122,7 @@ "width": null } }, - "b5ce17ab53824591a98f449e8b025965": { + "b09dbf3fce1a4e5491433501bc5e7751": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5730,56 +5175,87 @@ "width": null } }, - "b87e8a4c2ee14767a3f43e0ed5c95bef": { + "b3b164c849464da7870310f7bea566b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "b50a7b54767048a6a0ee15d292d150a5": { + "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_61c89909398349d4947e964970ec402c", - "max": 30931277.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cea842f314324ac09988392b914b2397", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_40b7f76f7c5040b99bb6d3b1b4aabd78", + "IPY_MODEL_286acff3d63f4b4a97ee007082ffc9d2", + "IPY_MODEL_3868e1a7559d457d96e83bd991cf34fd" + ], + "layout": "IPY_MODEL_6a18c90eae4440eb893c9f025767dddd", "tabbable": null, - "tooltip": null, - "value": 30931277.0 + "tooltip": null } }, - "b9e9ee891b2c4607a90164b2e9fd4a8d": { + "b6ab6ccea1a342ae815e4c1eb98b45d4": { "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": "" + } + }, + "ba2a00d2cf6a41309e5ef39c4e9854f3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a57b7f6524be4d1487e83f44670fbd99", - "placeholder": "​", - "style": "IPY_MODEL_1dde6247d89e422fae510063a9694bde", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_c72a0e72518444dc9d87a6311824912a", + "IPY_MODEL_6c2b6b6278f64121a4277ac53a3f4f9c", + "IPY_MODEL_c358e3106fa44fd7b23d0664abb7490e" + ], + "layout": "IPY_MODEL_f8b9efa53c0f4fb8bdbd667a9dc4d7d6", "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" + "tooltip": null } }, - "bc23d1163ae54391b6d1b095111b7b64": { + "bd520442648241aba2c0213d7bea8277": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5832,30 +5308,7 @@ "width": null } }, - "bea8e00f12f049ad985bf7dcd77d49fc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4ff17cf5ce1d4837a0986a522b40bcca", - "placeholder": "​", - "style": "IPY_MODEL_aad5e096b2544d48870ed3fc61b6b5c2", - "tabbable": null, - "tooltip": null, - "value": " 5.18M/5.18M [00:00<00:00, 26.7MB/s]" - } - }, - "c181f62b4468453a89ff5289910a3a65": { + "bf535745edea45cfac37c688118391e0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5871,85 +5324,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_801f7cce1d194f9b9c33149aef36026d", + "layout": "IPY_MODEL_1c538754e2c94b6c8cc8835413e8c193", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a4cd2589ffb34e0a9fe14c70c848ca2e", + "style": "IPY_MODEL_56b4f5069edb439582d40c62a1b11bab", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "c229dd5792804b6a88e3be57058cd2b5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c35b5ab6fc2c4e4985a34b5cd00d779e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c3853c49ef2340458b92a49f587b4960": { - "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": "" - } - }, - "c6930b35722c4bc1b618491699bf7322": { - "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": "" - } - }, - "c932e3e7a73e4533acbe1af723e40e3b": { + "c010c260788e44cca6942cd5c1a20a87": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6002,57 +5387,7 @@ "width": null } }, - "cea842f314324ac09988392b914b2397": { - "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": "" - } - }, - "ced1e34cf86f4b69a1e90969c5bfcfce": { - "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": "" - } - }, - "cf534b6635784368915fadd5cbb13ef7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "cf5c2930549d4fbe8250517f5696e479": { + "c0f595a8925341fd91b6fa8d56f70231": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6070,60 +5405,7 @@ "text_color": null } }, - "d1c62c6e888d44e4be1c96009d1e1537": { - "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 - } - }, - "d29be32abfeb45e38ab62bf213c3c810": { + "c10bd78b3a524ca098f9b1d758f653b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6176,7 +5458,7 @@ "width": null } }, - "d39b3c56fc0643fa90446413b6ae1c01": { + "c358e3106fa44fd7b23d0664abb7490e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6191,15 +5473,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e31ab863cb26461ab15a486e91c8be0d", + "layout": "IPY_MODEL_9259e5716ecc44169ffc9bfcb2347eee", "placeholder": "​", - "style": "IPY_MODEL_3584d39ca68a4c4ba99d6ca4346077bb", + "style": "IPY_MODEL_1157e6644cf94a369b0bc0c79b48c0c3", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 56.87it/s]" } }, - "d5935e90376a42bebcfd4ccbd45c7d9a": { + "c466561275334474bb5df6065049149b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6252,49 +5534,76 @@ "width": null } }, - "d70fb12f289249e6b2d96f5899ec7593": { + "c613b29260064809a8c4178ed6147c38": { "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_abfc1d43df3449a99634921972eb546c", + "placeholder": "​", + "style": "IPY_MODEL_518259e3f72a4db68b3a4b1c66321dd3", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "da48984a4c04483e88ca66d17f185796": { + "c72a0e72518444dc9d87a6311824912a": { "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_2dd5b78c91654388ac491464dc21b064", - "IPY_MODEL_56de5e02bd01436b8edc20836384169f", - "IPY_MODEL_20d5d6d7592245cb8e6ae6637e124962" - ], - "layout": "IPY_MODEL_55052c746d8f42be983228e459b49afe", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_66123941a1764558997a9752673ab190", + "placeholder": "​", + "style": "IPY_MODEL_eb54dee925e3409d9850aa1fcc1b48b5", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" + } + }, + "caf7d35738c6472c950d3543d4f3eaf7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cd5d376c47d44680ad380c3818a21fa0", + "placeholder": "​", + "style": "IPY_MODEL_13dd0f8d07254c1e8c1c8fce7499453f", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "da48a4f7c7cb408096c49e6124bf70e4": { + "cb06124a899d43a0a1959a132eeb5c5a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6312,7 +5621,33 @@ "text_color": null } }, - "dacec0f83fe048a7a0cef5a9a98e4b81": { + "cb1128114ec44703baed9028494b5a52": { + "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_e6e0df3778dd4adcba513dac2d339069", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a2d8a1f3b9974266876dc362568d194b", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "cd5d376c47d44680ad380c3818a21fa0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6365,7 +5700,43 @@ "width": null } }, - "dafa2013e9ee488dad70944f3e7d06ae": { + "ce4a09a003454d74b4417a7ebdbbd50f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "cf7dcf12b151404e896e8bdb62460655": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d1705d9417414e228d2b64d20c5d00ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6418,7 +5789,7 @@ "width": null } }, - "dc6d200dd499407da1c62711cafeca7a": { + "d70005fa09074d9fab741afc9900399e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6433,15 +5804,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b46cd35a553d401c8d614aaa77b758c5", + "layout": "IPY_MODEL_41c91ad69758433799e5eda8ba8446ce", "placeholder": "​", - "style": "IPY_MODEL_547ab24770b14064ac8dae5e0841c9e7", + "style": "IPY_MODEL_05f112c96d29414fa42131bc8603da0e", "tabbable": null, "tooltip": null, - "value": " 2/2 [00:00<00:00, 565.23it/s]" + "value": " 60000/60000 [00:00<00:00, 272073.32 examples/s]" } }, - "de630eedaa6d434582d62b17e7c8e009": { + "da3b8eaa8bfa4de78ce5bf1e9c7d73cb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6456,16 +5827,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_b9e9ee891b2c4607a90164b2e9fd4a8d", - "IPY_MODEL_41eddd2244a74684986dd50abd365d34", - "IPY_MODEL_f2e62f490b8a41e7adb6c88eb7644d4a" + "IPY_MODEL_caf7d35738c6472c950d3543d4f3eaf7", + "IPY_MODEL_1b1616f669014ec88ea42a507fc66b38", + "IPY_MODEL_7455f99c8036436092de8d7492d73268" ], - "layout": "IPY_MODEL_23c9db1ca6ee4834bd0db0e311214d5f", + "layout": "IPY_MODEL_0386e13f4a2941548963bd69cdaa9593", "tabbable": null, "tooltip": null } }, - "def8761350f347f58474faf97a0ce8bb": { + "daa10eea47ee4dd185c055d03ea53de6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6483,7 +5854,7 @@ "text_color": null } }, - "e31ab863cb26461ab15a486e91c8be0d": { + "dabb292c869d416bb677733a64a4bc31": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6536,7 +5907,7 @@ "width": null } }, - "e4ff32be7a5847b891bae956c99642ed": { + "dc0af5e6d82348d69e1094d9659e5f15": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6589,192 +5960,115 @@ "width": null } }, - "e7e978c332654878b944425a2cce7431": { - "model_module": "@jupyter-widgets/base", + "de26db0302f94535950aced09b5e1d00": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1868860131654264bd9a733aac14b217", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f335fbafaacb4563bee9fa4ddaf06be4", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "de2a9078248149578085749e3dfea820": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_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 } }, - "ea94ee3f1fa044819811fc2417e237a5": { + "deb611609efa4c2db0570414b2d76844": { "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_8e80e402c8e544be8f3665d48bb331b1", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c6930b35722c4bc1b618491699bf7322", + "layout": "IPY_MODEL_bd520442648241aba2c0213d7bea8277", + "placeholder": "​", + "style": "IPY_MODEL_ce4a09a003454d74b4417a7ebdbbd50f", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "Computing checksums: 100%" } }, - "ea9766b70dbb45bdac1e2bc0bb2273ff": { - "model_module": "@jupyter-widgets/base", + "e17fd520bbdc4e70b092658dcb682abe": { + "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 } }, - "ee24cb9ae816475d87eaff5a37eafbe2": { - "model_module": "@jupyter-widgets/base", + "e630651efa774777b84bec8783ca86e1": { + "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_f0c48e7a92584111a0c8860ced295150", + "placeholder": "​", + "style": "IPY_MODEL_de2a9078248149578085749e3dfea820", + "tabbable": null, + "tooltip": null, + "value": "Generating train split: 100%" } }, - "efdb227f3798423092002d9ff1033b04": { + "e6e0df3778dd4adcba513dac2d339069": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6827,60 +6121,30 @@ "width": null } }, - "f259d9b670d14c18adbf4e4088c33b4c": { - "model_module": "@jupyter-widgets/base", + "e7583439721a41c9aec978c1bdbdeef9": { + "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_a3b36ce8ee7f4f819f3ed5fb5a98933e", + "placeholder": "​", + "style": "IPY_MODEL_e17fd520bbdc4e70b092658dcb682abe", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "f2e62f490b8a41e7adb6c88eb7644d4a": { + "ea3ebf4b767442e281dfd166c4d944af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6895,15 +6159,57 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ea9766b70dbb45bdac1e2bc0bb2273ff", + "layout": "IPY_MODEL_a346f15cb5134231b6e007dcf577d223", "placeholder": "​", - "style": "IPY_MODEL_1f39f77cae9a4b8289093a19d2b053ce", + "style": "IPY_MODEL_cf7dcf12b151404e896e8bdb62460655", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:10<00:00, 6452.30 examples/s]" + "value": " 2/2 [00:00<00:00, 578.21it/s]" + } + }, + "eb54dee925e3409d9850aa1fcc1b48b5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ee14e2e007734aa6a367ee52a32693dc": { + "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_4c192cd329c54205a7c62f4df4cbb056", + "IPY_MODEL_98efed1f73804d8b998a51e41a7b91fd", + "IPY_MODEL_4fd94f96dc754593acfa96a01d4e99a1" + ], + "layout": "IPY_MODEL_9b3fbd16257049c7a45c921d39d3eaf5", + "tabbable": null, + "tooltip": null } }, - "f3388fc2449142fab5c93b916b11ec17": { + "ee680731bdcb4b61bede18e265e6df46": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6956,7 +6262,47 @@ "width": null } }, - "f6e05afaf00440a7830ad926432b4f83": { + "ef77a024e7724e369dc1c8d4f9e368dc": { + "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_e7583439721a41c9aec978c1bdbdeef9", + "IPY_MODEL_03271711edad450897e85e06eb6cb127", + "IPY_MODEL_a4463a06d49648eea5841f1b1c26666e" + ], + "layout": "IPY_MODEL_287287177d414d93adcc6f20d070dc87", + "tabbable": null, + "tooltip": null + } + }, + "f03f41af5b5447f9bcca654369f6a2ca": { + "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": "" + } + }, + "f0c48e7a92584111a0c8860ced295150": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7009,7 +6355,47 @@ "width": null } }, - "f85f0334db114aa59b010e361bb4fbbd": { + "f335fbafaacb4563bee9fa4ddaf06be4": { + "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": "" + } + }, + "f536096ae2354577a855edd0b00768f8": { + "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_e630651efa774777b84bec8783ca86e1", + "IPY_MODEL_cb1128114ec44703baed9028494b5a52", + "IPY_MODEL_d70005fa09074d9fab741afc9900399e" + ], + "layout": "IPY_MODEL_40cf93e1ad9c40329463ec8680702b75", + "tabbable": null, + "tooltip": null + } + }, + "f53af9199dd543cc90ef5fbb55d8ec83": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7062,7 +6448,7 @@ "width": null } }, - "f867132b50104b83b0babfb88b2684b3": { + "f73fd148bfd14a059bf19d9a0008bf30": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7115,31 +6501,7 @@ "width": null } }, - "f8b0487fce084fbbb8bb7ec13ff93904": { - "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_8970c3588e8b4775ae0c5198bb726b6c", - "IPY_MODEL_c181f62b4468453a89ff5289910a3a65", - "IPY_MODEL_643564843e7c4d559d15bce8b09e60bf" - ], - "layout": "IPY_MODEL_d5935e90376a42bebcfd4ccbd45c7d9a", - "tabbable": null, - "tooltip": null - } - }, - "fb58bed5d89f4661babe6b776dac767d": { + "f8b9efa53c0f4fb8bdbd667a9dc4d7d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7191,30 +6553,6 @@ "visibility": null, "width": null } - }, - "fe021de5ecdc4eaaa8a1a9bc5fc1da98": { - "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_188195b2866e46598594d40c79b6011b", - "IPY_MODEL_5d784a31f1c34dfd9151df789b1bab99", - "IPY_MODEL_dc6d200dd499407da1c62711cafeca7a" - ], - "layout": "IPY_MODEL_7521a6e0209d4bcfa4688c2f87743e34", - "tabbable": null, - "tooltip": null - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index b1d74a80c..4251dc171 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": "2024-02-27T04:11:14.797503Z", - "iopub.status.busy": "2024-02-27T04:11:14.797171Z", - "iopub.status.idle": "2024-02-27T04:11:15.872433Z", - "shell.execute_reply": "2024-02-27T04:11:15.871920Z" + "iopub.execute_input": "2024-03-05T17:26:25.913974Z", + "iopub.status.busy": "2024-03-05T17:26:25.913793Z", + "iopub.status.idle": "2024-03-05T17:26:27.120147Z", + "shell.execute_reply": "2024-03-05T17:26:27.119495Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:11:15.874932Z", - "iopub.status.busy": "2024-02-27T04:11:15.874600Z", - "iopub.status.idle": "2024-02-27T04:11:16.050372Z", - "shell.execute_reply": "2024-02-27T04:11:16.049843Z" + "iopub.execute_input": "2024-03-05T17:26:27.122822Z", + "iopub.status.busy": "2024-03-05T17:26:27.122428Z", + "iopub.status.idle": "2024-03-05T17:26:27.308983Z", + "shell.execute_reply": "2024-03-05T17:26:27.308368Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:16.053004Z", - "iopub.status.busy": "2024-02-27T04:11:16.052608Z", - "iopub.status.idle": "2024-02-27T04:11:16.064097Z", - "shell.execute_reply": "2024-02-27T04:11:16.063546Z" + "iopub.execute_input": "2024-03-05T17:26:27.311690Z", + "iopub.status.busy": "2024-03-05T17:26:27.311307Z", + "iopub.status.idle": "2024-03-05T17:26:27.323761Z", + "shell.execute_reply": "2024-03-05T17:26:27.323176Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:16.066283Z", - "iopub.status.busy": "2024-02-27T04:11:16.065903Z", - "iopub.status.idle": "2024-02-27T04:11:16.300106Z", - "shell.execute_reply": "2024-02-27T04:11:16.299548Z" + "iopub.execute_input": "2024-03-05T17:26:27.326197Z", + "iopub.status.busy": "2024-03-05T17:26:27.325856Z", + "iopub.status.idle": "2024-03-05T17:26:27.569749Z", + "shell.execute_reply": "2024-03-05T17:26:27.569143Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:16.302603Z", - "iopub.status.busy": "2024-02-27T04:11:16.302218Z", - "iopub.status.idle": "2024-02-27T04:11:16.329576Z", - "shell.execute_reply": "2024-02-27T04:11:16.329164Z" + "iopub.execute_input": "2024-03-05T17:26:27.572005Z", + "iopub.status.busy": "2024-03-05T17:26:27.571722Z", + "iopub.status.idle": "2024-03-05T17:26:27.599205Z", + "shell.execute_reply": "2024-03-05T17:26:27.598661Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:16.331602Z", - "iopub.status.busy": "2024-02-27T04:11:16.331279Z", - "iopub.status.idle": "2024-02-27T04:11:17.949443Z", - "shell.execute_reply": "2024-02-27T04:11:17.948926Z" + "iopub.execute_input": "2024-03-05T17:26:27.601935Z", + "iopub.status.busy": "2024-03-05T17:26:27.601446Z", + "iopub.status.idle": "2024-03-05T17:26:29.470338Z", + "shell.execute_reply": "2024-03-05T17:26:29.469673Z" } }, "outputs": [ @@ -475,10 +475,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:17.952199Z", - "iopub.status.busy": "2024-02-27T04:11:17.951567Z", - "iopub.status.idle": "2024-02-27T04:11:17.970482Z", - "shell.execute_reply": "2024-02-27T04:11:17.970030Z" + "iopub.execute_input": "2024-03-05T17:26:29.473388Z", + "iopub.status.busy": "2024-03-05T17:26:29.472356Z", + "iopub.status.idle": "2024-03-05T17:26:29.493442Z", + "shell.execute_reply": "2024-03-05T17:26:29.492883Z" }, "scrolled": true }, @@ -603,10 +603,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:17.972450Z", - "iopub.status.busy": "2024-02-27T04:11:17.972150Z", - "iopub.status.idle": "2024-02-27T04:11:19.336299Z", - "shell.execute_reply": "2024-02-27T04:11:19.335777Z" + "iopub.execute_input": "2024-03-05T17:26:29.495698Z", + "iopub.status.busy": "2024-03-05T17:26:29.495318Z", + "iopub.status.idle": "2024-03-05T17:26:31.024996Z", + "shell.execute_reply": "2024-03-05T17:26:31.024329Z" }, "id": "AaHC5MRKjruT" }, @@ -725,10 +725,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.338968Z", - "iopub.status.busy": "2024-02-27T04:11:19.338212Z", - "iopub.status.idle": "2024-02-27T04:11:19.351594Z", - "shell.execute_reply": "2024-02-27T04:11:19.351150Z" + "iopub.execute_input": "2024-03-05T17:26:31.028261Z", + "iopub.status.busy": "2024-03-05T17:26:31.027357Z", + "iopub.status.idle": "2024-03-05T17:26:31.043398Z", + "shell.execute_reply": "2024-03-05T17:26:31.042833Z" }, "id": "Wy27rvyhjruU" }, @@ -777,10 +777,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.353609Z", - "iopub.status.busy": "2024-02-27T04:11:19.353270Z", - "iopub.status.idle": "2024-02-27T04:11:19.422073Z", - "shell.execute_reply": "2024-02-27T04:11:19.421532Z" + "iopub.execute_input": "2024-03-05T17:26:31.045811Z", + "iopub.status.busy": "2024-03-05T17:26:31.045436Z", + "iopub.status.idle": "2024-03-05T17:26:31.135388Z", + "shell.execute_reply": "2024-03-05T17:26:31.134826Z" }, "id": "Db8YHnyVjruU" }, @@ -887,10 +887,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.424260Z", - "iopub.status.busy": "2024-02-27T04:11:19.423971Z", - "iopub.status.idle": "2024-02-27T04:11:19.631539Z", - "shell.execute_reply": "2024-02-27T04:11:19.631015Z" + "iopub.execute_input": "2024-03-05T17:26:31.137715Z", + "iopub.status.busy": "2024-03-05T17:26:31.137407Z", + "iopub.status.idle": "2024-03-05T17:26:31.354491Z", + "shell.execute_reply": "2024-03-05T17:26:31.353914Z" }, "id": "iJqAHuS2jruV" }, @@ -927,10 +927,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.633648Z", - "iopub.status.busy": "2024-02-27T04:11:19.633311Z", - "iopub.status.idle": "2024-02-27T04:11:19.649889Z", - "shell.execute_reply": "2024-02-27T04:11:19.649466Z" + "iopub.execute_input": "2024-03-05T17:26:31.356854Z", + "iopub.status.busy": "2024-03-05T17:26:31.356506Z", + "iopub.status.idle": "2024-03-05T17:26:31.374002Z", + "shell.execute_reply": "2024-03-05T17:26:31.373435Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1396,10 +1396,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.651815Z", - "iopub.status.busy": "2024-02-27T04:11:19.651501Z", - "iopub.status.idle": "2024-02-27T04:11:19.660394Z", - "shell.execute_reply": "2024-02-27T04:11:19.659948Z" + "iopub.execute_input": "2024-03-05T17:26:31.376365Z", + "iopub.status.busy": "2024-03-05T17:26:31.376010Z", + "iopub.status.idle": "2024-03-05T17:26:31.386117Z", + "shell.execute_reply": "2024-03-05T17:26:31.385574Z" }, "id": "0lonvOYvjruV" }, @@ -1546,10 +1546,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.662491Z", - "iopub.status.busy": "2024-02-27T04:11:19.662099Z", - "iopub.status.idle": "2024-02-27T04:11:19.741536Z", - "shell.execute_reply": "2024-02-27T04:11:19.740945Z" + "iopub.execute_input": "2024-03-05T17:26:31.388376Z", + "iopub.status.busy": "2024-03-05T17:26:31.388008Z", + "iopub.status.idle": "2024-03-05T17:26:31.487199Z", + "shell.execute_reply": "2024-03-05T17:26:31.486574Z" }, "id": "MfqTCa3kjruV" }, @@ -1630,10 +1630,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.743988Z", - "iopub.status.busy": "2024-02-27T04:11:19.743652Z", - "iopub.status.idle": "2024-02-27T04:11:19.860324Z", - "shell.execute_reply": "2024-02-27T04:11:19.859746Z" + "iopub.execute_input": "2024-03-05T17:26:31.490166Z", + "iopub.status.busy": "2024-03-05T17:26:31.489873Z", + "iopub.status.idle": "2024-03-05T17:26:31.635282Z", + "shell.execute_reply": "2024-03-05T17:26:31.634636Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1693,10 +1693,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.862767Z", - "iopub.status.busy": "2024-02-27T04:11:19.862440Z", - "iopub.status.idle": "2024-02-27T04:11:19.866266Z", - "shell.execute_reply": "2024-02-27T04:11:19.865804Z" + "iopub.execute_input": "2024-03-05T17:26:31.637910Z", + "iopub.status.busy": "2024-03-05T17:26:31.637471Z", + "iopub.status.idle": "2024-03-05T17:26:31.641593Z", + "shell.execute_reply": "2024-03-05T17:26:31.641043Z" }, "id": "0rXP3ZPWjruW" }, @@ -1734,10 +1734,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.868195Z", - "iopub.status.busy": "2024-02-27T04:11:19.867937Z", - "iopub.status.idle": "2024-02-27T04:11:19.871611Z", - "shell.execute_reply": "2024-02-27T04:11:19.871094Z" + "iopub.execute_input": "2024-03-05T17:26:31.643787Z", + "iopub.status.busy": "2024-03-05T17:26:31.643459Z", + "iopub.status.idle": "2024-03-05T17:26:31.647384Z", + "shell.execute_reply": "2024-03-05T17:26:31.646842Z" }, "id": "-iRPe8KXjruW" }, @@ -1792,10 +1792,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.873567Z", - "iopub.status.busy": "2024-02-27T04:11:19.873211Z", - "iopub.status.idle": "2024-02-27T04:11:19.910384Z", - "shell.execute_reply": "2024-02-27T04:11:19.909960Z" + "iopub.execute_input": "2024-03-05T17:26:31.649608Z", + "iopub.status.busy": "2024-03-05T17:26:31.649282Z", + "iopub.status.idle": "2024-03-05T17:26:31.688906Z", + "shell.execute_reply": "2024-03-05T17:26:31.688352Z" }, "id": "ZpipUliyjruW" }, @@ -1804,7 +1804,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "pred_probs is a (250, 4) matrix of predicted probabilities\n" + "pred_probs is a (250, 4) matrix of predicted probabilities" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -1846,10 +1853,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.912313Z", - "iopub.status.busy": "2024-02-27T04:11:19.912020Z", - "iopub.status.idle": "2024-02-27T04:11:19.954874Z", - "shell.execute_reply": "2024-02-27T04:11:19.954347Z" + "iopub.execute_input": "2024-03-05T17:26:31.691110Z", + "iopub.status.busy": "2024-03-05T17:26:31.690899Z", + "iopub.status.idle": "2024-03-05T17:26:31.735848Z", + "shell.execute_reply": "2024-03-05T17:26:31.735209Z" }, "id": "SLq-3q4xjruX" }, @@ -1918,10 +1925,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.956813Z", - "iopub.status.busy": "2024-02-27T04:11:19.956444Z", - "iopub.status.idle": "2024-02-27T04:11:20.045340Z", - "shell.execute_reply": "2024-02-27T04:11:20.044815Z" + "iopub.execute_input": "2024-03-05T17:26:31.738311Z", + "iopub.status.busy": "2024-03-05T17:26:31.737938Z", + "iopub.status.idle": "2024-03-05T17:26:31.849062Z", + "shell.execute_reply": "2024-03-05T17:26:31.848352Z" }, "id": "g5LHhhuqFbXK" }, @@ -1953,10 +1960,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.047806Z", - "iopub.status.busy": "2024-02-27T04:11:20.047455Z", - "iopub.status.idle": "2024-02-27T04:11:20.124961Z", - "shell.execute_reply": "2024-02-27T04:11:20.124406Z" + "iopub.execute_input": "2024-03-05T17:26:31.851797Z", + "iopub.status.busy": "2024-03-05T17:26:31.851586Z", + "iopub.status.idle": "2024-03-05T17:26:31.969993Z", + "shell.execute_reply": "2024-03-05T17:26:31.969392Z" }, "id": "p7w8F8ezBcet" }, @@ -2013,10 +2020,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.127202Z", - "iopub.status.busy": "2024-02-27T04:11:20.126910Z", - "iopub.status.idle": "2024-02-27T04:11:20.336685Z", - "shell.execute_reply": "2024-02-27T04:11:20.336163Z" + "iopub.execute_input": "2024-03-05T17:26:31.972627Z", + "iopub.status.busy": "2024-03-05T17:26:31.972225Z", + "iopub.status.idle": "2024-03-05T17:26:32.191363Z", + "shell.execute_reply": "2024-03-05T17:26:32.190882Z" }, "id": "WETRL74tE_sU" }, @@ -2051,10 +2058,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.338863Z", - "iopub.status.busy": "2024-02-27T04:11:20.338518Z", - "iopub.status.idle": "2024-02-27T04:11:20.499507Z", - "shell.execute_reply": "2024-02-27T04:11:20.498940Z" + "iopub.execute_input": "2024-03-05T17:26:32.193719Z", + "iopub.status.busy": "2024-03-05T17:26:32.193362Z", + "iopub.status.idle": "2024-03-05T17:26:32.441992Z", + "shell.execute_reply": "2024-03-05T17:26:32.441363Z" }, "id": "kCfdx2gOLmXS" }, @@ -2216,10 +2223,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.501687Z", - "iopub.status.busy": "2024-02-27T04:11:20.501498Z", - "iopub.status.idle": "2024-02-27T04:11:20.507903Z", - "shell.execute_reply": "2024-02-27T04:11:20.507342Z" + "iopub.execute_input": "2024-03-05T17:26:32.444521Z", + "iopub.status.busy": "2024-03-05T17:26:32.444263Z", + "iopub.status.idle": "2024-03-05T17:26:32.451465Z", + "shell.execute_reply": "2024-03-05T17:26:32.450911Z" }, "id": "-uogYRWFYnuu" }, @@ -2273,10 +2280,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.509836Z", - "iopub.status.busy": "2024-02-27T04:11:20.509594Z", - "iopub.status.idle": "2024-02-27T04:11:20.723906Z", - "shell.execute_reply": "2024-02-27T04:11:20.723363Z" + "iopub.execute_input": "2024-03-05T17:26:32.453590Z", + "iopub.status.busy": "2024-03-05T17:26:32.453389Z", + "iopub.status.idle": "2024-03-05T17:26:32.678650Z", + "shell.execute_reply": "2024-03-05T17:26:32.678005Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2323,10 +2330,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.726056Z", - "iopub.status.busy": "2024-02-27T04:11:20.725621Z", - "iopub.status.idle": "2024-02-27T04:11:21.797128Z", - "shell.execute_reply": "2024-02-27T04:11:21.796614Z" + "iopub.execute_input": "2024-03-05T17:26:32.681109Z", + "iopub.status.busy": "2024-03-05T17:26:32.680713Z", + "iopub.status.idle": "2024-03-05T17:26:33.758319Z", + "shell.execute_reply": "2024-03-05T17:26:33.757744Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index d6474214f..a5eab3b4f 100644 --- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb @@ -89,10 +89,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:24.965144Z", - "iopub.status.busy": "2024-02-27T04:11:24.964979Z", - "iopub.status.idle": "2024-02-27T04:11:25.986628Z", - "shell.execute_reply": "2024-02-27T04:11:25.986025Z" + "iopub.execute_input": "2024-03-05T17:26:38.410635Z", + "iopub.status.busy": "2024-03-05T17:26:38.410144Z", + "iopub.status.idle": "2024-03-05T17:26:39.538780Z", + "shell.execute_reply": "2024-03-05T17:26:39.538156Z" }, "nbsphinx": "hidden" }, @@ -102,7 +102,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -136,10 +136,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:25.989118Z", - "iopub.status.busy": "2024-02-27T04:11:25.988838Z", - "iopub.status.idle": "2024-02-27T04:11:25.992192Z", - "shell.execute_reply": "2024-02-27T04:11:25.991753Z" + "iopub.execute_input": "2024-03-05T17:26:39.542285Z", + "iopub.status.busy": "2024-03-05T17:26:39.541793Z", + "iopub.status.idle": "2024-03-05T17:26:39.544910Z", + "shell.execute_reply": "2024-03-05T17:26:39.544471Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:25.994242Z", - "iopub.status.busy": "2024-02-27T04:11:25.994064Z", - "iopub.status.idle": "2024-02-27T04:11:26.001825Z", - "shell.execute_reply": "2024-02-27T04:11:26.001382Z" + "iopub.execute_input": "2024-03-05T17:26:39.546890Z", + "iopub.status.busy": "2024-03-05T17:26:39.546713Z", + "iopub.status.idle": "2024-03-05T17:26:39.554759Z", + "shell.execute_reply": "2024-03-05T17:26:39.554223Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.003744Z", - "iopub.status.busy": "2024-02-27T04:11:26.003397Z", - "iopub.status.idle": "2024-02-27T04:11:26.050244Z", - "shell.execute_reply": "2024-02-27T04:11:26.049766Z" + "iopub.execute_input": "2024-03-05T17:26:39.557146Z", + "iopub.status.busy": "2024-03-05T17:26:39.556707Z", + "iopub.status.idle": "2024-03-05T17:26:39.605516Z", + "shell.execute_reply": "2024-03-05T17:26:39.605020Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.052581Z", - "iopub.status.busy": "2024-02-27T04:11:26.052189Z", - "iopub.status.idle": "2024-02-27T04:11:26.069298Z", - "shell.execute_reply": "2024-02-27T04:11:26.068826Z" + "iopub.execute_input": "2024-03-05T17:26:39.607894Z", + "iopub.status.busy": "2024-03-05T17:26:39.607701Z", + "iopub.status.idle": "2024-03-05T17:26:39.626581Z", + "shell.execute_reply": "2024-03-05T17:26:39.626098Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.071234Z", - "iopub.status.busy": "2024-02-27T04:11:26.071056Z", - "iopub.status.idle": "2024-02-27T04:11:26.075098Z", - "shell.execute_reply": "2024-02-27T04:11:26.074678Z" + "iopub.execute_input": "2024-03-05T17:26:39.628779Z", + "iopub.status.busy": "2024-03-05T17:26:39.628440Z", + "iopub.status.idle": "2024-03-05T17:26:39.632432Z", + "shell.execute_reply": "2024-03-05T17:26:39.631869Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.077104Z", - "iopub.status.busy": "2024-02-27T04:11:26.076785Z", - "iopub.status.idle": "2024-02-27T04:11:26.103793Z", - "shell.execute_reply": "2024-02-27T04:11:26.103386Z" + "iopub.execute_input": "2024-03-05T17:26:39.634490Z", + "iopub.status.busy": "2024-03-05T17:26:39.634309Z", + "iopub.status.idle": "2024-03-05T17:26:39.664088Z", + "shell.execute_reply": "2024-03-05T17:26:39.663607Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.105914Z", - "iopub.status.busy": "2024-02-27T04:11:26.105409Z", - "iopub.status.idle": "2024-02-27T04:11:26.132048Z", - "shell.execute_reply": "2024-02-27T04:11:26.131536Z" + "iopub.execute_input": "2024-03-05T17:26:39.666426Z", + "iopub.status.busy": "2024-03-05T17:26:39.666199Z", + "iopub.status.idle": "2024-03-05T17:26:39.693767Z", + "shell.execute_reply": "2024-03-05T17:26:39.693266Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.134207Z", - "iopub.status.busy": "2024-02-27T04:11:26.133842Z", - "iopub.status.idle": "2024-02-27T04:11:27.825507Z", - "shell.execute_reply": "2024-02-27T04:11:27.824974Z" + "iopub.execute_input": "2024-03-05T17:26:39.696423Z", + "iopub.status.busy": "2024-03-05T17:26:39.695989Z", + "iopub.status.idle": "2024-03-05T17:26:41.565574Z", + "shell.execute_reply": "2024-03-05T17:26:41.565008Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.827897Z", - "iopub.status.busy": "2024-02-27T04:11:27.827612Z", - "iopub.status.idle": "2024-02-27T04:11:27.834365Z", - "shell.execute_reply": "2024-02-27T04:11:27.833863Z" + "iopub.execute_input": "2024-03-05T17:26:41.568243Z", + "iopub.status.busy": "2024-03-05T17:26:41.567892Z", + "iopub.status.idle": "2024-03-05T17:26:41.574559Z", + "shell.execute_reply": "2024-03-05T17:26:41.574055Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.836382Z", - "iopub.status.busy": "2024-02-27T04:11:27.836001Z", - "iopub.status.idle": "2024-02-27T04:11:27.848356Z", - "shell.execute_reply": "2024-02-27T04:11:27.847833Z" + "iopub.execute_input": "2024-03-05T17:26:41.576745Z", + "iopub.status.busy": "2024-03-05T17:26:41.576422Z", + "iopub.status.idle": "2024-03-05T17:26:41.589414Z", + "shell.execute_reply": "2024-03-05T17:26:41.588758Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.850387Z", - "iopub.status.busy": "2024-02-27T04:11:27.850033Z", - "iopub.status.idle": "2024-02-27T04:11:27.856141Z", - "shell.execute_reply": "2024-02-27T04:11:27.855627Z" + "iopub.execute_input": "2024-03-05T17:26:41.591894Z", + "iopub.status.busy": "2024-03-05T17:26:41.591351Z", + "iopub.status.idle": "2024-03-05T17:26:41.598472Z", + "shell.execute_reply": "2024-03-05T17:26:41.597990Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.858133Z", - "iopub.status.busy": "2024-02-27T04:11:27.857774Z", - "iopub.status.idle": "2024-02-27T04:11:27.860433Z", - "shell.execute_reply": "2024-02-27T04:11:27.859922Z" + "iopub.execute_input": "2024-03-05T17:26:41.600752Z", + "iopub.status.busy": "2024-03-05T17:26:41.600488Z", + "iopub.status.idle": "2024-03-05T17:26:41.603120Z", + "shell.execute_reply": "2024-03-05T17:26:41.602690Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.862335Z", - "iopub.status.busy": "2024-02-27T04:11:27.862091Z", - "iopub.status.idle": "2024-02-27T04:11:27.865596Z", - "shell.execute_reply": "2024-02-27T04:11:27.865170Z" + "iopub.execute_input": "2024-03-05T17:26:41.605153Z", + "iopub.status.busy": "2024-03-05T17:26:41.604836Z", + "iopub.status.idle": "2024-03-05T17:26:41.608242Z", + "shell.execute_reply": "2024-03-05T17:26:41.607724Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.867491Z", - "iopub.status.busy": "2024-02-27T04:11:27.867250Z", - "iopub.status.idle": "2024-02-27T04:11:27.869808Z", - "shell.execute_reply": "2024-02-27T04:11:27.869355Z" + "iopub.execute_input": "2024-03-05T17:26:41.610371Z", + "iopub.status.busy": "2024-03-05T17:26:41.610047Z", + "iopub.status.idle": "2024-03-05T17:26:41.612774Z", + "shell.execute_reply": "2024-03-05T17:26:41.612284Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.871783Z", - "iopub.status.busy": "2024-02-27T04:11:27.871406Z", - "iopub.status.idle": "2024-02-27T04:11:27.875464Z", - "shell.execute_reply": "2024-02-27T04:11:27.874914Z" + "iopub.execute_input": "2024-03-05T17:26:41.614738Z", + "iopub.status.busy": "2024-03-05T17:26:41.614477Z", + "iopub.status.idle": "2024-03-05T17:26:41.618840Z", + "shell.execute_reply": "2024-03-05T17:26:41.618393Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.877446Z", - "iopub.status.busy": "2024-02-27T04:11:27.877159Z", - "iopub.status.idle": "2024-02-27T04:11:27.906313Z", - "shell.execute_reply": "2024-02-27T04:11:27.905793Z" + "iopub.execute_input": "2024-03-05T17:26:41.620901Z", + "iopub.status.busy": "2024-03-05T17:26:41.620576Z", + "iopub.status.idle": "2024-03-05T17:26:41.649873Z", + "shell.execute_reply": "2024-03-05T17:26:41.649396Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.908369Z", - "iopub.status.busy": "2024-02-27T04:11:27.908054Z", - "iopub.status.idle": "2024-02-27T04:11:27.912335Z", - "shell.execute_reply": "2024-02-27T04:11:27.911931Z" + "iopub.execute_input": "2024-03-05T17:26:41.652481Z", + "iopub.status.busy": "2024-03-05T17:26:41.652090Z", + "iopub.status.idle": "2024-03-05T17:26:41.657110Z", + "shell.execute_reply": "2024-03-05T17:26:41.656656Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index b4048672e..d1e1a339c 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": "2024-02-27T04:11:30.504197Z", - "iopub.status.busy": "2024-02-27T04:11:30.504023Z", - "iopub.status.idle": "2024-02-27T04:11:31.574534Z", - "shell.execute_reply": "2024-02-27T04:11:31.574018Z" + "iopub.execute_input": "2024-03-05T17:26:44.746120Z", + "iopub.status.busy": "2024-03-05T17:26:44.745938Z", + "iopub.status.idle": "2024-03-05T17:26:45.959393Z", + "shell.execute_reply": "2024-03-05T17:26:45.958752Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:11:31.577082Z", - "iopub.status.busy": "2024-02-27T04:11:31.576604Z", - "iopub.status.idle": "2024-02-27T04:11:31.771076Z", - "shell.execute_reply": "2024-02-27T04:11:31.770560Z" + "iopub.execute_input": "2024-03-05T17:26:45.962143Z", + "iopub.status.busy": "2024-03-05T17:26:45.961823Z", + "iopub.status.idle": "2024-03-05T17:26:46.172359Z", + "shell.execute_reply": "2024-03-05T17:26:46.171835Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:31.773699Z", - "iopub.status.busy": "2024-02-27T04:11:31.773178Z", - "iopub.status.idle": "2024-02-27T04:11:31.785926Z", - "shell.execute_reply": "2024-02-27T04:11:31.785461Z" + "iopub.execute_input": "2024-03-05T17:26:46.175349Z", + "iopub.status.busy": "2024-03-05T17:26:46.174824Z", + "iopub.status.idle": "2024-03-05T17:26:46.188517Z", + "shell.execute_reply": "2024-03-05T17:26:46.187888Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:31.787872Z", - "iopub.status.busy": "2024-02-27T04:11:31.787553Z", - "iopub.status.idle": "2024-02-27T04:11:34.432855Z", - "shell.execute_reply": "2024-02-27T04:11:34.432293Z" + "iopub.execute_input": "2024-03-05T17:26:46.190918Z", + "iopub.status.busy": "2024-03-05T17:26:46.190461Z", + "iopub.status.idle": "2024-03-05T17:26:48.936990Z", + "shell.execute_reply": "2024-03-05T17:26:48.936426Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:34.435059Z", - "iopub.status.busy": "2024-02-27T04:11:34.434724Z", - "iopub.status.idle": "2024-02-27T04:11:35.777890Z", - "shell.execute_reply": "2024-02-27T04:11:35.777352Z" + "iopub.execute_input": "2024-03-05T17:26:48.939340Z", + "iopub.status.busy": "2024-03-05T17:26:48.938917Z", + "iopub.status.idle": "2024-03-05T17:26:50.296100Z", + "shell.execute_reply": "2024-03-05T17:26:50.295554Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:35.780283Z", - "iopub.status.busy": "2024-02-27T04:11:35.779934Z", - "iopub.status.idle": "2024-02-27T04:11:35.783955Z", - "shell.execute_reply": "2024-02-27T04:11:35.783514Z" + "iopub.execute_input": "2024-03-05T17:26:50.298773Z", + "iopub.status.busy": "2024-03-05T17:26:50.298318Z", + "iopub.status.idle": "2024-03-05T17:26:50.302356Z", + "shell.execute_reply": "2024-03-05T17:26:50.301899Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:35.785855Z", - "iopub.status.busy": "2024-02-27T04:11:35.785523Z", - "iopub.status.idle": "2024-02-27T04:11:37.505693Z", - "shell.execute_reply": "2024-02-27T04:11:37.505072Z" + "iopub.execute_input": "2024-03-05T17:26:50.304511Z", + "iopub.status.busy": "2024-03-05T17:26:50.304083Z", + "iopub.status.idle": "2024-03-05T17:26:52.275481Z", + "shell.execute_reply": "2024-03-05T17:26:52.274862Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:37.508200Z", - "iopub.status.busy": "2024-02-27T04:11:37.507651Z", - "iopub.status.idle": "2024-02-27T04:11:37.515666Z", - "shell.execute_reply": "2024-02-27T04:11:37.515153Z" + "iopub.execute_input": "2024-03-05T17:26:52.278347Z", + "iopub.status.busy": "2024-03-05T17:26:52.277719Z", + "iopub.status.idle": "2024-03-05T17:26:52.286605Z", + "shell.execute_reply": "2024-03-05T17:26:52.286012Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:37.517477Z", - "iopub.status.busy": "2024-02-27T04:11:37.517304Z", - "iopub.status.idle": "2024-02-27T04:11:40.082105Z", - "shell.execute_reply": "2024-02-27T04:11:40.081554Z" + "iopub.execute_input": "2024-03-05T17:26:52.288970Z", + "iopub.status.busy": "2024-03-05T17:26:52.288583Z", + "iopub.status.idle": "2024-03-05T17:26:54.978883Z", + "shell.execute_reply": "2024-03-05T17:26:54.978296Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:40.084350Z", - "iopub.status.busy": "2024-02-27T04:11:40.083987Z", - "iopub.status.idle": "2024-02-27T04:11:40.087200Z", - "shell.execute_reply": "2024-02-27T04:11:40.086726Z" + "iopub.execute_input": "2024-03-05T17:26:54.981244Z", + "iopub.status.busy": "2024-03-05T17:26:54.980834Z", + "iopub.status.idle": "2024-03-05T17:26:54.985045Z", + "shell.execute_reply": "2024-03-05T17:26:54.984569Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:40.089279Z", - "iopub.status.busy": "2024-02-27T04:11:40.088963Z", - "iopub.status.idle": "2024-02-27T04:11:40.093263Z", - "shell.execute_reply": "2024-02-27T04:11:40.092874Z" + "iopub.execute_input": "2024-03-05T17:26:54.987214Z", + "iopub.status.busy": "2024-03-05T17:26:54.986873Z", + "iopub.status.idle": "2024-03-05T17:26:54.991161Z", + "shell.execute_reply": "2024-03-05T17:26:54.990649Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:40.095213Z", - "iopub.status.busy": "2024-02-27T04:11:40.094894Z", - "iopub.status.idle": "2024-02-27T04:11:40.097856Z", - "shell.execute_reply": "2024-02-27T04:11:40.097419Z" + "iopub.execute_input": "2024-03-05T17:26:54.993453Z", + "iopub.status.busy": "2024-03-05T17:26:54.993046Z", + "iopub.status.idle": "2024-03-05T17:26:54.996386Z", + "shell.execute_reply": "2024-03-05T17:26:54.995868Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index dcc7ec89b..e3448c246 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": "2024-02-27T04:11:42.306563Z", - "iopub.status.busy": "2024-02-27T04:11:42.306138Z", - "iopub.status.idle": "2024-02-27T04:11:43.382966Z", - "shell.execute_reply": "2024-02-27T04:11:43.382442Z" + "iopub.execute_input": "2024-03-05T17:26:57.524008Z", + "iopub.status.busy": "2024-03-05T17:26:57.523826Z", + "iopub.status.idle": "2024-03-05T17:26:58.725796Z", + "shell.execute_reply": "2024-03-05T17:26:58.725204Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:11:43.385619Z", - "iopub.status.busy": "2024-02-27T04:11:43.385198Z", - "iopub.status.idle": "2024-02-27T04:11:45.888818Z", - "shell.execute_reply": "2024-02-27T04:11:45.888209Z" + "iopub.execute_input": "2024-03-05T17:26:58.728445Z", + "iopub.status.busy": "2024-03-05T17:26:58.728150Z", + "iopub.status.idle": "2024-03-05T17:27:01.598915Z", + "shell.execute_reply": "2024-03-05T17:27:01.598216Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:45.891348Z", - "iopub.status.busy": "2024-02-27T04:11:45.891009Z", - "iopub.status.idle": "2024-02-27T04:11:45.894217Z", - "shell.execute_reply": "2024-02-27T04:11:45.893754Z" + "iopub.execute_input": "2024-03-05T17:27:01.601413Z", + "iopub.status.busy": "2024-03-05T17:27:01.601208Z", + "iopub.status.idle": "2024-03-05T17:27:01.604543Z", + "shell.execute_reply": "2024-03-05T17:27:01.604094Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:45.896274Z", - "iopub.status.busy": "2024-02-27T04:11:45.895937Z", - "iopub.status.idle": "2024-02-27T04:11:45.902367Z", - "shell.execute_reply": "2024-02-27T04:11:45.901866Z" + "iopub.execute_input": "2024-03-05T17:27:01.606448Z", + "iopub.status.busy": "2024-03-05T17:27:01.606277Z", + "iopub.status.idle": "2024-03-05T17:27:01.612922Z", + "shell.execute_reply": "2024-03-05T17:27:01.612512Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:45.904439Z", - "iopub.status.busy": "2024-02-27T04:11:45.904139Z", - "iopub.status.idle": "2024-02-27T04:11:46.391039Z", - "shell.execute_reply": "2024-02-27T04:11:46.390438Z" + "iopub.execute_input": "2024-03-05T17:27:01.614983Z", + "iopub.status.busy": "2024-03-05T17:27:01.614783Z", + "iopub.status.idle": "2024-03-05T17:27:02.122175Z", + "shell.execute_reply": "2024-03-05T17:27:02.121541Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:46.393498Z", - "iopub.status.busy": "2024-02-27T04:11:46.393056Z", - "iopub.status.idle": "2024-02-27T04:11:46.398285Z", - "shell.execute_reply": "2024-02-27T04:11:46.397749Z" + "iopub.execute_input": "2024-03-05T17:27:02.125008Z", + "iopub.status.busy": "2024-03-05T17:27:02.124616Z", + "iopub.status.idle": "2024-03-05T17:27:02.130169Z", + "shell.execute_reply": "2024-03-05T17:27:02.129720Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:46.400495Z", - "iopub.status.busy": "2024-02-27T04:11:46.400077Z", - "iopub.status.idle": "2024-02-27T04:11:46.404012Z", - "shell.execute_reply": "2024-02-27T04:11:46.403603Z" + "iopub.execute_input": "2024-03-05T17:27:02.132310Z", + "iopub.status.busy": "2024-03-05T17:27:02.131969Z", + "iopub.status.idle": "2024-03-05T17:27:02.135946Z", + "shell.execute_reply": "2024-03-05T17:27:02.135384Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:46.405920Z", - "iopub.status.busy": "2024-02-27T04:11:46.405608Z", - "iopub.status.idle": "2024-02-27T04:11:47.134326Z", - "shell.execute_reply": "2024-02-27T04:11:47.133795Z" + "iopub.execute_input": "2024-03-05T17:27:02.138118Z", + "iopub.status.busy": "2024-03-05T17:27:02.137723Z", + "iopub.status.idle": "2024-03-05T17:27:02.901172Z", + "shell.execute_reply": "2024-03-05T17:27:02.900567Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:47.136690Z", - "iopub.status.busy": "2024-02-27T04:11:47.136327Z", - "iopub.status.idle": "2024-02-27T04:11:47.306932Z", - "shell.execute_reply": "2024-02-27T04:11:47.306383Z" + "iopub.execute_input": "2024-03-05T17:27:02.903649Z", + "iopub.status.busy": "2024-03-05T17:27:02.903255Z", + "iopub.status.idle": "2024-03-05T17:27:03.076763Z", + "shell.execute_reply": "2024-03-05T17:27:03.076198Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:47.309102Z", - "iopub.status.busy": "2024-02-27T04:11:47.308790Z", - "iopub.status.idle": "2024-02-27T04:11:47.312934Z", - "shell.execute_reply": "2024-02-27T04:11:47.312519Z" + "iopub.execute_input": "2024-03-05T17:27:03.079164Z", + "iopub.status.busy": "2024-03-05T17:27:03.078769Z", + "iopub.status.idle": "2024-03-05T17:27:03.083122Z", + "shell.execute_reply": "2024-03-05T17:27:03.082620Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:47.314963Z", - "iopub.status.busy": "2024-02-27T04:11:47.314635Z", - "iopub.status.idle": "2024-02-27T04:11:47.759434Z", - "shell.execute_reply": "2024-02-27T04:11:47.758867Z" + "iopub.execute_input": "2024-03-05T17:27:03.085402Z", + "iopub.status.busy": "2024-03-05T17:27:03.085057Z", + "iopub.status.idle": "2024-03-05T17:27:03.556083Z", + "shell.execute_reply": "2024-03-05T17:27:03.555501Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:47.762576Z", - "iopub.status.busy": "2024-02-27T04:11:47.762139Z", - "iopub.status.idle": "2024-02-27T04:11:48.066037Z", - "shell.execute_reply": "2024-02-27T04:11:48.065385Z" + "iopub.execute_input": "2024-03-05T17:27:03.558955Z", + "iopub.status.busy": "2024-03-05T17:27:03.558579Z", + "iopub.status.idle": "2024-03-05T17:27:03.899216Z", + "shell.execute_reply": "2024-03-05T17:27:03.898670Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:48.068646Z", - "iopub.status.busy": "2024-02-27T04:11:48.068183Z", - "iopub.status.idle": "2024-02-27T04:11:48.402796Z", - "shell.execute_reply": "2024-02-27T04:11:48.402206Z" + "iopub.execute_input": "2024-03-05T17:27:03.901856Z", + "iopub.status.busy": "2024-03-05T17:27:03.901656Z", + "iopub.status.idle": "2024-03-05T17:27:04.275751Z", + "shell.execute_reply": "2024-03-05T17:27:04.275075Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:48.405600Z", - "iopub.status.busy": "2024-02-27T04:11:48.405277Z", - "iopub.status.idle": "2024-02-27T04:11:48.848393Z", - "shell.execute_reply": "2024-02-27T04:11:48.847814Z" + "iopub.execute_input": "2024-03-05T17:27:04.278898Z", + "iopub.status.busy": "2024-03-05T17:27:04.278502Z", + "iopub.status.idle": "2024-03-05T17:27:04.703652Z", + "shell.execute_reply": "2024-03-05T17:27:04.703071Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:48.852279Z", - "iopub.status.busy": "2024-02-27T04:11:48.852044Z", - "iopub.status.idle": "2024-02-27T04:11:49.298159Z", - "shell.execute_reply": "2024-02-27T04:11:49.297549Z" + "iopub.execute_input": "2024-03-05T17:27:04.707903Z", + "iopub.status.busy": "2024-03-05T17:27:04.707505Z", + "iopub.status.idle": "2024-03-05T17:27:05.168729Z", + "shell.execute_reply": "2024-03-05T17:27:05.168201Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:49.301031Z", - "iopub.status.busy": "2024-02-27T04:11:49.300630Z", - "iopub.status.idle": "2024-02-27T04:11:49.514596Z", - "shell.execute_reply": "2024-02-27T04:11:49.514046Z" + "iopub.execute_input": "2024-03-05T17:27:05.171143Z", + "iopub.status.busy": "2024-03-05T17:27:05.170786Z", + "iopub.status.idle": "2024-03-05T17:27:05.392298Z", + "shell.execute_reply": "2024-03-05T17:27:05.391759Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:49.516815Z", - "iopub.status.busy": "2024-02-27T04:11:49.516622Z", - "iopub.status.idle": "2024-02-27T04:11:49.715001Z", - "shell.execute_reply": "2024-02-27T04:11:49.714515Z" + "iopub.execute_input": "2024-03-05T17:27:05.395123Z", + "iopub.status.busy": "2024-03-05T17:27:05.394728Z", + "iopub.status.idle": "2024-03-05T17:27:05.576097Z", + "shell.execute_reply": "2024-03-05T17:27:05.575566Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:49.717353Z", - "iopub.status.busy": "2024-02-27T04:11:49.716944Z", - "iopub.status.idle": "2024-02-27T04:11:49.719774Z", - "shell.execute_reply": "2024-02-27T04:11:49.719359Z" + "iopub.execute_input": "2024-03-05T17:27:05.578735Z", + "iopub.status.busy": "2024-03-05T17:27:05.578359Z", + "iopub.status.idle": "2024-03-05T17:27:05.581268Z", + "shell.execute_reply": "2024-03-05T17:27:05.580803Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:49.721693Z", - "iopub.status.busy": "2024-02-27T04:11:49.721405Z", - "iopub.status.idle": "2024-02-27T04:11:50.753324Z", - "shell.execute_reply": "2024-02-27T04:11:50.752830Z" + "iopub.execute_input": "2024-03-05T17:27:05.583273Z", + "iopub.status.busy": "2024-03-05T17:27:05.582924Z", + "iopub.status.idle": "2024-03-05T17:27:06.565448Z", + "shell.execute_reply": "2024-03-05T17:27:06.564894Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:50.755934Z", - "iopub.status.busy": "2024-02-27T04:11:50.755585Z", - "iopub.status.idle": "2024-02-27T04:11:50.911975Z", - "shell.execute_reply": "2024-02-27T04:11:50.911445Z" + "iopub.execute_input": "2024-03-05T17:27:06.568408Z", + "iopub.status.busy": "2024-03-05T17:27:06.568009Z", + "iopub.status.idle": "2024-03-05T17:27:06.687121Z", + "shell.execute_reply": "2024-03-05T17:27:06.686521Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:50.914131Z", - "iopub.status.busy": "2024-02-27T04:11:50.913716Z", - "iopub.status.idle": "2024-02-27T04:11:51.118016Z", - "shell.execute_reply": "2024-02-27T04:11:51.117448Z" + "iopub.execute_input": "2024-03-05T17:27:06.689382Z", + "iopub.status.busy": "2024-03-05T17:27:06.689059Z", + "iopub.status.idle": "2024-03-05T17:27:06.876674Z", + "shell.execute_reply": "2024-03-05T17:27:06.876140Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:51.120073Z", - "iopub.status.busy": "2024-02-27T04:11:51.119896Z", - "iopub.status.idle": "2024-02-27T04:11:51.870570Z", - "shell.execute_reply": "2024-02-27T04:11:51.869902Z" + "iopub.execute_input": "2024-03-05T17:27:06.878856Z", + "iopub.status.busy": "2024-03-05T17:27:06.878515Z", + "iopub.status.idle": "2024-03-05T17:27:07.606125Z", + "shell.execute_reply": "2024-03-05T17:27:07.605521Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:51.872897Z", - "iopub.status.busy": "2024-02-27T04:11:51.872468Z", - "iopub.status.idle": "2024-02-27T04:11:51.876382Z", - "shell.execute_reply": "2024-02-27T04:11:51.875831Z" + "iopub.execute_input": "2024-03-05T17:27:07.608309Z", + "iopub.status.busy": "2024-03-05T17:27:07.607971Z", + "iopub.status.idle": "2024-03-05T17:27:07.611428Z", + "shell.execute_reply": "2024-03-05T17:27:07.610996Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 67d259469..397ed20fc 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": "2024-02-27T04:11:53.890073Z", - "iopub.status.busy": "2024-02-27T04:11:53.889643Z", - "iopub.status.idle": "2024-02-27T04:11:56.480945Z", - "shell.execute_reply": "2024-02-27T04:11:56.480341Z" + "iopub.execute_input": "2024-03-05T17:27:10.060270Z", + "iopub.status.busy": "2024-03-05T17:27:10.060087Z", + "iopub.status.idle": "2024-03-05T17:27:12.905020Z", + "shell.execute_reply": "2024-03-05T17:27:12.904476Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:11:56.483928Z", - "iopub.status.busy": "2024-02-27T04:11:56.483350Z", - "iopub.status.idle": "2024-02-27T04:11:56.791675Z", - "shell.execute_reply": "2024-02-27T04:11:56.791161Z" + "iopub.execute_input": "2024-03-05T17:27:12.907590Z", + "iopub.status.busy": "2024-03-05T17:27:12.907171Z", + "iopub.status.idle": "2024-03-05T17:27:13.251719Z", + "shell.execute_reply": "2024-03-05T17:27:13.251094Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:56.794084Z", - "iopub.status.busy": "2024-02-27T04:11:56.793693Z", - "iopub.status.idle": "2024-02-27T04:11:56.797959Z", - "shell.execute_reply": "2024-02-27T04:11:56.797543Z" + "iopub.execute_input": "2024-03-05T17:27:13.254293Z", + "iopub.status.busy": "2024-03-05T17:27:13.253816Z", + "iopub.status.idle": "2024-03-05T17:27:13.257780Z", + "shell.execute_reply": "2024-03-05T17:27:13.257381Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:56.800012Z", - "iopub.status.busy": "2024-02-27T04:11:56.799740Z", - "iopub.status.idle": "2024-02-27T04:12:04.551750Z", - "shell.execute_reply": "2024-02-27T04:12:04.551191Z" + "iopub.execute_input": "2024-03-05T17:27:13.259723Z", + "iopub.status.busy": "2024-03-05T17:27:13.259443Z", + "iopub.status.idle": "2024-03-05T17:27:23.723006Z", + "shell.execute_reply": "2024-03-05T17:27:23.722515Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 32768/170498071 [00:00<11:45, 241646.66it/s]" + " 0%| | 32768/170498071 [00:00<10:31, 269919.49it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 229376/170498071 [00:00<03:00, 942515.06it/s]" + " 0%| | 196608/170498071 [00:00<02:56, 966458.45it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 884736/170498071 [00:00<00:58, 2890541.33it/s]" + " 0%| | 655360/170498071 [00:00<01:07, 2504105.17it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 1%|▏ | 2490368/170498071 [00:00<00:22, 7320877.52it/s]" + " 1%| | 1736704/170498071 [00:00<00:30, 5572547.07it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 6127616/170498071 [00:00<00:09, 16751733.74it/s]" + " 3%|▎ | 5505024/170498071 [00:00<00:09, 16680254.08it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 10059776/170498071 [00:00<00:06, 23756599.96it/s]" + " 4%|▍ | 7241728/170498071 [00:00<00:09, 16684410.09it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▊ | 14843904/170498071 [00:00<00:05, 30057088.98it/s]" + " 7%|▋ | 12484608/170498071 [00:00<00:05, 28026407.31it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█▏ | 19398656/170498071 [00:00<00:04, 33683657.53it/s]" + " 10%|█ | 17203200/170498071 [00:00<00:04, 33985714.22it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 23756800/170498071 [00:01<00:04, 36544386.70it/s]" + " 13%|█▎ | 21561344/170498071 [00:00<00:04, 36903939.66it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 28573696/170498071 [00:01<00:03, 39948941.41it/s]" + " 15%|█▌ | 26116096/170498071 [00:01<00:03, 38463753.10it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32636928/170498071 [00:01<00:03, 38882711.64it/s]" + " 18%|█▊ | 30965760/170498071 [00:01<00:03, 41421312.96it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 36995072/170498071 [00:01<00:03, 40240064.28it/s]" + " 21%|██ | 35127296/170498071 [00:01<00:03, 36384427.00it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 41320448/170498071 [00:01<00:03, 39602414.48it/s]" + " 23%|██▎ | 38895616/170498071 [00:01<00:03, 33119504.43it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 46071808/170498071 [00:01<00:03, 41387825.17it/s]" + " 25%|██▌ | 42893312/170498071 [00:01<00:03, 34811837.39it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 50561024/170498071 [00:01<00:02, 42078718.25it/s]" + " 27%|██▋ | 46497792/170498071 [00:01<00:03, 33172830.75it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 55345152/170498071 [00:01<00:02, 43195246.79it/s]" + " 29%|██▉ | 49905664/170498071 [00:01<00:03, 32287601.62it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 59736064/170498071 [00:01<00:02, 43303052.00it/s]" + " 31%|███ | 53215232/170498071 [00:01<00:03, 31491349.41it/s]" ] }, { @@ -388,7 +388,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 64094208/170498071 [00:01<00:02, 41709323.75it/s]" + " 33%|███▎ | 56426496/170498071 [00:01<00:03, 30902122.74it/s]" ] }, { @@ -396,7 +396,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 68517888/170498071 [00:02<00:02, 42407506.23it/s]" + " 35%|███▍ | 59637760/170498071 [00:02<00:03, 31163304.47it/s]" ] }, { @@ -404,7 +404,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 72777728/170498071 [00:02<00:02, 41273394.63it/s]" + " 37%|███▋ | 62783488/170498071 [00:02<00:03, 31030223.75it/s]" ] }, { @@ -412,7 +412,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 77398016/170498071 [00:02<00:02, 42027004.91it/s]" + " 39%|███▊ | 65929216/170498071 [00:02<00:03, 30910743.30it/s]" ] }, { @@ -420,7 +420,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 82018304/170498071 [00:02<00:02, 42597732.49it/s]" + " 41%|████ | 69107712/170498071 [00:02<00:03, 31135201.25it/s]" ] }, { @@ -428,7 +428,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 86474752/170498071 [00:02<00:01, 43118930.00it/s]" + " 42%|████▏ | 72253440/170498071 [00:02<00:03, 30936489.35it/s]" ] }, { @@ -436,7 +436,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 90800128/170498071 [00:02<00:01, 43094745.01it/s]" + " 44%|████▍ | 75399168/170498071 [00:02<00:03, 31081474.88it/s]" ] }, { @@ -444,7 +444,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 95125504/170498071 [00:02<00:01, 42129672.37it/s]" + " 46%|████▌ | 78643200/170498071 [00:02<00:02, 31408686.11it/s]" ] }, { @@ -452,7 +452,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 99352576/170498071 [00:02<00:01, 42038446.91it/s]" + " 48%|████▊ | 81788928/170498071 [00:02<00:02, 31218015.50it/s]" ] }, { @@ -460,7 +460,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 103579648/170498071 [00:02<00:01, 40923143.11it/s]" + " 50%|████▉ | 84934656/170498071 [00:02<00:02, 30990506.94it/s]" ] }, { @@ -468,7 +468,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 108232704/170498071 [00:03<00:01, 41876534.80it/s]" + " 52%|█████▏ | 88408064/170498071 [00:03<00:02, 31608333.33it/s]" ] }, { @@ -476,7 +476,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 112820224/170498071 [00:03<00:01, 42863873.07it/s]" + " 54%|█████▍ | 91750400/170498071 [00:03<00:02, 32012594.43it/s]" ] }, { @@ -484,7 +484,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 117145600/170498071 [00:03<00:01, 42769270.38it/s]" + " 56%|█████▌ | 94961664/170498071 [00:03<00:02, 27308309.34it/s]" ] }, { @@ -492,7 +492,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████▏ | 121536512/170498071 [00:03<00:01, 43068079.06it/s]" + " 58%|█████▊ | 99090432/170498071 [00:03<00:02, 30994857.35it/s]" ] }, { @@ -500,7 +500,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 125861888/170498071 [00:03<00:01, 42150861.57it/s]" + " 60%|██████ | 102334464/170498071 [00:03<00:02, 27659707.88it/s]" ] }, { @@ -508,7 +508,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 130252800/170498071 [00:03<00:00, 42564252.43it/s]" + " 62%|██████▏ | 105250816/170498071 [00:03<00:02, 26502385.23it/s]" ] }, { @@ -516,7 +516,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 134610944/170498071 [00:03<00:00, 42852568.36it/s]" + " 63%|██████▎ | 108036096/170498071 [00:03<00:02, 25478374.75it/s]" ] }, { @@ -524,7 +524,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 138903552/170498071 [00:03<00:00, 41982253.90it/s]" + " 65%|██████▍ | 110657536/170498071 [00:03<00:02, 24538449.99it/s]" ] }, { @@ -532,7 +532,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 143523840/170498071 [00:03<00:00, 43185844.07it/s]" + " 66%|██████▋ | 113311744/170498071 [00:03<00:02, 25021856.35it/s]" ] }, { @@ -540,7 +540,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 147881984/170498071 [00:03<00:00, 41691783.52it/s]" + " 68%|██████▊ | 115867648/170498071 [00:04<00:02, 24774854.76it/s]" ] }, { @@ -548,7 +548,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 152076288/170498071 [00:04<00:00, 41706123.81it/s]" + " 69%|██████▉ | 118390784/170498071 [00:04<00:02, 24241470.52it/s]" ] }, { @@ -556,7 +556,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 156663808/170498071 [00:04<00:00, 42215760.73it/s]" + " 71%|███████ | 120848384/170498071 [00:04<00:02, 23753247.36it/s]" ] }, { @@ -564,7 +564,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 161284096/170498071 [00:04<00:00, 41673431.13it/s]" + " 72%|███████▏ | 123568128/170498071 [00:04<00:01, 24261379.86it/s]" ] }, { @@ -572,7 +572,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 166002688/170498071 [00:04<00:00, 43084659.05it/s]" + " 74%|███████▍ | 126025728/170498071 [00:04<00:01, 23594480.68it/s]" ] }, { @@ -580,7 +580,199 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:04<00:00, 38191430.08it/s]" + " 75%|███████▌ | 128417792/170498071 [00:04<00:02, 20773011.98it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 77%|███████▋ | 130580480/170498071 [00:04<00:02, 19800177.67it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 78%|███████▊ | 132612096/170498071 [00:04<00:01, 19005187.83it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 79%|███████▉ | 134643712/170498071 [00:05<00:01, 19153138.38it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 80%|████████ | 136609792/170498071 [00:05<00:01, 18831371.51it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 81%|████████ | 138510336/170498071 [00:05<00:01, 18803029.53it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 82%|████████▏ | 140410880/170498071 [00:05<00:01, 17960198.33it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 84%|████████▎ | 142376960/170498071 [00:05<00:01, 17805565.42it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 85%|████████▍ | 144572416/170498071 [00:05<00:01, 18743454.87it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 86%|████████▌ | 146472960/170498071 [00:05<00:01, 18532386.26it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|████████▋ | 148340736/170498071 [00:05<00:01, 18299463.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 88%|████████▊ | 150306816/170498071 [00:05<00:01, 18100949.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 89%|████████▉ | 152535040/170498071 [00:05<00:00, 19156709.97it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 91%|█████████ | 154468352/170498071 [00:06<00:00, 18598590.74it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████▏| 156336128/170498071 [00:06<00:00, 16302393.04it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 93%|█████████▎| 158040064/170498071 [00:06<00:00, 15540613.82it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 94%|█████████▎| 159645696/170498071 [00:06<00:00, 14932144.14it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 95%|█████████▍| 161284096/170498071 [00:06<00:00, 15088330.71it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 95%|█████████▌| 162824192/170498071 [00:06<00:00, 14696848.62it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 96%|█████████▋| 164331520/170498071 [00:06<00:00, 14580244.32it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 97%|█████████▋| 165806080/170498071 [00:06<00:00, 13947217.56it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 167313408/170498071 [00:07<00:00, 13840984.98it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 168886272/170498071 [00:07<00:00, 13546385.58it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 170262528/170498071 [00:07<00:00, 12509763.03it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:07<00:00, 23313635.10it/s]" ] }, { @@ -698,10 +890,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:04.554095Z", - "iopub.status.busy": "2024-02-27T04:12:04.553755Z", - "iopub.status.idle": "2024-02-27T04:12:04.558353Z", - "shell.execute_reply": "2024-02-27T04:12:04.557935Z" + "iopub.execute_input": "2024-03-05T17:27:23.725574Z", + "iopub.status.busy": "2024-03-05T17:27:23.725091Z", + "iopub.status.idle": "2024-03-05T17:27:23.730246Z", + "shell.execute_reply": "2024-03-05T17:27:23.729780Z" }, "nbsphinx": "hidden" }, @@ -752,10 +944,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:04.560450Z", - "iopub.status.busy": "2024-02-27T04:12:04.560130Z", - "iopub.status.idle": "2024-02-27T04:12:05.107980Z", - "shell.execute_reply": "2024-02-27T04:12:05.107432Z" + "iopub.execute_input": "2024-03-05T17:27:23.732335Z", + "iopub.status.busy": "2024-03-05T17:27:23.732023Z", + "iopub.status.idle": "2024-03-05T17:27:24.276403Z", + "shell.execute_reply": "2024-03-05T17:27:24.275782Z" } }, "outputs": [ @@ -788,10 +980,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:05.110242Z", - "iopub.status.busy": "2024-02-27T04:12:05.109917Z", - "iopub.status.idle": "2024-02-27T04:12:05.627298Z", - "shell.execute_reply": "2024-02-27T04:12:05.626734Z" + "iopub.execute_input": "2024-03-05T17:27:24.278697Z", + "iopub.status.busy": "2024-03-05T17:27:24.278480Z", + "iopub.status.idle": "2024-03-05T17:27:24.785506Z", + "shell.execute_reply": "2024-03-05T17:27:24.784888Z" } }, "outputs": [ @@ -829,10 +1021,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:05.629650Z", - "iopub.status.busy": "2024-02-27T04:12:05.629100Z", - "iopub.status.idle": "2024-02-27T04:12:05.632692Z", - "shell.execute_reply": "2024-02-27T04:12:05.632165Z" + "iopub.execute_input": "2024-03-05T17:27:24.787916Z", + "iopub.status.busy": "2024-03-05T17:27:24.787553Z", + "iopub.status.idle": "2024-03-05T17:27:24.791293Z", + "shell.execute_reply": "2024-03-05T17:27:24.790804Z" } }, "outputs": [], @@ -855,17 +1047,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:05.634654Z", - "iopub.status.busy": "2024-02-27T04:12:05.634297Z", - "iopub.status.idle": "2024-02-27T04:12:18.330648Z", - "shell.execute_reply": "2024-02-27T04:12:18.329900Z" + "iopub.execute_input": "2024-03-05T17:27:24.793269Z", + "iopub.status.busy": "2024-03-05T17:27:24.793064Z", + "iopub.status.idle": "2024-03-05T17:27:37.401129Z", + "shell.execute_reply": "2024-03-05T17:27:37.400469Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21697d09e82c4f0694b3284b917d2713", + "model_id": "40a7d20076bd4bb6bc9a80f7cef43d4a", "version_major": 2, "version_minor": 0 }, @@ -924,10 +1116,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:18.333033Z", - "iopub.status.busy": "2024-02-27T04:12:18.332657Z", - "iopub.status.idle": "2024-02-27T04:12:19.912387Z", - "shell.execute_reply": "2024-02-27T04:12:19.911850Z" + "iopub.execute_input": "2024-03-05T17:27:37.403862Z", + "iopub.status.busy": "2024-03-05T17:27:37.403383Z", + "iopub.status.idle": "2024-03-05T17:27:39.011875Z", + "shell.execute_reply": "2024-03-05T17:27:39.011271Z" } }, "outputs": [ @@ -971,10 +1163,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:19.915001Z", - "iopub.status.busy": "2024-02-27T04:12:19.914703Z", - "iopub.status.idle": "2024-02-27T04:12:20.330306Z", - "shell.execute_reply": "2024-02-27T04:12:20.329792Z" + "iopub.execute_input": "2024-03-05T17:27:39.014598Z", + "iopub.status.busy": "2024-03-05T17:27:39.014383Z", + "iopub.status.idle": "2024-03-05T17:27:39.493962Z", + "shell.execute_reply": "2024-03-05T17:27:39.493379Z" } }, "outputs": [ @@ -1010,10 +1202,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:20.333139Z", - "iopub.status.busy": "2024-02-27T04:12:20.332800Z", - "iopub.status.idle": "2024-02-27T04:12:20.985461Z", - "shell.execute_reply": "2024-02-27T04:12:20.984928Z" + "iopub.execute_input": "2024-03-05T17:27:39.496796Z", + "iopub.status.busy": "2024-03-05T17:27:39.496584Z", + "iopub.status.idle": "2024-03-05T17:27:40.174170Z", + "shell.execute_reply": "2024-03-05T17:27:40.173675Z" } }, "outputs": [ @@ -1063,10 +1255,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:20.988138Z", - "iopub.status.busy": "2024-02-27T04:12:20.987853Z", - "iopub.status.idle": "2024-02-27T04:12:21.328838Z", - "shell.execute_reply": "2024-02-27T04:12:21.328341Z" + "iopub.execute_input": "2024-03-05T17:27:40.176859Z", + "iopub.status.busy": "2024-03-05T17:27:40.176650Z", + "iopub.status.idle": "2024-03-05T17:27:40.529313Z", + "shell.execute_reply": "2024-03-05T17:27:40.528714Z" } }, "outputs": [ @@ -1114,10 +1306,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:21.331122Z", - "iopub.status.busy": "2024-02-27T04:12:21.330689Z", - "iopub.status.idle": "2024-02-27T04:12:21.572116Z", - "shell.execute_reply": "2024-02-27T04:12:21.571610Z" + "iopub.execute_input": "2024-03-05T17:27:40.531734Z", + "iopub.status.busy": "2024-03-05T17:27:40.531496Z", + "iopub.status.idle": "2024-03-05T17:27:40.787735Z", + "shell.execute_reply": "2024-03-05T17:27:40.787157Z" } }, "outputs": [ @@ -1173,10 +1365,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:21.575035Z", - "iopub.status.busy": "2024-02-27T04:12:21.574607Z", - "iopub.status.idle": "2024-02-27T04:12:21.664289Z", - "shell.execute_reply": "2024-02-27T04:12:21.663820Z" + "iopub.execute_input": "2024-03-05T17:27:40.790049Z", + "iopub.status.busy": "2024-03-05T17:27:40.789728Z", + "iopub.status.idle": "2024-03-05T17:27:40.885361Z", + "shell.execute_reply": "2024-03-05T17:27:40.884838Z" } }, "outputs": [], @@ -1197,10 +1389,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:21.666659Z", - "iopub.status.busy": "2024-02-27T04:12:21.666281Z", - "iopub.status.idle": "2024-02-27T04:12:31.701389Z", - "shell.execute_reply": "2024-02-27T04:12:31.700785Z" + "iopub.execute_input": "2024-03-05T17:27:40.888238Z", + "iopub.status.busy": "2024-03-05T17:27:40.887827Z", + "iopub.status.idle": "2024-03-05T17:27:51.442460Z", + "shell.execute_reply": "2024-03-05T17:27:51.441852Z" } }, "outputs": [ @@ -1237,10 +1429,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:31.703572Z", - "iopub.status.busy": "2024-02-27T04:12:31.703343Z", - "iopub.status.idle": "2024-02-27T04:12:33.344974Z", - "shell.execute_reply": "2024-02-27T04:12:33.344389Z" + "iopub.execute_input": "2024-03-05T17:27:51.445411Z", + "iopub.status.busy": "2024-03-05T17:27:51.444969Z", + "iopub.status.idle": "2024-03-05T17:27:53.315567Z", + "shell.execute_reply": "2024-03-05T17:27:53.314998Z" } }, "outputs": [ @@ -1271,10 +1463,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:33.347503Z", - "iopub.status.busy": "2024-02-27T04:12:33.347128Z", - "iopub.status.idle": "2024-02-27T04:12:33.550522Z", - "shell.execute_reply": "2024-02-27T04:12:33.549972Z" + "iopub.execute_input": "2024-03-05T17:27:53.318306Z", + "iopub.status.busy": "2024-03-05T17:27:53.317725Z", + "iopub.status.idle": "2024-03-05T17:27:53.522749Z", + "shell.execute_reply": "2024-03-05T17:27:53.522213Z" } }, "outputs": [], @@ -1288,10 +1480,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:33.553081Z", - "iopub.status.busy": "2024-02-27T04:12:33.552707Z", - "iopub.status.idle": "2024-02-27T04:12:33.555899Z", - "shell.execute_reply": "2024-02-27T04:12:33.555465Z" + "iopub.execute_input": "2024-03-05T17:27:53.525253Z", + "iopub.status.busy": "2024-03-05T17:27:53.524898Z", + "iopub.status.idle": "2024-03-05T17:27:53.528019Z", + "shell.execute_reply": "2024-03-05T17:27:53.527574Z" } }, "outputs": [], @@ -1313,10 +1505,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:33.557800Z", - "iopub.status.busy": "2024-02-27T04:12:33.557464Z", - "iopub.status.idle": "2024-02-27T04:12:33.565685Z", - "shell.execute_reply": "2024-02-27T04:12:33.565265Z" + "iopub.execute_input": "2024-03-05T17:27:53.529967Z", + "iopub.status.busy": "2024-03-05T17:27:53.529787Z", + "iopub.status.idle": "2024-03-05T17:27:53.537988Z", + "shell.execute_reply": "2024-03-05T17:27:53.537466Z" }, "nbsphinx": "hidden" }, @@ -1361,7 +1553,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0530181c0c294208bb7bf878ec5d73f9": { + "02ad6743bcb14967bba613ba0fab06e1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1414,54 +1606,7 @@ "width": null } }, - "0f987cfe47de43cda334806f4fa21fdd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_254ceb3633674bf2acf63fa769db8c0e", - "placeholder": "​", - "style": "IPY_MODEL_f5519d4051ae4c8980e4173760fa5956", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 157MB/s]" - } - }, - "21697d09e82c4f0694b3284b917d2713": { - "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_8cc1e75554854ca3b6d3221ffcb880db", - "IPY_MODEL_8a85a371ff684b5196fa52a91f0f8bf7", - "IPY_MODEL_0f987cfe47de43cda334806f4fa21fdd" - ], - "layout": "IPY_MODEL_f8c2f65c3ff748a1bb0b85c7e3c719e2", - "tabbable": null, - "tooltip": null - } - }, - "254ceb3633674bf2acf63fa769db8c0e": { + "1941b1ff05234d928ff8c5feaa8741bd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1514,7 +1659,7 @@ "width": null } }, - "414afe5146db419ca1be9cd94251168d": { + "35c07a7a884c4d3bbc1dc341e804c9b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1530,7 +1675,49 @@ "description_width": "" } }, - "7353808876674515925c27ab5ffe65d8": { + "40a7d20076bd4bb6bc9a80f7cef43d4a": { + "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_ea36e9ce6c804b34b30d1d46d5f7c286", + "IPY_MODEL_9e1e2fee078d403f83a945883fc505f0", + "IPY_MODEL_f30d40e671d34726b65020af61a31e4a" + ], + "layout": "IPY_MODEL_1941b1ff05234d928ff8c5feaa8741bd", + "tabbable": null, + "tooltip": null + } + }, + "4f44bc5b21144fe99756eaacf7811665": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "906cf214b75842f6b344cb9999f83b29": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1583,7 +1770,7 @@ "width": null } }, - "8a85a371ff684b5196fa52a91f0f8bf7": { + "9e1e2fee078d403f83a945883fc505f0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1599,76 +1786,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0530181c0c294208bb7bf878ec5d73f9", + "layout": "IPY_MODEL_906cf214b75842f6b344cb9999f83b29", "max": 102469840.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_414afe5146db419ca1be9cd94251168d", + "style": "IPY_MODEL_35c07a7a884c4d3bbc1dc341e804c9b7", "tabbable": null, "tooltip": null, "value": 102469840.0 } }, - "8cc1e75554854ca3b6d3221ffcb880db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7353808876674515925c27ab5ffe65d8", - "placeholder": "​", - "style": "IPY_MODEL_8f2bd469441e43b9bf2a396ccdb117e3", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" - } - }, - "8f2bd469441e43b9bf2a396ccdb117e3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f5519d4051ae4c8980e4173760fa5956": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f8c2f65c3ff748a1bb0b85c7e3c719e2": { + "af93eb4797944c12bcae89167179894b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1720,6 +1848,70 @@ "visibility": null, "width": null } + }, + "ce3a5efacbbe4258a211b2151cf8b998": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ea36e9ce6c804b34b30d1d46d5f7c286": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_af93eb4797944c12bcae89167179894b", + "placeholder": "​", + "style": "IPY_MODEL_4f44bc5b21144fe99756eaacf7811665", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "f30d40e671d34726b65020af61a31e4a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_02ad6743bcb14967bba613ba0fab06e1", + "placeholder": "​", + "style": "IPY_MODEL_ce3a5efacbbe4258a211b2151cf8b998", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 336MB/s]" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index f7df14f68..ed9b4ecc9 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": "2024-02-27T04:12:37.966414Z", - "iopub.status.busy": "2024-02-27T04:12:37.965886Z", - "iopub.status.idle": "2024-02-27T04:12:39.042191Z", - "shell.execute_reply": "2024-02-27T04:12:39.041580Z" + "iopub.execute_input": "2024-03-05T17:27:57.694523Z", + "iopub.status.busy": "2024-03-05T17:27:57.694324Z", + "iopub.status.idle": "2024-03-05T17:27:58.863092Z", + "shell.execute_reply": "2024-03-05T17:27:58.862510Z" }, "nbsphinx": "hidden" }, @@ -117,7 +117,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.044785Z", - "iopub.status.busy": "2024-02-27T04:12:39.044459Z", - "iopub.status.idle": "2024-02-27T04:12:39.062671Z", - "shell.execute_reply": "2024-02-27T04:12:39.062262Z" + "iopub.execute_input": "2024-03-05T17:27:58.866019Z", + "iopub.status.busy": "2024-03-05T17:27:58.865498Z", + "iopub.status.idle": "2024-03-05T17:27:58.884994Z", + "shell.execute_reply": "2024-03-05T17:27:58.884490Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.064674Z", - "iopub.status.busy": "2024-02-27T04:12:39.064333Z", - "iopub.status.idle": "2024-02-27T04:12:39.067417Z", - "shell.execute_reply": "2024-02-27T04:12:39.066975Z" + "iopub.execute_input": "2024-03-05T17:27:58.887835Z", + "iopub.status.busy": "2024-03-05T17:27:58.887268Z", + "iopub.status.idle": "2024-03-05T17:27:58.890503Z", + "shell.execute_reply": "2024-03-05T17:27:58.890039Z" }, "nbsphinx": "hidden" }, @@ -199,10 +199,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.069387Z", - "iopub.status.busy": "2024-02-27T04:12:39.069068Z", - "iopub.status.idle": "2024-02-27T04:12:39.350236Z", - "shell.execute_reply": "2024-02-27T04:12:39.349670Z" + "iopub.execute_input": "2024-03-05T17:27:58.892698Z", + "iopub.status.busy": "2024-03-05T17:27:58.892380Z", + "iopub.status.idle": "2024-03-05T17:27:59.131908Z", + "shell.execute_reply": "2024-03-05T17:27:59.131354Z" } }, "outputs": [ @@ -375,10 +375,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.352572Z", - "iopub.status.busy": "2024-02-27T04:12:39.352225Z", - "iopub.status.idle": "2024-02-27T04:12:39.532631Z", - "shell.execute_reply": "2024-02-27T04:12:39.532141Z" + "iopub.execute_input": "2024-03-05T17:27:59.134275Z", + "iopub.status.busy": "2024-03-05T17:27:59.133938Z", + "iopub.status.idle": "2024-03-05T17:27:59.320629Z", + "shell.execute_reply": "2024-03-05T17:27:59.319986Z" }, "nbsphinx": "hidden" }, @@ -418,10 +418,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.534933Z", - "iopub.status.busy": "2024-02-27T04:12:39.534606Z", - "iopub.status.idle": "2024-02-27T04:12:39.745869Z", - "shell.execute_reply": "2024-02-27T04:12:39.745306Z" + "iopub.execute_input": "2024-03-05T17:27:59.323434Z", + "iopub.status.busy": "2024-03-05T17:27:59.323158Z", + "iopub.status.idle": "2024-03-05T17:27:59.544216Z", + "shell.execute_reply": "2024-03-05T17:27:59.543584Z" } }, "outputs": [ @@ -457,10 +457,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.748004Z", - "iopub.status.busy": "2024-02-27T04:12:39.747671Z", - "iopub.status.idle": "2024-02-27T04:12:39.752047Z", - "shell.execute_reply": "2024-02-27T04:12:39.751620Z" + "iopub.execute_input": "2024-03-05T17:27:59.546524Z", + "iopub.status.busy": "2024-03-05T17:27:59.546152Z", + "iopub.status.idle": "2024-03-05T17:27:59.550728Z", + "shell.execute_reply": "2024-03-05T17:27:59.550272Z" } }, "outputs": [], @@ -478,10 +478,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.753965Z", - "iopub.status.busy": "2024-02-27T04:12:39.753630Z", - "iopub.status.idle": "2024-02-27T04:12:39.759434Z", - "shell.execute_reply": "2024-02-27T04:12:39.759005Z" + "iopub.execute_input": "2024-03-05T17:27:59.552734Z", + "iopub.status.busy": "2024-03-05T17:27:59.552396Z", + "iopub.status.idle": "2024-03-05T17:27:59.558578Z", + "shell.execute_reply": "2024-03-05T17:27:59.558172Z" } }, "outputs": [], @@ -528,10 +528,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.761361Z", - "iopub.status.busy": "2024-02-27T04:12:39.761049Z", - "iopub.status.idle": "2024-02-27T04:12:39.763638Z", - "shell.execute_reply": "2024-02-27T04:12:39.763222Z" + "iopub.execute_input": "2024-03-05T17:27:59.560662Z", + "iopub.status.busy": "2024-03-05T17:27:59.560276Z", + "iopub.status.idle": "2024-03-05T17:27:59.562888Z", + "shell.execute_reply": "2024-03-05T17:27:59.562445Z" } }, "outputs": [], @@ -546,10 +546,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.765462Z", - "iopub.status.busy": "2024-02-27T04:12:39.765151Z", - "iopub.status.idle": "2024-02-27T04:12:47.938730Z", - "shell.execute_reply": "2024-02-27T04:12:47.938110Z" + "iopub.execute_input": "2024-03-05T17:27:59.564819Z", + "iopub.status.busy": "2024-03-05T17:27:59.564644Z", + "iopub.status.idle": "2024-03-05T17:28:08.265948Z", + "shell.execute_reply": "2024-03-05T17:28:08.265396Z" } }, "outputs": [], @@ -573,10 +573,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.941524Z", - "iopub.status.busy": "2024-02-27T04:12:47.941119Z", - "iopub.status.idle": "2024-02-27T04:12:47.948227Z", - "shell.execute_reply": "2024-02-27T04:12:47.947795Z" + "iopub.execute_input": "2024-03-05T17:28:08.269045Z", + "iopub.status.busy": "2024-03-05T17:28:08.268497Z", + "iopub.status.idle": "2024-03-05T17:28:08.276034Z", + "shell.execute_reply": "2024-03-05T17:28:08.275487Z" } }, "outputs": [ @@ -611,35 +611,35 @@ " \n", " 0\n", " False\n", - " 0.636197\n", + " 0.385101\n", " 73.3\n", " 76.499503\n", " \n", " \n", " 1\n", " False\n", - " 0.843478\n", + " 0.698255\n", " 83.8\n", " 82.776647\n", " \n", " \n", " 2\n", " True\n", - " 0.350358\n", + " 0.109373\n", " 73.5\n", " 63.170547\n", " \n", " \n", " 3\n", " False\n", - " 0.706969\n", + " 0.481096\n", " 78.6\n", " 75.984759\n", " \n", " \n", " 4\n", " False\n", - " 0.812515\n", + " 0.645270\n", " 74.1\n", " 75.795928\n", " \n", @@ -649,11 +649,11 @@ ], "text/plain": [ " is_label_issue label_quality given_label predicted_label\n", - "0 False 0.636197 73.3 76.499503\n", - "1 False 0.843478 83.8 82.776647\n", - "2 True 0.350358 73.5 63.170547\n", - "3 False 0.706969 78.6 75.984759\n", - "4 False 0.812515 74.1 75.795928" + "0 False 0.385101 73.3 76.499503\n", + "1 False 0.698255 83.8 82.776647\n", + "2 True 0.109373 73.5 63.170547\n", + "3 False 0.481096 78.6 75.984759\n", + "4 False 0.645270 74.1 75.795928" ] }, "execution_count": 11, @@ -679,10 +679,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.950317Z", - "iopub.status.busy": "2024-02-27T04:12:47.949997Z", - "iopub.status.idle": "2024-02-27T04:12:47.953528Z", - "shell.execute_reply": "2024-02-27T04:12:47.953129Z" + "iopub.execute_input": "2024-03-05T17:28:08.278170Z", + "iopub.status.busy": "2024-03-05T17:28:08.277969Z", + "iopub.status.idle": "2024-03-05T17:28:08.282203Z", + "shell.execute_reply": "2024-03-05T17:28:08.281637Z" } }, "outputs": [], @@ -697,10 +697,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.955484Z", - "iopub.status.busy": "2024-02-27T04:12:47.955162Z", - "iopub.status.idle": "2024-02-27T04:12:47.958513Z", - "shell.execute_reply": "2024-02-27T04:12:47.958007Z" + "iopub.execute_input": "2024-03-05T17:28:08.284667Z", + "iopub.status.busy": "2024-03-05T17:28:08.284167Z", + "iopub.status.idle": "2024-03-05T17:28:08.287737Z", + "shell.execute_reply": "2024-03-05T17:28:08.287176Z" } }, "outputs": [ @@ -735,10 +735,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.960428Z", - "iopub.status.busy": "2024-02-27T04:12:47.960108Z", - "iopub.status.idle": "2024-02-27T04:12:47.962924Z", - "shell.execute_reply": "2024-02-27T04:12:47.962517Z" + "iopub.execute_input": "2024-03-05T17:28:08.289867Z", + "iopub.status.busy": "2024-03-05T17:28:08.289680Z", + "iopub.status.idle": "2024-03-05T17:28:08.292718Z", + "shell.execute_reply": "2024-03-05T17:28:08.292286Z" } }, "outputs": [], @@ -757,10 +757,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.964790Z", - "iopub.status.busy": "2024-02-27T04:12:47.964470Z", - "iopub.status.idle": "2024-02-27T04:12:47.972257Z", - "shell.execute_reply": "2024-02-27T04:12:47.971731Z" + "iopub.execute_input": "2024-03-05T17:28:08.294795Z", + "iopub.status.busy": "2024-03-05T17:28:08.294462Z", + "iopub.status.idle": "2024-03-05T17:28:08.303025Z", + "shell.execute_reply": "2024-03-05T17:28:08.302479Z" } }, "outputs": [ @@ -884,10 +884,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.974208Z", - "iopub.status.busy": "2024-02-27T04:12:47.974046Z", - "iopub.status.idle": "2024-02-27T04:12:47.976325Z", - "shell.execute_reply": "2024-02-27T04:12:47.975918Z" + "iopub.execute_input": "2024-03-05T17:28:08.305276Z", + "iopub.status.busy": "2024-03-05T17:28:08.304919Z", + "iopub.status.idle": "2024-03-05T17:28:08.307548Z", + "shell.execute_reply": "2024-03-05T17:28:08.307139Z" }, "nbsphinx": "hidden" }, @@ -922,10 +922,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.978267Z", - "iopub.status.busy": "2024-02-27T04:12:47.977965Z", - "iopub.status.idle": "2024-02-27T04:12:48.098737Z", - "shell.execute_reply": "2024-02-27T04:12:48.098194Z" + "iopub.execute_input": "2024-03-05T17:28:08.309684Z", + "iopub.status.busy": "2024-03-05T17:28:08.309343Z", + "iopub.status.idle": "2024-03-05T17:28:08.434590Z", + "shell.execute_reply": "2024-03-05T17:28:08.433983Z" } }, "outputs": [ @@ -964,10 +964,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.100998Z", - "iopub.status.busy": "2024-02-27T04:12:48.100820Z", - "iopub.status.idle": "2024-02-27T04:12:48.204625Z", - "shell.execute_reply": "2024-02-27T04:12:48.204047Z" + "iopub.execute_input": "2024-03-05T17:28:08.437150Z", + "iopub.status.busy": "2024-03-05T17:28:08.436715Z", + "iopub.status.idle": "2024-03-05T17:28:08.545259Z", + "shell.execute_reply": "2024-03-05T17:28:08.544672Z" } }, "outputs": [ @@ -1023,10 +1023,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.206849Z", - "iopub.status.busy": "2024-02-27T04:12:48.206667Z", - "iopub.status.idle": "2024-02-27T04:12:48.695182Z", - "shell.execute_reply": "2024-02-27T04:12:48.694573Z" + "iopub.execute_input": "2024-03-05T17:28:08.547738Z", + "iopub.status.busy": "2024-03-05T17:28:08.547331Z", + "iopub.status.idle": "2024-03-05T17:28:09.072557Z", + "shell.execute_reply": "2024-03-05T17:28:09.072007Z" } }, "outputs": [], @@ -1042,10 +1042,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.698020Z", - "iopub.status.busy": "2024-02-27T04:12:48.697619Z", - "iopub.status.idle": "2024-02-27T04:12:48.792695Z", - "shell.execute_reply": "2024-02-27T04:12:48.792091Z" + "iopub.execute_input": "2024-03-05T17:28:09.075513Z", + "iopub.status.busy": "2024-03-05T17:28:09.074977Z", + "iopub.status.idle": "2024-03-05T17:28:09.174305Z", + "shell.execute_reply": "2024-03-05T17:28:09.173666Z" } }, "outputs": [ @@ -1080,10 +1080,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.795039Z", - "iopub.status.busy": "2024-02-27T04:12:48.794655Z", - "iopub.status.idle": "2024-02-27T04:12:48.803011Z", - "shell.execute_reply": "2024-02-27T04:12:48.802567Z" + "iopub.execute_input": "2024-03-05T17:28:09.176814Z", + "iopub.status.busy": "2024-03-05T17:28:09.176434Z", + "iopub.status.idle": "2024-03-05T17:28:09.186020Z", + "shell.execute_reply": "2024-03-05T17:28:09.185562Z" } }, "outputs": [ @@ -1190,10 +1190,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.805011Z", - "iopub.status.busy": "2024-02-27T04:12:48.804681Z", - "iopub.status.idle": "2024-02-27T04:12:48.807400Z", - "shell.execute_reply": "2024-02-27T04:12:48.806959Z" + "iopub.execute_input": "2024-03-05T17:28:09.188258Z", + "iopub.status.busy": "2024-03-05T17:28:09.187902Z", + "iopub.status.idle": "2024-03-05T17:28:09.190624Z", + "shell.execute_reply": "2024-03-05T17:28:09.190181Z" }, "nbsphinx": "hidden" }, @@ -1218,10 +1218,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.809322Z", - "iopub.status.busy": "2024-02-27T04:12:48.809004Z", - "iopub.status.idle": "2024-02-27T04:12:54.245843Z", - "shell.execute_reply": "2024-02-27T04:12:54.245180Z" + "iopub.execute_input": "2024-03-05T17:28:09.192753Z", + "iopub.status.busy": "2024-03-05T17:28:09.192409Z", + "iopub.status.idle": "2024-03-05T17:28:14.860363Z", + "shell.execute_reply": "2024-03-05T17:28:14.859804Z" } }, "outputs": [ @@ -1265,10 +1265,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:54.248043Z", - "iopub.status.busy": "2024-02-27T04:12:54.247864Z", - "iopub.status.idle": "2024-02-27T04:12:54.256305Z", - "shell.execute_reply": "2024-02-27T04:12:54.255899Z" + "iopub.execute_input": "2024-03-05T17:28:14.862653Z", + "iopub.status.busy": "2024-03-05T17:28:14.862257Z", + "iopub.status.idle": "2024-03-05T17:28:14.871090Z", + "shell.execute_reply": "2024-03-05T17:28:14.870586Z" } }, "outputs": [ @@ -1303,35 +1303,35 @@ " \n", " 659\n", " True\n", - " 0.000005\n", + " 5.791186e-12\n", " 17.4\n", " 84.110719\n", " \n", " \n", " 367\n", " True\n", - " 0.000044\n", + " 6.485156e-10\n", " 0.0\n", " 56.670640\n", " \n", " \n", " 56\n", " True\n", - " 0.000060\n", + " 1.225300e-09\n", " 8.9\n", " 71.749976\n", " \n", " \n", " 318\n", " True\n", - " 0.000066\n", + " 1.499679e-09\n", " 0.0\n", " 71.947007\n", " \n", " \n", " 305\n", " True\n", - " 0.000314\n", + " 4.067882e-08\n", " 19.1\n", " 61.648396\n", " \n", @@ -1340,12 +1340,12 @@ "" ], "text/plain": [ - " is_label_issue label_score given_label predicted_label\n", - "659 True 0.000005 17.4 84.110719\n", - "367 True 0.000044 0.0 56.670640\n", - "56 True 0.000060 8.9 71.749976\n", - "318 True 0.000066 0.0 71.947007\n", - "305 True 0.000314 19.1 61.648396" + " is_label_issue label_score given_label predicted_label\n", + "659 True 5.791186e-12 17.4 84.110719\n", + "367 True 6.485156e-10 0.0 56.670640\n", + "56 True 1.225300e-09 8.9 71.749976\n", + "318 True 1.499679e-09 0.0 71.947007\n", + "305 True 4.067882e-08 19.1 61.648396" ] }, "execution_count": 24, @@ -1377,10 +1377,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:54.258376Z", - "iopub.status.busy": "2024-02-27T04:12:54.258083Z", - "iopub.status.idle": "2024-02-27T04:12:54.322695Z", - "shell.execute_reply": "2024-02-27T04:12:54.322098Z" + "iopub.execute_input": "2024-03-05T17:28:14.873550Z", + "iopub.status.busy": "2024-03-05T17:28:14.873117Z", + "iopub.status.idle": "2024-03-05T17:28:14.939007Z", + "shell.execute_reply": "2024-03-05T17:28:14.938371Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index a6a857792..3c7b2e571 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": "2024-02-27T04:12:57.207694Z", - "iopub.status.busy": "2024-02-27T04:12:57.207532Z", - "iopub.status.idle": "2024-02-27T04:12:59.279928Z", - "shell.execute_reply": "2024-02-27T04:12:59.279246Z" + "iopub.execute_input": "2024-03-05T17:28:18.147705Z", + "iopub.status.busy": "2024-03-05T17:28:18.147191Z", + "iopub.status.idle": "2024-03-05T17:28:20.582426Z", + "shell.execute_reply": "2024-03-05T17:28:20.581741Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:59.282497Z", - "iopub.status.busy": "2024-02-27T04:12:59.282159Z", - "iopub.status.idle": "2024-02-27T04:13:53.069895Z", - "shell.execute_reply": "2024-02-27T04:13:53.069266Z" + "iopub.execute_input": "2024-03-05T17:28:20.585272Z", + "iopub.status.busy": "2024-03-05T17:28:20.584882Z", + "iopub.status.idle": "2024-03-05T17:45:40.610320Z", + "shell.execute_reply": "2024-03-05T17:45:40.609609Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:53.072806Z", - "iopub.status.busy": "2024-02-27T04:13:53.072123Z", - "iopub.status.idle": "2024-02-27T04:13:54.114174Z", - "shell.execute_reply": "2024-02-27T04:13:54.113575Z" + "iopub.execute_input": "2024-03-05T17:45:40.612947Z", + "iopub.status.busy": "2024-03-05T17:45:40.612757Z", + "iopub.status.idle": "2024-03-05T17:45:41.757970Z", + "shell.execute_reply": "2024-03-05T17:45:41.757383Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:13:54.116665Z", - "iopub.status.busy": "2024-02-27T04:13:54.116394Z", - "iopub.status.idle": "2024-02-27T04:13:54.119670Z", - "shell.execute_reply": "2024-02-27T04:13:54.119164Z" + "iopub.execute_input": "2024-03-05T17:45:41.760770Z", + "iopub.status.busy": "2024-03-05T17:45:41.760440Z", + "iopub.status.idle": "2024-03-05T17:45:41.763885Z", + "shell.execute_reply": "2024-03-05T17:45:41.763386Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:54.121737Z", - "iopub.status.busy": "2024-02-27T04:13:54.121337Z", - "iopub.status.idle": "2024-02-27T04:13:54.125111Z", - "shell.execute_reply": "2024-02-27T04:13:54.124597Z" + "iopub.execute_input": "2024-03-05T17:45:41.766023Z", + "iopub.status.busy": "2024-03-05T17:45:41.765791Z", + "iopub.status.idle": "2024-03-05T17:45:41.769948Z", + "shell.execute_reply": "2024-03-05T17:45:41.769469Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:54.127305Z", - "iopub.status.busy": "2024-02-27T04:13:54.127002Z", - "iopub.status.idle": "2024-02-27T04:13:54.130549Z", - "shell.execute_reply": "2024-02-27T04:13:54.130136Z" + "iopub.execute_input": "2024-03-05T17:45:41.772615Z", + "iopub.status.busy": "2024-03-05T17:45:41.772136Z", + "iopub.status.idle": "2024-03-05T17:45:41.776655Z", + "shell.execute_reply": "2024-03-05T17:45:41.776065Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:54.132513Z", - "iopub.status.busy": "2024-02-27T04:13:54.132263Z", - "iopub.status.idle": "2024-02-27T04:13:54.135127Z", - "shell.execute_reply": "2024-02-27T04:13:54.134716Z" + "iopub.execute_input": "2024-03-05T17:45:41.778817Z", + "iopub.status.busy": "2024-03-05T17:45:41.778595Z", + "iopub.status.idle": "2024-03-05T17:45:41.781542Z", + "shell.execute_reply": "2024-03-05T17:45:41.781115Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:54.136872Z", - "iopub.status.busy": "2024-02-27T04:13:54.136696Z", - "iopub.status.idle": "2024-02-27T04:15:09.458723Z", - "shell.execute_reply": "2024-02-27T04:15:09.458208Z" + "iopub.execute_input": "2024-03-05T17:45:41.783628Z", + "iopub.status.busy": "2024-03-05T17:45:41.783296Z", + "iopub.status.idle": "2024-03-05T17:46:58.949548Z", + "shell.execute_reply": "2024-03-05T17:46:58.948943Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f35bc28e6c854036845115d0c80f19d5", + "model_id": "b162807487ed4294915f30fd4e3c76e0", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "296d85fcd35c431788201d553a1a5a6f", + "model_id": "9ace8d45462a43e3acb62ffd2dbfeae6", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:15:09.461479Z", - "iopub.status.busy": "2024-02-27T04:15:09.460992Z", - "iopub.status.idle": "2024-02-27T04:15:10.130417Z", - "shell.execute_reply": "2024-02-27T04:15:10.129849Z" + "iopub.execute_input": "2024-03-05T17:46:58.952327Z", + "iopub.status.busy": "2024-03-05T17:46:58.951998Z", + "iopub.status.idle": "2024-03-05T17:46:59.639571Z", + "shell.execute_reply": "2024-03-05T17:46:59.639056Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:15:10.132563Z", - "iopub.status.busy": "2024-02-27T04:15:10.132277Z", - "iopub.status.idle": "2024-02-27T04:15:12.733259Z", - "shell.execute_reply": "2024-02-27T04:15:12.732682Z" + "iopub.execute_input": "2024-03-05T17:46:59.641915Z", + "iopub.status.busy": "2024-03-05T17:46:59.641441Z", + "iopub.status.idle": "2024-03-05T17:47:02.397012Z", + "shell.execute_reply": "2024-03-05T17:47:02.396451Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:15:12.735338Z", - "iopub.status.busy": "2024-02-27T04:15:12.735158Z", - "iopub.status.idle": "2024-02-27T04:15:45.577670Z", - "shell.execute_reply": "2024-02-27T04:15:45.577252Z" + "iopub.execute_input": "2024-03-05T17:47:02.399110Z", + "iopub.status.busy": "2024-03-05T17:47:02.398893Z", + "iopub.status.idle": "2024-03-05T17:47:35.738739Z", + "shell.execute_reply": "2024-03-05T17:47:35.738229Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cef6a8d6978b42028d2a2ab034ec55eb", + "model_id": "6cc004502aa044fe903fd61c1b15e1b1", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:15:45.579888Z", - "iopub.status.busy": "2024-02-27T04:15:45.579488Z", - "iopub.status.idle": "2024-02-27T04:16:00.523676Z", - "shell.execute_reply": "2024-02-27T04:16:00.523078Z" + "iopub.execute_input": "2024-03-05T17:47:35.741383Z", + "iopub.status.busy": "2024-03-05T17:47:35.740959Z", + "iopub.status.idle": "2024-03-05T17:47:50.208175Z", + "shell.execute_reply": "2024-03-05T17:47:50.207672Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:00.526352Z", - "iopub.status.busy": "2024-02-27T04:16:00.526156Z", - "iopub.status.idle": "2024-02-27T04:16:04.264366Z", - "shell.execute_reply": "2024-02-27T04:16:04.263812Z" + "iopub.execute_input": "2024-03-05T17:47:50.210787Z", + "iopub.status.busy": "2024-03-05T17:47:50.210374Z", + "iopub.status.idle": "2024-03-05T17:47:54.047662Z", + "shell.execute_reply": "2024-03-05T17:47:54.047067Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:04.266493Z", - "iopub.status.busy": "2024-02-27T04:16:04.266173Z", - "iopub.status.idle": "2024-02-27T04:16:05.581814Z", - "shell.execute_reply": "2024-02-27T04:16:05.581240Z" + "iopub.execute_input": "2024-03-05T17:47:54.049846Z", + "iopub.status.busy": "2024-03-05T17:47:54.049497Z", + "iopub.status.idle": "2024-03-05T17:47:55.459288Z", + "shell.execute_reply": "2024-03-05T17:47:55.458735Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9da4c9cbb0384e2aadaed1e9a5ac86c9", + "model_id": "5ffa05d5e3254d5485cde2ec919c0753", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:05.584272Z", - "iopub.status.busy": "2024-02-27T04:16:05.583899Z", - "iopub.status.idle": "2024-02-27T04:16:06.141736Z", - "shell.execute_reply": "2024-02-27T04:16:06.141168Z" + "iopub.execute_input": "2024-03-05T17:47:55.461782Z", + "iopub.status.busy": "2024-03-05T17:47:55.461446Z", + "iopub.status.idle": "2024-03-05T17:47:56.021511Z", + "shell.execute_reply": "2024-03-05T17:47:56.020919Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:06.144288Z", - "iopub.status.busy": "2024-02-27T04:16:06.143856Z", - "iopub.status.idle": "2024-02-27T04:16:12.281675Z", - "shell.execute_reply": "2024-02-27T04:16:12.281106Z" + "iopub.execute_input": "2024-03-05T17:47:56.023926Z", + "iopub.status.busy": "2024-03-05T17:47:56.023736Z", + "iopub.status.idle": "2024-03-05T17:48:02.253088Z", + "shell.execute_reply": "2024-03-05T17:48:02.252504Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:12.284059Z", - "iopub.status.busy": "2024-02-27T04:16:12.283678Z", - "iopub.status.idle": "2024-02-27T04:16:12.339573Z", - "shell.execute_reply": "2024-02-27T04:16:12.338971Z" + "iopub.execute_input": "2024-03-05T17:48:02.255140Z", + "iopub.status.busy": "2024-03-05T17:48:02.254968Z", + "iopub.status.idle": "2024-03-05T17:48:02.310981Z", + "shell.execute_reply": "2024-03-05T17:48:02.310446Z" }, "nbsphinx": "hidden" }, @@ -1038,125 +1038,82 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "09b26f4b3b9a47a2805cf4f2a46f9d69": { + "0e78779c254948ca955970bd699f05e4": { "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": "" } }, - "12d018fa4ae846b8a54e374c7019f13f": { - "model_module": "@jupyter-widgets/base", + "1f495e4964a148feb4e60d20e8452c6e": { + "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_e94ffb07e0db4adb805c9bc580a3e3e4", + "placeholder": "​", + "style": "IPY_MODEL_e13ede49da8b47288f20a176abd303de", + "tabbable": null, + "tooltip": null, + "value": " 4997817/4997817 [00:33<00:00, 150552.43it/s]" } }, - "296d85fcd35c431788201d553a1a5a6f": { + "22dd2e443884472987903e12456a6afc": { "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_bdd76556372a4e9493160aa23c770516", - "IPY_MODEL_afc228943c0c4f7da0a08beb16ce10d8", - "IPY_MODEL_6dd49c305ab748c7a93943e1588862e6" - ], - "layout": "IPY_MODEL_f24324799ab14c45bd97e44a8aafc700", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2c70b8eef5404b779ee375b744cce580": { + "3037b33d74a246529b444447f531d14a": { "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_731a6803de2943de9fe59e135467352c", - "placeholder": "​", - "style": "IPY_MODEL_dc09f94a59704a56b35e0ae9b670dff3", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "384a4505d6a24950b928aac1dc443147": { + "32f943c5ccf24606a0682b14df6b8813": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1209,48 +1166,7 @@ "width": null } }, - "3f2145734072403ca5a67549fab4f063": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "463e6bd1ba434320a64292f487ede2d8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fadf065818134007af3b6127fc18a147", - "placeholder": "​", - "style": "IPY_MODEL_09b26f4b3b9a47a2805cf4f2a46f9d69", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "50a5c3532d8a4018ac6a0cf17ea51c7b": { + "3968d811886943a58fcd6d72287a87c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1268,102 +1184,7 @@ "text_color": null } }, - "52595c826b444c2ebc76e3c515902b05": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_67c1e03df8e04d0faf0c596ee30c7c3f", - "placeholder": "​", - "style": "IPY_MODEL_d6735c14323d4b459e3e3c0bae6e2633", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "57b231e2b47b4bd9aebaa4374be7cdf6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d27b521ce1bf4fd39a9949ea8f5c2d4c", - "placeholder": "​", - "style": "IPY_MODEL_3f2145734072403ca5a67549fab4f063", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 409.01it/s]" - } - }, - "592e152ed2a34bd9baec5bafb7b23e0b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e8ac72205cde41498bb49c0af38523a2", - "placeholder": "​", - "style": "IPY_MODEL_e1b022c8165a4bf188893ed01dda6872", - "tabbable": null, - "tooltip": null, - "value": " 4997817/4997817 [00:32<00:00, 153315.47it/s]" - } - }, - "61f7722029cb43cb818c03e75f945cbb": { - "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_791fb20963684d658cbb4f2356804315", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9cd946793cc44581acb428f9f883ce63", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "62b097660a12458c9f94a0db9841f36f": { + "3e2f5c5b14d04e7eab77c1da047eaf37": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1416,33 +1237,41 @@ "width": null } }, - "6747a844f55e402b8ce37f9146b436da": { + "3e422b6f9c924f8b90c52984c29f2eba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_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_6852741653514354a11f29cdbeb0785d", - "max": 4997817.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e1972f5c0ada424e8875fc5447db0e05", - "tabbable": null, - "tooltip": null, - "value": 4997817.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3f852fa32ca24e6bb53931e24dadc044": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "67c1e03df8e04d0faf0c596ee30c7c3f": { + "405315e1b55c4805b3c835dd9b07aad0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1495,7 +1324,7 @@ "width": null } }, - "6852741653514354a11f29cdbeb0785d": { + "47224c6e6ce747dbb5c436862e7fc083": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1548,7 +1377,51 @@ "width": null } }, - "6dd49c305ab748c7a93943e1588862e6": { + "47db57068b3e4989a7681de8ad28cc95": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4859445022f1466492847acbc9218820": { + "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_6614dba8fffc4486948833eb2cb48453", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3e422b6f9c924f8b90c52984c29f2eba", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "48dad57e5d1a47b8bd13601d5d9c86cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1563,15 +1436,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9462c72d042744399edccd9fe0e5604e", + "layout": "IPY_MODEL_47224c6e6ce747dbb5c436862e7fc083", "placeholder": "​", - "style": "IPY_MODEL_6f35fa660d314dcf83ab10d7f6446013", + "style": "IPY_MODEL_3037b33d74a246529b444447f531d14a", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:20<00:00,  1.43it/s]" + "value": " 30/30 [00:22<00:00,  1.35it/s]" } }, - "6e9177833aa44fe4a3b3364d0ca3a407": { + "4aa157fdd2534b229ea93d063d9b0b18": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1624,7 +1497,7 @@ "width": null } }, - "6f35fa660d314dcf83ab10d7f6446013": { + "5b863241a0d04ead91d8344e1a316a03": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1642,7 +1515,31 @@ "text_color": null } }, - "731a6803de2943de9fe59e135467352c": { + "5ffa05d5e3254d5485cde2ec919c0753": { + "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_e6b05918df3841d8b0e4559dc4c908b4", + "IPY_MODEL_a27bbce248eb48fa85aa97fe6cc9367b", + "IPY_MODEL_cfa4014b382543c9b3b6454daaba56ea" + ], + "layout": "IPY_MODEL_c9f1ffe82f3d413d901770786743b252", + "tabbable": null, + "tooltip": null + } + }, + "6614dba8fffc4486948833eb2cb48453": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1695,7 +1592,57 @@ "width": null } }, - "791fb20963684d658cbb4f2356804315": { + "6cc004502aa044fe903fd61c1b15e1b1": { + "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_a7ab9defd92845acb59e41d2d3fd53d0", + "IPY_MODEL_712e47d99ef54b18b2c7ecd143fbf9b1", + "IPY_MODEL_1f495e4964a148feb4e60d20e8452c6e" + ], + "layout": "IPY_MODEL_405315e1b55c4805b3c835dd9b07aad0", + "tabbable": null, + "tooltip": null + } + }, + "712e47d99ef54b18b2c7ecd143fbf9b1": { + "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_a5ddf96f8e974675a30c27da41f7b7a8", + "max": 4997817.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9abea605e6474573bcb7c31e57c24686", + "tabbable": null, + "tooltip": null, + "value": 4997817.0 + } + }, + "7d352113995b40caa7c8490d9201ba46": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1748,25 +1695,7 @@ "width": null } }, - "89c18b2d1deb4f3686561f36964504fd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "932538e731be4078b2cdcf6fa4d50caf": { + "826f982e152b493fa16f4022915cc507": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1819,7 +1748,7 @@ "width": null } }, - "9462c72d042744399edccd9fe0e5604e": { + "84cd9c7192a24ffbabecc9aae62510f4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1859,112 +1788,20 @@ "margin": null, "max_height": null, "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "9cd946793cc44581acb428f9f883ce63": { - "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": "" - } - }, - "9da4c9cbb0384e2aadaed1e9a5ac86c9": { - "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_463e6bd1ba434320a64292f487ede2d8", - "IPY_MODEL_61f7722029cb43cb818c03e75f945cbb", - "IPY_MODEL_de7d2fcc6436422e84c48615a8b1a169" - ], - "layout": "IPY_MODEL_12d018fa4ae846b8a54e374c7019f13f", - "tabbable": null, - "tooltip": null - } - }, - "afc228943c0c4f7da0a08beb16ce10d8": { - "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_62b097660a12458c9f94a0db9841f36f", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_bb4f39000f154f8e937001f942d79b8b", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "b527ce48830a47aba867ef2f10cd2329": { - "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_6e9177833aa44fe4a3b3364d0ca3a407", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f5219d4ec3d24d008bb0e11b485f1850", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "bb4f39000f154f8e937001f942d79b8b": { + "9abea605e6474573bcb7c31e57c24686": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1980,54 +1817,57 @@ "description_width": "" } }, - "bdd76556372a4e9493160aa23c770516": { + "9ace8d45462a43e3acb62ffd2dbfeae6": { "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_932538e731be4078b2cdcf6fa4d50caf", - "placeholder": "​", - "style": "IPY_MODEL_50a5c3532d8a4018ac6a0cf17ea51c7b", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fc947703ddb343b58ccc14eaba3bcca0", + "IPY_MODEL_edda8d6aface48258c3afff4ab0438dc", + "IPY_MODEL_48dad57e5d1a47b8bd13601d5d9c86cc" + ], + "layout": "IPY_MODEL_7d352113995b40caa7c8490d9201ba46", "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" + "tooltip": null } }, - "cef6a8d6978b42028d2a2ab034ec55eb": { + "a27bbce248eb48fa85aa97fe6cc9367b": { "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_2c70b8eef5404b779ee375b744cce580", - "IPY_MODEL_6747a844f55e402b8ce37f9146b436da", - "IPY_MODEL_592e152ed2a34bd9baec5bafb7b23e0b" - ], - "layout": "IPY_MODEL_f7400852642549e6a9cfb66d9efd8518", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c15a753a5cb2429281e5608ff1b5bb25", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0e78779c254948ca955970bd699f05e4", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 30.0 } }, - "d27b521ce1bf4fd39a9949ea8f5c2d4c": { + "a5ddf96f8e974675a30c27da41f7b7a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2080,66 +1920,107 @@ "width": null } }, - "d6735c14323d4b459e3e3c0bae6e2633": { - "model_module": "@jupyter-widgets/controls", + "a63c0c1d48b24b9b86990f8442e7a986": { + "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 } }, - "dc09f94a59704a56b35e0ae9b670dff3": { + "a7ab9defd92845acb59e41d2d3fd53d0": { "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_4aa157fdd2534b229ea93d063d9b0b18", + "placeholder": "​", + "style": "IPY_MODEL_47db57068b3e4989a7681de8ad28cc95", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "de7d2fcc6436422e84c48615a8b1a169": { + "b162807487ed4294915f30fd4e3c76e0": { "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_384a4505d6a24950b928aac1dc443147", - "placeholder": "​", - "style": "IPY_MODEL_89c18b2d1deb4f3686561f36964504fd", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cd1eb2f2a1b54b0fa870a8cedc781785", + "IPY_MODEL_4859445022f1466492847acbc9218820", + "IPY_MODEL_ece37f209eae46d499da542dc36ff270" + ], + "layout": "IPY_MODEL_cba7ec7ff68e4d2b99b2c419a5f61998", "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:01<00:00, 23.54it/s]" + "tooltip": null } }, - "e1972f5c0ada424e8875fc5447db0e05": { + "b1d722c462df4200ab2f28159c75e2ae": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2155,7 +2036,7 @@ "description_width": "" } }, - "e1b022c8165a4bf188893ed01dda6872": { + "bdacb14f809d4e719720b76b5446738c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2173,7 +2054,7 @@ "text_color": null } }, - "e8ac72205cde41498bb49c0af38523a2": { + "c15a753a5cb2429281e5608ff1b5bb25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2226,7 +2107,7 @@ "width": null } }, - "e911eabd389a4c078fec14d183589ffa": { + "c9f1ffe82f3d413d901770786743b252": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2279,7 +2160,7 @@ "width": null } }, - "f24324799ab14c45bd97e44a8aafc700": { + "cba7ec7ff68e4d2b99b2c419a5f61998": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2332,47 +2213,94 @@ "width": null } }, - "f35bc28e6c854036845115d0c80f19d5": { + "cd1eb2f2a1b54b0fa870a8cedc781785": { "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_52595c826b444c2ebc76e3c515902b05", - "IPY_MODEL_b527ce48830a47aba867ef2f10cd2329", - "IPY_MODEL_57b231e2b47b4bd9aebaa4374be7cdf6" - ], - "layout": "IPY_MODEL_e911eabd389a4c078fec14d183589ffa", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3e2f5c5b14d04e7eab77c1da047eaf37", + "placeholder": "​", + "style": "IPY_MODEL_3f852fa32ca24e6bb53931e24dadc044", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" } }, - "f5219d4ec3d24d008bb0e11b485f1850": { + "cfa4014b382543c9b3b6454daaba56ea": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_efc88340d53c45efa6cfd84134127283", + "placeholder": "​", + "style": "IPY_MODEL_3968d811886943a58fcd6d72287a87c6", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 21.68it/s]" + } + }, + "e13ede49da8b47288f20a176abd303de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "e6b05918df3841d8b0e4559dc4c908b4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_826f982e152b493fa16f4022915cc507", + "placeholder": "​", + "style": "IPY_MODEL_bdacb14f809d4e719720b76b5446738c", + "tabbable": null, + "tooltip": null, + "value": "images processed using softmin: 100%" } }, - "f7400852642549e6a9cfb66d9efd8518": { + "e94ffb07e0db4adb805c9bc580a3e3e4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2425,7 +2353,56 @@ "width": null } }, - "fadf065818134007af3b6127fc18a147": { + "ece37f209eae46d499da542dc36ff270": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a63c0c1d48b24b9b86990f8442e7a986", + "placeholder": "​", + "style": "IPY_MODEL_5b863241a0d04ead91d8344e1a316a03", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 413.45it/s]" + } + }, + "edda8d6aface48258c3afff4ab0438dc": { + "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_32f943c5ccf24606a0682b14df6b8813", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b1d722c462df4200ab2f28159c75e2ae", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "efc88340d53c45efa6cfd84134127283": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2477,6 +2454,29 @@ "visibility": null, "width": null } + }, + "fc947703ddb343b58ccc14eaba3bcca0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_84cd9c7192a24ffbabecc9aae62510f4", + "placeholder": "​", + "style": "IPY_MODEL_22dd2e443884472987903e12456a6afc", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: 100%" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb index 4db6b2fb5..c33c4f908 100644 --- a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb @@ -112,10 +112,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:14.737131Z", - "iopub.status.busy": "2024-02-27T04:16:14.736964Z", - "iopub.status.idle": "2024-02-27T04:16:15.945502Z", - "shell.execute_reply": "2024-02-27T04:16:15.945037Z" + "iopub.execute_input": "2024-03-05T17:48:04.856587Z", + "iopub.status.busy": "2024-03-05T17:48:04.856409Z", + "iopub.status.idle": "2024-03-05T17:48:05.988531Z", + "shell.execute_reply": "2024-03-05T17:48:05.987868Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -150,10 +150,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:15.947855Z", - "iopub.status.busy": "2024-02-27T04:16:15.947480Z", - "iopub.status.idle": "2024-02-27T04:16:15.973123Z", - "shell.execute_reply": "2024-02-27T04:16:15.972599Z" + "iopub.execute_input": "2024-03-05T17:48:05.991418Z", + "iopub.status.busy": "2024-03-05T17:48:05.990791Z", + "iopub.status.idle": "2024-03-05T17:48:06.011423Z", + "shell.execute_reply": "2024-03-05T17:48:06.010921Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:15.975475Z", - "iopub.status.busy": "2024-02-27T04:16:15.975065Z", - "iopub.status.idle": "2024-02-27T04:16:16.125117Z", - "shell.execute_reply": "2024-02-27T04:16:16.124586Z" + "iopub.execute_input": "2024-03-05T17:48:06.014009Z", + "iopub.status.busy": "2024-03-05T17:48:06.013708Z", + "iopub.status.idle": "2024-03-05T17:48:06.128945Z", + "shell.execute_reply": "2024-03-05T17:48:06.128378Z" } }, "outputs": [ @@ -304,10 +304,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.127227Z", - "iopub.status.busy": "2024-02-27T04:16:16.126946Z", - "iopub.status.idle": "2024-02-27T04:16:16.130584Z", - "shell.execute_reply": "2024-02-27T04:16:16.130059Z" + "iopub.execute_input": "2024-03-05T17:48:06.131142Z", + "iopub.status.busy": "2024-03-05T17:48:06.130841Z", + "iopub.status.idle": "2024-03-05T17:48:06.134314Z", + "shell.execute_reply": "2024-03-05T17:48:06.133885Z" } }, "outputs": [], @@ -328,10 +328,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.132634Z", - "iopub.status.busy": "2024-02-27T04:16:16.132250Z", - "iopub.status.idle": "2024-02-27T04:16:16.140835Z", - "shell.execute_reply": "2024-02-27T04:16:16.140417Z" + "iopub.execute_input": "2024-03-05T17:48:06.136444Z", + "iopub.status.busy": "2024-03-05T17:48:06.136115Z", + "iopub.status.idle": "2024-03-05T17:48:06.144438Z", + "shell.execute_reply": "2024-03-05T17:48:06.143865Z" } }, "outputs": [], @@ -383,10 +383,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.142989Z", - "iopub.status.busy": "2024-02-27T04:16:16.142569Z", - "iopub.status.idle": "2024-02-27T04:16:16.145030Z", - "shell.execute_reply": "2024-02-27T04:16:16.144617Z" + "iopub.execute_input": "2024-03-05T17:48:06.146732Z", + "iopub.status.busy": "2024-03-05T17:48:06.146414Z", + "iopub.status.idle": "2024-03-05T17:48:06.149109Z", + "shell.execute_reply": "2024-03-05T17:48:06.148562Z" } }, "outputs": [], @@ -408,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.146938Z", - "iopub.status.busy": "2024-02-27T04:16:16.146628Z", - "iopub.status.idle": "2024-02-27T04:16:16.658427Z", - "shell.execute_reply": "2024-02-27T04:16:16.657906Z" + "iopub.execute_input": "2024-03-05T17:48:06.151171Z", + "iopub.status.busy": "2024-03-05T17:48:06.150864Z", + "iopub.status.idle": "2024-03-05T17:48:06.678736Z", + "shell.execute_reply": "2024-03-05T17:48:06.678202Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.660726Z", - "iopub.status.busy": "2024-02-27T04:16:16.660536Z", - "iopub.status.idle": "2024-02-27T04:16:18.271783Z", - "shell.execute_reply": "2024-02-27T04:16:18.271203Z" + "iopub.execute_input": "2024-03-05T17:48:06.681076Z", + "iopub.status.busy": "2024-03-05T17:48:06.680880Z", + "iopub.status.idle": "2024-03-05T17:48:08.453121Z", + "shell.execute_reply": "2024-03-05T17:48:08.452497Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.274531Z", - "iopub.status.busy": "2024-02-27T04:16:18.273765Z", - "iopub.status.idle": "2024-02-27T04:16:18.283860Z", - "shell.execute_reply": "2024-02-27T04:16:18.283431Z" + "iopub.execute_input": "2024-03-05T17:48:08.455931Z", + "iopub.status.busy": "2024-03-05T17:48:08.455186Z", + "iopub.status.idle": "2024-03-05T17:48:08.465833Z", + "shell.execute_reply": "2024-03-05T17:48:08.465310Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.285733Z", - "iopub.status.busy": "2024-02-27T04:16:18.285537Z", - "iopub.status.idle": "2024-02-27T04:16:18.289535Z", - "shell.execute_reply": "2024-02-27T04:16:18.289115Z" + "iopub.execute_input": "2024-03-05T17:48:08.467945Z", + "iopub.status.busy": "2024-03-05T17:48:08.467767Z", + "iopub.status.idle": "2024-03-05T17:48:08.471808Z", + "shell.execute_reply": "2024-03-05T17:48:08.471372Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.291513Z", - "iopub.status.busy": "2024-02-27T04:16:18.291204Z", - "iopub.status.idle": "2024-02-27T04:16:18.298162Z", - "shell.execute_reply": "2024-02-27T04:16:18.297735Z" + "iopub.execute_input": "2024-03-05T17:48:08.473687Z", + "iopub.status.busy": "2024-03-05T17:48:08.473515Z", + "iopub.status.idle": "2024-03-05T17:48:08.481125Z", + "shell.execute_reply": "2024-03-05T17:48:08.480648Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.300199Z", - "iopub.status.busy": "2024-02-27T04:16:18.299889Z", - "iopub.status.idle": "2024-02-27T04:16:18.410879Z", - "shell.execute_reply": "2024-02-27T04:16:18.410323Z" + "iopub.execute_input": "2024-03-05T17:48:08.483006Z", + "iopub.status.busy": "2024-03-05T17:48:08.482829Z", + "iopub.status.idle": "2024-03-05T17:48:08.595386Z", + "shell.execute_reply": "2024-03-05T17:48:08.594890Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.412932Z", - "iopub.status.busy": "2024-02-27T04:16:18.412621Z", - "iopub.status.idle": "2024-02-27T04:16:18.415398Z", - "shell.execute_reply": "2024-02-27T04:16:18.414881Z" + "iopub.execute_input": "2024-03-05T17:48:08.597533Z", + "iopub.status.busy": "2024-03-05T17:48:08.597339Z", + "iopub.status.idle": "2024-03-05T17:48:08.600276Z", + "shell.execute_reply": "2024-03-05T17:48:08.599817Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.417564Z", - "iopub.status.busy": "2024-02-27T04:16:18.417159Z", - "iopub.status.idle": "2024-02-27T04:16:20.369816Z", - "shell.execute_reply": "2024-02-27T04:16:20.369175Z" + "iopub.execute_input": "2024-03-05T17:48:08.602300Z", + "iopub.status.busy": "2024-03-05T17:48:08.602114Z", + "iopub.status.idle": "2024-03-05T17:48:10.652590Z", + "shell.execute_reply": "2024-03-05T17:48:10.651973Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:20.372807Z", - "iopub.status.busy": "2024-02-27T04:16:20.372225Z", - "iopub.status.idle": "2024-02-27T04:16:20.383278Z", - "shell.execute_reply": "2024-02-27T04:16:20.382741Z" + "iopub.execute_input": "2024-03-05T17:48:10.655736Z", + "iopub.status.busy": "2024-03-05T17:48:10.654924Z", + "iopub.status.idle": "2024-03-05T17:48:10.666973Z", + "shell.execute_reply": "2024-03-05T17:48:10.666399Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:20.385229Z", - "iopub.status.busy": "2024-02-27T04:16:20.385051Z", - "iopub.status.idle": "2024-02-27T04:16:20.514805Z", - "shell.execute_reply": "2024-02-27T04:16:20.514319Z" + "iopub.execute_input": "2024-03-05T17:48:10.669111Z", + "iopub.status.busy": "2024-03-05T17:48:10.668780Z", + "iopub.status.idle": "2024-03-05T17:48:10.779840Z", + "shell.execute_reply": "2024-03-05T17:48:10.779359Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb index eeeda813b..7b751ea82 100644 --- a/master/.doctrees/nbsphinx/tutorials/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:23.372370Z", - "iopub.status.busy": "2024-02-27T04:16:23.372190Z", - "iopub.status.idle": "2024-02-27T04:16:25.962294Z", - "shell.execute_reply": "2024-02-27T04:16:25.961803Z" + "iopub.execute_input": "2024-03-05T17:48:14.638665Z", + "iopub.status.busy": "2024-03-05T17:48:14.638226Z", + "iopub.status.idle": "2024-03-05T17:48:17.428257Z", + "shell.execute_reply": "2024-03-05T17:48:17.427698Z" }, "nbsphinx": "hidden" }, @@ -134,7 +134,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:25.964716Z", - "iopub.status.busy": "2024-02-27T04:16:25.964409Z", - "iopub.status.idle": "2024-02-27T04:16:25.967890Z", - "shell.execute_reply": "2024-02-27T04:16:25.967445Z" + "iopub.execute_input": "2024-03-05T17:48:17.430961Z", + "iopub.status.busy": "2024-03-05T17:48:17.430423Z", + "iopub.status.idle": "2024-03-05T17:48:17.433889Z", + "shell.execute_reply": "2024-03-05T17:48:17.433420Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:25.969673Z", - "iopub.status.busy": "2024-02-27T04:16:25.969495Z", - "iopub.status.idle": "2024-02-27T04:16:25.972738Z", - "shell.execute_reply": "2024-02-27T04:16:25.972220Z" + "iopub.execute_input": "2024-03-05T17:48:17.435905Z", + "iopub.status.busy": "2024-03-05T17:48:17.435631Z", + "iopub.status.idle": "2024-03-05T17:48:17.438735Z", + "shell.execute_reply": "2024-03-05T17:48:17.438293Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:25.974775Z", - "iopub.status.busy": "2024-02-27T04:16:25.974597Z", - "iopub.status.idle": "2024-02-27T04:16:26.114274Z", - "shell.execute_reply": "2024-02-27T04:16:26.113793Z" + "iopub.execute_input": "2024-03-05T17:48:17.440824Z", + "iopub.status.busy": "2024-03-05T17:48:17.440490Z", + "iopub.status.idle": "2024-03-05T17:48:17.558800Z", + "shell.execute_reply": "2024-03-05T17:48:17.558202Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.116290Z", - "iopub.status.busy": "2024-02-27T04:16:26.116099Z", - "iopub.status.idle": "2024-02-27T04:16:26.119649Z", - "shell.execute_reply": "2024-02-27T04:16:26.119216Z" + "iopub.execute_input": "2024-03-05T17:48:17.561117Z", + "iopub.status.busy": "2024-03-05T17:48:17.560769Z", + "iopub.status.idle": "2024-03-05T17:48:17.564365Z", + "shell.execute_reply": "2024-03-05T17:48:17.563876Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.121568Z", - "iopub.status.busy": "2024-02-27T04:16:26.121379Z", - "iopub.status.idle": "2024-02-27T04:16:26.124791Z", - "shell.execute_reply": "2024-02-27T04:16:26.124322Z" + "iopub.execute_input": "2024-03-05T17:48:17.566433Z", + "iopub.status.busy": "2024-03-05T17:48:17.566089Z", + "iopub.status.idle": "2024-03-05T17:48:17.569334Z", + "shell.execute_reply": "2024-03-05T17:48:17.568816Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'getting_spare_card', 'cancel_transfer', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'card_about_to_expire', 'visa_or_mastercard'}\n" + "Classes: {'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_about_to_expire', 'getting_spare_card'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.126916Z", - "iopub.status.busy": "2024-02-27T04:16:26.126572Z", - "iopub.status.idle": "2024-02-27T04:16:26.129643Z", - "shell.execute_reply": "2024-02-27T04:16:26.129127Z" + "iopub.execute_input": "2024-03-05T17:48:17.571385Z", + "iopub.status.busy": "2024-03-05T17:48:17.571040Z", + "iopub.status.idle": "2024-03-05T17:48:17.574329Z", + "shell.execute_reply": "2024-03-05T17:48:17.573852Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.131708Z", - "iopub.status.busy": "2024-02-27T04:16:26.131379Z", - "iopub.status.idle": "2024-02-27T04:16:26.134572Z", - "shell.execute_reply": "2024-02-27T04:16:26.134163Z" + "iopub.execute_input": "2024-03-05T17:48:17.576440Z", + "iopub.status.busy": "2024-03-05T17:48:17.576097Z", + "iopub.status.idle": "2024-03-05T17:48:17.579381Z", + "shell.execute_reply": "2024-03-05T17:48:17.578941Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.136586Z", - "iopub.status.busy": "2024-02-27T04:16:26.136261Z", - "iopub.status.idle": "2024-02-27T04:16:30.548466Z", - "shell.execute_reply": "2024-02-27T04:16:30.547934Z" + "iopub.execute_input": "2024-03-05T17:48:17.581610Z", + "iopub.status.busy": "2024-03-05T17:48:17.581251Z", + "iopub.status.idle": "2024-03-05T17:48:21.861401Z", + "shell.execute_reply": "2024-03-05T17:48:21.860860Z" } }, "outputs": [ @@ -510,10 +510,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:30.551323Z", - "iopub.status.busy": "2024-02-27T04:16:30.550849Z", - "iopub.status.idle": "2024-02-27T04:16:30.553720Z", - "shell.execute_reply": "2024-02-27T04:16:30.553169Z" + "iopub.execute_input": "2024-03-05T17:48:21.864411Z", + "iopub.status.busy": "2024-03-05T17:48:21.863952Z", + "iopub.status.idle": "2024-03-05T17:48:21.867754Z", + "shell.execute_reply": "2024-03-05T17:48:21.867131Z" } }, "outputs": [], @@ -535,10 +535,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:30.555772Z", - "iopub.status.busy": "2024-02-27T04:16:30.555380Z", - "iopub.status.idle": "2024-02-27T04:16:30.557923Z", - "shell.execute_reply": "2024-02-27T04:16:30.557487Z" + "iopub.execute_input": "2024-03-05T17:48:21.870196Z", + "iopub.status.busy": "2024-03-05T17:48:21.869822Z", + "iopub.status.idle": "2024-03-05T17:48:21.872712Z", + "shell.execute_reply": "2024-03-05T17:48:21.872255Z" } }, "outputs": [], @@ -553,10 +553,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:30.559936Z", - "iopub.status.busy": "2024-02-27T04:16:30.559556Z", - "iopub.status.idle": "2024-02-27T04:16:32.861702Z", - "shell.execute_reply": "2024-02-27T04:16:32.861107Z" + "iopub.execute_input": "2024-03-05T17:48:21.874737Z", + "iopub.status.busy": "2024-03-05T17:48:21.874406Z", + "iopub.status.idle": "2024-03-05T17:48:24.259147Z", + "shell.execute_reply": "2024-03-05T17:48:24.258462Z" }, "scrolled": true }, @@ -579,10 +579,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.864510Z", - "iopub.status.busy": "2024-02-27T04:16:32.863947Z", - "iopub.status.idle": "2024-02-27T04:16:32.871671Z", - "shell.execute_reply": "2024-02-27T04:16:32.871121Z" + "iopub.execute_input": "2024-03-05T17:48:24.262237Z", + "iopub.status.busy": "2024-03-05T17:48:24.261594Z", + "iopub.status.idle": "2024-03-05T17:48:24.269892Z", + "shell.execute_reply": "2024-03-05T17:48:24.269350Z" } }, "outputs": [ @@ -683,10 +683,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.873982Z", - "iopub.status.busy": "2024-02-27T04:16:32.873436Z", - "iopub.status.idle": "2024-02-27T04:16:32.877504Z", - "shell.execute_reply": "2024-02-27T04:16:32.876984Z" + "iopub.execute_input": "2024-03-05T17:48:24.271990Z", + "iopub.status.busy": "2024-03-05T17:48:24.271656Z", + "iopub.status.idle": "2024-03-05T17:48:24.275810Z", + "shell.execute_reply": "2024-03-05T17:48:24.275167Z" } }, "outputs": [], @@ -700,10 +700,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.879587Z", - "iopub.status.busy": "2024-02-27T04:16:32.879408Z", - "iopub.status.idle": "2024-02-27T04:16:32.882469Z", - "shell.execute_reply": "2024-02-27T04:16:32.881967Z" + "iopub.execute_input": "2024-03-05T17:48:24.277825Z", + "iopub.status.busy": "2024-03-05T17:48:24.277502Z", + "iopub.status.idle": "2024-03-05T17:48:24.280798Z", + "shell.execute_reply": "2024-03-05T17:48:24.280252Z" } }, "outputs": [ @@ -738,10 +738,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.884542Z", - "iopub.status.busy": "2024-02-27T04:16:32.884147Z", - "iopub.status.idle": "2024-02-27T04:16:32.887188Z", - "shell.execute_reply": "2024-02-27T04:16:32.886670Z" + "iopub.execute_input": "2024-03-05T17:48:24.282844Z", + "iopub.status.busy": "2024-03-05T17:48:24.282543Z", + "iopub.status.idle": "2024-03-05T17:48:24.285556Z", + "shell.execute_reply": "2024-03-05T17:48:24.285026Z" } }, "outputs": [], @@ -761,10 +761,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.889084Z", - "iopub.status.busy": "2024-02-27T04:16:32.888790Z", - "iopub.status.idle": "2024-02-27T04:16:32.895757Z", - "shell.execute_reply": "2024-02-27T04:16:32.895258Z" + "iopub.execute_input": "2024-03-05T17:48:24.287578Z", + "iopub.status.busy": "2024-03-05T17:48:24.287182Z", + "iopub.status.idle": "2024-03-05T17:48:24.294884Z", + "shell.execute_reply": "2024-03-05T17:48:24.294339Z" } }, "outputs": [ @@ -889,10 +889,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.897819Z", - "iopub.status.busy": "2024-02-27T04:16:32.897482Z", - "iopub.status.idle": "2024-02-27T04:16:33.119938Z", - "shell.execute_reply": "2024-02-27T04:16:33.119443Z" + "iopub.execute_input": "2024-03-05T17:48:24.297174Z", + "iopub.status.busy": "2024-03-05T17:48:24.296794Z", + "iopub.status.idle": "2024-03-05T17:48:24.521401Z", + "shell.execute_reply": "2024-03-05T17:48:24.520874Z" }, "scrolled": true }, @@ -931,10 +931,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:33.123324Z", - "iopub.status.busy": "2024-02-27T04:16:33.122406Z", - "iopub.status.idle": "2024-02-27T04:16:33.326081Z", - "shell.execute_reply": "2024-02-27T04:16:33.325571Z" + "iopub.execute_input": "2024-03-05T17:48:24.524018Z", + "iopub.status.busy": "2024-03-05T17:48:24.523639Z", + "iopub.status.idle": "2024-03-05T17:48:24.700538Z", + "shell.execute_reply": "2024-03-05T17:48:24.700015Z" }, "scrolled": true }, @@ -967,10 +967,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:33.329701Z", - "iopub.status.busy": "2024-02-27T04:16:33.328790Z", - "iopub.status.idle": "2024-02-27T04:16:33.333589Z", - "shell.execute_reply": "2024-02-27T04:16:33.333123Z" + "iopub.execute_input": "2024-03-05T17:48:24.703138Z", + "iopub.status.busy": "2024-03-05T17:48:24.702742Z", + "iopub.status.idle": "2024-03-05T17:48:24.706703Z", + "shell.execute_reply": "2024-03-05T17:48:24.706199Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 047ae0f88..f1711449b 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": "2024-02-27T04:16:36.282082Z", - "iopub.status.busy": "2024-02-27T04:16:36.281908Z", - "iopub.status.idle": "2024-02-27T04:16:38.062551Z", - "shell.execute_reply": "2024-02-27T04:16:38.061998Z" + "iopub.execute_input": "2024-03-05T17:48:28.698096Z", + "iopub.status.busy": "2024-03-05T17:48:28.697888Z", + "iopub.status.idle": "2024-03-05T17:48:30.515374Z", + "shell.execute_reply": "2024-03-05T17:48:30.514704Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-02-27 04:16:36-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-03-05 17:48:28-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,9 +94,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "143.244.50.210, 2400:52e0:1a01::984:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|143.244.50.210|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", + "169.150.249.168, 2400:52e0:1a01::953:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.249.168|:443... connected.\r\n", + "HTTP request sent, awaiting response... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -109,9 +116,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.05s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.06s \r\n", "\r\n", - "2024-02-27 04:16:36 (19.2 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-03-05 17:48:28 (16.9 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -124,16 +131,23 @@ " inflating: data/metadata \r\n", " inflating: data/test.txt \r\n", " inflating: data/train.txt \r\n", - " inflating: data/valid.txt \r\n" + " inflating: data/valid.txt " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "--2024-02-27 04:16:36-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.98.57, 52.216.213.49, 54.231.226.249, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.98.57|:443... " + "--2024-03-05 17:48:29-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.107.17, 52.216.54.57, 52.217.88.28, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.107.17|:443... " ] }, { @@ -167,7 +181,15 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 1%[ ] 279.53K 1.21MB/s " + "pred_probs.npz 0%[ ] 160.53K 782KB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 8%[> ] 1.38M 3.37MB/s " ] }, { @@ -175,7 +197,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 28%[====> ] 4.67M 10.3MB/s " + "pred_probs.npz 52%[=========> ] 8.61M 13.9MB/s " ] }, { @@ -183,9 +205,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 25.5MB/s in 0.6s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 21.2MB/s in 0.8s \r\n", "\r\n", - "2024-02-27 04:16:37 (25.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-03-05 17:48:30 (21.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -202,10 +224,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:38.064998Z", - "iopub.status.busy": "2024-02-27T04:16:38.064642Z", - "iopub.status.idle": "2024-02-27T04:16:39.102453Z", - "shell.execute_reply": "2024-02-27T04:16:39.101931Z" + "iopub.execute_input": "2024-03-05T17:48:30.518071Z", + "iopub.status.busy": "2024-03-05T17:48:30.517872Z", + "iopub.status.idle": "2024-03-05T17:48:31.647064Z", + "shell.execute_reply": "2024-03-05T17:48:31.646513Z" }, "nbsphinx": "hidden" }, @@ -216,7 +238,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -242,10 +264,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:39.104901Z", - "iopub.status.busy": "2024-02-27T04:16:39.104521Z", - "iopub.status.idle": "2024-02-27T04:16:39.107823Z", - "shell.execute_reply": "2024-02-27T04:16:39.107397Z" + "iopub.execute_input": "2024-03-05T17:48:31.649495Z", + "iopub.status.busy": "2024-03-05T17:48:31.649168Z", + "iopub.status.idle": "2024-03-05T17:48:31.652598Z", + "shell.execute_reply": "2024-03-05T17:48:31.652075Z" } }, "outputs": [], @@ -295,10 +317,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:39.109717Z", - "iopub.status.busy": "2024-02-27T04:16:39.109446Z", - "iopub.status.idle": "2024-02-27T04:16:39.112479Z", - "shell.execute_reply": "2024-02-27T04:16:39.112052Z" + "iopub.execute_input": "2024-03-05T17:48:31.654619Z", + "iopub.status.busy": "2024-03-05T17:48:31.654313Z", + "iopub.status.idle": "2024-03-05T17:48:31.657329Z", + "shell.execute_reply": "2024-03-05T17:48:31.656798Z" }, "nbsphinx": "hidden" }, @@ -316,10 +338,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:39.114415Z", - "iopub.status.busy": "2024-02-27T04:16:39.114091Z", - "iopub.status.idle": "2024-02-27T04:16:48.203347Z", - "shell.execute_reply": "2024-02-27T04:16:48.202798Z" + "iopub.execute_input": "2024-03-05T17:48:31.659406Z", + "iopub.status.busy": "2024-03-05T17:48:31.659023Z", + "iopub.status.idle": "2024-03-05T17:48:40.758067Z", + "shell.execute_reply": "2024-03-05T17:48:40.757525Z" } }, "outputs": [], @@ -393,10 +415,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:48.205929Z", - "iopub.status.busy": "2024-02-27T04:16:48.205544Z", - "iopub.status.idle": "2024-02-27T04:16:48.211248Z", - "shell.execute_reply": "2024-02-27T04:16:48.210768Z" + "iopub.execute_input": "2024-03-05T17:48:40.760490Z", + "iopub.status.busy": "2024-03-05T17:48:40.760276Z", + "iopub.status.idle": "2024-03-05T17:48:40.765876Z", + "shell.execute_reply": "2024-03-05T17:48:40.765342Z" }, "nbsphinx": "hidden" }, @@ -436,10 +458,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:48.213236Z", - "iopub.status.busy": "2024-02-27T04:16:48.212898Z", - "iopub.status.idle": "2024-02-27T04:16:48.570838Z", - "shell.execute_reply": "2024-02-27T04:16:48.570212Z" + "iopub.execute_input": "2024-03-05T17:48:40.767925Z", + "iopub.status.busy": "2024-03-05T17:48:40.767584Z", + "iopub.status.idle": "2024-03-05T17:48:41.150882Z", + "shell.execute_reply": "2024-03-05T17:48:41.150386Z" } }, "outputs": [], @@ -476,10 +498,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:48.573370Z", - "iopub.status.busy": "2024-02-27T04:16:48.573173Z", - "iopub.status.idle": "2024-02-27T04:16:48.577651Z", - "shell.execute_reply": "2024-02-27T04:16:48.577110Z" + "iopub.execute_input": "2024-03-05T17:48:41.153344Z", + "iopub.status.busy": "2024-03-05T17:48:41.152994Z", + "iopub.status.idle": "2024-03-05T17:48:41.157237Z", + "shell.execute_reply": "2024-03-05T17:48:41.156689Z" } }, "outputs": [ @@ -551,10 +573,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:48.579676Z", - "iopub.status.busy": "2024-02-27T04:16:48.579369Z", - "iopub.status.idle": "2024-02-27T04:16:50.930572Z", - "shell.execute_reply": "2024-02-27T04:16:50.929786Z" + "iopub.execute_input": "2024-03-05T17:48:41.159296Z", + "iopub.status.busy": "2024-03-05T17:48:41.159119Z", + "iopub.status.idle": "2024-03-05T17:48:43.661974Z", + "shell.execute_reply": "2024-03-05T17:48:43.660985Z" } }, "outputs": [], @@ -576,10 +598,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.934005Z", - "iopub.status.busy": "2024-02-27T04:16:50.932996Z", - "iopub.status.idle": "2024-02-27T04:16:50.937367Z", - "shell.execute_reply": "2024-02-27T04:16:50.936914Z" + "iopub.execute_input": "2024-03-05T17:48:43.665593Z", + "iopub.status.busy": "2024-03-05T17:48:43.664859Z", + "iopub.status.idle": "2024-03-05T17:48:43.669727Z", + "shell.execute_reply": "2024-03-05T17:48:43.669246Z" } }, "outputs": [ @@ -615,10 +637,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.939294Z", - "iopub.status.busy": "2024-02-27T04:16:50.938996Z", - "iopub.status.idle": "2024-02-27T04:16:50.944549Z", - "shell.execute_reply": "2024-02-27T04:16:50.944017Z" + "iopub.execute_input": "2024-03-05T17:48:43.671802Z", + "iopub.status.busy": "2024-03-05T17:48:43.671467Z", + "iopub.status.idle": "2024-03-05T17:48:43.677333Z", + "shell.execute_reply": "2024-03-05T17:48:43.676755Z" } }, "outputs": [ @@ -796,10 +818,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.946645Z", - "iopub.status.busy": "2024-02-27T04:16:50.946254Z", - "iopub.status.idle": "2024-02-27T04:16:50.971737Z", - "shell.execute_reply": "2024-02-27T04:16:50.971206Z" + "iopub.execute_input": "2024-03-05T17:48:43.679642Z", + "iopub.status.busy": "2024-03-05T17:48:43.679214Z", + "iopub.status.idle": "2024-03-05T17:48:43.707101Z", + "shell.execute_reply": "2024-03-05T17:48:43.706480Z" } }, "outputs": [ @@ -901,10 +923,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.973819Z", - "iopub.status.busy": "2024-02-27T04:16:50.973397Z", - "iopub.status.idle": "2024-02-27T04:16:50.977629Z", - "shell.execute_reply": "2024-02-27T04:16:50.977093Z" + "iopub.execute_input": "2024-03-05T17:48:43.709407Z", + "iopub.status.busy": "2024-03-05T17:48:43.709012Z", + "iopub.status.idle": "2024-03-05T17:48:43.714442Z", + "shell.execute_reply": "2024-03-05T17:48:43.713895Z" } }, "outputs": [ @@ -978,10 +1000,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.979555Z", - "iopub.status.busy": "2024-02-27T04:16:50.979258Z", - "iopub.status.idle": "2024-02-27T04:16:52.383789Z", - "shell.execute_reply": "2024-02-27T04:16:52.383184Z" + "iopub.execute_input": "2024-03-05T17:48:43.716670Z", + "iopub.status.busy": "2024-03-05T17:48:43.716289Z", + "iopub.status.idle": "2024-03-05T17:48:45.220835Z", + "shell.execute_reply": "2024-03-05T17:48:45.220199Z" } }, "outputs": [ @@ -1153,10 +1175,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:52.386055Z", - "iopub.status.busy": "2024-02-27T04:16:52.385862Z", - "iopub.status.idle": "2024-02-27T04:16:52.389934Z", - "shell.execute_reply": "2024-02-27T04:16:52.389502Z" + "iopub.execute_input": "2024-03-05T17:48:45.223167Z", + "iopub.status.busy": "2024-03-05T17:48:45.222742Z", + "iopub.status.idle": "2024-03-05T17:48:45.226964Z", + "shell.execute_reply": "2024-03-05T17:48:45.226451Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/audio.doctree b/master/.doctrees/tutorials/audio.doctree index 38a93600601d4ea338a042f05bc434604bc03b65..cafeb627895606177ce2e990adea18cb0a399b85 100644 GIT binary patch delta 8933 zcmeHLOQ>CE6?XURP|^%q@uqcR&NkYp^d|Wq>wi(CB08uxB%u>S8|uIQ2Q5NOabks% zUK|vwg9yTgf~W(PMo=gAOw<;N+KEOGq$LA&WI#|vaNx*qpS^E;Z;-Rc!nts{m%}+{ z?e+K`Yh8b1=NoVA{Pf0A>8@M}S8V#qS&^Jc&J`g&%UZqAmYt*+wDG}^wpMFpG=25f z(HCzWZSZ^7>ekW4zaDB9tD9zV;>G5#{ojASc(h}uqF}|f6ybt)r79w4qOnPPBH{4BRgI5$ z{ZjkHkc|l5r7Tj_B#n+vX=4RG$ANTRee>Uo*P9bQXOmS7t~#o^1gnZN$z)@-HM-w> zxqbhupKpKAbTg4n|N8G10;y~a%0ns_PZ*TQbCyozNQ4qgZ>9izVOMjU%Gtq`R8}LlPY>+N)VQb0hE3ENnMJ0kC0FMnDQ#0h1GHGlm)dVP*W~8z_>+kE=!Hv9ojd=jPe1n5+)N&R z_guM7Uf%8h(6sk=b1C&NJky>UzPlJFvkzLTBuG1nSy1Q{C6l5xdDVQUy^66EqG(Pv zGFs_`7YTq?q^d!?bm#%a_l+Q1YNdns&e`Y%Mm#fd7D}>b7dhs!`9Jwt`&QG<#E$)g zw-=9hG#7jgwBOub+}ru=icDxqih_I*J}Q?j3x&xAjKb7NDV!&rb-^r?V^D&`JMq{V zA4)m(7`g{m-LWG-Y?i=NvdBZaT1g2T3rZ6mixdibwTk~({IZ!1JkIBA-1U6E_RqzG zT`f_^8YO}6DAeLnc&U>Uc%JNBn#hkc67x!70MWQAkwRuyLNy>9)&pI<)+~j}syGiu zF_j`d;;a(*`jn+2@24E*LXJ7iL|k))=zsg4#pzvTrfO&N-<;##FxEI!~eX`F`;@)Nh)5ag4P69S=gd<76nAs${U`?y@3bF15meM5tfCxjgeVwO0KUj-Fr7lKo;}un zv0W17=(8;FFbUdSVwE74Qev`DU>+avySLjL&C+;73Cf+c8)Qv#LRlbk7E_0f%eR<)(2G0g^adaAqOHB(MOk&4jGTcR4OZ|Fp|Q@(tfLqRF)n9w2{m2Ig zO$ULH76ObB2+oBIIG$zErOFceNF&`m*`8pdvV`XTZK+)Pi zF1@W_Rgt2jNKO@)6)hcv6R-GM4@%qS@D%$~r`yM2Yc|HtqlbL+bbIfz0_q9ox%j~g zO1P>76_c#pAP>2R4(&|#3u70f)TBy=4`cE|4h=OT;6=k@Mx*^IP&7Ex!CS(jOA{5o z9tIQ^-RU6q?bl3jH4f@b+}vNc?S-XDKDdy4Mvg0q>D&S>YS{{ zz94<^#KKfvMKVD|c87G@q9g=e^8lEjfql@)NCi=H9&N{~e_Y&vm)!gSQ!=x$*IEso zySxAWBkid(pz{M_m%Z2QGSFWWY`lzUUJPhQkZSld84=Vvm{ehl*bzsH##@L%)Es@l z2u?~j*QLWynwio&l+JFhTipjSNnhS5MhvUB96=Gt7J4nuHeUhlA`ai($;1Rq$=WHli0$@^}s z-n0IB`tHl$diuSe+a6Q4$CT|cWjgR3M(OsL@;@#)gYxy;W6JiJa_G!;hhlAyDVt6? t+hfZ1n6f>lY>z41V~W}yQw|=Xw#St1F=cy9`TselJh6KF{v)e<{tNIw)CK?m delta 8963 zcmeHM%d4GN75C(Pv8Fc|ikH+$bG}?`NX=`nwck!E5(kBrv^cRyEc;2Z(l*qAgBDvl z=)eprSiX_gkqIJFC!UE|q4*dXhou=694LrVgpMls`|f%4+)KFIKi~ucfxGjqz4lta z$6EL1d%JJGxBJd7PSAIrQ{~0{z3*O;(K;tf7O@B$z0xrxnS^Fixfm5(4v~$DuiriK z#@!PW{5>|mdt&;Z3zOsXJ9>KNjmbZTfBkm)SnsIF;sTfIt1_{ARz?=jMiie+5%bsX zOrP)i{Q zzA%61bKU!sX5`^lf0$lt1|D9!*?nmcFLyh`YbcH6iN=g?jbQ6#2d3Xdhm4X?e?x;9Il=|e!6dD z6vj7sqz^u7A$&zo0k_F|>tymOlt9eMtU5I5P+T@iiCXbIGKdJ=G`UDO4d+1P{e3eUgBZ zCR9mVlhgdh4jOf4ob}4p8bMDrdoNJqic19# zE^t)PxRD9KcHi*VTiv-!PG8nfdQxZ z$c&zIDHPC;&<>}Q;N+HIYZQqO!l%`thfX(MZV9#rXGf;PdVw_!AxtT71dA$@a*U)? zGE5qLD7dcFAX$~Yl*&e-v^L&WTQ%rccyLb?Bi7!Y%#xxalB>`%Rke&BUry36qnoo! zn^0>79&X>?U7MAo;|{Oh>>l3NwhNL4T^9SisYM3Y;|5+7Ii`e_G1Mp<=X0^~$m3B#?)tPyw7s;#ga zt@@SS?);emu}!%IXs{yOBeOw5j+01K@tYYPx+GD|-M)qQc^ zf4jRi>1E~+9XJe#QP8{K9PB`l9u@OMb%i|Sb9Cq>^ctc4Mm`Ijd#F2q!Rig2qny39 zcw~Y(gGBhuqq0}ZvT&GRJ2nCjtt3M7cnns>DxWKuBNi7`WeO{mYZdDtlyakA51wv4 z+)CwUN9PmArv36mK}t9#Iob@E7mFS&s22=b(mu@3-X3#?WdVS)W!N$mw09wn)=D^b z9<>?3^}F*I{x<#LWaf3vXi-g6G~Un$uVt3X8Cw{iT^wE5$|ECZw!_&EAaae^ye=z5 z?oxNE7ry99(IFwJjKB>>z(o#FUR~l!v}=T$DprC_AlylTY!#u@)k!NW+l9*YK@uc8 z3lkIg)q#_Bz_TjD+2P&73UsiQt-uS^wc@VRfz+9<9|48#s;**)P~b)|x@HT+04T77 z15|8x{^rZwx4K^2;2BmCy=RnxJUUT=WDTB%8>W?07Se!VoGFBq*+3l0nj(X$!bn)o zjg%U}<07S2+>Mm>HSf-&-MK49U*-+XbI{%ZJYG$ zFp?FD(0U1}TFwrEP(TkzR`i}`h&=SKU^ozxG)B0k#kA4dq^zpwVg)G;1lW>Ime`Wh zklIg&x<+6g-u|R}s&55e*LG)k^uOKbXYiF#6;1@5;-q0ww8_PLpQ*rN&YwEA?=WS= z&oNOzBvOPUb`eKX7FA$qkPGb1WU6i)H!#gcl{k%#%of4`tzvR==`d>}yVBi{zsefdXC?$O@>>q912)`9c_9kmpD?(P757xgs5@iw;YV znE_NNS`wbzXiQ>saH$2x#*W*7O~ke0E)jQh*h4P!!WFt~Hw~syc*X27hr3Y=@5KcEqV}|t(RMat(DSP-*dyy zP9N6~y!FfZXZHU*d*AgRKl{Kpx9gSddS$y_x%U?EAR4#pl>>X7wPtMBE8F$Ty>?)S zq;I=kxyN2-yIwgtY}YH>^~!d=vR$tn+U;!DEBDw=ttYT-v2)X_Y1NB diff --git a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree index cecad7e6658a357698ab99e4acb3e5e8be9662f7..1356ec9d083a89648c172372f1bc2844cf50312f 100644 GIT binary patch delta 1925 zcmeH{y=#>*6vlZ&5v(0cy9j+LPFie|bMiH~*+KAgtRUs&YrKM^i>Q--KnXbL=BnE( z4h1KNF5>MZRVm`uL2$T>pt;zhf)LybS8M8lrjl18%s`QUyI2E ziJ6A82kc3=A}Cr%N=MKft*-Zi2f_d@M6O-wcGzOs|IC)k<8N%UJUwK0rpHNhin9Bf zUG1SUMWHXu^-(JmA}Jz3gbXgLn1l3`DVdbilxQ^92NksPG9U+uhGZ4o>41dNf=0xl zf5UdVKJx&YB)3)xL`S3~H&$vvT*fMUsb$NEao(uBYhiV(CkFi=l zuhK6LufDTK-M!W`t*-T}m#%MaUAcB~ZUOdFm%kkwIGk delta 1921 zcmeHHJ8M=!5aljGFs2N~LcEuNjRv!i*~b)irVxCV5hc#P3KbQx7q#*Sgar%jY-4xD zB4U*yjhIvtl_2R-2o_%>=zhjRZ7%ldcHj(i&dfQxxjePGJoWP3L2=_Iq`UF{t$F3G zb1Fsd6Sp2gdkQLWEd|76fLtUm4fw~+WZz6Ss~eqC=nOX?lTv`z5E2KILFZ^ukSd=) zHjf5T{libq#)l4#_GpN!3<;2GyCwsHv>|P|<^9)9c6*}B)+ZE|CxQZr4z<^jC*^`M zh>-ge+w57>Rw`gH$lx(T#rT*pOB*W3#GNV4Dw{4_U)bUD^c!0&&v)6qaXA{KQP$tG z3xg~Q?J(@JLsZd_bDBU2jXYSdq$Z!VQC^HC-I-{mw#j6!c#?o9JSQD%Pcl9#jM#T; zY_;ilw3;ZAh}MDD(t{7$#;RRLmz*sXHq)QpWiOh+!G({Q7@9lQFkh~$vxUxyEP=e> z8U=7yUshT24!EW)c-*^9iz!lwF{@lnU@9r4jj*9!)hS1zZ`atHrWNR7s;&|`BoM~9 z+QREmypge%rJ|1f)-g19tb=z0V_{Fo%81P!K*UqhmhBOnX_W{$t0aB25qu^xoabEq zo^5bK|FTHGvc;}Wv_n3xtDo*)e`imc2SaC?o$F@LURqo_fAP%1xupS{(%F?ksR0DTb@YybcN delta 72 zcmX^AjpO_`jty5h4YRUMQVUW_a`cUp%q=X8Q&S9*QVqF?ksR0Jqx~SpWb4 diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree index 605c5efeb517abd0eb44a6ef97bbd7707c302add..b357bcda324f6e0d44ed5b1fbfe4e5c9e22bd3d7 100644 GIT binary patch delta 62 zcmZpbY?a)Q$Y^MumuH!0mRzN8kZf+6Y;K-vU}%wKn3iT?U~HCTZfKHdX>4j?X=-9@ RW@c<`WNK`(c@pDiE&ww75o!Pc delta 62 zcmZpbY?a)Q$Y_|AZIW7$Qj()@oMdicVVs&`kd$g*o@8lanv|GqoNQofWN4C-YG#sX Rk&>8XWM-1Ic@pDiE&yI#6ITEL diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree index 804b76a3250ebdb5e8a515400952f88bbed515d7..ac101b3bb55a3fffe7eb8d311c802b58bae6e6ec 100644 GIT binary patch delta 68 zcmdnf%f6?VeZxLZL-V{m%RIB>Dt&`wbJJvV^Hc*vizLIeGz$Y`vm|pvlSE5nQwvK| Y6Js+oV`C#zW0U6hoZH`XGM?Q70QCG7qyPW_ delta 68 zcmdnf%f6?VeZxLZ!>nwR)Pj_f9DU;?a|;XO)D(lHR0H!QOB2(i#AM@S15+bIlay35 YlSGS@#3Um#lceVNoZH`XGM?Q704O0BkpKVy diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree index a818b3ac610f9ad0247eea1debeddf0bdba413a1..8b5ae160cb73719ebb73f2cd0fd03021b41d0a4e 100644 GIT binary patch delta 13524 zcmeHNTZ~;*8O}L-q)IEcIxQ92IlC=XgLAggBM1iudr4|Qhi5e9fV1Mf~e7YL4uG54=T#-(WK}b4@TMt6OATX6QeIizkT+ZI#c6WFZy6|CQaIzSzZ5H z>%V;8|NZBo?74m4;_yBDwl%+eckiv6-nOHD+un5N zo%@7TYgdGYVPg>%|deq!H2 zx97G4dp_$Jm3}&=5%0q(SG&6*!Sp7x9(i1 z71z;m<~1Qf=;R%bNk*AXbc8w{hOd3=^W{Z%A6b9Y-*<9VZ5Y)-(ZEBZA_;0OwI&;x zjBrVFH{PzesNL>@hhAG%6KhC_BpQ#PHOU(yJ&RGLNCj0nkGCr>YPUQ7D7K@DWXiR) z%2=U@ax#%bBjrTeY_%S5S6X!R?jze@UrX&=N;y%Dt9cr2ay&6_jgTBB(s--lqV{B; zd-AWVYEM+kSu&oy7bX%zxk%AjM|IXo$MJT>MeTN1jIQg?3A2j~kH2*8@cAEIwDiq+ z!>vDlzAw!1_Mg0cc>Lv6!#DrrhVI)Rc1vD)WaIGC=XQ19ob{D+j(+L66a6|6VO?Zd zc$I}E%2FMLAdC|p@x#sqrA7D97e@4j-o_UP0$)-%F1W+LLiQp%go;oa4$-tZ{EKRS z^rw^kkIvQ6IqL%rt=olB%th;SP=+fbGGTG_^e3ywdIQ54aZV@2i4`GGBWNZX#gDZc zu;%IOs`=&zC#nsjUw@(hQm-zYKsPUZv42(3cyo7E?QU+ow|{0GnaMUAC$b`fYUQE~ zcq%R#3%I8lxy{w$T%BFUp^{)BJKUbwltTzgSuHf8GLA0!X7w{%M+lyimkg0FNHRtz zXHpX0n5?ZG;emeEE81*mRgt2}_7bGIX4oIpibjNPR#v(wtWiE|QyABiC6r57N2yqvPAE z$9qMy&0$y3LBVKy4b|NAX#c!jbeGzH>F$F!ZoTWS9raeJG{GN6B|!o-Il*b%`rMJ@L|d&mCof5N(O~y5>FwcRC`nmmw-}jm~uf`Oo=t?F09r!@99@-Mi(EczKV=19AKPrgX#8j zbNHG5SqMl0YNJtZ(WX&WIjtLvAuolrPeG=MoPCS6DEtMNb zf6CQ&dIK!cfW@FVGa|-dxtCesUOw?m^=QkJZP6&%?XFNVdvX(ea_FgQ=T2_PvShO& z<|wt)j0Yhp^T67qHBM?Ajc#<9&fPXwBnZoMt>B`(GJ8sKxpH;k#b}#r^AA@W28C<2 z9zB1YYJ*G&V<`PrG71cLUIQs}k|q(vm0bpH1D+i*5DCrMV?z`Q%s@#W0t0Zie}OBYhIQ#yFs_@g9|Nit?9vX^-t`Jljl&*=0aO7kNG3K@ zsS%!FBgl0VM_XR3Zkrp}G1j>lEn?ItYXMrs5K1FKu%-^wYOh@OGI$e}ZQ2M+U!s*7HoE?Mqcf28f3*YV@?tS3IS&8NeaMH7QAYkp)oh;*v7!K z?7g=r8$qB32%CZDhHGPlRzY`5yV3!|87;@rQviv{7BHA2ml{<>Wox=@E81R%+Ey2= ziVoT?I?+FGC$-CxjoD;aQf;{bvnJdMLs}|^VRXr7;hlx)^m&9D_gL>x$vG<% zhZW*+F5xBNw5JfN}*iW6HP* zgO&<(LogSzlED7p5N`~iMx|_}1vzQe&=G)?MT%w~$S4qyh-A<$4fxZ{k72KC)vkKH z|8j3oFy7q%mg-uN;pw(JOxrPEy|G3k%e}(6fR=*zwyzB`8m2|2)HyP_jq@P~n;{LL zkJE%&gsDRi+r-f2YuhzB3pB+*f`Kjps6Yeg1(s0Hi1#ef(+T9DV7xi}0Pvt>y2G|# zoi#V84G`Jk=*Ti9ESSAj4g?=9XXYGLp#$gzg|OfOzm-gAfnrIR0ZEIX4>*4VRyIFL zINXv00zK6P?GjfGKp1u4u0=W{x}{yhm27v#13Jxg+t2jJO!AC|Y_HDWrFKcREcFt& zJ2V>fm*|kahJLA0S_7I4u%YahRmY+jbBa_V0jGQIG)`FvSh_L+3lG@#Z*}wStE=^O z;cmG@yS1yUGX{nVC@>uKqU{#EgD97Y0cnZ`KAiM#3yLuX@xv?Ns6cH3C`j*v6u>;1 zzI-_v?yh?yaBd(OTAx9mk3@Fb3ubs=#wcRK=7CF#iJW=g=Y6Ede2fEd_JkPKR*iZW@y#Ifp}(anGBqf06n?NEXijCL7#1)bYI zuuM=9pw$V08N~x;hL$IAnF3|fE@PK9gBqVu&4GmNJyA|IN)K4P*BQ-$S>nmj;VoLl z6gwMaBMrC&Jg%)K4zzB}lZ%EyE!o0!i_M7#D!r<8$Zojs$;+yR-Da1d%P6LAaTaZ7 z5}c#-8*8CT!4K%Fql2P5DV8Ns@>K3YJele#puoy>DuwFG)i~GGr<*i%XAcV3>Qb-O zP6kF2&sYjpD<}-oAgd?=%q5>Ac;!+@%XySwjx8^M_zN8b6v84PiD9T&6Pa>>Na(vc zXc4g#(u>QyPZQgw5!@xc;x~aCCI~2&~rt7OUbvp@SE(r*8 zs1F&pu{Z;?J%uVk=_!HFLLf@gAxy16r{^R?a}y{>FBnlXM&CFS0^a~6j6yJHU~f=i zK;5DOHO<*E2`MEl^>YQAow`@B*qm_H2L~MQI~M9GR1?{fWd@=NjTObfB)J?7lu1xb zyx@RGArf)as1S0tkuZzWH_0`PA44~W_>$UX0xf_9!x1Q*nE~mS?l9eO$CK-l&E}5F zD&3sBA226&iDf9qodbIe?T9WSdV(RKsTRcykR4GNyDS?N1ojbHg|GDv6}+-CVWtOU zJWa_gz1AsmS(k3qCD@KQrU8CXsrXA^;T z09R08m(w-3~`JaRS>K#t8O0{=FSm`w!6(p5n_pG^e1Pdk-V`PoDO zA3>Quj+jjZW)p#>X~%3LFq;U>CIYjGz<-`b%q9Y}iNLbY<^SJJ1V)$${C4#}0T#Lb Aga7~l delta 13344 zcmeHNTg+Tl8O_WYsa%8>+ESqX=TK@PQ2y@wMKlVcK>>lt10Y!TwP3N7LZXR5ti%UE zjLm@@Ob{hVj7qAc;HV%M@eKp7JZNa&Fc5(!P1MA7X6A4IA@Xk@jK0iE(`k10xA*?O z^{w@-(+3W%dEn5RU;OG=`lVeW?TL5XvYmTv3{R2xM6|a;dKY*gl2H+p63#^DnG*is zpO1a|+UDbsOn|<-D=96)uc`9saejnF1-;ZZEE}quB7C-$Tue~O} z(%iOlLv!`cO(%t$-0q#}`W<_2-hKAA`s{Yo-+E^@f7*Fga{vqE&f)6dc87G!^*ip^ z>vrFs)86JEJBQ81H*e@)e`j;mtDBljZ@TusvRO;F_J_^OH{ZQ}XS(B#n|JTrar+*( zH|+>+ZycX&|5LqFPwxIUIr5PczxdDI!l3(iyv?c2>Rp58FT2*aXI|dhaLRb~`20tY zzk1ikW^l{8=A*mh|0dGG9k*;-sJ${=hCp*nUIc5s*U@W94Rb6z!kX8A^v&|3eUGm@ z=R5po^+@@;xpcYX^Djm~-AV!i-WMmpq9v*siI%94vFAHRlhW?+u+JI$Jba)Q|0I zJViy4=G0Ons2kmO-Qc&qQpk4y{S_@n?5;rCE1UkXwk$Yh0z=#^h=_(qnH9;!-OWr8Lyc`ew8wm zq`Y*GmE*RLZp1xSjw(!d57mD0;r^CIv&eQ`wEgxQHs5*YwtBM_W}9!= z)~dc&2&s+K&_ zzoHoS8(nd}@~|#(#XBa%*mu}d*%V8=bd-^UlU9nUq0r z9gNaw8p_*DZSZmQiN`8nOqv{+Q9+|>1&h`bnZN;xp=Sh6N7(Q=l<>-7X-jd!Qp9%u z)BQ__GArGk6#zzCml@bby98tN8hS$9Sj9n%vzPAAx1b&ZPY9&7r7%+fndY7;^*gG5elPzu|U3g{|Cr4w|${9OO+^9AF}&_!(I z+6$2gibg2tMMyTd22kRlatb7ThFExR*KpF@Rg3jRt_1vOKfjH_9`e$L!c!ky(gLo ziej~nXKfWYhh!p`=Q2t#UCNm;vZsvF zc)>_k3Hwj_e_e>1#u%$hA-ju^mb}*ASbOOe3Cwcb1YL@F7O8a(Vpe3H6PvdDWEqGq z;~f>C26B>-0Y=m$pB;MH2rVBifGJEppmC+4NK%p>=mNb%!72xBr>D0l1)X$?Ld^E? z{grMnINaZ|joD=|gOKsO>l(o%5jfh0&uHl`p!;RQmZfG`(7s&*SvNYgad2<%u0ncd zVNx^xIbl4cdCJ0P>Wm^>Yn|{GMzRq|3L1-o69Tjq zL<^Ky3f7N*vmh~uNam+PQDzR&uS^G1VYTyuThI#_MP%YpoD-o~h$-3=-tB+B7;}QM z1Ml`vs=GeUoh2Yl2-IXO)q-=wLANw8KxZm<2`9lbDPl*!Sm#4Pqcu^2zj4AT!cZ2d z*PJ2SL^q&g_{bsil#?)^foW})VoqukQhue7t^0UI+Z*4iHrGON!V-E>H`8HgvQ~pO zB?PPukcCSC)0TjLrBF^AAO4Lj!8>#jN`Hw)h#>55vZVcrlis=wCEKGte zRQ}r0>l+96_3kQUW)?1`Vw_!#(R`&Bvq!)+EL^J-CLf+Kn59|T%zQ|`D@)<<3SLsr z1UC>(OALlQMs!lJKD3p`pxh1f3R%PC%vqQ`z(gmiL?lXAqb$41?1%&-wxX1227M-{ z04oJgJ6Snkx3#|MvKDC!lqNpkS4|%h?*FNL}AoQS}9x{yz5{VELV#b)RvjH zG$t-2W@048VGfNvF>U-r;ZI;z3O>0_AvSGqy98YJ=Ba}%i+oYoWnjY_qiuK@nDl~J z0|$plhfyE>+SoX{aTAnq(N8&|7r|tOEX~5FRG71?3-2b5fUPcE%XR4;>&0Uhcm#f z4O|2K1IBK}bAbQwcK@y3c`L_F-Oa!3zPkA%HM8w%Rb5yNKPI+wI&*0}Y!fJ7I5%J( zkp)Z)lfA+C*cBUB%6AOCXD~Fvplo4oV42U-B={hK!l;2Pw%SjrO&SIj=VE8jaS6$l zFsX45o(4?kM?VI)|eaS$gI3GSmnDwF0>iaCK>DP(sE zwW`a+b|mk>CDp=qxhQ64wG5{XVgr^vhIb_li6mjt0%=1;`P?ZFG{%DOIDy?nKo(Lo z3#3ws&aS3~w>t_uQn=Pp*bPIZkU}oOk%i_WXzd{|p5O{_AWKdjU?D}Rq5mxdlqio@ z>KKCUG#Uz8EVDuuvWipzp}Dg{Bcl)u9q3RCn2$tTLu$7_KU}RJJ@<$HOTAL~_G{Nv zR}Et!{MZsX1Y$ub>HrhU6ZC zGAfUfqAR|}=P#<|DiZ2fQcE0xr6V*B0e32caWPcT zltV|yQyc(yUjzBQ`W4_Ch0*df}|Zi@rm{$y`;UD04) z8gu5Z{@m4nzi$b1SN}ad#&MxvM{S_22)))2!T_yZZ0*QDN@t W&t3i54=%I7`G30lBe?o+9RDxw1;0a<;$aWCUU+AZFhFmXpQjH2_KT8khh8 delta 80 zcmdn_N_gih;SGB@4YRUMQVUW_a`cUp%q=X8Q&S9*QVqUMsA?1p>G;!FN>?S%Ms{0fBVh6^j=)Si_KQ|;Yu_*07)cOJak zjlURi9(qq%S}kh`T472$1?@1}2s#irlH&%6^+P=ZLM9(ds)?tSBAV)@#H3YD=;gp= z&khgY6-S1LKZ$X6wbfTpn2iH#t;AqVfUKROlB5il$G!c~c>k=p*ERPse1BVId~i){ zb-kwOv;hh-ODt4E$_5D(IBT?tZs4*P#{Rsx-ZeW9up#7Pqwl~-U|8J}v6qRvMHd27 z4|(c|QGn$UTGY}RnvfX2hq#5AiqYswvCuKE1 zk9(1{I?s?m-)cOA@;2)ToeU=(zVpG#`)f-VFD~~>(bQsDT*dGKK}|kTtxBdCD@#S` z30X^uvn@Y2-uzU2)_vSMs3mTD>W#O~oL+tPwb#$I7`nf@HM*s><>B*nainkInoZ>R z@$C_)pfMM$7!4sGw3p08$15nw6&f#*ICp5#=E7cNqA?DQtzOr}4rbX{bas-qh4e4= z9gSqPmJmzGh|Xu9LsVd+@(@%Re*0EDKhxe1-O+=$Fno7gtTuPBE4)RWbL75IO9{+C z9b+*?$y!ZeH{C|`-l5)Cp9mNS6B%o+Ov6}h#6-5#5OHEF-;JS4&2p^lI}p6*8DwRG zEKd58rhdDTMj6f*b?{nNUQUNr_p#vedqzuo-}@s z`hu~FjoE@V>?Fx2*-*I`n12gHM(8(wQy~# zZ@BThaKj%r#GB3NndUH$zSA_EjoJZh10-x~3vOm=R&j=hj0`EWl EH)pPRx&QzG delta 3895 zcmeHJJ%}Au6lN}4xCF6KW2JAhs1R7P_uPB#Um6=N0>;%+WeMm0fo4ephNO^SHdzEo zVG-ErMN$~hK-4LO1q<60siIhHDhrX2E{YJ(?0dvrkeyn1uX%6g-8&7B{cpA&Oe13gJN9VmRS;it=3}_-HoupBKxmYBhDJqNc`lDx#+mWFdS>{X|TVlI;$zyamA;dvIS=qqqNF{S4q(a+md@i2vSAP;;HKVwl+z@gU zwqqi)Tiq7adR2T$DV6{R6VyoLGFU!4l@vrhBN8HN3QVL}iz-#p)#|E4(Ip~gGbPe` z#N-oePf@_YGzh#&0|uXcpxQCt7y9;sxY3LvPpOOn4^-Ym&_R1V7W@b2S3X)>x^!u| zU6LdTvSW8uHV1c9X0z)K(h73L8M84r4kfcrKNX)fpB#KnJ}xoriMQW9b#nFfH{Luo zCeZz>$6B|vw%mPjMjUR(cnuwS`IX%kcw>!}#-?D2{iI{@#yAr*IG#{R4J|mkFsT&0 z)2uurkub$5tI@X98nZ9PbpPc(kk!giGO!7Fiwz}dufWZYCpD4CZ@l zw1_9(o9LjtXMZTq(ERb)Yn}q=Y;e(}{_;SoHQquoGFvJYy@_60rKEyV2t5yqnPOuq z+zi7vfrpNDF{ca>3h@MWkZG@CM}T2aEKEjlpLp+h_u74Nqzz=bb0o8K+(;gMu@tUB z1yw9x%9%`+;W#NlDys>#jTE8`TFPj1;pm|m9fjbLy&#RV+z{}dEgN_$I+e1qaW9=Q zy1QeKKDqk9qUP4meU2gdrEb zal_SJ{Qsh)By<>4#{_?qrwGDWK^S`0k^_P-qmqi;Ax=GL%wlipS^5*cV3<-GsfRu zL+ZW72afc%|E^zb%FltrPXVWIWdF`3-p;35$&c}`j;ed0s_0r1@irgXmt5gZ8sAJ; zN4%Y_`+Lt!iF>CSP2{KEjDtF}xc)JRE8P{CQZI4Tf6;FO<&RI)HRW?l%pNtK zw|dsGNpa_o#C>?G^KOK0hHoE^NBb9Y)(?5ZdwagTto$ zO1}(O`o|CfOumnm9syLwdgd8TbVkfidh}p=bc|DeN-SJh&n@k=trh_b*K0Bg&DLJD?8bfr_2g9k@_)z^>m_Cu8EZ@KvLIIj;Kkdtf`VLE0Q70 zhTzJMAPIU(QFL$N#P60J#5G_ODDwJ*7+d|unAiSMsIv5)82CIoEBtGKWqrtGx;n=7 zmPvmrIn5L2pP9vZ7sr{t#@7BJ-2>V#m=d4MX8$6#MOrwTkZNzCJ2AePH+gq0x)Z{( z&>iky)=K)vE(n?fV1ebJpJ9e+7<@Lvw$L;71IQ_mof|D)*C%eK69j^h>$GXbfexAyv*lkU& zD}nh6@i#m|B{I!37Y+9=d#8VVR|vQHQEHIWbIot^X78@6ucGrSJz@E=^xVo|DeH%l z3N-m2^^A;&|0#U>zsJ%8$hEnr-{s9)&b+3j5sGgvN?bp(!nd7wrf;#~0@}Po?>dHc)%A=tdRy;WOTT12`}7v@1DzsynFX!>|=FOs+`)% zkb0sr;7V-?patIU2M+Mgy|RQ-PL(?U;9P0hHMJZ-Iz!B`yslkfiAaKbWT-(bII)gjoC*c zGaH(<2`^U#c8tDQL}tz(eTVCl?vU^tG$)~mGsVkd$_mhaeZH1`a5;>Gr)lr zd6q5d<0|i_ccyt?|8{(C^vK>bPhVo5>CPLQlsm#XTm8#oq%DZ~y?g#N>7N@vj8;?! z?%`Dq$^R%u1{Nb0!TjF1_Xc_!-)-7w&q&&@n)#F1JHZ?vR_Ms-2UrCx*^7*RVHm+E zXg+;#T?qw0rUg+DJ*=9$fyvc-xdBTNOwCf@1vEj{P1O`!S5%yo0q=k=C2#}f25~nq z3oCzpeEchh^{Lebe&FP4<_EH2HUz zUK1k2oI>T)Z#Xjk>NaAOlD9sBP-#S=gpwLAq2C60`cL;E=h3E`0MQl(yrITh|HU3M z(tGCliSdD9%kD4d(gQFEvBU7NDUV@4bO1w4n8$G6%(Hf)PVZ15NXKW;nqSl zHiu|%CXeN4?32mIlrkBfa_KI3$(}Gm)4fOE4Xll-!4UQJ8r~ZSW29w`k;zQUzPgw( zn$$Cpi<24b@jYRTRu+vO;GO)Zn(So7R_M|)1BcP?^bE|8!{_GaXD>`)g0#>CeWz%~ zfnm=`Y5_-x<@^xG98JjqpaDxJ7#Ye5=6QvJN!BvzRbJn7FR_Hhk=>Mw#8s1!k*TJn zYObrKOev)rl4A!psNA5y29ck5&ma=LjA4B*z~ytXyBOLm7t5NV>kHApu4kcaUceqN zl1=hD!=1j;GiQXpz?zd0PHH zM21V~6&#s;O6W~{N$50h_j_R#^Qiq8vfL8Q1_m&hx3Rh0{g@@9=W}H537eQHS{~pT z+Z;4`?eFKQXJOh2eOsb!MGXIC4w=gP_uGr>mYFgI!AaR^)s#g^FkHn= zONK4GhOWqIiF=+hgSh8;9&34heC#2H__F|-NBn90YlOG`n2Kx`a(}@Zw(VEQ_50Y< zMO>%RUfihDUOB$E^$IBYI2+vDi}QUkY3zz(NDhQ94a+5X`1xQITyam6lygaZ#=dWLhdSh|BahAO@nT$_lVxPp<+O z`$ez9ta?|$I$rBlD9@BVSuD?^dUJUm-a9AHhx88Qxi1@hP;V~J$MnX3p>PRFJ~Sv~@C17TIuNN~*1QoL+MDCT#knWPWuM|aq@p;MY9S$(HwIX81GbH$ zIf~7*rFUS!9u2UJr@|&?z+U7S8evn;(9%sDL(G`RaNk@X&$#Y{oF>)2*@vi=@!Aqb zFG#v+lw5_B>}neRNUGu3qOCh}+Q2{btj{Vlh;w>SZD73)WmqQ}z?_{Rt?ZvgZYy3} zu!M@*LWWOd6BIF=924MAr`P8AeO7J2?-{|^TF&o_fdDF6C-Ans{aT5!VEAcadybqW z9iYV=7iP{=bZUqW=lPKwoqh7$F5!7I`7e&?jsR1CTY^lduW?l8uz6mt^-oTcN9l(g z)%juDoOW^u{g$J74sXlLC3!K2CZ=WB^9o~_GL`E4MVZeAl3NHJS;zDJO@qmBBKecr z$Z&5#zj(BBWF5?aIG{d)Z~2k{IV3kE=-4oX)ME}5IvV|*L&yelLeTH88cQpy*%a9H zs#`JGUq6Tp-fs=xHP|03EShsp@th|OBiFGxgG$2Ekglx0Q<|47tMDgnqK5zD{ZUh1 zRmY2oE9-bM@tQhFK~N?2=MN`0(!1+|V&WZjLDA>>x}cUoF>1$o%0EcVulM&KNggHF z`5%uY3sAde#PKISLMv;3kChdaq}F9_8AVPdmUxLUve6H ziN;~%?*+?6dw&s`TVa2lZ;m3Py@_{Ku;yQh#4YL`RMdS_iX52j_CHUN(L^cgKVBkp z!v4wRu%a$QCTp^f28f+h)P1l*PD3|m!w>mtg6c?J$?9(n+2E>R!)1Lq8!qm{*>FLh z92@r6$U6G{J^>qU=o7HvyL~ttFp9C^_jN&iq-6e$nLp^HlG5#cxE!KnHikTw#}(}^ zs%W|XK$Kt3^;c}~la=J`46L|C-p`F-Pku9wbmnGQdLSTto9nM;GX#QM?~}2lzEF_y zC@YHj!^V^Kw6<@qD(3GQPp)DUE}a1OW&tzQ=@2$bNaQB1OwM(e%%1Ob6zIqHKAi49 z>w@n0g>>&XiL{WT{2dxLGgh4;&?EYC(jVG4NBS2hk*kQyh8#()z5(fv?#oGkC_b@E z5YoPsw>wHr7rm}8_o^${>`G?FWDV!LiSMdIPX~S%Bc8Tyd!VO=q-H%2MYjsV++YZ-KSLVJ}q=#^c#*@<(|HK`7g(m zEZsFt-AbFX<7&F?Dpp!_4O??G$qq^ZZCwxYAF3e@q~70oG(m{{yZZRo4Dmkw3SyPs z{R+zgdkXRYqHm$^`U`s$ODd@R9MUf!D>=yDJc%@V!gGE7Sq?n1bu3v%PV>b{pd%x2 zG>S)0>4%tLoVVbOe%{1sb^fN~Nz~i)e1BfO6JG8p?MaaN3~#N`=v%%3?(Is{wINy@;WX22;o7zouPt-* zN#qbhp3X0>m7(@Tzsx%)lLraemB)EV*zx{MVi58%>zEMl;Ee^CKh81V!dDaeKQ20@ zf~`hbiP&F$I>9bneZEHAT3=+@$N68JPM)M4^+AnzJcHN@8DN_Y_;gPs#toxrZ-1G| zb~Wp{={vlh>)#>uIsH4*BBQ8NADBL~KG46Z^<4iB!6(zd*7{)Mp#=WaQeu+t`MXbq zMY)zuu4FnLvUUKy6VD>yJ(i(&TYcu3i^-%$dLLGWIOndg@zop1vH|olj6{(0H{O^h z9ofL94YG60oYy;G#p$JufrE0 z*sH(4!KW>Jx0crQFVvaO%fmjY6gHjIpIfBy{Xr5n^Zl)49`SEALyI&k2AC)G&z!Q2 zyhZ4l{R5(OhK*O>M@&L5>>m*2Y~GkB3OyDb2=y^>If`7e7gqOjtSaxqs+X z=6ZUif7vg|hPDLyE?(2wj*rf#@u!eXJBrBkP!XB#!oTtX9_LYQ36GZf2RuX$E*1N6 zTCMf*Ay<=` zdT{9>P}dPTR;PSSf9@Cv4aGnM&qQ&yfic=ubcfX5l{z9&X57_#$~YLaO|wJGtSm|CVN6kyojKl4rU zHoY{Q?5wwlNSE;k(6;j}a!jr-0QCJ_-z{&GQ_#mJYY8U{(u5K@7#QdB_SCzOx9yBX zT;3iWz~$|M0XccQ;!osF@)94y2Enr zZV!jDy>H}eO2YQ~r|5?j3%1waZRFeQ8^ghDd!5Fb0z&_k zw`JDF==G7RwKS-6_2`iqQB7AwX#XZ0&1x&$ZQsJjd)RHKL-`a>h12d!$X{j`U_r-p z;K?@FUuURHXyW#@1jd`8yr0;Iw$Rm0fhZzu5Z)tW>9tKcS#R!3uc7xe1u@B;O@UqY znm8Y?#V50?6PkjUq-6dN;w9dn{;nzTz?D0(*dkw-fv+1%iu~MH7*puy-fb!j2i|Ea z3m5|~61_cteK zKF`U#F^Uq3@(Kp^4HBIbf`?l&+8BB{C1ZI9%OtI^gQn=`gdE8`SSC4-cNEyCK`q=q z^={z=i{syUWGh-YvfMsR50MS*Qwb^0uMDK-R)ro$m$u}jruLGSBB{AFGfJh)2)zzq zaH3=>oAHtsuA?PGmsRouMR#!Yg=0p=SA@fZ6Ghwd?W@9e-JrjxkF*3v?H+kcKIBv0G3(Htg~skiJ|-}BI9JC+;MH)tz}S^w|7Iw_ z>NE&&dn*^RtnB(EA9A+WaT zGwEyi-pEC?1fj|*Zl#Wz4<-+8;}K1J8;_aV+Ok~E44*|G zBlN(wz>$n?3mnPNHqPNTd@>GiYYjMD0-+udn1z}$9~NqE2%(chs63-B&)_kc5e{8Z zMVGfR(WGrSS#=C;VCS|2+e9w+>%IfQV22Rv=CkMW_UQ~PG_UebTcLew2v-mLG|dl| z`Cy2|1K~0=Y?(@?Z2`5T^rf~SYC3~G^+H?b`Lk(FjJ^w$5H+12Htv5BJt0cJ#z+KA zD|lnxAd%We-+1)L<9G(We{W&WEt(3^|Q&j^ewG(RoL0`o%~lZDYvBw0{q07A_0lpyrYUI90 z%2C?mv~xG8Y@}o8!)y|PwXNGom*o0}{RAQM&TL=i>YvaK_8?!3!lw%y9$nY& z-})oUD!8@&g`42zzG~-Q?(=qTM?P(5b|fQgrXrzr0|O7&YhYkNzHa9Rn7#6Vfq8itleuv#ok{%KZM4DPHJHZyS@+X^{@(`E z4*%i%X}v$|FQmVJ);3`J$}_0*+`TQ^HsXP7+uu-r)sn2sx<6wX>Dx=_NC}dWN>wXDN)v*%{aBl<=z6teNUFbd9_iN;P3(A=Uad)z zZ*Cy%5s@K>)Q^^-A15=2oid8hT+aqJD(>LmZ~-;7jqDN?yt{5B2@;+_?Z zr1uQ;-+GuHI&jY7nE}aW7Se3yzmjN`S^T^mlUw}C9aR61A~C~F(1#$g&rdke=Y(O( zoUhBS_|1QYjarsW*;h*cLwNMd;gOT0ryxg&if`rQ%16cY&OpB-o!&rhQ=ys#ihXsc z*gPV>GF0p!A}+DEKdDAUobZ438>CUuK{*doM+X&on61?;BCaiIeZXJ;2$e~Tzwj4y zLi}L{h7RJv@F?E)>#z+a$Y<%eK|vrqnS)`0Fd65IzeFJV%sm6*kA_2oSolQ#_~X1S zbI;@SBD^=q8jIrKCwYGm2cO3uc{iLUaJ?n?e#*j}k%8~J@>ztzK6`|R!D|L__qBRZ zE(|{GIeHb@#D}mjc;leJi+z7kKyk7I;2=n- zs^Q$)7oB?hzj}$ztt}Cit^S0U>0Y~IA`_rCfAh;oqYoXN6QJ6I2NwxYeMWwTenRN6 z9C`zTe(>Oo^%{MTP-`#`QD*-E#lcQCiNM-+{(&ya^_{j0Z#`ZV^i^A!o1HB=akRhc zC-evY&9BoN{9bR+YK-PFOCz5@h*_{-cEGdrNPpGaVEL{7hG$VyTR%8pH@V4ge~u2# z-26v+4I#f|ef&MQ`v(WSzI`y)uAA_Qhy^pP{vAD}u4HYaYIwwQ-P_oreV+sPGY3%l zc7Q5>v3N|zdWVG!>G$bC9-(mP2vdUE>MSlLF;e08dcTx>q#HKV-KzJXY46#I_(oQE&Zgh>4$98!$qSyK4TAl zRxqk6DRhW}u868X^i$MW{yk7za!|7&QqGHEFbarYsHAFt|AJ2-2fR z5a-;mhc^A|g|R-5Ku#XchddyvSkB3jk1G7yZ)t`1Ykf$hbNmo)`Jg=oS|<%DEdQSw zYF!Xll-R^Iu!-!P>9>DH7PlqT$W2@$H!_Xn$4qy8Ls!rzh6LsA$60S!?*4fQej`5r zvEjX`rnJ`d#SrdfKO3@_ldY#$_z(WQq}1IswAjh^99rOHagbp1*CnOy&x+`A@ZiAH zmaP2_|GOk|Kh=i@j`rS=N`a#-8M)d24UL>sn)TP$1TJ-IBr=SiJT&i8PXMw7S$`(8 zEfQH!L1z!;g^KH{A~s#X#s`IpX{;kr6*;bWz?HcHjr*-V1ow8Z|7PF40;hDNk>NFK zU~y;ruoJU~X69B$aN3zqzkuGDd$7{q(lauabp$VF-_I6!!qC9iCQESKUQ-z0pE&qJ z|Dh)<4o_&dn-XmquamkG?$$6&!4fUJ++{gwJ#EV`cSrt^p-=Kg`d_AryxmH@X@Io;N<)(DeNjsvd+OmTyCd^>zKc61Ghm?gJE+^eJ z=-t1m(EQ;Y(c~CSN16;%}j}^s42MK1*cpM(cke? zFd^9xIi;cvw}QEhv=58VF&2bQ!9p2cA00AEZS4Gop31D93LXz*HAZbkdTh>5e0tj%}IXu46*HB*_p| zBeV35==_Sdl%tzsTCxm7gG{TsraHEyrWCxuA{ug`_Su#wf|Ynr6RM{Pl4ZJ%A*6J? zM{Fsk;@6!CS_axz7PjAUd31WhECV~xxhF4=_DrM&Th?98G*XH#i=t=>wyvv!4Rc^9 zBF^>?&9V`?lrjxLgKp}gY$%$JFin&3Mvjwa`k65gj6F<%pahu}p>LWdYcNZ?Y`HeB z1Cnf0S6$JsyFAj6S=Ac3gCxrU7XlegmVt}X7!=cAd013T$RfLH$WaXqtC1wlV6Qzo zIEHQNuz@!+M9AzPyllQu^a1dN2a5j3b+ z+O#xSrnGGsus+5OTceHPQp!Mfxw@4B>aSiA75!DuN1FYai=%z2Qd&yTqz)iS>P^vTLc~(R9L4kK zW^)%gON}uX*)?o`_26i#txLeEoCenUMrSmE_cTRUa}C^v0LKLVGa$!~?E-5}Q(Z0a z4RGW$D-uE)^fq-^A4wL|jsXXmHg!i)(-3J%%)nSYL=pzBPr#y0Re*amQ-*0M_y=AR zlr^Y;892yo8>9DoPS1TVU=PuPoOA zRUtZf^VCVpSr(PK)GUMG>__C%GTcd*Xj0b#Din_{Ln|fc>&mT&`#N3Hz#7X`MN^gq zH)U8lGv~T%L%FaC!9)|j&U9Q?7X@4fn#SAaDNAuI7CN%)uE13u!EkkhSR+hO;XRZz zW^@ckK`6#9iDUP~$%VeI9Bl6EQe|Mneh5DEb=YUIEL^<;i)lErs9Kf^{{;Jii|S;k zl$bHk##-PKnO!tex{HfEY{!K7iUJI;?248phccOzZPU=PW?geNY{@8i#Rq|aoWjbP z+c66LTsgqpeqGc7wl3F66Vm@pgOS{4LXXYa}CPRdCM3WzCjTagrT)1I78J5m~1 z0uO@*IS{irK$Z5yM^JzD&N+~-f*oqw}Di0^ifn`g8 zndlOxzhQ0kr~zXB^46JWrw|P*lBRYjI`?t9Wax$TgaZJc6tdeNzwxim}hhT9F^)98Wrkzepjw&I4z@5Et zHXuH(qe~;4aSHWL>>9r1|MK?e)Pz_DcA$5g9*b5b99uBc%uebl>~A7EbK%(#O{og} zQCj42OA?nwres(K!8Fo%XHJ35NolHONTP0AjuS{Md;xL@Ev>;n*b2M>f@`R;BOwLG z-6ocUB=O+r03MT-gX^1fBVt*wzF1c=HerhbCf6;=R8o*8?6;=jj2(oTX;qP|!qCH% z(#Ukus)0xvQ5y1eU6=3*yD6(|l0q*nA)o=rG(~|yQZ!J+)p3cCVTg7bp1(l-%D~tB z-L(~BqHD+q5Ee&O{|7He+s7zIhpHB2bE*j{o%MmE-w`& zSGPqYG^+_kwP28tiaBUD$1qBYI*zj#SybqX}vgB0Cz_nDV%sTv~cF~~to_zi(AI8?VXQ2l4> zW2v%018r*q<;=7pB6@XjtGLYsGmUWGQWRimXO>=q(p^F`frXJ$ZCgysNcBv^L2`f~ z4QlEtC_p?=0nBc$xQn#Hqos9KV+KQZa925=v?NDJ!^eoCzgw!Phm%7n+$C6DVxh3u zRt7B0xEAPGhl0#aDbO(%sK}0;awSx`1SF-Fl1`h7VA&$_XMc4q^0L6Hw}L1C_#!cX zp%v*LCU7Xbbj2O3Si#CjWJw??c1bl`%sIBM*98&DIX1-Yn!{39*GS2d37>^qJ=8Sr zC`u_PloS*jlDOzaRApFDB)PIXCs}S2D;9Y?AVK5Fz!k5X4kw)YW;u&yLt{~fRu1Gp`6Bx4!B?5d)Kz<4{ z0i6Wr1XDB=!$hR-;_e+ACCcL6dl|>g=Y$v zQ4Tn-njrA$68OTMQSpW?kyz;3^bS!gP&>ndTSSc%z6=YrHD-*Bl;}tZc3m?aMjz!a zK+BkGBR9dLmElpb!j%zBr+9QZ!sQgM%!+u$Wc|=sEMXuYgSUbk!reItrd3;VHA})J zN+})Hjj*m@sU|$0?Lr|@ku{OjC@vQ2;?_bGPn?2cNfKoSOGRN?L);^aDQIvC0V>=V zq8>2a&eV;Ly^n;wYq+77bq!Yb11(d*dR+G;nj*49+?d!>A7TDqETQo5)efM zj<78XavlMysH2pF;29pmf|~Nkgx5!G4cDW&T1pX77=%%V@6b$Df^SaAhM3~=%=ZP# zAi#^1ZC%6m+pmhH{8<;rsuREtQLiSdu8m@^s2VK%w$iqR*cc%>FHr_=AR#K6tE#e$ zs-KKo`wR=N0zbiFU@`cVn1e<@Y{Q|su#G63$Pgh76FCANL$ZtNEYRDU_0Y*Ou!AC{ zSPpDHiiAz-&f3t0fKWPeA|_M?`s3N9i4To{6ew3UM6vh3pJG;Cm$LbPe3) zx+a$c%ycbjLJnc3QP9Vw~3Q<=DcsBoh`u1F9}Z1|$S zm}Xm}aOl{I;#n8pYfKApMA)HGAh5EAB%KKW`)MKr7h$G+q(1dw)QAlfP1HmY^=TyE z0xoSutmFzRc8sypftohkN9T3_GVsHKNY|iIbKe;i{GcELm6uf8MaHGUPU@nOmQb!w zW3LazFC618*rswtEzP#Yv9pa}4g!e6q^jTwJg8mDwD>kv(nTQxTLMtjAf`qTXB)V5 z2-Ru}4l!L+j4T5@+^H%9m+w?Dhn5uan@<8tNxF^P7%7^Cy(k0OA@W{acBB}*s@jIM zT7Y##)dU_5*(SIsDXxvE%tZy0Z^Q=yWCCssMSVd<^%Zsl>dAH{(@3zP)%dT#q2V^= zE@AtVu%CvQrAyE#IfrGDfgPz1rBLDUD<=5Ah^=}Ub_b!XW7}#P)iKn~9J|Prl)JGo zRJwAEDpsR1B3iP`U50L1pVmK7ZdpOX#^T2|85s^l3WYW39ggJ*sKljVSJNoe3A{WU zZl@wTaaj=`zYB(Zf^GXklaK=-=hRY0ni>1-h8sAkiYk1b!32WsB_N7K9;~I|L9D

AptkwpxSK9;HZoZ9h5(?CJ9x19A8MAJecH_RSCcL zgS|HaF(QkFp-i?8%ZreT?GhHAF~LrVhN=RxPg|8W)Q=Pa|AZICHM=aqKq2BGk}Lx{ z2qVR|GEnoUOM+NZM5L=5tR#sDA3s%ODh?`ADEeT#23dAy>7S9hC9q!bLSS2e7OqM~ z;OnqMCZdWh3(0_fvVHpmDpg9_K&2f&>IQQOGlK&|C_>{CXMxlLZ;iu3U4r%7k&h+I zKo!_#z?8{K!B4sI-2jB(T8EMhc#0ng8xS_SJpt3tJN1$G-M?){Lcr-N(Mv?qgrw$G+Iv{qAF5 z#Rn5hj-PZN`}zk*&bp6%bszic`iNdh*`)i}*FQRQ)_v@&``B0R_anQHeRUuEN_HRn zDmZi2eeA2~AY1pbukK@C-N(L44QU#;C9DH!mGg!{a$Qx=?{@+{2RX) zJDaS_)Ls{(5n3}W)9?D&TM_z)q5faj#vJ;3+&6ECN%6-kd-Pb*>Ua;`)*9(dSNXrV zA*RHi$BzlGm~8t8j3kpIoi7gc$NRDNj!*H6d@E|snd6>2dXc+e{-PyoItSsWcUJT~ z%e36HMh90_FJ9Bx%-TktuBO5!n;5S89^qPP6lC$Qxi*jS`ZS{Bhu|#4mZdzYa zKhHH6jdspnaMm2#T;k>it!(#uuZ!(pKM^-ouBc&GV2_?N*F3`o41KVF)VkQvOOM5! zZ7cepHUHevbLOSzFPdvEnKOT0Zq(DPx5_mapFeu;e8*jL_KK=`CVtuW`~{ff;x+qs z{s9A)cm9mBzpuyHUq+7Qpac8MsIVNXXMfq+mj}yT9xQNqu&_&)cixNjE$?LOSl+oZ z_{-2;4h-2}hTrl|hR5==XE!pCM-Rh&aEEtJ!$)T&0C!Gj-+s=%&0ybl$MNSo?AsGS zrSll}Z4S`sJeGZ9KWy509Q($8Td~tTo_%G%Gu3$l`^J8Vs`Etljr|Tx=Sl1v`=OZ5 zli4?RCw}KC>>K;BgwC1l8@q(I^HlbYUFO?)8vDj>(d#^&eLG=jX6N@~$C3X5LD&Rh delta 43531 zcmeIb36xdEwLfgt?e5#%fY1z0(?IuSo|`idDwCo{5e0`h;J_J$1{Fk{q5_$GL`^gY zJ&7}#Xfzr{)EkYnF>%63@?xA7pR@ST48Azz`Ttebx%ZxX!9Mw~_kAzx`&PS_aH*$@u73Gw&%VUSOG$6{@PGX|8Ko zQt0WJN^wlZ_Z=nL^tEqh50d;LWZ!7hUX!D>2MwwJ(UR8gy`zg~5008<>>jo3*G^~U z;!)qH(;8>wcH0e|8Sol}syBx$-Tb=mU^izI0eNgBWWv^{4{EzH_` z`l{oa_nOY0+^{5n+=eAB7gpw({Ljx5Io1$z33fLqdi_aj=)CK(gYRP_tKB7iCav3W z?uLm=>MvXqZG38K)Ue0Ec;i4asI?ZRy*NP8$4?E5);^yn|Lo||za8t z?P}c_b5A+r34dhZM(YkK za{~EVH0O|6V9Rxf3{LoWdR;aDJ}mh6#R~tDn@W7pcmqdwnuQCJtvZTs+ks+Anqh=a z;24sj$e}MAw(L7x>1GaMJp4$Chu;9?52ksT+=qX4m6YxcsSR9T+JIo-ms1S<=Mvy_ zhhX4n+1|e%OdL|UQlxK0Q~;ChPBc@J4a#INshQ|&N?3wO^=~xmiR1YI#qKA%~OA8J0s2izG zM>YtEj#&76I;$aC_u0Js6t==%4V%wCbUL968lsJ#OoYuDn%6{o^zmUg(#5QKW`1VU zRMGHu1Bf~y*F6pc?jMTjx%Lm^tv)S8x6L?=&fhmm-3Lwc(qS?ofi+*?HCFD`G4~{<44Ej(*ys(Q*rMHNMe$o(= zm+5pY(Hu=XdR{cZt&a{@4L;dNe6kP4Wc2-H$KN?dp?&jw#~#~8Q$Fgm=q5TcU*7Q2 zr1^_Q6X-P7JTt!{X{v0vAul%kX936iwHwaOKV59=r^mI^#rf#r ze38<#So0vht<&>jTQ1fE{*K94ZHugrT3kP|iZ|rBBwU}bm4puXd#V%!zT+6arfRyS z_=W;02u#lkqzZa=lA7PqHqs(0Y9!J3fJdTq$bx! z>sC&p-7B~#T#y!p9UN@BJuetaq}4`P9tJjaZK$)Wwa0Q0D+35DJ6V-K>|`532a$Q&!RYD{JJRVi4ck zxOw)9*@Qlo>ewPWqWf1~MErSS~7Y5E1NH0 z6%$ID%8=(@V-J7S7;iPmjFy_=ZwbEp+v;}O(ZqH{hDQ%?+Jm+=ZNB96IStfgjeIji zL&w%RDiMY3oZYl^=ZPKL#&NOVv7UA~cqQ}8U>3ty1Q`5~2w_N13r<`|l^ zUn0}i&5x~Z7)EbqogF`%QWx#`@#OsQ%5$xearroUZ{^wZlRk!{T9p1E+Hv_=^c4~> zJd1Wj|8~V$^wlPT`m5Z4U;H6Y$ITD)mEy+J^I zeFF8h0_xFs)b*hcR-nEwf%<0x>T8?1hNGCBGh5B>&n)&d|WXg398_AR%(S4r>U)j?U-9ER848m+IL>roK%8A(~|6}wn!Psk;t)rvc zf87Kdy>aWT{NQAUvXUcTM6JrmuSbjjU^E`q zr`AdoLd%sj%M3llOJt^7`U`oY zg!=}Aw?=bbSPp-Rp>jqttc*%}M;~r4u1v%8;}SgFUgReUfFiQMu0`19(yshLTkLk;xB>m7dh<1lOVBR&!7gb13&*cy`p51pRn%kahDS_oUe&A|aXxK&& zxRUOo_M1~ZG7Tk{?RI_jIE`Ne&!GCXIep+BQgA~A-M^BLI66<`4SnP3gUB+nTXe~G9&=1>_=Xr*!RHGC zpO2;?dZ+}Ep!$gdq+L;cZHnq|g{S54g1$DPFsPFBU;@4uoDjl^k4gJ;q()@ZOdv+K6 z`k~;4_Zc?`Zf%Ixes)m)mu!4)cdnj>M%Th!Vy58%(JI(=0-9&#A4>+76%=FRzZas# zTRQ3cX?Wf#!6UeJlK^N}-1=mNTgma5%%F>Quc)XqIDA{#<~aQPH3rdq8jUJov7~!$iFEdhyFUW4r)Y;R=zek zn*ZOUbGj@gs>}JRj+EBAy0Yi~4D>7db02E-=QGm}w?!9!-Y1!vX|L;h>J*CWHa79T z6!T5~$1&;QB?wR3Rin^T4_5FwwggmJPyI;X^P6dW3O!YeyJ>Z)S>4je@`H9HJk-&F$La(zWHoxpP$+jjIkC|>b3GL-a*Nq;gcuQQMv2c(o#{eUVZ zwIdDs_7W?wa63PpJ-Z8b-E#nES8G6uT@Q~pwvdDJv)LmH2XIym7zA8V#k2d9da^L? z8bp?p)vS3k%j^%y&rgO|81&!)oI$Mt6$T}GybVu?#C}KK7EtYqKM&oBKW`DB-XuVc zm{0YXfb?iK%_9RU8tb_-xcL*3KCZIfPQe?Bj*8-<4+?m1O2LZ|g!$6746rQC0K-}s z3L9Hnc47d-!_h-vxTYZmnyG2FrAU(Pdxq;smhSmnZ)FaG-l9rNIj&yC;GW)+;)1Cy zRb0^8Qo%PJ)RI`?hH2=89g) z+Gpl3NQPE8V08=UfD_UX9b1A(sHigpNV{Tzi+5syKL|LVP2l`&2w6b43pg)h^Ssv* zU)@T6OaD;;^vb0D)}c@u{RVPCuNQ48(!VVLeYPc)uS#fN>*n87G>4Jv2*pVr3;UzO z;EB2Jy$ABJA8iYVleSWyNC_Oy`-bf?knso37-?5wgNQUI0%Sab?8m({WTU3?#0Q$* zA3@COSN9;1^p%K5;>o{;ssquVr&GOkiWtpmOKEz@Z>?w><`AvM*-FMiR?jq*l4nlzCTbT zO5VXzDJ(m&Ht7G{?5SS+Ofo^v|*4yX6LX8boW6VmuNa>0x_z( z3gxbbi6p4%Qp#Q1CXyMYuGpT0{md=*T|S8{E%$XyCR6Cw%YElgCL2n95d&mNx$FEX z9YdmH%|r1e=vxn{K7#3 zxdcf9pd5o3%HJ6%P+l|pi^Vb1ksGJK6WEIPlgV`YCjlkeZj#9br9Q!y@0I&FTb7>V znX1preX~{4U3xBlOvRl226GZ%{6vjRWPL1+6))4swX}0EJC&E>89Mnc>v>EEkxDaj zh_v$4XBoi5?-IPd)&t(&5_mh>#P~&nIo^&MEbw;3V2-zTi=0nSV_hQAwQ{gP98&)b zaYqamDWe2)Ej5_?&stSN;GZ0T%qBaCy#hOWn;ON-_8@gRS{%&v-rpFscMdN8=M3^z zjy{f663v&Z7aha*CZAIJJnNX5A1E3t8ij0P^G}Wrd1DCXPbuVg26BT^17Bx-#iM|^ zpK}Z`8XJe~WKbj<`4}hEedY`+vTj*U5Nd{F1&(LPn#v7I<{)NJ#ts48JW<;)fx$kC ztyyt}JK|9{0QzSiuPdDnHpb2kq?BQ*ACk5zqu2yMd~K5EaJ~<^a0s(1_tBGvaKtKX zboUTutUsmaF))Nhv6A*j4k9b*_gVYQhFQFgty}@^L@9TP%>! z`j1IR;UaQ=8~qdOn3wY)A?gG9poj;U& zQN>qoCMOfR6qCS>x`=Hq99q2Q7V;LMXAdo-B2GG<{snOe{T}O>nZHgnrcmJx<*1mS z4bsokAl)laQB0v?933?@-gqJXcD!gaX^Nk|gZ#Lo3;pMA=$7zT_pA6LjiP6(P_(5Q zMa7|MhiTWexiemKC)uY~BQH%j|KmH!G?I@Wet}FIJP$akYpY`Zdy9+jA~q#M_z1!M z_c83$wH2Q$lEVoZ6R&)Mw8#72gL$T~Q8FFV7T<6Wxf_2Ai~WFjK!{7t0>5VHni4J8 zG$20a30R*)+qm^Pq)iCTL2X=U-uM++PnWSSVSSdg386W>jSI~|ZNmE0wTT0Znl+wj zfOelzS>p<^#*f4rjhAA}Rdox^#p@p+qnc6Z>IR2XWzXsEbViZOep# z-B+>4!O}?~(xq)Fl8_%VjFb*t^Uo%TfTlh;F8qp&pdScO-c7dqR+|vxX-|@wIhr5F zdAE>f_#Re#a0_`bN85&#d3Q?E@tdtg&C!XhV`knIjTPP{BwqOe(jT`U+e$wa_WU|{|0_$AHA87vmFh&wM>$BrmUnH~R<6b6x$^-eT zKNB*0{q|Mz7JZj> z2{wCcm|(N5KrNh{Cx-RNW=H4CY{u7UYv*eWY3FM+4s73v&EDKb_HUwEdzsB#2EN>$ zX0xt#!Dj93@d}#_Y)`Qn?cbhu2o@zs;B5BLcCgtJ0ZJf1p>Dfivke~-B}c#A&e`ne z?5TC_#rBWM<{Z700VOZ?k57`UGdzO_=!)6Mqgmdh?DK#i@|Z_Wc$5J=W|KtYxh$%Ui>*! z(u@b_O!mv5pSD*{x*-*FTIe($@S&ZbbiY`ccuzWHczMB>lXiB}U3~8ga!xM3?NOS) zYc>5&j*esF%7@;sSEdk$-g;%4&y&7g@n#OC3pnnMXVlYW@U0kVLzp@~czBgMZfcA_ zsHa!c6NWPZ91-8xK-B3dg{ zGAk3ZVgww~pUTu4!9B?q z9WbTh@JWrui~G~t3Egi*#h0AIkgks?p4&n%CiL(TLbFaBM0I*B8z!Nxc*!7|W1Wmn z`T5iMBk{*`>CDHr+b1`0;3JP->0jTJGEYfsw^;jHBP8^nj6oM`*bpBpv%rq<)nM_4MCRX_LiZ;Z zv&ZBC7mcjqm|Kb~rqc5XqGj|>6Yke9*o+sA6y8+L_&eiuT~woYG3bS|yeAo5QI?NY zI`2<9&zVl2kLwPlEpeX_q?uO&kyrSV^{f);k9>>q#V?Y!{KJ*`_M{(Irg~Jg7oj7+ zH;|GJ08mznzpXqYlwu3(7VtGSef4fy;uS~GJ>#?Y1ush-jI3l@{Bc0{pp!eezS(m> z8qot-kI*;!#p{lOzELne^v&cBp>KdIUdB>`tM;a9eKVY{>fj1zMMs5uD+*`k0W}II z#;~-bzP=-^aK777rEpesq!rFZBe}w<0ccE9{P}_O_yIL+a!b5)9zCE|-%LuFXWKkF zi$31L_07n~08(1tG{F`eTY3eec*%T3TEz?I)2W0$*OAgdj0BH$6#sfK-9qS_9YO=W zynyaQx3ggqYddTq-Bj-DJe1C%|IPcF-{jbWtM+94>LK*0zLnJKn;pWeOiSkf#bMMU zw6(Lc@^sN7Sh)sxoe}@*RPE$+mty4Bgsqv@$<^5MuhS<8J&1J+;vc|8HFm|+u9UuddzI8Y5h}^s8c+W!6p>I%WQ=ig_JjUqUc<3 zUTZ_rH~$;-O?pP5;=R!|1;$0(dqX?--rlTCv?q-!3%s4QbMNij=Tg6!Ue9{V>goN; zgF-!Rt~}hoSwP2pHVP>J8dF=?z5UliEB(H}xRd^_AdJn^1+JDFzejJSAFwWAVBf{= zSY7nLV_xW`rwf8LF&)=SH8?6X*XpGqqqtrgII6;xOfMCYMHo-2oFE%R(mH7h@`{!G z%EVDsI%(jjRDNYffvcn%SSIZR%P|6$BWqw`8IAK}{bG7e6+GwFz_X$Xo|CHKIWij_ zlvUQ09$q8hxpJ5A>>bs>^Jon`cU8f2M>RZG?hFrOfzxjQ3#5u4-xLsR+a&~tTvh`? zRdg`^7WU!Ll#JER zc2xY#RdnTm8r8EkKICe8EH%wK=-256J~7_Lp~#t3bKl2hiduf36uBecN!ggO~H zMyQh}l%ztPAOpuwVI9l2bIe%~1Ga^1rydC!=5(%p1FzQev(hw`jF@v#I_Mw;7LGOGd`? zi`4x8!DgaO^E>GlCcE4VUh``~E2h;_?|(m*aQy%4QnrRI^wnbaKcrmgh48Q5&YETa zYn-@CMzj+|3(b_JHIf7+r0W$%!I}AI1@%(l%H3S?=DM%L0G36pQn+zR;fiqO4xw>* zwxz}nzJzyNSc0zpK6(TlJXQoPEn}cW z=SN8=QqaGr^Twv4mcz!DqZYLDsO7Jfnf9b7Rwnr*Y3ET(-9t3Y(=%9aIcoW=@}P)X z&aXT?w2uIfMJ=Qy{?qgH!1#&B;bGh`mU|f2j};!qwPSg_^1UbM`Sd~7B^-?VurU^| z{0uW9!*%UtFH;HA$S>18zl8ZB*p|c2`GOpsy87X88s2RUk-0&U73EZynE^!S8<{`M@AA4iPi3^d?S>>%Y|?p-`{EB%;IFSY($w*H~xiZ}d$en9BT zal)It<&Q}4p2dbq%_}hp&)T05Kd=!V)%HKo{o-?;qkYPwuX_&3m<#x5v=xW_iSEOu z68`0WVgcb_)&O)6UeRIx<-flS>fbSrQ~$Pcg8H|N`-xSgG-Ee1oYs!<&mgeTDg$x4r>~o%xuT zJw)o_^>08v;b-m>kNJ<1UkG2ZJwD_=;47YyZW|VNzK*sh%Wd54{Dh6W;`P$AyzRg@ z(RS*ar8e$oPTI!&%zw$^iLW_yd|8*cjC_N~r+v+q@gfoa4~DmjulYAdiE<+R&)}p= zB79N81l-r;G?+J@Q~PV<1-17b&r(VI$9um+htNVHp7SDYYiQP0NtfdDcEG{^)^=d} zqj%xVcfLpQw(I+}y}sGjZ7tq-0U1dxOKucj|QHe3FfLTM6)ty+EQZg@3}{RjP&RC5iVkT&0K6RONNosh=Hq2svuuG#q?;`{%L z-b44FP_f?63p|M@rM`?pW&1iEyBFw-GGPMPU9iywbVSJcrn z(ETMnhpw5x(o<)!e;@ND+{04)hI0E|n{uc*7wyC1lWFcqdK2%I;{T$#Jy;KS=iX-E zh7&lrg_>1A?30d2j+**V{BSP!wSpmgmTluU8QW4d+t+Mdk765|ZAyWsNs3oo`Rlrq z>pH!_u{0%AQD&-WjwjhRt`@Tu*OoO`QSI1SQ#Uw%?TNb9;_HpM)uhw4OuSU8c}ft- znrWDh8ft1F1-|atx*NpLw&VsEpMA3Kd7@-NFnVfX^hwT{qxUEvb)d?MX~rve)Xgbm zfMmv(Y_6M*tG~oEUDCPdVj{~Alt7jO$8p&v4c84V-}D{X2(-Y|)MUF#5URRu$#}oe z*6hGWp{;^

-2`GTgBE@cwlVl7cPUK2jf{>ngY&O_!Vy7p19zE#ZzC-N0lIkI3Cm z3R!E$b2isWof%8UE9d0oeN)$Y9lIK@lK^tu=_P*3k~D5K-=u0ICFA17BTI9&(86s( zC>B;U)eRiqwt~1NS2wtL`}o|Vx=z{jLQhgOWN36obZ~-eNsjJHc(X1DIbDm3wcO81 zr(%SG9!iEOgFie;##K6s=LNb1)P$-lSO7!oino7KcLOP8K%w((7cu}a<2hsNjXwMW zH*t+yy!i6E5z|$(OPbHFa1%S0y->y*p_Xamayv%_aaG+@L-4FC*tnxl`K8XvYS*ILNh7Gv?TV>!)z z#|u0O#0~?z!|#}msphqC&iYV(c zBt%XmITIiz$y1X@5v>MX+<1jw4gA4%WoM7AH~a7l)~546x%_r1MwgkAUqd%T+;nExrsZprC0S4`O5n(jZ+cpwrZrZ^ z<$Xe92^r3ytWhnMcF%k+Rgca>VM&HNv$nQ%N8LCf!a<;Do(o$8y8{E_!-T@3Ku+1; zu$e+TakcFQz8t8U7g|QBxrXh?usXJ3<8sar3Oy!c!LuoItotTj8d2p?_EgOeeOrb! zho)siJvuJT_X8z!T*!bziP!ec9Rz=%X8>TWqzOtQs{vdJ)ACeRfswIH3m1rLnxZ?* ze8A?Wb(0DI1F*@irF!6X%eBDxhT+NvG?uBkMd+pnNI|wN8GeLf84zRxmKuQ~7{@bh z7jdHOCJdahuq;ea)|y;aY7xaT|edd1*e?H51pX zYNjc%z9JF`F)XhGNRH=Y)X=Ri5l7WAVhG zW~?g=YJA+~bp^@LyA*ZlX=N*F;%ZpO2qXossp87K5WWsxwpT*S1eL&UOwpWD*NFkL zkDYivWD}dWAg8wL>7fS$U|Y<5KwYFCfWLS0D=3t4+h znNldrKnC%26;6|GV{=xB*LMTlj)Q9%4MF6GF0VVbZe)N#xE@!BmeN$19#ac#57Z2O zPd8%uiQJH~6vB|g4jY&|2rPXOD|}GTYJg z_?p-2`xZhFRd+%slyHTDdA0)ai|IB-N7xJP#}he&<6M4An*~+d)A^tg=DSCz1aAoKjYQ8`{(2aHrb|vbJ8x& zj9c!n8(J_O=n2__i|#0}=`5@QQVlbNatpk$w6m-`KX;Nmt4C6qX0szx3>z0{ifZYBtyuV1AtjRE`5M=s>ujDeRhW{OgxMr6UxMSGhxM8s43|-bO_=w6aFpE-E9C3 zHs(%faYa|%Eu_?n{REwFLHxCwc6@lPvo z!x&!q5D`rlbhy`{1uf|!J~1R*NqG8aIX~X&x5|pdhe-8pQ*vnhsPHuFY2hJM`Ht`b^>;Ae}ugYjaNRlzU#0XL;iL z&&`c51P;KV)QgJ*mJBWEyM|{$CQXCIiX{&-Yt3X2Juk`jaL_QBU$oC}+@C7)Q--E1 zU8*E3o}zj-WDYkuA(Md2qw8az5Ik9CUnkI$-DTce`#`QW?P+FFs#=<|n6!Fy77EL< zm$kK&>{*f=nh1hqUq%wfc5TE+G6HgB9}P({O4AC!C~|mxb0?fe_E`)WxRy0TfdTiC zBSqY*Ae2+(RJhs+=v>?}ZiF6G15#oZqIC__OAx`jh-S**k zA{7Z08W@sWe2D3fLdLQ(&nw}^sGb&NvWHW=XX{5#m*8EdVmn{A;V2{OFb!ND0rd?h zU-LZI*L2VHZEnC3+hyDUy0eFwL859I%3w^rdUOWb!m`9=?Tt&E$gzNxa2u1Pz^??K zAteY&gGJK7QxHA3inaz)hpq}o3$%4T7)IpNZCt?*-^WxHo{mY`yiO=jEeO<54jpz; z9r#p+pNMZ!nDClRM}&=u)b$L7%%!fw>k)wX<70BN5IWsQ0u-5f)M3KtF{Plx0YKIk zI^RRSQ<#c^gL44Mw*oM>ufeN!R9#geRx-H3wL+&Tax1D2Re@s`ASFcjhrmfzeOP)! zN3Iaeo;a9UiwddBSTZGbJ|Z4Pl5u>(ECO;RKD=Pa1cb(y45!#~Cvp!3G=?tHmH-il z7RdN2zHNlijdtLfU~#dBPB==Mt0HNHKpTl|I0Q1{Z39VcIL#1YSj9GWOz|@^vd2~9 zQ8;zUTr}QzYyDAOYL~911de?($71vc&*0*SK;YA~tCF4%rB2LU!;9RZe+gEpz zw=CjROHc+&*2r51(t@(QWo(U~yp0tR9TJo#0tdw7>?;hG>PpBrhWL;WzC47(nbLWi zhgc2?KO4tv_$~u#h+Tv=$VBUwiHOkP{&h;$3Kp{8zTtW*@`{>`lRzAMXr_b{4feqd z*?BUNE29#{!HszK+w0n&L$KR~GmvaT|^LXRSA2}5Au z7zzNY1_VkE9k{@r3lma(uExH`1O==?z2Mem1Sj}V1(JWJjEx|1!xM$&gvyPGPU7DQ|y8wq4;5WFq3_zE8?Qo5C!?ST}sRW4w($Vt`v3HNnU4y)3&xEPTo@B9P zwPa>ssbt%Rc4llRFBN7F3IT3AYz7{MF7Vy>*=42NNv9&=+!I+B9WP@5 z3!sSM1kg2RAj2pc!ZRtQU~j|$ZjZ~yvp3htpmI;EtMS8c*NuxMKR1wjmt9(yEvKYT z$HA(CEUV!&pTP}KOrj{v@rSuZu1y&4&WuZXMUqQoQL;!?Eln9LT6_H7Gy0U0o$Jil z6?3-72{Q-12+s;?6Q&KRX&hU@0SgQhfjO|DC9>D4;66}f{}l{}uc!kvxMUp{TB`_7 zaGYE1QsThCg72fLo{q{5c&I8;QZ~F+cIxFxj>Rp*-DlJzYnidUkh_f46~?jCxkuTd z;f&vdqokpR0wEAP4v=+Ua!{@b(Tf*90AA!LAe}m-MaBsl4m=Pt;ZRQ1u@1}~+-00- zC6Wiv6wClG(_@B4m62wHYv)@M3^poGN|M*}vXaQ5Af?*#nia`|#E0q^|QoQ@A!=qXu)I`-iuS-#{U3m^yZb8!lc^B6=h zP|ot3+|s^Ng&@yUw- zsZJEbL0{vAGaT}2NKA_`?)G0n>NjcC)<< z@DQ{>;!x@$m2xLYh(Ls3)(lo)r@3rD1_-aR+@vY|um?#87#f-bi~++2DaPkJVCiJl zg{hD|i3Pem0z#oy*1CdMvzARso#h!g#Kx_Z5`Y3*qV5IYvleB??J<*4kN>%F1 zz^*+pWlbQLg|V`fLz@}sNIBZhRVc=u&p9_ zAwv}*TOKMQ%vzx1)KYlLSxbtPWseKO4(&i>y<%*Ao}Z7;Rdx1--t@tNjP!Arna-^s zr{W>O4c&;^8X4(7l*1vVg}sAo5Cmu$FnLld*;~n7vbMdI+`X0DJ(Q61!ui#pCRNySI}2tBcfA$0J#F)X70}Rv~rmd%GD0vsE>v z8MWl1%qaFDNt>oUR+P>Pg?5*VPt>msMAWzkl zW))TUR&tXyy_MYJ%HND~*xpL+p3BHnQkPX$Eu^luk{iYAc*z7eF!C#Ft6paM|5hdU z0rZIpQ|G;igQ1&r#ae=kCy#+{aZQupA+Kgw877R z*tpwn7fuVK<4+wLZ=wxD>GSyHC2bf(S2xEyXoH@Ab07xH_2M<7$lS*6w{bD~g~M0J z+diuwKDnE;;a`1DUmBb-?bKk!DW{&cp<5Y&roPMZ8pHBw{0md7Hgr!O5#N_|03FmY(FC7+})<$Hi;w8ph{622tzsc)+K$HP`*>g!ng@Y|Ve7b$$08l>N!J zxW4=OCids*X7(rB(|W#_^?VQOIoRs~C~Ie6uJ68=|H*(_&w*Lbfmn}qSqocrJyye? ztCtKrcf-W)<0j(D_OEpxkH5Mb*I?o96WGtY*ssOx*GG-`!(qQ(0w~=s`*kM3>Gs$! z_HDp!pZ%KJia!GOs}AVx4%sjEsqgL+*)R67-R>pq7yBA-_fqzYeQmJ2oBd+nHtb$D maktUC;d_Aixtuk!Z^(3?#D1~+=(|s5zrqQ{`i6!h$o~QPjE!yp diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree index c0635ea5367528f94d82908d6f100ae9ab40e674..b93ce10ff15f8c00ad063050279db029d347584b 100644 GIT binary patch delta 76 zcmdnLo_GIx-VFyh4bAiNEc48gtMm<$%}tZd%~K5wEs_k=(ku*&&63OwO%g4QO)V@< fO^nUVjE#*h<%K~k!Ld6K1xX;Nacak7D_k)cUSs+mcm RMM`3lk(o)-<|altZUAM%5}5!1 diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree index 62d00160317cbd4eb4f4c4e8a8d6328154197daa..2e2fb10f21140451439c85d2cedb932fa88d9199 100644 GIT binary patch delta 72 zcmZ3mkz>I|jtzS_4bAiNEc48gtMm<$%}tZd%~K5wEs_k=(ku*&&63OwO%g4QO)V@< cO^nUVjE#*0a<;$aWZeFilW8*_08hsjX8-^I delta 72 zcmZ3mkz>I|jtzS_4YRUMQVUW_a`cUp%q=X8Q&S9*QVqk delta 72 zcmbPyoMZBFjtvJm4YRUMQVUW_a`cUp%q=X8Q&S9*QVq1NY5 zm47%yy_R0`7&;L+xlj6iP7OHFdJz1bjBM+zZ5 z)lNHOz(Wv-o&_&}MMJ?`)H}W7o;)XojV$sMBh@AV75Ok{K^h~HmsBRY-CO>Oq_u8H;iBE*i^}X4)DH$Py`{6DnFCmCyKjY$#}80aTFL2yRq6 zZ}>!t3JF-AzDH^}3{-HyjJM21<3m0t8Bd8}5phangW+|zxOERO9mE0PZXhN6t_GioQ zk@U2RNIsHUg^rR;ld(zxn-9s!@>zo%ELWz<;X(^3kRocOG)N{5m!50uWJ*!VfVk^J`>@)X;ktwUiQ2g15q{AUMy!-XYRZ zej3SUOx+=GNVSgRbA(na_&iBYwCVZ_p8pt~P(oy|wo;#zNLDKo9C{jefz^`C?gyZf z*$5sD(g;iot!SQzk>&6#IkuaNCWG3i2s&gVe6T_QQk5XtOc!>Byu(!~W1Fi&fGU4+ zb(OF0lGFW6>c;tj+mrQ$g}$Pm(DS6E7b$7Lym5{?ls-?yYi1(fg<84s?cVP4{vzpW ztv?_OWU*p~uAT*4ygEMBxH58KbgF8nfA6mFG1Mpf%B?ZdQ&bXWe^&Q%x^o9rr({2; U`#Jsh+w&iDI?+1&h=WNz&$QMq?)cmo5{L diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree index 86472f12d114f611674494dc0c6b902a33861ca0..3d169f035ef7acc2942c17b094174d9a90f8a39f 100644 GIT binary patch delta 1522 zcmc(eKWI}?6vjD0UkyZv6gx=z;^3e|(|g~2_n!_bHW-cC!J$|XG-+z4)V5lK)EK07 z6!Z$mNn(nBplU1cNoVO?u%HD82SIV|;I6l!5i=U$xm~_{zH@%x<=jWB&ev6EIgC?T zspyI*-W()F$%|5!2vr3-KCTE81xfwOnsKGM&xByi{-d7L^H`uddM|rnxl@dtRT$GX4Mfs_arg1SvSZn6k`9@!sHeX2S1;G{M}|f z&`yhln`Efl7{m6sjd0H%w-MGt*Zf$VY7_jWHMsjK23>m1i>1Zxt$k+md)z#D6L!}k zZLS1kU08#)@Ms?1zugV5Ct~wEg5mX1Fy+PpJTeK76Feg|rAm|rsE1!B;YLDae7hZd zOCk7f8hUYa1_}w9@$CK(QW_u{KTpAoReY_5)GcMJ(9}OAu{I6o6MlvWsg!50lu2zI z#99@Inb_$0UjQo>pc%u)2{?_H=3sAU?)n_~ow@g)gFd`^3*64!jk8u>vhuEWc)Xv7 zeVDA<%0ENn>^!)5C<_-N9LH;L4f#p9j{Uczia1h-esoSnJ+t+f@c11VL6V1|$R0WE WlzT2dEbqa=JY*x8x?(pemHGwlPPB*s delta 1543 zcmaEIm~F;kwhdc24YRUMQVUW_a`cUp%q=X8Q&S9*QVqi3ln1#bBoCv7nZXbn;ICKT1=LAk(=zZ zD2mP8z|7Lja`Ku*Kt+Z|riP}IYh2|vdn`_3qCo#-kHuD-|1ZA^H{;@FOPCu3wpha4 z;16U%?e7CJq4uBMA~*TV>LQr+Bx>E9fligP=ktCX+YZ zQl2hT4i_;qm|j)RSO%3hGXu)2PgkyB^oNKT8=6e7t6+?noPUjdbLV23Sg;*v!6_!j zw&@F<8RaG`tYMq}qlwXyTR}kq?vDgtMzQJlS{UUJq9!KO75spra;?asK<_L7sf|Mw zHJh#g5}k)E3i6sQ&`1F;8~uW!RJ$n|?6Ffa1S1B@9n&-G7)vJqT5CLApq^1=vi&;t z>7kyCYLnkQ;h*m3%gBcCIxu2s86eY>nt);T--D5T^3nB!!Zl@#mD3Hq871hRIMfEp NV)^aQTN(dK0|4bnv$FsI diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree index 14acd2b20998c8915a77bdc8772ffe17829f0e61..30dc71fc8602d1fd9984ce450cdc7ec91ed8f62c 100644 GIT binary patch delta 7700 zcmeHLORHr^73OMdx=rFE=|@cN-e>nU9fe%_)??MnpdG{@4N241z~FE0(2SqL&h#VCks2}0LwCCemD!N=lZ z%~SK&J2~#3|LI_AjlyJ8&~(a%yy09_;UEJGXY}HU%~P`{{_e2*+xdx=lvDJzRwnW` zkoQ`bq&Sf$qo;4*>~5SGbRngZt$~B0>QfMGRPe+qR8>-(?)|*`#omC|2{fx$RY}%T zP@Wt*i2st_D>33}k4}egcYoPyB_1c2`xlyd$9JxFexi%2hD|~#f{H6KIma@i=lH$q z>AQEjucL^YEQIun5T+jH%?e5(4%`f%8+8a1a^q{IJlc6MP^iD+2 zCA0I=c{9b?Kkni6wkCGtTVL%|-$;uRj>aE8+&?oOzS2ECM5|fg?a3!aDJ0xU5Y-c@ z;$rZ2c_RjY(28UfvzlWLGJ8!_WYp3|q1US^T(ql71OS+9rVw&8q%vz;t8!vlO)K-P z8Zl`k9^by!$?@6i-6xKuzDVZ!t4H5_`QX;A!{LBabcJ25))&eug#eh54AE38Lm1!P z>rYRI7rU2E{11%1-JKra`g8Zt^zIY=t-bHIF6;pLFMr|7*IxMiXPSr$4|PmL!&@HL+g_toA*Mi8c4HYu8OR{jajc|0IoK9KmPXh?##VbWT((4t8yhJ zM4aK$OA|1di;87Qy^?eE*~W_bMP3=tI)c;`lOXLg-=p3gz; zj8~CCfmOApmzUih;6Z49J!1E!Psx!f_RmMzc&koc1i2(2o+n5 z!9=qkSB;h#n{mAq@)$106_aHomwd`nTOqSS&phV-l6kGnvr^=wm3LmrrR&|<&nflP z*Is=_h))V}_?2$~G;Z-JFY0wdx~z?oQios?369aRryz1rLZ(Q|QPXc8Kd8)qxRkA(t zc>d%4rzRAmG_Hba5M)Uy%=@6TusVk%kalgr&hTvjC7(9Ok;_cb}f{)F_GZa64AceTF*cv6w#)^Of&52@8UKkFE zjTaH}tkCouqQ7~f*CnG6%)kUPd5>)urL!?*jX_e@%aN6W!C6aDlCLE}42Y~mL2(N7 zUzXF#yC|o~M&cDI9~hqv{o~K+`-v%yFh*S@y|ySxV3f=RqQL}0y-BmzKq+p~#fXM4 zL|I!m-nS%M7rDu`7K%4dx4ODw=A=>F_@}Ggg$oP4AWej%jfSCw6u<|fSplhv#-yao zvJIj*?_6LAVyIV+!I;qjD)Rin%lu?Zk8V{`yLPX(*Lp4M>3UOs3Rm zxn{h9CaJ)<<#wFtV14!yavxp-cjqf-I};M#K}7TT@toUtj7UIg%HxUt=7H3Ugb$syd^j9E5}dao#!z z5M|U7YHeS2ALCk=waK9tL^n@!qkSvJ4XxtlyKp?>-i_b8(_Lvfvt=)KmT+eY?R*LD zEa87R>VI=@yXW0m!ks1DSwgY7*jis@Y@Xg(!vF3?cb2f>%+|i{ZJC#q5^Y$~oh97( hmbKN@)soyUwG1$jp+ptf-J_)-Eb^+u`E-uX%U)QyWf4^RL7%+3cV z2I)<9x~8mjVkHo~B$2gNK?EI4UEJ6@HN}l@o*z=F1{pHmy2z5G%QDCiNpVS9m$kA zDk^0d84M~g+3DY|bl=?`D(dYsUahjkKgr<%MTp2qm2jG-TfgakyEQ~DZ7K}FP+%Kk zaK@G@tDuynl77T(4^4+3b~m>M)9^e_F7(ef9giPe?A%~%5`|Q*t`MGAL#3p>DMeAT zN!3LZlWZ)iDp*OCIkNUe6<|4rVv00s{OD$PYWn8iyFYFXT57M8lO{5Ak|icqh=| zU)~%4^~>(`j8Vr1`2NN2?AduL43OllF%*Pk?UJva1Lf!)i>k~Vw%^;Hpp?oJp`&RA z!o9LY0!L-GKDs!)`gZrbtw~K%I?up!;%alC0G5_03}8grtx>PxcR{__@VlU%S5hli ztP-&Rg)YjJQ#Cbk)@UM2>J6HLPD(489omX;A(=p_3R{8}$;J_-JTrak#r_ZIA~{>5 zwNX|EEa$th)D3l(jM%d&|W zpt1%OEi1q_Ri{Ekf^9X-hQuo6OwR0+A|0Y-mu&C~1u8;2=Zq}318F#3P_E(k4&_dN zP0|yuU3-e?i$r@by)KFD;;%TzYK4haq7$<99szKIvS)7#L()U22^d=h+*ZV+ENs

;a&4o?fN?%69LqME@ly zE=w-XSxgC<*=9%~1Yxd#6Jw!!4pItvXDEeBMcL{FLr9d#%kH)iz=qrL&rkF(Oo}+x z?+j^A}LqkPKn6zE>6+>t$iwS7l|3PG zr7R9=W8dgZt{7<`wn{=)Z87UIJcWq6?bQ?-)%` z_$m%JiS`r0b_!>ATIoR&aCnjr*y$&-4NeRhq~DfH@o3xH~jBKetp*bf9{4; J?1pdO{U6?kzHk5l diff --git a/master/.doctrees/tutorials/tabular.doctree b/master/.doctrees/tutorials/tabular.doctree index 6eb709fc28127ffc97f145d5cd57d42c7eff5306..446d9ae6a33de19bde9c9a8cde9743681852d6dc 100644 GIT binary patch delta 64 zcmeCX%-naGdBZwRL-V{m%RIB>Dt&`wbJJvV^Hc*vizLIeGz$Y`vm|pvlSE5nQwvK| U6Js+oV`C#zW0TGIIoIC@0H}i$F#rGn delta 64 zcmeCX%-naGdBZwR!>nwR)Pj_f9DU;?a|;XO)D(lHR0H!QOB2(i#AM@S15+bIlay35 UlSGS@#3Um#lcde}IoIC@0Mq3b9smFU diff --git a/master/.doctrees/tutorials/text.doctree b/master/.doctrees/tutorials/text.doctree index 42db94586f7102802f0e0e65b3ce999dc532f8f1..316785d9dac02e4cd48db2315db15cfea0f0c29b 100644 GIT binary patch delta 459 zcmexxnf1eE)(tB-4bAiNEc48gtMm<$%}tZd%~K5wEs_k=(ku*&&63OwO%g4QO)V@< zO^nUVjE#*S>{_KT*{)igT}MGp+1S#0^1DX4$(B_-llN3fPySVnqKjpEfDWSwvwBSJPiy RJb7oMDVbU~-)Rzg4gleIq8I=G delta 645 zcmexxnf1eE)(tB-4YRUMQVUW_a`cUp%q=X8Q&S9*QVqSEYl>%qo!sI)=N(})L@k4SJzQcPtHip zOHYl7FUZWBJfTif93+&OmzM)7}HL-}7 zWfmvK#OD{q#OEd!m!uXYCl;ls$J9t68K$LK7#N!+nH!oU zS{j>LSelv`o0%CK8<`rLZ2rml+f?3`m4U&gpeWUjOIO#(z{o_`z*yJ7RKd{P%EZFT z$YQdtcj)8|uB^&tmU@P!26{#&K-$bgN5RO%z`)AXDAmBqFwwx!%F5Ezcyhmk2_LGu z$rl`M$eI;%abYo9LBr5Y&vG)a`%hkED=jBa40fF?;GC>vXke~qXs)ARYNTfbRBCFX zXA0w)>seUn0bO_BBc2tkZ1Vc}eUrQdbPe^4e4X@*6@VZ!Pr*RX0-N2&1_~NRK$Vki zy&Tjs*kh+;2u6(Xa9GVaW%~KQjQXOuQb9bqDd4^L12R&;Ivv8XucJ^MlarEPQd~PZ zFg}V6qG9rTV1n)QG@rcF-FcK8VZvQ1N z8X20Tq?(x|TBIZ<8JU?RZT`vm+f?3$m4U&gpeWUjOIO#(z{o_`z)07~T*1J^%FxWp z*lhBJ=qM!T_x zo-FIwD{ETJ#f8OS1r0+>J)_Buu|IiHEj;VALdDWT&(vH;!PE$7p&1Yx>zP;rxh8r> z#)f)EMnEAG%gKT9@hl*PlN+M;P2TAuplhmU>g%LmtN;X=c?t%4W>{@DSI{s5s+?@^ z<)D_q9y=vNFk%FU!D^<-tdkSI=!xQr{ZO6in4FaSlHytgLzD<6HXRuFhXT_<_~iLM zCS;@q*U8p?nR>*e2OR~NLnkXnML|-AzW)spQ-w)D*l37=vpy`JTnSw#sR1k`a3v6m ziie!ZZn1J?Bn#4uh|?q{1bZ*@$?KyzMyS^}KS|J>uaqI!BcPB}TB4AXn3S56nxc?c atT3f@O5>ENP0Wl8AkeDMxK*D?Y!d*`fuS@2 diff --git a/master/_modules/cleanlab/regression/learn.html b/master/_modules/cleanlab/regression/learn.html index c3d8e6823..39f79e2e7 100644 --- a/master/_modules/cleanlab/regression/learn.html +++ b/master/_modules/cleanlab/regression/learn.html @@ -1036,7 +1036,13 @@

Source code for cleanlab.regression.learn

                     "If uncertainty is passed in as an array, it must have the same length as y."
                 )
 
-        label_quality_scores = np.exp(-abs(residual) / (uncertainty + TINY_VALUE))
+        residual_adjusted = abs(residual / (uncertainty + TINY_VALUE))
+
+        # adjust lqs by the median (for more human-readable scores)
+        residual_median = max(
+            np.median(residual_adjusted), TINY_VALUE
+        )  # take the max to prevent median = 0
+        label_quality_scores = np.exp(-residual_adjusted / residual_median)
 
         label_issues_mask = np.zeros(len(y), dtype=bool)
         num_issues = math.ceil(len(y) * self.k)
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index 4a8b6dcf1..fe6c44641 100644
--- a/master/_sources/tutorials/audio.ipynb
+++ b/master/_sources/tutorials/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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 38b93dcd1..4eb0d4204 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 36f9fac31..f93e27614 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 11f50c954..d1942f653 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -81,7 +81,7 @@
     "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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 2d8044eef..6d0141f4c 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 3e566a600..6bca40d39 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -77,7 +77,7 @@
     "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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 0a45d987c..ac6872e5d 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 2e14ceb1a..9b3f96c38 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -96,7 +96,7 @@
     "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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 215311453..e6248c330 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 baeccd14c..3c6cb62b1 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 a4a60d16b..901e61116 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 5240df809..289269a01 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -111,7 +111,7 @@
     "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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 06f85a512..d31503619 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb
index 37c02bfd6..8c42720b0 100644
--- a/master/_sources/tutorials/tabular.ipynb
+++ b/master/_sources/tutorials/tabular.ipynb
@@ -119,7 +119,7 @@
     "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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb
index ff822ee3f..1423e4b9b 100644
--- a/master/_sources/tutorials/text.ipynb
+++ b/master/_sources/tutorials/text.ipynb
@@ -128,7 +128,7 @@
     "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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 d09ca8d1f..3ce3448fc 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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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 f2c3c8598..26e680224 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/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/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/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/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "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/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.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/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/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/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/image.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/tabular.ipynb", "tutorials/text.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "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", "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", "Audio Classification with SpeechBrain and Cleanlab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Find Dataset-level Issues for Dataset Curation", "FAQ", "Image Classification with PyTorch and Cleanlab", "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", "Classification with Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 79, 81, 82, 89, 91, 92], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 81, 82, 89, 91, 92], "generate_noise_matrix_from_trac": [0, 1, 81, 82, 89, 91, 92], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 15, 39, 44, 46, 47, 48, 49, 50, 51, 63, 86, 88, 100], "method": [1, 2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 84, 85, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "ar": [1, 2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 17, 19, 20, 21, 22, 23, 25, 28, 29, 31, 33, 35, 36, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "us": [1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 78, 79, 81, 86, 90, 95], "benchmark": [1, 36, 78, 79, 81, 82, 89, 91, 92], "cleanlab": [1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 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, 51, 52, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 79, 81, 82, 86, 90, 95], "": [1, 2, 3, 9, 17, 31, 35, 36, 40, 44, 47, 49, 51, 56, 57, 61, 63, 64, 65, 66, 68, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "core": [1, 39, 42, 70, 72], "algorithm": [1, 2, 7, 9, 30, 37, 41, 51, 56, 65, 74, 76, 78, 87, 89, 91, 100], "These": [1, 2, 3, 4, 7, 9, 20, 36, 38, 40, 41, 42, 43, 54, 56, 57, 60, 64, 65, 69, 73, 74, 76, 77, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "introduc": [1, 80, 87, 89], "synthet": [1, 91, 92, 97], "nois": [1, 2, 3, 35, 42, 45, 51, 57, 81, 82, 86, 91, 96], "label": [1, 2, 3, 4, 6, 7, 8, 11, 13, 14, 15, 19, 20, 21, 23, 28, 30, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 51, 52, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 81, 86, 90, 94, 95], "classif": [1, 3, 4, 6, 9, 11, 13, 15, 31, 33, 35, 39, 41, 42, 45, 47, 48, 51, 56, 57, 58, 59, 60, 65, 66, 74, 75, 76, 77, 78, 79, 81, 82, 90, 91, 94, 95, 96, 97], "dataset": [1, 2, 3, 4, 6, 9, 11, 12, 13, 15, 17, 18, 19, 21, 24, 25, 26, 27, 29, 30, 38, 39, 40, 41, 42, 45, 47, 51, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 90, 91, 95, 98], "specif": [1, 3, 4, 8, 13, 14, 15, 26, 32, 33, 38, 54, 58, 61, 64, 73, 77, 84, 85, 88, 89, 93, 100], "thi": [1, 2, 3, 4, 5, 6, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "modul": [1, 3, 12, 13, 14, 15, 20, 23, 28, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 47, 49, 51, 54, 56, 61, 64, 65, 66, 78, 87, 88, 92], "provid": [1, 2, 3, 4, 6, 7, 9, 13, 15, 17, 22, 29, 33, 35, 36, 37, 39, 40, 42, 45, 51, 55, 56, 57, 58, 63, 64, 65, 66, 68, 70, 72, 73, 76, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 94, 95, 96, 97, 98, 99, 100], "gener": [1, 2, 3, 6, 9, 17, 22, 24, 32, 35, 47, 51, 52, 65, 66, 68, 73, 80, 81, 82, 85, 86, 87, 88, 89, 91, 92, 94, 95, 97, 99, 100], "valid": [1, 2, 3, 4, 9, 11, 31, 33, 35, 42, 43, 45, 46, 47, 49, 51, 56, 58, 61, 64, 66, 68, 69, 77, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93, 96, 97, 98, 99, 100], "matric": [1, 3, 45, 87], "which": [1, 2, 3, 4, 6, 9, 11, 12, 13, 15, 17, 21, 25, 31, 32, 33, 35, 36, 40, 41, 42, 45, 47, 50, 51, 56, 57, 58, 61, 63, 64, 65, 66, 68, 69, 72, 73, 74, 76, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "learn": [1, 2, 3, 4, 9, 13, 15, 21, 29, 32, 37, 38, 39, 40, 42, 44, 46, 51, 54, 56, 58, 65, 67, 69, 72, 76, 78, 80, 81, 84, 85, 86, 88, 90, 91, 92, 96, 99], "i": [1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "possibl": [1, 2, 3, 6, 9, 35, 36, 40, 42, 44, 45, 47, 58, 59, 60, 61, 63, 64, 65, 66, 68, 74, 76, 77, 82, 87, 89, 91, 92, 93, 96, 97, 100], "noisi": [1, 2, 3, 9, 35, 37, 40, 42, 45, 51, 57, 58, 60, 66, 68, 69, 70, 72, 73, 79, 81, 82, 84, 85, 87, 90, 91], "given": [1, 2, 3, 4, 9, 13, 29, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 50, 51, 56, 57, 58, 61, 63, 64, 65, 66, 68, 69, 73, 74, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98, 99, 100], "matrix": [1, 2, 3, 4, 9, 15, 17, 30, 35, 42, 44, 45, 48, 51, 52, 58, 61, 63, 64, 65, 66, 84, 93, 94], "trace": [1, 81, 82, 89, 91, 92], "valu": [1, 2, 3, 4, 9, 11, 12, 15, 17, 21, 25, 26, 31, 33, 35, 36, 37, 39, 40, 42, 44, 45, 47, 49, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 77, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 99, 100], "more": [1, 2, 3, 4, 6, 9, 12, 13, 15, 17, 25, 35, 36, 39, 40, 41, 44, 47, 49, 51, 56, 57, 58, 59, 60, 61, 63, 64, 66, 68, 69, 72, 73, 74, 76, 78, 80, 81, 84, 85, 86, 87, 88, 91, 92, 93, 94, 97, 100], "function": [1, 2, 3, 4, 6, 12, 13, 15, 22, 25, 29, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 82, 86, 87, 89, 91, 92, 93, 97, 98, 99, 100], "noise_matrix": [1, 2, 3, 9, 45, 51, 81, 82, 89, 91, 92], "py": [1, 3, 32, 36, 37, 42, 45, 47, 80, 81, 82, 85, 87, 89, 91, 92, 99], "verbos": [1, 2, 4, 6, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 39, 42, 56, 57, 58, 63, 65, 66, 68, 70, 72, 73, 77, 81, 89, 91], "fals": [1, 2, 3, 4, 6, 11, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 33, 35, 36, 39, 40, 42, 46, 50, 51, 52, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 74, 80, 81, 82, 84, 85, 87, 88, 89, 91, 93, 94, 96, 97, 99], "sourc": [1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77], "prior": [1, 2, 3, 35, 42, 45, 47], "repres": [1, 2, 3, 6, 9, 11, 15, 17, 25, 31, 33, 35, 39, 42, 45, 48, 49, 51, 56, 57, 58, 61, 63, 64, 65, 66, 68, 70, 72, 73, 77, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "p": [1, 2, 3, 4, 9, 35, 42, 44, 45, 51, 56, 64, 65, 66, 70, 84, 85, 89, 91, 100], "true_label": [1, 2, 3, 35, 45, 51, 89, 91], "k": [1, 2, 3, 4, 7, 9, 11, 15, 17, 18, 22, 25, 27, 30, 35, 39, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 56, 57, 58, 59, 60, 61, 64, 65, 66, 68, 70, 72, 73, 74, 76, 77, 80, 81, 82, 87, 89, 91, 92, 93, 94, 97, 98, 100], "check": [1, 2, 4, 5, 8, 9, 11, 15, 26, 33, 36, 39, 40, 46, 52, 55, 61, 64, 68, 78, 80, 81, 82, 87, 88, 89, 91, 92, 96, 98, 99], "learnabl": 1, "mean": [1, 2, 6, 7, 11, 12, 21, 25, 37, 40, 45, 47, 49, 63, 68, 82, 85, 87, 89, 91, 92, 93, 94, 96, 99], "achiev": [1, 2, 36, 37, 40, 68, 87, 91, 100], "better": [1, 4, 42, 56, 58, 66, 68, 69, 78, 80, 82, 84, 85, 87, 89, 92, 93, 94, 99, 100], "than": [1, 2, 3, 6, 9, 25, 27, 30, 35, 42, 51, 55, 56, 61, 63, 65, 66, 68, 72, 76, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 100], "random": [1, 2, 3, 6, 9, 17, 30, 39, 47, 56, 66, 68, 80, 81, 82, 84, 87, 88, 89, 91, 92, 94, 98], "perform": [1, 2, 6, 9, 25, 27, 30, 36, 40, 47, 64, 68, 78, 81, 87, 89, 91, 92, 95, 96, 98, 99], "averag": [1, 3, 4, 9, 21, 27, 35, 36, 40, 47, 49, 56, 57, 64, 65, 66, 87, 91, 94], "amount": [1, 3, 88], "paramet": [1, 2, 3, 4, 8, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 82, 85, 88, 98, 99], "np": [1, 2, 3, 4, 6, 15, 17, 30, 35, 37, 39, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 73, 74, 76, 77, 80, 81, 82, 84, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "ndarrai": [1, 2, 3, 4, 15, 22, 24, 25, 29, 30, 31, 35, 37, 39, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 76, 100], "an": [1, 2, 3, 4, 6, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 49, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "arrai": [1, 2, 3, 4, 6, 9, 11, 15, 17, 25, 31, 35, 37, 39, 40, 41, 42, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 81, 82, 85, 87, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "shape": [1, 2, 3, 4, 15, 17, 35, 37, 39, 41, 42, 44, 45, 46, 47, 49, 50, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 86, 87, 89, 92, 93, 94, 97, 100], "condit": [1, 2, 3, 45, 50, 51, 66, 88, 89, 100], "probabl": [1, 2, 3, 4, 7, 9, 15, 22, 24, 27, 31, 35, 39, 40, 41, 42, 44, 45, 47, 48, 50, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 74, 76, 77, 78, 79, 86, 87, 89, 90, 92, 93, 94, 97, 100], "k_": [1, 2, 3, 45, 51], "k_y": [1, 2, 3, 45, 51], "contain": [1, 2, 3, 4, 9, 11, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 42, 44, 45, 50, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 72, 73, 74, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99], "fraction": [1, 2, 3, 9, 19, 37, 45, 51, 56, 68, 84, 87], "exampl": [1, 2, 3, 4, 6, 7, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 91, 92, 93, 95, 96, 97, 98, 99, 100], "everi": [1, 2, 3, 4, 15, 36, 40, 42, 45, 50, 51, 58, 66, 68, 69, 80, 81, 82, 84, 85, 87, 88, 91, 93, 95, 97, 98, 100], "class": [1, 2, 3, 4, 6, 8, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 98, 99, 100], "other": [1, 2, 3, 4, 9, 15, 21, 26, 35, 36, 38, 39, 40, 42, 45, 48, 51, 52, 54, 56, 57, 60, 64, 65, 66, 68, 73, 80, 81, 82, 84, 85, 87, 88, 89, 92, 94, 97, 100], "assum": [1, 2, 3, 11, 42, 45, 50, 51, 66, 70, 73, 87, 92, 94, 96, 97, 100], "column": [1, 2, 3, 4, 9, 11, 12, 29, 35, 39, 42, 45, 47, 48, 50, 51, 56, 57, 58, 60, 61, 64, 65, 66, 68, 73, 74, 76, 77, 80, 81, 82, 85, 86, 87, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100], "sum": [1, 2, 3, 25, 30, 31, 35, 45, 47, 51, 57, 58, 60, 63, 68, 81, 82, 87, 88, 89, 91, 92, 97, 100], "1": [1, 2, 3, 4, 6, 9, 11, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 86, 87, 95], "each": [1, 2, 3, 4, 6, 7, 8, 11, 12, 13, 15, 19, 21, 22, 24, 25, 30, 31, 32, 35, 36, 37, 39, 40, 41, 42, 44, 45, 47, 48, 49, 51, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "true": [1, 2, 3, 4, 6, 9, 11, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 33, 35, 36, 37, 39, 40, 42, 45, 47, 50, 51, 52, 55, 56, 57, 58, 61, 63, 64, 65, 66, 68, 70, 72, 73, 77, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "return": [1, 2, 3, 4, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 80, 81, 82, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 99, 100], "type": [1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 79, 80, 81, 82, 83, 84, 85, 87, 88, 92, 93, 96, 97, 98, 100], "bool": [1, 2, 3, 4, 11, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 33, 35, 36, 39, 40, 42, 47, 50, 51, 56, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 77], "is_valid": 1, "whether": [1, 3, 4, 9, 11, 12, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 36, 39, 40, 42, 51, 56, 57, 58, 60, 61, 77, 80, 82, 84, 85, 86, 87, 88, 89, 96, 99, 100], "from": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 22, 26, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 41, 42, 45, 47, 48, 49, 50, 51, 56, 58, 60, 63, 64, 65, 66, 68, 69, 74, 76, 77, 78, 80, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 97, 100], "perfect": [1, 2, 35, 68, 89, 93], "exactli": [1, 3, 9, 35, 36, 40, 42, 59, 65, 81, 82, 84, 85, 88, 89], "yield": [1, 36, 40], "between": [1, 4, 9, 14, 15, 20, 21, 23, 25, 28, 31, 35, 36, 37, 38, 39, 40, 42, 43, 44, 46, 49, 54, 56, 57, 60, 63, 65, 66, 68, 69, 72, 76, 77, 79, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 96, 97, 99, 100], "below": [1, 3, 4, 9, 35, 36, 39, 40, 42, 44, 47, 56, 57, 58, 63, 64, 72, 76, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "we": [1, 2, 3, 4, 6, 9, 12, 21, 36, 39, 40, 42, 47, 51, 52, 56, 63, 64, 66, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "loop": [1, 3, 45, 51, 88, 93], "implement": [1, 2, 3, 8, 13, 21, 36, 37, 39, 40, 45, 51, 68, 78, 80, 81, 84, 94, 95, 98], "what": [1, 4, 8, 9, 15, 32, 35, 37, 39, 42, 56, 57, 61, 63, 80, 81, 82, 84, 85, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "doe": [1, 2, 3, 6, 9, 39, 40, 42, 47, 52, 63, 64, 68, 70, 72, 76, 80, 81, 82, 84, 85, 88, 92, 96, 97, 99], "do": [1, 2, 4, 9, 35, 39, 40, 51, 52, 65, 66, 70, 80, 81, 82, 84, 85, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "fast": 1, "explain": [1, 9], "python": [1, 2, 40, 55, 68, 80, 81, 82, 85, 86, 94, 99], "pseudocod": [1, 95], "happen": [1, 9, 42, 58, 85, 91, 97], "n": [1, 2, 3, 4, 6, 35, 36, 39, 40, 42, 44, 45, 46, 47, 49, 50, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 76, 80, 85, 86, 87, 88, 91, 92, 93, 96, 97, 98, 99, 100], "without": [1, 2, 4, 9, 11, 13, 19, 36, 40, 60, 68, 78, 80, 85, 89, 93, 94, 99], "ani": [1, 2, 3, 4, 6, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 39, 40, 42, 44, 46, 49, 50, 51, 55, 56, 58, 60, 61, 63, 64, 66, 68, 70, 72, 73, 78, 80, 81, 82, 84, 85, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99], "distinct": [1, 17, 51, 100], "natur": [1, 9, 91, 94], "number": [1, 2, 3, 4, 6, 7, 9, 11, 12, 15, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 33, 35, 36, 37, 39, 40, 42, 45, 46, 47, 48, 49, 50, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 76, 77, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 97, 100], "0": [1, 2, 3, 4, 6, 9, 11, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "count_joint": 1, "len": [1, 2, 3, 6, 35, 39, 45, 50, 51, 52, 65, 66, 68, 81, 82, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "y": [1, 2, 3, 4, 7, 17, 29, 30, 40, 45, 47, 51, 52, 55, 64, 68, 69, 80, 81, 82, 84, 87, 89, 91, 92, 94, 96, 99], "round": [1, 39, 42, 51, 68, 87, 96], "astyp": [1, 91], "int": [1, 2, 3, 4, 6, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 36, 37, 39, 40, 42, 47, 48, 49, 50, 51, 52, 57, 58, 60, 64, 65, 66, 68, 70, 72, 73, 74, 77, 80, 81, 88, 93, 94], "rang": [1, 3, 4, 6, 11, 45, 47, 49, 51, 64, 68, 69, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 100], "idx_flip": 1, "where": [1, 2, 3, 4, 6, 9, 11, 12, 15, 21, 35, 39, 42, 45, 46, 47, 48, 49, 50, 51, 52, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 80, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 99, 100], "pragma": 1, "cover": [1, 3, 79, 86], "choic": [1, 7, 42, 49, 87, 88, 92, 94], "replac": [1, 50, 55, 66, 81, 82, 85, 86, 87, 88, 91, 94, 98, 99], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 66, 80, 81, 82], "05": [1, 9, 25, 29, 50, 64, 68, 74, 76, 84, 86, 87, 89, 93, 100], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 81, 82, 89, 91, 92], "none": [1, 2, 3, 4, 6, 11, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 52, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 68, 70, 72, 73, 76, 77, 81, 82, 87, 88, 89, 91, 92, 97], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 9, 25, 38, 40, 47, 68, 80, 81, 82, 84, 86, 89, 91, 92, 98], "max_it": [1, 80, 85, 94, 99], "10000": [1, 39, 86, 87], "x": [1, 2, 3, 4, 9, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 35, 36, 37, 40, 42, 44, 45, 47, 50, 51, 52, 55, 56, 58, 64, 65, 66, 68, 70, 80, 81, 82, 84, 86, 87, 88, 89, 91, 92, 94, 96, 98, 99], "diagon": [1, 3, 4, 42, 45, 51], "equal": [1, 3, 9, 11, 58, 63, 73, 95], "creat": [1, 2, 8, 15, 17, 36, 39, 40, 42, 51, 68, 78, 80, 84, 85, 87, 88, 97, 99, 100], "impli": [1, 9, 35, 57, 64], "float": [1, 2, 9, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 37, 38, 39, 40, 42, 44, 46, 47, 49, 50, 51, 56, 57, 58, 60, 63, 64, 68, 72, 76, 80, 81, 82, 89, 91, 92], "entri": [1, 3, 4, 35, 36, 40, 42, 44, 48, 49, 51, 56, 57, 58, 61, 84, 85, 89, 92, 93, 96, 98, 99], "maximum": [1, 9, 65, 73, 77, 97], "minimum": [1, 7, 9, 19, 42, 44, 58, 63, 76], "noise_r": 1, "non": [1, 2, 3, 4, 6, 8, 15, 25, 36, 40, 42, 63, 68, 81, 87, 89, 91, 93, 94], "default": [1, 2, 3, 4, 6, 9, 13, 15, 27, 29, 32, 35, 36, 37, 39, 40, 42, 44, 45, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 81, 87, 88, 96, 97], "If": [1, 2, 3, 4, 9, 11, 12, 15, 25, 27, 33, 35, 36, 39, 40, 42, 44, 45, 47, 50, 51, 55, 56, 57, 58, 61, 63, 64, 65, 68, 69, 70, 72, 73, 76, 77, 78, 79, 80, 81, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "have": [1, 2, 3, 4, 6, 9, 15, 20, 23, 25, 28, 35, 36, 38, 39, 40, 42, 45, 47, 51, 55, 56, 57, 58, 61, 63, 64, 65, 66, 68, 69, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "all": [1, 2, 3, 4, 6, 7, 9, 12, 13, 15, 21, 32, 35, 36, 39, 40, 41, 42, 45, 47, 48, 50, 51, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 68, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "necessari": [1, 2, 3, 6, 9, 11, 50, 81], "In": [1, 2, 3, 4, 9, 35, 36, 39, 40, 56, 57, 59, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 94, 95, 96, 97, 98, 99, 100], "particular": [1, 4, 5, 9, 12, 13, 15, 18, 19, 21, 25, 26, 27, 30, 36, 40, 51, 56, 60, 64, 68, 73, 77, 78, 80, 82, 85, 87, 91, 92, 94, 96, 98, 99], "satisfi": [1, 3, 35], "requir": [1, 2, 4, 6, 7, 8, 9, 10, 11, 29, 34, 36, 37, 38, 39, 40, 42, 45, 51, 54, 55, 58, 65, 66, 68, 70, 78, 79, 80, 86, 87, 89, 95], "argument": [1, 2, 3, 4, 9, 15, 22, 26, 29, 30, 31, 36, 39, 40, 41, 42, 47, 52, 55, 56, 57, 58, 60, 63, 64, 65, 66, 68, 72, 73, 74, 76, 82, 85, 86, 87, 88, 92, 93, 96, 99, 100], "when": [1, 2, 3, 4, 9, 11, 13, 22, 25, 36, 40, 42, 45, 47, 51, 55, 58, 60, 61, 63, 65, 66, 68, 69, 81, 82, 84, 85, 88, 91, 95, 96, 97, 98, 99, 100], "The": [1, 2, 3, 4, 6, 7, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 51, 55, 56, 57, 58, 61, 63, 64, 65, 66, 68, 70, 73, 74, 76, 78, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "rate": [1, 2, 3, 9, 37, 51, 80, 100], "set": [1, 2, 3, 4, 8, 9, 11, 12, 15, 16, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 36, 39, 40, 42, 46, 47, 49, 51, 55, 56, 58, 61, 63, 64, 65, 66, 68, 70, 72, 73, 81, 82, 84, 85, 87, 91, 92, 94, 95, 96, 97, 98, 99, 100], "note": [1, 2, 3, 6, 7, 9, 11, 26, 30, 33, 36, 39, 40, 41, 42, 47, 51, 56, 61, 63, 64, 65, 66, 68, 69, 73, 79, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "you": [1, 2, 3, 4, 6, 9, 13, 15, 35, 36, 38, 39, 40, 42, 47, 54, 55, 56, 58, 61, 63, 64, 65, 66, 68, 69, 70, 73, 74, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "high": [1, 2, 15, 39, 42, 51, 63, 66, 68, 81, 82, 86, 88, 89, 93, 96, 97, 98, 99, 100], "mai": [1, 2, 3, 4, 9, 12, 20, 21, 23, 28, 31, 35, 36, 38, 39, 40, 42, 45, 47, 51, 56, 57, 61, 63, 64, 65, 66, 68, 70, 73, 77, 79, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 95, 96, 97, 99, 100], "imposs": [1, 9, 89], "also": [1, 2, 3, 4, 6, 9, 21, 33, 35, 36, 39, 40, 42, 47, 50, 55, 56, 65, 68, 73, 76, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100], "low": [1, 9, 51, 56, 78, 81, 82, 85, 89, 93, 97], "zero": [1, 3, 4, 36, 40, 44, 51, 52, 81, 88, 92, 93, 94], "forc": [1, 2, 3, 4, 40, 81, 100], "instead": [1, 2, 3, 9, 12, 15, 32, 35, 36, 39, 40, 42, 45, 51, 55, 56, 58, 60, 64, 65, 66, 68, 69, 72, 74, 76, 79, 80, 84, 85, 87, 88, 89, 92, 93, 94, 96, 97, 98, 99, 100], "onli": [1, 2, 3, 4, 6, 9, 15, 22, 25, 29, 35, 36, 39, 40, 41, 42, 44, 45, 50, 51, 52, 55, 56, 65, 66, 68, 70, 72, 76, 77, 78, 80, 81, 82, 85, 88, 91, 92, 93, 94, 95, 96, 97, 99, 100], "guarante": [1, 3, 4, 14, 20, 23, 28, 36, 38, 40, 43, 45, 54, 79], "produc": [1, 2, 4, 9, 15, 47, 56, 66, 68, 70, 72, 78, 80, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 99, 100], "higher": [1, 4, 9, 35, 42, 44, 45, 47, 49, 56, 57, 68, 82, 85, 87, 93], "opposit": [1, 100], "occur": [1, 3, 9, 35, 50, 63, 81, 82, 87, 88, 94], "small": [1, 3, 9, 35, 39, 47, 51, 57, 64, 85, 86, 88, 92, 94, 99], "numpi": [1, 3, 4, 6, 9, 11, 17, 30, 31, 39, 40, 41, 47, 49, 50, 52, 55, 60, 63, 68, 69, 74, 76, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "max": [1, 42, 65, 66, 82, 88, 94], "tri": [1, 36, 40, 95], "befor": [1, 2, 3, 36, 40, 49, 51, 65, 68, 73, 85, 87, 89, 91, 94, 96, 98, 99], "option": [1, 2, 3, 4, 6, 7, 8, 11, 12, 15, 22, 25, 29, 35, 36, 39, 40, 42, 45, 47, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 76, 77, 78, 80, 81, 82, 84, 87, 88, 89, 92, 96, 97, 98], "left": [1, 2, 42, 44, 49, 51, 58, 61, 64, 81, 82, 92, 93, 94, 97], "stochast": 1, "exceed": 1, "m": [1, 4, 36, 40, 46, 47, 56, 61, 63, 64, 65, 81, 82, 86, 91, 92, 93, 100], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 36, 40, 55, 87, 89, 97], "length": [1, 4, 11, 25, 26, 35, 37, 42, 51, 58, 61, 65, 66, 68, 70, 73, 77, 80, 92, 94, 97, 98, 100], "must": [1, 2, 3, 4, 6, 15, 35, 36, 37, 38, 40, 42, 45, 47, 48, 51, 54, 55, 56, 57, 58, 65, 66, 68, 70, 72, 73, 74, 76, 77, 80, 91, 95, 97, 100], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 9, 11, 35, 39, 42, 48, 51, 52, 56, 58, 64, 70, 72, 73, 74, 76, 77, 80, 87, 91, 92, 93, 97, 98, 99, 100], "ball": [1, 86], "bin": [1, 3, 58, 81, 82, 94], "ensur": [1, 2, 9, 36, 40, 51, 52, 63, 66, 68, 80, 81, 82, 85, 87, 88, 89, 94, 95, 96, 98, 99], "most": [1, 3, 4, 6, 9, 15, 35, 39, 42, 47, 55, 56, 57, 58, 61, 63, 64, 65, 66, 69, 72, 76, 77, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92, 93, 94, 96, 97, 98, 99], "least": [1, 9, 17, 30, 35, 39, 56, 57, 63, 66, 76, 82, 87, 88, 91, 94, 97], "int_arrai": [1, 51], "can": [2, 3, 4, 6, 7, 8, 12, 13, 15, 32, 33, 35, 36, 37, 38, 39, 40, 42, 46, 47, 48, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 73, 74, 77, 78, 79, 80, 81, 84, 85, 88, 92, 93, 94, 95, 96, 97, 98, 99, 100], "model": [2, 3, 4, 9, 15, 17, 29, 31, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 79, 81, 82, 86, 90, 95, 97, 100], "For": [2, 3, 4, 6, 8, 9, 10, 15, 21, 34, 35, 36, 39, 40, 42, 45, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 74, 76, 77, 78, 80, 82, 84, 86, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 99, 100], "regular": [2, 3, 39, 55], "multi": [2, 3, 9, 31, 35, 36, 39, 40, 42, 46, 47, 48, 51, 52, 57, 58, 59, 60, 65, 66, 78, 87, 89, 90], "task": [2, 4, 6, 9, 10, 11, 13, 14, 15, 24, 29, 32, 35, 39, 45, 47, 48, 49, 51, 56, 58, 66, 68, 78, 80, 85, 86, 87, 89, 92, 94, 96, 97, 99, 100], "cleanlearn": [2, 3, 9, 22, 29, 36, 51, 55, 67, 68, 69, 78, 79, 96, 98, 99], "wrap": [2, 36, 40, 55, 65, 68, 78, 81, 82, 84, 85, 89, 96, 98, 99], "instanc": [2, 3, 4, 5, 6, 9, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 40, 47, 55, 64, 65, 68, 73, 80, 81, 82, 84, 85, 88, 89, 93, 98, 99], "sklearn": [2, 3, 4, 7, 9, 17, 30, 35, 40, 47, 51, 55, 65, 68, 69, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99], "classifi": [2, 3, 40, 47, 51, 56, 59, 65, 66, 78, 79, 80, 84, 85, 87, 91, 92, 94, 95, 97, 98, 99, 100], "adher": [2, 40, 68], "estim": [2, 3, 4, 8, 12, 21, 35, 39, 40, 42, 45, 51, 56, 57, 58, 63, 65, 68, 70, 72, 76, 78, 79, 80, 81, 82, 84, 85, 87, 88, 90, 93, 94, 95, 96, 97, 100], "api": [2, 3, 13, 55, 61, 64, 65, 68, 79, 87, 96], "defin": [2, 3, 4, 6, 9, 13, 21, 35, 36, 37, 39, 40, 42, 66, 68, 70, 80, 81, 82, 84, 87, 91, 94, 100], "four": [2, 9, 86, 89, 100], "clf": [2, 3, 4, 47, 68, 78, 84, 87, 89, 92, 98], "fit": [2, 3, 4, 7, 9, 17, 38, 40, 54, 55, 65, 67, 68, 78, 81, 82, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 100], "sample_weight": [2, 40, 68, 89], "predict_proba": [2, 4, 35, 38, 40, 47, 54, 55, 80, 81, 82, 84, 85, 87, 89, 91, 92, 94, 98], "predict": [2, 3, 4, 7, 9, 15, 21, 22, 24, 27, 29, 31, 33, 35, 38, 39, 40, 41, 42, 44, 45, 47, 48, 50, 51, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 86, 87, 89, 90, 94, 96, 97, 99, 100], "score": [2, 3, 4, 6, 9, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 39, 41, 42, 44, 47, 49, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 69, 72, 74, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 94, 96, 98, 99], "data": [2, 3, 4, 6, 7, 8, 10, 12, 13, 14, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 35, 37, 38, 39, 40, 41, 42, 47, 48, 51, 54, 55, 56, 57, 58, 59, 63, 65, 66, 67, 68, 73, 74, 75, 76, 77, 79, 83, 88, 90, 95, 99], "e": [2, 3, 4, 9, 11, 21, 31, 35, 36, 39, 40, 42, 45, 47, 48, 51, 52, 56, 57, 58, 59, 61, 64, 65, 66, 68, 70, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99], "featur": [2, 3, 4, 7, 9, 15, 17, 18, 22, 25, 26, 27, 29, 30, 47, 51, 65, 68, 78, 81, 82, 84, 85, 87, 89, 91, 92, 96, 98], "element": [2, 3, 4, 35, 41, 42, 44, 51, 56, 58, 66, 73, 74, 76, 80, 85, 87, 99, 100], "first": [2, 4, 9, 16, 25, 26, 35, 39, 47, 51, 56, 57, 61, 64, 66, 68, 80, 81, 84, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "index": [2, 9, 25, 35, 42, 50, 51, 52, 57, 66, 68, 73, 76, 77, 80, 81, 82, 84, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 99, 100], "should": [2, 3, 4, 6, 9, 13, 21, 25, 30, 31, 35, 36, 39, 40, 42, 44, 45, 47, 49, 50, 51, 55, 56, 57, 60, 61, 63, 64, 65, 66, 68, 69, 73, 74, 76, 77, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "correspond": [2, 3, 4, 9, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 33, 35, 36, 39, 40, 41, 42, 44, 45, 47, 50, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 73, 74, 76, 77, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "differ": [2, 4, 6, 9, 12, 14, 20, 23, 25, 26, 28, 35, 36, 38, 39, 40, 42, 43, 47, 51, 52, 54, 56, 61, 63, 65, 68, 80, 81, 82, 84, 85, 88, 89, 91, 92, 94, 95, 96, 98], "sampl": [2, 3, 4, 7, 9, 15, 19, 42, 44, 47, 58, 61, 64, 66, 68, 69, 78, 79, 86, 87, 89, 90, 92, 93, 96, 97, 99, 100], "size": [2, 9, 30, 36, 39, 40, 42, 47, 58, 63, 64, 68, 70, 72, 84, 87, 88, 89, 91, 92, 93, 95, 97, 99], "here": [2, 4, 6, 9, 13, 39, 42, 45, 55, 56, 57, 58, 60, 61, 64, 65, 76, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "re": [2, 4, 36, 40, 50, 56, 68, 78, 80, 81, 84, 85, 87, 96, 97, 98, 99, 100], "weight": [2, 9, 36, 37, 40, 47, 56, 63, 66, 68, 80, 81, 82, 85, 99], "loss": [2, 37, 55, 66, 68, 88], "while": [2, 3, 9, 36, 39, 40, 46, 47, 51, 68, 78, 87, 88, 89, 91, 92, 96], "train": [2, 3, 4, 9, 15, 17, 31, 36, 37, 38, 40, 47, 51, 55, 56, 61, 64, 65, 68, 69, 79, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 95, 97, 100], "support": [2, 3, 4, 11, 13, 32, 33, 39, 41, 47, 51, 52, 65, 66, 76, 78, 79, 80, 81, 82, 87, 88], "your": [2, 3, 4, 8, 9, 15, 35, 36, 38, 39, 40, 42, 47, 51, 54, 55, 56, 57, 58, 60, 65, 66, 68, 69, 70, 72, 73, 79, 80, 84, 86, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "recommend": [2, 4, 6, 9, 12, 15, 39, 42, 56, 81, 82, 87, 88, 95, 96], "furthermor": 2, "correctli": [2, 3, 9, 35, 36, 40, 42, 45, 52, 57, 58, 63, 64, 68, 70, 85, 87, 92, 93, 96, 97, 99], "clonabl": [2, 68], "via": [2, 4, 6, 9, 12, 15, 17, 21, 35, 37, 39, 40, 47, 51, 56, 61, 64, 65, 66, 68, 69, 72, 76, 80, 81, 82, 84, 85, 86, 87, 88, 92, 93, 94, 95, 96, 97, 98, 99, 100], "base": [2, 3, 4, 6, 9, 11, 12, 15, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 36, 39, 40, 41, 42, 45, 46, 47, 49, 50, 51, 52, 55, 56, 57, 58, 60, 63, 65, 66, 68, 69, 72, 74, 76, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 100], "clone": [2, 68, 92], "intern": [2, 3, 6, 9, 10, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 39, 44, 45, 46, 47, 48, 49, 50, 51, 52, 60, 64, 68, 74, 79, 80, 81, 87, 89, 91, 92, 93, 94, 96, 100], "multipl": [2, 3, 4, 11, 12, 33, 35, 42, 50, 56, 57, 58, 60, 63, 64, 68, 78, 81, 82, 87, 88, 90, 92, 93, 96], "g": [2, 3, 4, 9, 11, 21, 31, 35, 36, 40, 42, 48, 51, 58, 59, 61, 64, 65, 66, 68, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99], "manual": [2, 68, 80, 87, 94, 95, 96, 98, 99, 100], "pytorch": [2, 36, 37, 40, 68, 78, 80, 87, 90, 92, 97], "call": [2, 3, 4, 5, 9, 12, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 47, 51, 55, 65, 68, 80, 81, 82, 85, 87, 89, 92, 94, 95, 96, 97, 99, 100], "__init__": [2, 37, 68, 88], "independ": [2, 3, 9, 57, 68, 85, 95, 96, 100], "compat": [2, 36, 39, 40, 55, 68, 69, 72, 76, 78, 87, 95, 96, 98, 99], "neural": [2, 37, 55, 65, 68, 80, 87, 88, 92, 94, 96], "network": [2, 36, 37, 40, 55, 65, 68, 80, 85, 87, 88, 92, 94, 96, 99], "typic": [2, 36, 40, 65, 68, 80, 82, 84, 85, 88, 94, 95, 98, 99], "initi": [2, 3, 12, 17, 36, 40, 56, 68, 85, 87, 98], "insid": [2, 40, 68, 87, 89], "There": [2, 3, 6, 78, 89, 91], "two": [2, 3, 9, 17, 25, 35, 36, 39, 40, 48, 51, 61, 63, 64, 79, 81, 82, 84, 85, 87, 88, 89, 92, 96, 97, 99, 100], "new": [2, 6, 13, 21, 36, 39, 40, 46, 50, 51, 56, 68, 80, 81, 85, 86, 87, 94, 95, 99, 100], "notion": 2, "confid": [2, 3, 9, 21, 35, 39, 42, 45, 47, 51, 56, 57, 58, 61, 63, 64, 65, 66, 68, 72, 76, 78, 84, 85, 88, 89, 91, 92, 93, 95, 97, 98, 100], "packag": [2, 4, 6, 8, 9, 10, 14, 34, 38, 42, 43, 51, 54, 61, 64, 68, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "prune": [2, 3, 42, 58, 68, 79, 93], "everyth": [2, 64, 89], "els": [2, 64, 81, 86, 87, 88, 91, 92, 93], "mathemat": [2, 3, 9, 45, 92], "keep": [2, 12, 13, 51, 78, 81, 86, 87, 97], "belong": [2, 3, 9, 35, 42, 44, 45, 57, 58, 59, 60, 65, 66, 70, 74, 76, 77, 82, 88, 89, 92, 94, 97, 100], "2": [2, 3, 4, 6, 11, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 37, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 51, 55, 57, 58, 60, 61, 64, 65, 66, 68, 69, 73, 74, 76, 77, 86, 87, 95], "error": [2, 3, 4, 9, 36, 40, 41, 42, 44, 45, 51, 57, 58, 60, 61, 63, 64, 66, 68, 70, 72, 73, 76, 79, 80, 81, 82, 84, 85, 86, 90, 98], "erron": [2, 3, 35, 42, 45, 51, 57, 58, 66, 68, 69, 70, 94, 96], "import": [2, 3, 4, 6, 7, 9, 11, 12, 13, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 35, 39, 41, 47, 49, 50, 56, 60, 63, 68, 69, 74, 76, 77, 78, 84, 85, 87, 92, 93, 94, 96, 97, 98, 99, 100], "linear_model": [2, 4, 35, 51, 68, 78, 80, 81, 82, 85, 87, 89, 91, 94, 99], "logisticregress": [2, 3, 4, 35, 51, 78, 80, 81, 82, 85, 87, 89, 91, 94, 99], "logreg": 2, "cl": [2, 13, 29, 68, 78, 87, 89, 96, 98, 99], "pass": [2, 3, 4, 7, 9, 11, 12, 13, 15, 22, 29, 32, 36, 39, 40, 42, 46, 47, 51, 55, 56, 58, 65, 66, 68, 74, 78, 80, 81, 82, 85, 86, 87, 89, 91, 93, 94, 96, 99], "x_train": [2, 81, 82, 89, 91, 92, 96, 98], "labels_maybe_with_error": 2, "had": [2, 3, 68, 93], "issu": [2, 3, 4, 5, 7, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 35, 36, 38, 39, 40, 41, 42, 54, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 83, 90, 91, 95, 96, 99], "pred": [2, 42, 51, 95, 96, 98, 99], "x_test": [2, 81, 82, 89, 92, 96, 98], "might": [2, 4, 56, 68, 73, 81, 82, 87, 88, 93, 98, 99], "case": [2, 3, 12, 35, 47, 56, 68, 80, 81, 82, 84, 86, 87, 88, 89, 94, 96, 98, 99, 100], "standard": [2, 3, 4, 29, 35, 42, 55, 57, 58, 60, 66, 68, 78, 81, 82, 84, 86, 89, 93, 98], "adapt": [2, 36, 38, 51, 54, 68, 94], "skorch": [2, 68, 78, 87], "kera": [2, 54, 61, 64, 68, 78, 87, 93], "scikera": [2, 55, 68, 87], "open": [2, 39, 86, 93, 100], "doesn": [2, 68, 78], "t": [2, 3, 6, 9, 16, 26, 36, 37, 39, 40, 41, 42, 47, 49, 50, 60, 65, 66, 68, 74, 76, 77, 78, 81, 82, 84, 85, 86, 88, 89, 92, 93, 96, 100], "alreadi": [2, 4, 9, 15, 36, 39, 40, 45, 55, 56, 68, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 98, 99], "exist": [2, 4, 9, 11, 17, 36, 39, 40, 50, 55, 61, 63, 65, 68, 78, 79, 81, 82, 85, 91, 99, 100], "made": [2, 4, 15, 36, 40, 68, 85, 87, 88, 91, 93, 95, 96, 98, 99], "easi": [2, 45, 68, 81, 82, 86, 87, 89, 92], "inherit": [2, 6, 37, 68], "baseestim": [2, 40, 68], "yourmodel": [2, 68], "def": [2, 6, 13, 36, 40, 55, 68, 80, 81, 82, 86, 87, 88, 89, 91, 92, 94, 96, 99, 100], "self": [2, 3, 4, 6, 9, 11, 12, 13, 15, 30, 36, 37, 39, 40, 42, 47, 65, 66, 68, 81, 85, 86, 88, 92, 97, 98, 99, 100], "refer": [2, 9, 15, 36, 40, 41, 57, 58, 60, 61, 63, 64, 68, 72, 73, 81, 82, 84, 85, 87, 88, 89, 92, 95, 96], "origin": [2, 4, 9, 40, 41, 42, 50, 51, 55, 57, 58, 61, 64, 65, 68, 69, 72, 74, 76, 81, 84, 85, 87, 88, 89, 93, 94, 96, 98, 99, 100], "total": [2, 3, 35, 39, 51, 57, 77, 87, 88, 97], "state": [2, 3, 4, 36, 37, 40, 46, 68, 89, 92, 93, 100], "art": [2, 37, 89, 92], "northcutt": [2, 3, 35, 65, 66], "et": [2, 3, 35, 37, 65, 66], "al": [2, 3, 35, 37, 65, 66], "2021": [2, 3, 35, 65, 66], "weak": [2, 64], "supervis": [2, 9, 81, 82, 87, 91], "find": [2, 4, 9, 12, 13, 15, 18, 19, 21, 22, 24, 25, 26, 27, 30, 31, 35, 36, 38, 39, 40, 41, 42, 46, 50, 51, 54, 61, 64, 65, 66, 68, 70, 74, 76, 79, 81, 90, 95], "uncertainti": [2, 9, 44, 65, 68, 87, 94, 96], "It": [2, 3, 4, 6, 9, 11, 12, 15, 21, 26, 29, 31, 32, 33, 36, 40, 42, 45, 47, 56, 63, 64, 68, 78, 81, 82, 85, 87, 88, 89, 92, 95, 99], "work": [2, 3, 6, 9, 11, 29, 35, 36, 39, 40, 42, 45, 50, 51, 52, 55, 56, 66, 68, 78, 79, 81, 82, 86, 94, 96, 99], "includ": [2, 3, 4, 6, 9, 12, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 35, 36, 38, 39, 40, 50, 51, 54, 56, 57, 60, 61, 65, 66, 68, 72, 73, 74, 76, 78, 79, 81, 82, 84, 85, 87, 88, 89, 92, 93, 94, 100], "deep": [2, 38, 40, 54, 55, 68, 85], "see": [2, 3, 4, 6, 9, 12, 13, 32, 35, 36, 39, 40, 41, 42, 47, 51, 55, 57, 58, 60, 61, 64, 65, 66, 68, 74, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "subfield": 2, "theori": [2, 89], "machin": [2, 4, 13, 15, 32, 38, 54, 68, 81, 82, 86, 91], "across": [2, 3, 4, 6, 9, 12, 21, 35, 39, 47, 57, 64, 65, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 95, 96], "varieti": [2, 87, 98, 99], "like": [2, 3, 4, 5, 6, 9, 13, 31, 35, 36, 39, 40, 42, 45, 51, 55, 56, 57, 60, 61, 63, 66, 68, 69, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "pu": [2, 51], "input": [2, 3, 4, 9, 15, 25, 35, 36, 39, 40, 45, 47, 50, 51, 52, 55, 64, 68, 78, 79, 80, 82, 85, 86, 87, 88, 89, 91, 92, 93, 96, 97, 99, 100], "discret": [2, 33, 42, 45, 51, 65, 66, 70, 72, 73], "vector": [2, 3, 4, 9, 15, 42, 45, 47, 48, 51, 65, 66, 78, 80, 81, 82, 84, 85, 88, 89, 92, 93, 94, 97, 99, 100], "would": [2, 3, 4, 36, 39, 40, 42, 51, 58, 68, 78, 81, 87, 88, 89, 94, 96, 99, 100], "obtain": [2, 4, 7, 9, 15, 42, 56, 58, 61, 64, 66, 69, 80, 82, 85, 87, 91, 93, 95, 97, 100], "been": [2, 35, 42, 45, 50, 51, 56, 57, 61, 63, 65, 66, 68, 80, 81, 84, 87, 89, 91, 92, 93, 94, 97, 100], "dure": [2, 9, 15, 65, 68, 80, 84, 85, 87, 89, 92, 95, 96, 98, 99, 100], "denot": [2, 3, 45, 47, 51, 58, 65, 66, 76], "tild": 2, "paper": [2, 9, 56, 65, 74, 76, 86, 89, 91, 94, 96, 100], "cv_n_fold": [2, 3, 68, 99], "5": [2, 3, 4, 7, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 35, 40, 42, 44, 46, 47, 51, 56, 57, 60, 61, 64, 68, 69, 76, 81, 85, 86, 87, 92, 93, 94, 95, 97, 99, 100], "converge_latent_estim": [2, 3], "pulearn": [2, 51], "find_label_issues_kwarg": [2, 9, 68, 79, 87, 89], "label_quality_scores_kwarg": [2, 9], "low_memori": [2, 58, 74, 87], "clean": [2, 63, 66, 68, 69, 78, 81, 82, 86, 96, 98, 99], "even": [2, 3, 6, 35, 39, 44, 45, 51, 68, 80, 87, 89, 91, 92, 93], "messi": [2, 68, 89], "ridden": [2, 68], "autom": [2, 68, 78, 82, 86, 87], "robust": [2, 45, 68, 82, 87], "prone": [2, 68], "out": [2, 3, 4, 9, 15, 27, 36, 40, 42, 47, 55, 58, 59, 61, 64, 65, 66, 68, 69, 77, 78, 79, 86, 87, 89, 90, 92, 93, 94, 96, 97, 99, 100], "current": [2, 3, 6, 9, 12, 13, 21, 36, 40, 41, 42, 47, 56, 63, 68, 81, 82, 87, 91, 93], "intend": [2, 12, 13, 14, 15, 31, 32, 33, 43, 56, 72, 76, 80, 81, 82, 85, 89], "A": [2, 3, 4, 6, 9, 11, 12, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 35, 36, 37, 40, 42, 45, 46, 47, 48, 49, 50, 51, 55, 56, 57, 60, 63, 64, 65, 66, 68, 70, 72, 73, 77, 79, 80, 81, 84, 85, 86, 87, 88, 89, 91, 93, 95, 98, 99, 100], "follow": [2, 3, 9, 13, 29, 33, 35, 36, 39, 40, 47, 49, 56, 57, 61, 63, 64, 65, 68, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "experiment": [2, 36, 37, 39, 40, 41, 58, 79, 87], "wrapper": [2, 55, 80, 96, 98, 99], "around": [2, 63, 81, 82, 93, 94, 100], "fasttext": [2, 54], "store": [2, 4, 9, 11, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 39, 40, 65, 68, 84, 85, 86, 87, 97, 98, 99, 100], "along": [2, 47, 58, 76, 81, 82, 87, 88, 94], "dimens": [2, 51, 70, 73, 87, 88, 94, 97], "select": [2, 8, 9, 25, 56, 66, 87, 88, 91, 94], "split": [2, 3, 4, 9, 11, 39, 47, 50, 51, 68, 80, 81, 82, 84, 85, 86, 88, 89, 92, 95, 98, 100], "cross": [2, 3, 9, 35, 42, 45, 46, 47, 58, 61, 64, 66, 68, 69, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93, 96, 97, 98, 99, 100], "fold": [2, 3, 35, 42, 45, 68, 80, 84, 86, 87, 93, 97, 98], "By": [2, 35, 57, 58, 68, 81, 87, 97], "need": [2, 3, 9, 35, 36, 39, 40, 42, 57, 58, 60, 65, 68, 78, 80, 81, 82, 85, 87, 89, 91, 92, 93, 97, 99], "holdout": [2, 3, 68], "comput": [2, 3, 4, 6, 7, 9, 18, 19, 21, 22, 25, 26, 27, 30, 35, 36, 37, 39, 40, 42, 44, 45, 46, 47, 51, 56, 57, 58, 60, 63, 64, 65, 66, 68, 69, 70, 72, 78, 79, 81, 82, 86, 89, 90, 93, 94, 96, 97, 99], "them": [2, 3, 4, 6, 8, 9, 10, 11, 26, 31, 34, 36, 38, 39, 40, 42, 54, 56, 65, 68, 79, 81, 82, 84, 85, 87, 88, 91, 92, 94, 96, 97, 98, 99, 100], "numer": [2, 3, 4, 9, 12, 21, 29, 33, 47, 63, 65, 68, 73, 78, 79, 80, 81, 82, 83, 85, 88, 89, 91, 92, 94, 96, 98, 99], "consist": [2, 3, 36, 40, 51, 56, 97, 100], "latent": [2, 3, 45], "thei": [2, 3, 4, 14, 20, 23, 25, 28, 36, 37, 38, 40, 42, 43, 49, 51, 55, 58, 63, 66, 68, 69, 72, 76, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 94, 96, 99, 100], "relat": [2, 3, 12, 18, 19, 25, 26, 27, 30, 45, 51, 57, 68, 82, 85], "close": [2, 3, 9, 39, 45, 65, 80, 81, 82, 84, 85, 87, 88, 89, 93], "form": [2, 3, 9, 36, 37, 40, 45, 50, 51, 66, 68, 87], "equival": [2, 3, 36, 40, 45, 65, 94, 96], "iter": [2, 3, 35, 36, 40, 42, 51, 57, 58, 68, 87, 91, 97], "enforc": [2, 36, 40, 51], "perfectli": [2, 35, 57, 89], "certain": [2, 3, 4, 36, 40, 55, 64, 68, 81, 82, 86, 93, 94], "dict": [2, 3, 4, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 39, 40, 42, 46, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 76, 81, 82, 87, 88, 100], "keyword": [2, 3, 4, 9, 15, 22, 26, 29, 36, 39, 40, 42, 44, 47, 50, 55, 56, 58, 65, 66, 68, 74, 76, 81], "filter": [2, 3, 9, 39, 41, 50, 57, 59, 60, 62, 64, 71, 72, 73, 75, 76, 77, 78, 79, 80, 82, 85, 86, 87, 88, 92, 93, 96, 97, 98, 99, 100], "find_label_issu": [2, 3, 9, 29, 38, 39, 41, 42, 57, 58, 59, 60, 61, 62, 63, 64, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 87, 92, 93, 96, 97, 98, 99, 100], "particularli": [2, 78, 91, 94], "filter_bi": [2, 3, 39, 42, 58, 79, 87], "frac_nois": [2, 42, 58, 74, 87], "min_examples_per_class": [2, 42, 58, 82, 87, 89], "impact": [2, 9, 81, 82, 88], "ml": [2, 4, 9, 14, 68, 78, 81, 82, 84, 85, 88, 91, 92, 96, 98, 99], "accuraci": [2, 37, 66, 80, 87, 88, 89, 91, 94, 96, 97, 98, 99], "n_job": [2, 39, 42, 58, 70, 72, 74, 87, 94, 97], "disabl": [2, 36, 40, 42, 94], "process": [2, 3, 6, 12, 15, 31, 36, 39, 40, 42, 50, 56, 58, 64, 70, 72, 74, 80, 81, 87, 91, 95, 99], "caus": [2, 42, 47, 81, 82, 87], "rank": [2, 3, 9, 35, 39, 41, 42, 47, 57, 58, 59, 61, 62, 64, 65, 67, 71, 73, 74, 75, 77, 78, 79, 81, 82, 86, 87, 92, 93, 94, 97, 98, 99, 100], "get_label_quality_scor": [2, 38, 39, 41, 42, 43, 47, 56, 58, 59, 60, 61, 62, 63, 66, 67, 69, 71, 72, 74, 75, 76, 79, 89, 92, 93, 97, 100], "adjust_pred_prob": [2, 9, 60, 65, 66, 89], "control": [2, 4, 8, 9, 15, 39, 42, 56, 64, 65, 68, 74, 76, 81, 82, 86, 87], "how": [2, 3, 4, 9, 11, 12, 13, 15, 21, 35, 36, 37, 39, 40, 45, 51, 56, 57, 60, 61, 63, 65, 66, 68, 72, 76, 78, 81, 82, 84, 85, 86, 88, 93, 94, 95, 96, 97, 98, 99], "much": [2, 9, 35, 39, 42, 68, 87, 89, 91, 94], "output": [2, 3, 4, 9, 15, 31, 36, 37, 40, 45, 51, 55, 56, 57, 61, 63, 64, 65, 68, 72, 73, 76, 77, 78, 79, 80, 81, 85, 86, 87, 88, 93, 94, 95, 96, 99], "print": [2, 4, 6, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 39, 40, 42, 51, 56, 57, 58, 63, 65, 66, 68, 70, 72, 73, 77, 79, 80, 82, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "suppress": [2, 39, 56, 63, 65, 66, 68, 70, 72, 73, 97, 100], "statement": [2, 39, 56, 63, 65, 66, 68, 70, 72, 73], "big": [2, 39, 58, 64, 68, 89], "limit": [2, 4, 15, 39, 58, 93, 97, 100], "memori": [2, 36, 39, 40, 58, 64, 70, 72, 81, 97], "label_issues_batch": [2, 38, 58, 87], "find_label_issues_batch": [2, 38, 39, 58, 87], "pred_prob": [2, 3, 4, 7, 9, 15, 22, 24, 25, 27, 30, 31, 35, 39, 41, 42, 44, 45, 46, 47, 48, 51, 52, 56, 57, 58, 60, 61, 64, 65, 66, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 96, 98, 99], "threshold": [2, 3, 6, 9, 17, 18, 19, 21, 27, 29, 30, 39, 63, 64, 65, 66, 72, 76, 81, 93, 94, 97, 100], "inverse_noise_matrix": [2, 3, 9, 45, 51, 79, 89], "label_issu": [2, 39, 42, 58, 61, 68, 70, 79, 80, 85, 87, 88, 89, 92, 96, 98, 99], "clf_kwarg": [2, 3, 9, 68], "clf_final_kwarg": [2, 68], "validation_func": [2, 3, 9], "correct": [2, 4, 9, 35, 39, 42, 44, 56, 57, 58, 60, 61, 63, 64, 66, 68, 69, 72, 76, 78, 80, 84, 85, 88, 89, 91, 93, 95, 96], "result": [2, 3, 9, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 39, 40, 42, 44, 49, 51, 58, 60, 61, 64, 66, 68, 69, 70, 72, 76, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 96, 97, 98, 99, 100], "identifi": [2, 3, 4, 6, 9, 11, 15, 26, 32, 35, 39, 41, 42, 58, 61, 64, 66, 68, 69, 70, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89, 92, 94, 96, 97, 98, 99, 100], "final": [2, 9, 68, 84, 93, 95, 96, 98], "remain": [2, 68, 79, 88, 92, 96, 98, 99, 100], "datasetlik": [2, 51, 68], "beyond": [2, 4, 6, 8, 10, 34, 78, 96, 97], "pd": [2, 3, 4, 6, 12, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 35, 46, 55, 56, 57, 68, 76, 80, 81, 82, 84, 85, 87, 89, 91, 96, 98, 99, 100], "datafram": [2, 3, 4, 6, 11, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 39, 46, 51, 52, 55, 56, 57, 68, 73, 77, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 96, 97, 99, 100], "scipi": [2, 4, 12, 51], "spars": [2, 4, 9, 12, 15, 17, 30, 51, 52, 84], "csr_matrix": [2, 4, 12, 15, 17, 30], "torch": [2, 36, 37, 40, 80, 85, 86, 88, 94, 99], "util": [2, 4, 9, 15, 32, 36, 37, 40, 43, 56, 61, 64, 68, 78, 79, 80, 81, 82, 87, 88, 89, 94], "tensorflow": [2, 51, 55, 78, 80, 87], "object": [2, 4, 9, 11, 12, 15, 31, 32, 36, 37, 39, 40, 47, 51, 52, 55, 58, 61, 62, 63, 64, 65, 68, 76, 78, 80, 82, 84, 88, 89, 90, 92, 96, 99], "list": [2, 3, 4, 11, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 37, 39, 40, 41, 42, 48, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 72, 73, 74, 76, 77, 79, 80, 81, 82, 86, 87, 88, 89, 92, 93, 96, 99, 100], "index_list": 2, "subset": [2, 3, 4, 15, 35, 39, 42, 51, 66, 73, 77, 80, 84, 85, 87, 88, 92, 93, 94, 95, 96, 98, 99, 100], "wa": [2, 3, 11, 13, 39, 51, 56, 57, 63, 65, 77, 80, 81, 82, 84, 85, 87, 89, 92, 93, 95, 97, 98, 99, 100], "abl": [2, 3, 9, 68, 80, 87, 89, 91, 92], "format": [2, 3, 4, 9, 11, 31, 36, 39, 40, 42, 45, 46, 47, 48, 51, 52, 55, 56, 57, 58, 61, 64, 65, 66, 68, 70, 72, 73, 76, 77, 80, 81, 82, 84, 86, 88, 91, 96, 97, 98, 100], "make": [2, 3, 4, 17, 36, 39, 40, 47, 55, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99], "sure": [2, 4, 39, 42, 47, 80, 81, 82, 84, 85, 86, 88, 91, 92, 93, 94, 96, 98, 99], "shuffl": [2, 9, 51, 80, 85, 88, 92, 94], "ha": [2, 3, 4, 5, 9, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 41, 45, 47, 50, 51, 56, 61, 63, 68, 74, 76, 77, 78, 80, 81, 82, 84, 85, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "batch": [2, 39, 51, 55, 56, 70, 72, 87, 88, 94], "order": [2, 4, 9, 33, 35, 36, 40, 41, 42, 45, 46, 47, 51, 56, 57, 58, 61, 64, 65, 66, 70, 73, 74, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 96, 97, 99, 100], "destroi": [2, 51], "oper": [2, 36, 39, 40, 51, 55, 66, 78, 85, 87, 94, 98, 99], "eg": [2, 4, 9, 51, 61, 64, 81, 82, 87], "repeat": [2, 51, 56, 91, 94], "appli": [2, 33, 36, 38, 40, 42, 47, 48, 50, 51, 60, 65, 74, 80, 81, 82, 84, 87, 88, 91, 92, 94, 95, 96, 97, 98, 99], "array_lik": [2, 3, 35, 42, 51, 58, 65, 69], "some": [2, 3, 4, 9, 13, 21, 35, 36, 38, 40, 42, 45, 50, 51, 54, 56, 57, 58, 60, 61, 64, 65, 66, 68, 70, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100], "seri": [2, 3, 39, 51, 52, 68, 76, 87], "row": [2, 3, 4, 9, 12, 26, 31, 35, 39, 42, 44, 45, 51, 56, 57, 58, 60, 65, 66, 68, 73, 74, 76, 77, 80, 81, 84, 85, 86, 87, 88, 91, 92, 94, 98, 100], "rather": [2, 3, 4, 25, 35, 51, 55, 56, 63, 72, 76, 91, 95, 96, 97, 99, 100], "leav": [2, 42], "per": [2, 3, 4, 6, 12, 35, 39, 42, 47, 50, 56, 57, 58, 60, 63, 64, 66, 69, 70, 72, 76, 82, 87, 93, 100], "determin": [2, 3, 9, 11, 15, 21, 25, 29, 35, 39, 42, 47, 51, 56, 58, 61, 63, 66, 72, 76, 81, 87, 91, 93, 94, 96], "cutoff": [2, 3, 94], "consid": [2, 3, 4, 9, 12, 15, 22, 25, 27, 30, 35, 36, 40, 42, 51, 56, 63, 65, 66, 69, 72, 76, 80, 84, 85, 87, 88, 89, 93, 94, 95, 96, 97, 98, 99], "section": [2, 3, 6, 9, 79, 84, 88, 93], "3": [2, 3, 4, 6, 9, 33, 35, 36, 40, 42, 45, 46, 47, 48, 49, 50, 51, 55, 58, 65, 66, 68, 69, 74, 76, 86, 87, 95], "equat": [2, 3, 45], "advanc": [2, 3, 4, 8, 9, 15, 63, 65, 76, 79, 82, 83, 89], "user": [2, 3, 4, 9, 13, 15, 26, 31, 32, 33, 36, 40, 42, 63, 65, 66, 68, 72, 76, 89], "specifi": [2, 3, 4, 7, 9, 12, 13, 15, 17, 30, 32, 36, 39, 40, 42, 47, 50, 56, 57, 58, 61, 63, 65, 66, 68, 69, 77, 79, 80, 82, 85, 88, 91, 93, 96, 99], "automat": [2, 3, 4, 25, 35, 78, 84, 85, 86, 87, 88, 91, 92, 93, 96, 97, 98, 99, 100], "greater": [2, 3, 4, 6, 8, 9, 27, 39, 51, 63, 82, 86, 87, 100], "count": [2, 21, 25, 35, 39, 42, 45, 51, 57, 58, 64, 79, 87, 88, 93], "observ": [2, 3, 45, 80, 81, 82, 91, 94, 96], "mislabel": [2, 9, 35, 39, 41, 42, 45, 56, 57, 58, 61, 63, 66, 72, 74, 76, 78, 80, 84, 85, 87, 88, 89, 93, 96, 98, 99], "one": [2, 3, 4, 6, 9, 25, 35, 36, 39, 40, 41, 42, 47, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 88, 91, 94, 95, 96, 98, 99, 100], "get_label_issu": [2, 38, 39, 67, 68, 89, 96, 98, 99], "either": [2, 3, 6, 9, 36, 39, 40, 42, 56, 58, 63, 65, 66, 70, 72, 82, 87, 92, 93], "boolean": [2, 6, 9, 21, 39, 42, 50, 56, 58, 61, 66, 68, 70, 72, 73, 78, 80, 82, 85, 87, 88, 93, 96, 97, 99], "label_issues_mask": [2, 42, 66, 68, 79], "indic": [2, 3, 4, 6, 9, 12, 21, 35, 39, 40, 41, 42, 44, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 72, 74, 76, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "its": [2, 4, 6, 8, 9, 15, 36, 39, 40, 42, 49, 50, 58, 61, 64, 65, 66, 68, 70, 74, 76, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 95, 96, 97, 99, 100], "return_indices_ranked_bi": [2, 39, 42, 58, 74, 79, 87, 89, 98, 99], "significantli": [2, 88, 89, 91, 95], "reduc": [2, 39, 42, 51, 80, 87], "time": [2, 9, 36, 39, 40, 51, 56, 79, 81, 86, 87, 88, 89, 93, 94, 96, 97, 98, 99, 100], "take": [2, 4, 9, 35, 36, 40, 46, 47, 51, 55, 66, 84, 88, 91, 92, 93, 98, 100], "run": [2, 4, 5, 6, 8, 9, 10, 13, 15, 25, 26, 31, 34, 36, 39, 40, 68, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "skip": [2, 9, 36, 40, 68, 80, 87, 92, 100], "slow": [2, 3], "step": [2, 6, 25, 47, 64, 87, 88, 89, 91, 95], "caution": [2, 4, 87], "previous": [2, 4, 12, 51, 65, 68, 79, 80, 81, 84, 85, 91, 95, 98], "assign": [2, 6, 9, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 36, 40, 46, 47, 51, 68, 81, 84, 87, 88, 96, 97, 98, 100], "individu": [2, 6, 9, 12, 25, 36, 40, 41, 56, 60, 63, 66, 68, 74, 76, 79, 82, 84, 87, 91, 92, 93, 98, 100], "still": [2, 39, 40, 51, 65, 80, 87, 88, 94, 98], "extra": [2, 36, 40, 51, 55, 56, 57, 68, 85, 87, 88, 91, 94], "receiv": [2, 9, 36, 40, 41, 57, 60, 61, 68, 70, 74, 82, 93], "overwritten": [2, 68], "callabl": [2, 3, 36, 40, 47, 50, 55, 60, 87], "x_val": 2, "y_val": 2, "map": [2, 3, 11, 39, 40, 43, 46, 50, 51, 64, 66, 68, 73, 80, 81, 82, 87, 88, 89, 92, 100], "appropri": [2, 9, 15, 33, 58, 66, 81, 84, 92, 93], "earli": [2, 88], "stop": [2, 88], "x_valid": 2, "y_valid": 2, "could": [2, 6, 9, 21, 35, 51, 65, 81, 84, 88, 92, 96, 98, 100], "f": [2, 6, 80, 81, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99], "ignor": [2, 36, 40, 50, 55, 68, 73, 77, 80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "allow": [2, 35, 36, 39, 40, 44, 51, 56, 64, 65, 68, 70, 72, 80, 87, 88, 95, 97, 99], "access": [2, 9, 12, 36, 40, 68, 82, 85, 88, 92, 99], "hyperparamet": [2, 60, 65, 88], "purpos": [2, 81, 82, 87, 92, 96], "want": [2, 4, 9, 35, 39, 52, 56, 58, 68, 81, 85, 86, 88, 91, 93, 94, 95, 97, 99, 100], "explicitli": [2, 7, 9, 40, 68, 87], "yourself": [2, 4, 39, 82], "altern": [2, 6, 9, 47, 51, 55, 56, 66, 79, 80, 84, 85, 87, 88, 89, 91, 92, 94, 96, 99], "same": [2, 3, 4, 6, 9, 11, 13, 15, 25, 29, 36, 39, 40, 42, 51, 55, 56, 58, 65, 66, 68, 72, 73, 76, 77, 78, 81, 82, 84, 85, 87, 88, 92, 93, 94, 95, 96, 97, 98, 99], "effect": [2, 9, 26, 36, 40, 56, 65, 68, 84, 85, 87, 88, 94], "offer": [2, 4, 80, 81, 82, 85, 87, 89, 92, 99], "after": [2, 3, 4, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 51, 56, 68, 81, 85, 87, 88, 89, 91, 93, 94, 95, 96, 97, 99], "attribut": [2, 4, 6, 9, 11, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 36, 39, 40, 47, 65, 68, 81, 98], "label_issues_df": [2, 68, 88], "similar": [2, 9, 35, 36, 40, 51, 56, 60, 61, 63, 65, 68, 72, 76, 81, 82, 84, 85, 87, 88, 89, 93, 94, 97], "document": [2, 3, 4, 9, 13, 15, 35, 36, 39, 40, 41, 42, 47, 50, 55, 57, 58, 60, 63, 64, 65, 68, 72, 73, 74, 76, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "descript": [2, 4, 6, 9, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 35, 41, 51, 61, 68, 81, 82], "were": [2, 3, 4, 35, 40, 57, 63, 76, 80, 84, 87, 89, 91, 93, 95, 97, 98], "present": [2, 3, 4, 9, 11, 12, 19, 35, 51, 65, 73, 78, 84, 87, 88, 94], "actual": [2, 3, 4, 35, 56, 57, 66, 82, 87, 89, 100], "num_class": [2, 35, 39, 51, 55, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 94, 98, 99], "uniqu": [2, 30, 51, 73, 81, 87, 92, 94], "given_label": [2, 4, 24, 29, 35, 45, 68, 73, 77, 80, 81, 82, 84, 85, 88, 89, 96, 97, 99, 100], "normal": [2, 3, 17, 25, 30, 42, 44, 47, 49, 50, 51, 66, 87, 89, 94], "trick": [2, 87], "distribut": [2, 3, 4, 9, 25, 27, 35, 40, 42, 46, 49, 56, 64, 65, 66, 78, 81, 82, 84, 85, 88, 93, 94], "account": [2, 35, 56, 60, 65, 66, 85, 87, 89, 91, 92, 94, 96, 99], "word": [2, 3, 50, 76, 77, 87], "remov": [2, 9, 30, 35, 36, 40, 42, 68, 78, 85, 86, 87, 88, 92, 94, 96, 98, 99], "so": [2, 3, 4, 5, 6, 9, 13, 25, 33, 35, 36, 39, 40, 42, 51, 56, 57, 63, 66, 68, 72, 76, 80, 81, 82, 85, 88, 89, 92, 94, 97], "proportion": [2, 9, 42], "just": [2, 3, 4, 9, 12, 31, 35, 37, 39, 51, 55, 66, 68, 70, 78, 79, 80, 82, 84, 85, 87, 88, 89, 92, 93, 94, 95, 96, 97, 98, 99], "procedur": 2, "get": [2, 3, 4, 7, 12, 30, 36, 37, 40, 42, 47, 50, 51, 56, 58, 60, 65, 66, 68, 69, 70, 78, 80, 85, 86, 87, 88, 89, 94, 95, 96, 98, 99], "detect": [2, 4, 6, 8, 12, 13, 15, 17, 21, 27, 41, 49, 59, 61, 62, 63, 64, 65, 66, 67, 68, 71, 75, 78, 81, 83, 88, 90, 92, 96, 97, 98, 99, 100], "arg": [2, 11, 21, 26, 30, 36, 37, 40, 47, 51, 66, 68], "kwarg": [2, 6, 9, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 39, 40, 41, 47, 55, 68, 70, 72, 74, 87], "test": [2, 4, 9, 25, 40, 47, 55, 68, 78, 81, 82, 84, 85, 88, 95, 96, 98, 99, 100], "expect": [2, 3, 36, 40, 42, 47, 56, 65, 66, 68, 87, 89, 91, 92, 93, 96, 98, 99, 100], "class_predict": 2, "evalu": [2, 9, 36, 37, 38, 39, 40, 64, 68, 80, 81, 82, 87, 88, 89, 91, 95, 96, 97, 98, 99], "simpli": [2, 35, 66, 81, 82, 84, 85, 87, 89, 92, 96, 97, 99, 100], "quantifi": [2, 4, 6, 9, 12, 42, 60, 65, 68, 78, 82, 84, 85, 88, 89, 93], "save_spac": [2, 9, 67, 68], "potenti": [2, 9, 35, 42, 50, 58, 61, 64, 66, 68, 70, 72, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 97, 98, 100], "cach": [2, 85, 99], "panda": [2, 4, 6, 11, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 35, 51, 52, 55, 56, 57, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 96, 97, 98, 99], "unlik": [2, 9, 42, 44, 47, 55, 57, 58, 60, 76, 81, 91, 92, 94, 96], "both": [2, 4, 9, 15, 25, 35, 36, 40, 42, 51, 56, 58, 66, 70, 72, 77, 78, 81, 87, 88, 89, 91, 100], "mask": [2, 39, 42, 50, 51, 58, 61, 66, 68, 70, 72, 73, 78, 86, 87, 91, 93, 97, 100], "prefer": [2, 66, 74, 92], "plan": 2, "subsequ": [2, 3, 36, 40, 85, 87, 89, 93, 99], "invok": [2, 36, 40, 89, 95], "scratch": [2, 68], "To": [2, 4, 6, 8, 9, 10, 12, 15, 25, 34, 36, 39, 40, 41, 42, 55, 56, 58, 60, 64, 65, 66, 68, 69, 70, 72, 78, 80, 81, 82, 84, 85, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "share": [2, 66, 68], "mostli": [2, 51, 63, 68, 92, 96], "longer": [2, 33, 46, 47, 50, 68, 79, 85, 87, 93, 99], "info": [2, 4, 6, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 57, 68, 76, 81, 82, 86, 87, 100], "about": [2, 3, 4, 6, 9, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 37, 39, 44, 56, 57, 60, 64, 68, 73, 76, 80, 81, 84, 85, 86, 87, 88, 89, 91, 94], "docstr": [2, 35, 36, 40, 51, 68, 86, 89], "unless": [2, 36, 40, 68, 87], "our": [2, 3, 9, 55, 56, 66, 68, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "is_label_issu": [2, 29, 68, 80, 81, 82, 84, 85, 88, 89, 92, 96, 99], "entir": [2, 9, 25, 39, 42, 45, 57, 58, 63, 66, 68, 70, 72, 73, 78, 81, 82, 87, 93, 94, 95, 97, 100], "accur": [2, 3, 4, 9, 15, 35, 39, 42, 56, 57, 58, 61, 64, 66, 68, 69, 70, 72, 73, 79, 82, 84, 85, 87, 88, 91, 96], "label_qu": [2, 56, 68, 89, 91, 96, 99], "measur": [2, 4, 35, 56, 57, 68, 78, 86, 87, 89, 91, 92, 96, 97, 98, 100], "qualiti": [2, 3, 4, 6, 9, 12, 29, 30, 35, 39, 41, 42, 44, 47, 56, 57, 58, 60, 61, 63, 66, 68, 69, 72, 74, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 90, 96, 98, 99], "lower": [2, 4, 6, 9, 12, 27, 39, 47, 49, 56, 57, 60, 63, 64, 66, 68, 69, 72, 76, 80, 82, 84, 85, 88, 91, 92, 93, 94, 96, 97, 99, 100], "eas": 2, "comparison": [2, 36, 40, 64, 89, 91], "against": [2, 36, 40, 81, 84, 87, 91, 92], "predicted_label": [2, 4, 24, 29, 68, 73, 77, 80, 81, 82, 84, 85, 88, 89, 96, 97, 99], "ad": [2, 36, 40, 82, 91, 96], "precis": [2, 58, 61, 64, 87, 89, 97, 100], "definit": [2, 6, 33, 47, 68, 84, 98], "accessor": [2, 68], "describ": [2, 9, 17, 56, 65, 66, 68, 74, 76, 89, 91, 92, 93, 95, 100], "precomput": [2, 4, 45, 68, 86], "clear": [2, 36, 40, 68, 85, 96, 99], "save": [2, 4, 15, 36, 39, 40, 64, 68, 87, 93, 97, 100], "space": [2, 4, 9, 65, 68, 84, 86, 88], "place": [2, 36, 40, 51, 68, 91, 98], "larg": [2, 39, 68, 84, 85, 87, 88, 93, 94, 97, 100], "deploi": [2, 68, 84, 85, 87, 88], "care": [2, 9, 36, 40, 68, 85, 87, 89], "avail": [2, 4, 6, 11, 13, 32, 40, 68, 87, 89, 91, 93, 96], "cannot": [2, 4, 11, 13, 51, 95, 100], "anymor": 2, "classmethod": [2, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 33, 40, 47, 68], "__init_subclass__": [2, 38, 40, 67, 68], "set_": [2, 40, 68], "_request": [2, 40, 68], "pep": [2, 40, 68], "487": [2, 40, 68], "look": [2, 4, 6, 15, 36, 40, 51, 68, 73, 81, 82, 84, 85, 87, 89, 91, 92, 93, 94, 97, 98, 100], "inform": [2, 4, 6, 9, 12, 15, 32, 36, 40, 51, 56, 57, 61, 64, 68, 73, 76, 77, 78, 80, 81, 84, 85, 89, 91, 94, 97, 100], "__metadata_request__": [2, 40, 68], "infer": [2, 40, 51, 68, 73, 77, 88, 91, 92, 98, 99], "signatur": [2, 36, 40, 68], "accept": [2, 36, 40, 66, 68, 81, 82], "metadata": [2, 40, 68, 84, 85, 88, 100], "through": [2, 4, 6, 40, 68, 80, 82, 85, 86, 87, 91, 93, 94, 99], "develop": [2, 8, 40, 68, 87, 89, 100], "request": [2, 40, 68, 82, 85, 86, 92, 98, 99, 100], "those": [2, 3, 9, 39, 40, 42, 55, 56, 58, 64, 68, 72, 76, 77, 78, 80, 87, 88, 93, 97], "http": [2, 4, 6, 8, 9, 10, 17, 34, 36, 37, 39, 40, 44, 51, 61, 64, 65, 68, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "www": [2, 40, 68, 94], "org": [2, 17, 36, 37, 40, 51, 65, 68, 87, 89, 100], "dev": [2, 40, 68], "0487": [2, 40, 68], "get_metadata_rout": [2, 38, 40, 67, 68], "rout": [2, 40, 68], "pleas": [2, 36, 40, 55, 68, 78, 80, 81, 82, 85, 86, 87, 88, 89, 91, 92, 94, 96, 99, 100], "guid": [2, 6, 40, 68, 79, 83, 88], "mechan": [2, 36, 40, 68], "metadatarequest": [2, 40, 68], "encapsul": [2, 15, 40, 63, 68], "get_param": [2, 38, 40, 54, 55, 67, 68], "subobject": [2, 40, 68], "param": [2, 9, 36, 40, 55, 65, 68, 87], "name": [2, 4, 5, 6, 9, 11, 12, 31, 33, 35, 36, 40, 46, 47, 51, 55, 56, 57, 64, 68, 73, 77, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 96, 97, 99, 100], "set_fit_request": [2, 38, 40, 67, 68], "str": [2, 3, 4, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 39, 40, 42, 45, 47, 50, 51, 55, 56, 57, 61, 63, 64, 66, 68, 73, 77, 80, 81, 87, 91, 92, 93, 100], "unchang": [2, 36, 40, 68, 100], "relev": [2, 15, 25, 40, 68, 88], "enable_metadata_rout": [2, 40, 68], "set_config": [2, 40, 68], "meta": [2, 40, 68], "rais": [2, 4, 11, 12, 33, 36, 40, 44, 47, 68, 80, 87], "alia": [2, 36, 40, 68], "metadata_rout": [2, 40, 68], "retain": [2, 40, 51, 68], "chang": [2, 31, 33, 36, 39, 40, 44, 68, 76, 80, 81, 85, 87, 93, 94, 99, 100], "version": [2, 4, 6, 8, 9, 10, 14, 20, 23, 28, 34, 36, 38, 40, 43, 44, 51, 54, 55, 66, 68, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "sub": [2, 40, 63, 68], "pipelin": [2, 40, 68, 96], "otherwis": [2, 6, 9, 33, 35, 36, 39, 40, 42, 48, 50, 51, 58, 65, 68, 70, 72, 73, 77, 85, 87, 99], "updat": [2, 12, 36, 39, 40, 68, 79, 81, 88], "set_param": [2, 38, 40, 54, 55, 67, 68], "simpl": [2, 36, 40, 42, 56, 66, 68, 81, 82, 84, 85, 88, 91, 94, 96, 98, 99], "well": [2, 3, 9, 36, 40, 44, 45, 56, 58, 64, 66, 68, 73, 76, 77, 79, 81, 82, 84, 85, 87, 88, 89, 91, 93, 94], "nest": [2, 36, 40, 41, 52, 68, 74, 76, 77, 100], "latter": [2, 36, 40, 68, 94], "compon": [2, 40, 68], "__": [2, 40, 68], "set_score_request": [2, 67, 68], "structur": [3, 65, 84, 98], "unobserv": 3, "less": [3, 4, 9, 30, 39, 47, 56, 65, 66, 70, 72, 76, 82, 84, 86, 87, 88, 89, 93, 100], "channel": [3, 80, 89], "character": 3, "flip": 3, "nm": 3, "invers": [3, 9, 35, 45, 51, 57, 82, 86, 99], "inv": 3, "confident_joint": [3, 21, 35, 42, 51, 57, 58, 79, 87, 89], "un": 3, "under": [3, 9, 36, 40, 57, 64, 65, 82, 94], "joint": [3, 35, 42, 45, 51, 57, 58, 86], "num_label_issu": [3, 39, 42, 58, 73, 77, 79], "estimation_method": [3, 39], "off_diagon": 3, "multi_label": [3, 35, 42, 51, 52, 58, 92], "don": [3, 78, 82, 84, 85, 88, 89, 93, 96], "statis": 3, "compute_confident_joint": [3, 35, 42, 51, 58, 89], "off": [3, 42, 51, 63, 88, 89, 93, 94], "j": [3, 4, 35, 36, 40, 41, 42, 58, 61, 64, 65, 74, 76, 77, 81, 82, 89, 97, 100], "confident_learn": [3, 42, 58, 89], "off_diagonal_calibr": 3, "calibr": [3, 42, 51, 56, 91], "cj": [3, 45, 51], "axi": [3, 30, 45, 47, 49, 70, 73, 80, 81, 82, 87, 88, 89, 91, 92, 94, 96, 97], "bincount": [3, 81, 82, 89, 91, 92], "alwai": [3, 9, 36, 40, 51, 80, 89, 96, 98, 99], "estimate_issu": 3, "over": [3, 4, 9, 36, 39, 40, 63, 64, 70, 72, 82, 84, 86, 87, 88, 89, 94, 96, 98], "As": [3, 6, 78, 81, 82, 85, 89, 96, 100], "add": [3, 4, 6, 11, 12, 36, 40, 55, 64, 80, 81, 82, 85, 87, 88, 89, 92, 99], "approach": [3, 35, 39, 42, 84, 89, 92, 94, 96, 98], "custom": [3, 6, 9, 10, 29, 36, 39, 40, 47, 50, 66, 82, 85, 89, 96, 99], "know": [3, 81, 82, 84, 85, 87, 88, 89, 91, 96], "cut": [3, 63, 78, 89], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 31, 93, 94, 100], "underestim": 3, "few": [3, 64, 78, 82, 87, 91, 92, 93, 94, 100], "4": [3, 4, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 46, 47, 50, 60, 61, 63, 64, 66, 69, 76, 86, 87, 92, 97, 100], "detail": [3, 4, 9, 13, 15, 32, 35, 36, 40, 41, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 72, 73, 74, 78, 79, 80, 92, 94, 100], "num_issu": [3, 6, 39, 80, 81, 82, 84, 85, 88, 89], "calibrate_confident_joint": 3, "up": [3, 6, 9, 16, 25, 26, 29, 42, 47, 56, 86, 87, 93, 96, 99, 100], "p_": [3, 35, 42], "pair": [3, 4, 9, 35, 42, 89], "v": [3, 9, 39, 57, 58, 60, 66, 81, 82, 92, 93, 94, 95], "rest": [3, 4, 6, 8, 9, 10, 34, 57, 58, 60, 68, 81, 82, 84, 85, 87, 88, 89, 91, 96, 98, 99], "fashion": [3, 4, 70, 98], "2x2": 3, "incorrectli": [3, 35, 57, 58, 61, 84, 100], "calibrated_cj": 3, "c": [3, 9, 50, 58, 66, 78, 80, 81, 82, 84, 85, 87, 89, 92, 93, 94, 95, 96, 98], "whose": [3, 4, 9, 27, 36, 40, 45, 50, 56, 60, 63, 69, 72, 76, 77, 80, 81, 82, 84, 85, 87, 88, 89, 92, 93, 94, 97, 100], "truli": [3, 94, 97], "estimate_joint": [3, 35, 89], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 58, 64, 89, 93, 95, 97, 100], "return_indices_of_off_diagon": 3, "frequenc": [3, 25, 56, 57, 64, 73, 93, 94], "done": [3, 9, 68, 81, 87, 89, 92, 94, 95], "overfit": [3, 9, 61, 64, 80, 81, 82, 84, 85, 88, 95, 98], "classifict": 3, "singl": [3, 4, 11, 25, 35, 36, 40, 41, 47, 48, 51, 56, 57, 63, 64, 65, 66, 76, 80, 81, 87, 89, 92, 93, 98], "baselin": [3, 36, 42, 94, 96, 99], "proxi": 3, "union": [3, 4, 11, 47, 51, 52, 58, 64, 68, 72, 76, 87], "tupl": [3, 30, 36, 40, 41, 45, 46, 48, 50, 51, 56, 58, 64, 72, 74, 76, 77, 80, 100], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 9, 39, 45, 56, 70, 72, 78, 87, 88, 97, 99], "practic": [3, 82, 88, 89, 94, 96, 98, 99], "complet": [3, 80, 81, 82, 84, 85, 87, 88, 89, 92, 93, 96], "gist": 3, "cj_ish": 3, "guess": [3, 45, 89, 91], "8": [3, 4, 6, 7, 46, 47, 48, 50, 60, 74, 76, 80, 81, 82, 84, 85, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "parallel": [3, 42, 64, 74, 86], "again": [3, 55, 87, 94, 98], "simplifi": [3, 13], "understand": [3, 8, 35, 57, 64, 82, 89, 96, 97, 100], "100": [3, 36, 40, 66, 81, 82, 84, 86, 87, 88, 89, 92, 93, 94, 98, 99, 100], "optim": [3, 36, 37, 40, 55, 88, 91], "speed": [3, 42, 86, 87, 96, 99], "dtype": [3, 22, 24, 25, 30, 36, 40, 50, 51, 60, 76, 80, 93], "enumer": [3, 36, 40, 80, 81, 82, 88, 100], "s_label": 3, "confident_bin": 3, "6": [3, 4, 40, 47, 51, 76, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "num_confident_bin": 3, "argmax": [3, 42, 66, 70, 73, 80, 87, 89, 93, 94, 97], "elif": 3, "estimate_lat": 3, "py_method": [3, 45], "cnt": [3, 45], "1d": [3, 4, 11, 15, 31, 39, 42, 47, 48, 51, 52, 60, 69, 80, 98], "eqn": [3, 45], "margin": [3, 42, 45, 47, 66], "marginal_p": [3, 45], "shorthand": [3, 12], "proport": [3, 9, 35, 57, 89, 95], "poorli": [3, 45, 98], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 89], "variabl": [3, 6, 13, 26, 51, 68, 69, 80, 81, 84, 89, 92, 96], "exact": [3, 45, 81, 82, 84, 88, 98], "within": [3, 4, 9, 14, 31, 36, 37, 40, 41, 43, 58, 63, 72, 74, 76, 81, 82, 87, 88, 93, 97], "percent": 3, "often": [3, 35, 45, 57, 87, 89, 95, 97], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 51, 52, 64, 80, 81, 84, 85, 87, 88, 92, 93, 94, 96, 99], "wai": [3, 4, 55, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92, 93, 95, 98, 99], "pro": 3, "con": 3, "pred_proba": [3, 95], "combin": [3, 35, 81, 86, 87, 88, 89, 95, 96], "becaus": [3, 45, 51, 63, 85, 87, 89, 91, 93], "littl": [3, 39, 86, 93, 100], "uniform": [3, 66, 86, 87, 89], "20": [3, 6, 41, 77, 80, 85, 86, 87, 88, 89, 93, 96, 97, 100], "Such": [3, 88, 94], "bound": [3, 22, 24, 36, 40, 50, 60, 61, 63, 64, 93], "reason": [3, 21, 36, 40], "comment": [3, 50, 100], "end": [3, 4, 36, 40, 64], "file": [3, 4, 11, 38, 39, 54, 64, 80, 81, 84, 85, 86, 87, 93, 94, 97, 98, 100], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 89], "handl": [3, 4, 6, 9, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 39, 40, 79, 81, 82, 84, 85, 88, 89, 97, 98, 100], "five": [3, 61, 64, 89, 93], "estimate_cv_predicted_prob": [3, 89], "estimate_noise_matric": 3, "get_confident_threshold": [3, 38, 39], "amongst": [3, 9, 93], "confident_threshold": [3, 9, 21, 39, 65], "unifi": 4, "audit": [4, 8, 11, 12, 15, 80, 83, 84, 85, 87, 88, 89, 92, 93, 96], "kind": [4, 5, 6, 9, 80, 81, 84, 85, 86, 88, 89], "addit": [4, 6, 8, 9, 10, 12, 32, 34, 36, 40, 47, 52, 56, 64, 74, 80, 81, 84, 85, 89, 91, 94, 95, 98, 99], "depend": [4, 6, 8, 9, 10, 11, 12, 34, 38, 42, 44, 51, 54, 58, 65, 68, 69, 78], "instal": [4, 6, 8, 9, 10, 34, 36, 38, 39, 40, 42, 54, 55, 70, 72], "pip": [4, 6, 8, 10, 34, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "development": [4, 6, 8, 10, 34], "git": [4, 6, 8, 10, 34, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 98, 99], "github": [4, 6, 8, 10, 34, 36, 37, 51, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99], "com": [4, 6, 8, 10, 34, 36, 37, 39, 44, 51, 65, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "egg": [4, 6, 8, 10, 34, 78, 86], "label_nam": [4, 6, 7, 9, 11, 17, 30, 78, 80, 81, 82, 84, 85, 87, 88, 89, 92, 93, 96], "image_kei": [4, 88], "interfac": [4, 78, 87, 89], "librari": [4, 9, 40, 61, 64, 65, 78, 81, 85, 86, 87, 99], "goal": [4, 96], "track": [4, 6, 12, 13, 78, 81, 86, 87, 89], "intermedi": [4, 8, 82], "statist": [4, 9, 12, 21, 25, 35, 56, 57, 64, 82, 84, 85, 89], "convert": [4, 11, 33, 36, 40, 48, 49, 52, 56, 63, 72, 76, 79, 80, 85, 86, 87, 88, 91, 92, 93, 99], "hug": [4, 11, 88], "face": [4, 11, 15, 86, 88, 92], "kei": [4, 6, 9, 11, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 40, 47, 56, 57, 63, 65, 81, 82, 85, 87, 88, 89, 91, 93], "string": [4, 9, 11, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 40, 51, 56, 57, 69, 73, 76, 77, 84, 85, 87, 91, 92, 99, 100], "dictionari": [4, 6, 9, 11, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 40, 46, 51, 56, 57, 60, 61, 63, 64, 81, 82, 84, 85, 89, 91, 92, 93], "path": [4, 11, 36, 39, 40, 64, 80, 81, 87, 93], "local": [4, 6, 11, 36, 37, 40, 80, 81, 82, 86, 87, 88, 89, 91, 92, 94, 96, 100], "text": [4, 6, 9, 11, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 41, 47, 65, 74, 76, 77, 78, 81, 82, 83, 86, 87, 89, 90, 91, 94], "txt": [4, 11, 100], "csv": [4, 11, 84, 85, 96, 98, 99], "json": [4, 11], "hub": [4, 11], "multiclass": [4, 11, 14, 47, 51, 56, 92], "regress": [4, 6, 9, 11, 13, 15, 20, 29, 31, 33, 81, 82, 85, 90, 91, 94, 99], "multilabel": [4, 11, 13, 14, 20, 24, 31, 33, 48, 92], "imag": [4, 8, 35, 40, 61, 63, 64, 65, 70, 72, 73, 78, 81, 82, 86, 87, 90, 91, 92, 93, 95, 97], "point": [4, 6, 9, 17, 25, 36, 40, 81, 82, 84, 85, 87, 88, 89, 91], "field": [4, 9, 36, 40], "themselv": [4, 96, 98, 99], "cleanvis": [4, 9], "level": [4, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 50, 74, 76, 82, 88, 90, 92, 97], "load_dataset": [4, 11, 88], "glue": 4, "sst2": 4, "properti": [4, 11, 12, 33, 36, 40], "has_label": [4, 11], "class_nam": [4, 11, 19, 35, 41, 57, 64, 73, 77, 78, 86, 89, 93, 97, 100], "empti": [4, 11, 45, 56, 82, 87, 92], "find_issu": [4, 5, 6, 7, 9, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 78, 80, 81, 82, 84, 85, 87, 88, 89, 92, 96], "knn_graph": [4, 9, 15, 17, 18, 25, 27, 30, 84], "issue_typ": [4, 5, 6, 7, 9, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 80, 81, 82, 84, 85, 87, 88, 89, 92, 96], "sort": [4, 15, 39, 42, 47, 56, 58, 61, 63, 64, 66, 72, 74, 76, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100], "common": [4, 12, 15, 82, 83, 86, 87, 89, 92, 93, 97], "real": [4, 15, 78, 81, 82, 87, 89, 91, 96, 97], "world": [4, 15, 78, 81, 82, 87, 89, 91, 96, 97], "interact": [4, 15, 85, 87], "embed": [4, 9, 15, 65, 78, 80, 81, 82, 84, 85, 89, 92, 96, 99], "thereof": [4, 15], "insight": [4, 15, 64, 91], "best": [4, 15, 46, 56, 66, 81, 82, 84, 85, 87, 88, 91, 92, 94, 96, 98, 99, 100], "2d": [4, 15, 31, 39, 47, 48, 50, 51, 56, 80, 92, 98], "num_exampl": [4, 15, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 35, 57, 80, 81, 82, 84, 85, 88, 89], "properli": [4, 9, 39, 46, 51, 52, 70, 80, 81, 82, 84, 85, 87, 88, 89, 92, 94, 96, 97], "respect": [4, 36, 40, 61, 64, 80, 81, 82, 84, 85, 88, 89, 92, 93], "lexicograph": [4, 46, 51, 80, 81, 82, 84, 85, 88, 89, 92], "represent": [4, 9, 15, 33, 36, 40, 48, 58, 78, 80, 81, 82, 85, 87, 88, 89, 94, 99], "num_featur": [4, 15, 36, 40, 55], "distanc": [4, 9, 15, 17, 25, 27, 30, 49, 63, 65, 84, 94], "nearest": [4, 9, 15, 22, 25, 27, 49, 65, 82, 85, 94], "neighbor": [4, 9, 15, 17, 22, 25, 27, 49, 65, 81, 82, 84, 85, 87, 88, 94], "graph": [4, 9, 12, 15, 17, 25, 30], "squar": [4, 51, 68, 86, 96], "csr": 4, "evenli": 4, "omit": [4, 63, 64, 88, 93], "itself": [4, 31, 36, 40, 93], "three": [4, 9, 35, 56, 57, 68, 73, 80, 81, 82, 84, 86, 89, 91, 95, 96, 97, 98, 100], "indptr": 4, "wise": 4, "start": [4, 6, 9, 33, 36, 37, 40, 47, 78, 84, 92, 100], "th": [4, 41, 46, 50, 51, 56, 58, 61, 63, 64, 65, 74, 76, 77, 85, 92, 93, 100], "ascend": [4, 35, 57, 88, 89], "segment": [4, 70, 72, 73, 90], "reflect": [4, 84, 85, 91, 93, 94, 96, 98, 99], "maintain": 4, "posit": [4, 36, 40, 49, 51, 64, 86, 94], "nearestneighbor": [4, 9, 17, 65, 84, 94], "kneighbors_graph": [4, 17, 84], "illustr": 4, "todens": 4, "second": [4, 47, 51, 64, 66, 81, 87, 89, 100], "duplic": [4, 8, 20, 21, 36, 40, 78, 81, 89, 96], "explicit": 4, "precend": 4, "construct": [4, 6, 9, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 40, 47, 55], "neither": [4, 9, 13, 93], "nor": [4, 9, 13], "collect": [4, 9, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 56, 87, 91, 100], "unspecifi": [4, 15, 42, 58], "interest": [4, 15, 21, 73, 77, 85, 89, 96, 97, 98, 99, 100], "constructor": [4, 9, 15, 22, 29], "issuemanag": [4, 8, 12, 13, 15, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 32], "respons": [4, 15, 21, 68, 69, 86, 96, 100], "random_st": [4, 80, 81, 82, 88, 89, 92, 94, 98], "lab": [4, 5, 7, 9, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 39, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 96], "comprehens": [4, 78, 88, 92, 96], "nbr": 4, "n_neighbor": [4, 9, 17, 65], "metric": [4, 9, 17, 18, 25, 30, 51, 55, 64, 65, 80, 84, 85, 88, 89, 96, 98, 99], "euclidean": [4, 9, 63, 65, 84], "mode": [4, 17, 36, 39, 40, 94], "4x4": 4, "float64": [4, 25, 36, 40, 76], "compress": [4, 9, 51, 70, 72], "toarrai": 4, "NOT": [4, 39, 85], "23606798": 4, "41421356": 4, "configur": [4, 15, 47, 82], "suppos": [4, 9, 61, 94, 96, 98, 99], "who": [4, 63, 84, 89, 98, 100], "manag": [4, 7, 8, 9, 12, 13, 14, 15, 16, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 81], "clean_learning_kwarg": [4, 9, 22, 29, 96], "labelissuemanag": [4, 9, 13, 20, 22], "prune_method": [4, 79], "prune_by_noise_r": [4, 42, 58, 89], "report": [4, 6, 10, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 57, 77, 78, 80, 81, 82, 84, 85, 89, 92, 96, 100], "include_descript": [4, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32], "show_summary_scor": [4, 32], "show_all_issu": [4, 32], "summari": [4, 6, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 41, 54, 55, 57, 62, 71, 72, 74, 75, 76, 79, 80, 81, 82, 84, 85, 86, 88, 89, 93, 96, 97, 100], "show": [4, 6, 25, 36, 40, 46, 51, 64, 73, 77, 82, 84, 85, 86, 87, 88, 89, 91, 94, 96, 97, 98, 100], "top": [4, 9, 35, 39, 41, 42, 51, 58, 61, 64, 66, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 93, 94, 96, 99, 100], "suffer": [4, 9, 12, 21, 58, 66, 77, 100], "onc": [4, 21, 35, 36, 40, 81, 87, 89, 92, 93, 98], "familiar": 4, "overal": [4, 6, 9, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 41, 47, 56, 57, 60, 63, 64, 68, 72, 73, 74, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 93, 100], "sever": [4, 6, 9, 11, 12, 21, 36, 39, 40, 42, 60, 63, 65, 66, 72, 76, 78, 80, 81, 82, 84, 85, 86, 87, 89, 93, 94, 98, 99, 100], "compar": [4, 56, 65, 76, 81, 82, 84, 89, 93], "issue_summari": [4, 6, 9, 12, 81], "With": [4, 39, 85, 89, 91, 96, 97, 99, 100], "usag": [4, 39, 55], "found": [4, 6, 9, 12, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 51, 78, 80, 81, 82, 84, 85, 87, 88, 92, 94, 96, 98, 99, 100], "usual": [4, 11, 31, 32, 88, 91, 96], "directli": [4, 13, 15, 32, 33, 39, 55, 56, 82, 85, 92, 93, 96, 99], "ti": [4, 56], "exhibit": [4, 6, 9, 12, 73, 82, 84, 85, 88, 89, 93], "ie": [4, 68], "likelihood": [4, 9, 39, 41, 42, 58, 63, 65, 66, 70, 74], "wherea": [4, 51, 58, 95], "outlier": [4, 8, 13, 20, 21, 30, 43, 66, 78, 81, 82, 89, 90, 96], "fundament": [4, 9], "incompar": 4, "quantiti": [4, 89, 96], "global": [4, 6, 21, 36, 40], "non_iid": [4, 9, 13, 25, 82, 84, 85, 88, 89], "hypothesi": 4, "iid": [4, 6, 8, 25, 84, 89], "never": [4, 80, 89, 92, 94, 95], "someth": [4, 6, 36, 40, 66, 93], "123": [4, 81, 82], "456": [4, 80, 98, 99], "nearest_neighbor": 4, "7": [4, 47, 48, 55, 74, 76, 80, 81, 82, 84, 85, 86, 87, 91, 92, 93, 94, 96, 97, 98, 99, 100], "9": [4, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 41, 47, 48, 60, 74, 76, 80, 81, 82, 84, 85, 86, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "distance_to_nearest_neighbor": [4, 81, 82, 84, 85, 88, 89], "789": 4, "get_issu": [4, 9, 12, 80, 82, 84, 85, 87, 88, 92, 96], "issue_nam": [4, 5, 6, 9, 12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 81, 82], "focu": [4, 12, 85, 97, 100], "full": [4, 9, 12, 39, 64, 88, 100], "summar": [4, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 57, 73, 77, 78, 97], "valueerror": [4, 11, 12, 33, 44, 47, 87], "specific_issu": [4, 12], "lie": [4, 9, 65, 66, 80, 81, 82, 84, 85, 88, 89, 99], "get_issue_summari": [4, 12, 82], "get_info": [4, 12, 82, 85], "yet": [4, 16, 26, 86, 91], "list_possible_issue_typ": [4, 13, 14], "regist": [4, 6, 13, 14, 16, 26, 36, 40, 81], "rtype": [4, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40], "registri": [4, 13, 14], "list_default_issue_typ": [4, 13, 14], "folder": [4, 80, 81, 88], "load": [4, 11, 39, 64, 86, 87, 88, 89, 93, 94, 97, 100], "futur": [4, 9, 21, 36, 40, 56, 78, 80, 81, 85, 87, 99], "overwrit": [4, 81], "separ": [4, 35, 47, 60, 81, 82, 87, 88, 93, 95], "static": 4, "rememb": [4, 85, 87, 89], "part": [4, 9, 36, 40, 42, 61, 63, 64, 80, 81, 86, 97, 100], "ident": [4, 9, 21, 51, 85], "datalab": [5, 7, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 78, 80, 87, 88, 91, 96, 98, 99], "walk": 6, "alongsid": [6, 36, 40, 81, 87], "pre": [6, 7, 9, 36, 40, 81, 82, 96], "runtim": [6, 36, 39, 40, 68, 70, 72, 80, 87, 88], "issue_manager_factori": [6, 13, 81], "myissuemanag": [6, 13], "myissuemanagerforregress": 6, "decor": [6, 13], "ll": [6, 47, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "thing": [6, 40, 89, 96, 99], "next": [6, 56, 78, 80, 84, 85, 87, 91, 93, 96, 98, 99, 100], "dummi": 6, "randint": [6, 30, 47, 81, 82, 87], "mark": [6, 9, 79, 93, 94, 96], "regard": [6, 82, 89], "rand": [6, 47, 81, 82], "is_": [6, 9, 81], "_issu": [6, 9, 81], "issue_score_kei": [6, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 81], "whole": [6, 25, 36, 40, 82], "make_summari": [6, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 81], "popul": [6, 82, 85], "verbosity_level": [6, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], "std": [6, 93], "raw_scor": 6, "bit": 6, "involv": [6, 39, 73, 77, 87, 92], "intermediate_arg": 6, "min": [6, 47, 63, 76, 81, 87, 94], "sin_filt": 6, "sin": 6, "arang": 6, "kernel": 6, "affect": [6, 9, 36, 40, 70, 76, 85, 87], "easili": [6, 45, 79, 80, 82, 84, 85, 89, 91, 92, 94, 95, 96, 97, 98, 99], "hard": [6, 40, 86, 94], "sai": [6, 9, 36, 40, 92, 97], "anoth": [6, 21, 35, 39, 50, 63, 66, 84, 85, 87, 89, 91, 94, 99], "try": [6, 9, 39, 42, 55, 56, 70, 72, 78, 82, 84, 85, 87, 88, 89, 97], "won": [6, 36, 40, 81, 82, 87, 92], "issue_manag": [6, 9, 10, 12, 14, 17, 18, 19, 22, 24, 25, 26, 27, 29, 30, 81], "instanti": [6, 15, 39, 55, 65, 80, 82, 84, 99], "477762": 6, "286455": 6, "term": [6, 9, 45, 51, 64, 80, 81, 82, 84, 85, 88, 89], "4778": 6, "is_basic_issu": 6, "basic_scor": 6, "13": [6, 18, 27, 80, 81, 82, 84, 85, 86, 88, 89, 91, 93, 94, 96, 97, 98, 99, 100], "003042": 6, "058117": 6, "11": [6, 55, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "121908": 6, "15": [6, 49, 68, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98, 99, 100], "169312": 6, "17": [6, 80, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 99, 100], "229044": 6, "2865": 6, "is_intermediate_issu": 6, "intermediate_scor": 6, "000000": [6, 81, 82, 86, 89], "007059": 6, "009967": 6, "010995": 6, "087332": 6, "016296": 6, "03947": 6, "019459": 6, "794251": 6, "underperform": [7, 8, 30], "group": [7, 8, 25, 30, 86, 93, 100], "dbscan": [7, 9, 30, 87], "hdbscan": [7, 87], "etc": [7, 9, 21, 31, 36, 40, 45, 55, 56, 74, 78, 81, 82, 84, 85, 87, 89, 92, 96], "sensit": [7, 9, 49], "ep": [7, 30, 64], "radiu": 7, "min_sampl": [7, 30], "kmean": [7, 87], "your_data": 7, "get_pred_prob": 7, "n_cluster": [7, 30, 87], "cluster_id": [7, 9, 30, 87], "labels_": 7, "underperforming_group": [7, 9, 13, 20, 82, 84, 85, 87, 88, 89], "search": [8, 9, 19, 25, 26, 50, 68, 87, 95], "nondefault": 8, "Near": [8, 87], "imbal": [8, 20, 60, 65, 66, 82], "null": [8, 13, 20, 82, 85, 88, 89], "valuat": [8, 17], "togeth": [8, 9, 45, 81, 82, 84, 85, 88, 89, 96, 99, 100], "built": [8, 47], "own": [8, 36, 38, 40, 54, 60, 61, 64, 70, 74, 80, 82, 84, 85, 87, 88, 91, 92, 96, 97, 98, 99, 100], "prerequisit": 8, "basic": [8, 40, 55, 84, 85, 94], "page": [9, 82, 87, 89], "variou": [9, 12, 29, 38, 52, 54, 78, 81, 82, 84, 85, 86, 89, 91, 93, 98], "why": [9, 85], "matter": [9, 35, 57, 85, 99], "_score": 9, "flag": [9, 21, 25, 42, 47, 57, 58, 61, 68, 78, 80, 81, 82, 84, 85, 86, 88, 89, 93, 94, 96, 97, 99], "badli": [9, 63, 100], "code": [9, 36, 40, 45, 51, 55, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "issue_scor": 9, "outlier_scor": [9, 27, 81, 82, 84, 85, 88, 89, 94], "atyp": [9, 65, 81, 82, 84, 85, 88, 89, 94], "datapoint": [9, 30, 42, 47, 51, 66, 69, 78, 80, 81, 82, 84, 85, 87, 95, 96, 98, 99], "is_issu": [9, 21], "is_outlier_issu": [9, 81, 82, 84, 85, 88, 89], "annot": [9, 35, 46, 56, 57, 58, 60, 61, 63, 64, 73, 76, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 90, 93, 97], "transform": [9, 31, 47, 49, 51, 65, 66, 82, 85, 88, 94, 98, 99, 100], "dissimilar": [9, 84, 85], "preced": 9, "cosin": [9, 65, 94], "incorrect": [9, 63, 66, 69, 80, 81, 82, 84, 85, 88, 89, 93, 96, 98], "due": [9, 39, 42, 66, 70, 72, 80, 81, 82, 84, 85, 88, 89, 96], "appear": [9, 35, 46, 57, 58, 61, 69, 82, 84, 85, 88, 96, 97], "now": [9, 39, 79, 80, 82, 91, 93, 94, 96, 98, 99, 100], "u": [9, 80, 81, 84, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 100], "token": [9, 41, 50, 72, 73, 74, 75, 76, 77, 87, 89, 90], "calcul": [9, 17, 25, 39, 47, 56, 60, 61, 63, 64, 65, 68, 72, 86, 88], "hamper": [9, 86, 88], "analyt": [9, 78, 87, 91], "lead": [9, 63, 66, 88, 93], "draw": [9, 81, 82], "conclus": [9, 85], "veri": [9, 35, 57, 61, 63, 81, 82, 84, 85, 87, 88, 89, 91, 94, 96, 99], "rare": [9, 42, 64, 81, 82, 84, 85, 87, 88, 89], "anomal": [9, 66, 81, 82, 84, 85, 88, 89], "articl": [9, 39, 87], "ai": [9, 78, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 98, 99, 100], "blog": 9, "unexpect": [9, 36, 40, 85], "consequ": 9, "inspect": [9, 80, 82, 88, 89, 93, 96, 99], "neg": [9, 63, 64, 81, 82, 86], "extrem": [9, 81, 82, 84, 85, 87, 88, 89], "rel": [9, 35, 56, 57, 65, 81, 82, 84, 85, 88, 89, 94], "record": [9, 36, 40, 80, 84, 96], "abbrevi": 9, "misspel": 9, "typo": [9, 77], "resolut": 9, "video": [9, 86], "audio": [9, 81, 82, 87, 90], "minor": [9, 50], "variat": 9, "translat": 9, "d": [9, 49, 84, 85, 89, 92, 96, 98, 100], "constant": [9, 30, 68], "median": [9, 29, 49], "question": [9, 21, 78, 89], "nearli": [9, 21, 82, 84, 85, 88], "awar": [9, 79, 89], "presenc": [9, 89], "near_dupl": [9, 13, 18, 81, 82, 84, 85, 87, 88, 89], "signific": [9, 84, 85, 89], "violat": [9, 84, 85, 89], "assumpt": [9, 84, 85, 89], "changepoint": [9, 84, 85, 89], "shift": [9, 84, 85, 89], "drift": [9, 82, 84, 89], "autocorrel": [9, 84, 85, 89], "almost": [9, 84, 85, 89], "adjac": [9, 84, 85, 89], "tend": [9, 35, 45, 84, 85, 89, 97, 100], "sequenti": [9, 36, 40, 55, 88], "gap": 9, "b": [9, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 35, 50, 51, 76, 84, 85, 86, 89, 95, 98, 100], "x1": [9, 61, 64, 93], "x2": [9, 61, 64, 93], "10th": 9, "100th": 9, "90": [9, 76, 84, 89, 95, 96, 98], "similarli": [9, 36, 40, 81, 84, 87, 88, 93], "math": [9, 88], "behind": [9, 65, 89], "proper": [9, 51, 56, 61, 64, 85, 88, 91, 93, 98], "closer": [9, 63, 93], "scenario": [9, 66, 81, 82], "underli": [9, 41, 65, 74, 76, 100], "stem": [9, 65, 94], "evolv": 9, "influenc": 9, "act": [9, 63, 81], "accordingli": [9, 31], "emploi": [9, 92, 94], "partit": [9, 95], "ahead": 9, "good": [9, 36, 40, 49, 55, 57, 63, 66, 70, 72, 73, 78, 84, 85, 88], "fix": [9, 56, 85, 89, 96, 99], "problem": [9, 31, 39, 47, 73, 78, 81, 82, 85, 87, 88], "deploy": [9, 89, 96, 98, 99], "overlook": [9, 63, 93], "fact": 9, "thu": [9, 35, 40, 57, 80, 84, 85, 89, 95, 98, 100], "diagnos": [9, 82, 87], "rarest": [9, 82], "q": [9, 93], "fall": [9, 63, 72, 76, 89, 94], "subpar": 9, "special": [9, 50], "techniqu": [9, 93], "smote": 9, "asymmetr": [9, 35], "class_imbal": [9, 13, 19, 82, 84, 85, 88, 89], "too": [9, 42, 47, 65, 82, 87, 88, 93], "dark": [9, 97], "bright": [9, 100], "blurri": [9, 88], "abnorm": [9, 64, 88, 93], "cluster": [9, 17, 30], "slice": 9, "poor": 9, "subpopul": 9, "faq": [9, 78, 82, 84, 85, 88, 90], "lowest": [9, 56, 64, 82, 87, 88, 91, 92, 93, 97], "get_self_confidence_for_each_label": [9, 47, 66], "power": [9, 84, 85, 86, 88, 89, 100], "r": [9, 39, 68, 81, 82, 96, 97], "tabular": [9, 78, 81, 82, 83, 87, 90, 91], "categor": [9, 65, 81, 82, 83, 87, 96, 98], "encod": [9, 48, 64, 70, 73, 84, 85, 87, 96, 97, 98, 99], "miss": [9, 26, 36, 40, 51, 61, 63, 84, 87, 93, 96], "pattern": 9, "contribut": [9, 17, 93], "isn": [9, 16, 26], "approxim": [9, 17, 39, 65, 91], "shaplei": [9, 17], "knn": [9, 12, 17, 25, 30, 65, 84, 94], "scalabl": 9, "sacrific": 9, "One": [9, 51, 65, 87], "quantif": 9, "data_valu": [9, 13, 20], "exert": [9, 82], "possible_issue_typ": 9, "label_kwarg": 9, "outlier_kwarg": 9, "near_duplicate_kwarg": 9, "non_iid_kwarg": 9, "class_imbalance_kwarg": 9, "underperforming_group_kwarg": 9, "null_kwarg": 9, "data_valuation_kwarg": 9, "health_summary_paramet": [9, 20, 22, 29], "health_summari": [9, 22, 35, 78, 86], "health_summary_kwarg": 9, "tandem": [9, 86], "view": [9, 36, 40, 41, 42, 72, 74, 76, 78, 80, 81, 82, 84, 85, 86, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "ood_kwarg": 9, "outofdistribut": [9, 27, 65, 94], "outsid": [9, 92], "outlierissuemanag": [9, 13, 20, 27, 81], "nearduplicateissuemanag": [9, 13, 18, 20], "noniidissuemanag": [9, 13, 20, 25], "num_permut": [9, 25], "permut": [9, 25], "significance_threshold": [9, 25], "signic": 9, "noniid": [9, 20], "classimbalanceissuemanag": [9, 13, 19, 20], "underperforminggroupissuemanag": [9, 13, 20, 30], "determinin": 9, "neighbour": 9, "min_cluster_sampl": [9, 30], "filter_cluster_id": [9, 20, 30], "clustering_kwarg": [9, 30], "nullissuemanag": [9, 13, 20, 26], "datavaluationissuemanag": [9, 13, 17, 20], "codeblock": 9, "demonstr": [9, 39, 81, 82, 85, 87, 88, 89, 91, 92, 93, 96, 97], "howev": [9, 36, 40, 51, 80, 84, 85, 88, 91, 95, 97, 98, 99], "mandatori": 9, "image_issue_types_kwarg": 9, "32": [9, 80, 81, 86, 91, 93], "fewer": [9, 42, 51, 93], "vice": [9, 57], "versa": [9, 57], "light": [9, 86, 88, 93, 97], "29": [9, 86, 88, 91, 92, 93, 97, 100], "low_inform": [9, 88], "odd_aspect_ratio": [9, 88], "35": [9, 81, 86, 91, 92, 93], "odd_siz": [9, 88], "10": [9, 17, 18, 22, 25, 30, 36, 37, 64, 65, 66, 77, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "doc": [9, 36, 40, 78, 80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "data_issu": [10, 14, 15, 32, 81], "issue_find": [10, 14], "factori": [10, 14, 15], "model_output": [10, 14], "except": [11, 36, 40, 55, 66, 81, 82, 88, 91], "dataformaterror": [11, 14], "add_not": 11, "with_traceback": 11, "tb": 11, "__traceback__": 11, "datasetdicterror": [11, 14], "datasetdict": 11, "datasetloaderror": [11, 14], "dataset_typ": 11, "fail": 11, "hold": 11, "associ": [11, 15, 31, 33, 36, 40, 64, 91], "sublist": 11, "map_to_int": 11, "abc": [11, 21, 31], "is_avail": [11, 88], "dataissu": [12, 14, 15, 32], "serv": [12, 15, 91], "central": [12, 100], "repositori": 12, "strategi": [12, 47, 87], "being": [12, 35, 36, 40, 42, 47, 50, 51, 66, 84, 87, 89, 96, 97, 98], "_infostrategi": 12, "basi": 12, "collect_statist": 12, "reus": [12, 21], "avoid": [12, 36, 39, 40, 42, 51, 58, 61, 64, 68, 70, 72, 81, 82, 87], "recomput": [12, 99], "weighted_knn_graph": 12, "issue_manager_that_computes_knn_graph": 12, "collect_issues_from_issue_manag": 12, "collect_issues_from_imagelab": 12, "imagelab": 12, "set_health_scor": 12, "health": [12, 22, 35, 57, 78], "get_data_statist": [12, 14], "concret": 13, "subclass": [13, 36, 40, 65, 81], "regressionlabelissuemanag": [13, 20, 28, 29], "multilabelissuemanag": [13, 20, 23, 24], "from_str": [13, 33, 43, 47], "my_issu": 13, "logic": [13, 33, 39, 42, 70, 72], "stabl": [14, 20, 23, 28, 38, 43, 51, 54, 65, 79], "issuefind": [14, 15, 32], "modeloutput": [14, 31], "multiclasspredprob": [14, 31], "regressionpredict": [14, 31], "multilabelpredprob": [14, 31], "instati": 15, "public": [15, 89, 93, 97, 100], "creation": [15, 40], "execut": [15, 36, 40, 81, 87, 93], "coordin": [15, 61, 63, 64, 93, 100], "behavior": [15, 35, 36, 40, 64, 87], "At": [15, 64, 87], "get_available_issue_typ": 15, "direct": [16, 26, 36, 40], "valuabl": 17, "vstack": [17, 51, 86, 87, 88, 89, 91, 92], "25": [17, 25, 36, 47, 49, 82, 86, 88, 89, 91, 92, 93, 100], "classvar": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30], "short": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 50, 51], "data_valuation_scor": 17, "item": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 51, 81, 82, 87, 88, 89, 91, 92], "some_info_kei": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30], "additional_info_kei": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30], "default_threshold": [17, 20, 27], "arxiv": [17, 89], "ab": [17, 89, 93], "1911": 17, "07128": 17, "larger": [17, 68, 70, 72, 85, 86, 87, 88], "collect_info": [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], "info_to_omit": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "compos": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 36, 40, 85, 94, 99], "is_x_issu": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "x_score": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "val_a": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "val_b1": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "val_b2": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "report_str": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32], "_": [18, 21, 22, 24, 25, 26, 29, 30, 47, 50, 51, 80, 81, 86, 88, 89, 92, 98], "near_duplicate_set": [18, 20, 81, 82, 84, 85, 87, 88, 89], "occurr": [18, 19, 21, 25, 26, 27, 30, 50], "median_nn_dist": 18, "near_duplicate_scor": [18, 81, 82, 84, 85, 87, 88, 89], "class_imbalance_scor": [19, 82], "bleed": [20, 23, 28, 38], "edg": [20, 23, 28, 38, 63, 78, 89, 100], "sharp": [20, 23, 28, 38], "get_health_summari": [20, 22], "ood": [20, 27, 65, 66, 81, 82, 85, 88, 89, 94], "simplified_kolmogorov_smirnov_test": [20, 25], "outlier_cluster_label": [20, 30], "no_underperforming_cluster_id": [20, 30], "set_knn_graph": [20, 30], "perform_clust": [20, 30], "get_worst_clust": [20, 30], "find_issues_with_predict": [20, 28, 29], "find_issues_with_featur": [20, 28, 29], "believ": [21, 97], "priori": [21, 89], "abstract": [21, 31], "applic": [22, 56, 87, 89, 91, 100], "typevar": [22, 24, 36, 40, 50, 60, 63, 64], "scalartyp": [22, 24], "covari": [22, 24, 68, 96], "summary_dict": 22, "label_scor": [22, 24, 29, 80, 81, 82, 84, 85, 88, 89, 92, 96], "neighbor_histogram": 25, "non_neighbor_histogram": 25, "kolmogorov": 25, "smirnov": 25, "largest": [25, 39, 47, 66, 70, 72, 93, 97], "empir": [25, 46, 56], "cumul": 25, "ecdf": 25, "histogram": [25, 84, 96], "absolut": [25, 29], "dimension": [25, 51, 80, 89, 94], "trial": 25, "non_iid_scor": [25, 82, 84, 85, 89], "null_track": 26, "extend": [26, 48, 88, 93, 94, 100], "superclass": 26, "arbitrari": [26, 35, 72, 76, 81, 94, 96], "prompt": 26, "address": [26, 81, 82, 85, 87, 99], "enabl": [26, 40], "null_scor": [26, 82], "37037": 27, "q3_avg_dist": 27, "iqr_avg_dist": 27, "median_outlier_scor": 27, "multipli": 29, "deleg": 29, "confus": [30, 31, 35, 36, 40, 42, 51, 64, 99, 100], "50": [30, 40, 87, 89, 91, 93, 94, 96, 100], "keepdim": [30, 87], "signifi": 30, "absenc": 30, "find_issues_kwarg": 30, "int64": [30, 80, 91], "npt": 30, "int_": 30, "id": [30, 56, 81, 87, 88, 91], "unique_cluster_id": 30, "_description_": 30, "performed_clust": 30, "worst_cluster_id": 30, "underperforming_group_scor": [30, 82], "convent": [31, 33], "subject": [31, 33], "meant": [31, 33], "Not": 31, "mainli": [31, 94, 100], "content": [31, 65, 80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "fetch": [31, 39, 80, 82], "datset": 32, "exclud": [32, 41, 73, 77, 81, 87, 100], "get_report": 32, "enum": [33, 47], "qualnam": [33, 47], "boundari": [33, 47, 81, 82], "continu": [33, 85, 87, 88, 91, 93, 96, 98, 99, 100], "binari": [33, 47, 51, 58, 60, 89, 100], "simultan": [33, 96], "task_str": 33, "is_classif": 33, "__contains__": [33, 43, 47], "member": [33, 36, 40, 47, 81, 82], "typeerror": [33, 47], "12": [33, 47, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98, 99, 100], "__getitem__": [33, 43, 47], "match": [33, 35, 36, 40, 42, 47, 56, 57, 66, 81, 82, 86, 88, 93, 95, 97], "__iter__": [33, 43, 47], "__len__": [33, 43, 47], "alias": [33, 47], "is_regress": 33, "is_multilabel": 33, "overview": [35, 80, 82, 84, 85, 88, 91, 93, 94, 96, 98, 99, 100], "modifi": [35, 36, 39, 40, 51, 87, 89], "help": [35, 36, 40, 64, 78, 79, 80, 81, 84, 85, 86, 87, 88, 91, 92, 96, 97, 98, 99, 100], "rank_classes_by_label_qu": [35, 82], "merg": [35, 50, 78, 86, 87, 100], "find_overlapping_class": [35, 87, 89], "problemat": [35, 57, 73, 77, 80, 93, 100], "unnorm": [35, 57, 89], "abov": [35, 36, 39, 40, 51, 56, 63, 64, 66, 72, 76, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100], "model_select": [35, 47, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 94, 96, 98, 99], "cross_val_predict": [35, 40, 80, 81, 82, 84, 85, 87, 89, 91, 95, 96, 98, 99], "get_data_labels_from_dataset": 35, "yourfavoritemodel": [35, 89], "cv": [35, 47, 80, 81, 82, 84, 89, 91, 98], "df": [35, 51, 77, 80, 87], "overall_label_qu": [35, 57], "col": 35, "prob": [35, 50, 89, 95], "divid": [35, 57, 66], "label_nois": [35, 57], "human": [35, 86, 97, 100], "clearli": [35, 66, 88, 93, 97], "num": [35, 57, 86, 89], "overlap": [35, 78, 86, 87, 89], "ontolog": 35, "publish": [35, 100], "therefor": [35, 66], "vehicl": [35, 86], "truck": [35, 86, 94, 97], "intuit": [35, 57], "car": [35, 86, 93, 97], "frequent": [35, 56, 84, 87, 96], "characterist": 35, "l": [35, 36, 40, 61, 63, 64], "class1": 35, "class2": 35, "relationship": 35, "dog": [35, 51, 57, 59, 73, 86, 87, 94, 95, 100], "cat": [35, 51, 57, 59, 86, 87, 94, 95], "captur": [35, 80, 93, 94, 97], "co": [35, 36, 37], "noisy_label": [35, 81, 82, 92], "overlapping_class": 35, "descend": [35, 36, 40, 47, 57, 64], "overall_label_health_scor": [35, 57, 89], "suggest": [35, 56, 57, 63, 85, 87, 88, 96, 99], "half": [35, 36, 38, 40, 57, 86, 100], "health_scor": [35, 57], "classes_by_label_qu": [35, 82], "cnn": [36, 38, 40, 88], "cifar": [36, 37, 86, 94], "teach": [36, 37], "bhanml": 36, "blob": 36, "master": [36, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 98, 99], "call_bn": [36, 38], "bn": 36, "input_channel": 36, "n_output": 36, "dropout_r": 36, "top_bn": 36, "architectur": [36, 40], "shown": [36, 64, 81, 87, 91, 94, 95, 97, 100], "forward": [36, 37, 38, 40, 88, 91], "overridden": [36, 40], "although": [36, 40, 65, 84, 98], "recip": [36, 40], "afterward": [36, 40], "sinc": [36, 40, 44, 52, 57, 64, 72, 76, 87, 91, 92, 93, 95, 100], "former": [36, 40], "hook": [36, 40, 86], "silent": [36, 39, 40], "t_destin": [36, 38, 40], "__call__": [36, 38, 40, 43, 47], "add_modul": [36, 38, 40], "child": [36, 40], "fn": [36, 40, 64], "recurs": [36, 40, 47], "submodul": [36, 40], "children": [36, 38, 40, 100], "nn": [36, 37, 40, 88], "init": [36, 40, 89], "no_grad": [36, 40, 88, 94], "init_weight": [36, 40], "linear": [36, 40, 85, 88, 99], "fill_": [36, 40], "net": [36, 40, 80, 86, 88], "in_featur": [36, 40], "out_featur": [36, 40], "bia": [36, 40, 88], "tensor": [36, 37, 40, 80, 85, 88, 94, 99], "requires_grad": [36, 40], "bfloat16": [36, 38, 40], "cast": [36, 40, 80], "buffer": [36, 38, 40], "datatyp": [36, 40], "xdoctest": [36, 40], "undefin": [36, 40], "var": [36, 40], "buf": [36, 40], "20l": [36, 40], "1l": [36, 40], "5l": [36, 40], "call_super_init": [36, 38, 40], "immedi": [36, 40, 94], "compil": [36, 38, 40, 55], "cpu": [36, 38, 40, 42, 80, 88], "move": [36, 40, 47, 79, 86], "cuda": [36, 38, 40, 80, 88], "devic": [36, 40, 80, 88], "gpu": [36, 40, 80, 85, 99], "live": [36, 40], "copi": [36, 40, 68, 80, 81, 82, 84, 87, 92, 95, 96, 98], "doubl": [36, 38, 40], "dump_patch": [36, 38, 40], "eval": [36, 38, 40, 88, 92, 94], "dropout": [36, 40], "batchnorm": [36, 40], "grad": [36, 40], "extra_repr": [36, 38, 40], "line": [36, 40, 78, 81, 86, 91, 94, 100], "get_buff": [36, 38, 40], "target": [36, 37, 40, 68, 69, 94, 96], "throw": [36, 40], "get_submodul": [36, 38, 40], "explan": [36, 40], "fulli": [36, 40, 55, 87], "qualifi": [36, 40], "referenc": [36, 40], "attributeerror": [36, 40], "invalid": [36, 40, 85], "resolv": [36, 40, 100], "get_extra_st": [36, 38, 40], "state_dict": [36, 38, 40], "set_extra_st": [36, 38, 40], "build": [36, 40, 88, 97], "picklabl": [36, 40], "serial": [36, 40], "backward": [36, 40, 88], "break": [36, 40, 88, 93], "pickl": [36, 40, 93], "get_paramet": [36, 38, 40], "let": [36, 40, 65, 66, 80, 82, 84, 85, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "net_b": [36, 40], "net_c": [36, 40], "conv": [36, 40], "conv2d": [36, 40, 88], "16": [36, 40, 47, 72, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 97, 99, 100], "33": [36, 40, 86, 93], "kernel_s": [36, 40], "stride": [36, 40], "200": [36, 40, 66, 86, 93, 100], "diagram": [36, 40, 95], "degre": [36, 40], "queri": [36, 40, 82, 87, 88, 92], "named_modul": [36, 38, 40], "o": [36, 40, 49, 50, 80, 81, 82, 86, 87, 89, 92, 93, 100], "transit": [36, 40], "ipu": [36, 38, 40], "load_state_dict": [36, 38, 40], "strict": [36, 40, 47], "persist": [36, 40], "strictli": [36, 40], "inplac": [36, 40, 91], "preserv": [36, 40, 51], "namedtupl": [36, 40], "missing_kei": [36, 40], "unexpected_kei": [36, 40], "runtimeerror": [36, 40], "idx": [36, 40, 51, 52, 64, 81, 87, 88, 89, 91, 93, 94], "named_buff": [36, 38, 40], "prefix": [36, 40, 80, 100], "remove_dupl": [36, 40], "prepend": [36, 40], "running_var": [36, 40], "named_children": [36, 38, 40], "conv4": [36, 40], "conv5": [36, 40], "memo": [36, 40], "named_paramet": [36, 38, 40], "register_backward_hook": [36, 38, 40], "deprec": [36, 40, 44, 80, 85, 87, 99], "favor": [36, 40], "register_full_backward_hook": [36, 38, 40], "removablehandl": [36, 40], "register_buff": [36, 38, 40], "running_mean": [36, 40], "register_forward_hook": [36, 38, 40], "with_kwarg": [36, 40], "always_cal": [36, 40], "possibli": [36, 40, 84, 98], "fire": [36, 40, 86], "register_module_forward_hook": [36, 40], "regardless": [36, 40, 81, 82], "register_forward_pre_hook": [36, 38, 40], "And": [36, 40], "forward_pr": [36, 40], "register_module_forward_pre_hook": [36, 40], "gradient": [36, 40, 84, 88, 96], "grad_input": [36, 40], "grad_output": [36, 40], "technic": [36, 40], "caller": [36, 40], "register_module_full_backward_hook": [36, 40], "register_full_backward_pre_hook": [36, 38, 40], "backward_pr": [36, 40], "register_module_full_backward_pre_hook": [36, 40], "register_load_state_dict_post_hook": [36, 38, 40], "post": [36, 40], "incompatible_kei": [36, 40], "modif": [36, 40], "thrown": [36, 40], "register_modul": [36, 38, 40], "register_paramet": [36, 38, 40], "register_state_dict_pre_hook": [36, 38, 40], "keep_var": [36, 40], "requires_grad_": [36, 38, 40], "autograd": [36, 40], "freez": [36, 40, 80, 85, 99], "finetun": [36, 40], "gan": [36, 40], "share_memori": [36, 38, 40], "share_memory_": [36, 40], "destin": [36, 40], "shallow": [36, 40], "releas": [36, 40, 79, 80, 87], "design": [36, 40], "ordereddict": [36, 40], "detach": [36, 40, 88], "non_block": [36, 40], "memory_format": [36, 40], "channels_last": [36, 40], "Its": [36, 40, 47, 57, 63], "complex": [36, 40, 80], "integr": [36, 40, 78], "asynchron": [36, 40], "host": [36, 40], "pin": [36, 40, 85, 86, 99], "desir": [36, 40, 50, 64], "4d": [36, 40], "ignore_w": [36, 40], "determinist": [36, 40, 80], "1913": [36, 40], "3420": [36, 40], "5113": [36, 40], "2325": [36, 40], "env": [36, 40], "torch_doctest_cuda1": [36, 40], "gpu1": [36, 40], "1914": [36, 40], "5112": [36, 40], "2324": [36, 40], "float16": [36, 40], "cdoubl": [36, 40], "3741": [36, 40], "2382": [36, 40], "5593": [36, 40], "4443": [36, 40], "complex128": [36, 40], "6122": [36, 40], "1150": [36, 40], "to_empti": [36, 38, 40], "storag": [36, 40, 85, 99], "dst_type": [36, 40], "xpu": [36, 38, 40], "zero_grad": [36, 38, 40, 88], "set_to_non": [36, 40], "reset": [36, 40], "context": [36, 40, 93], "noisili": [37, 89], "han": 37, "2018": 37, "cifar_cnn": [37, 38], "loss_coteach": [37, 38], "y_1": 37, "y_2": 37, "forget_r": 37, "class_weight": 37, "logit": [37, 55, 88], "decim": [37, 51], "quickli": [37, 80, 84, 85, 87, 88, 92, 94, 97, 98, 100], "forget": [37, 47, 100], "rate_schedul": 37, "epoch": [37, 38, 40, 87, 88], "initialize_lr_schedul": [37, 38], "lr": [37, 38, 40], "001": [37, 66, 87], "250": [37, 81, 82, 89, 93], "epoch_decay_start": 37, "80": [37, 84, 92, 96, 98], "schedul": 37, "adjust": [37, 42, 60, 65, 66, 78, 89], "beta": 37, "adam": 37, "adjust_learning_r": [37, 38], "alpha_plan": 37, "beta1_plan": 37, "forget_rate_schedul": [37, 38], "num_gradu": 37, "expon": 37, "tell": [37, 85, 88, 89, 99], "train_load": [37, 40], "model1": [37, 89], "optimizer1": 37, "model2": [37, 89], "optimizer2": 37, "dataload": [37, 88, 94], "parser": 37, "parse_arg": 37, "num_iter_per_epoch": 37, "print_freq": 37, "topk": 37, "top1": 37, "top5": 37, "test_load": 37, "offici": [38, 54, 100], "wish": [38, 54, 94, 97, 100], "adj_confident_thresholds_shar": [38, 39], "labels_shar": [38, 39], "pred_probs_shar": [38, 39], "labelinspector": [38, 39, 87], "get_num_issu": [38, 39], "get_quality_scor": [38, 39], "update_confident_threshold": [38, 39], "score_label_qu": [38, 39], "split_arr": [38, 39], "span_classif": 38, "display_issu": [38, 41, 71, 72, 73, 74, 75, 76, 77, 97, 100], "mnist_pytorch": 38, "get_mnist_dataset": [38, 40], "get_sklearn_digits_dataset": [38, 40], "simplenet": [38, 40], "batch_siz": [38, 39, 40, 70, 72, 87, 88, 94, 97], "log_interv": [38, 40], "momentum": [38, 40], "no_cuda": [38, 40], "test_batch_s": [38, 40, 88], "loader": [38, 40, 88], "set_predict_proba_request": [38, 40], "set_predict_request": [38, 40], "coteach": [38, 79], "mini": [39, 70, 72, 87], "low_self_confid": [39, 42, 58], "self_confid": [39, 42, 43, 47, 58, 60, 66, 74, 76, 87, 89, 98, 99], "conveni": [39, 80, 85, 99], "script": 39, "labels_fil": [39, 87], "pred_probs_fil": [39, 87], "quality_score_kwarg": 39, "num_issue_kwarg": 39, "return_mask": 39, "variant": [39, 56, 97], "read": [39, 44, 82, 87, 89, 94, 100], "zarr": [39, 87], "memmap": [39, 97], "pythonspe": 39, "mmap": [39, 87], "hdf5": 39, "further": [39, 41, 57, 58, 60, 63, 64, 72, 73, 80, 87], "yourfil": 39, "npy": [39, 86, 87, 97], "mmap_mod": [39, 97], "tip": [39, 42, 55, 87], "save_arrai": 39, "your_arrai": 39, "disk": [39, 86, 87], "npz": [39, 100], "maxim": [39, 56, 70, 72, 97], "multiprocess": [39, 42, 58, 70, 72, 87, 88], "linux": [39, 70, 72], "physic": [39, 42, 70, 72, 93], "psutil": [39, 42, 70, 72], "labels_arrai": [39, 52], "predprob": 39, "pred_probs_arrai": 39, "back": [39, 64, 81, 87, 93, 94], "store_result": 39, "becom": [39, 94], "verifi": [39, 87, 91, 94], "long": [39, 56, 65, 91], "enough": [39, 51, 87], "chunk": [39, 95], "ram": [39, 86], "faster": [39, 65, 68, 70, 72, 87, 89], "end_index": 39, "labels_batch": 39, "pred_probs_batch": 39, "batch_result": 39, "indices_of_examples_with_issu": [39, 87], "shortcut": 39, "encount": [39, 42, 70], "1000": [39, 80, 85, 87, 94], "aggreg": [39, 43, 47, 56, 60, 63, 66, 76, 87, 89, 91], "seen": [39, 87, 94, 100], "far": [39, 56], "label_quality_scor": [39, 60, 63, 66, 69, 89, 93], "method1": 39, "method2": 39, "normalized_margin": [39, 42, 43, 47, 58, 60, 66, 74, 76], "low_normalized_margin": [39, 42, 58], "issue_indic": [39, 63, 88], "update_num_issu": 39, "arr": [39, 87], "chunksiz": 39, "convnet": 40, "bespok": [40, 55], "download": [40, 80, 87, 94], "mnist": [40, 78, 80, 86], "handwritten": 40, "digit": [40, 80, 86], "last": [40, 47, 61, 64, 81, 82, 87, 91, 93, 100], "sklearn_digits_test_s": 40, "64": [40, 84, 88, 89, 93, 98], "01": [40, 66, 68, 80, 89, 92, 93], "templat": 40, "flexibli": 40, "among": [40, 56, 89], "test_set": 40, "Be": 40, "overrid": 40, "train_idx": [40, 51, 94], "train_label": [40, 94, 99], "scikit": [40, 51, 65, 78, 80, 81, 82, 84, 85, 87, 90, 96, 99], "span": 41, "sentenc": [41, 50, 74, 76, 77, 85, 99], "token_classif": [41, 50, 74, 76, 77, 87], "encourag": [42, 58, 66, 69], "multilabel_classif": [42, 57, 58, 60, 66, 87, 92], "pred_probs_by_class": 42, "prune_count_matrix_col": 42, "rank_by_kwarg": [42, 58, 66, 89], "num_to_remove_per_class": [42, 58], "bad": [42, 58, 63, 66, 85, 87, 99], "seem": [42, 89, 92], "aren": 42, "confidence_weighted_entropi": [42, 43, 47, 58, 60, 66, 74, 76], "label_issues_idx": [42, 66], "entropi": [42, 44, 46, 47, 65, 66], "prune_by_class": [42, 58, 89], "predicted_neq_given": [42, 58, 89], "prune_counts_matrix": 42, "smallest": [42, 66], "unus": 42, "number_of_mislabeled_examples_in_class_k": 42, "delet": [42, 78, 87, 99], "thread": [42, 58], "window": [42, 80, 86], "shorter": [42, 61], "find_predicted_neq_given": 42, "find_label_issues_using_argmax_confusion_matrix": 42, "remove_noise_from_class": [43, 51], "clip_noise_r": [43, 51], "clip_valu": [43, 51], "value_count": [43, 51, 87], "value_counts_fill_missing_class": [43, 51], "get_missing_class": [43, 51], "round_preserving_sum": [43, 51], "round_preserving_row_tot": [43, 51], "estimate_pu_f1": [43, 51], "confusion_matrix": [43, 51], "print_square_matrix": [43, 51], "print_noise_matrix": [43, 51, 89], "print_inverse_noise_matrix": [43, 51], "print_joint_matrix": [43, 51, 89], "compress_int_arrai": [43, 51], "train_val_split": [43, 51], "subset_x_i": [43, 51], "subset_label": [43, 51], "subset_data": [43, 51], "extract_indices_tf": [43, 51], "unshuffle_tensorflow_dataset": [43, 51], "is_torch_dataset": [43, 51], "is_tensorflow_dataset": [43, 51], "csr_vstack": [43, 51], "append_extra_datapoint": [43, 51], "get_num_class": [43, 51], "num_unique_class": [43, 51], "get_unique_class": [43, 51], "format_label": [43, 51], "smart_display_datafram": [43, 51], "force_two_dimens": [43, 51], "latent_algebra": [43, 79], "compute_ps_py_inv_noise_matrix": [43, 45], "compute_py_inv_noise_matrix": [43, 45], "compute_inv_noise_matrix": [43, 45], "compute_noise_matrix_from_invers": [43, 45], "compute_pi": [43, 45], "compute_pyx": [43, 45], "label_quality_util": 43, "get_normalized_entropi": [43, 44], "multilabel_util": [43, 92], "stack_compl": [43, 48], "get_onehot_num_class": [43, 48], "int2onehot": [43, 48, 92], "onehot2int": [43, 48, 92], "multilabel_scor": [43, 60], "classlabelscor": [43, 47], "exponential_moving_averag": [43, 47, 60], "softmin": [43, 47, 60, 63, 72, 76], "possible_method": [43, 47], "multilabelscor": [43, 47], "get_class_label_quality_scor": [43, 47], "multilabel_pi": [43, 47], "get_cross_validated_multilabel_pred_prob": [43, 47], "transform_distances_to_scor": [43, 49], "token_classification_util": [43, 100], "get_sent": [43, 50, 100], "filter_sent": [43, 50, 100], "process_token": [43, 50], "merge_prob": [43, 50], "color_sent": [43, 50], "assert_valid_input": [43, 52], "assert_valid_class_label": [43, 52], "assert_nonempty_input": [43, 52], "assert_indexing_work": [43, 52], "labels_to_arrai": [43, 52], "labels_to_list_multilabel": [43, 52], "min_allowed_prob": 44, "wikipedia": 44, "activ": [44, 46, 56, 78, 91], "towardsdatasci": 44, "cheatsheet": 44, "ec57bc067c0b": 44, "clip": [44, 51, 80], "behav": 44, "unnecessari": [44, 87], "slightli": [44, 98, 99], "interv": [44, 47, 94], "herein": 45, "inexact": 45, "cours": 45, "propag": 45, "throughout": [45, 51, 68, 80, 91, 97, 100], "increas": [45, 63, 65, 66, 80, 81, 87, 91, 92, 100], "dot": [45, 76, 87], "true_labels_class_count": 45, "pyx": 45, "multiannot": 46, "assert_valid_inputs_multiannot": 46, "labels_multiannot": [46, 56], "ensembl": [46, 47, 56, 66, 84, 87, 92, 94, 96, 98], "allow_single_label": 46, "annotator_id": 46, "assert_valid_pred_prob": 46, "pred_probs_unlabel": [46, 56], "format_multiannotator_label": [46, 56, 91], "formatted_label": [46, 51], "old": [46, 51, 79, 80, 86], "check_consensus_label_class": 46, "consensus_label": [46, 56, 91], "consensus_method": [46, 56], "consensu": [46, 56, 78, 90, 100], "establish": [46, 96, 99], "compute_soft_cross_entropi": 46, "soft": [46, 86], "find_best_temp_scal": 46, "coarse_search_rang": [46, 68, 87], "fine_search_s": [46, 68, 87], "temperatur": [46, 47, 63, 72, 76], "scale": [46, 49, 86, 87, 94, 97, 98], "factor": [46, 47, 49, 70, 72], "minim": [46, 63, 94], "temp_scale_pred_prob": 46, "temp": 46, "sharpen": [46, 86], "smoothen": 46, "get_normalized_margin_for_each_label": [47, 66], "get_confidence_weighted_entropy_for_each_label": [47, 66], "75": [47, 81, 82, 86, 91, 92, 93, 96, 100], "scorer": 47, "alpha": [47, 60, 63, 81, 82, 89, 92, 96], "exponenti": 47, "ema": 47, "s_1": 47, "s_k": 47, "ema_k": 47, "accord": [47, 58, 84, 85, 89, 100], "formula": [47, 49], "_t": 47, "cdot": 47, "s_t": 47, "qquad": 47, "leq": 47, "_1": 47, "give": [47, 66, 89, 91, 97], "recent": [47, 100], "success": 47, "previou": [47, 87, 88, 93], "discount": 47, "s_ema": 47, "175": [47, 88, 89, 93], "underflow": 47, "nan": [47, 56, 84, 91, 96, 98], "aggregated_scor": 47, "base_scor": 47, "base_scorer_kwarg": 47, "aggregator_kwarg": [47, 60], "n_sampl": 47, "n_label": 47, "worst": [47, 91], "class_label_quality_scor": 47, "42": [47, 85, 86, 93, 100], "452": 47, "new_scor": 47, "575": 47, "get_label_quality_scores_per_class": [47, 59, 60], "ml_scorer": 47, "binar": [47, 48], "reformat": [47, 80], "wider": 47, "splitter": 47, "kfold": [47, 88], "onevsrestclassifi": [47, 92], "randomforestclassifi": [47, 89, 92], "n_split": [47, 82, 88, 92], "pred_prob_slic": 48, "onehot": 48, "hot": [48, 58, 64, 70, 73, 84, 86, 87, 96, 97, 98], "onehot_matrix": 48, "avg_dist": 49, "scaling_factor": 49, "exp": [49, 65, 66, 81], "dt": 49, "right": [49, 61, 64, 85, 92, 93, 94, 99], "strength": [49, 64], "pronounc": 49, "differenti": 49, "ly": 49, "rule": [49, 50, 86], "thumb": 49, "ood_features_scor": [49, 65, 94], "88988177": 49, "80519832": 49, "readabl": 50, "lambda": [50, 80, 81, 87, 91], "long_sent": 50, "headlin": 50, "charact": [50, 51], "s1": 50, "s2": 50, "processed_token": 50, "alecnlcb": 50, "entiti": [50, 78, 87, 100], "mapped_ent": 50, "unique_ident": 50, "loc": [50, 81, 82, 88, 100], "nbitbas": [50, 60], "probs_merg": 50, "55": [50, 86, 93, 96], "0125": [50, 76], "0375": 50, "075": 50, "025": 50, "color": [50, 73, 81, 82, 84, 89, 92, 94, 96, 97], "red": [50, 64, 81, 82, 86, 89, 92, 93, 94, 97], "colored_sent": 50, "termcolor": 50, "31msentenc": 50, "0m": 50, "ancillari": 51, "class_without_nois": 51, "any_other_class": 51, "choos": [51, 66, 84, 87, 89, 96, 98], "tradition": 51, "new_sum": 51, "fill": 51, "come": [51, 81, 82, 87, 97], "major": [51, 56, 79, 88, 94], "versu": [51, 89], "obviou": 51, "cgdeboer": 51, "iteround": 51, "reach": 51, "prob_s_eq_1": 51, "claesen": 51, "f1": [51, 64, 85, 89], "BE": 51, "left_nam": 51, "top_nam": 51, "titl": [51, 81, 82, 89, 92, 94], "short_titl": 51, "round_plac": 51, "pretti": [51, 89], "joint_matrix": 51, "num_possible_valu": 51, "holdout_idx": 51, "extract": [51, 65, 80, 85, 91, 94, 97, 99], "allow_shuffl": 51, "turn": [51, 78, 93], "shuffledataset": 51, "histori": 51, "pre_x": 51, "buffer_s": 51, "csr_matric": 51, "append": [51, 80, 86, 87, 88, 89, 91, 92, 93, 94, 100], "bottom": [51, 61, 64, 93], "to_data": 51, "from_data": 51, "taken": 51, "label_matrix": 51, "canon": 51, "displai": [51, 64, 73, 77, 80, 85, 89, 99, 100], "jupyt": [51, 80, 81, 82, 86, 87, 88, 89, 91, 92, 94, 96, 100], "notebook": [51, 56, 80, 82, 86, 87, 89, 91, 92, 93, 97, 100], "consol": 51, "html": [51, 61, 64, 65, 84, 87, 89], "allow_missing_class": 52, "allow_one_class": 52, "length_x": 52, "labellik": 52, "labels_list": [52, 58], "keraswrappermodel": [54, 55, 78], "keraswrappersequenti": [54, 55], "tf": [55, 80], "legaci": 55, "lack": 55, "keraswrapp": 55, "huggingface_keras_imdb": 55, "unit": [55, 100], "model_kwarg": [55, 68], "compile_kwarg": 55, "sparsecategoricalcrossentropi": 55, "layer": [55, 80, 85, 94, 99], "dens": 55, "my_keras_model": 55, "from_logit": 55, "declar": 55, "apply_softmax": 55, "analysi": 56, "analyz": [56, 78, 89, 91, 92], "get_label_quality_multiannot": [56, 91], "vote": 56, "crowdsourc": [56, 78, 91], "dawid": [56, 91], "skene": [56, 91], "analog": [56, 86, 91], "chosen": [56, 66, 87, 91], "crowdlab": [56, 91], "unlabel": [56, 84, 85, 88, 91, 94, 97], "decid": [56, 85, 86, 91, 96, 99, 100], "get_active_learning_scor": [56, 91], "activelab": [56, 91], "priorit": [56, 63, 93, 97, 100], "showcas": 56, "main": 56, "best_qual": 56, "quality_method": 56, "calibrate_prob": 56, "return_detailed_qu": 56, "return_annotator_stat": 56, "return_weight": 56, "label_quality_score_kwarg": 56, "necessarili": [56, 64, 85, 89], "did": [56, 57, 80, 84, 89, 91, 96, 98, 99], "majority_vot": 56, "broken": [56, 64, 86, 96], "highest": [56, 64, 81, 88, 95], "0th": 56, "consensus_quality_scor": [56, 91], "annotator_agr": [56, 91], "reman": 56, "1st": 56, "2nd": [56, 70], "3rd": 56, "consensus_label_suffix": 56, "consensus_quality_score_suffix": 56, "suffix": 56, "emsembl": 56, "weigh": [56, 86], "agreement": [56, 91], "agre": 56, "prevent": [56, 87], "overconfid": [56, 95], "wrong": [56, 61, 63, 79, 81, 82, 85, 87, 89, 93, 99], "detailed_label_qu": [56, 91], "annotator_stat": [56, 91], "model_weight": 56, "annotator_weight": 56, "warn": [56, 81, 82, 87], "labels_info": 56, "num_annot": [56, 91], "deriv": [56, 91], "quality_annotator_1": 56, "quality_annotator_2": 56, "quality_annotator_m": 56, "annotator_qu": [56, 91], "num_examples_label": [56, 91], "agreement_with_consensu": [56, 91], "worst_class": [56, 91], "trustworthi": [56, 91, 96], "get_label_quality_multiannotator_ensembl": 56, "weigtht": 56, "budget": 56, "retrain": [56, 96, 99], "active_learning_scor": 56, "improv": [56, 82, 86, 87, 88, 89, 96, 97, 98, 99], "active_learning_scores_unlabel": 56, "get_active_learning_scores_ensembl": 56, "henc": [56, 80, 81, 91], "get_majority_vote_label": [56, 91], "event": 56, "lastli": [56, 84], "convert_long_to_wide_dataset": 56, "labels_multiannotator_long": 56, "wide": [56, 80, 98, 99], "suitabl": [56, 84, 98], "labels_multiannotator_wid": 56, "common_multilabel_issu": [57, 59], "mutual": [57, 92], "exclus": [57, 92], "rank_classes_by_multilabel_qu": [57, 59], "overall_multilabel_health_scor": [57, 59], "multilabel_health_summari": [57, 59], "classes_by_multilabel_qu": 57, "inner": [58, 72], "find_multilabel_issues_per_class": [58, 59], "per_class_label_issu": 58, "label_issues_list": 58, "pred_probs_list": [58, 66, 88, 89], "anim": [59, 94], "rat": 59, "predat": 59, "pet": 59, "reptil": 59, "manner": [60, 91, 96, 98, 99], "box": [61, 63, 64, 86, 93], "object_detect": [61, 63, 64, 93], "return_indices_ranked_by_scor": [61, 93], "overlapping_label_check": [61, 63], "suboptim": [61, 63], "locat": [61, 63, 93, 97, 100], "bbox": [61, 64, 93], "image_nam": [61, 64], "y1": [61, 64, 93], "y2": [61, 64, 93], "later": [61, 64, 65, 99, 100], "corner": [61, 64, 93], "xyxi": [61, 64, 93], "io": [61, 64, 80, 86], "keras_cv": [61, 64], "bounding_box": [61, 64, 93], "detectron": [61, 64, 93], "detectron2": [61, 64, 93], "readthedoc": [61, 64], "en": [61, 64], "latest": [61, 64], "visual": [61, 62, 64, 81, 82, 88, 96, 98, 100], "draw_box": [61, 64], "mmdetect": [61, 64, 93], "swap": [61, 63, 73, 77], "penal": [61, 63], "concern": [61, 63, 78, 82], "issues_from_scor": [62, 63, 71, 72, 73, 75, 76, 77, 93, 97, 100], "compute_overlooked_box_scor": [62, 63], "compute_badloc_box_scor": [62, 63], "compute_swap_box_scor": [62, 63], "pool_box_scores_per_imag": [62, 63], "object_counts_per_imag": [62, 64, 93], "bounding_box_size_distribut": [62, 64, 93], "class_label_distribut": [62, 64, 93], "get_sorted_bbox_count_idx": [62, 64], "plot_class_size_distribut": [62, 64], "plot_class_distribut": [62, 64], "get_average_per_class_confusion_matrix": [62, 64], "calculate_per_class_metr": [62, 64], "aggregation_weight": 63, "imperfect": [63, 87], "chose": [63, 91, 93], "imperfectli": [63, 93], "dirti": [63, 66, 69, 96], "subtyp": 63, "badloc": 63, "nonneg": 63, "high_probability_threshold": 63, "auxiliary_input": [63, 64], "vari": [63, 82], "iou": [63, 64], "heavili": 63, "auxiliarytypesdict": 63, "pred_label": [63, 99], "pred_label_prob": 63, "pred_bbox": 63, "lab_label": 63, "lab_bbox": 63, "similarity_matrix": 63, "min_possible_similar": 63, "scores_overlook": 63, "low_probability_threshold": 63, "scores_badloc": 63, "accident": [63, 84, 85, 87, 99], "scores_swap": 63, "box_scor": 63, "image_scor": [63, 72, 97], "discov": [64, 82, 100], "auxiliari": [64, 94, 97], "_get_valid_inputs_for_compute_scor": 64, "object_count": 64, "down": 64, "bbox_siz": 64, "class_distribut": 64, "plot": [64, 81, 82, 89, 92, 94, 96, 97], "sorted_idx": [64, 94], "class_to_show": 64, "hidden": [64, 94], "max_class_to_show": 64, "prediction_threshold": 64, "overlai": [64, 93], "figsiz": [64, 81, 82, 88, 89, 92, 94], "save_path": [64, 93], "blue": [64, 86, 89, 93], "overlaid": 64, "side": [64, 86, 93], "figur": [64, 89, 92, 94, 96], "extens": [64, 89, 91], "png": [64, 93], "pdf": [64, 65], "svg": 64, "matplotlib": [64, 81, 82, 88, 89, 92, 93, 94, 96], "num_proc": [64, 88], "intersect": [64, 87], "tp": 64, "fp": 64, "ground": [64, 86, 89, 91, 96], "truth": [64, 89, 91, 96], "bias": 64, "avg_metr": 64, "distionari": 64, "95": [64, 74, 76, 84, 86, 89, 96], "per_class_metr": 64, "Of": 65, "li": 65, "smaller": [65, 92, 93], "find_top_issu": [65, 66, 94], "reli": [65, 80, 81, 82, 85, 93, 94, 96, 99], "dist_metr": 65, "dim": [65, 88, 97], "subtract": [65, 66], "renorm": [65, 66, 87], "least_confid": 65, "sum_": 65, "log": [65, 66, 79], "softmax": [65, 72, 76, 88], "literatur": 65, "gen": 65, "liu": 65, "lochman": 65, "zach": 65, "openaccess": 65, "thecvf": 65, "cvpr2023": 65, "liu_gen_pushing_the_limits_of_softmax": 65, "based_out": 65, "distribution_detection_cvpr_2023_pap": 65, "fit_scor": [65, 94], "ood_predictions_scor": 65, "pretrain": [65, 80, 85, 94, 99], "adjust_confident_threshold": 65, "probabilist": [65, 80, 81, 82, 84, 85, 94, 95, 98], "order_label_issu": [66, 79], "whichev": [66, 95], "argsort": [66, 85, 88, 89, 93, 94, 96, 99], "max_": 66, "get_label_quality_ensemble_scor": [66, 87, 89], "weight_ensemble_members_bi": 66, "custom_weight": 66, "log_loss_search_t_valu": 66, "0001": [66, 86], "scheme": 66, "log_loss_search": 66, "log_loss": [66, 85], "1e0": 66, "1e1": 66, "1e2": 66, "2e2": 66, "quality_scor": [66, 94], "forth": 66, "top_issue_indic": 66, "rank_bi": [66, 79], "weird": [66, 77], "minu": 66, "prob_label": 66, "max_prob_not_label": 66, "idea": [66, 93], "AND": [66, 85], "get_epistemic_uncertainti": [67, 68], "get_aleatoric_uncertainti": [67, 68], "corrupt": [68, 96], "linearregress": [68, 87, 96], "y_with_nois": 68, "n_boot": [68, 87], "include_aleatoric_uncertainti": [68, 87], "sole": [68, 81, 91, 94, 98], "bootstrap": [68, 87, 96], "resampl": [68, 80, 87], "epistem": [68, 87, 94, 96], "aleator": [68, 87, 96], "model_final_kwarg": 68, "coars": 68, "thorough": [68, 87], "fine": [68, 80, 85, 94, 99], "grain": 68, "grid": 68, "varianc": [68, 89], "epistemic_uncertainti": 68, "residu": [68, 69, 87], "deviat": [68, 93, 96], "aleatoric_uncertainti": 68, "outr": 69, "contin": 69, "raw": [69, 78, 79, 82, 86, 88, 91, 93, 94, 96], "aka": [69, 80, 89, 93, 96, 100], "00323821": 69, "33692597": 69, "00191686": 69, "semant": [70, 72, 73, 90], "pixel": [70, 72, 73, 94, 97], "h": [70, 72, 73, 97], "height": [70, 72, 73, 97], "w": [70, 72, 73, 97], "width": [70, 72, 73, 97], "labels_one_hot": [70, 73, 97], "stream": [70, 94, 100], "downsampl": [70, 72, 97], "shrink": [70, 72], "divis": [70, 72, 81], "common_label_issu": [71, 73, 75, 77, 97, 100], "filter_by_class": [71, 73, 97], "segmant": [72, 73], "num_pixel_issu": [72, 97], "product": [72, 87, 88], "pixel_scor": [72, 97], "highlight": [73, 77, 81, 82, 84, 97], "enter": 73, "legend": [73, 81, 82, 92, 93, 96, 97], "colormap": 73, "background": 73, "person": [73, 87, 93, 97, 100], "ambigu": [73, 77, 80, 85, 86, 89, 99, 100], "systemat": [73, 77, 91], "misunderstood": [73, 77], "issues_df": [73, 88], "class_index": 73, "issues_subset": [73, 77], "filter_by_token": [75, 77, 100], "token_score_method": 76, "sentence_score_method": 76, "sentence_score_kwarg": 76, "compris": [76, 77], "token_scor": [76, 100], "converg": 76, "toward": 76, "_softmin_sentence_scor": 76, "sentence_scor": [76, 100], "token_info": 76, "70": [76, 84], "02": [76, 81, 82, 89, 93, 100], "03": [76, 84, 86, 89, 93, 100], "04": [76, 84, 93, 94, 100], "08": [76, 89, 93, 96, 100], "commonli": [77, 79, 81, 82, 92, 100], "But": [77, 85, 89, 96, 100], "restrict": [77, 87], "reliabl": [78, 80, 87, 91, 97, 98], "thousand": 78, "imagenet": [78, 86], "popular": [78, 91, 93], "centric": [78, 84, 85, 88, 90], "capabl": 78, "minut": [78, 80, 84, 85, 86, 91, 92, 93, 96, 97, 98, 99, 100], "conda": 78, "feature_embed": [78, 94], "Then": [78, 87, 88, 98, 99], "your_dataset": [78, 80, 81, 82, 84, 85, 87, 88], "column_name_of_label": [78, 80, 81, 82, 84, 85, 88], "plagu": [78, 82], "untrain": 78, "\u30c4": 78, "label_issues_info": [78, 82], "sklearn_compatible_model": 78, "framework": [78, 92, 93], "complianc": 78, "tag": [78, 92, 100], "sequenc": 78, "recognit": [78, 80, 87, 100], "train_data": [78, 94, 96, 98, 99], "gotten": 78, "test_data": [78, 89, 92, 94, 96, 98, 99], "deal": [78, 82], "tutori": [78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "feel": [78, 80, 82, 87], "free": [78, 80, 82, 84, 85, 87, 88, 89], "ask": [78, 87], "slack": [78, 87], "project": [78, 96], "welcom": 78, "commun": [78, 87], "guidelin": [78, 93], "piec": 78, "studio": [78, 82, 84, 85, 87, 88], "platform": [78, 84, 85, 87, 88], "automl": [78, 87], "foundat": 78, "smart": [78, 84, 85, 87, 88], "edit": [78, 87], "easier": [78, 89], "unreli": [78, 80, 84, 85, 98], "link": [78, 80, 86, 93], "older": 79, "outlin": 79, "substitut": 79, "v2": [79, 84, 98], "get_noise_indic": 79, "psx": 79, "sorted_index_method": 79, "order_label_error": 79, "label_errors_bool": 79, "latent_estim": 79, "num_label_error": 79, "learningwithnoisylabel": 79, "neatli": 79, "organ": [79, 84, 86, 98, 100], "reorgan": 79, "baseline_method": 79, "incorpor": [79, 89], "research": [79, 89], "polyplex": 79, "terminologi": 79, "label_error": 79, "quickstart": [80, 81, 82, 84, 85, 86, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "spoken": 80, "500": [80, 94, 100], "english": [80, 86], "pronunci": 80, "wav": 80, "huggingfac": [80, 81, 82, 88], "voxceleb": 80, "speech": [80, 100], "your_pred_prob": [80, 81, 82, 84, 85], "tensorflow_io": 80, "huggingface_hub": 80, "branch": [80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 98, 99], "reproduc": [80, 84, 89, 91], "command": 80, "wget": [80, 93, 97, 100], "navig": 80, "browser": 80, "jakobovski": 80, "archiv": [80, 100], "v1": 80, "tar": [80, 94], "gz": [80, 94], "mkdir": [80, 100], "spoken_digit": 80, "xf": 80, "6_nicolas_32": 80, "data_path": 80, "listdir": 80, "nondeterminist": 80, "file_nam": 80, "endswith": 80, "file_path": 80, "join": [80, 87, 88], "39": [80, 81, 85, 86, 87, 88, 93, 96, 97, 99, 100], "7_george_26": 80, "0_nicolas_24": 80, "0_nicolas_6": 80, "listen": 80, "display_exampl": 80, "click": [80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "expand": [80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "pulldown": [80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "colab": [80, 81, 82, 86, 87, 88, 89, 91, 92, 94, 96, 100], "tfio": 80, "pathlib": 80, "ipython": 80, "load_wav_16k_mono": 80, "filenam": 80, "khz": 80, "file_cont": 80, "read_fil": 80, "sample_r": 80, "decode_wav": 80, "desired_channel": 80, "squeez": 80, "rate_in": 80, "rate_out": 80, "16000": 80, "wav_file_nam": 80, "audio_r": 80, "wav_file_exampl": 80, "plai": [80, 86, 87], "button": 80, "wav_file_name_exampl": 80, "7_jackson_43": 80, "hear": 80, "extractor": 80, "encoderclassifi": 80, "spkrec": 80, "xvect": 80, "feature_extractor": 80, "from_hparam": 80, "run_opt": 80, "uncom": 80, "ffmpeg": 80, "system": [80, 84, 85, 88, 97], "backend": 80, "wav_audio_file_path": 80, "head": [80, 82, 84, 85, 86, 88, 89, 91, 96, 98, 99], "torchaudio": 80, "extract_audio_embed": 80, "emb": [80, 88], "signal": 80, "encode_batch": 80, "embeddings_list": [80, 88], "embeddings_arrai": 80, "opt": [80, 82, 85, 99], "hostedtoolcach": [80, 82, 85, 99], "x64": [80, 82, 85, 99], "lib": [80, 82, 85, 99], "python3": [80, 82, 85, 99], "site": [80, 82, 85, 99], "650": 80, "userwarn": [80, 81, 82, 85, 99], "stft": 80, "return_complex": 80, "view_as_r": 80, "recov": 80, "trigger": 80, "aten": 80, "src": 80, "nativ": 80, "spectralop": 80, "cpp": 80, "863": [80, 99], "_vf": 80, "n_fft": 80, "hop_length": 80, "win_length": 80, "attr": 80, "512": [80, 88], "14": [80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "196311": 80, "319459": 80, "478975": 80, "2890875": 80, "8170238": 80, "89265": 80, "24": [80, 86, 89, 91, 93, 96], "898056": 80, "256195": 80, "559641": 80, "559721": 80, "62067": 80, "285245": 80, "21": [80, 81, 86, 87, 89, 93, 96, 100], "709627": 80, "5033693": 80, "913803": 80, "819831": 80, "1831515": 80, "208763": 80, "084257": 80, "3210397": 80, "005453": 80, "216152": 80, "478235": 80, "6821785": 80, "053807": 80, "242471": 80, "091424": 80, "78334856": 80, "03954": 80, "23": [80, 86, 88, 89, 93, 96], "569176": 80, "19": [80, 85, 86, 87, 88, 89, 93, 94, 96, 97, 99, 100], "761097": 80, "1258295": 80, "753237": 80, "3508866": 80, "598274": 80, "23712": 80, "2500": 80, "leverag": [80, 85, 87, 89, 91, 99], "tune": [80, 85, 86, 94, 99], "computation": [80, 85, 99], "intens": [80, 85, 99], "held": [80, 84, 85, 86, 93, 94, 95, 98], "straightforward": [80, 84, 98], "benefit": [80, 95, 97, 98], "tol": 80, "num_crossval_fold": [80, 84, 91, 98], "decreas": [80, 87], "accuracy_scor": [80, 85, 89, 98, 99], "cv_accuraci": 80, "9708": 80, "probabilit": [80, 99], "9976": 80, "986": 80, "002161": 80, "176": [80, 86, 89, 92], "002483": 80, "2318": 80, "004411": 80, "1005": 80, "004857": 80, "1871": 80, "007494": 80, "investig": 80, "040587": 80, "999207": 80, "999377": 80, "975220": 80, "999367": 80, "18": [80, 85, 86, 87, 89, 93, 94, 96, 97, 99], "identified_label_issu": [80, 85], "lowest_quality_label": [80, 85, 89, 96, 99], "sort_valu": [80, 82, 84, 85, 87, 88, 89, 91, 92, 96], "516": 80, "1946": 80, "469": 80, "2132": 80, "worth": [80, 89], "iloc": [80, 84, 85, 96, 98, 99], "6_yweweler_25": 80, "7_nicolas_43": 80, "6_theo_27": 80, "6_yweweler_36": 80, "6_yweweler_14": 80, "6_yweweler_35": 80, "6_nicolas_8": 80, "sound": 80, "quit": [80, 94], "22": [80, 81, 86, 88, 89, 92, 93, 100], "blindli": [80, 87, 96, 98, 99], "trust": [80, 87, 89, 91, 95, 96, 98, 99], "underneath": 81, "hood": 81, "alert": 81, "introduct": 81, "mayb": [81, 82, 85], "examin": [81, 82, 84, 93, 98], "your_feature_matrix": [81, 82], "toi": [81, 82, 86, 88, 89, 91], "train_test_split": [81, 82, 94, 98, 99], "inf": [81, 82], "mid": [81, 82], "bins_map": [81, 82], "create_data": [81, 82], "y_bin": [81, 82], "y_i": [81, 82], "y_bin_idx": [81, 82], "y_train": [81, 82, 89, 96], "y_test": [81, 82, 89, 96], "y_train_idx": [81, 82], "y_test_idx": [81, 82], "test_siz": [81, 82, 98, 99], "slide": [81, 82, 86], "decis": [81, 82, 98], "frame": [81, 82], "x_out": [81, 82], "tini": [81, 82], "concaten": [81, 82, 87, 95], "y_out": [81, 82], "y_out_bin": [81, 82], "y_out_bin_idx": [81, 82], "exact_duplicate_idx": [81, 82], "x_duplic": [81, 82], "y_duplic": [81, 82], "y_duplicate_idx": [81, 82], "noisy_labels_idx": [81, 82, 92], "scatter": [81, 82, 89, 92, 96], "black": [81, 82, 86, 96], "cyan": [81, 82], "pyplot": [81, 82, 88, 89, 92, 94, 96], "plt": [81, 82, 88, 89, 92, 94, 96], "plot_data": [81, 82, 89, 92, 96], "fig": [81, 82, 86, 88, 94, 96], "ax": [81, 82, 88, 94, 96], "subplot": [81, 82, 88, 94], "set_titl": [81, 82, 88, 94], "set_xlabel": [81, 82], "x_1": [81, 82], "fontsiz": [81, 82, 88, 89, 92], "set_ylabel": [81, 82], "x_2": [81, 82], "set_xlim": [81, 82], "set_ylim": [81, 82], "linestyl": [81, 82], "circl": [81, 82, 89, 92], "misclassifi": [81, 82], "zip": [81, 82, 88, 93, 100], "label_err": [81, 82], "180": [81, 82, 93], "marker": [81, 82], "facecolor": [81, 82], "edgecolor": [81, 82], "linewidth": [81, 82, 94], "dup": [81, 82], "first_legend": [81, 82], "align": [81, 82], "title_fontproperti": [81, 82], "semibold": [81, 82], "second_legend": [81, 82], "45": [81, 82, 86, 89, 93], "gca": [81, 82], "add_artist": [81, 82], "tight_layout": [81, 82], "ideal": [81, 82], "logist": [81, 82, 85, 91, 94, 99], "remaind": 81, "modal": [81, 82, 87, 91], "132": [81, 82, 89, 93], "9318": 81, "77": [81, 82, 84, 93, 98], "006940": 81, "007830": 81, "40": [81, 82, 85, 86], "014828": 81, "107": [81, 82, 89, 92], "021241": 81, "120": [81, 82, 98], "026407": 81, "notic": [81, 89, 91, 93], "3558": [81, 82], "126": [81, 82, 89, 93], "006636": [81, 82], "130": [81, 82], "012571": [81, 82], "129": [81, 82], "127": [81, 82], "014909": [81, 82], "128": [81, 82, 88], "017443": [81, 82], "6160": [81, 82], "is_near_duplicate_issu": [81, 82, 84, 85, 87, 88, 89], "131": [81, 82, 97], "000000e": [81, 82], "00": [81, 82, 84, 86, 94, 98], "000002": [81, 82], "463180e": [81, 82], "07": [81, 82, 84, 88, 89, 93, 96], "51": [81, 82, 84, 86, 89, 93], "161148": [81, 82], "859087e": [81, 82], "30": [81, 82, 86, 87, 88, 92, 97, 100], "3453": 81, "029542": 81, "031182": 81, "057961": 81, "058244": 81, "home": [81, 82, 85, 86, 99], "runner": [81, 82, 85, 99], "348": 81, "378": 81, "357": 81, "34": [81, 86, 89, 91, 93, 100], "54": [81, 86, 89, 93, 100], "039122": 81, "53": [81, 82, 84, 86, 92, 93, 98], "044598": 81, "105": [81, 93], "105196": 81, "133654": 81, "43": [81, 86, 89, 93], "168033": 81, "125": 81, "101107": 81, "37": [81, 86, 100], "183382": 81, "109": [81, 86, 93], "209259": 81, "211042": 81, "221316": 81, "average_ood_scor": 81, "34530442089193386": 81, "52": [81, 86, 93, 100], "169820": 81, "087324e": 81, "89": [81, 84, 93, 96, 99], "92": [81, 89, 93, 98], "259024": 81, "583757e": 81, "91": [81, 93], "346458": 81, "341292e": 81, "specfi": 81, "new_lab": 81, "scoring_funct": 81, "div": 81, "rem": 81, "inv_scal": 81, "49": [81, 86, 89, 93, 100], "superstitionissuemanag": 81, "unlucki": 81, "superstit": 81, "to_seri": 81, "issues_mask": 81, "summary_scor": 81, "9242": 81, "is_superstition_issu": 81, "superstition_scor": 81, "26": [81, 86, 88, 89, 91, 93], "047581": 81, "090635": 81, "129591": 81, "65": [81, 93, 98], "164840": 81, "demo": [82, 84, 92, 98], "lurk": [82, 88, 89], "_split": 82, "737": 82, "thoroughli": 82, "preprocess": [82, 84, 94, 96, 98, 99], "904": 82, "review": [82, 84, 85, 86, 87, 89, 93, 96, 97, 98, 99, 100], "8561": 82, "001908": 82, "58": [82, 84, 86, 89, 93, 98], "003564": 82, "007331": 82, "008963": 82, "009664": 82, "0227": 82, "is_class_imbalance_issu": 82, "022727": 82, "86": [82, 84, 88, 89, 93, 96, 98], "87": [82, 88, 93, 96, 99], "auto": [82, 86, 87, 96, 98, 99], "conceptu": 82, "856061": 82, "355772": 82, "616034": 82, "821750": 82, "901562": 82, "betweeen": 82, "is_null_issu": 82, "is_non_iid_issu": [82, 84, 85, 89], "is_underperforming_group_issu": 82, "859131": 82, "417707": 82, "664083": 82, "970324": 82, "816953": 82, "375317": 82, "641516": 82, "890575": 82, "531021": 82, "460593": 82, "601188": 82, "826147": 82, "752808": 82, "321635": 82, "562539": 82, "948362": 82, "090243": 82, "472909": 82, "746763": 82, "878267": 82, "examples_w_issu": [82, 87], "013445": 82, "025184": 82, "026376": 82, "inde": [82, 85], "miscellan": [82, 100], "428571": 82, "111111": 82, "571429": 82, "407407": 82, "592593": 82, "337838": 82, "092593": 82, "662162": 82, "333333": [82, 86], "952381": 82, "666667": 82, "portion": 82, "huge": [82, 89], "worri": [82, 85], "critic": 82, "highli": [82, 88], "sql": [84, 98], "databas": [84, 98], "excel": [84, 98], "parquet": [84, 98], "student": [84, 96, 98, 100], "grade": [84, 96, 98], "900": [84, 96, 98], "exam": [84, 96, 98], "letter": [84, 98, 100], "hundr": [84, 98], "histgradientboostingclassifi": 84, "standardscal": [84, 94, 98], "grades_data": [84, 98], "read_csv": [84, 85, 96, 98, 99], "stud_id": [84, 98], "exam_1": [84, 96, 98], "exam_2": [84, 96, 98], "exam_3": [84, 96, 98], "letter_grad": [84, 98], "f48f73": [84, 98], "0bd4e7": [84, 98], "81": [84, 85, 93, 96, 98, 100], "great": [84, 86, 98], "particip": [84, 98], "cb9d7a": [84, 98], "61": [84, 89, 93, 96, 98], "94": [84, 86, 89, 93, 96, 98], "78": [84, 86, 89, 93, 96, 98], "9acca4": [84, 98], "48": [84, 86, 89, 93, 98], "x_raw": [84, 98], "cat_featur": 84, "x_encod": [84, 98], "get_dummi": [84, 96, 98], "drop_first": [84, 98], "numeric_featur": [84, 98], "scaler": [84, 94, 98], "x_process": [84, 98], "fit_transform": [84, 98], "bring": [84, 85, 88, 91, 96, 98, 99], "byod": [84, 85, 88, 91, 96, 98, 99], "boost": [84, 87, 91, 96], "xgboost": [84, 87, 96], "think": [84, 85, 87, 92, 97, 100], "carefulli": [84, 85, 88, 98], "nonzero": 84, "suspici": [84, 98], "tabl": [84, 86, 91, 98], "358": 84, "294": [84, 93], "46": [84, 86, 89, 93], "941": 84, "7109": 84, "000005": [84, 85, 96], "886": 84, "000059": 84, "709": 84, "000104": 84, "723": [84, 88], "000169": 84, "689": 84, "000181": 84, "3590": 84, "051882e": 84, "683133e": 84, "536582e": 84, "406589e": 84, "324246e": 84, "6165": 84, "582": 84, "185": [84, 86, 93], "187": [84, 86], "27": [84, 86, 89, 93, 100], "898": 84, "637": [84, 98], "0014": [84, 86], "595": 84, "702427": 84, "147": [84, 89, 93], "711186": 84, "157": [84, 89], "721394": 84, "771": 84, "731979": 84, "740335": 84, "0014153602099278074": 84, "issue_result": 84, "000842": 84, "555944": 84, "004374": 84, "sorted_issu": 84, "73": [84, 86, 92, 93, 96], "deserv": 84, "outlier_result": 84, "sorted_outli": 84, "56": [84, 86, 96], "96": [84, 86, 89, 92, 93, 96], "lt": [84, 85, 86, 91, 94], "style": [84, 97], "font": 84, "18px": 84, "ff00ff": 84, "bac": 84, "unintend": [84, 85], "mistak": [84, 85, 88, 98, 99], "duplicate_result": 84, "690": 84, "246": [84, 93], "perhap": [84, 89, 91], "twice": 84, "67": [84, 86, 93, 96], "wari": [84, 85, 87], "super": [84, 85, 88], "intent": [85, 99], "servic": [85, 87, 99], "onlin": [85, 99], "bank": [85, 86, 99], "banking77": [85, 99], "oo": [85, 99], "000": [85, 86, 88, 99, 100], "categori": [85, 88, 99], "scope": [85, 99], "dive": 85, "your_featur": 85, "sentence_transform": [85, 99], "sentencetransform": [85, 99], "payment": [85, 99], "cancel_transf": [85, 99], "transfer": [85, 99], "fund": [85, 99], "cancel": [85, 99], "transact": [85, 99], "my": [85, 99], "revert": [85, 99], "morn": [85, 99], "realis": [85, 99], "yesterdai": [85, 99], "rent": [85, 99], "realli": [85, 91, 97, 99], "tomorrow": [85, 99], "raw_text": [85, 99], "getting_spare_card": [85, 99], "beneficiary_not_allow": [85, 99], "change_pin": [85, 99], "supported_cards_and_curr": [85, 99], "apple_pay_or_google_pai": [85, 99], "visa_or_mastercard": [85, 99], "card_payment_fee_charg": [85, 99], "card_about_to_expir": [85, 99], "lost_or_stolen_phon": [85, 99], "utter": [85, 99], "suit": [85, 86, 87, 99], "electra": [85, 99], "discrimin": [85, 99], "googl": [85, 99], "text_embed": 85, "No": [85, 87, 99], "google_electra": [85, 99], "pool": [85, 87, 94, 99], "_util": [85, 99], "831": [85, 99], "typedstorag": [85, 99], "untypedstorag": [85, 99], "untyped_storag": [85, 99], "fget": [85, 99], "__get__": [85, 99], "owner": [85, 99], "400": [85, 99], "data_dict": [85, 89, 91], "85": [85, 93], "38": [85, 86, 93], "9710": 85, "981": 85, "974": 85, "000146": 85, "982": [85, 86], "000224": 85, "971": 85, "000507": 85, "980": [85, 86], "000960": 85, "3584": 85, "994": 85, "009642": 85, "999": 85, "013067": 85, "013841": 85, "433": 85, "014722": 85, "989": 85, "018224": 85, "6070": 85, "160": [85, 96], "095724": 85, "148": 85, "006237": 85, "546": 85, "099341": 85, "514": 85, "006485": 85, "481": 85, "123418": 85, "008165": 85, "0000": [85, 86, 89], "313": [85, 93], "564102": 85, "572258": 85, "28": [85, 86, 88, 89, 91, 100], "574915": 85, "31": [85, 86, 89, 91, 93], "575507": 85, "575874": 85, "792090": 85, "257611": 85, "698710": 85, "182121": 85, "771619": 85, "to_numpi": [85, 87, 96, 99], "data_with_suggested_label": 85, "suggested_label": 85, "charg": [85, 99], "cash": [85, 99], "holidai": [85, 99], "sent": [85, 99, 100], "card": [85, 86, 99], "mine": [85, 99], "expir": [85, 99], "me": [85, 99], "withdraw": 85, "monei": 85, "whoever": [85, 99], "outlier_issu": [85, 88], "lowest_quality_outli": 85, "OR": 85, "636c65616e6c616220697320617765736f6d6521": 85, "phone": [85, 86], "gone": 85, "gt": [85, 91, 100], "samp": 85, "br": 85, "press": [85, 100], "nonsens": 85, "sens": 85, "detriment": 85, "duplicate_issu": 85, "fee": 85, "pai": 85, "go": [85, 86, 89], "strongli": 85, "p_valu": 85, "benign": 85, "shortlist": [85, 96, 99], "curat": [85, 90], "mnist_test_set": 86, "imagenet_val_set": 86, "tench": 86, "goldfish": 86, "white": [86, 100], "shark": 86, "tiger": 86, "hammerhead": 86, "electr": 86, "rai": 86, "stingrai": 86, "cock": 86, "hen": 86, "ostrich": 86, "brambl": 86, "goldfinch": 86, "hous": 86, "finch": 86, "junco": 86, "indigo": 86, "bunt": 86, "american": [86, 100], "robin": 86, "bulbul": 86, "jai": 86, "magpi": 86, "chickade": 86, "dipper": 86, "kite": 86, "bald": 86, "eagl": 86, "vultur": 86, "grei": 86, "owl": 86, "salamand": 86, "smooth": 86, "newt": 86, "spot": [86, 93], "axolotl": 86, "bullfrog": 86, "tree": 86, "frog": [86, 94], "tail": 86, "loggerhead": 86, "sea": 86, "turtl": 86, "leatherback": 86, "mud": 86, "terrapin": 86, "band": 86, "gecko": 86, "green": [86, 100], "iguana": 86, "carolina": 86, "anol": 86, "desert": 86, "grassland": 86, "whiptail": 86, "lizard": 86, "agama": 86, "frill": 86, "neck": 86, "allig": 86, "gila": 86, "monster": 86, "european": 86, "chameleon": 86, "komodo": 86, "dragon": 86, "nile": 86, "crocodil": 86, "triceratop": 86, "worm": 86, "snake": 86, "ring": 86, "eastern": 86, "hog": 86, "nose": 86, "kingsnak": 86, "garter": 86, "water": 86, "vine": 86, "night": 86, "boa": 86, "constrictor": 86, "african": 86, "rock": 86, "indian": 86, "cobra": 86, "mamba": 86, "saharan": 86, "horn": 86, "viper": 86, "diamondback": 86, "rattlesnak": 86, "sidewind": 86, "trilobit": 86, "harvestman": 86, "scorpion": 86, "yellow": 86, "garden": 86, "spider": 86, "barn": 86, "southern": 86, "widow": 86, "tarantula": 86, "wolf": 86, "tick": 86, "centiped": 86, "grous": 86, "ptarmigan": 86, "ruf": 86, "prairi": 86, "peacock": 86, "quail": 86, "partridg": 86, "parrot": 86, "macaw": 86, "sulphur": 86, "crest": 86, "cockatoo": 86, "lorikeet": 86, "coucal": 86, "bee": 86, "eater": 86, "hornbil": 86, "hummingbird": 86, "jacamar": 86, "toucan": 86, "duck": [86, 99], "breast": 86, "mergans": 86, "goos": 86, "swan": 86, "tusker": 86, "echidna": 86, "platypu": 86, "wallabi": 86, "koala": 86, "wombat": 86, "jellyfish": 86, "anemon": 86, "brain": 86, "coral": 86, "flatworm": 86, "nematod": 86, "conch": 86, "snail": 86, "slug": 86, "chiton": 86, "chamber": 86, "nautilu": 86, "dung": 86, "crab": 86, "fiddler": 86, "king": 86, "lobster": 86, "spini": 86, "crayfish": 86, "hermit": 86, "isopod": 86, "stork": 86, "spoonbil": 86, "flamingo": 86, "heron": 86, "egret": 86, "bittern": 86, "crane": 86, "bird": [86, 94], "limpkin": 86, "gallinul": 86, "coot": 86, "bustard": 86, "ruddi": 86, "turnston": 86, "dunlin": 86, "redshank": 86, "dowitch": 86, "oystercatch": 86, "pelican": 86, "penguin": 86, "albatross": 86, "whale": 86, "killer": 86, "dugong": 86, "lion": 86, "chihuahua": 86, "japanes": 86, "chin": 86, "maltes": 86, "pekinges": 86, "shih": 86, "tzu": 86, "charl": 86, "spaniel": 86, "papillon": 86, "terrier": 86, "rhodesian": 86, "ridgeback": 86, "afghan": [86, 100], "hound": 86, "basset": 86, "beagl": 86, "bloodhound": 86, "bluetick": 86, "coonhound": 86, "tan": 86, "walker": 86, "foxhound": 86, "redbon": 86, "borzoi": 86, "irish": 86, "wolfhound": 86, "italian": 86, "greyhound": 86, "whippet": 86, "ibizan": 86, "norwegian": 86, "elkhound": 86, "otterhound": 86, "saluki": 86, "scottish": 86, "deerhound": 86, "weimaran": 86, "staffordshir": 86, "bull": 86, "bedlington": 86, "border": 86, "kerri": 86, "norfolk": 86, "norwich": 86, "yorkshir": 86, "wire": 86, "fox": 86, "lakeland": 86, "sealyham": 86, "airedal": 86, "cairn": 86, "australian": 86, "dandi": 86, "dinmont": 86, "boston": 86, "miniatur": 86, "schnauzer": 86, "giant": 86, "tibetan": 86, "silki": 86, "coat": [86, 88], "wheaten": 86, "west": 86, "highland": 86, "lhasa": 86, "apso": 86, "flat": 86, "retriev": 86, "curli": 86, "golden": 86, "labrador": 86, "chesapeak": 86, "bai": 86, "german": [86, 100], "shorthair": 86, "pointer": 86, "vizsla": 86, "setter": 86, "gordon": 86, "brittani": 86, "clumber": 86, "springer": 86, "welsh": 86, "cocker": 86, "sussex": 86, "kuvasz": 86, "schipperk": 86, "groenendael": 86, "malinoi": 86, "briard": 86, "kelpi": 86, "komondor": 86, "sheepdog": 86, "shetland": 86, "colli": 86, "bouvier": 86, "de": 86, "flandr": 86, "rottweil": 86, "shepherd": 86, "dobermann": 86, "pinscher": 86, "swiss": [86, 100], "mountain": 86, "bernes": 86, "appenzel": 86, "sennenhund": 86, "entlebuch": 86, "boxer": 86, "bullmastiff": 86, "mastiff": 86, "french": 86, "bulldog": 86, "dane": 86, "st": 86, "bernard": 86, "huski": 86, "alaskan": 86, "malamut": 86, "siberian": 86, "dalmatian": 86, "affenpinsch": 86, "basenji": 86, "pug": 86, "leonberg": 86, "newfoundland": 86, "pyrenean": 86, "samoi": 86, "pomeranian": 86, "chow": 86, "keeshond": 86, "griffon": 86, "bruxelloi": 86, "pembrok": 86, "corgi": 86, "cardigan": 86, "poodl": 86, "mexican": 86, "hairless": 86, "tundra": 86, "coyot": 86, "dingo": 86, "dhole": 86, "wild": 86, "hyena": 86, "kit": 86, "arctic": 86, "tabbi": 86, "persian": 86, "siames": 86, "egyptian": 86, "mau": 86, "cougar": 86, "lynx": 86, "leopard": 86, "snow": 86, "jaguar": 86, "cheetah": 86, "brown": [86, 97], "bear": 86, "polar": 86, "sloth": 86, "mongoos": 86, "meerkat": 86, "beetl": 86, "ladybug": 86, "longhorn": 86, "leaf": 86, "rhinocero": 86, "weevil": 86, "fly": 86, "ant": 86, "grasshopp": 86, "cricket": 86, "stick": 86, "insect": 86, "cockroach": 86, "manti": 86, "cicada": 86, "leafhopp": 86, "lacew": 86, "dragonfli": 86, "damselfli": 86, "admir": 86, "ringlet": 86, "monarch": 86, "butterfli": 86, "gossam": 86, "wing": 86, "starfish": 86, "urchin": 86, "cucumb": 86, "cottontail": 86, "rabbit": 86, "hare": 86, "angora": 86, "hamster": 86, "porcupin": 86, "squirrel": 86, "marmot": 86, "beaver": 86, "guinea": 86, "pig": 86, "sorrel": 86, "zebra": 86, "boar": 86, "warthog": 86, "hippopotamu": 86, "ox": 86, "buffalo": 86, "bison": 86, "bighorn": 86, "sheep": 86, "alpin": 86, "ibex": 86, "hartebeest": 86, "impala": 86, "gazel": 86, "dromedari": 86, "llama": 86, "weasel": 86, "mink": 86, "polecat": 86, "foot": 86, "ferret": 86, "otter": 86, "skunk": 86, "badger": 86, "armadillo": 86, "toed": 86, "orangutan": 86, "gorilla": 86, "chimpanze": 86, "gibbon": 86, "siamang": 86, "guenon": 86, "pata": 86, "monkei": 86, "baboon": 86, "macaqu": 86, "langur": 86, "colobu": 86, "probosci": 86, "marmoset": 86, "capuchin": 86, "howler": 86, "titi": 86, "geoffroi": 86, "lemur": 86, "indri": 86, "asian": 86, "eleph": 86, "bush": 86, "snoek": 86, "eel": 86, "coho": 86, "salmon": 86, "beauti": 86, "clownfish": 86, "sturgeon": 86, "garfish": 86, "lionfish": 86, "pufferfish": 86, "abacu": 86, "abaya": 86, "academ": 86, "gown": 86, "accordion": 86, "acoust": 86, "guitar": 86, "aircraft": 86, "carrier": 86, "airlin": 86, "airship": 86, "altar": 86, "ambul": 86, "amphibi": 86, "clock": [86, 100], "apiari": 86, "apron": 86, "wast": 86, "assault": 86, "rifl": 86, "backpack": 86, "bakeri": 86, "balanc": 86, "beam": 86, "balloon": 86, "ballpoint": 86, "pen": 86, "aid": 86, "banjo": 86, "balust": 86, "barbel": 86, "barber": 86, "chair": [86, 93], "barbershop": 86, "baromet": 86, "barrel": 86, "wheelbarrow": 86, "basebal": 86, "basketbal": 86, "bassinet": 86, "bassoon": 86, "swim": 86, "cap": 86, "bath": 86, "towel": 86, "bathtub": 86, "station": 86, "wagon": 86, "lighthous": 86, "beaker": 86, "militari": 86, "beer": 86, "bottl": 86, "glass": 86, "bell": 86, "cot": 86, "bib": 86, "bicycl": [86, 97], "bikini": 86, "binder": 86, "binocular": 86, "birdhous": 86, "boathous": 86, "bobsleigh": 86, "bolo": 86, "tie": 86, "poke": 86, "bonnet": 86, "bookcas": 86, "bookstor": 86, "bow": 86, "brass": 86, "bra": 86, "breakwat": 86, "breastplat": 86, "broom": 86, "bucket": 86, "buckl": 86, "bulletproof": 86, "vest": 86, "butcher": 86, "shop": 86, "taxicab": 86, "cauldron": 86, "candl": 86, "cannon": 86, "cano": 86, "mirror": [86, 93], "carousel": 86, "tool": [86, 89, 91], "carton": 86, "wheel": 86, "teller": 86, "cassett": 86, "player": 86, "castl": 86, "catamaran": 86, "cd": 86, "cello": 86, "mobil": [86, 100], "chain": 86, "fenc": [86, 97], "mail": 86, "chainsaw": 86, "chest": 86, "chiffoni": 86, "chime": 86, "china": 86, "cabinet": 86, "christma": 86, "stock": 86, "church": 86, "movi": 86, "theater": 86, "cleaver": 86, "cliff": 86, "dwell": 86, "cloak": 86, "clog": 86, "cocktail": 86, "shaker": 86, "coffe": 86, "mug": 86, "coffeemak": 86, "coil": 86, "lock": 86, "keyboard": 86, "confectioneri": 86, "ship": [86, 94], "corkscrew": 86, "cornet": 86, "cowboi": 86, "boot": 86, "hat": 86, "cradl": 86, "crash": 86, "helmet": 86, "crate": 86, "infant": 86, "bed": 86, "crock": 86, "pot": 86, "croquet": 86, "crutch": 86, "cuirass": 86, "dam": 86, "desk": 86, "desktop": 86, "rotari": 86, "dial": 86, "telephon": 86, "diaper": 86, "watch": 86, "dine": 86, "dishcloth": 86, "dishwash": 86, "disc": 86, "brake": 86, "dock": 86, "sled": 86, "dome": 86, "doormat": 86, "drill": 86, "rig": 86, "drum": 86, "drumstick": 86, "dumbbel": 86, "dutch": 86, "oven": 86, "fan": 86, "locomot": 86, "entertain": 86, "center": 86, "envelop": 86, "espresso": 86, "powder": 86, "feather": 86, "fireboat": 86, "engin": [86, 97], "screen": 86, "sheet": 86, "flagpol": 86, "flute": 86, "footbal": 86, "forklift": 86, "fountain": 86, "poster": 86, "freight": 86, "fry": 86, "pan": 86, "fur": 86, "garbag": 86, "ga": 86, "pump": 86, "goblet": 86, "kart": 86, "golf": 86, "cart": 86, "gondola": 86, "gong": 86, "grand": 86, "piano": 86, "greenhous": 86, "grill": 86, "groceri": 86, "guillotin": 86, "barrett": 86, "hair": 86, "sprai": 86, "hammer": 86, "dryer": 86, "hand": [86, 89], "handkerchief": 86, "drive": 86, "harmonica": 86, "harp": 86, "harvest": 86, "hatchet": 86, "holster": 86, "honeycomb": 86, "hoop": 86, "skirt": 86, "horizont": 86, "bar": 86, "hors": [86, 94, 99], "drawn": 86, "hourglass": 86, "ipod": 86, "cloth": 86, "iron": 86, "jack": 86, "lantern": 86, "jean": 86, "jeep": 86, "shirt": [86, 88], "jigsaw": 86, "puzzl": 86, "pull": 86, "rickshaw": 86, "joystick": 86, "kimono": 86, "knee": 86, "pad": 86, "knot": 86, "ladl": 86, "lampshad": 86, "laptop": 86, "lawn": 86, "mower": 86, "knife": 86, "lifeboat": 86, "lighter": 86, "limousin": 86, "ocean": 86, "liner": 86, "lipstick": 86, "slip": 86, "shoe": 86, "lotion": 86, "speaker": 86, "loup": 86, "sawmil": 86, "magnet": 86, "compass": 86, "bag": [86, 88, 94, 95], "mailbox": 86, "tight": 86, "tank": 86, "manhol": 86, "maraca": 86, "marimba": 86, "maypol": 86, "maze": 86, "cup": [86, 93], "medicin": 86, "megalith": 86, "microphon": 86, "microwav": 86, "milk": 86, "minibu": 86, "miniskirt": 86, "minivan": 86, "missil": 86, "mitten": 86, "mix": 86, "bowl": 86, "modem": 86, "monasteri": 86, "monitor": 86, "mope": 86, "mortar": 86, "mosqu": 86, "mosquito": 86, "scooter": 86, "bike": 86, "tent": 86, "mous": [86, 87], "mousetrap": 86, "van": 86, "muzzl": 86, "nail": 86, "brace": 86, "necklac": 86, "nippl": 86, "obelisk": 86, "obo": 86, "ocarina": 86, "odomet": 86, "oil": 86, "oscilloscop": 86, "overskirt": 86, "bullock": 86, "oxygen": 86, "packet": 86, "paddl": 86, "padlock": 86, "paintbrush": 86, "pajama": 86, "palac": [86, 100], "parachut": 86, "park": 86, "bench": 86, "meter": 86, "passeng": 86, "patio": 86, "payphon": 86, "pedest": 86, "pencil": 86, "perfum": 86, "petri": 86, "dish": 86, "photocopi": 86, "plectrum": 86, "pickelhaub": 86, "picket": 86, "pickup": 86, "pier": 86, "piggi": 86, "pill": 86, "pillow": 86, "ping": 86, "pong": 86, "pinwheel": 86, "pirat": 86, "pitcher": 86, "plane": 86, "planetarium": 86, "plastic": 86, "plate": 86, "rack": 86, "plow": 86, "plunger": 86, "polaroid": 86, "camera": 86, "pole": [86, 97], "polic": 86, "poncho": 86, "billiard": 86, "soda": 86, "potter": 86, "prayer": 86, "rug": 86, "printer": 86, "prison": 86, "projectil": 86, "projector": 86, "hockei": 86, "puck": 86, "punch": 86, "purs": 86, "quill": 86, "quilt": 86, "race": 86, "racket": 86, "radiat": 86, "radio": 86, "telescop": 86, "rain": 86, "recreat": 86, "reel": 86, "reflex": 86, "refriger": 86, "remot": 86, "restaur": 86, "revolv": 86, "rotisseri": 86, "eras": 86, "rugbi": 86, "ruler": 86, "safe": 86, "safeti": 86, "salt": 86, "sandal": [86, 88], "sarong": 86, "saxophon": 86, "scabbard": 86, "school": 86, "bu": [86, 97], "schooner": 86, "scoreboard": 86, "crt": 86, "screw": 86, "screwdriv": 86, "seat": 86, "belt": 86, "sew": 86, "shield": 86, "shoji": 86, "basket": 86, "shovel": 86, "shower": 86, "curtain": 86, "ski": 86, "sleep": 86, "door": 86, "slot": 86, "snorkel": 86, "snowmobil": 86, "snowplow": 86, "soap": 86, "dispens": 86, "soccer": [86, 100], "sock": 86, "solar": 86, "thermal": 86, "collector": 86, "sombrero": 86, "soup": 86, "heater": 86, "shuttl": 86, "spatula": 86, "motorboat": 86, "web": 86, "spindl": 86, "sport": [86, 100], "spotlight": 86, "stage": 86, "steam": 86, "arch": 86, "bridg": 86, "steel": 86, "stethoscop": 86, "scarf": 86, "stone": 86, "wall": [86, 97], "stopwatch": 86, "stove": 86, "strainer": 86, "tram": 86, "stretcher": 86, "couch": 86, "stupa": 86, "submarin": 86, "sundial": 86, "sunglass": 86, "sunscreen": 86, "suspens": 86, "mop": 86, "sweatshirt": 86, "swimsuit": 86, "swing": 86, "switch": 86, "syring": 86, "lamp": 86, "tape": 86, "teapot": 86, "teddi": 86, "televis": [86, 100], "tenni": 86, "thatch": 86, "roof": 86, "front": 86, "thimbl": 86, "thresh": 86, "throne": 86, "tile": 86, "toaster": 86, "tobacco": 86, "toilet": 86, "totem": 86, "tow": 86, "tractor": 86, "semi": 86, "trailer": 86, "trai": 86, "trench": 86, "tricycl": 86, "trimaran": 86, "tripod": 86, "triumphal": 86, "trolleybu": 86, "trombon": 86, "tub": 86, "turnstil": 86, "typewrit": 86, "umbrella": 86, "unicycl": 86, "upright": 86, "vacuum": 86, "cleaner": 86, "vase": 86, "vault": 86, "velvet": 86, "vend": 86, "vestment": 86, "viaduct": 86, "violin": 86, "volleybal": 86, "waffl": 86, "wallet": 86, "wardrob": 86, "sink": 86, "wash": 86, "jug": 86, "tower": 86, "whiskei": 86, "whistl": 86, "wig": 86, "shade": [86, 97], "windsor": 86, "wine": 86, "wok": 86, "wooden": 86, "spoon": 86, "wool": 86, "rail": 86, "shipwreck": 86, "yawl": 86, "yurt": 86, "websit": 86, "comic": 86, "book": 86, "crossword": 86, "traffic": [86, 93, 97], "sign": [86, 97, 100], "dust": 86, "jacket": [86, 93], "menu": 86, "guacamol": 86, "consomm": 86, "trifl": 86, "ic": 86, "cream": 86, "pop": 86, "baguett": 86, "bagel": 86, "pretzel": 86, "cheeseburg": 86, "mash": 86, "potato": 86, "cabbag": 86, "broccoli": 86, "cauliflow": 86, "zucchini": 86, "spaghetti": 86, "squash": 86, "acorn": 86, "butternut": 86, "artichok": 86, "pepper": 86, "cardoon": 86, "mushroom": 86, "granni": 86, "smith": 86, "strawberri": 86, "orang": 86, "lemon": 86, "pineappl": 86, "banana": 86, "jackfruit": 86, "custard": 86, "appl": 86, "pomegran": 86, "hai": 86, "carbonara": 86, "chocol": 86, "syrup": 86, "dough": 86, "meatloaf": 86, "pizza": 86, "pie": 86, "burrito": 86, "eggnog": 86, "alp": 86, "bubbl": 86, "reef": 86, "geyser": 86, "lakeshor": 86, "promontori": 86, "shoal": 86, "seashor": 86, "vallei": 86, "volcano": 86, "bridegroom": 86, "scuba": 86, "diver": 86, "rapese": 86, "daisi": 86, "ladi": 86, "slipper": 86, "corn": 86, "rose": 86, "hip": 86, "chestnut": 86, "fungu": 86, "agar": 86, "gyromitra": 86, "stinkhorn": 86, "earth": 86, "star": 86, "wood": 86, "bolet": 86, "ear": 86, "cifar10_test_set": 86, "airplan": [86, 94], "automobil": [86, 94], "deer": [86, 94], "cifar100_test_set": 86, "aquarium_fish": 86, "babi": 86, "boi": 86, "camel": 86, "caterpillar": 86, "cattl": [86, 100], "cloud": 86, "dinosaur": 86, "dolphin": 86, "flatfish": 86, "forest": 86, "girl": 86, "kangaroo": 86, "lawn_mow": 86, "man": 86, "maple_tre": 86, "motorcycl": [86, 97], "oak_tre": 86, "orchid": 86, "palm_tre": 86, "pear": 86, "pickup_truck": 86, "pine_tre": 86, "plain": 86, "poppi": 86, "possum": 86, "raccoon": 86, "road": [86, 97], "rocket": 86, "seal": 86, "shrew": 86, "skyscrap": 86, "streetcar": 86, "sunflow": 86, "sweet_pepp": 86, "trout": 86, "tulip": 86, "willow_tre": 86, "woman": [86, 93], "caltech256": 86, "ak47": 86, "bat": 86, "glove": 86, "birdbath": 86, "blimp": 86, "bonsai": 86, "boom": 86, "breadmak": 86, "buddha": 86, "bulldoz": 86, "cactu": 86, "cake": 86, "tire": 86, "cartman": 86, "cereal": 86, "chandeli": 86, "chess": 86, "board": 86, "chimp": 86, "chopstick": 86, "coffin": 86, "coin": 86, "comet": 86, "cormor": 86, "globe": 86, "diamond": 86, "dice": 86, "doorknob": 86, "drink": 86, "straw": 86, "dumb": 86, "eiffel": 86, "elk": 86, "ewer": 86, "eyeglass": 86, "fern": 86, "fighter": 86, "jet": [86, 96], "extinguish": 86, "hydrant": 86, "firework": 86, "flashlight": 86, "floppi": 86, "fri": 86, "frisbe": 86, "galaxi": 86, "giraff": 86, "goat": 86, "gate": 86, "grape": 86, "pick": [86, 87], "hamburg": 86, "hammock": 86, "harpsichord": 86, "hawksbil": 86, "helicopt": 86, "hibiscu": 86, "homer": 86, "simpson": 86, "horsesho": 86, "air": 86, "skeleton": 86, "ibi": 86, "cone": 86, "iri": 86, "jesu": 86, "christ": 86, "joi": 86, "kayak": 86, "ketch": 86, "ladder": 86, "lath": 86, "licens": 86, "lightbulb": 86, "lightn": 86, "mandolin": 86, "mar": 86, "mattress": 86, "megaphon": 86, "menorah": 86, "microscop": 86, "minaret": 86, "minotaur": 86, "motorbik": 86, "mussel": 86, "neckti": 86, "octopu": 86, "palm": 86, "pilot": 86, "paperclip": 86, "shredder": 86, "pci": 86, "peopl": [86, 93], "pez": 86, "picnic": 86, "pram": 86, "prai": 86, "pyramid": 86, "rainbow": 86, "roulett": 86, "saddl": 86, "saturn": 86, "segwai": 86, "propel": 86, "sextant": 86, "music": 86, "skateboard": 86, "smokestack": 86, "sneaker": 86, "boat": 86, "stain": 86, "steer": 86, "stirrup": 86, "superman": 86, "sushi": 86, "armi": [86, 100], "sword": 86, "tambourin": 86, "teepe": 86, "court": 86, "theodolit": 86, "tomato": 86, "tombston": 86, "tour": 86, "pisa": 86, "treadmil": 86, "fork": 86, "tweezer": 86, "unicorn": 86, "vcr": 86, "waterfal": 86, "watermelon": 86, "weld": 86, "windmil": 86, "xylophon": 86, "yarmulk": 86, "yo": 86, "toad": 86, "twenty_news_test_set": 86, "alt": 86, "atheism": 86, "comp": 86, "graphic": [86, 97], "misc": [86, 100], "sy": 86, "ibm": 86, "pc": 86, "hardwar": 86, "mac": 86, "forsal": 86, "rec": 86, "sci": 86, "crypt": 86, "electron": 86, "med": 86, "soc": 86, "religion": 86, "christian": [86, 100], "talk": [86, 100], "polit": 86, "gun": 86, "mideast": 86, "amazon": 86, "neutral": 86, "imdb_test_set": 86, "all_class": 86, "20news_test_set": 86, "_load_classes_predprobs_label": 86, "dataset_nam": 86, "labelerror": 86, "url_bas": 86, "5392f6c71473055060be3044becdde1cbc18284d": 86, "url_label": 86, "original_test_label": 86, "_original_label": 86, "url_prob": 86, "cross_validated_predicted_prob": 86, "_pyx": 86, "num_part": 86, "datatset": 86, "bytesio": 86, "allow_pickl": 86, "pred_probs_part": 86, "url": 86, "_of_": 86, "nload": 86, "imdb": 86, "ve": [86, 87, 89, 91, 93], "interpret": [86, 87, 89, 92, 96], "capit": 86, "29780": 86, "256": [86, 87, 93], "780": 86, "medic": [86, 100], "doctor": 86, "254": [86, 93], "359223": 86, "640777": 86, "184": [86, 89], "258427": 86, "341176": 86, "263158": 86, "658824": 86, "337349": 86, "246575": 86, "662651": 86, "248": 86, "330000": 86, "355769": 86, "670000": 86, "251": [86, 93], "167": [86, 89, 93], "252": 86, "112": 86, "253": [86, 93], "022989": 86, "255": [86, 88], "049505": 86, "190": [86, 89, 93], "66": 86, "002216": 86, "000974": 86, "59": [86, 93], "88": [86, 87, 88, 89, 92, 93, 96], "000873": 86, "000739": 86, "79": [86, 93, 98], "32635": 86, "32636": 86, "47": [86, 93], "32637": 86, "32638": 86, "32639": 86, "32640": 86, "051": 86, "93": [86, 93, 96, 98], "002242": 86, "997758": 86, "002088": 86, "001045": 86, "997912": 86, "002053": 86, "997947": 86, "001980": 86, "000991": 86, "998020": 86, "001946": 86, "002915": 86, "998054": 86, "001938": 86, "002904": 86, "998062": 86, "001020": 86, "998980": 86, "001018": 86, "002035": 86, "998982": 86, "999009": 86, "0003": 86, "0002": 86, "36": [86, 100], "41": [86, 93, 96], "44": [86, 92, 93, 99], "71": [86, 89, 93, 96], "071": 86, "067269": 86, "929": 86, "046": 86, "058243": 86, "954": 86, "035": 86, "032096": 86, "965": 86, "031": 86, "012232": 86, "969": 86, "022": 86, "025896": 86, "978": 86, "020": [86, 89], "013092": 86, "018": 86, "013065": 86, "016": 86, "030542": 86, "984": [86, 100], "013": 86, "020833": 86, "987": 86, "012": 86, "010020": 86, "988": 86, "0073": 86, "0020": 86, "0016": 86, "0015": 86, "0013": 86, "0012": 86, "0010": 86, "0008": 86, "0007": 86, "0006": 86, "0005": 86, "0004": 86, "244": [86, 93, 100], "98": [86, 87, 96, 100], "452381": 86, "459770": 86, "72": [86, 89, 92, 96], "523364": 86, "460784": 86, "446602": 86, "57": [86, 89, 100], "68": [86, 89, 93, 98], "103774": 86, "030612": 86, "97": [86, 87, 89, 93, 96, 98, 100], "110092": 86, "049020": 86, "99": [86, 89, 98], "0034": 86, "0032": 86, "0026": 86, "0025": 86, "4945": 86, "4946": 86, "4947": 86, "4948": 86, "4949": 86, "4950": 86, "846": 86, "82": [86, 89, 93, 96], "7532": 86, "532": 86, "034483": 86, "009646": 86, "965517": 86, "030457": 86, "020513": 86, "969543": 86, "028061": 86, "035443": 86, "971939": 86, "025316": 86, "005168": 86, "974684": 86, "049751": 86, "979487": 86, "019920": 86, "042802": 86, "980080": 86, "017677": 86, "005115": 86, "982323": 86, "012987": 86, "005236": 86, "987013": 86, "012723": 86, "025126": 86, "987277": 86, "010989": 86, "008264": 86, "989011": 86, "010283": 86, "027778": 86, "989717": 86, "009677": 86, "990323": 86, "007614": 86, "010127": 86, "992386": 86, "005051": 86, "994949": 86, "005025": 86, "994975": 86, "005013": 86, "994987": 86, "001859": 86, "001328": 86, "000929": 86, "000664": 86, "186": [86, 89], "188": [86, 89, 92], "189": [86, 89], "snippet": 87, "nlp": [87, 100], "mind": [87, 89], "number_of_class": 87, "total_number_of_data_point": 87, "drop": [87, 91, 96, 99], "feed": 87, "alphabet": 87, "labels_proper_format": 87, "your_classifi": 87, "issues_datafram": 87, "class_predicted_for_flagged_exampl": 87, "class_predicted_for_all_exampl": 87, "grant": 87, "datataset": 87, "fair": [87, 89], "game": 87, "speedup": [87, 94], "flexibl": 87, "tempfil": 87, "mkdtemp": 87, "sped": 87, "anywai": 87, "pred_probs_merg": 87, "merge_rare_class": 87, "count_threshold": 87, "class_mapping_orig2new": 87, "heath_summari": 87, "num_examples_per_class": 87, "rare_class": 87, "num_classes_merg": 87, "other_class": 87, "labels_merg": 87, "new_c": 87, "merged_prob": 87, "hstack": [87, 88, 89, 91], "new_class": 87, "original_class": 87, "num_check": 87, "ones_array_ref": 87, "isclos": 87, "though": [87, 89, 100], "successfulli": 87, "meaning": [87, 94], "virtuou": [87, 91], "cycl": [87, 91], "jointli": 87, "junk": 87, "clutter": 87, "unknown": 87, "caltech": 87, "combined_boolean_mask": 87, "mask1": 87, "mask2": 87, "gradientboostingclassifi": [87, 89], "true_error": [87, 89, 92], "101": [87, 93], "102": [87, 92, 93], "104": [87, 89, 93], "model_to_find_error": 87, "model_to_return": 87, "cl0": 87, "randomizedsearchcv": 87, "expens": 87, "param_distribut": 87, "learning_r": [87, 89], "max_depth": [87, 89], "magnitud": 87, "coeffici": [87, 96], "optin": 87, "environ": [87, 89], "rerun": [87, 89], "cell": [87, 89], "On": [87, 89, 93], "unabl": [87, 89], "render": [87, 89], "nbviewer": [87, 89], "nbsp": [87, 89], "cleanlearninginot": [87, 89], "fittedcleanlearn": [87, 89], "linearregressionlinearregress": 87, "n_init": 87, "fit_predict": 87, "continuous_column": 87, "categorical_column": 87, "data_df": 87, "feature_a": 87, "feature_b": 87, "unexpectedli": 87, "emphas": 87, "especi": [87, 88, 96, 98, 99], "crucial": 87, "merge_duplicate_set": 87, "merge_kei": 87, "construct_group_kei": 87, "merged_set": 87, "consolidate_set": 87, "tolist": [87, 92], "issubset": 87, "frozenset": 87, "sets_list": 87, "mutabl": 87, "new_set": 87, "current_set": 87, "intersecting_set": 87, "lowest_score_strategi": 87, "sub_df": 87, "idxmin": 87, "filter_near_dupl": 87, "strategy_fn": 87, "strategy_kwarg": 87, "duplicate_row": 87, "group_kei": 87, "to_keep_indic": 87, "groupbi": 87, "explod": 87, "to_remov": 87, "isin": [87, 94], "kept": 87, "near_duplicate_issu": [87, 88], "ids_to_remove_seri": 87, "tmp": 87, "ipykernel_5939": 87, "1995098996": 87, "deprecationwarn": 87, "dataframegroupbi": 87, "include_group": 87, "silenc": 87, "assist": 87, "streamlin": 87, "ux": 87, "agpl": 87, "compani": 87, "commerci": 87, "alter": 87, "email": 87, "discuss": 87, "anywher": 87, "profession": 87, "expert": 87, "60": [88, 89, 96], "excess": 88, "torchvis": [88, 94], "tensordataset": 88, "stratifiedkfold": [88, 92], "tqdm": 88, "autonotebook": 88, "fashion_mnist": 88, "num_row": 88, "60000": 88, "pil": 88, "transformed_dataset": 88, "with_format": 88, "unsqueez": 88, "cpu_count": 88, "torch_dataset": 88, "quick": [88, 92], "relu": 88, "batchnorm2d": 88, "maxpool2d": 88, "lazylinear": 88, "flatten": 88, "get_test_accuraci": 88, "testload": [88, 94], "energi": 88, "trainload": [88, 94], "n_epoch": 88, "patienc": 88, "criterion": 88, "crossentropyloss": 88, "adamw": 88, "best_test_accuraci": 88, "start_epoch": 88, "running_loss": 88, "best_epoch": 88, "end_epoch": 88, "3f": [88, 96], "acc": [88, 89], "time_taken": 88, "compute_embed": 88, "compute_pred_prob": 88, "train_batch_s": 88, "num_work": 88, "worker": [88, 100], "train_id_list": 88, "test_id_list": 88, "train_id": 88, "test_id": 88, "embeddings_model": 88, "ntrain": 88, "trainset": 88, "testset": 88, "pin_memori": 88, "fold_embed": 88, "fold_pred_prob": 88, "finish": 88, "482": 88, "720": 88, "329": [88, 93], "195": 88, "435": 88, "493": 88, "060": 88, "680": 88, "330": [88, 93], "505": 88, "601": 88, "476": 88, "340": 88, "647": 88, "328": [88, 93], "310": 88, "510": 88, "reorder": 88, "vision": 88, "grayscal": 88, "exce": 88, "max_preval": 88, "7714": 88, "3772": 88, "3585": 88, "166": 88, "3651": 88, "27080": 88, "873833e": 88, "40378": 88, "915575e": 88, "25316": 88, "390277e": 88, "06": [88, 89, 93, 100], "2090": 88, "751164e": 88, "14999": 88, "881301e": 88, "9569": 88, "11262": 88, "000003": 88, "19228": 88, "000010": 88, "dress": 88, "32657": 88, "000013": 88, "21282": 88, "000016": 88, "53564": 88, "000018": 88, "pullov": 88, "6321": 88, "30968": 88, "001267": 88, "30659": 88, "000022": [88, 100], "47824": 88, "001454": 88, "3370": 88, "000026": 88, "54565": 88, "001854": 88, "9762": 88, "258": 88, "47139": 88, "000033": 88, "166980": 88, "986195": 88, "997205": 88, "948781": 88, "999358": 88, "54078": 88, "17371": 88, "000025": 88, "plot_label_issue_exampl": 88, "ncol": [88, 94], "nrow": [88, 94], "ceil": 88, "axes_list": 88, "label_issue_indic": 88, "gl": 88, "sl": 88, "fontdict": 88, "imshow": [88, 94], "cmap": [88, 96], "grai": 88, "subplots_adjust": 88, "hspace": 88, "outsiz": 88, "outlier_issues_df": 88, "depict": [88, 92, 93, 94, 95, 97], "plot_outlier_issues_exampl": 88, "n_comparison_imag": 88, "sample_from_class": 88, "number_of_sampl": 88, "non_outlier_indic": 88, "isnul": 88, "non_outlier_indices_excluding_curr": 88, "sampled_indic": 88, "label_scores_of_sampl": 88, "top_score_indic": 88, "top_label_indic": 88, "sampled_imag": 88, "get_image_given_label_and_sampl": 88, "image_from_dataset": 88, "corresponding_label": 88, "comparison_imag": 88, "images_to_plot": 88, "idlist": 88, "iterrow": 88, "closest": 88, "counterpart": 88, "near_duplicate_issues_df": 88, "plot_near_duplicate_issue_exampl": 88, "seen_id_pair": 88, "get_image_and_given_label_and_predicted_label": 88, "duplicate_imag": 88, "nd_set": 88, "challeng": 88, "dark_issu": 88, "reveal": [88, 93, 97], "dark_scor": 88, "dark_issues_df": 88, "is_dark_issu": 88, "34848": 88, "203922": 88, "50270": 88, "204588": 88, "3936": 88, "213098": 88, "733": 88, "217686": 88, "8094": 88, "230118": 88, "plot_image_issue_exampl": 88, "difficult": 88, "disproportion": 88, "lowinfo_issu": 88, "low_information_scor": 88, "lowinfo_issues_df": 88, "is_low_information_issu": 88, "53050": 88, "067975": 88, "40875": 88, "089929": 88, "9594": 88, "092601": 88, "34825": 88, "107744": 88, "37530": 88, "108516": 88, "lot": 88, "depth": 89, "survei": [89, 100], "focus": [89, 91, 92, 96], "scienc": 89, "multivariate_norm": [89, 91, 92], "make_data": [89, 91], "cov": [89, 91, 92], "avg_trac": [89, 92], "test_label": [89, 92, 94, 99], "py_tru": 89, "noise_matrix_tru": 89, "noise_marix": 89, "s_test": 89, "noisy_test_label": 89, "purpl": 89, "val": 89, "namespac": 89, "exec": 89, "markerfacecolor": [89, 92], "markeredgecolor": [89, 92, 96], "markers": [89, 92, 96], "markeredgewidth": [89, 92, 96], "realist": 89, "7560": 89, "637318e": 89, "896262e": 89, "548391e": 89, "923417e": 89, "375075e": 89, "3454": 89, "014051": 89, "020451": 89, "249": [89, 93, 100], "042594": 89, "043859": 89, "045954": 89, "6120": 89, "023714": 89, "007136": 89, "119": [89, 93], "107266": 89, "103": [89, 93], "033738": 89, "238": [89, 93], "119505": 89, "236": [89, 93], "037843": 89, "222": 89, "614915": 89, "122": [89, 93], "624422": 89, "625965": 89, "626079": 89, "118": 89, "627675": 89, "695223": 89, "323529": 89, "523015": 89, "013720": 89, "675727": 89, "646521": 89, "anyth": 89, "enhanc": [89, 91, 93], "magic": 89, "83": [89, 93, 96, 98, 100], "liter": 89, "identif": 89, "x27": 89, "logisticregressionlogisticregress": 89, "ever": 89, "092": 89, "040": 89, "024": 89, "004": 89, "surpris": 89, "1705": 89, "01936": 89, "ton": 89, "yourfavoritemodel1": 89, "merged_label": 89, "merged_test_label": 89, "newli": [89, 91], "yourfavoritemodel2": 89, "yourfavoritemodel3": 89, "cl3": 89, "takeawai": 89, "That": [89, 92], "randomli": 89, "my_test_pred_prob": 89, "my_test_pr": 89, "issues_test": 89, "corrected_test_label": 89, "pretend": 89, "cl_test_pr": 89, "69": [89, 96], "fairli": 89, "label_acc": 89, "percentag": 89, "offset": 89, "nquestion": 89, "overestim": 89, "answer": 89, "experienc": 89, "76": [89, 92, 93, 96, 98], "knowledg": 89, "prioiri": 89, "known": 89, "versatil": 89, "label_issues_indic": 89, "213": [89, 93, 100], "212": [89, 98], "218": [89, 93], "152": 89, "197": [89, 93], "196": [89, 93], "170": 89, "214": 89, "164": [89, 92], "198": [89, 93], "191": [89, 93], "63": [89, 93, 96], "121": [89, 99], "117": [89, 96], "62": [89, 93, 96], "206": [89, 93], "115": [89, 93], "193": 89, "194": 89, "201": [89, 93], "174": 89, "163": 89, "150": [89, 91, 93], "169": 89, "151": [89, 93], "168": 89, "precision_scor": 89, "recall_scor": 89, "f1_score": 89, "true_label_issu": 89, "filter_by_list": 89, "718750": [89, 91], "807018": 89, "912": 89, "733333": 89, "800000": 89, "721311": 89, "792793": 89, "908": 89, "676923": 89, "765217": 89, "892": 89, "567901": 89, "702290": 89, "844": 89, "gaug": 89, "label_issues_count": 89, "155": [89, 93], "156": 89, "172": [89, 92], "easiest": 89, "modular": 89, "penalti": 89, "l2": 89, "model3": 89, "n_estim": 89, "cv_pred_probs_1": 89, "cv_pred_probs_2": 89, "cv_pred_probs_3": 89, "label_quality_scores_best": 89, "cv_pred_probs_ensembl": 89, "label_quality_scores_bett": 89, "superior": [89, 95], "workflow": [90, 96], "speechbrain": 90, "timm": 90, "glad": 91, "multiannotator_label": 91, "300": [91, 100], "noisier": 91, "111": [91, 96], "local_data": [91, 92], "true_labels_train": [91, 92], "noise_matrix_bett": 91, "noise_matrix_wors": 91, "transpos": [91, 94], "dropna": 91, "zfill": 91, "row_na_check": 91, "notna": 91, "reset_index": 91, "a0001": 91, "a0002": 91, "a0003": 91, "a0004": 91, "a0005": 91, "a0006": 91, "a0007": 91, "a0008": 91, "a0009": 91, "a0010": 91, "a0041": 91, "a0042": 91, "a0043": 91, "a0044": 91, "a0045": 91, "a0046": 91, "a0047": 91, "a0048": 91, "a0049": 91, "a0050": 91, "na": 91, "60856743": 91, "41693214": 91, "40908785": 91, "87147629": 91, "64941785": 91, "10774851": 91, "0524466": 91, "71853246": 91, "37169848": 91, "66031048": 91, "multiannotator_util": 91, "crude": 91, "straight": 91, "majority_vote_label": 91, "736118": 91, "757751": 91, "782232": 91, "715565": 91, "824256": 91, "quality_annotator_a0001": 91, "quality_annotator_a0002": 91, "quality_annotator_a0003": 91, "quality_annotator_a0004": 91, "quality_annotator_a0005": 91, "quality_annotator_a0006": 91, "quality_annotator_a0007": 91, "quality_annotator_a0008": 91, "quality_annotator_a0009": 91, "quality_annotator_a0010": 91, "quality_annotator_a0041": 91, "quality_annotator_a0042": 91, "quality_annotator_a0043": 91, "quality_annotator_a0044": 91, "quality_annotator_a0045": 91, "quality_annotator_a0046": 91, "quality_annotator_a0047": 91, "quality_annotator_a0048": 91, "quality_annotator_a0049": 91, "quality_annotator_a0050": 91, "070564": 91, "216078": 91, "119188": 91, "alongisd": 91, "244981": 91, "208333": 91, "295979": 91, "294118": 91, "324197": 91, "310345": 91, "355316": 91, "346154": 91, "439732": 91, "480000": 91, "a0031": 91, "523205": 91, "580645": 91, "a0034": 91, "535313": 91, "607143": 91, "a0021": 91, "606999": 91, "a0015": 91, "609526": 91, "678571": 91, "a0011": 91, "621103": 91, "692308": 91, "wors": 91, "improved_consensus_label": 91, "majority_vote_accuraci": 91, "cleanlab_label_accuraci": 91, "8581081081081081": 91, "9797297297297297": 91, "besid": 91, "sorted_consensus_quality_scor": 91, "worst_qual": 91, "better_qu": 91, "worst_quality_accuraci": 91, "better_quality_accuraci": 91, "9893238434163701": 91, "improved_pred_prob": 91, "treat": [91, 92, 96, 100], "analzi": 91, "copyright": 92, "advertis": 92, "violenc": 92, "nsfw": 92, "suppli": [92, 93, 96], "celeba": 92, "make_multilabel_data": 92, "boxes_coordin": 92, "box_multilabel": 92, "make_multi": 92, "bx1": 92, "by1": 92, "bx2": 92, "by2": 92, "label_list": 92, "ur": 92, "upper": 92, "inidx": 92, "logical_and": 92, "inv_d": 92, "labels_idx": 92, "true_labels_test": 92, "dict_unique_label": 92, "get_color_arrai": 92, "dcolor": 92, "aa4400": 92, "55227f": 92, "55a100": 92, "00ff00": 92, "007f7f": 92, "386b55": 92, "0000ff": 92, "simplic": 92, "advis": 92, "y_onehot": 92, "single_class_label": 92, "stratifi": [92, 95], "kf": 92, "train_index": 92, "test_index": 92, "clf_cv": 92, "x_train_cv": 92, "x_test_cv": 92, "y_train_cv": 92, "y_test_cv": 92, "y_pred_cv": 92, "saw": 92, "num_to_displai": 92, "09": [92, 93, 96], "275": 92, "267": 92, "225": 92, "171": 92, "234": 92, "165": 92, "227": [92, 93], "262": [92, 93], "263": [92, 93], "266": [92, 93], "139": 92, "143": [92, 93, 100], "216": [92, 93, 100], "265": 92, "159": [92, 93], "despit": [92, 100], "suspect": 92, "888": 92, "8224": 92, "9632": 92, "968": 92, "6512": 92, "0444": 92, "774": 92, "labels_binary_format": 92, "labels_list_format": 92, "surround": 93, "scene": 93, "coco": 93, "everydai": 93, "has_label_issu": 93, "insal": 93, "nc": [93, 97, 100], "s3": [93, 97, 100], "amazonaw": [93, 97, 100], "objectdetectionbenchmark": 93, "tutorial_obj": 93, "pkl": 93, "example_imag": 93, "unzip": [93, 100], "_separate_label": 93, "_separate_predict": 93, "begin": 93, "image_path": 93, "rb": 93, "image_to_visu": 93, "seg_map": 93, "334": 93, "float32": 93, "bboxes_ignor": 93, "290": 93, "286": 93, "285": 93, "224": 93, "231": [93, 100], "293": 93, "235": 93, "289": 93, "282": 93, "74": [93, 96, 98], "281": 93, "271": 93, "280": 93, "277": 93, "279": 93, "287": 93, "299": 93, "276": 93, "307": 93, "321": 93, "326": 93, "333": 93, "261": 93, "319": 93, "257": 93, "295": 93, "283": 93, "243": 93, "303": 93, "316": 93, "247": 93, "323": 93, "327": 93, "226": [93, 100], "228": 93, "232": 93, "219": 93, "239": 93, "240": 93, "209": 93, "242": 93, "202": 93, "230": 93, "215": 93, "220": 93, "229": 93, "217": 93, "237": 93, "207": 93, "204": 93, "84": [93, 96], "205": 93, "223": 93, "153": 93, "149": 93, "140": 93, "124": 93, "268": 93, "273": 93, "108": 93, "284": 93, "110": 93, "136": 93, "145": 93, "173": 93, "297": 93, "317": 93, "192": 93, "332": 93, "324": 93, "203": 93, "320": 93, "314": 93, "199": 93, "291": 93, "000000481413": 93, "jpg": 93, "42398": 93, "44503": 93, "337": [93, 99], "29968": 93, "336": 93, "21005": 93, "9978472": 93, "forgot": 93, "drew": 93, "label_issue_idx": 93, "num_examples_to_show": 93, "138": 93, "candid": 93, "97489622": 93, "70610878": 93, "98764951": 93, "88899237": 93, "99085805": 93, "issue_idx": 93, "95569726e": 93, "03354841e": 93, "57510169e": 93, "58447666e": 93, "39755858e": 93, "issue_to_visu": 93, "000000009483": 93, "95569726168054e": 93, "addition": [93, 97], "visibl": 93, "missmatch": 93, "likelei": 93, "agnost": 93, "vaidat": 93, "inconsist": 93, "000000395701": 93, "033548411774308e": 93, "armchair": 93, "tv": 93, "000000154004": 93, "38300759625496356": 93, "foreground": 93, "000000448410": 93, "0008575101690203273": 93, "crowd": 93, "alon": 93, "explor": [93, 94], "resembl": [93, 94], "000000499768": 93, "9748962231208227": 93, "000000521141": 93, "8889923658893665": 93, "000000143931": 93, "9876495074395956": 93, "bonu": 93, "uncov": 93, "irregular": 93, "anomali": 93, "unusu": [93, 94], "object_detection_util": 93, "calculate_bounding_box_area": 93, "num_imgs_to_show": 93, "lab_object_count": 93, "pred_object_count": 93, "000000430073": 93, "000000183709": 93, "000000189475": 93, "studi": 93, "label_norm": 93, "pred_norm": 93, "area": [93, 97], "assess": 93, "lab_area": 93, "pred_area": 93, "lab_area_mean": 93, "lab_area_std": 93, "max_deviation_valu": 93, "max_deviation_class": 93, "deviation_valu": 93, "deviation_class": 93, "mean_area": 93, "std_area": 93, "class_area": 93, "deviations_awai": 93, "max_deviation_index": 93, "num_imgs_to_show_per_class": 93, "class_num": 93, "sorted_indic": 93, "000000422886": 93, "000000341828": 93, "000000461009": 93, "train_feature_embed": 94, "ood_train_feature_scor": 94, "test_feature_embed": 94, "ood_test_feature_scor": 94, "ood_train_predictions_scor": 94, "train_pred_prob": 94, "ood_test_predictions_scor": 94, "test_pred_prob": 94, "pylab": 94, "rcparam": 94, "baggingclassifi": 94, "therebi": 94, "rescal": 94, "transform_norm": 94, "totensor": 94, "root": 94, "animal_class": 94, "non_animal_class": 94, "animal_idx": 94, "test_idx": 94, "toronto": 94, "edu": 94, "kriz": 94, "170498071": 94, "38191430": 94, "08it": 94, "5000": 94, "plot_imag": 94, "visualize_outli": 94, "txt_class": 94, "img": [94, 96], "npimg": 94, "show_label": 94, "data_subset": 94, "resnet50": 94, "corpu": 94, "2048": 94, "embed_imag": 94, "create_model": 94, "strang": 94, "odd": 94, "train_ood_features_scor": 94, "top_train_ood_features_idx": 94, "fun": 94, "negat": 94, "homogen": 94, "bottom_train_ood_features_idx": 94, "test_ood_features_scor": 94, "top_ood_features_idx": 94, "inevit": 94, "trade": 94, "5th": 94, "percentil": 94, "fifth_percentil": 94, "plt_rang": 94, "hist": 94, "train_outlier_scor": 94, "ylabel": 94, "axvlin": 94, "test_outlier_scor": 94, "ood_features_indic": 94, "revisit": 94, "return_invers": 94, "train_feature_embeddings_sc": 94, "test_feature_embeddings_sc": 94, "train_pred_label": 94, "9702": 94, "train_ood_predictions_scor": 94, "test_ood_predictions_scor": 94, "lost": 94, "unsuit": 95, "ok": [95, 100], "convention": 95, "aforement": 95, "hypothet": 95, "contrast": 95, "tradit": 95, "disjoint": 95, "out_of_sample_pred_probs_for_a": 95, "out_of_sample_pred_probs_for_b": 95, "out_of_sample_pred_probs_for_c": 95, "out_of_sample_pred_prob": 95, "price": 96, "incom": 96, "ag": 96, "sensor": 96, "histgradientboostingregressor": 96, "r2_score": 96, "student_grades_r": 96, "final_scor": 96, "true_final_scor": 96, "homework": 96, "3d": 96, "hue": 96, "mpl_toolkit": 96, "mplot3d": 96, "axes3d": 96, "errors_idx": 96, "add_subplot": 96, "z": 96, "colorbar": 96, "errors_mask": 96, "feature_column": 96, "predicted_column": 96, "x_train_raw": 96, "x_test_raw": 96, "categorical_featur": [96, 98], "randomforestregressor": 96, "636197": 96, "499503": 96, "843478": 96, "776647": 96, "350358": 96, "170547": 96, "706969": 96, "984759": 96, "812515": 96, "795928": 96, "identified_issu": [96, 99], "141": 96, "659": 96, "367": 96, "318": 96, "305": 96, "560": 96, "657": 96, "688": 96, "view_datapoint": 96, "concat": 96, "consum": [96, 99], "baseline_model": [96, 99], "preds_og": 96, "r2_og": 96, "838": 96, "robustli": [96, 98, 99], "acceler": [96, 99], "found_label_issu": 96, "preds_cl": 96, "r2_cl": 96, "926": 96, "effort": [96, 98, 99], "favorit": 96, "968627e": 96, "228799": 96, "646674e": 96, "402962": 96, "323818e": 96, "952758": 96, "422144e": 96, "456908": 96, "465815e": 96, "753968": 96, "110719": 96, "000044": 96, "670640": 96, "000060": 96, "749976": 96, "000066": 96, "947007": 96, "000314": 96, "648396": 96, "plu": 96, "synthia": 97, "imagesegment": 97, "given_mask": 97, "predicted_mask": 97, "set_printopt": [97, 100], "sky": 97, "sidewalk": 97, "veget": 97, "terrain": 97, "rider": 97, "pred_probs_filepath": 97, "1088": 97, "1920": 97, "label_filepath": 97, "synthia_class": 97, "maunal": 97, "100000": 97, "244800": 97, "leftmost": 97, "middl": [97, 100], "infact": 97, "rightmost": 97, "discrep": 97, "3263230": 97, "783379": 97, "275110": 97, "255792": 97, "78225": 97, "55990": 97, "54427": 97, "33591": 97, "24645": 97, "21308": 97, "15045": 97, "14171": 97, "13832": 97, "13498": 97, "11490": 97, "9164": 97, "8769": 97, "6999": 97, "6031": 97, "5011": 97, "mistakenli": 97, "class_issu": 97, "aim": [97, 100], "domin": 97, "extratreesclassifi": 98, "extratre": 98, "ranked_label_issu": [98, 99], "labelencod": [98, 99], "labels_raw": 98, "interg": [98, 99], "tress": 98, "827": 98, "cheat": 98, "0pt": 98, "233": 98, "labels_train": 98, "labels_test": 98, "acc_og": [98, 99], "783068783068783": 98, "acc_cl": [98, 99], "8095238095238095": 98, "earlier": [99, 100], "raw_label": 99, "raw_train_text": 99, "raw_test_text": 99, "raw_train_label": 99, "raw_test_label": 99, "encond": 99, "train_text": 99, "test_text": 99, "858371": 99, "547274": 99, "826228": 99, "966008": 99, "792449": 99, "646": 99, "390": 99, "628": 99, "702": 99, "135": 99, "735": 99, "print_as_df": 99, "inverse_transform": 99, "fight": 99, "bunch": 100, "conll": 100, "2003": 100, "love": 100, "n_i": 100, "optional_list_of_ordered_class_nam": 100, "deepai": 100, "conll2003": 100, "rm": 100, "tokenclassif": 100, "2024": 100, "210": 100, "2400": 100, "52e0": 100, "1a01": 100, "connect": 100, "443": 100, "await": 100, "982975": 100, "960k": 100, "959": 100, "94k": 100, "kb": 100, "mb": 100, "directori": 100, "inflat": 100, "182": 100, "17045998": 100, "16m": 100, "octet": 100, "26m": 100, "5mb": 100, "bert": 100, "read_npz": 100, "filepath": 100, "corrsespond": 100, "iob2": 100, "given_ent": 100, "entity_map": 100, "readfil": 100, "sep": 100, "startswith": 100, "docstart": 100, "isalpha": 100, "isupp": 100, "indices_to_preview": 100, "nsentenc": 100, "eu": 100, "reject": 100, "boycott": 100, "british": 100, "lamb": 100, "00030412": 100, "00023826": 100, "99936208": 100, "00007009": 100, "00002545": 100, "99998795": 100, "00000401": 100, "00000218": 100, "00000455": 100, "00000131": 100, "00000749": 100, "99996115": 100, "00001371": 100, "0000087": 100, "00000895": 100, "99998936": 100, "00000382": 100, "00000178": 100, "00000366": 100, "00000137": 100, "99999101": 100, "00000266": 100, "00000174": 100, "0000035": 100, "00000109": 100, "99998768": 100, "00000482": 100, "00000202": 100, "00000438": 100, "0000011": 100, "00000465": 100, "99996392": 100, "00001105": 100, "0000116": 100, "00000878": 100, "99998671": 100, "00000364": 100, "00000213": 100, "00000472": 100, "00000281": 100, "99999073": 100, "00000211": 100, "00000159": 100, "00000442": 100, "00000115": 100, "peter": 100, "blackburn": 100, "00000358": 100, "00000529": 100, "99995623": 100, "0000129": 100, "0000024": 100, "00001812": 100, "99994141": 100, "00001645": 100, "00002162": 100, "brussel": 100, "1996": 100, "00001172": 100, "00000821": 100, "00004661": 100, "0000618": 100, "99987167": 100, "99999061": 100, "00000201": 100, "00000195": 100, "00000408": 100, "00000135": 100, "2254": 100, "2907": 100, "19392": 100, "9962": 100, "8904": 100, "19303": 100, "12918": 100, "9256": 100, "11855": 100, "18392": 100, "20426": 100, "19402": 100, "14744": 100, "19371": 100, "4645": 100, "10331": 100, "9430": 100, "6143": 100, "18367": 100, "12914": 100, "todai": 100, "weather": 100, "march": 100, "scalfaro": 100, "northern": 100, "himself": 100, "said": 100, "germani": 100, "nastja": 100, "rysich": 100, "north": 100, "spla": 100, "fought": 100, "khartoum": 100, "govern": 100, "south": 100, "1983": 100, "autonomi": 100, "animist": 100, "region": 100, "moslem": 100, "arabis": 100, "mayor": 100, "antonio": 100, "gonzalez": 100, "garcia": 100, "revolutionari": 100, "parti": 100, "wednesdai": 100, "troop": 100, "raid": 100, "farm": 100, "stole": 100, "rape": 100, "women": 100, "spring": 100, "chg": 100, "hrw": 100, "12pct": 100, "princ": 100, "photo": 100, "moment": 100, "spokeswoman": 100, "rainier": 100, "told": 100, "reuter": 100, "danila": 100, "carib": 100, "w224": 100, "equip": 100, "radiomet": 100, "earn": 100, "19996": 100, "london": 100, "denom": 100, "sale": 100, "uk": 100, "jp": 100, "fr": 100, "maccabi": 100, "hapoel": 100, "haifa": 100, "tel": 100, "aviv": 100, "hospit": 100, "rever": 100, "roman": 100, "cathol": 100, "nun": 100, "admit": 100, "calcutta": 100, "week": 100, "ago": 100, "fever": 100, "vomit": 100, "allianc": 100, "embattl": 100, "kabul": 100, "salang": 100, "highwai": 100, "mondai": 100, "tuesdai": 100, "suprem": 100, "council": 100, "led": 100, "jumbish": 100, "milli": 100, "movement": 100, "warlord": 100, "abdul": 100, "rashid": 100, "dostum": 100, "dollar": 100, "exchang": 100, "3570": 100, "12049": 100, "born": 100, "1937": 100, "provinc": 100, "anhui": 100, "dai": 100, "came": 100, "shanghai": 100, "citi": 100, "prolif": 100, "author": 100, "teacher": 100, "chines": 100, "16764": 100, "1990": 100, "historian": 100, "alan": 100, "john": 100, "percival": 100, "taylor": 100, "di": 100, "20446": 100, "pace": 100, "bowler": 100, "ian": 100, "harvei": 100, "claim": 100, "victoria": 100, "15514": 100, "cotti": 100, "osc": 100, "foreign": 100, "minist": 100, "7525": 100, "sultan": 100, "specter": 100, "met": 100, "crown": 100, "abdullah": 100, "defenc": 100, "aviat": 100, "jeddah": 100, "saudi": 100, "agenc": 100, "2288": 100, "hi": 100, "customari": 100, "outfit": 100, "champion": 100, "damp": 100, "scalp": 100, "canada": 100, "reign": 100, "olymp": 100, "donovan": 100, "bailei": 100, "1992": 100, "linford": 100, "christi": 100, "britain": 100, "1984": 100, "1988": 100, "carl": 100, "lewi": 100, "ambigi": 100, "punctuat": 100, "chicago": 100, "digest": 100, "philadelphia": 100, "usda": 100, "york": 100, "token_issu": 100, "471": 100, "kean": 100, "year": 100, "contract": 100, "manchest": 100, "19072": 100, "societi": 100, "million": 100, "bite": 100, "deliv": 100, "19910": 100, "father": 100, "clarenc": 100, "woolmer": 100, "renam": 100, "uttar": 100, "pradesh": 100, "india": 100, "ranji": 100, "trophi": 100, "nation": 100, "championship": 100, "captain": 100, "1949": 100, "15658": 100, "19879": 100, "iii": 100, "brian": 100, "shimer": 100, "randi": 100, "jone": 100, "19104": 100}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [10, 0, 0, "-", "datalab"], [35, 0, 0, "-", "dataset"], [38, 0, 0, "-", "experimental"], [42, 0, 0, "-", "filter"], [43, 0, 0, "-", "internal"], [54, 0, 0, "-", "models"], [56, 0, 0, "-", "multiannotator"], [59, 0, 0, "-", "multilabel_classification"], [62, 0, 0, "-", "object_detection"], [65, 0, 0, "-", "outlier"], [66, 0, 0, "-", "rank"], [67, 0, 0, "-", "regression"], [71, 0, 0, "-", "segmentation"], [75, 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.datalab": [[4, 0, 0, "-", "datalab"], [14, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[4, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[4, 4, 1, "", "class_names"], [4, 3, 1, "", "find_issues"], [4, 3, 1, "", "get_info"], [4, 3, 1, "", "get_issue_summary"], [4, 3, 1, "", "get_issues"], [4, 4, 1, "", "has_labels"], [4, 4, 1, "", "info"], [4, 4, 1, "", "issue_summary"], [4, 4, 1, "", "issues"], [4, 4, 1, "", "labels"], [4, 3, 1, "", "list_default_issue_types"], [4, 3, 1, "", "list_possible_issue_types"], [4, 3, 1, "", "load"], [4, 3, 1, "", "report"], [4, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[11, 0, 0, "-", "data"], [12, 0, 0, "-", "data_issues"], [15, 0, 0, "-", "issue_finder"], [13, 0, 0, "-", "issue_manager_factory"], [31, 0, 0, "-", "model_outputs"], [32, 0, 0, "-", "report"], [33, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[11, 2, 1, "", "Data"], [11, 5, 1, "", "DataFormatError"], [11, 5, 1, "", "DatasetDictError"], [11, 5, 1, "", "DatasetLoadError"], [11, 2, 1, "", "Label"], [11, 2, 1, "", "MultiClass"], [11, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[11, 4, 1, "", "class_names"], [11, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[11, 3, 1, "", "add_note"], [11, 6, 1, "", "args"], [11, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[11, 3, 1, "", "add_note"], [11, 6, 1, "", "args"], [11, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[11, 3, 1, "", "add_note"], [11, 6, 1, "", "args"], [11, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[11, 4, 1, "", "class_names"], [11, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[11, 4, 1, "", "class_names"], [11, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[11, 4, 1, "", "class_names"], [11, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[12, 2, 1, "", "DataIssues"], [12, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[12, 3, 1, "", "collect_issues_from_imagelab"], [12, 3, 1, "", "collect_issues_from_issue_manager"], [12, 3, 1, "", "collect_statistics"], [12, 3, 1, "", "get_info"], [12, 3, 1, "", "get_issue_summary"], [12, 3, 1, "", "get_issues"], [12, 6, 1, "", "info"], [12, 6, 1, "", "issue_summary"], [12, 6, 1, "", "issues"], [12, 3, 1, "", "set_health_score"], [12, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[15, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[15, 3, 1, "", "find_issues"], [15, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[17, 0, 0, "-", "data_valuation"], [18, 0, 0, "-", "duplicate"], [19, 0, 0, "-", "imbalance"], [21, 0, 0, "-", "issue_manager"], [22, 0, 0, "-", "label"], [25, 0, 0, "-", "noniid"], [26, 0, 0, "-", "null"], [27, 0, 0, "-", "outlier"], [30, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[17, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[17, 6, 1, "", "DEFAULT_THRESHOLD"], [17, 3, 1, "", "collect_info"], [17, 6, 1, "", "description"], [17, 3, 1, "", "find_issues"], [17, 6, 1, "", "info"], [17, 6, 1, "", "issue_name"], [17, 6, 1, "", "issue_score_key"], [17, 6, 1, "", "issues"], [17, 3, 1, "", "make_summary"], [17, 3, 1, "", "report"], [17, 6, 1, "", "summary"], [17, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[18, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[18, 3, 1, "", "collect_info"], [18, 6, 1, "", "description"], [18, 3, 1, "", "find_issues"], [18, 6, 1, "", "info"], [18, 6, 1, "", "issue_name"], [18, 6, 1, "", "issue_score_key"], [18, 6, 1, "", "issues"], [18, 3, 1, "", "make_summary"], [18, 6, 1, "", "near_duplicate_sets"], [18, 3, 1, "", "report"], [18, 6, 1, "", "summary"], [18, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[19, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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.issue_manager": [[21, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[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.label": [[22, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 3, 1, "", "get_health_summary"], [22, 6, 1, "", "health_summary_parameters"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[24, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[24, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [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.noniid": [[25, 2, 1, "", "NonIIDIssueManager"], [25, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[25, 3, 1, "", "collect_info"], [25, 6, 1, "", "description"], [25, 3, 1, "", "find_issues"], [25, 6, 1, "", "info"], [25, 6, 1, "", "issue_name"], [25, 6, 1, "", "issue_score_key"], [25, 6, 1, "", "issues"], [25, 3, 1, "", "make_summary"], [25, 3, 1, "", "report"], [25, 6, 1, "", "summary"], [25, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[26, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[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.outlier": [[27, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[27, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "ood"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[29, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[29, 2, 1, "", "RegressionLabelIssueManager"], [29, 1, 1, "", "find_issues_with_features"], [29, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[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, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[30, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[30, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [30, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "filter_cluster_ids"], [30, 3, 1, "", "find_issues"], [30, 3, 1, "", "get_worst_cluster"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "perform_clustering"], [30, 3, 1, "", "report"], [30, 3, 1, "", "set_knn_graph"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[13, 7, 1, "", "REGISTRY"], [13, 1, 1, "", "list_default_issue_types"], [13, 1, 1, "", "list_possible_issue_types"], [13, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[31, 2, 1, "", "ModelOutput"], [31, 2, 1, "", "MultiClassPredProbs"], [31, 2, 1, "", "MultiLabelPredProbs"], [31, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[31, 3, 1, "", "collect"], [31, 6, 1, "", "data"], [31, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[31, 6, 1, "", "argument"], [31, 3, 1, "", "collect"], [31, 6, 1, "", "data"], [31, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[31, 6, 1, "", "argument"], [31, 3, 1, "", "collect"], [31, 6, 1, "", "data"], [31, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[31, 6, 1, "", "argument"], [31, 3, 1, "", "collect"], [31, 6, 1, "", "data"], [31, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[32, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[32, 3, 1, "", "get_report"], [32, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[33, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[33, 6, 1, "", "CLASSIFICATION"], [33, 6, 1, "", "MULTILABEL"], [33, 6, 1, "", "REGRESSION"], [33, 3, 1, "", "__contains__"], [33, 3, 1, "", "__getitem__"], [33, 3, 1, "", "__iter__"], [33, 3, 1, "", "__len__"], [33, 3, 1, "", "from_str"], [33, 4, 1, "", "is_classification"], [33, 4, 1, "", "is_multilabel"], [33, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[35, 1, 1, "", "find_overlapping_classes"], [35, 1, 1, "", "health_summary"], [35, 1, 1, "", "overall_label_health_score"], [35, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[36, 0, 0, "-", "cifar_cnn"], [37, 0, 0, "-", "coteaching"], [39, 0, 0, "-", "label_issues_batched"], [40, 0, 0, "-", "mnist_pytorch"], [41, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[36, 2, 1, "", "CNN"], [36, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[36, 6, 1, "", "T_destination"], [36, 3, 1, "", "__call__"], [36, 3, 1, "", "add_module"], [36, 3, 1, "", "apply"], [36, 3, 1, "", "bfloat16"], [36, 3, 1, "", "buffers"], [36, 6, 1, "", "call_super_init"], [36, 3, 1, "", "children"], [36, 3, 1, "", "compile"], [36, 3, 1, "", "cpu"], [36, 3, 1, "", "cuda"], [36, 3, 1, "", "double"], [36, 6, 1, "", "dump_patches"], [36, 3, 1, "", "eval"], [36, 3, 1, "", "extra_repr"], [36, 3, 1, "", "float"], [36, 3, 1, "id0", "forward"], [36, 3, 1, "", "get_buffer"], [36, 3, 1, "", "get_extra_state"], [36, 3, 1, "", "get_parameter"], [36, 3, 1, "", "get_submodule"], [36, 3, 1, "", "half"], [36, 3, 1, "", "ipu"], [36, 3, 1, "", "load_state_dict"], [36, 3, 1, "", "modules"], [36, 3, 1, "", "named_buffers"], [36, 3, 1, "", "named_children"], [36, 3, 1, "", "named_modules"], [36, 3, 1, "", "named_parameters"], [36, 3, 1, "", "parameters"], [36, 3, 1, "", "register_backward_hook"], [36, 3, 1, "", "register_buffer"], [36, 3, 1, "", "register_forward_hook"], [36, 3, 1, "", "register_forward_pre_hook"], [36, 3, 1, "", "register_full_backward_hook"], [36, 3, 1, "", "register_full_backward_pre_hook"], [36, 3, 1, "", "register_load_state_dict_post_hook"], [36, 3, 1, "", "register_module"], [36, 3, 1, "", "register_parameter"], [36, 3, 1, "", "register_state_dict_pre_hook"], [36, 3, 1, "", "requires_grad_"], [36, 3, 1, "", "set_extra_state"], [36, 3, 1, "", "share_memory"], [36, 3, 1, "", "state_dict"], [36, 3, 1, "", "to"], [36, 3, 1, "", "to_empty"], [36, 3, 1, "", "train"], [36, 6, 1, "", "training"], [36, 3, 1, "", "type"], [36, 3, 1, "", "xpu"], [36, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[37, 1, 1, "", "adjust_learning_rate"], [37, 1, 1, "", "evaluate"], [37, 1, 1, "", "forget_rate_scheduler"], [37, 1, 1, "", "initialize_lr_scheduler"], [37, 1, 1, "", "loss_coteaching"], [37, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[39, 2, 1, "", "LabelInspector"], [39, 7, 1, "", "adj_confident_thresholds_shared"], [39, 1, 1, "", "find_label_issues_batched"], [39, 7, 1, "", "labels_shared"], [39, 7, 1, "", "pred_probs_shared"], [39, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[39, 3, 1, "", "get_confident_thresholds"], [39, 3, 1, "", "get_label_issues"], [39, 3, 1, "", "get_num_issues"], [39, 3, 1, "", "get_quality_scores"], [39, 3, 1, "", "score_label_quality"], [39, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[40, 2, 1, "", "CNN"], [40, 2, 1, "", "SimpleNet"], [40, 1, 1, "", "get_mnist_dataset"], [40, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[40, 3, 1, "", "__init_subclass__"], [40, 6, 1, "", "batch_size"], [40, 6, 1, "", "dataset"], [40, 6, 1, "", "epochs"], [40, 3, 1, "id0", "fit"], [40, 3, 1, "", "get_metadata_routing"], [40, 3, 1, "", "get_params"], [40, 6, 1, "", "loader"], [40, 6, 1, "", "log_interval"], [40, 6, 1, "", "lr"], [40, 6, 1, "", "momentum"], [40, 6, 1, "", "no_cuda"], [40, 3, 1, "id1", "predict"], [40, 3, 1, "id4", "predict_proba"], [40, 6, 1, "", "seed"], [40, 3, 1, "", "set_fit_request"], [40, 3, 1, "", "set_params"], [40, 3, 1, "", "set_predict_proba_request"], [40, 3, 1, "", "set_predict_request"], [40, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[40, 6, 1, "", "T_destination"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "add_module"], [40, 3, 1, "", "apply"], [40, 3, 1, "", "bfloat16"], [40, 3, 1, "", "buffers"], [40, 6, 1, "", "call_super_init"], [40, 3, 1, "", "children"], [40, 3, 1, "", "compile"], [40, 3, 1, "", "cpu"], [40, 3, 1, "", "cuda"], [40, 3, 1, "", "double"], [40, 6, 1, "", "dump_patches"], [40, 3, 1, "", "eval"], [40, 3, 1, "", "extra_repr"], [40, 3, 1, "", "float"], [40, 3, 1, "", "forward"], [40, 3, 1, "", "get_buffer"], [40, 3, 1, "", "get_extra_state"], [40, 3, 1, "", "get_parameter"], [40, 3, 1, "", "get_submodule"], [40, 3, 1, "", "half"], [40, 3, 1, "", "ipu"], [40, 3, 1, "", "load_state_dict"], [40, 3, 1, "", "modules"], [40, 3, 1, "", "named_buffers"], [40, 3, 1, "", "named_children"], [40, 3, 1, "", "named_modules"], [40, 3, 1, "", "named_parameters"], [40, 3, 1, "", "parameters"], [40, 3, 1, "", "register_backward_hook"], [40, 3, 1, "", "register_buffer"], [40, 3, 1, "", "register_forward_hook"], [40, 3, 1, "", "register_forward_pre_hook"], [40, 3, 1, "", "register_full_backward_hook"], [40, 3, 1, "", "register_full_backward_pre_hook"], [40, 3, 1, "", "register_load_state_dict_post_hook"], [40, 3, 1, "", "register_module"], [40, 3, 1, "", "register_parameter"], [40, 3, 1, "", "register_state_dict_pre_hook"], [40, 3, 1, "", "requires_grad_"], [40, 3, 1, "", "set_extra_state"], [40, 3, 1, "", "share_memory"], [40, 3, 1, "", "state_dict"], [40, 3, 1, "", "to"], [40, 3, 1, "", "to_empty"], [40, 3, 1, "", "train"], [40, 6, 1, "", "training"], [40, 3, 1, "", "type"], [40, 3, 1, "", "xpu"], [40, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[41, 1, 1, "", "display_issues"], [41, 1, 1, "", "find_label_issues"], [41, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[42, 1, 1, "", "find_label_issues"], [42, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [42, 1, 1, "", "find_predicted_neq_given"], [42, 7, 1, "", "pred_probs_by_class"], [42, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[44, 0, 0, "-", "label_quality_utils"], [45, 0, 0, "-", "latent_algebra"], [46, 0, 0, "-", "multiannotator_utils"], [47, 0, 0, "-", "multilabel_scorer"], [48, 0, 0, "-", "multilabel_utils"], [49, 0, 0, "-", "outlier"], [50, 0, 0, "-", "token_classification_utils"], [51, 0, 0, "-", "util"], [52, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[44, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[45, 1, 1, "", "compute_inv_noise_matrix"], [45, 1, 1, "", "compute_noise_matrix_from_inverse"], [45, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [45, 1, 1, "", "compute_py"], [45, 1, 1, "", "compute_py_inv_noise_matrix"], [45, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[46, 1, 1, "", "assert_valid_inputs_multiannotator"], [46, 1, 1, "", "assert_valid_pred_probs"], [46, 1, 1, "", "check_consensus_label_classes"], [46, 1, 1, "", "compute_soft_cross_entropy"], [46, 1, 1, "", "find_best_temp_scaler"], [46, 1, 1, "", "format_multiannotator_labels"], [46, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[47, 2, 1, "", "Aggregator"], [47, 2, 1, "", "ClassLabelScorer"], [47, 2, 1, "", "MultilabelScorer"], [47, 1, 1, "", "exponential_moving_average"], [47, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [47, 1, 1, "", "get_label_quality_scores"], [47, 1, 1, "", "multilabel_py"], [47, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[47, 3, 1, "", "__call__"], [47, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[47, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [47, 6, 1, "", "NORMALIZED_MARGIN"], [47, 6, 1, "", "SELF_CONFIDENCE"], [47, 3, 1, "", "__call__"], [47, 3, 1, "", "__contains__"], [47, 3, 1, "", "__getitem__"], [47, 3, 1, "", "__iter__"], [47, 3, 1, "", "__len__"], [47, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[47, 3, 1, "", "__call__"], [47, 3, 1, "", "aggregate"], [47, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[48, 1, 1, "", "get_onehot_num_classes"], [48, 1, 1, "", "int2onehot"], [48, 1, 1, "", "onehot2int"], [48, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[49, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[50, 1, 1, "", "color_sentence"], [50, 1, 1, "", "filter_sentence"], [50, 1, 1, "", "get_sentence"], [50, 1, 1, "", "mapping"], [50, 1, 1, "", "merge_probs"], [50, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[51, 1, 1, "", "append_extra_datapoint"], [51, 1, 1, "", "clip_noise_rates"], [51, 1, 1, "", "clip_values"], [51, 1, 1, "", "compress_int_array"], [51, 1, 1, "", "confusion_matrix"], [51, 1, 1, "", "csr_vstack"], [51, 1, 1, "", "estimate_pu_f1"], [51, 1, 1, "", "extract_indices_tf"], [51, 1, 1, "", "force_two_dimensions"], [51, 1, 1, "", "format_labels"], [51, 1, 1, "", "get_missing_classes"], [51, 1, 1, "", "get_num_classes"], [51, 1, 1, "", "get_unique_classes"], [51, 1, 1, "", "is_tensorflow_dataset"], [51, 1, 1, "", "is_torch_dataset"], [51, 1, 1, "", "num_unique_classes"], [51, 1, 1, "", "print_inverse_noise_matrix"], [51, 1, 1, "", "print_joint_matrix"], [51, 1, 1, "", "print_noise_matrix"], [51, 1, 1, "", "print_square_matrix"], [51, 1, 1, "", "remove_noise_from_class"], [51, 1, 1, "", "round_preserving_row_totals"], [51, 1, 1, "", "round_preserving_sum"], [51, 1, 1, "", "smart_display_dataframe"], [51, 1, 1, "", "subset_X_y"], [51, 1, 1, "", "subset_data"], [51, 1, 1, "", "subset_labels"], [51, 1, 1, "", "train_val_split"], [51, 1, 1, "", "unshuffle_tensorflow_dataset"], [51, 1, 1, "", "value_counts"], [51, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[52, 1, 1, "", "assert_indexing_works"], [52, 1, 1, "", "assert_nonempty_input"], [52, 1, 1, "", "assert_valid_class_labels"], [52, 1, 1, "", "assert_valid_inputs"], [52, 1, 1, "", "labels_to_array"], [52, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[55, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[55, 2, 1, "", "KerasWrapperModel"], [55, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[55, 3, 1, "", "fit"], [55, 3, 1, "", "get_params"], [55, 3, 1, "", "predict"], [55, 3, 1, "", "predict_proba"], [55, 3, 1, "", "set_params"], [55, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[55, 3, 1, "", "fit"], [55, 3, 1, "", "get_params"], [55, 3, 1, "", "predict"], [55, 3, 1, "", "predict_proba"], [55, 3, 1, "", "set_params"], [55, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[56, 1, 1, "", "convert_long_to_wide_dataset"], [56, 1, 1, "", "get_active_learning_scores"], [56, 1, 1, "", "get_active_learning_scores_ensemble"], [56, 1, 1, "", "get_label_quality_multiannotator"], [56, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [56, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[57, 0, 0, "-", "dataset"], [58, 0, 0, "-", "filter"], [60, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[57, 1, 1, "", "common_multilabel_issues"], [57, 1, 1, "", "multilabel_health_summary"], [57, 1, 1, "", "overall_multilabel_health_score"], [57, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[58, 1, 1, "", "find_label_issues"], [58, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[60, 1, 1, "", "get_label_quality_scores"], [60, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[61, 0, 0, "-", "filter"], [63, 0, 0, "-", "rank"], [64, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[61, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[63, 1, 1, "", "compute_badloc_box_scores"], [63, 1, 1, "", "compute_overlooked_box_scores"], [63, 1, 1, "", "compute_swap_box_scores"], [63, 1, 1, "", "get_label_quality_scores"], [63, 1, 1, "", "issues_from_scores"], [63, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[64, 1, 1, "", "bounding_box_size_distribution"], [64, 1, 1, "", "calculate_per_class_metrics"], [64, 1, 1, "", "class_label_distribution"], [64, 1, 1, "", "get_average_per_class_confusion_matrix"], [64, 1, 1, "", "get_sorted_bbox_count_idxs"], [64, 1, 1, "", "object_counts_per_image"], [64, 1, 1, "", "plot_class_distribution"], [64, 1, 1, "", "plot_class_size_distributions"], [64, 1, 1, "", "visualize"]], "cleanlab.outlier": [[65, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[65, 3, 1, "", "fit"], [65, 3, 1, "", "fit_score"], [65, 3, 1, "", "score"]], "cleanlab.rank": [[66, 1, 1, "", "find_top_issues"], [66, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [66, 1, 1, "", "get_label_quality_ensemble_scores"], [66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_normalized_margin_for_each_label"], [66, 1, 1, "", "get_self_confidence_for_each_label"], [66, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[68, 0, 0, "-", "learn"], [69, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[68, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[68, 3, 1, "", "__init_subclass__"], [68, 3, 1, "", "find_label_issues"], [68, 3, 1, "", "fit"], [68, 3, 1, "", "get_aleatoric_uncertainty"], [68, 3, 1, "", "get_epistemic_uncertainty"], [68, 3, 1, "", "get_label_issues"], [68, 3, 1, "", "get_metadata_routing"], [68, 3, 1, "", "get_params"], [68, 3, 1, "", "predict"], [68, 3, 1, "", "save_space"], [68, 3, 1, "", "score"], [68, 3, 1, "", "set_fit_request"], [68, 3, 1, "", "set_params"], [68, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[69, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[70, 0, 0, "-", "filter"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[70, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[73, 1, 1, "", "common_label_issues"], [73, 1, 1, "", "display_issues"], [73, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[74, 0, 0, "-", "filter"], [76, 0, 0, "-", "rank"], [77, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[74, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[76, 1, 1, "", "get_label_quality_scores"], [76, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[77, 1, 1, "", "common_label_issues"], [77, 1, 1, "", "display_issues"], [77, 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, 80, 84, 85, 87, 88, 89, 92, 98, 99, 100], "count": [3, 89], "datalab": [4, 6, 8, 9, 10, 81, 82, 83, 84, 85, 89, 92], "creat": [6, 81, 82, 89, 91], "your": [6, 78, 81, 82, 85, 87, 89], "own": 6, "issu": [6, 8, 9, 20, 29, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 97, 98, 100], "manag": [6, 20], "prerequisit": 6, "implement": 6, "issuemanag": [6, 81], "basic": 6, "check": 6, "intermedi": 6, "advanc": [6, 81], "us": [6, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "gener": 7, "cluster": [7, 87], "id": 7, "guid": [8, 10], "type": [8, 9, 89], "custom": [8, 81], "can": [9, 82, 86, 87, 89, 91], "detect": [9, 82, 84, 85, 87, 89, 93, 94], "estim": [9, 89, 91, 92], "each": 9, "label": [9, 22, 24, 29, 78, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100], "outlier": [9, 27, 49, 65, 84, 85, 88, 92, 94], "Near": [9, 82, 84, 85, 88], "duplic": [9, 18, 82, 84, 85, 87, 88, 92], "non": [9, 85], "iid": [9, 85], "class": [9, 79, 89, 97], "imbal": [9, 19], "imag": [9, 88, 94], "specif": [9, 20, 97], "underperform": [9, 87], "group": [9, 87], "null": [9, 26], "data": [9, 11, 78, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93, 94, 96, 97, 98, 100], "valuat": 9, "option": 9, "paramet": [9, 89], "get": [10, 81, 82, 91, 92, 93, 97, 100], "start": [10, 86], "api": 10, "refer": 10, "data_issu": 12, "factori": 13, "intern": [14, 43], "issue_find": 15, "data_valu": 17, "issue_manag": [20, 21], "regist": 20, "ml": [20, 87, 89], "task": [20, 33], "multilabel": 23, "noniid": 25, "regress": [28, 67, 68, 69, 87, 96], "prioriti": 29, "order": 29, "find": [29, 78, 80, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "underperforming_group": 30, "model_output": 31, "report": [32, 88], "dataset": [35, 57, 78, 82, 85, 86, 87, 88, 89, 92, 93, 94, 96, 97, 99, 100], "cifar_cnn": 36, "coteach": 37, "experiment": 38, "label_issues_batch": 39, "mnist_pytorch": 40, "span_classif": 41, "filter": [42, 58, 61, 70, 74, 89], "label_quality_util": 44, "latent_algebra": 45, "multiannotator_util": 46, "multilabel_scor": 47, "multilabel_util": 48, "token_classification_util": 50, "util": 51, "valid": [52, 88, 95], "fasttext": 53, "model": [54, 78, 80, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99], "kera": 55, "multiannot": [56, 91], "multilabel_classif": 59, "rank": [60, 63, 66, 69, 72, 76, 89], "object_detect": 62, "summari": [64, 73, 77], "learn": [68, 82, 87, 89, 98], "segment": [71, 97], "token_classif": [75, 100], "cleanlab": [78, 80, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "open": [78, 87], "sourc": [78, 87], "document": 78, "quickstart": 78, "1": [78, 79, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "instal": [78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "2": [78, 79, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "common": [78, 79, 100], "3": [78, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "handl": [78, 87], "error": [78, 87, 88, 89, 91, 92, 93, 96, 97, 99, 100], "train": [78, 80, 87, 94, 96, 98, 99], "robust": [78, 89, 96, 98, 99], "noisi": [78, 89, 96, 98, 99], "4": [78, 80, 81, 82, 84, 85, 88, 89, 91, 93, 94, 96, 98, 99], "curat": [78, 86], "fix": [78, 87], "level": [78, 86, 89, 100], "5": [78, 80, 82, 84, 88, 89, 91, 96, 98], "improv": [78, 91], "via": [78, 89, 91], "mani": [78, 89], "other": [78, 91, 93, 96], "techniqu": 78, "contribut": 78, "easi": [78, 84, 85, 88], "mode": [78, 84, 85, 88], "how": [79, 87, 89, 91, 92, 100], "migrat": 79, "version": 79, "0": 79, "from": [79, 81, 82, 89, 96, 98, 99], "pre": [79, 80, 87, 94], "function": [79, 81], "name": 79, "chang": 79, "modul": [79, 89], "new": 79, "remov": 79, "argument": [79, 81], "variabl": 79, "audio": 80, "speechbrain": 80, "depend": [80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "import": [80, 81, 82, 86, 88, 89, 91], "them": [80, 86, 89], "load": [80, 81, 82, 84, 85, 96, 98, 99], "featur": [80, 88, 94], "fit": 80, "linear": 80, "comput": [80, 84, 85, 87, 88, 91, 95, 98], "out": [80, 81, 82, 84, 85, 88, 91, 95, 98], "sampl": [80, 81, 82, 84, 85, 88, 91, 95, 98], "predict": [80, 81, 82, 84, 85, 88, 91, 92, 93, 95, 98], "probabl": [80, 81, 82, 84, 85, 88, 91, 95, 98], "workflow": [81, 89], "audit": [81, 82], "requir": [81, 82, 84, 85, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "classifi": [81, 82], "instanti": 81, "object": [81, 93], "increment": 81, "search": 81, "specifi": [81, 87], "nondefault": 81, "save": 81, "ad": 81, "A": 82, "unifi": 82, "all": [82, 89], "kind": [82, 93], "skip": [82, 86, 89, 91], "detail": [82, 86, 89, 91], "more": [82, 89, 96, 98, 99], "about": 82, "addit": 82, "inform": [82, 88], "tutori": [83, 86, 90], "tabular": [84, 98], "numer": 84, "categor": 84, "column": 84, "process": [84, 94, 96, 98], "select": [84, 98], "construct": 84, "k": [84, 88, 95], "nearest": 84, "neighbour": 84, "graph": 84, "text": [85, 99, 100], "format": [85, 87, 92, 93, 99], "defin": [85, 88, 96, 99], "drift": [85, 92], "fetch": [86, 88], "evalu": 86, "health": [86, 89], "8": [86, 89], "popular": 86, "faq": 87, "what": [87, 89, 95], "do": [87, 89], "i": [87, 89, 95], "infer": 87, "correct": 87, "exampl": [87, 88, 89, 94], "ha": 87, "flag": 87, "should": 87, "v": 87, "test": [87, 89, 94], "big": 87, "limit": 87, "memori": 87, "why": 87, "isn": 87, "t": 87, "cleanlearn": [87, 89], "work": [87, 89, 91, 100], "me": 87, "differ": [87, 93], "clean": [87, 89], "final": 87, "hyperparamet": 87, "tune": 87, "onli": 87, "one": [87, 89, 92, 97], "doe": [87, 91, 100], "take": 87, "so": 87, "long": 87, "slice": 87, "when": [87, 89], "identifi": [87, 93], "run": 87, "licens": 87, "under": 87, "an": 87, "answer": 87, "question": 87, "pytorch": [88, 94], "normal": 88, "fashion": 88, "mnist": 88, "prepar": 88, "fold": [88, 95], "cross": [88, 95], "embed": [88, 94], "7": [88, 89], "view": 88, "most": [88, 100], "like": 88, "sever": 88, "set": [88, 89], "dark": 88, "top": [88, 97], "low": 88, "The": 89, "centric": 89, "ai": 89, "machin": 89, "find_label_issu": 89, "line": 89, "code": 89, "visual": [89, 93, 94, 97], "twenti": 89, "lowest": 89, "qualiti": [89, 91, 92, 93, 97, 100], "see": 89, "now": 89, "let": 89, "": 89, "happen": 89, "we": 89, "merg": 89, "seafoam": 89, "green": 89, "yellow": 89, "too": 89, "you": 89, "re": 89, "6": 89, "One": 89, "score": [89, 91, 92, 93, 97, 100], "rule": 89, "overal": [89, 97], "accur": 89, "thi": 89, "directli": 89, "fulli": 89, "character": 89, "nois": 89, "matrix": [89, 92], "joint": 89, "prior": 89, "true": 89, "distribut": 89, "flip": 89, "rate": 89, "ani": 89, "again": 89, "support": 89, "lot": 89, "method": 89, "filter_bi": 89, "automat": 89, "everi": 89, "uniqu": 89, "num_label_issu": 89, "threshold": 89, "found": 89, "Not": 89, "sure": 89, "ensembl": 89, "multipl": [89, 91], "predictor": 89, "consensu": 91, "annot": 91, "initi": 91, "major": 91, "vote": 91, "better": 91, "statist": 91, "compar": 91, "inspect": 91, "potenti": [91, 96, 99], "retrain": 91, "further": 91, "multi": 92, "beyond": 92, "mislabel": [92, 97, 100], "given": 92, "hot": 92, "binari": 92, "without": 92, "applic": 92, "real": 92, "download": [93, 97, 100], "objectlab": 93, "exploratori": 93, "analysi": 93, "timm": 94, "cifar10": 94, "some": 94, "pred_prob": [94, 97, 100], "wai": 96, "semant": 97, "which": 97, "ar": 97, "commonli": 97, "focus": 97, "scikit": 98, "token": 100, "word": 100, "sentenc": 100, "contain": 100, "particular": 100}, "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"]], "datalab": [[4, "module-cleanlab.datalab.datalab"], [10, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[6, "creating-your-own-issues-manager"]], "Prerequisites": [[6, "prerequisites"]], "Implementing IssueManagers": [[6, "implementing-issuemanagers"]], "Basic Issue Check": [[6, "basic-issue-check"]], "Intermediate Issue Check": [[6, "intermediate-issue-check"]], "Advanced Issue Check": [[6, "advanced-issue-check"]], "Use with Datalab": [[6, "use-with-datalab"]], "Generating Cluster IDs": [[7, "generating-cluster-ids"]], "Datalab guides": [[8, "datalab-guides"]], "Types of issues": [[8, "types-of-issues"]], "Customizing issue types": [[8, "customizing-issue-types"]], "Datalab Issue Types": [[9, "datalab-issue-types"]], "Types of issues Datalab can detect": [[9, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[9, "estimates-for-each-issue-type"]], "Label Issue": [[9, "label-issue"]], "Outlier Issue": [[9, "outlier-issue"]], "(Near) Duplicate Issue": [[9, "near-duplicate-issue"]], "Non-IID Issue": [[9, "non-iid-issue"]], "Class Imbalance Issue": [[9, "class-imbalance-issue"]], "Image-specific Issues": [[9, "image-specific-issues"]], "Underperforming Group Issue": [[9, "underperforming-group-issue"]], "Null Issue": [[9, "null-issue"]], "Data Valuation Issue": [[9, "data-valuation-issue"]], "Optional Issue Parameters": [[9, "optional-issue-parameters"]], "Label Issue Parameters": [[9, "label-issue-parameters"]], "Outlier Issue Parameters": [[9, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[9, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[9, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[9, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[9, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[9, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[9, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[9, "image-issue-parameters"]], "Getting Started": [[10, "getting-started"]], "Guides": [[10, "guides"]], "API Reference": [[10, "api-reference"]], "data": [[11, "module-cleanlab.datalab.internal.data"]], "data_issues": [[12, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[13, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[14, "internal"], [43, "internal"]], "issue_finder": [[15, "issue-finder"]], "data_valuation": [[17, "data-valuation"]], "duplicate": [[18, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[19, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[20, "issue-manager"], [21, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[20, "registered-issue-managers"]], "ML task-specific issue managers": [[20, "ml-task-specific-issue-managers"]], "label": [[22, "module-cleanlab.datalab.internal.issue_manager.label"], [24, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [29, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[23, "multilabel"]], "noniid": [[25, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[26, "null"]], "outlier": [[27, "module-cleanlab.datalab.internal.issue_manager.outlier"], [49, "module-cleanlab.internal.outlier"], [65, "module-cleanlab.outlier"]], "regression": [[28, "regression"], [67, "regression"]], "Priority Order for finding issues:": [[29, null]], "underperforming_group": [[30, "underperforming-group"]], "model_outputs": [[31, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[32, "report"]], "task": [[33, "task"]], "dataset": [[35, "module-cleanlab.dataset"], [57, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[36, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[37, "module-cleanlab.experimental.coteaching"]], "experimental": [[38, "experimental"]], "label_issues_batched": [[39, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[40, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[41, "module-cleanlab.experimental.span_classification"]], "filter": [[42, "module-cleanlab.filter"], [58, "module-cleanlab.multilabel_classification.filter"], [61, "filter"], [70, "filter"], [74, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[44, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[45, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[46, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[47, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[48, "module-cleanlab.internal.multilabel_utils"]], "token_classification_utils": [[50, "module-cleanlab.internal.token_classification_utils"]], "util": [[51, "module-cleanlab.internal.util"]], "validation": [[52, "module-cleanlab.internal.validation"]], "fasttext": [[53, "fasttext"]], "models": [[54, "models"]], "keras": [[55, "module-cleanlab.models.keras"]], "multiannotator": [[56, "module-cleanlab.multiannotator"]], "multilabel_classification": [[59, "multilabel-classification"]], "rank": [[60, "module-cleanlab.multilabel_classification.rank"], [63, "module-cleanlab.object_detection.rank"], [66, "module-cleanlab.rank"], [72, "module-cleanlab.segmentation.rank"], [76, "module-cleanlab.token_classification.rank"]], "object_detection": [[62, "object-detection"]], "summary": [[64, "summary"], [73, "module-cleanlab.segmentation.summary"], [77, "module-cleanlab.token_classification.summary"]], "regression.learn": [[68, "module-cleanlab.regression.learn"]], "regression.rank": [[69, "module-cleanlab.regression.rank"]], "segmentation": [[71, "segmentation"]], "token_classification": [[75, "token-classification"]], "cleanlab open-source documentation": [[78, "cleanlab-open-source-documentation"]], "Quickstart": [[78, "quickstart"]], "1. Install cleanlab": [[78, "install-cleanlab"]], "2. Find common issues in your data": [[78, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[78, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[78, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[78, "improve-your-data-via-many-other-techniques"]], "Contributing": [[78, "contributing"]], "Easy Mode": [[78, "easy-mode"], [84, "Easy-Mode"], [85, "Easy-Mode"], [88, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[79, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[79, "function-and-class-name-changes"]], "Module name changes": [[79, "module-name-changes"]], "New modules": [[79, "new-modules"]], "Removed modules": [[79, "removed-modules"]], "Common argument and variable name changes": [[79, "common-argument-and-variable-name-changes"]], "Audio Classification with SpeechBrain and Cleanlab": [[80, "Audio-Classification-with-SpeechBrain-and-Cleanlab"]], "1. Install dependencies and import them": [[80, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[80, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[80, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[80, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[80, "5.-Use-cleanlab-to-find-label-issues"], [84, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[81, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[81, "Install-and-import-required-dependencies"]], "Create and load the data": [[81, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[81, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[81, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[81, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[81, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[81, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[81, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[82, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[82, "1.-Install-and-import-required-dependencies"], [88, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[82, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[82, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[82, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[82, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[82, "Get-additional-information"]], "Near duplicate issues": [[82, "Near-duplicate-issues"], [88, "Near-duplicate-issues"]], "Datalab Tutorials": [[83, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[84, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[84, "1.-Install-required-dependencies"], [85, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [98, "1.-Install-required-dependencies"], [99, "1.-Install-required-dependencies"]], "2. Load and process the data": [[84, "2.-Load-and-process-the-data"], [96, "2.-Load-and-process-the-data"], [98, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[84, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [98, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[84, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[84, "Label-issues"], [85, "Label-issues"], [88, "Label-issues"]], "Outlier issues": [[84, "Outlier-issues"], [85, "Outlier-issues"], [88, "Outlier-issues"]], "Near-duplicate issues": [[84, "Near-duplicate-issues"], [85, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[85, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[85, "2.-Load-and-format-the-text-dataset"], [99, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[85, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[85, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[85, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[86, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[86, "Install-dependencies-and-import-them"], [89, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[86, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[86, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[87, "FAQ"]], "What data can cleanlab detect issues in?": [[87, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[87, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[87, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[87, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[87, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[87, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[87, "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?": [[87, "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?": [[87, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[87, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[87, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[87, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[87, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[87, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[88, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[88, "7.-Use-cleanlab-to-find-issues"]], "View report": [[88, "View-report"]], "View most likely examples with label errors": [[88, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[88, "View-most-severe-outliers"]], "View sets of near duplicate images": [[88, "View-sets-of-near-duplicate-images"]], "Dark images": [[88, "Dark-images"]], "View top examples of dark images": [[88, "View-top-examples-of-dark-images"]], "Low information images": [[88, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[89, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[89, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[89, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[89, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[89, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[89, "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.": [[89, "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": [[89, "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": [[89, "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!": [[89, "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": [[89, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[89, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[89, "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)": [[89, "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:": [[89, "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": [[89, "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.": [[89, "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.": [[89, "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.": [[89, "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.": [[89, "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?": [[89, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[89, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[90, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[91, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[91, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[91, "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": [[91, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[91, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[91, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[91, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[91, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[91, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[92, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[92, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[92, "2.-Format-data,-labels,-and-model-predictions"], [93, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[92, "3.-Use-cleanlab-to-find-label-issues"], [93, "3.-Use-cleanlab-to-find-label-issues"], [97, "3.-Use-cleanlab-to-find-label-issues"], [100, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[92, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[92, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[92, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[92, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[92, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[93, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[93, "1.-Install-required-dependencies-and-download-data"], [97, "1.-Install-required-dependencies-and-download-data"], [100, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[93, "Get-label-quality-scores"], [97, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[93, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[93, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[93, "Other-uses-of-visualize"]], "Exploratory data analysis": [[93, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[94, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[94, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[94, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[94, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[94, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[94, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[95, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[95, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[95, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[96, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[96, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[96, "4.-Train-a-more-robust-model-from-noisy-labels"], [99, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[96, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[97, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[97, "2.-Get-data,-labels,-and-pred_probs"], [100, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[97, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[97, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[97, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[98, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[98, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[98, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[99, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[99, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[100, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[100, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[100, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[100, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[100, "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.datalab.datalab"], [10, "module-cleanlab.datalab"], [11, "module-cleanlab.datalab.internal.data"], [12, "module-cleanlab.datalab.internal.data_issues"], [13, "module-cleanlab.datalab.internal.issue_manager_factory"], [14, "module-cleanlab.datalab.internal"], [15, "module-cleanlab.datalab.internal.issue_finder"], [17, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [18, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [19, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [21, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [22, "module-cleanlab.datalab.internal.issue_manager.label"], [24, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [25, "module-cleanlab.datalab.internal.issue_manager.noniid"], [26, "module-cleanlab.datalab.internal.issue_manager.null"], [27, "module-cleanlab.datalab.internal.issue_manager.outlier"], [29, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [30, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [31, "module-cleanlab.datalab.internal.model_outputs"], [32, "module-cleanlab.datalab.internal.report"], [33, "module-cleanlab.datalab.internal.task"], [35, "module-cleanlab.dataset"], [36, "module-cleanlab.experimental.cifar_cnn"], [37, "module-cleanlab.experimental.coteaching"], [38, "module-cleanlab.experimental"], [39, "module-cleanlab.experimental.label_issues_batched"], [40, "module-cleanlab.experimental.mnist_pytorch"], [41, "module-cleanlab.experimental.span_classification"], [42, "module-cleanlab.filter"], [43, "module-cleanlab.internal"], [44, "module-cleanlab.internal.label_quality_utils"], [45, "module-cleanlab.internal.latent_algebra"], [46, "module-cleanlab.internal.multiannotator_utils"], [47, "module-cleanlab.internal.multilabel_scorer"], [48, "module-cleanlab.internal.multilabel_utils"], [49, "module-cleanlab.internal.outlier"], [50, "module-cleanlab.internal.token_classification_utils"], [51, "module-cleanlab.internal.util"], [52, "module-cleanlab.internal.validation"], [54, "module-cleanlab.models"], [55, "module-cleanlab.models.keras"], [56, "module-cleanlab.multiannotator"], [57, "module-cleanlab.multilabel_classification.dataset"], [58, "module-cleanlab.multilabel_classification.filter"], [59, "module-cleanlab.multilabel_classification"], [60, "module-cleanlab.multilabel_classification.rank"], [61, "module-cleanlab.object_detection.filter"], [62, "module-cleanlab.object_detection"], [63, "module-cleanlab.object_detection.rank"], [64, "module-cleanlab.object_detection.summary"], [65, "module-cleanlab.outlier"], [66, "module-cleanlab.rank"], [67, "module-cleanlab.regression"], [68, "module-cleanlab.regression.learn"], [69, "module-cleanlab.regression.rank"], [70, "module-cleanlab.segmentation.filter"], [71, "module-cleanlab.segmentation"], [72, "module-cleanlab.segmentation.rank"], [73, "module-cleanlab.segmentation.summary"], [74, "module-cleanlab.token_classification.filter"], [75, "module-cleanlab.token_classification"], [76, "module-cleanlab.token_classification.rank"], [77, "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"]], "datalab (class in cleanlab.datalab.datalab)": [[4, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[4, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[4, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[10, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[11, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[11, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[11, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[11, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[11, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[11, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[11, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[11, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[11, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[11, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[11, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[11, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[11, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[11, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[11, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[11, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[11, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[11, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[11, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[11, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[11, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[11, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[11, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[11, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[11, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[12, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[12, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[13, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[13, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[13, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[13, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[13, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[14, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[15, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[15, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[15, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[15, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[17, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[18, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[19, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[21, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[22, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[24, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[25, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[26, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[27, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[29, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[30, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "set_knn_graph() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.set_knn_graph"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[31, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[31, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[31, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[31, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[31, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[32, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[32, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[32, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[32, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[33, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[33, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[33, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[33, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[33, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[33, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[33, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[33, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[35, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[35, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[35, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[35, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[35, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[36, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[36, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[36, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[36, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[36, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[36, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.forward"], [36, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[36, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[37, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[38, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[39, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[40, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[40, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [40, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[40, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[40, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [40, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [40, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[41, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[41, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[41, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[41, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[42, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[42, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[42, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[42, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[42, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[42, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[43, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[44, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[44, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[45, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[46, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[47, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[47, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[47, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[47, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[47, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[47, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[48, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[48, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[48, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[48, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[48, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.outlier": [[49, "module-cleanlab.internal.outlier"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[49, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[50, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[51, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[52, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[54, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[55, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[55, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[55, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[56, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[57, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[57, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[57, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[57, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[57, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[58, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[58, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[58, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[59, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[60, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[60, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[60, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[61, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[61, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[62, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[63, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[64, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[65, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[65, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[65, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[65, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[65, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[66, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[66, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[66, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[67, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[68, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[68, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[68, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[69, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[69, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[70, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[70, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[71, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[72, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[72, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[72, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[73, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[73, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[73, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[73, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[74, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[74, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[75, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[76, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[76, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[76, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[77, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[77, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[77, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[77, "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/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/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/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/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "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/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.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/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/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/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/image.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/tabular.ipynb", "tutorials/text.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "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", "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", "Audio Classification with SpeechBrain and Cleanlab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Find Dataset-level Issues for Dataset Curation", "FAQ", "Image Classification with PyTorch and Cleanlab", "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", "Classification with Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 79, 81, 82, 89, 91, 92], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 81, 82, 89, 91, 92], "generate_noise_matrix_from_trac": [0, 1, 81, 82, 89, 91, 92], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 15, 39, 44, 46, 47, 48, 49, 50, 51, 63, 86, 88, 100], "method": [1, 2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 84, 85, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "ar": [1, 2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 17, 19, 20, 21, 22, 23, 25, 28, 29, 31, 33, 35, 36, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "us": [1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 78, 79, 81, 86, 90, 95], "benchmark": [1, 36, 78, 79, 81, 82, 89, 91, 92], "cleanlab": [1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 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, 51, 52, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 79, 81, 82, 86, 90, 95], "": [1, 2, 3, 9, 17, 31, 35, 36, 40, 44, 47, 49, 51, 56, 57, 61, 63, 64, 65, 66, 68, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "core": [1, 39, 42, 70, 72], "algorithm": [1, 2, 7, 9, 30, 37, 41, 51, 56, 65, 74, 76, 78, 87, 89, 91, 100], "These": [1, 2, 3, 4, 7, 9, 20, 36, 38, 40, 41, 42, 43, 54, 56, 57, 60, 64, 65, 69, 73, 74, 76, 77, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "introduc": [1, 80, 87, 89], "synthet": [1, 91, 92, 97], "nois": [1, 2, 3, 35, 42, 45, 51, 57, 81, 82, 86, 91, 96], "label": [1, 2, 3, 4, 6, 7, 8, 11, 13, 14, 15, 19, 20, 21, 23, 28, 30, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 51, 52, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 81, 86, 90, 94, 95], "classif": [1, 3, 4, 6, 9, 11, 13, 15, 31, 33, 35, 39, 41, 42, 45, 47, 48, 51, 56, 57, 58, 59, 60, 65, 66, 74, 75, 76, 77, 78, 79, 81, 82, 90, 91, 94, 95, 96, 97], "dataset": [1, 2, 3, 4, 6, 9, 11, 12, 13, 15, 17, 18, 19, 21, 24, 25, 26, 27, 29, 30, 38, 39, 40, 41, 42, 45, 47, 51, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 90, 91, 95, 98], "specif": [1, 3, 4, 8, 13, 14, 15, 26, 32, 33, 38, 54, 58, 61, 64, 73, 77, 84, 85, 88, 89, 93, 100], "thi": [1, 2, 3, 4, 5, 6, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "modul": [1, 3, 12, 13, 14, 15, 20, 23, 28, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 47, 49, 51, 54, 56, 61, 64, 65, 66, 78, 87, 88, 92], "provid": [1, 2, 3, 4, 6, 7, 9, 13, 15, 17, 22, 29, 33, 35, 36, 37, 39, 40, 42, 45, 51, 55, 56, 57, 58, 63, 64, 65, 66, 68, 70, 72, 73, 76, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 94, 95, 96, 97, 98, 99, 100], "gener": [1, 2, 3, 6, 9, 17, 22, 24, 32, 35, 47, 51, 52, 65, 66, 68, 73, 80, 81, 82, 85, 86, 87, 88, 89, 91, 92, 94, 95, 97, 99, 100], "valid": [1, 2, 3, 4, 9, 11, 31, 33, 35, 42, 43, 45, 46, 47, 49, 51, 56, 58, 61, 64, 66, 68, 69, 77, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93, 96, 97, 98, 99, 100], "matric": [1, 3, 45, 87], "which": [1, 2, 3, 4, 6, 9, 11, 12, 13, 15, 17, 21, 25, 31, 32, 33, 35, 36, 40, 41, 42, 45, 47, 50, 51, 56, 57, 58, 61, 63, 64, 65, 66, 68, 69, 72, 73, 74, 76, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "learn": [1, 2, 3, 4, 9, 13, 15, 21, 29, 32, 37, 38, 39, 40, 42, 44, 46, 51, 54, 56, 58, 65, 67, 69, 72, 76, 78, 80, 81, 84, 85, 86, 88, 90, 91, 92, 96, 99], "i": [1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "possibl": [1, 2, 3, 6, 9, 35, 36, 40, 42, 44, 45, 47, 58, 59, 60, 61, 63, 64, 65, 66, 68, 74, 76, 77, 82, 87, 89, 91, 92, 93, 96, 97, 100], "noisi": [1, 2, 3, 9, 35, 37, 40, 42, 45, 51, 57, 58, 60, 66, 68, 69, 70, 72, 73, 79, 81, 82, 84, 85, 87, 90, 91], "given": [1, 2, 3, 4, 9, 13, 29, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 50, 51, 56, 57, 58, 61, 63, 64, 65, 66, 68, 69, 73, 74, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98, 99, 100], "matrix": [1, 2, 3, 4, 9, 15, 17, 30, 35, 42, 44, 45, 48, 51, 52, 58, 61, 63, 64, 65, 66, 84, 93, 94], "trace": [1, 81, 82, 89, 91, 92], "valu": [1, 2, 3, 4, 9, 11, 12, 15, 17, 21, 25, 26, 31, 33, 35, 36, 37, 39, 40, 42, 44, 45, 47, 49, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 77, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 99, 100], "more": [1, 2, 3, 4, 6, 9, 12, 13, 15, 17, 25, 35, 36, 39, 40, 41, 44, 47, 49, 51, 56, 57, 58, 59, 60, 61, 63, 64, 66, 68, 69, 72, 73, 74, 76, 78, 80, 81, 84, 85, 86, 87, 88, 91, 92, 93, 94, 97, 100], "function": [1, 2, 3, 4, 6, 12, 13, 15, 22, 25, 29, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 82, 86, 87, 89, 91, 92, 93, 97, 98, 99, 100], "noise_matrix": [1, 2, 3, 9, 45, 51, 81, 82, 89, 91, 92], "py": [1, 3, 32, 36, 37, 42, 45, 47, 80, 81, 82, 85, 87, 89, 91, 92, 99], "verbos": [1, 2, 4, 6, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 39, 42, 56, 57, 58, 63, 65, 66, 68, 70, 72, 73, 77, 81, 89, 91], "fals": [1, 2, 3, 4, 6, 11, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 33, 35, 36, 39, 40, 42, 46, 50, 51, 52, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 74, 80, 81, 82, 84, 85, 87, 88, 89, 91, 93, 94, 96, 97, 99], "sourc": [1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77], "prior": [1, 2, 3, 35, 42, 45, 47], "repres": [1, 2, 3, 6, 9, 11, 15, 17, 25, 31, 33, 35, 39, 42, 45, 48, 49, 51, 56, 57, 58, 61, 63, 64, 65, 66, 68, 70, 72, 73, 77, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "p": [1, 2, 3, 4, 9, 35, 42, 44, 45, 51, 56, 64, 65, 66, 70, 84, 85, 89, 91, 100], "true_label": [1, 2, 3, 35, 45, 51, 89, 91], "k": [1, 2, 3, 4, 7, 9, 11, 15, 17, 18, 22, 25, 27, 30, 35, 39, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 56, 57, 58, 59, 60, 61, 64, 65, 66, 68, 70, 72, 73, 74, 76, 77, 80, 81, 82, 87, 89, 91, 92, 93, 94, 97, 98, 100], "check": [1, 2, 4, 5, 8, 9, 11, 15, 26, 33, 36, 39, 40, 46, 52, 55, 61, 64, 68, 78, 80, 81, 82, 87, 88, 89, 91, 92, 96, 98, 99], "learnabl": 1, "mean": [1, 2, 6, 7, 11, 12, 21, 25, 37, 40, 45, 47, 49, 63, 68, 82, 85, 87, 89, 91, 92, 93, 94, 96, 99], "achiev": [1, 2, 36, 37, 40, 68, 87, 91, 100], "better": [1, 4, 42, 56, 58, 66, 68, 69, 78, 80, 82, 84, 85, 87, 89, 92, 93, 94, 99, 100], "than": [1, 2, 3, 6, 9, 25, 27, 30, 35, 42, 51, 55, 56, 61, 63, 65, 66, 68, 72, 76, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 100], "random": [1, 2, 3, 6, 9, 17, 30, 39, 47, 56, 66, 68, 80, 81, 82, 84, 87, 88, 89, 91, 92, 94, 98], "perform": [1, 2, 6, 9, 25, 27, 30, 36, 40, 47, 64, 68, 78, 81, 87, 89, 91, 92, 95, 96, 98, 99], "averag": [1, 3, 4, 9, 21, 27, 35, 36, 40, 47, 49, 56, 57, 64, 65, 66, 87, 91, 94], "amount": [1, 3, 88], "paramet": [1, 2, 3, 4, 8, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 82, 85, 88, 98, 99], "np": [1, 2, 3, 4, 6, 15, 17, 30, 35, 37, 39, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 73, 74, 76, 77, 80, 81, 82, 84, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "ndarrai": [1, 2, 3, 4, 15, 22, 24, 25, 29, 30, 31, 35, 37, 39, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 76, 100], "an": [1, 2, 3, 4, 6, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 49, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "arrai": [1, 2, 3, 4, 6, 9, 11, 15, 17, 25, 31, 35, 37, 39, 40, 41, 42, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 81, 82, 85, 87, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "shape": [1, 2, 3, 4, 15, 17, 35, 37, 39, 41, 42, 44, 45, 46, 47, 49, 50, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 86, 87, 89, 92, 93, 94, 97, 100], "condit": [1, 2, 3, 45, 50, 51, 66, 88, 89, 100], "probabl": [1, 2, 3, 4, 7, 9, 15, 22, 24, 27, 31, 35, 39, 40, 41, 42, 44, 45, 47, 48, 50, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 74, 76, 77, 78, 79, 86, 87, 89, 90, 92, 93, 94, 97, 100], "k_": [1, 2, 3, 45, 51], "k_y": [1, 2, 3, 45, 51], "contain": [1, 2, 3, 4, 9, 11, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 42, 44, 45, 50, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 72, 73, 74, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99], "fraction": [1, 2, 3, 9, 19, 37, 45, 51, 56, 68, 84, 87], "exampl": [1, 2, 3, 4, 6, 7, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 91, 92, 93, 95, 96, 97, 98, 99, 100], "everi": [1, 2, 3, 4, 15, 36, 40, 42, 45, 50, 51, 58, 66, 68, 69, 80, 81, 82, 84, 85, 87, 88, 91, 93, 95, 97, 98, 100], "class": [1, 2, 3, 4, 6, 8, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 98, 99, 100], "other": [1, 2, 3, 4, 9, 15, 21, 26, 35, 36, 38, 39, 40, 42, 45, 48, 51, 52, 54, 56, 57, 60, 64, 65, 66, 68, 73, 80, 81, 82, 84, 85, 87, 88, 89, 92, 94, 97, 100], "assum": [1, 2, 3, 11, 42, 45, 50, 51, 66, 70, 73, 87, 92, 94, 96, 97, 100], "column": [1, 2, 3, 4, 9, 11, 12, 29, 35, 39, 42, 45, 47, 48, 50, 51, 56, 57, 58, 60, 61, 64, 65, 66, 68, 73, 74, 76, 77, 80, 81, 82, 85, 86, 87, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100], "sum": [1, 2, 3, 25, 30, 31, 35, 45, 47, 51, 57, 58, 60, 63, 68, 81, 82, 87, 88, 89, 91, 92, 97, 100], "1": [1, 2, 3, 4, 6, 9, 11, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 86, 87, 95], "each": [1, 2, 3, 4, 6, 7, 8, 11, 12, 13, 15, 19, 21, 22, 24, 25, 30, 31, 32, 35, 36, 37, 39, 40, 41, 42, 44, 45, 47, 48, 49, 51, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "true": [1, 2, 3, 4, 6, 9, 11, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 33, 35, 36, 37, 39, 40, 42, 45, 47, 50, 51, 52, 55, 56, 57, 58, 61, 63, 64, 65, 66, 68, 70, 72, 73, 77, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "return": [1, 2, 3, 4, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 80, 81, 82, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 99, 100], "type": [1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 79, 80, 81, 82, 83, 84, 85, 87, 88, 92, 93, 96, 97, 98, 100], "bool": [1, 2, 3, 4, 11, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 33, 35, 36, 39, 40, 42, 47, 50, 51, 56, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 77], "is_valid": 1, "whether": [1, 3, 4, 9, 11, 12, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 36, 39, 40, 42, 51, 56, 57, 58, 60, 61, 77, 80, 82, 84, 85, 86, 87, 88, 89, 96, 99, 100], "from": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 22, 26, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 41, 42, 45, 47, 48, 49, 50, 51, 56, 58, 60, 63, 64, 65, 66, 68, 69, 74, 76, 77, 78, 80, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 97, 100], "perfect": [1, 2, 35, 68, 89, 93], "exactli": [1, 3, 9, 35, 36, 40, 42, 59, 65, 81, 82, 84, 85, 88, 89], "yield": [1, 36, 40], "between": [1, 4, 9, 14, 15, 20, 21, 23, 25, 28, 31, 35, 36, 37, 38, 39, 40, 42, 43, 44, 46, 49, 54, 56, 57, 60, 63, 65, 66, 68, 69, 72, 76, 77, 79, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 96, 97, 99, 100], "below": [1, 3, 4, 9, 35, 36, 39, 40, 42, 44, 47, 56, 57, 58, 63, 64, 72, 76, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "we": [1, 2, 3, 4, 6, 9, 12, 21, 36, 39, 40, 42, 47, 51, 52, 56, 63, 64, 66, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "loop": [1, 3, 45, 51, 88, 93], "implement": [1, 2, 3, 8, 13, 21, 36, 37, 39, 40, 45, 51, 68, 78, 80, 81, 84, 94, 95, 98], "what": [1, 4, 8, 9, 15, 32, 35, 37, 39, 42, 56, 57, 61, 63, 80, 81, 82, 84, 85, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "doe": [1, 2, 3, 6, 9, 39, 40, 42, 47, 52, 63, 64, 68, 70, 72, 76, 80, 81, 82, 84, 85, 88, 92, 96, 97, 99], "do": [1, 2, 4, 9, 35, 39, 40, 51, 52, 65, 66, 70, 80, 81, 82, 84, 85, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "fast": 1, "explain": [1, 9], "python": [1, 2, 40, 55, 68, 80, 81, 82, 85, 86, 94, 99], "pseudocod": [1, 95], "happen": [1, 9, 42, 58, 85, 91, 97], "n": [1, 2, 3, 4, 6, 35, 36, 39, 40, 42, 44, 45, 46, 47, 49, 50, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 76, 80, 85, 86, 87, 88, 91, 92, 93, 96, 97, 98, 99, 100], "without": [1, 2, 4, 9, 11, 13, 19, 36, 40, 60, 68, 78, 80, 85, 89, 93, 94, 99], "ani": [1, 2, 3, 4, 6, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 39, 40, 42, 44, 46, 49, 50, 51, 55, 56, 58, 60, 61, 63, 64, 66, 68, 70, 72, 73, 78, 80, 81, 82, 84, 85, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99], "distinct": [1, 17, 51, 100], "natur": [1, 9, 91, 94], "number": [1, 2, 3, 4, 6, 7, 9, 11, 12, 15, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 33, 35, 36, 37, 39, 40, 42, 45, 46, 47, 48, 49, 50, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 76, 77, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 97, 100], "0": [1, 2, 3, 4, 6, 9, 11, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "count_joint": 1, "len": [1, 2, 3, 6, 35, 39, 45, 50, 51, 52, 65, 66, 68, 81, 82, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "y": [1, 2, 3, 4, 7, 17, 29, 30, 40, 45, 47, 51, 52, 55, 64, 68, 69, 80, 81, 82, 84, 87, 89, 91, 92, 94, 96, 99], "round": [1, 39, 42, 51, 68, 87, 96], "astyp": [1, 91], "int": [1, 2, 3, 4, 6, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 36, 37, 39, 40, 42, 47, 48, 49, 50, 51, 52, 57, 58, 60, 64, 65, 66, 68, 70, 72, 73, 74, 77, 80, 81, 88, 93, 94], "rang": [1, 3, 4, 6, 11, 45, 47, 49, 51, 64, 68, 69, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 100], "idx_flip": 1, "where": [1, 2, 3, 4, 6, 9, 11, 12, 15, 21, 35, 39, 42, 45, 46, 47, 48, 49, 50, 51, 52, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 80, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 99, 100], "pragma": 1, "cover": [1, 3, 79, 86], "choic": [1, 7, 42, 49, 87, 88, 92, 94], "replac": [1, 50, 55, 66, 81, 82, 85, 86, 87, 88, 91, 94, 98, 99], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 66, 80, 81, 82], "05": [1, 9, 25, 29, 50, 64, 68, 74, 76, 84, 86, 87, 89, 93, 100], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 81, 82, 89, 91, 92], "none": [1, 2, 3, 4, 6, 11, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 52, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 68, 70, 72, 73, 76, 77, 81, 82, 87, 88, 89, 91, 92, 97], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 9, 25, 38, 40, 47, 68, 80, 81, 82, 84, 86, 89, 91, 92, 98], "max_it": [1, 80, 85, 94, 99], "10000": [1, 39, 86, 87], "x": [1, 2, 3, 4, 9, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 35, 36, 37, 40, 42, 44, 45, 47, 50, 51, 52, 55, 56, 58, 64, 65, 66, 68, 70, 80, 81, 82, 84, 86, 87, 88, 89, 91, 92, 94, 96, 98, 99], "diagon": [1, 3, 4, 42, 45, 51], "equal": [1, 3, 9, 11, 58, 63, 73, 95], "creat": [1, 2, 8, 15, 17, 36, 39, 40, 42, 51, 68, 78, 80, 84, 85, 87, 88, 97, 99, 100], "impli": [1, 9, 35, 57, 64], "float": [1, 2, 9, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 37, 38, 39, 40, 42, 44, 46, 47, 49, 50, 51, 56, 57, 58, 60, 63, 64, 68, 72, 76, 80, 81, 82, 89, 91, 92], "entri": [1, 3, 4, 35, 36, 40, 42, 44, 48, 49, 51, 56, 57, 58, 61, 84, 85, 89, 92, 93, 96, 98, 99], "maximum": [1, 9, 65, 73, 77, 97], "minimum": [1, 7, 9, 19, 42, 44, 58, 63, 76], "noise_r": 1, "non": [1, 2, 3, 4, 6, 8, 15, 25, 36, 40, 42, 63, 68, 81, 87, 89, 91, 93, 94], "default": [1, 2, 3, 4, 6, 9, 13, 15, 27, 29, 32, 35, 36, 37, 39, 40, 42, 44, 45, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 81, 87, 88, 96, 97], "If": [1, 2, 3, 4, 9, 11, 12, 15, 25, 27, 33, 35, 36, 39, 40, 42, 44, 45, 47, 50, 51, 55, 56, 57, 58, 61, 63, 64, 65, 68, 69, 70, 72, 73, 76, 77, 78, 79, 80, 81, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "have": [1, 2, 3, 4, 6, 9, 15, 20, 23, 25, 28, 35, 36, 38, 39, 40, 42, 45, 47, 51, 55, 56, 57, 58, 61, 63, 64, 65, 66, 68, 69, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "all": [1, 2, 3, 4, 6, 7, 9, 12, 13, 15, 21, 32, 35, 36, 39, 40, 41, 42, 45, 47, 48, 50, 51, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 68, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "necessari": [1, 2, 3, 6, 9, 11, 50, 81], "In": [1, 2, 3, 4, 9, 35, 36, 39, 40, 56, 57, 59, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 94, 95, 96, 97, 98, 99, 100], "particular": [1, 4, 5, 9, 12, 13, 15, 18, 19, 21, 25, 26, 27, 30, 36, 40, 51, 56, 60, 64, 68, 73, 77, 78, 80, 82, 85, 87, 91, 92, 94, 96, 98, 99], "satisfi": [1, 3, 35], "requir": [1, 2, 4, 6, 7, 8, 9, 10, 11, 29, 34, 36, 37, 38, 39, 40, 42, 45, 51, 54, 55, 58, 65, 66, 68, 70, 78, 79, 80, 86, 87, 89, 95], "argument": [1, 2, 3, 4, 9, 15, 22, 26, 29, 30, 31, 36, 39, 40, 41, 42, 47, 52, 55, 56, 57, 58, 60, 63, 64, 65, 66, 68, 72, 73, 74, 76, 82, 85, 86, 87, 88, 92, 93, 96, 99, 100], "when": [1, 2, 3, 4, 9, 11, 13, 22, 25, 36, 40, 42, 45, 47, 51, 55, 58, 60, 61, 63, 65, 66, 68, 69, 81, 82, 84, 85, 88, 91, 95, 96, 97, 98, 99, 100], "The": [1, 2, 3, 4, 6, 7, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 51, 55, 56, 57, 58, 61, 63, 64, 65, 66, 68, 70, 73, 74, 76, 78, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "rate": [1, 2, 3, 9, 37, 51, 80, 100], "set": [1, 2, 3, 4, 8, 9, 11, 12, 15, 16, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 36, 39, 40, 42, 46, 47, 49, 51, 55, 56, 58, 61, 63, 64, 65, 66, 68, 70, 72, 73, 81, 82, 84, 85, 87, 91, 92, 94, 95, 96, 97, 98, 99, 100], "note": [1, 2, 3, 6, 7, 9, 11, 26, 30, 33, 36, 39, 40, 41, 42, 47, 51, 56, 61, 63, 64, 65, 66, 68, 69, 73, 79, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "you": [1, 2, 3, 4, 6, 9, 13, 15, 35, 36, 38, 39, 40, 42, 47, 54, 55, 56, 58, 61, 63, 64, 65, 66, 68, 69, 70, 73, 74, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "high": [1, 2, 15, 39, 42, 51, 63, 66, 68, 81, 82, 86, 88, 89, 93, 96, 97, 98, 99, 100], "mai": [1, 2, 3, 4, 9, 12, 20, 21, 23, 28, 31, 35, 36, 38, 39, 40, 42, 45, 47, 51, 56, 57, 61, 63, 64, 65, 66, 68, 70, 73, 77, 79, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 95, 96, 97, 99, 100], "imposs": [1, 9, 89], "also": [1, 2, 3, 4, 6, 9, 21, 33, 35, 36, 39, 40, 42, 47, 50, 55, 56, 65, 68, 73, 76, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100], "low": [1, 9, 51, 56, 78, 81, 82, 85, 89, 93, 97], "zero": [1, 3, 4, 36, 40, 44, 51, 52, 81, 88, 92, 93, 94], "forc": [1, 2, 3, 4, 40, 81, 100], "instead": [1, 2, 3, 9, 12, 15, 32, 35, 36, 39, 40, 42, 45, 51, 55, 56, 58, 60, 64, 65, 66, 68, 69, 72, 74, 76, 79, 80, 84, 85, 87, 88, 89, 92, 93, 94, 96, 97, 98, 99, 100], "onli": [1, 2, 3, 4, 6, 9, 15, 22, 25, 29, 35, 36, 39, 40, 41, 42, 44, 45, 50, 51, 52, 55, 56, 65, 66, 68, 70, 72, 76, 77, 78, 80, 81, 82, 85, 88, 91, 92, 93, 94, 95, 96, 97, 99, 100], "guarante": [1, 3, 4, 14, 20, 23, 28, 36, 38, 40, 43, 45, 54, 79], "produc": [1, 2, 4, 9, 15, 47, 56, 66, 68, 70, 72, 78, 80, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 99, 100], "higher": [1, 4, 9, 35, 42, 44, 45, 47, 49, 56, 57, 68, 82, 85, 87, 93], "opposit": [1, 100], "occur": [1, 3, 9, 35, 50, 63, 81, 82, 87, 88, 94], "small": [1, 3, 9, 35, 39, 47, 51, 57, 64, 85, 86, 88, 92, 94, 99], "numpi": [1, 3, 4, 6, 9, 11, 17, 30, 31, 39, 40, 41, 47, 49, 50, 52, 55, 60, 63, 68, 69, 74, 76, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "max": [1, 42, 65, 66, 82, 88, 94], "tri": [1, 36, 40, 95], "befor": [1, 2, 3, 36, 40, 49, 51, 65, 68, 73, 85, 87, 89, 91, 94, 96, 98, 99], "option": [1, 2, 3, 4, 6, 7, 8, 11, 12, 15, 22, 25, 29, 35, 36, 39, 40, 42, 45, 47, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 73, 76, 77, 78, 80, 81, 82, 84, 87, 88, 89, 92, 96, 97, 98], "left": [1, 2, 42, 44, 49, 51, 58, 61, 64, 81, 82, 92, 93, 94, 97], "stochast": 1, "exceed": 1, "m": [1, 4, 36, 40, 46, 47, 56, 61, 63, 64, 65, 81, 82, 86, 91, 92, 93, 100], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 36, 40, 55, 87, 89, 97], "length": [1, 4, 11, 25, 26, 35, 37, 42, 51, 58, 61, 65, 66, 68, 70, 73, 77, 80, 92, 94, 97, 98, 100], "must": [1, 2, 3, 4, 6, 15, 35, 36, 37, 38, 40, 42, 45, 47, 48, 51, 54, 55, 56, 57, 58, 65, 66, 68, 70, 72, 73, 74, 76, 77, 80, 91, 95, 97, 100], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 9, 11, 35, 39, 42, 48, 51, 52, 56, 58, 64, 70, 72, 73, 74, 76, 77, 80, 87, 91, 92, 93, 97, 98, 99, 100], "ball": [1, 86], "bin": [1, 3, 58, 81, 82, 94], "ensur": [1, 2, 9, 36, 40, 51, 52, 63, 66, 68, 80, 81, 82, 85, 87, 88, 89, 94, 95, 96, 98, 99], "most": [1, 3, 4, 6, 9, 15, 35, 39, 42, 47, 55, 56, 57, 58, 61, 63, 64, 65, 66, 69, 72, 76, 77, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92, 93, 94, 96, 97, 98, 99], "least": [1, 9, 17, 30, 35, 39, 56, 57, 63, 66, 76, 82, 87, 88, 91, 94, 97], "int_arrai": [1, 51], "can": [2, 3, 4, 6, 7, 8, 12, 13, 15, 32, 33, 35, 36, 37, 38, 39, 40, 42, 46, 47, 48, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 73, 74, 77, 78, 79, 80, 81, 84, 85, 88, 92, 93, 94, 95, 96, 97, 98, 99, 100], "model": [2, 3, 4, 9, 15, 17, 29, 31, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 79, 81, 82, 86, 90, 95, 97, 100], "For": [2, 3, 4, 6, 8, 9, 10, 15, 21, 34, 35, 36, 39, 40, 42, 45, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 72, 74, 76, 77, 78, 80, 82, 84, 86, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 99, 100], "regular": [2, 3, 39, 55], "multi": [2, 3, 9, 31, 35, 36, 39, 40, 42, 46, 47, 48, 51, 52, 57, 58, 59, 60, 65, 66, 78, 87, 89, 90], "task": [2, 4, 6, 9, 10, 11, 13, 14, 15, 24, 29, 32, 35, 39, 45, 47, 48, 49, 51, 56, 58, 66, 68, 78, 80, 85, 86, 87, 89, 92, 94, 96, 97, 99, 100], "cleanlearn": [2, 3, 9, 22, 29, 36, 51, 55, 67, 68, 69, 78, 79, 96, 98, 99], "wrap": [2, 36, 40, 55, 65, 68, 78, 81, 82, 84, 85, 89, 96, 98, 99], "instanc": [2, 3, 4, 5, 6, 9, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 40, 47, 55, 64, 65, 68, 73, 80, 81, 82, 84, 85, 88, 89, 93, 98, 99], "sklearn": [2, 3, 4, 7, 9, 17, 30, 35, 40, 47, 51, 55, 65, 68, 69, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99], "classifi": [2, 3, 40, 47, 51, 56, 59, 65, 66, 78, 79, 80, 84, 85, 87, 91, 92, 94, 95, 97, 98, 99, 100], "adher": [2, 40, 68], "estim": [2, 3, 4, 8, 12, 21, 35, 39, 40, 42, 45, 51, 56, 57, 58, 63, 65, 68, 70, 72, 76, 78, 79, 80, 81, 82, 84, 85, 87, 88, 90, 93, 94, 95, 96, 97, 100], "api": [2, 3, 13, 55, 61, 64, 65, 68, 79, 87, 96], "defin": [2, 3, 4, 6, 9, 13, 21, 35, 36, 37, 39, 40, 42, 66, 68, 70, 80, 81, 82, 84, 87, 91, 94, 100], "four": [2, 9, 86, 89, 100], "clf": [2, 3, 4, 47, 68, 78, 84, 87, 89, 92, 98], "fit": [2, 3, 4, 7, 9, 17, 38, 40, 54, 55, 65, 67, 68, 78, 81, 82, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 100], "sample_weight": [2, 40, 68, 89], "predict_proba": [2, 4, 35, 38, 40, 47, 54, 55, 80, 81, 82, 84, 85, 87, 89, 91, 92, 94, 98], "predict": [2, 3, 4, 7, 9, 15, 21, 22, 24, 27, 29, 31, 33, 35, 38, 39, 40, 41, 42, 44, 45, 47, 48, 50, 51, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 86, 87, 89, 90, 94, 96, 97, 99, 100], "score": [2, 3, 4, 6, 9, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 39, 41, 42, 44, 47, 49, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 69, 72, 74, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 94, 96, 98, 99], "data": [2, 3, 4, 6, 7, 8, 10, 12, 13, 14, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 35, 37, 38, 39, 40, 41, 42, 47, 48, 51, 54, 55, 56, 57, 58, 59, 63, 65, 66, 67, 68, 73, 74, 75, 76, 77, 79, 83, 88, 90, 95, 99], "e": [2, 3, 4, 9, 11, 21, 31, 35, 36, 39, 40, 42, 45, 47, 48, 51, 52, 56, 57, 58, 59, 61, 64, 65, 66, 68, 70, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99], "featur": [2, 3, 4, 7, 9, 15, 17, 18, 22, 25, 26, 27, 29, 30, 47, 51, 65, 68, 78, 81, 82, 84, 85, 87, 89, 91, 92, 96, 98], "element": [2, 3, 4, 35, 41, 42, 44, 51, 56, 58, 66, 73, 74, 76, 80, 85, 87, 99, 100], "first": [2, 4, 9, 16, 25, 26, 35, 39, 47, 51, 56, 57, 61, 64, 66, 68, 80, 81, 84, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "index": [2, 9, 25, 35, 42, 50, 51, 52, 57, 66, 68, 73, 76, 77, 80, 81, 82, 84, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 99, 100], "should": [2, 3, 4, 6, 9, 13, 21, 25, 30, 31, 35, 36, 39, 40, 42, 44, 45, 47, 49, 50, 51, 55, 56, 57, 60, 61, 63, 64, 65, 66, 68, 69, 73, 74, 76, 77, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "correspond": [2, 3, 4, 9, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 33, 35, 36, 39, 40, 41, 42, 44, 45, 47, 50, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 70, 73, 74, 76, 77, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "differ": [2, 4, 6, 9, 12, 14, 20, 23, 25, 26, 28, 35, 36, 38, 39, 40, 42, 43, 47, 51, 52, 54, 56, 61, 63, 65, 68, 80, 81, 82, 84, 85, 88, 89, 91, 92, 94, 95, 96, 98], "sampl": [2, 3, 4, 7, 9, 15, 19, 42, 44, 47, 58, 61, 64, 66, 68, 69, 78, 79, 86, 87, 89, 90, 92, 93, 96, 97, 99, 100], "size": [2, 9, 30, 36, 39, 40, 42, 47, 58, 63, 64, 68, 70, 72, 84, 87, 88, 89, 91, 92, 93, 95, 97, 99], "here": [2, 4, 6, 9, 13, 39, 42, 45, 55, 56, 57, 58, 60, 61, 64, 65, 76, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "re": [2, 4, 36, 40, 50, 56, 68, 78, 80, 81, 84, 85, 87, 96, 97, 98, 99, 100], "weight": [2, 9, 36, 37, 40, 47, 56, 63, 66, 68, 80, 81, 82, 85, 99], "loss": [2, 37, 55, 66, 68, 88], "while": [2, 3, 9, 36, 39, 40, 46, 47, 51, 68, 78, 87, 88, 89, 91, 92, 96], "train": [2, 3, 4, 9, 15, 17, 31, 36, 37, 38, 40, 47, 51, 55, 56, 61, 64, 65, 68, 69, 79, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 95, 97, 100], "support": [2, 3, 4, 11, 13, 32, 33, 39, 41, 47, 51, 52, 65, 66, 76, 78, 79, 80, 81, 82, 87, 88], "your": [2, 3, 4, 8, 9, 15, 35, 36, 38, 39, 40, 42, 47, 51, 54, 55, 56, 57, 58, 60, 65, 66, 68, 69, 70, 72, 73, 79, 80, 84, 86, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "recommend": [2, 4, 6, 9, 12, 15, 39, 42, 56, 81, 82, 87, 88, 95, 96], "furthermor": 2, "correctli": [2, 3, 9, 35, 36, 40, 42, 45, 52, 57, 58, 63, 64, 68, 70, 85, 87, 92, 93, 96, 97, 99], "clonabl": [2, 68], "via": [2, 4, 6, 9, 12, 15, 17, 21, 35, 37, 39, 40, 47, 51, 56, 61, 64, 65, 66, 68, 69, 72, 76, 80, 81, 82, 84, 85, 86, 87, 88, 92, 93, 94, 95, 96, 97, 98, 99, 100], "base": [2, 3, 4, 6, 9, 11, 12, 15, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 36, 39, 40, 41, 42, 45, 46, 47, 49, 50, 51, 52, 55, 56, 57, 58, 60, 63, 65, 66, 68, 69, 72, 74, 76, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 100], "clone": [2, 68, 92], "intern": [2, 3, 6, 9, 10, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 39, 44, 45, 46, 47, 48, 49, 50, 51, 52, 60, 64, 68, 74, 79, 80, 81, 87, 89, 91, 92, 93, 94, 96, 100], "multipl": [2, 3, 4, 11, 12, 33, 35, 42, 50, 56, 57, 58, 60, 63, 64, 68, 78, 81, 82, 87, 88, 90, 92, 93, 96], "g": [2, 3, 4, 9, 11, 21, 31, 35, 36, 40, 42, 48, 51, 58, 59, 61, 64, 65, 66, 68, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99], "manual": [2, 68, 80, 87, 94, 95, 96, 98, 99, 100], "pytorch": [2, 36, 37, 40, 68, 78, 80, 87, 90, 92, 97], "call": [2, 3, 4, 5, 9, 12, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 47, 51, 55, 65, 68, 80, 81, 82, 85, 87, 89, 92, 94, 95, 96, 97, 99, 100], "__init__": [2, 37, 68, 88], "independ": [2, 3, 9, 57, 68, 85, 95, 96, 100], "compat": [2, 36, 39, 40, 55, 68, 69, 72, 76, 78, 87, 95, 96, 98, 99], "neural": [2, 37, 55, 65, 68, 80, 87, 88, 92, 94, 96], "network": [2, 36, 37, 40, 55, 65, 68, 80, 85, 87, 88, 92, 94, 96, 99], "typic": [2, 36, 40, 65, 68, 80, 82, 84, 85, 88, 94, 95, 98, 99], "initi": [2, 3, 12, 17, 36, 40, 56, 68, 85, 87, 98], "insid": [2, 40, 68, 87, 89], "There": [2, 3, 6, 78, 89, 91], "two": [2, 3, 9, 17, 25, 35, 36, 39, 40, 48, 51, 61, 63, 64, 79, 81, 82, 84, 85, 87, 88, 89, 92, 96, 97, 99, 100], "new": [2, 6, 13, 21, 36, 39, 40, 46, 50, 51, 56, 68, 80, 81, 85, 86, 87, 94, 95, 99, 100], "notion": 2, "confid": [2, 3, 9, 21, 35, 39, 42, 45, 47, 51, 56, 57, 58, 61, 63, 64, 65, 66, 68, 72, 76, 78, 84, 85, 88, 89, 91, 92, 93, 95, 97, 98, 100], "packag": [2, 4, 6, 8, 9, 10, 14, 34, 38, 42, 43, 51, 54, 61, 64, 68, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "prune": [2, 3, 42, 58, 68, 79, 93], "everyth": [2, 64, 89], "els": [2, 64, 81, 86, 87, 88, 91, 92, 93], "mathemat": [2, 3, 9, 45, 92], "keep": [2, 12, 13, 51, 78, 81, 86, 87, 97], "belong": [2, 3, 9, 35, 42, 44, 45, 57, 58, 59, 60, 65, 66, 70, 74, 76, 77, 82, 88, 89, 92, 94, 97, 100], "2": [2, 3, 4, 6, 11, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 37, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 51, 55, 57, 58, 60, 61, 64, 65, 66, 68, 69, 73, 74, 76, 77, 86, 87, 95], "error": [2, 3, 4, 9, 36, 40, 41, 42, 44, 45, 51, 57, 58, 60, 61, 63, 64, 66, 68, 70, 72, 73, 76, 79, 80, 81, 82, 84, 85, 86, 90, 98], "erron": [2, 3, 35, 42, 45, 51, 57, 58, 66, 68, 69, 70, 94, 96], "import": [2, 3, 4, 6, 7, 9, 11, 12, 13, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 35, 39, 41, 47, 49, 50, 56, 60, 63, 68, 69, 74, 76, 77, 78, 84, 85, 87, 92, 93, 94, 96, 97, 98, 99, 100], "linear_model": [2, 4, 35, 51, 68, 78, 80, 81, 82, 85, 87, 89, 91, 94, 99], "logisticregress": [2, 3, 4, 35, 51, 78, 80, 81, 82, 85, 87, 89, 91, 94, 99], "logreg": 2, "cl": [2, 13, 29, 68, 78, 87, 89, 96, 98, 99], "pass": [2, 3, 4, 7, 9, 11, 12, 13, 15, 22, 29, 32, 36, 39, 40, 42, 46, 47, 51, 55, 56, 58, 65, 66, 68, 74, 78, 80, 81, 82, 85, 86, 87, 89, 91, 93, 94, 96, 99], "x_train": [2, 81, 82, 89, 91, 92, 96, 98], "labels_maybe_with_error": 2, "had": [2, 3, 68, 93], "issu": [2, 3, 4, 5, 7, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 35, 36, 38, 39, 40, 41, 42, 54, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 83, 90, 91, 95, 96, 99], "pred": [2, 42, 51, 95, 96, 98, 99], "x_test": [2, 81, 82, 89, 92, 96, 98], "might": [2, 4, 56, 68, 73, 81, 82, 87, 88, 93, 98, 99], "case": [2, 3, 12, 35, 47, 56, 68, 80, 81, 82, 84, 86, 87, 88, 89, 94, 96, 98, 99, 100], "standard": [2, 3, 4, 29, 35, 42, 55, 57, 58, 60, 66, 68, 78, 81, 82, 84, 86, 89, 93, 98], "adapt": [2, 36, 38, 51, 54, 68, 94], "skorch": [2, 68, 78, 87], "kera": [2, 54, 61, 64, 68, 78, 87, 93], "scikera": [2, 55, 68, 87], "open": [2, 39, 86, 93, 100], "doesn": [2, 68, 78], "t": [2, 3, 6, 9, 16, 26, 36, 37, 39, 40, 41, 42, 47, 49, 50, 60, 65, 66, 68, 74, 76, 77, 78, 81, 82, 84, 85, 86, 88, 89, 92, 93, 96, 100], "alreadi": [2, 4, 9, 15, 36, 39, 40, 45, 55, 56, 68, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 98, 99], "exist": [2, 4, 9, 11, 17, 36, 39, 40, 50, 55, 61, 63, 65, 68, 78, 79, 81, 82, 85, 91, 99, 100], "made": [2, 4, 15, 36, 40, 68, 85, 87, 88, 91, 93, 95, 96, 98, 99], "easi": [2, 45, 68, 81, 82, 86, 87, 89, 92], "inherit": [2, 6, 37, 68], "baseestim": [2, 40, 68], "yourmodel": [2, 68], "def": [2, 6, 13, 36, 40, 55, 68, 80, 81, 82, 86, 87, 88, 89, 91, 92, 94, 96, 99, 100], "self": [2, 3, 4, 6, 9, 11, 12, 13, 15, 30, 36, 37, 39, 40, 42, 47, 65, 66, 68, 81, 85, 86, 88, 92, 97, 98, 99, 100], "refer": [2, 9, 15, 36, 40, 41, 57, 58, 60, 61, 63, 64, 68, 72, 73, 81, 82, 84, 85, 87, 88, 89, 92, 95, 96], "origin": [2, 4, 9, 40, 41, 42, 50, 51, 55, 57, 58, 61, 64, 65, 68, 69, 72, 74, 76, 81, 84, 85, 87, 88, 89, 93, 94, 96, 98, 99, 100], "total": [2, 3, 35, 39, 51, 57, 77, 87, 88, 97], "state": [2, 3, 4, 36, 37, 40, 46, 68, 89, 92, 93, 100], "art": [2, 37, 89, 92], "northcutt": [2, 3, 35, 65, 66], "et": [2, 3, 35, 37, 65, 66], "al": [2, 3, 35, 37, 65, 66], "2021": [2, 3, 35, 65, 66], "weak": [2, 64], "supervis": [2, 9, 81, 82, 87, 91], "find": [2, 4, 9, 12, 13, 15, 18, 19, 21, 22, 24, 25, 26, 27, 30, 31, 35, 36, 38, 39, 40, 41, 42, 46, 50, 51, 54, 61, 64, 65, 66, 68, 70, 74, 76, 79, 81, 90, 95], "uncertainti": [2, 9, 44, 65, 68, 87, 94, 96], "It": [2, 3, 4, 6, 9, 11, 12, 15, 21, 26, 29, 31, 32, 33, 36, 40, 42, 45, 47, 56, 63, 64, 68, 78, 81, 82, 85, 87, 88, 89, 92, 95, 99], "work": [2, 3, 6, 9, 11, 29, 35, 36, 39, 40, 42, 45, 50, 51, 52, 55, 56, 66, 68, 78, 79, 81, 82, 86, 94, 96, 99], "includ": [2, 3, 4, 6, 9, 12, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 35, 36, 38, 39, 40, 50, 51, 54, 56, 57, 60, 61, 65, 66, 68, 72, 73, 74, 76, 78, 79, 81, 82, 84, 85, 87, 88, 89, 92, 93, 94, 100], "deep": [2, 38, 40, 54, 55, 68, 85], "see": [2, 3, 4, 6, 9, 12, 13, 32, 35, 36, 39, 40, 41, 42, 47, 51, 55, 57, 58, 60, 61, 64, 65, 66, 68, 74, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "subfield": 2, "theori": [2, 89], "machin": [2, 4, 13, 15, 32, 38, 54, 68, 81, 82, 86, 91], "across": [2, 3, 4, 6, 9, 12, 21, 35, 39, 47, 57, 64, 65, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 95, 96], "varieti": [2, 87, 98, 99], "like": [2, 3, 4, 5, 6, 9, 13, 31, 35, 36, 39, 40, 42, 45, 51, 55, 56, 57, 60, 61, 63, 66, 68, 69, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "pu": [2, 51], "input": [2, 3, 4, 9, 15, 25, 35, 36, 39, 40, 45, 47, 50, 51, 52, 55, 64, 68, 78, 79, 80, 82, 85, 86, 87, 88, 89, 91, 92, 93, 96, 97, 99, 100], "discret": [2, 33, 42, 45, 51, 65, 66, 70, 72, 73], "vector": [2, 3, 4, 9, 15, 42, 45, 47, 48, 51, 65, 66, 78, 80, 81, 82, 84, 85, 88, 89, 92, 93, 94, 97, 99, 100], "would": [2, 3, 4, 36, 39, 40, 42, 51, 58, 68, 78, 81, 87, 88, 89, 94, 96, 99, 100], "obtain": [2, 4, 7, 9, 15, 42, 56, 58, 61, 64, 66, 69, 80, 82, 85, 87, 91, 93, 95, 97, 100], "been": [2, 35, 42, 45, 50, 51, 56, 57, 61, 63, 65, 66, 68, 80, 81, 84, 87, 89, 91, 92, 93, 94, 97, 100], "dure": [2, 9, 15, 65, 68, 80, 84, 85, 87, 89, 92, 95, 96, 98, 99, 100], "denot": [2, 3, 45, 47, 51, 58, 65, 66, 76], "tild": 2, "paper": [2, 9, 56, 65, 74, 76, 86, 89, 91, 94, 96, 100], "cv_n_fold": [2, 3, 68, 99], "5": [2, 3, 4, 7, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 35, 40, 42, 44, 46, 47, 51, 56, 57, 60, 61, 64, 68, 69, 76, 81, 85, 86, 87, 92, 93, 94, 95, 97, 99, 100], "converge_latent_estim": [2, 3], "pulearn": [2, 51], "find_label_issues_kwarg": [2, 9, 68, 79, 87, 89], "label_quality_scores_kwarg": [2, 9], "low_memori": [2, 58, 74, 87], "clean": [2, 63, 66, 68, 69, 78, 81, 82, 86, 96, 98, 99], "even": [2, 3, 6, 35, 39, 44, 45, 51, 68, 80, 87, 89, 91, 92, 93], "messi": [2, 68, 89], "ridden": [2, 68], "autom": [2, 68, 78, 82, 86, 87], "robust": [2, 45, 68, 82, 87], "prone": [2, 68], "out": [2, 3, 4, 9, 15, 27, 36, 40, 42, 47, 55, 58, 59, 61, 64, 65, 66, 68, 69, 77, 78, 79, 86, 87, 89, 90, 92, 93, 94, 96, 97, 99, 100], "current": [2, 3, 6, 9, 12, 13, 21, 36, 40, 41, 42, 47, 56, 63, 68, 81, 82, 87, 91, 93], "intend": [2, 12, 13, 14, 15, 31, 32, 33, 43, 56, 72, 76, 80, 81, 82, 85, 89], "A": [2, 3, 4, 6, 9, 11, 12, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 35, 36, 37, 40, 42, 45, 46, 47, 48, 49, 50, 51, 55, 56, 57, 60, 63, 64, 65, 66, 68, 70, 72, 73, 77, 79, 80, 81, 84, 85, 86, 87, 88, 89, 91, 93, 95, 98, 99, 100], "follow": [2, 3, 9, 13, 29, 33, 35, 36, 39, 40, 47, 49, 56, 57, 61, 63, 64, 65, 68, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "experiment": [2, 36, 37, 39, 40, 41, 58, 79, 87], "wrapper": [2, 55, 80, 96, 98, 99], "around": [2, 63, 81, 82, 93, 94, 100], "fasttext": [2, 54], "store": [2, 4, 9, 11, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 39, 40, 65, 68, 84, 85, 86, 87, 97, 98, 99, 100], "along": [2, 47, 58, 76, 81, 82, 87, 88, 94], "dimens": [2, 51, 70, 73, 87, 88, 94, 97], "select": [2, 8, 9, 25, 56, 66, 87, 88, 91, 94], "split": [2, 3, 4, 9, 11, 39, 47, 50, 51, 68, 80, 81, 82, 84, 85, 86, 88, 89, 92, 95, 98, 100], "cross": [2, 3, 9, 35, 42, 45, 46, 47, 58, 61, 64, 66, 68, 69, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93, 96, 97, 98, 99, 100], "fold": [2, 3, 35, 42, 45, 68, 80, 84, 86, 87, 93, 97, 98], "By": [2, 35, 57, 58, 68, 81, 87, 97], "need": [2, 3, 9, 35, 36, 39, 40, 42, 57, 58, 60, 65, 68, 78, 80, 81, 82, 85, 87, 89, 91, 92, 93, 97, 99], "holdout": [2, 3, 68], "comput": [2, 3, 4, 6, 7, 9, 18, 19, 21, 22, 25, 26, 27, 30, 35, 36, 37, 39, 40, 42, 44, 45, 46, 47, 51, 56, 57, 58, 60, 63, 64, 65, 66, 68, 69, 70, 72, 78, 79, 81, 82, 86, 89, 90, 93, 94, 96, 97, 99], "them": [2, 3, 4, 6, 8, 9, 10, 11, 26, 31, 34, 36, 38, 39, 40, 42, 54, 56, 65, 68, 79, 81, 82, 84, 85, 87, 88, 91, 92, 94, 96, 97, 98, 99, 100], "numer": [2, 3, 4, 9, 12, 21, 29, 33, 47, 63, 65, 68, 73, 78, 79, 80, 81, 82, 83, 85, 88, 89, 91, 92, 94, 96, 98, 99], "consist": [2, 3, 36, 40, 51, 56, 97, 100], "latent": [2, 3, 45], "thei": [2, 3, 4, 14, 20, 23, 25, 28, 36, 37, 38, 40, 42, 43, 49, 51, 55, 58, 63, 66, 68, 69, 72, 76, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 94, 96, 99, 100], "relat": [2, 3, 12, 18, 19, 25, 26, 27, 30, 45, 51, 57, 68, 82, 85], "close": [2, 3, 9, 39, 45, 65, 80, 81, 82, 84, 85, 87, 88, 89, 93], "form": [2, 3, 9, 36, 37, 40, 45, 50, 51, 66, 68, 87], "equival": [2, 3, 36, 40, 45, 65, 94, 96], "iter": [2, 3, 35, 36, 40, 42, 51, 57, 58, 68, 87, 91, 97], "enforc": [2, 36, 40, 51], "perfectli": [2, 35, 57, 89], "certain": [2, 3, 4, 36, 40, 55, 64, 68, 81, 82, 86, 93, 94], "dict": [2, 3, 4, 9, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 39, 40, 42, 46, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 76, 81, 82, 87, 88, 100], "keyword": [2, 3, 4, 9, 15, 22, 26, 29, 36, 39, 40, 42, 44, 47, 50, 55, 56, 58, 65, 66, 68, 74, 76, 81], "filter": [2, 3, 9, 39, 41, 50, 57, 59, 60, 62, 64, 71, 72, 73, 75, 76, 77, 78, 79, 80, 82, 85, 86, 87, 88, 92, 93, 96, 97, 98, 99, 100], "find_label_issu": [2, 3, 9, 29, 38, 39, 41, 42, 57, 58, 59, 60, 61, 62, 63, 64, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 87, 92, 93, 96, 97, 98, 99, 100], "particularli": [2, 78, 91, 94], "filter_bi": [2, 3, 39, 42, 58, 79, 87], "frac_nois": [2, 42, 58, 74, 87], "min_examples_per_class": [2, 42, 58, 82, 87, 89], "impact": [2, 9, 81, 82, 88], "ml": [2, 4, 9, 14, 68, 78, 81, 82, 84, 85, 88, 91, 92, 96, 98, 99], "accuraci": [2, 37, 66, 80, 87, 88, 89, 91, 94, 96, 97, 98, 99], "n_job": [2, 39, 42, 58, 70, 72, 74, 87, 94, 97], "disabl": [2, 36, 40, 42, 94], "process": [2, 3, 6, 12, 15, 31, 36, 39, 40, 42, 50, 56, 58, 64, 70, 72, 74, 80, 81, 87, 91, 95, 99], "caus": [2, 42, 47, 81, 82, 87], "rank": [2, 3, 9, 35, 39, 41, 42, 47, 57, 58, 59, 61, 62, 64, 65, 67, 71, 73, 74, 75, 77, 78, 79, 81, 82, 86, 87, 92, 93, 94, 97, 98, 99, 100], "get_label_quality_scor": [2, 38, 39, 41, 42, 43, 47, 56, 58, 59, 60, 61, 62, 63, 66, 67, 69, 71, 72, 74, 75, 76, 79, 89, 92, 93, 97, 100], "adjust_pred_prob": [2, 9, 60, 65, 66, 89], "control": [2, 4, 8, 9, 15, 39, 42, 56, 64, 65, 68, 74, 76, 81, 82, 86, 87], "how": [2, 3, 4, 9, 11, 12, 13, 15, 21, 35, 36, 37, 39, 40, 45, 51, 56, 57, 60, 61, 63, 65, 66, 68, 72, 76, 78, 81, 82, 84, 85, 86, 88, 93, 94, 95, 96, 97, 98, 99], "much": [2, 9, 35, 39, 42, 68, 87, 89, 91, 94], "output": [2, 3, 4, 9, 15, 31, 36, 37, 40, 45, 51, 55, 56, 57, 61, 63, 64, 65, 68, 72, 73, 76, 77, 78, 79, 80, 81, 85, 86, 87, 88, 93, 94, 95, 96, 99], "print": [2, 4, 6, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 39, 40, 42, 51, 56, 57, 58, 63, 65, 66, 68, 70, 72, 73, 77, 79, 80, 82, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "suppress": [2, 39, 56, 63, 65, 66, 68, 70, 72, 73, 97, 100], "statement": [2, 39, 56, 63, 65, 66, 68, 70, 72, 73], "big": [2, 39, 58, 64, 68, 89], "limit": [2, 4, 15, 39, 58, 93, 97, 100], "memori": [2, 36, 39, 40, 58, 64, 70, 72, 81, 97], "label_issues_batch": [2, 38, 58, 87], "find_label_issues_batch": [2, 38, 39, 58, 87], "pred_prob": [2, 3, 4, 7, 9, 15, 22, 24, 25, 27, 30, 31, 35, 39, 41, 42, 44, 45, 46, 47, 48, 51, 52, 56, 57, 58, 60, 61, 64, 65, 66, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 96, 98, 99], "threshold": [2, 3, 6, 9, 17, 18, 19, 21, 27, 29, 30, 39, 63, 64, 65, 66, 72, 76, 81, 93, 94, 97, 100], "inverse_noise_matrix": [2, 3, 9, 45, 51, 79, 89], "label_issu": [2, 39, 42, 58, 61, 68, 70, 79, 80, 85, 87, 88, 89, 92, 96, 98, 99], "clf_kwarg": [2, 3, 9, 68], "clf_final_kwarg": [2, 68], "validation_func": [2, 3, 9], "correct": [2, 4, 9, 35, 39, 42, 44, 56, 57, 58, 60, 61, 63, 64, 66, 68, 69, 72, 76, 78, 80, 84, 85, 88, 89, 91, 93, 95, 96], "result": [2, 3, 9, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 39, 40, 42, 44, 49, 51, 58, 60, 61, 64, 66, 68, 69, 70, 72, 76, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 96, 97, 98, 99, 100], "identifi": [2, 3, 4, 6, 9, 11, 15, 26, 32, 35, 39, 41, 42, 58, 61, 64, 66, 68, 69, 70, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89, 92, 94, 96, 97, 98, 99, 100], "final": [2, 9, 68, 84, 93, 95, 96, 98], "remain": [2, 68, 79, 88, 92, 96, 98, 99, 100], "datasetlik": [2, 51, 68], "beyond": [2, 4, 6, 8, 10, 34, 78, 96, 97], "pd": [2, 3, 4, 6, 12, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 35, 46, 55, 56, 57, 68, 76, 80, 81, 82, 84, 85, 87, 89, 91, 96, 98, 99, 100], "datafram": [2, 3, 4, 6, 11, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 39, 46, 51, 52, 55, 56, 57, 68, 73, 77, 79, 80, 81, 82, 84, 85, 87, 88, 89, 91, 96, 97, 99, 100], "scipi": [2, 4, 12, 51], "spars": [2, 4, 9, 12, 15, 17, 30, 51, 52, 84], "csr_matrix": [2, 4, 12, 15, 17, 30], "torch": [2, 36, 37, 40, 80, 85, 86, 88, 94, 99], "util": [2, 4, 9, 15, 32, 36, 37, 40, 43, 56, 61, 64, 68, 78, 79, 80, 81, 82, 87, 88, 89, 94], "tensorflow": [2, 51, 55, 78, 80, 87], "object": [2, 4, 9, 11, 12, 15, 31, 32, 36, 37, 39, 40, 47, 51, 52, 55, 58, 61, 62, 63, 64, 65, 68, 76, 78, 80, 82, 84, 88, 89, 90, 92, 96, 99], "list": [2, 3, 4, 11, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 37, 39, 40, 41, 42, 48, 50, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 72, 73, 74, 76, 77, 79, 80, 81, 82, 86, 87, 88, 89, 92, 93, 96, 99, 100], "index_list": 2, "subset": [2, 3, 4, 15, 35, 39, 42, 51, 66, 73, 77, 80, 84, 85, 87, 88, 92, 93, 94, 95, 96, 98, 99, 100], "wa": [2, 3, 11, 13, 39, 51, 56, 57, 63, 65, 77, 80, 81, 82, 84, 85, 87, 89, 92, 93, 95, 97, 98, 99, 100], "abl": [2, 3, 9, 68, 80, 87, 89, 91, 92], "format": [2, 3, 4, 9, 11, 31, 36, 39, 40, 42, 45, 46, 47, 48, 51, 52, 55, 56, 57, 58, 61, 64, 65, 66, 68, 70, 72, 73, 76, 77, 80, 81, 82, 84, 86, 88, 91, 96, 97, 98, 100], "make": [2, 3, 4, 17, 36, 39, 40, 47, 55, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99], "sure": [2, 4, 39, 42, 47, 80, 81, 82, 84, 85, 86, 88, 91, 92, 93, 94, 96, 98, 99], "shuffl": [2, 9, 51, 80, 85, 88, 92, 94], "ha": [2, 3, 4, 5, 9, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 41, 45, 47, 50, 51, 56, 61, 63, 68, 74, 76, 77, 78, 80, 81, 82, 84, 85, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "batch": [2, 39, 51, 55, 56, 70, 72, 87, 88, 94], "order": [2, 4, 9, 33, 35, 36, 40, 41, 42, 45, 46, 47, 51, 56, 57, 58, 61, 64, 65, 66, 70, 73, 74, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 96, 97, 99, 100], "destroi": [2, 51], "oper": [2, 36, 39, 40, 51, 55, 66, 78, 85, 87, 94, 98, 99], "eg": [2, 4, 9, 51, 61, 64, 81, 82, 87], "repeat": [2, 51, 56, 91, 94], "appli": [2, 33, 36, 38, 40, 42, 47, 48, 50, 51, 60, 65, 74, 80, 81, 82, 84, 87, 88, 91, 92, 94, 95, 96, 97, 98, 99], "array_lik": [2, 3, 35, 42, 51, 58, 65, 69], "some": [2, 3, 4, 9, 13, 21, 35, 36, 38, 40, 42, 45, 50, 51, 54, 56, 57, 58, 60, 61, 64, 65, 66, 68, 70, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100], "seri": [2, 3, 39, 51, 52, 68, 76, 87], "row": [2, 3, 4, 9, 12, 26, 31, 35, 39, 42, 44, 45, 51, 56, 57, 58, 60, 65, 66, 68, 73, 74, 76, 77, 80, 81, 84, 85, 86, 87, 88, 91, 92, 94, 98, 100], "rather": [2, 3, 4, 25, 35, 51, 55, 56, 63, 72, 76, 91, 95, 96, 97, 99, 100], "leav": [2, 42], "per": [2, 3, 4, 6, 12, 35, 39, 42, 47, 50, 56, 57, 58, 60, 63, 64, 66, 69, 70, 72, 76, 82, 87, 93, 100], "determin": [2, 3, 9, 11, 15, 21, 25, 29, 35, 39, 42, 47, 51, 56, 58, 61, 63, 66, 72, 76, 81, 87, 91, 93, 94, 96], "cutoff": [2, 3, 94], "consid": [2, 3, 4, 9, 12, 15, 22, 25, 27, 30, 35, 36, 40, 42, 51, 56, 63, 65, 66, 69, 72, 76, 80, 84, 85, 87, 88, 89, 93, 94, 95, 96, 97, 98, 99], "section": [2, 3, 6, 9, 79, 84, 88, 93], "3": [2, 3, 4, 6, 9, 33, 35, 36, 40, 42, 45, 46, 47, 48, 49, 50, 51, 55, 58, 65, 66, 68, 69, 74, 76, 86, 87, 95], "equat": [2, 3, 45], "advanc": [2, 3, 4, 8, 9, 15, 63, 65, 76, 79, 82, 83, 89], "user": [2, 3, 4, 9, 13, 15, 26, 31, 32, 33, 36, 40, 42, 63, 65, 66, 68, 72, 76, 89], "specifi": [2, 3, 4, 7, 9, 12, 13, 15, 17, 30, 32, 36, 39, 40, 42, 47, 50, 56, 57, 58, 61, 63, 65, 66, 68, 69, 77, 79, 80, 82, 85, 88, 91, 93, 96, 99], "automat": [2, 3, 4, 25, 35, 78, 84, 85, 86, 87, 88, 91, 92, 93, 96, 97, 98, 99, 100], "greater": [2, 3, 4, 6, 8, 9, 27, 39, 51, 63, 82, 86, 87, 100], "count": [2, 21, 25, 35, 39, 42, 45, 51, 57, 58, 64, 79, 87, 88, 93], "observ": [2, 3, 45, 80, 81, 82, 91, 94, 96], "mislabel": [2, 9, 35, 39, 41, 42, 45, 56, 57, 58, 61, 63, 66, 72, 74, 76, 78, 80, 84, 85, 87, 88, 89, 93, 96, 98, 99], "one": [2, 3, 4, 6, 9, 25, 35, 36, 39, 40, 41, 42, 47, 51, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 88, 91, 94, 95, 96, 98, 99, 100], "get_label_issu": [2, 38, 39, 67, 68, 89, 96, 98, 99], "either": [2, 3, 6, 9, 36, 39, 40, 42, 56, 58, 63, 65, 66, 70, 72, 82, 87, 92, 93], "boolean": [2, 6, 9, 21, 39, 42, 50, 56, 58, 61, 66, 68, 70, 72, 73, 78, 80, 82, 85, 87, 88, 93, 96, 97, 99], "label_issues_mask": [2, 42, 66, 68, 79], "indic": [2, 3, 4, 6, 9, 12, 21, 35, 39, 40, 41, 42, 44, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 68, 69, 72, 74, 76, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "its": [2, 4, 6, 8, 9, 15, 36, 39, 40, 42, 49, 50, 58, 61, 64, 65, 66, 68, 70, 74, 76, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 95, 96, 97, 99, 100], "return_indices_ranked_bi": [2, 39, 42, 58, 74, 79, 87, 89, 98, 99], "significantli": [2, 88, 89, 91, 95], "reduc": [2, 39, 42, 51, 80, 87], "time": [2, 9, 36, 39, 40, 51, 56, 79, 81, 86, 87, 88, 89, 93, 94, 96, 97, 98, 99, 100], "take": [2, 4, 9, 35, 36, 40, 46, 47, 51, 55, 66, 84, 88, 91, 92, 93, 98, 100], "run": [2, 4, 5, 6, 8, 9, 10, 13, 15, 25, 26, 31, 34, 36, 39, 40, 68, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "skip": [2, 9, 36, 40, 68, 80, 87, 92, 100], "slow": [2, 3], "step": [2, 6, 25, 47, 64, 87, 88, 89, 91, 95], "caution": [2, 4, 87], "previous": [2, 4, 12, 51, 65, 68, 79, 80, 81, 84, 85, 91, 95, 98], "assign": [2, 6, 9, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 36, 40, 46, 47, 51, 68, 81, 84, 87, 88, 96, 97, 98, 100], "individu": [2, 6, 9, 12, 25, 36, 40, 41, 56, 60, 63, 66, 68, 74, 76, 79, 82, 84, 87, 91, 92, 93, 98, 100], "still": [2, 39, 40, 51, 65, 80, 87, 88, 94, 98], "extra": [2, 36, 40, 51, 55, 56, 57, 68, 85, 87, 88, 91, 94], "receiv": [2, 9, 36, 40, 41, 57, 60, 61, 68, 70, 74, 82, 93], "overwritten": [2, 68], "callabl": [2, 3, 36, 40, 47, 50, 55, 60, 87], "x_val": 2, "y_val": 2, "map": [2, 3, 11, 39, 40, 43, 46, 50, 51, 64, 66, 68, 73, 80, 81, 82, 87, 88, 89, 92, 100], "appropri": [2, 9, 15, 33, 58, 66, 81, 84, 92, 93], "earli": [2, 88], "stop": [2, 88], "x_valid": 2, "y_valid": 2, "could": [2, 6, 9, 21, 35, 51, 65, 81, 84, 88, 92, 96, 98, 100], "f": [2, 6, 80, 81, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99], "ignor": [2, 36, 40, 50, 55, 68, 73, 77, 80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "allow": [2, 35, 36, 39, 40, 44, 51, 56, 64, 65, 68, 70, 72, 80, 87, 88, 95, 97, 99], "access": [2, 9, 12, 36, 40, 68, 82, 85, 88, 92, 99], "hyperparamet": [2, 60, 65, 88], "purpos": [2, 81, 82, 87, 92, 96], "want": [2, 4, 9, 35, 39, 52, 56, 58, 68, 81, 85, 86, 88, 91, 93, 94, 95, 97, 99, 100], "explicitli": [2, 7, 9, 40, 68, 87], "yourself": [2, 4, 39, 82], "altern": [2, 6, 9, 47, 51, 55, 56, 66, 79, 80, 84, 85, 87, 88, 89, 91, 92, 94, 96, 99], "same": [2, 3, 4, 6, 9, 11, 13, 15, 25, 29, 36, 39, 40, 42, 51, 55, 56, 58, 65, 66, 68, 72, 73, 76, 77, 78, 81, 82, 84, 85, 87, 88, 92, 93, 94, 95, 96, 97, 98, 99], "effect": [2, 9, 26, 36, 40, 56, 65, 68, 84, 85, 87, 88, 94], "offer": [2, 4, 80, 81, 82, 85, 87, 89, 92, 99], "after": [2, 3, 4, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 51, 56, 68, 81, 85, 87, 88, 89, 91, 93, 94, 95, 96, 97, 99], "attribut": [2, 4, 6, 9, 11, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 36, 39, 40, 47, 65, 68, 81, 98], "label_issues_df": [2, 68, 88], "similar": [2, 9, 35, 36, 40, 51, 56, 60, 61, 63, 65, 68, 72, 76, 81, 82, 84, 85, 87, 88, 89, 93, 94, 97], "document": [2, 3, 4, 9, 13, 15, 35, 36, 39, 40, 41, 42, 47, 50, 55, 57, 58, 60, 63, 64, 65, 68, 72, 73, 74, 76, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "descript": [2, 4, 6, 9, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 35, 41, 51, 61, 68, 81, 82], "were": [2, 3, 4, 35, 40, 57, 63, 76, 80, 84, 87, 89, 91, 93, 95, 97, 98], "present": [2, 3, 4, 9, 11, 12, 19, 35, 51, 65, 73, 78, 84, 87, 88, 94], "actual": [2, 3, 4, 35, 56, 57, 66, 82, 87, 89, 100], "num_class": [2, 35, 39, 51, 55, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 94, 98, 99], "uniqu": [2, 30, 51, 73, 81, 87, 92, 94], "given_label": [2, 4, 24, 29, 35, 45, 68, 73, 77, 80, 81, 82, 84, 85, 88, 89, 96, 97, 99, 100], "normal": [2, 3, 17, 25, 30, 42, 44, 47, 49, 50, 51, 66, 87, 89, 94], "trick": [2, 87], "distribut": [2, 3, 4, 9, 25, 27, 35, 40, 42, 46, 49, 56, 64, 65, 66, 78, 81, 82, 84, 85, 88, 93, 94], "account": [2, 35, 56, 60, 65, 66, 85, 87, 89, 91, 92, 94, 96, 99], "word": [2, 3, 50, 76, 77, 87], "remov": [2, 9, 30, 35, 36, 40, 42, 68, 78, 85, 86, 87, 88, 92, 94, 96, 98, 99], "so": [2, 3, 4, 5, 6, 9, 13, 25, 33, 35, 36, 39, 40, 42, 51, 56, 57, 63, 66, 68, 72, 76, 80, 81, 82, 85, 88, 89, 92, 94, 97], "proportion": [2, 9, 42], "just": [2, 3, 4, 9, 12, 31, 35, 37, 39, 51, 55, 66, 68, 70, 78, 79, 80, 82, 84, 85, 87, 88, 89, 92, 93, 94, 95, 96, 97, 98, 99], "procedur": 2, "get": [2, 3, 4, 7, 12, 30, 36, 37, 40, 42, 47, 50, 51, 56, 58, 60, 65, 66, 68, 69, 70, 78, 80, 85, 86, 87, 88, 89, 94, 95, 96, 98, 99], "detect": [2, 4, 6, 8, 12, 13, 15, 17, 21, 27, 41, 49, 59, 61, 62, 63, 64, 65, 66, 67, 68, 71, 75, 78, 81, 83, 88, 90, 92, 96, 97, 98, 99, 100], "arg": [2, 11, 21, 26, 30, 36, 37, 40, 47, 51, 66, 68], "kwarg": [2, 6, 9, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 39, 40, 41, 47, 55, 68, 70, 72, 74, 87], "test": [2, 4, 9, 25, 40, 47, 55, 68, 78, 81, 82, 84, 85, 88, 95, 96, 98, 99, 100], "expect": [2, 3, 36, 40, 42, 47, 56, 65, 66, 68, 87, 89, 91, 92, 93, 96, 98, 99, 100], "class_predict": 2, "evalu": [2, 9, 36, 37, 38, 39, 40, 64, 68, 80, 81, 82, 87, 88, 89, 91, 95, 96, 97, 98, 99], "simpli": [2, 35, 66, 81, 82, 84, 85, 87, 89, 92, 96, 97, 99, 100], "quantifi": [2, 4, 6, 9, 12, 42, 60, 65, 68, 78, 82, 84, 85, 88, 89, 93], "save_spac": [2, 9, 67, 68], "potenti": [2, 9, 35, 42, 50, 58, 61, 64, 66, 68, 70, 72, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 97, 98, 100], "cach": [2, 85, 99], "panda": [2, 4, 6, 11, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 35, 51, 52, 55, 56, 57, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 96, 97, 98, 99], "unlik": [2, 9, 42, 44, 47, 55, 57, 58, 60, 76, 81, 91, 92, 94, 96], "both": [2, 4, 9, 15, 25, 35, 36, 40, 42, 51, 56, 58, 66, 70, 72, 77, 78, 81, 87, 88, 89, 91, 100], "mask": [2, 39, 42, 50, 51, 58, 61, 66, 68, 70, 72, 73, 78, 86, 87, 91, 93, 97, 100], "prefer": [2, 66, 74, 92], "plan": 2, "subsequ": [2, 3, 36, 40, 85, 87, 89, 93, 99], "invok": [2, 36, 40, 89, 95], "scratch": [2, 68], "To": [2, 4, 6, 8, 9, 10, 12, 15, 25, 34, 36, 39, 40, 41, 42, 55, 56, 58, 60, 64, 65, 66, 68, 69, 70, 72, 78, 80, 81, 82, 84, 85, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "share": [2, 66, 68], "mostli": [2, 51, 63, 68, 92, 96], "longer": [2, 33, 46, 47, 50, 68, 79, 85, 87, 93, 99], "info": [2, 4, 6, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 57, 68, 76, 81, 82, 86, 87, 100], "about": [2, 3, 4, 6, 9, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 37, 39, 44, 56, 57, 60, 64, 68, 73, 76, 80, 81, 84, 85, 86, 87, 88, 89, 91, 94], "docstr": [2, 35, 36, 40, 51, 68, 86, 89], "unless": [2, 36, 40, 68, 87], "our": [2, 3, 9, 55, 56, 66, 68, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "is_label_issu": [2, 29, 68, 80, 81, 82, 84, 85, 88, 89, 92, 96, 99], "entir": [2, 9, 25, 39, 42, 45, 57, 58, 63, 66, 68, 70, 72, 73, 78, 81, 82, 87, 93, 94, 95, 97, 100], "accur": [2, 3, 4, 9, 15, 35, 39, 42, 56, 57, 58, 61, 64, 66, 68, 69, 70, 72, 73, 79, 82, 84, 85, 87, 88, 91, 96], "label_qu": [2, 56, 68, 89, 91, 96, 99], "measur": [2, 4, 35, 56, 57, 68, 78, 86, 87, 89, 91, 92, 96, 97, 98, 100], "qualiti": [2, 3, 4, 6, 9, 12, 29, 30, 35, 39, 41, 42, 44, 47, 56, 57, 58, 60, 61, 63, 66, 68, 69, 72, 74, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 90, 96, 98, 99], "lower": [2, 4, 6, 9, 12, 27, 39, 47, 49, 56, 57, 60, 63, 64, 66, 68, 69, 72, 76, 80, 82, 84, 85, 88, 91, 92, 93, 94, 96, 97, 99, 100], "eas": 2, "comparison": [2, 36, 40, 64, 89, 91], "against": [2, 36, 40, 81, 84, 87, 91, 92], "predicted_label": [2, 4, 24, 29, 68, 73, 77, 80, 81, 82, 84, 85, 88, 89, 96, 97, 99], "ad": [2, 36, 40, 82, 91, 96], "precis": [2, 58, 61, 64, 87, 89, 97, 100], "definit": [2, 6, 33, 47, 68, 84, 98], "accessor": [2, 68], "describ": [2, 9, 17, 56, 65, 66, 68, 74, 76, 89, 91, 92, 93, 95, 100], "precomput": [2, 4, 45, 68, 86], "clear": [2, 36, 40, 68, 85, 96, 99], "save": [2, 4, 15, 36, 39, 40, 64, 68, 87, 93, 97, 100], "space": [2, 4, 9, 65, 68, 84, 86, 88], "place": [2, 36, 40, 51, 68, 91, 98], "larg": [2, 39, 68, 84, 85, 87, 88, 93, 94, 97, 100], "deploi": [2, 68, 84, 85, 87, 88], "care": [2, 9, 36, 40, 68, 85, 87, 89], "avail": [2, 4, 6, 11, 13, 32, 40, 68, 87, 89, 91, 93, 96], "cannot": [2, 4, 11, 13, 51, 95, 100], "anymor": 2, "classmethod": [2, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 33, 40, 47, 68], "__init_subclass__": [2, 38, 40, 67, 68], "set_": [2, 40, 68], "_request": [2, 40, 68], "pep": [2, 40, 68], "487": [2, 40, 68], "look": [2, 4, 6, 15, 36, 40, 51, 68, 73, 81, 82, 84, 85, 87, 89, 91, 92, 93, 94, 97, 98, 100], "inform": [2, 4, 6, 9, 12, 15, 32, 36, 40, 51, 56, 57, 61, 64, 68, 73, 76, 77, 78, 80, 81, 84, 85, 89, 91, 94, 97, 100], "__metadata_request__": [2, 40, 68], "infer": [2, 40, 51, 68, 73, 77, 88, 91, 92, 98, 99], "signatur": [2, 36, 40, 68], "accept": [2, 36, 40, 66, 68, 81, 82], "metadata": [2, 40, 68, 84, 85, 88, 100], "through": [2, 4, 6, 40, 68, 80, 82, 85, 86, 87, 91, 93, 94, 99], "develop": [2, 8, 40, 68, 87, 89, 100], "request": [2, 40, 68, 82, 85, 86, 92, 98, 99, 100], "those": [2, 3, 9, 39, 40, 42, 55, 56, 58, 64, 68, 72, 76, 77, 78, 80, 87, 88, 93, 97], "http": [2, 4, 6, 8, 9, 10, 17, 34, 36, 37, 39, 40, 44, 51, 61, 64, 65, 68, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "www": [2, 40, 68, 94], "org": [2, 17, 36, 37, 40, 51, 65, 68, 87, 89, 100], "dev": [2, 40, 68], "0487": [2, 40, 68], "get_metadata_rout": [2, 38, 40, 67, 68], "rout": [2, 40, 68], "pleas": [2, 36, 40, 55, 68, 78, 80, 81, 82, 85, 86, 87, 88, 89, 91, 92, 94, 96, 99, 100], "guid": [2, 6, 40, 68, 79, 83, 88], "mechan": [2, 36, 40, 68], "metadatarequest": [2, 40, 68], "encapsul": [2, 15, 40, 63, 68], "get_param": [2, 38, 40, 54, 55, 67, 68], "subobject": [2, 40, 68], "param": [2, 9, 36, 40, 55, 65, 68, 87], "name": [2, 4, 5, 6, 9, 11, 12, 31, 33, 35, 36, 40, 46, 47, 51, 55, 56, 57, 64, 68, 73, 77, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 96, 97, 99, 100], "set_fit_request": [2, 38, 40, 67, 68], "str": [2, 3, 4, 11, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 39, 40, 42, 45, 47, 50, 51, 55, 56, 57, 61, 63, 64, 66, 68, 73, 77, 80, 81, 87, 91, 92, 93, 100], "unchang": [2, 36, 40, 68, 100], "relev": [2, 15, 25, 40, 68, 88], "enable_metadata_rout": [2, 40, 68], "set_config": [2, 40, 68], "meta": [2, 40, 68], "rais": [2, 4, 11, 12, 33, 36, 40, 44, 47, 68, 80, 87], "alia": [2, 36, 40, 68], "metadata_rout": [2, 40, 68], "retain": [2, 40, 51, 68], "chang": [2, 31, 33, 36, 39, 40, 44, 68, 76, 80, 81, 85, 87, 93, 94, 99, 100], "version": [2, 4, 6, 8, 9, 10, 14, 20, 23, 28, 34, 36, 38, 40, 43, 44, 51, 54, 55, 66, 68, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99, 100], "sub": [2, 40, 63, 68], "pipelin": [2, 40, 68, 96], "otherwis": [2, 6, 9, 33, 35, 36, 39, 40, 42, 48, 50, 51, 58, 65, 68, 70, 72, 73, 77, 85, 87, 99], "updat": [2, 12, 36, 39, 40, 68, 79, 81, 88], "set_param": [2, 38, 40, 54, 55, 67, 68], "simpl": [2, 36, 40, 42, 56, 66, 68, 81, 82, 84, 85, 88, 91, 94, 96, 98, 99], "well": [2, 3, 9, 36, 40, 44, 45, 56, 58, 64, 66, 68, 73, 76, 77, 79, 81, 82, 84, 85, 87, 88, 89, 91, 93, 94], "nest": [2, 36, 40, 41, 52, 68, 74, 76, 77, 100], "latter": [2, 36, 40, 68, 94], "compon": [2, 40, 68], "__": [2, 40, 68], "set_score_request": [2, 67, 68], "structur": [3, 65, 84, 98], "unobserv": 3, "less": [3, 4, 9, 30, 39, 47, 56, 65, 66, 70, 72, 76, 82, 84, 86, 87, 88, 89, 93, 100], "channel": [3, 80, 89], "character": 3, "flip": 3, "nm": 3, "invers": [3, 9, 35, 45, 51, 57, 82, 86, 99], "inv": 3, "confident_joint": [3, 21, 35, 42, 51, 57, 58, 79, 87, 89], "un": 3, "under": [3, 9, 36, 40, 57, 64, 65, 82, 94], "joint": [3, 35, 42, 45, 51, 57, 58, 86], "num_label_issu": [3, 39, 42, 58, 73, 77, 79], "estimation_method": [3, 39], "off_diagon": 3, "multi_label": [3, 35, 42, 51, 52, 58, 92], "don": [3, 78, 82, 84, 85, 88, 89, 93, 96], "statis": 3, "compute_confident_joint": [3, 35, 42, 51, 58, 89], "off": [3, 42, 51, 63, 88, 89, 93, 94], "j": [3, 4, 35, 36, 40, 41, 42, 58, 61, 64, 65, 74, 76, 77, 81, 82, 89, 97, 100], "confident_learn": [3, 42, 58, 89], "off_diagonal_calibr": 3, "calibr": [3, 42, 51, 56, 91], "cj": [3, 45, 51], "axi": [3, 30, 45, 47, 49, 70, 73, 80, 81, 82, 87, 88, 89, 91, 92, 94, 96, 97], "bincount": [3, 81, 82, 89, 91, 92], "alwai": [3, 9, 36, 40, 51, 80, 89, 96, 98, 99], "estimate_issu": 3, "over": [3, 4, 9, 36, 39, 40, 63, 64, 70, 72, 82, 84, 86, 87, 88, 89, 94, 96, 98], "As": [3, 6, 78, 81, 82, 85, 89, 96, 100], "add": [3, 4, 6, 11, 12, 36, 40, 55, 64, 80, 81, 82, 85, 87, 88, 89, 92, 99], "approach": [3, 35, 39, 42, 84, 89, 92, 94, 96, 98], "custom": [3, 6, 9, 10, 29, 36, 39, 40, 47, 50, 66, 82, 85, 89, 96, 99], "know": [3, 81, 82, 84, 85, 87, 88, 89, 91, 96], "cut": [3, 63, 78, 89], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 31, 93, 94, 100], "underestim": 3, "few": [3, 64, 78, 82, 87, 91, 92, 93, 94, 100], "4": [3, 4, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 46, 47, 50, 60, 61, 63, 64, 66, 69, 76, 86, 87, 92, 97, 100], "detail": [3, 4, 9, 13, 15, 32, 35, 36, 40, 41, 47, 51, 55, 56, 57, 58, 60, 61, 63, 64, 65, 72, 73, 74, 78, 79, 80, 92, 94, 100], "num_issu": [3, 6, 39, 80, 81, 82, 84, 85, 88, 89], "calibrate_confident_joint": 3, "up": [3, 6, 9, 16, 25, 26, 29, 42, 47, 56, 86, 87, 93, 96, 99, 100], "p_": [3, 35, 42], "pair": [3, 4, 9, 35, 42, 89], "v": [3, 9, 39, 57, 58, 60, 66, 81, 82, 92, 93, 94, 95], "rest": [3, 4, 6, 8, 9, 10, 34, 57, 58, 60, 68, 81, 82, 84, 85, 87, 88, 89, 91, 96, 98, 99], "fashion": [3, 4, 70, 98], "2x2": 3, "incorrectli": [3, 35, 57, 58, 61, 84, 100], "calibrated_cj": 3, "c": [3, 9, 50, 58, 66, 78, 80, 81, 82, 84, 85, 87, 89, 92, 93, 94, 95, 96, 98], "whose": [3, 4, 9, 27, 36, 40, 45, 50, 56, 60, 63, 69, 72, 76, 77, 80, 81, 82, 84, 85, 87, 88, 89, 92, 93, 94, 97, 100], "truli": [3, 94, 97], "estimate_joint": [3, 35, 89], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 58, 64, 89, 93, 95, 97, 100], "return_indices_of_off_diagon": 3, "frequenc": [3, 25, 56, 57, 64, 73, 93, 94], "done": [3, 9, 68, 81, 87, 89, 92, 94, 95], "overfit": [3, 9, 61, 64, 80, 81, 82, 84, 85, 88, 95, 98], "classifict": 3, "singl": [3, 4, 11, 25, 35, 36, 40, 41, 47, 48, 51, 56, 57, 63, 64, 65, 66, 76, 80, 81, 87, 89, 92, 93, 98], "baselin": [3, 36, 42, 94, 96, 99], "proxi": 3, "union": [3, 4, 11, 47, 51, 52, 58, 64, 68, 72, 76, 87], "tupl": [3, 30, 36, 40, 41, 45, 46, 48, 50, 51, 56, 58, 64, 72, 74, 76, 77, 80, 100], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 9, 39, 45, 56, 70, 72, 78, 87, 88, 97, 99], "practic": [3, 82, 88, 89, 94, 96, 98, 99], "complet": [3, 80, 81, 82, 84, 85, 87, 88, 89, 92, 93, 96], "gist": 3, "cj_ish": 3, "guess": [3, 45, 89, 91], "8": [3, 4, 6, 7, 46, 47, 48, 50, 60, 74, 76, 80, 81, 82, 84, 85, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "parallel": [3, 42, 64, 74, 86], "again": [3, 55, 87, 94, 98], "simplifi": [3, 13], "understand": [3, 8, 35, 57, 64, 82, 89, 96, 97, 100], "100": [3, 36, 40, 66, 81, 82, 84, 86, 87, 88, 89, 92, 93, 94, 98, 99, 100], "optim": [3, 36, 37, 40, 55, 88, 91], "speed": [3, 42, 86, 87, 96, 99], "dtype": [3, 22, 24, 25, 30, 36, 40, 50, 51, 60, 76, 80, 93], "enumer": [3, 36, 40, 80, 81, 82, 88, 100], "s_label": 3, "confident_bin": 3, "6": [3, 4, 40, 47, 51, 76, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "num_confident_bin": 3, "argmax": [3, 42, 66, 70, 73, 80, 87, 89, 93, 94, 97], "elif": 3, "estimate_lat": 3, "py_method": [3, 45], "cnt": [3, 45], "1d": [3, 4, 11, 15, 31, 39, 42, 47, 48, 51, 52, 60, 69, 80, 98], "eqn": [3, 45], "margin": [3, 42, 45, 47, 66], "marginal_p": [3, 45], "shorthand": [3, 12], "proport": [3, 9, 35, 57, 89, 95], "poorli": [3, 45, 98], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 89], "variabl": [3, 6, 13, 26, 51, 68, 69, 80, 81, 84, 89, 92, 96], "exact": [3, 45, 81, 82, 84, 88, 98], "within": [3, 4, 9, 14, 31, 36, 37, 40, 41, 43, 58, 63, 72, 74, 76, 81, 82, 87, 88, 93, 97], "percent": 3, "often": [3, 35, 45, 57, 87, 89, 95, 97], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 51, 52, 64, 80, 81, 84, 85, 87, 88, 92, 93, 94, 96, 99], "wai": [3, 4, 55, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92, 93, 95, 98, 99], "pro": 3, "con": 3, "pred_proba": [3, 95], "combin": [3, 35, 81, 86, 87, 88, 89, 95, 96], "becaus": [3, 45, 51, 63, 85, 87, 89, 91, 93], "littl": [3, 39, 86, 93, 100], "uniform": [3, 66, 86, 87, 89], "20": [3, 6, 41, 77, 80, 85, 86, 87, 88, 89, 93, 96, 97, 100], "Such": [3, 88, 94], "bound": [3, 22, 24, 36, 40, 50, 60, 61, 63, 64, 93], "reason": [3, 21, 36, 40], "comment": [3, 50, 100], "end": [3, 4, 36, 40, 64], "file": [3, 4, 11, 38, 39, 54, 64, 80, 81, 84, 85, 86, 87, 93, 94, 97, 98, 100], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 89], "handl": [3, 4, 6, 9, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 39, 40, 79, 81, 82, 84, 85, 88, 89, 97, 98, 100], "five": [3, 61, 64, 89, 93], "estimate_cv_predicted_prob": [3, 89], "estimate_noise_matric": 3, "get_confident_threshold": [3, 38, 39], "amongst": [3, 9, 93], "confident_threshold": [3, 9, 21, 39, 65], "unifi": 4, "audit": [4, 8, 11, 12, 15, 80, 83, 84, 85, 87, 88, 89, 92, 93, 96], "kind": [4, 5, 6, 9, 80, 81, 84, 85, 86, 88, 89], "addit": [4, 6, 8, 9, 10, 12, 32, 34, 36, 40, 47, 52, 56, 64, 74, 80, 81, 84, 85, 89, 91, 94, 95, 98, 99], "depend": [4, 6, 8, 9, 10, 11, 12, 34, 38, 42, 44, 51, 54, 58, 65, 68, 69, 78], "instal": [4, 6, 8, 9, 10, 34, 36, 38, 39, 40, 42, 54, 55, 70, 72], "pip": [4, 6, 8, 10, 34, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "development": [4, 6, 8, 10, 34], "git": [4, 6, 8, 10, 34, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 98, 99], "github": [4, 6, 8, 10, 34, 36, 37, 51, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99], "com": [4, 6, 8, 10, 34, 36, 37, 39, 44, 51, 65, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "egg": [4, 6, 8, 10, 34, 78, 86], "label_nam": [4, 6, 7, 9, 11, 17, 30, 78, 80, 81, 82, 84, 85, 87, 88, 89, 92, 93, 96], "image_kei": [4, 88], "interfac": [4, 78, 87, 89], "librari": [4, 9, 40, 61, 64, 65, 78, 81, 85, 86, 87, 99], "goal": [4, 96], "track": [4, 6, 12, 13, 78, 81, 86, 87, 89], "intermedi": [4, 8, 82], "statist": [4, 9, 12, 21, 25, 35, 56, 57, 64, 82, 84, 85, 89], "convert": [4, 11, 33, 36, 40, 48, 49, 52, 56, 63, 72, 76, 79, 80, 85, 86, 87, 88, 91, 92, 93, 99], "hug": [4, 11, 88], "face": [4, 11, 15, 86, 88, 92], "kei": [4, 6, 9, 11, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 40, 47, 56, 57, 63, 65, 81, 82, 85, 87, 88, 89, 91, 93], "string": [4, 9, 11, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 40, 51, 56, 57, 69, 73, 76, 77, 84, 85, 87, 91, 92, 99, 100], "dictionari": [4, 6, 9, 11, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 36, 40, 46, 51, 56, 57, 60, 61, 63, 64, 81, 82, 84, 85, 89, 91, 92, 93], "path": [4, 11, 36, 39, 40, 64, 80, 81, 87, 93], "local": [4, 6, 11, 36, 37, 40, 80, 81, 82, 86, 87, 88, 89, 91, 92, 94, 96, 100], "text": [4, 6, 9, 11, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 41, 47, 65, 74, 76, 77, 78, 81, 82, 83, 86, 87, 89, 90, 91, 94], "txt": [4, 11, 100], "csv": [4, 11, 84, 85, 96, 98, 99], "json": [4, 11], "hub": [4, 11], "multiclass": [4, 11, 14, 47, 51, 56, 92], "regress": [4, 6, 9, 11, 13, 15, 20, 29, 31, 33, 81, 82, 85, 90, 91, 94, 99], "multilabel": [4, 11, 13, 14, 20, 24, 31, 33, 48, 92], "imag": [4, 8, 35, 40, 61, 63, 64, 65, 70, 72, 73, 78, 81, 82, 86, 87, 90, 91, 92, 93, 95, 97], "point": [4, 6, 9, 17, 25, 36, 40, 81, 82, 84, 85, 87, 88, 89, 91], "field": [4, 9, 36, 40], "themselv": [4, 96, 98, 99], "cleanvis": [4, 9], "level": [4, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 35, 50, 74, 76, 82, 88, 90, 92, 97], "load_dataset": [4, 11, 88], "glue": 4, "sst2": 4, "properti": [4, 11, 12, 33, 36, 40], "has_label": [4, 11], "class_nam": [4, 11, 19, 35, 41, 57, 64, 73, 77, 78, 86, 89, 93, 97, 100], "empti": [4, 11, 45, 56, 82, 87, 92], "find_issu": [4, 5, 6, 7, 9, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 78, 80, 81, 82, 84, 85, 87, 88, 89, 92, 96], "knn_graph": [4, 9, 15, 17, 18, 25, 27, 30, 84], "issue_typ": [4, 5, 6, 7, 9, 12, 13, 15, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 80, 81, 82, 84, 85, 87, 88, 89, 92, 96], "sort": [4, 15, 39, 42, 47, 56, 58, 61, 63, 64, 66, 72, 74, 76, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100], "common": [4, 12, 15, 82, 83, 86, 87, 89, 92, 93, 97], "real": [4, 15, 78, 81, 82, 87, 89, 91, 96, 97], "world": [4, 15, 78, 81, 82, 87, 89, 91, 96, 97], "interact": [4, 15, 85, 87], "embed": [4, 9, 15, 65, 78, 80, 81, 82, 84, 85, 89, 92, 96, 99], "thereof": [4, 15], "insight": [4, 15, 64, 91], "best": [4, 15, 46, 56, 66, 81, 82, 84, 85, 87, 88, 91, 92, 94, 96, 98, 99, 100], "2d": [4, 15, 31, 39, 47, 48, 50, 51, 56, 80, 92, 98], "num_exampl": [4, 15, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32, 35, 57, 80, 81, 82, 84, 85, 88, 89], "properli": [4, 9, 39, 46, 51, 52, 70, 80, 81, 82, 84, 85, 87, 88, 89, 92, 94, 96, 97], "respect": [4, 36, 40, 61, 64, 80, 81, 82, 84, 85, 88, 89, 92, 93], "lexicograph": [4, 46, 51, 80, 81, 82, 84, 85, 88, 89, 92], "represent": [4, 9, 15, 33, 36, 40, 48, 58, 78, 80, 81, 82, 85, 87, 88, 89, 94, 99], "num_featur": [4, 15, 36, 40, 55], "distanc": [4, 9, 15, 17, 25, 27, 30, 49, 63, 65, 84, 94], "nearest": [4, 9, 15, 22, 25, 27, 49, 65, 82, 85, 94], "neighbor": [4, 9, 15, 17, 22, 25, 27, 49, 65, 81, 82, 84, 85, 87, 88, 94], "graph": [4, 9, 12, 15, 17, 25, 30], "squar": [4, 51, 68, 86, 96], "csr": 4, "evenli": 4, "omit": [4, 63, 64, 88, 93], "itself": [4, 31, 36, 40, 93], "three": [4, 9, 35, 56, 57, 68, 73, 80, 81, 82, 84, 86, 89, 91, 95, 96, 97, 98, 100], "indptr": 4, "wise": 4, "start": [4, 6, 9, 33, 36, 37, 40, 47, 78, 84, 92, 100], "th": [4, 41, 46, 50, 51, 56, 58, 61, 63, 64, 65, 74, 76, 77, 85, 92, 93, 100], "ascend": [4, 35, 57, 88, 89], "segment": [4, 70, 72, 73, 90], "reflect": [4, 84, 85, 91, 93, 94, 96, 98, 99], "maintain": 4, "posit": [4, 36, 40, 49, 51, 64, 86, 94], "nearestneighbor": [4, 9, 17, 65, 84, 94], "kneighbors_graph": [4, 17, 84], "illustr": 4, "todens": 4, "second": [4, 47, 51, 64, 66, 81, 87, 89, 100], "duplic": [4, 8, 20, 21, 36, 40, 78, 81, 89, 96], "explicit": 4, "precend": 4, "construct": [4, 6, 9, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32, 36, 40, 47, 55], "neither": [4, 9, 13, 93], "nor": [4, 9, 13], "collect": [4, 9, 12, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 56, 87, 91, 100], "unspecifi": [4, 15, 42, 58], "interest": [4, 15, 21, 73, 77, 85, 89, 96, 97, 98, 99, 100], "constructor": [4, 9, 15, 22, 29], "issuemanag": [4, 8, 12, 13, 15, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 32], "respons": [4, 15, 21, 68, 69, 86, 96, 100], "random_st": [4, 80, 81, 82, 88, 89, 92, 94, 98], "lab": [4, 5, 7, 9, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 39, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 96], "comprehens": [4, 78, 88, 92, 96], "nbr": 4, "n_neighbor": [4, 9, 17, 65], "metric": [4, 9, 17, 18, 25, 30, 51, 55, 64, 65, 80, 84, 85, 88, 89, 96, 98, 99], "euclidean": [4, 9, 63, 65, 84], "mode": [4, 17, 36, 39, 40, 94], "4x4": 4, "float64": [4, 25, 36, 40, 76], "compress": [4, 9, 51, 70, 72], "toarrai": 4, "NOT": [4, 39, 85], "23606798": 4, "41421356": 4, "configur": [4, 15, 47, 82], "suppos": [4, 9, 61, 94, 96, 98, 99], "who": [4, 63, 84, 89, 98, 100], "manag": [4, 7, 8, 9, 12, 13, 14, 15, 16, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 81], "clean_learning_kwarg": [4, 9, 22, 29, 96], "labelissuemanag": [4, 9, 13, 20, 22], "prune_method": [4, 79], "prune_by_noise_r": [4, 42, 58, 89], "report": [4, 6, 10, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 57, 77, 78, 80, 81, 82, 84, 85, 89, 92, 96, 100], "include_descript": [4, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 32], "show_summary_scor": [4, 32], "show_all_issu": [4, 32], "summari": [4, 6, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 41, 54, 55, 57, 62, 71, 72, 74, 75, 76, 79, 80, 81, 82, 84, 85, 86, 88, 89, 93, 96, 97, 100], "show": [4, 6, 25, 36, 40, 46, 51, 64, 73, 77, 82, 84, 85, 86, 87, 88, 89, 91, 94, 96, 97, 98, 100], "top": [4, 9, 35, 39, 41, 42, 51, 58, 61, 64, 66, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 93, 94, 96, 99, 100], "suffer": [4, 9, 12, 21, 58, 66, 77, 100], "onc": [4, 21, 35, 36, 40, 81, 87, 89, 92, 93, 98], "familiar": 4, "overal": [4, 6, 9, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 41, 47, 56, 57, 60, 63, 64, 68, 72, 73, 74, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 93, 100], "sever": [4, 6, 9, 11, 12, 21, 36, 39, 40, 42, 60, 63, 65, 66, 72, 76, 78, 80, 81, 82, 84, 85, 86, 87, 89, 93, 94, 98, 99, 100], "compar": [4, 56, 65, 76, 81, 82, 84, 89, 93], "issue_summari": [4, 6, 9, 12, 81], "With": [4, 39, 85, 89, 91, 96, 97, 99, 100], "usag": [4, 39, 55], "found": [4, 6, 9, 12, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 51, 78, 80, 81, 82, 84, 85, 87, 88, 92, 94, 96, 98, 99, 100], "usual": [4, 11, 31, 32, 88, 91, 96], "directli": [4, 13, 15, 32, 33, 39, 55, 56, 82, 85, 92, 93, 96, 99], "ti": [4, 56], "exhibit": [4, 6, 9, 12, 73, 82, 84, 85, 88, 89, 93], "ie": [4, 68], "likelihood": [4, 9, 39, 41, 42, 58, 63, 65, 66, 70, 74], "wherea": [4, 51, 58, 95], "outlier": [4, 8, 13, 20, 21, 30, 43, 66, 78, 81, 82, 89, 90, 96], "fundament": [4, 9], "incompar": 4, "quantiti": [4, 89, 96], "global": [4, 6, 21, 36, 40], "non_iid": [4, 9, 13, 25, 82, 84, 85, 88, 89], "hypothesi": 4, "iid": [4, 6, 8, 25, 84, 89], "never": [4, 80, 89, 92, 94, 95], "someth": [4, 6, 36, 40, 66, 93], "123": [4, 81, 82], "456": [4, 80, 98, 99], "nearest_neighbor": 4, "7": [4, 47, 48, 55, 74, 76, 80, 81, 82, 84, 85, 86, 87, 91, 92, 93, 94, 96, 97, 98, 99, 100], "9": [4, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 41, 47, 48, 60, 74, 76, 80, 81, 82, 84, 85, 86, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "distance_to_nearest_neighbor": [4, 81, 82, 84, 85, 88, 89], "789": 4, "get_issu": [4, 9, 12, 80, 82, 84, 85, 87, 88, 92, 96], "issue_nam": [4, 5, 6, 9, 12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 81, 82], "focu": [4, 12, 85, 97, 100], "full": [4, 9, 12, 39, 64, 88, 100], "summar": [4, 12, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 35, 57, 73, 77, 78, 97], "valueerror": [4, 11, 12, 33, 44, 47, 87], "specific_issu": [4, 12], "lie": [4, 9, 65, 66, 80, 81, 82, 84, 85, 88, 89, 99], "get_issue_summari": [4, 12, 82], "get_info": [4, 12, 82, 85], "yet": [4, 16, 26, 86, 91], "list_possible_issue_typ": [4, 13, 14], "regist": [4, 6, 13, 14, 16, 26, 36, 40, 81], "rtype": [4, 13, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40], "registri": [4, 13, 14], "list_default_issue_typ": [4, 13, 14], "folder": [4, 80, 81, 88], "load": [4, 11, 39, 64, 86, 87, 88, 89, 93, 94, 97, 100], "futur": [4, 9, 21, 36, 40, 56, 78, 80, 81, 85, 87, 99], "overwrit": [4, 81], "separ": [4, 35, 47, 60, 81, 82, 87, 88, 93, 95], "static": 4, "rememb": [4, 85, 87, 89], "part": [4, 9, 36, 40, 42, 61, 63, 64, 80, 81, 86, 97, 100], "ident": [4, 9, 21, 51, 85], "datalab": [5, 7, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 78, 80, 87, 88, 91, 96, 98, 99], "walk": 6, "alongsid": [6, 36, 40, 81, 87], "pre": [6, 7, 9, 36, 40, 81, 82, 96], "runtim": [6, 36, 39, 40, 68, 70, 72, 80, 87, 88], "issue_manager_factori": [6, 13, 81], "myissuemanag": [6, 13], "myissuemanagerforregress": 6, "decor": [6, 13], "ll": [6, 47, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "thing": [6, 40, 89, 96, 99], "next": [6, 56, 78, 80, 84, 85, 87, 91, 93, 96, 98, 99, 100], "dummi": 6, "randint": [6, 30, 47, 81, 82, 87], "mark": [6, 9, 79, 93, 94, 96], "regard": [6, 82, 89], "rand": [6, 47, 81, 82], "is_": [6, 9, 81], "_issu": [6, 9, 81], "issue_score_kei": [6, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 81], "whole": [6, 25, 36, 40, 82], "make_summari": [6, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 81], "popul": [6, 82, 85], "verbosity_level": [6, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], "std": [6, 93], "raw_scor": 6, "bit": 6, "involv": [6, 39, 73, 77, 87, 92], "intermediate_arg": 6, "min": [6, 47, 63, 76, 81, 87, 94], "sin_filt": 6, "sin": 6, "arang": 6, "kernel": 6, "affect": [6, 9, 36, 40, 70, 76, 85, 87], "easili": [6, 45, 79, 80, 82, 84, 85, 89, 91, 92, 94, 95, 96, 97, 98, 99], "hard": [6, 40, 86, 94], "sai": [6, 9, 36, 40, 92, 97], "anoth": [6, 21, 35, 39, 50, 63, 66, 84, 85, 87, 89, 91, 94, 99], "try": [6, 9, 39, 42, 55, 56, 70, 72, 78, 82, 84, 85, 87, 88, 89, 97], "won": [6, 36, 40, 81, 82, 87, 92], "issue_manag": [6, 9, 10, 12, 14, 17, 18, 19, 22, 24, 25, 26, 27, 29, 30, 81], "instanti": [6, 15, 39, 55, 65, 80, 82, 84, 99], "477762": 6, "286455": 6, "term": [6, 9, 45, 51, 64, 80, 81, 82, 84, 85, 88, 89], "4778": 6, "is_basic_issu": 6, "basic_scor": 6, "13": [6, 18, 27, 80, 81, 82, 84, 85, 86, 88, 89, 91, 93, 94, 96, 97, 98, 99, 100], "003042": 6, "058117": 6, "11": [6, 55, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "121908": 6, "15": [6, 49, 68, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98, 99, 100], "169312": 6, "17": [6, 80, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 99, 100], "229044": 6, "2865": 6, "is_intermediate_issu": 6, "intermediate_scor": 6, "000000": [6, 81, 82, 86, 89], "007059": 6, "009967": 6, "010995": 6, "087332": 6, "016296": 6, "03947": 6, "019459": 6, "794251": 6, "underperform": [7, 8, 30], "group": [7, 8, 25, 30, 86, 93, 100], "dbscan": [7, 9, 30, 87], "hdbscan": [7, 87], "etc": [7, 9, 21, 31, 36, 40, 45, 55, 56, 74, 78, 81, 82, 84, 85, 87, 89, 92, 96], "sensit": [7, 9, 49], "ep": [7, 30, 64], "radiu": 7, "min_sampl": [7, 30], "kmean": [7, 87], "your_data": 7, "get_pred_prob": 7, "n_cluster": [7, 30, 87], "cluster_id": [7, 9, 30, 87], "labels_": 7, "underperforming_group": [7, 9, 13, 20, 82, 84, 85, 87, 88, 89], "search": [8, 9, 19, 25, 26, 50, 68, 87, 95], "nondefault": 8, "Near": [8, 87], "imbal": [8, 20, 60, 65, 66, 82], "null": [8, 13, 20, 82, 85, 88, 89], "valuat": [8, 17], "togeth": [8, 9, 45, 81, 82, 84, 85, 88, 89, 96, 99, 100], "built": [8, 47], "own": [8, 36, 38, 40, 54, 60, 61, 64, 70, 74, 80, 82, 84, 85, 87, 88, 91, 92, 96, 97, 98, 99, 100], "prerequisit": 8, "basic": [8, 40, 55, 84, 85, 94], "page": [9, 82, 87, 89], "variou": [9, 12, 29, 38, 52, 54, 78, 81, 82, 84, 85, 86, 89, 91, 93, 98], "why": [9, 85], "matter": [9, 35, 57, 85, 99], "_score": 9, "flag": [9, 21, 25, 42, 47, 57, 58, 61, 68, 78, 80, 81, 82, 84, 85, 86, 88, 89, 93, 94, 96, 97, 99], "badli": [9, 63, 100], "code": [9, 36, 40, 45, 51, 55, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "issue_scor": 9, "outlier_scor": [9, 27, 81, 82, 84, 85, 88, 89, 94], "atyp": [9, 65, 81, 82, 84, 85, 88, 89, 94], "datapoint": [9, 30, 42, 47, 51, 66, 69, 78, 80, 81, 82, 84, 85, 87, 95, 96, 98, 99], "is_issu": [9, 21], "is_outlier_issu": [9, 81, 82, 84, 85, 88, 89], "annot": [9, 35, 46, 56, 57, 58, 60, 61, 63, 64, 73, 76, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 90, 93, 97], "transform": [9, 31, 47, 49, 51, 65, 66, 82, 85, 88, 94, 98, 99, 100], "dissimilar": [9, 84, 85], "preced": 9, "cosin": [9, 65, 94], "incorrect": [9, 63, 66, 69, 80, 81, 82, 84, 85, 88, 89, 93, 96, 98], "due": [9, 39, 42, 66, 70, 72, 80, 81, 82, 84, 85, 88, 89, 96], "appear": [9, 35, 46, 57, 58, 61, 69, 82, 84, 85, 88, 96, 97], "now": [9, 39, 79, 80, 82, 91, 93, 94, 96, 98, 99, 100], "u": [9, 80, 81, 84, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 100], "token": [9, 41, 50, 72, 73, 74, 75, 76, 77, 87, 89, 90], "calcul": [9, 17, 25, 39, 47, 56, 60, 61, 63, 64, 65, 68, 72, 86, 88], "hamper": [9, 86, 88], "analyt": [9, 78, 87, 91], "lead": [9, 63, 66, 88, 93], "draw": [9, 81, 82], "conclus": [9, 85], "veri": [9, 35, 57, 61, 63, 81, 82, 84, 85, 87, 88, 89, 91, 94, 96, 99], "rare": [9, 42, 64, 81, 82, 84, 85, 87, 88, 89], "anomal": [9, 66, 81, 82, 84, 85, 88, 89], "articl": [9, 39, 87], "ai": [9, 78, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 98, 99, 100], "blog": 9, "unexpect": [9, 36, 40, 85], "consequ": 9, "inspect": [9, 80, 82, 88, 89, 93, 96, 99], "neg": [9, 63, 64, 81, 82, 86], "extrem": [9, 81, 82, 84, 85, 87, 88, 89], "rel": [9, 35, 56, 57, 65, 81, 82, 84, 85, 88, 89, 94], "record": [9, 36, 40, 80, 84, 96], "abbrevi": 9, "misspel": 9, "typo": [9, 77], "resolut": 9, "video": [9, 86], "audio": [9, 81, 82, 87, 90], "minor": [9, 50], "variat": 9, "translat": 9, "d": [9, 49, 84, 85, 89, 92, 96, 98, 100], "constant": [9, 30, 68], "median": [9, 29, 49], "question": [9, 21, 78, 89], "nearli": [9, 21, 82, 84, 85, 88], "awar": [9, 79, 89], "presenc": [9, 89], "near_dupl": [9, 13, 18, 81, 82, 84, 85, 87, 88, 89], "signific": [9, 84, 85, 89], "violat": [9, 84, 85, 89], "assumpt": [9, 84, 85, 89], "changepoint": [9, 84, 85, 89], "shift": [9, 84, 85, 89], "drift": [9, 82, 84, 89], "autocorrel": [9, 84, 85, 89], "almost": [9, 84, 85, 89], "adjac": [9, 84, 85, 89], "tend": [9, 35, 45, 84, 85, 89, 97, 100], "sequenti": [9, 36, 40, 55, 88], "gap": 9, "b": [9, 17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 35, 50, 51, 76, 84, 85, 86, 89, 95, 98, 100], "x1": [9, 61, 64, 93], "x2": [9, 61, 64, 93], "10th": 9, "100th": 9, "90": [9, 76, 84, 89, 95, 96, 98], "similarli": [9, 36, 40, 81, 84, 87, 88, 93], "math": [9, 88], "behind": [9, 65, 89], "proper": [9, 51, 56, 61, 64, 85, 88, 91, 93, 98], "closer": [9, 63, 93], "scenario": [9, 66, 81, 82], "underli": [9, 41, 65, 74, 76, 100], "stem": [9, 65, 94], "evolv": 9, "influenc": 9, "act": [9, 63, 81], "accordingli": [9, 31], "emploi": [9, 92, 94], "partit": [9, 95], "ahead": 9, "good": [9, 36, 40, 49, 55, 57, 63, 66, 70, 72, 73, 78, 84, 85, 88], "fix": [9, 56, 85, 89, 96, 99], "problem": [9, 31, 39, 47, 73, 78, 81, 82, 85, 87, 88], "deploy": [9, 89, 96, 98, 99], "overlook": [9, 63, 93], "fact": 9, "thu": [9, 35, 40, 57, 80, 84, 85, 89, 95, 98, 100], "diagnos": [9, 82, 87], "rarest": [9, 82], "q": [9, 93], "fall": [9, 63, 72, 76, 89, 94], "subpar": 9, "special": [9, 50], "techniqu": [9, 93], "smote": 9, "asymmetr": [9, 35], "class_imbal": [9, 13, 19, 82, 84, 85, 88, 89], "too": [9, 42, 47, 65, 82, 87, 88, 93], "dark": [9, 97], "bright": [9, 100], "blurri": [9, 88], "abnorm": [9, 64, 88, 93], "cluster": [9, 17, 30], "slice": 9, "poor": 9, "subpopul": 9, "faq": [9, 78, 82, 84, 85, 88, 90], "lowest": [9, 56, 64, 82, 87, 88, 91, 92, 93, 97], "get_self_confidence_for_each_label": [9, 47, 66], "power": [9, 84, 85, 86, 88, 89, 100], "r": [9, 39, 68, 81, 82, 96, 97], "tabular": [9, 78, 81, 82, 83, 87, 90, 91], "categor": [9, 65, 81, 82, 83, 87, 96, 98], "encod": [9, 48, 64, 70, 73, 84, 85, 87, 96, 97, 98, 99], "miss": [9, 26, 36, 40, 51, 61, 63, 84, 87, 93, 96], "pattern": 9, "contribut": [9, 17, 93], "isn": [9, 16, 26], "approxim": [9, 17, 39, 65, 91], "shaplei": [9, 17], "knn": [9, 12, 17, 25, 30, 65, 84, 94], "scalabl": 9, "sacrific": 9, "One": [9, 51, 65, 87], "quantif": 9, "data_valu": [9, 13, 20], "exert": [9, 82], "possible_issue_typ": 9, "label_kwarg": 9, "outlier_kwarg": 9, "near_duplicate_kwarg": 9, "non_iid_kwarg": 9, "class_imbalance_kwarg": 9, "underperforming_group_kwarg": 9, "null_kwarg": 9, "data_valuation_kwarg": 9, "health_summary_paramet": [9, 20, 22, 29], "health_summari": [9, 22, 35, 78, 86], "health_summary_kwarg": 9, "tandem": [9, 86], "view": [9, 36, 40, 41, 42, 72, 74, 76, 78, 80, 81, 82, 84, 85, 86, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100], "ood_kwarg": 9, "outofdistribut": [9, 27, 65, 94], "outsid": [9, 92], "outlierissuemanag": [9, 13, 20, 27, 81], "nearduplicateissuemanag": [9, 13, 18, 20], "noniidissuemanag": [9, 13, 20, 25], "num_permut": [9, 25], "permut": [9, 25], "significance_threshold": [9, 25], "signic": 9, "noniid": [9, 20], "classimbalanceissuemanag": [9, 13, 19, 20], "underperforminggroupissuemanag": [9, 13, 20, 30], "determinin": 9, "neighbour": 9, "min_cluster_sampl": [9, 30], "filter_cluster_id": [9, 20, 30], "clustering_kwarg": [9, 30], "nullissuemanag": [9, 13, 20, 26], "datavaluationissuemanag": [9, 13, 17, 20], "codeblock": 9, "demonstr": [9, 39, 81, 82, 85, 87, 88, 89, 91, 92, 93, 96, 97], "howev": [9, 36, 40, 51, 80, 84, 85, 88, 91, 95, 97, 98, 99], "mandatori": 9, "image_issue_types_kwarg": 9, "32": [9, 80, 81, 86, 91, 93], "fewer": [9, 42, 51, 93], "vice": [9, 57], "versa": [9, 57], "light": [9, 86, 88, 93, 97], "29": [9, 86, 88, 91, 92, 93, 97, 100], "low_inform": [9, 88], "odd_aspect_ratio": [9, 88], "35": [9, 81, 86, 91, 92, 93], "odd_siz": [9, 88], "10": [9, 17, 18, 22, 25, 30, 36, 37, 64, 65, 66, 77, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], "doc": [9, 36, 40, 78, 80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "data_issu": [10, 14, 15, 32, 81], "issue_find": [10, 14], "factori": [10, 14, 15], "model_output": [10, 14], "except": [11, 36, 40, 55, 66, 81, 82, 88, 91], "dataformaterror": [11, 14], "add_not": 11, "with_traceback": 11, "tb": 11, "__traceback__": 11, "datasetdicterror": [11, 14], "datasetdict": 11, "datasetloaderror": [11, 14], "dataset_typ": 11, "fail": 11, "hold": 11, "associ": [11, 15, 31, 33, 36, 40, 64, 91], "sublist": 11, "map_to_int": 11, "abc": [11, 21, 31], "is_avail": [11, 88], "dataissu": [12, 14, 15, 32], "serv": [12, 15, 91], "central": [12, 100], "repositori": 12, "strategi": [12, 47, 87], "being": [12, 35, 36, 40, 42, 47, 50, 51, 66, 84, 87, 89, 96, 97, 98], "_infostrategi": 12, "basi": 12, "collect_statist": 12, "reus": [12, 21], "avoid": [12, 36, 39, 40, 42, 51, 58, 61, 64, 68, 70, 72, 81, 82, 87], "recomput": [12, 99], "weighted_knn_graph": 12, "issue_manager_that_computes_knn_graph": 12, "collect_issues_from_issue_manag": 12, "collect_issues_from_imagelab": 12, "imagelab": 12, "set_health_scor": 12, "health": [12, 22, 35, 57, 78], "get_data_statist": [12, 14], "concret": 13, "subclass": [13, 36, 40, 65, 81], "regressionlabelissuemanag": [13, 20, 28, 29], "multilabelissuemanag": [13, 20, 23, 24], "from_str": [13, 33, 43, 47], "my_issu": 13, "logic": [13, 33, 39, 42, 70, 72], "stabl": [14, 20, 23, 28, 38, 43, 51, 54, 65, 79], "issuefind": [14, 15, 32], "modeloutput": [14, 31], "multiclasspredprob": [14, 31], "regressionpredict": [14, 31], "multilabelpredprob": [14, 31], "instati": 15, "public": [15, 89, 93, 97, 100], "creation": [15, 40], "execut": [15, 36, 40, 81, 87, 93], "coordin": [15, 61, 63, 64, 93, 100], "behavior": [15, 35, 36, 40, 64, 87], "At": [15, 64, 87], "get_available_issue_typ": 15, "direct": [16, 26, 36, 40], "valuabl": 17, "vstack": [17, 51, 86, 87, 88, 89, 91, 92], "25": [17, 25, 36, 47, 49, 82, 86, 88, 89, 91, 92, 93, 100], "classvar": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30], "short": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 50, 51], "data_valuation_scor": 17, "item": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 36, 40, 51, 81, 82, 87, 88, 89, 91, 92], "some_info_kei": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30], "additional_info_kei": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30], "default_threshold": [17, 20, 27], "arxiv": [17, 89], "ab": [17, 89, 93], "1911": 17, "07128": 17, "larger": [17, 68, 70, 72, 85, 86, 87, 88], "collect_info": [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], "info_to_omit": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "compos": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30, 36, 40, 85, 94, 99], "is_x_issu": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "x_score": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "val_a": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "val_b1": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "val_b2": [17, 18, 19, 21, 22, 24, 25, 27, 29, 30], "report_str": [17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 32], "_": [18, 21, 22, 24, 25, 26, 29, 30, 47, 50, 51, 80, 81, 86, 88, 89, 92, 98], "near_duplicate_set": [18, 20, 81, 82, 84, 85, 87, 88, 89], "occurr": [18, 19, 21, 25, 26, 27, 30, 50], "median_nn_dist": 18, "near_duplicate_scor": [18, 81, 82, 84, 85, 87, 88, 89], "class_imbalance_scor": [19, 82], "bleed": [20, 23, 28, 38], "edg": [20, 23, 28, 38, 63, 78, 89, 100], "sharp": [20, 23, 28, 38], "get_health_summari": [20, 22], "ood": [20, 27, 65, 66, 81, 82, 85, 88, 89, 94], "simplified_kolmogorov_smirnov_test": [20, 25], "outlier_cluster_label": [20, 30], "no_underperforming_cluster_id": [20, 30], "set_knn_graph": [20, 30], "perform_clust": [20, 30], "get_worst_clust": [20, 30], "find_issues_with_predict": [20, 28, 29], "find_issues_with_featur": [20, 28, 29], "believ": [21, 97], "priori": [21, 89], "abstract": [21, 31], "applic": [22, 56, 87, 89, 91, 100], "typevar": [22, 24, 36, 40, 50, 60, 63, 64], "scalartyp": [22, 24], "covari": [22, 24, 68, 96], "summary_dict": 22, "label_scor": [22, 24, 29, 80, 81, 82, 84, 85, 88, 89, 92, 96], "neighbor_histogram": 25, "non_neighbor_histogram": 25, "kolmogorov": 25, "smirnov": 25, "largest": [25, 39, 47, 66, 70, 72, 93, 97], "empir": [25, 46, 56], "cumul": 25, "ecdf": 25, "histogram": [25, 84, 96], "absolut": [25, 29], "dimension": [25, 51, 80, 89, 94], "trial": 25, "non_iid_scor": [25, 82, 84, 85, 89], "null_track": 26, "extend": [26, 48, 88, 93, 94, 100], "superclass": 26, "arbitrari": [26, 35, 72, 76, 81, 94, 96], "prompt": 26, "address": [26, 81, 82, 85, 87, 99], "enabl": [26, 40], "null_scor": [26, 82], "37037": 27, "q3_avg_dist": 27, "iqr_avg_dist": 27, "median_outlier_scor": 27, "multipli": 29, "deleg": 29, "confus": [30, 31, 35, 36, 40, 42, 51, 64, 99, 100], "50": [30, 40, 87, 89, 91, 93, 94, 96], "keepdim": [30, 87], "signifi": 30, "absenc": 30, "find_issues_kwarg": 30, "int64": [30, 80, 91], "npt": 30, "int_": 30, "id": [30, 56, 81, 87, 88, 91], "unique_cluster_id": 30, "_description_": 30, "performed_clust": 30, "worst_cluster_id": 30, "underperforming_group_scor": [30, 82], "convent": [31, 33], "subject": [31, 33], "meant": [31, 33], "Not": 31, "mainli": [31, 94, 100], "content": [31, 65, 80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "fetch": [31, 39, 80, 82], "datset": 32, "exclud": [32, 41, 73, 77, 81, 87, 100], "get_report": 32, "enum": [33, 47], "qualnam": [33, 47], "boundari": [33, 47, 81, 82], "continu": [33, 85, 87, 88, 91, 93, 96, 98, 99, 100], "binari": [33, 47, 51, 58, 60, 89, 100], "simultan": [33, 96], "task_str": 33, "is_classif": 33, "__contains__": [33, 43, 47], "member": [33, 36, 40, 47, 81, 82], "typeerror": [33, 47], "12": [33, 47, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98, 99, 100], "__getitem__": [33, 43, 47], "match": [33, 35, 36, 40, 42, 47, 56, 57, 66, 81, 82, 86, 88, 93, 95, 97], "__iter__": [33, 43, 47], "__len__": [33, 43, 47], "alias": [33, 47], "is_regress": 33, "is_multilabel": 33, "overview": [35, 80, 82, 84, 85, 88, 91, 93, 94, 96, 98, 99, 100], "modifi": [35, 36, 39, 40, 51, 87, 89], "help": [35, 36, 40, 64, 78, 79, 80, 81, 84, 85, 86, 87, 88, 91, 92, 96, 97, 98, 99, 100], "rank_classes_by_label_qu": [35, 82], "merg": [35, 50, 78, 86, 87, 100], "find_overlapping_class": [35, 87, 89], "problemat": [35, 57, 73, 77, 80, 93, 100], "unnorm": [35, 57, 89], "abov": [35, 36, 39, 40, 51, 56, 63, 64, 66, 72, 76, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100], "model_select": [35, 47, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 94, 96, 98, 99], "cross_val_predict": [35, 40, 80, 81, 82, 84, 85, 87, 89, 91, 95, 96, 98, 99], "get_data_labels_from_dataset": 35, "yourfavoritemodel": [35, 89], "cv": [35, 47, 80, 81, 82, 84, 89, 91, 98], "df": [35, 51, 77, 80, 87], "overall_label_qu": [35, 57], "col": 35, "prob": [35, 50, 89, 95], "divid": [35, 57, 66], "label_nois": [35, 57], "human": [35, 86, 97, 100], "clearli": [35, 66, 88, 93, 97], "num": [35, 57, 86, 89], "overlap": [35, 78, 86, 87, 89], "ontolog": 35, "publish": [35, 100], "therefor": [35, 66], "vehicl": [35, 86], "truck": [35, 86, 94, 97], "intuit": [35, 57], "car": [35, 86, 93, 97], "frequent": [35, 56, 84, 87, 96], "characterist": 35, "l": [35, 36, 40, 61, 63, 64], "class1": 35, "class2": 35, "relationship": 35, "dog": [35, 51, 57, 59, 73, 86, 87, 94, 95, 100], "cat": [35, 51, 57, 59, 86, 87, 94, 95], "captur": [35, 80, 93, 94, 97], "co": [35, 36, 37], "noisy_label": [35, 81, 82, 92], "overlapping_class": 35, "descend": [35, 36, 40, 47, 57, 64], "overall_label_health_scor": [35, 57, 89], "suggest": [35, 56, 57, 63, 85, 87, 88, 96, 99], "half": [35, 36, 38, 40, 57, 86, 100], "health_scor": [35, 57], "classes_by_label_qu": [35, 82], "cnn": [36, 38, 40, 88], "cifar": [36, 37, 86, 94], "teach": [36, 37], "bhanml": 36, "blob": 36, "master": [36, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 98, 99], "call_bn": [36, 38], "bn": 36, "input_channel": 36, "n_output": 36, "dropout_r": 36, "top_bn": 36, "architectur": [36, 40], "shown": [36, 64, 81, 87, 91, 94, 95, 97, 100], "forward": [36, 37, 38, 40, 88, 91], "overridden": [36, 40], "although": [36, 40, 65, 84, 98], "recip": [36, 40], "afterward": [36, 40], "sinc": [36, 40, 44, 52, 57, 64, 72, 76, 87, 91, 92, 93, 95, 100], "former": [36, 40], "hook": [36, 40, 86], "silent": [36, 39, 40], "t_destin": [36, 38, 40], "__call__": [36, 38, 40, 43, 47], "add_modul": [36, 38, 40], "child": [36, 40], "fn": [36, 40, 64], "recurs": [36, 40, 47], "submodul": [36, 40], "children": [36, 38, 40, 100], "nn": [36, 37, 40, 88], "init": [36, 40, 89], "no_grad": [36, 40, 88, 94], "init_weight": [36, 40], "linear": [36, 40, 85, 88, 99], "fill_": [36, 40], "net": [36, 40, 80, 86, 88], "in_featur": [36, 40], "out_featur": [36, 40], "bia": [36, 40, 88], "tensor": [36, 37, 40, 80, 85, 88, 94, 99], "requires_grad": [36, 40], "bfloat16": [36, 38, 40], "cast": [36, 40, 80], "buffer": [36, 38, 40], "datatyp": [36, 40], "xdoctest": [36, 40], "undefin": [36, 40], "var": [36, 40], "buf": [36, 40], "20l": [36, 40], "1l": [36, 40], "5l": [36, 40], "call_super_init": [36, 38, 40], "immedi": [36, 40, 94], "compil": [36, 38, 40, 55], "cpu": [36, 38, 40, 42, 80, 88], "move": [36, 40, 47, 79, 86], "cuda": [36, 38, 40, 80, 88], "devic": [36, 40, 80, 88], "gpu": [36, 40, 80, 85, 99], "live": [36, 40], "copi": [36, 40, 68, 80, 81, 82, 84, 87, 92, 95, 96, 98], "doubl": [36, 38, 40], "dump_patch": [36, 38, 40], "eval": [36, 38, 40, 88, 92, 94], "dropout": [36, 40], "batchnorm": [36, 40], "grad": [36, 40], "extra_repr": [36, 38, 40], "line": [36, 40, 78, 81, 86, 91, 94, 100], "get_buff": [36, 38, 40], "target": [36, 37, 40, 68, 69, 94, 96], "throw": [36, 40], "get_submodul": [36, 38, 40], "explan": [36, 40], "fulli": [36, 40, 55, 87], "qualifi": [36, 40], "referenc": [36, 40], "attributeerror": [36, 40], "invalid": [36, 40, 85], "resolv": [36, 40, 100], "get_extra_st": [36, 38, 40], "state_dict": [36, 38, 40], "set_extra_st": [36, 38, 40], "build": [36, 40, 88, 97], "picklabl": [36, 40], "serial": [36, 40], "backward": [36, 40, 88], "break": [36, 40, 88, 93], "pickl": [36, 40, 93], "get_paramet": [36, 38, 40], "let": [36, 40, 65, 66, 80, 82, 84, 85, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "net_b": [36, 40], "net_c": [36, 40], "conv": [36, 40], "conv2d": [36, 40, 88], "16": [36, 40, 47, 72, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 93, 94, 97, 99, 100], "33": [36, 40, 86, 93], "kernel_s": [36, 40], "stride": [36, 40], "200": [36, 40, 66, 86, 93, 100], "diagram": [36, 40, 95], "degre": [36, 40], "queri": [36, 40, 82, 87, 88, 92], "named_modul": [36, 38, 40], "o": [36, 40, 49, 50, 80, 81, 82, 86, 87, 89, 92, 93, 100], "transit": [36, 40], "ipu": [36, 38, 40], "load_state_dict": [36, 38, 40], "strict": [36, 40, 47], "persist": [36, 40], "strictli": [36, 40], "inplac": [36, 40, 91], "preserv": [36, 40, 51], "namedtupl": [36, 40], "missing_kei": [36, 40], "unexpected_kei": [36, 40], "runtimeerror": [36, 40], "idx": [36, 40, 51, 52, 64, 81, 87, 88, 89, 91, 93, 94], "named_buff": [36, 38, 40], "prefix": [36, 40, 80, 100], "remove_dupl": [36, 40], "prepend": [36, 40], "running_var": [36, 40], "named_children": [36, 38, 40], "conv4": [36, 40], "conv5": [36, 40], "memo": [36, 40], "named_paramet": [36, 38, 40], "register_backward_hook": [36, 38, 40], "deprec": [36, 40, 44, 80, 85, 87, 99], "favor": [36, 40], "register_full_backward_hook": [36, 38, 40], "removablehandl": [36, 40], "register_buff": [36, 38, 40], "running_mean": [36, 40], "register_forward_hook": [36, 38, 40], "with_kwarg": [36, 40], "always_cal": [36, 40], "possibli": [36, 40, 84, 98], "fire": [36, 40, 86], "register_module_forward_hook": [36, 40], "regardless": [36, 40, 81, 82], "register_forward_pre_hook": [36, 38, 40], "And": [36, 40], "forward_pr": [36, 40], "register_module_forward_pre_hook": [36, 40], "gradient": [36, 40, 84, 88, 96], "grad_input": [36, 40], "grad_output": [36, 40], "technic": [36, 40], "caller": [36, 40], "register_module_full_backward_hook": [36, 40], "register_full_backward_pre_hook": [36, 38, 40], "backward_pr": [36, 40], "register_module_full_backward_pre_hook": [36, 40], "register_load_state_dict_post_hook": [36, 38, 40], "post": [36, 40], "incompatible_kei": [36, 40], "modif": [36, 40], "thrown": [36, 40], "register_modul": [36, 38, 40], "register_paramet": [36, 38, 40], "register_state_dict_pre_hook": [36, 38, 40], "keep_var": [36, 40], "requires_grad_": [36, 38, 40], "autograd": [36, 40], "freez": [36, 40, 80, 85, 99], "finetun": [36, 40], "gan": [36, 40], "share_memori": [36, 38, 40], "share_memory_": [36, 40], "destin": [36, 40], "shallow": [36, 40], "releas": [36, 40, 79, 80, 87], "design": [36, 40], "ordereddict": [36, 40], "detach": [36, 40, 88], "non_block": [36, 40], "memory_format": [36, 40], "channels_last": [36, 40], "Its": [36, 40, 47, 57, 63], "complex": [36, 40, 80], "integr": [36, 40, 78], "asynchron": [36, 40], "host": [36, 40], "pin": [36, 40, 85, 86, 99], "desir": [36, 40, 50, 64], "4d": [36, 40], "ignore_w": [36, 40], "determinist": [36, 40, 80], "1913": [36, 40], "3420": [36, 40], "5113": [36, 40], "2325": [36, 40], "env": [36, 40], "torch_doctest_cuda1": [36, 40], "gpu1": [36, 40], "1914": [36, 40], "5112": [36, 40], "2324": [36, 40], "float16": [36, 40], "cdoubl": [36, 40], "3741": [36, 40], "2382": [36, 40], "5593": [36, 40], "4443": [36, 40], "complex128": [36, 40], "6122": [36, 40], "1150": [36, 40], "to_empti": [36, 38, 40], "storag": [36, 40, 85, 99], "dst_type": [36, 40], "xpu": [36, 38, 40], "zero_grad": [36, 38, 40, 88], "set_to_non": [36, 40], "reset": [36, 40], "context": [36, 40, 93], "noisili": [37, 89], "han": 37, "2018": 37, "cifar_cnn": [37, 38], "loss_coteach": [37, 38], "y_1": 37, "y_2": 37, "forget_r": 37, "class_weight": 37, "logit": [37, 55, 88], "decim": [37, 51], "quickli": [37, 80, 84, 85, 87, 88, 92, 94, 97, 98, 100], "forget": [37, 47, 100], "rate_schedul": 37, "epoch": [37, 38, 40, 87, 88], "initialize_lr_schedul": [37, 38], "lr": [37, 38, 40], "001": [37, 66, 87], "250": [37, 81, 82, 89, 93], "epoch_decay_start": 37, "80": [37, 84, 92, 96, 98], "schedul": 37, "adjust": [37, 42, 60, 65, 66, 78, 89], "beta": 37, "adam": 37, "adjust_learning_r": [37, 38], "alpha_plan": 37, "beta1_plan": 37, "forget_rate_schedul": [37, 38], "num_gradu": 37, "expon": 37, "tell": [37, 85, 88, 89, 99], "train_load": [37, 40], "model1": [37, 89], "optimizer1": 37, "model2": [37, 89], "optimizer2": 37, "dataload": [37, 88, 94], "parser": 37, "parse_arg": 37, "num_iter_per_epoch": 37, "print_freq": 37, "topk": 37, "top1": 37, "top5": 37, "test_load": 37, "offici": [38, 54, 100], "wish": [38, 54, 94, 97, 100], "adj_confident_thresholds_shar": [38, 39], "labels_shar": [38, 39], "pred_probs_shar": [38, 39], "labelinspector": [38, 39, 87], "get_num_issu": [38, 39], "get_quality_scor": [38, 39], "update_confident_threshold": [38, 39], "score_label_qu": [38, 39], "split_arr": [38, 39], "span_classif": 38, "display_issu": [38, 41, 71, 72, 73, 74, 75, 76, 77, 97, 100], "mnist_pytorch": 38, "get_mnist_dataset": [38, 40], "get_sklearn_digits_dataset": [38, 40], "simplenet": [38, 40], "batch_siz": [38, 39, 40, 70, 72, 87, 88, 94, 97], "log_interv": [38, 40], "momentum": [38, 40], "no_cuda": [38, 40], "test_batch_s": [38, 40, 88], "loader": [38, 40, 88], "set_predict_proba_request": [38, 40], "set_predict_request": [38, 40], "coteach": [38, 79], "mini": [39, 70, 72, 87], "low_self_confid": [39, 42, 58], "self_confid": [39, 42, 43, 47, 58, 60, 66, 74, 76, 87, 89, 98, 99], "conveni": [39, 80, 85, 99], "script": 39, "labels_fil": [39, 87], "pred_probs_fil": [39, 87], "quality_score_kwarg": 39, "num_issue_kwarg": 39, "return_mask": 39, "variant": [39, 56, 97], "read": [39, 44, 82, 87, 89, 94, 100], "zarr": [39, 87], "memmap": [39, 97], "pythonspe": 39, "mmap": [39, 87], "hdf5": 39, "further": [39, 41, 57, 58, 60, 63, 64, 72, 73, 80, 87], "yourfil": 39, "npy": [39, 86, 87, 97], "mmap_mod": [39, 97], "tip": [39, 42, 55, 87], "save_arrai": 39, "your_arrai": 39, "disk": [39, 86, 87], "npz": [39, 100], "maxim": [39, 56, 70, 72, 97], "multiprocess": [39, 42, 58, 70, 72, 87, 88], "linux": [39, 70, 72], "physic": [39, 42, 70, 72, 93], "psutil": [39, 42, 70, 72], "labels_arrai": [39, 52], "predprob": 39, "pred_probs_arrai": 39, "back": [39, 64, 81, 87, 93, 94], "store_result": 39, "becom": [39, 94], "verifi": [39, 87, 91, 94], "long": [39, 56, 65, 91], "enough": [39, 51, 87], "chunk": [39, 95], "ram": [39, 86], "faster": [39, 65, 68, 70, 72, 87, 89], "end_index": 39, "labels_batch": 39, "pred_probs_batch": 39, "batch_result": 39, "indices_of_examples_with_issu": [39, 87], "shortcut": 39, "encount": [39, 42, 70], "1000": [39, 80, 85, 87, 94], "aggreg": [39, 43, 47, 56, 60, 63, 66, 76, 87, 89, 91], "seen": [39, 87, 94, 100], "far": [39, 56], "label_quality_scor": [39, 60, 63, 66, 69, 89, 93], "method1": 39, "method2": 39, "normalized_margin": [39, 42, 43, 47, 58, 60, 66, 74, 76], "low_normalized_margin": [39, 42, 58], "issue_indic": [39, 63, 88], "update_num_issu": 39, "arr": [39, 87], "chunksiz": 39, "convnet": 40, "bespok": [40, 55], "download": [40, 80, 87, 88, 94], "mnist": [40, 78, 80, 86], "handwritten": 40, "digit": [40, 80, 86], "last": [40, 47, 61, 64, 81, 82, 87, 91, 93, 100], "sklearn_digits_test_s": 40, "64": [40, 84, 88, 89, 93, 98], "01": [40, 66, 68, 80, 89, 92, 93], "templat": 40, "flexibli": 40, "among": [40, 56, 89], "test_set": 40, "Be": 40, "overrid": 40, "train_idx": [40, 51, 94], "train_label": [40, 94, 99], "scikit": [40, 51, 65, 78, 80, 81, 82, 84, 85, 87, 90, 96, 99], "span": 41, "sentenc": [41, 50, 74, 76, 77, 85, 99], "token_classif": [41, 50, 74, 76, 77, 87], "encourag": [42, 58, 66, 69], "multilabel_classif": [42, 57, 58, 60, 66, 87, 92], "pred_probs_by_class": 42, "prune_count_matrix_col": 42, "rank_by_kwarg": [42, 58, 66, 89], "num_to_remove_per_class": [42, 58], "bad": [42, 58, 63, 66, 85, 87, 99], "seem": [42, 89, 92], "aren": 42, "confidence_weighted_entropi": [42, 43, 47, 58, 60, 66, 74, 76], "label_issues_idx": [42, 66], "entropi": [42, 44, 46, 47, 65, 66], "prune_by_class": [42, 58, 89], "predicted_neq_given": [42, 58, 89], "prune_counts_matrix": 42, "smallest": [42, 66], "unus": 42, "number_of_mislabeled_examples_in_class_k": 42, "delet": [42, 78, 87, 99], "thread": [42, 58], "window": [42, 80, 86], "shorter": [42, 61], "find_predicted_neq_given": 42, "find_label_issues_using_argmax_confusion_matrix": 42, "remove_noise_from_class": [43, 51], "clip_noise_r": [43, 51], "clip_valu": [43, 51], "value_count": [43, 51, 87], "value_counts_fill_missing_class": [43, 51], "get_missing_class": [43, 51], "round_preserving_sum": [43, 51], "round_preserving_row_tot": [43, 51], "estimate_pu_f1": [43, 51], "confusion_matrix": [43, 51], "print_square_matrix": [43, 51], "print_noise_matrix": [43, 51, 89], "print_inverse_noise_matrix": [43, 51], "print_joint_matrix": [43, 51, 89], "compress_int_arrai": [43, 51], "train_val_split": [43, 51], "subset_x_i": [43, 51], "subset_label": [43, 51], "subset_data": [43, 51], "extract_indices_tf": [43, 51], "unshuffle_tensorflow_dataset": [43, 51], "is_torch_dataset": [43, 51], "is_tensorflow_dataset": [43, 51], "csr_vstack": [43, 51], "append_extra_datapoint": [43, 51], "get_num_class": [43, 51], "num_unique_class": [43, 51], "get_unique_class": [43, 51], "format_label": [43, 51], "smart_display_datafram": [43, 51], "force_two_dimens": [43, 51], "latent_algebra": [43, 79], "compute_ps_py_inv_noise_matrix": [43, 45], "compute_py_inv_noise_matrix": [43, 45], "compute_inv_noise_matrix": [43, 45], "compute_noise_matrix_from_invers": [43, 45], "compute_pi": [43, 45], "compute_pyx": [43, 45], "label_quality_util": 43, "get_normalized_entropi": [43, 44], "multilabel_util": [43, 92], "stack_compl": [43, 48], "get_onehot_num_class": [43, 48], "int2onehot": [43, 48, 92], "onehot2int": [43, 48, 92], "multilabel_scor": [43, 60], "classlabelscor": [43, 47], "exponential_moving_averag": [43, 47, 60], "softmin": [43, 47, 60, 63, 72, 76], "possible_method": [43, 47], "multilabelscor": [43, 47], "get_class_label_quality_scor": [43, 47], "multilabel_pi": [43, 47], "get_cross_validated_multilabel_pred_prob": [43, 47], "transform_distances_to_scor": [43, 49], "token_classification_util": [43, 100], "get_sent": [43, 50, 100], "filter_sent": [43, 50, 100], "process_token": [43, 50], "merge_prob": [43, 50], "color_sent": [43, 50], "assert_valid_input": [43, 52], "assert_valid_class_label": [43, 52], "assert_nonempty_input": [43, 52], "assert_indexing_work": [43, 52], "labels_to_arrai": [43, 52], "labels_to_list_multilabel": [43, 52], "min_allowed_prob": 44, "wikipedia": 44, "activ": [44, 46, 56, 78, 91], "towardsdatasci": 44, "cheatsheet": 44, "ec57bc067c0b": 44, "clip": [44, 51, 80], "behav": 44, "unnecessari": [44, 87], "slightli": [44, 98, 99], "interv": [44, 47, 94], "herein": 45, "inexact": 45, "cours": 45, "propag": 45, "throughout": [45, 51, 68, 80, 91, 97, 100], "increas": [45, 63, 65, 66, 80, 81, 87, 91, 92, 100], "dot": [45, 76, 87], "true_labels_class_count": 45, "pyx": 45, "multiannot": 46, "assert_valid_inputs_multiannot": 46, "labels_multiannot": [46, 56], "ensembl": [46, 47, 56, 66, 84, 87, 92, 94, 96, 98], "allow_single_label": 46, "annotator_id": 46, "assert_valid_pred_prob": 46, "pred_probs_unlabel": [46, 56], "format_multiannotator_label": [46, 56, 91], "formatted_label": [46, 51], "old": [46, 51, 79, 80, 86], "check_consensus_label_class": 46, "consensus_label": [46, 56, 91], "consensus_method": [46, 56], "consensu": [46, 56, 78, 90, 100], "establish": [46, 96, 99], "compute_soft_cross_entropi": 46, "soft": [46, 86], "find_best_temp_scal": 46, "coarse_search_rang": [46, 68, 87], "fine_search_s": [46, 68, 87], "temperatur": [46, 47, 63, 72, 76], "scale": [46, 49, 86, 87, 94, 97, 98], "factor": [46, 47, 49, 70, 72], "minim": [46, 63, 94], "temp_scale_pred_prob": 46, "temp": 46, "sharpen": [46, 86], "smoothen": 46, "get_normalized_margin_for_each_label": [47, 66], "get_confidence_weighted_entropy_for_each_label": [47, 66], "75": [47, 81, 82, 86, 91, 92, 93, 96, 100], "scorer": 47, "alpha": [47, 60, 63, 81, 82, 89, 92, 96], "exponenti": 47, "ema": 47, "s_1": 47, "s_k": 47, "ema_k": 47, "accord": [47, 58, 84, 85, 89, 100], "formula": [47, 49], "_t": 47, "cdot": 47, "s_t": 47, "qquad": 47, "leq": 47, "_1": 47, "give": [47, 66, 89, 91, 97], "recent": [47, 100], "success": 47, "previou": [47, 87, 88, 93], "discount": 47, "s_ema": 47, "175": [47, 88, 89, 93], "underflow": 47, "nan": [47, 56, 84, 91, 96, 98], "aggregated_scor": 47, "base_scor": 47, "base_scorer_kwarg": 47, "aggregator_kwarg": [47, 60], "n_sampl": 47, "n_label": 47, "worst": [47, 91], "class_label_quality_scor": 47, "42": [47, 85, 86, 93, 100], "452": 47, "new_scor": 47, "575": 47, "get_label_quality_scores_per_class": [47, 59, 60], "ml_scorer": 47, "binar": [47, 48], "reformat": [47, 80], "wider": 47, "splitter": 47, "kfold": [47, 88], "onevsrestclassifi": [47, 92], "randomforestclassifi": [47, 89, 92], "n_split": [47, 82, 88, 92], "pred_prob_slic": 48, "onehot": 48, "hot": [48, 58, 64, 70, 73, 84, 86, 87, 96, 97, 98], "onehot_matrix": 48, "avg_dist": 49, "scaling_factor": 49, "exp": [49, 65, 66, 81], "dt": 49, "right": [49, 61, 64, 85, 92, 93, 94, 99], "strength": [49, 64], "pronounc": 49, "differenti": 49, "ly": 49, "rule": [49, 50, 86], "thumb": 49, "ood_features_scor": [49, 65, 94], "88988177": 49, "80519832": 49, "readabl": 50, "lambda": [50, 80, 81, 87, 91], "long_sent": 50, "headlin": 50, "charact": [50, 51], "s1": 50, "s2": 50, "processed_token": 50, "alecnlcb": 50, "entiti": [50, 78, 87, 100], "mapped_ent": 50, "unique_ident": 50, "loc": [50, 81, 82, 88, 100], "nbitbas": [50, 60], "probs_merg": 50, "55": [50, 86, 93, 96], "0125": [50, 76], "0375": 50, "075": 50, "025": 50, "color": [50, 73, 81, 82, 84, 89, 92, 94, 96, 97], "red": [50, 64, 81, 82, 86, 89, 92, 93, 94, 97], "colored_sent": 50, "termcolor": 50, "31msentenc": 50, "0m": 50, "ancillari": 51, "class_without_nois": 51, "any_other_class": 51, "choos": [51, 66, 84, 87, 89, 96, 98], "tradition": 51, "new_sum": 51, "fill": 51, "come": [51, 81, 82, 87, 97], "major": [51, 56, 79, 88, 94], "versu": [51, 89], "obviou": 51, "cgdeboer": 51, "iteround": 51, "reach": 51, "prob_s_eq_1": 51, "claesen": 51, "f1": [51, 64, 85, 89], "BE": 51, "left_nam": 51, "top_nam": 51, "titl": [51, 81, 82, 89, 92, 94], "short_titl": 51, "round_plac": 51, "pretti": [51, 89], "joint_matrix": 51, "num_possible_valu": 51, "holdout_idx": 51, "extract": [51, 65, 80, 85, 91, 94, 97, 99], "allow_shuffl": 51, "turn": [51, 78, 93], "shuffledataset": 51, "histori": 51, "pre_x": 51, "buffer_s": 51, "csr_matric": 51, "append": [51, 80, 86, 87, 88, 89, 91, 92, 93, 94, 100], "bottom": [51, 61, 64, 93], "to_data": 51, "from_data": 51, "taken": 51, "label_matrix": 51, "canon": 51, "displai": [51, 64, 73, 77, 80, 85, 89, 99, 100], "jupyt": [51, 80, 81, 82, 86, 87, 88, 89, 91, 92, 94, 96, 100], "notebook": [51, 56, 80, 82, 86, 87, 89, 91, 92, 93, 97, 100], "consol": 51, "html": [51, 61, 64, 65, 84, 87, 89], "allow_missing_class": 52, "allow_one_class": 52, "length_x": 52, "labellik": 52, "labels_list": [52, 58], "keraswrappermodel": [54, 55, 78], "keraswrappersequenti": [54, 55], "tf": [55, 80], "legaci": 55, "lack": 55, "keraswrapp": 55, "huggingface_keras_imdb": 55, "unit": [55, 100], "model_kwarg": [55, 68], "compile_kwarg": 55, "sparsecategoricalcrossentropi": 55, "layer": [55, 80, 85, 94, 99], "dens": 55, "my_keras_model": 55, "from_logit": 55, "declar": 55, "apply_softmax": 55, "analysi": 56, "analyz": [56, 78, 89, 91, 92], "get_label_quality_multiannot": [56, 91], "vote": 56, "crowdsourc": [56, 78, 91], "dawid": [56, 91], "skene": [56, 91], "analog": [56, 86, 91], "chosen": [56, 66, 87, 91], "crowdlab": [56, 91], "unlabel": [56, 84, 85, 88, 91, 94, 97], "decid": [56, 85, 86, 91, 96, 99, 100], "get_active_learning_scor": [56, 91], "activelab": [56, 91], "priorit": [56, 63, 93, 97, 100], "showcas": 56, "main": 56, "best_qual": 56, "quality_method": 56, "calibrate_prob": 56, "return_detailed_qu": 56, "return_annotator_stat": 56, "return_weight": 56, "label_quality_score_kwarg": 56, "necessarili": [56, 64, 85, 89], "did": [56, 57, 80, 84, 89, 91, 96, 98, 99], "majority_vot": 56, "broken": [56, 64, 86, 96], "highest": [56, 64, 81, 88, 95], "0th": 56, "consensus_quality_scor": [56, 91], "annotator_agr": [56, 91], "reman": 56, "1st": 56, "2nd": [56, 70], "3rd": 56, "consensus_label_suffix": 56, "consensus_quality_score_suffix": 56, "suffix": 56, "emsembl": 56, "weigh": [56, 86], "agreement": [56, 91], "agre": 56, "prevent": [56, 87], "overconfid": [56, 95], "wrong": [56, 61, 63, 79, 81, 82, 85, 87, 89, 93, 99], "detailed_label_qu": [56, 91], "annotator_stat": [56, 91], "model_weight": 56, "annotator_weight": 56, "warn": [56, 81, 82, 87], "labels_info": 56, "num_annot": [56, 91], "deriv": [56, 91], "quality_annotator_1": 56, "quality_annotator_2": 56, "quality_annotator_m": 56, "annotator_qu": [56, 91], "num_examples_label": [56, 91], "agreement_with_consensu": [56, 91], "worst_class": [56, 91], "trustworthi": [56, 91, 96], "get_label_quality_multiannotator_ensembl": 56, "weigtht": 56, "budget": 56, "retrain": [56, 96, 99], "active_learning_scor": 56, "improv": [56, 82, 86, 87, 88, 89, 96, 97, 98, 99], "active_learning_scores_unlabel": 56, "get_active_learning_scores_ensembl": 56, "henc": [56, 80, 81, 91], "get_majority_vote_label": [56, 91], "event": 56, "lastli": [56, 84], "convert_long_to_wide_dataset": 56, "labels_multiannotator_long": 56, "wide": [56, 80, 98, 99], "suitabl": [56, 84, 98], "labels_multiannotator_wid": 56, "common_multilabel_issu": [57, 59], "mutual": [57, 92], "exclus": [57, 92], "rank_classes_by_multilabel_qu": [57, 59], "overall_multilabel_health_scor": [57, 59], "multilabel_health_summari": [57, 59], "classes_by_multilabel_qu": 57, "inner": [58, 72], "find_multilabel_issues_per_class": [58, 59], "per_class_label_issu": 58, "label_issues_list": 58, "pred_probs_list": [58, 66, 88, 89], "anim": [59, 94], "rat": 59, "predat": 59, "pet": 59, "reptil": 59, "manner": [60, 91, 96, 98, 99], "box": [61, 63, 64, 86, 93], "object_detect": [61, 63, 64, 93], "return_indices_ranked_by_scor": [61, 93], "overlapping_label_check": [61, 63], "suboptim": [61, 63], "locat": [61, 63, 93, 97, 100], "bbox": [61, 64, 93], "image_nam": [61, 64], "y1": [61, 64, 93], "y2": [61, 64, 93], "later": [61, 64, 65, 99, 100], "corner": [61, 64, 93], "xyxi": [61, 64, 93], "io": [61, 64, 80, 86], "keras_cv": [61, 64], "bounding_box": [61, 64, 93], "detectron": [61, 64, 93], "detectron2": [61, 64, 93], "readthedoc": [61, 64], "en": [61, 64], "latest": [61, 64], "visual": [61, 62, 64, 81, 82, 88, 96, 98, 100], "draw_box": [61, 64], "mmdetect": [61, 64, 93], "swap": [61, 63, 73, 77], "penal": [61, 63], "concern": [61, 63, 78, 82], "issues_from_scor": [62, 63, 71, 72, 73, 75, 76, 77, 93, 97, 100], "compute_overlooked_box_scor": [62, 63], "compute_badloc_box_scor": [62, 63], "compute_swap_box_scor": [62, 63], "pool_box_scores_per_imag": [62, 63], "object_counts_per_imag": [62, 64, 93], "bounding_box_size_distribut": [62, 64, 93], "class_label_distribut": [62, 64, 93], "get_sorted_bbox_count_idx": [62, 64], "plot_class_size_distribut": [62, 64], "plot_class_distribut": [62, 64], "get_average_per_class_confusion_matrix": [62, 64], "calculate_per_class_metr": [62, 64], "aggregation_weight": 63, "imperfect": [63, 87], "chose": [63, 91, 93], "imperfectli": [63, 93], "dirti": [63, 66, 69, 96], "subtyp": 63, "badloc": 63, "nonneg": 63, "high_probability_threshold": 63, "auxiliary_input": [63, 64], "vari": [63, 82], "iou": [63, 64], "heavili": 63, "auxiliarytypesdict": 63, "pred_label": [63, 99], "pred_label_prob": 63, "pred_bbox": 63, "lab_label": 63, "lab_bbox": 63, "similarity_matrix": 63, "min_possible_similar": 63, "scores_overlook": 63, "low_probability_threshold": 63, "scores_badloc": 63, "accident": [63, 84, 85, 87, 99], "scores_swap": 63, "box_scor": 63, "image_scor": [63, 72, 97], "discov": [64, 82, 100], "auxiliari": [64, 94, 97], "_get_valid_inputs_for_compute_scor": 64, "object_count": 64, "down": 64, "bbox_siz": 64, "class_distribut": 64, "plot": [64, 81, 82, 89, 92, 94, 96, 97], "sorted_idx": [64, 94], "class_to_show": 64, "hidden": [64, 94], "max_class_to_show": 64, "prediction_threshold": 64, "overlai": [64, 93], "figsiz": [64, 81, 82, 88, 89, 92, 94], "save_path": [64, 93], "blue": [64, 86, 89, 93], "overlaid": 64, "side": [64, 86, 93], "figur": [64, 89, 92, 94, 96], "extens": [64, 89, 91], "png": [64, 93], "pdf": [64, 65], "svg": 64, "matplotlib": [64, 81, 82, 88, 89, 92, 93, 94, 96], "num_proc": [64, 88], "intersect": [64, 87], "tp": 64, "fp": 64, "ground": [64, 86, 89, 91, 96], "truth": [64, 89, 91, 96], "bias": 64, "avg_metr": 64, "distionari": 64, "95": [64, 74, 76, 84, 86, 89, 96], "per_class_metr": 64, "Of": 65, "li": 65, "smaller": [65, 92, 93], "find_top_issu": [65, 66, 94], "reli": [65, 80, 81, 82, 85, 93, 94, 96, 99], "dist_metr": 65, "dim": [65, 88, 97], "subtract": [65, 66], "renorm": [65, 66, 87], "least_confid": 65, "sum_": 65, "log": [65, 66, 79], "softmax": [65, 72, 76, 88], "literatur": 65, "gen": 65, "liu": 65, "lochman": 65, "zach": 65, "openaccess": 65, "thecvf": 65, "cvpr2023": 65, "liu_gen_pushing_the_limits_of_softmax": 65, "based_out": 65, "distribution_detection_cvpr_2023_pap": 65, "fit_scor": [65, 94], "ood_predictions_scor": 65, "pretrain": [65, 80, 85, 94, 99], "adjust_confident_threshold": 65, "probabilist": [65, 80, 81, 82, 84, 85, 94, 95, 98], "order_label_issu": [66, 79], "whichev": [66, 95], "argsort": [66, 85, 88, 89, 93, 94, 96, 99], "max_": 66, "get_label_quality_ensemble_scor": [66, 87, 89], "weight_ensemble_members_bi": 66, "custom_weight": 66, "log_loss_search_t_valu": 66, "0001": [66, 86], "scheme": 66, "log_loss_search": 66, "log_loss": [66, 85], "1e0": 66, "1e1": 66, "1e2": 66, "2e2": 66, "quality_scor": [66, 94], "forth": 66, "top_issue_indic": 66, "rank_bi": [66, 79], "weird": [66, 77], "minu": 66, "prob_label": 66, "max_prob_not_label": 66, "idea": [66, 93], "AND": [66, 85], "get_epistemic_uncertainti": [67, 68], "get_aleatoric_uncertainti": [67, 68], "corrupt": [68, 96], "linearregress": [68, 87, 96], "y_with_nois": 68, "n_boot": [68, 87], "include_aleatoric_uncertainti": [68, 87], "sole": [68, 81, 91, 94, 98], "bootstrap": [68, 87, 96], "resampl": [68, 80, 87], "epistem": [68, 87, 94, 96], "aleator": [68, 87, 96], "model_final_kwarg": 68, "coars": 68, "thorough": [68, 87], "fine": [68, 80, 85, 94, 99], "grain": 68, "grid": 68, "varianc": [68, 89], "epistemic_uncertainti": 68, "residu": [68, 69, 87], "deviat": [68, 93, 96], "aleatoric_uncertainti": 68, "outr": 69, "contin": 69, "raw": [69, 78, 79, 82, 86, 88, 91, 93, 94, 96], "aka": [69, 80, 89, 93, 96, 100], "00323821": 69, "33692597": 69, "00191686": 69, "semant": [70, 72, 73, 90], "pixel": [70, 72, 73, 94, 97], "h": [70, 72, 73, 97], "height": [70, 72, 73, 97], "w": [70, 72, 73, 97], "width": [70, 72, 73, 97], "labels_one_hot": [70, 73, 97], "stream": [70, 94, 100], "downsampl": [70, 72, 97], "shrink": [70, 72], "divis": [70, 72, 81], "common_label_issu": [71, 73, 75, 77, 97, 100], "filter_by_class": [71, 73, 97], "segmant": [72, 73], "num_pixel_issu": [72, 97], "product": [72, 87, 88], "pixel_scor": [72, 97], "highlight": [73, 77, 81, 82, 84, 97], "enter": 73, "legend": [73, 81, 82, 92, 93, 96, 97], "colormap": 73, "background": 73, "person": [73, 87, 93, 97, 100], "ambigu": [73, 77, 80, 85, 86, 89, 99, 100], "systemat": [73, 77, 91], "misunderstood": [73, 77], "issues_df": [73, 88], "class_index": 73, "issues_subset": [73, 77], "filter_by_token": [75, 77, 100], "token_score_method": 76, "sentence_score_method": 76, "sentence_score_kwarg": 76, "compris": [76, 77], "token_scor": [76, 100], "converg": 76, "toward": 76, "_softmin_sentence_scor": 76, "sentence_scor": [76, 100], "token_info": 76, "70": [76, 84], "02": [76, 81, 82, 89, 93], "03": [76, 84, 86, 89, 93, 100], "04": [76, 84, 93], "08": [76, 89, 93, 96, 100], "commonli": [77, 79, 81, 82, 92, 100], "But": [77, 85, 89, 96, 100], "restrict": [77, 87], "reliabl": [78, 80, 87, 91, 97, 98], "thousand": 78, "imagenet": [78, 86], "popular": [78, 91, 93], "centric": [78, 84, 85, 88, 90], "capabl": 78, "minut": [78, 80, 84, 85, 86, 91, 92, 93, 96, 97, 98, 99, 100], "conda": 78, "feature_embed": [78, 94], "Then": [78, 87, 88, 98, 99], "your_dataset": [78, 80, 81, 82, 84, 85, 87, 88], "column_name_of_label": [78, 80, 81, 82, 84, 85, 88], "plagu": [78, 82], "untrain": 78, "\u30c4": 78, "label_issues_info": [78, 82], "sklearn_compatible_model": 78, "framework": [78, 92, 93], "complianc": 78, "tag": [78, 92, 100], "sequenc": 78, "recognit": [78, 80, 87, 100], "train_data": [78, 94, 96, 98, 99], "gotten": 78, "test_data": [78, 89, 92, 94, 96, 98, 99], "deal": [78, 82], "tutori": [78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "feel": [78, 80, 82, 87], "free": [78, 80, 82, 84, 85, 87, 88, 89], "ask": [78, 87], "slack": [78, 87], "project": [78, 96], "welcom": 78, "commun": [78, 87], "guidelin": [78, 93], "piec": 78, "studio": [78, 82, 84, 85, 87, 88], "platform": [78, 84, 85, 87, 88], "automl": [78, 87], "foundat": 78, "smart": [78, 84, 85, 87, 88], "edit": [78, 87], "easier": [78, 89], "unreli": [78, 80, 84, 85, 98], "link": [78, 80, 86, 93], "older": 79, "outlin": 79, "substitut": 79, "v2": [79, 84, 98], "get_noise_indic": 79, "psx": 79, "sorted_index_method": 79, "order_label_error": 79, "label_errors_bool": 79, "latent_estim": 79, "num_label_error": 79, "learningwithnoisylabel": 79, "neatli": 79, "organ": [79, 84, 86, 98, 100], "reorgan": 79, "baseline_method": 79, "incorpor": [79, 89], "research": [79, 89], "polyplex": 79, "terminologi": 79, "label_error": 79, "quickstart": [80, 81, 82, 84, 85, 86, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "spoken": 80, "500": [80, 94, 100], "english": [80, 86], "pronunci": 80, "wav": 80, "huggingfac": [80, 81, 82, 88], "voxceleb": 80, "speech": [80, 100], "your_pred_prob": [80, 81, 82, 84, 85], "tensorflow_io": 80, "huggingface_hub": 80, "branch": [80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 98, 99], "reproduc": [80, 84, 89, 91], "command": 80, "wget": [80, 93, 97, 100], "navig": 80, "browser": 80, "jakobovski": 80, "archiv": [80, 100], "v1": 80, "tar": [80, 94], "gz": [80, 94], "mkdir": [80, 100], "spoken_digit": 80, "xf": 80, "6_nicolas_32": 80, "data_path": 80, "listdir": 80, "nondeterminist": 80, "file_nam": 80, "endswith": 80, "file_path": 80, "join": [80, 87, 88], "39": [80, 81, 85, 86, 87, 88, 93, 96, 97, 99, 100], "7_george_26": 80, "0_nicolas_24": 80, "0_nicolas_6": 80, "listen": 80, "display_exampl": 80, "click": [80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "expand": [80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "pulldown": [80, 81, 82, 86, 88, 89, 91, 92, 94, 96, 100], "colab": [80, 81, 82, 86, 87, 88, 89, 91, 92, 94, 96, 100], "tfio": 80, "pathlib": 80, "ipython": 80, "load_wav_16k_mono": 80, "filenam": 80, "khz": 80, "file_cont": 80, "read_fil": 80, "sample_r": 80, "decode_wav": 80, "desired_channel": 80, "squeez": 80, "rate_in": 80, "rate_out": 80, "16000": 80, "wav_file_nam": 80, "audio_r": 80, "wav_file_exampl": 80, "plai": [80, 86, 87], "button": 80, "wav_file_name_exampl": 80, "7_jackson_43": 80, "hear": 80, "extractor": 80, "encoderclassifi": 80, "spkrec": 80, "xvect": 80, "feature_extractor": 80, "from_hparam": 80, "run_opt": 80, "uncom": 80, "ffmpeg": 80, "system": [80, 84, 85, 88, 97], "backend": 80, "wav_audio_file_path": 80, "head": [80, 82, 84, 85, 86, 88, 89, 91, 96, 98, 99], "torchaudio": 80, "extract_audio_embed": 80, "emb": [80, 88], "signal": 80, "encode_batch": 80, "embeddings_list": [80, 88], "embeddings_arrai": 80, "opt": [80, 82, 85, 99], "hostedtoolcach": [80, 82, 85, 99], "x64": [80, 82, 85, 99], "lib": [80, 82, 85, 99], "python3": [80, 82, 85, 99], "site": [80, 82, 85, 99], "650": 80, "userwarn": [80, 81, 82, 85, 99], "stft": 80, "return_complex": 80, "view_as_r": 80, "recov": 80, "trigger": 80, "aten": 80, "src": 80, "nativ": 80, "spectralop": 80, "cpp": 80, "863": [80, 99], "_vf": 80, "n_fft": 80, "hop_length": 80, "win_length": 80, "attr": 80, "512": [80, 88], "14": [80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "196311": 80, "319459": 80, "478975": 80, "2890875": 80, "8170238": 80, "89265": 80, "24": [80, 86, 89, 91, 93, 96], "898056": 80, "256195": 80, "559641": 80, "559721": 80, "62067": 80, "285245": 80, "21": [80, 81, 86, 87, 89, 93, 96, 100], "709627": 80, "5033693": 80, "913803": 80, "819831": 80, "1831515": 80, "208763": 80, "084257": 80, "3210397": 80, "005453": 80, "216152": 80, "478235": 80, "6821785": 80, "053807": 80, "242471": 80, "091424": 80, "78334856": 80, "03954": 80, "23": [80, 86, 88, 89, 93, 96], "569176": 80, "19": [80, 85, 86, 87, 88, 89, 93, 94, 96, 97, 99], "761097": 80, "1258295": 80, "753237": 80, "3508866": 80, "598274": 80, "23712": 80, "2500": 80, "leverag": [80, 85, 87, 89, 91, 99], "tune": [80, 85, 86, 94, 99], "computation": [80, 85, 99], "intens": [80, 85, 99], "held": [80, 84, 85, 86, 93, 94, 95, 98], "straightforward": [80, 84, 98], "benefit": [80, 95, 97, 98], "tol": 80, "num_crossval_fold": [80, 84, 91, 98], "decreas": [80, 87], "accuracy_scor": [80, 85, 89, 98, 99], "cv_accuraci": 80, "9708": 80, "probabilit": [80, 99], "9976": 80, "986": 80, "002161": 80, "176": [80, 86, 89, 92], "002483": 80, "2318": 80, "004411": 80, "1005": 80, "004857": 80, "1871": 80, "007494": 80, "investig": 80, "040587": 80, "999207": 80, "999377": 80, "975220": 80, "999367": 80, "18": [80, 85, 86, 87, 89, 93, 94, 96, 97, 99], "identified_label_issu": [80, 85], "lowest_quality_label": [80, 85, 89, 96, 99], "sort_valu": [80, 82, 84, 85, 87, 88, 89, 91, 92, 96], "516": 80, "1946": 80, "469": 80, "2132": 80, "worth": [80, 89], "iloc": [80, 84, 85, 96, 98, 99], "6_yweweler_25": 80, "7_nicolas_43": 80, "6_theo_27": 80, "6_yweweler_36": 80, "6_yweweler_14": 80, "6_yweweler_35": 80, "6_nicolas_8": 80, "sound": 80, "quit": [80, 94], "22": [80, 81, 86, 88, 89, 92, 93, 100], "blindli": [80, 87, 96, 98, 99], "trust": [80, 87, 89, 91, 95, 96, 98, 99], "underneath": 81, "hood": 81, "alert": 81, "introduct": 81, "mayb": [81, 82, 85], "examin": [81, 82, 84, 93, 98], "your_feature_matrix": [81, 82], "toi": [81, 82, 86, 88, 89, 91], "train_test_split": [81, 82, 94, 98, 99], "inf": [81, 82], "mid": [81, 82], "bins_map": [81, 82], "create_data": [81, 82], "y_bin": [81, 82], "y_i": [81, 82], "y_bin_idx": [81, 82], "y_train": [81, 82, 89, 96], "y_test": [81, 82, 89, 96], "y_train_idx": [81, 82], "y_test_idx": [81, 82], "test_siz": [81, 82, 98, 99], "slide": [81, 82, 86], "decis": [81, 82, 98], "frame": [81, 82], "x_out": [81, 82], "tini": [81, 82], "concaten": [81, 82, 87, 95], "y_out": [81, 82], "y_out_bin": [81, 82], "y_out_bin_idx": [81, 82], "exact_duplicate_idx": [81, 82], "x_duplic": [81, 82], "y_duplic": [81, 82], "y_duplicate_idx": [81, 82], "noisy_labels_idx": [81, 82, 92], "scatter": [81, 82, 89, 92, 96], "black": [81, 82, 86, 96], "cyan": [81, 82], "pyplot": [81, 82, 88, 89, 92, 94, 96], "plt": [81, 82, 88, 89, 92, 94, 96], "plot_data": [81, 82, 89, 92, 96], "fig": [81, 82, 86, 88, 94, 96], "ax": [81, 82, 88, 94, 96], "subplot": [81, 82, 88, 94], "set_titl": [81, 82, 88, 94], "set_xlabel": [81, 82], "x_1": [81, 82], "fontsiz": [81, 82, 88, 89, 92], "set_ylabel": [81, 82], "x_2": [81, 82], "set_xlim": [81, 82], "set_ylim": [81, 82], "linestyl": [81, 82], "circl": [81, 82, 89, 92], "misclassifi": [81, 82], "zip": [81, 82, 88, 93, 100], "label_err": [81, 82], "180": [81, 82, 93], "marker": [81, 82], "facecolor": [81, 82], "edgecolor": [81, 82], "linewidth": [81, 82, 94], "dup": [81, 82], "first_legend": [81, 82], "align": [81, 82], "title_fontproperti": [81, 82], "semibold": [81, 82], "second_legend": [81, 82], "45": [81, 82, 86, 89, 93], "gca": [81, 82], "add_artist": [81, 82], "tight_layout": [81, 82], "ideal": [81, 82], "logist": [81, 82, 85, 91, 94, 99], "remaind": 81, "modal": [81, 82, 87, 91], "132": [81, 82, 89, 93], "9318": 81, "77": [81, 82, 84, 93, 98], "006940": 81, "007830": 81, "40": [81, 82, 85, 86], "014828": 81, "107": [81, 82, 89, 92, 100], "021241": 81, "120": [81, 82, 98], "026407": 81, "notic": [81, 89, 91, 93], "3558": [81, 82], "126": [81, 82, 89, 93], "006636": [81, 82], "130": [81, 82], "012571": [81, 82], "129": [81, 82], "127": [81, 82], "014909": [81, 82], "128": [81, 82, 88], "017443": [81, 82], "6160": [81, 82], "is_near_duplicate_issu": [81, 82, 84, 85, 87, 88, 89], "131": [81, 82, 97], "000000e": [81, 82], "00": [81, 82, 84, 86, 88, 94, 98], "000002": [81, 82], "463180e": [81, 82], "07": [81, 82, 84, 88, 89, 93, 94, 96], "51": [81, 82, 84, 86, 89, 93], "161148": [81, 82], "859087e": [81, 82], "30": [81, 82, 86, 87, 88, 92, 97, 100], "3453": 81, "029542": 81, "031182": 81, "057961": 81, "058244": 81, "home": [81, 82, 85, 86, 99], "runner": [81, 82, 85, 99], "348": 81, "378": 81, "357": 81, "34": [81, 86, 89, 91, 93, 100], "54": [81, 86, 88, 89, 93, 100], "039122": 81, "53": [81, 82, 84, 86, 92, 93, 98], "044598": 81, "105": [81, 93], "105196": 81, "133654": 81, "43": [81, 86, 89, 93], "168033": 81, "125": 81, "101107": 81, "37": [81, 86], "183382": 81, "109": [81, 86, 93], "209259": 81, "211042": 81, "221316": 81, "average_ood_scor": 81, "34530442089193386": 81, "52": [81, 86, 93, 100], "169820": 81, "087324e": 81, "89": [81, 84, 93, 96, 99], "92": [81, 89, 93, 98], "259024": 81, "583757e": 81, "91": [81, 93], "346458": 81, "341292e": 81, "specfi": 81, "new_lab": 81, "scoring_funct": 81, "div": 81, "rem": 81, "inv_scal": 81, "49": [81, 86, 89, 93], "superstitionissuemanag": 81, "unlucki": 81, "superstit": 81, "to_seri": 81, "issues_mask": 81, "summary_scor": 81, "9242": 81, "is_superstition_issu": 81, "superstition_scor": 81, "26": [81, 86, 88, 89, 91, 93], "047581": 81, "090635": 81, "129591": 81, "65": [81, 93, 98], "164840": 81, "demo": [82, 84, 92, 98], "lurk": [82, 88, 89], "_split": 82, "737": 82, "thoroughli": 82, "preprocess": [82, 84, 94, 96, 98, 99], "904": 82, "review": [82, 84, 85, 86, 87, 89, 93, 96, 97, 98, 99, 100], "8561": 82, "001908": 82, "58": [82, 84, 86, 89, 93, 98], "003564": 82, "007331": 82, "008963": 82, "009664": 82, "0227": 82, "is_class_imbalance_issu": 82, "022727": 82, "86": [82, 84, 88, 89, 93, 96, 98], "87": [82, 88, 93, 96, 99], "auto": [82, 86, 87, 96, 98, 99], "conceptu": 82, "856061": 82, "355772": 82, "616034": 82, "821750": 82, "901562": 82, "betweeen": 82, "is_null_issu": 82, "is_non_iid_issu": [82, 84, 85, 89], "is_underperforming_group_issu": 82, "859131": 82, "417707": 82, "664083": 82, "970324": 82, "816953": 82, "375317": 82, "641516": 82, "890575": 82, "531021": 82, "460593": 82, "601188": 82, "826147": 82, "752808": 82, "321635": 82, "562539": 82, "948362": 82, "090243": 82, "472909": 82, "746763": 82, "878267": 82, "examples_w_issu": [82, 87], "013445": 82, "025184": 82, "026376": 82, "inde": [82, 85], "miscellan": [82, 100], "428571": 82, "111111": 82, "571429": 82, "407407": 82, "592593": 82, "337838": 82, "092593": 82, "662162": 82, "333333": [82, 86], "952381": 82, "666667": 82, "portion": 82, "huge": [82, 89], "worri": [82, 85], "critic": 82, "highli": [82, 88], "sql": [84, 98], "databas": [84, 98], "excel": [84, 98], "parquet": [84, 98], "student": [84, 96, 98, 100], "grade": [84, 96, 98], "900": [84, 96, 98], "exam": [84, 96, 98], "letter": [84, 98, 100], "hundr": [84, 98], "histgradientboostingclassifi": 84, "standardscal": [84, 94, 98], "grades_data": [84, 98], "read_csv": [84, 85, 96, 98, 99], "stud_id": [84, 98], "exam_1": [84, 96, 98], "exam_2": [84, 96, 98], "exam_3": [84, 96, 98], "letter_grad": [84, 98], "f48f73": [84, 98], "0bd4e7": [84, 98], "81": [84, 85, 93, 96, 98, 100], "great": [84, 86, 98], "particip": [84, 98], "cb9d7a": [84, 98], "61": [84, 89, 93, 96, 98], "94": [84, 86, 89, 93, 96, 98], "78": [84, 86, 89, 93, 96, 98], "9acca4": [84, 98], "48": [84, 86, 89, 93, 98, 100], "x_raw": [84, 98], "cat_featur": 84, "x_encod": [84, 98], "get_dummi": [84, 96, 98], "drop_first": [84, 98], "numeric_featur": [84, 98], "scaler": [84, 94, 98], "x_process": [84, 98], "fit_transform": [84, 98], "bring": [84, 85, 88, 91, 96, 98, 99], "byod": [84, 85, 88, 91, 96, 98, 99], "boost": [84, 87, 91, 96], "xgboost": [84, 87, 96], "think": [84, 85, 87, 92, 97, 100], "carefulli": [84, 85, 88, 98], "nonzero": 84, "suspici": [84, 98], "tabl": [84, 86, 91, 98], "358": 84, "294": [84, 93], "46": [84, 86, 89, 93], "941": 84, "7109": 84, "000005": [84, 85], "886": 84, "000059": 84, "709": 84, "000104": 84, "723": 84, "000169": 84, "689": 84, "000181": 84, "3590": 84, "051882e": 84, "683133e": 84, "536582e": 84, "406589e": 84, "324246e": 84, "6165": 84, "582": 84, "185": [84, 86, 93], "187": [84, 86], "27": [84, 86, 89, 93, 100], "898": [84, 88], "637": [84, 98], "0014": [84, 86], "595": 84, "702427": 84, "147": [84, 89, 93], "711186": 84, "157": [84, 89], "721394": 84, "771": 84, "731979": 84, "740335": 84, "0014153602099278074": 84, "issue_result": 84, "000842": 84, "555944": 84, "004374": 84, "sorted_issu": 84, "73": [84, 86, 92, 93, 96], "deserv": 84, "outlier_result": 84, "sorted_outli": 84, "56": [84, 86, 96], "96": [84, 86, 89, 92, 93, 96], "lt": [84, 85, 86, 88, 91, 94], "style": [84, 97], "font": 84, "18px": 84, "ff00ff": 84, "bac": 84, "unintend": [84, 85], "mistak": [84, 85, 88, 98, 99], "duplicate_result": 84, "690": 84, "246": [84, 93], "perhap": [84, 89, 91], "twice": 84, "67": [84, 86, 93, 96], "wari": [84, 85, 87], "super": [84, 85, 88], "intent": [85, 99], "servic": [85, 87, 99], "onlin": [85, 99], "bank": [85, 86, 99], "banking77": [85, 99], "oo": [85, 99], "000": [85, 86, 88, 99, 100], "categori": [85, 88, 99], "scope": [85, 99], "dive": 85, "your_featur": 85, "sentence_transform": [85, 99], "sentencetransform": [85, 99], "payment": [85, 99], "cancel_transf": [85, 99], "transfer": [85, 99], "fund": [85, 99], "cancel": [85, 99], "transact": [85, 99], "my": [85, 99], "revert": [85, 99], "morn": [85, 99], "realis": [85, 99], "yesterdai": [85, 99], "rent": [85, 99], "realli": [85, 91, 97, 99], "tomorrow": [85, 99], "raw_text": [85, 99], "change_pin": [85, 99], "card_payment_fee_charg": [85, 99], "beneficiary_not_allow": [85, 99], "getting_spare_card": [85, 99], "supported_cards_and_curr": [85, 99], "visa_or_mastercard": [85, 99], "lost_or_stolen_phon": [85, 99], "apple_pay_or_google_pai": [85, 99], "card_about_to_expir": [85, 99], "utter": [85, 99], "suit": [85, 86, 87, 99], "electra": [85, 99], "discrimin": [85, 99], "googl": [85, 99], "text_embed": 85, "No": [85, 87, 99], "google_electra": [85, 99], "pool": [85, 87, 94, 99], "_util": [85, 99], "831": [85, 99], "typedstorag": [85, 99], "untypedstorag": [85, 99], "untyped_storag": [85, 99], "fget": [85, 99], "__get__": [85, 99], "owner": [85, 99], "400": [85, 99], "data_dict": [85, 89, 91], "85": [85, 93], "38": [85, 86, 88, 93], "9710": 85, "981": 85, "974": 85, "000146": 85, "982": [85, 86], "000224": 85, "971": 85, "000507": 85, "980": [85, 86], "000960": 85, "3584": 85, "994": 85, "009642": 85, "999": 85, "013067": 85, "013841": 85, "433": 85, "014722": 85, "989": 85, "018224": 85, "6070": 85, "160": [85, 96], "095724": 85, "148": 85, "006237": 85, "546": 85, "099341": 85, "514": 85, "006485": 85, "481": 85, "123418": 85, "008165": 85, "0000": [85, 86, 89], "313": [85, 93], "564102": 85, "572258": 85, "28": [85, 86, 88, 89, 91, 100], "574915": 85, "31": [85, 86, 89, 91, 93], "575507": 85, "575874": 85, "792090": 85, "257611": 85, "698710": 85, "182121": 85, "771619": 85, "to_numpi": [85, 87, 96, 99], "data_with_suggested_label": 85, "suggested_label": 85, "charg": [85, 99], "cash": [85, 99], "holidai": [85, 99], "sent": [85, 99, 100], "card": [85, 86, 99], "mine": [85, 99], "expir": [85, 99], "me": [85, 99], "withdraw": 85, "monei": 85, "whoever": [85, 99], "outlier_issu": [85, 88], "lowest_quality_outli": 85, "OR": 85, "636c65616e6c616220697320617765736f6d6521": 85, "phone": [85, 86], "gone": 85, "gt": [85, 91, 100], "samp": 85, "br": 85, "press": [85, 100], "nonsens": 85, "sens": 85, "detriment": 85, "duplicate_issu": 85, "fee": 85, "pai": 85, "go": [85, 86, 89], "strongli": 85, "p_valu": 85, "benign": 85, "shortlist": [85, 96, 99], "curat": [85, 90], "mnist_test_set": 86, "imagenet_val_set": 86, "tench": 86, "goldfish": 86, "white": [86, 100], "shark": 86, "tiger": 86, "hammerhead": 86, "electr": 86, "rai": 86, "stingrai": 86, "cock": 86, "hen": 86, "ostrich": 86, "brambl": 86, "goldfinch": 86, "hous": 86, "finch": 86, "junco": 86, "indigo": 86, "bunt": 86, "american": [86, 100], "robin": 86, "bulbul": 86, "jai": 86, "magpi": 86, "chickade": 86, "dipper": 86, "kite": 86, "bald": 86, "eagl": 86, "vultur": 86, "grei": 86, "owl": 86, "salamand": 86, "smooth": 86, "newt": 86, "spot": [86, 93], "axolotl": 86, "bullfrog": 86, "tree": 86, "frog": [86, 94], "tail": 86, "loggerhead": 86, "sea": 86, "turtl": 86, "leatherback": 86, "mud": 86, "terrapin": 86, "band": 86, "gecko": 86, "green": [86, 100], "iguana": 86, "carolina": 86, "anol": 86, "desert": 86, "grassland": 86, "whiptail": 86, "lizard": 86, "agama": 86, "frill": 86, "neck": 86, "allig": 86, "gila": 86, "monster": 86, "european": 86, "chameleon": 86, "komodo": 86, "dragon": 86, "nile": 86, "crocodil": 86, "triceratop": 86, "worm": 86, "snake": 86, "ring": 86, "eastern": 86, "hog": 86, "nose": 86, "kingsnak": 86, "garter": 86, "water": 86, "vine": 86, "night": 86, "boa": 86, "constrictor": 86, "african": 86, "rock": 86, "indian": 86, "cobra": 86, "mamba": 86, "saharan": 86, "horn": 86, "viper": 86, "diamondback": 86, "rattlesnak": 86, "sidewind": 86, "trilobit": 86, "harvestman": 86, "scorpion": 86, "yellow": 86, "garden": 86, "spider": 86, "barn": 86, "southern": 86, "widow": 86, "tarantula": 86, "wolf": 86, "tick": 86, "centiped": 86, "grous": 86, "ptarmigan": 86, "ruf": 86, "prairi": 86, "peacock": 86, "quail": 86, "partridg": 86, "parrot": 86, "macaw": 86, "sulphur": 86, "crest": 86, "cockatoo": 86, "lorikeet": 86, "coucal": 86, "bee": 86, "eater": 86, "hornbil": 86, "hummingbird": 86, "jacamar": 86, "toucan": 86, "duck": [86, 99], "breast": 86, "mergans": 86, "goos": 86, "swan": 86, "tusker": 86, "echidna": 86, "platypu": 86, "wallabi": 86, "koala": 86, "wombat": 86, "jellyfish": 86, "anemon": 86, "brain": 86, "coral": 86, "flatworm": 86, "nematod": 86, "conch": 86, "snail": 86, "slug": 86, "chiton": 86, "chamber": 86, "nautilu": 86, "dung": 86, "crab": 86, "fiddler": 86, "king": 86, "lobster": 86, "spini": 86, "crayfish": 86, "hermit": 86, "isopod": 86, "stork": 86, "spoonbil": 86, "flamingo": 86, "heron": 86, "egret": 86, "bittern": 86, "crane": 86, "bird": [86, 94], "limpkin": 86, "gallinul": 86, "coot": 86, "bustard": 86, "ruddi": 86, "turnston": 86, "dunlin": 86, "redshank": 86, "dowitch": 86, "oystercatch": 86, "pelican": 86, "penguin": 86, "albatross": 86, "whale": 86, "killer": 86, "dugong": 86, "lion": 86, "chihuahua": 86, "japanes": 86, "chin": 86, "maltes": 86, "pekinges": 86, "shih": 86, "tzu": 86, "charl": 86, "spaniel": 86, "papillon": 86, "terrier": 86, "rhodesian": 86, "ridgeback": 86, "afghan": [86, 100], "hound": 86, "basset": 86, "beagl": 86, "bloodhound": 86, "bluetick": 86, "coonhound": 86, "tan": 86, "walker": 86, "foxhound": 86, "redbon": 86, "borzoi": 86, "irish": 86, "wolfhound": 86, "italian": 86, "greyhound": 86, "whippet": 86, "ibizan": 86, "norwegian": 86, "elkhound": 86, "otterhound": 86, "saluki": 86, "scottish": 86, "deerhound": 86, "weimaran": 86, "staffordshir": 86, "bull": 86, "bedlington": 86, "border": 86, "kerri": 86, "norfolk": 86, "norwich": 86, "yorkshir": 86, "wire": 86, "fox": 86, "lakeland": 86, "sealyham": 86, "airedal": 86, "cairn": 86, "australian": 86, "dandi": 86, "dinmont": 86, "boston": 86, "miniatur": 86, "schnauzer": 86, "giant": 86, "tibetan": 86, "silki": 86, "coat": [86, 88], "wheaten": 86, "west": 86, "highland": 86, "lhasa": 86, "apso": 86, "flat": 86, "retriev": 86, "curli": 86, "golden": 86, "labrador": 86, "chesapeak": 86, "bai": 86, "german": [86, 100], "shorthair": 86, "pointer": 86, "vizsla": 86, "setter": 86, "gordon": 86, "brittani": 86, "clumber": 86, "springer": 86, "welsh": 86, "cocker": 86, "sussex": 86, "kuvasz": 86, "schipperk": 86, "groenendael": 86, "malinoi": 86, "briard": 86, "kelpi": 86, "komondor": 86, "sheepdog": 86, "shetland": 86, "colli": 86, "bouvier": 86, "de": 86, "flandr": 86, "rottweil": 86, "shepherd": 86, "dobermann": 86, "pinscher": 86, "swiss": [86, 100], "mountain": 86, "bernes": 86, "appenzel": 86, "sennenhund": 86, "entlebuch": 86, "boxer": 86, "bullmastiff": 86, "mastiff": 86, "french": 86, "bulldog": 86, "dane": 86, "st": 86, "bernard": 86, "huski": 86, "alaskan": 86, "malamut": 86, "siberian": 86, "dalmatian": 86, "affenpinsch": 86, "basenji": 86, "pug": 86, "leonberg": 86, "newfoundland": 86, "pyrenean": 86, "samoi": 86, "pomeranian": 86, "chow": 86, "keeshond": 86, "griffon": 86, "bruxelloi": 86, "pembrok": 86, "corgi": 86, "cardigan": 86, "poodl": 86, "mexican": 86, "hairless": 86, "tundra": 86, "coyot": 86, "dingo": 86, "dhole": 86, "wild": 86, "hyena": 86, "kit": 86, "arctic": 86, "tabbi": 86, "persian": 86, "siames": 86, "egyptian": 86, "mau": 86, "cougar": 86, "lynx": 86, "leopard": 86, "snow": 86, "jaguar": 86, "cheetah": 86, "brown": [86, 97], "bear": 86, "polar": 86, "sloth": 86, "mongoos": 86, "meerkat": 86, "beetl": 86, "ladybug": 86, "longhorn": 86, "leaf": 86, "rhinocero": 86, "weevil": 86, "fly": 86, "ant": 86, "grasshopp": 86, "cricket": 86, "stick": 86, "insect": 86, "cockroach": 86, "manti": 86, "cicada": 86, "leafhopp": 86, "lacew": 86, "dragonfli": 86, "damselfli": 86, "admir": 86, "ringlet": 86, "monarch": 86, "butterfli": 86, "gossam": 86, "wing": 86, "starfish": 86, "urchin": 86, "cucumb": 86, "cottontail": 86, "rabbit": 86, "hare": 86, "angora": 86, "hamster": 86, "porcupin": 86, "squirrel": 86, "marmot": 86, "beaver": 86, "guinea": 86, "pig": 86, "sorrel": 86, "zebra": 86, "boar": 86, "warthog": 86, "hippopotamu": 86, "ox": 86, "buffalo": 86, "bison": 86, "bighorn": 86, "sheep": 86, "alpin": 86, "ibex": 86, "hartebeest": 86, "impala": 86, "gazel": 86, "dromedari": 86, "llama": 86, "weasel": 86, "mink": 86, "polecat": 86, "foot": 86, "ferret": 86, "otter": 86, "skunk": 86, "badger": 86, "armadillo": 86, "toed": 86, "orangutan": 86, "gorilla": 86, "chimpanze": 86, "gibbon": 86, "siamang": 86, "guenon": 86, "pata": 86, "monkei": 86, "baboon": 86, "macaqu": 86, "langur": 86, "colobu": 86, "probosci": 86, "marmoset": 86, "capuchin": 86, "howler": 86, "titi": 86, "geoffroi": 86, "lemur": 86, "indri": 86, "asian": 86, "eleph": 86, "bush": 86, "snoek": 86, "eel": 86, "coho": 86, "salmon": 86, "beauti": 86, "clownfish": 86, "sturgeon": 86, "garfish": 86, "lionfish": 86, "pufferfish": 86, "abacu": 86, "abaya": 86, "academ": 86, "gown": 86, "accordion": 86, "acoust": 86, "guitar": 86, "aircraft": 86, "carrier": 86, "airlin": 86, "airship": 86, "altar": 86, "ambul": 86, "amphibi": 86, "clock": [86, 100], "apiari": 86, "apron": 86, "wast": 86, "assault": 86, "rifl": 86, "backpack": 86, "bakeri": 86, "balanc": 86, "beam": 86, "balloon": 86, "ballpoint": 86, "pen": 86, "aid": 86, "banjo": 86, "balust": 86, "barbel": 86, "barber": 86, "chair": [86, 93], "barbershop": 86, "baromet": 86, "barrel": 86, "wheelbarrow": 86, "basebal": 86, "basketbal": 86, "bassinet": 86, "bassoon": 86, "swim": 86, "cap": 86, "bath": 86, "towel": 86, "bathtub": 86, "station": 86, "wagon": 86, "lighthous": 86, "beaker": 86, "militari": 86, "beer": 86, "bottl": 86, "glass": 86, "bell": 86, "cot": 86, "bib": 86, "bicycl": [86, 97], "bikini": 86, "binder": 86, "binocular": 86, "birdhous": 86, "boathous": 86, "bobsleigh": 86, "bolo": 86, "tie": 86, "poke": 86, "bonnet": 86, "bookcas": 86, "bookstor": 86, "bow": 86, "brass": 86, "bra": 86, "breakwat": 86, "breastplat": 86, "broom": 86, "bucket": 86, "buckl": 86, "bulletproof": 86, "vest": 86, "butcher": 86, "shop": 86, "taxicab": 86, "cauldron": 86, "candl": 86, "cannon": 86, "cano": 86, "mirror": [86, 93], "carousel": 86, "tool": [86, 89, 91], "carton": 86, "wheel": 86, "teller": 86, "cassett": 86, "player": 86, "castl": 86, "catamaran": 86, "cd": 86, "cello": 86, "mobil": [86, 100], "chain": 86, "fenc": [86, 97], "mail": 86, "chainsaw": 86, "chest": 86, "chiffoni": 86, "chime": 86, "china": 86, "cabinet": 86, "christma": 86, "stock": 86, "church": 86, "movi": 86, "theater": 86, "cleaver": 86, "cliff": 86, "dwell": 86, "cloak": 86, "clog": 86, "cocktail": 86, "shaker": 86, "coffe": 86, "mug": 86, "coffeemak": 86, "coil": 86, "lock": 86, "keyboard": 86, "confectioneri": 86, "ship": [86, 94], "corkscrew": 86, "cornet": 86, "cowboi": 86, "boot": 86, "hat": 86, "cradl": 86, "crash": 86, "helmet": 86, "crate": 86, "infant": 86, "bed": 86, "crock": 86, "pot": 86, "croquet": 86, "crutch": 86, "cuirass": 86, "dam": 86, "desk": 86, "desktop": 86, "rotari": 86, "dial": 86, "telephon": 86, "diaper": 86, "watch": 86, "dine": 86, "dishcloth": 86, "dishwash": 86, "disc": 86, "brake": 86, "dock": 86, "sled": 86, "dome": 86, "doormat": 86, "drill": 86, "rig": 86, "drum": 86, "drumstick": 86, "dumbbel": 86, "dutch": 86, "oven": 86, "fan": 86, "locomot": 86, "entertain": 86, "center": 86, "envelop": 86, "espresso": 86, "powder": 86, "feather": 86, "fireboat": 86, "engin": [86, 97], "screen": 86, "sheet": 86, "flagpol": 86, "flute": 86, "footbal": 86, "forklift": 86, "fountain": 86, "poster": 86, "freight": 86, "fry": 86, "pan": 86, "fur": 86, "garbag": 86, "ga": 86, "pump": 86, "goblet": 86, "kart": 86, "golf": 86, "cart": 86, "gondola": 86, "gong": 86, "grand": 86, "piano": 86, "greenhous": 86, "grill": 86, "groceri": 86, "guillotin": 86, "barrett": 86, "hair": 86, "sprai": 86, "hammer": 86, "dryer": 86, "hand": [86, 89], "handkerchief": 86, "drive": 86, "harmonica": 86, "harp": 86, "harvest": 86, "hatchet": 86, "holster": 86, "honeycomb": 86, "hoop": 86, "skirt": 86, "horizont": 86, "bar": 86, "hors": [86, 94, 99], "drawn": 86, "hourglass": 86, "ipod": 86, "cloth": 86, "iron": 86, "jack": 86, "lantern": 86, "jean": 86, "jeep": 86, "shirt": [86, 88], "jigsaw": 86, "puzzl": 86, "pull": 86, "rickshaw": 86, "joystick": 86, "kimono": 86, "knee": 86, "pad": 86, "knot": 86, "ladl": 86, "lampshad": 86, "laptop": 86, "lawn": 86, "mower": 86, "knife": 86, "lifeboat": 86, "lighter": 86, "limousin": 86, "ocean": 86, "liner": 86, "lipstick": 86, "slip": 86, "shoe": 86, "lotion": 86, "speaker": 86, "loup": 86, "sawmil": 86, "magnet": 86, "compass": 86, "bag": [86, 88, 94, 95], "mailbox": 86, "tight": 86, "tank": 86, "manhol": 86, "maraca": 86, "marimba": 86, "maypol": 86, "maze": 86, "cup": [86, 93], "medicin": 86, "megalith": 86, "microphon": 86, "microwav": 86, "milk": 86, "minibu": 86, "miniskirt": 86, "minivan": 86, "missil": 86, "mitten": 86, "mix": 86, "bowl": 86, "modem": 86, "monasteri": 86, "monitor": 86, "mope": 86, "mortar": 86, "mosqu": 86, "mosquito": 86, "scooter": 86, "bike": 86, "tent": 86, "mous": [86, 87], "mousetrap": 86, "van": 86, "muzzl": 86, "nail": 86, "brace": 86, "necklac": 86, "nippl": 86, "obelisk": 86, "obo": 86, "ocarina": 86, "odomet": 86, "oil": 86, "oscilloscop": 86, "overskirt": 86, "bullock": 86, "oxygen": 86, "packet": 86, "paddl": 86, "padlock": 86, "paintbrush": 86, "pajama": 86, "palac": [86, 100], "parachut": 86, "park": 86, "bench": 86, "meter": 86, "passeng": 86, "patio": 86, "payphon": 86, "pedest": 86, "pencil": 86, "perfum": 86, "petri": 86, "dish": 86, "photocopi": 86, "plectrum": 86, "pickelhaub": 86, "picket": 86, "pickup": 86, "pier": 86, "piggi": 86, "pill": 86, "pillow": 86, "ping": 86, "pong": 86, "pinwheel": 86, "pirat": 86, "pitcher": 86, "plane": 86, "planetarium": 86, "plastic": 86, "plate": 86, "rack": 86, "plow": 86, "plunger": 86, "polaroid": 86, "camera": 86, "pole": [86, 97], "polic": 86, "poncho": 86, "billiard": 86, "soda": 86, "potter": 86, "prayer": 86, "rug": 86, "printer": 86, "prison": 86, "projectil": 86, "projector": 86, "hockei": 86, "puck": 86, "punch": 86, "purs": 86, "quill": 86, "quilt": 86, "race": 86, "racket": 86, "radiat": 86, "radio": 86, "telescop": 86, "rain": 86, "recreat": 86, "reel": 86, "reflex": 86, "refriger": 86, "remot": 86, "restaur": 86, "revolv": 86, "rotisseri": 86, "eras": 86, "rugbi": 86, "ruler": 86, "safe": 86, "safeti": 86, "salt": 86, "sandal": [86, 88], "sarong": 86, "saxophon": 86, "scabbard": 86, "school": 86, "bu": [86, 97], "schooner": 86, "scoreboard": 86, "crt": 86, "screw": 86, "screwdriv": 86, "seat": 86, "belt": 86, "sew": 86, "shield": 86, "shoji": 86, "basket": 86, "shovel": 86, "shower": 86, "curtain": 86, "ski": 86, "sleep": 86, "door": 86, "slot": 86, "snorkel": 86, "snowmobil": 86, "snowplow": 86, "soap": 86, "dispens": 86, "soccer": [86, 100], "sock": 86, "solar": 86, "thermal": 86, "collector": 86, "sombrero": 86, "soup": 86, "heater": 86, "shuttl": 86, "spatula": 86, "motorboat": 86, "web": 86, "spindl": 86, "sport": [86, 100], "spotlight": 86, "stage": 86, "steam": 86, "arch": 86, "bridg": 86, "steel": 86, "stethoscop": 86, "scarf": 86, "stone": 86, "wall": [86, 97], "stopwatch": 86, "stove": 86, "strainer": 86, "tram": 86, "stretcher": 86, "couch": 86, "stupa": 86, "submarin": 86, "sundial": 86, "sunglass": 86, "sunscreen": 86, "suspens": 86, "mop": 86, "sweatshirt": 86, "swimsuit": 86, "swing": 86, "switch": 86, "syring": 86, "lamp": 86, "tape": 86, "teapot": 86, "teddi": 86, "televis": [86, 100], "tenni": 86, "thatch": 86, "roof": 86, "front": 86, "thimbl": 86, "thresh": 86, "throne": 86, "tile": 86, "toaster": 86, "tobacco": 86, "toilet": 86, "totem": 86, "tow": 86, "tractor": 86, "semi": 86, "trailer": 86, "trai": 86, "trench": 86, "tricycl": 86, "trimaran": 86, "tripod": 86, "triumphal": 86, "trolleybu": 86, "trombon": 86, "tub": 86, "turnstil": 86, "typewrit": 86, "umbrella": 86, "unicycl": 86, "upright": 86, "vacuum": 86, "cleaner": 86, "vase": 86, "vault": 86, "velvet": 86, "vend": 86, "vestment": 86, "viaduct": 86, "violin": 86, "volleybal": 86, "waffl": 86, "wallet": 86, "wardrob": 86, "sink": 86, "wash": 86, "jug": 86, "tower": 86, "whiskei": 86, "whistl": 86, "wig": 86, "shade": [86, 97], "windsor": 86, "wine": 86, "wok": 86, "wooden": 86, "spoon": 86, "wool": 86, "rail": 86, "shipwreck": 86, "yawl": 86, "yurt": 86, "websit": 86, "comic": 86, "book": 86, "crossword": 86, "traffic": [86, 93, 97], "sign": [86, 97, 100], "dust": 86, "jacket": [86, 93], "menu": 86, "guacamol": 86, "consomm": 86, "trifl": 86, "ic": 86, "cream": 86, "pop": 86, "baguett": 86, "bagel": 86, "pretzel": 86, "cheeseburg": 86, "mash": 86, "potato": 86, "cabbag": 86, "broccoli": 86, "cauliflow": 86, "zucchini": 86, "spaghetti": 86, "squash": 86, "acorn": 86, "butternut": 86, "artichok": 86, "pepper": 86, "cardoon": 86, "mushroom": 86, "granni": 86, "smith": 86, "strawberri": 86, "orang": 86, "lemon": 86, "pineappl": 86, "banana": 86, "jackfruit": 86, "custard": 86, "appl": 86, "pomegran": 86, "hai": 86, "carbonara": 86, "chocol": 86, "syrup": 86, "dough": 86, "meatloaf": 86, "pizza": 86, "pie": 86, "burrito": 86, "eggnog": 86, "alp": 86, "bubbl": 86, "reef": 86, "geyser": 86, "lakeshor": 86, "promontori": 86, "shoal": 86, "seashor": 86, "vallei": 86, "volcano": 86, "bridegroom": 86, "scuba": 86, "diver": 86, "rapese": 86, "daisi": 86, "ladi": 86, "slipper": 86, "corn": 86, "rose": 86, "hip": 86, "chestnut": 86, "fungu": 86, "agar": 86, "gyromitra": 86, "stinkhorn": 86, "earth": 86, "star": 86, "wood": 86, "bolet": 86, "ear": 86, "cifar10_test_set": 86, "airplan": [86, 94], "automobil": [86, 94], "deer": [86, 94], "cifar100_test_set": 86, "aquarium_fish": 86, "babi": 86, "boi": 86, "camel": 86, "caterpillar": 86, "cattl": [86, 100], "cloud": 86, "dinosaur": 86, "dolphin": 86, "flatfish": 86, "forest": 86, "girl": 86, "kangaroo": 86, "lawn_mow": 86, "man": 86, "maple_tre": 86, "motorcycl": [86, 97], "oak_tre": 86, "orchid": 86, "palm_tre": 86, "pear": 86, "pickup_truck": 86, "pine_tre": 86, "plain": 86, "poppi": 86, "possum": 86, "raccoon": 86, "road": [86, 97], "rocket": 86, "seal": 86, "shrew": 86, "skyscrap": 86, "streetcar": 86, "sunflow": 86, "sweet_pepp": 86, "trout": 86, "tulip": 86, "willow_tre": 86, "woman": [86, 93], "caltech256": 86, "ak47": 86, "bat": 86, "glove": 86, "birdbath": 86, "blimp": 86, "bonsai": 86, "boom": 86, "breadmak": 86, "buddha": 86, "bulldoz": 86, "cactu": 86, "cake": 86, "tire": 86, "cartman": 86, "cereal": 86, "chandeli": 86, "chess": 86, "board": 86, "chimp": 86, "chopstick": 86, "coffin": 86, "coin": 86, "comet": 86, "cormor": 86, "globe": 86, "diamond": 86, "dice": 86, "doorknob": 86, "drink": 86, "straw": 86, "dumb": 86, "eiffel": 86, "elk": 86, "ewer": 86, "eyeglass": 86, "fern": 86, "fighter": 86, "jet": [86, 96], "extinguish": 86, "hydrant": 86, "firework": 86, "flashlight": 86, "floppi": 86, "fri": 86, "frisbe": 86, "galaxi": 86, "giraff": 86, "goat": 86, "gate": 86, "grape": 86, "pick": [86, 87], "hamburg": 86, "hammock": 86, "harpsichord": 86, "hawksbil": 86, "helicopt": 86, "hibiscu": 86, "homer": 86, "simpson": 86, "horsesho": 86, "air": 86, "skeleton": 86, "ibi": 86, "cone": 86, "iri": 86, "jesu": 86, "christ": 86, "joi": 86, "kayak": 86, "ketch": 86, "ladder": 86, "lath": 86, "licens": 86, "lightbulb": 86, "lightn": 86, "mandolin": 86, "mar": 86, "mattress": 86, "megaphon": 86, "menorah": 86, "microscop": 86, "minaret": 86, "minotaur": 86, "motorbik": 86, "mussel": 86, "neckti": 86, "octopu": 86, "palm": 86, "pilot": 86, "paperclip": 86, "shredder": 86, "pci": 86, "peopl": [86, 93], "pez": 86, "picnic": 86, "pram": 86, "prai": 86, "pyramid": 86, "rainbow": 86, "roulett": 86, "saddl": 86, "saturn": 86, "segwai": 86, "propel": 86, "sextant": 86, "music": 86, "skateboard": 86, "smokestack": 86, "sneaker": 86, "boat": 86, "stain": 86, "steer": 86, "stirrup": 86, "superman": 86, "sushi": 86, "armi": [86, 100], "sword": 86, "tambourin": 86, "teepe": 86, "court": 86, "theodolit": 86, "tomato": 86, "tombston": 86, "tour": 86, "pisa": 86, "treadmil": 86, "fork": 86, "tweezer": 86, "unicorn": 86, "vcr": 86, "waterfal": 86, "watermelon": 86, "weld": 86, "windmil": 86, "xylophon": 86, "yarmulk": 86, "yo": 86, "toad": 86, "twenty_news_test_set": 86, "alt": 86, "atheism": 86, "comp": 86, "graphic": [86, 97], "misc": [86, 100], "sy": 86, "ibm": 86, "pc": 86, "hardwar": 86, "mac": 86, "forsal": 86, "rec": 86, "sci": 86, "crypt": 86, "electron": 86, "med": 86, "soc": 86, "religion": 86, "christian": [86, 100], "talk": [86, 100], "polit": 86, "gun": 86, "mideast": 86, "amazon": 86, "neutral": 86, "imdb_test_set": 86, "all_class": 86, "20news_test_set": 86, "_load_classes_predprobs_label": 86, "dataset_nam": 86, "labelerror": 86, "url_bas": 86, "5392f6c71473055060be3044becdde1cbc18284d": 86, "url_label": 86, "original_test_label": 86, "_original_label": 86, "url_prob": 86, "cross_validated_predicted_prob": 86, "_pyx": 86, "num_part": 86, "datatset": 86, "bytesio": 86, "allow_pickl": 86, "pred_probs_part": 86, "url": 86, "_of_": 86, "nload": 86, "imdb": 86, "ve": [86, 87, 89, 91, 93], "interpret": [86, 87, 89, 92, 96], "capit": 86, "29780": 86, "256": [86, 87, 93], "780": 86, "medic": [86, 100], "doctor": 86, "254": [86, 93], "359223": 86, "640777": 86, "184": [86, 89], "258427": 86, "341176": 86, "263158": 86, "658824": 86, "337349": 86, "246575": 86, "662651": 86, "248": 86, "330000": 86, "355769": 86, "670000": 86, "251": [86, 93], "167": [86, 88, 89, 93], "252": 86, "112": 86, "253": [86, 93], "022989": 86, "255": [86, 88], "049505": 86, "190": [86, 89, 93], "66": 86, "002216": 86, "000974": 86, "59": [86, 93], "88": [86, 87, 88, 89, 92, 93, 96, 100], "000873": 86, "000739": 86, "79": [86, 93, 98], "32635": 86, "32636": 86, "47": [86, 93], "32637": 86, "32638": 86, "32639": 86, "32640": 86, "051": 86, "93": [86, 93, 96, 98], "002242": 86, "997758": 86, "002088": 86, "001045": 86, "997912": 86, "002053": 86, "997947": 86, "001980": 86, "000991": 86, "998020": 86, "001946": 86, "002915": 86, "998054": 86, "001938": 86, "002904": 86, "998062": 86, "001020": 86, "998980": 86, "001018": 86, "002035": 86, "998982": 86, "999009": 86, "0003": 86, "0002": 86, "36": [86, 100], "41": [86, 93, 96], "44": [86, 92, 93, 99], "71": [86, 89, 93, 96], "071": 86, "067269": 86, "929": 86, "046": 86, "058243": 86, "954": 86, "035": 86, "032096": 86, "965": 86, "031": 86, "012232": 86, "969": 86, "022": 86, "025896": 86, "978": 86, "020": [86, 89], "013092": 86, "018": 86, "013065": 86, "016": 86, "030542": 86, "984": 86, "013": 86, "020833": 86, "987": 86, "012": 86, "010020": 86, "988": 86, "0073": 86, "0020": 86, "0016": 86, "0015": 86, "0013": 86, "0012": 86, "0010": 86, "0008": 86, "0007": 86, "0006": 86, "0005": 86, "0004": 86, "244": [86, 93], "98": [86, 87, 96], "452381": 86, "459770": 86, "72": [86, 89, 92, 96], "523364": 86, "460784": 86, "446602": 86, "57": [86, 89, 100], "68": [86, 89, 93, 98], "103774": 86, "030612": 86, "97": [86, 87, 89, 93, 96, 98, 100], "110092": 86, "049020": 86, "99": [86, 89, 98], "0034": 86, "0032": 86, "0026": 86, "0025": 86, "4945": 86, "4946": 86, "4947": 86, "4948": 86, "4949": 86, "4950": 86, "846": 86, "82": [86, 89, 93, 96], "7532": 86, "532": 86, "034483": 86, "009646": 86, "965517": 86, "030457": 86, "020513": 86, "969543": 86, "028061": 86, "035443": 86, "971939": 86, "025316": 86, "005168": 86, "974684": 86, "049751": 86, "979487": 86, "019920": 86, "042802": 86, "980080": 86, "017677": 86, "005115": 86, "982323": 86, "012987": 86, "005236": 86, "987013": 86, "012723": 86, "025126": 86, "987277": 86, "010989": 86, "008264": 86, "989011": 86, "010283": 86, "027778": 86, "989717": 86, "009677": 86, "990323": 86, "007614": 86, "010127": 86, "992386": 86, "005051": 86, "994949": 86, "005025": 86, "994975": 86, "005013": 86, "994987": 86, "001859": 86, "001328": 86, "000929": 86, "000664": 86, "186": [86, 89], "188": [86, 89, 92], "189": [86, 89], "snippet": 87, "nlp": [87, 100], "mind": [87, 89], "number_of_class": 87, "total_number_of_data_point": 87, "drop": [87, 91, 96, 99], "feed": 87, "alphabet": 87, "labels_proper_format": 87, "your_classifi": 87, "issues_datafram": 87, "class_predicted_for_flagged_exampl": 87, "class_predicted_for_all_exampl": 87, "grant": 87, "datataset": 87, "fair": [87, 89], "game": 87, "speedup": [87, 94], "flexibl": 87, "tempfil": 87, "mkdtemp": 87, "sped": 87, "anywai": 87, "pred_probs_merg": 87, "merge_rare_class": 87, "count_threshold": 87, "class_mapping_orig2new": 87, "heath_summari": 87, "num_examples_per_class": 87, "rare_class": 87, "num_classes_merg": 87, "other_class": 87, "labels_merg": 87, "new_c": 87, "merged_prob": 87, "hstack": [87, 88, 89, 91], "new_class": 87, "original_class": 87, "num_check": 87, "ones_array_ref": 87, "isclos": 87, "though": [87, 89, 100], "successfulli": 87, "meaning": [87, 94], "virtuou": [87, 91], "cycl": [87, 91], "jointli": 87, "junk": 87, "clutter": 87, "unknown": 87, "caltech": 87, "combined_boolean_mask": 87, "mask1": 87, "mask2": 87, "gradientboostingclassifi": [87, 89], "true_error": [87, 89, 92], "101": [87, 93], "102": [87, 92, 93], "104": [87, 89, 93], "model_to_find_error": 87, "model_to_return": 87, "cl0": 87, "randomizedsearchcv": 87, "expens": 87, "param_distribut": 87, "learning_r": [87, 89], "max_depth": [87, 89], "magnitud": 87, "coeffici": [87, 96], "optin": 87, "environ": [87, 89], "rerun": [87, 89], "cell": [87, 89], "On": [87, 89, 93], "unabl": [87, 89], "render": [87, 89], "nbviewer": [87, 89], "nbsp": [87, 89], "cleanlearninginot": [87, 89], "fittedcleanlearn": [87, 89], "linearregressionlinearregress": 87, "n_init": 87, "fit_predict": 87, "continuous_column": 87, "categorical_column": 87, "data_df": 87, "feature_a": 87, "feature_b": 87, "unexpectedli": 87, "emphas": 87, "especi": [87, 88, 96, 98, 99], "crucial": 87, "merge_duplicate_set": 87, "merge_kei": 87, "construct_group_kei": 87, "merged_set": 87, "consolidate_set": 87, "tolist": [87, 92], "issubset": 87, "frozenset": 87, "sets_list": 87, "mutabl": 87, "new_set": 87, "current_set": 87, "intersecting_set": 87, "lowest_score_strategi": 87, "sub_df": 87, "idxmin": 87, "filter_near_dupl": 87, "strategy_fn": 87, "strategy_kwarg": 87, "duplicate_row": 87, "group_kei": 87, "to_keep_indic": 87, "groupbi": 87, "explod": 87, "to_remov": 87, "isin": [87, 94], "kept": 87, "near_duplicate_issu": [87, 88], "ids_to_remove_seri": 87, "tmp": 87, "ipykernel_5903": 87, "1995098996": 87, "deprecationwarn": 87, "dataframegroupbi": 87, "include_group": 87, "silenc": 87, "assist": 87, "streamlin": 87, "ux": 87, "agpl": 87, "compani": 87, "commerci": 87, "alter": 87, "email": 87, "discuss": 87, "anywher": 87, "profession": 87, "expert": 87, "60": [88, 89, 96], "excess": 88, "torchvis": [88, 94], "tensordataset": 88, "stratifiedkfold": [88, 92], "tqdm": 88, "autonotebook": 88, "fashion_mnist": 88, "9m": 88, "1mb": 88, "18m": 88, "9mb": 88, "num_row": 88, "60000": 88, "pil": 88, "transformed_dataset": 88, "with_format": 88, "unsqueez": 88, "cpu_count": 88, "torch_dataset": 88, "quick": [88, 92], "relu": 88, "batchnorm2d": 88, "maxpool2d": 88, "lazylinear": 88, "flatten": 88, "get_test_accuraci": 88, "testload": [88, 94], "energi": 88, "trainload": [88, 94], "n_epoch": 88, "patienc": 88, "criterion": 88, "crossentropyloss": 88, "adamw": 88, "best_test_accuraci": 88, "start_epoch": 88, "running_loss": 88, "best_epoch": 88, "end_epoch": 88, "3f": [88, 96], "acc": [88, 89], "time_taken": 88, "compute_embed": 88, "compute_pred_prob": 88, "train_batch_s": 88, "num_work": 88, "worker": [88, 100], "train_id_list": 88, "test_id_list": 88, "train_id": 88, "test_id": 88, "embeddings_model": 88, "ntrain": 88, "trainset": 88, "testset": 88, "pin_memori": 88, "fold_embed": 88, "fold_pred_prob": 88, "finish": 88, "482": 88, "720": 88, "119": [88, 89, 93], "329": [88, 93], "195": 88, "493": 88, "060": 88, "330": [88, 93], "505": 88, "897": 88, "476": 88, "340": 88, "115": [88, 89, 93], "328": [88, 93], "310": 88, "879": 88, "reorder": 88, "vision": 88, "grayscal": 88, "exce": 88, "max_preval": 88, "7714": 88, "3772": 88, "3585": 88, "166": 88, "3651": 88, "27080": 88, "873833e": 88, "40378": 88, "915575e": 88, "25316": 88, "390277e": 88, "06": [88, 89, 93, 100], "2090": 88, "751164e": 88, "14999": 88, "881301e": 88, "9569": 88, "11262": 88, "000003": 88, "19228": 88, "000010": 88, "dress": 88, "32657": 88, "000013": 88, "21282": 88, "000016": 88, "53564": 88, "000018": 88, "pullov": 88, "6321": 88, "30968": 88, "001267": 88, "30659": 88, "000022": [88, 100], "47824": 88, "001454": 88, "3370": 88, "000026": 88, "54565": 88, "001854": 88, "9762": 88, "258": 88, "47139": 88, "000033": 88, "166980": 88, "986195": 88, "997205": 88, "948781": 88, "999358": 88, "54078": 88, "17371": 88, "000025": 88, "plot_label_issue_exampl": 88, "ncol": [88, 94], "nrow": [88, 94], "ceil": 88, "axes_list": 88, "label_issue_indic": 88, "gl": 88, "sl": 88, "fontdict": 88, "imshow": [88, 94], "cmap": [88, 96], "grai": 88, "subplots_adjust": 88, "hspace": 88, "outsiz": 88, "outlier_issues_df": 88, "depict": [88, 92, 93, 94, 95, 97], "plot_outlier_issues_exampl": 88, "n_comparison_imag": 88, "sample_from_class": 88, "number_of_sampl": 88, "non_outlier_indic": 88, "isnul": 88, "non_outlier_indices_excluding_curr": 88, "sampled_indic": 88, "label_scores_of_sampl": 88, "top_score_indic": 88, "top_label_indic": 88, "sampled_imag": 88, "get_image_given_label_and_sampl": 88, "image_from_dataset": 88, "corresponding_label": 88, "comparison_imag": 88, "images_to_plot": 88, "idlist": 88, "iterrow": 88, "closest": 88, "counterpart": 88, "near_duplicate_issues_df": 88, "plot_near_duplicate_issue_exampl": 88, "seen_id_pair": 88, "get_image_and_given_label_and_predicted_label": 88, "duplicate_imag": 88, "nd_set": 88, "challeng": 88, "dark_issu": 88, "reveal": [88, 93, 97], "dark_scor": 88, "dark_issues_df": 88, "is_dark_issu": 88, "34848": 88, "203922": 88, "50270": 88, "204588": 88, "3936": 88, "213098": 88, "733": 88, "217686": 88, "8094": 88, "230118": 88, "plot_image_issue_exampl": 88, "difficult": 88, "disproportion": 88, "lowinfo_issu": 88, "low_information_scor": 88, "lowinfo_issues_df": 88, "is_low_information_issu": 88, "53050": 88, "067975": 88, "40875": 88, "089929": 88, "9594": 88, "092601": 88, "34825": 88, "107744": 88, "37530": 88, "108516": 88, "lot": 88, "depth": 89, "survei": [89, 100], "focus": [89, 91, 92, 96], "scienc": 89, "multivariate_norm": [89, 91, 92], "make_data": [89, 91], "cov": [89, 91, 92], "avg_trac": [89, 92], "test_label": [89, 92, 94, 99], "py_tru": 89, "noise_matrix_tru": 89, "noise_marix": 89, "s_test": 89, "noisy_test_label": 89, "purpl": 89, "val": 89, "namespac": 89, "exec": 89, "markerfacecolor": [89, 92], "markeredgecolor": [89, 92, 96], "markers": [89, 92, 96], "markeredgewidth": [89, 92, 96], "realist": 89, "7560": 89, "637318e": 89, "896262e": 89, "548391e": 89, "923417e": 89, "375075e": 89, "3454": 89, "014051": 89, "020451": 89, "249": [89, 93, 100], "042594": 89, "043859": 89, "045954": 89, "6120": 89, "023714": 89, "007136": 89, "107266": 89, "103": [89, 93], "033738": 89, "238": [89, 93], "119505": 89, "236": [89, 93], "037843": 89, "222": 89, "614915": 89, "122": [89, 93], "624422": 89, "625965": 89, "626079": 89, "118": 89, "627675": 89, "695223": 89, "323529": 89, "523015": 89, "013720": 89, "675727": 89, "646521": 89, "anyth": 89, "enhanc": [89, 91, 93], "magic": 89, "83": [89, 93, 96, 98, 100], "liter": 89, "identif": 89, "x27": 89, "logisticregressionlogisticregress": 89, "ever": 89, "092": 89, "040": 89, "024": 89, "004": 89, "surpris": 89, "1705": 89, "01936": 89, "ton": 89, "yourfavoritemodel1": 89, "merged_label": 89, "merged_test_label": 89, "newli": [89, 91], "yourfavoritemodel2": 89, "yourfavoritemodel3": 89, "cl3": 89, "takeawai": 89, "That": [89, 92], "randomli": 89, "my_test_pred_prob": 89, "my_test_pr": 89, "issues_test": 89, "corrected_test_label": 89, "pretend": 89, "cl_test_pr": 89, "69": [89, 96], "fairli": 89, "label_acc": 89, "percentag": 89, "offset": 89, "nquestion": 89, "overestim": 89, "answer": 89, "experienc": 89, "76": [89, 92, 93, 96, 98], "knowledg": 89, "prioiri": 89, "known": 89, "versatil": 89, "label_issues_indic": 89, "213": [89, 93], "212": [89, 98], "218": [89, 93], "152": 89, "197": [89, 93], "196": [89, 93], "170": 89, "214": 89, "164": [89, 92], "198": [89, 93], "191": [89, 93], "63": [89, 93, 96], "121": [89, 99], "117": [89, 96], "62": [89, 93, 96], "206": [89, 93], "193": 89, "194": 89, "201": [89, 93], "174": 89, "163": 89, "150": [89, 91, 93, 100], "169": [89, 100], "151": [89, 93], "168": [89, 100], "precision_scor": 89, "recall_scor": 89, "f1_score": 89, "true_label_issu": 89, "filter_by_list": 89, "718750": [89, 91], "807018": 89, "912": 89, "733333": 89, "800000": 89, "721311": 89, "792793": 89, "908": 89, "676923": 89, "765217": 89, "892": 89, "567901": 89, "702290": 89, "844": 89, "gaug": 89, "label_issues_count": 89, "155": [89, 93], "156": 89, "172": [89, 92], "easiest": 89, "modular": 89, "penalti": 89, "l2": 89, "model3": 89, "n_estim": 89, "cv_pred_probs_1": 89, "cv_pred_probs_2": 89, "cv_pred_probs_3": 89, "label_quality_scores_best": 89, "cv_pred_probs_ensembl": 89, "label_quality_scores_bett": 89, "superior": [89, 95], "workflow": [90, 96], "speechbrain": 90, "timm": 90, "glad": 91, "multiannotator_label": 91, "300": [91, 100], "noisier": 91, "111": [91, 96], "local_data": [91, 92], "true_labels_train": [91, 92], "noise_matrix_bett": 91, "noise_matrix_wors": 91, "transpos": [91, 94], "dropna": 91, "zfill": 91, "row_na_check": 91, "notna": 91, "reset_index": 91, "a0001": 91, "a0002": 91, "a0003": 91, "a0004": 91, "a0005": 91, "a0006": 91, "a0007": 91, "a0008": 91, "a0009": 91, "a0010": 91, "a0041": 91, "a0042": 91, "a0043": 91, "a0044": 91, "a0045": 91, "a0046": 91, "a0047": 91, "a0048": 91, "a0049": 91, "a0050": 91, "na": 91, "60856743": 91, "41693214": 91, "40908785": 91, "87147629": 91, "64941785": 91, "10774851": 91, "0524466": 91, "71853246": 91, "37169848": 91, "66031048": 91, "multiannotator_util": 91, "crude": 91, "straight": 91, "majority_vote_label": 91, "736118": 91, "757751": 91, "782232": 91, "715565": 91, "824256": 91, "quality_annotator_a0001": 91, "quality_annotator_a0002": 91, "quality_annotator_a0003": 91, "quality_annotator_a0004": 91, "quality_annotator_a0005": 91, "quality_annotator_a0006": 91, "quality_annotator_a0007": 91, "quality_annotator_a0008": 91, "quality_annotator_a0009": 91, "quality_annotator_a0010": 91, "quality_annotator_a0041": 91, "quality_annotator_a0042": 91, "quality_annotator_a0043": 91, "quality_annotator_a0044": 91, "quality_annotator_a0045": 91, "quality_annotator_a0046": 91, "quality_annotator_a0047": 91, "quality_annotator_a0048": 91, "quality_annotator_a0049": 91, "quality_annotator_a0050": 91, "070564": 91, "216078": 91, "119188": 91, "alongisd": 91, "244981": 91, "208333": 91, "295979": 91, "294118": 91, "324197": 91, "310345": 91, "355316": 91, "346154": 91, "439732": 91, "480000": 91, "a0031": 91, "523205": 91, "580645": 91, "a0034": 91, "535313": 91, "607143": 91, "a0021": 91, "606999": 91, "a0015": 91, "609526": 91, "678571": 91, "a0011": 91, "621103": 91, "692308": 91, "wors": 91, "improved_consensus_label": 91, "majority_vote_accuraci": 91, "cleanlab_label_accuraci": 91, "8581081081081081": 91, "9797297297297297": 91, "besid": 91, "sorted_consensus_quality_scor": 91, "worst_qual": 91, "better_qu": 91, "worst_quality_accuraci": 91, "better_quality_accuraci": 91, "9893238434163701": 91, "improved_pred_prob": 91, "treat": [91, 92, 96, 100], "analzi": 91, "copyright": 92, "advertis": 92, "violenc": 92, "nsfw": 92, "suppli": [92, 93, 96], "celeba": 92, "make_multilabel_data": 92, "boxes_coordin": 92, "box_multilabel": 92, "make_multi": 92, "bx1": 92, "by1": 92, "bx2": 92, "by2": 92, "label_list": 92, "ur": 92, "upper": 92, "inidx": 92, "logical_and": 92, "inv_d": 92, "labels_idx": 92, "true_labels_test": 92, "dict_unique_label": 92, "get_color_arrai": 92, "dcolor": 92, "aa4400": 92, "55227f": 92, "55a100": 92, "00ff00": 92, "007f7f": 92, "386b55": 92, "0000ff": 92, "simplic": 92, "advis": 92, "y_onehot": 92, "single_class_label": 92, "stratifi": [92, 95], "kf": 92, "train_index": 92, "test_index": 92, "clf_cv": 92, "x_train_cv": 92, "x_test_cv": 92, "y_train_cv": 92, "y_test_cv": 92, "y_pred_cv": 92, "saw": 92, "num_to_displai": 92, "09": [92, 93, 96], "275": 92, "267": 92, "225": 92, "171": 92, "234": 92, "165": 92, "227": [92, 93], "262": [92, 93], "263": [92, 93], "266": [92, 93], "139": 92, "143": [92, 93], "216": [92, 93, 100], "265": 92, "159": [92, 93], "despit": [92, 100], "suspect": 92, "888": 92, "8224": 92, "9632": 92, "968": 92, "6512": 92, "0444": 92, "774": 92, "labels_binary_format": 92, "labels_list_format": 92, "surround": 93, "scene": 93, "coco": 93, "everydai": 93, "has_label_issu": 93, "insal": 93, "nc": [93, 97, 100], "s3": [93, 97, 100], "amazonaw": [93, 97, 100], "objectdetectionbenchmark": 93, "tutorial_obj": 93, "pkl": 93, "example_imag": 93, "unzip": [93, 100], "_separate_label": 93, "_separate_predict": 93, "begin": 93, "image_path": 93, "rb": 93, "image_to_visu": 93, "seg_map": 93, "334": 93, "float32": 93, "bboxes_ignor": 93, "290": 93, "286": 93, "285": 93, "224": 93, "231": 93, "293": 93, "235": 93, "289": 93, "282": 93, "74": [93, 96, 98], "281": 93, "271": 93, "280": 93, "277": 93, "279": 93, "287": 93, "299": 93, "276": 93, "307": 93, "321": 93, "326": 93, "333": 93, "261": 93, "319": 93, "257": 93, "295": 93, "283": 93, "243": 93, "303": 93, "316": 93, "247": 93, "323": 93, "327": 93, "226": 93, "228": 93, "232": 93, "219": 93, "239": 93, "240": 93, "209": 93, "242": 93, "202": 93, "230": 93, "215": 93, "220": 93, "229": 93, "217": [93, 100], "237": 93, "207": 93, "204": 93, "84": [93, 96], "205": 93, "223": 93, "153": 93, "149": 93, "140": 93, "124": 93, "268": 93, "273": 93, "108": 93, "284": 93, "110": 93, "136": 93, "145": 93, "173": 93, "297": 93, "317": 93, "192": 93, "332": 93, "324": 93, "203": 93, "320": 93, "314": 93, "199": 93, "291": 93, "000000481413": 93, "jpg": 93, "42398": 93, "44503": 93, "337": [93, 99], "29968": 93, "336": 93, "21005": 93, "9978472": 93, "forgot": 93, "drew": 93, "label_issue_idx": 93, "num_examples_to_show": 93, "138": 93, "candid": 93, "97489622": 93, "70610878": 93, "98764951": 93, "88899237": 93, "99085805": 93, "issue_idx": 93, "95569726e": 93, "03354841e": 93, "57510169e": 93, "58447666e": 93, "39755858e": 93, "issue_to_visu": 93, "000000009483": 93, "95569726168054e": 93, "addition": [93, 97], "visibl": 93, "missmatch": 93, "likelei": 93, "agnost": 93, "vaidat": 93, "inconsist": 93, "000000395701": 93, "033548411774308e": 93, "armchair": 93, "tv": 93, "000000154004": 93, "38300759625496356": 93, "foreground": 93, "000000448410": 93, "0008575101690203273": 93, "crowd": 93, "alon": 93, "explor": [93, 94], "resembl": [93, 94], "000000499768": 93, "9748962231208227": 93, "000000521141": 93, "8889923658893665": 93, "000000143931": 93, "9876495074395956": 93, "bonu": 93, "uncov": 93, "irregular": 93, "anomali": 93, "unusu": [93, 94], "object_detection_util": 93, "calculate_bounding_box_area": 93, "num_imgs_to_show": 93, "lab_object_count": 93, "pred_object_count": 93, "000000430073": 93, "000000183709": 93, "000000189475": 93, "studi": 93, "label_norm": 93, "pred_norm": 93, "area": [93, 97], "assess": 93, "lab_area": 93, "pred_area": 93, "lab_area_mean": 93, "lab_area_std": 93, "max_deviation_valu": 93, "max_deviation_class": 93, "deviation_valu": 93, "deviation_class": 93, "mean_area": 93, "std_area": 93, "class_area": 93, "deviations_awai": 93, "max_deviation_index": 93, "num_imgs_to_show_per_class": 93, "class_num": 93, "sorted_indic": 93, "000000422886": 93, "000000341828": 93, "000000461009": 93, "train_feature_embed": 94, "ood_train_feature_scor": 94, "test_feature_embed": 94, "ood_test_feature_scor": 94, "ood_train_predictions_scor": 94, "train_pred_prob": 94, "ood_test_predictions_scor": 94, "test_pred_prob": 94, "pylab": 94, "rcparam": 94, "baggingclassifi": 94, "therebi": 94, "rescal": 94, "transform_norm": 94, "totensor": 94, "root": 94, "animal_class": 94, "non_animal_class": 94, "animal_idx": 94, "test_idx": 94, "toronto": 94, "edu": 94, "kriz": 94, "170498071": 94, "23313635": 94, "10it": 94, "5000": 94, "plot_imag": 94, "visualize_outli": 94, "txt_class": 94, "img": [94, 96], "npimg": 94, "show_label": 94, "data_subset": 94, "resnet50": 94, "corpu": 94, "2048": 94, "embed_imag": 94, "create_model": 94, "strang": 94, "odd": 94, "train_ood_features_scor": 94, "top_train_ood_features_idx": 94, "fun": 94, "negat": 94, "homogen": 94, "bottom_train_ood_features_idx": 94, "test_ood_features_scor": 94, "top_ood_features_idx": 94, "inevit": 94, "trade": 94, "5th": 94, "percentil": 94, "fifth_percentil": 94, "plt_rang": 94, "hist": 94, "train_outlier_scor": 94, "ylabel": 94, "axvlin": 94, "test_outlier_scor": 94, "ood_features_indic": 94, "revisit": 94, "return_invers": 94, "train_feature_embeddings_sc": 94, "test_feature_embeddings_sc": 94, "train_pred_label": 94, "9702": 94, "train_ood_predictions_scor": 94, "test_ood_predictions_scor": 94, "lost": 94, "unsuit": 95, "ok": [95, 100], "convention": 95, "aforement": 95, "hypothet": 95, "contrast": 95, "tradit": 95, "disjoint": 95, "out_of_sample_pred_probs_for_a": 95, "out_of_sample_pred_probs_for_b": 95, "out_of_sample_pred_probs_for_c": 95, "out_of_sample_pred_prob": 95, "price": 96, "incom": 96, "ag": 96, "sensor": 96, "histgradientboostingregressor": 96, "r2_score": 96, "student_grades_r": 96, "final_scor": 96, "true_final_scor": 96, "homework": 96, "3d": 96, "hue": 96, "mpl_toolkit": 96, "mplot3d": 96, "axes3d": 96, "errors_idx": 96, "add_subplot": 96, "z": 96, "colorbar": 96, "errors_mask": 96, "feature_column": 96, "predicted_column": 96, "x_train_raw": 96, "x_test_raw": 96, "categorical_featur": [96, 98], "randomforestregressor": 96, "385101": 96, "499503": 96, "698255": 96, "776647": 96, "109373": 96, "170547": 96, "481096": 96, "984759": 96, "645270": 96, "795928": 96, "identified_issu": [96, 99], "141": 96, "659": 96, "367": 96, "318": 96, "305": 96, "560": 96, "657": 96, "688": 96, "view_datapoint": 96, "concat": 96, "consum": [96, 99], "baseline_model": [96, 99], "preds_og": 96, "r2_og": 96, "838": 96, "robustli": [96, 98, 99], "acceler": [96, 99], "found_label_issu": 96, "preds_cl": 96, "r2_cl": 96, "926": 96, "effort": [96, 98, 99], "favorit": 96, "968627e": 96, "228799": 96, "646674e": 96, "402962": 96, "323818e": 96, "952758": 96, "422144e": 96, "456908": 96, "465815e": 96, "753968": 96, "791186e": 96, "110719": 96, "485156e": 96, "670640": 96, "225300e": 96, "749976": 96, "499679e": 96, "947007": 96, "067882e": 96, "648396": 96, "plu": 96, "synthia": 97, "imagesegment": 97, "given_mask": 97, "predicted_mask": 97, "set_printopt": [97, 100], "sky": 97, "sidewalk": 97, "veget": 97, "terrain": 97, "rider": 97, "pred_probs_filepath": 97, "1088": 97, "1920": 97, "label_filepath": 97, "synthia_class": 97, "maunal": 97, "100000": 97, "244800": 97, "leftmost": 97, "middl": [97, 100], "infact": 97, "rightmost": 97, "discrep": 97, "3263230": 97, "783379": 97, "275110": 97, "255792": 97, "78225": 97, "55990": 97, "54427": 97, "33591": 97, "24645": 97, "21308": 97, "15045": 97, "14171": 97, "13832": 97, "13498": 97, "11490": 97, "9164": 97, "8769": 97, "6999": 97, "6031": 97, "5011": 97, "mistakenli": 97, "class_issu": 97, "aim": [97, 100], "domin": 97, "extratreesclassifi": 98, "extratre": 98, "ranked_label_issu": [98, 99], "labelencod": [98, 99], "labels_raw": 98, "interg": [98, 99], "tress": 98, "827": 98, "cheat": 98, "0pt": 98, "233": 98, "labels_train": 98, "labels_test": 98, "acc_og": [98, 99], "783068783068783": 98, "acc_cl": [98, 99], "8095238095238095": 98, "earlier": [99, 100], "raw_label": 99, "raw_train_text": 99, "raw_test_text": 99, "raw_train_label": 99, "raw_test_label": 99, "encond": 99, "train_text": 99, "test_text": 99, "858371": 99, "547274": 99, "826228": 99, "966008": 99, "792449": 99, "646": 99, "390": 99, "628": 99, "702": 99, "135": 99, "735": 99, "print_as_df": 99, "inverse_transform": 99, "fight": 99, "bunch": 100, "conll": 100, "2003": 100, "love": 100, "n_i": 100, "optional_list_of_ordered_class_nam": 100, "deepai": 100, "conll2003": 100, "rm": 100, "tokenclassif": 100, "2024": 100, "2400": 100, "52e0": 100, "1a01": 100, "953": 100, "connect": 100, "443": 100, "await": 100, "982975": 100, "960k": 100, "959": 100, "94k": 100, "kb": 100, "mb": 100, "directori": 100, "inflat": 100, "182": 100, "17045998": 100, "16m": 100, "octet": 100, "26m": 100, "2mb": 100, "bert": 100, "read_npz": 100, "filepath": 100, "corrsespond": 100, "iob2": 100, "given_ent": 100, "entity_map": 100, "readfil": 100, "sep": 100, "startswith": 100, "docstart": 100, "isalpha": 100, "isupp": 100, "indices_to_preview": 100, "nsentenc": 100, "eu": 100, "reject": 100, "boycott": 100, "british": 100, "lamb": 100, "00030412": 100, "00023826": 100, "99936208": 100, "00007009": 100, "00002545": 100, "99998795": 100, "00000401": 100, "00000218": 100, "00000455": 100, "00000131": 100, "00000749": 100, "99996115": 100, "00001371": 100, "0000087": 100, "00000895": 100, "99998936": 100, "00000382": 100, "00000178": 100, "00000366": 100, "00000137": 100, "99999101": 100, "00000266": 100, "00000174": 100, "0000035": 100, "00000109": 100, "99998768": 100, "00000482": 100, "00000202": 100, "00000438": 100, "0000011": 100, "00000465": 100, "99996392": 100, "00001105": 100, "0000116": 100, "00000878": 100, "99998671": 100, "00000364": 100, "00000213": 100, "00000472": 100, "00000281": 100, "99999073": 100, "00000211": 100, "00000159": 100, "00000442": 100, "00000115": 100, "peter": 100, "blackburn": 100, "00000358": 100, "00000529": 100, "99995623": 100, "0000129": 100, "0000024": 100, "00001812": 100, "99994141": 100, "00001645": 100, "00002162": 100, "brussel": 100, "1996": 100, "00001172": 100, "00000821": 100, "00004661": 100, "0000618": 100, "99987167": 100, "99999061": 100, "00000201": 100, "00000195": 100, "00000408": 100, "00000135": 100, "2254": 100, "2907": 100, "19392": 100, "9962": 100, "8904": 100, "19303": 100, "12918": 100, "9256": 100, "11855": 100, "18392": 100, "20426": 100, "19402": 100, "14744": 100, "19371": 100, "4645": 100, "10331": 100, "9430": 100, "6143": 100, "18367": 100, "12914": 100, "todai": 100, "weather": 100, "march": 100, "scalfaro": 100, "northern": 100, "himself": 100, "said": 100, "germani": 100, "nastja": 100, "rysich": 100, "north": 100, "spla": 100, "fought": 100, "khartoum": 100, "govern": 100, "south": 100, "1983": 100, "autonomi": 100, "animist": 100, "region": 100, "moslem": 100, "arabis": 100, "mayor": 100, "antonio": 100, "gonzalez": 100, "garcia": 100, "revolutionari": 100, "parti": 100, "wednesdai": 100, "troop": 100, "raid": 100, "farm": 100, "stole": 100, "rape": 100, "women": 100, "spring": 100, "chg": 100, "hrw": 100, "12pct": 100, "princ": 100, "photo": 100, "moment": 100, "spokeswoman": 100, "rainier": 100, "told": 100, "reuter": 100, "danila": 100, "carib": 100, "w224": 100, "equip": 100, "radiomet": 100, "earn": 100, "19996": 100, "london": 100, "denom": 100, "sale": 100, "uk": 100, "jp": 100, "fr": 100, "maccabi": 100, "hapoel": 100, "haifa": 100, "tel": 100, "aviv": 100, "hospit": 100, "rever": 100, "roman": 100, "cathol": 100, "nun": 100, "admit": 100, "calcutta": 100, "week": 100, "ago": 100, "fever": 100, "vomit": 100, "allianc": 100, "embattl": 100, "kabul": 100, "salang": 100, "highwai": 100, "mondai": 100, "tuesdai": 100, "suprem": 100, "council": 100, "led": 100, "jumbish": 100, "milli": 100, "movement": 100, "warlord": 100, "abdul": 100, "rashid": 100, "dostum": 100, "dollar": 100, "exchang": 100, "3570": 100, "12049": 100, "born": 100, "1937": 100, "provinc": 100, "anhui": 100, "dai": 100, "came": 100, "shanghai": 100, "citi": 100, "prolif": 100, "author": 100, "teacher": 100, "chines": 100, "16764": 100, "1990": 100, "historian": 100, "alan": 100, "john": 100, "percival": 100, "taylor": 100, "di": 100, "20446": 100, "pace": 100, "bowler": 100, "ian": 100, "harvei": 100, "claim": 100, "victoria": 100, "15514": 100, "cotti": 100, "osc": 100, "foreign": 100, "minist": 100, "7525": 100, "sultan": 100, "specter": 100, "met": 100, "crown": 100, "abdullah": 100, "defenc": 100, "aviat": 100, "jeddah": 100, "saudi": 100, "agenc": 100, "2288": 100, "hi": 100, "customari": 100, "outfit": 100, "champion": 100, "damp": 100, "scalp": 100, "canada": 100, "reign": 100, "olymp": 100, "donovan": 100, "bailei": 100, "1992": 100, "linford": 100, "christi": 100, "britain": 100, "1984": 100, "1988": 100, "carl": 100, "lewi": 100, "ambigi": 100, "punctuat": 100, "chicago": 100, "digest": 100, "philadelphia": 100, "usda": 100, "york": 100, "token_issu": 100, "471": 100, "kean": 100, "year": 100, "contract": 100, "manchest": 100, "19072": 100, "societi": 100, "million": 100, "bite": 100, "deliv": 100, "19910": 100, "father": 100, "clarenc": 100, "woolmer": 100, "renam": 100, "uttar": 100, "pradesh": 100, "india": 100, "ranji": 100, "trophi": 100, "nation": 100, "championship": 100, "captain": 100, "1949": 100, "15658": 100, "19879": 100, "iii": 100, "brian": 100, "shimer": 100, "randi": 100, "jone": 100, "19104": 100}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [10, 0, 0, "-", "datalab"], [35, 0, 0, "-", "dataset"], [38, 0, 0, "-", "experimental"], [42, 0, 0, "-", "filter"], [43, 0, 0, "-", "internal"], [54, 0, 0, "-", "models"], [56, 0, 0, "-", "multiannotator"], [59, 0, 0, "-", "multilabel_classification"], [62, 0, 0, "-", "object_detection"], [65, 0, 0, "-", "outlier"], [66, 0, 0, "-", "rank"], [67, 0, 0, "-", "regression"], [71, 0, 0, "-", "segmentation"], [75, 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.datalab": [[4, 0, 0, "-", "datalab"], [14, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[4, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[4, 4, 1, "", "class_names"], [4, 3, 1, "", "find_issues"], [4, 3, 1, "", "get_info"], [4, 3, 1, "", "get_issue_summary"], [4, 3, 1, "", "get_issues"], [4, 4, 1, "", "has_labels"], [4, 4, 1, "", "info"], [4, 4, 1, "", "issue_summary"], [4, 4, 1, "", "issues"], [4, 4, 1, "", "labels"], [4, 3, 1, "", "list_default_issue_types"], [4, 3, 1, "", "list_possible_issue_types"], [4, 3, 1, "", "load"], [4, 3, 1, "", "report"], [4, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[11, 0, 0, "-", "data"], [12, 0, 0, "-", "data_issues"], [15, 0, 0, "-", "issue_finder"], [13, 0, 0, "-", "issue_manager_factory"], [31, 0, 0, "-", "model_outputs"], [32, 0, 0, "-", "report"], [33, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[11, 2, 1, "", "Data"], [11, 5, 1, "", "DataFormatError"], [11, 5, 1, "", "DatasetDictError"], [11, 5, 1, "", "DatasetLoadError"], [11, 2, 1, "", "Label"], [11, 2, 1, "", "MultiClass"], [11, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[11, 4, 1, "", "class_names"], [11, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[11, 3, 1, "", "add_note"], [11, 6, 1, "", "args"], [11, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[11, 3, 1, "", "add_note"], [11, 6, 1, "", "args"], [11, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[11, 3, 1, "", "add_note"], [11, 6, 1, "", "args"], [11, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[11, 4, 1, "", "class_names"], [11, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[11, 4, 1, "", "class_names"], [11, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[11, 4, 1, "", "class_names"], [11, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[12, 2, 1, "", "DataIssues"], [12, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[12, 3, 1, "", "collect_issues_from_imagelab"], [12, 3, 1, "", "collect_issues_from_issue_manager"], [12, 3, 1, "", "collect_statistics"], [12, 3, 1, "", "get_info"], [12, 3, 1, "", "get_issue_summary"], [12, 3, 1, "", "get_issues"], [12, 6, 1, "", "info"], [12, 6, 1, "", "issue_summary"], [12, 6, 1, "", "issues"], [12, 3, 1, "", "set_health_score"], [12, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[15, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[15, 3, 1, "", "find_issues"], [15, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[17, 0, 0, "-", "data_valuation"], [18, 0, 0, "-", "duplicate"], [19, 0, 0, "-", "imbalance"], [21, 0, 0, "-", "issue_manager"], [22, 0, 0, "-", "label"], [25, 0, 0, "-", "noniid"], [26, 0, 0, "-", "null"], [27, 0, 0, "-", "outlier"], [30, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[17, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[17, 6, 1, "", "DEFAULT_THRESHOLD"], [17, 3, 1, "", "collect_info"], [17, 6, 1, "", "description"], [17, 3, 1, "", "find_issues"], [17, 6, 1, "", "info"], [17, 6, 1, "", "issue_name"], [17, 6, 1, "", "issue_score_key"], [17, 6, 1, "", "issues"], [17, 3, 1, "", "make_summary"], [17, 3, 1, "", "report"], [17, 6, 1, "", "summary"], [17, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[18, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[18, 3, 1, "", "collect_info"], [18, 6, 1, "", "description"], [18, 3, 1, "", "find_issues"], [18, 6, 1, "", "info"], [18, 6, 1, "", "issue_name"], [18, 6, 1, "", "issue_score_key"], [18, 6, 1, "", "issues"], [18, 3, 1, "", "make_summary"], [18, 6, 1, "", "near_duplicate_sets"], [18, 3, 1, "", "report"], [18, 6, 1, "", "summary"], [18, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[19, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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.issue_manager": [[21, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[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.label": [[22, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 3, 1, "", "get_health_summary"], [22, 6, 1, "", "health_summary_parameters"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[24, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[24, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [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.noniid": [[25, 2, 1, "", "NonIIDIssueManager"], [25, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[25, 3, 1, "", "collect_info"], [25, 6, 1, "", "description"], [25, 3, 1, "", "find_issues"], [25, 6, 1, "", "info"], [25, 6, 1, "", "issue_name"], [25, 6, 1, "", "issue_score_key"], [25, 6, 1, "", "issues"], [25, 3, 1, "", "make_summary"], [25, 3, 1, "", "report"], [25, 6, 1, "", "summary"], [25, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[26, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[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.outlier": [[27, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[27, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "ood"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[29, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[29, 2, 1, "", "RegressionLabelIssueManager"], [29, 1, 1, "", "find_issues_with_features"], [29, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[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, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[30, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[30, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [30, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "filter_cluster_ids"], [30, 3, 1, "", "find_issues"], [30, 3, 1, "", "get_worst_cluster"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "perform_clustering"], [30, 3, 1, "", "report"], [30, 3, 1, "", "set_knn_graph"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[13, 7, 1, "", "REGISTRY"], [13, 1, 1, "", "list_default_issue_types"], [13, 1, 1, "", "list_possible_issue_types"], [13, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[31, 2, 1, "", "ModelOutput"], [31, 2, 1, "", "MultiClassPredProbs"], [31, 2, 1, "", "MultiLabelPredProbs"], [31, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[31, 3, 1, "", "collect"], [31, 6, 1, "", "data"], [31, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[31, 6, 1, "", "argument"], [31, 3, 1, "", "collect"], [31, 6, 1, "", "data"], [31, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[31, 6, 1, "", "argument"], [31, 3, 1, "", "collect"], [31, 6, 1, "", "data"], [31, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[31, 6, 1, "", "argument"], [31, 3, 1, "", "collect"], [31, 6, 1, "", "data"], [31, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[32, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[32, 3, 1, "", "get_report"], [32, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[33, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[33, 6, 1, "", "CLASSIFICATION"], [33, 6, 1, "", "MULTILABEL"], [33, 6, 1, "", "REGRESSION"], [33, 3, 1, "", "__contains__"], [33, 3, 1, "", "__getitem__"], [33, 3, 1, "", "__iter__"], [33, 3, 1, "", "__len__"], [33, 3, 1, "", "from_str"], [33, 4, 1, "", "is_classification"], [33, 4, 1, "", "is_multilabel"], [33, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[35, 1, 1, "", "find_overlapping_classes"], [35, 1, 1, "", "health_summary"], [35, 1, 1, "", "overall_label_health_score"], [35, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[36, 0, 0, "-", "cifar_cnn"], [37, 0, 0, "-", "coteaching"], [39, 0, 0, "-", "label_issues_batched"], [40, 0, 0, "-", "mnist_pytorch"], [41, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[36, 2, 1, "", "CNN"], [36, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[36, 6, 1, "", "T_destination"], [36, 3, 1, "", "__call__"], [36, 3, 1, "", "add_module"], [36, 3, 1, "", "apply"], [36, 3, 1, "", "bfloat16"], [36, 3, 1, "", "buffers"], [36, 6, 1, "", "call_super_init"], [36, 3, 1, "", "children"], [36, 3, 1, "", "compile"], [36, 3, 1, "", "cpu"], [36, 3, 1, "", "cuda"], [36, 3, 1, "", "double"], [36, 6, 1, "", "dump_patches"], [36, 3, 1, "", "eval"], [36, 3, 1, "", "extra_repr"], [36, 3, 1, "", "float"], [36, 3, 1, "id0", "forward"], [36, 3, 1, "", "get_buffer"], [36, 3, 1, "", "get_extra_state"], [36, 3, 1, "", "get_parameter"], [36, 3, 1, "", "get_submodule"], [36, 3, 1, "", "half"], [36, 3, 1, "", "ipu"], [36, 3, 1, "", "load_state_dict"], [36, 3, 1, "", "modules"], [36, 3, 1, "", "named_buffers"], [36, 3, 1, "", "named_children"], [36, 3, 1, "", "named_modules"], [36, 3, 1, "", "named_parameters"], [36, 3, 1, "", "parameters"], [36, 3, 1, "", "register_backward_hook"], [36, 3, 1, "", "register_buffer"], [36, 3, 1, "", "register_forward_hook"], [36, 3, 1, "", "register_forward_pre_hook"], [36, 3, 1, "", "register_full_backward_hook"], [36, 3, 1, "", "register_full_backward_pre_hook"], [36, 3, 1, "", "register_load_state_dict_post_hook"], [36, 3, 1, "", "register_module"], [36, 3, 1, "", "register_parameter"], [36, 3, 1, "", "register_state_dict_pre_hook"], [36, 3, 1, "", "requires_grad_"], [36, 3, 1, "", "set_extra_state"], [36, 3, 1, "", "share_memory"], [36, 3, 1, "", "state_dict"], [36, 3, 1, "", "to"], [36, 3, 1, "", "to_empty"], [36, 3, 1, "", "train"], [36, 6, 1, "", "training"], [36, 3, 1, "", "type"], [36, 3, 1, "", "xpu"], [36, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[37, 1, 1, "", "adjust_learning_rate"], [37, 1, 1, "", "evaluate"], [37, 1, 1, "", "forget_rate_scheduler"], [37, 1, 1, "", "initialize_lr_scheduler"], [37, 1, 1, "", "loss_coteaching"], [37, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[39, 2, 1, "", "LabelInspector"], [39, 7, 1, "", "adj_confident_thresholds_shared"], [39, 1, 1, "", "find_label_issues_batched"], [39, 7, 1, "", "labels_shared"], [39, 7, 1, "", "pred_probs_shared"], [39, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[39, 3, 1, "", "get_confident_thresholds"], [39, 3, 1, "", "get_label_issues"], [39, 3, 1, "", "get_num_issues"], [39, 3, 1, "", "get_quality_scores"], [39, 3, 1, "", "score_label_quality"], [39, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[40, 2, 1, "", "CNN"], [40, 2, 1, "", "SimpleNet"], [40, 1, 1, "", "get_mnist_dataset"], [40, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[40, 3, 1, "", "__init_subclass__"], [40, 6, 1, "", "batch_size"], [40, 6, 1, "", "dataset"], [40, 6, 1, "", "epochs"], [40, 3, 1, "id0", "fit"], [40, 3, 1, "", "get_metadata_routing"], [40, 3, 1, "", "get_params"], [40, 6, 1, "", "loader"], [40, 6, 1, "", "log_interval"], [40, 6, 1, "", "lr"], [40, 6, 1, "", "momentum"], [40, 6, 1, "", "no_cuda"], [40, 3, 1, "id1", "predict"], [40, 3, 1, "id4", "predict_proba"], [40, 6, 1, "", "seed"], [40, 3, 1, "", "set_fit_request"], [40, 3, 1, "", "set_params"], [40, 3, 1, "", "set_predict_proba_request"], [40, 3, 1, "", "set_predict_request"], [40, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[40, 6, 1, "", "T_destination"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "add_module"], [40, 3, 1, "", "apply"], [40, 3, 1, "", "bfloat16"], [40, 3, 1, "", "buffers"], [40, 6, 1, "", "call_super_init"], [40, 3, 1, "", "children"], [40, 3, 1, "", "compile"], [40, 3, 1, "", "cpu"], [40, 3, 1, "", "cuda"], [40, 3, 1, "", "double"], [40, 6, 1, "", "dump_patches"], [40, 3, 1, "", "eval"], [40, 3, 1, "", "extra_repr"], [40, 3, 1, "", "float"], [40, 3, 1, "", "forward"], [40, 3, 1, "", "get_buffer"], [40, 3, 1, "", "get_extra_state"], [40, 3, 1, "", "get_parameter"], [40, 3, 1, "", "get_submodule"], [40, 3, 1, "", "half"], [40, 3, 1, "", "ipu"], [40, 3, 1, "", "load_state_dict"], [40, 3, 1, "", "modules"], [40, 3, 1, "", "named_buffers"], [40, 3, 1, "", "named_children"], [40, 3, 1, "", "named_modules"], [40, 3, 1, "", "named_parameters"], [40, 3, 1, "", "parameters"], [40, 3, 1, "", "register_backward_hook"], [40, 3, 1, "", "register_buffer"], [40, 3, 1, "", "register_forward_hook"], [40, 3, 1, "", "register_forward_pre_hook"], [40, 3, 1, "", "register_full_backward_hook"], [40, 3, 1, "", "register_full_backward_pre_hook"], [40, 3, 1, "", "register_load_state_dict_post_hook"], [40, 3, 1, "", "register_module"], [40, 3, 1, "", "register_parameter"], [40, 3, 1, "", "register_state_dict_pre_hook"], [40, 3, 1, "", "requires_grad_"], [40, 3, 1, "", "set_extra_state"], [40, 3, 1, "", "share_memory"], [40, 3, 1, "", "state_dict"], [40, 3, 1, "", "to"], [40, 3, 1, "", "to_empty"], [40, 3, 1, "", "train"], [40, 6, 1, "", "training"], [40, 3, 1, "", "type"], [40, 3, 1, "", "xpu"], [40, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[41, 1, 1, "", "display_issues"], [41, 1, 1, "", "find_label_issues"], [41, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[42, 1, 1, "", "find_label_issues"], [42, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [42, 1, 1, "", "find_predicted_neq_given"], [42, 7, 1, "", "pred_probs_by_class"], [42, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[44, 0, 0, "-", "label_quality_utils"], [45, 0, 0, "-", "latent_algebra"], [46, 0, 0, "-", "multiannotator_utils"], [47, 0, 0, "-", "multilabel_scorer"], [48, 0, 0, "-", "multilabel_utils"], [49, 0, 0, "-", "outlier"], [50, 0, 0, "-", "token_classification_utils"], [51, 0, 0, "-", "util"], [52, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[44, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[45, 1, 1, "", "compute_inv_noise_matrix"], [45, 1, 1, "", "compute_noise_matrix_from_inverse"], [45, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [45, 1, 1, "", "compute_py"], [45, 1, 1, "", "compute_py_inv_noise_matrix"], [45, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[46, 1, 1, "", "assert_valid_inputs_multiannotator"], [46, 1, 1, "", "assert_valid_pred_probs"], [46, 1, 1, "", "check_consensus_label_classes"], [46, 1, 1, "", "compute_soft_cross_entropy"], [46, 1, 1, "", "find_best_temp_scaler"], [46, 1, 1, "", "format_multiannotator_labels"], [46, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[47, 2, 1, "", "Aggregator"], [47, 2, 1, "", "ClassLabelScorer"], [47, 2, 1, "", "MultilabelScorer"], [47, 1, 1, "", "exponential_moving_average"], [47, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [47, 1, 1, "", "get_label_quality_scores"], [47, 1, 1, "", "multilabel_py"], [47, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[47, 3, 1, "", "__call__"], [47, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[47, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [47, 6, 1, "", "NORMALIZED_MARGIN"], [47, 6, 1, "", "SELF_CONFIDENCE"], [47, 3, 1, "", "__call__"], [47, 3, 1, "", "__contains__"], [47, 3, 1, "", "__getitem__"], [47, 3, 1, "", "__iter__"], [47, 3, 1, "", "__len__"], [47, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[47, 3, 1, "", "__call__"], [47, 3, 1, "", "aggregate"], [47, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[48, 1, 1, "", "get_onehot_num_classes"], [48, 1, 1, "", "int2onehot"], [48, 1, 1, "", "onehot2int"], [48, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[49, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[50, 1, 1, "", "color_sentence"], [50, 1, 1, "", "filter_sentence"], [50, 1, 1, "", "get_sentence"], [50, 1, 1, "", "mapping"], [50, 1, 1, "", "merge_probs"], [50, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[51, 1, 1, "", "append_extra_datapoint"], [51, 1, 1, "", "clip_noise_rates"], [51, 1, 1, "", "clip_values"], [51, 1, 1, "", "compress_int_array"], [51, 1, 1, "", "confusion_matrix"], [51, 1, 1, "", "csr_vstack"], [51, 1, 1, "", "estimate_pu_f1"], [51, 1, 1, "", "extract_indices_tf"], [51, 1, 1, "", "force_two_dimensions"], [51, 1, 1, "", "format_labels"], [51, 1, 1, "", "get_missing_classes"], [51, 1, 1, "", "get_num_classes"], [51, 1, 1, "", "get_unique_classes"], [51, 1, 1, "", "is_tensorflow_dataset"], [51, 1, 1, "", "is_torch_dataset"], [51, 1, 1, "", "num_unique_classes"], [51, 1, 1, "", "print_inverse_noise_matrix"], [51, 1, 1, "", "print_joint_matrix"], [51, 1, 1, "", "print_noise_matrix"], [51, 1, 1, "", "print_square_matrix"], [51, 1, 1, "", "remove_noise_from_class"], [51, 1, 1, "", "round_preserving_row_totals"], [51, 1, 1, "", "round_preserving_sum"], [51, 1, 1, "", "smart_display_dataframe"], [51, 1, 1, "", "subset_X_y"], [51, 1, 1, "", "subset_data"], [51, 1, 1, "", "subset_labels"], [51, 1, 1, "", "train_val_split"], [51, 1, 1, "", "unshuffle_tensorflow_dataset"], [51, 1, 1, "", "value_counts"], [51, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[52, 1, 1, "", "assert_indexing_works"], [52, 1, 1, "", "assert_nonempty_input"], [52, 1, 1, "", "assert_valid_class_labels"], [52, 1, 1, "", "assert_valid_inputs"], [52, 1, 1, "", "labels_to_array"], [52, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[55, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[55, 2, 1, "", "KerasWrapperModel"], [55, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[55, 3, 1, "", "fit"], [55, 3, 1, "", "get_params"], [55, 3, 1, "", "predict"], [55, 3, 1, "", "predict_proba"], [55, 3, 1, "", "set_params"], [55, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[55, 3, 1, "", "fit"], [55, 3, 1, "", "get_params"], [55, 3, 1, "", "predict"], [55, 3, 1, "", "predict_proba"], [55, 3, 1, "", "set_params"], [55, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[56, 1, 1, "", "convert_long_to_wide_dataset"], [56, 1, 1, "", "get_active_learning_scores"], [56, 1, 1, "", "get_active_learning_scores_ensemble"], [56, 1, 1, "", "get_label_quality_multiannotator"], [56, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [56, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[57, 0, 0, "-", "dataset"], [58, 0, 0, "-", "filter"], [60, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[57, 1, 1, "", "common_multilabel_issues"], [57, 1, 1, "", "multilabel_health_summary"], [57, 1, 1, "", "overall_multilabel_health_score"], [57, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[58, 1, 1, "", "find_label_issues"], [58, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[60, 1, 1, "", "get_label_quality_scores"], [60, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[61, 0, 0, "-", "filter"], [63, 0, 0, "-", "rank"], [64, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[61, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[63, 1, 1, "", "compute_badloc_box_scores"], [63, 1, 1, "", "compute_overlooked_box_scores"], [63, 1, 1, "", "compute_swap_box_scores"], [63, 1, 1, "", "get_label_quality_scores"], [63, 1, 1, "", "issues_from_scores"], [63, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[64, 1, 1, "", "bounding_box_size_distribution"], [64, 1, 1, "", "calculate_per_class_metrics"], [64, 1, 1, "", "class_label_distribution"], [64, 1, 1, "", "get_average_per_class_confusion_matrix"], [64, 1, 1, "", "get_sorted_bbox_count_idxs"], [64, 1, 1, "", "object_counts_per_image"], [64, 1, 1, "", "plot_class_distribution"], [64, 1, 1, "", "plot_class_size_distributions"], [64, 1, 1, "", "visualize"]], "cleanlab.outlier": [[65, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[65, 3, 1, "", "fit"], [65, 3, 1, "", "fit_score"], [65, 3, 1, "", "score"]], "cleanlab.rank": [[66, 1, 1, "", "find_top_issues"], [66, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [66, 1, 1, "", "get_label_quality_ensemble_scores"], [66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_normalized_margin_for_each_label"], [66, 1, 1, "", "get_self_confidence_for_each_label"], [66, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[68, 0, 0, "-", "learn"], [69, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[68, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[68, 3, 1, "", "__init_subclass__"], [68, 3, 1, "", "find_label_issues"], [68, 3, 1, "", "fit"], [68, 3, 1, "", "get_aleatoric_uncertainty"], [68, 3, 1, "", "get_epistemic_uncertainty"], [68, 3, 1, "", "get_label_issues"], [68, 3, 1, "", "get_metadata_routing"], [68, 3, 1, "", "get_params"], [68, 3, 1, "", "predict"], [68, 3, 1, "", "save_space"], [68, 3, 1, "", "score"], [68, 3, 1, "", "set_fit_request"], [68, 3, 1, "", "set_params"], [68, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[69, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[70, 0, 0, "-", "filter"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[70, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[73, 1, 1, "", "common_label_issues"], [73, 1, 1, "", "display_issues"], [73, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[74, 0, 0, "-", "filter"], [76, 0, 0, "-", "rank"], [77, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[74, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[76, 1, 1, "", "get_label_quality_scores"], [76, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[77, 1, 1, "", "common_label_issues"], [77, 1, 1, "", "display_issues"], [77, 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, 80, 84, 85, 87, 88, 89, 92, 98, 99, 100], "count": [3, 89], "datalab": [4, 6, 8, 9, 10, 81, 82, 83, 84, 85, 89, 92], "creat": [6, 81, 82, 89, 91], "your": [6, 78, 81, 82, 85, 87, 89], "own": 6, "issu": [6, 8, 9, 20, 29, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 92, 93, 97, 98, 100], "manag": [6, 20], "prerequisit": 6, "implement": 6, "issuemanag": [6, 81], "basic": 6, "check": 6, "intermedi": 6, "advanc": [6, 81], "us": [6, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "gener": 7, "cluster": [7, 87], "id": 7, "guid": [8, 10], "type": [8, 9, 89], "custom": [8, 81], "can": [9, 82, 86, 87, 89, 91], "detect": [9, 82, 84, 85, 87, 89, 93, 94], "estim": [9, 89, 91, 92], "each": 9, "label": [9, 22, 24, 29, 78, 80, 82, 84, 85, 87, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100], "outlier": [9, 27, 49, 65, 84, 85, 88, 92, 94], "Near": [9, 82, 84, 85, 88], "duplic": [9, 18, 82, 84, 85, 87, 88, 92], "non": [9, 85], "iid": [9, 85], "class": [9, 79, 89, 97], "imbal": [9, 19], "imag": [9, 88, 94], "specif": [9, 20, 97], "underperform": [9, 87], "group": [9, 87], "null": [9, 26], "data": [9, 11, 78, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93, 94, 96, 97, 98, 100], "valuat": 9, "option": 9, "paramet": [9, 89], "get": [10, 81, 82, 91, 92, 93, 97, 100], "start": [10, 86], "api": 10, "refer": 10, "data_issu": 12, "factori": 13, "intern": [14, 43], "issue_find": 15, "data_valu": 17, "issue_manag": [20, 21], "regist": 20, "ml": [20, 87, 89], "task": [20, 33], "multilabel": 23, "noniid": 25, "regress": [28, 67, 68, 69, 87, 96], "prioriti": 29, "order": 29, "find": [29, 78, 80, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "underperforming_group": 30, "model_output": 31, "report": [32, 88], "dataset": [35, 57, 78, 82, 85, 86, 87, 88, 89, 92, 93, 94, 96, 97, 99, 100], "cifar_cnn": 36, "coteach": 37, "experiment": 38, "label_issues_batch": 39, "mnist_pytorch": 40, "span_classif": 41, "filter": [42, 58, 61, 70, 74, 89], "label_quality_util": 44, "latent_algebra": 45, "multiannotator_util": 46, "multilabel_scor": 47, "multilabel_util": 48, "token_classification_util": 50, "util": 51, "valid": [52, 88, 95], "fasttext": 53, "model": [54, 78, 80, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 98, 99], "kera": 55, "multiannot": [56, 91], "multilabel_classif": 59, "rank": [60, 63, 66, 69, 72, 76, 89], "object_detect": 62, "summari": [64, 73, 77], "learn": [68, 82, 87, 89, 98], "segment": [71, 97], "token_classif": [75, 100], "cleanlab": [78, 80, 84, 85, 87, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "open": [78, 87], "sourc": [78, 87], "document": 78, "quickstart": 78, "1": [78, 79, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "instal": [78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "2": [78, 79, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "common": [78, 79, 100], "3": [78, 80, 81, 82, 84, 85, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "handl": [78, 87], "error": [78, 87, 88, 89, 91, 92, 93, 96, 97, 99, 100], "train": [78, 80, 87, 94, 96, 98, 99], "robust": [78, 89, 96, 98, 99], "noisi": [78, 89, 96, 98, 99], "4": [78, 80, 81, 82, 84, 85, 88, 89, 91, 93, 94, 96, 98, 99], "curat": [78, 86], "fix": [78, 87], "level": [78, 86, 89, 100], "5": [78, 80, 82, 84, 88, 89, 91, 96, 98], "improv": [78, 91], "via": [78, 89, 91], "mani": [78, 89], "other": [78, 91, 93, 96], "techniqu": 78, "contribut": 78, "easi": [78, 84, 85, 88], "mode": [78, 84, 85, 88], "how": [79, 87, 89, 91, 92, 100], "migrat": 79, "version": 79, "0": 79, "from": [79, 81, 82, 89, 96, 98, 99], "pre": [79, 80, 87, 94], "function": [79, 81], "name": 79, "chang": 79, "modul": [79, 89], "new": 79, "remov": 79, "argument": [79, 81], "variabl": 79, "audio": 80, "speechbrain": 80, "depend": [80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 93, 94, 96, 97, 98, 99, 100], "import": [80, 81, 82, 86, 88, 89, 91], "them": [80, 86, 89], "load": [80, 81, 82, 84, 85, 96, 98, 99], "featur": [80, 88, 94], "fit": 80, "linear": 80, "comput": [80, 84, 85, 87, 88, 91, 95, 98], "out": [80, 81, 82, 84, 85, 88, 91, 95, 98], "sampl": [80, 81, 82, 84, 85, 88, 91, 95, 98], "predict": [80, 81, 82, 84, 85, 88, 91, 92, 93, 95, 98], "probabl": [80, 81, 82, 84, 85, 88, 91, 95, 98], "workflow": [81, 89], "audit": [81, 82], "requir": [81, 82, 84, 85, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100], "classifi": [81, 82], "instanti": 81, "object": [81, 93], "increment": 81, "search": 81, "specifi": [81, 87], "nondefault": 81, "save": 81, "ad": 81, "A": 82, "unifi": 82, "all": [82, 89], "kind": [82, 93], "skip": [82, 86, 89, 91], "detail": [82, 86, 89, 91], "more": [82, 89, 96, 98, 99], "about": 82, "addit": 82, "inform": [82, 88], "tutori": [83, 86, 90], "tabular": [84, 98], "numer": 84, "categor": 84, "column": 84, "process": [84, 94, 96, 98], "select": [84, 98], "construct": 84, "k": [84, 88, 95], "nearest": 84, "neighbour": 84, "graph": 84, "text": [85, 99, 100], "format": [85, 87, 92, 93, 99], "defin": [85, 88, 96, 99], "drift": [85, 92], "fetch": [86, 88], "evalu": 86, "health": [86, 89], "8": [86, 89], "popular": 86, "faq": 87, "what": [87, 89, 95], "do": [87, 89], "i": [87, 89, 95], "infer": 87, "correct": 87, "exampl": [87, 88, 89, 94], "ha": 87, "flag": 87, "should": 87, "v": 87, "test": [87, 89, 94], "big": 87, "limit": 87, "memori": 87, "why": 87, "isn": 87, "t": 87, "cleanlearn": [87, 89], "work": [87, 89, 91, 100], "me": 87, "differ": [87, 93], "clean": [87, 89], "final": 87, "hyperparamet": 87, "tune": 87, "onli": 87, "one": [87, 89, 92, 97], "doe": [87, 91, 100], "take": 87, "so": 87, "long": 87, "slice": 87, "when": [87, 89], "identifi": [87, 93], "run": 87, "licens": 87, "under": 87, "an": 87, "answer": 87, "question": 87, "pytorch": [88, 94], "normal": 88, "fashion": 88, "mnist": 88, "prepar": 88, "fold": [88, 95], "cross": [88, 95], "embed": [88, 94], "7": [88, 89], "view": 88, "most": [88, 100], "like": 88, "sever": 88, "set": [88, 89], "dark": 88, "top": [88, 97], "low": 88, "The": 89, "centric": 89, "ai": 89, "machin": 89, "find_label_issu": 89, "line": 89, "code": 89, "visual": [89, 93, 94, 97], "twenti": 89, "lowest": 89, "qualiti": [89, 91, 92, 93, 97, 100], "see": 89, "now": 89, "let": 89, "": 89, "happen": 89, "we": 89, "merg": 89, "seafoam": 89, "green": 89, "yellow": 89, "too": 89, "you": 89, "re": 89, "6": 89, "One": 89, "score": [89, 91, 92, 93, 97, 100], "rule": 89, "overal": [89, 97], "accur": 89, "thi": 89, "directli": 89, "fulli": 89, "character": 89, "nois": 89, "matrix": [89, 92], "joint": 89, "prior": 89, "true": 89, "distribut": 89, "flip": 89, "rate": 89, "ani": 89, "again": 89, "support": 89, "lot": 89, "method": 89, "filter_bi": 89, "automat": 89, "everi": 89, "uniqu": 89, "num_label_issu": 89, "threshold": 89, "found": 89, "Not": 89, "sure": 89, "ensembl": 89, "multipl": [89, 91], "predictor": 89, "consensu": 91, "annot": 91, "initi": 91, "major": 91, "vote": 91, "better": 91, "statist": 91, "compar": 91, "inspect": 91, "potenti": [91, 96, 99], "retrain": 91, "further": 91, "multi": 92, "beyond": 92, "mislabel": [92, 97, 100], "given": 92, "hot": 92, "binari": 92, "without": 92, "applic": 92, "real": 92, "download": [93, 97, 100], "objectlab": 93, "exploratori": 93, "analysi": 93, "timm": 94, "cifar10": 94, "some": 94, "pred_prob": [94, 97, 100], "wai": 96, "semant": 97, "which": 97, "ar": 97, "commonli": 97, "focus": 97, "scikit": 98, "token": 100, "word": 100, "sentenc": 100, "contain": 100, "particular": 100}, "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"]], "datalab": [[4, "module-cleanlab.datalab.datalab"], [10, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[6, "creating-your-own-issues-manager"]], "Prerequisites": [[6, "prerequisites"]], "Implementing IssueManagers": [[6, "implementing-issuemanagers"]], "Basic Issue Check": [[6, "basic-issue-check"]], "Intermediate Issue Check": [[6, "intermediate-issue-check"]], "Advanced Issue Check": [[6, "advanced-issue-check"]], "Use with Datalab": [[6, "use-with-datalab"]], "Generating Cluster IDs": [[7, "generating-cluster-ids"]], "Datalab guides": [[8, "datalab-guides"]], "Types of issues": [[8, "types-of-issues"]], "Customizing issue types": [[8, "customizing-issue-types"]], "Datalab Issue Types": [[9, "datalab-issue-types"]], "Types of issues Datalab can detect": [[9, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[9, "estimates-for-each-issue-type"]], "Label Issue": [[9, "label-issue"]], "Outlier Issue": [[9, "outlier-issue"]], "(Near) Duplicate Issue": [[9, "near-duplicate-issue"]], "Non-IID Issue": [[9, "non-iid-issue"]], "Class Imbalance Issue": [[9, "class-imbalance-issue"]], "Image-specific Issues": [[9, "image-specific-issues"]], "Underperforming Group Issue": [[9, "underperforming-group-issue"]], "Null Issue": [[9, "null-issue"]], "Data Valuation Issue": [[9, "data-valuation-issue"]], "Optional Issue Parameters": [[9, "optional-issue-parameters"]], "Label Issue Parameters": [[9, "label-issue-parameters"]], "Outlier Issue Parameters": [[9, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[9, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[9, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[9, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[9, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[9, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[9, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[9, "image-issue-parameters"]], "Getting Started": [[10, "getting-started"]], "Guides": [[10, "guides"]], "API Reference": [[10, "api-reference"]], "data": [[11, "module-cleanlab.datalab.internal.data"]], "data_issues": [[12, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[13, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[14, "internal"], [43, "internal"]], "issue_finder": [[15, "issue-finder"]], "data_valuation": [[17, "data-valuation"]], "duplicate": [[18, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[19, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[20, "issue-manager"], [21, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[20, "registered-issue-managers"]], "ML task-specific issue managers": [[20, "ml-task-specific-issue-managers"]], "label": [[22, "module-cleanlab.datalab.internal.issue_manager.label"], [24, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [29, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[23, "multilabel"]], "noniid": [[25, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[26, "null"]], "outlier": [[27, "module-cleanlab.datalab.internal.issue_manager.outlier"], [49, "module-cleanlab.internal.outlier"], [65, "module-cleanlab.outlier"]], "regression": [[28, "regression"], [67, "regression"]], "Priority Order for finding issues:": [[29, null]], "underperforming_group": [[30, "underperforming-group"]], "model_outputs": [[31, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[32, "report"]], "task": [[33, "task"]], "dataset": [[35, "module-cleanlab.dataset"], [57, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[36, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[37, "module-cleanlab.experimental.coteaching"]], "experimental": [[38, "experimental"]], "label_issues_batched": [[39, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[40, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[41, "module-cleanlab.experimental.span_classification"]], "filter": [[42, "module-cleanlab.filter"], [58, "module-cleanlab.multilabel_classification.filter"], [61, "filter"], [70, "filter"], [74, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[44, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[45, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[46, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[47, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[48, "module-cleanlab.internal.multilabel_utils"]], "token_classification_utils": [[50, "module-cleanlab.internal.token_classification_utils"]], "util": [[51, "module-cleanlab.internal.util"]], "validation": [[52, "module-cleanlab.internal.validation"]], "fasttext": [[53, "fasttext"]], "models": [[54, "models"]], "keras": [[55, "module-cleanlab.models.keras"]], "multiannotator": [[56, "module-cleanlab.multiannotator"]], "multilabel_classification": [[59, "multilabel-classification"]], "rank": [[60, "module-cleanlab.multilabel_classification.rank"], [63, "module-cleanlab.object_detection.rank"], [66, "module-cleanlab.rank"], [72, "module-cleanlab.segmentation.rank"], [76, "module-cleanlab.token_classification.rank"]], "object_detection": [[62, "object-detection"]], "summary": [[64, "summary"], [73, "module-cleanlab.segmentation.summary"], [77, "module-cleanlab.token_classification.summary"]], "regression.learn": [[68, "module-cleanlab.regression.learn"]], "regression.rank": [[69, "module-cleanlab.regression.rank"]], "segmentation": [[71, "segmentation"]], "token_classification": [[75, "token-classification"]], "cleanlab open-source documentation": [[78, "cleanlab-open-source-documentation"]], "Quickstart": [[78, "quickstart"]], "1. Install cleanlab": [[78, "install-cleanlab"]], "2. Find common issues in your data": [[78, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[78, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[78, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[78, "improve-your-data-via-many-other-techniques"]], "Contributing": [[78, "contributing"]], "Easy Mode": [[78, "easy-mode"], [84, "Easy-Mode"], [85, "Easy-Mode"], [88, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[79, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[79, "function-and-class-name-changes"]], "Module name changes": [[79, "module-name-changes"]], "New modules": [[79, "new-modules"]], "Removed modules": [[79, "removed-modules"]], "Common argument and variable name changes": [[79, "common-argument-and-variable-name-changes"]], "Audio Classification with SpeechBrain and Cleanlab": [[80, "Audio-Classification-with-SpeechBrain-and-Cleanlab"]], "1. Install dependencies and import them": [[80, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[80, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[80, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[80, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[80, "5.-Use-cleanlab-to-find-label-issues"], [84, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[81, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[81, "Install-and-import-required-dependencies"]], "Create and load the data": [[81, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[81, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[81, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[81, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[81, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[81, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[81, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[82, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[82, "1.-Install-and-import-required-dependencies"], [88, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[82, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[82, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[82, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[82, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[82, "Get-additional-information"]], "Near duplicate issues": [[82, "Near-duplicate-issues"], [88, "Near-duplicate-issues"]], "Datalab Tutorials": [[83, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[84, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[84, "1.-Install-required-dependencies"], [85, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [98, "1.-Install-required-dependencies"], [99, "1.-Install-required-dependencies"]], "2. Load and process the data": [[84, "2.-Load-and-process-the-data"], [96, "2.-Load-and-process-the-data"], [98, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[84, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [98, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[84, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[84, "Label-issues"], [85, "Label-issues"], [88, "Label-issues"]], "Outlier issues": [[84, "Outlier-issues"], [85, "Outlier-issues"], [88, "Outlier-issues"]], "Near-duplicate issues": [[84, "Near-duplicate-issues"], [85, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[85, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[85, "2.-Load-and-format-the-text-dataset"], [99, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[85, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[85, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[85, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[86, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[86, "Install-dependencies-and-import-them"], [89, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[86, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[86, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[87, "FAQ"]], "What data can cleanlab detect issues in?": [[87, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[87, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[87, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[87, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[87, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[87, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[87, "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?": [[87, "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?": [[87, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[87, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[87, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[87, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[87, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[87, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[88, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[88, "7.-Use-cleanlab-to-find-issues"]], "View report": [[88, "View-report"]], "View most likely examples with label errors": [[88, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[88, "View-most-severe-outliers"]], "View sets of near duplicate images": [[88, "View-sets-of-near-duplicate-images"]], "Dark images": [[88, "Dark-images"]], "View top examples of dark images": [[88, "View-top-examples-of-dark-images"]], "Low information images": [[88, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[89, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[89, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[89, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[89, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[89, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[89, "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.": [[89, "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": [[89, "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": [[89, "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!": [[89, "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": [[89, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[89, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[89, "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)": [[89, "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:": [[89, "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": [[89, "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.": [[89, "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.": [[89, "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.": [[89, "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.": [[89, "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?": [[89, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[89, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[90, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[91, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[91, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[91, "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": [[91, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[91, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[91, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[91, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[91, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[91, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[92, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[92, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[92, "2.-Format-data,-labels,-and-model-predictions"], [93, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[92, "3.-Use-cleanlab-to-find-label-issues"], [93, "3.-Use-cleanlab-to-find-label-issues"], [97, "3.-Use-cleanlab-to-find-label-issues"], [100, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[92, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[92, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[92, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[92, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[92, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[93, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[93, "1.-Install-required-dependencies-and-download-data"], [97, "1.-Install-required-dependencies-and-download-data"], [100, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[93, "Get-label-quality-scores"], [97, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[93, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[93, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[93, "Other-uses-of-visualize"]], "Exploratory data analysis": [[93, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[94, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[94, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[94, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[94, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[94, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[94, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[95, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[95, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[95, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[96, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[96, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[96, "4.-Train-a-more-robust-model-from-noisy-labels"], [99, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[96, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[97, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[97, "2.-Get-data,-labels,-and-pred_probs"], [100, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[97, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[97, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[97, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[98, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[98, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[98, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[99, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[99, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[100, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[100, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[100, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[100, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[100, "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.datalab.datalab"], [10, "module-cleanlab.datalab"], [11, "module-cleanlab.datalab.internal.data"], [12, "module-cleanlab.datalab.internal.data_issues"], [13, "module-cleanlab.datalab.internal.issue_manager_factory"], [14, "module-cleanlab.datalab.internal"], [15, "module-cleanlab.datalab.internal.issue_finder"], [17, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [18, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [19, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [21, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [22, "module-cleanlab.datalab.internal.issue_manager.label"], [24, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [25, "module-cleanlab.datalab.internal.issue_manager.noniid"], [26, "module-cleanlab.datalab.internal.issue_manager.null"], [27, "module-cleanlab.datalab.internal.issue_manager.outlier"], [29, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [30, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [31, "module-cleanlab.datalab.internal.model_outputs"], [32, "module-cleanlab.datalab.internal.report"], [33, "module-cleanlab.datalab.internal.task"], [35, "module-cleanlab.dataset"], [36, "module-cleanlab.experimental.cifar_cnn"], [37, "module-cleanlab.experimental.coteaching"], [38, "module-cleanlab.experimental"], [39, "module-cleanlab.experimental.label_issues_batched"], [40, "module-cleanlab.experimental.mnist_pytorch"], [41, "module-cleanlab.experimental.span_classification"], [42, "module-cleanlab.filter"], [43, "module-cleanlab.internal"], [44, "module-cleanlab.internal.label_quality_utils"], [45, "module-cleanlab.internal.latent_algebra"], [46, "module-cleanlab.internal.multiannotator_utils"], [47, "module-cleanlab.internal.multilabel_scorer"], [48, "module-cleanlab.internal.multilabel_utils"], [49, "module-cleanlab.internal.outlier"], [50, "module-cleanlab.internal.token_classification_utils"], [51, "module-cleanlab.internal.util"], [52, "module-cleanlab.internal.validation"], [54, "module-cleanlab.models"], [55, "module-cleanlab.models.keras"], [56, "module-cleanlab.multiannotator"], [57, "module-cleanlab.multilabel_classification.dataset"], [58, "module-cleanlab.multilabel_classification.filter"], [59, "module-cleanlab.multilabel_classification"], [60, "module-cleanlab.multilabel_classification.rank"], [61, "module-cleanlab.object_detection.filter"], [62, "module-cleanlab.object_detection"], [63, "module-cleanlab.object_detection.rank"], [64, "module-cleanlab.object_detection.summary"], [65, "module-cleanlab.outlier"], [66, "module-cleanlab.rank"], [67, "module-cleanlab.regression"], [68, "module-cleanlab.regression.learn"], [69, "module-cleanlab.regression.rank"], [70, "module-cleanlab.segmentation.filter"], [71, "module-cleanlab.segmentation"], [72, "module-cleanlab.segmentation.rank"], [73, "module-cleanlab.segmentation.summary"], [74, "module-cleanlab.token_classification.filter"], [75, "module-cleanlab.token_classification"], [76, "module-cleanlab.token_classification.rank"], [77, "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"]], "datalab (class in cleanlab.datalab.datalab)": [[4, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[4, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[4, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[4, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[4, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[10, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[11, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[11, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[11, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[11, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[11, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[11, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[11, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[11, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[11, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[11, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[11, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[11, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[11, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[11, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[11, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[11, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[11, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[11, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[11, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[11, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[11, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[11, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[11, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[11, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[11, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[12, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[12, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[12, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[13, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[13, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[13, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[13, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[13, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[14, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[15, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[15, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[15, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[15, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[17, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[18, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[19, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[21, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[22, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[24, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[25, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[26, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[27, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[29, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[30, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "set_knn_graph() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.set_knn_graph"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[31, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[31, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[31, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[31, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[31, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[31, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[31, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[31, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[32, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[32, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[32, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[32, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[33, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[33, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[33, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[33, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[33, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[33, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[33, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[33, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[33, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[35, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[35, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[35, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[35, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[35, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[36, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[36, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[36, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[36, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[36, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[36, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.forward"], [36, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[36, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[36, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[37, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[37, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[38, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[39, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[39, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[39, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[40, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[40, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [40, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[40, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[40, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [40, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [40, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[40, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[40, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[41, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[41, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[41, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[41, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[42, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[42, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[42, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[42, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[42, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[42, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[43, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[44, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[44, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[45, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[45, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[46, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[46, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[47, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[47, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[47, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[47, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[47, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[47, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[47, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[47, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[48, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[48, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[48, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[48, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[48, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.outlier": [[49, "module-cleanlab.internal.outlier"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[49, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[50, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[50, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[51, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[51, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[52, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[52, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[54, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[55, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[55, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[55, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[55, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[55, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[56, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[56, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[57, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[57, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[57, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[57, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[57, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[58, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[58, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[58, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[59, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[60, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[60, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[60, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[61, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[61, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[62, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[63, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[63, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[64, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[64, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[65, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[65, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[65, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[65, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[65, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[66, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[66, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[66, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[66, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[67, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[68, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[68, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[68, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[68, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[69, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[69, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[70, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[70, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[71, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[72, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[72, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[72, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[73, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[73, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[73, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[73, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[74, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[74, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[75, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[76, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[76, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[76, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[77, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[77, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[77, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[77, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index 6120eb94c..5b5c55b17 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -1284,7 +1284,7 @@ 
5. Use cleanlab to find label issues
-{"state": {"81e3e8a2ef9f46bf92463e9260e9c6e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c996c8ded1c242519367283b82a57d1f": {"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": ""}}, "587d5afa681941929def8cb7ba71c68e": {"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_81e3e8a2ef9f46bf92463e9260e9c6e0", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c996c8ded1c242519367283b82a57d1f", "tabbable": null, "tooltip": null, "value": 2041.0}}, "da647cf9f1e14c11a29292e32cfaa2a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "edcd8a9c035f473c802a8aa421d746ea": {"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}}, "4317f602eb6448219730ee1cfaeedcf0": {"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_da647cf9f1e14c11a29292e32cfaa2a3", "placeholder": "\u200b", "style": "IPY_MODEL_edcd8a9c035f473c802a8aa421d746ea", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "b12ada2d417f46e88fe6fabd24838332": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6d8acd1de47f4f72959417e8c27be59f": {"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}}, "0ee410b3dfc14487b66d8b7fc20650c7": {"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_b12ada2d417f46e88fe6fabd24838332", "placeholder": "\u200b", "style": "IPY_MODEL_6d8acd1de47f4f72959417e8c27be59f", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007491kB/s]"}}, "bfe535d96fae496284f30fa9c1781a3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3c76a1fee86a4227be3d46a2a641f555": {"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_4317f602eb6448219730ee1cfaeedcf0", "IPY_MODEL_587d5afa681941929def8cb7ba71c68e", "IPY_MODEL_0ee410b3dfc14487b66d8b7fc20650c7"], "layout": "IPY_MODEL_bfe535d96fae496284f30fa9c1781a3a", "tabbable": null, "tooltip": null}}, "a9b5009feb6945c79783c94aea122c63": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c5ae8d6c40ff4963a0fbf5fcf6bfc93e": {"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": ""}}, "140b73aa892444faae014d00079e2788": {"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_a9b5009feb6945c79783c94aea122c63", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c5ae8d6c40ff4963a0fbf5fcf6bfc93e", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "c691af00cbb94bf39c3a8a5146a576dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1687948ffbe64fd9901a3f79e6268028": {"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}}, "b5cbbf51759d4c5c890e614cbfc65642": {"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_c691af00cbb94bf39c3a8a5146a576dc", "placeholder": "\u200b", "style": "IPY_MODEL_1687948ffbe64fd9901a3f79e6268028", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "49ee340afec24549ae70a7482414a46a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "414cb0b2dedb40cd8d7bdb5ce1fe2f7b": {"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}}, "1f26119746d940189864bee94ad71611": {"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_49ee340afec24549ae70a7482414a46a", "placeholder": "\u200b", "style": "IPY_MODEL_414cb0b2dedb40cd8d7bdb5ce1fe2f7b", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u200785.5MB/s]"}}, "3609813b80104388a2ee0bd9ee20c925": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a167bb6a0d2f498cae2d405888ac02c8": {"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_b5cbbf51759d4c5c890e614cbfc65642", "IPY_MODEL_140b73aa892444faae014d00079e2788", "IPY_MODEL_1f26119746d940189864bee94ad71611"], "layout": "IPY_MODEL_3609813b80104388a2ee0bd9ee20c925", "tabbable": null, "tooltip": null}}, "5a8dcca2bd4146cdbb15cc6215ee402f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ec74d93347b0455697f7f52ad09a1fb0": {"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": ""}}, "dc41e0c8f71c4f53b9ca2c3622990d8c": {"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_5a8dcca2bd4146cdbb15cc6215ee402f", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_ec74d93347b0455697f7f52ad09a1fb0", "tabbable": null, "tooltip": null, "value": 3201.0}}, "50d47fc4f93f4188bbf4ab29cd859f48": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b3c669058dac4292bef1f53bf12f3968": {"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}}, "84817a0a47f542158e62b3f5e21fc60c": {"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_50d47fc4f93f4188bbf4ab29cd859f48", "placeholder": "\u200b", "style": "IPY_MODEL_b3c669058dac4292bef1f53bf12f3968", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "4a6eacdea73c41a0ad26bbf82c0c1eda": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "698c826660b0488c82d677e9ae6d2399": {"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}}, "3d2ab8c5a4cb4b3da887cfa394f5911f": {"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_4a6eacdea73c41a0ad26bbf82c0c1eda", "placeholder": "\u200b", "style": "IPY_MODEL_698c826660b0488c82d677e9ae6d2399", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007775kB/s]"}}, "1dd97717a1214bfe9d212adf1d3e2ab0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "99ca0ac96ea24e749df6d9be8b4f21a5": {"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_84817a0a47f542158e62b3f5e21fc60c", "IPY_MODEL_dc41e0c8f71c4f53b9ca2c3622990d8c", "IPY_MODEL_3d2ab8c5a4cb4b3da887cfa394f5911f"], "layout": "IPY_MODEL_1dd97717a1214bfe9d212adf1d3e2ab0", "tabbable": null, "tooltip": null}}, "a336443b66df467dace7e94c1c59ad27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e47ca2d722a34a598abe1cf08289a6e2": {"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": ""}}, "95fda47511e54bf9a953d34867e2e942": {"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_a336443b66df467dace7e94c1c59ad27", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e47ca2d722a34a598abe1cf08289a6e2", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "09e5f1b8b663460f9a29cdb2aaa85eb3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fb650e73d93c40c480141df1f8373f27": {"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}}, "a313aad06bcd408b805a1dd462d8364a": {"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_09e5f1b8b663460f9a29cdb2aaa85eb3", "placeholder": "\u200b", "style": "IPY_MODEL_fb650e73d93c40c480141df1f8373f27", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "6989bef8f8834d41b5c527cb4a3950cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "57a928b784864640a80ea325f269731a": {"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}}, "036ef186e101426db4176e3a4f063144": {"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_6989bef8f8834d41b5c527cb4a3950cb", "placeholder": "\u200b", "style": "IPY_MODEL_57a928b784864640a80ea325f269731a", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u200765.2MB/s]"}}, "8401aafedf014a8ba251ccbd2b54b2f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "60b7cfc2292d43c684370b86ba28e91f": {"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_a313aad06bcd408b805a1dd462d8364a", "IPY_MODEL_95fda47511e54bf9a953d34867e2e942", "IPY_MODEL_036ef186e101426db4176e3a4f063144"], "layout": "IPY_MODEL_8401aafedf014a8ba251ccbd2b54b2f6", "tabbable": null, "tooltip": null}}, "7947273f2dcf4883b1a68e1be2030d82": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a173191c5fdd4528ac7b0e87b0315681": {"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": ""}}, "7b77c474ff8342bda1ca217c71271855": {"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_7947273f2dcf4883b1a68e1be2030d82", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a173191c5fdd4528ac7b0e87b0315681", "tabbable": null, "tooltip": null, "value": 128619.0}}, "f94bb4e2c10a42c5bb656de999d1e76d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "963b0a45d9b04cba898b999cd0f0e3d3": {"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}}, "3cf84962c8db4c2cad717e15ba4bc832": {"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_f94bb4e2c10a42c5bb656de999d1e76d", "placeholder": "\u200b", "style": "IPY_MODEL_963b0a45d9b04cba898b999cd0f0e3d3", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "2a30b6a9d0de4b19b30a1a834646a8e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5fc0069ebb63460b8e077df8b935e5ab": {"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}}, "d50c911c091841e0bb8a46eee475bac4": {"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_2a30b6a9d0de4b19b30a1a834646a8e1", "placeholder": "\u200b", "style": "IPY_MODEL_5fc0069ebb63460b8e077df8b935e5ab", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20071.75MB/s]"}}, "64e15d9dc2bb464bb5b168142752dc2f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b8a6f025d5624d209b93b9c7c3bb3c27": {"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_3cf84962c8db4c2cad717e15ba4bc832", "IPY_MODEL_7b77c474ff8342bda1ca217c71271855", "IPY_MODEL_d50c911c091841e0bb8a46eee475bac4"], "layout": "IPY_MODEL_64e15d9dc2bb464bb5b168142752dc2f", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0}
+{"state": {"b72a0b2bfed9412ea7c80b41aa396823": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a41d618c74d340b3925fe3a0851f1315": {"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": ""}}, "d7016839426a46a2b6291eeb633cb623": {"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_b72a0b2bfed9412ea7c80b41aa396823", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a41d618c74d340b3925fe3a0851f1315", "tabbable": null, "tooltip": null, "value": 2041.0}}, "319e07937ef24475a56c38440740bf25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a40987cd0cf34153a723560f24b943fb": {"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}}, "18dd5d2a97f74f3e962e25c5d5636538": {"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_319e07937ef24475a56c38440740bf25", "placeholder": "\u200b", "style": "IPY_MODEL_a40987cd0cf34153a723560f24b943fb", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "5b628bd170da44e3a185bb07f80354ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "49042ddee4684089a54e610adef8a59f": {"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}}, "af09bc54503944bc976b15fe5afdc119": {"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_5b628bd170da44e3a185bb07f80354ea", "placeholder": "\u200b", "style": "IPY_MODEL_49042ddee4684089a54e610adef8a59f", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007459kB/s]"}}, "2a855e906b6549dea0363c4d0be69b90": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "05b5a3d2ddab4a31bf77a4236ce62e30": {"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_18dd5d2a97f74f3e962e25c5d5636538", "IPY_MODEL_d7016839426a46a2b6291eeb633cb623", "IPY_MODEL_af09bc54503944bc976b15fe5afdc119"], "layout": "IPY_MODEL_2a855e906b6549dea0363c4d0be69b90", "tabbable": null, "tooltip": null}}, "e571d89312c04371a4020ad4c5ca65da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4a00e3bcfab54e7080c4a9242f56b139": {"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": ""}}, "323988776a80440db4bd021b8b7abadf": {"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_e571d89312c04371a4020ad4c5ca65da", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_4a00e3bcfab54e7080c4a9242f56b139", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "8d7e1c7a4ca048e08a27d6b026283569": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "107843d3e17047b68318a2398d48ecdc": {"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}}, "653c644017ff4206ae4c5a7b0c9eb822": {"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_8d7e1c7a4ca048e08a27d6b026283569", "placeholder": "\u200b", "style": "IPY_MODEL_107843d3e17047b68318a2398d48ecdc", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "fea3afa1441a49fe8b5413c70653d77c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bd283822e62a40f198ddb7e9f5e07b3b": {"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}}, "02d2e78f345f4ad2876203afcd124882": {"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_fea3afa1441a49fe8b5413c70653d77c", "placeholder": "\u200b", "style": "IPY_MODEL_bd283822e62a40f198ddb7e9f5e07b3b", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u2007251MB/s]"}}, "ebb668d2cc1f4a088059ce6f93bd3d27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0865ee4fc8bd4afbb16e5eb4a79326c9": {"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_653c644017ff4206ae4c5a7b0c9eb822", "IPY_MODEL_323988776a80440db4bd021b8b7abadf", "IPY_MODEL_02d2e78f345f4ad2876203afcd124882"], "layout": "IPY_MODEL_ebb668d2cc1f4a088059ce6f93bd3d27", "tabbable": null, "tooltip": null}}, "bf4345672517422b9259bcd86c2b92b0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9f8107ce83ce4889b2d06e23d0e09b73": {"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": ""}}, "5ba39a0eb224452cb1b284a7b5a6ed5a": {"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_bf4345672517422b9259bcd86c2b92b0", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_9f8107ce83ce4889b2d06e23d0e09b73", "tabbable": null, "tooltip": null, "value": 3201.0}}, "728e7befc82c47a5994fe90fa573fa70": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "11603c8c4e764015894aecf439fcd5c8": {"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}}, "15984c556e014d788e6c9dc91fbfc7f5": {"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_728e7befc82c47a5994fe90fa573fa70", "placeholder": "\u200b", "style": "IPY_MODEL_11603c8c4e764015894aecf439fcd5c8", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "492a36a8e2774d0e8d2017e1f593b018": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "44da8d1e436c407eaf1262eeac6962b5": {"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}}, "5854c235c37944b1bce904506805c460": {"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_492a36a8e2774d0e8d2017e1f593b018", "placeholder": "\u200b", "style": "IPY_MODEL_44da8d1e436c407eaf1262eeac6962b5", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007733kB/s]"}}, "79dbf4601a8040a8a7d5934587e12cef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ac6c9d197b7f40a1a4283df36f4b3cdb": {"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_15984c556e014d788e6c9dc91fbfc7f5", "IPY_MODEL_5ba39a0eb224452cb1b284a7b5a6ed5a", "IPY_MODEL_5854c235c37944b1bce904506805c460"], "layout": "IPY_MODEL_79dbf4601a8040a8a7d5934587e12cef", "tabbable": null, "tooltip": null}}, "2e4f430032694b62be18ab1e3ef1e94a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5b52d16b67a54c5880c38421e1a5a9ed": {"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": ""}}, "86f07b0ca74a472e9763172c0ca78fff": {"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_2e4f430032694b62be18ab1e3ef1e94a", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5b52d16b67a54c5880c38421e1a5a9ed", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "f39ac366e8f24b1089c0781b3749fed2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "35caa1342ed042b4801df365091345bd": {"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}}, "e206079b94314a15a28f298733273909": {"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_f39ac366e8f24b1089c0781b3749fed2", "placeholder": "\u200b", "style": "IPY_MODEL_35caa1342ed042b4801df365091345bd", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "5c8979c8d8424f9db924361ffad40322": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a37413dc6853418e84b05f3f0c590ab7": {"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}}, "66089462085c404fa7080ca4e424dddf": {"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_5c8979c8d8424f9db924361ffad40322", "placeholder": "\u200b", "style": "IPY_MODEL_a37413dc6853418e84b05f3f0c590ab7", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u2007259MB/s]"}}, "d883b7d379fc4151a9aca9d049129d04": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f2995cfbadb74ba4a58692ba3ba8921e": {"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_e206079b94314a15a28f298733273909", "IPY_MODEL_86f07b0ca74a472e9763172c0ca78fff", "IPY_MODEL_66089462085c404fa7080ca4e424dddf"], "layout": "IPY_MODEL_d883b7d379fc4151a9aca9d049129d04", "tabbable": null, "tooltip": null}}, "58e0ce45e41f42e884637a0768654e34": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "816aa489621e46a3b6dea042dde099b1": {"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": ""}}, "7f706b6971c14a0c82e30545bbc1b252": {"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_58e0ce45e41f42e884637a0768654e34", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_816aa489621e46a3b6dea042dde099b1", "tabbable": null, "tooltip": null, "value": 128619.0}}, "fe9f42f3c0354ff5b64d4b2ca255c7fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8325f1a964e6457dbe42f85472fa9a3e": {"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}}, "27497e44407c4f098595adec05befff7": {"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_fe9f42f3c0354ff5b64d4b2ca255c7fb", "placeholder": "\u200b", "style": "IPY_MODEL_8325f1a964e6457dbe42f85472fa9a3e", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "6b8f837473e74a9ca1bebd945c18ef66": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8e9de3d5b6604a3cbb8e952a2fe606cf": {"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}}, "a209b80893854821b090c57679d16594": {"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_6b8f837473e74a9ca1bebd945c18ef66", "placeholder": "\u200b", "style": "IPY_MODEL_8e9de3d5b6604a3cbb8e952a2fe606cf", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20073.97MB/s]"}}, "3f295ef4343b4f16bbc6f586c7534d19": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4299667108514635aa9f4719fe91026a": {"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_27497e44407c4f098595adec05befff7", "IPY_MODEL_7f706b6971c14a0c82e30545bbc1b252", "IPY_MODEL_a209b80893854821b090c57679d16594"], "layout": "IPY_MODEL_3f295ef4343b4f16bbc6f586c7534d19", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0}
 
 
  
diff --git a/master/tutorials/audio.ipynb b/master/tutorials/audio.ipynb
index 9611d88ac..43127d3de 100644
--- a/master/tutorials/audio.ipynb
+++ b/master/tutorials/audio.ipynb
@@ -78,10 +78,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:13.490591Z",
-     "iopub.status.busy": "2024-02-27T04:05:13.490425Z",
-     "iopub.status.idle": "2024-02-27T04:05:18.118922Z",
-     "shell.execute_reply": "2024-02-27T04:05:18.118386Z"
+     "iopub.execute_input": "2024-03-05T17:19:53.911896Z",
+     "iopub.status.busy": "2024-03-05T17:19:53.911718Z",
+     "iopub.status.idle": "2024-03-05T17:19:59.233816Z",
+     "shell.execute_reply": "2024-03-05T17:19:59.233252Z"
     },
     "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@09245a93829950109e7226b76e8ce2bf667da73f\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:05:18.121634Z",
-     "iopub.status.busy": "2024-02-27T04:05:18.121062Z",
-     "iopub.status.idle": "2024-02-27T04:05:18.124359Z",
-     "shell.execute_reply": "2024-02-27T04:05:18.123808Z"
+     "iopub.execute_input": "2024-03-05T17:19:59.236612Z",
+     "iopub.status.busy": "2024-03-05T17:19:59.236172Z",
+     "iopub.status.idle": "2024-03-05T17:19:59.240085Z",
+     "shell.execute_reply": "2024-03-05T17:19:59.239663Z"
     },
     "id": "LaEiwXUiVHCS"
    },
@@ -157,10 +157,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:18.126438Z",
-     "iopub.status.busy": "2024-02-27T04:05:18.126148Z",
-     "iopub.status.idle": "2024-02-27T04:05:18.130682Z",
-     "shell.execute_reply": "2024-02-27T04:05:18.130160Z"
+     "iopub.execute_input": "2024-03-05T17:19:59.241967Z",
+     "iopub.status.busy": "2024-03-05T17:19:59.241790Z",
+     "iopub.status.idle": "2024-03-05T17:19:59.246687Z",
+     "shell.execute_reply": "2024-03-05T17:19:59.246174Z"
     },
     "nbsphinx": "hidden"
    },
@@ -208,10 +208,10 @@
      "base_uri": "https://localhost:8080/"
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:18.132712Z",
-     "iopub.status.busy": "2024-02-27T04:05:18.132423Z",
-     "iopub.status.idle": "2024-02-27T04:05:20.130453Z",
-     "shell.execute_reply": "2024-02-27T04:05:20.129702Z"
+     "iopub.execute_input": "2024-03-05T17:19:59.248926Z",
+     "iopub.status.busy": "2024-03-05T17:19:59.248612Z",
+     "iopub.status.idle": "2024-03-05T17:20:01.022880Z",
+     "shell.execute_reply": "2024-03-05T17:20:01.022247Z"
     },
     "id": "GRDPEg7-VOQe",
     "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
      "base_uri": "https://localhost:8080/"
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:20.133354Z",
-     "iopub.status.busy": "2024-02-27T04:05:20.132874Z",
-     "iopub.status.idle": "2024-02-27T04:05:20.143387Z",
-     "shell.execute_reply": "2024-02-27T04:05:20.142872Z"
+     "iopub.execute_input": "2024-03-05T17:20:01.025405Z",
+     "iopub.status.busy": "2024-03-05T17:20:01.025203Z",
+     "iopub.status.idle": "2024-03-05T17:20:01.035872Z",
+     "shell.execute_reply": "2024-03-05T17:20:01.035326Z"
     },
     "id": "FDA5sGZwUSur",
     "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:20.172369Z",
-     "iopub.status.busy": "2024-02-27T04:05:20.171991Z",
-     "iopub.status.idle": "2024-02-27T04:05:20.177447Z",
-     "shell.execute_reply": "2024-02-27T04:05:20.176985Z"
+     "iopub.execute_input": "2024-03-05T17:20:01.068212Z",
+     "iopub.status.busy": "2024-03-05T17:20:01.067699Z",
+     "iopub.status.idle": "2024-03-05T17:20:01.073312Z",
+     "shell.execute_reply": "2024-03-05T17:20:01.072849Z"
     },
     "nbsphinx": "hidden"
    },
@@ -380,10 +380,10 @@
      "height": 92
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:20.179423Z",
-     "iopub.status.busy": "2024-02-27T04:05:20.179098Z",
-     "iopub.status.idle": "2024-02-27T04:05:20.596148Z",
-     "shell.execute_reply": "2024-02-27T04:05:20.595581Z"
+     "iopub.execute_input": "2024-03-05T17:20:01.075480Z",
+     "iopub.status.busy": "2024-03-05T17:20:01.075144Z",
+     "iopub.status.idle": "2024-03-05T17:20:01.586542Z",
+     "shell.execute_reply": "2024-03-05T17:20:01.586052Z"
     },
     "id": "dLBvUZLlII5w",
     "outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:20.598101Z",
-     "iopub.status.busy": "2024-02-27T04:05:20.597912Z",
-     "iopub.status.idle": "2024-02-27T04:05:22.606455Z",
-     "shell.execute_reply": "2024-02-27T04:05:22.605971Z"
+     "iopub.execute_input": "2024-03-05T17:20:01.588731Z",
+     "iopub.status.busy": "2024-03-05T17:20:01.588529Z",
+     "iopub.status.idle": "2024-03-05T17:20:02.976075Z",
+     "shell.execute_reply": "2024-03-05T17:20:02.975579Z"
     },
     "id": "vL9lkiKsHvKr"
    },
@@ -474,10 +474,10 @@
      "height": 143
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:22.608780Z",
-     "iopub.status.busy": "2024-02-27T04:05:22.608478Z",
-     "iopub.status.idle": "2024-02-27T04:05:22.626555Z",
-     "shell.execute_reply": "2024-02-27T04:05:22.626045Z"
+     "iopub.execute_input": "2024-03-05T17:20:02.978379Z",
+     "iopub.status.busy": "2024-03-05T17:20:02.978192Z",
+     "iopub.status.idle": "2024-03-05T17:20:03.000246Z",
+     "shell.execute_reply": "2024-03-05T17:20:02.999772Z"
     },
     "id": "obQYDKdLiUU6",
     "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -557,10 +557,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:22.628565Z",
-     "iopub.status.busy": "2024-02-27T04:05:22.628243Z",
-     "iopub.status.idle": "2024-02-27T04:05:22.631274Z",
-     "shell.execute_reply": "2024-02-27T04:05:22.630837Z"
+     "iopub.execute_input": "2024-03-05T17:20:03.002511Z",
+     "iopub.status.busy": "2024-03-05T17:20:03.002068Z",
+     "iopub.status.idle": "2024-03-05T17:20:03.005554Z",
+     "shell.execute_reply": "2024-03-05T17:20:03.005094Z"
     },
     "id": "I8JqhOZgi94g"
    },
@@ -582,10 +582,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:22.633263Z",
-     "iopub.status.busy": "2024-02-27T04:05:22.632938Z",
-     "iopub.status.idle": "2024-02-27T04:05:36.695564Z",
-     "shell.execute_reply": "2024-02-27T04:05:36.694959Z"
+     "iopub.execute_input": "2024-03-05T17:20:03.007697Z",
+     "iopub.status.busy": "2024-03-05T17:20:03.007325Z",
+     "iopub.status.idle": "2024-03-05T17:20:19.332306Z",
+     "shell.execute_reply": "2024-03-05T17:20:19.331685Z"
     },
     "id": "2FSQ2GR9R_YA"
    },
@@ -627,10 +627,10 @@
      "base_uri": "https://localhost:8080/"
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:36.698492Z",
-     "iopub.status.busy": "2024-02-27T04:05:36.697978Z",
-     "iopub.status.idle": "2024-02-27T04:05:36.701893Z",
-     "shell.execute_reply": "2024-02-27T04:05:36.701395Z"
+     "iopub.execute_input": "2024-03-05T17:20:19.335349Z",
+     "iopub.status.busy": "2024-03-05T17:20:19.334800Z",
+     "iopub.status.idle": "2024-03-05T17:20:19.338737Z",
+     "shell.execute_reply": "2024-03-05T17:20:19.338186Z"
     },
     "id": "kAkY31IVXyr8",
     "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632"
@@ -690,10 +690,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:36.703934Z",
-     "iopub.status.busy": "2024-02-27T04:05:36.703636Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.410033Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.409461Z"
+     "iopub.execute_input": "2024-03-05T17:20:19.340881Z",
+     "iopub.status.busy": "2024-03-05T17:20:19.340499Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.112976Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.112416Z"
     },
     "id": "i_drkY9YOcw4"
    },
@@ -727,10 +727,10 @@
      "base_uri": "https://localhost:8080/"
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.412903Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.412541Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.417176Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.416719Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.115868Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.115512Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.120120Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.119646Z"
     },
     "id": "_b-AQeoXOc7q",
     "outputId": "15ae534a-f517-4906-b177-ca91931a8954"
@@ -777,10 +777,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.419532Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.419191Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.512809Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.512279Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.123443Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.122522Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.259517Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.258903Z"
     }
    },
    "outputs": [
@@ -817,10 +817,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.515152Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.514781Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.526501Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.526047Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.262099Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.261701Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.274942Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.274471Z"
     },
     "scrolled": true
    },
@@ -875,10 +875,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.528471Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.528139Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.535823Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.535394Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.277216Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.276777Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.285125Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.284565Z"
     }
    },
    "outputs": [
@@ -982,10 +982,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.537820Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.537484Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.541593Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.541161Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.287373Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.286983Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.291496Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.291053Z"
     }
    },
    "outputs": [
@@ -1023,10 +1023,10 @@
      "height": 237
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.543573Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.543247Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.548773Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.548231Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.293771Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.293346Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.299248Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.298692Z"
     },
     "id": "FQwRHgbclpsO",
     "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182"
@@ -1153,10 +1153,10 @@
      "height": 92
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.550908Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.550600Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.661144Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.660651Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.301413Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.301106Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.418372Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.417843Z"
     },
     "id": "ff1NFVlDoysO",
     "outputId": "8141a036-44c1-4349-c338-880432513e37"
@@ -1210,10 +1210,10 @@
      "height": 92
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.663451Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.663016Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.768845Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.768348Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.420574Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.420379Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.530203Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.529692Z"
     },
     "id": "GZgovGkdiaiP",
     "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7"
@@ -1258,10 +1258,10 @@
      "height": 92
     },
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.771149Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.770785Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.874707Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.874076Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.532432Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.532007Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.639742Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.639205Z"
     },
     "id": "lfa2eHbMwG8R",
     "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c"
@@ -1302,10 +1302,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.877055Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.876643Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.979417Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.978890Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.641870Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.641591Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.748313Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.747714Z"
     }
    },
    "outputs": [
@@ -1353,10 +1353,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-27T04:05:37.981762Z",
-     "iopub.status.busy": "2024-02-27T04:05:37.981320Z",
-     "iopub.status.idle": "2024-02-27T04:05:37.984421Z",
-     "shell.execute_reply": "2024-02-27T04:05:37.984001Z"
+     "iopub.execute_input": "2024-03-05T17:20:20.750355Z",
+     "iopub.status.busy": "2024-03-05T17:20:20.750135Z",
+     "iopub.status.idle": "2024-03-05T17:20:20.754353Z",
+     "shell.execute_reply": "2024-03-05T17:20:20.753805Z"
     },
     "nbsphinx": "hidden"
    },
@@ -1397,7 +1397,7 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "036ef186e101426db4176e3a4f063144": {
+     "02d2e78f345f4ad2876203afcd124882": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -1412,68 +1412,99 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_6989bef8f8834d41b5c527cb4a3950cb",
+       "layout": "IPY_MODEL_fea3afa1441a49fe8b5413c70653d77c",
        "placeholder": "​",
-       "style": "IPY_MODEL_57a928b784864640a80ea325f269731a",
+       "style": "IPY_MODEL_bd283822e62a40f198ddb7e9f5e07b3b",
        "tabbable": null,
        "tooltip": null,
-       "value": " 15.9M/15.9M [00:00<00:00, 65.2MB/s]"
+       "value": " 16.9M/16.9M [00:00<00:00, 251MB/s]"
       }
      },
-     "09e5f1b8b663460f9a29cdb2aaa85eb3": {
-      "model_module": "@jupyter-widgets/base",
+     "05b5a3d2ddab4a31bf77a4236ce62e30": {
+      "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_18dd5d2a97f74f3e962e25c5d5636538",
+        "IPY_MODEL_d7016839426a46a2b6291eeb633cb623",
+        "IPY_MODEL_af09bc54503944bc976b15fe5afdc119"
+       ],
+       "layout": "IPY_MODEL_2a855e906b6549dea0363c4d0be69b90",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "0865ee4fc8bd4afbb16e5eb4a79326c9": {
+      "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_653c644017ff4206ae4c5a7b0c9eb822",
+        "IPY_MODEL_323988776a80440db4bd021b8b7abadf",
+        "IPY_MODEL_02d2e78f345f4ad2876203afcd124882"
+       ],
+       "layout": "IPY_MODEL_ebb668d2cc1f4a088059ce6f93bd3d27",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "107843d3e17047b68318a2398d48ecdc": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_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
       }
      },
-     "0ee410b3dfc14487b66d8b7fc20650c7": {
+     "11603c8c4e764015894aecf439fcd5c8": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "15984c556e014d788e6c9dc91fbfc7f5": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -1488,59 +1519,61 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_b12ada2d417f46e88fe6fabd24838332",
+       "layout": "IPY_MODEL_728e7befc82c47a5994fe90fa573fa70",
        "placeholder": "​",
-       "style": "IPY_MODEL_6d8acd1de47f4f72959417e8c27be59f",
+       "style": "IPY_MODEL_11603c8c4e764015894aecf439fcd5c8",
        "tabbable": null,
        "tooltip": null,
-       "value": " 2.04k/2.04k [00:00<00:00, 491kB/s]"
+       "value": "mean_var_norm_emb.ckpt: 100%"
       }
      },
-     "140b73aa892444faae014d00079e2788": {
+     "18dd5d2a97f74f3e962e25c5d5636538": {
       "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_a9b5009feb6945c79783c94aea122c63",
-       "max": 16887676.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_c5ae8d6c40ff4963a0fbf5fcf6bfc93e",
+       "layout": "IPY_MODEL_319e07937ef24475a56c38440740bf25",
+       "placeholder": "​",
+       "style": "IPY_MODEL_a40987cd0cf34153a723560f24b943fb",
        "tabbable": null,
        "tooltip": null,
-       "value": 16887676.0
+       "value": "hyperparams.yaml: 100%"
       }
      },
-     "1687948ffbe64fd9901a3f79e6268028": {
+     "27497e44407c4f098595adec05befff7": {
       "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_fe9f42f3c0354ff5b64d4b2ca255c7fb",
+       "placeholder": "​",
+       "style": "IPY_MODEL_8325f1a964e6457dbe42f85472fa9a3e",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "label_encoder.txt: 100%"
       }
      },
-     "1dd97717a1214bfe9d212adf1d3e2ab0": {
+     "2a855e906b6549dea0363c4d0be69b90": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -1593,30 +1626,7 @@
        "width": null
       }
      },
-     "1f26119746d940189864bee94ad71611": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_49ee340afec24549ae70a7482414a46a",
-       "placeholder": "​",
-       "style": "IPY_MODEL_414cb0b2dedb40cd8d7bdb5ce1fe2f7b",
-       "tabbable": null,
-       "tooltip": null,
-       "value": " 16.9M/16.9M [00:00<00:00, 85.5MB/s]"
-      }
-     },
-     "2a30b6a9d0de4b19b30a1a834646a8e1": {
+     "2e4f430032694b62be18ab1e3ef1e94a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -1669,7 +1679,7 @@
        "width": null
       }
      },
-     "3609813b80104388a2ee0bd9ee20c925": {
+     "319e07937ef24475a56c38440740bf25": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -1722,77 +1732,128 @@
        "width": null
       }
      },
-     "3c76a1fee86a4227be3d46a2a641f555": {
+     "323988776a80440db4bd021b8b7abadf": {
       "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_4317f602eb6448219730ee1cfaeedcf0",
-        "IPY_MODEL_587d5afa681941929def8cb7ba71c68e",
-        "IPY_MODEL_0ee410b3dfc14487b66d8b7fc20650c7"
-       ],
-       "layout": "IPY_MODEL_bfe535d96fae496284f30fa9c1781a3a",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_e571d89312c04371a4020ad4c5ca65da",
+       "max": 16887676.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_4a00e3bcfab54e7080c4a9242f56b139",
        "tabbable": null,
-       "tooltip": null
+       "tooltip": null,
+       "value": 16887676.0
       }
      },
-     "3cf84962c8db4c2cad717e15ba4bc832": {
+     "35caa1342ed042b4801df365091345bd": {
       "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_f94bb4e2c10a42c5bb656de999d1e76d",
-       "placeholder": "​",
-       "style": "IPY_MODEL_963b0a45d9b04cba898b999cd0f0e3d3",
-       "tabbable": null,
-       "tooltip": null,
-       "value": "label_encoder.txt: 100%"
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "3d2ab8c5a4cb4b3da887cfa394f5911f": {
+     "3f295ef4343b4f16bbc6f586c7534d19": {
+      "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
+      }
+     },
+     "4299667108514635aa9f4719fe91026a": {
       "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_4a6eacdea73c41a0ad26bbf82c0c1eda",
-       "placeholder": "​",
-       "style": "IPY_MODEL_698c826660b0488c82d677e9ae6d2399",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_27497e44407c4f098595adec05befff7",
+        "IPY_MODEL_7f706b6971c14a0c82e30545bbc1b252",
+        "IPY_MODEL_a209b80893854821b090c57679d16594"
+       ],
+       "layout": "IPY_MODEL_3f295ef4343b4f16bbc6f586c7534d19",
        "tabbable": null,
-       "tooltip": null,
-       "value": " 3.20k/3.20k [00:00<00:00, 775kB/s]"
+       "tooltip": null
       }
      },
-     "414cb0b2dedb40cd8d7bdb5ce1fe2f7b": {
+     "44da8d1e436c407eaf1262eeac6962b5": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -1810,30 +1871,25 @@
        "text_color": null
       }
      },
-     "4317f602eb6448219730ee1cfaeedcf0": {
+     "49042ddee4684089a54e610adef8a59f": {
       "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_da647cf9f1e14c11a29292e32cfaa2a3",
-       "placeholder": "​",
-       "style": "IPY_MODEL_edcd8a9c035f473c802a8aa421d746ea",
-       "tabbable": null,
-       "tooltip": null,
-       "value": "hyperparams.yaml: 100%"
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "49ee340afec24549ae70a7482414a46a": {
+     "492a36a8e2774d0e8d2017e1f593b018": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -1886,7 +1942,46 @@
        "width": null
       }
      },
-     "4a6eacdea73c41a0ad26bbf82c0c1eda": {
+     "4a00e3bcfab54e7080c4a9242f56b139": {
+      "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": ""
+      }
+     },
+     "5854c235c37944b1bce904506805c460": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_492a36a8e2774d0e8d2017e1f593b018",
+       "placeholder": "​",
+       "style": "IPY_MODEL_44da8d1e436c407eaf1262eeac6962b5",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 3.20k/3.20k [00:00<00:00, 733kB/s]"
+      }
+     },
+     "58e0ce45e41f42e884637a0768654e34": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -1939,7 +2034,23 @@
        "width": null
       }
      },
-     "50d47fc4f93f4188bbf4ab29cd859f48": {
+     "5b52d16b67a54c5880c38421e1a5a9ed": {
+      "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": ""
+      }
+     },
+     "5b628bd170da44e3a185bb07f80354ea": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -1982,35 +2093,17 @@
        "min_height": null,
        "min_width": null,
        "object_fit": null,
-       "object_position": null,
-       "order": null,
-       "overflow": null,
-       "padding": null,
-       "right": null,
-       "top": null,
-       "visibility": null,
-       "width": null
-      }
-     },
-     "57a928b784864640a80ea325f269731a": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "587d5afa681941929def8cb7ba71c68e": {
+     "5ba39a0eb224452cb1b284a7b5a6ed5a": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "FloatProgressModel",
@@ -2026,17 +2119,17 @@
        "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_81e3e8a2ef9f46bf92463e9260e9c6e0",
-       "max": 2041.0,
+       "layout": "IPY_MODEL_bf4345672517422b9259bcd86c2b92b0",
+       "max": 3201.0,
        "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_c996c8ded1c242519367283b82a57d1f",
+       "style": "IPY_MODEL_9f8107ce83ce4889b2d06e23d0e09b73",
        "tabbable": null,
        "tooltip": null,
-       "value": 2041.0
+       "value": 3201.0
       }
      },
-     "5a8dcca2bd4146cdbb15cc6215ee402f": {
+     "5c8979c8d8424f9db924361ffad40322": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2089,49 +2182,53 @@
        "width": null
       }
      },
-     "5fc0069ebb63460b8e077df8b935e5ab": {
+     "653c644017ff4206ae4c5a7b0c9eb822": {
       "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_8d7e1c7a4ca048e08a27d6b026283569",
+       "placeholder": "​",
+       "style": "IPY_MODEL_107843d3e17047b68318a2398d48ecdc",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "embedding_model.ckpt: 100%"
       }
      },
-     "60b7cfc2292d43c684370b86ba28e91f": {
+     "66089462085c404fa7080ca4e424dddf": {
       "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_a313aad06bcd408b805a1dd462d8364a",
-        "IPY_MODEL_95fda47511e54bf9a953d34867e2e942",
-        "IPY_MODEL_036ef186e101426db4176e3a4f063144"
-       ],
-       "layout": "IPY_MODEL_8401aafedf014a8ba251ccbd2b54b2f6",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_5c8979c8d8424f9db924361ffad40322",
+       "placeholder": "​",
+       "style": "IPY_MODEL_a37413dc6853418e84b05f3f0c590ab7",
        "tabbable": null,
-       "tooltip": null
+       "tooltip": null,
+       "value": " 15.9M/15.9M [00:00<00:00, 259MB/s]"
       }
      },
-     "64e15d9dc2bb464bb5b168142752dc2f": {
+     "6b8f837473e74a9ca1bebd945c18ef66": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2184,7 +2281,7 @@
        "width": null
       }
      },
-     "6989bef8f8834d41b5c527cb4a3950cb": {
+     "728e7befc82c47a5994fe90fa573fa70": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2237,43 +2334,7 @@
        "width": null
       }
      },
-     "698c826660b0488c82d677e9ae6d2399": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "6d8acd1de47f4f72959417e8c27be59f": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "7947273f2dcf4883b1a68e1be2030d82": {
+     "79dbf4601a8040a8a7d5934587e12cef": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2326,7 +2387,7 @@
        "width": null
       }
      },
-     "7b77c474ff8342bda1ca217c71271855": {
+     "7f706b6971c14a0c82e30545bbc1b252": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "FloatProgressModel",
@@ -2342,70 +2403,77 @@
        "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_7947273f2dcf4883b1a68e1be2030d82",
+       "layout": "IPY_MODEL_58e0ce45e41f42e884637a0768654e34",
        "max": 128619.0,
        "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_a173191c5fdd4528ac7b0e87b0315681",
+       "style": "IPY_MODEL_816aa489621e46a3b6dea042dde099b1",
        "tabbable": null,
        "tooltip": null,
        "value": 128619.0
       }
      },
-     "81e3e8a2ef9f46bf92463e9260e9c6e0": {
-      "model_module": "@jupyter-widgets/base",
+     "816aa489621e46a3b6dea042dde099b1": {
+      "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": ""
+      }
+     },
+     "8325f1a964e6457dbe42f85472fa9a3e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "86f07b0ca74a472e9763172c0ca78fff": {
+      "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_2e4f430032694b62be18ab1e3ef1e94a",
+       "max": 15856877.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_5b52d16b67a54c5880c38421e1a5a9ed",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 15856877.0
       }
      },
-     "8401aafedf014a8ba251ccbd2b54b2f6": {
+     "8d7e1c7a4ca048e08a27d6b026283569": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2458,56 +2526,64 @@
        "width": null
       }
      },
-     "84817a0a47f542158e62b3f5e21fc60c": {
+     "8e9de3d5b6604a3cbb8e952a2fe606cf": {
       "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_50d47fc4f93f4188bbf4ab29cd859f48",
-       "placeholder": "​",
-       "style": "IPY_MODEL_b3c669058dac4292bef1f53bf12f3968",
-       "tabbable": null,
-       "tooltip": null,
-       "value": "mean_var_norm_emb.ckpt: 100%"
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "95fda47511e54bf9a953d34867e2e942": {
+     "9f8107ce83ce4889b2d06e23d0e09b73": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "a209b80893854821b090c57679d16594": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
+       "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_a336443b66df467dace7e94c1c59ad27",
-       "max": 15856877.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_e47ca2d722a34a598abe1cf08289a6e2",
+       "layout": "IPY_MODEL_6b8f837473e74a9ca1bebd945c18ef66",
+       "placeholder": "​",
+       "style": "IPY_MODEL_8e9de3d5b6604a3cbb8e952a2fe606cf",
        "tabbable": null,
        "tooltip": null,
-       "value": 15856877.0
+       "value": " 129k/129k [00:00<00:00, 3.97MB/s]"
       }
      },
-     "963b0a45d9b04cba898b999cd0f0e3d3": {
+     "a37413dc6853418e84b05f3f0c590ab7": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -2525,71 +2601,65 @@
        "text_color": null
       }
      },
-     "99ca0ac96ea24e749df6d9be8b4f21a5": {
+     "a40987cd0cf34153a723560f24b943fb": {
       "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_84817a0a47f542158e62b3f5e21fc60c",
-        "IPY_MODEL_dc41e0c8f71c4f53b9ca2c3622990d8c",
-        "IPY_MODEL_3d2ab8c5a4cb4b3da887cfa394f5911f"
-       ],
-       "layout": "IPY_MODEL_1dd97717a1214bfe9d212adf1d3e2ab0",
-       "tabbable": null,
-       "tooltip": null
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "a167bb6a0d2f498cae2d405888ac02c8": {
+     "a41d618c74d340b3925fe3a0851f1315": {
       "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_b5cbbf51759d4c5c890e614cbfc65642",
-        "IPY_MODEL_140b73aa892444faae014d00079e2788",
-        "IPY_MODEL_1f26119746d940189864bee94ad71611"
-       ],
-       "layout": "IPY_MODEL_3609813b80104388a2ee0bd9ee20c925",
-       "tabbable": null,
-       "tooltip": null
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
       }
      },
-     "a173191c5fdd4528ac7b0e87b0315681": {
+     "ac6c9d197b7f40a1a4283df36f4b3cdb": {
       "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_15984c556e014d788e6c9dc91fbfc7f5",
+        "IPY_MODEL_5ba39a0eb224452cb1b284a7b5a6ed5a",
+        "IPY_MODEL_5854c235c37944b1bce904506805c460"
+       ],
+       "layout": "IPY_MODEL_79dbf4601a8040a8a7d5934587e12cef",
+       "tabbable": null,
+       "tooltip": null
       }
      },
-     "a313aad06bcd408b805a1dd462d8364a": {
+     "af09bc54503944bc976b15fe5afdc119": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -2604,15 +2674,15 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_09e5f1b8b663460f9a29cdb2aaa85eb3",
+       "layout": "IPY_MODEL_5b628bd170da44e3a185bb07f80354ea",
        "placeholder": "​",
-       "style": "IPY_MODEL_fb650e73d93c40c480141df1f8373f27",
+       "style": "IPY_MODEL_49042ddee4684089a54e610adef8a59f",
        "tabbable": null,
        "tooltip": null,
-       "value": "classifier.ckpt: 100%"
+       "value": " 2.04k/2.04k [00:00<00:00, 459kB/s]"
       }
      },
-     "a336443b66df467dace7e94c1c59ad27": {
+     "b72a0b2bfed9412ea7c80b41aa396823": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2665,7 +2735,25 @@
        "width": null
       }
      },
-     "a9b5009feb6945c79783c94aea122c63": {
+     "bd283822e62a40f198ddb7e9f5e07b3b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "bf4345672517422b9259bcd86c2b92b0": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2718,7 +2806,33 @@
        "width": null
       }
      },
-     "b12ada2d417f46e88fe6fabd24838332": {
+     "d7016839426a46a2b6291eeb633cb623": {
+      "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_b72a0b2bfed9412ea7c80b41aa396823",
+       "max": 2041.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_a41d618c74d340b3925fe3a0851f1315",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 2041.0
+      }
+     },
+     "d883b7d379fc4151a9aca9d049129d04": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2771,25 +2885,7 @@
        "width": null
       }
      },
-     "b3c669058dac4292bef1f53bf12f3968": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "b5cbbf51759d4c5c890e614cbfc65642": {
+     "e206079b94314a15a28f298733273909": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -2804,39 +2900,15 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_c691af00cbb94bf39c3a8a5146a576dc",
+       "layout": "IPY_MODEL_f39ac366e8f24b1089c0781b3749fed2",
        "placeholder": "​",
-       "style": "IPY_MODEL_1687948ffbe64fd9901a3f79e6268028",
+       "style": "IPY_MODEL_35caa1342ed042b4801df365091345bd",
        "tabbable": null,
        "tooltip": null,
-       "value": "embedding_model.ckpt: 100%"
-      }
-     },
-     "b8a6f025d5624d209b93b9c7c3bb3c27": {
-      "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_3cf84962c8db4c2cad717e15ba4bc832",
-        "IPY_MODEL_7b77c474ff8342bda1ca217c71271855",
-        "IPY_MODEL_d50c911c091841e0bb8a46eee475bac4"
-       ],
-       "layout": "IPY_MODEL_64e15d9dc2bb464bb5b168142752dc2f",
-       "tabbable": null,
-       "tooltip": null
+       "value": "classifier.ckpt: 100%"
       }
      },
-     "bfe535d96fae496284f30fa9c1781a3a": {
+     "e571d89312c04371a4020ad4c5ca65da": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2889,23 +2961,7 @@
        "width": null
       }
      },
-     "c5ae8d6c40ff4963a0fbf5fcf6bfc93e": {
-      "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": ""
-      }
-     },
-     "c691af00cbb94bf39c3a8a5146a576dc": {
+     "ebb668d2cc1f4a088059ce6f93bd3d27": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -2958,46 +3014,31 @@
        "width": null
       }
      },
-     "c996c8ded1c242519367283b82a57d1f": {
-      "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": ""
-      }
-     },
-     "d50c911c091841e0bb8a46eee475bac4": {
+     "f2995cfbadb74ba4a58692ba3ba8921e": {
       "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_2a30b6a9d0de4b19b30a1a834646a8e1",
-       "placeholder": "​",
-       "style": "IPY_MODEL_5fc0069ebb63460b8e077df8b935e5ab",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_e206079b94314a15a28f298733273909",
+        "IPY_MODEL_86f07b0ca74a472e9763172c0ca78fff",
+        "IPY_MODEL_66089462085c404fa7080ca4e424dddf"
+       ],
+       "layout": "IPY_MODEL_d883b7d379fc4151a9aca9d049129d04",
        "tabbable": null,
-       "tooltip": null,
-       "value": " 129k/129k [00:00<00:00, 1.75MB/s]"
+       "tooltip": null
       }
      },
-     "da647cf9f1e14c11a29292e32cfaa2a3": {
+     "f39ac366e8f24b1089c0781b3749fed2": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -3050,83 +3091,7 @@
        "width": null
       }
      },
-     "dc41e0c8f71c4f53b9ca2c3622990d8c": {
-      "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_5a8dcca2bd4146cdbb15cc6215ee402f",
-       "max": 3201.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_ec74d93347b0455697f7f52ad09a1fb0",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 3201.0
-      }
-     },
-     "e47ca2d722a34a598abe1cf08289a6e2": {
-      "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": ""
-      }
-     },
-     "ec74d93347b0455697f7f52ad09a1fb0": {
-      "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": ""
-      }
-     },
-     "edcd8a9c035f473c802a8aa421d746ea": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "f94bb4e2c10a42c5bb656de999d1e76d": {
+     "fe9f42f3c0354ff5b64d4b2ca255c7fb": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -3179,22 +3144,57 @@
        "width": null
       }
      },
-     "fb650e73d93c40c480141df1f8373f27": {
-      "model_module": "@jupyter-widgets/controls",
+     "fea3afa1441a49fe8b5413c70653d77c": {
+      "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
       }
      }
     },
diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html
index e57243765..249135ab2 100644
--- a/master/tutorials/datalab/datalab_advanced.html
+++ b/master/tutorials/datalab/datalab_advanced.html
@@ -1218,7 +1218,7 @@ 
Functionality 3: Save and load Datalab objects
-
+
@@ -1483,7 +1483,7 @@

Functionality 4: Adding a custom IssueManager -{"state": {"965b96a03f4e49a1a35a64ca56a57070": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f7be9183e943429895b5d0e38c66994f": {"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": ""}}, "a9a469c2cb9d495bbdf9f27c6a9e086a": {"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_965b96a03f4e49a1a35a64ca56a57070", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f7be9183e943429895b5d0e38c66994f", "tabbable": null, "tooltip": null, "value": 132.0}}, "f8993d6a885a431590c7b325abe563b1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c257e6f030e24991b0a5da88e6bd39d9": {"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}}, "0d75aada1d784552b4bc57dd0e2d8e87": {"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_f8993d6a885a431590c7b325abe563b1", "placeholder": "\u200b", "style": "IPY_MODEL_c257e6f030e24991b0a5da88e6bd39d9", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "81f24ab10591408ab1737ab8405af10d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9edacddf3f6f468e835617c96a838ac4": {"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}}, "198de52115ce41668cdd0e08b62d1664": {"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_81f24ab10591408ab1737ab8405af10d", "placeholder": "\u200b", "style": "IPY_MODEL_9edacddf3f6f468e835617c96a838ac4", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200710999.27\u2007examples/s]"}}, "31cff3e2bd7d4bfaa600ff052f7c815a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "339860642e334976a9a046f45fd791a3": {"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_0d75aada1d784552b4bc57dd0e2d8e87", "IPY_MODEL_a9a469c2cb9d495bbdf9f27c6a9e086a", "IPY_MODEL_198de52115ce41668cdd0e08b62d1664"], "layout": "IPY_MODEL_31cff3e2bd7d4bfaa600ff052f7c815a", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"e34c8492e0a54010be6bf703108cebd3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3297cbbe557e44f78a95fb0bea7c24fa": {"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": ""}}, "1ad89a56d3604860be10ca4c39cd069f": {"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_e34c8492e0a54010be6bf703108cebd3", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3297cbbe557e44f78a95fb0bea7c24fa", "tabbable": null, "tooltip": null, "value": 132.0}}, "d657cda5aa3443f39f5dfc2bafae7ee5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c5c65b2c403247a2a9396c4a42836444": {"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}}, "f0c36a208851449aa52078261a02d705": {"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_d657cda5aa3443f39f5dfc2bafae7ee5", "placeholder": "\u200b", "style": "IPY_MODEL_c5c65b2c403247a2a9396c4a42836444", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "345a0523363b498db8c90b3d4534316b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ca514b22286544a8aa112b337064ede8": {"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}}, "2eecee279b5c4b67b35ae8d108cc2ee0": {"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_345a0523363b498db8c90b3d4534316b", "placeholder": "\u200b", "style": "IPY_MODEL_ca514b22286544a8aa112b337064ede8", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200710513.24\u2007examples/s]"}}, "a9faa51cd3b447f2b2bb01ab6bc04826": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c0f9ed83b8d44aadb0570576e9326e9e": {"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_f0c36a208851449aa52078261a02d705", "IPY_MODEL_1ad89a56d3604860be10ca4c39cd069f", "IPY_MODEL_2eecee279b5c4b67b35ae8d108cc2ee0"], "layout": "IPY_MODEL_a9faa51cd3b447f2b2bb01ab6bc04826", "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 74ac3a011..6266d6219 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": "2024-02-27T04:05:41.387234Z", - "iopub.status.busy": "2024-02-27T04:05:41.386905Z", - "iopub.status.idle": "2024-02-27T04:05:42.482314Z", - "shell.execute_reply": "2024-02-27T04:05:42.481746Z" + "iopub.execute_input": "2024-03-05T17:20:25.003333Z", + "iopub.status.busy": "2024-03-05T17:20:25.003152Z", + "iopub.status.idle": "2024-03-05T17:20:26.185083Z", + "shell.execute_reply": "2024-03-05T17:20:26.184572Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:05:42.484862Z", - "iopub.status.busy": "2024-02-27T04:05:42.484602Z", - "iopub.status.idle": "2024-02-27T04:05:42.487596Z", - "shell.execute_reply": "2024-02-27T04:05:42.487153Z" + "iopub.execute_input": "2024-03-05T17:20:26.187945Z", + "iopub.status.busy": "2024-03-05T17:20:26.187337Z", + "iopub.status.idle": "2024-03-05T17:20:26.190682Z", + "shell.execute_reply": "2024-03-05T17:20:26.190110Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:42.489522Z", - "iopub.status.busy": "2024-02-27T04:05:42.489352Z", - "iopub.status.idle": "2024-02-27T04:05:42.497844Z", - "shell.execute_reply": "2024-02-27T04:05:42.497391Z" + "iopub.execute_input": "2024-03-05T17:20:26.192976Z", + "iopub.status.busy": "2024-03-05T17:20:26.192612Z", + "iopub.status.idle": "2024-03-05T17:20:26.202240Z", + "shell.execute_reply": "2024-03-05T17:20:26.201637Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:42.499598Z", - "iopub.status.busy": "2024-02-27T04:05:42.499429Z", - "iopub.status.idle": "2024-02-27T04:05:42.504259Z", - "shell.execute_reply": "2024-02-27T04:05:42.503862Z" + "iopub.execute_input": "2024-03-05T17:20:26.204394Z", + "iopub.status.busy": "2024-03-05T17:20:26.204171Z", + "iopub.status.idle": "2024-03-05T17:20:26.209462Z", + "shell.execute_reply": "2024-03-05T17:20:26.208897Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:42.506373Z", - "iopub.status.busy": "2024-02-27T04:05:42.506053Z", - "iopub.status.idle": "2024-02-27T04:05:42.686458Z", - "shell.execute_reply": "2024-02-27T04:05:42.685990Z" + "iopub.execute_input": "2024-03-05T17:20:26.211671Z", + "iopub.status.busy": "2024-03-05T17:20:26.211325Z", + "iopub.status.idle": "2024-03-05T17:20:26.402086Z", + "shell.execute_reply": "2024-03-05T17:20:26.401527Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:42.688795Z", - "iopub.status.busy": "2024-02-27T04:05:42.688517Z", - "iopub.status.idle": "2024-02-27T04:05:43.056220Z", - "shell.execute_reply": "2024-02-27T04:05:43.055676Z" + "iopub.execute_input": "2024-03-05T17:20:26.404796Z", + "iopub.status.busy": "2024-03-05T17:20:26.404409Z", + "iopub.status.idle": "2024-03-05T17:20:26.793091Z", + "shell.execute_reply": "2024-03-05T17:20:26.792484Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:43.058356Z", - "iopub.status.busy": "2024-02-27T04:05:43.058168Z", - "iopub.status.idle": "2024-02-27T04:05:43.082002Z", - "shell.execute_reply": "2024-02-27T04:05:43.081569Z" + "iopub.execute_input": "2024-03-05T17:20:26.795498Z", + "iopub.status.busy": "2024-03-05T17:20:26.795072Z", + "iopub.status.idle": "2024-03-05T17:20:26.819385Z", + "shell.execute_reply": "2024-03-05T17:20:26.818771Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:43.083945Z", - "iopub.status.busy": "2024-02-27T04:05:43.083766Z", - "iopub.status.idle": "2024-02-27T04:05:43.094596Z", - "shell.execute_reply": "2024-02-27T04:05:43.094195Z" + "iopub.execute_input": "2024-03-05T17:20:26.822118Z", + "iopub.status.busy": "2024-03-05T17:20:26.821758Z", + "iopub.status.idle": "2024-03-05T17:20:26.836988Z", + "shell.execute_reply": "2024-03-05T17:20:26.836497Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:43.096612Z", - "iopub.status.busy": "2024-02-27T04:05:43.096295Z", - "iopub.status.idle": "2024-02-27T04:05:44.675827Z", - "shell.execute_reply": "2024-02-27T04:05:44.675257Z" + "iopub.execute_input": "2024-03-05T17:20:26.839663Z", + "iopub.status.busy": "2024-03-05T17:20:26.839295Z", + "iopub.status.idle": "2024-03-05T17:20:28.625914Z", + "shell.execute_reply": "2024-03-05T17:20:28.625251Z" } }, "outputs": [ @@ -709,10 +709,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:44.678300Z", - "iopub.status.busy": "2024-02-27T04:05:44.677837Z", - "iopub.status.idle": "2024-02-27T04:05:44.699073Z", - "shell.execute_reply": "2024-02-27T04:05:44.698649Z" + "iopub.execute_input": "2024-03-05T17:20:28.628752Z", + "iopub.status.busy": "2024-03-05T17:20:28.628172Z", + "iopub.status.idle": "2024-03-05T17:20:28.654230Z", + "shell.execute_reply": "2024-03-05T17:20:28.653636Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:44.701104Z", - "iopub.status.busy": "2024-02-27T04:05:44.700795Z", - "iopub.status.idle": "2024-02-27T04:05:44.719782Z", - "shell.execute_reply": "2024-02-27T04:05:44.719241Z" + "iopub.execute_input": "2024-03-05T17:20:28.656640Z", + "iopub.status.busy": "2024-03-05T17:20:28.656291Z", + "iopub.status.idle": "2024-03-05T17:20:28.677261Z", + "shell.execute_reply": "2024-03-05T17:20:28.676662Z" } }, "outputs": [ @@ -936,10 +936,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:44.721588Z", - "iopub.status.busy": "2024-02-27T04:05:44.721421Z", - "iopub.status.idle": "2024-02-27T04:05:44.736162Z", - "shell.execute_reply": "2024-02-27T04:05:44.735750Z" + "iopub.execute_input": "2024-03-05T17:20:28.679426Z", + "iopub.status.busy": "2024-03-05T17:20:28.679093Z", + "iopub.status.idle": "2024-03-05T17:20:28.693568Z", + "shell.execute_reply": "2024-03-05T17:20:28.693120Z" } }, "outputs": [ @@ -1069,17 +1069,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:44.738135Z", - "iopub.status.busy": "2024-02-27T04:05:44.737963Z", - "iopub.status.idle": "2024-02-27T04:05:44.758464Z", - "shell.execute_reply": "2024-02-27T04:05:44.757916Z" + "iopub.execute_input": "2024-03-05T17:20:28.695769Z", + "iopub.status.busy": "2024-03-05T17:20:28.695363Z", + "iopub.status.idle": "2024-03-05T17:20:28.720069Z", + "shell.execute_reply": "2024-03-05T17:20:28.719450Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "339860642e334976a9a046f45fd791a3", + "model_id": "c0f9ed83b8d44aadb0570576e9326e9e", "version_major": 2, "version_minor": 0 }, @@ -1115,10 +1115,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:44.760781Z", - "iopub.status.busy": "2024-02-27T04:05:44.760473Z", - "iopub.status.idle": "2024-02-27T04:05:44.774984Z", - "shell.execute_reply": "2024-02-27T04:05:44.774571Z" + "iopub.execute_input": "2024-03-05T17:20:28.722376Z", + "iopub.status.busy": "2024-03-05T17:20:28.721942Z", + "iopub.status.idle": "2024-03-05T17:20:28.737874Z", + "shell.execute_reply": "2024-03-05T17:20:28.737311Z" } }, "outputs": [ @@ -1236,10 +1236,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:44.777034Z", - "iopub.status.busy": "2024-02-27T04:05:44.776664Z", - "iopub.status.idle": "2024-02-27T04:05:44.782288Z", - "shell.execute_reply": "2024-02-27T04:05:44.781750Z" + "iopub.execute_input": "2024-03-05T17:20:28.740256Z", + "iopub.status.busy": "2024-03-05T17:20:28.739849Z", + "iopub.status.idle": "2024-03-05T17:20:28.746001Z", + "shell.execute_reply": "2024-03-05T17:20:28.745413Z" } }, "outputs": [], @@ -1296,10 +1296,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:44.784189Z", - "iopub.status.busy": "2024-02-27T04:05:44.783894Z", - "iopub.status.idle": "2024-02-27T04:05:44.801508Z", - "shell.execute_reply": "2024-02-27T04:05:44.801050Z" + "iopub.execute_input": "2024-03-05T17:20:28.748231Z", + "iopub.status.busy": "2024-03-05T17:20:28.747881Z", + "iopub.status.idle": "2024-03-05T17:20:28.767639Z", + "shell.execute_reply": "2024-03-05T17:20:28.767061Z" } }, "outputs": [ @@ -1431,30 +1431,33 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0d75aada1d784552b4bc57dd0e2d8e87": { + "1ad89a56d3604860be10ca4c39cd069f": { "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_f8993d6a885a431590c7b325abe563b1", - "placeholder": "​", - "style": "IPY_MODEL_c257e6f030e24991b0a5da88e6bd39d9", + "layout": "IPY_MODEL_e34c8492e0a54010be6bf703108cebd3", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3297cbbe557e44f78a95fb0bea7c24fa", "tabbable": null, "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" + "value": 132.0 } }, - "198de52115ce41668cdd0e08b62d1664": { + "2eecee279b5c4b67b35ae8d108cc2ee0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1469,92 +1472,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_81f24ab10591408ab1737ab8405af10d", + "layout": "IPY_MODEL_345a0523363b498db8c90b3d4534316b", "placeholder": "​", - "style": "IPY_MODEL_9edacddf3f6f468e835617c96a838ac4", + "style": "IPY_MODEL_ca514b22286544a8aa112b337064ede8", "tabbable": null, "tooltip": null, - "value": " 132/132 [00:00<00:00, 10999.27 examples/s]" - } - }, - "31cff3e2bd7d4bfaa600ff052f7c815a": { - "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": " 132/132 [00:00<00:00, 10513.24 examples/s]" } }, - "339860642e334976a9a046f45fd791a3": { + "3297cbbe557e44f78a95fb0bea7c24fa": { "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_0d75aada1d784552b4bc57dd0e2d8e87", - "IPY_MODEL_a9a469c2cb9d495bbdf9f27c6a9e086a", - "IPY_MODEL_198de52115ce41668cdd0e08b62d1664" - ], - "layout": "IPY_MODEL_31cff3e2bd7d4bfaa600ff052f7c815a", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "81f24ab10591408ab1737ab8405af10d": { + "345a0523363b498db8c90b3d4534316b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1607,7 +1549,7 @@ "width": null } }, - "965b96a03f4e49a1a35a64ca56a57070": { + "a9faa51cd3b447f2b2bb01ab6bc04826": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1660,51 +1602,49 @@ "width": null } }, - "9edacddf3f6f468e835617c96a838ac4": { + "c0f9ed83b8d44aadb0570576e9326e9e": { "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_f0c36a208851449aa52078261a02d705", + "IPY_MODEL_1ad89a56d3604860be10ca4c39cd069f", + "IPY_MODEL_2eecee279b5c4b67b35ae8d108cc2ee0" + ], + "layout": "IPY_MODEL_a9faa51cd3b447f2b2bb01ab6bc04826", + "tabbable": null, + "tooltip": null } }, - "a9a469c2cb9d495bbdf9f27c6a9e086a": { + "c5c65b2c403247a2a9396c4a42836444": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_965b96a03f4e49a1a35a64ca56a57070", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f7be9183e943429895b5d0e38c66994f", - "tabbable": null, - "tooltip": null, - "value": 132.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c257e6f030e24991b0a5da88e6bd39d9": { + "ca514b22286544a8aa112b337064ede8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1722,23 +1662,60 @@ "text_color": null } }, - "f7be9183e943429895b5d0e38c66994f": { - "model_module": "@jupyter-widgets/controls", + "d657cda5aa3443f39f5dfc2bafae7ee5": { + "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 } }, - "f8993d6a885a431590c7b325abe563b1": { + "e34c8492e0a54010be6bf703108cebd3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1790,6 +1767,29 @@ "visibility": null, "width": null } + }, + "f0c36a208851449aa52078261a02d705": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d657cda5aa3443f39f5dfc2bafae7ee5", + "placeholder": "​", + "style": "IPY_MODEL_c5c65b2c403247a2a9396c4a42836444", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index d42b89cd8..3ce8e9b9b 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": "2024-02-27T04:05:47.283167Z", - "iopub.status.busy": "2024-02-27T04:05:47.282992Z", - "iopub.status.idle": "2024-02-27T04:05:48.349052Z", - "shell.execute_reply": "2024-02-27T04:05:48.348530Z" + "iopub.execute_input": "2024-03-05T17:20:31.597050Z", + "iopub.status.busy": "2024-03-05T17:20:31.596874Z", + "iopub.status.idle": "2024-03-05T17:20:32.783852Z", + "shell.execute_reply": "2024-03-05T17:20:32.783250Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:05:48.351439Z", - "iopub.status.busy": "2024-02-27T04:05:48.351143Z", - "iopub.status.idle": "2024-02-27T04:05:48.354037Z", - "shell.execute_reply": "2024-02-27T04:05:48.353552Z" + "iopub.execute_input": "2024-03-05T17:20:32.786619Z", + "iopub.status.busy": "2024-03-05T17:20:32.786107Z", + "iopub.status.idle": "2024-03-05T17:20:32.789369Z", + "shell.execute_reply": "2024-03-05T17:20:32.788841Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:48.356211Z", - "iopub.status.busy": "2024-02-27T04:05:48.355895Z", - "iopub.status.idle": "2024-02-27T04:05:48.364600Z", - "shell.execute_reply": "2024-02-27T04:05:48.364196Z" + "iopub.execute_input": "2024-03-05T17:20:32.791641Z", + "iopub.status.busy": "2024-03-05T17:20:32.791255Z", + "iopub.status.idle": "2024-03-05T17:20:32.800989Z", + "shell.execute_reply": "2024-03-05T17:20:32.800473Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:48.366616Z", - "iopub.status.busy": "2024-02-27T04:05:48.366293Z", - "iopub.status.idle": "2024-02-27T04:05:48.370947Z", - "shell.execute_reply": "2024-02-27T04:05:48.370512Z" + "iopub.execute_input": "2024-03-05T17:20:32.803297Z", + "iopub.status.busy": "2024-03-05T17:20:32.802933Z", + "iopub.status.idle": "2024-03-05T17:20:32.807870Z", + "shell.execute_reply": "2024-03-05T17:20:32.807285Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:48.373085Z", - "iopub.status.busy": "2024-02-27T04:05:48.372774Z", - "iopub.status.idle": "2024-02-27T04:05:48.552425Z", - "shell.execute_reply": "2024-02-27T04:05:48.551449Z" + "iopub.execute_input": "2024-03-05T17:20:32.809943Z", + "iopub.status.busy": "2024-03-05T17:20:32.809760Z", + "iopub.status.idle": "2024-03-05T17:20:32.999231Z", + "shell.execute_reply": "2024-03-05T17:20:32.998731Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:48.554861Z", - "iopub.status.busy": "2024-02-27T04:05:48.554513Z", - "iopub.status.idle": "2024-02-27T04:05:48.881826Z", - "shell.execute_reply": "2024-02-27T04:05:48.881254Z" + "iopub.execute_input": "2024-03-05T17:20:33.001628Z", + "iopub.status.busy": "2024-03-05T17:20:33.001405Z", + "iopub.status.idle": "2024-03-05T17:20:33.352056Z", + "shell.execute_reply": "2024-03-05T17:20:33.351467Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:48.884083Z", - "iopub.status.busy": "2024-02-27T04:05:48.883728Z", - "iopub.status.idle": "2024-02-27T04:05:48.886487Z", - "shell.execute_reply": "2024-02-27T04:05:48.886061Z" + "iopub.execute_input": "2024-03-05T17:20:33.354402Z", + "iopub.status.busy": "2024-03-05T17:20:33.353990Z", + "iopub.status.idle": "2024-03-05T17:20:33.356747Z", + "shell.execute_reply": "2024-03-05T17:20:33.356324Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:48.888476Z", - "iopub.status.busy": "2024-02-27T04:05:48.888145Z", - "iopub.status.idle": "2024-02-27T04:05:48.923241Z", - "shell.execute_reply": "2024-02-27T04:05:48.922712Z" + "iopub.execute_input": "2024-03-05T17:20:33.358830Z", + "iopub.status.busy": "2024-03-05T17:20:33.358512Z", + "iopub.status.idle": "2024-03-05T17:20:33.394203Z", + "shell.execute_reply": "2024-03-05T17:20:33.393636Z" } }, "outputs": [ @@ -647,10 +647,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:48.925298Z", - "iopub.status.busy": "2024-02-27T04:05:48.924964Z", - "iopub.status.idle": "2024-02-27T04:05:50.564886Z", - "shell.execute_reply": "2024-02-27T04:05:50.564350Z" + "iopub.execute_input": "2024-03-05T17:20:33.396541Z", + "iopub.status.busy": "2024-03-05T17:20:33.396191Z", + "iopub.status.idle": "2024-03-05T17:20:35.242061Z", + "shell.execute_reply": "2024-03-05T17:20:35.241403Z" } }, "outputs": [ @@ -703,10 +703,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:50.567530Z", - "iopub.status.busy": "2024-02-27T04:05:50.566890Z", - "iopub.status.idle": "2024-02-27T04:05:50.586109Z", - "shell.execute_reply": "2024-02-27T04:05:50.585606Z" + "iopub.execute_input": "2024-03-05T17:20:35.244816Z", + "iopub.status.busy": "2024-03-05T17:20:35.244075Z", + "iopub.status.idle": "2024-03-05T17:20:35.265901Z", + "shell.execute_reply": "2024-03-05T17:20:35.265357Z" } }, "outputs": [ @@ -834,10 +834,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:50.588129Z", - "iopub.status.busy": "2024-02-27T04:05:50.587798Z", - "iopub.status.idle": "2024-02-27T04:05:50.593918Z", - "shell.execute_reply": "2024-02-27T04:05:50.593475Z" + "iopub.execute_input": "2024-03-05T17:20:35.268062Z", + "iopub.status.busy": "2024-03-05T17:20:35.267783Z", + "iopub.status.idle": "2024-03-05T17:20:35.274791Z", + "shell.execute_reply": "2024-03-05T17:20:35.274271Z" } }, "outputs": [ @@ -948,10 +948,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:50.595920Z", - "iopub.status.busy": "2024-02-27T04:05:50.595664Z", - "iopub.status.idle": "2024-02-27T04:05:50.601302Z", - "shell.execute_reply": "2024-02-27T04:05:50.600771Z" + "iopub.execute_input": "2024-03-05T17:20:35.276947Z", + "iopub.status.busy": "2024-03-05T17:20:35.276610Z", + "iopub.status.idle": "2024-03-05T17:20:35.282401Z", + "shell.execute_reply": "2024-03-05T17:20:35.281975Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:50.603461Z", - "iopub.status.busy": "2024-02-27T04:05:50.603081Z", - "iopub.status.idle": "2024-02-27T04:05:50.613302Z", - "shell.execute_reply": "2024-02-27T04:05:50.612770Z" + "iopub.execute_input": "2024-03-05T17:20:35.284680Z", + "iopub.status.busy": "2024-03-05T17:20:35.284312Z", + "iopub.status.idle": "2024-03-05T17:20:35.297372Z", + "shell.execute_reply": "2024-03-05T17:20:35.296834Z" } }, "outputs": [ @@ -1213,10 +1213,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:50.615369Z", - "iopub.status.busy": "2024-02-27T04:05:50.615055Z", - "iopub.status.idle": "2024-02-27T04:05:50.623959Z", - "shell.execute_reply": "2024-02-27T04:05:50.623440Z" + "iopub.execute_input": "2024-03-05T17:20:35.299714Z", + "iopub.status.busy": "2024-03-05T17:20:35.299325Z", + "iopub.status.idle": "2024-03-05T17:20:35.309657Z", + "shell.execute_reply": "2024-03-05T17:20:35.309205Z" } }, "outputs": [ @@ -1332,10 +1332,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:50.625922Z", - "iopub.status.busy": "2024-02-27T04:05:50.625618Z", - "iopub.status.idle": "2024-02-27T04:05:50.632194Z", - "shell.execute_reply": "2024-02-27T04:05:50.631776Z" + "iopub.execute_input": "2024-03-05T17:20:35.311732Z", + "iopub.status.busy": "2024-03-05T17:20:35.311381Z", + "iopub.status.idle": "2024-03-05T17:20:35.318398Z", + "shell.execute_reply": "2024-03-05T17:20:35.317936Z" }, "scrolled": true }, @@ -1460,10 +1460,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:50.634258Z", - "iopub.status.busy": "2024-02-27T04:05:50.633943Z", - "iopub.status.idle": "2024-02-27T04:05:50.642913Z", - "shell.execute_reply": "2024-02-27T04:05:50.642484Z" + "iopub.execute_input": "2024-03-05T17:20:35.320615Z", + "iopub.status.busy": "2024-03-05T17:20:35.320289Z", + "iopub.status.idle": "2024-03-05T17:20:35.329915Z", + "shell.execute_reply": "2024-03-05T17:20:35.329337Z" } }, "outputs": [ diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 545ca5a09..09b2aa32f 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:53.195675Z", - "iopub.status.busy": "2024-02-27T04:05:53.195508Z", - "iopub.status.idle": "2024-02-27T04:05:54.213531Z", - "shell.execute_reply": "2024-02-27T04:05:54.212938Z" + "iopub.execute_input": "2024-03-05T17:20:38.324379Z", + "iopub.status.busy": "2024-03-05T17:20:38.323914Z", + "iopub.status.idle": "2024-03-05T17:20:39.449137Z", + "shell.execute_reply": "2024-03-05T17:20:39.448616Z" }, "nbsphinx": "hidden" }, @@ -87,7 +87,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:54.216112Z", - "iopub.status.busy": "2024-02-27T04:05:54.215792Z", - "iopub.status.idle": "2024-02-27T04:05:54.234342Z", - "shell.execute_reply": "2024-02-27T04:05:54.233813Z" + "iopub.execute_input": "2024-03-05T17:20:39.451641Z", + "iopub.status.busy": "2024-03-05T17:20:39.451319Z", + "iopub.status.idle": "2024-03-05T17:20:39.500378Z", + "shell.execute_reply": "2024-03-05T17:20:39.499694Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:54.236418Z", - "iopub.status.busy": "2024-02-27T04:05:54.236051Z", - "iopub.status.idle": "2024-02-27T04:05:54.488803Z", - "shell.execute_reply": "2024-02-27T04:05:54.488301Z" + "iopub.execute_input": "2024-03-05T17:20:39.503373Z", + "iopub.status.busy": "2024-03-05T17:20:39.502840Z", + "iopub.status.idle": "2024-03-05T17:20:39.837627Z", + "shell.execute_reply": "2024-03-05T17:20:39.837070Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:54.490837Z", - "iopub.status.busy": "2024-02-27T04:05:54.490507Z", - "iopub.status.idle": "2024-02-27T04:05:54.493788Z", - "shell.execute_reply": "2024-02-27T04:05:54.493362Z" + "iopub.execute_input": "2024-03-05T17:20:39.839739Z", + "iopub.status.busy": "2024-03-05T17:20:39.839553Z", + "iopub.status.idle": "2024-03-05T17:20:39.843046Z", + "shell.execute_reply": "2024-03-05T17:20:39.842626Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:54.495817Z", - "iopub.status.busy": "2024-02-27T04:05:54.495514Z", - "iopub.status.idle": "2024-02-27T04:05:54.503082Z", - "shell.execute_reply": "2024-02-27T04:05:54.502683Z" + "iopub.execute_input": "2024-03-05T17:20:39.844999Z", + "iopub.status.busy": "2024-03-05T17:20:39.844818Z", + "iopub.status.idle": "2024-03-05T17:20:39.853451Z", + "shell.execute_reply": "2024-03-05T17:20:39.852908Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:54.505050Z", - "iopub.status.busy": "2024-02-27T04:05:54.504787Z", - "iopub.status.idle": "2024-02-27T04:05:54.507404Z", - "shell.execute_reply": "2024-02-27T04:05:54.506875Z" + "iopub.execute_input": "2024-03-05T17:20:39.855824Z", + "iopub.status.busy": "2024-03-05T17:20:39.855377Z", + "iopub.status.idle": "2024-03-05T17:20:39.858012Z", + "shell.execute_reply": "2024-03-05T17:20:39.857590Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:54.509498Z", - "iopub.status.busy": "2024-02-27T04:05:54.509025Z", - "iopub.status.idle": "2024-02-27T04:05:57.470871Z", - "shell.execute_reply": "2024-02-27T04:05:57.470369Z" + "iopub.execute_input": "2024-03-05T17:20:39.860396Z", + "iopub.status.busy": "2024-03-05T17:20:39.859967Z", + "iopub.status.idle": "2024-03-05T17:20:42.949636Z", + "shell.execute_reply": "2024-03-05T17:20:42.948999Z" } }, "outputs": [], @@ -402,10 +402,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:57.473399Z", - "iopub.status.busy": "2024-02-27T04:05:57.473010Z", - "iopub.status.idle": "2024-02-27T04:05:57.482991Z", - "shell.execute_reply": "2024-02-27T04:05:57.482581Z" + "iopub.execute_input": "2024-03-05T17:20:42.952893Z", + "iopub.status.busy": "2024-03-05T17:20:42.952245Z", + "iopub.status.idle": "2024-03-05T17:20:42.962189Z", + "shell.execute_reply": "2024-03-05T17:20:42.961618Z" } }, "outputs": [], @@ -437,10 +437,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:57.484952Z", - "iopub.status.busy": "2024-02-27T04:05:57.484628Z", - "iopub.status.idle": "2024-02-27T04:05:59.232925Z", - "shell.execute_reply": "2024-02-27T04:05:59.232292Z" + "iopub.execute_input": "2024-03-05T17:20:42.964615Z", + "iopub.status.busy": "2024-03-05T17:20:42.964268Z", + "iopub.status.idle": "2024-03-05T17:20:44.896121Z", + "shell.execute_reply": "2024-03-05T17:20:44.895455Z" } }, "outputs": [ @@ -477,10 +477,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:59.236752Z", - "iopub.status.busy": "2024-02-27T04:05:59.235340Z", - "iopub.status.idle": "2024-02-27T04:05:59.262008Z", - "shell.execute_reply": "2024-02-27T04:05:59.261516Z" + "iopub.execute_input": "2024-03-05T17:20:44.899563Z", + "iopub.status.busy": "2024-03-05T17:20:44.898714Z", + "iopub.status.idle": "2024-03-05T17:20:44.923777Z", + "shell.execute_reply": "2024-03-05T17:20:44.923232Z" }, "scrolled": true }, @@ -605,10 +605,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:59.265463Z", - "iopub.status.busy": "2024-02-27T04:05:59.264570Z", - "iopub.status.idle": "2024-02-27T04:05:59.275678Z", - "shell.execute_reply": "2024-02-27T04:05:59.275218Z" + "iopub.execute_input": "2024-03-05T17:20:44.926341Z", + "iopub.status.busy": "2024-03-05T17:20:44.925966Z", + "iopub.status.idle": "2024-03-05T17:20:44.935427Z", + "shell.execute_reply": "2024-03-05T17:20:44.934951Z" } }, "outputs": [ @@ -712,10 +712,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:59.279208Z", - "iopub.status.busy": "2024-02-27T04:05:59.278194Z", - "iopub.status.idle": "2024-02-27T04:05:59.290903Z", - "shell.execute_reply": "2024-02-27T04:05:59.290447Z" + "iopub.execute_input": "2024-03-05T17:20:44.937925Z", + "iopub.status.busy": "2024-03-05T17:20:44.937557Z", + "iopub.status.idle": "2024-03-05T17:20:44.948863Z", + "shell.execute_reply": "2024-03-05T17:20:44.948363Z" } }, "outputs": [ @@ -844,10 +844,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:59.294320Z", - "iopub.status.busy": "2024-02-27T04:05:59.293396Z", - "iopub.status.idle": "2024-02-27T04:05:59.304198Z", - "shell.execute_reply": "2024-02-27T04:05:59.303812Z" + "iopub.execute_input": "2024-03-05T17:20:44.951408Z", + "iopub.status.busy": "2024-03-05T17:20:44.951038Z", + "iopub.status.idle": "2024-03-05T17:20:44.960643Z", + "shell.execute_reply": "2024-03-05T17:20:44.960144Z" } }, "outputs": [ @@ -961,10 +961,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:59.306903Z", - "iopub.status.busy": "2024-02-27T04:05:59.306190Z", - "iopub.status.idle": "2024-02-27T04:05:59.315253Z", - "shell.execute_reply": "2024-02-27T04:05:59.314831Z" + "iopub.execute_input": "2024-03-05T17:20:44.963214Z", + "iopub.status.busy": "2024-03-05T17:20:44.962836Z", + "iopub.status.idle": "2024-03-05T17:20:44.971838Z", + "shell.execute_reply": "2024-03-05T17:20:44.971419Z" } }, "outputs": [ @@ -1075,10 +1075,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:59.317228Z", - "iopub.status.busy": "2024-02-27T04:05:59.316922Z", - "iopub.status.idle": "2024-02-27T04:05:59.323118Z", - "shell.execute_reply": "2024-02-27T04:05:59.322591Z" + "iopub.execute_input": "2024-03-05T17:20:44.973897Z", + "iopub.status.busy": "2024-03-05T17:20:44.973595Z", + "iopub.status.idle": "2024-03-05T17:20:44.979761Z", + "shell.execute_reply": "2024-03-05T17:20:44.979327Z" } }, "outputs": [ @@ -1162,10 +1162,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:59.325111Z", - "iopub.status.busy": "2024-02-27T04:05:59.324821Z", - "iopub.status.idle": "2024-02-27T04:05:59.330955Z", - "shell.execute_reply": "2024-02-27T04:05:59.330435Z" + "iopub.execute_input": "2024-03-05T17:20:44.981729Z", + "iopub.status.busy": "2024-03-05T17:20:44.981441Z", + "iopub.status.idle": "2024-03-05T17:20:44.987334Z", + "shell.execute_reply": "2024-03-05T17:20:44.986939Z" } }, "outputs": [ @@ -1258,10 +1258,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:05:59.332907Z", - "iopub.status.busy": "2024-02-27T04:05:59.332605Z", - "iopub.status.idle": "2024-02-27T04:05:59.339035Z", - "shell.execute_reply": "2024-02-27T04:05:59.338505Z" + "iopub.execute_input": "2024-03-05T17:20:44.989372Z", + "iopub.status.busy": "2024-03-05T17:20:44.989066Z", + "iopub.status.idle": "2024-03-05T17:20:44.996029Z", + "shell.execute_reply": "2024-03-05T17:20:44.995450Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 0c0523639..b53985b8e 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -723,7 +723,7 @@

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

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

@@ -770,43 +770,43 @@

2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1525,7 +1525,7 @@

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/text.ipynb b/master/tutorials/datalab/text.ipynb index 18ece56e9..1f0963528 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": "2024-02-27T04:06:01.970290Z", - "iopub.status.busy": "2024-02-27T04:06:01.970119Z", - "iopub.status.idle": "2024-02-27T04:06:04.661347Z", - "shell.execute_reply": "2024-02-27T04:06:04.660786Z" + "iopub.execute_input": "2024-03-05T17:20:47.739695Z", + "iopub.status.busy": "2024-03-05T17:20:47.739469Z", + "iopub.status.idle": "2024-03-05T17:20:51.008654Z", + "shell.execute_reply": "2024-03-05T17:20:51.008041Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:06:04.663765Z", - "iopub.status.busy": "2024-02-27T04:06:04.663479Z", - "iopub.status.idle": "2024-02-27T04:06:04.666928Z", - "shell.execute_reply": "2024-02-27T04:06:04.666458Z" + "iopub.execute_input": "2024-03-05T17:20:51.011463Z", + "iopub.status.busy": "2024-03-05T17:20:51.011114Z", + "iopub.status.idle": "2024-03-05T17:20:51.014558Z", + "shell.execute_reply": "2024-03-05T17:20:51.014115Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:04.668736Z", - "iopub.status.busy": "2024-02-27T04:06:04.668563Z", - "iopub.status.idle": "2024-02-27T04:06:04.671428Z", - "shell.execute_reply": "2024-02-27T04:06:04.671011Z" + "iopub.execute_input": "2024-03-05T17:20:51.016692Z", + "iopub.status.busy": "2024-03-05T17:20:51.016344Z", + "iopub.status.idle": "2024-03-05T17:20:51.019473Z", + "shell.execute_reply": "2024-03-05T17:20:51.019018Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:04.673415Z", - "iopub.status.busy": "2024-02-27T04:06:04.673093Z", - "iopub.status.idle": "2024-02-27T04:06:04.815931Z", - "shell.execute_reply": "2024-02-27T04:06:04.815443Z" + "iopub.execute_input": "2024-03-05T17:20:51.021497Z", + "iopub.status.busy": "2024-03-05T17:20:51.021175Z", + "iopub.status.idle": "2024-03-05T17:20:51.181745Z", + "shell.execute_reply": "2024-03-05T17:20:51.181219Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:04.818017Z", - "iopub.status.busy": "2024-02-27T04:06:04.817646Z", - "iopub.status.idle": "2024-02-27T04:06:04.821192Z", - "shell.execute_reply": "2024-02-27T04:06:04.820676Z" + "iopub.execute_input": "2024-03-05T17:20:51.183916Z", + "iopub.status.busy": "2024-03-05T17:20:51.183557Z", + "iopub.status.idle": "2024-03-05T17:20:51.187914Z", + "shell.execute_reply": "2024-03-05T17:20:51.187427Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'getting_spare_card', 'beneficiary_not_allowed', 'change_pin', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'card_payment_fee_charged', 'cancel_transfer', 'card_about_to_expire', 'lost_or_stolen_phone'}\n" + "Classes: {'change_pin', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'getting_spare_card', 'supported_cards_and_currencies', 'visa_or_mastercard', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'card_about_to_expire', 'cancel_transfer'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:04.823148Z", - "iopub.status.busy": "2024-02-27T04:06:04.822816Z", - "iopub.status.idle": "2024-02-27T04:06:04.826002Z", - "shell.execute_reply": "2024-02-27T04:06:04.825474Z" + "iopub.execute_input": "2024-03-05T17:20:51.190048Z", + "iopub.status.busy": "2024-03-05T17:20:51.189701Z", + "iopub.status.idle": "2024-03-05T17:20:51.192989Z", + "shell.execute_reply": "2024-03-05T17:20:51.192521Z" } }, "outputs": [ @@ -365,17 +365,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:04.827989Z", - "iopub.status.busy": "2024-02-27T04:06:04.827689Z", - "iopub.status.idle": "2024-02-27T04:06:10.183883Z", - "shell.execute_reply": "2024-02-27T04:06:10.183261Z" + "iopub.execute_input": "2024-03-05T17:20:51.195081Z", + "iopub.status.busy": "2024-03-05T17:20:51.194749Z", + "iopub.status.idle": "2024-03-05T17:20:56.822368Z", + "shell.execute_reply": "2024-03-05T17:20:56.821726Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6835cc1fdeb4c99bb7db75182a2fa02", + "model_id": "637da32b700c47eba3de5d5f8da01a3c", "version_major": 2, "version_minor": 0 }, @@ -389,7 +389,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f12ce9dadb3544109f98d413818761d5", + "model_id": "8217c61c3ce14e4199198f85e84ae73a", "version_major": 2, "version_minor": 0 }, @@ -403,7 +403,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "98c39a4b261040afa0a1feaa34a5aea1", + "model_id": "0980cd0d8b5348eb805b2dd6ed1416ff", "version_major": 2, "version_minor": 0 }, @@ -417,7 +417,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "71f8d408515f4ecabd1c462b22bd9666", + "model_id": "160f137596894606a5e0e1d13059f977", "version_major": 2, "version_minor": 0 }, @@ -431,7 +431,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9e93629d98964af9a8378a4e1660117f", + "model_id": "57aaeefe17e84b1d9ea3e2b98453137e", "version_major": 2, "version_minor": 0 }, @@ -445,12 +445,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db1b0344cfa64455886e8435c5568f4c", + "model_id": "b06eff523ebb48d08134eda9a17f7e7d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/29.0 [00:00How can I find label issues in big datasets with limited memory?

-
+
-
+
@@ -1595,7 +1595,7 @@

How to handle near-duplicate data identified by cleanlab? 
-/tmp/ipykernel_5939/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+/tmp/ipykernel_5903/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
   to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values

@@ -1637,7 +1637,7 @@

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: info@cleanlab.ai

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 779ce5eed..280c7c07a 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:24.777366Z", - "iopub.status.busy": "2024-02-27T04:06:24.776828Z", - "iopub.status.idle": "2024-02-27T04:06:25.797404Z", - "shell.execute_reply": "2024-02-27T04:06:25.796822Z" + "iopub.execute_input": "2024-03-05T17:21:12.764085Z", + "iopub.status.busy": "2024-03-05T17:21:12.763902Z", + "iopub.status.idle": "2024-03-05T17:21:13.888000Z", + "shell.execute_reply": "2024-03-05T17:21:13.887410Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:25.799916Z", - "iopub.status.busy": "2024-02-27T04:06:25.799559Z", - "iopub.status.idle": "2024-02-27T04:06:25.802800Z", - "shell.execute_reply": "2024-02-27T04:06:25.802367Z" + "iopub.execute_input": "2024-03-05T17:21:13.890687Z", + "iopub.status.busy": "2024-03-05T17:21:13.890322Z", + "iopub.status.idle": "2024-03-05T17:21:13.894013Z", + "shell.execute_reply": "2024-03-05T17:21:13.893570Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:25.804698Z", - "iopub.status.busy": "2024-02-27T04:06:25.804395Z", - "iopub.status.idle": "2024-02-27T04:06:28.687278Z", - "shell.execute_reply": "2024-02-27T04:06:28.686707Z" + "iopub.execute_input": "2024-03-05T17:21:13.896255Z", + "iopub.status.busy": "2024-03-05T17:21:13.895915Z", + "iopub.status.idle": "2024-03-05T17:21:17.074366Z", + "shell.execute_reply": "2024-03-05T17:21:17.073730Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.690428Z", - "iopub.status.busy": "2024-02-27T04:06:28.689569Z", - "iopub.status.idle": "2024-02-27T04:06:28.718152Z", - "shell.execute_reply": "2024-02-27T04:06:28.717464Z" + "iopub.execute_input": "2024-03-05T17:21:17.077518Z", + "iopub.status.busy": "2024-03-05T17:21:17.076764Z", + "iopub.status.idle": "2024-03-05T17:21:17.124643Z", + "shell.execute_reply": "2024-03-05T17:21:17.124010Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.720515Z", - "iopub.status.busy": "2024-02-27T04:06:28.720288Z", - "iopub.status.idle": "2024-02-27T04:06:28.745208Z", - "shell.execute_reply": "2024-02-27T04:06:28.744547Z" + "iopub.execute_input": "2024-03-05T17:21:17.127208Z", + "iopub.status.busy": "2024-03-05T17:21:17.126966Z", + "iopub.status.idle": "2024-03-05T17:21:17.173443Z", + "shell.execute_reply": "2024-03-05T17:21:17.172771Z" } }, "outputs": [], @@ -213,10 +213,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.747965Z", - "iopub.status.busy": "2024-02-27T04:06:28.747444Z", - "iopub.status.idle": "2024-02-27T04:06:28.750447Z", - "shell.execute_reply": "2024-02-27T04:06:28.750019Z" + "iopub.execute_input": "2024-03-05T17:21:17.176293Z", + "iopub.status.busy": "2024-03-05T17:21:17.175958Z", + "iopub.status.idle": "2024-03-05T17:21:17.179061Z", + "shell.execute_reply": "2024-03-05T17:21:17.178612Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.752409Z", - "iopub.status.busy": "2024-02-27T04:06:28.752047Z", - "iopub.status.idle": "2024-02-27T04:06:28.754568Z", - "shell.execute_reply": "2024-02-27T04:06:28.754142Z" + "iopub.execute_input": "2024-03-05T17:21:17.181408Z", + "iopub.status.busy": "2024-03-05T17:21:17.181050Z", + "iopub.status.idle": "2024-03-05T17:21:17.183903Z", + "shell.execute_reply": "2024-03-05T17:21:17.183298Z" } }, "outputs": [], @@ -298,10 +298,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.756710Z", - "iopub.status.busy": "2024-02-27T04:06:28.756324Z", - "iopub.status.idle": "2024-02-27T04:06:28.781321Z", - "shell.execute_reply": "2024-02-27T04:06:28.780814Z" + "iopub.execute_input": "2024-03-05T17:21:17.186166Z", + "iopub.status.busy": "2024-03-05T17:21:17.185828Z", + "iopub.status.idle": "2024-03-05T17:21:17.209818Z", + "shell.execute_reply": "2024-03-05T17:21:17.209311Z" } }, "outputs": [ @@ -315,7 +315,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f61dd3b983a24f74ab208519c173e22e", + "model_id": "ba57cbf4050946d0a8e0f0a46ffb9147", "version_major": 2, "version_minor": 0 }, @@ -329,7 +329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a4f612a2a8e4161a7158f6f22b634c4", + "model_id": "65ea16b53bb94f76ac68f0fce0e54778", "version_major": 2, "version_minor": 0 }, @@ -387,10 +387,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.786975Z", - "iopub.status.busy": "2024-02-27T04:06:28.786603Z", - "iopub.status.idle": "2024-02-27T04:06:28.792913Z", - "shell.execute_reply": "2024-02-27T04:06:28.792410Z" + "iopub.execute_input": "2024-03-05T17:21:17.223969Z", + "iopub.status.busy": "2024-03-05T17:21:17.223480Z", + "iopub.status.idle": "2024-03-05T17:21:17.230131Z", + "shell.execute_reply": "2024-03-05T17:21:17.229711Z" }, "nbsphinx": "hidden" }, @@ -421,10 +421,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.795256Z", - "iopub.status.busy": "2024-02-27T04:06:28.794883Z", - "iopub.status.idle": "2024-02-27T04:06:28.798222Z", - "shell.execute_reply": "2024-02-27T04:06:28.797731Z" + "iopub.execute_input": "2024-03-05T17:21:17.232256Z", + "iopub.status.busy": "2024-03-05T17:21:17.231933Z", + "iopub.status.idle": "2024-03-05T17:21:17.235340Z", + "shell.execute_reply": "2024-03-05T17:21:17.234901Z" }, "nbsphinx": "hidden" }, @@ -447,10 +447,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.800117Z", - "iopub.status.busy": "2024-02-27T04:06:28.799763Z", - "iopub.status.idle": "2024-02-27T04:06:28.805832Z", - "shell.execute_reply": "2024-02-27T04:06:28.805384Z" + "iopub.execute_input": "2024-03-05T17:21:17.237360Z", + "iopub.status.busy": "2024-03-05T17:21:17.237045Z", + "iopub.status.idle": "2024-03-05T17:21:17.244947Z", + "shell.execute_reply": "2024-03-05T17:21:17.244461Z" } }, "outputs": [], @@ -500,10 +500,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.807630Z", - "iopub.status.busy": "2024-02-27T04:06:28.807456Z", - "iopub.status.idle": "2024-02-27T04:06:28.837350Z", - "shell.execute_reply": "2024-02-27T04:06:28.836843Z" + "iopub.execute_input": "2024-03-05T17:21:17.247282Z", + "iopub.status.busy": "2024-03-05T17:21:17.246715Z", + "iopub.status.idle": "2024-03-05T17:21:17.288259Z", + "shell.execute_reply": "2024-03-05T17:21:17.287650Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.839510Z", - "iopub.status.busy": "2024-02-27T04:06:28.839230Z", - "iopub.status.idle": "2024-02-27T04:06:28.866880Z", - "shell.execute_reply": "2024-02-27T04:06:28.866365Z" + "iopub.execute_input": "2024-03-05T17:21:17.290838Z", + "iopub.status.busy": "2024-03-05T17:21:17.290583Z", + "iopub.status.idle": "2024-03-05T17:21:17.335796Z", + "shell.execute_reply": "2024-03-05T17:21:17.335150Z" }, "nbsphinx": "hidden" }, @@ -602,10 +602,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.869188Z", - "iopub.status.busy": "2024-02-27T04:06:28.868906Z", - "iopub.status.idle": "2024-02-27T04:06:28.985567Z", - "shell.execute_reply": "2024-02-27T04:06:28.984999Z" + "iopub.execute_input": "2024-03-05T17:21:17.338557Z", + "iopub.status.busy": "2024-03-05T17:21:17.338302Z", + "iopub.status.idle": "2024-03-05T17:21:17.475626Z", + "shell.execute_reply": "2024-03-05T17:21:17.474982Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:28.988263Z", - "iopub.status.busy": "2024-02-27T04:06:28.987608Z", - "iopub.status.idle": "2024-02-27T04:06:32.060909Z", - "shell.execute_reply": "2024-02-27T04:06:32.060376Z" + "iopub.execute_input": "2024-03-05T17:21:17.478305Z", + "iopub.status.busy": "2024-03-05T17:21:17.477699Z", + "iopub.status.idle": "2024-03-05T17:21:20.566180Z", + "shell.execute_reply": "2024-03-05T17:21:20.565536Z" } }, "outputs": [ @@ -761,10 +761,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:32.063259Z", - "iopub.status.busy": "2024-02-27T04:06:32.062923Z", - "iopub.status.idle": "2024-02-27T04:06:32.116888Z", - "shell.execute_reply": "2024-02-27T04:06:32.116338Z" + "iopub.execute_input": "2024-03-05T17:21:20.568681Z", + "iopub.status.busy": "2024-03-05T17:21:20.568273Z", + "iopub.status.idle": "2024-03-05T17:21:20.629912Z", + "shell.execute_reply": "2024-03-05T17:21:20.629320Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "101765eb", + "id": "3dc5af82", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1214,7 +1214,7 @@ }, { "cell_type": "markdown", - "id": "3624ae93", + "id": "ba7adcc4", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -1227,13 +1227,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "b666a099", + "id": "e3530e17", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:32.118924Z", - "iopub.status.busy": "2024-02-27T04:06:32.118745Z", - "iopub.status.idle": "2024-02-27T04:06:32.229180Z", - "shell.execute_reply": "2024-02-27T04:06:32.228630Z" + "iopub.execute_input": "2024-03-05T17:21:20.632248Z", + "iopub.status.busy": "2024-03-05T17:21:20.631904Z", + "iopub.status.idle": "2024-03-05T17:21:20.730909Z", + "shell.execute_reply": "2024-03-05T17:21:20.730355Z" } }, "outputs": [ @@ -1274,7 +1274,7 @@ }, { "cell_type": "markdown", - "id": "c006dde4", + "id": "0b1e80f7", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -1283,13 +1283,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "5fa75ecb", + "id": "2a110f04", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:32.232278Z", - "iopub.status.busy": "2024-02-27T04:06:32.231683Z", - "iopub.status.idle": "2024-02-27T04:06:32.318246Z", - "shell.execute_reply": "2024-02-27T04:06:32.317795Z" + "iopub.execute_input": "2024-03-05T17:21:20.733722Z", + "iopub.status.busy": "2024-03-05T17:21:20.733506Z", + "iopub.status.idle": "2024-03-05T17:21:20.803250Z", + "shell.execute_reply": "2024-03-05T17:21:20.802621Z" } }, "outputs": [ @@ -1325,7 +1325,7 @@ }, { "cell_type": "markdown", - "id": "6257667b", + "id": "a64850da", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -1336,13 +1336,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "11ca1fa3", + "id": "89de76b4", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:32.321673Z", - "iopub.status.busy": "2024-02-27T04:06:32.320675Z", - "iopub.status.idle": "2024-02-27T04:06:32.329356Z", - "shell.execute_reply": "2024-02-27T04:06:32.328932Z" + "iopub.execute_input": "2024-03-05T17:21:20.805616Z", + "iopub.status.busy": "2024-03-05T17:21:20.805423Z", + "iopub.status.idle": "2024-03-05T17:21:20.814113Z", + "shell.execute_reply": "2024-03-05T17:21:20.813503Z" } }, "outputs": [], @@ -1444,7 +1444,7 @@ }, { "cell_type": "markdown", - "id": "c9ff9667", + "id": "12d58d5c", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1459,13 +1459,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "5a3878ed", + "id": "938f1fc7", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:32.331708Z", - "iopub.status.busy": "2024-02-27T04:06:32.331510Z", - "iopub.status.idle": "2024-02-27T04:06:32.351481Z", - "shell.execute_reply": "2024-02-27T04:06:32.350828Z" + "iopub.execute_input": "2024-03-05T17:21:20.816418Z", + "iopub.status.busy": "2024-03-05T17:21:20.816235Z", + "iopub.status.idle": "2024-03-05T17:21:20.839034Z", + "shell.execute_reply": "2024-03-05T17:21:20.838449Z" } }, "outputs": [ @@ -1482,7 +1482,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_5939/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_5903/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n" ] } @@ -1516,13 +1516,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "48a4654d", + "id": "485cce24", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:32.353878Z", - "iopub.status.busy": "2024-02-27T04:06:32.353506Z", - "iopub.status.idle": "2024-02-27T04:06:32.356727Z", - "shell.execute_reply": "2024-02-27T04:06:32.356096Z" + "iopub.execute_input": "2024-03-05T17:21:20.841459Z", + "iopub.status.busy": "2024-03-05T17:21:20.841035Z", + "iopub.status.idle": "2024-03-05T17:21:20.844604Z", + "shell.execute_reply": "2024-03-05T17:21:20.844035Z" } }, "outputs": [ @@ -1617,7 +1617,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0737cf5b16bb4e38b91e0306ffc8c9a0": { + "1b9bca7996ff4e81bff106196643175f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1670,7 +1670,7 @@ "width": null } }, - "0a0a1d7ab2fe413b90d558f6fa321df4": { + "2934cd4e4a5f4b9b81e597cd441f7df7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1723,74 +1723,7 @@ "width": null } }, - "10f609ed10d94cb69ec382166d00741a": { - "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_85650056c96a4433be85575bd17c05c2", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4fa987394d504a4cbb0f536effbd8e55", - "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "241849380576476188b5e9c8672cd56b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9a6c38a0d7794c8babeedcd292e6c8b0", - "placeholder": "​", - "style": "IPY_MODEL_254dee90263a436ba9b09300b6de0bea", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1234671.93it/s]" - } - }, - "254dee90263a436ba9b09300b6de0bea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "2e0ee7334cf3469595a9015b20c143e7": { + "3cca3100edd94c43aad5d21ee5df40f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1843,138 +1776,87 @@ "width": null } }, - "392889ce28d944448d8373f47697b5c1": { - "model_module": "@jupyter-widgets/base", + "3fa7350f91c14dfd9bc54f02076535a7": { + "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_bc2ed99cfb5d42e490f52ff1efc4a7c9", + "placeholder": "​", + "style": "IPY_MODEL_9dee086ce59d4d1f842b43469adbd3da", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: " } }, - "3dc778ab52e94c7d923eaad32ef1ff72": { + "4040cf89dbeb436ba45e8114b32cb481": { "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": "" } }, - "4fa987394d504a4cbb0f536effbd8e55": { + "575f106f4e164e57844c4c185ef15b27": { "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 } }, - "52efe7e73ff145a798171c8646405a3c": { + "619a9e139ade4360988557c2593d893e": { "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_2e0ee7334cf3469595a9015b20c143e7", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_719734d0dda04838ba42443daa016a0e", + "layout": "IPY_MODEL_3cca3100edd94c43aad5d21ee5df40f7", + "placeholder": "​", + "style": "IPY_MODEL_8e8bf03eb6194ece8a8ae6a4bf6de9a4", "tabbable": null, "tooltip": null, - "value": 50.0 - } - }, - "5861e50d6aef485b80622021eea4871c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": "number of examples processed for checking labels: " } }, - "6a4f612a2a8e4161a7158f6f22b634c4": { + "65ea16b53bb94f76ac68f0fce0e54778": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1989,16 +1871,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_bce17c5ea41a4267bdcd1b1eab6371da", - "IPY_MODEL_10f609ed10d94cb69ec382166d00741a", - "IPY_MODEL_241849380576476188b5e9c8672cd56b" + "IPY_MODEL_619a9e139ade4360988557c2593d893e", + "IPY_MODEL_af53310ad9c4478c8c9a206a28092d8b", + "IPY_MODEL_7d0afbe9fe2148bdbea0f56b43027b25" ], - "layout": "IPY_MODEL_0737cf5b16bb4e38b91e0306ffc8c9a0", + "layout": "IPY_MODEL_95626146065643b8b47c2de9c31f19d8", "tabbable": null, "tooltip": null } }, - "719734d0dda04838ba42443daa016a0e": { + "76cdcbf5a557456e83eb03665ea3c71f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2014,60 +1896,7 @@ "description_width": "" } }, - "82e8eccbe9154c1fbaa7d16e0e2c21b3": { - "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 - } - }, - "844c8c6342a94993ba185c82e8a909af": { + "7d0afbe9fe2148bdbea0f56b43027b25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2082,15 +1911,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_82e8eccbe9154c1fbaa7d16e0e2c21b3", + "layout": "IPY_MODEL_db056e9dbad3466087f91faf70501314", "placeholder": "​", - "style": "IPY_MODEL_9b43a9ab5e6642cfa3f63f39e3eb44d5", + "style": "IPY_MODEL_8f608f352fe340bc89b0c5f63fcd64c6", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1139384.98it/s]" + "value": " 10000/? [00:00<00:00, 1480046.58it/s]" } }, - "84b7dc41b7b94d2693b9ced38b02468b": { + "7d3f25e411fd4d1d8a8965b2b263f104": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2105,15 +1934,51 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9cf720d9d674465fa8b42ddd76797e96", + "layout": "IPY_MODEL_1b9bca7996ff4e81bff106196643175f", "placeholder": "​", - "style": "IPY_MODEL_3dc778ab52e94c7d923eaad32ef1ff72", + "style": "IPY_MODEL_575f106f4e164e57844c4c185ef15b27", "tabbable": null, "tooltip": null, - "value": "number of examples processed for estimating thresholds: " + "value": " 10000/? [00:00<00:00, 1054719.74it/s]" + } + }, + "8e8bf03eb6194ece8a8ae6a4bf6de9a4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "85650056c96a4433be85575bd17c05c2": { + "8f608f352fe340bc89b0c5f63fcd64c6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8fc1631e99324bb3b9374a6a039f07f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2166,7 +2031,7 @@ "width": null } }, - "9a6c38a0d7794c8babeedcd292e6c8b0": { + "95626146065643b8b47c2de9c31f19d8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2219,7 +2084,7 @@ "width": null } }, - "9b43a9ab5e6642cfa3f63f39e3eb44d5": { + "9dee086ce59d4d1f842b43469adbd3da": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2237,7 +2102,7 @@ "text_color": null } }, - "9cf720d9d674465fa8b42ddd76797e96": { + "a5ed646dd52e40eb89fee21baaeb9423": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2290,30 +2155,33 @@ "width": null } }, - "bce17c5ea41a4267bdcd1b1eab6371da": { + "af53310ad9c4478c8c9a206a28092d8b": { "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_392889ce28d944448d8373f47697b5c1", - "placeholder": "​", - "style": "IPY_MODEL_5861e50d6aef485b80622021eea4871c", + "layout": "IPY_MODEL_2934cd4e4a5f4b9b81e597cd441f7df7", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_76cdcbf5a557456e83eb03665ea3c71f", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: " + "value": 50.0 } }, - "f61dd3b983a24f74ab208519c173e22e": { + "ba57cbf4050946d0a8e0f0a46ffb9147": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2328,14 +2196,146 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_84b7dc41b7b94d2693b9ced38b02468b", - "IPY_MODEL_52efe7e73ff145a798171c8646405a3c", - "IPY_MODEL_844c8c6342a94993ba185c82e8a909af" + "IPY_MODEL_3fa7350f91c14dfd9bc54f02076535a7", + "IPY_MODEL_e024c5706661495590c372fdb1196325", + "IPY_MODEL_7d3f25e411fd4d1d8a8965b2b263f104" ], - "layout": "IPY_MODEL_0a0a1d7ab2fe413b90d558f6fa321df4", + "layout": "IPY_MODEL_a5ed646dd52e40eb89fee21baaeb9423", "tabbable": null, "tooltip": null } + }, + "bc2ed99cfb5d42e490f52ff1efc4a7c9": { + "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 + } + }, + "db056e9dbad3466087f91faf70501314": { + "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 + } + }, + "e024c5706661495590c372fdb1196325": { + "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_8fc1631e99324bb3b9374a6a039f07f1", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4040cf89dbeb436ba45e8114b32cb481", + "tabbable": null, + "tooltip": null, + "value": 50.0 + } } }, "version_major": 2, diff --git a/master/tutorials/image.html b/master/tutorials/image.html index d3ea4ee74..146ed5dc4 100644 --- a/master/tutorials/image.html +++ b/master/tutorials/image.html @@ -658,26 +658,23 @@

2. Fetch and normalize the Fashion-MNIST dataset
-
-
- -
-
-
-
-
+
+
+Downloading data: 100%|██████████| 30.9M/30.9M [00:00<00:00, 54.1MB/s]
+Downloading data: 100%|██████████| 5.18M/5.18M [00:00<00:00, 38.9MB/s]
+
-
+
-
+
[3]:
@@ -732,7 +729,7 @@ 
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.

@@ -981,8 +978,8 @@

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: 4.723
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.435
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.119
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.898
 Computing feature embeddings ...

@@ -990,7 +987,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1004,7 +1001,7 @@

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

-
+
@@ -1013,8 +1010,8 @@

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: 4.680
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.601
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.167
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.897
 Computing feature embeddings ...

@@ -1022,7 +1019,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1036,7 +1033,7 @@

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

-
+
@@ -1045,8 +1042,8 @@

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: 4.647
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.510
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.115
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.879
 Computing feature embeddings ...

@@ -1054,7 +1051,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1068,7 +1065,7 @@

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

-
+
@@ -1131,7 +1128,7 @@

7. Use cleanlab to find issues

-
+
@@ -1841,35 +1838,35 @@

Dark images - dark_score is_dark_issue + dark_score 34848 - 0.203922 True + 0.203922 50270 - 0.204588 True + 0.204588 3936 - 0.213098 True + 0.213098 733 - 0.217686 True + 0.217686 8094 - 0.230118 True + 0.230118 @@ -1963,35 +1960,35 @@

Low information images - low_information_score is_low_information_issue + low_information_score 53050 - 0.067975 True + 0.067975 40875 - 0.089929 True + 0.089929 9594 - 0.092601 True + 0.092601 34825 - 0.107744 True + 0.107744 37530 - 0.108516 True + 0.108516 @@ -2019,7 +2016,7 @@

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/image.ipynb b/master/tutorials/image.ipynb index 487a682cb..cf2c1cf6b 100644 --- a/master/tutorials/image.ipynb +++ b/master/tutorials/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:35.637554Z", - "iopub.status.busy": "2024-02-27T04:06:35.637386Z", - "iopub.status.idle": "2024-02-27T04:06:38.388920Z", - "shell.execute_reply": "2024-02-27T04:06:38.388400Z" + "iopub.execute_input": "2024-03-05T17:21:25.239896Z", + "iopub.status.busy": "2024-03-05T17:21:25.239467Z", + "iopub.status.idle": "2024-03-05T17:21:28.218902Z", + "shell.execute_reply": "2024-03-05T17:21:28.218348Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:38.391535Z", - "iopub.status.busy": "2024-02-27T04:06:38.391048Z", - "iopub.status.idle": "2024-02-27T04:06:38.394625Z", - "shell.execute_reply": "2024-02-27T04:06:38.394093Z" + "iopub.execute_input": "2024-03-05T17:21:28.221620Z", + "iopub.status.busy": "2024-03-05T17:21:28.221140Z", + "iopub.status.idle": "2024-03-05T17:21:28.224667Z", + "shell.execute_reply": "2024-03-05T17:21:28.224238Z" } }, "outputs": [], @@ -152,45 +152,103 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:06:38.396672Z", - "iopub.status.busy": "2024-02-27T04:06:38.396385Z", - "iopub.status.idle": "2024-02-27T04:06:42.885953Z", - "shell.execute_reply": "2024-02-27T04:06:42.885403Z" + "iopub.execute_input": "2024-03-05T17:21:28.226756Z", + "iopub.status.busy": "2024-03-05T17:21:28.226442Z", + "iopub.status.idle": "2024-03-05T17:21:31.877898Z", + "shell.execute_reply": "2024-03-05T17:21:31.877423Z" } }, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2ce8708f33844bb880fc5b942a72dda2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Downloading data: 0%| | 0.00/30.9M [00:00\n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 34848\n", - " 0.203922\n", " True\n", + " 0.203922\n", " \n", " \n", " 50270\n", - " 0.204588\n", " True\n", + " 0.204588\n", " \n", " \n", " 3936\n", - " 0.213098\n", " True\n", + " 0.213098\n", " \n", " \n", " 733\n", - " 0.217686\n", " True\n", + " 0.217686\n", " \n", " \n", " 8094\n", - " 0.230118\n", " True\n", + " 0.230118\n", " \n", " \n", "\n", "

" ], "text/plain": [ - " dark_score is_dark_issue\n", - "34848 0.203922 True\n", - "50270 0.204588 True\n", - "3936 0.213098 True\n", - "733 0.217686 True\n", - "8094 0.230118 True" + " is_dark_issue dark_score\n", + "34848 True 0.203922\n", + "50270 True 0.204588\n", + "3936 True 0.213098\n", + "733 True 0.217686\n", + "8094 True 0.230118" ] }, "execution_count": 26, @@ -2279,10 +2337,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.560278Z", - "iopub.status.busy": "2024-02-27T04:11:10.560104Z", - "iopub.status.idle": "2024-02-27T04:11:10.564723Z", - "shell.execute_reply": "2024-02-27T04:11:10.564082Z" + "iopub.execute_input": "2024-03-05T17:26:21.208808Z", + "iopub.status.busy": "2024-03-05T17:26:21.208568Z", + "iopub.status.idle": "2024-03-05T17:26:21.214798Z", + "shell.execute_reply": "2024-03-05T17:26:21.214202Z" }, "nbsphinx": "hidden" }, @@ -2319,10 +2377,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.566856Z", - "iopub.status.busy": "2024-02-27T04:11:10.566684Z", - "iopub.status.idle": "2024-02-27T04:11:10.741038Z", - "shell.execute_reply": "2024-02-27T04:11:10.740486Z" + "iopub.execute_input": "2024-03-05T17:26:21.217468Z", + "iopub.status.busy": "2024-03-05T17:26:21.217250Z", + "iopub.status.idle": "2024-03-05T17:26:21.432838Z", + "shell.execute_reply": "2024-03-05T17:26:21.432261Z" } }, "outputs": [ @@ -2364,10 +2422,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.743278Z", - "iopub.status.busy": "2024-02-27T04:11:10.742977Z", - "iopub.status.idle": "2024-02-27T04:11:10.750387Z", - "shell.execute_reply": "2024-02-27T04:11:10.749877Z" + "iopub.execute_input": "2024-03-05T17:26:21.435255Z", + "iopub.status.busy": "2024-03-05T17:26:21.434873Z", + "iopub.status.idle": "2024-03-05T17:26:21.443496Z", + "shell.execute_reply": "2024-03-05T17:26:21.443041Z" } }, "outputs": [ @@ -2392,47 +2450,47 @@ " \n", " \n", " \n", - " low_information_score\n", " is_low_information_issue\n", + " low_information_score\n", " \n", " \n", " \n", " \n", " 53050\n", - " 0.067975\n", " True\n", + " 0.067975\n", " \n", " \n", " 40875\n", - " 0.089929\n", " True\n", + " 0.089929\n", " \n", " \n", " 9594\n", - " 0.092601\n", " True\n", + " 0.092601\n", " \n", " \n", " 34825\n", - " 0.107744\n", " True\n", + " 0.107744\n", " \n", " \n", " 37530\n", - " 0.108516\n", " True\n", + " 0.108516\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " low_information_score is_low_information_issue\n", - "53050 0.067975 True\n", - "40875 0.089929 True\n", - "9594 0.092601 True\n", - "34825 0.107744 True\n", - "37530 0.108516 True" + " is_low_information_issue low_information_score\n", + "53050 True 0.067975\n", + "40875 True 0.089929\n", + "9594 True 0.092601\n", + "34825 True 0.107744\n", + "37530 True 0.108516" ] }, "execution_count": 29, @@ -2453,10 +2511,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.752334Z", - "iopub.status.busy": "2024-02-27T04:11:10.751944Z", - "iopub.status.idle": "2024-02-27T04:11:10.924530Z", - "shell.execute_reply": "2024-02-27T04:11:10.923931Z" + "iopub.execute_input": "2024-03-05T17:26:21.445632Z", + "iopub.status.busy": "2024-03-05T17:26:21.445274Z", + "iopub.status.idle": "2024-03-05T17:26:21.651921Z", + "shell.execute_reply": "2024-03-05T17:26:21.651295Z" } }, "outputs": [ @@ -2496,10 +2554,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:10.926763Z", - "iopub.status.busy": "2024-02-27T04:11:10.926334Z", - "iopub.status.idle": "2024-02-27T04:11:10.930611Z", - "shell.execute_reply": "2024-02-27T04:11:10.930192Z" + "iopub.execute_input": "2024-03-05T17:26:21.654676Z", + "iopub.status.busy": "2024-03-05T17:26:21.654097Z", + "iopub.status.idle": "2024-03-05T17:26:21.658861Z", + "shell.execute_reply": "2024-03-05T17:26:21.658395Z" }, "nbsphinx": "hidden" }, @@ -2536,101 +2594,59 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "027e45bb8c5a4d319b044fd806e409b4": { + "00117567805f440d9d5d764e0ce0c942": { "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_455fc43143fa462da4615d0a05091c09", - "placeholder": "​", - "style": "IPY_MODEL_659e01794dc54b11953523e915b610dd", + "layout": "IPY_MODEL_837534ba61a440e29b8bec88ecafa936", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2adee810714f4132b5db9e250308fd95", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "093a2599e270466faea60621fd1691da": { - "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_811410c9f7a8407c9afab9994a295677", - "IPY_MODEL_14b865ceefa7490384e4edcbc80229e0", - "IPY_MODEL_3763c15b0203445aaea40c64099b052c" - ], - "layout": "IPY_MODEL_7a4771c243a240b2b91afa67fe46ecff", - "tabbable": null, - "tooltip": null - } - }, - "098bbaf5d84a421588aef46a8eac6143": { - "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_1a3831876daa4f518f667abecbbfe437", - "IPY_MODEL_37b7a9f319b54f61a56201aab953a926", - "IPY_MODEL_3fd8ad114b9342f1b1541ef2fdf7e55a" - ], - "layout": "IPY_MODEL_449cf6286eb141d596a5d4eb92953c88", - "tabbable": null, - "tooltip": null + "value": 60000.0 } }, - "0e3ca28ce7344406851f80d680a12506": { + "03271711edad450897e85e06eb6cb127": { "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_fb58bed5d89f4661babe6b776dac767d", - "placeholder": "​", - "style": "IPY_MODEL_cf5c2930549d4fbe8250517f5696e479", + "layout": "IPY_MODEL_c466561275334474bb5df6065049149b", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f03f41af5b5447f9bcca654369f6a2ca", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": 40.0 } }, - "11b49017a0b64344bec8ee657afc5c4f": { + "0386e13f4a2941548963bd69cdaa9593": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2683,95 +2699,25 @@ "width": null } }, - "12cb341fdb1044cab97410fd09a1540a": { + "05f112c96d29414fa42131bc8603da0e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8c67daeac0434fb382558a823e32902b", - "placeholder": "​", - "style": "IPY_MODEL_da48a4f7c7cb408096c49e6124bf70e4", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.93it/s]" - } - }, - "14578e5a36ee46cf94f7f04c06ba4a53": { - "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": "" - } - }, - "14b865ceefa7490384e4edcbc80229e0": { - "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_b5ce17ab53824591a98f449e8b025965", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6eb5308f57c4436da74f9cc6dfdc56c1", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "14ded9a461884f059d39711716f1f6e7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d1c62c6e888d44e4be1c96009d1e1537", - "placeholder": "​", - "style": "IPY_MODEL_cf534b6635784368915fadd5cbb13ef7", - "tabbable": null, - "tooltip": null, - "value": "100%" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "17cfc02493654970909b80a5b0796ef7": { + "1157e6644cf94a369b0bc0c79b48c0c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2789,79 +2735,139 @@ "text_color": null } }, - "188195b2866e46598594d40c79b6011b": { - "model_module": "@jupyter-widgets/controls", + "120d1d74f90b477aa1f7c85cb04f2a8b": { + "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_e7e978c332654878b944425a2cce7431", - "placeholder": "​", - "style": "IPY_MODEL_79471511b83c4838b8cf566b168e574b", - "tabbable": null, - "tooltip": null, - "value": "Computing checksums: 100%" - } - }, - "18e7d43c53be48b3b4f95442866c0a87": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "125e89a524574e23a5d582ea6a6193ef": { + "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": "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_7610d89b3fb84ed8b8f85e6777c2be44", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_23e6efa276194e59af80b0da3f3d22b2", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "1a3831876daa4f518f667abecbbfe437": { + "12ed270f15a14c4487328aa39e253957": { "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_706ef0ce82a848d5a2b5a1cfeff3b66d", - "placeholder": "​", - "style": "IPY_MODEL_3db1ad2d9bd3486d81c82e7d825fefab", + "layout": "IPY_MODEL_7ce21c448ae34e09a0b1b077bdf8f9c7", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9f08aebc732446de872f662bd327955b", "tabbable": null, "tooltip": null, - "value": "100%" + "value": 2.0 } }, - "1cf137b879214a3ab635c3fa84b1bdbf": { + "13320e008c6b4b55b02b9da6ede7d759": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2914,25 +2920,31 @@ "width": null } }, - "1dde6247d89e422fae510063a9694bde": { + "13bdb84d4d494962babbcc3d785b981b": { "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_87a35075be054c99a6917be1ada6b70b", + "IPY_MODEL_bf535745edea45cfac37c688118391e0", + "IPY_MODEL_1f63f86750404d358ae1b3d0fc4211c1" + ], + "layout": "IPY_MODEL_2ade4dc2e32040a8bb5ffa50bc1ea807", + "tabbable": null, + "tooltip": null } }, - "1f39f77cae9a4b8289093a19d2b053ce": { + "13dd0f8d07254c1e8c1c8fce7499453f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2950,46 +2962,7 @@ "text_color": null } }, - "1fcfe42385e9486293f75bb99e009cbc": { - "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": "" - } - }, - "20d5d6d7592245cb8e6ae6637e124962": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d29be32abfeb45e38ab62bf213c3c810", - "placeholder": "​", - "style": "IPY_MODEL_564b4d4343944d9aa89b5c29b117db36", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.91it/s]" - } - }, - "23c9db1ca6ee4834bd0db0e311214d5f": { + "1754f6bec411400e8a0248d2d205d9d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3042,23 +3015,7 @@ "width": null } }, - "23e6efa276194e59af80b0da3f3d22b2": { - "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": "" - } - }, - "25fc4b8a75354f75a538e45656f2b4dd": { + "1868860131654264bd9a733aac14b217": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3111,53 +3068,7 @@ "width": null } }, - "26fe5f0671964c01a572cce50f2bf3b7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_39502ba2184b4f83a2158243bead98e5", - "placeholder": "​", - "style": "IPY_MODEL_17cfc02493654970909b80a5b0796ef7", - "tabbable": null, - "tooltip": null, - "value": " 30.9M/30.9M [00:00<00:00, 37.0MB/s]" - } - }, - "273c33e7bc0e454e972f56f0ef99215f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5fbb4d5a4d0044699dec9b3b12696dc1", - "placeholder": "​", - "style": "IPY_MODEL_def8761350f347f58474faf97a0ce8bb", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 57.49it/s]" - } - }, - "2b36aa46f93e41ce9638a118aa74e2f5": { + "1b1616f669014ec88ea42a507fc66b38": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3173,17 +3084,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c932e3e7a73e4533acbe1af723e40e3b", - "max": 40.0, + "layout": "IPY_MODEL_743fd21aabd3441080cbdf88ef4750cd", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2cc4ddad126c41428a99dd2b3bf5f5a4", + "style": "IPY_MODEL_69be4e0cb1f94577b806e6dcbf5729f8", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 60000.0 } }, - "2bfb9fa1816c4ed1b4fa5a535ee7600f": { + "1c538754e2c94b6c8cc8835413e8c193": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3236,7 +3147,7 @@ "width": null } }, - "2cc4ddad126c41428a99dd2b3bf5f5a4": { + "1d9fd4ce611a49c1be1a8cd6c17a0dc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3252,31 +3163,25 @@ "description_width": "" } }, - "2ce8708f33844bb880fc5b942a72dda2": { + "1dbf4ffd3fe34a0e82fbab2708457009": { "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_919d270fbb284450aff333e907475643", - "IPY_MODEL_b87e8a4c2ee14767a3f43e0ed5c95bef", - "IPY_MODEL_26fe5f0671964c01a572cce50f2bf3b7" - ], - "layout": "IPY_MODEL_1cf137b879214a3ab635c3fa84b1bdbf", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2dd5b78c91654388ac491464dc21b064": { + "1f63f86750404d358ae1b3d0fc4211c1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3291,15 +3196,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9e0d2440a9254b05bc81e16591cb45ca", + "layout": "IPY_MODEL_f53af9199dd543cc90ef5fbb55d8ec83", "placeholder": "​", - "style": "IPY_MODEL_92681c8858d448d49fca40635dc49cac", + "style": "IPY_MODEL_7ae1a926cc1f4379a9d86080778be517", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 53.35it/s]" } }, - "2df710401168425db8bcf3af9d77a3ca": { + "221e7e9db86c4eda9720dcec34776e7d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3352,25 +3257,33 @@ "width": null } }, - "2ef3598161a949e9aef2bb7578a46bfe": { + "286acff3d63f4b4a97ee007082ffc9d2": { "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_120d1d74f90b477aa1f7c85cb04f2a8b", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1d9fd4ce611a49c1be1a8cd6c17a0dc5", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "302ea6b92b3b4650ad94b51b2022da44": { + "287287177d414d93adcc6f20d070dc87": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3423,114 +3336,7 @@ "width": null } }, - "3229250625af4d5a9210d0ea9b5fa16d": { - "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": "" - } - }, - "3584d39ca68a4c4ba99d6ca4346077bb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "35ddd41ea21749febdb2e58bd227aecb": { - "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_027e45bb8c5a4d319b044fd806e409b4", - "IPY_MODEL_18e7d43c53be48b3b4f95442866c0a87", - "IPY_MODEL_acd9fc454b7a441bad4a264362ca582c" - ], - "layout": "IPY_MODEL_6ded2348d0c14c1eb3186fc9fb33ca4a", - "tabbable": null, - "tooltip": null - } - }, - "3763c15b0203445aaea40c64099b052c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2bfb9fa1816c4ed1b4fa5a535ee7600f", - "placeholder": "​", - "style": "IPY_MODEL_4906b500667e44f6b41c7438c7ed301d", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:00<00:00, 972588.47 examples/s]" - } - }, - "37b7a9f319b54f61a56201aab953a926": { - "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_f259d9b670d14c18adbf4e4088c33b4c", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3229250625af4d5a9210d0ea9b5fa16d", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "39502ba2184b4f83a2158243bead98e5": { + "2ade4dc2e32040a8bb5ffa50bc1ea807": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3583,48 +3389,47 @@ "width": null } }, - "3d289127d48f42a38c13d633650495ab": { + "2adee810714f4132b5db9e250308fd95": { "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_5d661f0602724b848c5f85cf262fc17f", - "placeholder": "​", - "style": "IPY_MODEL_aa210d8e134d4e65a35337751bebb8fa", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "3db1ad2d9bd3486d81c82e7d825fefab": { + "322b3bcbb5394c948154c32f4eb5e609": { "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_c613b29260064809a8c4178ed6147c38", + "IPY_MODEL_968457e5d1c1482a94cea2642422460c", + "IPY_MODEL_98cdb184470b45209302dfb69e4de525" + ], + "layout": "IPY_MODEL_b09dbf3fce1a4e5491433501bc5e7751", + "tabbable": null, + "tooltip": null } }, - "3fd8ad114b9342f1b1541ef2fdf7e55a": { + "3868e1a7559d457d96e83bd991cf34fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3639,65 +3444,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_302ea6b92b3b4650ad94b51b2022da44", + "layout": "IPY_MODEL_c10bd78b3a524ca098f9b1d758f653b7", "placeholder": "​", - "style": "IPY_MODEL_c229dd5792804b6a88e3be57058cd2b5", + "style": "IPY_MODEL_a3fb24f690c840348a883369a4610bd2", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:35<00:00, 1565.74it/s]" - } - }, - "40daa0fc37b74d61ab0f77a5b7507dee": { - "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_14ded9a461884f059d39711716f1f6e7", - "IPY_MODEL_8522d6bc397849cea3fe47036e466f1a", - "IPY_MODEL_273c33e7bc0e454e972f56f0ef99215f" - ], - "layout": "IPY_MODEL_e4ff32be7a5847b891bae956c99642ed", - "tabbable": null, - "tooltip": null + "value": " 40/40 [00:00<00:00, 59.66it/s]" } }, - "41eddd2244a74684986dd50abd365d34": { + "40b7f76f7c5040b99bb6d3b1b4aabd78": { "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_bc23d1163ae54391b6d1b095111b7b64", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_56ad411b8db441c7abeb093633bccdc9", + "layout": "IPY_MODEL_dc0af5e6d82348d69e1094d9659e5f15", + "placeholder": "​", + "style": "IPY_MODEL_aaa247560a9f4de5ba6276b9318cbdd9", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": "100%" } }, - "449cf6286eb141d596a5d4eb92953c88": { + "40cf93e1ad9c40329463ec8680702b75": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3750,7 +3528,7 @@ "width": null } }, - "44f264b134e14625bbdfc087820b1311": { + "41c91ad69758433799e5eda8ba8446ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3803,7 +3581,53 @@ "width": null } }, - "455fc43143fa462da4615d0a05091c09": { + "4c192cd329c54205a7c62f4df4cbb056": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f73fd148bfd14a059bf19d9a0008bf30", + "placeholder": "​", + "style": "IPY_MODEL_9fc4e85f7c89462f8e41f451f28c165b", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "4ca98e8187e745858cecba425db799e9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dabb292c869d416bb677733a64a4bc31", + "placeholder": "​", + "style": "IPY_MODEL_6f0e630928ed49cdaa9710f934e1bb41", + "tabbable": null, + "tooltip": null, + "value": "Map (num_proc=4): 100%" + } + }, + "4dd4b6127ee54c23b831b0010934f8d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3856,31 +3680,46 @@ "width": null } }, - "4739241d01104e08a7e32ab4bd340c07": { + "4eac37c8dfde423abb9aa3bf66ee4869": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "4fd94f96dc754593acfa96a01d4e99a1": { + "model_module": "@jupyter-widgets/controls", + "model_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_0e3ca28ce7344406851f80d680a12506", - "IPY_MODEL_92cf2be851da4d64855fbee49ea41f00", - "IPY_MODEL_bea8e00f12f049ad985bf7dcd77d49fc" - ], - "layout": "IPY_MODEL_657f66f6978d40808cce2ea1ad7c4e35", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a09459f714804969ae04c7c5e268af7c", + "placeholder": "​", + "style": "IPY_MODEL_cb06124a899d43a0a1959a132eeb5c5a", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 40/40 [00:00<00:00, 58.17it/s]" } }, - "4906b500667e44f6b41c7438c7ed301d": { + "518259e3f72a4db68b3a4b1c66321dd3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3898,60 +3737,47 @@ "text_color": null } }, - "4ff17cf5ce1d4837a0986a522b40bcca": { - "model_module": "@jupyter-widgets/base", + "56b4f5069edb439582d40c62a1b11bab": { + "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": "" + } + }, + "633e645b240b406a822c9ceed34219ee": { + "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_deb611609efa4c2db0570414b2d76844", + "IPY_MODEL_12ed270f15a14c4487328aa39e253957", + "IPY_MODEL_ea3ebf4b767442e281dfd166c4d944af" + ], + "layout": "IPY_MODEL_a343830a4e5b4d87a216175fbee9c91d", + "tabbable": null, + "tooltip": null } }, - "5049b4cd31d447ca8d4c523ad88cd284": { + "66123941a1764558997a9752673ab190": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4004,25 +3830,23 @@ "width": null } }, - "547ab24770b14064ac8dae5e0841c9e7": { + "69be4e0cb1f94577b806e6dcbf5729f8": { "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": "" } }, - "55052c746d8f42be983228e459b49afe": { + "6a18c90eae4440eb893c9f025767dddd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4075,41 +3899,7 @@ "width": null } }, - "564b4d4343944d9aa89b5c29b117db36": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "56ad411b8db441c7abeb093633bccdc9": { - "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": "" - } - }, - "56de5e02bd01436b8edc20836384169f": { + "6c2b6b6278f64121a4277ac53a3f4f9c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4125,63 +3915,53 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_efdb227f3798423092002d9ff1033b04", + "layout": "IPY_MODEL_7914f506574d4f4196c3e7b33bc285e1", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_9c3c72650c7d4960aeb4524159bb68ea", + "style": "IPY_MODEL_b6ab6ccea1a342ae815e4c1eb98b45d4", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "5804b31687494c07b710386e859b431e": { + "6c94014e9b16448d874b35ec257ea024": { "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_dafa2013e9ee488dad70944f3e7d06ae", - "placeholder": "​", - "style": "IPY_MODEL_d70fb12f289249e6b2d96f5899ec7593", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.54it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "580af8c56b8e460ab0acfbec8fb3e2fb": { + "6f0e630928ed49cdaa9710f934e1bb41": { "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_6b91322e23f945039bf15f4d04da6d7a", - "placeholder": "​", - "style": "IPY_MODEL_8cace21a20524e9dab7a18b2058ebf7d", - "tabbable": null, - "tooltip": null, - "value": " 10000/10000 [00:00<00:00, 636059.57 examples/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5d661f0602724b848c5f85cf262fc17f": { + "743fd21aabd3441080cbdf88ef4750cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4234,86 +4014,48 @@ "width": null } }, - "5d784a31f1c34dfd9151df789b1bab99": { + "7455f99c8036436092de8d7492d73268": { "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_ee24cb9ae816475d87eaff5a37eafbe2", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ced1e34cf86f4b69a1e90969c5bfcfce", + "layout": "IPY_MODEL_125e89a524574e23a5d582ea6a6193ef", + "placeholder": "​", + "style": "IPY_MODEL_76c06e0e535b4eb2a0d04e8168ce3990", "tabbable": null, "tooltip": null, - "value": 2.0 + "value": " 60000/60000 [00:50<00:00, 1187.52it/s]" } }, - "5fbb4d5a4d0044699dec9b3b12696dc1": { - "model_module": "@jupyter-widgets/base", + "76c06e0e535b4eb2a0d04e8168ce3990": { + "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 } }, - "61c89909398349d4947e964970ec402c": { + "7914f506574d4f4196c3e7b33bc285e1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4366,7 +4108,7 @@ "width": null } }, - "63039be8332146a99b88b787eadd7730": { + "79b1180eabc3436fac65786d11656c22": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4384,30 +4126,25 @@ "text_color": null } }, - "643564843e7c4d559d15bce8b09e60bf": { + "7ae1a926cc1f4379a9d86080778be517": { "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_44f264b134e14625bbdfc087820b1311", - "placeholder": "​", - "style": "IPY_MODEL_c35b5ab6fc2c4e4985a34b5cd00d779e", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 60.47it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "657f66f6978d40808cce2ea1ad7c4e35": { + "7ce21c448ae34e09a0b1b077bdf8f9c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4460,25 +4197,31 @@ "width": null } }, - "659e01794dc54b11953523e915b610dd": { + "8287864aaa354e0c91aae0254dbfbe57": { "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_a7ed57099d7d4be183532589e53557cf", + "IPY_MODEL_de26db0302f94535950aced09b5e1d00", + "IPY_MODEL_9981abca60404a29a95ca6ef747463a5" + ], + "layout": "IPY_MODEL_ee680731bdcb4b61bede18e265e6df46", + "tabbable": null, + "tooltip": null } }, - "65bb8dd226b741aabd691de81b94e05a": { + "837534ba61a440e29b8bec88ecafa936": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4531,7 +4274,7 @@ "width": null } }, - "6b91322e23f945039bf15f4d04da6d7a": { + "8398904f0c42454194f5d9c89dce2cc7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4584,7 +4327,7 @@ "width": null } }, - "6c7e3e6b760e4744982009dc6ba085cd": { + "872d173a4763445394490e4d03082558": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4599,39 +4342,85 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_d39b3c56fc0643fa90446413b6ae1c01", - "IPY_MODEL_2b36aa46f93e41ce9638a118aa74e2f5", - "IPY_MODEL_12cb341fdb1044cab97410fd09a1540a" + "IPY_MODEL_4ca98e8187e745858cecba425db799e9", + "IPY_MODEL_00117567805f440d9d5d764e0ce0c942", + "IPY_MODEL_8e7816b7f5d24fdc9d3fef632c20c5cd" ], - "layout": "IPY_MODEL_5049b4cd31d447ca8d4c523ad88cd284", + "layout": "IPY_MODEL_8398904f0c42454194f5d9c89dce2cc7", "tabbable": null, "tooltip": null } }, - "6ded2348d0c14c1eb3186fc9fb33ca4a": { - "model_module": "@jupyter-widgets/base", + "87a35075be054c99a6917be1ada6b70b": { + "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, + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a5252d7ceea648dab3c92b42299622ca", + "placeholder": "​", + "style": "IPY_MODEL_a8ebead3bd874a8d8eef78eacf045d09", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "8e7816b7f5d24fdc9d3fef632c20c5cd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1754f6bec411400e8a0248d2d205d9d3", + "placeholder": "​", + "style": "IPY_MODEL_1dbf4ffd3fe34a0e82fbab2708457009", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 5376.58 examples/s]" + } + }, + "9259e5716ecc44169ffc9bfcb2347eee": { + "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, @@ -4661,23 +4450,105 @@ "width": null } }, - "6eb5308f57c4436da74f9cc6dfdc56c1": { + "968457e5d1c1482a94cea2642422460c": { "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_d1705d9417414e228d2b64d20c5d00ee", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4eac37c8dfde423abb9aa3bf66ee4869", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "98cdb184470b45209302dfb69e4de525": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c010c260788e44cca6942cd5c1a20a87", + "placeholder": "​", + "style": "IPY_MODEL_daa10eea47ee4dd185c055d03ea53de6", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 61.29it/s]" + } + }, + "98efed1f73804d8b998a51e41a7b91fd": { + "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_a957df93ee9243a1a41c86fcf625f3b0", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b3b164c849464da7870310f7bea566b9", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "9981abca60404a29a95ca6ef747463a5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_221e7e9db86c4eda9720dcec34776e7d", + "placeholder": "​", + "style": "IPY_MODEL_c0f595a8925341fd91b6fa8d56f70231", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:00<00:00, 267427.36 examples/s]" } }, - "706ef0ce82a848d5a2b5a1cfeff3b66d": { + "9b3fbd16257049c7a45c921d39d3eaf5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4730,7 +4601,23 @@ "width": null } }, - "7465a94905974f7097b588489de27e46": { + "9f08aebc732446de872f662bd327955b": { + "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": "" + } + }, + "9fc4e85f7c89462f8e41f451f28c165b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4748,7 +4635,7 @@ "text_color": null } }, - "7521a6e0209d4bcfa4688c2f87743e34": { + "a09459f714804969ae04c7c5e268af7c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4801,7 +4688,23 @@ "width": null } }, - "7610d89b3fb84ed8b8f85e6777c2be44": { + "a2d8a1f3b9974266876dc362568d194b": { + "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": "" + } + }, + "a343830a4e5b4d87a216175fbee9c91d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4854,25 +4757,7 @@ "width": null } }, - "79471511b83c4838b8cf566b168e574b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "7a4771c243a240b2b91afa67fe46ecff": { + "a346f15cb5134231b6e007dcf577d223": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4925,7 +4810,7 @@ "width": null } }, - "801f7cce1d194f9b9c33149aef36026d": { + "a3b36ce8ee7f4f819f3ed5fb5a98933e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4978,56 +4863,25 @@ "width": null } }, - "811410c9f7a8407c9afab9994a295677": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f6e05afaf00440a7830ad926432b4f83", - "placeholder": "​", - "style": "IPY_MODEL_a31c4401cfda463da2542a23380c4208", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" - } - }, - "8522d6bc397849cea3fe47036e466f1a": { + "a3fb24f690c840348a883369a4610bd2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f867132b50104b83b0babfb88b2684b3", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c3853c49ef2340458b92a49f587b4960", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8970c3588e8b4775ae0c5198bb726b6c": { + "a4463a06d49648eea5841f1b1c26666e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5042,15 +4896,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_25fc4b8a75354f75a538e45656f2b4dd", + "layout": "IPY_MODEL_13320e008c6b4b55b02b9da6ede7d759", "placeholder": "​", - "style": "IPY_MODEL_af3de8894f9941dfaa69005524af544d", + "style": "IPY_MODEL_6c94014e9b16448d874b35ec257ea024", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 62.59it/s]" } }, - "8c67daeac0434fb382558a823e32902b": { + "a5252d7ceea648dab3c92b42299622ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5103,7 +4957,30 @@ "width": null } }, - "8cace21a20524e9dab7a18b2058ebf7d": { + "a7ed57099d7d4be183532589e53557cf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4dd4b6127ee54c23b831b0010934f8d6", + "placeholder": "​", + "style": "IPY_MODEL_79b1180eabc3436fac65786d11656c22", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" + } + }, + "a8ebead3bd874a8d8eef78eacf045d09": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5121,7 +4998,7 @@ "text_color": null } }, - "8de2e10eaab64976bbf8d7da16e4e743": { + "a957df93ee9243a1a41c86fcf625f3b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5174,446 +5051,14 @@ "width": null } }, - "8e80e402c8e544be8f3665d48bb331b1": { - "model_module": "@jupyter-widgets/base", + "aaa247560a9f4de5ba6276b9318cbdd9": { + "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8f5530de2e7a4f3f89af3a5ce58a19fe": { - "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_11b49017a0b64344bec8ee657afc5c4f", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1fcfe42385e9486293f75bb99e009cbc", - "tabbable": null, - "tooltip": null, - "value": 10000.0 - } - }, - "8f5abaf104bc43d39f6a5747eb17b340": { - "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_9a0a4a25729b4c8792a119ad77f36c3c", - "IPY_MODEL_8f5530de2e7a4f3f89af3a5ce58a19fe", - "IPY_MODEL_580af8c56b8e460ab0acfbec8fb3e2fb" - ], - "layout": "IPY_MODEL_f85f0334db114aa59b010e361bb4fbbd", - "tabbable": null, - "tooltip": null - } - }, - "919d270fbb284450aff333e907475643": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8de2e10eaab64976bbf8d7da16e4e743", - "placeholder": "​", - "style": "IPY_MODEL_2ef3598161a949e9aef2bb7578a46bfe", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "92681c8858d448d49fca40635dc49cac": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "92cf2be851da4d64855fbee49ea41f00": { - "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_dacec0f83fe048a7a0cef5a9a98e4b81", - "max": 5175617.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_14578e5a36ee46cf94f7f04c06ba4a53", - "tabbable": null, - "tooltip": null, - "value": 5175617.0 - } - }, - "9a0a4a25729b4c8792a119ad77f36c3c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_65bb8dd226b741aabd691de81b94e05a", - "placeholder": "​", - "style": "IPY_MODEL_7465a94905974f7097b588489de27e46", - "tabbable": null, - "tooltip": null, - "value": "Generating test split: 100%" - } - }, - "9c3c72650c7d4960aeb4524159bb68ea": { - "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": "" - } - }, - "9cb82e8aadeb44f696eeb05cba80bf38": { - "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_3d289127d48f42a38c13d633650495ab", - "IPY_MODEL_ea94ee3f1fa044819811fc2417e237a5", - "IPY_MODEL_5804b31687494c07b710386e859b431e" - ], - "layout": "IPY_MODEL_f3388fc2449142fab5c93b916b11ec17", - "tabbable": null, - "tooltip": null - } - }, - "9e0d2440a9254b05bc81e16591cb45ca": { - "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 - } - }, - "a31c4401cfda463da2542a23380c4208": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a4cd2589ffb34e0a9fe14c70c848ca2e": { - "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": "" - } - }, - "a57b7f6524be4d1487e83f44670fbd99": { - "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 - } - }, - "aa210d8e134d4e65a35337751bebb8fa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "aad5e096b2544d48870ed3fc61b6b5c2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "acd9fc454b7a441bad4a264362ca582c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2df710401168425db8bcf3af9d77a3ca", - "placeholder": "​", - "style": "IPY_MODEL_63039be8332146a99b88b787eadd7730", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.75it/s]" - } - }, - "af3de8894f9941dfaa69005524af544d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -5624,7 +5069,7 @@ "text_color": null } }, - "b46cd35a553d401c8d614aaa77b758c5": { + "abfc1d43df3449a99634921972eb546c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5677,7 +5122,7 @@ "width": null } }, - "b5ce17ab53824591a98f449e8b025965": { + "b09dbf3fce1a4e5491433501bc5e7751": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5730,56 +5175,87 @@ "width": null } }, - "b87e8a4c2ee14767a3f43e0ed5c95bef": { + "b3b164c849464da7870310f7bea566b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "b50a7b54767048a6a0ee15d292d150a5": { + "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_61c89909398349d4947e964970ec402c", - "max": 30931277.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cea842f314324ac09988392b914b2397", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_40b7f76f7c5040b99bb6d3b1b4aabd78", + "IPY_MODEL_286acff3d63f4b4a97ee007082ffc9d2", + "IPY_MODEL_3868e1a7559d457d96e83bd991cf34fd" + ], + "layout": "IPY_MODEL_6a18c90eae4440eb893c9f025767dddd", "tabbable": null, - "tooltip": null, - "value": 30931277.0 + "tooltip": null } }, - "b9e9ee891b2c4607a90164b2e9fd4a8d": { + "b6ab6ccea1a342ae815e4c1eb98b45d4": { "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": "" + } + }, + "ba2a00d2cf6a41309e5ef39c4e9854f3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a57b7f6524be4d1487e83f44670fbd99", - "placeholder": "​", - "style": "IPY_MODEL_1dde6247d89e422fae510063a9694bde", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_c72a0e72518444dc9d87a6311824912a", + "IPY_MODEL_6c2b6b6278f64121a4277ac53a3f4f9c", + "IPY_MODEL_c358e3106fa44fd7b23d0664abb7490e" + ], + "layout": "IPY_MODEL_f8b9efa53c0f4fb8bdbd667a9dc4d7d6", "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" + "tooltip": null } }, - "bc23d1163ae54391b6d1b095111b7b64": { + "bd520442648241aba2c0213d7bea8277": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5832,30 +5308,7 @@ "width": null } }, - "bea8e00f12f049ad985bf7dcd77d49fc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4ff17cf5ce1d4837a0986a522b40bcca", - "placeholder": "​", - "style": "IPY_MODEL_aad5e096b2544d48870ed3fc61b6b5c2", - "tabbable": null, - "tooltip": null, - "value": " 5.18M/5.18M [00:00<00:00, 26.7MB/s]" - } - }, - "c181f62b4468453a89ff5289910a3a65": { + "bf535745edea45cfac37c688118391e0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5871,85 +5324,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_801f7cce1d194f9b9c33149aef36026d", + "layout": "IPY_MODEL_1c538754e2c94b6c8cc8835413e8c193", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a4cd2589ffb34e0a9fe14c70c848ca2e", + "style": "IPY_MODEL_56b4f5069edb439582d40c62a1b11bab", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "c229dd5792804b6a88e3be57058cd2b5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c35b5ab6fc2c4e4985a34b5cd00d779e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c3853c49ef2340458b92a49f587b4960": { - "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": "" - } - }, - "c6930b35722c4bc1b618491699bf7322": { - "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": "" - } - }, - "c932e3e7a73e4533acbe1af723e40e3b": { + "c010c260788e44cca6942cd5c1a20a87": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6002,57 +5387,7 @@ "width": null } }, - "cea842f314324ac09988392b914b2397": { - "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": "" - } - }, - "ced1e34cf86f4b69a1e90969c5bfcfce": { - "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": "" - } - }, - "cf534b6635784368915fadd5cbb13ef7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "cf5c2930549d4fbe8250517f5696e479": { + "c0f595a8925341fd91b6fa8d56f70231": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6070,60 +5405,7 @@ "text_color": null } }, - "d1c62c6e888d44e4be1c96009d1e1537": { - "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 - } - }, - "d29be32abfeb45e38ab62bf213c3c810": { + "c10bd78b3a524ca098f9b1d758f653b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6176,7 +5458,7 @@ "width": null } }, - "d39b3c56fc0643fa90446413b6ae1c01": { + "c358e3106fa44fd7b23d0664abb7490e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6191,15 +5473,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e31ab863cb26461ab15a486e91c8be0d", + "layout": "IPY_MODEL_9259e5716ecc44169ffc9bfcb2347eee", "placeholder": "​", - "style": "IPY_MODEL_3584d39ca68a4c4ba99d6ca4346077bb", + "style": "IPY_MODEL_1157e6644cf94a369b0bc0c79b48c0c3", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 56.87it/s]" } }, - "d5935e90376a42bebcfd4ccbd45c7d9a": { + "c466561275334474bb5df6065049149b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6252,49 +5534,76 @@ "width": null } }, - "d70fb12f289249e6b2d96f5899ec7593": { + "c613b29260064809a8c4178ed6147c38": { "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_abfc1d43df3449a99634921972eb546c", + "placeholder": "​", + "style": "IPY_MODEL_518259e3f72a4db68b3a4b1c66321dd3", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "da48984a4c04483e88ca66d17f185796": { + "c72a0e72518444dc9d87a6311824912a": { "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_2dd5b78c91654388ac491464dc21b064", - "IPY_MODEL_56de5e02bd01436b8edc20836384169f", - "IPY_MODEL_20d5d6d7592245cb8e6ae6637e124962" - ], - "layout": "IPY_MODEL_55052c746d8f42be983228e459b49afe", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_66123941a1764558997a9752673ab190", + "placeholder": "​", + "style": "IPY_MODEL_eb54dee925e3409d9850aa1fcc1b48b5", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" + } + }, + "caf7d35738c6472c950d3543d4f3eaf7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cd5d376c47d44680ad380c3818a21fa0", + "placeholder": "​", + "style": "IPY_MODEL_13dd0f8d07254c1e8c1c8fce7499453f", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "da48a4f7c7cb408096c49e6124bf70e4": { + "cb06124a899d43a0a1959a132eeb5c5a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6312,7 +5621,33 @@ "text_color": null } }, - "dacec0f83fe048a7a0cef5a9a98e4b81": { + "cb1128114ec44703baed9028494b5a52": { + "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_e6e0df3778dd4adcba513dac2d339069", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a2d8a1f3b9974266876dc362568d194b", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "cd5d376c47d44680ad380c3818a21fa0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6365,7 +5700,43 @@ "width": null } }, - "dafa2013e9ee488dad70944f3e7d06ae": { + "ce4a09a003454d74b4417a7ebdbbd50f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "cf7dcf12b151404e896e8bdb62460655": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d1705d9417414e228d2b64d20c5d00ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6418,7 +5789,7 @@ "width": null } }, - "dc6d200dd499407da1c62711cafeca7a": { + "d70005fa09074d9fab741afc9900399e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6433,15 +5804,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b46cd35a553d401c8d614aaa77b758c5", + "layout": "IPY_MODEL_41c91ad69758433799e5eda8ba8446ce", "placeholder": "​", - "style": "IPY_MODEL_547ab24770b14064ac8dae5e0841c9e7", + "style": "IPY_MODEL_05f112c96d29414fa42131bc8603da0e", "tabbable": null, "tooltip": null, - "value": " 2/2 [00:00<00:00, 565.23it/s]" + "value": " 60000/60000 [00:00<00:00, 272073.32 examples/s]" } }, - "de630eedaa6d434582d62b17e7c8e009": { + "da3b8eaa8bfa4de78ce5bf1e9c7d73cb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6456,16 +5827,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_b9e9ee891b2c4607a90164b2e9fd4a8d", - "IPY_MODEL_41eddd2244a74684986dd50abd365d34", - "IPY_MODEL_f2e62f490b8a41e7adb6c88eb7644d4a" + "IPY_MODEL_caf7d35738c6472c950d3543d4f3eaf7", + "IPY_MODEL_1b1616f669014ec88ea42a507fc66b38", + "IPY_MODEL_7455f99c8036436092de8d7492d73268" ], - "layout": "IPY_MODEL_23c9db1ca6ee4834bd0db0e311214d5f", + "layout": "IPY_MODEL_0386e13f4a2941548963bd69cdaa9593", "tabbable": null, "tooltip": null } }, - "def8761350f347f58474faf97a0ce8bb": { + "daa10eea47ee4dd185c055d03ea53de6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6483,7 +5854,7 @@ "text_color": null } }, - "e31ab863cb26461ab15a486e91c8be0d": { + "dabb292c869d416bb677733a64a4bc31": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6536,7 +5907,7 @@ "width": null } }, - "e4ff32be7a5847b891bae956c99642ed": { + "dc0af5e6d82348d69e1094d9659e5f15": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6589,192 +5960,115 @@ "width": null } }, - "e7e978c332654878b944425a2cce7431": { - "model_module": "@jupyter-widgets/base", + "de26db0302f94535950aced09b5e1d00": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1868860131654264bd9a733aac14b217", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f335fbafaacb4563bee9fa4ddaf06be4", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "de2a9078248149578085749e3dfea820": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_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 } }, - "ea94ee3f1fa044819811fc2417e237a5": { + "deb611609efa4c2db0570414b2d76844": { "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_8e80e402c8e544be8f3665d48bb331b1", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c6930b35722c4bc1b618491699bf7322", + "layout": "IPY_MODEL_bd520442648241aba2c0213d7bea8277", + "placeholder": "​", + "style": "IPY_MODEL_ce4a09a003454d74b4417a7ebdbbd50f", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "Computing checksums: 100%" } }, - "ea9766b70dbb45bdac1e2bc0bb2273ff": { - "model_module": "@jupyter-widgets/base", + "e17fd520bbdc4e70b092658dcb682abe": { + "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 } }, - "ee24cb9ae816475d87eaff5a37eafbe2": { - "model_module": "@jupyter-widgets/base", + "e630651efa774777b84bec8783ca86e1": { + "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_f0c48e7a92584111a0c8860ced295150", + "placeholder": "​", + "style": "IPY_MODEL_de2a9078248149578085749e3dfea820", + "tabbable": null, + "tooltip": null, + "value": "Generating train split: 100%" } }, - "efdb227f3798423092002d9ff1033b04": { + "e6e0df3778dd4adcba513dac2d339069": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6827,60 +6121,30 @@ "width": null } }, - "f259d9b670d14c18adbf4e4088c33b4c": { - "model_module": "@jupyter-widgets/base", + "e7583439721a41c9aec978c1bdbdeef9": { + "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_a3b36ce8ee7f4f819f3ed5fb5a98933e", + "placeholder": "​", + "style": "IPY_MODEL_e17fd520bbdc4e70b092658dcb682abe", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "f2e62f490b8a41e7adb6c88eb7644d4a": { + "ea3ebf4b767442e281dfd166c4d944af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6895,15 +6159,57 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ea9766b70dbb45bdac1e2bc0bb2273ff", + "layout": "IPY_MODEL_a346f15cb5134231b6e007dcf577d223", "placeholder": "​", - "style": "IPY_MODEL_1f39f77cae9a4b8289093a19d2b053ce", + "style": "IPY_MODEL_cf7dcf12b151404e896e8bdb62460655", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:10<00:00, 6452.30 examples/s]" + "value": " 2/2 [00:00<00:00, 578.21it/s]" + } + }, + "eb54dee925e3409d9850aa1fcc1b48b5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ee14e2e007734aa6a367ee52a32693dc": { + "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_4c192cd329c54205a7c62f4df4cbb056", + "IPY_MODEL_98efed1f73804d8b998a51e41a7b91fd", + "IPY_MODEL_4fd94f96dc754593acfa96a01d4e99a1" + ], + "layout": "IPY_MODEL_9b3fbd16257049c7a45c921d39d3eaf5", + "tabbable": null, + "tooltip": null } }, - "f3388fc2449142fab5c93b916b11ec17": { + "ee680731bdcb4b61bede18e265e6df46": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6956,7 +6262,47 @@ "width": null } }, - "f6e05afaf00440a7830ad926432b4f83": { + "ef77a024e7724e369dc1c8d4f9e368dc": { + "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_e7583439721a41c9aec978c1bdbdeef9", + "IPY_MODEL_03271711edad450897e85e06eb6cb127", + "IPY_MODEL_a4463a06d49648eea5841f1b1c26666e" + ], + "layout": "IPY_MODEL_287287177d414d93adcc6f20d070dc87", + "tabbable": null, + "tooltip": null + } + }, + "f03f41af5b5447f9bcca654369f6a2ca": { + "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": "" + } + }, + "f0c48e7a92584111a0c8860ced295150": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7009,7 +6355,47 @@ "width": null } }, - "f85f0334db114aa59b010e361bb4fbbd": { + "f335fbafaacb4563bee9fa4ddaf06be4": { + "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": "" + } + }, + "f536096ae2354577a855edd0b00768f8": { + "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_e630651efa774777b84bec8783ca86e1", + "IPY_MODEL_cb1128114ec44703baed9028494b5a52", + "IPY_MODEL_d70005fa09074d9fab741afc9900399e" + ], + "layout": "IPY_MODEL_40cf93e1ad9c40329463ec8680702b75", + "tabbable": null, + "tooltip": null + } + }, + "f53af9199dd543cc90ef5fbb55d8ec83": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7062,7 +6448,7 @@ "width": null } }, - "f867132b50104b83b0babfb88b2684b3": { + "f73fd148bfd14a059bf19d9a0008bf30": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7115,31 +6501,7 @@ "width": null } }, - "f8b0487fce084fbbb8bb7ec13ff93904": { - "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_8970c3588e8b4775ae0c5198bb726b6c", - "IPY_MODEL_c181f62b4468453a89ff5289910a3a65", - "IPY_MODEL_643564843e7c4d559d15bce8b09e60bf" - ], - "layout": "IPY_MODEL_d5935e90376a42bebcfd4ccbd45c7d9a", - "tabbable": null, - "tooltip": null - } - }, - "fb58bed5d89f4661babe6b776dac767d": { + "f8b9efa53c0f4fb8bdbd667a9dc4d7d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7191,30 +6553,6 @@ "visibility": null, "width": null } - }, - "fe021de5ecdc4eaaa8a1a9bc5fc1da98": { - "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_188195b2866e46598594d40c79b6011b", - "IPY_MODEL_5d784a31f1c34dfd9151df789b1bab99", - "IPY_MODEL_dc6d200dd499407da1c62711cafeca7a" - ], - "layout": "IPY_MODEL_7521a6e0209d4bcfa4688c2f87743e34", - "tabbable": null, - "tooltip": null - } } }, "version_major": 2, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index b1d74a80c..4251dc171 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": "2024-02-27T04:11:14.797503Z", - "iopub.status.busy": "2024-02-27T04:11:14.797171Z", - "iopub.status.idle": "2024-02-27T04:11:15.872433Z", - "shell.execute_reply": "2024-02-27T04:11:15.871920Z" + "iopub.execute_input": "2024-03-05T17:26:25.913974Z", + "iopub.status.busy": "2024-03-05T17:26:25.913793Z", + "iopub.status.idle": "2024-03-05T17:26:27.120147Z", + "shell.execute_reply": "2024-03-05T17:26:27.119495Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:11:15.874932Z", - "iopub.status.busy": "2024-02-27T04:11:15.874600Z", - "iopub.status.idle": "2024-02-27T04:11:16.050372Z", - "shell.execute_reply": "2024-02-27T04:11:16.049843Z" + "iopub.execute_input": "2024-03-05T17:26:27.122822Z", + "iopub.status.busy": "2024-03-05T17:26:27.122428Z", + "iopub.status.idle": "2024-03-05T17:26:27.308983Z", + "shell.execute_reply": "2024-03-05T17:26:27.308368Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:16.053004Z", - "iopub.status.busy": "2024-02-27T04:11:16.052608Z", - "iopub.status.idle": "2024-02-27T04:11:16.064097Z", - "shell.execute_reply": "2024-02-27T04:11:16.063546Z" + "iopub.execute_input": "2024-03-05T17:26:27.311690Z", + "iopub.status.busy": "2024-03-05T17:26:27.311307Z", + "iopub.status.idle": "2024-03-05T17:26:27.323761Z", + "shell.execute_reply": "2024-03-05T17:26:27.323176Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:16.066283Z", - "iopub.status.busy": "2024-02-27T04:11:16.065903Z", - "iopub.status.idle": "2024-02-27T04:11:16.300106Z", - "shell.execute_reply": "2024-02-27T04:11:16.299548Z" + "iopub.execute_input": "2024-03-05T17:26:27.326197Z", + "iopub.status.busy": "2024-03-05T17:26:27.325856Z", + "iopub.status.idle": "2024-03-05T17:26:27.569749Z", + "shell.execute_reply": "2024-03-05T17:26:27.569143Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:16.302603Z", - "iopub.status.busy": "2024-02-27T04:11:16.302218Z", - "iopub.status.idle": "2024-02-27T04:11:16.329576Z", - "shell.execute_reply": "2024-02-27T04:11:16.329164Z" + "iopub.execute_input": "2024-03-05T17:26:27.572005Z", + "iopub.status.busy": "2024-03-05T17:26:27.571722Z", + "iopub.status.idle": "2024-03-05T17:26:27.599205Z", + "shell.execute_reply": "2024-03-05T17:26:27.598661Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:16.331602Z", - "iopub.status.busy": "2024-02-27T04:11:16.331279Z", - "iopub.status.idle": "2024-02-27T04:11:17.949443Z", - "shell.execute_reply": "2024-02-27T04:11:17.948926Z" + "iopub.execute_input": "2024-03-05T17:26:27.601935Z", + "iopub.status.busy": "2024-03-05T17:26:27.601446Z", + "iopub.status.idle": "2024-03-05T17:26:29.470338Z", + "shell.execute_reply": "2024-03-05T17:26:29.469673Z" } }, "outputs": [ @@ -475,10 +475,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:17.952199Z", - "iopub.status.busy": "2024-02-27T04:11:17.951567Z", - "iopub.status.idle": "2024-02-27T04:11:17.970482Z", - "shell.execute_reply": "2024-02-27T04:11:17.970030Z" + "iopub.execute_input": "2024-03-05T17:26:29.473388Z", + "iopub.status.busy": "2024-03-05T17:26:29.472356Z", + "iopub.status.idle": "2024-03-05T17:26:29.493442Z", + "shell.execute_reply": "2024-03-05T17:26:29.492883Z" }, "scrolled": true }, @@ -603,10 +603,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:17.972450Z", - "iopub.status.busy": "2024-02-27T04:11:17.972150Z", - "iopub.status.idle": "2024-02-27T04:11:19.336299Z", - "shell.execute_reply": "2024-02-27T04:11:19.335777Z" + "iopub.execute_input": "2024-03-05T17:26:29.495698Z", + "iopub.status.busy": "2024-03-05T17:26:29.495318Z", + "iopub.status.idle": "2024-03-05T17:26:31.024996Z", + "shell.execute_reply": "2024-03-05T17:26:31.024329Z" }, "id": "AaHC5MRKjruT" }, @@ -725,10 +725,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.338968Z", - "iopub.status.busy": "2024-02-27T04:11:19.338212Z", - "iopub.status.idle": "2024-02-27T04:11:19.351594Z", - "shell.execute_reply": "2024-02-27T04:11:19.351150Z" + "iopub.execute_input": "2024-03-05T17:26:31.028261Z", + "iopub.status.busy": "2024-03-05T17:26:31.027357Z", + "iopub.status.idle": "2024-03-05T17:26:31.043398Z", + "shell.execute_reply": "2024-03-05T17:26:31.042833Z" }, "id": "Wy27rvyhjruU" }, @@ -777,10 +777,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.353609Z", - "iopub.status.busy": "2024-02-27T04:11:19.353270Z", - "iopub.status.idle": "2024-02-27T04:11:19.422073Z", - "shell.execute_reply": "2024-02-27T04:11:19.421532Z" + "iopub.execute_input": "2024-03-05T17:26:31.045811Z", + "iopub.status.busy": "2024-03-05T17:26:31.045436Z", + "iopub.status.idle": "2024-03-05T17:26:31.135388Z", + "shell.execute_reply": "2024-03-05T17:26:31.134826Z" }, "id": "Db8YHnyVjruU" }, @@ -887,10 +887,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.424260Z", - "iopub.status.busy": "2024-02-27T04:11:19.423971Z", - "iopub.status.idle": "2024-02-27T04:11:19.631539Z", - "shell.execute_reply": "2024-02-27T04:11:19.631015Z" + "iopub.execute_input": "2024-03-05T17:26:31.137715Z", + "iopub.status.busy": "2024-03-05T17:26:31.137407Z", + "iopub.status.idle": "2024-03-05T17:26:31.354491Z", + "shell.execute_reply": "2024-03-05T17:26:31.353914Z" }, "id": "iJqAHuS2jruV" }, @@ -927,10 +927,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.633648Z", - "iopub.status.busy": "2024-02-27T04:11:19.633311Z", - "iopub.status.idle": "2024-02-27T04:11:19.649889Z", - "shell.execute_reply": "2024-02-27T04:11:19.649466Z" + "iopub.execute_input": "2024-03-05T17:26:31.356854Z", + "iopub.status.busy": "2024-03-05T17:26:31.356506Z", + "iopub.status.idle": "2024-03-05T17:26:31.374002Z", + "shell.execute_reply": "2024-03-05T17:26:31.373435Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1396,10 +1396,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.651815Z", - "iopub.status.busy": "2024-02-27T04:11:19.651501Z", - "iopub.status.idle": "2024-02-27T04:11:19.660394Z", - "shell.execute_reply": "2024-02-27T04:11:19.659948Z" + "iopub.execute_input": "2024-03-05T17:26:31.376365Z", + "iopub.status.busy": "2024-03-05T17:26:31.376010Z", + "iopub.status.idle": "2024-03-05T17:26:31.386117Z", + "shell.execute_reply": "2024-03-05T17:26:31.385574Z" }, "id": "0lonvOYvjruV" }, @@ -1546,10 +1546,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.662491Z", - "iopub.status.busy": "2024-02-27T04:11:19.662099Z", - "iopub.status.idle": "2024-02-27T04:11:19.741536Z", - "shell.execute_reply": "2024-02-27T04:11:19.740945Z" + "iopub.execute_input": "2024-03-05T17:26:31.388376Z", + "iopub.status.busy": "2024-03-05T17:26:31.388008Z", + "iopub.status.idle": "2024-03-05T17:26:31.487199Z", + "shell.execute_reply": "2024-03-05T17:26:31.486574Z" }, "id": "MfqTCa3kjruV" }, @@ -1630,10 +1630,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.743988Z", - "iopub.status.busy": "2024-02-27T04:11:19.743652Z", - "iopub.status.idle": "2024-02-27T04:11:19.860324Z", - "shell.execute_reply": "2024-02-27T04:11:19.859746Z" + "iopub.execute_input": "2024-03-05T17:26:31.490166Z", + "iopub.status.busy": "2024-03-05T17:26:31.489873Z", + "iopub.status.idle": "2024-03-05T17:26:31.635282Z", + "shell.execute_reply": "2024-03-05T17:26:31.634636Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1693,10 +1693,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.862767Z", - "iopub.status.busy": "2024-02-27T04:11:19.862440Z", - "iopub.status.idle": "2024-02-27T04:11:19.866266Z", - "shell.execute_reply": "2024-02-27T04:11:19.865804Z" + "iopub.execute_input": "2024-03-05T17:26:31.637910Z", + "iopub.status.busy": "2024-03-05T17:26:31.637471Z", + "iopub.status.idle": "2024-03-05T17:26:31.641593Z", + "shell.execute_reply": "2024-03-05T17:26:31.641043Z" }, "id": "0rXP3ZPWjruW" }, @@ -1734,10 +1734,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.868195Z", - "iopub.status.busy": "2024-02-27T04:11:19.867937Z", - "iopub.status.idle": "2024-02-27T04:11:19.871611Z", - "shell.execute_reply": "2024-02-27T04:11:19.871094Z" + "iopub.execute_input": "2024-03-05T17:26:31.643787Z", + "iopub.status.busy": "2024-03-05T17:26:31.643459Z", + "iopub.status.idle": "2024-03-05T17:26:31.647384Z", + "shell.execute_reply": "2024-03-05T17:26:31.646842Z" }, "id": "-iRPe8KXjruW" }, @@ -1792,10 +1792,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.873567Z", - "iopub.status.busy": "2024-02-27T04:11:19.873211Z", - "iopub.status.idle": "2024-02-27T04:11:19.910384Z", - "shell.execute_reply": "2024-02-27T04:11:19.909960Z" + "iopub.execute_input": "2024-03-05T17:26:31.649608Z", + "iopub.status.busy": "2024-03-05T17:26:31.649282Z", + "iopub.status.idle": "2024-03-05T17:26:31.688906Z", + "shell.execute_reply": "2024-03-05T17:26:31.688352Z" }, "id": "ZpipUliyjruW" }, @@ -1804,7 +1804,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "pred_probs is a (250, 4) matrix of predicted probabilities\n" + "pred_probs is a (250, 4) matrix of predicted probabilities" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -1846,10 +1853,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.912313Z", - "iopub.status.busy": "2024-02-27T04:11:19.912020Z", - "iopub.status.idle": "2024-02-27T04:11:19.954874Z", - "shell.execute_reply": "2024-02-27T04:11:19.954347Z" + "iopub.execute_input": "2024-03-05T17:26:31.691110Z", + "iopub.status.busy": "2024-03-05T17:26:31.690899Z", + "iopub.status.idle": "2024-03-05T17:26:31.735848Z", + "shell.execute_reply": "2024-03-05T17:26:31.735209Z" }, "id": "SLq-3q4xjruX" }, @@ -1918,10 +1925,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:19.956813Z", - "iopub.status.busy": "2024-02-27T04:11:19.956444Z", - "iopub.status.idle": "2024-02-27T04:11:20.045340Z", - "shell.execute_reply": "2024-02-27T04:11:20.044815Z" + "iopub.execute_input": "2024-03-05T17:26:31.738311Z", + "iopub.status.busy": "2024-03-05T17:26:31.737938Z", + "iopub.status.idle": "2024-03-05T17:26:31.849062Z", + "shell.execute_reply": "2024-03-05T17:26:31.848352Z" }, "id": "g5LHhhuqFbXK" }, @@ -1953,10 +1960,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.047806Z", - "iopub.status.busy": "2024-02-27T04:11:20.047455Z", - "iopub.status.idle": "2024-02-27T04:11:20.124961Z", - "shell.execute_reply": "2024-02-27T04:11:20.124406Z" + "iopub.execute_input": "2024-03-05T17:26:31.851797Z", + "iopub.status.busy": "2024-03-05T17:26:31.851586Z", + "iopub.status.idle": "2024-03-05T17:26:31.969993Z", + "shell.execute_reply": "2024-03-05T17:26:31.969392Z" }, "id": "p7w8F8ezBcet" }, @@ -2013,10 +2020,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.127202Z", - "iopub.status.busy": "2024-02-27T04:11:20.126910Z", - "iopub.status.idle": "2024-02-27T04:11:20.336685Z", - "shell.execute_reply": "2024-02-27T04:11:20.336163Z" + "iopub.execute_input": "2024-03-05T17:26:31.972627Z", + "iopub.status.busy": "2024-03-05T17:26:31.972225Z", + "iopub.status.idle": "2024-03-05T17:26:32.191363Z", + "shell.execute_reply": "2024-03-05T17:26:32.190882Z" }, "id": "WETRL74tE_sU" }, @@ -2051,10 +2058,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.338863Z", - "iopub.status.busy": "2024-02-27T04:11:20.338518Z", - "iopub.status.idle": "2024-02-27T04:11:20.499507Z", - "shell.execute_reply": "2024-02-27T04:11:20.498940Z" + "iopub.execute_input": "2024-03-05T17:26:32.193719Z", + "iopub.status.busy": "2024-03-05T17:26:32.193362Z", + "iopub.status.idle": "2024-03-05T17:26:32.441992Z", + "shell.execute_reply": "2024-03-05T17:26:32.441363Z" }, "id": "kCfdx2gOLmXS" }, @@ -2216,10 +2223,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.501687Z", - "iopub.status.busy": "2024-02-27T04:11:20.501498Z", - "iopub.status.idle": "2024-02-27T04:11:20.507903Z", - "shell.execute_reply": "2024-02-27T04:11:20.507342Z" + "iopub.execute_input": "2024-03-05T17:26:32.444521Z", + "iopub.status.busy": "2024-03-05T17:26:32.444263Z", + "iopub.status.idle": "2024-03-05T17:26:32.451465Z", + "shell.execute_reply": "2024-03-05T17:26:32.450911Z" }, "id": "-uogYRWFYnuu" }, @@ -2273,10 +2280,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.509836Z", - "iopub.status.busy": "2024-02-27T04:11:20.509594Z", - "iopub.status.idle": "2024-02-27T04:11:20.723906Z", - "shell.execute_reply": "2024-02-27T04:11:20.723363Z" + "iopub.execute_input": "2024-03-05T17:26:32.453590Z", + "iopub.status.busy": "2024-03-05T17:26:32.453389Z", + "iopub.status.idle": "2024-03-05T17:26:32.678650Z", + "shell.execute_reply": "2024-03-05T17:26:32.678005Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2323,10 +2330,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:20.726056Z", - "iopub.status.busy": "2024-02-27T04:11:20.725621Z", - "iopub.status.idle": "2024-02-27T04:11:21.797128Z", - "shell.execute_reply": "2024-02-27T04:11:21.796614Z" + "iopub.execute_input": "2024-03-05T17:26:32.681109Z", + "iopub.status.busy": "2024-03-05T17:26:32.680713Z", + "iopub.status.idle": "2024-03-05T17:26:33.758319Z", + "shell.execute_reply": "2024-03-05T17:26:33.757744Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index d6474214f..a5eab3b4f 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -89,10 +89,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:24.965144Z", - "iopub.status.busy": "2024-02-27T04:11:24.964979Z", - "iopub.status.idle": "2024-02-27T04:11:25.986628Z", - "shell.execute_reply": "2024-02-27T04:11:25.986025Z" + "iopub.execute_input": "2024-03-05T17:26:38.410635Z", + "iopub.status.busy": "2024-03-05T17:26:38.410144Z", + "iopub.status.idle": "2024-03-05T17:26:39.538780Z", + "shell.execute_reply": "2024-03-05T17:26:39.538156Z" }, "nbsphinx": "hidden" }, @@ -102,7 +102,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -136,10 +136,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:25.989118Z", - "iopub.status.busy": "2024-02-27T04:11:25.988838Z", - "iopub.status.idle": "2024-02-27T04:11:25.992192Z", - "shell.execute_reply": "2024-02-27T04:11:25.991753Z" + "iopub.execute_input": "2024-03-05T17:26:39.542285Z", + "iopub.status.busy": "2024-03-05T17:26:39.541793Z", + "iopub.status.idle": "2024-03-05T17:26:39.544910Z", + "shell.execute_reply": "2024-03-05T17:26:39.544471Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:25.994242Z", - "iopub.status.busy": "2024-02-27T04:11:25.994064Z", - "iopub.status.idle": "2024-02-27T04:11:26.001825Z", - "shell.execute_reply": "2024-02-27T04:11:26.001382Z" + "iopub.execute_input": "2024-03-05T17:26:39.546890Z", + "iopub.status.busy": "2024-03-05T17:26:39.546713Z", + "iopub.status.idle": "2024-03-05T17:26:39.554759Z", + "shell.execute_reply": "2024-03-05T17:26:39.554223Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.003744Z", - "iopub.status.busy": "2024-02-27T04:11:26.003397Z", - "iopub.status.idle": "2024-02-27T04:11:26.050244Z", - "shell.execute_reply": "2024-02-27T04:11:26.049766Z" + "iopub.execute_input": "2024-03-05T17:26:39.557146Z", + "iopub.status.busy": "2024-03-05T17:26:39.556707Z", + "iopub.status.idle": "2024-03-05T17:26:39.605516Z", + "shell.execute_reply": "2024-03-05T17:26:39.605020Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.052581Z", - "iopub.status.busy": "2024-02-27T04:11:26.052189Z", - "iopub.status.idle": "2024-02-27T04:11:26.069298Z", - "shell.execute_reply": "2024-02-27T04:11:26.068826Z" + "iopub.execute_input": "2024-03-05T17:26:39.607894Z", + "iopub.status.busy": "2024-03-05T17:26:39.607701Z", + "iopub.status.idle": "2024-03-05T17:26:39.626581Z", + "shell.execute_reply": "2024-03-05T17:26:39.626098Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.071234Z", - "iopub.status.busy": "2024-02-27T04:11:26.071056Z", - "iopub.status.idle": "2024-02-27T04:11:26.075098Z", - "shell.execute_reply": "2024-02-27T04:11:26.074678Z" + "iopub.execute_input": "2024-03-05T17:26:39.628779Z", + "iopub.status.busy": "2024-03-05T17:26:39.628440Z", + "iopub.status.idle": "2024-03-05T17:26:39.632432Z", + "shell.execute_reply": "2024-03-05T17:26:39.631869Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.077104Z", - "iopub.status.busy": "2024-02-27T04:11:26.076785Z", - "iopub.status.idle": "2024-02-27T04:11:26.103793Z", - "shell.execute_reply": "2024-02-27T04:11:26.103386Z" + "iopub.execute_input": "2024-03-05T17:26:39.634490Z", + "iopub.status.busy": "2024-03-05T17:26:39.634309Z", + "iopub.status.idle": "2024-03-05T17:26:39.664088Z", + "shell.execute_reply": "2024-03-05T17:26:39.663607Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.105914Z", - "iopub.status.busy": "2024-02-27T04:11:26.105409Z", - "iopub.status.idle": "2024-02-27T04:11:26.132048Z", - "shell.execute_reply": "2024-02-27T04:11:26.131536Z" + "iopub.execute_input": "2024-03-05T17:26:39.666426Z", + "iopub.status.busy": "2024-03-05T17:26:39.666199Z", + "iopub.status.idle": "2024-03-05T17:26:39.693767Z", + "shell.execute_reply": "2024-03-05T17:26:39.693266Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:26.134207Z", - "iopub.status.busy": "2024-02-27T04:11:26.133842Z", - "iopub.status.idle": "2024-02-27T04:11:27.825507Z", - "shell.execute_reply": "2024-02-27T04:11:27.824974Z" + "iopub.execute_input": "2024-03-05T17:26:39.696423Z", + "iopub.status.busy": "2024-03-05T17:26:39.695989Z", + "iopub.status.idle": "2024-03-05T17:26:41.565574Z", + "shell.execute_reply": "2024-03-05T17:26:41.565008Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.827897Z", - "iopub.status.busy": "2024-02-27T04:11:27.827612Z", - "iopub.status.idle": "2024-02-27T04:11:27.834365Z", - "shell.execute_reply": "2024-02-27T04:11:27.833863Z" + "iopub.execute_input": "2024-03-05T17:26:41.568243Z", + "iopub.status.busy": "2024-03-05T17:26:41.567892Z", + "iopub.status.idle": "2024-03-05T17:26:41.574559Z", + "shell.execute_reply": "2024-03-05T17:26:41.574055Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.836382Z", - "iopub.status.busy": "2024-02-27T04:11:27.836001Z", - "iopub.status.idle": "2024-02-27T04:11:27.848356Z", - "shell.execute_reply": "2024-02-27T04:11:27.847833Z" + "iopub.execute_input": "2024-03-05T17:26:41.576745Z", + "iopub.status.busy": "2024-03-05T17:26:41.576422Z", + "iopub.status.idle": "2024-03-05T17:26:41.589414Z", + "shell.execute_reply": "2024-03-05T17:26:41.588758Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.850387Z", - "iopub.status.busy": "2024-02-27T04:11:27.850033Z", - "iopub.status.idle": "2024-02-27T04:11:27.856141Z", - "shell.execute_reply": "2024-02-27T04:11:27.855627Z" + "iopub.execute_input": "2024-03-05T17:26:41.591894Z", + "iopub.status.busy": "2024-03-05T17:26:41.591351Z", + "iopub.status.idle": "2024-03-05T17:26:41.598472Z", + "shell.execute_reply": "2024-03-05T17:26:41.597990Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.858133Z", - "iopub.status.busy": "2024-02-27T04:11:27.857774Z", - "iopub.status.idle": "2024-02-27T04:11:27.860433Z", - "shell.execute_reply": "2024-02-27T04:11:27.859922Z" + "iopub.execute_input": "2024-03-05T17:26:41.600752Z", + "iopub.status.busy": "2024-03-05T17:26:41.600488Z", + "iopub.status.idle": "2024-03-05T17:26:41.603120Z", + "shell.execute_reply": "2024-03-05T17:26:41.602690Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.862335Z", - "iopub.status.busy": "2024-02-27T04:11:27.862091Z", - "iopub.status.idle": "2024-02-27T04:11:27.865596Z", - "shell.execute_reply": "2024-02-27T04:11:27.865170Z" + "iopub.execute_input": "2024-03-05T17:26:41.605153Z", + "iopub.status.busy": "2024-03-05T17:26:41.604836Z", + "iopub.status.idle": "2024-03-05T17:26:41.608242Z", + "shell.execute_reply": "2024-03-05T17:26:41.607724Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.867491Z", - "iopub.status.busy": "2024-02-27T04:11:27.867250Z", - "iopub.status.idle": "2024-02-27T04:11:27.869808Z", - "shell.execute_reply": "2024-02-27T04:11:27.869355Z" + "iopub.execute_input": "2024-03-05T17:26:41.610371Z", + "iopub.status.busy": "2024-03-05T17:26:41.610047Z", + "iopub.status.idle": "2024-03-05T17:26:41.612774Z", + "shell.execute_reply": "2024-03-05T17:26:41.612284Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.871783Z", - "iopub.status.busy": "2024-02-27T04:11:27.871406Z", - "iopub.status.idle": "2024-02-27T04:11:27.875464Z", - "shell.execute_reply": "2024-02-27T04:11:27.874914Z" + "iopub.execute_input": "2024-03-05T17:26:41.614738Z", + "iopub.status.busy": "2024-03-05T17:26:41.614477Z", + "iopub.status.idle": "2024-03-05T17:26:41.618840Z", + "shell.execute_reply": "2024-03-05T17:26:41.618393Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.877446Z", - "iopub.status.busy": "2024-02-27T04:11:27.877159Z", - "iopub.status.idle": "2024-02-27T04:11:27.906313Z", - "shell.execute_reply": "2024-02-27T04:11:27.905793Z" + "iopub.execute_input": "2024-03-05T17:26:41.620901Z", + "iopub.status.busy": "2024-03-05T17:26:41.620576Z", + "iopub.status.idle": "2024-03-05T17:26:41.649873Z", + "shell.execute_reply": "2024-03-05T17:26:41.649396Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:27.908369Z", - "iopub.status.busy": "2024-02-27T04:11:27.908054Z", - "iopub.status.idle": "2024-02-27T04:11:27.912335Z", - "shell.execute_reply": "2024-02-27T04:11:27.911931Z" + "iopub.execute_input": "2024-03-05T17:26:41.652481Z", + "iopub.status.busy": "2024-03-05T17:26:41.652090Z", + "iopub.status.idle": "2024-03-05T17:26:41.657110Z", + "shell.execute_reply": "2024-03-05T17:26:41.656656Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index b4048672e..d1e1a339c 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": "2024-02-27T04:11:30.504197Z", - "iopub.status.busy": "2024-02-27T04:11:30.504023Z", - "iopub.status.idle": "2024-02-27T04:11:31.574534Z", - "shell.execute_reply": "2024-02-27T04:11:31.574018Z" + "iopub.execute_input": "2024-03-05T17:26:44.746120Z", + "iopub.status.busy": "2024-03-05T17:26:44.745938Z", + "iopub.status.idle": "2024-03-05T17:26:45.959393Z", + "shell.execute_reply": "2024-03-05T17:26:45.958752Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:11:31.577082Z", - "iopub.status.busy": "2024-02-27T04:11:31.576604Z", - "iopub.status.idle": "2024-02-27T04:11:31.771076Z", - "shell.execute_reply": "2024-02-27T04:11:31.770560Z" + "iopub.execute_input": "2024-03-05T17:26:45.962143Z", + "iopub.status.busy": "2024-03-05T17:26:45.961823Z", + "iopub.status.idle": "2024-03-05T17:26:46.172359Z", + "shell.execute_reply": "2024-03-05T17:26:46.171835Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:31.773699Z", - "iopub.status.busy": "2024-02-27T04:11:31.773178Z", - "iopub.status.idle": "2024-02-27T04:11:31.785926Z", - "shell.execute_reply": "2024-02-27T04:11:31.785461Z" + "iopub.execute_input": "2024-03-05T17:26:46.175349Z", + "iopub.status.busy": "2024-03-05T17:26:46.174824Z", + "iopub.status.idle": "2024-03-05T17:26:46.188517Z", + "shell.execute_reply": "2024-03-05T17:26:46.187888Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:31.787872Z", - "iopub.status.busy": "2024-02-27T04:11:31.787553Z", - "iopub.status.idle": "2024-02-27T04:11:34.432855Z", - "shell.execute_reply": "2024-02-27T04:11:34.432293Z" + "iopub.execute_input": "2024-03-05T17:26:46.190918Z", + "iopub.status.busy": "2024-03-05T17:26:46.190461Z", + "iopub.status.idle": "2024-03-05T17:26:48.936990Z", + "shell.execute_reply": "2024-03-05T17:26:48.936426Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:34.435059Z", - "iopub.status.busy": "2024-02-27T04:11:34.434724Z", - "iopub.status.idle": "2024-02-27T04:11:35.777890Z", - "shell.execute_reply": "2024-02-27T04:11:35.777352Z" + "iopub.execute_input": "2024-03-05T17:26:48.939340Z", + "iopub.status.busy": "2024-03-05T17:26:48.938917Z", + "iopub.status.idle": "2024-03-05T17:26:50.296100Z", + "shell.execute_reply": "2024-03-05T17:26:50.295554Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:35.780283Z", - "iopub.status.busy": "2024-02-27T04:11:35.779934Z", - "iopub.status.idle": "2024-02-27T04:11:35.783955Z", - "shell.execute_reply": "2024-02-27T04:11:35.783514Z" + "iopub.execute_input": "2024-03-05T17:26:50.298773Z", + "iopub.status.busy": "2024-03-05T17:26:50.298318Z", + "iopub.status.idle": "2024-03-05T17:26:50.302356Z", + "shell.execute_reply": "2024-03-05T17:26:50.301899Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:35.785855Z", - "iopub.status.busy": "2024-02-27T04:11:35.785523Z", - "iopub.status.idle": "2024-02-27T04:11:37.505693Z", - "shell.execute_reply": "2024-02-27T04:11:37.505072Z" + "iopub.execute_input": "2024-03-05T17:26:50.304511Z", + "iopub.status.busy": "2024-03-05T17:26:50.304083Z", + "iopub.status.idle": "2024-03-05T17:26:52.275481Z", + "shell.execute_reply": "2024-03-05T17:26:52.274862Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:37.508200Z", - "iopub.status.busy": "2024-02-27T04:11:37.507651Z", - "iopub.status.idle": "2024-02-27T04:11:37.515666Z", - "shell.execute_reply": "2024-02-27T04:11:37.515153Z" + "iopub.execute_input": "2024-03-05T17:26:52.278347Z", + "iopub.status.busy": "2024-03-05T17:26:52.277719Z", + "iopub.status.idle": "2024-03-05T17:26:52.286605Z", + "shell.execute_reply": "2024-03-05T17:26:52.286012Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:37.517477Z", - "iopub.status.busy": "2024-02-27T04:11:37.517304Z", - "iopub.status.idle": "2024-02-27T04:11:40.082105Z", - "shell.execute_reply": "2024-02-27T04:11:40.081554Z" + "iopub.execute_input": "2024-03-05T17:26:52.288970Z", + "iopub.status.busy": "2024-03-05T17:26:52.288583Z", + "iopub.status.idle": "2024-03-05T17:26:54.978883Z", + "shell.execute_reply": "2024-03-05T17:26:54.978296Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:40.084350Z", - "iopub.status.busy": "2024-02-27T04:11:40.083987Z", - "iopub.status.idle": "2024-02-27T04:11:40.087200Z", - "shell.execute_reply": "2024-02-27T04:11:40.086726Z" + "iopub.execute_input": "2024-03-05T17:26:54.981244Z", + "iopub.status.busy": "2024-03-05T17:26:54.980834Z", + "iopub.status.idle": "2024-03-05T17:26:54.985045Z", + "shell.execute_reply": "2024-03-05T17:26:54.984569Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:40.089279Z", - "iopub.status.busy": "2024-02-27T04:11:40.088963Z", - "iopub.status.idle": "2024-02-27T04:11:40.093263Z", - "shell.execute_reply": "2024-02-27T04:11:40.092874Z" + "iopub.execute_input": "2024-03-05T17:26:54.987214Z", + "iopub.status.busy": "2024-03-05T17:26:54.986873Z", + "iopub.status.idle": "2024-03-05T17:26:54.991161Z", + "shell.execute_reply": "2024-03-05T17:26:54.990649Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:40.095213Z", - "iopub.status.busy": "2024-02-27T04:11:40.094894Z", - "iopub.status.idle": "2024-02-27T04:11:40.097856Z", - "shell.execute_reply": "2024-02-27T04:11:40.097419Z" + "iopub.execute_input": "2024-03-05T17:26:54.993453Z", + "iopub.status.busy": "2024-03-05T17:26:54.993046Z", + "iopub.status.idle": "2024-03-05T17:26:54.996386Z", + "shell.execute_reply": "2024-03-05T17:26:54.995868Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index dcc7ec89b..e3448c246 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:42.306563Z", - "iopub.status.busy": "2024-02-27T04:11:42.306138Z", - "iopub.status.idle": "2024-02-27T04:11:43.382966Z", - "shell.execute_reply": "2024-02-27T04:11:43.382442Z" + "iopub.execute_input": "2024-03-05T17:26:57.524008Z", + "iopub.status.busy": "2024-03-05T17:26:57.523826Z", + "iopub.status.idle": "2024-03-05T17:26:58.725796Z", + "shell.execute_reply": "2024-03-05T17:26:58.725204Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:11:43.385619Z", - "iopub.status.busy": "2024-02-27T04:11:43.385198Z", - "iopub.status.idle": "2024-02-27T04:11:45.888818Z", - "shell.execute_reply": "2024-02-27T04:11:45.888209Z" + "iopub.execute_input": "2024-03-05T17:26:58.728445Z", + "iopub.status.busy": "2024-03-05T17:26:58.728150Z", + "iopub.status.idle": "2024-03-05T17:27:01.598915Z", + "shell.execute_reply": "2024-03-05T17:27:01.598216Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:45.891348Z", - "iopub.status.busy": "2024-02-27T04:11:45.891009Z", - "iopub.status.idle": "2024-02-27T04:11:45.894217Z", - "shell.execute_reply": "2024-02-27T04:11:45.893754Z" + "iopub.execute_input": "2024-03-05T17:27:01.601413Z", + "iopub.status.busy": "2024-03-05T17:27:01.601208Z", + "iopub.status.idle": "2024-03-05T17:27:01.604543Z", + "shell.execute_reply": "2024-03-05T17:27:01.604094Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:45.896274Z", - "iopub.status.busy": "2024-02-27T04:11:45.895937Z", - "iopub.status.idle": "2024-02-27T04:11:45.902367Z", - "shell.execute_reply": "2024-02-27T04:11:45.901866Z" + "iopub.execute_input": "2024-03-05T17:27:01.606448Z", + "iopub.status.busy": "2024-03-05T17:27:01.606277Z", + "iopub.status.idle": "2024-03-05T17:27:01.612922Z", + "shell.execute_reply": "2024-03-05T17:27:01.612512Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:45.904439Z", - "iopub.status.busy": "2024-02-27T04:11:45.904139Z", - "iopub.status.idle": "2024-02-27T04:11:46.391039Z", - "shell.execute_reply": "2024-02-27T04:11:46.390438Z" + "iopub.execute_input": "2024-03-05T17:27:01.614983Z", + "iopub.status.busy": "2024-03-05T17:27:01.614783Z", + "iopub.status.idle": "2024-03-05T17:27:02.122175Z", + "shell.execute_reply": "2024-03-05T17:27:02.121541Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:46.393498Z", - "iopub.status.busy": "2024-02-27T04:11:46.393056Z", - "iopub.status.idle": "2024-02-27T04:11:46.398285Z", - "shell.execute_reply": "2024-02-27T04:11:46.397749Z" + "iopub.execute_input": "2024-03-05T17:27:02.125008Z", + "iopub.status.busy": "2024-03-05T17:27:02.124616Z", + "iopub.status.idle": "2024-03-05T17:27:02.130169Z", + "shell.execute_reply": "2024-03-05T17:27:02.129720Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:46.400495Z", - "iopub.status.busy": "2024-02-27T04:11:46.400077Z", - "iopub.status.idle": "2024-02-27T04:11:46.404012Z", - "shell.execute_reply": "2024-02-27T04:11:46.403603Z" + "iopub.execute_input": "2024-03-05T17:27:02.132310Z", + "iopub.status.busy": "2024-03-05T17:27:02.131969Z", + "iopub.status.idle": "2024-03-05T17:27:02.135946Z", + "shell.execute_reply": "2024-03-05T17:27:02.135384Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:46.405920Z", - "iopub.status.busy": "2024-02-27T04:11:46.405608Z", - "iopub.status.idle": "2024-02-27T04:11:47.134326Z", - "shell.execute_reply": "2024-02-27T04:11:47.133795Z" + "iopub.execute_input": "2024-03-05T17:27:02.138118Z", + "iopub.status.busy": "2024-03-05T17:27:02.137723Z", + "iopub.status.idle": "2024-03-05T17:27:02.901172Z", + "shell.execute_reply": "2024-03-05T17:27:02.900567Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:47.136690Z", - "iopub.status.busy": "2024-02-27T04:11:47.136327Z", - "iopub.status.idle": "2024-02-27T04:11:47.306932Z", - "shell.execute_reply": "2024-02-27T04:11:47.306383Z" + "iopub.execute_input": "2024-03-05T17:27:02.903649Z", + "iopub.status.busy": "2024-03-05T17:27:02.903255Z", + "iopub.status.idle": "2024-03-05T17:27:03.076763Z", + "shell.execute_reply": "2024-03-05T17:27:03.076198Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:47.309102Z", - "iopub.status.busy": "2024-02-27T04:11:47.308790Z", - "iopub.status.idle": "2024-02-27T04:11:47.312934Z", - "shell.execute_reply": "2024-02-27T04:11:47.312519Z" + "iopub.execute_input": "2024-03-05T17:27:03.079164Z", + "iopub.status.busy": "2024-03-05T17:27:03.078769Z", + "iopub.status.idle": "2024-03-05T17:27:03.083122Z", + "shell.execute_reply": "2024-03-05T17:27:03.082620Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:47.314963Z", - "iopub.status.busy": "2024-02-27T04:11:47.314635Z", - "iopub.status.idle": "2024-02-27T04:11:47.759434Z", - "shell.execute_reply": "2024-02-27T04:11:47.758867Z" + "iopub.execute_input": "2024-03-05T17:27:03.085402Z", + "iopub.status.busy": "2024-03-05T17:27:03.085057Z", + "iopub.status.idle": "2024-03-05T17:27:03.556083Z", + "shell.execute_reply": "2024-03-05T17:27:03.555501Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:47.762576Z", - "iopub.status.busy": "2024-02-27T04:11:47.762139Z", - "iopub.status.idle": "2024-02-27T04:11:48.066037Z", - "shell.execute_reply": "2024-02-27T04:11:48.065385Z" + "iopub.execute_input": "2024-03-05T17:27:03.558955Z", + "iopub.status.busy": "2024-03-05T17:27:03.558579Z", + "iopub.status.idle": "2024-03-05T17:27:03.899216Z", + "shell.execute_reply": "2024-03-05T17:27:03.898670Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:48.068646Z", - "iopub.status.busy": "2024-02-27T04:11:48.068183Z", - "iopub.status.idle": "2024-02-27T04:11:48.402796Z", - "shell.execute_reply": "2024-02-27T04:11:48.402206Z" + "iopub.execute_input": "2024-03-05T17:27:03.901856Z", + "iopub.status.busy": "2024-03-05T17:27:03.901656Z", + "iopub.status.idle": "2024-03-05T17:27:04.275751Z", + "shell.execute_reply": "2024-03-05T17:27:04.275075Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:48.405600Z", - "iopub.status.busy": "2024-02-27T04:11:48.405277Z", - "iopub.status.idle": "2024-02-27T04:11:48.848393Z", - "shell.execute_reply": "2024-02-27T04:11:48.847814Z" + "iopub.execute_input": "2024-03-05T17:27:04.278898Z", + "iopub.status.busy": "2024-03-05T17:27:04.278502Z", + "iopub.status.idle": "2024-03-05T17:27:04.703652Z", + "shell.execute_reply": "2024-03-05T17:27:04.703071Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:48.852279Z", - "iopub.status.busy": "2024-02-27T04:11:48.852044Z", - "iopub.status.idle": "2024-02-27T04:11:49.298159Z", - "shell.execute_reply": "2024-02-27T04:11:49.297549Z" + "iopub.execute_input": "2024-03-05T17:27:04.707903Z", + "iopub.status.busy": "2024-03-05T17:27:04.707505Z", + "iopub.status.idle": "2024-03-05T17:27:05.168729Z", + "shell.execute_reply": "2024-03-05T17:27:05.168201Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:49.301031Z", - "iopub.status.busy": "2024-02-27T04:11:49.300630Z", - "iopub.status.idle": "2024-02-27T04:11:49.514596Z", - "shell.execute_reply": "2024-02-27T04:11:49.514046Z" + "iopub.execute_input": "2024-03-05T17:27:05.171143Z", + "iopub.status.busy": "2024-03-05T17:27:05.170786Z", + "iopub.status.idle": "2024-03-05T17:27:05.392298Z", + "shell.execute_reply": "2024-03-05T17:27:05.391759Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:49.516815Z", - "iopub.status.busy": "2024-02-27T04:11:49.516622Z", - "iopub.status.idle": "2024-02-27T04:11:49.715001Z", - "shell.execute_reply": "2024-02-27T04:11:49.714515Z" + "iopub.execute_input": "2024-03-05T17:27:05.395123Z", + "iopub.status.busy": "2024-03-05T17:27:05.394728Z", + "iopub.status.idle": "2024-03-05T17:27:05.576097Z", + "shell.execute_reply": "2024-03-05T17:27:05.575566Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:49.717353Z", - "iopub.status.busy": "2024-02-27T04:11:49.716944Z", - "iopub.status.idle": "2024-02-27T04:11:49.719774Z", - "shell.execute_reply": "2024-02-27T04:11:49.719359Z" + "iopub.execute_input": "2024-03-05T17:27:05.578735Z", + "iopub.status.busy": "2024-03-05T17:27:05.578359Z", + "iopub.status.idle": "2024-03-05T17:27:05.581268Z", + "shell.execute_reply": "2024-03-05T17:27:05.580803Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:49.721693Z", - "iopub.status.busy": "2024-02-27T04:11:49.721405Z", - "iopub.status.idle": "2024-02-27T04:11:50.753324Z", - "shell.execute_reply": "2024-02-27T04:11:50.752830Z" + "iopub.execute_input": "2024-03-05T17:27:05.583273Z", + "iopub.status.busy": "2024-03-05T17:27:05.582924Z", + "iopub.status.idle": "2024-03-05T17:27:06.565448Z", + "shell.execute_reply": "2024-03-05T17:27:06.564894Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:50.755934Z", - "iopub.status.busy": "2024-02-27T04:11:50.755585Z", - "iopub.status.idle": "2024-02-27T04:11:50.911975Z", - "shell.execute_reply": "2024-02-27T04:11:50.911445Z" + "iopub.execute_input": "2024-03-05T17:27:06.568408Z", + "iopub.status.busy": "2024-03-05T17:27:06.568009Z", + "iopub.status.idle": "2024-03-05T17:27:06.687121Z", + "shell.execute_reply": "2024-03-05T17:27:06.686521Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:50.914131Z", - "iopub.status.busy": "2024-02-27T04:11:50.913716Z", - "iopub.status.idle": "2024-02-27T04:11:51.118016Z", - "shell.execute_reply": "2024-02-27T04:11:51.117448Z" + "iopub.execute_input": "2024-03-05T17:27:06.689382Z", + "iopub.status.busy": "2024-03-05T17:27:06.689059Z", + "iopub.status.idle": "2024-03-05T17:27:06.876674Z", + "shell.execute_reply": "2024-03-05T17:27:06.876140Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:51.120073Z", - "iopub.status.busy": "2024-02-27T04:11:51.119896Z", - "iopub.status.idle": "2024-02-27T04:11:51.870570Z", - "shell.execute_reply": "2024-02-27T04:11:51.869902Z" + "iopub.execute_input": "2024-03-05T17:27:06.878856Z", + "iopub.status.busy": "2024-03-05T17:27:06.878515Z", + "iopub.status.idle": "2024-03-05T17:27:07.606125Z", + "shell.execute_reply": "2024-03-05T17:27:07.605521Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:51.872897Z", - "iopub.status.busy": "2024-02-27T04:11:51.872468Z", - "iopub.status.idle": "2024-02-27T04:11:51.876382Z", - "shell.execute_reply": "2024-02-27T04:11:51.875831Z" + "iopub.execute_input": "2024-03-05T17:27:07.608309Z", + "iopub.status.busy": "2024-03-05T17:27:07.607971Z", + "iopub.status.idle": "2024-03-05T17:27:07.611428Z", + "shell.execute_reply": "2024-03-05T17:27:07.610996Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 7d3fbec3b..52dcd7d5b 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -712,7 +712,7 @@

2. Pre-process the Cifar10 dataset 
-100%|██████████| 170498071/170498071 [00:04<00:00, 38191430.08it/s]
+100%|██████████| 170498071/170498071 [00:07<00:00, 23313635.10it/s]
@@ -842,7 +842,7 @@

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

-
+
@@ -1056,7 +1056,7 @@

4. Use cleanlab and here.

diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index 67d259469..397ed20fc 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:53.890073Z", - "iopub.status.busy": "2024-02-27T04:11:53.889643Z", - "iopub.status.idle": "2024-02-27T04:11:56.480945Z", - "shell.execute_reply": "2024-02-27T04:11:56.480341Z" + "iopub.execute_input": "2024-03-05T17:27:10.060270Z", + "iopub.status.busy": "2024-03-05T17:27:10.060087Z", + "iopub.status.idle": "2024-03-05T17:27:12.905020Z", + "shell.execute_reply": "2024-03-05T17:27:12.904476Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:11:56.483928Z", - "iopub.status.busy": "2024-02-27T04:11:56.483350Z", - "iopub.status.idle": "2024-02-27T04:11:56.791675Z", - "shell.execute_reply": "2024-02-27T04:11:56.791161Z" + "iopub.execute_input": "2024-03-05T17:27:12.907590Z", + "iopub.status.busy": "2024-03-05T17:27:12.907171Z", + "iopub.status.idle": "2024-03-05T17:27:13.251719Z", + "shell.execute_reply": "2024-03-05T17:27:13.251094Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:56.794084Z", - "iopub.status.busy": "2024-02-27T04:11:56.793693Z", - "iopub.status.idle": "2024-02-27T04:11:56.797959Z", - "shell.execute_reply": "2024-02-27T04:11:56.797543Z" + "iopub.execute_input": "2024-03-05T17:27:13.254293Z", + "iopub.status.busy": "2024-03-05T17:27:13.253816Z", + "iopub.status.idle": "2024-03-05T17:27:13.257780Z", + "shell.execute_reply": "2024-03-05T17:27:13.257381Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:11:56.800012Z", - "iopub.status.busy": "2024-02-27T04:11:56.799740Z", - "iopub.status.idle": "2024-02-27T04:12:04.551750Z", - "shell.execute_reply": "2024-02-27T04:12:04.551191Z" + "iopub.execute_input": "2024-03-05T17:27:13.259723Z", + "iopub.status.busy": "2024-03-05T17:27:13.259443Z", + "iopub.status.idle": "2024-03-05T17:27:23.723006Z", + "shell.execute_reply": "2024-03-05T17:27:23.722515Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 32768/170498071 [00:00<11:45, 241646.66it/s]" + " 0%| | 32768/170498071 [00:00<10:31, 269919.49it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 229376/170498071 [00:00<03:00, 942515.06it/s]" + " 0%| | 196608/170498071 [00:00<02:56, 966458.45it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 884736/170498071 [00:00<00:58, 2890541.33it/s]" + " 0%| | 655360/170498071 [00:00<01:07, 2504105.17it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 1%|▏ | 2490368/170498071 [00:00<00:22, 7320877.52it/s]" + " 1%| | 1736704/170498071 [00:00<00:30, 5572547.07it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 6127616/170498071 [00:00<00:09, 16751733.74it/s]" + " 3%|▎ | 5505024/170498071 [00:00<00:09, 16680254.08it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 10059776/170498071 [00:00<00:06, 23756599.96it/s]" + " 4%|▍ | 7241728/170498071 [00:00<00:09, 16684410.09it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▊ | 14843904/170498071 [00:00<00:05, 30057088.98it/s]" + " 7%|▋ | 12484608/170498071 [00:00<00:05, 28026407.31it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█▏ | 19398656/170498071 [00:00<00:04, 33683657.53it/s]" + " 10%|█ | 17203200/170498071 [00:00<00:04, 33985714.22it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 23756800/170498071 [00:01<00:04, 36544386.70it/s]" + " 13%|█▎ | 21561344/170498071 [00:00<00:04, 36903939.66it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 28573696/170498071 [00:01<00:03, 39948941.41it/s]" + " 15%|█▌ | 26116096/170498071 [00:01<00:03, 38463753.10it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32636928/170498071 [00:01<00:03, 38882711.64it/s]" + " 18%|█▊ | 30965760/170498071 [00:01<00:03, 41421312.96it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 36995072/170498071 [00:01<00:03, 40240064.28it/s]" + " 21%|██ | 35127296/170498071 [00:01<00:03, 36384427.00it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 41320448/170498071 [00:01<00:03, 39602414.48it/s]" + " 23%|██▎ | 38895616/170498071 [00:01<00:03, 33119504.43it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 46071808/170498071 [00:01<00:03, 41387825.17it/s]" + " 25%|██▌ | 42893312/170498071 [00:01<00:03, 34811837.39it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 50561024/170498071 [00:01<00:02, 42078718.25it/s]" + " 27%|██▋ | 46497792/170498071 [00:01<00:03, 33172830.75it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 55345152/170498071 [00:01<00:02, 43195246.79it/s]" + " 29%|██▉ | 49905664/170498071 [00:01<00:03, 32287601.62it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 59736064/170498071 [00:01<00:02, 43303052.00it/s]" + " 31%|███ | 53215232/170498071 [00:01<00:03, 31491349.41it/s]" ] }, { @@ -388,7 +388,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 64094208/170498071 [00:01<00:02, 41709323.75it/s]" + " 33%|███▎ | 56426496/170498071 [00:01<00:03, 30902122.74it/s]" ] }, { @@ -396,7 +396,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 68517888/170498071 [00:02<00:02, 42407506.23it/s]" + " 35%|███▍ | 59637760/170498071 [00:02<00:03, 31163304.47it/s]" ] }, { @@ -404,7 +404,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 72777728/170498071 [00:02<00:02, 41273394.63it/s]" + " 37%|███▋ | 62783488/170498071 [00:02<00:03, 31030223.75it/s]" ] }, { @@ -412,7 +412,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 77398016/170498071 [00:02<00:02, 42027004.91it/s]" + " 39%|███▊ | 65929216/170498071 [00:02<00:03, 30910743.30it/s]" ] }, { @@ -420,7 +420,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 82018304/170498071 [00:02<00:02, 42597732.49it/s]" + " 41%|████ | 69107712/170498071 [00:02<00:03, 31135201.25it/s]" ] }, { @@ -428,7 +428,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 86474752/170498071 [00:02<00:01, 43118930.00it/s]" + " 42%|████▏ | 72253440/170498071 [00:02<00:03, 30936489.35it/s]" ] }, { @@ -436,7 +436,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 90800128/170498071 [00:02<00:01, 43094745.01it/s]" + " 44%|████▍ | 75399168/170498071 [00:02<00:03, 31081474.88it/s]" ] }, { @@ -444,7 +444,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 95125504/170498071 [00:02<00:01, 42129672.37it/s]" + " 46%|████▌ | 78643200/170498071 [00:02<00:02, 31408686.11it/s]" ] }, { @@ -452,7 +452,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 99352576/170498071 [00:02<00:01, 42038446.91it/s]" + " 48%|████▊ | 81788928/170498071 [00:02<00:02, 31218015.50it/s]" ] }, { @@ -460,7 +460,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 103579648/170498071 [00:02<00:01, 40923143.11it/s]" + " 50%|████▉ | 84934656/170498071 [00:02<00:02, 30990506.94it/s]" ] }, { @@ -468,7 +468,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 108232704/170498071 [00:03<00:01, 41876534.80it/s]" + " 52%|█████▏ | 88408064/170498071 [00:03<00:02, 31608333.33it/s]" ] }, { @@ -476,7 +476,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 112820224/170498071 [00:03<00:01, 42863873.07it/s]" + " 54%|█████▍ | 91750400/170498071 [00:03<00:02, 32012594.43it/s]" ] }, { @@ -484,7 +484,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 117145600/170498071 [00:03<00:01, 42769270.38it/s]" + " 56%|█████▌ | 94961664/170498071 [00:03<00:02, 27308309.34it/s]" ] }, { @@ -492,7 +492,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████▏ | 121536512/170498071 [00:03<00:01, 43068079.06it/s]" + " 58%|█████▊ | 99090432/170498071 [00:03<00:02, 30994857.35it/s]" ] }, { @@ -500,7 +500,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 125861888/170498071 [00:03<00:01, 42150861.57it/s]" + " 60%|██████ | 102334464/170498071 [00:03<00:02, 27659707.88it/s]" ] }, { @@ -508,7 +508,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 130252800/170498071 [00:03<00:00, 42564252.43it/s]" + " 62%|██████▏ | 105250816/170498071 [00:03<00:02, 26502385.23it/s]" ] }, { @@ -516,7 +516,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 134610944/170498071 [00:03<00:00, 42852568.36it/s]" + " 63%|██████▎ | 108036096/170498071 [00:03<00:02, 25478374.75it/s]" ] }, { @@ -524,7 +524,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 138903552/170498071 [00:03<00:00, 41982253.90it/s]" + " 65%|██████▍ | 110657536/170498071 [00:03<00:02, 24538449.99it/s]" ] }, { @@ -532,7 +532,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 143523840/170498071 [00:03<00:00, 43185844.07it/s]" + " 66%|██████▋ | 113311744/170498071 [00:03<00:02, 25021856.35it/s]" ] }, { @@ -540,7 +540,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 147881984/170498071 [00:03<00:00, 41691783.52it/s]" + " 68%|██████▊ | 115867648/170498071 [00:04<00:02, 24774854.76it/s]" ] }, { @@ -548,7 +548,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 152076288/170498071 [00:04<00:00, 41706123.81it/s]" + " 69%|██████▉ | 118390784/170498071 [00:04<00:02, 24241470.52it/s]" ] }, { @@ -556,7 +556,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 156663808/170498071 [00:04<00:00, 42215760.73it/s]" + " 71%|███████ | 120848384/170498071 [00:04<00:02, 23753247.36it/s]" ] }, { @@ -564,7 +564,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 161284096/170498071 [00:04<00:00, 41673431.13it/s]" + " 72%|███████▏ | 123568128/170498071 [00:04<00:01, 24261379.86it/s]" ] }, { @@ -572,7 +572,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 166002688/170498071 [00:04<00:00, 43084659.05it/s]" + " 74%|███████▍ | 126025728/170498071 [00:04<00:01, 23594480.68it/s]" ] }, { @@ -580,7 +580,199 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:04<00:00, 38191430.08it/s]" + " 75%|███████▌ | 128417792/170498071 [00:04<00:02, 20773011.98it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 77%|███████▋ | 130580480/170498071 [00:04<00:02, 19800177.67it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 78%|███████▊ | 132612096/170498071 [00:04<00:01, 19005187.83it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 79%|███████▉ | 134643712/170498071 [00:05<00:01, 19153138.38it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 80%|████████ | 136609792/170498071 [00:05<00:01, 18831371.51it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 81%|████████ | 138510336/170498071 [00:05<00:01, 18803029.53it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 82%|████████▏ | 140410880/170498071 [00:05<00:01, 17960198.33it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 84%|████████▎ | 142376960/170498071 [00:05<00:01, 17805565.42it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 85%|████████▍ | 144572416/170498071 [00:05<00:01, 18743454.87it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 86%|████████▌ | 146472960/170498071 [00:05<00:01, 18532386.26it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|████████▋ | 148340736/170498071 [00:05<00:01, 18299463.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 88%|████████▊ | 150306816/170498071 [00:05<00:01, 18100949.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 89%|████████▉ | 152535040/170498071 [00:05<00:00, 19156709.97it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 91%|█████████ | 154468352/170498071 [00:06<00:00, 18598590.74it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████▏| 156336128/170498071 [00:06<00:00, 16302393.04it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 93%|█████████▎| 158040064/170498071 [00:06<00:00, 15540613.82it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 94%|█████████▎| 159645696/170498071 [00:06<00:00, 14932144.14it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 95%|█████████▍| 161284096/170498071 [00:06<00:00, 15088330.71it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 95%|█████████▌| 162824192/170498071 [00:06<00:00, 14696848.62it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 96%|█████████▋| 164331520/170498071 [00:06<00:00, 14580244.32it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 97%|█████████▋| 165806080/170498071 [00:06<00:00, 13947217.56it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 167313408/170498071 [00:07<00:00, 13840984.98it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 168886272/170498071 [00:07<00:00, 13546385.58it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 170262528/170498071 [00:07<00:00, 12509763.03it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:07<00:00, 23313635.10it/s]" ] }, { @@ -698,10 +890,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:04.554095Z", - "iopub.status.busy": "2024-02-27T04:12:04.553755Z", - "iopub.status.idle": "2024-02-27T04:12:04.558353Z", - "shell.execute_reply": "2024-02-27T04:12:04.557935Z" + "iopub.execute_input": "2024-03-05T17:27:23.725574Z", + "iopub.status.busy": "2024-03-05T17:27:23.725091Z", + "iopub.status.idle": "2024-03-05T17:27:23.730246Z", + "shell.execute_reply": "2024-03-05T17:27:23.729780Z" }, "nbsphinx": "hidden" }, @@ -752,10 +944,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:04.560450Z", - "iopub.status.busy": "2024-02-27T04:12:04.560130Z", - "iopub.status.idle": "2024-02-27T04:12:05.107980Z", - "shell.execute_reply": "2024-02-27T04:12:05.107432Z" + "iopub.execute_input": "2024-03-05T17:27:23.732335Z", + "iopub.status.busy": "2024-03-05T17:27:23.732023Z", + "iopub.status.idle": "2024-03-05T17:27:24.276403Z", + "shell.execute_reply": "2024-03-05T17:27:24.275782Z" } }, "outputs": [ @@ -788,10 +980,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:05.110242Z", - "iopub.status.busy": "2024-02-27T04:12:05.109917Z", - "iopub.status.idle": "2024-02-27T04:12:05.627298Z", - "shell.execute_reply": "2024-02-27T04:12:05.626734Z" + "iopub.execute_input": "2024-03-05T17:27:24.278697Z", + "iopub.status.busy": "2024-03-05T17:27:24.278480Z", + "iopub.status.idle": "2024-03-05T17:27:24.785506Z", + "shell.execute_reply": "2024-03-05T17:27:24.784888Z" } }, "outputs": [ @@ -829,10 +1021,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:05.629650Z", - "iopub.status.busy": "2024-02-27T04:12:05.629100Z", - "iopub.status.idle": "2024-02-27T04:12:05.632692Z", - "shell.execute_reply": "2024-02-27T04:12:05.632165Z" + "iopub.execute_input": "2024-03-05T17:27:24.787916Z", + "iopub.status.busy": "2024-03-05T17:27:24.787553Z", + "iopub.status.idle": "2024-03-05T17:27:24.791293Z", + "shell.execute_reply": "2024-03-05T17:27:24.790804Z" } }, "outputs": [], @@ -855,17 +1047,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:05.634654Z", - "iopub.status.busy": "2024-02-27T04:12:05.634297Z", - "iopub.status.idle": "2024-02-27T04:12:18.330648Z", - "shell.execute_reply": "2024-02-27T04:12:18.329900Z" + "iopub.execute_input": "2024-03-05T17:27:24.793269Z", + "iopub.status.busy": "2024-03-05T17:27:24.793064Z", + "iopub.status.idle": "2024-03-05T17:27:37.401129Z", + "shell.execute_reply": "2024-03-05T17:27:37.400469Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21697d09e82c4f0694b3284b917d2713", + "model_id": "40a7d20076bd4bb6bc9a80f7cef43d4a", "version_major": 2, "version_minor": 0 }, @@ -924,10 +1116,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:18.333033Z", - "iopub.status.busy": "2024-02-27T04:12:18.332657Z", - "iopub.status.idle": "2024-02-27T04:12:19.912387Z", - "shell.execute_reply": "2024-02-27T04:12:19.911850Z" + "iopub.execute_input": "2024-03-05T17:27:37.403862Z", + "iopub.status.busy": "2024-03-05T17:27:37.403383Z", + "iopub.status.idle": "2024-03-05T17:27:39.011875Z", + "shell.execute_reply": "2024-03-05T17:27:39.011271Z" } }, "outputs": [ @@ -971,10 +1163,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:19.915001Z", - "iopub.status.busy": "2024-02-27T04:12:19.914703Z", - "iopub.status.idle": "2024-02-27T04:12:20.330306Z", - "shell.execute_reply": "2024-02-27T04:12:20.329792Z" + "iopub.execute_input": "2024-03-05T17:27:39.014598Z", + "iopub.status.busy": "2024-03-05T17:27:39.014383Z", + "iopub.status.idle": "2024-03-05T17:27:39.493962Z", + "shell.execute_reply": "2024-03-05T17:27:39.493379Z" } }, "outputs": [ @@ -1010,10 +1202,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:20.333139Z", - "iopub.status.busy": "2024-02-27T04:12:20.332800Z", - "iopub.status.idle": "2024-02-27T04:12:20.985461Z", - "shell.execute_reply": "2024-02-27T04:12:20.984928Z" + "iopub.execute_input": "2024-03-05T17:27:39.496796Z", + "iopub.status.busy": "2024-03-05T17:27:39.496584Z", + "iopub.status.idle": "2024-03-05T17:27:40.174170Z", + "shell.execute_reply": "2024-03-05T17:27:40.173675Z" } }, "outputs": [ @@ -1063,10 +1255,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:20.988138Z", - "iopub.status.busy": "2024-02-27T04:12:20.987853Z", - "iopub.status.idle": "2024-02-27T04:12:21.328838Z", - "shell.execute_reply": "2024-02-27T04:12:21.328341Z" + "iopub.execute_input": "2024-03-05T17:27:40.176859Z", + "iopub.status.busy": "2024-03-05T17:27:40.176650Z", + "iopub.status.idle": "2024-03-05T17:27:40.529313Z", + "shell.execute_reply": "2024-03-05T17:27:40.528714Z" } }, "outputs": [ @@ -1114,10 +1306,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:21.331122Z", - "iopub.status.busy": "2024-02-27T04:12:21.330689Z", - "iopub.status.idle": "2024-02-27T04:12:21.572116Z", - "shell.execute_reply": "2024-02-27T04:12:21.571610Z" + "iopub.execute_input": "2024-03-05T17:27:40.531734Z", + "iopub.status.busy": "2024-03-05T17:27:40.531496Z", + "iopub.status.idle": "2024-03-05T17:27:40.787735Z", + "shell.execute_reply": "2024-03-05T17:27:40.787157Z" } }, "outputs": [ @@ -1173,10 +1365,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:21.575035Z", - "iopub.status.busy": "2024-02-27T04:12:21.574607Z", - "iopub.status.idle": "2024-02-27T04:12:21.664289Z", - "shell.execute_reply": "2024-02-27T04:12:21.663820Z" + "iopub.execute_input": "2024-03-05T17:27:40.790049Z", + "iopub.status.busy": "2024-03-05T17:27:40.789728Z", + "iopub.status.idle": "2024-03-05T17:27:40.885361Z", + "shell.execute_reply": "2024-03-05T17:27:40.884838Z" } }, "outputs": [], @@ -1197,10 +1389,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:21.666659Z", - "iopub.status.busy": "2024-02-27T04:12:21.666281Z", - "iopub.status.idle": "2024-02-27T04:12:31.701389Z", - "shell.execute_reply": "2024-02-27T04:12:31.700785Z" + "iopub.execute_input": "2024-03-05T17:27:40.888238Z", + "iopub.status.busy": "2024-03-05T17:27:40.887827Z", + "iopub.status.idle": "2024-03-05T17:27:51.442460Z", + "shell.execute_reply": "2024-03-05T17:27:51.441852Z" } }, "outputs": [ @@ -1237,10 +1429,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:31.703572Z", - "iopub.status.busy": "2024-02-27T04:12:31.703343Z", - "iopub.status.idle": "2024-02-27T04:12:33.344974Z", - "shell.execute_reply": "2024-02-27T04:12:33.344389Z" + "iopub.execute_input": "2024-03-05T17:27:51.445411Z", + "iopub.status.busy": "2024-03-05T17:27:51.444969Z", + "iopub.status.idle": "2024-03-05T17:27:53.315567Z", + "shell.execute_reply": "2024-03-05T17:27:53.314998Z" } }, "outputs": [ @@ -1271,10 +1463,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:33.347503Z", - "iopub.status.busy": "2024-02-27T04:12:33.347128Z", - "iopub.status.idle": "2024-02-27T04:12:33.550522Z", - "shell.execute_reply": "2024-02-27T04:12:33.549972Z" + "iopub.execute_input": "2024-03-05T17:27:53.318306Z", + "iopub.status.busy": "2024-03-05T17:27:53.317725Z", + "iopub.status.idle": "2024-03-05T17:27:53.522749Z", + "shell.execute_reply": "2024-03-05T17:27:53.522213Z" } }, "outputs": [], @@ -1288,10 +1480,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:33.553081Z", - "iopub.status.busy": "2024-02-27T04:12:33.552707Z", - "iopub.status.idle": "2024-02-27T04:12:33.555899Z", - "shell.execute_reply": "2024-02-27T04:12:33.555465Z" + "iopub.execute_input": "2024-03-05T17:27:53.525253Z", + "iopub.status.busy": "2024-03-05T17:27:53.524898Z", + "iopub.status.idle": "2024-03-05T17:27:53.528019Z", + "shell.execute_reply": "2024-03-05T17:27:53.527574Z" } }, "outputs": [], @@ -1313,10 +1505,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:33.557800Z", - "iopub.status.busy": "2024-02-27T04:12:33.557464Z", - "iopub.status.idle": "2024-02-27T04:12:33.565685Z", - "shell.execute_reply": "2024-02-27T04:12:33.565265Z" + "iopub.execute_input": "2024-03-05T17:27:53.529967Z", + "iopub.status.busy": "2024-03-05T17:27:53.529787Z", + "iopub.status.idle": "2024-03-05T17:27:53.537988Z", + "shell.execute_reply": "2024-03-05T17:27:53.537466Z" }, "nbsphinx": "hidden" }, @@ -1361,7 +1553,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0530181c0c294208bb7bf878ec5d73f9": { + "02ad6743bcb14967bba613ba0fab06e1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1414,54 +1606,7 @@ "width": null } }, - "0f987cfe47de43cda334806f4fa21fdd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_254ceb3633674bf2acf63fa769db8c0e", - "placeholder": "​", - "style": "IPY_MODEL_f5519d4051ae4c8980e4173760fa5956", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 157MB/s]" - } - }, - "21697d09e82c4f0694b3284b917d2713": { - "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_8cc1e75554854ca3b6d3221ffcb880db", - "IPY_MODEL_8a85a371ff684b5196fa52a91f0f8bf7", - "IPY_MODEL_0f987cfe47de43cda334806f4fa21fdd" - ], - "layout": "IPY_MODEL_f8c2f65c3ff748a1bb0b85c7e3c719e2", - "tabbable": null, - "tooltip": null - } - }, - "254ceb3633674bf2acf63fa769db8c0e": { + "1941b1ff05234d928ff8c5feaa8741bd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1514,7 +1659,7 @@ "width": null } }, - "414afe5146db419ca1be9cd94251168d": { + "35c07a7a884c4d3bbc1dc341e804c9b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1530,7 +1675,49 @@ "description_width": "" } }, - "7353808876674515925c27ab5ffe65d8": { + "40a7d20076bd4bb6bc9a80f7cef43d4a": { + "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_ea36e9ce6c804b34b30d1d46d5f7c286", + "IPY_MODEL_9e1e2fee078d403f83a945883fc505f0", + "IPY_MODEL_f30d40e671d34726b65020af61a31e4a" + ], + "layout": "IPY_MODEL_1941b1ff05234d928ff8c5feaa8741bd", + "tabbable": null, + "tooltip": null + } + }, + "4f44bc5b21144fe99756eaacf7811665": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "906cf214b75842f6b344cb9999f83b29": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1583,7 +1770,7 @@ "width": null } }, - "8a85a371ff684b5196fa52a91f0f8bf7": { + "9e1e2fee078d403f83a945883fc505f0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1599,76 +1786,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0530181c0c294208bb7bf878ec5d73f9", + "layout": "IPY_MODEL_906cf214b75842f6b344cb9999f83b29", "max": 102469840.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_414afe5146db419ca1be9cd94251168d", + "style": "IPY_MODEL_35c07a7a884c4d3bbc1dc341e804c9b7", "tabbable": null, "tooltip": null, "value": 102469840.0 } }, - "8cc1e75554854ca3b6d3221ffcb880db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7353808876674515925c27ab5ffe65d8", - "placeholder": "​", - "style": "IPY_MODEL_8f2bd469441e43b9bf2a396ccdb117e3", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" - } - }, - "8f2bd469441e43b9bf2a396ccdb117e3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f5519d4051ae4c8980e4173760fa5956": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f8c2f65c3ff748a1bb0b85c7e3c719e2": { + "af93eb4797944c12bcae89167179894b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1720,6 +1848,70 @@ "visibility": null, "width": null } + }, + "ce3a5efacbbe4258a211b2151cf8b998": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ea36e9ce6c804b34b30d1d46d5f7c286": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_af93eb4797944c12bcae89167179894b", + "placeholder": "​", + "style": "IPY_MODEL_4f44bc5b21144fe99756eaacf7811665", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "f30d40e671d34726b65020af61a31e4a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_02ad6743bcb14967bba613ba0fab06e1", + "placeholder": "​", + "style": "IPY_MODEL_ce3a5efacbbe4258a211b2151cf8b998", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 336MB/s]" + } } }, "version_major": 2, diff --git a/master/tutorials/regression.html b/master/tutorials/regression.html index 823f15fdc..37d8a81ef 100644 --- a/master/tutorials/regression.html +++ b/master/tutorials/regression.html @@ -882,35 +882,35 @@

3. Define a regression model and use cleanlab to find potential label errors 0 False - 0.636197 + 0.385101 73.3 76.499503 1 False - 0.843478 + 0.698255 83.8 82.776647 2 True - 0.350358 + 0.109373 73.5 63.170547 3 False - 0.706969 + 0.481096 78.6 75.984759 4 False - 0.812515 + 0.645270 74.1 75.795928 @@ -1312,35 +1312,35 @@

5. Other ways to find noisy labels in regression datasets=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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.044785Z", - "iopub.status.busy": "2024-02-27T04:12:39.044459Z", - "iopub.status.idle": "2024-02-27T04:12:39.062671Z", - "shell.execute_reply": "2024-02-27T04:12:39.062262Z" + "iopub.execute_input": "2024-03-05T17:27:58.866019Z", + "iopub.status.busy": "2024-03-05T17:27:58.865498Z", + "iopub.status.idle": "2024-03-05T17:27:58.884994Z", + "shell.execute_reply": "2024-03-05T17:27:58.884490Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.064674Z", - "iopub.status.busy": "2024-02-27T04:12:39.064333Z", - "iopub.status.idle": "2024-02-27T04:12:39.067417Z", - "shell.execute_reply": "2024-02-27T04:12:39.066975Z" + "iopub.execute_input": "2024-03-05T17:27:58.887835Z", + "iopub.status.busy": "2024-03-05T17:27:58.887268Z", + "iopub.status.idle": "2024-03-05T17:27:58.890503Z", + "shell.execute_reply": "2024-03-05T17:27:58.890039Z" }, "nbsphinx": "hidden" }, @@ -199,10 +199,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.069387Z", - "iopub.status.busy": "2024-02-27T04:12:39.069068Z", - "iopub.status.idle": "2024-02-27T04:12:39.350236Z", - "shell.execute_reply": "2024-02-27T04:12:39.349670Z" + "iopub.execute_input": "2024-03-05T17:27:58.892698Z", + "iopub.status.busy": "2024-03-05T17:27:58.892380Z", + "iopub.status.idle": "2024-03-05T17:27:59.131908Z", + "shell.execute_reply": "2024-03-05T17:27:59.131354Z" } }, "outputs": [ @@ -375,10 +375,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.352572Z", - "iopub.status.busy": "2024-02-27T04:12:39.352225Z", - "iopub.status.idle": "2024-02-27T04:12:39.532631Z", - "shell.execute_reply": "2024-02-27T04:12:39.532141Z" + "iopub.execute_input": "2024-03-05T17:27:59.134275Z", + "iopub.status.busy": "2024-03-05T17:27:59.133938Z", + "iopub.status.idle": "2024-03-05T17:27:59.320629Z", + "shell.execute_reply": "2024-03-05T17:27:59.319986Z" }, "nbsphinx": "hidden" }, @@ -418,10 +418,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.534933Z", - "iopub.status.busy": "2024-02-27T04:12:39.534606Z", - "iopub.status.idle": "2024-02-27T04:12:39.745869Z", - "shell.execute_reply": "2024-02-27T04:12:39.745306Z" + "iopub.execute_input": "2024-03-05T17:27:59.323434Z", + "iopub.status.busy": "2024-03-05T17:27:59.323158Z", + "iopub.status.idle": "2024-03-05T17:27:59.544216Z", + "shell.execute_reply": "2024-03-05T17:27:59.543584Z" } }, "outputs": [ @@ -457,10 +457,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.748004Z", - "iopub.status.busy": "2024-02-27T04:12:39.747671Z", - "iopub.status.idle": "2024-02-27T04:12:39.752047Z", - "shell.execute_reply": "2024-02-27T04:12:39.751620Z" + "iopub.execute_input": "2024-03-05T17:27:59.546524Z", + "iopub.status.busy": "2024-03-05T17:27:59.546152Z", + "iopub.status.idle": "2024-03-05T17:27:59.550728Z", + "shell.execute_reply": "2024-03-05T17:27:59.550272Z" } }, "outputs": [], @@ -478,10 +478,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.753965Z", - "iopub.status.busy": "2024-02-27T04:12:39.753630Z", - "iopub.status.idle": "2024-02-27T04:12:39.759434Z", - "shell.execute_reply": "2024-02-27T04:12:39.759005Z" + "iopub.execute_input": "2024-03-05T17:27:59.552734Z", + "iopub.status.busy": "2024-03-05T17:27:59.552396Z", + "iopub.status.idle": "2024-03-05T17:27:59.558578Z", + "shell.execute_reply": "2024-03-05T17:27:59.558172Z" } }, "outputs": [], @@ -528,10 +528,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.761361Z", - "iopub.status.busy": "2024-02-27T04:12:39.761049Z", - "iopub.status.idle": "2024-02-27T04:12:39.763638Z", - "shell.execute_reply": "2024-02-27T04:12:39.763222Z" + "iopub.execute_input": "2024-03-05T17:27:59.560662Z", + "iopub.status.busy": "2024-03-05T17:27:59.560276Z", + "iopub.status.idle": "2024-03-05T17:27:59.562888Z", + "shell.execute_reply": "2024-03-05T17:27:59.562445Z" } }, "outputs": [], @@ -546,10 +546,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:39.765462Z", - "iopub.status.busy": "2024-02-27T04:12:39.765151Z", - "iopub.status.idle": "2024-02-27T04:12:47.938730Z", - "shell.execute_reply": "2024-02-27T04:12:47.938110Z" + "iopub.execute_input": "2024-03-05T17:27:59.564819Z", + "iopub.status.busy": "2024-03-05T17:27:59.564644Z", + "iopub.status.idle": "2024-03-05T17:28:08.265948Z", + "shell.execute_reply": "2024-03-05T17:28:08.265396Z" } }, "outputs": [], @@ -573,10 +573,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.941524Z", - "iopub.status.busy": "2024-02-27T04:12:47.941119Z", - "iopub.status.idle": "2024-02-27T04:12:47.948227Z", - "shell.execute_reply": "2024-02-27T04:12:47.947795Z" + "iopub.execute_input": "2024-03-05T17:28:08.269045Z", + "iopub.status.busy": "2024-03-05T17:28:08.268497Z", + "iopub.status.idle": "2024-03-05T17:28:08.276034Z", + "shell.execute_reply": "2024-03-05T17:28:08.275487Z" } }, "outputs": [ @@ -611,35 +611,35 @@ " \n", " 0\n", " False\n", - " 0.636197\n", + " 0.385101\n", " 73.3\n", " 76.499503\n", " \n", " \n", " 1\n", " False\n", - " 0.843478\n", + " 0.698255\n", " 83.8\n", " 82.776647\n", " \n", " \n", " 2\n", " True\n", - " 0.350358\n", + " 0.109373\n", " 73.5\n", " 63.170547\n", " \n", " \n", " 3\n", " False\n", - " 0.706969\n", + " 0.481096\n", " 78.6\n", " 75.984759\n", " \n", " \n", " 4\n", " False\n", - " 0.812515\n", + " 0.645270\n", " 74.1\n", " 75.795928\n", " \n", @@ -649,11 +649,11 @@ ], "text/plain": [ " is_label_issue label_quality given_label predicted_label\n", - "0 False 0.636197 73.3 76.499503\n", - "1 False 0.843478 83.8 82.776647\n", - "2 True 0.350358 73.5 63.170547\n", - "3 False 0.706969 78.6 75.984759\n", - "4 False 0.812515 74.1 75.795928" + "0 False 0.385101 73.3 76.499503\n", + "1 False 0.698255 83.8 82.776647\n", + "2 True 0.109373 73.5 63.170547\n", + "3 False 0.481096 78.6 75.984759\n", + "4 False 0.645270 74.1 75.795928" ] }, "execution_count": 11, @@ -679,10 +679,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.950317Z", - "iopub.status.busy": "2024-02-27T04:12:47.949997Z", - "iopub.status.idle": "2024-02-27T04:12:47.953528Z", - "shell.execute_reply": "2024-02-27T04:12:47.953129Z" + "iopub.execute_input": "2024-03-05T17:28:08.278170Z", + "iopub.status.busy": "2024-03-05T17:28:08.277969Z", + "iopub.status.idle": "2024-03-05T17:28:08.282203Z", + "shell.execute_reply": "2024-03-05T17:28:08.281637Z" } }, "outputs": [], @@ -697,10 +697,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.955484Z", - "iopub.status.busy": "2024-02-27T04:12:47.955162Z", - "iopub.status.idle": "2024-02-27T04:12:47.958513Z", - "shell.execute_reply": "2024-02-27T04:12:47.958007Z" + "iopub.execute_input": "2024-03-05T17:28:08.284667Z", + "iopub.status.busy": "2024-03-05T17:28:08.284167Z", + "iopub.status.idle": "2024-03-05T17:28:08.287737Z", + "shell.execute_reply": "2024-03-05T17:28:08.287176Z" } }, "outputs": [ @@ -735,10 +735,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.960428Z", - "iopub.status.busy": "2024-02-27T04:12:47.960108Z", - "iopub.status.idle": "2024-02-27T04:12:47.962924Z", - "shell.execute_reply": "2024-02-27T04:12:47.962517Z" + "iopub.execute_input": "2024-03-05T17:28:08.289867Z", + "iopub.status.busy": "2024-03-05T17:28:08.289680Z", + "iopub.status.idle": "2024-03-05T17:28:08.292718Z", + "shell.execute_reply": "2024-03-05T17:28:08.292286Z" } }, "outputs": [], @@ -757,10 +757,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.964790Z", - "iopub.status.busy": "2024-02-27T04:12:47.964470Z", - "iopub.status.idle": "2024-02-27T04:12:47.972257Z", - "shell.execute_reply": "2024-02-27T04:12:47.971731Z" + "iopub.execute_input": "2024-03-05T17:28:08.294795Z", + "iopub.status.busy": "2024-03-05T17:28:08.294462Z", + "iopub.status.idle": "2024-03-05T17:28:08.303025Z", + "shell.execute_reply": "2024-03-05T17:28:08.302479Z" } }, "outputs": [ @@ -884,10 +884,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.974208Z", - "iopub.status.busy": "2024-02-27T04:12:47.974046Z", - "iopub.status.idle": "2024-02-27T04:12:47.976325Z", - "shell.execute_reply": "2024-02-27T04:12:47.975918Z" + "iopub.execute_input": "2024-03-05T17:28:08.305276Z", + "iopub.status.busy": "2024-03-05T17:28:08.304919Z", + "iopub.status.idle": "2024-03-05T17:28:08.307548Z", + "shell.execute_reply": "2024-03-05T17:28:08.307139Z" }, "nbsphinx": "hidden" }, @@ -922,10 +922,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:47.978267Z", - "iopub.status.busy": "2024-02-27T04:12:47.977965Z", - "iopub.status.idle": "2024-02-27T04:12:48.098737Z", - "shell.execute_reply": "2024-02-27T04:12:48.098194Z" + "iopub.execute_input": "2024-03-05T17:28:08.309684Z", + "iopub.status.busy": "2024-03-05T17:28:08.309343Z", + "iopub.status.idle": "2024-03-05T17:28:08.434590Z", + "shell.execute_reply": "2024-03-05T17:28:08.433983Z" } }, "outputs": [ @@ -964,10 +964,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.100998Z", - "iopub.status.busy": "2024-02-27T04:12:48.100820Z", - "iopub.status.idle": "2024-02-27T04:12:48.204625Z", - "shell.execute_reply": "2024-02-27T04:12:48.204047Z" + "iopub.execute_input": "2024-03-05T17:28:08.437150Z", + "iopub.status.busy": "2024-03-05T17:28:08.436715Z", + "iopub.status.idle": "2024-03-05T17:28:08.545259Z", + "shell.execute_reply": "2024-03-05T17:28:08.544672Z" } }, "outputs": [ @@ -1023,10 +1023,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.206849Z", - "iopub.status.busy": "2024-02-27T04:12:48.206667Z", - "iopub.status.idle": "2024-02-27T04:12:48.695182Z", - "shell.execute_reply": "2024-02-27T04:12:48.694573Z" + "iopub.execute_input": "2024-03-05T17:28:08.547738Z", + "iopub.status.busy": "2024-03-05T17:28:08.547331Z", + "iopub.status.idle": "2024-03-05T17:28:09.072557Z", + "shell.execute_reply": "2024-03-05T17:28:09.072007Z" } }, "outputs": [], @@ -1042,10 +1042,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.698020Z", - "iopub.status.busy": "2024-02-27T04:12:48.697619Z", - "iopub.status.idle": "2024-02-27T04:12:48.792695Z", - "shell.execute_reply": "2024-02-27T04:12:48.792091Z" + "iopub.execute_input": "2024-03-05T17:28:09.075513Z", + "iopub.status.busy": "2024-03-05T17:28:09.074977Z", + "iopub.status.idle": "2024-03-05T17:28:09.174305Z", + "shell.execute_reply": "2024-03-05T17:28:09.173666Z" } }, "outputs": [ @@ -1080,10 +1080,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.795039Z", - "iopub.status.busy": "2024-02-27T04:12:48.794655Z", - "iopub.status.idle": "2024-02-27T04:12:48.803011Z", - "shell.execute_reply": "2024-02-27T04:12:48.802567Z" + "iopub.execute_input": "2024-03-05T17:28:09.176814Z", + "iopub.status.busy": "2024-03-05T17:28:09.176434Z", + "iopub.status.idle": "2024-03-05T17:28:09.186020Z", + "shell.execute_reply": "2024-03-05T17:28:09.185562Z" } }, "outputs": [ @@ -1190,10 +1190,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.805011Z", - "iopub.status.busy": "2024-02-27T04:12:48.804681Z", - "iopub.status.idle": "2024-02-27T04:12:48.807400Z", - "shell.execute_reply": "2024-02-27T04:12:48.806959Z" + "iopub.execute_input": "2024-03-05T17:28:09.188258Z", + "iopub.status.busy": "2024-03-05T17:28:09.187902Z", + "iopub.status.idle": "2024-03-05T17:28:09.190624Z", + "shell.execute_reply": "2024-03-05T17:28:09.190181Z" }, "nbsphinx": "hidden" }, @@ -1218,10 +1218,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:48.809322Z", - "iopub.status.busy": "2024-02-27T04:12:48.809004Z", - "iopub.status.idle": "2024-02-27T04:12:54.245843Z", - "shell.execute_reply": "2024-02-27T04:12:54.245180Z" + "iopub.execute_input": "2024-03-05T17:28:09.192753Z", + "iopub.status.busy": "2024-03-05T17:28:09.192409Z", + "iopub.status.idle": "2024-03-05T17:28:14.860363Z", + "shell.execute_reply": "2024-03-05T17:28:14.859804Z" } }, "outputs": [ @@ -1265,10 +1265,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:54.248043Z", - "iopub.status.busy": "2024-02-27T04:12:54.247864Z", - "iopub.status.idle": "2024-02-27T04:12:54.256305Z", - "shell.execute_reply": "2024-02-27T04:12:54.255899Z" + "iopub.execute_input": "2024-03-05T17:28:14.862653Z", + "iopub.status.busy": "2024-03-05T17:28:14.862257Z", + "iopub.status.idle": "2024-03-05T17:28:14.871090Z", + "shell.execute_reply": "2024-03-05T17:28:14.870586Z" } }, "outputs": [ @@ -1303,35 +1303,35 @@ " \n", " 659\n", " True\n", - " 0.000005\n", + " 5.791186e-12\n", " 17.4\n", " 84.110719\n", " \n", " \n", " 367\n", " True\n", - " 0.000044\n", + " 6.485156e-10\n", " 0.0\n", " 56.670640\n", " \n", " \n", " 56\n", " True\n", - " 0.000060\n", + " 1.225300e-09\n", " 8.9\n", " 71.749976\n", " \n", " \n", " 318\n", " True\n", - " 0.000066\n", + " 1.499679e-09\n", " 0.0\n", " 71.947007\n", " \n", " \n", " 305\n", " True\n", - " 0.000314\n", + " 4.067882e-08\n", " 19.1\n", " 61.648396\n", " \n", @@ -1340,12 +1340,12 @@ "

" ], "text/plain": [ - " is_label_issue label_score given_label predicted_label\n", - "659 True 0.000005 17.4 84.110719\n", - "367 True 0.000044 0.0 56.670640\n", - "56 True 0.000060 8.9 71.749976\n", - "318 True 0.000066 0.0 71.947007\n", - "305 True 0.000314 19.1 61.648396" + " is_label_issue label_score given_label predicted_label\n", + "659 True 5.791186e-12 17.4 84.110719\n", + "367 True 6.485156e-10 0.0 56.670640\n", + "56 True 1.225300e-09 8.9 71.749976\n", + "318 True 1.499679e-09 0.0 71.947007\n", + "305 True 4.067882e-08 19.1 61.648396" ] }, "execution_count": 24, @@ -1377,10 +1377,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:54.258376Z", - "iopub.status.busy": "2024-02-27T04:12:54.258083Z", - "iopub.status.idle": "2024-02-27T04:12:54.322695Z", - "shell.execute_reply": "2024-02-27T04:12:54.322098Z" + "iopub.execute_input": "2024-03-05T17:28:14.873550Z", + "iopub.status.busy": "2024-03-05T17:28:14.873117Z", + "iopub.status.idle": "2024-03-05T17:28:14.939007Z", + "shell.execute_reply": "2024-03-05T17:28:14.938371Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index d880d25c0..ba8f057ba 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -732,13 +732,13 @@

3. Use cleanlab to find label issues

-
+
-
+
@@ -823,7 +823,7 @@

Classes which are commonly mislabeled overall

-
+
@@ -1072,7 +1072,7 @@

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().

@@ -1128,7 +1128,7 @@

Get label quality scores -{"state": {"6e9177833aa44fe4a3b3364d0ca3a407": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f5219d4ec3d24d008bb0e11b485f1850": {"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": ""}}, "b527ce48830a47aba867ef2f10cd2329": {"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_6e9177833aa44fe4a3b3364d0ca3a407", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f5219d4ec3d24d008bb0e11b485f1850", "tabbable": null, "tooltip": null, "value": 30.0}}, "67c1e03df8e04d0faf0c596ee30c7c3f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d6735c14323d4b459e3e3c0bae6e2633": {"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}}, "52595c826b444c2ebc76e3c515902b05": {"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_67c1e03df8e04d0faf0c596ee30c7c3f", "placeholder": "\u200b", "style": "IPY_MODEL_d6735c14323d4b459e3e3c0bae6e2633", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "d27b521ce1bf4fd39a9949ea8f5c2d4c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3f2145734072403ca5a67549fab4f063": {"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}}, "57b231e2b47b4bd9aebaa4374be7cdf6": {"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_d27b521ce1bf4fd39a9949ea8f5c2d4c", "placeholder": "\u200b", "style": "IPY_MODEL_3f2145734072403ca5a67549fab4f063", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007409.01it/s]"}}, "e911eabd389a4c078fec14d183589ffa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f35bc28e6c854036845115d0c80f19d5": {"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_52595c826b444c2ebc76e3c515902b05", "IPY_MODEL_b527ce48830a47aba867ef2f10cd2329", "IPY_MODEL_57b231e2b47b4bd9aebaa4374be7cdf6"], "layout": "IPY_MODEL_e911eabd389a4c078fec14d183589ffa", "tabbable": null, "tooltip": null}}, "62b097660a12458c9f94a0db9841f36f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bb4f39000f154f8e937001f942d79b8b": {"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": ""}}, "afc228943c0c4f7da0a08beb16ce10d8": {"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_62b097660a12458c9f94a0db9841f36f", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_bb4f39000f154f8e937001f942d79b8b", "tabbable": null, "tooltip": null, "value": 30.0}}, "932538e731be4078b2cdcf6fa4d50caf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "50a5c3532d8a4018ac6a0cf17ea51c7b": {"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}}, "bdd76556372a4e9493160aa23c770516": {"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_932538e731be4078b2cdcf6fa4d50caf", "placeholder": "\u200b", "style": "IPY_MODEL_50a5c3532d8a4018ac6a0cf17ea51c7b", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "9462c72d042744399edccd9fe0e5604e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6f35fa660d314dcf83ab10d7f6446013": {"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}}, "6dd49c305ab748c7a93943e1588862e6": {"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_9462c72d042744399edccd9fe0e5604e", "placeholder": "\u200b", "style": "IPY_MODEL_6f35fa660d314dcf83ab10d7f6446013", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:20<00:00,\u2007\u20071.43it/s]"}}, "f24324799ab14c45bd97e44a8aafc700": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "296d85fcd35c431788201d553a1a5a6f": {"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_bdd76556372a4e9493160aa23c770516", "IPY_MODEL_afc228943c0c4f7da0a08beb16ce10d8", "IPY_MODEL_6dd49c305ab748c7a93943e1588862e6"], "layout": "IPY_MODEL_f24324799ab14c45bd97e44a8aafc700", "tabbable": null, "tooltip": null}}, "6852741653514354a11f29cdbeb0785d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e1972f5c0ada424e8875fc5447db0e05": {"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": ""}}, "6747a844f55e402b8ce37f9146b436da": {"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_6852741653514354a11f29cdbeb0785d", "max": 4997817.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e1972f5c0ada424e8875fc5447db0e05", "tabbable": null, "tooltip": null, "value": 4997817.0}}, "731a6803de2943de9fe59e135467352c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "dc09f94a59704a56b35e0ae9b670dff3": {"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}}, "2c70b8eef5404b779ee375b744cce580": {"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_731a6803de2943de9fe59e135467352c", "placeholder": "\u200b", "style": "IPY_MODEL_dc09f94a59704a56b35e0ae9b670dff3", "tabbable": null, "tooltip": null, "value": "100%"}}, "e8ac72205cde41498bb49c0af38523a2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e1b022c8165a4bf188893ed01dda6872": {"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}}, "592e152ed2a34bd9baec5bafb7b23e0b": {"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_e8ac72205cde41498bb49c0af38523a2", "placeholder": "\u200b", "style": "IPY_MODEL_e1b022c8165a4bf188893ed01dda6872", "tabbable": null, "tooltip": null, "value": "\u20074997817/4997817\u2007[00:32<00:00,\u2007153315.47it/s]"}}, "f7400852642549e6a9cfb66d9efd8518": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cef6a8d6978b42028d2a2ab034ec55eb": {"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_2c70b8eef5404b779ee375b744cce580", "IPY_MODEL_6747a844f55e402b8ce37f9146b436da", "IPY_MODEL_592e152ed2a34bd9baec5bafb7b23e0b"], "layout": "IPY_MODEL_f7400852642549e6a9cfb66d9efd8518", "tabbable": null, "tooltip": null}}, "791fb20963684d658cbb4f2356804315": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9cd946793cc44581acb428f9f883ce63": {"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": ""}}, "61f7722029cb43cb818c03e75f945cbb": {"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_791fb20963684d658cbb4f2356804315", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_9cd946793cc44581acb428f9f883ce63", "tabbable": null, "tooltip": null, "value": 30.0}}, "fadf065818134007af3b6127fc18a147": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "09b26f4b3b9a47a2805cf4f2a46f9d69": {"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}}, "463e6bd1ba434320a64292f487ede2d8": {"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_fadf065818134007af3b6127fc18a147", "placeholder": "\u200b", "style": "IPY_MODEL_09b26f4b3b9a47a2805cf4f2a46f9d69", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "384a4505d6a24950b928aac1dc443147": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "89c18b2d1deb4f3686561f36964504fd": {"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}}, "de7d2fcc6436422e84c48615a8b1a169": {"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_384a4505d6a24950b928aac1dc443147", "placeholder": "\u200b", "style": "IPY_MODEL_89c18b2d1deb4f3686561f36964504fd", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200723.54it/s]"}}, "12d018fa4ae846b8a54e374c7019f13f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9da4c9cbb0384e2aadaed1e9a5ac86c9": {"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_463e6bd1ba434320a64292f487ede2d8", "IPY_MODEL_61f7722029cb43cb818c03e75f945cbb", "IPY_MODEL_de7d2fcc6436422e84c48615a8b1a169"], "layout": "IPY_MODEL_12d018fa4ae846b8a54e374c7019f13f", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"6614dba8fffc4486948833eb2cb48453": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3e422b6f9c924f8b90c52984c29f2eba": {"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": ""}}, "4859445022f1466492847acbc9218820": {"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_6614dba8fffc4486948833eb2cb48453", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3e422b6f9c924f8b90c52984c29f2eba", "tabbable": null, "tooltip": null, "value": 30.0}}, "3e2f5c5b14d04e7eab77c1da047eaf37": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3f852fa32ca24e6bb53931e24dadc044": {"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}}, "cd1eb2f2a1b54b0fa870a8cedc781785": {"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_3e2f5c5b14d04e7eab77c1da047eaf37", "placeholder": "\u200b", "style": "IPY_MODEL_3f852fa32ca24e6bb53931e24dadc044", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "a63c0c1d48b24b9b86990f8442e7a986": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5b863241a0d04ead91d8344e1a316a03": {"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}}, "ece37f209eae46d499da542dc36ff270": {"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_a63c0c1d48b24b9b86990f8442e7a986", "placeholder": "\u200b", "style": "IPY_MODEL_5b863241a0d04ead91d8344e1a316a03", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007413.45it/s]"}}, "cba7ec7ff68e4d2b99b2c419a5f61998": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b162807487ed4294915f30fd4e3c76e0": {"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_cd1eb2f2a1b54b0fa870a8cedc781785", "IPY_MODEL_4859445022f1466492847acbc9218820", "IPY_MODEL_ece37f209eae46d499da542dc36ff270"], "layout": "IPY_MODEL_cba7ec7ff68e4d2b99b2c419a5f61998", "tabbable": null, "tooltip": null}}, "32f943c5ccf24606a0682b14df6b8813": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b1d722c462df4200ab2f28159c75e2ae": {"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": ""}}, "edda8d6aface48258c3afff4ab0438dc": {"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_32f943c5ccf24606a0682b14df6b8813", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b1d722c462df4200ab2f28159c75e2ae", "tabbable": null, "tooltip": null, "value": 30.0}}, "84cd9c7192a24ffbabecc9aae62510f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "22dd2e443884472987903e12456a6afc": {"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}}, "fc947703ddb343b58ccc14eaba3bcca0": {"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_84cd9c7192a24ffbabecc9aae62510f4", "placeholder": "\u200b", "style": "IPY_MODEL_22dd2e443884472987903e12456a6afc", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "47224c6e6ce747dbb5c436862e7fc083": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3037b33d74a246529b444447f531d14a": {"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}}, "48dad57e5d1a47b8bd13601d5d9c86cc": {"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_47224c6e6ce747dbb5c436862e7fc083", "placeholder": "\u200b", "style": "IPY_MODEL_3037b33d74a246529b444447f531d14a", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:22<00:00,\u2007\u20071.35it/s]"}}, "7d352113995b40caa7c8490d9201ba46": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9ace8d45462a43e3acb62ffd2dbfeae6": {"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_fc947703ddb343b58ccc14eaba3bcca0", "IPY_MODEL_edda8d6aface48258c3afff4ab0438dc", "IPY_MODEL_48dad57e5d1a47b8bd13601d5d9c86cc"], "layout": "IPY_MODEL_7d352113995b40caa7c8490d9201ba46", "tabbable": null, "tooltip": null}}, "a5ddf96f8e974675a30c27da41f7b7a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9abea605e6474573bcb7c31e57c24686": {"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": ""}}, "712e47d99ef54b18b2c7ecd143fbf9b1": {"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_a5ddf96f8e974675a30c27da41f7b7a8", "max": 4997817.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_9abea605e6474573bcb7c31e57c24686", "tabbable": null, "tooltip": null, "value": 4997817.0}}, "4aa157fdd2534b229ea93d063d9b0b18": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "47db57068b3e4989a7681de8ad28cc95": {"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}}, "a7ab9defd92845acb59e41d2d3fd53d0": {"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_4aa157fdd2534b229ea93d063d9b0b18", "placeholder": "\u200b", "style": "IPY_MODEL_47db57068b3e4989a7681de8ad28cc95", "tabbable": null, "tooltip": null, "value": "100%"}}, "e94ffb07e0db4adb805c9bc580a3e3e4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e13ede49da8b47288f20a176abd303de": {"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}}, "1f495e4964a148feb4e60d20e8452c6e": {"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_e94ffb07e0db4adb805c9bc580a3e3e4", "placeholder": "\u200b", "style": "IPY_MODEL_e13ede49da8b47288f20a176abd303de", "tabbable": null, "tooltip": null, "value": "\u20074997817/4997817\u2007[00:33<00:00,\u2007150552.43it/s]"}}, "405315e1b55c4805b3c835dd9b07aad0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6cc004502aa044fe903fd61c1b15e1b1": {"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_a7ab9defd92845acb59e41d2d3fd53d0", "IPY_MODEL_712e47d99ef54b18b2c7ecd143fbf9b1", "IPY_MODEL_1f495e4964a148feb4e60d20e8452c6e"], "layout": "IPY_MODEL_405315e1b55c4805b3c835dd9b07aad0", "tabbable": null, "tooltip": null}}, "c15a753a5cb2429281e5608ff1b5bb25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0e78779c254948ca955970bd699f05e4": {"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": ""}}, "a27bbce248eb48fa85aa97fe6cc9367b": {"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_c15a753a5cb2429281e5608ff1b5bb25", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_0e78779c254948ca955970bd699f05e4", "tabbable": null, "tooltip": null, "value": 30.0}}, "826f982e152b493fa16f4022915cc507": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bdacb14f809d4e719720b76b5446738c": {"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}}, "e6b05918df3841d8b0e4559dc4c908b4": {"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_826f982e152b493fa16f4022915cc507", "placeholder": "\u200b", "style": "IPY_MODEL_bdacb14f809d4e719720b76b5446738c", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "efc88340d53c45efa6cfd84134127283": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3968d811886943a58fcd6d72287a87c6": {"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}}, "cfa4014b382543c9b3b6454daaba56ea": {"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_efc88340d53c45efa6cfd84134127283", "placeholder": "\u200b", "style": "IPY_MODEL_3968d811886943a58fcd6d72287a87c6", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200721.68it/s]"}}, "c9f1ffe82f3d413d901770786743b252": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5ffa05d5e3254d5485cde2ec919c0753": {"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_e6b05918df3841d8b0e4559dc4c908b4", "IPY_MODEL_a27bbce248eb48fa85aa97fe6cc9367b", "IPY_MODEL_cfa4014b382543c9b3b6454daaba56ea"], "layout": "IPY_MODEL_c9f1ffe82f3d413d901770786743b252", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index a6a857792..3c7b2e571 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:57.207694Z", - "iopub.status.busy": "2024-02-27T04:12:57.207532Z", - "iopub.status.idle": "2024-02-27T04:12:59.279928Z", - "shell.execute_reply": "2024-02-27T04:12:59.279246Z" + "iopub.execute_input": "2024-03-05T17:28:18.147705Z", + "iopub.status.busy": "2024-03-05T17:28:18.147191Z", + "iopub.status.idle": "2024-03-05T17:28:20.582426Z", + "shell.execute_reply": "2024-03-05T17:28:20.581741Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:12:59.282497Z", - "iopub.status.busy": "2024-02-27T04:12:59.282159Z", - "iopub.status.idle": "2024-02-27T04:13:53.069895Z", - "shell.execute_reply": "2024-02-27T04:13:53.069266Z" + "iopub.execute_input": "2024-03-05T17:28:20.585272Z", + "iopub.status.busy": "2024-03-05T17:28:20.584882Z", + "iopub.status.idle": "2024-03-05T17:45:40.610320Z", + "shell.execute_reply": "2024-03-05T17:45:40.609609Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:53.072806Z", - "iopub.status.busy": "2024-02-27T04:13:53.072123Z", - "iopub.status.idle": "2024-02-27T04:13:54.114174Z", - "shell.execute_reply": "2024-02-27T04:13:54.113575Z" + "iopub.execute_input": "2024-03-05T17:45:40.612947Z", + "iopub.status.busy": "2024-03-05T17:45:40.612757Z", + "iopub.status.idle": "2024-03-05T17:45:41.757970Z", + "shell.execute_reply": "2024-03-05T17:45:41.757383Z" }, "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\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": "2024-02-27T04:13:54.116665Z", - "iopub.status.busy": "2024-02-27T04:13:54.116394Z", - "iopub.status.idle": "2024-02-27T04:13:54.119670Z", - "shell.execute_reply": "2024-02-27T04:13:54.119164Z" + "iopub.execute_input": "2024-03-05T17:45:41.760770Z", + "iopub.status.busy": "2024-03-05T17:45:41.760440Z", + "iopub.status.idle": "2024-03-05T17:45:41.763885Z", + "shell.execute_reply": "2024-03-05T17:45:41.763386Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:54.121737Z", - "iopub.status.busy": "2024-02-27T04:13:54.121337Z", - "iopub.status.idle": "2024-02-27T04:13:54.125111Z", - "shell.execute_reply": "2024-02-27T04:13:54.124597Z" + "iopub.execute_input": "2024-03-05T17:45:41.766023Z", + "iopub.status.busy": "2024-03-05T17:45:41.765791Z", + "iopub.status.idle": "2024-03-05T17:45:41.769948Z", + "shell.execute_reply": "2024-03-05T17:45:41.769469Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:54.127305Z", - "iopub.status.busy": "2024-02-27T04:13:54.127002Z", - "iopub.status.idle": "2024-02-27T04:13:54.130549Z", - "shell.execute_reply": "2024-02-27T04:13:54.130136Z" + "iopub.execute_input": "2024-03-05T17:45:41.772615Z", + "iopub.status.busy": "2024-03-05T17:45:41.772136Z", + "iopub.status.idle": "2024-03-05T17:45:41.776655Z", + "shell.execute_reply": "2024-03-05T17:45:41.776065Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:54.132513Z", - "iopub.status.busy": "2024-02-27T04:13:54.132263Z", - "iopub.status.idle": "2024-02-27T04:13:54.135127Z", - "shell.execute_reply": "2024-02-27T04:13:54.134716Z" + "iopub.execute_input": "2024-03-05T17:45:41.778817Z", + "iopub.status.busy": "2024-03-05T17:45:41.778595Z", + "iopub.status.idle": "2024-03-05T17:45:41.781542Z", + "shell.execute_reply": "2024-03-05T17:45:41.781115Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:13:54.136872Z", - "iopub.status.busy": "2024-02-27T04:13:54.136696Z", - "iopub.status.idle": "2024-02-27T04:15:09.458723Z", - "shell.execute_reply": "2024-02-27T04:15:09.458208Z" + "iopub.execute_input": "2024-03-05T17:45:41.783628Z", + "iopub.status.busy": "2024-03-05T17:45:41.783296Z", + "iopub.status.idle": "2024-03-05T17:46:58.949548Z", + "shell.execute_reply": "2024-03-05T17:46:58.948943Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f35bc28e6c854036845115d0c80f19d5", + "model_id": "b162807487ed4294915f30fd4e3c76e0", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "296d85fcd35c431788201d553a1a5a6f", + "model_id": "9ace8d45462a43e3acb62ffd2dbfeae6", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:15:09.461479Z", - "iopub.status.busy": "2024-02-27T04:15:09.460992Z", - "iopub.status.idle": "2024-02-27T04:15:10.130417Z", - "shell.execute_reply": "2024-02-27T04:15:10.129849Z" + "iopub.execute_input": "2024-03-05T17:46:58.952327Z", + "iopub.status.busy": "2024-03-05T17:46:58.951998Z", + "iopub.status.idle": "2024-03-05T17:46:59.639571Z", + "shell.execute_reply": "2024-03-05T17:46:59.639056Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:15:10.132563Z", - "iopub.status.busy": "2024-02-27T04:15:10.132277Z", - "iopub.status.idle": "2024-02-27T04:15:12.733259Z", - "shell.execute_reply": "2024-02-27T04:15:12.732682Z" + "iopub.execute_input": "2024-03-05T17:46:59.641915Z", + "iopub.status.busy": "2024-03-05T17:46:59.641441Z", + "iopub.status.idle": "2024-03-05T17:47:02.397012Z", + "shell.execute_reply": "2024-03-05T17:47:02.396451Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:15:12.735338Z", - "iopub.status.busy": "2024-02-27T04:15:12.735158Z", - "iopub.status.idle": "2024-02-27T04:15:45.577670Z", - "shell.execute_reply": "2024-02-27T04:15:45.577252Z" + "iopub.execute_input": "2024-03-05T17:47:02.399110Z", + "iopub.status.busy": "2024-03-05T17:47:02.398893Z", + "iopub.status.idle": "2024-03-05T17:47:35.738739Z", + "shell.execute_reply": "2024-03-05T17:47:35.738229Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cef6a8d6978b42028d2a2ab034ec55eb", + "model_id": "6cc004502aa044fe903fd61c1b15e1b1", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:15:45.579888Z", - "iopub.status.busy": "2024-02-27T04:15:45.579488Z", - "iopub.status.idle": "2024-02-27T04:16:00.523676Z", - "shell.execute_reply": "2024-02-27T04:16:00.523078Z" + "iopub.execute_input": "2024-03-05T17:47:35.741383Z", + "iopub.status.busy": "2024-03-05T17:47:35.740959Z", + "iopub.status.idle": "2024-03-05T17:47:50.208175Z", + "shell.execute_reply": "2024-03-05T17:47:50.207672Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:00.526352Z", - "iopub.status.busy": "2024-02-27T04:16:00.526156Z", - "iopub.status.idle": "2024-02-27T04:16:04.264366Z", - "shell.execute_reply": "2024-02-27T04:16:04.263812Z" + "iopub.execute_input": "2024-03-05T17:47:50.210787Z", + "iopub.status.busy": "2024-03-05T17:47:50.210374Z", + "iopub.status.idle": "2024-03-05T17:47:54.047662Z", + "shell.execute_reply": "2024-03-05T17:47:54.047067Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:04.266493Z", - "iopub.status.busy": "2024-02-27T04:16:04.266173Z", - "iopub.status.idle": "2024-02-27T04:16:05.581814Z", - "shell.execute_reply": "2024-02-27T04:16:05.581240Z" + "iopub.execute_input": "2024-03-05T17:47:54.049846Z", + "iopub.status.busy": "2024-03-05T17:47:54.049497Z", + "iopub.status.idle": "2024-03-05T17:47:55.459288Z", + "shell.execute_reply": "2024-03-05T17:47:55.458735Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9da4c9cbb0384e2aadaed1e9a5ac86c9", + "model_id": "5ffa05d5e3254d5485cde2ec919c0753", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:05.584272Z", - "iopub.status.busy": "2024-02-27T04:16:05.583899Z", - "iopub.status.idle": "2024-02-27T04:16:06.141736Z", - "shell.execute_reply": "2024-02-27T04:16:06.141168Z" + "iopub.execute_input": "2024-03-05T17:47:55.461782Z", + "iopub.status.busy": "2024-03-05T17:47:55.461446Z", + "iopub.status.idle": "2024-03-05T17:47:56.021511Z", + "shell.execute_reply": "2024-03-05T17:47:56.020919Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:06.144288Z", - "iopub.status.busy": "2024-02-27T04:16:06.143856Z", - "iopub.status.idle": "2024-02-27T04:16:12.281675Z", - "shell.execute_reply": "2024-02-27T04:16:12.281106Z" + "iopub.execute_input": "2024-03-05T17:47:56.023926Z", + "iopub.status.busy": "2024-03-05T17:47:56.023736Z", + "iopub.status.idle": "2024-03-05T17:48:02.253088Z", + "shell.execute_reply": "2024-03-05T17:48:02.252504Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:12.284059Z", - "iopub.status.busy": "2024-02-27T04:16:12.283678Z", - "iopub.status.idle": "2024-02-27T04:16:12.339573Z", - "shell.execute_reply": "2024-02-27T04:16:12.338971Z" + "iopub.execute_input": "2024-03-05T17:48:02.255140Z", + "iopub.status.busy": "2024-03-05T17:48:02.254968Z", + "iopub.status.idle": "2024-03-05T17:48:02.310981Z", + "shell.execute_reply": "2024-03-05T17:48:02.310446Z" }, "nbsphinx": "hidden" }, @@ -1038,125 +1038,82 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "09b26f4b3b9a47a2805cf4f2a46f9d69": { + "0e78779c254948ca955970bd699f05e4": { "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": "" } }, - "12d018fa4ae846b8a54e374c7019f13f": { - "model_module": "@jupyter-widgets/base", + "1f495e4964a148feb4e60d20e8452c6e": { + "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_e94ffb07e0db4adb805c9bc580a3e3e4", + "placeholder": "​", + "style": "IPY_MODEL_e13ede49da8b47288f20a176abd303de", + "tabbable": null, + "tooltip": null, + "value": " 4997817/4997817 [00:33<00:00, 150552.43it/s]" } }, - "296d85fcd35c431788201d553a1a5a6f": { + "22dd2e443884472987903e12456a6afc": { "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_bdd76556372a4e9493160aa23c770516", - "IPY_MODEL_afc228943c0c4f7da0a08beb16ce10d8", - "IPY_MODEL_6dd49c305ab748c7a93943e1588862e6" - ], - "layout": "IPY_MODEL_f24324799ab14c45bd97e44a8aafc700", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2c70b8eef5404b779ee375b744cce580": { + "3037b33d74a246529b444447f531d14a": { "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_731a6803de2943de9fe59e135467352c", - "placeholder": "​", - "style": "IPY_MODEL_dc09f94a59704a56b35e0ae9b670dff3", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "384a4505d6a24950b928aac1dc443147": { + "32f943c5ccf24606a0682b14df6b8813": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1209,48 +1166,7 @@ "width": null } }, - "3f2145734072403ca5a67549fab4f063": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "463e6bd1ba434320a64292f487ede2d8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fadf065818134007af3b6127fc18a147", - "placeholder": "​", - "style": "IPY_MODEL_09b26f4b3b9a47a2805cf4f2a46f9d69", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "50a5c3532d8a4018ac6a0cf17ea51c7b": { + "3968d811886943a58fcd6d72287a87c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1268,102 +1184,7 @@ "text_color": null } }, - "52595c826b444c2ebc76e3c515902b05": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_67c1e03df8e04d0faf0c596ee30c7c3f", - "placeholder": "​", - "style": "IPY_MODEL_d6735c14323d4b459e3e3c0bae6e2633", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "57b231e2b47b4bd9aebaa4374be7cdf6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d27b521ce1bf4fd39a9949ea8f5c2d4c", - "placeholder": "​", - "style": "IPY_MODEL_3f2145734072403ca5a67549fab4f063", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 409.01it/s]" - } - }, - "592e152ed2a34bd9baec5bafb7b23e0b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e8ac72205cde41498bb49c0af38523a2", - "placeholder": "​", - "style": "IPY_MODEL_e1b022c8165a4bf188893ed01dda6872", - "tabbable": null, - "tooltip": null, - "value": " 4997817/4997817 [00:32<00:00, 153315.47it/s]" - } - }, - "61f7722029cb43cb818c03e75f945cbb": { - "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_791fb20963684d658cbb4f2356804315", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9cd946793cc44581acb428f9f883ce63", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "62b097660a12458c9f94a0db9841f36f": { + "3e2f5c5b14d04e7eab77c1da047eaf37": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1416,33 +1237,41 @@ "width": null } }, - "6747a844f55e402b8ce37f9146b436da": { + "3e422b6f9c924f8b90c52984c29f2eba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_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_6852741653514354a11f29cdbeb0785d", - "max": 4997817.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e1972f5c0ada424e8875fc5447db0e05", - "tabbable": null, - "tooltip": null, - "value": 4997817.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3f852fa32ca24e6bb53931e24dadc044": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "67c1e03df8e04d0faf0c596ee30c7c3f": { + "405315e1b55c4805b3c835dd9b07aad0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1495,7 +1324,7 @@ "width": null } }, - "6852741653514354a11f29cdbeb0785d": { + "47224c6e6ce747dbb5c436862e7fc083": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1548,7 +1377,51 @@ "width": null } }, - "6dd49c305ab748c7a93943e1588862e6": { + "47db57068b3e4989a7681de8ad28cc95": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4859445022f1466492847acbc9218820": { + "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_6614dba8fffc4486948833eb2cb48453", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3e422b6f9c924f8b90c52984c29f2eba", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "48dad57e5d1a47b8bd13601d5d9c86cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1563,15 +1436,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9462c72d042744399edccd9fe0e5604e", + "layout": "IPY_MODEL_47224c6e6ce747dbb5c436862e7fc083", "placeholder": "​", - "style": "IPY_MODEL_6f35fa660d314dcf83ab10d7f6446013", + "style": "IPY_MODEL_3037b33d74a246529b444447f531d14a", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:20<00:00,  1.43it/s]" + "value": " 30/30 [00:22<00:00,  1.35it/s]" } }, - "6e9177833aa44fe4a3b3364d0ca3a407": { + "4aa157fdd2534b229ea93d063d9b0b18": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1624,7 +1497,7 @@ "width": null } }, - "6f35fa660d314dcf83ab10d7f6446013": { + "5b863241a0d04ead91d8344e1a316a03": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1642,7 +1515,31 @@ "text_color": null } }, - "731a6803de2943de9fe59e135467352c": { + "5ffa05d5e3254d5485cde2ec919c0753": { + "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_e6b05918df3841d8b0e4559dc4c908b4", + "IPY_MODEL_a27bbce248eb48fa85aa97fe6cc9367b", + "IPY_MODEL_cfa4014b382543c9b3b6454daaba56ea" + ], + "layout": "IPY_MODEL_c9f1ffe82f3d413d901770786743b252", + "tabbable": null, + "tooltip": null + } + }, + "6614dba8fffc4486948833eb2cb48453": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1695,7 +1592,57 @@ "width": null } }, - "791fb20963684d658cbb4f2356804315": { + "6cc004502aa044fe903fd61c1b15e1b1": { + "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_a7ab9defd92845acb59e41d2d3fd53d0", + "IPY_MODEL_712e47d99ef54b18b2c7ecd143fbf9b1", + "IPY_MODEL_1f495e4964a148feb4e60d20e8452c6e" + ], + "layout": "IPY_MODEL_405315e1b55c4805b3c835dd9b07aad0", + "tabbable": null, + "tooltip": null + } + }, + "712e47d99ef54b18b2c7ecd143fbf9b1": { + "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_a5ddf96f8e974675a30c27da41f7b7a8", + "max": 4997817.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9abea605e6474573bcb7c31e57c24686", + "tabbable": null, + "tooltip": null, + "value": 4997817.0 + } + }, + "7d352113995b40caa7c8490d9201ba46": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1748,25 +1695,7 @@ "width": null } }, - "89c18b2d1deb4f3686561f36964504fd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "932538e731be4078b2cdcf6fa4d50caf": { + "826f982e152b493fa16f4022915cc507": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1819,7 +1748,7 @@ "width": null } }, - "9462c72d042744399edccd9fe0e5604e": { + "84cd9c7192a24ffbabecc9aae62510f4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1859,112 +1788,20 @@ "margin": null, "max_height": null, "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "9cd946793cc44581acb428f9f883ce63": { - "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": "" - } - }, - "9da4c9cbb0384e2aadaed1e9a5ac86c9": { - "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_463e6bd1ba434320a64292f487ede2d8", - "IPY_MODEL_61f7722029cb43cb818c03e75f945cbb", - "IPY_MODEL_de7d2fcc6436422e84c48615a8b1a169" - ], - "layout": "IPY_MODEL_12d018fa4ae846b8a54e374c7019f13f", - "tabbable": null, - "tooltip": null - } - }, - "afc228943c0c4f7da0a08beb16ce10d8": { - "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_62b097660a12458c9f94a0db9841f36f", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_bb4f39000f154f8e937001f942d79b8b", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "b527ce48830a47aba867ef2f10cd2329": { - "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_6e9177833aa44fe4a3b3364d0ca3a407", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f5219d4ec3d24d008bb0e11b485f1850", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "bb4f39000f154f8e937001f942d79b8b": { + "9abea605e6474573bcb7c31e57c24686": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1980,54 +1817,57 @@ "description_width": "" } }, - "bdd76556372a4e9493160aa23c770516": { + "9ace8d45462a43e3acb62ffd2dbfeae6": { "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_932538e731be4078b2cdcf6fa4d50caf", - "placeholder": "​", - "style": "IPY_MODEL_50a5c3532d8a4018ac6a0cf17ea51c7b", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fc947703ddb343b58ccc14eaba3bcca0", + "IPY_MODEL_edda8d6aface48258c3afff4ab0438dc", + "IPY_MODEL_48dad57e5d1a47b8bd13601d5d9c86cc" + ], + "layout": "IPY_MODEL_7d352113995b40caa7c8490d9201ba46", "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" + "tooltip": null } }, - "cef6a8d6978b42028d2a2ab034ec55eb": { + "a27bbce248eb48fa85aa97fe6cc9367b": { "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_2c70b8eef5404b779ee375b744cce580", - "IPY_MODEL_6747a844f55e402b8ce37f9146b436da", - "IPY_MODEL_592e152ed2a34bd9baec5bafb7b23e0b" - ], - "layout": "IPY_MODEL_f7400852642549e6a9cfb66d9efd8518", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c15a753a5cb2429281e5608ff1b5bb25", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0e78779c254948ca955970bd699f05e4", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 30.0 } }, - "d27b521ce1bf4fd39a9949ea8f5c2d4c": { + "a5ddf96f8e974675a30c27da41f7b7a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2080,66 +1920,107 @@ "width": null } }, - "d6735c14323d4b459e3e3c0bae6e2633": { - "model_module": "@jupyter-widgets/controls", + "a63c0c1d48b24b9b86990f8442e7a986": { + "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 } }, - "dc09f94a59704a56b35e0ae9b670dff3": { + "a7ab9defd92845acb59e41d2d3fd53d0": { "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_4aa157fdd2534b229ea93d063d9b0b18", + "placeholder": "​", + "style": "IPY_MODEL_47db57068b3e4989a7681de8ad28cc95", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "de7d2fcc6436422e84c48615a8b1a169": { + "b162807487ed4294915f30fd4e3c76e0": { "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_384a4505d6a24950b928aac1dc443147", - "placeholder": "​", - "style": "IPY_MODEL_89c18b2d1deb4f3686561f36964504fd", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cd1eb2f2a1b54b0fa870a8cedc781785", + "IPY_MODEL_4859445022f1466492847acbc9218820", + "IPY_MODEL_ece37f209eae46d499da542dc36ff270" + ], + "layout": "IPY_MODEL_cba7ec7ff68e4d2b99b2c419a5f61998", "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:01<00:00, 23.54it/s]" + "tooltip": null } }, - "e1972f5c0ada424e8875fc5447db0e05": { + "b1d722c462df4200ab2f28159c75e2ae": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2155,7 +2036,7 @@ "description_width": "" } }, - "e1b022c8165a4bf188893ed01dda6872": { + "bdacb14f809d4e719720b76b5446738c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2173,7 +2054,7 @@ "text_color": null } }, - "e8ac72205cde41498bb49c0af38523a2": { + "c15a753a5cb2429281e5608ff1b5bb25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2226,7 +2107,7 @@ "width": null } }, - "e911eabd389a4c078fec14d183589ffa": { + "c9f1ffe82f3d413d901770786743b252": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2279,7 +2160,7 @@ "width": null } }, - "f24324799ab14c45bd97e44a8aafc700": { + "cba7ec7ff68e4d2b99b2c419a5f61998": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2332,47 +2213,94 @@ "width": null } }, - "f35bc28e6c854036845115d0c80f19d5": { + "cd1eb2f2a1b54b0fa870a8cedc781785": { "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_52595c826b444c2ebc76e3c515902b05", - "IPY_MODEL_b527ce48830a47aba867ef2f10cd2329", - "IPY_MODEL_57b231e2b47b4bd9aebaa4374be7cdf6" - ], - "layout": "IPY_MODEL_e911eabd389a4c078fec14d183589ffa", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3e2f5c5b14d04e7eab77c1da047eaf37", + "placeholder": "​", + "style": "IPY_MODEL_3f852fa32ca24e6bb53931e24dadc044", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" } }, - "f5219d4ec3d24d008bb0e11b485f1850": { + "cfa4014b382543c9b3b6454daaba56ea": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_efc88340d53c45efa6cfd84134127283", + "placeholder": "​", + "style": "IPY_MODEL_3968d811886943a58fcd6d72287a87c6", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 21.68it/s]" + } + }, + "e13ede49da8b47288f20a176abd303de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "e6b05918df3841d8b0e4559dc4c908b4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_826f982e152b493fa16f4022915cc507", + "placeholder": "​", + "style": "IPY_MODEL_bdacb14f809d4e719720b76b5446738c", + "tabbable": null, + "tooltip": null, + "value": "images processed using softmin: 100%" } }, - "f7400852642549e6a9cfb66d9efd8518": { + "e94ffb07e0db4adb805c9bc580a3e3e4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2425,7 +2353,56 @@ "width": null } }, - "fadf065818134007af3b6127fc18a147": { + "ece37f209eae46d499da542dc36ff270": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a63c0c1d48b24b9b86990f8442e7a986", + "placeholder": "​", + "style": "IPY_MODEL_5b863241a0d04ead91d8344e1a316a03", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 413.45it/s]" + } + }, + "edda8d6aface48258c3afff4ab0438dc": { + "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_32f943c5ccf24606a0682b14df6b8813", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b1d722c462df4200ab2f28159c75e2ae", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "efc88340d53c45efa6cfd84134127283": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2477,6 +2454,29 @@ "visibility": null, "width": null } + }, + "fc947703ddb343b58ccc14eaba3bcca0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_84cd9c7192a24ffbabecc9aae62510f4", + "placeholder": "​", + "style": "IPY_MODEL_22dd2e443884472987903e12456a6afc", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: 100%" + } } }, "version_major": 2, diff --git a/master/tutorials/tabular.ipynb b/master/tutorials/tabular.ipynb index 4db6b2fb5..c33c4f908 100644 --- a/master/tutorials/tabular.ipynb +++ b/master/tutorials/tabular.ipynb @@ -112,10 +112,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:14.737131Z", - "iopub.status.busy": "2024-02-27T04:16:14.736964Z", - "iopub.status.idle": "2024-02-27T04:16:15.945502Z", - "shell.execute_reply": "2024-02-27T04:16:15.945037Z" + "iopub.execute_input": "2024-03-05T17:48:04.856587Z", + "iopub.status.busy": "2024-03-05T17:48:04.856409Z", + "iopub.status.idle": "2024-03-05T17:48:05.988531Z", + "shell.execute_reply": "2024-03-05T17:48:05.987868Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -150,10 +150,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:15.947855Z", - "iopub.status.busy": "2024-02-27T04:16:15.947480Z", - "iopub.status.idle": "2024-02-27T04:16:15.973123Z", - "shell.execute_reply": "2024-02-27T04:16:15.972599Z" + "iopub.execute_input": "2024-03-05T17:48:05.991418Z", + "iopub.status.busy": "2024-03-05T17:48:05.990791Z", + "iopub.status.idle": "2024-03-05T17:48:06.011423Z", + "shell.execute_reply": "2024-03-05T17:48:06.010921Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:15.975475Z", - "iopub.status.busy": "2024-02-27T04:16:15.975065Z", - "iopub.status.idle": "2024-02-27T04:16:16.125117Z", - "shell.execute_reply": "2024-02-27T04:16:16.124586Z" + "iopub.execute_input": "2024-03-05T17:48:06.014009Z", + "iopub.status.busy": "2024-03-05T17:48:06.013708Z", + "iopub.status.idle": "2024-03-05T17:48:06.128945Z", + "shell.execute_reply": "2024-03-05T17:48:06.128378Z" } }, "outputs": [ @@ -304,10 +304,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.127227Z", - "iopub.status.busy": "2024-02-27T04:16:16.126946Z", - "iopub.status.idle": "2024-02-27T04:16:16.130584Z", - "shell.execute_reply": "2024-02-27T04:16:16.130059Z" + "iopub.execute_input": "2024-03-05T17:48:06.131142Z", + "iopub.status.busy": "2024-03-05T17:48:06.130841Z", + "iopub.status.idle": "2024-03-05T17:48:06.134314Z", + "shell.execute_reply": "2024-03-05T17:48:06.133885Z" } }, "outputs": [], @@ -328,10 +328,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.132634Z", - "iopub.status.busy": "2024-02-27T04:16:16.132250Z", - "iopub.status.idle": "2024-02-27T04:16:16.140835Z", - "shell.execute_reply": "2024-02-27T04:16:16.140417Z" + "iopub.execute_input": "2024-03-05T17:48:06.136444Z", + "iopub.status.busy": "2024-03-05T17:48:06.136115Z", + "iopub.status.idle": "2024-03-05T17:48:06.144438Z", + "shell.execute_reply": "2024-03-05T17:48:06.143865Z" } }, "outputs": [], @@ -383,10 +383,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.142989Z", - "iopub.status.busy": "2024-02-27T04:16:16.142569Z", - "iopub.status.idle": "2024-02-27T04:16:16.145030Z", - "shell.execute_reply": "2024-02-27T04:16:16.144617Z" + "iopub.execute_input": "2024-03-05T17:48:06.146732Z", + "iopub.status.busy": "2024-03-05T17:48:06.146414Z", + "iopub.status.idle": "2024-03-05T17:48:06.149109Z", + "shell.execute_reply": "2024-03-05T17:48:06.148562Z" } }, "outputs": [], @@ -408,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.146938Z", - "iopub.status.busy": "2024-02-27T04:16:16.146628Z", - "iopub.status.idle": "2024-02-27T04:16:16.658427Z", - "shell.execute_reply": "2024-02-27T04:16:16.657906Z" + "iopub.execute_input": "2024-03-05T17:48:06.151171Z", + "iopub.status.busy": "2024-03-05T17:48:06.150864Z", + "iopub.status.idle": "2024-03-05T17:48:06.678736Z", + "shell.execute_reply": "2024-03-05T17:48:06.678202Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:16.660726Z", - "iopub.status.busy": "2024-02-27T04:16:16.660536Z", - "iopub.status.idle": "2024-02-27T04:16:18.271783Z", - "shell.execute_reply": "2024-02-27T04:16:18.271203Z" + "iopub.execute_input": "2024-03-05T17:48:06.681076Z", + "iopub.status.busy": "2024-03-05T17:48:06.680880Z", + "iopub.status.idle": "2024-03-05T17:48:08.453121Z", + "shell.execute_reply": "2024-03-05T17:48:08.452497Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.274531Z", - "iopub.status.busy": "2024-02-27T04:16:18.273765Z", - "iopub.status.idle": "2024-02-27T04:16:18.283860Z", - "shell.execute_reply": "2024-02-27T04:16:18.283431Z" + "iopub.execute_input": "2024-03-05T17:48:08.455931Z", + "iopub.status.busy": "2024-03-05T17:48:08.455186Z", + "iopub.status.idle": "2024-03-05T17:48:08.465833Z", + "shell.execute_reply": "2024-03-05T17:48:08.465310Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.285733Z", - "iopub.status.busy": "2024-02-27T04:16:18.285537Z", - "iopub.status.idle": "2024-02-27T04:16:18.289535Z", - "shell.execute_reply": "2024-02-27T04:16:18.289115Z" + "iopub.execute_input": "2024-03-05T17:48:08.467945Z", + "iopub.status.busy": "2024-03-05T17:48:08.467767Z", + "iopub.status.idle": "2024-03-05T17:48:08.471808Z", + "shell.execute_reply": "2024-03-05T17:48:08.471372Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.291513Z", - "iopub.status.busy": "2024-02-27T04:16:18.291204Z", - "iopub.status.idle": "2024-02-27T04:16:18.298162Z", - "shell.execute_reply": "2024-02-27T04:16:18.297735Z" + "iopub.execute_input": "2024-03-05T17:48:08.473687Z", + "iopub.status.busy": "2024-03-05T17:48:08.473515Z", + "iopub.status.idle": "2024-03-05T17:48:08.481125Z", + "shell.execute_reply": "2024-03-05T17:48:08.480648Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.300199Z", - "iopub.status.busy": "2024-02-27T04:16:18.299889Z", - "iopub.status.idle": "2024-02-27T04:16:18.410879Z", - "shell.execute_reply": "2024-02-27T04:16:18.410323Z" + "iopub.execute_input": "2024-03-05T17:48:08.483006Z", + "iopub.status.busy": "2024-03-05T17:48:08.482829Z", + "iopub.status.idle": "2024-03-05T17:48:08.595386Z", + "shell.execute_reply": "2024-03-05T17:48:08.594890Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.412932Z", - "iopub.status.busy": "2024-02-27T04:16:18.412621Z", - "iopub.status.idle": "2024-02-27T04:16:18.415398Z", - "shell.execute_reply": "2024-02-27T04:16:18.414881Z" + "iopub.execute_input": "2024-03-05T17:48:08.597533Z", + "iopub.status.busy": "2024-03-05T17:48:08.597339Z", + "iopub.status.idle": "2024-03-05T17:48:08.600276Z", + "shell.execute_reply": "2024-03-05T17:48:08.599817Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:18.417564Z", - "iopub.status.busy": "2024-02-27T04:16:18.417159Z", - "iopub.status.idle": "2024-02-27T04:16:20.369816Z", - "shell.execute_reply": "2024-02-27T04:16:20.369175Z" + "iopub.execute_input": "2024-03-05T17:48:08.602300Z", + "iopub.status.busy": "2024-03-05T17:48:08.602114Z", + "iopub.status.idle": "2024-03-05T17:48:10.652590Z", + "shell.execute_reply": "2024-03-05T17:48:10.651973Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:20.372807Z", - "iopub.status.busy": "2024-02-27T04:16:20.372225Z", - "iopub.status.idle": "2024-02-27T04:16:20.383278Z", - "shell.execute_reply": "2024-02-27T04:16:20.382741Z" + "iopub.execute_input": "2024-03-05T17:48:10.655736Z", + "iopub.status.busy": "2024-03-05T17:48:10.654924Z", + "iopub.status.idle": "2024-03-05T17:48:10.666973Z", + "shell.execute_reply": "2024-03-05T17:48:10.666399Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:20.385229Z", - "iopub.status.busy": "2024-02-27T04:16:20.385051Z", - "iopub.status.idle": "2024-02-27T04:16:20.514805Z", - "shell.execute_reply": "2024-02-27T04:16:20.514319Z" + "iopub.execute_input": "2024-03-05T17:48:10.669111Z", + "iopub.status.busy": "2024-03-05T17:48:10.668780Z", + "iopub.status.idle": "2024-03-05T17:48:10.779840Z", + "shell.execute_reply": "2024-03-05T17:48:10.779359Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/text.html b/master/tutorials/text.html index 291f19940..1fd68cc86 100644 --- a/master/tutorials/text.html +++ b/master/tutorials/text.html @@ -749,7 +749,7 @@

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

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

diff --git a/master/tutorials/text.ipynb b/master/tutorials/text.ipynb index eeeda813b..7b751ea82 100644 --- a/master/tutorials/text.ipynb +++ b/master/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:23.372370Z", - "iopub.status.busy": "2024-02-27T04:16:23.372190Z", - "iopub.status.idle": "2024-02-27T04:16:25.962294Z", - "shell.execute_reply": "2024-02-27T04:16:25.961803Z" + "iopub.execute_input": "2024-03-05T17:48:14.638665Z", + "iopub.status.busy": "2024-03-05T17:48:14.638226Z", + "iopub.status.idle": "2024-03-05T17:48:17.428257Z", + "shell.execute_reply": "2024-03-05T17:48:17.427698Z" }, "nbsphinx": "hidden" }, @@ -134,7 +134,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:25.964716Z", - "iopub.status.busy": "2024-02-27T04:16:25.964409Z", - "iopub.status.idle": "2024-02-27T04:16:25.967890Z", - "shell.execute_reply": "2024-02-27T04:16:25.967445Z" + "iopub.execute_input": "2024-03-05T17:48:17.430961Z", + "iopub.status.busy": "2024-03-05T17:48:17.430423Z", + "iopub.status.idle": "2024-03-05T17:48:17.433889Z", + "shell.execute_reply": "2024-03-05T17:48:17.433420Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:25.969673Z", - "iopub.status.busy": "2024-02-27T04:16:25.969495Z", - "iopub.status.idle": "2024-02-27T04:16:25.972738Z", - "shell.execute_reply": "2024-02-27T04:16:25.972220Z" + "iopub.execute_input": "2024-03-05T17:48:17.435905Z", + "iopub.status.busy": "2024-03-05T17:48:17.435631Z", + "iopub.status.idle": "2024-03-05T17:48:17.438735Z", + "shell.execute_reply": "2024-03-05T17:48:17.438293Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:25.974775Z", - "iopub.status.busy": "2024-02-27T04:16:25.974597Z", - "iopub.status.idle": "2024-02-27T04:16:26.114274Z", - "shell.execute_reply": "2024-02-27T04:16:26.113793Z" + "iopub.execute_input": "2024-03-05T17:48:17.440824Z", + "iopub.status.busy": "2024-03-05T17:48:17.440490Z", + "iopub.status.idle": "2024-03-05T17:48:17.558800Z", + "shell.execute_reply": "2024-03-05T17:48:17.558202Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.116290Z", - "iopub.status.busy": "2024-02-27T04:16:26.116099Z", - "iopub.status.idle": "2024-02-27T04:16:26.119649Z", - "shell.execute_reply": "2024-02-27T04:16:26.119216Z" + "iopub.execute_input": "2024-03-05T17:48:17.561117Z", + "iopub.status.busy": "2024-03-05T17:48:17.560769Z", + "iopub.status.idle": "2024-03-05T17:48:17.564365Z", + "shell.execute_reply": "2024-03-05T17:48:17.563876Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.121568Z", - "iopub.status.busy": "2024-02-27T04:16:26.121379Z", - "iopub.status.idle": "2024-02-27T04:16:26.124791Z", - "shell.execute_reply": "2024-02-27T04:16:26.124322Z" + "iopub.execute_input": "2024-03-05T17:48:17.566433Z", + "iopub.status.busy": "2024-03-05T17:48:17.566089Z", + "iopub.status.idle": "2024-03-05T17:48:17.569334Z", + "shell.execute_reply": "2024-03-05T17:48:17.568816Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'getting_spare_card', 'cancel_transfer', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'card_about_to_expire', 'visa_or_mastercard'}\n" + "Classes: {'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_about_to_expire', 'getting_spare_card'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.126916Z", - "iopub.status.busy": "2024-02-27T04:16:26.126572Z", - "iopub.status.idle": "2024-02-27T04:16:26.129643Z", - "shell.execute_reply": "2024-02-27T04:16:26.129127Z" + "iopub.execute_input": "2024-03-05T17:48:17.571385Z", + "iopub.status.busy": "2024-03-05T17:48:17.571040Z", + "iopub.status.idle": "2024-03-05T17:48:17.574329Z", + "shell.execute_reply": "2024-03-05T17:48:17.573852Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.131708Z", - "iopub.status.busy": "2024-02-27T04:16:26.131379Z", - "iopub.status.idle": "2024-02-27T04:16:26.134572Z", - "shell.execute_reply": "2024-02-27T04:16:26.134163Z" + "iopub.execute_input": "2024-03-05T17:48:17.576440Z", + "iopub.status.busy": "2024-03-05T17:48:17.576097Z", + "iopub.status.idle": "2024-03-05T17:48:17.579381Z", + "shell.execute_reply": "2024-03-05T17:48:17.578941Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:26.136586Z", - "iopub.status.busy": "2024-02-27T04:16:26.136261Z", - "iopub.status.idle": "2024-02-27T04:16:30.548466Z", - "shell.execute_reply": "2024-02-27T04:16:30.547934Z" + "iopub.execute_input": "2024-03-05T17:48:17.581610Z", + "iopub.status.busy": "2024-03-05T17:48:17.581251Z", + "iopub.status.idle": "2024-03-05T17:48:21.861401Z", + "shell.execute_reply": "2024-03-05T17:48:21.860860Z" } }, "outputs": [ @@ -510,10 +510,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:30.551323Z", - "iopub.status.busy": "2024-02-27T04:16:30.550849Z", - "iopub.status.idle": "2024-02-27T04:16:30.553720Z", - "shell.execute_reply": "2024-02-27T04:16:30.553169Z" + "iopub.execute_input": "2024-03-05T17:48:21.864411Z", + "iopub.status.busy": "2024-03-05T17:48:21.863952Z", + "iopub.status.idle": "2024-03-05T17:48:21.867754Z", + "shell.execute_reply": "2024-03-05T17:48:21.867131Z" } }, "outputs": [], @@ -535,10 +535,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:30.555772Z", - "iopub.status.busy": "2024-02-27T04:16:30.555380Z", - "iopub.status.idle": "2024-02-27T04:16:30.557923Z", - "shell.execute_reply": "2024-02-27T04:16:30.557487Z" + "iopub.execute_input": "2024-03-05T17:48:21.870196Z", + "iopub.status.busy": "2024-03-05T17:48:21.869822Z", + "iopub.status.idle": "2024-03-05T17:48:21.872712Z", + "shell.execute_reply": "2024-03-05T17:48:21.872255Z" } }, "outputs": [], @@ -553,10 +553,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:30.559936Z", - "iopub.status.busy": "2024-02-27T04:16:30.559556Z", - "iopub.status.idle": "2024-02-27T04:16:32.861702Z", - "shell.execute_reply": "2024-02-27T04:16:32.861107Z" + "iopub.execute_input": "2024-03-05T17:48:21.874737Z", + "iopub.status.busy": "2024-03-05T17:48:21.874406Z", + "iopub.status.idle": "2024-03-05T17:48:24.259147Z", + "shell.execute_reply": "2024-03-05T17:48:24.258462Z" }, "scrolled": true }, @@ -579,10 +579,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.864510Z", - "iopub.status.busy": "2024-02-27T04:16:32.863947Z", - "iopub.status.idle": "2024-02-27T04:16:32.871671Z", - "shell.execute_reply": "2024-02-27T04:16:32.871121Z" + "iopub.execute_input": "2024-03-05T17:48:24.262237Z", + "iopub.status.busy": "2024-03-05T17:48:24.261594Z", + "iopub.status.idle": "2024-03-05T17:48:24.269892Z", + "shell.execute_reply": "2024-03-05T17:48:24.269350Z" } }, "outputs": [ @@ -683,10 +683,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.873982Z", - "iopub.status.busy": "2024-02-27T04:16:32.873436Z", - "iopub.status.idle": "2024-02-27T04:16:32.877504Z", - "shell.execute_reply": "2024-02-27T04:16:32.876984Z" + "iopub.execute_input": "2024-03-05T17:48:24.271990Z", + "iopub.status.busy": "2024-03-05T17:48:24.271656Z", + "iopub.status.idle": "2024-03-05T17:48:24.275810Z", + "shell.execute_reply": "2024-03-05T17:48:24.275167Z" } }, "outputs": [], @@ -700,10 +700,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.879587Z", - "iopub.status.busy": "2024-02-27T04:16:32.879408Z", - "iopub.status.idle": "2024-02-27T04:16:32.882469Z", - "shell.execute_reply": "2024-02-27T04:16:32.881967Z" + "iopub.execute_input": "2024-03-05T17:48:24.277825Z", + "iopub.status.busy": "2024-03-05T17:48:24.277502Z", + "iopub.status.idle": "2024-03-05T17:48:24.280798Z", + "shell.execute_reply": "2024-03-05T17:48:24.280252Z" } }, "outputs": [ @@ -738,10 +738,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.884542Z", - "iopub.status.busy": "2024-02-27T04:16:32.884147Z", - "iopub.status.idle": "2024-02-27T04:16:32.887188Z", - "shell.execute_reply": "2024-02-27T04:16:32.886670Z" + "iopub.execute_input": "2024-03-05T17:48:24.282844Z", + "iopub.status.busy": "2024-03-05T17:48:24.282543Z", + "iopub.status.idle": "2024-03-05T17:48:24.285556Z", + "shell.execute_reply": "2024-03-05T17:48:24.285026Z" } }, "outputs": [], @@ -761,10 +761,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.889084Z", - "iopub.status.busy": "2024-02-27T04:16:32.888790Z", - "iopub.status.idle": "2024-02-27T04:16:32.895757Z", - "shell.execute_reply": "2024-02-27T04:16:32.895258Z" + "iopub.execute_input": "2024-03-05T17:48:24.287578Z", + "iopub.status.busy": "2024-03-05T17:48:24.287182Z", + "iopub.status.idle": "2024-03-05T17:48:24.294884Z", + "shell.execute_reply": "2024-03-05T17:48:24.294339Z" } }, "outputs": [ @@ -889,10 +889,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:32.897819Z", - "iopub.status.busy": "2024-02-27T04:16:32.897482Z", - "iopub.status.idle": "2024-02-27T04:16:33.119938Z", - "shell.execute_reply": "2024-02-27T04:16:33.119443Z" + "iopub.execute_input": "2024-03-05T17:48:24.297174Z", + "iopub.status.busy": "2024-03-05T17:48:24.296794Z", + "iopub.status.idle": "2024-03-05T17:48:24.521401Z", + "shell.execute_reply": "2024-03-05T17:48:24.520874Z" }, "scrolled": true }, @@ -931,10 +931,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:33.123324Z", - "iopub.status.busy": "2024-02-27T04:16:33.122406Z", - "iopub.status.idle": "2024-02-27T04:16:33.326081Z", - "shell.execute_reply": "2024-02-27T04:16:33.325571Z" + "iopub.execute_input": "2024-03-05T17:48:24.524018Z", + "iopub.status.busy": "2024-03-05T17:48:24.523639Z", + "iopub.status.idle": "2024-03-05T17:48:24.700538Z", + "shell.execute_reply": "2024-03-05T17:48:24.700015Z" }, "scrolled": true }, @@ -967,10 +967,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:33.329701Z", - "iopub.status.busy": "2024-02-27T04:16:33.328790Z", - "iopub.status.idle": "2024-02-27T04:16:33.333589Z", - "shell.execute_reply": "2024-02-27T04:16:33.333123Z" + "iopub.execute_input": "2024-03-05T17:48:24.703138Z", + "iopub.status.busy": "2024-03-05T17:48:24.702742Z", + "iopub.status.idle": "2024-03-05T17:48:24.706703Z", + "shell.execute_reply": "2024-03-05T17:48:24.706199Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 7941aef63..ff68427ac 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -642,16 +642,16 @@

1. Install required dependencies and download data 
---2024-02-27 04:16:36--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 143.244.50.210, 2400:52e0:1a01::984:1
-Connecting to data.deepai.org (data.deepai.org)|143.244.50.210|:443... connected.
+--2024-03-05 17:48:28--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 169.150.249.168, 2400:52e0:1a01::953:1
+Connecting to data.deepai.org (data.deepai.org)|169.150.249.168|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
-conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.05s
+conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.06s
 
-2024-02-27 04:16:36 (19.2 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-03-05 17:48:28 (16.9 MB/s) - ‘conll2003.zip’ saved [982975/982975]
 
 mkdir: cannot create directory ‘data’: File exists
 Archive:  conll2003.zip
@@ -659,16 +659,16 @@ 
1. Install required dependencies and download data
diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index 047ae0f88..f1711449b 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:36.282082Z", - "iopub.status.busy": "2024-02-27T04:16:36.281908Z", - "iopub.status.idle": "2024-02-27T04:16:38.062551Z", - "shell.execute_reply": "2024-02-27T04:16:38.061998Z" + "iopub.execute_input": "2024-03-05T17:48:28.698096Z", + "iopub.status.busy": "2024-03-05T17:48:28.697888Z", + "iopub.status.idle": "2024-03-05T17:48:30.515374Z", + "shell.execute_reply": "2024-03-05T17:48:30.514704Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-02-27 04:16:36-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-03-05 17:48:28-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,9 +94,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "143.244.50.210, 2400:52e0:1a01::984:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|143.244.50.210|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", + "169.150.249.168, 2400:52e0:1a01::953:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.249.168|:443... connected.\r\n", + "HTTP request sent, awaiting response... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -109,9 +116,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.05s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.06s \r\n", "\r\n", - "2024-02-27 04:16:36 (19.2 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-03-05 17:48:28 (16.9 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -124,16 +131,23 @@ " inflating: data/metadata \r\n", " inflating: data/test.txt \r\n", " inflating: data/train.txt \r\n", - " inflating: data/valid.txt \r\n" + " inflating: data/valid.txt " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "--2024-02-27 04:16:36-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.98.57, 52.216.213.49, 54.231.226.249, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.98.57|:443... " + "--2024-03-05 17:48:29-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.107.17, 52.216.54.57, 52.217.88.28, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.107.17|:443... " ] }, { @@ -167,7 +181,15 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 1%[ ] 279.53K 1.21MB/s " + "pred_probs.npz 0%[ ] 160.53K 782KB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 8%[> ] 1.38M 3.37MB/s " ] }, { @@ -175,7 +197,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 28%[====> ] 4.67M 10.3MB/s " + "pred_probs.npz 52%[=========> ] 8.61M 13.9MB/s " ] }, { @@ -183,9 +205,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 25.5MB/s in 0.6s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 21.2MB/s in 0.8s \r\n", "\r\n", - "2024-02-27 04:16:37 (25.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-03-05 17:48:30 (21.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -202,10 +224,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:38.064998Z", - "iopub.status.busy": "2024-02-27T04:16:38.064642Z", - "iopub.status.idle": "2024-02-27T04:16:39.102453Z", - "shell.execute_reply": "2024-02-27T04:16:39.101931Z" + "iopub.execute_input": "2024-03-05T17:48:30.518071Z", + "iopub.status.busy": "2024-03-05T17:48:30.517872Z", + "iopub.status.idle": "2024-03-05T17:48:31.647064Z", + "shell.execute_reply": "2024-03-05T17:48:31.646513Z" }, "nbsphinx": "hidden" }, @@ -216,7 +238,7 @@ "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@09245a93829950109e7226b76e8ce2bf667da73f\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@90ac7962130e2a11bea5dbdc98b03c02744b63ab\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -242,10 +264,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:39.104901Z", - "iopub.status.busy": "2024-02-27T04:16:39.104521Z", - "iopub.status.idle": "2024-02-27T04:16:39.107823Z", - "shell.execute_reply": "2024-02-27T04:16:39.107397Z" + "iopub.execute_input": "2024-03-05T17:48:31.649495Z", + "iopub.status.busy": "2024-03-05T17:48:31.649168Z", + "iopub.status.idle": "2024-03-05T17:48:31.652598Z", + "shell.execute_reply": "2024-03-05T17:48:31.652075Z" } }, "outputs": [], @@ -295,10 +317,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:39.109717Z", - "iopub.status.busy": "2024-02-27T04:16:39.109446Z", - "iopub.status.idle": "2024-02-27T04:16:39.112479Z", - "shell.execute_reply": "2024-02-27T04:16:39.112052Z" + "iopub.execute_input": "2024-03-05T17:48:31.654619Z", + "iopub.status.busy": "2024-03-05T17:48:31.654313Z", + "iopub.status.idle": "2024-03-05T17:48:31.657329Z", + "shell.execute_reply": "2024-03-05T17:48:31.656798Z" }, "nbsphinx": "hidden" }, @@ -316,10 +338,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:39.114415Z", - "iopub.status.busy": "2024-02-27T04:16:39.114091Z", - "iopub.status.idle": "2024-02-27T04:16:48.203347Z", - "shell.execute_reply": "2024-02-27T04:16:48.202798Z" + "iopub.execute_input": "2024-03-05T17:48:31.659406Z", + "iopub.status.busy": "2024-03-05T17:48:31.659023Z", + "iopub.status.idle": "2024-03-05T17:48:40.758067Z", + "shell.execute_reply": "2024-03-05T17:48:40.757525Z" } }, "outputs": [], @@ -393,10 +415,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:48.205929Z", - "iopub.status.busy": "2024-02-27T04:16:48.205544Z", - "iopub.status.idle": "2024-02-27T04:16:48.211248Z", - "shell.execute_reply": "2024-02-27T04:16:48.210768Z" + "iopub.execute_input": "2024-03-05T17:48:40.760490Z", + "iopub.status.busy": "2024-03-05T17:48:40.760276Z", + "iopub.status.idle": "2024-03-05T17:48:40.765876Z", + "shell.execute_reply": "2024-03-05T17:48:40.765342Z" }, "nbsphinx": "hidden" }, @@ -436,10 +458,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:48.213236Z", - "iopub.status.busy": "2024-02-27T04:16:48.212898Z", - "iopub.status.idle": "2024-02-27T04:16:48.570838Z", - "shell.execute_reply": "2024-02-27T04:16:48.570212Z" + "iopub.execute_input": "2024-03-05T17:48:40.767925Z", + "iopub.status.busy": "2024-03-05T17:48:40.767584Z", + "iopub.status.idle": "2024-03-05T17:48:41.150882Z", + "shell.execute_reply": "2024-03-05T17:48:41.150386Z" } }, "outputs": [], @@ -476,10 +498,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:48.573370Z", - "iopub.status.busy": "2024-02-27T04:16:48.573173Z", - "iopub.status.idle": "2024-02-27T04:16:48.577651Z", - "shell.execute_reply": "2024-02-27T04:16:48.577110Z" + "iopub.execute_input": "2024-03-05T17:48:41.153344Z", + "iopub.status.busy": "2024-03-05T17:48:41.152994Z", + "iopub.status.idle": "2024-03-05T17:48:41.157237Z", + "shell.execute_reply": "2024-03-05T17:48:41.156689Z" } }, "outputs": [ @@ -551,10 +573,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:48.579676Z", - "iopub.status.busy": "2024-02-27T04:16:48.579369Z", - "iopub.status.idle": "2024-02-27T04:16:50.930572Z", - "shell.execute_reply": "2024-02-27T04:16:50.929786Z" + "iopub.execute_input": "2024-03-05T17:48:41.159296Z", + "iopub.status.busy": "2024-03-05T17:48:41.159119Z", + "iopub.status.idle": "2024-03-05T17:48:43.661974Z", + "shell.execute_reply": "2024-03-05T17:48:43.660985Z" } }, "outputs": [], @@ -576,10 +598,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.934005Z", - "iopub.status.busy": "2024-02-27T04:16:50.932996Z", - "iopub.status.idle": "2024-02-27T04:16:50.937367Z", - "shell.execute_reply": "2024-02-27T04:16:50.936914Z" + "iopub.execute_input": "2024-03-05T17:48:43.665593Z", + "iopub.status.busy": "2024-03-05T17:48:43.664859Z", + "iopub.status.idle": "2024-03-05T17:48:43.669727Z", + "shell.execute_reply": "2024-03-05T17:48:43.669246Z" } }, "outputs": [ @@ -615,10 +637,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.939294Z", - "iopub.status.busy": "2024-02-27T04:16:50.938996Z", - "iopub.status.idle": "2024-02-27T04:16:50.944549Z", - "shell.execute_reply": "2024-02-27T04:16:50.944017Z" + "iopub.execute_input": "2024-03-05T17:48:43.671802Z", + "iopub.status.busy": "2024-03-05T17:48:43.671467Z", + "iopub.status.idle": "2024-03-05T17:48:43.677333Z", + "shell.execute_reply": "2024-03-05T17:48:43.676755Z" } }, "outputs": [ @@ -796,10 +818,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.946645Z", - "iopub.status.busy": "2024-02-27T04:16:50.946254Z", - "iopub.status.idle": "2024-02-27T04:16:50.971737Z", - "shell.execute_reply": "2024-02-27T04:16:50.971206Z" + "iopub.execute_input": "2024-03-05T17:48:43.679642Z", + "iopub.status.busy": "2024-03-05T17:48:43.679214Z", + "iopub.status.idle": "2024-03-05T17:48:43.707101Z", + "shell.execute_reply": "2024-03-05T17:48:43.706480Z" } }, "outputs": [ @@ -901,10 +923,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.973819Z", - "iopub.status.busy": "2024-02-27T04:16:50.973397Z", - "iopub.status.idle": "2024-02-27T04:16:50.977629Z", - "shell.execute_reply": "2024-02-27T04:16:50.977093Z" + "iopub.execute_input": "2024-03-05T17:48:43.709407Z", + "iopub.status.busy": "2024-03-05T17:48:43.709012Z", + "iopub.status.idle": "2024-03-05T17:48:43.714442Z", + "shell.execute_reply": "2024-03-05T17:48:43.713895Z" } }, "outputs": [ @@ -978,10 +1000,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:50.979555Z", - "iopub.status.busy": "2024-02-27T04:16:50.979258Z", - "iopub.status.idle": "2024-02-27T04:16:52.383789Z", - "shell.execute_reply": "2024-02-27T04:16:52.383184Z" + "iopub.execute_input": "2024-03-05T17:48:43.716670Z", + "iopub.status.busy": "2024-03-05T17:48:43.716289Z", + "iopub.status.idle": "2024-03-05T17:48:45.220835Z", + "shell.execute_reply": "2024-03-05T17:48:45.220199Z" } }, "outputs": [ @@ -1153,10 +1175,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-02-27T04:16:52.386055Z", - "iopub.status.busy": "2024-02-27T04:16:52.385862Z", - "iopub.status.idle": "2024-02-27T04:16:52.389934Z", - "shell.execute_reply": "2024-02-27T04:16:52.389502Z" + "iopub.execute_input": "2024-03-05T17:48:45.223167Z", + "iopub.status.busy": "2024-03-05T17:48:45.222742Z", + "iopub.status.idle": "2024-03-05T17:48:45.226964Z", + "shell.execute_reply": "2024-03-05T17:48:45.226451Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 71466a4fc..90d8c6d24 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.0", - commit_hash: "09245a93829950109e7226b76e8ce2bf667da73f", + commit_hash: "90ac7962130e2a11bea5dbdc98b03c02744b63ab", }; \ No newline at end of file