diff --git a/master/.buildinfo b/master/.buildinfo index ece896686..1528dbe1c 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: f88f7c566ae1cd182e4d434608355dfc +config: 7819fe37108463a4b3bf57a557ea29c3 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index 93bdf1944..f2f9be38a 100644 Binary files a/master/.doctrees/cleanlab/benchmarking/index.doctree and b/master/.doctrees/cleanlab/benchmarking/index.doctree differ diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree index 7a1b4c8d9..1297471b6 100644 Binary files a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree and b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree differ diff --git a/master/.doctrees/cleanlab/classification.doctree b/master/.doctrees/cleanlab/classification.doctree index b12b09390..5892d7f6c 100644 Binary files a/master/.doctrees/cleanlab/classification.doctree and b/master/.doctrees/cleanlab/classification.doctree differ diff --git a/master/.doctrees/cleanlab/count.doctree b/master/.doctrees/cleanlab/count.doctree index faeab0cb7..cd3e3af01 100644 Binary files a/master/.doctrees/cleanlab/count.doctree and b/master/.doctrees/cleanlab/count.doctree differ diff --git a/master/.doctrees/cleanlab/data_valuation.doctree b/master/.doctrees/cleanlab/data_valuation.doctree index e58ae052c..3ba409355 100644 Binary files a/master/.doctrees/cleanlab/data_valuation.doctree and b/master/.doctrees/cleanlab/data_valuation.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/datalab.doctree b/master/.doctrees/cleanlab/datalab/datalab.doctree index ad83842b7..535bff933 100644 Binary files a/master/.doctrees/cleanlab/datalab/datalab.doctree and b/master/.doctrees/cleanlab/datalab/datalab.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree b/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree index a3e5fc77c..8606b7265 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree and b/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree index c7333af93..cd451c3de 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree and b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index cf94a16fa..54000009c 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree and b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index da2481882..707397a2a 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/index.doctree and b/master/.doctrees/cleanlab/datalab/guide/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree index 0c69dfb33..564fe19f9 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree and b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/table.doctree b/master/.doctrees/cleanlab/datalab/guide/table.doctree index e056aa3f9..8128d3837 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/table.doctree and b/master/.doctrees/cleanlab/datalab/guide/table.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree index 2c66cec54..928635efb 100644 Binary files a/master/.doctrees/cleanlab/datalab/index.doctree and b/master/.doctrees/cleanlab/datalab/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index 6614df31d..72541bba7 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/data.doctree and b/master/.doctrees/cleanlab/datalab/internal/data.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree index f73717754..d822d4d7f 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree and b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/factory.doctree b/master/.doctrees/cleanlab/datalab/internal/factory.doctree index be038bd61..d0616f966 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/factory.doctree and b/master/.doctrees/cleanlab/datalab/internal/factory.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree index f11566337..6607e7215 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree index 4d30f0cf8..c553152f1 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree differ 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 5bcc6541d..3ee414b80 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree differ 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 9856fab71..8714f1596 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index 32f4935bf..358423bd0 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index 2fd1ef72e..ac00aae28 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree index e30e726ba..5dc2ad54f 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree differ 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 b0cbcd36e..04d5848fa 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree index 27ca03d50..133ffbdc0 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree differ 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 a901c0b3d..b9b7fbc63 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree differ 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 356826588..b6461efd2 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree index 4137f2627..8b26ef911 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree index bf0137099..d5110b613 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree index 2b0de15e9..49952bcfe 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree index 0a487b459..1386e1183 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree differ 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 dde318df1..a37a3a079 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree index fa79cd57a..420bc1027 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree b/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree index c6927a9c2..271aef037 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree and b/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/report.doctree b/master/.doctrees/cleanlab/datalab/internal/report.doctree index 2d828a71c..a410c0fe7 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/report.doctree and b/master/.doctrees/cleanlab/datalab/internal/report.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/task.doctree b/master/.doctrees/cleanlab/datalab/internal/task.doctree index be59b70b3..9522ad98d 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/task.doctree and b/master/.doctrees/cleanlab/datalab/internal/task.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree index aa2ee0d19..8d840e159 100644 Binary files a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree and b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree differ diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index 68b865db7..a8b3f2ff1 100644 Binary files a/master/.doctrees/cleanlab/dataset.doctree and b/master/.doctrees/cleanlab/dataset.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree index b5b5429ba..663fd9e82 100644 Binary files a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree and b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/coteaching.doctree b/master/.doctrees/cleanlab/experimental/coteaching.doctree index 77a2e5ed2..d4bd56e92 100644 Binary files a/master/.doctrees/cleanlab/experimental/coteaching.doctree and b/master/.doctrees/cleanlab/experimental/coteaching.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree index e0f560144..04f5dacfe 100644 Binary files a/master/.doctrees/cleanlab/experimental/index.doctree and b/master/.doctrees/cleanlab/experimental/index.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree index f24c7e357..318ceb948 100644 Binary files a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree and b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree index cb1adf705..d834197f0 100644 Binary files a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree and b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/span_classification.doctree b/master/.doctrees/cleanlab/experimental/span_classification.doctree index f948c4e2a..5070d5daf 100644 Binary files a/master/.doctrees/cleanlab/experimental/span_classification.doctree and b/master/.doctrees/cleanlab/experimental/span_classification.doctree differ diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree index 18654fab8..c3d2f52c2 100644 Binary files a/master/.doctrees/cleanlab/filter.doctree and b/master/.doctrees/cleanlab/filter.doctree differ diff --git a/master/.doctrees/cleanlab/internal/index.doctree b/master/.doctrees/cleanlab/internal/index.doctree index 50f9ee200..ae3450488 100644 Binary files a/master/.doctrees/cleanlab/internal/index.doctree and b/master/.doctrees/cleanlab/internal/index.doctree differ diff --git a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree index 433d63a13..7491affd7 100644 Binary files a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree and b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree differ diff --git a/master/.doctrees/cleanlab/internal/latent_algebra.doctree b/master/.doctrees/cleanlab/internal/latent_algebra.doctree index a0fb65aab..419d5dc0c 100644 Binary files a/master/.doctrees/cleanlab/internal/latent_algebra.doctree and b/master/.doctrees/cleanlab/internal/latent_algebra.doctree differ diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index 95be350da..3a22599f2 100644 Binary files a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree and b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree differ diff --git a/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree b/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree index 48e51288c..882f87797 100644 Binary files a/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree and b/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree differ diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree index fd08f4f41..c86f4bf25 100644 Binary files a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree and b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree differ diff --git a/master/.doctrees/cleanlab/internal/neighbor/index.doctree b/master/.doctrees/cleanlab/internal/neighbor/index.doctree index 2a7b435d3..8ae072e71 100644 Binary files a/master/.doctrees/cleanlab/internal/neighbor/index.doctree and b/master/.doctrees/cleanlab/internal/neighbor/index.doctree differ diff --git a/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree b/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree index 1e62d13b7..0b1cf46b1 100644 Binary files a/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree and b/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree differ diff --git a/master/.doctrees/cleanlab/internal/neighbor/metric.doctree b/master/.doctrees/cleanlab/internal/neighbor/metric.doctree index cafbb1c22..932138a14 100644 Binary files a/master/.doctrees/cleanlab/internal/neighbor/metric.doctree and b/master/.doctrees/cleanlab/internal/neighbor/metric.doctree differ diff --git a/master/.doctrees/cleanlab/internal/neighbor/search.doctree b/master/.doctrees/cleanlab/internal/neighbor/search.doctree index 06dcdb4e2..e58907802 100644 Binary files a/master/.doctrees/cleanlab/internal/neighbor/search.doctree and b/master/.doctrees/cleanlab/internal/neighbor/search.doctree differ diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index a35db5f97..2a67afdcc 100644 Binary files a/master/.doctrees/cleanlab/internal/outlier.doctree and b/master/.doctrees/cleanlab/internal/outlier.doctree differ diff --git a/master/.doctrees/cleanlab/internal/token_classification_utils.doctree b/master/.doctrees/cleanlab/internal/token_classification_utils.doctree index 956e5ac4a..689e6a4b4 100644 Binary files a/master/.doctrees/cleanlab/internal/token_classification_utils.doctree and b/master/.doctrees/cleanlab/internal/token_classification_utils.doctree differ diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree index 46a43d3bd..b67a94de2 100644 Binary files a/master/.doctrees/cleanlab/internal/util.doctree and b/master/.doctrees/cleanlab/internal/util.doctree differ diff --git a/master/.doctrees/cleanlab/internal/validation.doctree b/master/.doctrees/cleanlab/internal/validation.doctree index 1583d867d..81223f728 100644 Binary files a/master/.doctrees/cleanlab/internal/validation.doctree and b/master/.doctrees/cleanlab/internal/validation.doctree differ diff --git a/master/.doctrees/cleanlab/models/fasttext.doctree b/master/.doctrees/cleanlab/models/fasttext.doctree index db77b1cf8..54fe765bc 100644 Binary files a/master/.doctrees/cleanlab/models/fasttext.doctree and b/master/.doctrees/cleanlab/models/fasttext.doctree differ diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree index b61cf77d3..54b49d0c9 100644 Binary files a/master/.doctrees/cleanlab/models/index.doctree and b/master/.doctrees/cleanlab/models/index.doctree differ diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree index e15a32363..f24ceecfc 100644 Binary files a/master/.doctrees/cleanlab/models/keras.doctree and b/master/.doctrees/cleanlab/models/keras.doctree differ diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree index 2dfc29470..620134ba8 100644 Binary files a/master/.doctrees/cleanlab/multiannotator.doctree and b/master/.doctrees/cleanlab/multiannotator.doctree differ diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree index cf80c6e76..a0e8c007e 100644 Binary files a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree and b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree differ diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree index d41aea481..1a7ca9e30 100644 Binary files a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree and b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree differ diff --git a/master/.doctrees/cleanlab/multilabel_classification/index.doctree b/master/.doctrees/cleanlab/multilabel_classification/index.doctree index 911273f6c..4e57ce061 100644 Binary files a/master/.doctrees/cleanlab/multilabel_classification/index.doctree and b/master/.doctrees/cleanlab/multilabel_classification/index.doctree differ diff --git a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree index b5de209c2..d38021d17 100644 Binary files a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree and b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree differ diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree index 98871ac4d..11baa8f14 100644 Binary files a/master/.doctrees/cleanlab/object_detection/filter.doctree and b/master/.doctrees/cleanlab/object_detection/filter.doctree differ diff --git a/master/.doctrees/cleanlab/object_detection/index.doctree b/master/.doctrees/cleanlab/object_detection/index.doctree index b0d70258e..5f489a614 100644 Binary files a/master/.doctrees/cleanlab/object_detection/index.doctree and b/master/.doctrees/cleanlab/object_detection/index.doctree differ diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index 12e5e8093..2c5ecb97c 100644 Binary files a/master/.doctrees/cleanlab/object_detection/rank.doctree and b/master/.doctrees/cleanlab/object_detection/rank.doctree differ diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree index dd2c6af7d..dd2cc837f 100644 Binary files a/master/.doctrees/cleanlab/object_detection/summary.doctree and b/master/.doctrees/cleanlab/object_detection/summary.doctree differ diff --git a/master/.doctrees/cleanlab/outlier.doctree b/master/.doctrees/cleanlab/outlier.doctree index be21abad2..236f97966 100644 Binary files a/master/.doctrees/cleanlab/outlier.doctree and b/master/.doctrees/cleanlab/outlier.doctree differ diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree index 9e5500b96..11b2bb3cb 100644 Binary files a/master/.doctrees/cleanlab/rank.doctree and b/master/.doctrees/cleanlab/rank.doctree differ diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree index f86cf34ce..71283bb30 100644 Binary files a/master/.doctrees/cleanlab/regression/index.doctree and b/master/.doctrees/cleanlab/regression/index.doctree differ diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree index 7bc42e086..80abbf89c 100644 Binary files a/master/.doctrees/cleanlab/regression/learn.doctree and b/master/.doctrees/cleanlab/regression/learn.doctree differ diff --git a/master/.doctrees/cleanlab/regression/rank.doctree b/master/.doctrees/cleanlab/regression/rank.doctree index 504424416..53867e3ed 100644 Binary files a/master/.doctrees/cleanlab/regression/rank.doctree and b/master/.doctrees/cleanlab/regression/rank.doctree differ diff --git a/master/.doctrees/cleanlab/segmentation/filter.doctree b/master/.doctrees/cleanlab/segmentation/filter.doctree index 8d836875b..380acc620 100644 Binary files a/master/.doctrees/cleanlab/segmentation/filter.doctree and b/master/.doctrees/cleanlab/segmentation/filter.doctree differ diff --git a/master/.doctrees/cleanlab/segmentation/index.doctree b/master/.doctrees/cleanlab/segmentation/index.doctree index 145a4abd4..f0c53b8df 100644 Binary files a/master/.doctrees/cleanlab/segmentation/index.doctree and b/master/.doctrees/cleanlab/segmentation/index.doctree differ diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree index be999a02b..ba46ae6f8 100644 Binary files a/master/.doctrees/cleanlab/segmentation/rank.doctree and b/master/.doctrees/cleanlab/segmentation/rank.doctree differ diff --git a/master/.doctrees/cleanlab/segmentation/summary.doctree b/master/.doctrees/cleanlab/segmentation/summary.doctree index 8e6e1ab39..eb015f3b4 100644 Binary files a/master/.doctrees/cleanlab/segmentation/summary.doctree and b/master/.doctrees/cleanlab/segmentation/summary.doctree differ diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree index fbafed6d1..9d8d729bb 100644 Binary files a/master/.doctrees/cleanlab/token_classification/filter.doctree and b/master/.doctrees/cleanlab/token_classification/filter.doctree differ diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index a4003b367..35a83f837 100644 Binary files a/master/.doctrees/cleanlab/token_classification/index.doctree and b/master/.doctrees/cleanlab/token_classification/index.doctree differ diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index 4b5f191e0..8bf1f8be0 100644 Binary files a/master/.doctrees/cleanlab/token_classification/rank.doctree and b/master/.doctrees/cleanlab/token_classification/rank.doctree differ diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree index e6738df0f..657b757c7 100644 Binary files a/master/.doctrees/cleanlab/token_classification/summary.doctree and b/master/.doctrees/cleanlab/token_classification/summary.doctree differ diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle index 9a3aebce5..709e2a87a 100644 Binary files a/master/.doctrees/environment.pickle and b/master/.doctrees/environment.pickle differ diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index fb0d88c54..c0cd39dc3 100644 Binary files a/master/.doctrees/index.doctree and b/master/.doctrees/index.doctree differ diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index ac1eecb0d..813266fcf 100644 Binary files a/master/.doctrees/migrating/migrate_v2.doctree and b/master/.doctrees/migrating/migrate_v2.doctree differ diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb index cc8956d28..f3d888536 100644 --- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb @@ -113,10 +113,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:27.766466Z", - "iopub.status.busy": "2024-06-25T19:31:27.766073Z", - "iopub.status.idle": "2024-06-25T19:31:28.950995Z", - "shell.execute_reply": "2024-06-25T19:31:28.950453Z" + "iopub.execute_input": "2024-06-25T23:13:19.683650Z", + "iopub.status.busy": "2024-06-25T23:13:19.683483Z", + "iopub.status.idle": "2024-06-25T23:13:20.876411Z", + "shell.execute_reply": "2024-06-25T23:13:20.875863Z" }, "nbsphinx": "hidden" }, @@ -126,7 +126,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -151,10 +151,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:28.953618Z", - "iopub.status.busy": "2024-06-25T19:31:28.953345Z", - "iopub.status.idle": "2024-06-25T19:31:28.970797Z", - "shell.execute_reply": "2024-06-25T19:31:28.970252Z" + "iopub.execute_input": "2024-06-25T23:13:20.879016Z", + "iopub.status.busy": "2024-06-25T23:13:20.878582Z", + "iopub.status.idle": "2024-06-25T23:13:20.895831Z", + "shell.execute_reply": "2024-06-25T23:13:20.895402Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:28.973223Z", - "iopub.status.busy": "2024-06-25T19:31:28.972835Z", - "iopub.status.idle": "2024-06-25T19:31:29.167625Z", - "shell.execute_reply": "2024-06-25T19:31:29.167053Z" + "iopub.execute_input": "2024-06-25T23:13:20.897855Z", + "iopub.status.busy": "2024-06-25T23:13:20.897628Z", + "iopub.status.idle": "2024-06-25T23:13:21.010572Z", + "shell.execute_reply": "2024-06-25T23:13:21.009996Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.197486Z", - "iopub.status.busy": "2024-06-25T19:31:29.197079Z", - "iopub.status.idle": "2024-06-25T19:31:29.200622Z", - "shell.execute_reply": "2024-06-25T19:31:29.200145Z" + "iopub.execute_input": "2024-06-25T23:13:21.037181Z", + "iopub.status.busy": "2024-06-25T23:13:21.036568Z", + "iopub.status.idle": "2024-06-25T23:13:21.040405Z", + "shell.execute_reply": "2024-06-25T23:13:21.039967Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.202620Z", - "iopub.status.busy": "2024-06-25T19:31:29.202441Z", - "iopub.status.idle": "2024-06-25T19:31:29.210646Z", - "shell.execute_reply": "2024-06-25T19:31:29.210233Z" + "iopub.execute_input": "2024-06-25T23:13:21.042333Z", + "iopub.status.busy": "2024-06-25T23:13:21.042161Z", + "iopub.status.idle": "2024-06-25T23:13:21.050408Z", + "shell.execute_reply": "2024-06-25T23:13:21.049993Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.212637Z", - "iopub.status.busy": "2024-06-25T19:31:29.212443Z", - "iopub.status.idle": "2024-06-25T19:31:29.214911Z", - "shell.execute_reply": "2024-06-25T19:31:29.214495Z" + "iopub.execute_input": "2024-06-25T23:13:21.052411Z", + "iopub.status.busy": "2024-06-25T23:13:21.052111Z", + "iopub.status.idle": "2024-06-25T23:13:21.054810Z", + "shell.execute_reply": "2024-06-25T23:13:21.054263Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.216761Z", - "iopub.status.busy": "2024-06-25T19:31:29.216593Z", - "iopub.status.idle": "2024-06-25T19:31:29.731597Z", - "shell.execute_reply": "2024-06-25T19:31:29.730952Z" + "iopub.execute_input": "2024-06-25T23:13:21.056799Z", + "iopub.status.busy": "2024-06-25T23:13:21.056479Z", + "iopub.status.idle": "2024-06-25T23:13:21.584928Z", + "shell.execute_reply": "2024-06-25T23:13:21.584385Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.733935Z", - "iopub.status.busy": "2024-06-25T19:31:29.733740Z", - "iopub.status.idle": "2024-06-25T19:31:31.552423Z", - "shell.execute_reply": "2024-06-25T19:31:31.551801Z" + "iopub.execute_input": "2024-06-25T23:13:21.587427Z", + "iopub.status.busy": "2024-06-25T23:13:21.587080Z", + "iopub.status.idle": "2024-06-25T23:13:23.402116Z", + "shell.execute_reply": "2024-06-25T23:13:23.401472Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.554814Z", - "iopub.status.busy": "2024-06-25T19:31:31.554296Z", - "iopub.status.idle": "2024-06-25T19:31:31.564323Z", - "shell.execute_reply": "2024-06-25T19:31:31.563854Z" + "iopub.execute_input": "2024-06-25T23:13:23.404837Z", + "iopub.status.busy": "2024-06-25T23:13:23.404191Z", + "iopub.status.idle": "2024-06-25T23:13:23.414068Z", + "shell.execute_reply": "2024-06-25T23:13:23.413559Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.566389Z", - "iopub.status.busy": "2024-06-25T19:31:31.566065Z", - "iopub.status.idle": "2024-06-25T19:31:31.570002Z", - "shell.execute_reply": "2024-06-25T19:31:31.569569Z" + "iopub.execute_input": "2024-06-25T23:13:23.416257Z", + "iopub.status.busy": "2024-06-25T23:13:23.415941Z", + "iopub.status.idle": "2024-06-25T23:13:23.420056Z", + "shell.execute_reply": "2024-06-25T23:13:23.419521Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.572029Z", - "iopub.status.busy": "2024-06-25T19:31:31.571709Z", - "iopub.status.idle": "2024-06-25T19:31:31.579030Z", - "shell.execute_reply": "2024-06-25T19:31:31.578475Z" + "iopub.execute_input": "2024-06-25T23:13:23.422287Z", + "iopub.status.busy": "2024-06-25T23:13:23.421904Z", + "iopub.status.idle": "2024-06-25T23:13:23.429186Z", + "shell.execute_reply": "2024-06-25T23:13:23.428630Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.581187Z", - "iopub.status.busy": "2024-06-25T19:31:31.580887Z", - "iopub.status.idle": "2024-06-25T19:31:31.691824Z", - "shell.execute_reply": "2024-06-25T19:31:31.691204Z" + "iopub.execute_input": "2024-06-25T23:13:23.431342Z", + "iopub.status.busy": "2024-06-25T23:13:23.431023Z", + "iopub.status.idle": "2024-06-25T23:13:23.542534Z", + "shell.execute_reply": "2024-06-25T23:13:23.542044Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.694170Z", - "iopub.status.busy": "2024-06-25T19:31:31.693686Z", - "iopub.status.idle": "2024-06-25T19:31:31.696628Z", - "shell.execute_reply": "2024-06-25T19:31:31.696102Z" + "iopub.execute_input": "2024-06-25T23:13:23.544624Z", + "iopub.status.busy": "2024-06-25T23:13:23.544286Z", + "iopub.status.idle": "2024-06-25T23:13:23.546943Z", + "shell.execute_reply": "2024-06-25T23:13:23.546515Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.698847Z", - "iopub.status.busy": "2024-06-25T19:31:31.698415Z", - "iopub.status.idle": "2024-06-25T19:31:33.679358Z", - "shell.execute_reply": "2024-06-25T19:31:33.678623Z" + "iopub.execute_input": "2024-06-25T23:13:23.548943Z", + "iopub.status.busy": "2024-06-25T23:13:23.548635Z", + "iopub.status.idle": "2024-06-25T23:13:25.510005Z", + "shell.execute_reply": "2024-06-25T23:13:25.509395Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:33.682516Z", - "iopub.status.busy": "2024-06-25T19:31:33.681890Z", - "iopub.status.idle": "2024-06-25T19:31:33.693245Z", - "shell.execute_reply": "2024-06-25T19:31:33.692694Z" + "iopub.execute_input": "2024-06-25T23:13:25.513097Z", + "iopub.status.busy": "2024-06-25T23:13:25.512371Z", + "iopub.status.idle": "2024-06-25T23:13:25.523496Z", + "shell.execute_reply": "2024-06-25T23:13:25.522944Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:33.695397Z", - "iopub.status.busy": "2024-06-25T19:31:33.695096Z", - "iopub.status.idle": "2024-06-25T19:31:33.841440Z", - "shell.execute_reply": "2024-06-25T19:31:33.840949Z" + "iopub.execute_input": "2024-06-25T23:13:25.525641Z", + "iopub.status.busy": "2024-06-25T23:13:25.525323Z", + "iopub.status.idle": "2024-06-25T23:13:25.545176Z", + "shell.execute_reply": "2024-06-25T23:13:25.544739Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb index a83013185..9af680b6f 100644 --- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:37.218802Z", - "iopub.status.busy": "2024-06-25T19:31:37.218626Z", - "iopub.status.idle": "2024-06-25T19:31:40.132819Z", - "shell.execute_reply": "2024-06-25T19:31:40.132198Z" + "iopub.execute_input": "2024-06-25T23:13:28.905676Z", + "iopub.status.busy": "2024-06-25T23:13:28.905503Z", + "iopub.status.idle": "2024-06-25T23:13:31.555296Z", + "shell.execute_reply": "2024-06-25T23:13:31.554730Z" }, "nbsphinx": "hidden" }, @@ -135,7 +135,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -160,10 +160,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.135382Z", - "iopub.status.busy": "2024-06-25T19:31:40.135098Z", - "iopub.status.idle": "2024-06-25T19:31:40.138344Z", - "shell.execute_reply": "2024-06-25T19:31:40.137917Z" + "iopub.execute_input": "2024-06-25T23:13:31.557860Z", + "iopub.status.busy": "2024-06-25T23:13:31.557469Z", + "iopub.status.idle": "2024-06-25T23:13:31.560897Z", + "shell.execute_reply": "2024-06-25T23:13:31.560352Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.140291Z", - "iopub.status.busy": "2024-06-25T19:31:40.139985Z", - "iopub.status.idle": "2024-06-25T19:31:40.143618Z", - "shell.execute_reply": "2024-06-25T19:31:40.143162Z" + "iopub.execute_input": "2024-06-25T23:13:31.562942Z", + "iopub.status.busy": "2024-06-25T23:13:31.562629Z", + "iopub.status.idle": "2024-06-25T23:13:31.565542Z", + "shell.execute_reply": "2024-06-25T23:13:31.565096Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.145468Z", - "iopub.status.busy": "2024-06-25T19:31:40.145298Z", - "iopub.status.idle": "2024-06-25T19:31:40.303499Z", - "shell.execute_reply": "2024-06-25T19:31:40.302894Z" + "iopub.execute_input": "2024-06-25T23:13:31.567524Z", + "iopub.status.busy": "2024-06-25T23:13:31.567195Z", + "iopub.status.idle": "2024-06-25T23:13:31.589244Z", + "shell.execute_reply": "2024-06-25T23:13:31.588737Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.305557Z", - "iopub.status.busy": "2024-06-25T19:31:40.305379Z", - "iopub.status.idle": "2024-06-25T19:31:40.309091Z", - "shell.execute_reply": "2024-06-25T19:31:40.308646Z" + "iopub.execute_input": "2024-06-25T23:13:31.591105Z", + "iopub.status.busy": "2024-06-25T23:13:31.590840Z", + "iopub.status.idle": "2024-06-25T23:13:31.594215Z", + "shell.execute_reply": "2024-06-25T23:13:31.593789Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.311111Z", - "iopub.status.busy": "2024-06-25T19:31:40.310718Z", - "iopub.status.idle": "2024-06-25T19:31:40.314252Z", - "shell.execute_reply": "2024-06-25T19:31:40.313796Z" + "iopub.execute_input": "2024-06-25T23:13:31.596064Z", + "iopub.status.busy": "2024-06-25T23:13:31.595883Z", + "iopub.status.idle": "2024-06-25T23:13:31.599153Z", + "shell.execute_reply": "2024-06-25T23:13:31.598670Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'change_pin', 'apple_pay_or_google_pay', 'cancel_transfer', 'lost_or_stolen_phone', 'visa_or_mastercard'}\n" + "Classes: {'card_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_payment_fee_charged'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.316289Z", - "iopub.status.busy": "2024-06-25T19:31:40.315953Z", - "iopub.status.idle": "2024-06-25T19:31:40.318817Z", - "shell.execute_reply": "2024-06-25T19:31:40.318324Z" + "iopub.execute_input": "2024-06-25T23:13:31.601175Z", + "iopub.status.busy": "2024-06-25T23:13:31.600751Z", + "iopub.status.idle": "2024-06-25T23:13:31.603901Z", + "shell.execute_reply": "2024-06-25T23:13:31.603365Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.320894Z", - "iopub.status.busy": "2024-06-25T19:31:40.320580Z", - "iopub.status.idle": "2024-06-25T19:31:40.323708Z", - "shell.execute_reply": "2024-06-25T19:31:40.323263Z" + "iopub.execute_input": "2024-06-25T23:13:31.606046Z", + "iopub.status.busy": "2024-06-25T23:13:31.605618Z", + "iopub.status.idle": "2024-06-25T23:13:31.608973Z", + "shell.execute_reply": "2024-06-25T23:13:31.608424Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.325657Z", - "iopub.status.busy": "2024-06-25T19:31:40.325357Z", - "iopub.status.idle": "2024-06-25T19:31:46.067731Z", - "shell.execute_reply": "2024-06-25T19:31:46.067125Z" + "iopub.execute_input": "2024-06-25T23:13:31.610942Z", + "iopub.status.busy": "2024-06-25T23:13:31.610641Z", + "iopub.status.idle": "2024-06-25T23:13:35.909329Z", + "shell.execute_reply": "2024-06-25T23:13:35.908695Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e9ebd3cab6ee4b38af6e19b1c2a2b7a0", + "model_id": "6477ae421e3e43aa814150445e014ac0", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8fe72969fe348a99c98be80dccd6c53", + "model_id": "e62034a9e6c043cc997861592486168a", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e5e14c62e1a4cf09b6fb8b0bb5ca451", + "model_id": "1e2c610098e54fea94839bb48d055f22", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "293b01a69e094447aeebb1e7e866fd51", + "model_id": "5b6e029c7f61484d880737df09cb3291", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9bbf8e629233461d84330aac6c38bc36", + "model_id": "e750160df873483c9096b064baeab112", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38046751c5324a119490bbe8a5ec326c", + "model_id": "ddf577727f0e42a1b074bcb455a4258a", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f84e049288f438cbb050b771815ee1a", + "model_id": "97155a33cf37454f8282b21b3806031d", "version_major": 2, "version_minor": 0 }, @@ -609,10 +609,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:46.070234Z", - "iopub.status.busy": "2024-06-25T19:31:46.070036Z", - "iopub.status.idle": "2024-06-25T19:31:46.072782Z", - "shell.execute_reply": "2024-06-25T19:31:46.072301Z" + "iopub.execute_input": "2024-06-25T23:13:35.912144Z", + "iopub.status.busy": "2024-06-25T23:13:35.911799Z", + "iopub.status.idle": "2024-06-25T23:13:35.914630Z", + "shell.execute_reply": "2024-06-25T23:13:35.914095Z" } }, "outputs": [], @@ -634,10 +634,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:46.074901Z", - "iopub.status.busy": "2024-06-25T19:31:46.074488Z", - "iopub.status.idle": "2024-06-25T19:31:46.077148Z", - "shell.execute_reply": "2024-06-25T19:31:46.076714Z" + "iopub.execute_input": "2024-06-25T23:13:35.916621Z", + "iopub.status.busy": "2024-06-25T23:13:35.916300Z", + "iopub.status.idle": "2024-06-25T23:13:35.918968Z", + "shell.execute_reply": "2024-06-25T23:13:35.918524Z" } }, "outputs": [], @@ -652,10 +652,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:46.078970Z", - "iopub.status.busy": "2024-06-25T19:31:46.078798Z", - "iopub.status.idle": "2024-06-25T19:31:48.698188Z", - "shell.execute_reply": "2024-06-25T19:31:48.697474Z" + "iopub.execute_input": "2024-06-25T23:13:35.920827Z", + "iopub.status.busy": "2024-06-25T23:13:35.920512Z", + "iopub.status.idle": "2024-06-25T23:13:38.614446Z", + "shell.execute_reply": "2024-06-25T23:13:38.613776Z" }, "scrolled": true }, @@ -678,10 +678,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.701194Z", - "iopub.status.busy": "2024-06-25T19:31:48.700490Z", - "iopub.status.idle": "2024-06-25T19:31:48.707774Z", - "shell.execute_reply": "2024-06-25T19:31:48.707224Z" + "iopub.execute_input": "2024-06-25T23:13:38.617773Z", + "iopub.status.busy": "2024-06-25T23:13:38.616881Z", + "iopub.status.idle": "2024-06-25T23:13:38.624576Z", + "shell.execute_reply": "2024-06-25T23:13:38.624128Z" } }, "outputs": [ @@ -782,10 +782,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.709878Z", - "iopub.status.busy": "2024-06-25T19:31:48.709556Z", - "iopub.status.idle": "2024-06-25T19:31:48.713210Z", - "shell.execute_reply": "2024-06-25T19:31:48.712781Z" + "iopub.execute_input": "2024-06-25T23:13:38.626671Z", + "iopub.status.busy": "2024-06-25T23:13:38.626274Z", + "iopub.status.idle": "2024-06-25T23:13:38.630173Z", + "shell.execute_reply": "2024-06-25T23:13:38.629644Z" } }, "outputs": [], @@ -799,10 +799,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.715198Z", - "iopub.status.busy": "2024-06-25T19:31:48.714884Z", - "iopub.status.idle": "2024-06-25T19:31:48.717917Z", - "shell.execute_reply": "2024-06-25T19:31:48.717397Z" + "iopub.execute_input": "2024-06-25T23:13:38.632131Z", + "iopub.status.busy": "2024-06-25T23:13:38.631753Z", + "iopub.status.idle": "2024-06-25T23:13:38.634921Z", + "shell.execute_reply": "2024-06-25T23:13:38.634406Z" } }, "outputs": [ @@ -837,10 +837,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.719920Z", - "iopub.status.busy": "2024-06-25T19:31:48.719615Z", - "iopub.status.idle": "2024-06-25T19:31:48.722447Z", - "shell.execute_reply": "2024-06-25T19:31:48.722011Z" + "iopub.execute_input": "2024-06-25T23:13:38.636934Z", + "iopub.status.busy": "2024-06-25T23:13:38.636530Z", + "iopub.status.idle": "2024-06-25T23:13:38.639393Z", + "shell.execute_reply": "2024-06-25T23:13:38.638964Z" } }, "outputs": [], @@ -860,10 +860,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.724470Z", - "iopub.status.busy": "2024-06-25T19:31:48.724080Z", - "iopub.status.idle": "2024-06-25T19:31:48.730838Z", - "shell.execute_reply": "2024-06-25T19:31:48.730303Z" + "iopub.execute_input": "2024-06-25T23:13:38.641436Z", + "iopub.status.busy": "2024-06-25T23:13:38.641098Z", + "iopub.status.idle": "2024-06-25T23:13:38.647659Z", + "shell.execute_reply": "2024-06-25T23:13:38.647204Z" } }, "outputs": [ @@ -988,10 +988,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.733056Z", - "iopub.status.busy": "2024-06-25T19:31:48.732753Z", - "iopub.status.idle": "2024-06-25T19:31:48.956488Z", - "shell.execute_reply": "2024-06-25T19:31:48.955930Z" + "iopub.execute_input": "2024-06-25T23:13:38.649778Z", + "iopub.status.busy": "2024-06-25T23:13:38.649479Z", + "iopub.status.idle": "2024-06-25T23:13:38.874003Z", + "shell.execute_reply": "2024-06-25T23:13:38.873478Z" }, "scrolled": true }, @@ -1030,10 +1030,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.958874Z", - "iopub.status.busy": "2024-06-25T19:31:48.958385Z", - "iopub.status.idle": "2024-06-25T19:31:49.133338Z", - "shell.execute_reply": "2024-06-25T19:31:49.132807Z" + "iopub.execute_input": "2024-06-25T23:13:38.876436Z", + "iopub.status.busy": "2024-06-25T23:13:38.876051Z", + "iopub.status.idle": "2024-06-25T23:13:39.048915Z", + "shell.execute_reply": "2024-06-25T23:13:39.048432Z" }, "scrolled": true }, @@ -1066,10 +1066,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:49.135767Z", - "iopub.status.busy": "2024-06-25T19:31:49.135370Z", - "iopub.status.idle": "2024-06-25T19:31:49.139272Z", - "shell.execute_reply": "2024-06-25T19:31:49.138786Z" + "iopub.execute_input": "2024-06-25T23:13:39.051340Z", + "iopub.status.busy": "2024-06-25T23:13:39.050964Z", + "iopub.status.idle": "2024-06-25T23:13:39.054620Z", + "shell.execute_reply": "2024-06-25T23:13:39.054132Z" }, "nbsphinx": "hidden" }, @@ -1113,30 +1113,33 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "007989e5e6cd44a5ada30dda1923d065": { + "02a072ed32924507837162ad70da9612": { "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_8b998a37935c4d30856e95a2cd3f1439", - "placeholder": "​", - "style": "IPY_MODEL_200586c52a4549678a949ca57213c920", + "layout": "IPY_MODEL_1aab5456eec04063a5df26734e017b79", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d7c835da9be44280b48466c92cb851d6", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 121kB/s]" + "value": 231508.0 } }, - "054e6f2fd5d14973972b7db9b5e4ed03": { + "03d9becbdd8046bf81525afd016ed573": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1152,23 +1155,53 @@ "description_width": "" } }, - "0bd860a7540947f98823d4a40bdafb86": { + "075ca50b902b46989673d1147902560d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_65b932c28ff3495098c0db8b56dd18de", + "placeholder": "​", + "style": "IPY_MODEL_e098c9499e5142759ba844e4486a6da6", + "tabbable": null, + "tooltip": null, + "value": ".gitattributes: 100%" + } + }, + "0ee27b719c824572adc4d14d07e72cf6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_68a06976e74d4773829c8f1f5d4cc2ab", + "placeholder": "​", + "style": "IPY_MODEL_7bc89cc511714eb3b9db0329b0ad4f47", + "tabbable": null, + "tooltip": null, + "value": "vocab.txt: 100%" } }, - "1047a464f8ad4f08a9c39e32ef88f77b": { + "197c89430b124236a2a7ca6374f1edf0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1221,132 +1254,7 @@ "width": null } }, - "200586c52a4549678a949ca57213c920": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "226f358553304154b872be466bcda2cc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "2606990b2b654e949895c6ea13bace1b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "293b01a69e094447aeebb1e7e866fd51": { - "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_f9e7d310eb764ed9abf54e3695e98058", - "IPY_MODEL_acd50c68e1bb474ca7e642fdbecd2231", - "IPY_MODEL_a4ea888fe4004256885e50c60385b700" - ], - "layout": "IPY_MODEL_91bc4e78a8934597a32b965150f632af", - "tabbable": null, - "tooltip": null - } - }, - "2db6e61311574d2799f341e0f2b280db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e95f58118fca41f79754cf64dc768e55", - "placeholder": "​", - "style": "IPY_MODEL_5d0e81a247724335969fb0549b56194b", - "tabbable": null, - "tooltip": null, - "value": "README.md: 100%" - } - }, - "2e5e14c62e1a4cf09b6fb8b0bb5ca451": { - "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_5ccb993dd72c4674bf4c5129e4e19f7b", - "IPY_MODEL_4105d4a519b74c618417f38830a92826", - "IPY_MODEL_007989e5e6cd44a5ada30dda1923d065" - ], - "layout": "IPY_MODEL_811eb3464f8b4658af7b92cb22fd5db1", - "tabbable": null, - "tooltip": null - } - }, - "2ed7ba0d1cb94817b121646f00c5a089": { + "1a996c0fb30c4181aef76b9d0b39bd20": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1399,33 +1307,7 @@ "width": null } }, - "3156c56a2d1e46779554f6e680768b77": { - "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_6da0de8fbedc40cca53799f8e45cf74b", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b0f309cd65ee48e4902fba4c141424a9", - "tabbable": null, - "tooltip": null, - "value": 466062.0 - } - }, - "371271d754f04722b9ba2102ec6453b3": { + "1aab5456eec04063a5df26734e017b79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1478,31 +1360,7 @@ "width": null } }, - "38046751c5324a119490bbe8a5ec326c": { - "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_684d44672b7e4f3e87afb7ff19a14a7d", - "IPY_MODEL_703a1805a6e14459bff5e7bc196e2924", - "IPY_MODEL_9678a77b61324383a27e704a34123184" - ], - "layout": "IPY_MODEL_7f83efe5fa43477c83b7dd0eab30b115", - "tabbable": null, - "tooltip": null - } - }, - "3b3726b7d15f4242bbe2a0e0323f5edd": { + "1b04ac435f764f288a38e492f152ec20": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1555,25 +1413,54 @@ "width": null } }, - "3c288e9049ba4814a9020bbda1512944": { + "1d787eed2c504168b62c71e3e8b823c8": { "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_c363fe3444424a62b964efb6ec305f7f", + "placeholder": "​", + "style": "IPY_MODEL_d622adb2d6724d848794f6e7c40de0b3", + "tabbable": null, + "tooltip": null, + "value": " 2.21k/2.21k [00:00<00:00, 422kB/s]" + } + }, + "1e2c610098e54fea94839bb48d055f22": { + "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_e7466595e3ae479d9ee9e989b0fa9efb", + "IPY_MODEL_8784e3b2891144468a5d989adaf62ae7", + "IPY_MODEL_a6280db66f3e434bbdb20ebab4989dec" + ], + "layout": "IPY_MODEL_32522284f75645f69b58819ffe34d2a7", + "tabbable": null, + "tooltip": null } }, - "3e906d417956421a978812281c844e7e": { + "20471efe14204b3fa973ce7c173612f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1588,15 +1475,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4c673f7ac4ec44149b7f219298381ca5", + "layout": "IPY_MODEL_5dc7272ce64644afa609af3b2046a38e", "placeholder": "​", - "style": "IPY_MODEL_e3282f6972624465bd15f84c183446ed", + "style": "IPY_MODEL_c220349ac77649e3bffdb0998b40b102", "tabbable": null, "tooltip": null, - "value": " 391/391 [00:00<00:00, 64.5kB/s]" + "value": " 54.2M/54.2M [00:00<00:00, 298MB/s]" } }, - "4105d4a519b74c618417f38830a92826": { + "2808a2dd85f840dc8384e96b6e31d382": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1612,81 +1499,24 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a7fc4b58838f4350b7cce239e6cf7c4d", - "max": 665.0, + "layout": "IPY_MODEL_8f55a6cbd0184173b7c3ecca3b2be6a1", + "max": 466062.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a7755505c8604626b66bd90ac49165ab", + "style": "IPY_MODEL_670785469ef5401e82177c280f877928", "tabbable": null, "tooltip": null, - "value": 665.0 + "value": 466062.0 } }, - "43e54781cf104522a0a7479b4684f176": { - "model_module": "@jupyter-widgets/controls", + "2b78fa97669e4067a4bd3de6ccaaa88a": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "47c94c5344ac480795afc89535d19275": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4c280f6c890c468db24cfa085162f8eb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d98e8d3d73134c40b8256b723de4c9ba", - "placeholder": "​", - "style": "IPY_MODEL_55cc185655a64110894ebe751347ee97", - "tabbable": null, - "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 422kB/s]" - } - }, - "4c673f7ac4ec44149b7f219298381ca5": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -1732,7 +1562,33 @@ "width": null } }, - "52f1b4fab3d74ad180e90f63881f6b32": { + "2e901b505de34984a7c06e4ff7be8ebe": { + "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_ab64f8b595ef4648a47b1b2da8eac241", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_03d9becbdd8046bf81525afd016ed573", + "tabbable": null, + "tooltip": null, + "value": 54245363.0 + } + }, + "2fe0de05b5e847e89ba80b8e4d398c4c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1785,25 +1641,7 @@ "width": null } }, - "55cc185655a64110894ebe751347ee97": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "5c2455143c7b4c9d8179735a17395106": { + "32522284f75645f69b58819ffe34d2a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1856,30 +1694,7 @@ "width": null } }, - "5ccb993dd72c4674bf4c5129e4e19f7b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c693822d0e9f45a5a0c3d5598c8464f4", - "placeholder": "​", - "style": "IPY_MODEL_6c14e14271b4450f89db48de72dffebe", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" - } - }, - "5d0e81a247724335969fb0549b56194b": { + "328a64a36aa14cc4a13e8c232d198977": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1897,7 +1712,7 @@ "text_color": null } }, - "5e1fb144070e4665b06631fd83e8a769": { + "35b5e0b171c7498d8e1a3633c745e073": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1912,84 +1727,77 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2ed7ba0d1cb94817b121646f00c5a089", + "layout": "IPY_MODEL_93beb5b9f7914314ac973bada268bcc2", "placeholder": "​", - "style": "IPY_MODEL_e45f1523c8d6445cacf72f4752609a80", + "style": "IPY_MODEL_71736b2d04e142cea126cbddad9801e2", "tabbable": null, "tooltip": null, - "value": " 466k/466k [00:00<00:00, 3.42MB/s]" + "value": "tokenizer.json: 100%" } }, - "65fb96fdd89c4a7aa6863a61cd8c8281": { + "3c2efa0519364cd7ad5d4149de79bec8": { "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_a2f3a938880745e29369216068f395dc", - "placeholder": "​", - "style": "IPY_MODEL_79bb28d92fc14400815dc3c2c98b31e6", - "tabbable": null, - "tooltip": null, - "value": "tokenizer.json: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "684d44672b7e4f3e87afb7ff19a14a7d": { + "3c700601fd7d4148894afa4961283a88": { "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_69f6ccf772ad48fa86cea5e7b57f6c92", - "placeholder": "​", - "style": "IPY_MODEL_2606990b2b654e949895c6ea13bace1b", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "6854a7c0f0b846a99e2cc07332f5e2ad": { + "42243b174d064f20a73f6c60315575eb": { "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_1047a464f8ad4f08a9c39e32ef88f77b", - "placeholder": "​", - "style": "IPY_MODEL_d22816db94ed4b3aa623fb9f9eb42646", + "layout": "IPY_MODEL_de1cc30d88f74d2299519f8846818183", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6e166f17ea384c918f9777404649330c", "tabbable": null, "tooltip": null, - "value": "vocab.txt: 100%" + "value": 391.0 } }, - "68caf9f94f8b49e29f2522245cfc1967": { + "485477b3f18244cb8e82240fdfc321c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2042,7 +1850,7 @@ "width": null } }, - "68d5eef2d9154625b81ca5a98ad95302": { + "4a7d8e7b7ba3464fa41165f58db05e12": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2095,7 +1903,7 @@ "width": null } }, - "69f6ccf772ad48fa86cea5e7b57f6c92": { + "4d312e2d3f524fb1bf8dba710216c761": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2148,25 +1956,7 @@ "width": null } }, - "6c14e14271b4450f89db48de72dffebe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6d917afdd1bf47ea943ba43a44498f21": { + "537d23f9aefe4d35aa82e5454ab78815": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2219,7 +2009,31 @@ "width": null } }, - "6da0de8fbedc40cca53799f8e45cf74b": { + "5b6e029c7f61484d880737df09cb3291": { + "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_fdddfa973dd841b39245eda900cf0339", + "IPY_MODEL_2e901b505de34984a7c06e4ff7be8ebe", + "IPY_MODEL_20471efe14204b3fa973ce7c173612f3" + ], + "layout": "IPY_MODEL_b0d211d5585d414d93c249182a668daf", + "tabbable": null, + "tooltip": null + } + }, + "5da1ee68b4f848c98bea7614ebf85f59": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2272,69 +2086,7 @@ "width": null } }, - "703a1805a6e14459bff5e7bc196e2924": { - "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_c467bba142d440348c677b9b88920801", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9e515a523986480cbbe1094ca437f513", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } - }, - "72118120122f4623822582e300065947": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "76977b7a679849439d4159579b51c480": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "77399e1417a148bbaaf8c99c6e4b2bb5": { + "5dc7272ce64644afa609af3b2046a38e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2387,7 +2139,7 @@ "width": null } }, - "7930ae36275240238ba62db1afd25ce2": { + "5f117a6515e1436cad7028680580b6a5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2440,7 +2192,7 @@ "width": null } }, - "79bb28d92fc14400815dc3c2c98b31e6": { + "634f7b4ddf0e46f3b98d4b3c9ce87c86": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2458,63 +2210,10 @@ "text_color": null } }, - "7f83efe5fa43477c83b7dd0eab30b115": { - "model_module": "@jupyter-widgets/base", + "6477ae421e3e43aa814150445e014ac0": { + "model_module": "@jupyter-widgets/controls", "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 - } - }, - "7f84e049288f438cbb050b771815ee1a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", @@ -2526,16 +2225,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_6854a7c0f0b846a99e2cc07332f5e2ad", - "IPY_MODEL_f674728a058e4ebc9cda63400ca9a97a", - "IPY_MODEL_b780cdac15fd4ff5bf0b64d9369ef63b" + "IPY_MODEL_075ca50b902b46989673d1147902560d", + "IPY_MODEL_42243b174d064f20a73f6c60315575eb", + "IPY_MODEL_bbe4f79369114ed9839dd57535856697" ], - "layout": "IPY_MODEL_52f1b4fab3d74ad180e90f63881f6b32", + "layout": "IPY_MODEL_1b04ac435f764f288a38e492f152ec20", "tabbable": null, "tooltip": null } }, - "811eb3464f8b4658af7b92cb22fd5db1": { + "65b932c28ff3495098c0db8b56dd18de": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2588,7 +2287,7 @@ "width": null } }, - "8b998a37935c4d30856e95a2cd3f1439": { + "66d43ed9bf3f4ef6aedf76e3d08d36c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2641,7 +2340,7 @@ "width": null } }, - "8f663dc8059d42e4a1c16507affd40e3": { + "670785469ef5401e82177c280f877928": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2657,7 +2356,7 @@ "description_width": "" } }, - "91bc4e78a8934597a32b965150f632af": { + "68a06976e74d4773829c8f1f5d4cc2ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2710,7 +2409,59 @@ "width": null } }, - "9678a77b61324383a27e704a34123184": { + "6e166f17ea384c918f9777404649330c": { + "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": "" + } + }, + "6f576e9c9374443588b496e63f781624": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "71736b2d04e142cea126cbddad9801e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7261cd19786a4a0b95b770c51f033b0c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2725,55 +2476,100 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7930ae36275240238ba62db1afd25ce2", + "layout": "IPY_MODEL_1a996c0fb30c4181aef76b9d0b39bd20", "placeholder": "​", - "style": "IPY_MODEL_226f358553304154b872be466bcda2cc", + "style": "IPY_MODEL_8d98dfc145534a779ad6c609609abd31", "tabbable": null, "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 9.10kB/s]" + "value": " 466k/466k [00:00<00:00, 14.0MB/s]" } }, - "9bbf8e629233461d84330aac6c38bc36": { + "7bc89cc511714eb3b9db0329b0ad4f47": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7dbd67a2cb264612b0d638e5e63a9a07": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8674027fdc534d3791107a43a5e491ab": { + "model_module": "@jupyter-widgets/controls", + "model_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_65fb96fdd89c4a7aa6863a61cd8c8281", - "IPY_MODEL_3156c56a2d1e46779554f6e680768b77", - "IPY_MODEL_5e1fb144070e4665b06631fd83e8a769" - ], - "layout": "IPY_MODEL_5c2455143c7b4c9d8179735a17395106", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dfd5800b7e4c41fb90cf34888c17c960", + "placeholder": "​", + "style": "IPY_MODEL_9bdcb01ac1254e4ca46f786f1f48635e", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "README.md: 100%" } }, - "9e515a523986480cbbe1094ca437f513": { + "8784e3b2891144468a5d989adaf62ae7": { "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_e98f8f44d5174617bc22e566a83f2e14", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9e9b474bbbd44555ac3344b5d173ecd4", + "tabbable": null, + "tooltip": null, + "value": 665.0 } }, - "a2f3a938880745e29369216068f395dc": { + "8c2a54a50b174504b67275f6fc1758ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2826,46 +2622,25 @@ "width": null } }, - "a4ea888fe4004256885e50c60385b700": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_371271d754f04722b9ba2102ec6453b3", - "placeholder": "​", - "style": "IPY_MODEL_72118120122f4623822582e300065947", - "tabbable": null, - "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 137MB/s]" - } - }, - "a7755505c8604626b66bd90ac49165ab": { + "8d98dfc145534a779ad6c609609abd31": { "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 } }, - "a7fc4b58838f4350b7cce239e6cf7c4d": { + "8f55a6cbd0184173b7c3ecca3b2be6a1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2918,31 +2693,7 @@ "width": null } }, - "a8fe72969fe348a99c98be80dccd6c53": { - "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_2db6e61311574d2799f341e0f2b280db", - "IPY_MODEL_c768456d220342ec8cc60cc2528c3732", - "IPY_MODEL_4c280f6c890c468db24cfa085162f8eb" - ], - "layout": "IPY_MODEL_dd1d6b6bcb61451a9984bb2bddd1826e", - "tabbable": null, - "tooltip": null - } - }, - "acd50c68e1bb474ca7e642fdbecd2231": { + "937b241a390b4aa1b76cb3c07905b257": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2958,59 +2709,131 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_68caf9f94f8b49e29f2522245cfc1967", - "max": 54245363.0, + "layout": "IPY_MODEL_4d312e2d3f524fb1bf8dba710216c761", + "max": 48.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_0bd860a7540947f98823d4a40bdafb86", + "style": "IPY_MODEL_b11b3d0461c94c138ecf254a642eb544", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": 48.0 } }, - "b0f309cd65ee48e4902fba4c141424a9": { - "model_module": "@jupyter-widgets/controls", + "93beb5b9f7914314ac973bada268bcc2": { + "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": "" - } - }, - "b780cdac15fd4ff5bf0b64d9369ef63b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d76c41fd805e466081dfeb1d93477c92", - "placeholder": "​", - "style": "IPY_MODEL_76977b7a679849439d4159579b51c480", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 3.55MB/s]" - } - }, - "baa0ee8552c34f9f808dc9985cb43d88": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "97155a33cf37454f8282b21b3806031d": { + "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_0ee27b719c824572adc4d14d07e72cf6", + "IPY_MODEL_02a072ed32924507837162ad70da9612", + "IPY_MODEL_c73c253ce5ce4c0c9744f5c0cb9d0530" + ], + "layout": "IPY_MODEL_bd8af8fcf5f34f848183718d176ce55f", + "tabbable": null, + "tooltip": null + } + }, + "9bdcb01ac1254e4ca46f786f1f48635e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "9e9b474bbbd44555ac3344b5d173ecd4": { + "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": "" + } + }, + "a6280db66f3e434bbdb20ebab4989dec": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", @@ -3022,15 +2845,64 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_77399e1417a148bbaaf8c99c6e4b2bb5", + "layout": "IPY_MODEL_2b78fa97669e4067a4bd3de6ccaaa88a", "placeholder": "​", - "style": "IPY_MODEL_3c288e9049ba4814a9020bbda1512944", + "style": "IPY_MODEL_7dbd67a2cb264612b0d638e5e63a9a07", "tabbable": null, "tooltip": null, - "value": ".gitattributes: 100%" + "value": " 665/665 [00:00<00:00, 129kB/s]" } }, - "c467bba142d440348c677b9b88920801": { + "a859632225cf4c338d075b33f4abd2e0": { + "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_4a7d8e7b7ba3464fa41165f58db05e12", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c7cdf638b52549a3923f4d9f4dacf40d", + "tabbable": null, + "tooltip": null, + "value": 2211.0 + } + }, + "aa703043d8574c99856909333746a558": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_537d23f9aefe4d35aa82e5454ab78815", + "placeholder": "​", + "style": "IPY_MODEL_3c700601fd7d4148894afa4961283a88", + "tabbable": null, + "tooltip": null, + "value": "tokenizer_config.json: 100%" + } + }, + "ab64f8b595ef4648a47b1b2da8eac241": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3083,7 +2955,7 @@ "width": null } }, - "c693822d0e9f45a5a0c3d5598c8464f4": { + "b0d211d5585d414d93c249182a668daf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3136,77 +3008,46 @@ "width": null } }, - "c768456d220342ec8cc60cc2528c3732": { + "b11b3d0461c94c138ecf254a642eb544": { "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_3b3726b7d15f4242bbe2a0e0323f5edd", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_054e6f2fd5d14973972b7db9b5e4ed03", - "tabbable": null, - "tooltip": null, - "value": 2211.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "ca2793614f594cf4a384748bfd36b073": { + "bbe4f79369114ed9839dd57535856697": { "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_ff59fb88d4aa46d481bfabbfb12c3c08", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8f663dc8059d42e4a1c16507affd40e3", + "layout": "IPY_MODEL_f933a441f363402d9fc2e796940cf4d3", + "placeholder": "​", + "style": "IPY_MODEL_328a64a36aa14cc4a13e8c232d198977", "tabbable": null, "tooltip": null, - "value": 391.0 - } - }, - "d22816db94ed4b3aa623fb9f9eb42646": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": " 391/391 [00:00<00:00, 68.8kB/s]" } }, - "d76c41fd805e466081dfeb1d93477c92": { + "bd8af8fcf5f34f848183718d176ce55f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3259,7 +3100,25 @@ "width": null } }, - "d8c1ad41f4fe4cf0a3f44d363086f32b": { + "c220349ac77649e3bffdb0998b40b102": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c363fe3444424a62b964efb6ec305f7f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3312,7 +3171,127 @@ "width": null } }, - "d98e8d3d73134c40b8256b723de4c9ba": { + "c73c253ce5ce4c0c9744f5c0cb9d0530": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_66d43ed9bf3f4ef6aedf76e3d08d36c7", + "placeholder": "​", + "style": "IPY_MODEL_3c2efa0519364cd7ad5d4149de79bec8", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 16.1MB/s]" + } + }, + "c7cdf638b52549a3923f4d9f4dacf40d": { + "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": "" + } + }, + "c9a979755e9a403eb0ca342120826ee6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_197c89430b124236a2a7ca6374f1edf0", + "placeholder": "​", + "style": "IPY_MODEL_634f7b4ddf0e46f3b98d4b3c9ce87c86", + "tabbable": null, + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 9.85kB/s]" + } + }, + "d622adb2d6724d848794f6e7c40de0b3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d7c835da9be44280b48466c92cb851d6": { + "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": "" + } + }, + "ddf577727f0e42a1b074bcb455a4258a": { + "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_aa703043d8574c99856909333746a558", + "IPY_MODEL_937b241a390b4aa1b76cb3c07905b257", + "IPY_MODEL_c9a979755e9a403eb0ca342120826ee6" + ], + "layout": "IPY_MODEL_5f117a6515e1436cad7028680580b6a5", + "tabbable": null, + "tooltip": null + } + }, + "de1cc30d88f74d2299519f8846818183": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3365,7 +3344,7 @@ "width": null } }, - "dd1d6b6bcb61451a9984bb2bddd1826e": { + "dfd5800b7e4c41fb90cf34888c17c960": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3418,7 +3397,7 @@ "width": null } }, - "e3282f6972624465bd15f84c183446ed": { + "e098c9499e5142759ba844e4486a6da6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3436,25 +3415,78 @@ "text_color": null } }, - "e45f1523c8d6445cacf72f4752609a80": { + "e62034a9e6c043cc997861592486168a": { "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_8674027fdc534d3791107a43a5e491ab", + "IPY_MODEL_a859632225cf4c338d075b33f4abd2e0", + "IPY_MODEL_1d787eed2c504168b62c71e3e8b823c8" + ], + "layout": "IPY_MODEL_5da1ee68b4f848c98bea7614ebf85f59", + "tabbable": null, + "tooltip": null } }, - "e95f58118fca41f79754cf64dc768e55": { + "e7466595e3ae479d9ee9e989b0fa9efb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8c2a54a50b174504b67275f6fc1758ea", + "placeholder": "​", + "style": "IPY_MODEL_fd9acfb5facf4aeaa71d2088d344d085", + "tabbable": null, + "tooltip": null, + "value": "config.json: 100%" + } + }, + "e750160df873483c9096b064baeab112": { + "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_35b5e0b171c7498d8e1a3633c745e073", + "IPY_MODEL_2808a2dd85f840dc8384e96b6e31d382", + "IPY_MODEL_7261cd19786a4a0b95b770c51f033b0c" + ], + "layout": "IPY_MODEL_485477b3f18244cb8e82240fdfc321c6", + "tabbable": null, + "tooltip": null + } + }, + "e98f8f44d5174617bc22e566a83f2e14": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3507,80 +3539,7 @@ "width": null } }, - "e9ebd3cab6ee4b38af6e19b1c2a2b7a0": { - "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_baa0ee8552c34f9f808dc9985cb43d88", - "IPY_MODEL_ca2793614f594cf4a384748bfd36b073", - "IPY_MODEL_3e906d417956421a978812281c844e7e" - ], - "layout": "IPY_MODEL_68d5eef2d9154625b81ca5a98ad95302", - "tabbable": null, - "tooltip": null - } - }, - "f674728a058e4ebc9cda63400ca9a97a": { - "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_d8c1ad41f4fe4cf0a3f44d363086f32b", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_43e54781cf104522a0a7479b4684f176", - "tabbable": null, - "tooltip": null, - "value": 231508.0 - } - }, - "f9e7d310eb764ed9abf54e3695e98058": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6d917afdd1bf47ea943ba43a44498f21", - "placeholder": "​", - "style": "IPY_MODEL_47c94c5344ac480795afc89535d19275", - "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" - } - }, - "ff59fb88d4aa46d481bfabbfb12c3c08": { + "f933a441f363402d9fc2e796940cf4d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3632,6 +3591,47 @@ "visibility": null, "width": null } + }, + "fd9acfb5facf4aeaa71d2088d344d085": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fdddfa973dd841b39245eda900cf0339": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2fe0de05b5e847e89ba80b8e4d398c4c", + "placeholder": "​", + "style": "IPY_MODEL_6f576e9c9374443588b496e63f781624", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb index 411158583..d40b1db54 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:53.133508Z", - "iopub.status.busy": "2024-06-25T19:31:53.133336Z", - "iopub.status.idle": "2024-06-25T19:31:58.248746Z", - "shell.execute_reply": "2024-06-25T19:31:58.248110Z" + "iopub.execute_input": "2024-06-25T23:13:42.048585Z", + "iopub.status.busy": "2024-06-25T23:13:42.048169Z", + "iopub.status.idle": "2024-06-25T23:13:47.015851Z", + "shell.execute_reply": "2024-06-25T23:13:47.015219Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:31:58.251604Z", - "iopub.status.busy": "2024-06-25T19:31:58.251034Z", - "iopub.status.idle": "2024-06-25T19:31:58.254389Z", - "shell.execute_reply": "2024-06-25T19:31:58.253843Z" + "iopub.execute_input": "2024-06-25T23:13:47.018616Z", + "iopub.status.busy": "2024-06-25T23:13:47.018295Z", + "iopub.status.idle": "2024-06-25T23:13:47.021447Z", + "shell.execute_reply": "2024-06-25T23:13:47.020989Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:58.256549Z", - "iopub.status.busy": "2024-06-25T19:31:58.256239Z", - "iopub.status.idle": "2024-06-25T19:31:58.260899Z", - "shell.execute_reply": "2024-06-25T19:31:58.260338Z" + "iopub.execute_input": "2024-06-25T23:13:47.023397Z", + "iopub.status.busy": "2024-06-25T23:13:47.023066Z", + "iopub.status.idle": "2024-06-25T23:13:47.027579Z", + "shell.execute_reply": "2024-06-25T23:13:47.027038Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T19:31:58.263210Z", - "iopub.status.busy": "2024-06-25T19:31:58.262770Z", - "iopub.status.idle": "2024-06-25T19:32:00.256796Z", - "shell.execute_reply": "2024-06-25T19:32:00.256144Z" + "iopub.execute_input": "2024-06-25T23:13:47.029706Z", + "iopub.status.busy": "2024-06-25T23:13:47.029408Z", + "iopub.status.idle": "2024-06-25T23:13:48.557949Z", + "shell.execute_reply": "2024-06-25T23:13:48.557324Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:00.259356Z", - "iopub.status.busy": "2024-06-25T19:32:00.259045Z", - "iopub.status.idle": "2024-06-25T19:32:00.269498Z", - "shell.execute_reply": "2024-06-25T19:32:00.269022Z" + "iopub.execute_input": "2024-06-25T23:13:48.560586Z", + "iopub.status.busy": "2024-06-25T23:13:48.560204Z", + "iopub.status.idle": "2024-06-25T23:13:48.570753Z", + "shell.execute_reply": "2024-06-25T23:13:48.570316Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:00.271550Z", - "iopub.status.busy": "2024-06-25T19:32:00.271221Z", - "iopub.status.idle": "2024-06-25T19:32:00.276417Z", - "shell.execute_reply": "2024-06-25T19:32:00.275932Z" + "iopub.execute_input": "2024-06-25T23:13:48.572948Z", + "iopub.status.busy": "2024-06-25T23:13:48.572614Z", + "iopub.status.idle": "2024-06-25T23:13:48.578335Z", + "shell.execute_reply": "2024-06-25T23:13:48.577906Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:00.278484Z", - "iopub.status.busy": "2024-06-25T19:32:00.278163Z", - "iopub.status.idle": "2024-06-25T19:32:00.762955Z", - "shell.execute_reply": "2024-06-25T19:32:00.762362Z" + "iopub.execute_input": "2024-06-25T23:13:48.580333Z", + "iopub.status.busy": "2024-06-25T23:13:48.580014Z", + "iopub.status.idle": "2024-06-25T23:13:49.044116Z", + "shell.execute_reply": "2024-06-25T23:13:49.043554Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:00.765136Z", - "iopub.status.busy": "2024-06-25T19:32:00.764811Z", - "iopub.status.idle": "2024-06-25T19:32:03.050183Z", - "shell.execute_reply": "2024-06-25T19:32:03.049698Z" + "iopub.execute_input": "2024-06-25T23:13:49.046459Z", + "iopub.status.busy": "2024-06-25T23:13:49.046048Z", + "iopub.status.idle": "2024-06-25T23:13:49.682286Z", + "shell.execute_reply": "2024-06-25T23:13:49.681791Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:03.052760Z", - "iopub.status.busy": "2024-06-25T19:32:03.052414Z", - "iopub.status.idle": "2024-06-25T19:32:03.070136Z", - "shell.execute_reply": "2024-06-25T19:32:03.069620Z" + "iopub.execute_input": "2024-06-25T23:13:49.685227Z", + "iopub.status.busy": "2024-06-25T23:13:49.684826Z", + "iopub.status.idle": "2024-06-25T23:13:49.703315Z", + "shell.execute_reply": "2024-06-25T23:13:49.702790Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:03.072148Z", - "iopub.status.busy": "2024-06-25T19:32:03.071949Z", - "iopub.status.idle": "2024-06-25T19:32:03.075039Z", - "shell.execute_reply": "2024-06-25T19:32:03.074605Z" + "iopub.execute_input": "2024-06-25T23:13:49.705384Z", + "iopub.status.busy": "2024-06-25T23:13:49.705205Z", + "iopub.status.idle": "2024-06-25T23:13:49.708482Z", + "shell.execute_reply": "2024-06-25T23:13:49.708013Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:03.077054Z", - "iopub.status.busy": "2024-06-25T19:32:03.076730Z", - "iopub.status.idle": "2024-06-25T19:32:17.091941Z", - "shell.execute_reply": "2024-06-25T19:32:17.091336Z" + "iopub.execute_input": "2024-06-25T23:13:49.710490Z", + "iopub.status.busy": "2024-06-25T23:13:49.710159Z", + "iopub.status.idle": "2024-06-25T23:14:03.836426Z", + "shell.execute_reply": "2024-06-25T23:14:03.835865Z" }, "id": "2FSQ2GR9R_YA" }, @@ -627,10 +627,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.094601Z", - "iopub.status.busy": "2024-06-25T19:32:17.094212Z", - "iopub.status.idle": "2024-06-25T19:32:17.098282Z", - "shell.execute_reply": "2024-06-25T19:32:17.097813Z" + "iopub.execute_input": "2024-06-25T23:14:03.839037Z", + "iopub.status.busy": "2024-06-25T23:14:03.838661Z", + "iopub.status.idle": "2024-06-25T23:14:03.842744Z", + "shell.execute_reply": "2024-06-25T23:14:03.842282Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.100415Z", - "iopub.status.busy": "2024-06-25T19:32:17.100030Z", - "iopub.status.idle": "2024-06-25T19:32:17.781950Z", - "shell.execute_reply": "2024-06-25T19:32:17.781387Z" + "iopub.execute_input": "2024-06-25T23:14:03.844717Z", + "iopub.status.busy": "2024-06-25T23:14:03.844392Z", + "iopub.status.idle": "2024-06-25T23:14:04.554198Z", + "shell.execute_reply": "2024-06-25T23:14:04.553609Z" }, "id": "i_drkY9YOcw4" }, @@ -727,10 +727,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.785604Z", - "iopub.status.busy": "2024-06-25T19:32:17.784675Z", - "iopub.status.idle": "2024-06-25T19:32:17.791417Z", - "shell.execute_reply": "2024-06-25T19:32:17.790891Z" + "iopub.execute_input": "2024-06-25T23:14:04.557144Z", + "iopub.status.busy": "2024-06-25T23:14:04.556722Z", + "iopub.status.idle": "2024-06-25T23:14:04.561566Z", + "shell.execute_reply": "2024-06-25T23:14:04.561058Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -777,10 +777,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.794921Z", - "iopub.status.busy": "2024-06-25T19:32:17.794005Z", - "iopub.status.idle": "2024-06-25T19:32:17.890859Z", - "shell.execute_reply": "2024-06-25T19:32:17.890238Z" + "iopub.execute_input": "2024-06-25T23:14:04.563987Z", + "iopub.status.busy": "2024-06-25T23:14:04.563613Z", + "iopub.status.idle": "2024-06-25T23:14:04.661144Z", + "shell.execute_reply": "2024-06-25T23:14:04.660555Z" } }, "outputs": [ @@ -817,10 +817,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.893215Z", - "iopub.status.busy": "2024-06-25T19:32:17.892852Z", - "iopub.status.idle": "2024-06-25T19:32:17.904591Z", - "shell.execute_reply": "2024-06-25T19:32:17.904119Z" + "iopub.execute_input": "2024-06-25T23:14:04.663549Z", + "iopub.status.busy": "2024-06-25T23:14:04.663180Z", + "iopub.status.idle": "2024-06-25T23:14:04.675655Z", + "shell.execute_reply": "2024-06-25T23:14:04.675200Z" }, "scrolled": true }, @@ -880,10 +880,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.906706Z", - "iopub.status.busy": "2024-06-25T19:32:17.906385Z", - "iopub.status.idle": "2024-06-25T19:32:17.914138Z", - "shell.execute_reply": "2024-06-25T19:32:17.913686Z" + "iopub.execute_input": "2024-06-25T23:14:04.677803Z", + "iopub.status.busy": "2024-06-25T23:14:04.677480Z", + "iopub.status.idle": "2024-06-25T23:14:04.685163Z", + "shell.execute_reply": "2024-06-25T23:14:04.684654Z" } }, "outputs": [ @@ -987,10 +987,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.916135Z", - "iopub.status.busy": "2024-06-25T19:32:17.915799Z", - "iopub.status.idle": "2024-06-25T19:32:17.919765Z", - "shell.execute_reply": "2024-06-25T19:32:17.919222Z" + "iopub.execute_input": "2024-06-25T23:14:04.687288Z", + "iopub.status.busy": "2024-06-25T23:14:04.686965Z", + "iopub.status.idle": "2024-06-25T23:14:04.691175Z", + "shell.execute_reply": "2024-06-25T23:14:04.690734Z" } }, "outputs": [ @@ -1028,10 +1028,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.921837Z", - "iopub.status.busy": "2024-06-25T19:32:17.921500Z", - "iopub.status.idle": "2024-06-25T19:32:17.926898Z", - "shell.execute_reply": "2024-06-25T19:32:17.926399Z" + "iopub.execute_input": "2024-06-25T23:14:04.693198Z", + "iopub.status.busy": "2024-06-25T23:14:04.692832Z", + "iopub.status.idle": "2024-06-25T23:14:04.698386Z", + "shell.execute_reply": "2024-06-25T23:14:04.697923Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1158,10 +1158,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.929118Z", - "iopub.status.busy": "2024-06-25T19:32:17.928697Z", - "iopub.status.idle": "2024-06-25T19:32:18.039116Z", - "shell.execute_reply": "2024-06-25T19:32:18.038547Z" + "iopub.execute_input": "2024-06-25T23:14:04.700375Z", + "iopub.status.busy": "2024-06-25T23:14:04.700060Z", + "iopub.status.idle": "2024-06-25T23:14:04.810591Z", + "shell.execute_reply": "2024-06-25T23:14:04.810025Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1215,10 +1215,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:18.041334Z", - "iopub.status.busy": "2024-06-25T19:32:18.040985Z", - "iopub.status.idle": "2024-06-25T19:32:18.143028Z", - "shell.execute_reply": "2024-06-25T19:32:18.142549Z" + "iopub.execute_input": "2024-06-25T23:14:04.812699Z", + "iopub.status.busy": "2024-06-25T23:14:04.812492Z", + "iopub.status.idle": "2024-06-25T23:14:04.914526Z", + "shell.execute_reply": "2024-06-25T23:14:04.913959Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1263,10 +1263,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:18.145023Z", - "iopub.status.busy": "2024-06-25T19:32:18.144735Z", - "iopub.status.idle": "2024-06-25T19:32:18.245208Z", - "shell.execute_reply": "2024-06-25T19:32:18.244749Z" + "iopub.execute_input": "2024-06-25T23:14:04.916775Z", + "iopub.status.busy": "2024-06-25T23:14:04.916438Z", + "iopub.status.idle": "2024-06-25T23:14:05.017283Z", + "shell.execute_reply": "2024-06-25T23:14:05.016746Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1307,10 +1307,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:18.247314Z", - "iopub.status.busy": "2024-06-25T19:32:18.246984Z", - "iopub.status.idle": "2024-06-25T19:32:18.345698Z", - "shell.execute_reply": "2024-06-25T19:32:18.345236Z" + "iopub.execute_input": "2024-06-25T23:14:05.019268Z", + "iopub.status.busy": "2024-06-25T23:14:05.019087Z", + "iopub.status.idle": "2024-06-25T23:14:05.125584Z", + "shell.execute_reply": "2024-06-25T23:14:05.124990Z" } }, "outputs": [ @@ -1358,10 +1358,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:18.347691Z", - "iopub.status.busy": "2024-06-25T19:32:18.347361Z", - "iopub.status.idle": "2024-06-25T19:32:18.350505Z", - "shell.execute_reply": "2024-06-25T19:32:18.349977Z" + "iopub.execute_input": "2024-06-25T23:14:05.127830Z", + "iopub.status.busy": "2024-06-25T23:14:05.127392Z", + "iopub.status.idle": "2024-06-25T23:14:05.130680Z", + "shell.execute_reply": "2024-06-25T23:14:05.130142Z" }, "nbsphinx": "hidden" }, @@ -1402,30 +1402,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0160f38ecac34b8a9a6e60b79941cb42": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6c2f4eab88df4fcfbf60ea1461b407fb", - "placeholder": "​", - "style": "IPY_MODEL_47f71961c8254da1a0cb8fe2f90ef9f5", - "tabbable": null, - "tooltip": null, - "value": "classifier.ckpt: 100%" - } - }, - "09e511f313fe4deaa3db651dc2ad1f38": { + "01d773c5fc084c74bf3898f08469f8f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1478,7 +1455,7 @@ "width": null } }, - "0f81bdbdfded4d61a103fbc884fdb8b7": { + "048952d61d4143eeb7a3064dac9fe4ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1531,83 +1508,69 @@ "width": null } }, - "13b1cd45cfea4f85bfb6caa46af5a63a": { - "model_module": "@jupyter-widgets/base", + "065d70114b74423d9f2fa424de1b7a1e": { + "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 } }, - "1965785719184160b13d07134d9c01cd": { + "0c2f7ca60ab147c99ae03f759db46297": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "11546fd8d68648da9c138662014585e5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9be1debd539947d495510d3e71e91a33", - "placeholder": "​", - "style": "IPY_MODEL_9593182600264e2aa5c5035fced2937b", + "layout": "IPY_MODEL_048952d61d4143eeb7a3064dac9fe4ac", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cd596207fea4447b96658e0f41eec607", "tabbable": null, "tooltip": null, - "value": "label_encoder.txt: 100%" + "value": 128619.0 } }, - "1aea1d112c054eec950bd686a0ca1d14": { + "12f57656dd504d218c054ef511da9ab4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1660,30 +1623,7 @@ "width": null } }, - "1b43dce850cb49019716fe7f052cd3f9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7b142eabfac84901b7edb8e5cf1db87e", - "placeholder": "​", - "style": "IPY_MODEL_751e8bbb861243c0b292b81647f5a7c8", - "tabbable": null, - "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 32.9MB/s]" - } - }, - "1c18c2753518415ea3cf39b985a054ad": { + "14c9df2de7cc4e1bbaf4f1a58d5e3037": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1736,7 +1676,7 @@ "width": null } }, - "295a0df817014d6fbb6377dcc33be2b3": { + "1510d16cc79a49c88e3542daf3152481": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1751,32 +1691,42 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_bfc1a00c4e8f4270af1f947f378411f7", - "IPY_MODEL_dcd54291f5a341bb9379d41671c00d95", - "IPY_MODEL_bec49e231d374b619cca6a3dbb6c9b4f" + "IPY_MODEL_bd56af9d1df24f959c2e27458bf28564", + "IPY_MODEL_b83e50a6f4c94c46b81bc9ccac82d8ce", + "IPY_MODEL_d74890bb53b24653a178d14fa9fb1027" ], - "layout": "IPY_MODEL_5375b29786b24fc182c5b06dc46c21f5", + "layout": "IPY_MODEL_12f57656dd504d218c054ef511da9ab4", "tabbable": null, "tooltip": null } }, - "386b0038b80f44249027e46144d33c35": { + "186fee51c3124d608de6a5c71a289764": { "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_8ca4da4898bb4451994f9434ae2ffbed", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_98428c59f17b40cdb1c0a7543994cae1", + "tabbable": null, + "tooltip": null, + "value": 15856877.0 } }, - "47f71961c8254da1a0cb8fe2f90ef9f5": { + "1dc5d0b009d545d1922ea03e17fa99b3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1794,7 +1744,56 @@ "text_color": null } }, - "48203e358e5e4c599815faec66f84717": { + "1e7ea41fcad145359a3915227cd3a572": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_50e8d183b2e146bfa57d74e4574d6bf6", + "placeholder": "​", + "style": "IPY_MODEL_7936b993c44f45fc8f53ff71690b204e", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 328MB/s]" + } + }, + "227af90915cb483c9537459db20c2dcc": { + "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_78b9bcb39b554c0290d9800dc940ff71", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5869d031c1004d448f247c32b8c702e1", + "tabbable": null, + "tooltip": null, + "value": 2041.0 + } + }, + "3740245a44544188bb5216cfbbe7c096": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1809,16 +1808,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0160f38ecac34b8a9a6e60b79941cb42", - "IPY_MODEL_cfb6db0858744f68a8074c3433dec014", - "IPY_MODEL_1b43dce850cb49019716fe7f052cd3f9" + "IPY_MODEL_ae1c35581029419bac126538cc5ba8f3", + "IPY_MODEL_186fee51c3124d608de6a5c71a289764", + "IPY_MODEL_1e7ea41fcad145359a3915227cd3a572" ], - "layout": "IPY_MODEL_5f9ac971b36144c99f166c6afca8b98c", + "layout": "IPY_MODEL_fe755d796bba4ebfa0a4fd20368887fa", "tabbable": null, "tooltip": null } }, - "5375b29786b24fc182c5b06dc46c21f5": { + "3d0bc58be6a64dbb9402e4f43247bbd9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1871,7 +1870,30 @@ "width": null } }, - "5a7fcc571ae3417ca7f32e5ac2e0f7ed": { + "40d6d738f9e542e98ecc6d953d704d68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ecd21cd3f9be4e8587ead71ef6eddacf", + "placeholder": "​", + "style": "IPY_MODEL_a873413b3495435bb8797c848d57b59a", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 508kB/s]" + } + }, + "4673aa7828d449fdb54b14aa87a8046b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1924,54 +1946,7 @@ "width": null } }, - "5b63774c214c467f836bfdcb44bf9b9f": { - "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_7b1eae32f9cd4561b35edcb6a450623c", - "IPY_MODEL_e4e0fafef42746d1a3f83998a48e68fb", - "IPY_MODEL_b61aaac35e2a498c8712ccc1a79e72b4" - ], - "layout": "IPY_MODEL_13b1cd45cfea4f85bfb6caa46af5a63a", - "tabbable": null, - "tooltip": null - } - }, - "5de7ce2185e149b199420cef3937b004": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d05aa5c2f7ed4f049aeb55cefb06f57f", - "placeholder": "​", - "style": "IPY_MODEL_df36694caa324dc08d075a690e55fed5", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 860kB/s]" - } - }, - "5f9ac971b36144c99f166c6afca8b98c": { + "4bdd74d6b6f54d1fb924effdb6c35e73": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2024,26 +1999,34 @@ "width": null } }, - "67c2bbd993f544be9aaa55f1059ee0c5": { + "4c68ee845d584f78b9ec889e427c9ab8": { "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": "" - } - }, - "6c2f4eab88df4fcfbf60ea1461b407fb": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ab9f826f70f04ba3b63e2e5db1d325f2", + "IPY_MODEL_11546fd8d68648da9c138662014585e5", + "IPY_MODEL_b5a2a7d91c7b447f8108b8896269f607" + ], + "layout": "IPY_MODEL_8954d8479e4b48759b6e0dbf92041c0c", + "tabbable": null, + "tooltip": null + } + }, + "4e926992584a4181a8551035087cce4c": { + "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", @@ -2093,25 +2076,7 @@ "width": null } }, - "751e8bbb861243c0b292b81647f5a7c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "7b142eabfac84901b7edb8e5cf1db87e": { + "50e8d183b2e146bfa57d74e4574d6bf6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2164,30 +2129,39 @@ "width": null } }, - "7b1eae32f9cd4561b35edcb6a450623c": { + "54403dba12ff4f26beb5b09e37a94253": { "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_8a3fcca569d54278b1f243ca468eb2e8", - "placeholder": "​", - "style": "IPY_MODEL_e8bef6ec94b841649328908a1e03f796", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "5869d031c1004d448f247c32b8c702e1": { + "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": "" } }, - "7c6fa0df6a7a4993ae1942f5e66eb942": { + "59bb523ac7f646b8b418eac0ba2650ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2240,7 +2214,7 @@ "width": null } }, - "8a3fcca569d54278b1f243ca468eb2e8": { + "5f72fda67a4f475ebe8560f39a8c3d76": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2293,49 +2267,7 @@ "width": null } }, - "8d0cc257082a482b8a9c20f2623d1dd0": { - "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_d3aabef313ae48b184adb3612e11f7c0", - "IPY_MODEL_9926def0468e4ef78a40becf734e408b", - "IPY_MODEL_5de7ce2185e149b199420cef3937b004" - ], - "layout": "IPY_MODEL_cad71209689044bf95c6a56cd4254c5d", - "tabbable": null, - "tooltip": null - } - }, - "9593182600264e2aa5c5035fced2937b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "95a7d374743e47b785481b53802557f9": { + "67850b52aee848a899a4c1cc7561c6dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2353,33 +2285,7 @@ "text_color": null } }, - "9926def0468e4ef78a40becf734e408b": { - "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_feef464c625b4ce98e733ec61ac7047a", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_67c2bbd993f544be9aaa55f1059ee0c5", - "tabbable": null, - "tooltip": null, - "value": 3201.0 - } - }, - "9be1debd539947d495510d3e71e91a33": { + "78b9bcb39b554c0290d9800dc940ff71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2432,7 +2338,7 @@ "width": null } }, - "b61aaac35e2a498c8712ccc1a79e72b4": { + "7906a23ebb7146d593158f78421f5ed4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2447,77 +2353,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f8e6a2a4f4ac44968d7ab7e8b41cd9e5", + "layout": "IPY_MODEL_4e926992584a4181a8551035087cce4c", "placeholder": "​", - "style": "IPY_MODEL_fd7987d5664246479c5c48a19962bcff", + "style": "IPY_MODEL_065d70114b74423d9f2fa424de1b7a1e", "tabbable": null, "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 33.4MB/s]" + "value": "hyperparams.yaml: 100%" } }, - "bc925d23337a4990bcbb6ad1d3ba0714": { + "7936b993c44f45fc8f53ff71690b204e": { "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": "" - } - }, - "bec49e231d374b619cca6a3dbb6c9b4f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_cec08439b11541149be6a96ed609992c", - "placeholder": "​", - "style": "IPY_MODEL_95a7d374743e47b785481b53802557f9", - "tabbable": null, - "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 514kB/s]" - } - }, - "bfc1a00c4e8f4270af1f947f378411f7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0f81bdbdfded4d61a103fbc884fdb8b7", - "placeholder": "​", - "style": "IPY_MODEL_d7ac88820da9447ebde38eee5408d525", - "tabbable": null, - "tooltip": null, - "value": "hyperparams.yaml: 100%" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "cad71209689044bf95c6a56cd4254c5d": { + "8954d8479e4b48759b6e0dbf92041c0c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2570,7 +2432,7 @@ "width": null } }, - "cec08439b11541149be6a96ed609992c": { + "8ca4da4898bb4451994f9434ae2ffbed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2623,33 +2485,116 @@ "width": null } }, - "cfb6db0858744f68a8074c3433dec014": { + "91f8c32305604aa6ad4753866f6428a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "98428c59f17b40cdb1c0a7543994cae1": { + "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": "" + } + }, + "a172e9a2a9464f0597dc89df0bc665f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a7376840cbcd4df89be2e35d1d83b7a7": { + "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": "" + } + }, + "a873413b3495435bb8797c848d57b59a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ab9f826f70f04ba3b63e2e5db1d325f2": { + "model_module": "@jupyter-widgets/controls", + "model_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_1c18c2753518415ea3cf39b985a054ad", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_bc925d23337a4990bcbb6ad1d3ba0714", + "layout": "IPY_MODEL_01d773c5fc084c74bf3898f08469f8f7", + "placeholder": "​", + "style": "IPY_MODEL_1dc5d0b009d545d1922ea03e17fa99b3", "tabbable": null, "tooltip": null, - "value": 15856877.0 + "value": "label_encoder.txt: 100%" } }, - "d05aa5c2f7ed4f049aeb55cefb06f57f": { + "acd3ca854a9b41b0a7e7c7c976d08dce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2702,7 +2647,7 @@ "width": null } }, - "d3aabef313ae48b184adb3612e11f7c0": { + "ae1c35581029419bac126538cc5ba8f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2717,33 +2662,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_efde3254ff184e58b1e14968ab98b2eb", + "layout": "IPY_MODEL_59bb523ac7f646b8b418eac0ba2650ec", "placeholder": "​", - "style": "IPY_MODEL_e51bddc0c26e45c28f6280a03fa6c570", + "style": "IPY_MODEL_91f8c32305604aa6ad4753866f6428a8", "tabbable": null, "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "d7ac88820da9447ebde38eee5408d525": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": "classifier.ckpt: 100%" } }, - "dc42c71988f64ceba56aa56cc9c662d3": { + "b5a2a7d91c7b447f8108b8896269f607": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2758,15 +2685,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7c6fa0df6a7a4993ae1942f5e66eb942", + "layout": "IPY_MODEL_14c9df2de7cc4e1bbaf4f1a58d5e3037", "placeholder": "​", - "style": "IPY_MODEL_fe2e22def9dd4fb9b35244ac0e211f6d", + "style": "IPY_MODEL_c805d0141fdb44038dfd6b1b6edf7abe", "tabbable": null, "tooltip": null, - "value": " 129k/129k [00:00<00:00, 7.29MB/s]" + "value": " 129k/129k [00:00<00:00, 7.47MB/s]" } }, - "dcd54291f5a341bb9379d41671c00d95": { + "b83e50a6f4c94c46b81bc9ccac82d8ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2782,35 +2709,93 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1aea1d112c054eec950bd686a0ca1d14", - "max": 2041.0, + "layout": "IPY_MODEL_acd3ca854a9b41b0a7e7c7c976d08dce", + "max": 16887676.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e83b02ee750444739a17f78b4d3f184f", + "style": "IPY_MODEL_a7376840cbcd4df89be2e35d1d83b7a7", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": 16887676.0 } }, - "df36694caa324dc08d075a690e55fed5": { + "bd56af9d1df24f959c2e27458bf28564": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3d0bc58be6a64dbb9402e4f43247bbd9", + "placeholder": "​", + "style": "IPY_MODEL_a172e9a2a9464f0597dc89df0bc665f5", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "c25723012a2d443ba16eb7f6c52bc3a5": { + "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": "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 } }, - "e4e0fafef42746d1a3f83998a48e68fb": { + "c7c838e69efb43a2b0ac48deb78c922d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2826,17 +2811,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_09e511f313fe4deaa3db651dc2ad1f38", - "max": 16887676.0, + "layout": "IPY_MODEL_f1d6704797ff4e7da238df4899d6eb75", + "max": 3201.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_ff0fbdda3a2745cab371f835eea2071a", + "style": "IPY_MODEL_54403dba12ff4f26beb5b09e37a94253", "tabbable": null, "tooltip": null, - "value": 16887676.0 + "value": 3201.0 } }, - "e51bddc0c26e45c28f6280a03fa6c570": { + "c805d0141fdb44038dfd6b1b6edf7abe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2854,7 +2839,7 @@ "text_color": null } }, - "e83b02ee750444739a17f78b4d3f184f": { + "cd596207fea4447b96658e0f41eec607": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2870,51 +2855,77 @@ "description_width": "" } }, - "e8bef6ec94b841649328908a1e03f796": { + "d74890bb53b24653a178d14fa9fb1027": { "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_4673aa7828d449fdb54b14aa87a8046b", + "placeholder": "​", + "style": "IPY_MODEL_f63a5476d3574388b856445769573334", + "tabbable": null, + "tooltip": null, + "value": " 16.9M/16.9M [00:00<00:00, 194MB/s]" } }, - "ec7119530ac242a8bf5cd0417e5460c5": { + "d8e5131818de460ba01fb92aed10a852": { "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_edb975a882cd4029ab886291208a5a5c", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_386b0038b80f44249027e46144d33c35", + "layout": "IPY_MODEL_f3b0e8a956184e818936ebd0416f606e", + "placeholder": "​", + "style": "IPY_MODEL_67850b52aee848a899a4c1cc7561c6dd", "tabbable": null, "tooltip": null, - "value": 128619.0 + "value": " 3.20k/3.20k [00:00<00:00, 795kB/s]" + } + }, + "e4e22d23b27f43a5843b7ba91eb92b25": { + "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_7906a23ebb7146d593158f78421f5ed4", + "IPY_MODEL_227af90915cb483c9537459db20c2dcc", + "IPY_MODEL_40d6d738f9e542e98ecc6d953d704d68" + ], + "layout": "IPY_MODEL_4bdd74d6b6f54d1fb924effdb6c35e73", + "tabbable": null, + "tooltip": null } }, - "edb975a882cd4029ab886291208a5a5c": { + "ecd21cd3f9be4e8587ead71ef6eddacf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2967,7 +2978,7 @@ "width": null } }, - "efde3254ff184e58b1e14968ab98b2eb": { + "f1d6704797ff4e7da238df4899d6eb75": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3020,31 +3031,7 @@ "width": null } }, - "f26aa1a5503c462e89477a33079f160a": { - "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_1965785719184160b13d07134d9c01cd", - "IPY_MODEL_ec7119530ac242a8bf5cd0417e5460c5", - "IPY_MODEL_dc42c71988f64ceba56aa56cc9c662d3" - ], - "layout": "IPY_MODEL_5a7fcc571ae3417ca7f32e5ac2e0f7ed", - "tabbable": null, - "tooltip": null - } - }, - "f8e6a2a4f4ac44968d7ab7e8b41cd9e5": { + "f3b0e8a956184e818936ebd0416f606e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3097,7 +3084,7 @@ "width": null } }, - "fd7987d5664246479c5c48a19962bcff": { + "f63a5476d3574388b856445769573334": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3115,25 +3102,54 @@ "text_color": null } }, - "fe2e22def9dd4fb9b35244ac0e211f6d": { + "fb4875c647a74da78ce0b565e117d890": { "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_fe276545fde34f708d296c6f254d81e8", + "IPY_MODEL_c7c838e69efb43a2b0ac48deb78c922d", + "IPY_MODEL_d8e5131818de460ba01fb92aed10a852" + ], + "layout": "IPY_MODEL_5f72fda67a4f475ebe8560f39a8c3d76", + "tabbable": null, + "tooltip": null + } + }, + "fe276545fde34f708d296c6f254d81e8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c25723012a2d443ba16eb7f6c52bc3a5", + "placeholder": "​", + "style": "IPY_MODEL_0c2f7ca60ab147c99ae03f759db46297", + "tabbable": null, + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" } }, - "feef464c625b4ce98e733ec61ac7047a": { + "fe755d796bba4ebfa0a4fd20368887fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3185,22 +3201,6 @@ "visibility": null, "width": null } - }, - "ff0fbdda3a2745cab371f835eea2071a": { - "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": "" - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index 33af481ea..17bb19429 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:21.525415Z", - "iopub.status.busy": "2024-06-25T19:32:21.525221Z", - "iopub.status.idle": "2024-06-25T19:32:22.681975Z", - "shell.execute_reply": "2024-06-25T19:32:22.681418Z" + "iopub.execute_input": "2024-06-25T23:14:09.178246Z", + "iopub.status.busy": "2024-06-25T23:14:09.177763Z", + "iopub.status.idle": "2024-06-25T23:14:10.319594Z", + "shell.execute_reply": "2024-06-25T23:14:10.319043Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:32:22.684626Z", - "iopub.status.busy": "2024-06-25T19:32:22.684217Z", - "iopub.status.idle": "2024-06-25T19:32:22.687052Z", - "shell.execute_reply": "2024-06-25T19:32:22.686634Z" + "iopub.execute_input": "2024-06-25T23:14:10.322187Z", + "iopub.status.busy": "2024-06-25T23:14:10.321743Z", + "iopub.status.idle": "2024-06-25T23:14:10.324748Z", + "shell.execute_reply": "2024-06-25T23:14:10.324303Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:22.689175Z", - "iopub.status.busy": "2024-06-25T19:32:22.688918Z", - "iopub.status.idle": "2024-06-25T19:32:22.697425Z", - "shell.execute_reply": "2024-06-25T19:32:22.696900Z" + "iopub.execute_input": "2024-06-25T23:14:10.326836Z", + "iopub.status.busy": "2024-06-25T23:14:10.326547Z", + "iopub.status.idle": "2024-06-25T23:14:10.335582Z", + "shell.execute_reply": "2024-06-25T23:14:10.335001Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:22.699485Z", - "iopub.status.busy": "2024-06-25T19:32:22.699153Z", - "iopub.status.idle": "2024-06-25T19:32:22.703881Z", - "shell.execute_reply": "2024-06-25T19:32:22.703445Z" + "iopub.execute_input": "2024-06-25T23:14:10.337605Z", + "iopub.status.busy": "2024-06-25T23:14:10.337298Z", + "iopub.status.idle": "2024-06-25T23:14:10.342294Z", + "shell.execute_reply": "2024-06-25T23:14:10.341736Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:22.706034Z", - "iopub.status.busy": "2024-06-25T19:32:22.705704Z", - "iopub.status.idle": "2024-06-25T19:32:22.888024Z", - "shell.execute_reply": "2024-06-25T19:32:22.887415Z" + "iopub.execute_input": "2024-06-25T23:14:10.344309Z", + "iopub.status.busy": "2024-06-25T23:14:10.344016Z", + "iopub.status.idle": "2024-06-25T23:14:10.523981Z", + "shell.execute_reply": "2024-06-25T23:14:10.523494Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:22.890902Z", - "iopub.status.busy": "2024-06-25T19:32:22.890533Z", - "iopub.status.idle": "2024-06-25T19:32:23.256766Z", - "shell.execute_reply": "2024-06-25T19:32:23.256201Z" + "iopub.execute_input": "2024-06-25T23:14:10.526345Z", + "iopub.status.busy": "2024-06-25T23:14:10.525993Z", + "iopub.status.idle": "2024-06-25T23:14:10.892857Z", + "shell.execute_reply": "2024-06-25T23:14:10.892276Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:23.259088Z", - "iopub.status.busy": "2024-06-25T19:32:23.258748Z", - "iopub.status.idle": "2024-06-25T19:32:23.281774Z", - "shell.execute_reply": "2024-06-25T19:32:23.281210Z" + "iopub.execute_input": "2024-06-25T23:14:10.895029Z", + "iopub.status.busy": "2024-06-25T23:14:10.894803Z", + "iopub.status.idle": "2024-06-25T23:14:10.917811Z", + "shell.execute_reply": "2024-06-25T23:14:10.917259Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:23.284159Z", - "iopub.status.busy": "2024-06-25T19:32:23.283828Z", - "iopub.status.idle": "2024-06-25T19:32:23.294542Z", - "shell.execute_reply": "2024-06-25T19:32:23.294124Z" + "iopub.execute_input": "2024-06-25T23:14:10.920262Z", + "iopub.status.busy": "2024-06-25T23:14:10.919694Z", + "iopub.status.idle": "2024-06-25T23:14:10.930815Z", + "shell.execute_reply": "2024-06-25T23:14:10.930406Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:23.296628Z", - "iopub.status.busy": "2024-06-25T19:32:23.296301Z", - "iopub.status.idle": "2024-06-25T19:32:25.256890Z", - "shell.execute_reply": "2024-06-25T19:32:25.256295Z" + "iopub.execute_input": "2024-06-25T23:14:10.932988Z", + "iopub.status.busy": "2024-06-25T23:14:10.932566Z", + "iopub.status.idle": "2024-06-25T23:14:12.886828Z", + "shell.execute_reply": "2024-06-25T23:14:12.886199Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.259406Z", - "iopub.status.busy": "2024-06-25T19:32:25.258951Z", - "iopub.status.idle": "2024-06-25T19:32:25.279987Z", - "shell.execute_reply": "2024-06-25T19:32:25.279554Z" + "iopub.execute_input": "2024-06-25T23:14:12.889582Z", + "iopub.status.busy": "2024-06-25T23:14:12.888947Z", + "iopub.status.idle": "2024-06-25T23:14:12.909728Z", + "shell.execute_reply": "2024-06-25T23:14:12.909257Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.281917Z", - "iopub.status.busy": "2024-06-25T19:32:25.281742Z", - "iopub.status.idle": "2024-06-25T19:32:25.300001Z", - "shell.execute_reply": "2024-06-25T19:32:25.299441Z" + "iopub.execute_input": "2024-06-25T23:14:12.911863Z", + "iopub.status.busy": "2024-06-25T23:14:12.911538Z", + "iopub.status.idle": "2024-06-25T23:14:12.929776Z", + "shell.execute_reply": "2024-06-25T23:14:12.929180Z" } }, "outputs": [ @@ -949,10 +949,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.302199Z", - "iopub.status.busy": "2024-06-25T19:32:25.301790Z", - "iopub.status.idle": "2024-06-25T19:32:25.315854Z", - "shell.execute_reply": "2024-06-25T19:32:25.315290Z" + "iopub.execute_input": "2024-06-25T23:14:12.931746Z", + "iopub.status.busy": "2024-06-25T23:14:12.931414Z", + "iopub.status.idle": "2024-06-25T23:14:12.945573Z", + "shell.execute_reply": "2024-06-25T23:14:12.945130Z" } }, "outputs": [ @@ -1087,17 +1087,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.317886Z", - "iopub.status.busy": "2024-06-25T19:32:25.317705Z", - "iopub.status.idle": "2024-06-25T19:32:25.337431Z", - "shell.execute_reply": "2024-06-25T19:32:25.336840Z" + "iopub.execute_input": "2024-06-25T23:14:12.947591Z", + "iopub.status.busy": "2024-06-25T23:14:12.947261Z", + "iopub.status.idle": "2024-06-25T23:14:12.966119Z", + "shell.execute_reply": "2024-06-25T23:14:12.965554Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9160678898ef4935b4c5e9badf645887", + "model_id": "a7755a803c3a40ef93dda582347c1c91", "version_major": 2, "version_minor": 0 }, @@ -1133,10 +1133,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.339545Z", - "iopub.status.busy": "2024-06-25T19:32:25.339254Z", - "iopub.status.idle": "2024-06-25T19:32:25.353979Z", - "shell.execute_reply": "2024-06-25T19:32:25.353546Z" + "iopub.execute_input": "2024-06-25T23:14:12.967941Z", + "iopub.status.busy": "2024-06-25T23:14:12.967765Z", + "iopub.status.idle": "2024-06-25T23:14:12.982515Z", + "shell.execute_reply": "2024-06-25T23:14:12.981981Z" } }, "outputs": [ @@ -1259,10 +1259,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.355891Z", - "iopub.status.busy": "2024-06-25T19:32:25.355714Z", - "iopub.status.idle": "2024-06-25T19:32:25.361530Z", - "shell.execute_reply": "2024-06-25T19:32:25.360999Z" + "iopub.execute_input": "2024-06-25T23:14:12.984568Z", + "iopub.status.busy": "2024-06-25T23:14:12.984190Z", + "iopub.status.idle": "2024-06-25T23:14:12.989904Z", + "shell.execute_reply": "2024-06-25T23:14:12.989416Z" } }, "outputs": [], @@ -1319,10 +1319,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.363640Z", - "iopub.status.busy": "2024-06-25T19:32:25.363214Z", - "iopub.status.idle": "2024-06-25T19:32:25.380614Z", - "shell.execute_reply": "2024-06-25T19:32:25.380174Z" + "iopub.execute_input": "2024-06-25T23:14:12.991867Z", + "iopub.status.busy": "2024-06-25T23:14:12.991560Z", + "iopub.status.idle": "2024-06-25T23:14:13.010114Z", + "shell.execute_reply": "2024-06-25T23:14:13.009557Z" } }, "outputs": [ @@ -1459,7 +1459,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "066581bfe5e048229e24f4456c525a9f": { + "1300012c40154e1da99babb9fe20b16e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1512,25 +1512,30 @@ "width": null } }, - "1c1065c6e3be428aa1ed6d3ef05adb52": { + "261be93bad834528939faa8054552fd1": { "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_2b2ed3b3a7ae4284a9bdf276850e3b28", + "placeholder": "​", + "style": "IPY_MODEL_ce12a71d028f4bb7883a05d9bc842498", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" } }, - "31beebcbb1ed40e98e72f51b7111d288": { + "2b2ed3b3a7ae4284a9bdf276850e3b28": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1583,7 +1588,30 @@ "width": null } }, - "495eb3b3071f43e1bf30631ee18ec38f": { + "6489f0184cc74d83bb9ddc2889d4a3e0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1300012c40154e1da99babb9fe20b16e", + "placeholder": "​", + "style": "IPY_MODEL_a579e009a0944dd19902993fdacfd504", + "tabbable": null, + "tooltip": null, + "value": " 132/132 [00:00<00:00, 13503.61 examples/s]" + } + }, + "781b877c2bbc46b7969db8529c1eb5c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1599,53 +1627,49 @@ "description_width": "" } }, - "514a0c7d656e41c7a0ef7156f7db70a4": { + "a579e009a0944dd19902993fdacfd504": { "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_066581bfe5e048229e24f4456c525a9f", - "placeholder": "​", - "style": "IPY_MODEL_e9790c7defa446cc93cb40beb3946950", - "tabbable": null, - "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5b75a7f5ee634ecbb513ceba0cf27697": { + "a7755a803c3a40ef93dda582347c1c91": { "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_74ffd90dc17c436bb5ca21cab1e64b31", - "placeholder": "​", - "style": "IPY_MODEL_1c1065c6e3be428aa1ed6d3ef05adb52", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_261be93bad834528939faa8054552fd1", + "IPY_MODEL_f70f2f9233844bc59865eab3649c0e10", + "IPY_MODEL_6489f0184cc74d83bb9ddc2889d4a3e0" + ], + "layout": "IPY_MODEL_c6f6f871cbae4bd6b342d6cfded8728e", "tabbable": null, - "tooltip": null, - "value": " 132/132 [00:00<00:00, 12588.35 examples/s]" + "tooltip": null } }, - "74ffd90dc17c436bb5ca21cab1e64b31": { + "c6f6f871cbae4bd6b342d6cfded8728e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1698,31 +1722,25 @@ "width": null } }, - "9160678898ef4935b4c5e9badf645887": { + "ce12a71d028f4bb7883a05d9bc842498": { "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_514a0c7d656e41c7a0ef7156f7db70a4", - "IPY_MODEL_e905864ed9704e29b5c76d93504b2f9f", - "IPY_MODEL_5b75a7f5ee634ecbb513ceba0cf27697" - ], - "layout": "IPY_MODEL_e0f1e9f09fb84534aee2fa9a795b3c91", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e0f1e9f09fb84534aee2fa9a795b3c91": { + "daa1004e74fa4197b88715988591c621": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1775,7 +1793,7 @@ "width": null } }, - "e905864ed9704e29b5c76d93504b2f9f": { + "f70f2f9233844bc59865eab3649c0e10": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1791,33 +1809,15 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_31beebcbb1ed40e98e72f51b7111d288", + "layout": "IPY_MODEL_daa1004e74fa4197b88715988591c621", "max": 132.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_495eb3b3071f43e1bf30631ee18ec38f", + "style": "IPY_MODEL_781b877c2bbc46b7969db8529c1eb5c3", "tabbable": null, "tooltip": null, "value": 132.0 } - }, - "e9790c7defa446cc93cb40beb3946950": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index 701b2fb18..4fb163767 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:28.076768Z", - "iopub.status.busy": "2024-06-25T19:32:28.076417Z", - "iopub.status.idle": "2024-06-25T19:32:29.230065Z", - "shell.execute_reply": "2024-06-25T19:32:29.229522Z" + "iopub.execute_input": "2024-06-25T23:14:15.711188Z", + "iopub.status.busy": "2024-06-25T23:14:15.711012Z", + "iopub.status.idle": "2024-06-25T23:14:16.870873Z", + "shell.execute_reply": "2024-06-25T23:14:16.870268Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:32:29.232655Z", - "iopub.status.busy": "2024-06-25T19:32:29.232386Z", - "iopub.status.idle": "2024-06-25T19:32:29.235452Z", - "shell.execute_reply": "2024-06-25T19:32:29.234935Z" + "iopub.execute_input": "2024-06-25T23:14:16.873481Z", + "iopub.status.busy": "2024-06-25T23:14:16.873232Z", + "iopub.status.idle": "2024-06-25T23:14:16.876287Z", + "shell.execute_reply": "2024-06-25T23:14:16.875762Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.237717Z", - "iopub.status.busy": "2024-06-25T19:32:29.237325Z", - "iopub.status.idle": "2024-06-25T19:32:29.246331Z", - "shell.execute_reply": "2024-06-25T19:32:29.245844Z" + "iopub.execute_input": "2024-06-25T23:14:16.878379Z", + "iopub.status.busy": "2024-06-25T23:14:16.878075Z", + "iopub.status.idle": "2024-06-25T23:14:16.887427Z", + "shell.execute_reply": "2024-06-25T23:14:16.886901Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.248332Z", - "iopub.status.busy": "2024-06-25T19:32:29.248005Z", - "iopub.status.idle": "2024-06-25T19:32:29.252728Z", - "shell.execute_reply": "2024-06-25T19:32:29.252172Z" + "iopub.execute_input": "2024-06-25T23:14:16.889377Z", + "iopub.status.busy": "2024-06-25T23:14:16.889034Z", + "iopub.status.idle": "2024-06-25T23:14:16.893463Z", + "shell.execute_reply": "2024-06-25T23:14:16.893025Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.254880Z", - "iopub.status.busy": "2024-06-25T19:32:29.254709Z", - "iopub.status.idle": "2024-06-25T19:32:29.437885Z", - "shell.execute_reply": "2024-06-25T19:32:29.437386Z" + "iopub.execute_input": "2024-06-25T23:14:16.895454Z", + "iopub.status.busy": "2024-06-25T23:14:16.895124Z", + "iopub.status.idle": "2024-06-25T23:14:17.076668Z", + "shell.execute_reply": "2024-06-25T23:14:17.076135Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.440194Z", - "iopub.status.busy": "2024-06-25T19:32:29.440000Z", - "iopub.status.idle": "2024-06-25T19:32:29.811011Z", - "shell.execute_reply": "2024-06-25T19:32:29.810430Z" + "iopub.execute_input": "2024-06-25T23:14:17.079016Z", + "iopub.status.busy": "2024-06-25T23:14:17.078687Z", + "iopub.status.idle": "2024-06-25T23:14:17.444945Z", + "shell.execute_reply": "2024-06-25T23:14:17.444376Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.813249Z", - "iopub.status.busy": "2024-06-25T19:32:29.812907Z", - "iopub.status.idle": "2024-06-25T19:32:29.815709Z", - "shell.execute_reply": "2024-06-25T19:32:29.815239Z" + "iopub.execute_input": "2024-06-25T23:14:17.447239Z", + "iopub.status.busy": "2024-06-25T23:14:17.446903Z", + "iopub.status.idle": "2024-06-25T23:14:17.449525Z", + "shell.execute_reply": "2024-06-25T23:14:17.449111Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.817838Z", - "iopub.status.busy": "2024-06-25T19:32:29.817412Z", - "iopub.status.idle": "2024-06-25T19:32:29.852590Z", - "shell.execute_reply": "2024-06-25T19:32:29.852033Z" + "iopub.execute_input": "2024-06-25T23:14:17.451586Z", + "iopub.status.busy": "2024-06-25T23:14:17.451263Z", + "iopub.status.idle": "2024-06-25T23:14:17.486312Z", + "shell.execute_reply": "2024-06-25T23:14:17.485793Z" } }, "outputs": [ @@ -647,10 +647,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.854728Z", - "iopub.status.busy": "2024-06-25T19:32:29.854340Z", - "iopub.status.idle": "2024-06-25T19:32:31.848165Z", - "shell.execute_reply": "2024-06-25T19:32:31.847549Z" + "iopub.execute_input": "2024-06-25T23:14:17.488380Z", + "iopub.status.busy": "2024-06-25T23:14:17.488048Z", + "iopub.status.idle": "2024-06-25T23:14:19.486184Z", + "shell.execute_reply": "2024-06-25T23:14:19.485493Z" } }, "outputs": [ @@ -710,10 +710,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.850423Z", - "iopub.status.busy": "2024-06-25T19:32:31.850093Z", - "iopub.status.idle": "2024-06-25T19:32:31.868614Z", - "shell.execute_reply": "2024-06-25T19:32:31.868087Z" + "iopub.execute_input": "2024-06-25T23:14:19.488814Z", + "iopub.status.busy": "2024-06-25T23:14:19.488306Z", + "iopub.status.idle": "2024-06-25T23:14:19.506666Z", + "shell.execute_reply": "2024-06-25T23:14:19.506238Z" } }, "outputs": [ @@ -846,10 +846,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.870858Z", - "iopub.status.busy": "2024-06-25T19:32:31.870547Z", - "iopub.status.idle": "2024-06-25T19:32:31.877139Z", - "shell.execute_reply": "2024-06-25T19:32:31.876698Z" + "iopub.execute_input": "2024-06-25T23:14:19.508882Z", + "iopub.status.busy": "2024-06-25T23:14:19.508469Z", + "iopub.status.idle": "2024-06-25T23:14:19.514832Z", + "shell.execute_reply": "2024-06-25T23:14:19.514311Z" } }, "outputs": [ @@ -960,10 +960,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.879087Z", - "iopub.status.busy": "2024-06-25T19:32:31.878912Z", - "iopub.status.idle": "2024-06-25T19:32:31.884810Z", - "shell.execute_reply": "2024-06-25T19:32:31.884314Z" + "iopub.execute_input": "2024-06-25T23:14:19.516791Z", + "iopub.status.busy": "2024-06-25T23:14:19.516482Z", + "iopub.status.idle": "2024-06-25T23:14:19.522090Z", + "shell.execute_reply": "2024-06-25T23:14:19.521611Z" } }, "outputs": [ @@ -1030,10 +1030,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.886777Z", - "iopub.status.busy": "2024-06-25T19:32:31.886603Z", - "iopub.status.idle": "2024-06-25T19:32:31.897515Z", - "shell.execute_reply": "2024-06-25T19:32:31.897079Z" + "iopub.execute_input": "2024-06-25T23:14:19.524150Z", + "iopub.status.busy": "2024-06-25T23:14:19.523753Z", + "iopub.status.idle": "2024-06-25T23:14:19.533902Z", + "shell.execute_reply": "2024-06-25T23:14:19.533440Z" } }, "outputs": [ @@ -1225,10 +1225,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.899532Z", - "iopub.status.busy": "2024-06-25T19:32:31.899178Z", - "iopub.status.idle": "2024-06-25T19:32:31.908000Z", - "shell.execute_reply": "2024-06-25T19:32:31.907553Z" + "iopub.execute_input": "2024-06-25T23:14:19.535870Z", + "iopub.status.busy": "2024-06-25T23:14:19.535545Z", + "iopub.status.idle": "2024-06-25T23:14:19.544125Z", + "shell.execute_reply": "2024-06-25T23:14:19.543654Z" } }, "outputs": [ @@ -1344,10 +1344,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.910005Z", - "iopub.status.busy": "2024-06-25T19:32:31.909678Z", - "iopub.status.idle": "2024-06-25T19:32:31.916574Z", - "shell.execute_reply": "2024-06-25T19:32:31.916117Z" + "iopub.execute_input": "2024-06-25T23:14:19.546177Z", + "iopub.status.busy": "2024-06-25T23:14:19.545853Z", + "iopub.status.idle": "2024-06-25T23:14:19.552700Z", + "shell.execute_reply": "2024-06-25T23:14:19.552255Z" }, "scrolled": true }, @@ -1472,10 +1472,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.918557Z", - "iopub.status.busy": "2024-06-25T19:32:31.918225Z", - "iopub.status.idle": "2024-06-25T19:32:31.927581Z", - "shell.execute_reply": "2024-06-25T19:32:31.927120Z" + "iopub.execute_input": "2024-06-25T23:14:19.554580Z", + "iopub.status.busy": "2024-06-25T23:14:19.554407Z", + "iopub.status.idle": "2024-06-25T23:14:19.563718Z", + "shell.execute_reply": "2024-06-25T23:14:19.563190Z" } }, "outputs": [ @@ -1578,10 +1578,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.929562Z", - "iopub.status.busy": "2024-06-25T19:32:31.929235Z", - "iopub.status.idle": "2024-06-25T19:32:31.940775Z", - "shell.execute_reply": "2024-06-25T19:32:31.940218Z" + "iopub.execute_input": "2024-06-25T23:14:19.565768Z", + "iopub.status.busy": "2024-06-25T23:14:19.565442Z", + "iopub.status.idle": "2024-06-25T23:14:19.576977Z", + "shell.execute_reply": "2024-06-25T23:14:19.576554Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb index a549a7040..da3ecdeb8 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:34.714453Z", - "iopub.status.busy": "2024-06-25T19:32:34.714061Z", - "iopub.status.idle": "2024-06-25T19:32:37.483269Z", - "shell.execute_reply": "2024-06-25T19:32:37.482729Z" + "iopub.execute_input": "2024-06-25T23:14:22.349033Z", + "iopub.status.busy": "2024-06-25T23:14:22.348862Z", + "iopub.status.idle": "2024-06-25T23:14:25.155777Z", + "shell.execute_reply": "2024-06-25T23:14:25.155231Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:37.485856Z", - "iopub.status.busy": "2024-06-25T19:32:37.485436Z", - "iopub.status.idle": "2024-06-25T19:32:37.489039Z", - "shell.execute_reply": "2024-06-25T19:32:37.488503Z" + "iopub.execute_input": "2024-06-25T23:14:25.158288Z", + "iopub.status.busy": "2024-06-25T23:14:25.158017Z", + "iopub.status.idle": "2024-06-25T23:14:25.161499Z", + "shell.execute_reply": "2024-06-25T23:14:25.161043Z" } }, "outputs": [], @@ -152,10 +152,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:37.491139Z", - "iopub.status.busy": "2024-06-25T19:32:37.490837Z", - "iopub.status.idle": "2024-06-25T19:32:52.986261Z", - "shell.execute_reply": "2024-06-25T19:32:52.985738Z" + "iopub.execute_input": "2024-06-25T23:14:25.163549Z", + "iopub.status.busy": "2024-06-25T23:14:25.163223Z", + "iopub.status.idle": "2024-06-25T23:14:35.757240Z", + "shell.execute_reply": "2024-06-25T23:14:35.756685Z" } }, "outputs": [ @@ -172,7 +172,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e85af83531bc4182b052d4cfe7f1020e", + "model_id": "99fb59566db2452bab382261d05e2879", "version_major": 2, "version_minor": 0 }, @@ -186,7 +186,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9abe31b01bc04cc89ff967d26e368fdf", + "model_id": "cacaca4358c34e93a46a3e2019d188d4", "version_major": 2, "version_minor": 0 }, @@ -200,7 +200,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1585cf2dc2e448068ac19676773a2a4b", + "model_id": "46c5f1e4a9ca403d83a2aa33da63b600", "version_major": 2, "version_minor": 0 }, @@ -214,7 +214,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9c34fb99987402ba4f521a988475574", + "model_id": "cc7010cd50844e48a3db713a6ea5f850", "version_major": 2, "version_minor": 0 }, @@ -228,7 +228,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "815effa183cf4ca4a7160696d4e9eb83", + "model_id": "1e806f052f23419ba6ec80aa76644ed5", "version_major": 2, "version_minor": 0 }, @@ -242,7 +242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79a15df271d14bfa8e4ed6dbe1c37a8a", + "model_id": "3590fcc9756749e0b9130b8809114216", "version_major": 2, "version_minor": 0 }, @@ -256,7 +256,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4d2025fc902f41b2b7c3474d4e9cd2fb", + "model_id": "489746a2a7db4406b7ebfd5f2a155361", "version_major": 2, "version_minor": 0 }, @@ -270,7 +270,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdbb1b6b96824b1ba8715b85852886fe", + "model_id": "b9c41de7ac0442aabfb15bbf3b5308c8", "version_major": 2, "version_minor": 0 }, @@ -312,10 +312,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:52.988486Z", - "iopub.status.busy": "2024-06-25T19:32:52.988150Z", - "iopub.status.idle": "2024-06-25T19:32:52.992110Z", - "shell.execute_reply": "2024-06-25T19:32:52.991660Z" + "iopub.execute_input": "2024-06-25T23:14:35.759372Z", + "iopub.status.busy": "2024-06-25T23:14:35.759148Z", + "iopub.status.idle": "2024-06-25T23:14:35.763037Z", + "shell.execute_reply": "2024-06-25T23:14:35.762503Z" } }, "outputs": [ @@ -340,17 +340,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:52.994131Z", - "iopub.status.busy": "2024-06-25T19:32:52.993820Z", - "iopub.status.idle": "2024-06-25T19:33:03.871847Z", - "shell.execute_reply": "2024-06-25T19:33:03.871235Z" + "iopub.execute_input": "2024-06-25T23:14:35.765199Z", + "iopub.status.busy": "2024-06-25T23:14:35.764868Z", + "iopub.status.idle": "2024-06-25T23:14:46.667044Z", + "shell.execute_reply": "2024-06-25T23:14:46.666518Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f2d29dc28e7140b792fc1ee3fcb857cb", + "model_id": "e075f5bd416a447eb67433e0d225370f", "version_major": 2, "version_minor": 0 }, @@ -388,10 +388,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:03.874363Z", - "iopub.status.busy": "2024-06-25T19:33:03.874071Z", - "iopub.status.idle": "2024-06-25T19:33:22.807154Z", - "shell.execute_reply": "2024-06-25T19:33:22.806523Z" + "iopub.execute_input": "2024-06-25T23:14:46.669519Z", + "iopub.status.busy": "2024-06-25T23:14:46.669228Z", + "iopub.status.idle": "2024-06-25T23:15:05.072765Z", + "shell.execute_reply": "2024-06-25T23:15:05.072224Z" } }, "outputs": [], @@ -424,10 +424,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.809827Z", - "iopub.status.busy": "2024-06-25T19:33:22.809607Z", - "iopub.status.idle": "2024-06-25T19:33:22.814504Z", - "shell.execute_reply": "2024-06-25T19:33:22.813958Z" + "iopub.execute_input": "2024-06-25T23:15:05.075378Z", + "iopub.status.busy": "2024-06-25T23:15:05.075000Z", + "iopub.status.idle": "2024-06-25T23:15:05.080668Z", + "shell.execute_reply": "2024-06-25T23:15:05.080229Z" } }, "outputs": [], @@ -465,10 +465,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.816419Z", - "iopub.status.busy": "2024-06-25T19:33:22.816239Z", - "iopub.status.idle": "2024-06-25T19:33:22.820245Z", - "shell.execute_reply": "2024-06-25T19:33:22.819819Z" + "iopub.execute_input": "2024-06-25T23:15:05.082696Z", + "iopub.status.busy": "2024-06-25T23:15:05.082377Z", + "iopub.status.idle": "2024-06-25T23:15:05.086277Z", + "shell.execute_reply": "2024-06-25T23:15:05.085865Z" }, "nbsphinx": "hidden" }, @@ -605,10 +605,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.822196Z", - "iopub.status.busy": "2024-06-25T19:33:22.822022Z", - "iopub.status.idle": "2024-06-25T19:33:22.831142Z", - "shell.execute_reply": "2024-06-25T19:33:22.830697Z" + "iopub.execute_input": "2024-06-25T23:15:05.088252Z", + "iopub.status.busy": "2024-06-25T23:15:05.087933Z", + "iopub.status.idle": "2024-06-25T23:15:05.096769Z", + "shell.execute_reply": "2024-06-25T23:15:05.096319Z" }, "nbsphinx": "hidden" }, @@ -733,10 +733,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.833164Z", - "iopub.status.busy": "2024-06-25T19:33:22.832846Z", - "iopub.status.idle": "2024-06-25T19:33:22.860883Z", - "shell.execute_reply": "2024-06-25T19:33:22.860460Z" + "iopub.execute_input": "2024-06-25T23:15:05.098773Z", + "iopub.status.busy": "2024-06-25T23:15:05.098471Z", + "iopub.status.idle": "2024-06-25T23:15:05.125306Z", + "shell.execute_reply": "2024-06-25T23:15:05.124855Z" } }, "outputs": [], @@ -773,10 +773,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.862805Z", - "iopub.status.busy": "2024-06-25T19:33:22.862633Z", - "iopub.status.idle": "2024-06-25T19:33:54.927685Z", - "shell.execute_reply": "2024-06-25T19:33:54.927065Z" + "iopub.execute_input": "2024-06-25T23:15:05.127482Z", + "iopub.status.busy": "2024-06-25T23:15:05.127151Z", + "iopub.status.idle": "2024-06-25T23:15:37.033092Z", + "shell.execute_reply": "2024-06-25T23:15:37.032511Z" } }, "outputs": [ @@ -792,21 +792,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.704\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.649\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.525\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.481\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b56125fc059b47e3b228dc3ed3b629c0", + "model_id": "8835da69dbeb4826a96baa0561232a18", "version_major": 2, "version_minor": 0 }, @@ -827,7 +827,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c565e132b5a46d398435caf4df461d4", + "model_id": "1141e88c1cd549c1ad36f5867b926978", "version_major": 2, "version_minor": 0 }, @@ -850,21 +850,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.714\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.663\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.460\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.663\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "63e4117109d44d79bcece5146781039a", + "model_id": "2a3f5349b34148209445198c9ae64559", "version_major": 2, "version_minor": 0 }, @@ -885,7 +885,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "09c8fb8f5f2945a4948b758b41efb311", + "model_id": "2c1834764c78450699f4a69ba292fe8e", "version_major": 2, "version_minor": 0 }, @@ -908,21 +908,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.742\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.680\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.468\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.450\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85c627e125a94180abe254acf928a1fc", + "model_id": "02ef28fe5e5647e49f15e9889ac88c8f", "version_major": 2, "version_minor": 0 }, @@ -943,7 +943,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85af3d53abef4aa8a6046017943dc826", + "model_id": "ebc081ac7cef42f58f0c46bdca672b27", "version_major": 2, "version_minor": 0 }, @@ -1022,10 +1022,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:54.930258Z", - "iopub.status.busy": "2024-06-25T19:33:54.929870Z", - "iopub.status.idle": "2024-06-25T19:33:54.943872Z", - "shell.execute_reply": "2024-06-25T19:33:54.943339Z" + "iopub.execute_input": "2024-06-25T23:15:37.035751Z", + "iopub.status.busy": "2024-06-25T23:15:37.035236Z", + "iopub.status.idle": "2024-06-25T23:15:37.049525Z", + "shell.execute_reply": "2024-06-25T23:15:37.049035Z" } }, "outputs": [], @@ -1050,10 +1050,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:54.946038Z", - "iopub.status.busy": "2024-06-25T19:33:54.945618Z", - "iopub.status.idle": "2024-06-25T19:33:55.403627Z", - "shell.execute_reply": "2024-06-25T19:33:55.402981Z" + "iopub.execute_input": "2024-06-25T23:15:37.051460Z", + "iopub.status.busy": "2024-06-25T23:15:37.051284Z", + "iopub.status.idle": "2024-06-25T23:15:37.533678Z", + "shell.execute_reply": "2024-06-25T23:15:37.533181Z" } }, "outputs": [], @@ -1073,10 +1073,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:55.406220Z", - "iopub.status.busy": "2024-06-25T19:33:55.406041Z", - "iopub.status.idle": "2024-06-25T19:35:30.535430Z", - "shell.execute_reply": "2024-06-25T19:35:30.534808Z" + "iopub.execute_input": "2024-06-25T23:15:37.536010Z", + "iopub.status.busy": "2024-06-25T23:15:37.535826Z", + "iopub.status.idle": "2024-06-25T23:17:13.081610Z", + "shell.execute_reply": "2024-06-25T23:17:13.080989Z" } }, "outputs": [ @@ -1123,7 +1123,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d65cb8246aa14189b49a0eeae6f3bad0", + "model_id": "55c0a386d760485f92009bb75259396b", "version_major": 2, "version_minor": 0 }, @@ -1162,10 +1162,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:30.537781Z", - "iopub.status.busy": "2024-06-25T19:35:30.537412Z", - "iopub.status.idle": "2024-06-25T19:35:30.983712Z", - "shell.execute_reply": "2024-06-25T19:35:30.983121Z" + "iopub.execute_input": "2024-06-25T23:17:13.084039Z", + "iopub.status.busy": "2024-06-25T23:17:13.083667Z", + "iopub.status.idle": "2024-06-25T23:17:13.530568Z", + "shell.execute_reply": "2024-06-25T23:17:13.530038Z" } }, "outputs": [ @@ -1311,10 +1311,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:30.986665Z", - "iopub.status.busy": "2024-06-25T19:35:30.986208Z", - "iopub.status.idle": "2024-06-25T19:35:31.048426Z", - "shell.execute_reply": "2024-06-25T19:35:31.047866Z" + "iopub.execute_input": "2024-06-25T23:17:13.532958Z", + "iopub.status.busy": "2024-06-25T23:17:13.532616Z", + "iopub.status.idle": "2024-06-25T23:17:13.595525Z", + "shell.execute_reply": "2024-06-25T23:17:13.594969Z" } }, "outputs": [ @@ -1418,10 +1418,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.050749Z", - "iopub.status.busy": "2024-06-25T19:35:31.050363Z", - "iopub.status.idle": "2024-06-25T19:35:31.060546Z", - "shell.execute_reply": "2024-06-25T19:35:31.060026Z" + "iopub.execute_input": "2024-06-25T23:17:13.597922Z", + "iopub.status.busy": "2024-06-25T23:17:13.597476Z", + "iopub.status.idle": "2024-06-25T23:17:13.606785Z", + "shell.execute_reply": "2024-06-25T23:17:13.606218Z" } }, "outputs": [ @@ -1551,10 +1551,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.062742Z", - "iopub.status.busy": "2024-06-25T19:35:31.062469Z", - "iopub.status.idle": "2024-06-25T19:35:31.068251Z", - "shell.execute_reply": "2024-06-25T19:35:31.067801Z" + "iopub.execute_input": "2024-06-25T23:17:13.609099Z", + "iopub.status.busy": "2024-06-25T23:17:13.608903Z", + "iopub.status.idle": "2024-06-25T23:17:13.613602Z", + "shell.execute_reply": "2024-06-25T23:17:13.613147Z" }, "nbsphinx": "hidden" }, @@ -1600,10 +1600,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.070241Z", - "iopub.status.busy": "2024-06-25T19:35:31.069928Z", - "iopub.status.idle": "2024-06-25T19:35:31.828844Z", - "shell.execute_reply": "2024-06-25T19:35:31.828271Z" + "iopub.execute_input": "2024-06-25T23:17:13.615408Z", + "iopub.status.busy": "2024-06-25T23:17:13.615233Z", + "iopub.status.idle": "2024-06-25T23:17:14.118370Z", + "shell.execute_reply": "2024-06-25T23:17:14.117787Z" } }, "outputs": [ @@ -1638,10 +1638,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.831222Z", - "iopub.status.busy": "2024-06-25T19:35:31.830895Z", - "iopub.status.idle": "2024-06-25T19:35:31.839311Z", - "shell.execute_reply": "2024-06-25T19:35:31.838857Z" + "iopub.execute_input": "2024-06-25T23:17:14.120491Z", + "iopub.status.busy": "2024-06-25T23:17:14.120304Z", + "iopub.status.idle": "2024-06-25T23:17:14.128885Z", + "shell.execute_reply": "2024-06-25T23:17:14.128442Z" } }, "outputs": [ @@ -1808,10 +1808,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.841482Z", - "iopub.status.busy": "2024-06-25T19:35:31.841163Z", - "iopub.status.idle": "2024-06-25T19:35:31.848176Z", - "shell.execute_reply": "2024-06-25T19:35:31.847749Z" + "iopub.execute_input": "2024-06-25T23:17:14.131053Z", + "iopub.status.busy": "2024-06-25T23:17:14.130639Z", + "iopub.status.idle": "2024-06-25T23:17:14.433754Z", + "shell.execute_reply": "2024-06-25T23:17:14.433138Z" }, "nbsphinx": "hidden" }, @@ -1887,10 +1887,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.850195Z", - "iopub.status.busy": "2024-06-25T19:35:31.849881Z", - "iopub.status.idle": "2024-06-25T19:35:32.292692Z", - "shell.execute_reply": "2024-06-25T19:35:32.292043Z" + "iopub.execute_input": "2024-06-25T23:17:14.437185Z", + "iopub.status.busy": "2024-06-25T23:17:14.436581Z", + "iopub.status.idle": "2024-06-25T23:17:14.910179Z", + "shell.execute_reply": "2024-06-25T23:17:14.909590Z" } }, "outputs": [ @@ -1927,10 +1927,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.295116Z", - "iopub.status.busy": "2024-06-25T19:35:32.294759Z", - "iopub.status.idle": "2024-06-25T19:35:32.310913Z", - "shell.execute_reply": "2024-06-25T19:35:32.310451Z" + "iopub.execute_input": "2024-06-25T23:17:14.912393Z", + "iopub.status.busy": "2024-06-25T23:17:14.912024Z", + "iopub.status.idle": "2024-06-25T23:17:14.927515Z", + "shell.execute_reply": "2024-06-25T23:17:14.926933Z" } }, "outputs": [ @@ -2087,10 +2087,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.313089Z", - "iopub.status.busy": "2024-06-25T19:35:32.312752Z", - "iopub.status.idle": "2024-06-25T19:35:32.318396Z", - "shell.execute_reply": "2024-06-25T19:35:32.317860Z" + "iopub.execute_input": "2024-06-25T23:17:14.929547Z", + "iopub.status.busy": "2024-06-25T23:17:14.929372Z", + "iopub.status.idle": "2024-06-25T23:17:14.935923Z", + "shell.execute_reply": "2024-06-25T23:17:14.935427Z" }, "nbsphinx": "hidden" }, @@ -2135,10 +2135,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.320370Z", - "iopub.status.busy": "2024-06-25T19:35:32.320196Z", - "iopub.status.idle": "2024-06-25T19:35:32.779377Z", - "shell.execute_reply": "2024-06-25T19:35:32.778856Z" + "iopub.execute_input": "2024-06-25T23:17:14.937944Z", + "iopub.status.busy": "2024-06-25T23:17:14.937612Z", + "iopub.status.idle": "2024-06-25T23:17:15.400691Z", + "shell.execute_reply": "2024-06-25T23:17:15.399712Z" } }, "outputs": [ @@ -2220,10 +2220,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.782553Z", - "iopub.status.busy": "2024-06-25T19:35:32.782090Z", - "iopub.status.idle": "2024-06-25T19:35:32.791666Z", - "shell.execute_reply": "2024-06-25T19:35:32.790923Z" + "iopub.execute_input": "2024-06-25T23:17:15.403380Z", + "iopub.status.busy": "2024-06-25T23:17:15.403170Z", + "iopub.status.idle": "2024-06-25T23:17:15.412375Z", + "shell.execute_reply": "2024-06-25T23:17:15.411801Z" } }, "outputs": [ @@ -2248,47 +2248,47 @@ " \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, @@ -2351,10 +2351,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.794003Z", - "iopub.status.busy": "2024-06-25T19:35:32.793805Z", - "iopub.status.idle": "2024-06-25T19:35:32.799849Z", - "shell.execute_reply": "2024-06-25T19:35:32.799106Z" + "iopub.execute_input": "2024-06-25T23:17:15.414938Z", + "iopub.status.busy": "2024-06-25T23:17:15.414744Z", + "iopub.status.idle": "2024-06-25T23:17:15.420451Z", + "shell.execute_reply": "2024-06-25T23:17:15.419881Z" }, "nbsphinx": "hidden" }, @@ -2391,10 +2391,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.802393Z", - "iopub.status.busy": "2024-06-25T19:35:32.802198Z", - "iopub.status.idle": "2024-06-25T19:35:33.003653Z", - "shell.execute_reply": "2024-06-25T19:35:33.003206Z" + "iopub.execute_input": "2024-06-25T23:17:15.422902Z", + "iopub.status.busy": "2024-06-25T23:17:15.422710Z", + "iopub.status.idle": "2024-06-25T23:17:15.624779Z", + "shell.execute_reply": "2024-06-25T23:17:15.624291Z" } }, "outputs": [ @@ -2436,10 +2436,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:33.005778Z", - "iopub.status.busy": "2024-06-25T19:35:33.005613Z", - "iopub.status.idle": "2024-06-25T19:35:33.013113Z", - "shell.execute_reply": "2024-06-25T19:35:33.012647Z" + "iopub.execute_input": "2024-06-25T23:17:15.627227Z", + "iopub.status.busy": "2024-06-25T23:17:15.626865Z", + "iopub.status.idle": "2024-06-25T23:17:15.634605Z", + "shell.execute_reply": "2024-06-25T23:17:15.634166Z" } }, "outputs": [ @@ -2464,47 +2464,47 @@ " \n", " \n", " \n", - " is_low_information_issue\n", " low_information_score\n", + " is_low_information_issue\n", " \n", " \n", " \n", " \n", " 53050\n", - " True\n", " 0.067975\n", + " True\n", " \n", " \n", " 40875\n", - " True\n", " 0.089929\n", + " True\n", " \n", " \n", " 9594\n", - " True\n", " 0.092601\n", + " True\n", " \n", " \n", " 34825\n", - " True\n", " 0.107744\n", + " True\n", " \n", " \n", " 37530\n", - " True\n", " 0.108516\n", + " True\n", " \n", " \n", "\n", "" ], "text/plain": [ - " 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" + " 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" ] }, "execution_count": 29, @@ -2525,10 +2525,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:33.015062Z", - "iopub.status.busy": "2024-06-25T19:35:33.014721Z", - "iopub.status.idle": "2024-06-25T19:35:33.209360Z", - "shell.execute_reply": "2024-06-25T19:35:33.208767Z" + "iopub.execute_input": "2024-06-25T23:17:15.636681Z", + "iopub.status.busy": "2024-06-25T23:17:15.636363Z", + "iopub.status.idle": "2024-06-25T23:17:15.834587Z", + "shell.execute_reply": "2024-06-25T23:17:15.834003Z" } }, "outputs": [ @@ -2568,10 +2568,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:33.211913Z", - "iopub.status.busy": "2024-06-25T19:35:33.211538Z", - "iopub.status.idle": "2024-06-25T19:35:33.216052Z", - "shell.execute_reply": "2024-06-25T19:35:33.215616Z" + "iopub.execute_input": "2024-06-25T23:17:15.836769Z", + "iopub.status.busy": "2024-06-25T23:17:15.836460Z", + "iopub.status.idle": "2024-06-25T23:17:15.840847Z", + "shell.execute_reply": "2024-06-25T23:17:15.840296Z" }, "nbsphinx": "hidden" }, @@ -2608,7 +2608,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0011fb26fc5d4baa896547da4133122f": { + "003b69c44f834dd6bd767bd85d0282c1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "01e9581b8f904ce9bd99cf2093d8e0b6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2661,48 +2679,31 @@ "width": null } }, - "00644b1a57b2451e9cdebb4eb7250f78": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "007876448c144ee39084540ed6eb06b9": { + "02ef28fe5e5647e49f15e9889ac88c8f": { "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_5745328a69db48878efba6c4bdb99305", - "placeholder": "​", - "style": "IPY_MODEL_b54eac280dff4e5dbe0c1d2b48743535", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_17898a71a5eb4556a9c02c78df55aeb3", + "IPY_MODEL_aae89dad290345f5acc2526904d6d4e5", + "IPY_MODEL_18560e45d46d48bf99bbf1a153e2502c" + ], + "layout": "IPY_MODEL_22708ad181354289ac5fa640bd1c4121", "tabbable": null, - "tooltip": null, - "value": " 8.85k/8.85k [00:00<00:00, 1.45MB/s]" + "tooltip": null } }, - "0120148eaefd42fd9a38edc77bc35ac7": { + "0302bede64084627a211292664570913": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2720,7 +2721,7 @@ "text_color": null } }, - "0261398aaa894092b6eca7f630c39440": { + "040097e76c3b414292ef486207e132ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2773,113 +2774,76 @@ "width": null } }, - "031211ba610a4802b4a54cdf1bba56a7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b9a030c7a18e43e0a9ade7e2489ba818", - "placeholder": "​", - "style": "IPY_MODEL_7824f895073b4989aabcc1118fe1e570", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "03ab3894bf4242d8999349575ac3ab13": { + "04bfeccaeea6416e98641eb5e663ae0b": { "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 - } - }, - "03b60960744f469c8562f78a5948c4a9": { - "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_46f3bc1415b94874ba19257d22e4bfed", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_44210013b27e41639717a483f6daeb8c", - "tabbable": null, - "tooltip": null, - "value": 10000.0 - } - }, - "0419dce41e82427eba5f201dcd3224ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fe37a821e1f04c6f9903e3722f9ab4c2", - "placeholder": "​", - "style": "IPY_MODEL_7f8b4b75f0c545ac966463807f510230", - "tabbable": null, - "tooltip": null, - "value": "100%" + "bar_color": null, + "description_width": "" } }, - "05b056eafe984430926b52278f208393": { - "model_module": "@jupyter-widgets/controls", + "08971fe179644eda8bcd42e21d601e92": { + "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 } }, - "0637c05b667541e782a074923de85b45": { + "08fa3b07fef4433e8a950677af858234": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2894,39 +2858,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_74f4d829ffbb4508957bfb49f70fe734", + "layout": "IPY_MODEL_72b19493a1ca42c793bcda945b230961", "placeholder": "​", - "style": "IPY_MODEL_13a871b41cce4be6bd9b0c8fb1116aec", + "style": "IPY_MODEL_afdd5bafd5ba49909abaaef0bb7f6038", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.59it/s]" - } - }, - "09c8fb8f5f2945a4948b758b41efb311": { - "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_031211ba610a4802b4a54cdf1bba56a7", - "IPY_MODEL_42fc637a41cf41029345fe4e9b68d6c9", - "IPY_MODEL_c1899c2c83a146b4b5eccb8d3c0892ae" - ], - "layout": "IPY_MODEL_44661251f61148bab2d1f3993f625718", - "tabbable": null, - "tooltip": null + "value": " 26.4M/26.4M [00:00<00:00, 114MB/s]" } }, - "0b157a2c7cf7434fbc4448eaf5ad92d7": { + "0ada4c3d2a88412599516fae13401849": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2942,48 +2882,7 @@ "description_width": "" } }, - "0d79223cae9246eaaa4f673e81a780a8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0f7c857000414450bdc92196b4466489": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_88408a65c5eb4eb48911a14aadbc74cf", - "placeholder": "​", - "style": "IPY_MODEL_26f5985a24fd4b3cb9fb40bc0c1b22a2", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "112a5fd388db49e4b8a0f9dffab06426": { + "0fc5778c044d4fcd9e8f745663d100c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3036,94 +2935,10 @@ "width": null } }, - "12693e79b18144d2be2b4b4399f4789e": { - "model_module": "@jupyter-widgets/controls", + "103b52da5fd24c24a14b4605ba5c3120": { + "model_module": "@jupyter-widgets/base", "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_bf14a805f3944138b263cb978e9fcf8b", - "max": 29515.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_51c208e957a24da184c9f20ebc42f546", - "tabbable": null, - "tooltip": null, - "value": 29515.0 - } - }, - "12da0c3d2bd5449dbb811de7fd8b2093": { - "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": "" - } - }, - "13a871b41cce4be6bd9b0c8fb1116aec": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "1585cf2dc2e448068ac19676773a2a4b": { - "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_744802ca32e849ab9fea4e8e3861b154", - "IPY_MODEL_2cca5f207862410483ac41718382a443", - "IPY_MODEL_5b650c7c7114407ca633a6f913edbef2" - ], - "layout": "IPY_MODEL_90c7589ac13b443eb422dd2f647490a3", - "tabbable": null, - "tooltip": null - } - }, - "175a6c09ad6c4cbfbdd9543952ef9ffc": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", @@ -3173,30 +2988,7 @@ "width": null } }, - "1a2d0d43f24f4a179401f7bde3fcbf11": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ec23491fb0024cb8a1b7dfd205775551", - "placeholder": "​", - "style": "IPY_MODEL_8946f270b2954b22801e92ce10bf7c63", - "tabbable": null, - "tooltip": null, - "value": "Downloading readme: 100%" - } - }, - "1b72621c613b4da3999f4f434c596d23": { + "10833dd9b558436e8141086cd6457744": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3249,82 +3041,49 @@ "width": null } }, - "1bb8a6dd435048b28340d4eac6730019": { - "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_c1f07c0e6cb7453cb234322863c6974b", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_494b87319f8d45ab8596d3cc2028f7f6", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "1c96211615924c808f262fae38d4ab01": { + "1141e88c1cd549c1ad36f5867b926978": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1b72621c613b4da3999f4f434c596d23", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b461446aa899462d865a83e0c727b5c5", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7a70b6d736914713b75013fd280cd1fb", + "IPY_MODEL_233edc846cd2411699f747246a5c017b", + "IPY_MODEL_9b8b5db0d4ec47ae8f14fbae0720d3e0" + ], + "layout": "IPY_MODEL_32715003f9374a25a98d8e94a71e934d", "tabbable": null, - "tooltip": null, - "value": 40.0 + "tooltip": null } }, - "1d5a9e2ed91b479f9781063cdce66b7e": { + "11828cd7a97146ac81748612b0601834": { "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_4266637c8f4c47fbb4c9e7bd9cc2cabf", - "placeholder": "​", - "style": "IPY_MODEL_2047417b76614aad92d50e9d57fa28a2", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:06<00:00, 8928.67 examples/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1f1d67e48a1c45258572b6fbf8ebe4e2": { + "11e93782fa6f464e9f6900e0c646f5cf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3377,7 +3136,7 @@ "width": null } }, - "1fbec845f4cf42bb8efb9b83163e0b9b": { + "1232a69e4cd748539b2d4cdde35b417c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3430,25 +3189,60 @@ "width": null } }, - "2047417b76614aad92d50e9d57fa28a2": { - "model_module": "@jupyter-widgets/controls", + "12fb080c89ea44b788b8036e7c269e2e": { + "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 } }, - "2426b36b4bcd4538a7a1a6c6f443b609": { + "176f4fe537ab44709eed5f4771e5a748": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3501,67 +3295,48 @@ "width": null } }, - "26f5985a24fd4b3cb9fb40bc0c1b22a2": { + "17898a71a5eb4556a9c02c78df55aeb3": { "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_a805af9f6458409cbfcf1ef1829794dc", + "placeholder": "​", + "style": "IPY_MODEL_7f159f23ebfd44699351ad51261161f4", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "2854eeec578a4c3e9a9828ec08c1652c": { + "17af51078a0c4c0f92db9850c8c773d7": { "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": "" - } - }, - "2a37f3b6e2e64eda9d5aad4e57bb6475": { - "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_1f1d67e48a1c45258572b6fbf8ebe4e2", - "max": 5148.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9b58b8eff8e745eb9e937354fedb1807", - "tabbable": null, - "tooltip": null, - "value": 5148.0 + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2c916b2e66ff458bae959988d3edfc7f": { + "18560e45d46d48bf99bbf1a153e2502c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3576,15 +3351,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0011fb26fc5d4baa896547da4133122f", + "layout": "IPY_MODEL_040097e76c3b414292ef486207e132ee", "placeholder": "​", - "style": "IPY_MODEL_9d3b7c0890814f2c9985eb79f97a04ac", + "style": "IPY_MODEL_0302bede64084627a211292664570913", "tabbable": null, "tooltip": null, - "value": "Generating train split: 100%" + "value": " 40/40 [00:00<00:00, 60.88it/s]" } }, - "2cca5f207862410483ac41718382a443": { + "18e5b3625d2a4b3abbfea651a20eef56": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3600,74 +3375,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ecde01d38be144db9d996498b953e5b4", - "max": 26421880.0, + "layout": "IPY_MODEL_01e9581b8f904ce9bd99cf2093d8e0b6", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_555d30c920d344059abb5d85084268de", - "tabbable": null, - "tooltip": null, - "value": 26421880.0 - } - }, - "2f9e9f6f7ebe42afa59802bdf7306ccb": { - "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": "" - } - }, - "30a335f232c04fe7b668ed6a418d27b2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "3253c6b3e3464da88809a4ce236555f1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_495d2ed12e8446bc80ad465a9f89a896", - "placeholder": "​", - "style": "IPY_MODEL_00644b1a57b2451e9cdebb4eb7250f78", + "style": "IPY_MODEL_293b9b52ffaa471e9c10ec006eefc31e", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.41it/s]" + "value": 40.0 } }, - "32b6d99a486e461eacd6e063c5420eff": { + "1a2ffe0f3d68433d9fc120a9b894e762": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3720,7 +3438,7 @@ "width": null } }, - "38eb030b83c64212b25df93cfe516570": { + "1cdb544e05fe49e5b616044f82615b00": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3735,33 +3453,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_77b7d71484644232938d588a6fbcc4ed", + "layout": "IPY_MODEL_12fb080c89ea44b788b8036e7c269e2e", "placeholder": "​", - "style": "IPY_MODEL_cd436773aeeb4e5f811c805cd47f7071", + "style": "IPY_MODEL_2b67454e45604716a3d08831b42d274a", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "39314c631f444d01903b4f50ccfc66df": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": "Downloading data: 100%" } }, - "420adb2a809a4b7693bda83cb040e60c": { + "1e4148a5a5464b22b94684bad558cbec": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3814,86 +3514,49 @@ "width": null } }, - "4266637c8f4c47fbb4c9e7bd9cc2cabf": { - "model_module": "@jupyter-widgets/base", + "1e806f052f23419ba6ec80aa76644ed5": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1cdb544e05fe49e5b616044f82615b00", + "IPY_MODEL_b602e59393ab410ba774d7c2419c556d", + "IPY_MODEL_d6337601ed54490eb0d452da36303706" + ], + "layout": "IPY_MODEL_1a2ffe0f3d68433d9fc120a9b894e762", + "tabbable": null, + "tooltip": null } }, - "4268b194fef1470096c0d165c23da82f": { + "1f7f1fac2dd34d07b7fc07f799729df6": { "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_b218eb25d1544b2198406c8d0daa0439", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_53d0e7a8e50247a88566f4b59b14d553", - "tabbable": null, - "tooltip": null, - "value": 60000.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "42c998dc50734d8c93d723bb78939244": { + "20376214a3ff45e29c894539ac53a963": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3946,109 +3609,30 @@ "width": null } }, - "42e73c1ad6b74b238d722d1c7ee516b1": { - "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": "" - } - }, - "42fc637a41cf41029345fe4e9b68d6c9": { + "20aa2cb116fa42218107305be2c9121a": { "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_175a6c09ad6c4cbfbdd9543952ef9ffc", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0b157a2c7cf7434fbc4448eaf5ad92d7", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "434f1175a8614296b63392ddd951670e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "44210013b27e41639717a483f6daeb8c": { - "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": "" - } - }, - "4439d6c4067d42b2bc7b5b49e651de05": { - "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_70c9b3de67694dc4a8c38a542842ae3c", - "max": 4.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2854eeec578a4c3e9a9828ec08c1652c", + "layout": "IPY_MODEL_dd549dafe96d457a9f3e32fa6dda0ce5", + "placeholder": "​", + "style": "IPY_MODEL_6f6e9c74347e4652b414406bd4c67238", "tabbable": null, "tooltip": null, - "value": 4.0 + "value": " 29.5k/29.5k [00:00<00:00, 4.38MB/s]" } }, - "44661251f61148bab2d1f3993f625718": { + "20e8c6a9ab644e3da69655bfbc794894": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4101,7 +3685,33 @@ "width": null } }, - "46692e2429834f6b8ad6d2e3bd669ac5": { + "2253900d1539484ea6c5fb5f87e34fae": { + "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_8b39eaec8f5843a3942f7b65e5320363", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e675357d5efc42b3968d61c273ca5f7f", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "22708ad181354289ac5fa640bd1c4121": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4154,7 +3764,7 @@ "width": null } }, - "46f3bc1415b94874ba19257d22e4bfed": { + "22e4e1da97fe49e290e9dac292880148": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4207,7 +3817,127 @@ "width": null } }, - "470cbbcbb3d44bb493031e8387fe6adc": { + "22ece7076054425f8784c9f44cd9c512": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "231c040b6a13479f944b5bef41c9994a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "233edc846cd2411699f747246a5c017b": { + "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_816719582fe14c11a648b648bb9e2cbb", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_474d936989bb4423af34fd74a8db3b23", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "293b9b52ffaa471e9c10ec006eefc31e": { + "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": "" + } + }, + "2a3f5349b34148209445198c9ae64559": { + "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_bf0ad0d606f04b7aa5013f43b6f32049", + "IPY_MODEL_5788b965507d455ca17eabfc155de530", + "IPY_MODEL_453bcb1f0a7e4e41aa72dab72cbeed75" + ], + "layout": "IPY_MODEL_10833dd9b558436e8141086cd6457744", + "tabbable": null, + "tooltip": null + } + }, + "2b67454e45604716a3d08831b42d274a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "2bf71c3624ce4413bc347a20e07c26d5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4260,23 +3990,31 @@ "width": null } }, - "494b87319f8d45ab8596d3cc2028f7f6": { + "2c1834764c78450699f4a69ba292fe8e": { "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_c6eb4afdd2a54d899795e4359d8c989d", + "IPY_MODEL_caabab78e31d438eb521e9c3f2e1c0a6", + "IPY_MODEL_e9b9c9bbe0374dd9b98a3a4c1553822f" + ], + "layout": "IPY_MODEL_20376214a3ff45e29c894539ac53a963", + "tabbable": null, + "tooltip": null } }, - "495d2ed12e8446bc80ad465a9f89a896": { + "2d69655ca2a545e9a4b05f6865cdf2a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4329,7 +4067,53 @@ "width": null } }, - "4b1c1c0a8eb2477b91cae9a52c47e58c": { + "307355aaf24e41138019598af025ac1b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_715dab8821f5465eade13b7f8765ed47", + "placeholder": "​", + "style": "IPY_MODEL_71217b3eed354f73861bde4bfaf66ffd", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:36<00:00, 1535.58it/s]" + } + }, + "311422b25c3d435280a7ae2e6c1b5cbc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_485dd48b720d4e1dbfce8f76953b154e", + "placeholder": "​", + "style": "IPY_MODEL_8c6b20c613cf48bd9f826dbeb5124369", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "32715003f9374a25a98d8e94a71e934d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4382,7 +4166,7 @@ "width": null } }, - "4b9e51775e974654a075cfdb1d1b699c": { + "32d10bf33ee54d2bac909d0748596dac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4435,25 +4219,7 @@ "width": null } }, - "4cfef00083ee41c18334522b0aaafbf6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4d2025fc902f41b2b7c3474d4e9cd2fb": { + "3590fcc9756749e0b9130b8809114216": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4468,16 +4234,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2c916b2e66ff458bae959988d3edfc7f", - "IPY_MODEL_f3a3a4eba3674c53882dfe2a1c081653", - "IPY_MODEL_1d5a9e2ed91b479f9781063cdce66b7e" + "IPY_MODEL_9c8e988dcc76462ea4a6543f9f1dd759", + "IPY_MODEL_61530f93052c4966a3ed9c90ec90c547", + "IPY_MODEL_3c58f7da1e954e5f85811b8c931dddf3" ], - "layout": "IPY_MODEL_e7dae7ac77284ca7a938a5140aea14ba", + "layout": "IPY_MODEL_839cc7aeb6cf41cba2328b772dda1354", "tabbable": null, "tooltip": null } }, - "4f814c4fdc474d7e8305f032c2caaf1d": { + "37059badcffc423fbad95494b9745859": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4492,81 +4258,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7437bf57da394059a3d162f5d7a1a77a", + "layout": "IPY_MODEL_50aef71653f04eb49e220d1870cbf7ff", "placeholder": "​", - "style": "IPY_MODEL_434f1175a8614296b63392ddd951670e", + "style": "IPY_MODEL_eb33301487694b0f9f4a664fce743134", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "51c208e957a24da184c9f20ebc42f546": { - "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": "" - } - }, - "53d0e7a8e50247a88566f4b59b14d553": { - "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": "" - } - }, - "555d30c920d344059abb5d85084268de": { - "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": "" - } - }, - "569d3453747c444a9c2661732c8b1fb5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 60000/60000 [00:06<00:00, 8813.66 examples/s]" } }, - "5745328a69db48878efba6c4bdb99305": { + "373768b5fd4c42118d61459d55c65ad2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4619,7 +4319,46 @@ "width": null } }, - "594f571cc61a464d8934a2a6ebaff1ed": { + "3764b604f5ba43288217fe3f4372b542": { + "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": "" + } + }, + "3779e0b1881642b8b80007dfa03126bb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1e4148a5a5464b22b94684bad558cbec", + "placeholder": "​", + "style": "IPY_MODEL_885a1c18bad74f47ae49ef86de2c3a67", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" + } + }, + "38bd0231f7c34338a8ccd2536adb5809": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4672,7 +4411,48 @@ "width": null } }, - "5a36ed53de5c4a9c8637a7b4013dfb54": { + "3c58f7da1e954e5f85811b8c931dddf3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d8441cb494324dad83d4a88dd3c805e6", + "placeholder": "​", + "style": "IPY_MODEL_3e99f4f8639445f0810c65d14dccca89", + "tabbable": null, + "tooltip": null, + "value": " 5.15k/5.15k [00:00<00:00, 906kB/s]" + } + }, + "3c76ee6a2244430cbb3e8e8416d1a23c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3e86a20ac6b044d4adccfeaba4245d25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4725,86 +4505,77 @@ "width": null } }, - "5b650c7c7114407ca633a6f913edbef2": { + "3e99f4f8639445f0810c65d14dccca89": { "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_d6b409257aab47b888b2954d7b04073e", - "placeholder": "​", - "style": "IPY_MODEL_d067169b2a1745bd893cbaa56705df57", - "tabbable": null, - "tooltip": null, - "value": " 26.4M/26.4M [00:00<00:00, 124MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5c565e132b5a46d398435caf4df461d4": { + "40e314c496664e698c489a3f5c279e1c": { "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_4f814c4fdc474d7e8305f032c2caaf1d", - "IPY_MODEL_bb91a65b9f7c4bcbae0545c6e0c0c603", - "IPY_MODEL_e39cdb7fe88f4310bfd8b1dcd8ac5ae2" - ], - "layout": "IPY_MODEL_a7668c39f7324f3abf6dee2fb270336f", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_08971fe179644eda8bcd42e21d601e92", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0ada4c3d2a88412599516fae13401849", "tabbable": null, - "tooltip": null - } - }, - "5c5b477c793d4588a25705aece5a2a2e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "tooltip": null, + "value": 10000.0 } }, - "5d69e819f44b493a8d07e58cb39a6a69": { + "41aa359adc6640ab9d5ea2369493623a": { "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_4ae6fd916d6d470190a53d061fb0b02f", + "max": 8845.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_63f97e8188cb4370abab083114492da0", + "tabbable": null, + "tooltip": null, + "value": 8845.0 } }, - "5efe1df058ed48ae9674a8448d855c64": { + "4251756ae8d344e8923025afa5b3be9a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4857,31 +4628,23 @@ "width": null } }, - "63e4117109d44d79bcece5146781039a": { + "42c5eeb19b2a4542b4159e63110273e4": { "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_eb56ed8bd1a74a87a2a7cf25c0992594", - "IPY_MODEL_ee448c322caa4ddbae258419893e01e8", - "IPY_MODEL_3253c6b3e3464da88809a4ce236555f1" - ], - "layout": "IPY_MODEL_659e46eb96e64415a9a947aea4256de6", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "6566ace6c0aa4a21abbf6d708cb457b0": { + "42f688e5fa0a4f698ca681a4433e115d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4934,7 +4697,7 @@ "width": null } }, - "659e46eb96e64415a9a947aea4256de6": { + "431d46eafa8c450b8c8f1bf1f7883cf3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4987,7 +4750,7 @@ "width": null } }, - "670532c752594999bbc5db2f86da0bfa": { + "4536c7080de841acaaec729cdf955c0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5040,51 +4803,70 @@ "width": null } }, - "67bb2dfe0bed499ea09cbd03e6cf7fd1": { + "453bcb1f0a7e4e41aa72dab72cbeed75": { "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_470cbbcbb3d44bb493031e8387fe6adc", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_42e73c1ad6b74b238d722d1c7ee516b1", + "layout": "IPY_MODEL_103b52da5fd24c24a14b4605ba5c3120", + "placeholder": "​", + "style": "IPY_MODEL_9d8907e922884b4c98182bd90146f805", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": " 40/40 [00:00<00:00, 60.08it/s]" } }, - "6b4971a3c52247978439a6e52e61bca9": { + "46c5f1e4a9ca403d83a2aa33da63b600": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_667ac6e91c614fc09ad352122a696161", + "IPY_MODEL_59358b3c018e40c1b73065b80810af12", + "IPY_MODEL_08fa3b07fef4433e8a950677af858234" + ], + "layout": "IPY_MODEL_4251756ae8d344e8923025afa5b3be9a", + "tabbable": null, + "tooltip": null + } + }, + "474d936989bb4423af34fd74a8db3b23": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "6bf38f634a0d459bbfabdc8331eb6e3b": { + "485dd48b720d4e1dbfce8f76953b154e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5137,60 +4919,49 @@ "width": null } }, - "70c9b3de67694dc4a8c38a542842ae3c": { - "model_module": "@jupyter-widgets/base", + "489746a2a7db4406b7ebfd5f2a155361": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ae4bdbf2d6494345acc4f7003fcd9cd9", + "IPY_MODEL_dd4b960c1fc54808ac7ec7abfbe1ccbc", + "IPY_MODEL_37059badcffc423fbad95494b9745859" + ], + "layout": "IPY_MODEL_73593795a4ac4b21a0677dccf1a525cb", + "tabbable": null, + "tooltip": null + } + }, + "4a74983359df462095cf64e3c2706a4b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "71c3b5aba9c94572b460be6639753985": { + "4ae6fd916d6d470190a53d061fb0b02f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5243,7 +5014,23 @@ "width": null } }, - "720932772aa64aba973045693c189137": { + "4d2f1756b094437bb3ff9f30ea93cbb2": { + "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": "" + } + }, + "4de2163b87c545ed89dfc257d6a7e304": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5296,7 +5083,7 @@ "width": null } }, - "7437bf57da394059a3d162f5d7a1a77a": { + "50aef71653f04eb49e220d1870cbf7ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5349,7 +5136,7 @@ "width": null } }, - "744802ca32e849ab9fea4e8e3861b154": { + "52638684784d4b5fb05e436a7f614243": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5364,38 +5151,107 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f19d1922e1974c3fa19988e4d4701f69", + "layout": "IPY_MODEL_ed7068ef4b08440eaae3bd1ba5cca147", "placeholder": "​", - "style": "IPY_MODEL_f924af4748c740ce948540ad741071d9", + "style": "IPY_MODEL_91c032ac4bfb4eceba18d1021672143e", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": " 4.83k/4.83k [00:00<00:00, 586kB/s]" } }, - "74a2396570a8494a948c33421fd20593": { + "55c0a386d760485f92009bb75259396b": { "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", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ed6f311b0f784e268d2c2f765d55a94a", + "IPY_MODEL_9e8ad22d82bc455da31ccd997a15763c", + "IPY_MODEL_307355aaf24e41138019598af025ac1b" + ], + "layout": "IPY_MODEL_1232a69e4cd748539b2d4cdde35b417c", + "tabbable": null, + "tooltip": null + } + }, + "5788b965507d455ca17eabfc155de530": { + "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_32b6d99a486e461eacd6e063c5420eff", - "placeholder": "​", - "style": "IPY_MODEL_8ec14b76495d43b599a2c06778d3e9c7", + "layout": "IPY_MODEL_b37bca5ee3994510a0c5bf88495f1a10", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6c1210fd37c047b69d6bb77d7a585576", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "59358b3c018e40c1b73065b80810af12": { + "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_6b3ea7da7caf4dc294c20842ed8cac54", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3764b604f5ba43288217fe3f4372b542", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:35<00:00, 1632.10it/s]" + "value": 26421880.0 + } + }, + "5a5ab3c1640f48e7953b2212cf787f29": { + "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": "" } }, - "74f4d829ffbb4508957bfb49f70fe734": { + "5aed3d8999204da4abd99966f9859da8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5448,7 +5304,7 @@ "width": null } }, - "757e89f53d174fc6b18a1a8380eb5700": { + "5c2cc530fc7746919e9df959510c6dc7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5501,23 +5357,51 @@ "width": null } }, - "778338997ef84774b5fc1b3e363f63c9": { + "5cbd075f38af4a35bd49255f7c6854c3": { "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 + } + }, + "61530f93052c4966a3ed9c90ec90c547": { + "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_0fc5778c044d4fcd9e8f745663d100c5", + "max": 5148.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bbcf2512f0a048e99a7d2ad46cb7b594", + "tabbable": null, + "tooltip": null, + "value": 5148.0 } }, - "77b7d71484644232938d588a6fbcc4ed": { + "629ccac58fe74198b172012097674576": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5570,102 +5454,69 @@ "width": null } }, - "7824f895073b4989aabcc1118fe1e570": { + "63e3c00666534cf78aef58362ab798ef": { "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_8abe4aff024b461ea93d3d85dd84f1f7", + "placeholder": "​", + "style": "IPY_MODEL_5cbd075f38af4a35bd49255f7c6854c3", + "tabbable": null, + "tooltip": null, + "value": "Downloading builder script: 100%" } }, - "789ca2ff29a44242935c400b234b1969": { - "model_module": "@jupyter-widgets/base", + "63f97e8188cb4370abab083114492da0": { + "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": "" } }, - "79a15df271d14bfa8e4ed6dbe1c37a8a": { + "667ac6e91c614fc09ad352122a696161": { "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_0f7c857000414450bdc92196b4466489", - "IPY_MODEL_2a37f3b6e2e64eda9d5aad4e57bb6475", - "IPY_MODEL_beba078ac8d64eb893f533808fb9cfdd" - ], - "layout": "IPY_MODEL_1fbec845f4cf42bb8efb9b83163e0b9b", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f8852f488ece4717923d24f0f949869a", + "placeholder": "​", + "style": "IPY_MODEL_11828cd7a97146ac81748612b0601834", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Downloading data: 100%" } }, - "7a1637c606824542971bdd53178cb547": { + "6a96595df722450495b69e3d9407c566": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5683,7 +5534,7 @@ "text_color": null } }, - "7ea218ed3bac4e3fb0038d64392dab79": { + "6b3ea7da7caf4dc294c20842ed8cac54": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5736,166 +5587,93 @@ "width": null } }, - "7f8b4b75f0c545ac966463807f510230": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "815effa183cf4ca4a7160696d4e9eb83": { - "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_a36a0824e2bc4b84adf3d6f24bae08e7", - "IPY_MODEL_f05de778ce8641daad6aaad095cd0a12", - "IPY_MODEL_fa8ee62999bf48be85571066708c3a70" - ], - "layout": "IPY_MODEL_c22ca0c1ac314ae3b7526a4ab19f5d59", - "tabbable": null, - "tooltip": null - } - }, - "826306b901dc424696b95e114b1441ca": { + "6c1210fd37c047b69d6bb77d7a585576": { "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_5efe1df058ed48ae9674a8448d855c64", - "placeholder": "​", - "style": "IPY_MODEL_569d3453747c444a9c2661732c8b1fb5", - "tabbable": null, - "tooltip": null, - "value": " 29.5k/29.5k [00:00<00:00, 4.61MB/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "8310ed48648242c0a8109e15f13f0f84": { + "6c53714694714ec184a3175a51eca22d": { "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_bf1140bdc30747b7875150c89a9b56d2", - "placeholder": "​", - "style": "IPY_MODEL_c9d9f8c0348541bc93ce2df33d9b1138", - "tabbable": null, - "tooltip": null, - "value": " 4/4 [00:00<00:00, 1391.72it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "85af3d53abef4aa8a6046017943dc826": { + "6f6e9c74347e4652b414406bd4c67238": { "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_f293407e061d47a9b2a576f458cfc91a", - "IPY_MODEL_67bb2dfe0bed499ea09cbd03e6cf7fd1", - "IPY_MODEL_0637c05b667541e782a074923de85b45" - ], - "layout": "IPY_MODEL_db5a222bfbda444ca14ba447f3de1c6b", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "85c627e125a94180abe254acf928a1fc": { + "6fa798d1f46540b9bd3e051f98c21430": { "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_0419dce41e82427eba5f201dcd3224ce", - "IPY_MODEL_fe9dd2d447a247b29fd9568449e4c772", - "IPY_MODEL_f1b29538d40a44f88d457938817aa9ca" - ], - "layout": "IPY_MODEL_ddf398ca235a4371928fa707d2edfab3", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "877f435c18c84cdb9acff7996168eee1": { + "71217b3eed354f73861bde4bfaf66ffd": { "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_ff64e02006ba4b3b90e1f252f3c58e25", - "placeholder": "​", - "style": "IPY_MODEL_8dcbfb9f172b4a8296952470fd844435", - "tabbable": null, - "tooltip": null, - "value": "Generating test split: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "88408a65c5eb4eb48911a14aadbc74cf": { + "715dab8821f5465eade13b7f8765ed47": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5948,25 +5726,7 @@ "width": null } }, - "8946f270b2954b22801e92ce10bf7c63": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "895c5faa034242eeb651e5c6bf3b1a51": { + "72b19493a1ca42c793bcda945b230961": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6019,7 +5779,7 @@ "width": null } }, - "8a47898db9234aa796ec7fb93a91d6e9": { + "73593795a4ac4b21a0677dccf1a525cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6072,25 +5832,7 @@ "width": null } }, - "8dcbfb9f172b4a8296952470fd844435": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "8ec14b76495d43b599a2c06778d3e9c7": { + "7439b162e309483580266f93e7ac4add": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6108,7 +5850,7 @@ "text_color": null } }, - "90c7589ac13b443eb422dd2f647490a3": { + "7461d590e55e4b9389cb1fa4b8b01858": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6161,7 +5903,30 @@ "width": null } }, - "965ba01b14474336b39a9ed4b2b5dcb5": { + "7a70b6d736914713b75013fd280cd1fb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5aed3d8999204da4abd99966f9859da8", + "placeholder": "​", + "style": "IPY_MODEL_003b69c44f834dd6bd767bd85d0282c1", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "7b9f6a000ece4e85bd677afa0c44a483": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6176,15 +5941,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f984f1911a3a4d3d97fb914d1b6abbce", + "layout": "IPY_MODEL_a261db39bdb34321bc03d5b7cdb18bd6", "placeholder": "​", - "style": "IPY_MODEL_03ab3894bf4242d8999349575ac3ab13", + "style": "IPY_MODEL_4a74983359df462095cf64e3c2706a4b", "tabbable": null, "tooltip": null, - "value": " 10000/10000 [00:01<00:00, 8813.96 examples/s]" + "value": " 40/40 [00:00<00:00, 61.37it/s]" } }, - "96d31dd7865d4b63adc5a9c556d6d472": { + "7ba456d195e144f7ba05947f8e466206": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6237,7 +6002,30 @@ "width": null } }, - "9a2bf66dd6c64060b9a09eaba40a26a8": { + "7c3c537f030046e892febf09fbb3953f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_42f688e5fa0a4f698ca681a4433e115d", + "placeholder": "​", + "style": "IPY_MODEL_a034a2ade0914168baff132569eeeb46", + "tabbable": null, + "tooltip": null, + "value": "Downloading readme: 100%" + } + }, + "7defd4db8e0e4d9d806b06b0ad18bad9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6290,70 +6078,7 @@ "width": null } }, - "9abe31b01bc04cc89ff967d26e368fdf": { - "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_1a2d0d43f24f4a179401f7bde3fcbf11", - "IPY_MODEL_e87b060be03d4626b55c248e0abe4a42", - "IPY_MODEL_007876448c144ee39084540ed6eb06b9" - ], - "layout": "IPY_MODEL_9a2bf66dd6c64060b9a09eaba40a26a8", - "tabbable": null, - "tooltip": null - } - }, - "9b58b8eff8e745eb9e937354fedb1807": { - "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": "" - } - }, - "9bb5412853cd41ffb7c03f1615108a7c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d69fd7a89a8640babdbb4265c990aff2", - "placeholder": "​", - "style": "IPY_MODEL_a40425de9d3b4923a12d5dfc419b6cf3", - "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" - } - }, - "9c72cef284e84cdaa2856f28c3593985": { + "7f159f23ebfd44699351ad51261161f4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6371,25 +6096,60 @@ "text_color": null } }, - "9d3b7c0890814f2c9985eb79f97a04ac": { - "model_module": "@jupyter-widgets/controls", + "816719582fe14c11a648b648bb9e2cbb": { + "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 } }, - "9dd47964eda34050b58a0ab2b3415ef0": { + "839cc7aeb6cf41cba2328b772dda1354": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6442,7 +6202,7 @@ "width": null } }, - "9fb3ff4fed8c462793870fe1a08d713c": { + "83fe2423c4494c2b9394412c402eca60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6460,7 +6220,7 @@ "text_color": null } }, - "a36a0824e2bc4b84adf3d6f24bae08e7": { + "865a8adbaefe44eb930f2df86dcd3a25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6475,15 +6235,39 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_46692e2429834f6b8ad6d2e3bd669ac5", + "layout": "IPY_MODEL_f35f77b3dc2d4f87b2f6435eb17b5f51", "placeholder": "​", - "style": "IPY_MODEL_0120148eaefd42fd9a38edc77bc35ac7", + "style": "IPY_MODEL_231c040b6a13479f944b5bef41c9994a", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": "Map (num_proc=4): 100%" + } + }, + "8835da69dbeb4826a96baa0561232a18": { + "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_f99e5e0dbb254dd0a57ef096794a0277", + "IPY_MODEL_9444514d4589415d940c94d2a40f0c7a", + "IPY_MODEL_7b9f6a000ece4e85bd677afa0c44a483" + ], + "layout": "IPY_MODEL_8a730989a49141659124fe8758fe6b9f", + "tabbable": null, + "tooltip": null } }, - "a40425de9d3b4923a12d5dfc419b6cf3": { + "885a1c18bad74f47ae49ef86de2c3a67": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6501,7 +6285,7 @@ "text_color": null } }, - "a67a308439454f8eb0f02917f9c68472": { + "8a730989a49141659124fe8758fe6b9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6554,25 +6338,7 @@ "width": null } }, - "a75ebf7ef9174d5e9cc59c872badcf44": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a7668c39f7324f3abf6dee2fb270336f": { + "8abe4aff024b461ea93d3d85dd84f1f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6625,55 +6391,7 @@ "width": null } }, - "a9c34fb99987402ba4f521a988475574": { - "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_d76da02ff0a84d74ad2c21a60fdbf6a8", - "IPY_MODEL_12693e79b18144d2be2b4b4399f4789e", - "IPY_MODEL_826306b901dc424696b95e114b1441ca" - ], - "layout": "IPY_MODEL_594f571cc61a464d8934a2a6ebaff1ed", - "tabbable": null, - "tooltip": null - } - }, - "abcdd0c8b869449f849ce66d0123d168": { - "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_c336f45d284843d786cfce257d33793c", - "IPY_MODEL_4439d6c4067d42b2bc7b5b49e651de05", - "IPY_MODEL_8310ed48648242c0a8109e15f13f0f84" - ], - "layout": "IPY_MODEL_cb33ab7c5bf847199febe8a7955b0a6b", - "tabbable": null, - "tooltip": null - } - }, - "af23bba915a74358b250ef2bb1a7d577": { + "8b39eaec8f5843a3942f7b65e5320363": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6726,7 +6444,25 @@ "width": null } }, - "b218eb25d1544b2198406c8d0daa0439": { + "8c6b20c613cf48bd9f826dbeb5124369": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8ce5df9efdba4fd1bad996ee729dec94": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6779,23 +6515,25 @@ "width": null } }, - "b461446aa899462d865a83e0c727b5c5": { + "91c032ac4bfb4eceba18d1021672143e": { "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 } }, - "b496abba866a4243bc5d3e643a27bfaa": { + "91e935457d624042a2d02c107985c146": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6810,57 +6548,41 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5a36ed53de5c4a9c8637a7b4013dfb54", + "layout": "IPY_MODEL_3e86a20ac6b044d4adccfeaba4245d25", "placeholder": "​", - "style": "IPY_MODEL_c5dd4307fb0e435f9e6eecf7eba58ea3", + "style": "IPY_MODEL_e522f4fa76294f30ae3dc6a49425837b", "tabbable": null, "tooltip": null, - "value": " 4.83k/4.83k [00:00<00:00, 604kB/s]" - } - }, - "b54eac280dff4e5dbe0c1d2b48743535": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 40/40 [00:00<00:00, 65.93it/s]" } }, - "b56125fc059b47e3b228dc3ed3b629c0": { + "92f49f3012f849ceaece9962b9690310": { "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_38eb030b83c64212b25df93cfe516570", - "IPY_MODEL_1c96211615924c808f262fae38d4ab01", - "IPY_MODEL_bed8037a62494179a7e38b4603feef04" - ], - "layout": "IPY_MODEL_8a47898db9234aa796ec7fb93a91d6e9", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_373768b5fd4c42118d61459d55c65ad2", + "max": 29515.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d3230f794b7e47c9beebfa7400ee3522", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 29515.0 } }, - "b9a030c7a18e43e0a9ade7e2489ba818": { + "92fcc9156b2a48328c1fb11582bdbb81": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6913,23 +6635,7 @@ "width": null } }, - "bb0480efe0bc4bd3801a7f7315323cf5": { - "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": "" - } - }, - "bb91a65b9f7c4bcbae0545c6e0c0c603": { + "9444514d4589415d940c94d2a40f0c7a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -6945,17 +6651,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_757e89f53d174fc6b18a1a8380eb5700", + "layout": "IPY_MODEL_7ba456d195e144f7ba05947f8e466206", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_05b056eafe984430926b52278f208393", + "style": "IPY_MODEL_d837ff833a594ea2b02ce7f7523c9dcd", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "bdbb1b6b96824b1ba8715b85852886fe": { + "99fb59566db2452bab382261d05e2879": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6970,16 +6676,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_877f435c18c84cdb9acff7996168eee1", - "IPY_MODEL_03b60960744f469c8562f78a5948c4a9", - "IPY_MODEL_965ba01b14474336b39a9ed4b2b5dcb5" + "IPY_MODEL_63e3c00666534cf78aef58362ab798ef", + "IPY_MODEL_bee58d3e475246fba7aa8b971564b1a7", + "IPY_MODEL_52638684784d4b5fb05e436a7f614243" ], - "layout": "IPY_MODEL_4b1c1c0a8eb2477b91cae9a52c47e58c", + "layout": "IPY_MODEL_dbc28ec812a84d3f80f20f8fcab2939a", "tabbable": null, "tooltip": null } }, - "beba078ac8d64eb893f533808fb9cfdd": { + "9b8b5db0d4ec47ae8f14fbae0720d3e0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6994,38 +6700,142 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_789ca2ff29a44242935c400b234b1969", + "layout": "IPY_MODEL_d1333e62f1304c11beca8dd72e8aa9f9", "placeholder": "​", - "style": "IPY_MODEL_0d79223cae9246eaaa4f673e81a780a8", + "style": "IPY_MODEL_c2680fc6a8544c869d7830d31ed37cea", "tabbable": null, "tooltip": null, - "value": " 5.15k/5.15k [00:00<00:00, 823kB/s]" + "value": " 40/40 [00:00<00:00, 59.41it/s]" } }, - "bed8037a62494179a7e38b4603feef04": { + "9c8e988dcc76462ea4a6543f9f1dd759": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { - "_dom_classes": [], + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f08b9948f05248b3b3ef10bd15ffc7d8", + "placeholder": "​", + "style": "IPY_MODEL_bcf73aedcb8442be80cd3903dca5729d", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "9d8907e922884b4c98182bd90146f805": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "9e8ad22d82bc455da31ccd997a15763c": { + "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_b9a6301405d54fc48fe5e9c1fcb3ff2f", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4d2f1756b094437bb3ff9f30ea93cbb2", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "9f427bf1023e48e693afcd4ee468c279": { + "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": "" + } + }, + "9f7de506f89c48b29c2cbb938a4e7210": { + "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_7461d590e55e4b9389cb1fa4b8b01858", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_42c5eeb19b2a4542b4159e63110273e4", + "tabbable": null, + "tooltip": null, + "value": 4.0 + } + }, + "a034a2ade0914168baff132569eeeb46": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { "_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_af23bba915a74358b250ef2bb1a7d577", - "placeholder": "​", - "style": "IPY_MODEL_4cfef00083ee41c18334522b0aaafbf6", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.66it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bf1140bdc30747b7875150c89a9b56d2": { + "a261db39bdb34321bc03d5b7cdb18bd6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7078,7 +6888,48 @@ "width": null } }, - "bf14a805f3944138b263cb978e9fcf8b": { + "a3d6f09afbca4e6482b56fd6d71d9b25": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a518055f2d994914bf1de3d11ea03a82": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c32f5e002a7a4c4ea804958d4deec82a", + "placeholder": "​", + "style": "IPY_MODEL_f332684f2b70406ab76c95d24f2f40ed", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "a805af9f6458409cbfcf1ef1829794dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7131,7 +6982,33 @@ "width": null } }, - "c1899c2c83a146b4b5eccb8d3c0892ae": { + "aae89dad290345f5acc2526904d6d4e5": { + "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_38bd0231f7c34338a8ccd2536adb5809", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c78a7456a9d940dd9a6d4e769ce24dc3", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "ae4bdbf2d6494345acc4f7003fcd9cd9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7146,15 +7023,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9dd47964eda34050b58a0ab2b3415ef0", + "layout": "IPY_MODEL_32d10bf33ee54d2bac909d0748596dac", "placeholder": "​", - "style": "IPY_MODEL_da7255e4a0a1407392cd379cdb23eb3c", + "style": "IPY_MODEL_bfc04f78f5024cc4b45fce30bcc4e7b2", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.06it/s]" + "value": "Generating train split: 100%" + } + }, + "afdd5bafd5ba49909abaaef0bb7f6038": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c1f07c0e6cb7453cb234322863c6974b": { + "b37bca5ee3994510a0c5bf88495f1a10": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7207,7 +7102,25 @@ "width": null } }, - "c22ca0c1ac314ae3b7526a4ab19f5d59": { + "b45281dbfb444428b286e76808d4a658": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "b5852aa7675d4ad7ab4bb8a846b7eead": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7260,48 +7173,33 @@ "width": null } }, - "c336f45d284843d786cfce257d33793c": { + "b602e59393ab410ba774d7c2419c556d": { "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_670532c752594999bbc5db2f86da0bfa", - "placeholder": "​", - "style": "IPY_MODEL_9fb3ff4fed8c462793870fe1a08d713c", + "layout": "IPY_MODEL_2d69655ca2a545e9a4b05f6865cdf2a8", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9f427bf1023e48e693afcd4ee468c279", "tabbable": null, "tooltip": null, - "value": "Computing checksums: 100%" - } - }, - "c5dd4307fb0e435f9e6eecf7eba58ea3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": 4422102.0 } }, - "c63bcc18843a48cd9b4c1b91951a299e": { + "b6bcf233e0084809823abddff92adea2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7316,15 +7214,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_71c3b5aba9c94572b460be6639753985", + "layout": "IPY_MODEL_4de2163b87c545ed89dfc257d6a7e304", "placeholder": "​", - "style": "IPY_MODEL_7a1637c606824542971bdd53178cb547", + "style": "IPY_MODEL_7439b162e309483580266f93e7ac4add", "tabbable": null, "tooltip": null, - "value": "Downloading builder script: 100%" + "value": " 8.85k/8.85k [00:00<00:00, 1.46MB/s]" } }, - "c6b17708d8404f668a926cfeef0a478f": { + "b9a6301405d54fc48fe5e9c1fcb3ff2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7377,25 +7275,47 @@ "width": null } }, - "c8922b8978bf4fc58458e70640f1b5c5": { + "b9c41de7ac0442aabfb15bbf3b5308c8": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3779e0b1881642b8b80007dfa03126bb", + "IPY_MODEL_40e314c496664e698c489a3f5c279e1c", + "IPY_MODEL_c6f60e04093c46f981b6f7b80f94e2d4" + ], + "layout": "IPY_MODEL_d6092c5dc4c74213a968d6db282bed10", + "tabbable": null, + "tooltip": null + } + }, + "bbcf2512f0a048e99a7d2ad46cb7b594": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "c9d9f8c0348541bc93ce2df33d9b1138": { + "bcf73aedcb8442be80cd3903dca5729d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7413,78 +7333,56 @@ "text_color": null } }, - "cb33ab7c5bf847199febe8a7955b0a6b": { - "model_module": "@jupyter-widgets/base", + "bee58d3e475246fba7aa8b971564b1a7": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b5852aa7675d4ad7ab4bb8a846b7eead", + "max": 4833.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5a5ab3c1640f48e7953b2212cf787f29", + "tabbable": null, + "tooltip": null, + "value": 4833.0 } }, - "cd436773aeeb4e5f811c805cd47f7071": { + "bf0ad0d606f04b7aa5013f43b6f32049": { "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_4536c7080de841acaaec729cdf955c0a", + "placeholder": "​", + "style": "IPY_MODEL_6a96595df722450495b69e3d9407c566", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "d067169b2a1745bd893cbaa56705df57": { + "bfc04f78f5024cc4b45fce30bcc4e7b2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7502,7 +7400,7 @@ "text_color": null } }, - "d10a94f6f1994a14b0ce8071ec84d170": { + "c1f41731ec4247e3ab4c57371cd20bf3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7555,7 +7453,25 @@ "width": null } }, - "d50d3c5c4ecd49cfbbcb44e787a993ba": { + "c2680fc6a8544c869d7830d31ed37cea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c32f5e002a7a4c4ea804958d4deec82a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7608,7 +7524,53 @@ "width": null } }, - "d65cb8246aa14189b49a0eeae6f3bad0": { + "c6eb4afdd2a54d899795e4359d8c989d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5c2cc530fc7746919e9df959510c6dc7", + "placeholder": "​", + "style": "IPY_MODEL_22ece7076054425f8784c9f44cd9c512", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "c6f60e04093c46f981b6f7b80f94e2d4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_92fcc9156b2a48328c1fb11582bdbb81", + "placeholder": "​", + "style": "IPY_MODEL_e06347bb350642c28c5755357c9f6b4f", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:01<00:00, 8738.82 examples/s]" + } + }, + "c75b1e8b04fc4bbe8b789d1cfa5fe576": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -7623,16 +7585,32 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_ee7c6e7d3d1b43138e41e0146c75cd98", - "IPY_MODEL_4268b194fef1470096c0d165c23da82f", - "IPY_MODEL_74a2396570a8494a948c33421fd20593" + "IPY_MODEL_d8736cab840549168545a3a77aa8e714", + "IPY_MODEL_9f7de506f89c48b29c2cbb938a4e7210", + "IPY_MODEL_dc708bb2c5314b17862c251eeb9db099" ], - "layout": "IPY_MODEL_d10a94f6f1994a14b0ce8071ec84d170", + "layout": "IPY_MODEL_22e4e1da97fe49e290e9dac292880148", "tabbable": null, "tooltip": null } }, - "d69fd7a89a8640babdbb4265c990aff2": { + "c78a7456a9d940dd9a6d4e769ce24dc3": { + "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": "" + } + }, + "c82a1f05b0ec418a9670fcf0e44708f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7685,7 +7663,7 @@ "width": null } }, - "d6b409257aab47b888b2954d7b04073e": { + "c8e6913e68754e7a9e614de447103505": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7738,101 +7716,81 @@ "width": null } }, - "d76da02ff0a84d74ad2c21a60fdbf6a8": { + "caabab78e31d438eb521e9c3f2e1c0a6": { "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_2426b36b4bcd4538a7a1a6c6f443b609", - "placeholder": "​", - "style": "IPY_MODEL_c8922b8978bf4fc58458e70640f1b5c5", + "layout": "IPY_MODEL_c1f41731ec4247e3ab4c57371cd20bf3", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6fa798d1f46540b9bd3e051f98c21430", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": 40.0 } }, - "da7255e4a0a1407392cd379cdb23eb3c": { + "cacaca4358c34e93a46a3e2019d188d4": { "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_7c3c537f030046e892febf09fbb3953f", + "IPY_MODEL_41aa359adc6640ab9d5ea2369493623a", + "IPY_MODEL_b6bcf233e0084809823abddff92adea2" + ], + "layout": "IPY_MODEL_fb9b135b97bd45978b3759750aac7be4", + "tabbable": null, + "tooltip": null } }, - "db5a222bfbda444ca14ba447f3de1c6b": { - "model_module": "@jupyter-widgets/base", + "cc7010cd50844e48a3db713a6ea5f850": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_311422b25c3d435280a7ae2e6c1b5cbc", + "IPY_MODEL_92f49f3012f849ceaece9962b9690310", + "IPY_MODEL_20aa2cb116fa42218107305be2c9121a" + ], + "layout": "IPY_MODEL_20e8c6a9ab644e3da69655bfbc794894", + "tabbable": null, + "tooltip": null } }, - "ddf398ca235a4371928fa707d2edfab3": { + "d1333e62f1304c11beca8dd72e8aa9f9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7885,30 +7843,23 @@ "width": null } }, - "e0619bd42a814114974327c4c92cbc96": { + "d3230f794b7e47c9beebfa7400ee3522": { "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_6566ace6c0aa4a21abbf6d708cb457b0", - "placeholder": "​", - "style": "IPY_MODEL_e45644a5a73d43e8a12aaa8ea0e71934", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:10<00:00, 7472.24 examples/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "e2caa9efa27847b7b3dd441f4a803cb2": { + "d6092c5dc4c74213a968d6db282bed10": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7961,7 +7912,7 @@ "width": null } }, - "e39cdb7fe88f4310bfd8b1dcd8ac5ae2": { + "d6337601ed54490eb0d452da36303706": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7976,33 +7927,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_720932772aa64aba973045693c189137", + "layout": "IPY_MODEL_c8e6913e68754e7a9e614de447103505", "placeholder": "​", - "style": "IPY_MODEL_39314c631f444d01903b4f50ccfc66df", + "style": "IPY_MODEL_6c53714694714ec184a3175a51eca22d", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.47it/s]" + "value": " 4.42M/4.42M [00:00<00:00, 95.7MB/s]" } }, - "e45644a5a73d43e8a12aaa8ea0e71934": { + "d837ff833a594ea2b02ce7f7523c9dcd": { "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": "" } }, - "e7dae7ac77284ca7a938a5140aea14ba": { + "d8441cb494324dad83d4a88dd3c805e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8055,57 +8004,7 @@ "width": null } }, - "e85af83531bc4182b052d4cfe7f1020e": { - "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_c63bcc18843a48cd9b4c1b91951a299e", - "IPY_MODEL_f0a66657e7724cf4a18612d673b0df8e", - "IPY_MODEL_b496abba866a4243bc5d3e643a27bfaa" - ], - "layout": "IPY_MODEL_420adb2a809a4b7693bda83cb040e60c", - "tabbable": null, - "tooltip": null - } - }, - "e87b060be03d4626b55c248e0abe4a42": { - "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_0261398aaa894092b6eca7f630c39440", - "max": 8845.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5d69e819f44b493a8d07e58cb39a6a69", - "tabbable": null, - "tooltip": null, - "value": 8845.0 - } - }, - "eb56ed8bd1a74a87a2a7cf25c0992594": { + "d8736cab840549168545a3a77aa8e714": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8120,15 +8019,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4b9e51775e974654a075cfdb1d1b699c", + "layout": "IPY_MODEL_11e93782fa6f464e9f6900e0c646f5cf", "placeholder": "​", - "style": "IPY_MODEL_a75ebf7ef9174d5e9cc59c872badcf44", + "style": "IPY_MODEL_83fe2423c4494c2b9394412c402eca60", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Computing checksums: 100%" } }, - "ec23491fb0024cb8a1b7dfd205775551": { + "dbc28ec812a84d3f80f20f8fcab2939a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8181,7 +8080,56 @@ "width": null } }, - "ecde01d38be144db9d996498b953e5b4": { + "dc708bb2c5314b17862c251eeb9db099": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c82a1f05b0ec418a9670fcf0e44708f1", + "placeholder": "​", + "style": "IPY_MODEL_3c76ee6a2244430cbb3e8e8416d1a23c", + "tabbable": null, + "tooltip": null, + "value": " 4/4 [00:00<00:00, 1332.58it/s]" + } + }, + "dd4b960c1fc54808ac7ec7abfbe1ccbc": { + "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_7defd4db8e0e4d9d806b06b0ad18bad9", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_04bfeccaeea6416e98641eb5e663ae0b", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "dd549dafe96d457a9f3e32fa6dda0ce5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8234,108 +8182,83 @@ "width": null } }, - "ee448c322caa4ddbae258419893e01e8": { + "e06347bb350642c28c5755357c9f6b4f": { "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_c6b17708d8404f668a926cfeef0a478f", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_778338997ef84774b5fc1b3e363f63c9", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ee7c6e7d3d1b43138e41e0146c75cd98": { + "e075f5bd416a447eb67433e0d225370f": { "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_96d31dd7865d4b63adc5a9c556d6d472", - "placeholder": "​", - "style": "IPY_MODEL_6b4971a3c52247978439a6e52e61bca9", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_865a8adbaefe44eb930f2df86dcd3a25", + "IPY_MODEL_2253900d1539484ea6c5fb5f87e34fae", + "IPY_MODEL_f669341590db4e77b2110a49a3fa808c" + ], + "layout": "IPY_MODEL_431d46eafa8c450b8c8f1bf1f7883cf3", "tabbable": null, - "tooltip": null, - "value": "100%" + "tooltip": null } }, - "f05de778ce8641daad6aaad095cd0a12": { + "e522f4fa76294f30ae3dc6a49425837b": { "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_e2caa9efa27847b7b3dd441f4a803cb2", - "max": 4422102.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_12da0c3d2bd5449dbb811de7fd8b2093", - "tabbable": null, - "tooltip": null, - "value": 4422102.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f0a66657e7724cf4a18612d673b0df8e": { + "e675357d5efc42b3968d61c273ca5f7f": { "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_6bf38f634a0d459bbfabdc8331eb6e3b", - "max": 4833.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5c5b477c793d4588a25705aece5a2a2e", - "tabbable": null, - "tooltip": null, - "value": 4833.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f19d1922e1974c3fa19988e4d4701f69": { + "e7e60d48deb84f8681aa2b4aaa1ad387": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8388,7 +8311,7 @@ "width": null } }, - "f1b29538d40a44f88d457938817aa9ca": { + "e9b9c9bbe0374dd9b98a3a4c1553822f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8403,38 +8326,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d50d3c5c4ecd49cfbbcb44e787a993ba", + "layout": "IPY_MODEL_629ccac58fe74198b172012097674576", "placeholder": "​", - "style": "IPY_MODEL_fe0a6190f0794548839615a5f024d71f", + "style": "IPY_MODEL_1f7f1fac2dd34d07b7fc07f799729df6", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 60.94it/s]" + "value": " 40/40 [00:00<00:00, 59.81it/s]" } }, - "f293407e061d47a9b2a576f458cfc91a": { + "eb33301487694b0f9f4a664fce743134": { "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_42c998dc50734d8c93d723bb78939244", - "placeholder": "​", - "style": "IPY_MODEL_30a335f232c04fe7b668ed6a418d27b2", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f2d29dc28e7140b792fc1ee3fcb857cb": { + "ebc081ac7cef42f58f0c46bdca672b27": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -8449,42 +8367,145 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_9bb5412853cd41ffb7c03f1615108a7c", - "IPY_MODEL_1bb8a6dd435048b28340d4eac6730019", - "IPY_MODEL_e0619bd42a814114974327c4c92cbc96" + "IPY_MODEL_a518055f2d994914bf1de3d11ea03a82", + "IPY_MODEL_18e5b3625d2a4b3abbfea651a20eef56", + "IPY_MODEL_91e935457d624042a2d02c107985c146" ], - "layout": "IPY_MODEL_7ea218ed3bac4e3fb0038d64392dab79", + "layout": "IPY_MODEL_2bf71c3624ce4413bc347a20e07c26d5", "tabbable": null, "tooltip": null } }, - "f3a3a4eba3674c53882dfe2a1c081653": { + "ed6f311b0f784e268d2c2f765d55a94a": { "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_a67a308439454f8eb0f02917f9c68472", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2f9e9f6f7ebe42afa59802bdf7306ccb", + "layout": "IPY_MODEL_e7e60d48deb84f8681aa2b4aaa1ad387", + "placeholder": "​", + "style": "IPY_MODEL_17af51078a0c4c0f92db9850c8c773d7", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": "100%" + } + }, + "ed7068ef4b08440eaae3bd1ba5cca147": { + "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 + } + }, + "f08b9948f05248b3b3ef10bd15ffc7d8": { + "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 } }, - "f924af4748c740ce948540ad741071d9": { + "f332684f2b70406ab76c95d24f2f40ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -8502,7 +8523,7 @@ "text_color": null } }, - "f984f1911a3a4d3d97fb914d1b6abbce": { + "f35f77b3dc2d4f87b2f6435eb17b5f51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8555,7 +8576,7 @@ "width": null } }, - "fa8ee62999bf48be85571066708c3a70": { + "f669341590db4e77b2110a49a3fa808c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8570,33 +8591,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_895c5faa034242eeb651e5c6bf3b1a51", + "layout": "IPY_MODEL_8ce5df9efdba4fd1bad996ee729dec94", "placeholder": "​", - "style": "IPY_MODEL_9c72cef284e84cdaa2856f28c3593985", + "style": "IPY_MODEL_a3d6f09afbca4e6482b56fd6d71d9b25", "tabbable": null, "tooltip": null, - "value": " 4.42M/4.42M [00:00<00:00, 61.8MB/s]" - } - }, - "fe0a6190f0794548839615a5f024d71f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 60000/60000 [00:10<00:00, 5815.53 examples/s]" } }, - "fe37a821e1f04c6f9903e3722f9ab4c2": { + "f8852f488ece4717923d24f0f949869a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8649,33 +8652,30 @@ "width": null } }, - "fe9dd2d447a247b29fd9568449e4c772": { + "f99e5e0dbb254dd0a57ef096794a0277": { "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_112a5fd388db49e4b8a0f9dffab06426", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_bb0480efe0bc4bd3801a7f7315323cf5", + "layout": "IPY_MODEL_176f4fe537ab44709eed5f4771e5a748", + "placeholder": "​", + "style": "IPY_MODEL_b45281dbfb444428b286e76808d4a658", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "100%" } }, - "ff64e02006ba4b3b90e1f252f3c58e25": { + "fb9b135b97bd45978b3759750aac7be4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index d470496b0..7f5df08d9 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:36.731110Z", - "iopub.status.busy": "2024-06-25T19:35:36.730936Z", - "iopub.status.idle": "2024-06-25T19:35:37.834580Z", - "shell.execute_reply": "2024-06-25T19:35:37.833954Z" + "iopub.execute_input": "2024-06-25T23:17:19.488251Z", + "iopub.status.busy": "2024-06-25T23:17:19.488091Z", + "iopub.status.idle": "2024-06-25T23:17:20.586301Z", + "shell.execute_reply": "2024-06-25T23:17:20.585756Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.837363Z", - "iopub.status.busy": "2024-06-25T19:35:37.837076Z", - "iopub.status.idle": "2024-06-25T19:35:37.855298Z", - "shell.execute_reply": "2024-06-25T19:35:37.854810Z" + "iopub.execute_input": "2024-06-25T23:17:20.589007Z", + "iopub.status.busy": "2024-06-25T23:17:20.588566Z", + "iopub.status.idle": "2024-06-25T23:17:20.607142Z", + "shell.execute_reply": "2024-06-25T23:17:20.606704Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.857546Z", - "iopub.status.busy": "2024-06-25T19:35:37.857301Z", - "iopub.status.idle": "2024-06-25T19:35:37.902804Z", - "shell.execute_reply": "2024-06-25T19:35:37.902282Z" + "iopub.execute_input": "2024-06-25T23:17:20.609262Z", + "iopub.status.busy": "2024-06-25T23:17:20.608896Z", + "iopub.status.idle": "2024-06-25T23:17:20.630509Z", + "shell.execute_reply": "2024-06-25T23:17:20.630057Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.904835Z", - "iopub.status.busy": "2024-06-25T19:35:37.904541Z", - "iopub.status.idle": "2024-06-25T19:35:37.907889Z", - "shell.execute_reply": "2024-06-25T19:35:37.907366Z" + "iopub.execute_input": "2024-06-25T23:17:20.632342Z", + "iopub.status.busy": "2024-06-25T23:17:20.632168Z", + "iopub.status.idle": "2024-06-25T23:17:20.635695Z", + "shell.execute_reply": "2024-06-25T23:17:20.635234Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.909808Z", - "iopub.status.busy": "2024-06-25T19:35:37.909561Z", - "iopub.status.idle": "2024-06-25T19:35:37.917137Z", - "shell.execute_reply": "2024-06-25T19:35:37.916719Z" + "iopub.execute_input": "2024-06-25T23:17:20.637844Z", + "iopub.status.busy": "2024-06-25T23:17:20.637544Z", + "iopub.status.idle": "2024-06-25T23:17:20.644982Z", + "shell.execute_reply": "2024-06-25T23:17:20.644551Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.919119Z", - "iopub.status.busy": "2024-06-25T19:35:37.918944Z", - "iopub.status.idle": "2024-06-25T19:35:37.921447Z", - "shell.execute_reply": "2024-06-25T19:35:37.921009Z" + "iopub.execute_input": "2024-06-25T23:17:20.646840Z", + "iopub.status.busy": "2024-06-25T23:17:20.646673Z", + "iopub.status.idle": "2024-06-25T23:17:20.649384Z", + "shell.execute_reply": "2024-06-25T23:17:20.648911Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.923229Z", - "iopub.status.busy": "2024-06-25T19:35:37.923058Z", - "iopub.status.idle": "2024-06-25T19:35:40.863311Z", - "shell.execute_reply": "2024-06-25T19:35:40.862782Z" + "iopub.execute_input": "2024-06-25T23:17:20.651376Z", + "iopub.status.busy": "2024-06-25T23:17:20.651062Z", + "iopub.status.idle": "2024-06-25T23:17:23.603750Z", + "shell.execute_reply": "2024-06-25T23:17:23.603132Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:40.866333Z", - "iopub.status.busy": "2024-06-25T19:35:40.865865Z", - "iopub.status.idle": "2024-06-25T19:35:40.875269Z", - "shell.execute_reply": "2024-06-25T19:35:40.874719Z" + "iopub.execute_input": "2024-06-25T23:17:23.606640Z", + "iopub.status.busy": "2024-06-25T23:17:23.606173Z", + "iopub.status.idle": "2024-06-25T23:17:23.615532Z", + "shell.execute_reply": "2024-06-25T23:17:23.614991Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:40.877815Z", - "iopub.status.busy": "2024-06-25T19:35:40.877412Z", - "iopub.status.idle": "2024-06-25T19:35:42.759452Z", - "shell.execute_reply": "2024-06-25T19:35:42.758773Z" + "iopub.execute_input": "2024-06-25T23:17:23.617787Z", + "iopub.status.busy": "2024-06-25T23:17:23.617408Z", + "iopub.status.idle": "2024-06-25T23:17:25.503397Z", + "shell.execute_reply": "2024-06-25T23:17:25.502726Z" } }, "outputs": [ @@ -484,10 +484,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.761931Z", - "iopub.status.busy": "2024-06-25T19:35:42.761498Z", - "iopub.status.idle": "2024-06-25T19:35:42.780225Z", - "shell.execute_reply": "2024-06-25T19:35:42.779777Z" + "iopub.execute_input": "2024-06-25T23:17:25.506132Z", + "iopub.status.busy": "2024-06-25T23:17:25.505476Z", + "iopub.status.idle": "2024-06-25T23:17:25.524117Z", + "shell.execute_reply": "2024-06-25T23:17:25.523676Z" }, "scrolled": true }, @@ -617,10 +617,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.782418Z", - "iopub.status.busy": "2024-06-25T19:35:42.782011Z", - "iopub.status.idle": "2024-06-25T19:35:42.789930Z", - "shell.execute_reply": "2024-06-25T19:35:42.789485Z" + "iopub.execute_input": "2024-06-25T23:17:25.526096Z", + "iopub.status.busy": "2024-06-25T23:17:25.525830Z", + "iopub.status.idle": "2024-06-25T23:17:25.533770Z", + "shell.execute_reply": "2024-06-25T23:17:25.533230Z" } }, "outputs": [ @@ -724,10 +724,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.791897Z", - "iopub.status.busy": "2024-06-25T19:35:42.791568Z", - "iopub.status.idle": "2024-06-25T19:35:42.800098Z", - "shell.execute_reply": "2024-06-25T19:35:42.799646Z" + "iopub.execute_input": "2024-06-25T23:17:25.535755Z", + "iopub.status.busy": "2024-06-25T23:17:25.535435Z", + "iopub.status.idle": "2024-06-25T23:17:25.544816Z", + "shell.execute_reply": "2024-06-25T23:17:25.544397Z" } }, "outputs": [ @@ -856,10 +856,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.802085Z", - "iopub.status.busy": "2024-06-25T19:35:42.801908Z", - "iopub.status.idle": "2024-06-25T19:35:42.809958Z", - "shell.execute_reply": "2024-06-25T19:35:42.809510Z" + "iopub.execute_input": "2024-06-25T23:17:25.546828Z", + "iopub.status.busy": "2024-06-25T23:17:25.546524Z", + "iopub.status.idle": "2024-06-25T23:17:25.554523Z", + "shell.execute_reply": "2024-06-25T23:17:25.554077Z" } }, "outputs": [ @@ -973,10 +973,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.811794Z", - "iopub.status.busy": "2024-06-25T19:35:42.811623Z", - "iopub.status.idle": "2024-06-25T19:35:42.820374Z", - "shell.execute_reply": "2024-06-25T19:35:42.819927Z" + "iopub.execute_input": "2024-06-25T23:17:25.556497Z", + "iopub.status.busy": "2024-06-25T23:17:25.556176Z", + "iopub.status.idle": "2024-06-25T23:17:25.564618Z", + "shell.execute_reply": "2024-06-25T23:17:25.564170Z" } }, "outputs": [ @@ -1087,10 +1087,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.822187Z", - "iopub.status.busy": "2024-06-25T19:35:42.822017Z", - "iopub.status.idle": "2024-06-25T19:35:42.829544Z", - "shell.execute_reply": "2024-06-25T19:35:42.829102Z" + "iopub.execute_input": "2024-06-25T23:17:25.566583Z", + "iopub.status.busy": "2024-06-25T23:17:25.566262Z", + "iopub.status.idle": "2024-06-25T23:17:25.573703Z", + "shell.execute_reply": "2024-06-25T23:17:25.573162Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.831790Z", - "iopub.status.busy": "2024-06-25T19:35:42.831383Z", - "iopub.status.idle": "2024-06-25T19:35:42.838578Z", - "shell.execute_reply": "2024-06-25T19:35:42.838124Z" + "iopub.execute_input": "2024-06-25T23:17:25.575840Z", + "iopub.status.busy": "2024-06-25T23:17:25.575524Z", + "iopub.status.idle": "2024-06-25T23:17:25.582660Z", + "shell.execute_reply": "2024-06-25T23:17:25.582224Z" } }, "outputs": [ @@ -1308,10 +1308,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.840523Z", - "iopub.status.busy": "2024-06-25T19:35:42.840354Z", - "iopub.status.idle": "2024-06-25T19:35:42.848877Z", - "shell.execute_reply": "2024-06-25T19:35:42.848311Z" + "iopub.execute_input": "2024-06-25T23:17:25.584694Z", + "iopub.status.busy": "2024-06-25T23:17:25.584373Z", + "iopub.status.idle": "2024-06-25T23:17:25.592350Z", + "shell.execute_reply": "2024-06-25T23:17:25.591901Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 5e2df2074..47d0847e3 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:45.390789Z", - "iopub.status.busy": "2024-06-25T19:35:45.390619Z", - "iopub.status.idle": "2024-06-25T19:35:48.008658Z", - "shell.execute_reply": "2024-06-25T19:35:48.008097Z" + "iopub.execute_input": "2024-06-25T23:17:28.279893Z", + "iopub.status.busy": "2024-06-25T23:17:28.279723Z", + "iopub.status.idle": "2024-06-25T23:17:30.902204Z", + "shell.execute_reply": "2024-06-25T23:17:30.901649Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:35:48.011088Z", - "iopub.status.busy": "2024-06-25T19:35:48.010783Z", - "iopub.status.idle": "2024-06-25T19:35:48.014230Z", - "shell.execute_reply": "2024-06-25T19:35:48.013782Z" + "iopub.execute_input": "2024-06-25T23:17:30.904858Z", + "iopub.status.busy": "2024-06-25T23:17:30.904404Z", + "iopub.status.idle": "2024-06-25T23:17:30.907555Z", + "shell.execute_reply": "2024-06-25T23:17:30.907124Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.016267Z", - "iopub.status.busy": "2024-06-25T19:35:48.015916Z", - "iopub.status.idle": "2024-06-25T19:35:48.019094Z", - "shell.execute_reply": "2024-06-25T19:35:48.018529Z" + "iopub.execute_input": "2024-06-25T23:17:30.909531Z", + "iopub.status.busy": "2024-06-25T23:17:30.909235Z", + "iopub.status.idle": "2024-06-25T23:17:30.912305Z", + "shell.execute_reply": "2024-06-25T23:17:30.911777Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.021232Z", - "iopub.status.busy": "2024-06-25T19:35:48.020813Z", - "iopub.status.idle": "2024-06-25T19:35:48.073023Z", - "shell.execute_reply": "2024-06-25T19:35:48.072456Z" + "iopub.execute_input": "2024-06-25T23:17:30.914377Z", + "iopub.status.busy": "2024-06-25T23:17:30.913988Z", + "iopub.status.idle": "2024-06-25T23:17:30.934290Z", + "shell.execute_reply": "2024-06-25T23:17:30.933773Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.075330Z", - "iopub.status.busy": "2024-06-25T19:35:48.074995Z", - "iopub.status.idle": "2024-06-25T19:35:48.078963Z", - "shell.execute_reply": "2024-06-25T19:35:48.078513Z" + "iopub.execute_input": "2024-06-25T23:17:30.936266Z", + "iopub.status.busy": "2024-06-25T23:17:30.935961Z", + "iopub.status.idle": "2024-06-25T23:17:30.939627Z", + "shell.execute_reply": "2024-06-25T23:17:30.939095Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies'}\n" + "Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_about_to_expire', 'getting_spare_card', 'change_pin', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'cancel_transfer'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.080913Z", - "iopub.status.busy": "2024-06-25T19:35:48.080733Z", - "iopub.status.idle": "2024-06-25T19:35:48.083997Z", - "shell.execute_reply": "2024-06-25T19:35:48.083535Z" + "iopub.execute_input": "2024-06-25T23:17:30.941560Z", + "iopub.status.busy": "2024-06-25T23:17:30.941250Z", + "iopub.status.idle": "2024-06-25T23:17:30.944331Z", + "shell.execute_reply": "2024-06-25T23:17:30.943818Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.086043Z", - "iopub.status.busy": "2024-06-25T19:35:48.085869Z", - "iopub.status.idle": "2024-06-25T19:35:52.539336Z", - "shell.execute_reply": "2024-06-25T19:35:52.538772Z" + "iopub.execute_input": "2024-06-25T23:17:30.946381Z", + "iopub.status.busy": "2024-06-25T23:17:30.946063Z", + "iopub.status.idle": "2024-06-25T23:17:34.606408Z", + "shell.execute_reply": "2024-06-25T23:17:34.605752Z" } }, "outputs": [ @@ -424,10 +424,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:52.541851Z", - "iopub.status.busy": "2024-06-25T19:35:52.541641Z", - "iopub.status.idle": "2024-06-25T19:35:53.417381Z", - "shell.execute_reply": "2024-06-25T19:35:53.416793Z" + "iopub.execute_input": "2024-06-25T23:17:34.609229Z", + "iopub.status.busy": "2024-06-25T23:17:34.608851Z", + "iopub.status.idle": "2024-06-25T23:17:35.466411Z", + "shell.execute_reply": "2024-06-25T23:17:35.465834Z" }, "scrolled": true }, @@ -459,10 +459,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:53.420304Z", - "iopub.status.busy": "2024-06-25T19:35:53.419913Z", - "iopub.status.idle": "2024-06-25T19:35:53.422789Z", - "shell.execute_reply": "2024-06-25T19:35:53.422303Z" + "iopub.execute_input": "2024-06-25T23:17:35.469450Z", + "iopub.status.busy": "2024-06-25T23:17:35.469026Z", + "iopub.status.idle": "2024-06-25T23:17:35.471951Z", + "shell.execute_reply": "2024-06-25T23:17:35.471467Z" } }, "outputs": [], @@ -482,10 +482,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:53.425167Z", - "iopub.status.busy": "2024-06-25T19:35:53.424776Z", - "iopub.status.idle": "2024-06-25T19:35:55.333188Z", - "shell.execute_reply": "2024-06-25T19:35:55.332528Z" + "iopub.execute_input": "2024-06-25T23:17:35.474346Z", + "iopub.status.busy": "2024-06-25T23:17:35.473954Z", + "iopub.status.idle": "2024-06-25T23:17:37.379211Z", + "shell.execute_reply": "2024-06-25T23:17:37.378561Z" }, "scrolled": true }, @@ -537,10 +537,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.336733Z", - "iopub.status.busy": "2024-06-25T19:35:55.336306Z", - "iopub.status.idle": "2024-06-25T19:35:55.363099Z", - "shell.execute_reply": "2024-06-25T19:35:55.362613Z" + "iopub.execute_input": "2024-06-25T23:17:37.383383Z", + "iopub.status.busy": "2024-06-25T23:17:37.382233Z", + "iopub.status.idle": "2024-06-25T23:17:37.408704Z", + "shell.execute_reply": "2024-06-25T23:17:37.408212Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.366640Z", - "iopub.status.busy": "2024-06-25T19:35:55.365705Z", - "iopub.status.idle": "2024-06-25T19:35:55.376030Z", - "shell.execute_reply": "2024-06-25T19:35:55.375622Z" + "iopub.execute_input": "2024-06-25T23:17:37.412193Z", + "iopub.status.busy": "2024-06-25T23:17:37.411277Z", + "iopub.status.idle": "2024-06-25T23:17:37.421651Z", + "shell.execute_reply": "2024-06-25T23:17:37.421256Z" }, "scrolled": true }, @@ -783,10 +783,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.378837Z", - "iopub.status.busy": "2024-06-25T19:35:55.378517Z", - "iopub.status.idle": "2024-06-25T19:35:55.382599Z", - "shell.execute_reply": "2024-06-25T19:35:55.382206Z" + "iopub.execute_input": "2024-06-25T23:17:37.424437Z", + "iopub.status.busy": "2024-06-25T23:17:37.423704Z", + "iopub.status.idle": "2024-06-25T23:17:37.428917Z", + "shell.execute_reply": "2024-06-25T23:17:37.428520Z" } }, "outputs": [ @@ -824,10 +824,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.384693Z", - "iopub.status.busy": "2024-06-25T19:35:55.384439Z", - "iopub.status.idle": "2024-06-25T19:35:55.390208Z", - "shell.execute_reply": "2024-06-25T19:35:55.389819Z" + "iopub.execute_input": "2024-06-25T23:17:37.430883Z", + "iopub.status.busy": "2024-06-25T23:17:37.430707Z", + "iopub.status.idle": "2024-06-25T23:17:37.438445Z", + "shell.execute_reply": "2024-06-25T23:17:37.437883Z" } }, "outputs": [ @@ -944,10 +944,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.392385Z", - "iopub.status.busy": "2024-06-25T19:35:55.392130Z", - "iopub.status.idle": "2024-06-25T19:35:55.398230Z", - "shell.execute_reply": "2024-06-25T19:35:55.397669Z" + "iopub.execute_input": "2024-06-25T23:17:37.440387Z", + "iopub.status.busy": "2024-06-25T23:17:37.440214Z", + "iopub.status.idle": "2024-06-25T23:17:37.446599Z", + "shell.execute_reply": "2024-06-25T23:17:37.446157Z" } }, "outputs": [ @@ -1030,10 +1030,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.400097Z", - "iopub.status.busy": "2024-06-25T19:35:55.399777Z", - "iopub.status.idle": "2024-06-25T19:35:55.405709Z", - "shell.execute_reply": "2024-06-25T19:35:55.405249Z" + "iopub.execute_input": "2024-06-25T23:17:37.448520Z", + "iopub.status.busy": "2024-06-25T23:17:37.448196Z", + "iopub.status.idle": "2024-06-25T23:17:37.454046Z", + "shell.execute_reply": "2024-06-25T23:17:37.453485Z" } }, "outputs": [ @@ -1141,10 +1141,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.407786Z", - "iopub.status.busy": "2024-06-25T19:35:55.407389Z", - "iopub.status.idle": "2024-06-25T19:35:55.415929Z", - "shell.execute_reply": "2024-06-25T19:35:55.415484Z" + "iopub.execute_input": "2024-06-25T23:17:37.456157Z", + "iopub.status.busy": "2024-06-25T23:17:37.455839Z", + "iopub.status.idle": "2024-06-25T23:17:37.464219Z", + "shell.execute_reply": "2024-06-25T23:17:37.463796Z" } }, "outputs": [ @@ -1255,10 +1255,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.417871Z", - "iopub.status.busy": "2024-06-25T19:35:55.417696Z", - "iopub.status.idle": "2024-06-25T19:35:55.422924Z", - "shell.execute_reply": "2024-06-25T19:35:55.422488Z" + "iopub.execute_input": "2024-06-25T23:17:37.466195Z", + "iopub.status.busy": "2024-06-25T23:17:37.465883Z", + "iopub.status.idle": "2024-06-25T23:17:37.471233Z", + "shell.execute_reply": "2024-06-25T23:17:37.470679Z" } }, "outputs": [ @@ -1326,10 +1326,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.424972Z", - "iopub.status.busy": "2024-06-25T19:35:55.424657Z", - "iopub.status.idle": "2024-06-25T19:35:55.429929Z", - "shell.execute_reply": "2024-06-25T19:35:55.429503Z" + "iopub.execute_input": "2024-06-25T23:17:37.473304Z", + "iopub.status.busy": "2024-06-25T23:17:37.472970Z", + "iopub.status.idle": "2024-06-25T23:17:37.478474Z", + "shell.execute_reply": "2024-06-25T23:17:37.478028Z" } }, "outputs": [ @@ -1408,10 +1408,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.431977Z", - "iopub.status.busy": "2024-06-25T19:35:55.431649Z", - "iopub.status.idle": "2024-06-25T19:35:55.435259Z", - "shell.execute_reply": "2024-06-25T19:35:55.434820Z" + "iopub.execute_input": "2024-06-25T23:17:37.480531Z", + "iopub.status.busy": "2024-06-25T23:17:37.480222Z", + "iopub.status.idle": "2024-06-25T23:17:37.483860Z", + "shell.execute_reply": "2024-06-25T23:17:37.483411Z" } }, "outputs": [ @@ -1459,10 +1459,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.437276Z", - "iopub.status.busy": "2024-06-25T19:35:55.436956Z", - "iopub.status.idle": "2024-06-25T19:35:55.441824Z", - "shell.execute_reply": "2024-06-25T19:35:55.441386Z" + "iopub.execute_input": "2024-06-25T23:17:37.485748Z", + "iopub.status.busy": "2024-06-25T23:17:37.485580Z", + "iopub.status.idle": "2024-06-25T23:17:37.490849Z", + "shell.execute_reply": "2024-06-25T23:17:37.490382Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index 073e233c2..05570c79a 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:59.467250Z", - "iopub.status.busy": "2024-06-25T19:35:59.467073Z", - "iopub.status.idle": "2024-06-25T19:35:59.885710Z", - "shell.execute_reply": "2024-06-25T19:35:59.885107Z" + "iopub.execute_input": "2024-06-25T23:17:40.853361Z", + "iopub.status.busy": "2024-06-25T23:17:40.852930Z", + "iopub.status.idle": "2024-06-25T23:17:41.272322Z", + "shell.execute_reply": "2024-06-25T23:17:41.271713Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:59.888637Z", - "iopub.status.busy": "2024-06-25T19:35:59.888151Z", - "iopub.status.idle": "2024-06-25T19:36:00.014649Z", - "shell.execute_reply": "2024-06-25T19:36:00.014148Z" + "iopub.execute_input": "2024-06-25T23:17:41.275299Z", + "iopub.status.busy": "2024-06-25T23:17:41.274749Z", + "iopub.status.idle": "2024-06-25T23:17:41.403175Z", + "shell.execute_reply": "2024-06-25T23:17:41.402663Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:00.016873Z", - "iopub.status.busy": "2024-06-25T19:36:00.016623Z", - "iopub.status.idle": "2024-06-25T19:36:00.039876Z", - "shell.execute_reply": "2024-06-25T19:36:00.039305Z" + "iopub.execute_input": "2024-06-25T23:17:41.405438Z", + "iopub.status.busy": "2024-06-25T23:17:41.405028Z", + "iopub.status.idle": "2024-06-25T23:17:41.427834Z", + "shell.execute_reply": "2024-06-25T23:17:41.427281Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:00.042285Z", - "iopub.status.busy": "2024-06-25T19:36:00.041898Z", - "iopub.status.idle": "2024-06-25T19:36:02.696869Z", - "shell.execute_reply": "2024-06-25T19:36:02.696318Z" + "iopub.execute_input": "2024-06-25T23:17:41.430652Z", + "iopub.status.busy": "2024-06-25T23:17:41.430206Z", + "iopub.status.idle": "2024-06-25T23:17:44.079438Z", + "shell.execute_reply": "2024-06-25T23:17:44.078785Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:02.699546Z", - "iopub.status.busy": "2024-06-25T19:36:02.698988Z", - "iopub.status.idle": "2024-06-25T19:36:11.210546Z", - "shell.execute_reply": "2024-06-25T19:36:11.209947Z" + "iopub.execute_input": "2024-06-25T23:17:44.082102Z", + "iopub.status.busy": "2024-06-25T23:17:44.081500Z", + "iopub.status.idle": "2024-06-25T23:17:51.711133Z", + "shell.execute_reply": "2024-06-25T23:17:51.710550Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:11.212912Z", - "iopub.status.busy": "2024-06-25T19:36:11.212489Z", - "iopub.status.idle": "2024-06-25T19:36:11.354224Z", - "shell.execute_reply": "2024-06-25T19:36:11.353605Z" + "iopub.execute_input": "2024-06-25T23:17:51.713313Z", + "iopub.status.busy": "2024-06-25T23:17:51.713127Z", + "iopub.status.idle": "2024-06-25T23:17:51.857400Z", + "shell.execute_reply": "2024-06-25T23:17:51.856753Z" } }, "outputs": [], @@ -854,10 +854,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:11.356684Z", - "iopub.status.busy": "2024-06-25T19:36:11.356497Z", - "iopub.status.idle": "2024-06-25T19:36:12.692416Z", - "shell.execute_reply": "2024-06-25T19:36:12.691867Z" + "iopub.execute_input": "2024-06-25T23:17:51.860009Z", + "iopub.status.busy": "2024-06-25T23:17:51.859627Z", + "iopub.status.idle": "2024-06-25T23:17:53.181642Z", + "shell.execute_reply": "2024-06-25T23:17:53.181004Z" } }, "outputs": [ @@ -1016,10 +1016,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:12.694507Z", - "iopub.status.busy": "2024-06-25T19:36:12.694321Z", - "iopub.status.idle": "2024-06-25T19:36:13.110943Z", - "shell.execute_reply": "2024-06-25T19:36:13.110403Z" + "iopub.execute_input": "2024-06-25T23:17:53.183695Z", + "iopub.status.busy": "2024-06-25T23:17:53.183507Z", + "iopub.status.idle": "2024-06-25T23:17:53.614506Z", + "shell.execute_reply": "2024-06-25T23:17:53.613154Z" } }, "outputs": [ @@ -1098,10 +1098,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.113354Z", - "iopub.status.busy": "2024-06-25T19:36:13.112876Z", - "iopub.status.idle": "2024-06-25T19:36:13.121876Z", - "shell.execute_reply": "2024-06-25T19:36:13.121426Z" + "iopub.execute_input": "2024-06-25T23:17:53.617165Z", + "iopub.status.busy": "2024-06-25T23:17:53.616488Z", + "iopub.status.idle": "2024-06-25T23:17:53.625569Z", + "shell.execute_reply": "2024-06-25T23:17:53.625088Z" } }, "outputs": [], @@ -1131,10 +1131,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.123927Z", - "iopub.status.busy": "2024-06-25T19:36:13.123749Z", - "iopub.status.idle": "2024-06-25T19:36:13.143234Z", - "shell.execute_reply": "2024-06-25T19:36:13.142805Z" + "iopub.execute_input": "2024-06-25T23:17:53.627646Z", + "iopub.status.busy": "2024-06-25T23:17:53.627333Z", + "iopub.status.idle": "2024-06-25T23:17:53.647292Z", + "shell.execute_reply": "2024-06-25T23:17:53.646870Z" } }, "outputs": [], @@ -1162,10 +1162,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.145167Z", - "iopub.status.busy": "2024-06-25T19:36:13.144993Z", - "iopub.status.idle": "2024-06-25T19:36:13.369942Z", - "shell.execute_reply": "2024-06-25T19:36:13.369417Z" + "iopub.execute_input": "2024-06-25T23:17:53.649278Z", + "iopub.status.busy": "2024-06-25T23:17:53.648956Z", + "iopub.status.idle": "2024-06-25T23:17:53.876935Z", + "shell.execute_reply": "2024-06-25T23:17:53.876376Z" } }, "outputs": [], @@ -1205,10 +1205,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.372709Z", - "iopub.status.busy": "2024-06-25T19:36:13.372266Z", - "iopub.status.idle": "2024-06-25T19:36:13.391271Z", - "shell.execute_reply": "2024-06-25T19:36:13.390786Z" + "iopub.execute_input": "2024-06-25T23:17:53.879777Z", + "iopub.status.busy": "2024-06-25T23:17:53.879575Z", + "iopub.status.idle": "2024-06-25T23:17:53.898417Z", + "shell.execute_reply": "2024-06-25T23:17:53.897956Z" } }, "outputs": [ @@ -1406,10 +1406,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.393275Z", - "iopub.status.busy": "2024-06-25T19:36:13.392955Z", - "iopub.status.idle": "2024-06-25T19:36:13.562067Z", - "shell.execute_reply": "2024-06-25T19:36:13.561518Z" + "iopub.execute_input": "2024-06-25T23:17:53.900637Z", + "iopub.status.busy": "2024-06-25T23:17:53.900291Z", + "iopub.status.idle": "2024-06-25T23:17:54.067010Z", + "shell.execute_reply": "2024-06-25T23:17:54.066325Z" } }, "outputs": [ @@ -1476,10 +1476,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.564551Z", - "iopub.status.busy": "2024-06-25T19:36:13.564210Z", - "iopub.status.idle": "2024-06-25T19:36:13.574249Z", - "shell.execute_reply": "2024-06-25T19:36:13.573705Z" + "iopub.execute_input": "2024-06-25T23:17:54.069491Z", + "iopub.status.busy": "2024-06-25T23:17:54.069138Z", + "iopub.status.idle": "2024-06-25T23:17:54.080042Z", + "shell.execute_reply": "2024-06-25T23:17:54.079594Z" } }, "outputs": [ @@ -1745,10 +1745,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.576275Z", - "iopub.status.busy": "2024-06-25T19:36:13.575975Z", - "iopub.status.idle": "2024-06-25T19:36:13.585430Z", - "shell.execute_reply": "2024-06-25T19:36:13.584885Z" + "iopub.execute_input": "2024-06-25T23:17:54.083209Z", + "iopub.status.busy": "2024-06-25T23:17:54.082726Z", + "iopub.status.idle": "2024-06-25T23:17:54.092500Z", + "shell.execute_reply": "2024-06-25T23:17:54.092040Z" } }, "outputs": [ @@ -1935,10 +1935,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.587370Z", - "iopub.status.busy": "2024-06-25T19:36:13.587068Z", - "iopub.status.idle": "2024-06-25T19:36:13.629038Z", - "shell.execute_reply": "2024-06-25T19:36:13.628478Z" + "iopub.execute_input": "2024-06-25T23:17:54.094651Z", + "iopub.status.busy": "2024-06-25T23:17:54.094321Z", + "iopub.status.idle": "2024-06-25T23:17:54.125818Z", + "shell.execute_reply": "2024-06-25T23:17:54.122177Z" } }, "outputs": [], @@ -1972,10 +1972,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.631050Z", - "iopub.status.busy": "2024-06-25T19:36:13.630746Z", - "iopub.status.idle": "2024-06-25T19:36:13.633461Z", - "shell.execute_reply": "2024-06-25T19:36:13.632931Z" + "iopub.execute_input": "2024-06-25T23:17:54.128194Z", + "iopub.status.busy": "2024-06-25T23:17:54.127850Z", + "iopub.status.idle": "2024-06-25T23:17:54.130729Z", + "shell.execute_reply": "2024-06-25T23:17:54.130269Z" } }, "outputs": [], @@ -1997,10 +1997,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.635387Z", - "iopub.status.busy": "2024-06-25T19:36:13.635196Z", - "iopub.status.idle": "2024-06-25T19:36:13.655022Z", - "shell.execute_reply": "2024-06-25T19:36:13.654546Z" + "iopub.execute_input": "2024-06-25T23:17:54.132753Z", + "iopub.status.busy": "2024-06-25T23:17:54.132426Z", + "iopub.status.idle": "2024-06-25T23:17:54.151669Z", + "shell.execute_reply": "2024-06-25T23:17:54.151107Z" } }, "outputs": [ @@ -2158,10 +2158,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.657280Z", - "iopub.status.busy": "2024-06-25T19:36:13.656950Z", - "iopub.status.idle": "2024-06-25T19:36:13.661121Z", - "shell.execute_reply": "2024-06-25T19:36:13.660700Z" + "iopub.execute_input": "2024-06-25T23:17:54.153875Z", + "iopub.status.busy": "2024-06-25T23:17:54.153542Z", + "iopub.status.idle": "2024-06-25T23:17:54.157885Z", + "shell.execute_reply": "2024-06-25T23:17:54.157427Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.663070Z", - "iopub.status.busy": "2024-06-25T19:36:13.662753Z", - "iopub.status.idle": "2024-06-25T19:36:13.690582Z", - "shell.execute_reply": "2024-06-25T19:36:13.690034Z" + "iopub.execute_input": "2024-06-25T23:17:54.159942Z", + "iopub.status.busy": "2024-06-25T23:17:54.159537Z", + "iopub.status.idle": "2024-06-25T23:17:54.187254Z", + "shell.execute_reply": "2024-06-25T23:17:54.186748Z" } }, "outputs": [ @@ -2343,10 +2343,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.692558Z", - "iopub.status.busy": "2024-06-25T19:36:13.692385Z", - "iopub.status.idle": "2024-06-25T19:36:14.062207Z", - "shell.execute_reply": "2024-06-25T19:36:14.061647Z" + "iopub.execute_input": "2024-06-25T23:17:54.189370Z", + "iopub.status.busy": "2024-06-25T23:17:54.189020Z", + "iopub.status.idle": "2024-06-25T23:17:54.563581Z", + "shell.execute_reply": "2024-06-25T23:17:54.563004Z" } }, "outputs": [ @@ -2413,10 +2413,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.064536Z", - "iopub.status.busy": "2024-06-25T19:36:14.064346Z", - "iopub.status.idle": "2024-06-25T19:36:14.067724Z", - "shell.execute_reply": "2024-06-25T19:36:14.067250Z" + "iopub.execute_input": "2024-06-25T23:17:54.566043Z", + "iopub.status.busy": "2024-06-25T23:17:54.565580Z", + "iopub.status.idle": "2024-06-25T23:17:54.568905Z", + "shell.execute_reply": "2024-06-25T23:17:54.568452Z" } }, "outputs": [ @@ -2467,10 +2467,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.069713Z", - "iopub.status.busy": "2024-06-25T19:36:14.069543Z", - "iopub.status.idle": "2024-06-25T19:36:14.082545Z", - "shell.execute_reply": "2024-06-25T19:36:14.082110Z" + "iopub.execute_input": "2024-06-25T23:17:54.570928Z", + "iopub.status.busy": "2024-06-25T23:17:54.570747Z", + "iopub.status.idle": "2024-06-25T23:17:54.584558Z", + "shell.execute_reply": "2024-06-25T23:17:54.584061Z" } }, "outputs": [ @@ -2749,10 +2749,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.084372Z", - "iopub.status.busy": "2024-06-25T19:36:14.084199Z", - "iopub.status.idle": "2024-06-25T19:36:14.097558Z", - "shell.execute_reply": "2024-06-25T19:36:14.097135Z" + "iopub.execute_input": "2024-06-25T23:17:54.586622Z", + "iopub.status.busy": "2024-06-25T23:17:54.586423Z", + "iopub.status.idle": "2024-06-25T23:17:54.600724Z", + "shell.execute_reply": "2024-06-25T23:17:54.600241Z" } }, "outputs": [ @@ -3019,10 +3019,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.099340Z", - "iopub.status.busy": "2024-06-25T19:36:14.099173Z", - "iopub.status.idle": "2024-06-25T19:36:14.108741Z", - "shell.execute_reply": "2024-06-25T19:36:14.108314Z" + "iopub.execute_input": "2024-06-25T23:17:54.602957Z", + "iopub.status.busy": "2024-06-25T23:17:54.602518Z", + "iopub.status.idle": "2024-06-25T23:17:54.612377Z", + "shell.execute_reply": "2024-06-25T23:17:54.611952Z" } }, "outputs": [], @@ -3047,10 +3047,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.110562Z", - "iopub.status.busy": "2024-06-25T19:36:14.110394Z", - "iopub.status.idle": "2024-06-25T19:36:14.119786Z", - "shell.execute_reply": "2024-06-25T19:36:14.119280Z" + "iopub.execute_input": "2024-06-25T23:17:54.614486Z", + "iopub.status.busy": "2024-06-25T23:17:54.614156Z", + "iopub.status.idle": "2024-06-25T23:17:54.623497Z", + "shell.execute_reply": "2024-06-25T23:17:54.622945Z" } }, "outputs": [ @@ -3222,10 +3222,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.121705Z", - "iopub.status.busy": "2024-06-25T19:36:14.121535Z", - "iopub.status.idle": "2024-06-25T19:36:14.125253Z", - "shell.execute_reply": "2024-06-25T19:36:14.124849Z" + "iopub.execute_input": "2024-06-25T23:17:54.625637Z", + "iopub.status.busy": "2024-06-25T23:17:54.625295Z", + "iopub.status.idle": "2024-06-25T23:17:54.630830Z", + "shell.execute_reply": "2024-06-25T23:17:54.629019Z" } }, "outputs": [], @@ -3257,10 +3257,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.127233Z", - "iopub.status.busy": "2024-06-25T19:36:14.126914Z", - "iopub.status.idle": "2024-06-25T19:36:14.177262Z", - "shell.execute_reply": "2024-06-25T19:36:14.176812Z" + "iopub.execute_input": "2024-06-25T23:17:54.633124Z", + "iopub.status.busy": "2024-06-25T23:17:54.632790Z", + "iopub.status.idle": "2024-06-25T23:17:54.684363Z", + "shell.execute_reply": "2024-06-25T23:17:54.683802Z" } }, "outputs": [ @@ -3268,230 +3268,230 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3567,10 +3567,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.179445Z", - "iopub.status.busy": "2024-06-25T19:36:14.179018Z", - "iopub.status.idle": "2024-06-25T19:36:14.184786Z", - "shell.execute_reply": "2024-06-25T19:36:14.184224Z" + "iopub.execute_input": "2024-06-25T23:17:54.686913Z", + "iopub.status.busy": "2024-06-25T23:17:54.686475Z", + "iopub.status.idle": "2024-06-25T23:17:54.692178Z", + "shell.execute_reply": "2024-06-25T23:17:54.691645Z" } }, "outputs": [], @@ -3609,10 +3609,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.186887Z", - "iopub.status.busy": "2024-06-25T19:36:14.186471Z", - "iopub.status.idle": "2024-06-25T19:36:14.196806Z", - "shell.execute_reply": "2024-06-25T19:36:14.196244Z" + "iopub.execute_input": "2024-06-25T23:17:54.694316Z", + "iopub.status.busy": "2024-06-25T23:17:54.693981Z", + "iopub.status.idle": "2024-06-25T23:17:54.705261Z", + "shell.execute_reply": "2024-06-25T23:17:54.704802Z" } }, "outputs": [ @@ -3648,10 +3648,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.198752Z", - "iopub.status.busy": "2024-06-25T19:36:14.198440Z", - "iopub.status.idle": "2024-06-25T19:36:14.412825Z", - "shell.execute_reply": "2024-06-25T19:36:14.412259Z" + "iopub.execute_input": "2024-06-25T23:17:54.707234Z", + "iopub.status.busy": "2024-06-25T23:17:54.707059Z", + "iopub.status.idle": "2024-06-25T23:17:54.923905Z", + "shell.execute_reply": "2024-06-25T23:17:54.923350Z" } }, "outputs": [ @@ -3703,10 +3703,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.414958Z", - "iopub.status.busy": "2024-06-25T19:36:14.414688Z", - "iopub.status.idle": "2024-06-25T19:36:14.422114Z", - "shell.execute_reply": "2024-06-25T19:36:14.421663Z" + "iopub.execute_input": "2024-06-25T23:17:54.926218Z", + "iopub.status.busy": "2024-06-25T23:17:54.925878Z", + "iopub.status.idle": "2024-06-25T23:17:54.933331Z", + "shell.execute_reply": "2024-06-25T23:17:54.932869Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 6a954c6b0..d462fdaea 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:17.536909Z", - "iopub.status.busy": "2024-06-25T19:36:17.536739Z", - "iopub.status.idle": "2024-06-25T19:36:18.659278Z", - "shell.execute_reply": "2024-06-25T19:36:18.658730Z" + "iopub.execute_input": "2024-06-25T23:17:58.501344Z", + "iopub.status.busy": "2024-06-25T23:17:58.500004Z", + "iopub.status.idle": "2024-06-25T23:17:59.801482Z", + "shell.execute_reply": "2024-06-25T23:17:59.800950Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:18.661733Z", - "iopub.status.busy": "2024-06-25T19:36:18.661422Z", - "iopub.status.idle": "2024-06-25T19:36:18.664275Z", - "shell.execute_reply": "2024-06-25T19:36:18.663748Z" + "iopub.execute_input": "2024-06-25T23:17:59.804004Z", + "iopub.status.busy": "2024-06-25T23:17:59.803708Z", + "iopub.status.idle": "2024-06-25T23:17:59.806736Z", + "shell.execute_reply": "2024-06-25T23:17:59.806281Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:18.666336Z", - "iopub.status.busy": "2024-06-25T19:36:18.666027Z", - "iopub.status.idle": "2024-06-25T19:36:18.678092Z", - "shell.execute_reply": "2024-06-25T19:36:18.677567Z" + "iopub.execute_input": "2024-06-25T23:17:59.808933Z", + "iopub.status.busy": "2024-06-25T23:17:59.808705Z", + "iopub.status.idle": "2024-06-25T23:17:59.821999Z", + "shell.execute_reply": "2024-06-25T23:17:59.821381Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:18.680164Z", - "iopub.status.busy": "2024-06-25T19:36:18.679860Z", - "iopub.status.idle": "2024-06-25T19:36:28.874863Z", - "shell.execute_reply": "2024-06-25T19:36:28.874371Z" + "iopub.execute_input": "2024-06-25T23:17:59.824481Z", + "iopub.status.busy": "2024-06-25T23:17:59.824047Z", + "iopub.status.idle": "2024-06-25T23:18:03.535596Z", + "shell.execute_reply": "2024-06-25T23:18:03.535061Z" }, "id": "dhTHOg8Pyv5G" }, @@ -694,13 +694,7 @@ "\n", "\n", "🎯 Mnist_test_set 🎯\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "\n", "Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n", "\n", @@ -2565,7 +2559,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " * Overall, about 18% (1,846 of the 10,000) labels in your dataset have potential issues.\n", " ** The overall label health score for this dataset is: 0.82.\n", "\n", diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 649612439..713861397 100644 --- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:31.054579Z", - "iopub.status.busy": "2024-06-25T19:36:31.054404Z", - "iopub.status.idle": "2024-06-25T19:36:32.183683Z", - "shell.execute_reply": "2024-06-25T19:36:32.183056Z" + "iopub.execute_input": "2024-06-25T23:18:05.926443Z", + "iopub.status.busy": "2024-06-25T23:18:05.926263Z", + "iopub.status.idle": "2024-06-25T23:18:07.103304Z", + "shell.execute_reply": "2024-06-25T23:18:07.102799Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:32.186495Z", - "iopub.status.busy": "2024-06-25T19:36:32.186073Z", - "iopub.status.idle": "2024-06-25T19:36:32.189610Z", - "shell.execute_reply": "2024-06-25T19:36:32.189148Z" + "iopub.execute_input": "2024-06-25T23:18:07.106148Z", + "iopub.status.busy": "2024-06-25T23:18:07.105603Z", + "iopub.status.idle": "2024-06-25T23:18:07.109155Z", + "shell.execute_reply": "2024-06-25T23:18:07.108679Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:32.191776Z", - "iopub.status.busy": "2024-06-25T19:36:32.191309Z", - "iopub.status.idle": "2024-06-25T19:36:35.412500Z", - "shell.execute_reply": "2024-06-25T19:36:35.411739Z" + "iopub.execute_input": "2024-06-25T23:18:07.111219Z", + "iopub.status.busy": "2024-06-25T23:18:07.110877Z", + "iopub.status.idle": "2024-06-25T23:18:10.366450Z", + "shell.execute_reply": "2024-06-25T23:18:10.365818Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.415868Z", - "iopub.status.busy": "2024-06-25T19:36:35.414996Z", - "iopub.status.idle": "2024-06-25T19:36:35.452492Z", - "shell.execute_reply": "2024-06-25T19:36:35.451863Z" + "iopub.execute_input": "2024-06-25T23:18:10.369846Z", + "iopub.status.busy": "2024-06-25T23:18:10.369009Z", + "iopub.status.idle": "2024-06-25T23:18:10.408435Z", + "shell.execute_reply": "2024-06-25T23:18:10.407723Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.455265Z", - "iopub.status.busy": "2024-06-25T19:36:35.454795Z", - "iopub.status.idle": "2024-06-25T19:36:35.489174Z", - "shell.execute_reply": "2024-06-25T19:36:35.488560Z" + "iopub.execute_input": "2024-06-25T23:18:10.411187Z", + "iopub.status.busy": "2024-06-25T23:18:10.410945Z", + "iopub.status.idle": "2024-06-25T23:18:10.447524Z", + "shell.execute_reply": "2024-06-25T23:18:10.446786Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.491931Z", - "iopub.status.busy": "2024-06-25T19:36:35.491449Z", - "iopub.status.idle": "2024-06-25T19:36:35.494631Z", - "shell.execute_reply": "2024-06-25T19:36:35.494157Z" + "iopub.execute_input": "2024-06-25T23:18:10.450344Z", + "iopub.status.busy": "2024-06-25T23:18:10.450101Z", + "iopub.status.idle": "2024-06-25T23:18:10.453289Z", + "shell.execute_reply": "2024-06-25T23:18:10.452762Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.496822Z", - "iopub.status.busy": "2024-06-25T19:36:35.496395Z", - "iopub.status.idle": "2024-06-25T19:36:35.499017Z", - "shell.execute_reply": "2024-06-25T19:36:35.498537Z" + "iopub.execute_input": "2024-06-25T23:18:10.455428Z", + "iopub.status.busy": "2024-06-25T23:18:10.455099Z", + "iopub.status.idle": "2024-06-25T23:18:10.457834Z", + "shell.execute_reply": "2024-06-25T23:18:10.457357Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.501249Z", - "iopub.status.busy": "2024-06-25T19:36:35.500816Z", - "iopub.status.idle": "2024-06-25T19:36:35.525422Z", - "shell.execute_reply": "2024-06-25T19:36:35.524821Z" + "iopub.execute_input": "2024-06-25T23:18:10.459894Z", + "iopub.status.busy": "2024-06-25T23:18:10.459627Z", + "iopub.status.idle": "2024-06-25T23:18:10.483748Z", + "shell.execute_reply": "2024-06-25T23:18:10.483202Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d8af54b634f1457680edc574c7fcb110", + "model_id": "558d7887a3b248ccbc78e41ae8f6a2ad", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84b64175499142ae9cf770d1e88b80ac", + "model_id": "633ecf7c235f443883ad78f8a1d748cd", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.532028Z", - "iopub.status.busy": "2024-06-25T19:36:35.531847Z", - "iopub.status.idle": "2024-06-25T19:36:35.538645Z", - "shell.execute_reply": "2024-06-25T19:36:35.538198Z" + "iopub.execute_input": "2024-06-25T23:18:10.488896Z", + "iopub.status.busy": "2024-06-25T23:18:10.488605Z", + "iopub.status.idle": "2024-06-25T23:18:10.495342Z", + "shell.execute_reply": "2024-06-25T23:18:10.494804Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.540612Z", - "iopub.status.busy": "2024-06-25T19:36:35.540437Z", - "iopub.status.idle": "2024-06-25T19:36:35.543848Z", - "shell.execute_reply": "2024-06-25T19:36:35.543410Z" + "iopub.execute_input": "2024-06-25T23:18:10.497491Z", + "iopub.status.busy": "2024-06-25T23:18:10.497223Z", + "iopub.status.idle": "2024-06-25T23:18:10.500578Z", + "shell.execute_reply": "2024-06-25T23:18:10.500143Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.545806Z", - "iopub.status.busy": "2024-06-25T19:36:35.545508Z", - "iopub.status.idle": "2024-06-25T19:36:35.551703Z", - "shell.execute_reply": "2024-06-25T19:36:35.551260Z" + "iopub.execute_input": "2024-06-25T23:18:10.502533Z", + "iopub.status.busy": "2024-06-25T23:18:10.502242Z", + "iopub.status.idle": "2024-06-25T23:18:10.508483Z", + "shell.execute_reply": "2024-06-25T23:18:10.507959Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.553602Z", - "iopub.status.busy": "2024-06-25T19:36:35.553415Z", - "iopub.status.idle": "2024-06-25T19:36:35.589414Z", - "shell.execute_reply": "2024-06-25T19:36:35.588805Z" + "iopub.execute_input": "2024-06-25T23:18:10.510615Z", + "iopub.status.busy": "2024-06-25T23:18:10.510302Z", + "iopub.status.idle": "2024-06-25T23:18:10.546530Z", + "shell.execute_reply": "2024-06-25T23:18:10.545827Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.592001Z", - "iopub.status.busy": "2024-06-25T19:36:35.591752Z", - "iopub.status.idle": "2024-06-25T19:36:35.628128Z", - "shell.execute_reply": "2024-06-25T19:36:35.627508Z" + "iopub.execute_input": "2024-06-25T23:18:10.548998Z", + "iopub.status.busy": "2024-06-25T23:18:10.548767Z", + "iopub.status.idle": "2024-06-25T23:18:10.582483Z", + "shell.execute_reply": "2024-06-25T23:18:10.581909Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.630864Z", - "iopub.status.busy": "2024-06-25T19:36:35.630509Z", - "iopub.status.idle": "2024-06-25T19:36:35.751028Z", - "shell.execute_reply": "2024-06-25T19:36:35.750367Z" + "iopub.execute_input": "2024-06-25T23:18:10.585385Z", + "iopub.status.busy": "2024-06-25T23:18:10.584868Z", + "iopub.status.idle": "2024-06-25T23:18:10.710386Z", + "shell.execute_reply": "2024-06-25T23:18:10.709794Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.753981Z", - "iopub.status.busy": "2024-06-25T19:36:35.753115Z", - "iopub.status.idle": "2024-06-25T19:36:38.820276Z", - "shell.execute_reply": "2024-06-25T19:36:38.819614Z" + "iopub.execute_input": "2024-06-25T23:18:10.713077Z", + "iopub.status.busy": "2024-06-25T23:18:10.712538Z", + "iopub.status.idle": "2024-06-25T23:18:13.846109Z", + "shell.execute_reply": "2024-06-25T23:18:13.845478Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.822817Z", - "iopub.status.busy": "2024-06-25T19:36:38.822359Z", - "iopub.status.idle": "2024-06-25T19:36:38.881135Z", - "shell.execute_reply": "2024-06-25T19:36:38.880677Z" + "iopub.execute_input": "2024-06-25T23:18:13.848642Z", + "iopub.status.busy": "2024-06-25T23:18:13.848179Z", + "iopub.status.idle": "2024-06-25T23:18:13.910214Z", + "shell.execute_reply": "2024-06-25T23:18:13.909621Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.883155Z", - "iopub.status.busy": "2024-06-25T19:36:38.882856Z", - "iopub.status.idle": "2024-06-25T19:36:38.922999Z", - "shell.execute_reply": "2024-06-25T19:36:38.922558Z" + "iopub.execute_input": "2024-06-25T23:18:13.912512Z", + "iopub.status.busy": "2024-06-25T23:18:13.912056Z", + "iopub.status.idle": "2024-06-25T23:18:13.955394Z", + "shell.execute_reply": "2024-06-25T23:18:13.954784Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "91d13c0b", + "id": "411cb3b4", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "838b0e29", + "id": "c0fc51ac", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "72c82160", + "id": "31d0af7b", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "c8ef0e49", + "id": "ddefd054", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.925175Z", - "iopub.status.busy": "2024-06-25T19:36:38.924869Z", - "iopub.status.idle": "2024-06-25T19:36:38.933100Z", - "shell.execute_reply": "2024-06-25T19:36:38.932519Z" + "iopub.execute_input": "2024-06-25T23:18:13.957642Z", + "iopub.status.busy": "2024-06-25T23:18:13.957445Z", + "iopub.status.idle": "2024-06-25T23:18:13.965853Z", + "shell.execute_reply": "2024-06-25T23:18:13.965258Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "bfd8eea7", + "id": "96a1ec22", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "7515c699", + "id": "d478ad17", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.935170Z", - "iopub.status.busy": "2024-06-25T19:36:38.934961Z", - "iopub.status.idle": "2024-06-25T19:36:38.958819Z", - "shell.execute_reply": "2024-06-25T19:36:38.958261Z" + "iopub.execute_input": "2024-06-25T23:18:13.968394Z", + "iopub.status.busy": "2024-06-25T23:18:13.968108Z", + "iopub.status.idle": "2024-06-25T23:18:13.989832Z", + "shell.execute_reply": "2024-06-25T23:18:13.989245Z" } }, "outputs": [ @@ -1495,7 +1495,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_7655/1995098996.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_7878/1995098996.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" ] } @@ -1529,13 +1529,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "0be681e4", + "id": "ff936017", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.960846Z", - "iopub.status.busy": "2024-06-25T19:36:38.960529Z", - "iopub.status.idle": "2024-06-25T19:36:38.963912Z", - "shell.execute_reply": "2024-06-25T19:36:38.963342Z" + "iopub.execute_input": "2024-06-25T23:18:13.992065Z", + "iopub.status.busy": "2024-06-25T23:18:13.991705Z", + "iopub.status.idle": "2024-06-25T23:18:13.994946Z", + "shell.execute_reply": "2024-06-25T23:18:13.994403Z" } }, "outputs": [ @@ -1630,7 +1630,75 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1ac8a486230942529a1f92b9b04d7e25": { + "01de1302b1ac41a68c4d605171741bc4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "05b22c53719c4c21a23fad4a52106f28": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0e5d155060264c219bd191119ba7e533": { + "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": "" + } + }, + "0f44c68a58214c4e8e72391024cc96e8": { + "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": "" + } + }, + "20b4f1fb000f40e69908d463dce3c07d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1683,7 +1751,7 @@ "width": null } }, - "1e692354f23845bf94b6ee7d9d7b2637": { + "21bd4c1e909b435f96eccf76ffa92cec": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1701,7 +1769,7 @@ "text_color": null } }, - "27e85dda33254222a2c4e9d42bf88ff4": { + "2414d070f8b94c0488b54d3ad24457fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1719,7 +1787,31 @@ "text_color": null } }, - "2f598ca1115244399eac8ba3ae5fcba3": { + "558d7887a3b248ccbc78e41ae8f6a2ad": { + "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_95142643f01f48508a522fc8ef8c8553", + "IPY_MODEL_e962ea686d414b5ca4d9f4f904cd468f", + "IPY_MODEL_db994fd696e94ac29224215d7f867ee2" + ], + "layout": "IPY_MODEL_5eb199074cca45eebcd2fae654f3d219", + "tabbable": null, + "tooltip": null + } + }, + "5baa096c30154543accc624858291e39": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1772,7 +1864,7 @@ "width": null } }, - "3e18aac5a76f4d2f8ce8f5acb53421d0": { + "5eb199074cca45eebcd2fae654f3d219": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1825,49 +1917,31 @@ "width": null } }, - "45a1fa5ae2f84e1b9e7c58b238c02698": { - "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": "" - } - }, - "58d31513ffc848d98369cbd1a5fadbf4": { + "633ecf7c235f443883ad78f8a1d748cd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2f598ca1115244399eac8ba3ae5fcba3", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_45a1fa5ae2f84e1b9e7c58b238c02698", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a6fc608ec49049b99b50be3158f92470", + "IPY_MODEL_b5ef166a707247e98779b17c09955c28", + "IPY_MODEL_9218df49202c42f186276c2b0c86c0ef" + ], + "layout": "IPY_MODEL_81ac2032ca0b474aba6329ab92b63efd", "tabbable": null, - "tooltip": null, - "value": 50.0 + "tooltip": null } }, - "83e8730e8f4b4919bc838506ae45e9f1": { + "80ffedccfbbc46d0bd395580acfaf87f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1920,31 +1994,60 @@ "width": null } }, - "84b64175499142ae9cf770d1e88b80ac": { - "model_module": "@jupyter-widgets/controls", + "81ac2032ca0b474aba6329ab92b63efd": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_89f81d1899184862ad07ccb4185e5559", - "IPY_MODEL_58d31513ffc848d98369cbd1a5fadbf4", - "IPY_MODEL_f724bd9057e2403697996412c3965090" - ], - "layout": "IPY_MODEL_1ac8a486230942529a1f92b9b04d7e25", - "tabbable": null, - "tooltip": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "89f81d1899184862ad07ccb4185e5559": { + "9218df49202c42f186276c2b0c86c0ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1959,41 +2062,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bd49361ee585455e9e0ea110e22cd11e", + "layout": "IPY_MODEL_5baa096c30154543accc624858291e39", "placeholder": "​", - "style": "IPY_MODEL_1e692354f23845bf94b6ee7d9d7b2637", + "style": "IPY_MODEL_05b22c53719c4c21a23fad4a52106f28", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: " + "value": " 10000/? [00:00<00:00, 1516982.17it/s]" } }, - "9e433498da854beaad7447e349a6c08e": { + "95142643f01f48508a522fc8ef8c8553": { "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_3e18aac5a76f4d2f8ce8f5acb53421d0", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_dcebb06a360d4208903b16c601b8b9f0", + "layout": "IPY_MODEL_c84021d7d3264151a5fb14b29eaf1cee", + "placeholder": "​", + "style": "IPY_MODEL_21bd4c1e909b435f96eccf76ffa92cec", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": "number of examples processed for estimating thresholds: " } }, - "a4ce410049214dbe89d19e4dbe853b25": { + "a6fc608ec49049b99b50be3158f92470": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2008,91 +2108,41 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bbe8dc417f9d43f1ae6a2da9fe46bcfd", + "layout": "IPY_MODEL_efd5cc9a580b4925b2b71b81f5bd69d9", "placeholder": "​", - "style": "IPY_MODEL_c0572681372e40379a108207eae59244", + "style": "IPY_MODEL_01de1302b1ac41a68c4d605171741bc4", "tabbable": null, "tooltip": null, - "value": "number of examples processed for estimating thresholds: " - } - }, - "b2b8153149be48568be2b6c09e02fa2f": { - "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": "number of examples processed for checking labels: " } }, - "ba90ec735ab140eda8d3905b17d24bf0": { + "b5ef166a707247e98779b17c09955c28": { "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_b2b8153149be48568be2b6c09e02fa2f", - "placeholder": "​", - "style": "IPY_MODEL_27e85dda33254222a2c4e9d42bf88ff4", + "layout": "IPY_MODEL_c4f061a7d9044c3cbc2d5dc4162c65ef", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0f44c68a58214c4e8e72391024cc96e8", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1015594.57it/s]" + "value": 50.0 } }, - "bbe8dc417f9d43f1ae6a2da9fe46bcfd": { + "c4f061a7d9044c3cbc2d5dc4162c65ef": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2145,7 +2195,7 @@ "width": null } }, - "bd49361ee585455e9e0ea110e22cd11e": { + "c84021d7d3264151a5fb14b29eaf1cee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2198,65 +2248,56 @@ "width": null } }, - "c0572681372e40379a108207eae59244": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d8af54b634f1457680edc574c7fcb110": { + "db994fd696e94ac29224215d7f867ee2": { "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_a4ce410049214dbe89d19e4dbe853b25", - "IPY_MODEL_9e433498da854beaad7447e349a6c08e", - "IPY_MODEL_ba90ec735ab140eda8d3905b17d24bf0" - ], - "layout": "IPY_MODEL_83e8730e8f4b4919bc838506ae45e9f1", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_20b4f1fb000f40e69908d463dce3c07d", + "placeholder": "​", + "style": "IPY_MODEL_2414d070f8b94c0488b54d3ad24457fb", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1059381.69it/s]" } }, - "dcebb06a360d4208903b16c601b8b9f0": { + "e962ea686d414b5ca4d9f4f904cd468f": { "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_80ffedccfbbc46d0bd395580acfaf87f", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0e5d155060264c219bd191119ba7e533", + "tabbable": null, + "tooltip": null, + "value": 50.0 } }, - "ef7c6e423a9041328b6cffec28d2d266": { + "efd5cc9a580b4925b2b71b81f5bd69d9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2308,47 +2349,6 @@ "visibility": null, "width": null } - }, - "f724bd9057e2403697996412c3965090": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ef7c6e423a9041328b6cffec28d2d266", - "placeholder": "​", - "style": "IPY_MODEL_f9185cf0646b4409bb846af3b144c5a1", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1581801.18it/s]" - } - }, - "f9185cf0646b4409bb846af3b144c5a1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 3a6310e80..902b836cf 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": "2024-06-25T19:36:41.976010Z", - "iopub.status.busy": "2024-06-25T19:36:41.975837Z", - "iopub.status.idle": "2024-06-25T19:36:43.122752Z", - "shell.execute_reply": "2024-06-25T19:36:43.122216Z" + "iopub.execute_input": "2024-06-25T23:18:17.256748Z", + "iopub.status.busy": "2024-06-25T23:18:17.256569Z", + "iopub.status.idle": "2024-06-25T23:18:18.418999Z", + "shell.execute_reply": "2024-06-25T23:18:18.418397Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:36:43.125429Z", - "iopub.status.busy": "2024-06-25T19:36:43.124947Z", - "iopub.status.idle": "2024-06-25T19:36:43.300656Z", - "shell.execute_reply": "2024-06-25T19:36:43.300064Z" + "iopub.execute_input": "2024-06-25T23:18:18.421550Z", + "iopub.status.busy": "2024-06-25T23:18:18.421304Z", + "iopub.status.idle": "2024-06-25T23:18:18.599266Z", + "shell.execute_reply": "2024-06-25T23:18:18.598641Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:43.303142Z", - "iopub.status.busy": "2024-06-25T19:36:43.302695Z", - "iopub.status.idle": "2024-06-25T19:36:43.314281Z", - "shell.execute_reply": "2024-06-25T19:36:43.313721Z" + "iopub.execute_input": "2024-06-25T23:18:18.601825Z", + "iopub.status.busy": "2024-06-25T23:18:18.601625Z", + "iopub.status.idle": "2024-06-25T23:18:18.613136Z", + "shell.execute_reply": "2024-06-25T23:18:18.612703Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:43.316604Z", - "iopub.status.busy": "2024-06-25T19:36:43.316167Z", - "iopub.status.idle": "2024-06-25T19:36:43.522010Z", - "shell.execute_reply": "2024-06-25T19:36:43.521428Z" + "iopub.execute_input": "2024-06-25T23:18:18.615067Z", + "iopub.status.busy": "2024-06-25T23:18:18.614888Z", + "iopub.status.idle": "2024-06-25T23:18:18.849624Z", + "shell.execute_reply": "2024-06-25T23:18:18.849023Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:43.524390Z", - "iopub.status.busy": "2024-06-25T19:36:43.524031Z", - "iopub.status.idle": "2024-06-25T19:36:43.550098Z", - "shell.execute_reply": "2024-06-25T19:36:43.549668Z" + "iopub.execute_input": "2024-06-25T23:18:18.851953Z", + "iopub.status.busy": "2024-06-25T23:18:18.851541Z", + "iopub.status.idle": "2024-06-25T23:18:18.877468Z", + "shell.execute_reply": "2024-06-25T23:18:18.877017Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:43.552184Z", - "iopub.status.busy": "2024-06-25T19:36:43.551843Z", - "iopub.status.idle": "2024-06-25T19:36:45.543682Z", - "shell.execute_reply": "2024-06-25T19:36:45.542976Z" + "iopub.execute_input": "2024-06-25T23:18:18.879561Z", + "iopub.status.busy": "2024-06-25T23:18:18.879211Z", + "iopub.status.idle": "2024-06-25T23:18:20.899666Z", + "shell.execute_reply": "2024-06-25T23:18:20.898976Z" } }, "outputs": [ @@ -482,10 +482,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:45.546502Z", - "iopub.status.busy": "2024-06-25T19:36:45.545811Z", - "iopub.status.idle": "2024-06-25T19:36:45.563579Z", - "shell.execute_reply": "2024-06-25T19:36:45.563096Z" + "iopub.execute_input": "2024-06-25T23:18:20.901960Z", + "iopub.status.busy": "2024-06-25T23:18:20.901648Z", + "iopub.status.idle": "2024-06-25T23:18:20.919398Z", + "shell.execute_reply": "2024-06-25T23:18:20.918920Z" }, "scrolled": true }, @@ -615,10 +615,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:45.565656Z", - "iopub.status.busy": "2024-06-25T19:36:45.565330Z", - "iopub.status.idle": "2024-06-25T19:36:46.995317Z", - "shell.execute_reply": "2024-06-25T19:36:46.994691Z" + "iopub.execute_input": "2024-06-25T23:18:20.921440Z", + "iopub.status.busy": "2024-06-25T23:18:20.921079Z", + "iopub.status.idle": "2024-06-25T23:18:22.361092Z", + "shell.execute_reply": "2024-06-25T23:18:22.360462Z" }, "id": "AaHC5MRKjruT" }, @@ -737,10 +737,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:46.998304Z", - "iopub.status.busy": "2024-06-25T19:36:46.997491Z", - "iopub.status.idle": "2024-06-25T19:36:47.010764Z", - "shell.execute_reply": "2024-06-25T19:36:47.010231Z" + "iopub.execute_input": "2024-06-25T23:18:22.363669Z", + "iopub.status.busy": "2024-06-25T23:18:22.363060Z", + "iopub.status.idle": "2024-06-25T23:18:22.376718Z", + "shell.execute_reply": "2024-06-25T23:18:22.376170Z" }, "id": "Wy27rvyhjruU" }, @@ -789,10 +789,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.012994Z", - "iopub.status.busy": "2024-06-25T19:36:47.012685Z", - "iopub.status.idle": "2024-06-25T19:36:47.092028Z", - "shell.execute_reply": "2024-06-25T19:36:47.091384Z" + "iopub.execute_input": "2024-06-25T23:18:22.378792Z", + "iopub.status.busy": "2024-06-25T23:18:22.378469Z", + "iopub.status.idle": "2024-06-25T23:18:22.452119Z", + "shell.execute_reply": "2024-06-25T23:18:22.451527Z" }, "id": "Db8YHnyVjruU" }, @@ -899,10 +899,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.094203Z", - "iopub.status.busy": "2024-06-25T19:36:47.093979Z", - "iopub.status.idle": "2024-06-25T19:36:47.306398Z", - "shell.execute_reply": "2024-06-25T19:36:47.305822Z" + "iopub.execute_input": "2024-06-25T23:18:22.454608Z", + "iopub.status.busy": "2024-06-25T23:18:22.454246Z", + "iopub.status.idle": "2024-06-25T23:18:22.662363Z", + "shell.execute_reply": "2024-06-25T23:18:22.661824Z" }, "id": "iJqAHuS2jruV" }, @@ -939,10 +939,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.308648Z", - "iopub.status.busy": "2024-06-25T19:36:47.308280Z", - "iopub.status.idle": "2024-06-25T19:36:47.324852Z", - "shell.execute_reply": "2024-06-25T19:36:47.324401Z" + "iopub.execute_input": "2024-06-25T23:18:22.664593Z", + "iopub.status.busy": "2024-06-25T23:18:22.664244Z", + "iopub.status.idle": "2024-06-25T23:18:22.681198Z", + "shell.execute_reply": "2024-06-25T23:18:22.680722Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1408,10 +1408,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.326839Z", - "iopub.status.busy": "2024-06-25T19:36:47.326575Z", - "iopub.status.idle": "2024-06-25T19:36:47.335814Z", - "shell.execute_reply": "2024-06-25T19:36:47.335352Z" + "iopub.execute_input": "2024-06-25T23:18:22.683372Z", + "iopub.status.busy": "2024-06-25T23:18:22.682962Z", + "iopub.status.idle": "2024-06-25T23:18:22.692419Z", + "shell.execute_reply": "2024-06-25T23:18:22.691897Z" }, "id": "0lonvOYvjruV" }, @@ -1558,10 +1558,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.337943Z", - "iopub.status.busy": "2024-06-25T19:36:47.337629Z", - "iopub.status.idle": "2024-06-25T19:36:47.419127Z", - "shell.execute_reply": "2024-06-25T19:36:47.418522Z" + "iopub.execute_input": "2024-06-25T23:18:22.694507Z", + "iopub.status.busy": "2024-06-25T23:18:22.694073Z", + "iopub.status.idle": "2024-06-25T23:18:22.776192Z", + "shell.execute_reply": "2024-06-25T23:18:22.775639Z" }, "id": "MfqTCa3kjruV" }, @@ -1642,10 +1642,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.421368Z", - "iopub.status.busy": "2024-06-25T19:36:47.421141Z", - "iopub.status.idle": "2024-06-25T19:36:47.538207Z", - "shell.execute_reply": "2024-06-25T19:36:47.537601Z" + "iopub.execute_input": "2024-06-25T23:18:22.778586Z", + "iopub.status.busy": "2024-06-25T23:18:22.778226Z", + "iopub.status.idle": "2024-06-25T23:18:22.894081Z", + "shell.execute_reply": "2024-06-25T23:18:22.893472Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1705,10 +1705,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.540745Z", - "iopub.status.busy": "2024-06-25T19:36:47.540377Z", - "iopub.status.idle": "2024-06-25T19:36:47.544346Z", - "shell.execute_reply": "2024-06-25T19:36:47.543812Z" + "iopub.execute_input": "2024-06-25T23:18:22.896294Z", + "iopub.status.busy": "2024-06-25T23:18:22.896067Z", + "iopub.status.idle": "2024-06-25T23:18:22.899817Z", + "shell.execute_reply": "2024-06-25T23:18:22.899290Z" }, "id": "0rXP3ZPWjruW" }, @@ -1746,10 +1746,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.546251Z", - "iopub.status.busy": "2024-06-25T19:36:47.546076Z", - "iopub.status.idle": "2024-06-25T19:36:47.549903Z", - "shell.execute_reply": "2024-06-25T19:36:47.549356Z" + "iopub.execute_input": "2024-06-25T23:18:22.901918Z", + "iopub.status.busy": "2024-06-25T23:18:22.901602Z", + "iopub.status.idle": "2024-06-25T23:18:22.905366Z", + "shell.execute_reply": "2024-06-25T23:18:22.904793Z" }, "id": "-iRPe8KXjruW" }, @@ -1804,10 +1804,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.552020Z", - "iopub.status.busy": "2024-06-25T19:36:47.551608Z", - "iopub.status.idle": "2024-06-25T19:36:47.587995Z", - "shell.execute_reply": "2024-06-25T19:36:47.587566Z" + "iopub.execute_input": "2024-06-25T23:18:22.907395Z", + "iopub.status.busy": "2024-06-25T23:18:22.907096Z", + "iopub.status.idle": "2024-06-25T23:18:22.943768Z", + "shell.execute_reply": "2024-06-25T23:18:22.943295Z" }, "id": "ZpipUliyjruW" }, @@ -1858,10 +1858,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.589852Z", - "iopub.status.busy": "2024-06-25T19:36:47.589680Z", - "iopub.status.idle": "2024-06-25T19:36:47.630699Z", - "shell.execute_reply": "2024-06-25T19:36:47.630137Z" + "iopub.execute_input": "2024-06-25T23:18:22.945705Z", + "iopub.status.busy": "2024-06-25T23:18:22.945390Z", + "iopub.status.idle": "2024-06-25T23:18:22.987000Z", + "shell.execute_reply": "2024-06-25T23:18:22.986556Z" }, "id": "SLq-3q4xjruX" }, @@ -1930,10 +1930,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.632515Z", - "iopub.status.busy": "2024-06-25T19:36:47.632349Z", - "iopub.status.idle": "2024-06-25T19:36:47.720647Z", - "shell.execute_reply": "2024-06-25T19:36:47.719956Z" + "iopub.execute_input": "2024-06-25T23:18:22.989099Z", + "iopub.status.busy": "2024-06-25T23:18:22.988778Z", + "iopub.status.idle": "2024-06-25T23:18:23.079367Z", + "shell.execute_reply": "2024-06-25T23:18:23.078808Z" }, "id": "g5LHhhuqFbXK" }, @@ -1965,10 +1965,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.723084Z", - "iopub.status.busy": "2024-06-25T19:36:47.722899Z", - "iopub.status.idle": "2024-06-25T19:36:47.802159Z", - "shell.execute_reply": "2024-06-25T19:36:47.801549Z" + "iopub.execute_input": "2024-06-25T23:18:23.081992Z", + "iopub.status.busy": "2024-06-25T23:18:23.081632Z", + "iopub.status.idle": "2024-06-25T23:18:23.163660Z", + "shell.execute_reply": "2024-06-25T23:18:23.163108Z" }, "id": "p7w8F8ezBcet" }, @@ -2025,10 +2025,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.804647Z", - "iopub.status.busy": "2024-06-25T19:36:47.804175Z", - "iopub.status.idle": "2024-06-25T19:36:48.012610Z", - "shell.execute_reply": "2024-06-25T19:36:48.012009Z" + "iopub.execute_input": "2024-06-25T23:18:23.166170Z", + "iopub.status.busy": "2024-06-25T23:18:23.165696Z", + "iopub.status.idle": "2024-06-25T23:18:23.373652Z", + "shell.execute_reply": "2024-06-25T23:18:23.373076Z" }, "id": "WETRL74tE_sU" }, @@ -2063,10 +2063,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:48.014974Z", - "iopub.status.busy": "2024-06-25T19:36:48.014733Z", - "iopub.status.idle": "2024-06-25T19:36:48.197734Z", - "shell.execute_reply": "2024-06-25T19:36:48.197109Z" + "iopub.execute_input": "2024-06-25T23:18:23.375920Z", + "iopub.status.busy": "2024-06-25T23:18:23.375563Z", + "iopub.status.idle": "2024-06-25T23:18:23.542133Z", + "shell.execute_reply": "2024-06-25T23:18:23.541601Z" }, "id": "kCfdx2gOLmXS" }, @@ -2228,10 +2228,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:48.200133Z", - "iopub.status.busy": "2024-06-25T19:36:48.199890Z", - "iopub.status.idle": "2024-06-25T19:36:48.206211Z", - "shell.execute_reply": "2024-06-25T19:36:48.205745Z" + "iopub.execute_input": "2024-06-25T23:18:23.544310Z", + "iopub.status.busy": "2024-06-25T23:18:23.544080Z", + "iopub.status.idle": "2024-06-25T23:18:23.550244Z", + "shell.execute_reply": "2024-06-25T23:18:23.549696Z" }, "id": "-uogYRWFYnuu" }, @@ -2285,10 +2285,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:48.208373Z", - "iopub.status.busy": "2024-06-25T19:36:48.207949Z", - "iopub.status.idle": "2024-06-25T19:36:48.423251Z", - "shell.execute_reply": "2024-06-25T19:36:48.422679Z" + "iopub.execute_input": "2024-06-25T23:18:23.552552Z", + "iopub.status.busy": "2024-06-25T23:18:23.552102Z", + "iopub.status.idle": "2024-06-25T23:18:23.765551Z", + "shell.execute_reply": "2024-06-25T23:18:23.764971Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2335,10 +2335,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:48.425569Z", - "iopub.status.busy": "2024-06-25T19:36:48.425133Z", - "iopub.status.idle": "2024-06-25T19:36:49.482076Z", - "shell.execute_reply": "2024-06-25T19:36:49.481529Z" + "iopub.execute_input": "2024-06-25T23:18:23.767794Z", + "iopub.status.busy": "2024-06-25T23:18:23.767426Z", + "iopub.status.idle": "2024-06-25T23:18:24.838654Z", + "shell.execute_reply": "2024-06-25T23:18:24.838036Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index 906c55fbe..b4c4a33f9 100644 --- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:52.983005Z", - "iopub.status.busy": "2024-06-25T19:36:52.982831Z", - "iopub.status.idle": "2024-06-25T19:36:54.092198Z", - "shell.execute_reply": "2024-06-25T19:36:54.091645Z" + "iopub.execute_input": "2024-06-25T23:18:28.410867Z", + "iopub.status.busy": "2024-06-25T23:18:28.410704Z", + "iopub.status.idle": "2024-06-25T23:18:29.523341Z", + "shell.execute_reply": "2024-06-25T23:18:29.522804Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.094787Z", - "iopub.status.busy": "2024-06-25T19:36:54.094431Z", - "iopub.status.idle": "2024-06-25T19:36:54.097617Z", - "shell.execute_reply": "2024-06-25T19:36:54.097173Z" + "iopub.execute_input": "2024-06-25T23:18:29.525967Z", + "iopub.status.busy": "2024-06-25T23:18:29.525510Z", + "iopub.status.idle": "2024-06-25T23:18:29.528645Z", + "shell.execute_reply": "2024-06-25T23:18:29.528187Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.099720Z", - "iopub.status.busy": "2024-06-25T19:36:54.099372Z", - "iopub.status.idle": "2024-06-25T19:36:54.107610Z", - "shell.execute_reply": "2024-06-25T19:36:54.107140Z" + "iopub.execute_input": "2024-06-25T23:18:29.530912Z", + "iopub.status.busy": "2024-06-25T23:18:29.530502Z", + "iopub.status.idle": "2024-06-25T23:18:29.538778Z", + "shell.execute_reply": "2024-06-25T23:18:29.538338Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.109674Z", - "iopub.status.busy": "2024-06-25T19:36:54.109247Z", - "iopub.status.idle": "2024-06-25T19:36:54.157412Z", - "shell.execute_reply": "2024-06-25T19:36:54.156840Z" + "iopub.execute_input": "2024-06-25T23:18:29.540895Z", + "iopub.status.busy": "2024-06-25T23:18:29.540489Z", + "iopub.status.idle": "2024-06-25T23:18:29.587259Z", + "shell.execute_reply": "2024-06-25T23:18:29.586733Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.159654Z", - "iopub.status.busy": "2024-06-25T19:36:54.159472Z", - "iopub.status.idle": "2024-06-25T19:36:54.177229Z", - "shell.execute_reply": "2024-06-25T19:36:54.176762Z" + "iopub.execute_input": "2024-06-25T23:18:29.589466Z", + "iopub.status.busy": "2024-06-25T23:18:29.589277Z", + "iopub.status.idle": "2024-06-25T23:18:29.606524Z", + "shell.execute_reply": "2024-06-25T23:18:29.606095Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.179344Z", - "iopub.status.busy": "2024-06-25T19:36:54.179010Z", - "iopub.status.idle": "2024-06-25T19:36:54.182993Z", - "shell.execute_reply": "2024-06-25T19:36:54.182561Z" + "iopub.execute_input": "2024-06-25T23:18:29.608443Z", + "iopub.status.busy": "2024-06-25T23:18:29.608267Z", + "iopub.status.idle": "2024-06-25T23:18:29.612218Z", + "shell.execute_reply": "2024-06-25T23:18:29.611771Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.185097Z", - "iopub.status.busy": "2024-06-25T19:36:54.184777Z", - "iopub.status.idle": "2024-06-25T19:36:54.198824Z", - "shell.execute_reply": "2024-06-25T19:36:54.198358Z" + "iopub.execute_input": "2024-06-25T23:18:29.614226Z", + "iopub.status.busy": "2024-06-25T23:18:29.614054Z", + "iopub.status.idle": "2024-06-25T23:18:29.631367Z", + "shell.execute_reply": "2024-06-25T23:18:29.630956Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.200845Z", - "iopub.status.busy": "2024-06-25T19:36:54.200664Z", - "iopub.status.idle": "2024-06-25T19:36:54.227151Z", - "shell.execute_reply": "2024-06-25T19:36:54.226585Z" + "iopub.execute_input": "2024-06-25T23:18:29.633306Z", + "iopub.status.busy": "2024-06-25T23:18:29.632964Z", + "iopub.status.idle": "2024-06-25T23:18:29.658440Z", + "shell.execute_reply": "2024-06-25T23:18:29.658012Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.229370Z", - "iopub.status.busy": "2024-06-25T19:36:54.228984Z", - "iopub.status.idle": "2024-06-25T19:36:56.088954Z", - "shell.execute_reply": "2024-06-25T19:36:56.088321Z" + "iopub.execute_input": "2024-06-25T23:18:29.660435Z", + "iopub.status.busy": "2024-06-25T23:18:29.660092Z", + "iopub.status.idle": "2024-06-25T23:18:31.561212Z", + "shell.execute_reply": "2024-06-25T23:18:31.560640Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.091797Z", - "iopub.status.busy": "2024-06-25T19:36:56.091365Z", - "iopub.status.idle": "2024-06-25T19:36:56.098121Z", - "shell.execute_reply": "2024-06-25T19:36:56.097667Z" + "iopub.execute_input": "2024-06-25T23:18:31.563955Z", + "iopub.status.busy": "2024-06-25T23:18:31.563327Z", + "iopub.status.idle": "2024-06-25T23:18:31.570324Z", + "shell.execute_reply": "2024-06-25T23:18:31.569880Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.100176Z", - "iopub.status.busy": "2024-06-25T19:36:56.099747Z", - "iopub.status.idle": "2024-06-25T19:36:56.112314Z", - "shell.execute_reply": "2024-06-25T19:36:56.111779Z" + "iopub.execute_input": "2024-06-25T23:18:31.572276Z", + "iopub.status.busy": "2024-06-25T23:18:31.571950Z", + "iopub.status.idle": "2024-06-25T23:18:31.584255Z", + "shell.execute_reply": "2024-06-25T23:18:31.583817Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.114307Z", - "iopub.status.busy": "2024-06-25T19:36:56.113989Z", - "iopub.status.idle": "2024-06-25T19:36:56.120308Z", - "shell.execute_reply": "2024-06-25T19:36:56.119759Z" + "iopub.execute_input": "2024-06-25T23:18:31.586203Z", + "iopub.status.busy": "2024-06-25T23:18:31.585878Z", + "iopub.status.idle": "2024-06-25T23:18:31.591999Z", + "shell.execute_reply": "2024-06-25T23:18:31.591576Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.122321Z", - "iopub.status.busy": "2024-06-25T19:36:56.122009Z", - "iopub.status.idle": "2024-06-25T19:36:56.124766Z", - "shell.execute_reply": "2024-06-25T19:36:56.124216Z" + "iopub.execute_input": "2024-06-25T23:18:31.594128Z", + "iopub.status.busy": "2024-06-25T23:18:31.593809Z", + "iopub.status.idle": "2024-06-25T23:18:31.596328Z", + "shell.execute_reply": "2024-06-25T23:18:31.595895Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.126666Z", - "iopub.status.busy": "2024-06-25T19:36:56.126364Z", - "iopub.status.idle": "2024-06-25T19:36:56.129930Z", - "shell.execute_reply": "2024-06-25T19:36:56.129387Z" + "iopub.execute_input": "2024-06-25T23:18:31.598281Z", + "iopub.status.busy": "2024-06-25T23:18:31.597974Z", + "iopub.status.idle": "2024-06-25T23:18:31.601541Z", + "shell.execute_reply": "2024-06-25T23:18:31.600983Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.132039Z", - "iopub.status.busy": "2024-06-25T19:36:56.131738Z", - "iopub.status.idle": "2024-06-25T19:36:56.134411Z", - "shell.execute_reply": "2024-06-25T19:36:56.133864Z" + "iopub.execute_input": "2024-06-25T23:18:31.603595Z", + "iopub.status.busy": "2024-06-25T23:18:31.603264Z", + "iopub.status.idle": "2024-06-25T23:18:31.605889Z", + "shell.execute_reply": "2024-06-25T23:18:31.605456Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.136459Z", - "iopub.status.busy": "2024-06-25T19:36:56.136150Z", - "iopub.status.idle": "2024-06-25T19:36:56.140438Z", - "shell.execute_reply": "2024-06-25T19:36:56.139976Z" + "iopub.execute_input": "2024-06-25T23:18:31.607856Z", + "iopub.status.busy": "2024-06-25T23:18:31.607558Z", + "iopub.status.idle": "2024-06-25T23:18:31.611501Z", + "shell.execute_reply": "2024-06-25T23:18:31.611048Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.142440Z", - "iopub.status.busy": "2024-06-25T19:36:56.142121Z", - "iopub.status.idle": "2024-06-25T19:36:56.170976Z", - "shell.execute_reply": "2024-06-25T19:36:56.170425Z" + "iopub.execute_input": "2024-06-25T23:18:31.613408Z", + "iopub.status.busy": "2024-06-25T23:18:31.613238Z", + "iopub.status.idle": "2024-06-25T23:18:31.641822Z", + "shell.execute_reply": "2024-06-25T23:18:31.641266Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.173162Z", - "iopub.status.busy": "2024-06-25T19:36:56.172858Z", - "iopub.status.idle": "2024-06-25T19:36:56.177426Z", - "shell.execute_reply": "2024-06-25T19:36:56.176864Z" + "iopub.execute_input": "2024-06-25T23:18:31.644000Z", + "iopub.status.busy": "2024-06-25T23:18:31.643674Z", + "iopub.status.idle": "2024-06-25T23:18:31.648272Z", + "shell.execute_reply": "2024-06-25T23:18:31.647708Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 9e634f2f3..9593cdb90 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": "2024-06-25T19:36:58.919980Z", - "iopub.status.busy": "2024-06-25T19:36:58.919807Z", - "iopub.status.idle": "2024-06-25T19:37:00.071287Z", - "shell.execute_reply": "2024-06-25T19:37:00.070749Z" + "iopub.execute_input": "2024-06-25T23:18:34.388005Z", + "iopub.status.busy": "2024-06-25T23:18:34.387509Z", + "iopub.status.idle": "2024-06-25T23:18:35.555688Z", + "shell.execute_reply": "2024-06-25T23:18:35.555141Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:00.073825Z", - "iopub.status.busy": "2024-06-25T19:37:00.073418Z", - "iopub.status.idle": "2024-06-25T19:37:00.265456Z", - "shell.execute_reply": "2024-06-25T19:37:00.264849Z" + "iopub.execute_input": "2024-06-25T23:18:35.558285Z", + "iopub.status.busy": "2024-06-25T23:18:35.557842Z", + "iopub.status.idle": "2024-06-25T23:18:35.751397Z", + "shell.execute_reply": "2024-06-25T23:18:35.750860Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:00.268256Z", - "iopub.status.busy": "2024-06-25T19:37:00.267860Z", - "iopub.status.idle": "2024-06-25T19:37:00.281177Z", - "shell.execute_reply": "2024-06-25T19:37:00.280743Z" + "iopub.execute_input": "2024-06-25T23:18:35.754209Z", + "iopub.status.busy": "2024-06-25T23:18:35.753733Z", + "iopub.status.idle": "2024-06-25T23:18:35.767096Z", + "shell.execute_reply": "2024-06-25T23:18:35.766635Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:00.283272Z", - "iopub.status.busy": "2024-06-25T19:37:00.282948Z", - "iopub.status.idle": "2024-06-25T19:37:02.915319Z", - "shell.execute_reply": "2024-06-25T19:37:02.914720Z" + "iopub.execute_input": "2024-06-25T23:18:35.769292Z", + "iopub.status.busy": "2024-06-25T23:18:35.768939Z", + "iopub.status.idle": "2024-06-25T23:18:38.460798Z", + "shell.execute_reply": "2024-06-25T23:18:38.460293Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:02.917655Z", - "iopub.status.busy": "2024-06-25T19:37:02.917303Z", - "iopub.status.idle": "2024-06-25T19:37:04.262113Z", - "shell.execute_reply": "2024-06-25T19:37:04.261389Z" + "iopub.execute_input": "2024-06-25T23:18:38.463138Z", + "iopub.status.busy": "2024-06-25T23:18:38.462688Z", + "iopub.status.idle": "2024-06-25T23:18:39.817391Z", + "shell.execute_reply": "2024-06-25T23:18:39.816843Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:04.264665Z", - "iopub.status.busy": "2024-06-25T19:37:04.264273Z", - "iopub.status.idle": "2024-06-25T19:37:04.268776Z", - "shell.execute_reply": "2024-06-25T19:37:04.268171Z" + "iopub.execute_input": "2024-06-25T23:18:39.819916Z", + "iopub.status.busy": "2024-06-25T23:18:39.819475Z", + "iopub.status.idle": "2024-06-25T23:18:39.823477Z", + "shell.execute_reply": "2024-06-25T23:18:39.822931Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:04.271017Z", - "iopub.status.busy": "2024-06-25T19:37:04.270694Z", - "iopub.status.idle": "2024-06-25T19:37:06.209152Z", - "shell.execute_reply": "2024-06-25T19:37:06.208542Z" + "iopub.execute_input": "2024-06-25T23:18:39.825523Z", + "iopub.status.busy": "2024-06-25T23:18:39.825189Z", + "iopub.status.idle": "2024-06-25T23:18:41.816360Z", + "shell.execute_reply": "2024-06-25T23:18:41.815747Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:06.211688Z", - "iopub.status.busy": "2024-06-25T19:37:06.211198Z", - "iopub.status.idle": "2024-06-25T19:37:06.218564Z", - "shell.execute_reply": "2024-06-25T19:37:06.218036Z" + "iopub.execute_input": "2024-06-25T23:18:41.818930Z", + "iopub.status.busy": "2024-06-25T23:18:41.818429Z", + "iopub.status.idle": "2024-06-25T23:18:41.826321Z", + "shell.execute_reply": "2024-06-25T23:18:41.825851Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:06.220591Z", - "iopub.status.busy": "2024-06-25T19:37:06.220264Z", - "iopub.status.idle": "2024-06-25T19:37:08.793564Z", - "shell.execute_reply": "2024-06-25T19:37:08.792970Z" + "iopub.execute_input": "2024-06-25T23:18:41.828406Z", + "iopub.status.busy": "2024-06-25T23:18:41.828097Z", + "iopub.status.idle": "2024-06-25T23:18:44.431218Z", + "shell.execute_reply": "2024-06-25T23:18:44.430687Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:08.795901Z", - "iopub.status.busy": "2024-06-25T19:37:08.795549Z", - "iopub.status.idle": "2024-06-25T19:37:08.798884Z", - "shell.execute_reply": "2024-06-25T19:37:08.798350Z" + "iopub.execute_input": "2024-06-25T23:18:44.433395Z", + "iopub.status.busy": "2024-06-25T23:18:44.433032Z", + "iopub.status.idle": "2024-06-25T23:18:44.436462Z", + "shell.execute_reply": "2024-06-25T23:18:44.435934Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:08.800984Z", - "iopub.status.busy": "2024-06-25T19:37:08.800677Z", - "iopub.status.idle": "2024-06-25T19:37:08.804151Z", - "shell.execute_reply": "2024-06-25T19:37:08.803635Z" + "iopub.execute_input": "2024-06-25T23:18:44.438430Z", + "iopub.status.busy": "2024-06-25T23:18:44.438123Z", + "iopub.status.idle": "2024-06-25T23:18:44.441587Z", + "shell.execute_reply": "2024-06-25T23:18:44.441125Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:08.806163Z", - "iopub.status.busy": "2024-06-25T19:37:08.805988Z", - "iopub.status.idle": "2024-06-25T19:37:08.809167Z", - "shell.execute_reply": "2024-06-25T19:37:08.808609Z" + "iopub.execute_input": "2024-06-25T23:18:44.443586Z", + "iopub.status.busy": "2024-06-25T23:18:44.443248Z", + "iopub.status.idle": "2024-06-25T23:18:44.446272Z", + "shell.execute_reply": "2024-06-25T23:18:44.445845Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index aebe787bb..ceb7220d6 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": "2024-06-25T19:37:11.308794Z", - "iopub.status.busy": "2024-06-25T19:37:11.308627Z", - "iopub.status.idle": "2024-06-25T19:37:12.452711Z", - "shell.execute_reply": "2024-06-25T19:37:12.452159Z" + "iopub.execute_input": "2024-06-25T23:18:46.821534Z", + "iopub.status.busy": "2024-06-25T23:18:46.821356Z", + "iopub.status.idle": "2024-06-25T23:18:47.991566Z", + "shell.execute_reply": "2024-06-25T23:18:47.991020Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:12.455258Z", - "iopub.status.busy": "2024-06-25T19:37:12.454827Z", - "iopub.status.idle": "2024-06-25T19:37:14.890620Z", - "shell.execute_reply": "2024-06-25T19:37:14.889969Z" + "iopub.execute_input": "2024-06-25T23:18:47.994044Z", + "iopub.status.busy": "2024-06-25T23:18:47.993746Z", + "iopub.status.idle": "2024-06-25T23:18:49.077383Z", + "shell.execute_reply": "2024-06-25T23:18:49.076740Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:14.893309Z", - "iopub.status.busy": "2024-06-25T19:37:14.892942Z", - "iopub.status.idle": "2024-06-25T19:37:14.896049Z", - "shell.execute_reply": "2024-06-25T19:37:14.895624Z" + "iopub.execute_input": "2024-06-25T23:18:49.079931Z", + "iopub.status.busy": "2024-06-25T23:18:49.079715Z", + "iopub.status.idle": "2024-06-25T23:18:49.083128Z", + "shell.execute_reply": "2024-06-25T23:18:49.082576Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:14.898040Z", - "iopub.status.busy": "2024-06-25T19:37:14.897713Z", - "iopub.status.idle": "2024-06-25T19:37:14.903653Z", - "shell.execute_reply": "2024-06-25T19:37:14.903185Z" + "iopub.execute_input": "2024-06-25T23:18:49.085315Z", + "iopub.status.busy": "2024-06-25T23:18:49.084875Z", + "iopub.status.idle": "2024-06-25T23:18:49.090995Z", + "shell.execute_reply": "2024-06-25T23:18:49.090565Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:14.905688Z", - "iopub.status.busy": "2024-06-25T19:37:14.905360Z", - "iopub.status.idle": "2024-06-25T19:37:15.391751Z", - "shell.execute_reply": "2024-06-25T19:37:15.391128Z" + "iopub.execute_input": "2024-06-25T23:18:49.092987Z", + "iopub.status.busy": "2024-06-25T23:18:49.092664Z", + "iopub.status.idle": "2024-06-25T23:18:49.578049Z", + "shell.execute_reply": "2024-06-25T23:18:49.577480Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:15.394507Z", - "iopub.status.busy": "2024-06-25T19:37:15.394142Z", - "iopub.status.idle": "2024-06-25T19:37:15.399398Z", - "shell.execute_reply": "2024-06-25T19:37:15.398860Z" + "iopub.execute_input": "2024-06-25T23:18:49.581141Z", + "iopub.status.busy": "2024-06-25T23:18:49.580804Z", + "iopub.status.idle": "2024-06-25T23:18:49.586187Z", + "shell.execute_reply": "2024-06-25T23:18:49.585728Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:15.401545Z", - "iopub.status.busy": "2024-06-25T19:37:15.401225Z", - "iopub.status.idle": "2024-06-25T19:37:15.404995Z", - "shell.execute_reply": "2024-06-25T19:37:15.404569Z" + "iopub.execute_input": "2024-06-25T23:18:49.588207Z", + "iopub.status.busy": "2024-06-25T23:18:49.587912Z", + "iopub.status.idle": "2024-06-25T23:18:49.592364Z", + "shell.execute_reply": "2024-06-25T23:18:49.591919Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:15.407039Z", - "iopub.status.busy": "2024-06-25T19:37:15.406711Z", - "iopub.status.idle": "2024-06-25T19:37:16.295944Z", - "shell.execute_reply": "2024-06-25T19:37:16.295383Z" + "iopub.execute_input": "2024-06-25T23:18:49.594287Z", + "iopub.status.busy": "2024-06-25T23:18:49.594112Z", + "iopub.status.idle": "2024-06-25T23:18:50.586165Z", + "shell.execute_reply": "2024-06-25T23:18:50.585507Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:16.298196Z", - "iopub.status.busy": "2024-06-25T19:37:16.297999Z", - "iopub.status.idle": "2024-06-25T19:37:16.525061Z", - "shell.execute_reply": "2024-06-25T19:37:16.524590Z" + "iopub.execute_input": "2024-06-25T23:18:50.588520Z", + "iopub.status.busy": "2024-06-25T23:18:50.588324Z", + "iopub.status.idle": "2024-06-25T23:18:50.808698Z", + "shell.execute_reply": "2024-06-25T23:18:50.808228Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:16.527288Z", - "iopub.status.busy": "2024-06-25T19:37:16.526859Z", - "iopub.status.idle": "2024-06-25T19:37:16.531244Z", - "shell.execute_reply": "2024-06-25T19:37:16.530747Z" + "iopub.execute_input": "2024-06-25T23:18:50.810921Z", + "iopub.status.busy": "2024-06-25T23:18:50.810585Z", + "iopub.status.idle": "2024-06-25T23:18:50.815013Z", + "shell.execute_reply": "2024-06-25T23:18:50.814577Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:16.533265Z", - "iopub.status.busy": "2024-06-25T19:37:16.533088Z", - "iopub.status.idle": "2024-06-25T19:37:16.979069Z", - "shell.execute_reply": "2024-06-25T19:37:16.978477Z" + "iopub.execute_input": "2024-06-25T23:18:50.816841Z", + "iopub.status.busy": "2024-06-25T23:18:50.816666Z", + "iopub.status.idle": "2024-06-25T23:18:51.264514Z", + "shell.execute_reply": "2024-06-25T23:18:51.263937Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:16.981738Z", - "iopub.status.busy": "2024-06-25T19:37:16.981547Z", - "iopub.status.idle": "2024-06-25T19:37:17.310927Z", - "shell.execute_reply": "2024-06-25T19:37:17.310336Z" + "iopub.execute_input": "2024-06-25T23:18:51.267205Z", + "iopub.status.busy": "2024-06-25T23:18:51.266984Z", + "iopub.status.idle": "2024-06-25T23:18:51.597569Z", + "shell.execute_reply": "2024-06-25T23:18:51.596965Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:17.313292Z", - "iopub.status.busy": "2024-06-25T19:37:17.312887Z", - "iopub.status.idle": "2024-06-25T19:37:17.645849Z", - "shell.execute_reply": "2024-06-25T19:37:17.645269Z" + "iopub.execute_input": "2024-06-25T23:18:51.599806Z", + "iopub.status.busy": "2024-06-25T23:18:51.599595Z", + "iopub.status.idle": "2024-06-25T23:18:51.933374Z", + "shell.execute_reply": "2024-06-25T23:18:51.932766Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:17.649071Z", - "iopub.status.busy": "2024-06-25T19:37:17.648711Z", - "iopub.status.idle": "2024-06-25T19:37:18.056258Z", - "shell.execute_reply": "2024-06-25T19:37:18.055723Z" + "iopub.execute_input": "2024-06-25T23:18:51.936579Z", + "iopub.status.busy": "2024-06-25T23:18:51.936094Z", + "iopub.status.idle": "2024-06-25T23:18:52.348181Z", + "shell.execute_reply": "2024-06-25T23:18:52.347588Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.060462Z", - "iopub.status.busy": "2024-06-25T19:37:18.060093Z", - "iopub.status.idle": "2024-06-25T19:37:18.505775Z", - "shell.execute_reply": "2024-06-25T19:37:18.505169Z" + "iopub.execute_input": "2024-06-25T23:18:52.352428Z", + "iopub.status.busy": "2024-06-25T23:18:52.351994Z", + "iopub.status.idle": "2024-06-25T23:18:52.773521Z", + "shell.execute_reply": "2024-06-25T23:18:52.772929Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.508548Z", - "iopub.status.busy": "2024-06-25T19:37:18.508203Z", - "iopub.status.idle": "2024-06-25T19:37:18.698418Z", - "shell.execute_reply": "2024-06-25T19:37:18.697831Z" + "iopub.execute_input": "2024-06-25T23:18:52.776870Z", + "iopub.status.busy": "2024-06-25T23:18:52.776447Z", + "iopub.status.idle": "2024-06-25T23:18:52.965633Z", + "shell.execute_reply": "2024-06-25T23:18:52.965014Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.700790Z", - "iopub.status.busy": "2024-06-25T19:37:18.700610Z", - "iopub.status.idle": "2024-06-25T19:37:18.880703Z", - "shell.execute_reply": "2024-06-25T19:37:18.880186Z" + "iopub.execute_input": "2024-06-25T23:18:52.968518Z", + "iopub.status.busy": "2024-06-25T23:18:52.968035Z", + "iopub.status.idle": "2024-06-25T23:18:53.169696Z", + "shell.execute_reply": "2024-06-25T23:18:53.169139Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.882941Z", - "iopub.status.busy": "2024-06-25T19:37:18.882765Z", - "iopub.status.idle": "2024-06-25T19:37:18.885792Z", - "shell.execute_reply": "2024-06-25T19:37:18.885246Z" + "iopub.execute_input": "2024-06-25T23:18:53.171908Z", + "iopub.status.busy": "2024-06-25T23:18:53.171701Z", + "iopub.status.idle": "2024-06-25T23:18:53.174679Z", + "shell.execute_reply": "2024-06-25T23:18:53.174135Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.887722Z", - "iopub.status.busy": "2024-06-25T19:37:18.887391Z", - "iopub.status.idle": "2024-06-25T19:37:19.791276Z", - "shell.execute_reply": "2024-06-25T19:37:19.790730Z" + "iopub.execute_input": "2024-06-25T23:18:53.176658Z", + "iopub.status.busy": "2024-06-25T23:18:53.176332Z", + "iopub.status.idle": "2024-06-25T23:18:54.151841Z", + "shell.execute_reply": "2024-06-25T23:18:54.151257Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:19.793943Z", - "iopub.status.busy": "2024-06-25T19:37:19.793573Z", - "iopub.status.idle": "2024-06-25T19:37:19.935555Z", - "shell.execute_reply": "2024-06-25T19:37:19.935101Z" + "iopub.execute_input": "2024-06-25T23:18:54.153970Z", + "iopub.status.busy": "2024-06-25T23:18:54.153788Z", + "iopub.status.idle": "2024-06-25T23:18:54.367334Z", + "shell.execute_reply": "2024-06-25T23:18:54.366782Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:19.937552Z", - "iopub.status.busy": "2024-06-25T19:37:19.937378Z", - "iopub.status.idle": "2024-06-25T19:37:20.088397Z", - "shell.execute_reply": "2024-06-25T19:37:20.087796Z" + "iopub.execute_input": "2024-06-25T23:18:54.369532Z", + "iopub.status.busy": "2024-06-25T23:18:54.369222Z", + "iopub.status.idle": "2024-06-25T23:18:54.583472Z", + "shell.execute_reply": "2024-06-25T23:18:54.582875Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:20.090556Z", - "iopub.status.busy": "2024-06-25T19:37:20.090235Z", - "iopub.status.idle": "2024-06-25T19:37:20.751985Z", - "shell.execute_reply": "2024-06-25T19:37:20.751385Z" + "iopub.execute_input": "2024-06-25T23:18:54.585760Z", + "iopub.status.busy": "2024-06-25T23:18:54.585359Z", + "iopub.status.idle": "2024-06-25T23:18:55.323353Z", + "shell.execute_reply": "2024-06-25T23:18:55.322814Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:20.754413Z", - "iopub.status.busy": "2024-06-25T19:37:20.753942Z", - "iopub.status.idle": "2024-06-25T19:37:20.757882Z", - "shell.execute_reply": "2024-06-25T19:37:20.757342Z" + "iopub.execute_input": "2024-06-25T23:18:55.325548Z", + "iopub.status.busy": "2024-06-25T23:18:55.325207Z", + "iopub.status.idle": "2024-06-25T23:18:55.329284Z", + "shell.execute_reply": "2024-06-25T23:18:55.328852Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 3359ecfd0..4aeee095a 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": "2024-06-25T19:37:22.937714Z", - "iopub.status.busy": "2024-06-25T19:37:22.937546Z", - "iopub.status.idle": "2024-06-25T19:37:25.620183Z", - "shell.execute_reply": "2024-06-25T19:37:25.619593Z" + "iopub.execute_input": "2024-06-25T23:18:57.455185Z", + "iopub.status.busy": "2024-06-25T23:18:57.455007Z", + "iopub.status.idle": "2024-06-25T23:19:00.140522Z", + "shell.execute_reply": "2024-06-25T23:19:00.139964Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:25.622737Z", - "iopub.status.busy": "2024-06-25T19:37:25.622414Z", - "iopub.status.idle": "2024-06-25T19:37:25.936079Z", - "shell.execute_reply": "2024-06-25T19:37:25.935452Z" + "iopub.execute_input": "2024-06-25T23:19:00.143299Z", + "iopub.status.busy": "2024-06-25T23:19:00.142777Z", + "iopub.status.idle": "2024-06-25T23:19:00.459330Z", + "shell.execute_reply": "2024-06-25T23:19:00.458710Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:25.938723Z", - "iopub.status.busy": "2024-06-25T19:37:25.938422Z", - "iopub.status.idle": "2024-06-25T19:37:25.942622Z", - "shell.execute_reply": "2024-06-25T19:37:25.942185Z" + "iopub.execute_input": "2024-06-25T23:19:00.461903Z", + "iopub.status.busy": "2024-06-25T23:19:00.461603Z", + "iopub.status.idle": "2024-06-25T23:19:00.465997Z", + "shell.execute_reply": "2024-06-25T23:19:00.465462Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:25.944514Z", - "iopub.status.busy": "2024-06-25T19:37:25.944341Z", - "iopub.status.idle": "2024-06-25T19:37:33.410224Z", - "shell.execute_reply": "2024-06-25T19:37:33.409701Z" + "iopub.execute_input": "2024-06-25T23:19:00.468073Z", + "iopub.status.busy": "2024-06-25T23:19:00.467652Z", + "iopub.status.idle": "2024-06-25T23:19:04.713802Z", + "shell.execute_reply": "2024-06-25T23:19:04.713212Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 32768/170498071 [00:00<10:33, 269061.34it/s]" + " 1%| | 1867776/170498071 [00:00<00:09, 18674661.14it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 229376/170498071 [00:00<02:43, 1044330.69it/s]" + " 8%|▊ | 13533184/170498071 [00:00<00:02, 76238255.65it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 884736/170498071 [00:00<00:56, 2986468.56it/s]" + " 15%|█▍ | 25133056/170498071 [00:00<00:01, 94330786.80it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 3506176/170498071 [00:00<00:15, 10508236.75it/s]" + " 22%|██▏ | 36732928/170498071 [00:00<00:01, 102749472.78it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 8552448/170498071 [00:00<00:06, 23273913.94it/s]" + " 28%|██▊ | 48431104/170498071 [00:00<00:01, 107856210.55it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 12877824/170498071 [00:00<00:05, 29531831.70it/s]" + " 35%|███▍ | 59244544/170498071 [00:00<00:01, 104969542.54it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 17661952/170498071 [00:00<00:04, 34683944.45it/s]" + " 41%|████▏ | 70385664/170498071 [00:00<00:00, 106967167.13it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22478848/170498071 [00:00<00:03, 38419021.56it/s]" + " 48%|████▊ | 82051072/170498071 [00:00<00:00, 109967427.14it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 27590656/170498071 [00:00<00:03, 42218756.72it/s]" + " 55%|█████▍ | 93618176/170498071 [00:00<00:00, 111587704.91it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32145408/170498071 [00:01<00:03, 42979378.57it/s]" + " 62%|██████▏ | 105381888/170498071 [00:01<00:00, 113341455.44it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 37552128/170498071 [00:01<00:02, 46248329.44it/s]" + " 69%|██████▊ | 116850688/170498071 [00:01<00:00, 113640126.52it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 42237952/170498071 [00:01<00:02, 45894868.35it/s]" + " 75%|███████▌ | 128221184/170498071 [00:01<00:00, 112577089.48it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 47546368/170498071 [00:01<00:02, 47987784.85it/s]" + " 82%|████████▏ | 139886592/170498071 [00:01<00:00, 113733636.99it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 52396032/170498071 [00:01<00:02, 47660760.18it/s]" + " 89%|████████▉ | 151355392/170498071 [00:01<00:00, 113996738.39it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 57212928/170498071 [00:01<00:02, 46221672.73it/s]" + " 96%|█████████▌| 163020800/170498071 [00:01<00:00, 114693076.37it/s]" ] }, { @@ -372,183 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 62259200/170498071 [00:01<00:02, 47420663.64it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 39%|███▉ | 67043328/170498071 [00:01<00:02, 46870615.91it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 42%|████▏ | 72187904/170498071 [00:01<00:02, 48207181.06it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 45%|████▌ | 77037568/170498071 [00:02<00:01, 47426096.98it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 48%|████▊ | 82477056/170498071 [00:02<00:01, 48903231.41it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 51%|█████▏ | 87392256/170498071 [00:02<00:01, 48876811.20it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 54%|█████▍ | 92307456/170498071 [00:02<00:01, 46456343.45it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 57%|█████▋ | 97714176/170498071 [00:02<00:01, 48622672.55it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 60%|██████ | 102629376/170498071 [00:02<00:01, 47303583.33it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 63%|██████▎ | 107839488/170498071 [00:02<00:01, 48660290.70it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 66%|██████▌ | 112754688/170498071 [00:02<00:01, 48170138.42it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 69%|██████▉ | 117604352/170498071 [00:02<00:01, 48147632.32it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 72%|███████▏ | 122552320/170498071 [00:02<00:01, 47622185.83it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 75%|███████▍ | 127336448/170498071 [00:03<00:00, 46393005.90it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 78%|███████▊ | 133300224/170498071 [00:03<00:00, 50178503.28it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 81%|████████ | 138346496/170498071 [00:03<00:00, 48020765.51it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 84%|████████▍ | 143196160/170498071 [00:03<00:00, 46334061.22it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 87%|████████▋ | 149061632/170498071 [00:03<00:00, 49353288.69it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 90%|█████████ | 154042368/170498071 [00:03<00:00, 48340337.16it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 93%|█████████▎| 158924800/170498071 [00:03<00:00, 46140400.23it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 97%|█████████▋| 164921344/170498071 [00:03<00:00, 49140811.61it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|█████████▉| 169869312/170498071 [00:03<00:00, 48444737.50it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:03<00:00, 43010872.29it/s]" + "100%|██████████| 170498071/170498071 [00:01<00:00, 107997102.42it/s]" ] }, { @@ -666,10 +490,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:33.412458Z", - "iopub.status.busy": "2024-06-25T19:37:33.412129Z", - "iopub.status.idle": "2024-06-25T19:37:33.416824Z", - "shell.execute_reply": "2024-06-25T19:37:33.416305Z" + "iopub.execute_input": "2024-06-25T23:19:04.715884Z", + "iopub.status.busy": "2024-06-25T23:19:04.715703Z", + "iopub.status.idle": "2024-06-25T23:19:04.720331Z", + "shell.execute_reply": "2024-06-25T23:19:04.719901Z" }, "nbsphinx": "hidden" }, @@ -720,10 +544,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:33.418809Z", - "iopub.status.busy": "2024-06-25T19:37:33.418495Z", - "iopub.status.idle": "2024-06-25T19:37:33.960026Z", - "shell.execute_reply": "2024-06-25T19:37:33.959496Z" + "iopub.execute_input": "2024-06-25T23:19:04.722383Z", + "iopub.status.busy": "2024-06-25T23:19:04.722067Z", + "iopub.status.idle": "2024-06-25T23:19:05.264665Z", + "shell.execute_reply": "2024-06-25T23:19:05.264156Z" } }, "outputs": [ @@ -756,10 +580,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:33.962172Z", - "iopub.status.busy": "2024-06-25T19:37:33.961834Z", - "iopub.status.idle": "2024-06-25T19:37:34.466908Z", - "shell.execute_reply": "2024-06-25T19:37:34.466308Z" + "iopub.execute_input": "2024-06-25T23:19:05.266862Z", + "iopub.status.busy": "2024-06-25T23:19:05.266533Z", + "iopub.status.idle": "2024-06-25T23:19:05.782683Z", + "shell.execute_reply": "2024-06-25T23:19:05.782187Z" } }, "outputs": [ @@ -797,10 +621,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:34.469197Z", - "iopub.status.busy": "2024-06-25T19:37:34.468825Z", - "iopub.status.idle": "2024-06-25T19:37:34.472416Z", - "shell.execute_reply": "2024-06-25T19:37:34.471962Z" + "iopub.execute_input": "2024-06-25T23:19:05.784979Z", + "iopub.status.busy": "2024-06-25T23:19:05.784496Z", + "iopub.status.idle": "2024-06-25T23:19:05.788141Z", + "shell.execute_reply": "2024-06-25T23:19:05.787582Z" } }, "outputs": [], @@ -823,17 +647,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:34.474255Z", - "iopub.status.busy": "2024-06-25T19:37:34.474084Z", - "iopub.status.idle": "2024-06-25T19:37:46.991980Z", - "shell.execute_reply": "2024-06-25T19:37:46.991418Z" + "iopub.execute_input": "2024-06-25T23:19:05.790197Z", + "iopub.status.busy": "2024-06-25T23:19:05.789889Z", + "iopub.status.idle": "2024-06-25T23:19:18.262418Z", + "shell.execute_reply": "2024-06-25T23:19:18.261753Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3a6eebd9a9694b07864d194c78cdb317", + "model_id": "c487c0c381e74a26a4f49ff121b50dc9", "version_major": 2, "version_minor": 0 }, @@ -892,10 +716,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:46.994340Z", - "iopub.status.busy": "2024-06-25T19:37:46.994149Z", - "iopub.status.idle": "2024-06-25T19:37:49.075191Z", - "shell.execute_reply": "2024-06-25T19:37:49.074609Z" + "iopub.execute_input": "2024-06-25T23:19:18.264580Z", + "iopub.status.busy": "2024-06-25T23:19:18.264398Z", + "iopub.status.idle": "2024-06-25T23:19:20.351765Z", + "shell.execute_reply": "2024-06-25T23:19:20.351216Z" } }, "outputs": [ @@ -939,10 +763,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:49.077233Z", - "iopub.status.busy": "2024-06-25T19:37:49.077055Z", - "iopub.status.idle": "2024-06-25T19:37:49.302913Z", - "shell.execute_reply": "2024-06-25T19:37:49.302334Z" + "iopub.execute_input": "2024-06-25T23:19:20.354001Z", + "iopub.status.busy": "2024-06-25T23:19:20.353823Z", + "iopub.status.idle": "2024-06-25T23:19:20.594457Z", + "shell.execute_reply": "2024-06-25T23:19:20.593862Z" } }, "outputs": [ @@ -978,10 +802,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:49.305112Z", - "iopub.status.busy": "2024-06-25T19:37:49.304932Z", - "iopub.status.idle": "2024-06-25T19:37:49.943529Z", - "shell.execute_reply": "2024-06-25T19:37:49.942935Z" + "iopub.execute_input": "2024-06-25T23:19:20.597502Z", + "iopub.status.busy": "2024-06-25T23:19:20.596997Z", + "iopub.status.idle": "2024-06-25T23:19:21.266452Z", + "shell.execute_reply": "2024-06-25T23:19:21.265865Z" } }, "outputs": [ @@ -1031,10 +855,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:49.945968Z", - "iopub.status.busy": "2024-06-25T19:37:49.945641Z", - "iopub.status.idle": "2024-06-25T19:37:50.264955Z", - "shell.execute_reply": "2024-06-25T19:37:50.264439Z" + "iopub.execute_input": "2024-06-25T23:19:21.269364Z", + "iopub.status.busy": "2024-06-25T23:19:21.269161Z", + "iopub.status.idle": "2024-06-25T23:19:21.610132Z", + "shell.execute_reply": "2024-06-25T23:19:21.609580Z" } }, "outputs": [ @@ -1082,10 +906,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:50.267076Z", - "iopub.status.busy": "2024-06-25T19:37:50.266888Z", - "iopub.status.idle": "2024-06-25T19:37:50.495188Z", - "shell.execute_reply": "2024-06-25T19:37:50.494600Z" + "iopub.execute_input": "2024-06-25T23:19:21.612438Z", + "iopub.status.busy": "2024-06-25T23:19:21.612030Z", + "iopub.status.idle": "2024-06-25T23:19:21.854392Z", + "shell.execute_reply": "2024-06-25T23:19:21.853886Z" } }, "outputs": [ @@ -1141,10 +965,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:50.497736Z", - "iopub.status.busy": "2024-06-25T19:37:50.497230Z", - "iopub.status.idle": "2024-06-25T19:37:50.573192Z", - "shell.execute_reply": "2024-06-25T19:37:50.572575Z" + "iopub.execute_input": "2024-06-25T23:19:21.857100Z", + "iopub.status.busy": "2024-06-25T23:19:21.856718Z", + "iopub.status.idle": "2024-06-25T23:19:21.949451Z", + "shell.execute_reply": "2024-06-25T23:19:21.948923Z" } }, "outputs": [], @@ -1165,10 +989,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:50.575685Z", - "iopub.status.busy": "2024-06-25T19:37:50.575502Z", - "iopub.status.idle": "2024-06-25T19:38:00.831598Z", - "shell.execute_reply": "2024-06-25T19:38:00.830941Z" + "iopub.execute_input": "2024-06-25T23:19:21.951971Z", + "iopub.status.busy": "2024-06-25T23:19:21.951792Z", + "iopub.status.idle": "2024-06-25T23:19:32.299281Z", + "shell.execute_reply": "2024-06-25T23:19:32.298639Z" } }, "outputs": [ @@ -1205,10 +1029,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:00.834016Z", - "iopub.status.busy": "2024-06-25T19:38:00.833627Z", - "iopub.status.idle": "2024-06-25T19:38:02.973271Z", - "shell.execute_reply": "2024-06-25T19:38:02.972713Z" + "iopub.execute_input": "2024-06-25T23:19:32.301554Z", + "iopub.status.busy": "2024-06-25T23:19:32.301299Z", + "iopub.status.idle": "2024-06-25T23:19:34.462906Z", + "shell.execute_reply": "2024-06-25T23:19:34.462279Z" } }, "outputs": [ @@ -1239,10 +1063,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:02.975964Z", - "iopub.status.busy": "2024-06-25T19:38:02.975476Z", - "iopub.status.idle": "2024-06-25T19:38:03.180118Z", - "shell.execute_reply": "2024-06-25T19:38:03.179626Z" + "iopub.execute_input": "2024-06-25T23:19:34.465814Z", + "iopub.status.busy": "2024-06-25T23:19:34.465269Z", + "iopub.status.idle": "2024-06-25T23:19:34.668011Z", + "shell.execute_reply": "2024-06-25T23:19:34.667512Z" } }, "outputs": [], @@ -1256,10 +1080,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:03.182510Z", - "iopub.status.busy": "2024-06-25T19:38:03.182166Z", - "iopub.status.idle": "2024-06-25T19:38:03.185175Z", - "shell.execute_reply": "2024-06-25T19:38:03.184750Z" + "iopub.execute_input": "2024-06-25T23:19:34.670373Z", + "iopub.status.busy": "2024-06-25T23:19:34.670183Z", + "iopub.status.idle": "2024-06-25T23:19:34.673433Z", + "shell.execute_reply": "2024-06-25T23:19:34.672977Z" } }, "outputs": [], @@ -1281,10 +1105,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:03.187201Z", - "iopub.status.busy": "2024-06-25T19:38:03.186922Z", - "iopub.status.idle": "2024-06-25T19:38:03.194967Z", - "shell.execute_reply": "2024-06-25T19:38:03.194449Z" + "iopub.execute_input": "2024-06-25T23:19:34.675236Z", + "iopub.status.busy": "2024-06-25T23:19:34.675068Z", + "iopub.status.idle": "2024-06-25T23:19:34.683440Z", + "shell.execute_reply": "2024-06-25T23:19:34.682879Z" }, "nbsphinx": "hidden" }, @@ -1329,31 +1153,67 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "3a6eebd9a9694b07864d194c78cdb317": { + "2a6c0688ba264a97aee90eb6e37e9dc2": { "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": "" + } + }, + "2ed8f22a488e446d9dd09c9e760c1fb2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3a433760fb114684b1ce550e725410f3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d314e5a0eb894d12bc8321b569f74cd0", - "IPY_MODEL_932ee016f6a74520b9da6cc833f044b5", - "IPY_MODEL_de1dfc7949d842468505de0b2a43b4f7" - ], - "layout": "IPY_MODEL_8876ddc2f4f248ff81bbe8f98f0e2826", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cac77136c4f843fea7898f1735deb973", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2a6c0688ba264a97aee90eb6e37e9dc2", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 102469840.0 } }, - "8876ddc2f4f248ff81bbe8f98f0e2826": { + "55e4c9a3e7c7475b9b79a0916ea74432": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1406,49 +1266,48 @@ "width": null } }, - "9117500d199d44088b40d306a6001f86": { + "7b2c005a31c74359a9e6040ef88246ae": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_55e4c9a3e7c7475b9b79a0916ea74432", + "placeholder": "​", + "style": "IPY_MODEL_2ed8f22a488e446d9dd09c9e760c1fb2", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" } }, - "932ee016f6a74520b9da6cc833f044b5": { + "acf4b18dbb8e42a391b0dfa7d26e3612": { "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_be482c428a914ad9bc5accbfcda59810", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9117500d199d44088b40d306a6001f86", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "9345c22a7d26444e94e7dc281c6f7083": { + "b8d3a0d6f70d45e596f645995e60a137": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1501,7 +1360,31 @@ "width": null } }, - "a0c0e9532ae04b61941f4f996fe124d2": { + "c487c0c381e74a26a4f49ff121b50dc9": { + "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_7b2c005a31c74359a9e6040ef88246ae", + "IPY_MODEL_3a433760fb114684b1ce550e725410f3", + "IPY_MODEL_d732fe2e90af4c978675ca6771e1718c" + ], + "layout": "IPY_MODEL_d9309721bf15440c9369f140e0453a0f", + "tabbable": null, + "tooltip": null + } + }, + "cac77136c4f843fea7898f1735deb973": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1554,7 +1437,30 @@ "width": null } }, - "be482c428a914ad9bc5accbfcda59810": { + "d732fe2e90af4c978675ca6771e1718c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b8d3a0d6f70d45e596f645995e60a137", + "placeholder": "​", + "style": "IPY_MODEL_acf4b18dbb8e42a391b0dfa7d26e3612", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 218MB/s]" + } + }, + "d9309721bf15440c9369f140e0453a0f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1606,88 +1512,6 @@ "visibility": null, "width": null } - }, - "d314e5a0eb894d12bc8321b569f74cd0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9345c22a7d26444e94e7dc281c6f7083", - "placeholder": "​", - "style": "IPY_MODEL_ed999755dbfa4b068fb1bc1851f1d2ea", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" - } - }, - "d8c964fe861d44eeb4663c74d1331470": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "de1dfc7949d842468505de0b2a43b4f7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a0c0e9532ae04b61941f4f996fe124d2", - "placeholder": "​", - "style": "IPY_MODEL_d8c964fe861d44eeb4663c74d1331470", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 265MB/s]" - } - }, - "ed999755dbfa4b068fb1bc1851f1d2ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 1e51dfcdc..4dccd9a0a 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": "2024-06-25T19:38:07.555838Z", - "iopub.status.busy": "2024-06-25T19:38:07.555668Z", - "iopub.status.idle": "2024-06-25T19:38:08.722369Z", - "shell.execute_reply": "2024-06-25T19:38:08.721811Z" + "iopub.execute_input": "2024-06-25T23:19:38.796252Z", + "iopub.status.busy": "2024-06-25T23:19:38.796082Z", + "iopub.status.idle": "2024-06-25T23:19:39.953258Z", + "shell.execute_reply": "2024-06-25T23:19:39.952691Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:08.724901Z", - "iopub.status.busy": "2024-06-25T19:38:08.724626Z", - "iopub.status.idle": "2024-06-25T19:38:08.741782Z", - "shell.execute_reply": "2024-06-25T19:38:08.741233Z" + "iopub.execute_input": "2024-06-25T23:19:39.955862Z", + "iopub.status.busy": "2024-06-25T23:19:39.955512Z", + "iopub.status.idle": "2024-06-25T23:19:39.972881Z", + "shell.execute_reply": "2024-06-25T23:19:39.972463Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:08.744094Z", - "iopub.status.busy": "2024-06-25T19:38:08.743687Z", - "iopub.status.idle": "2024-06-25T19:38:08.746763Z", - "shell.execute_reply": "2024-06-25T19:38:08.746228Z" + "iopub.execute_input": "2024-06-25T23:19:39.975108Z", + "iopub.status.busy": "2024-06-25T23:19:39.974726Z", + "iopub.status.idle": "2024-06-25T23:19:39.977547Z", + "shell.execute_reply": "2024-06-25T23:19:39.977124Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:08.748783Z", - "iopub.status.busy": "2024-06-25T19:38:08.748471Z", - "iopub.status.idle": "2024-06-25T19:38:09.023742Z", - "shell.execute_reply": "2024-06-25T19:38:09.023127Z" + "iopub.execute_input": "2024-06-25T23:19:39.979571Z", + "iopub.status.busy": "2024-06-25T23:19:39.979249Z", + "iopub.status.idle": "2024-06-25T23:19:40.010006Z", + "shell.execute_reply": "2024-06-25T23:19:40.009548Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.025867Z", - "iopub.status.busy": "2024-06-25T19:38:09.025685Z", - "iopub.status.idle": "2024-06-25T19:38:09.204489Z", - "shell.execute_reply": "2024-06-25T19:38:09.203970Z" + "iopub.execute_input": "2024-06-25T23:19:40.012066Z", + "iopub.status.busy": "2024-06-25T23:19:40.011740Z", + "iopub.status.idle": "2024-06-25T23:19:40.191233Z", + "shell.execute_reply": "2024-06-25T23:19:40.190672Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.206625Z", - "iopub.status.busy": "2024-06-25T19:38:09.206444Z", - "iopub.status.idle": "2024-06-25T19:38:09.445281Z", - "shell.execute_reply": "2024-06-25T19:38:09.444670Z" + "iopub.execute_input": "2024-06-25T23:19:40.193662Z", + "iopub.status.busy": "2024-06-25T23:19:40.193313Z", + "iopub.status.idle": "2024-06-25T23:19:40.401417Z", + "shell.execute_reply": "2024-06-25T23:19:40.400809Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.447540Z", - "iopub.status.busy": "2024-06-25T19:38:09.447186Z", - "iopub.status.idle": "2024-06-25T19:38:09.451599Z", - "shell.execute_reply": "2024-06-25T19:38:09.451044Z" + "iopub.execute_input": "2024-06-25T23:19:40.403764Z", + "iopub.status.busy": "2024-06-25T23:19:40.403425Z", + "iopub.status.idle": "2024-06-25T23:19:40.407638Z", + "shell.execute_reply": "2024-06-25T23:19:40.407217Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.453555Z", - "iopub.status.busy": "2024-06-25T19:38:09.453375Z", - "iopub.status.idle": "2024-06-25T19:38:09.460592Z", - "shell.execute_reply": "2024-06-25T19:38:09.460157Z" + "iopub.execute_input": "2024-06-25T23:19:40.409677Z", + "iopub.status.busy": "2024-06-25T23:19:40.409360Z", + "iopub.status.idle": "2024-06-25T23:19:40.415770Z", + "shell.execute_reply": "2024-06-25T23:19:40.415356Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.462899Z", - "iopub.status.busy": "2024-06-25T19:38:09.462366Z", - "iopub.status.idle": "2024-06-25T19:38:09.465304Z", - "shell.execute_reply": "2024-06-25T19:38:09.464836Z" + "iopub.execute_input": "2024-06-25T23:19:40.417772Z", + "iopub.status.busy": "2024-06-25T23:19:40.417455Z", + "iopub.status.idle": "2024-06-25T23:19:40.420042Z", + "shell.execute_reply": "2024-06-25T23:19:40.419591Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.467150Z", - "iopub.status.busy": "2024-06-25T19:38:09.466976Z", - "iopub.status.idle": "2024-06-25T19:38:18.068771Z", - "shell.execute_reply": "2024-06-25T19:38:18.068131Z" + "iopub.execute_input": "2024-06-25T23:19:40.421960Z", + "iopub.status.busy": "2024-06-25T23:19:40.421649Z", + "iopub.status.idle": "2024-06-25T23:19:48.997759Z", + "shell.execute_reply": "2024-06-25T23:19:48.997063Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.071591Z", - "iopub.status.busy": "2024-06-25T19:38:18.071196Z", - "iopub.status.idle": "2024-06-25T19:38:18.078371Z", - "shell.execute_reply": "2024-06-25T19:38:18.077824Z" + "iopub.execute_input": "2024-06-25T23:19:49.000433Z", + "iopub.status.busy": "2024-06-25T23:19:49.000048Z", + "iopub.status.idle": "2024-06-25T23:19:49.007281Z", + "shell.execute_reply": "2024-06-25T23:19:49.006704Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.080333Z", - "iopub.status.busy": "2024-06-25T19:38:18.080152Z", - "iopub.status.idle": "2024-06-25T19:38:18.083810Z", - "shell.execute_reply": "2024-06-25T19:38:18.083366Z" + "iopub.execute_input": "2024-06-25T23:19:49.009612Z", + "iopub.status.busy": "2024-06-25T23:19:49.009171Z", + "iopub.status.idle": "2024-06-25T23:19:49.013898Z", + "shell.execute_reply": "2024-06-25T23:19:49.013343Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.085821Z", - "iopub.status.busy": "2024-06-25T19:38:18.085497Z", - "iopub.status.idle": "2024-06-25T19:38:18.088621Z", - "shell.execute_reply": "2024-06-25T19:38:18.088109Z" + "iopub.execute_input": "2024-06-25T23:19:49.016095Z", + "iopub.status.busy": "2024-06-25T23:19:49.015919Z", + "iopub.status.idle": "2024-06-25T23:19:49.019068Z", + "shell.execute_reply": "2024-06-25T23:19:49.018547Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.090576Z", - "iopub.status.busy": "2024-06-25T19:38:18.090262Z", - "iopub.status.idle": "2024-06-25T19:38:18.093389Z", - "shell.execute_reply": "2024-06-25T19:38:18.092821Z" + "iopub.execute_input": "2024-06-25T23:19:49.020914Z", + "iopub.status.busy": "2024-06-25T23:19:49.020745Z", + "iopub.status.idle": "2024-06-25T23:19:49.023808Z", + "shell.execute_reply": "2024-06-25T23:19:49.023350Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.095470Z", - "iopub.status.busy": "2024-06-25T19:38:18.095154Z", - "iopub.status.idle": "2024-06-25T19:38:18.103228Z", - "shell.execute_reply": "2024-06-25T19:38:18.102775Z" + "iopub.execute_input": "2024-06-25T23:19:49.025803Z", + "iopub.status.busy": "2024-06-25T23:19:49.025488Z", + "iopub.status.idle": "2024-06-25T23:19:49.033564Z", + "shell.execute_reply": "2024-06-25T23:19:49.033138Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.105003Z", - "iopub.status.busy": "2024-06-25T19:38:18.104832Z", - "iopub.status.idle": "2024-06-25T19:38:18.107625Z", - "shell.execute_reply": "2024-06-25T19:38:18.107128Z" + "iopub.execute_input": "2024-06-25T23:19:49.035573Z", + "iopub.status.busy": "2024-06-25T23:19:49.035256Z", + "iopub.status.idle": "2024-06-25T23:19:49.037707Z", + "shell.execute_reply": "2024-06-25T23:19:49.037270Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.109671Z", - "iopub.status.busy": "2024-06-25T19:38:18.109367Z", - "iopub.status.idle": "2024-06-25T19:38:18.236233Z", - "shell.execute_reply": "2024-06-25T19:38:18.235732Z" + "iopub.execute_input": "2024-06-25T23:19:49.039639Z", + "iopub.status.busy": "2024-06-25T23:19:49.039383Z", + "iopub.status.idle": "2024-06-25T23:19:49.162747Z", + "shell.execute_reply": "2024-06-25T23:19:49.162268Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.238299Z", - "iopub.status.busy": "2024-06-25T19:38:18.237942Z", - "iopub.status.idle": "2024-06-25T19:38:18.347132Z", - "shell.execute_reply": "2024-06-25T19:38:18.346641Z" + "iopub.execute_input": "2024-06-25T23:19:49.164799Z", + "iopub.status.busy": "2024-06-25T23:19:49.164444Z", + "iopub.status.idle": "2024-06-25T23:19:49.269361Z", + "shell.execute_reply": "2024-06-25T23:19:49.268836Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.349402Z", - "iopub.status.busy": "2024-06-25T19:38:18.349044Z", - "iopub.status.idle": "2024-06-25T19:38:18.839672Z", - "shell.execute_reply": "2024-06-25T19:38:18.839073Z" + "iopub.execute_input": "2024-06-25T23:19:49.271927Z", + "iopub.status.busy": "2024-06-25T23:19:49.271573Z", + "iopub.status.idle": "2024-06-25T23:19:49.761626Z", + "shell.execute_reply": "2024-06-25T23:19:49.760979Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.841931Z", - "iopub.status.busy": "2024-06-25T19:38:18.841755Z", - "iopub.status.idle": "2024-06-25T19:38:18.912662Z", - "shell.execute_reply": "2024-06-25T19:38:18.912091Z" + "iopub.execute_input": "2024-06-25T23:19:49.764216Z", + "iopub.status.busy": "2024-06-25T23:19:49.763834Z", + "iopub.status.idle": "2024-06-25T23:19:49.843367Z", + "shell.execute_reply": "2024-06-25T23:19:49.842743Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.915065Z", - "iopub.status.busy": "2024-06-25T19:38:18.914579Z", - "iopub.status.idle": "2024-06-25T19:38:18.923159Z", - "shell.execute_reply": "2024-06-25T19:38:18.922730Z" + "iopub.execute_input": "2024-06-25T23:19:49.845464Z", + "iopub.status.busy": "2024-06-25T23:19:49.845235Z", + "iopub.status.idle": "2024-06-25T23:19:49.853788Z", + "shell.execute_reply": "2024-06-25T23:19:49.853340Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.925120Z", - "iopub.status.busy": "2024-06-25T19:38:18.924947Z", - "iopub.status.idle": "2024-06-25T19:38:18.927502Z", - "shell.execute_reply": "2024-06-25T19:38:18.927067Z" + "iopub.execute_input": "2024-06-25T23:19:49.855684Z", + "iopub.status.busy": "2024-06-25T23:19:49.855513Z", + "iopub.status.idle": "2024-06-25T23:19:49.858498Z", + "shell.execute_reply": "2024-06-25T23:19:49.857932Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.929453Z", - "iopub.status.busy": "2024-06-25T19:38:18.929127Z", - "iopub.status.idle": "2024-06-25T19:38:24.397527Z", - "shell.execute_reply": "2024-06-25T19:38:24.396937Z" + "iopub.execute_input": "2024-06-25T23:19:49.860435Z", + "iopub.status.busy": "2024-06-25T23:19:49.860125Z", + "iopub.status.idle": "2024-06-25T23:19:55.315583Z", + "shell.execute_reply": "2024-06-25T23:19:55.315005Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:24.400077Z", - "iopub.status.busy": "2024-06-25T19:38:24.399563Z", - "iopub.status.idle": "2024-06-25T19:38:24.408142Z", - "shell.execute_reply": "2024-06-25T19:38:24.407603Z" + "iopub.execute_input": "2024-06-25T23:19:55.317789Z", + "iopub.status.busy": "2024-06-25T23:19:55.317611Z", + "iopub.status.idle": "2024-06-25T23:19:55.326379Z", + "shell.execute_reply": "2024-06-25T23:19:55.325834Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:24.410281Z", - "iopub.status.busy": "2024-06-25T19:38:24.409820Z", - "iopub.status.idle": "2024-06-25T19:38:24.473861Z", - "shell.execute_reply": "2024-06-25T19:38:24.473281Z" + "iopub.execute_input": "2024-06-25T23:19:55.328528Z", + "iopub.status.busy": "2024-06-25T23:19:55.328125Z", + "iopub.status.idle": "2024-06-25T23:19:55.396039Z", + "shell.execute_reply": "2024-06-25T23:19:55.395563Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 253b92cf5..d70cfaf4e 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": "2024-06-25T19:38:27.445776Z", - "iopub.status.busy": "2024-06-25T19:38:27.445616Z", - "iopub.status.idle": "2024-06-25T19:38:29.357688Z", - "shell.execute_reply": "2024-06-25T19:38:29.356961Z" + "iopub.execute_input": "2024-06-25T23:19:58.399516Z", + "iopub.status.busy": "2024-06-25T23:19:58.399339Z", + "iopub.status.idle": "2024-06-25T23:19:59.729255Z", + "shell.execute_reply": "2024-06-25T23:19:59.728521Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:29.360485Z", - "iopub.status.busy": "2024-06-25T19:38:29.360106Z", - "iopub.status.idle": "2024-06-25T19:39:24.167594Z", - "shell.execute_reply": "2024-06-25T19:39:24.166933Z" + "iopub.execute_input": "2024-06-25T23:19:59.731986Z", + "iopub.status.busy": "2024-06-25T23:19:59.731605Z", + "iopub.status.idle": "2024-06-25T23:20:48.710370Z", + "shell.execute_reply": "2024-06-25T23:20:48.709721Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:24.170328Z", - "iopub.status.busy": "2024-06-25T19:39:24.169968Z", - "iopub.status.idle": "2024-06-25T19:39:25.274825Z", - "shell.execute_reply": "2024-06-25T19:39:25.274283Z" + "iopub.execute_input": "2024-06-25T23:20:48.712844Z", + "iopub.status.busy": "2024-06-25T23:20:48.712648Z", + "iopub.status.idle": "2024-06-25T23:20:49.819754Z", + "shell.execute_reply": "2024-06-25T23:20:49.819207Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:39:25.277334Z", - "iopub.status.busy": "2024-06-25T19:39:25.276961Z", - "iopub.status.idle": "2024-06-25T19:39:25.280274Z", - "shell.execute_reply": "2024-06-25T19:39:25.279814Z" + "iopub.execute_input": "2024-06-25T23:20:49.822551Z", + "iopub.status.busy": "2024-06-25T23:20:49.821983Z", + "iopub.status.idle": "2024-06-25T23:20:49.825360Z", + "shell.execute_reply": "2024-06-25T23:20:49.824898Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:25.282318Z", - "iopub.status.busy": "2024-06-25T19:39:25.282060Z", - "iopub.status.idle": "2024-06-25T19:39:25.285902Z", - "shell.execute_reply": "2024-06-25T19:39:25.285458Z" + "iopub.execute_input": "2024-06-25T23:20:49.827409Z", + "iopub.status.busy": "2024-06-25T23:20:49.827081Z", + "iopub.status.idle": "2024-06-25T23:20:49.830739Z", + "shell.execute_reply": "2024-06-25T23:20:49.830321Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:25.287764Z", - "iopub.status.busy": "2024-06-25T19:39:25.287595Z", - "iopub.status.idle": "2024-06-25T19:39:25.291186Z", - "shell.execute_reply": "2024-06-25T19:39:25.290735Z" + "iopub.execute_input": "2024-06-25T23:20:49.832814Z", + "iopub.status.busy": "2024-06-25T23:20:49.832481Z", + "iopub.status.idle": "2024-06-25T23:20:49.835986Z", + "shell.execute_reply": "2024-06-25T23:20:49.835546Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:25.293004Z", - "iopub.status.busy": "2024-06-25T19:39:25.292834Z", - "iopub.status.idle": "2024-06-25T19:39:25.296491Z", - "shell.execute_reply": "2024-06-25T19:39:25.296049Z" + "iopub.execute_input": "2024-06-25T23:20:49.837816Z", + "iopub.status.busy": "2024-06-25T23:20:49.837650Z", + "iopub.status.idle": "2024-06-25T23:20:49.841360Z", + "shell.execute_reply": "2024-06-25T23:20:49.840870Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:25.298372Z", - "iopub.status.busy": "2024-06-25T19:39:25.298196Z", - "iopub.status.idle": "2024-06-25T19:39:58.536591Z", - "shell.execute_reply": "2024-06-25T19:39:58.535983Z" + "iopub.execute_input": "2024-06-25T23:20:49.843348Z", + "iopub.status.busy": "2024-06-25T23:20:49.843045Z", + "iopub.status.idle": "2024-06-25T23:21:23.312097Z", + "shell.execute_reply": "2024-06-25T23:21:23.311395Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "944591b9a0384c6388bc6a076330ac62", + "model_id": "198f978c68c04b42bb7f505400e75581", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "456e1a39f8a0484d84df60d119f7d9b3", + "model_id": "4b186141820047419c3ae004111754f6", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:58.539357Z", - "iopub.status.busy": "2024-06-25T19:39:58.538990Z", - "iopub.status.idle": "2024-06-25T19:39:59.206448Z", - "shell.execute_reply": "2024-06-25T19:39:59.205970Z" + "iopub.execute_input": "2024-06-25T23:21:23.314740Z", + "iopub.status.busy": "2024-06-25T23:21:23.314519Z", + "iopub.status.idle": "2024-06-25T23:21:23.985247Z", + "shell.execute_reply": "2024-06-25T23:21:23.984646Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:59.208781Z", - "iopub.status.busy": "2024-06-25T19:39:59.208330Z", - "iopub.status.idle": "2024-06-25T19:40:01.948266Z", - "shell.execute_reply": "2024-06-25T19:40:01.947672Z" + "iopub.execute_input": "2024-06-25T23:21:23.987564Z", + "iopub.status.busy": "2024-06-25T23:21:23.987136Z", + "iopub.status.idle": "2024-06-25T23:21:26.705173Z", + "shell.execute_reply": "2024-06-25T23:21:26.704585Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:01.950510Z", - "iopub.status.busy": "2024-06-25T19:40:01.950173Z", - "iopub.status.idle": "2024-06-25T19:40:34.744210Z", - "shell.execute_reply": "2024-06-25T19:40:34.743718Z" + "iopub.execute_input": "2024-06-25T23:21:26.707473Z", + "iopub.status.busy": "2024-06-25T23:21:26.707134Z", + "iopub.status.idle": "2024-06-25T23:22:00.356055Z", + "shell.execute_reply": "2024-06-25T23:22:00.355524Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f91d1545f3254e83bb88ef07ebe6e9fe", + "model_id": "28a8baec191044f3831c5b052b050cba", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:34.746367Z", - "iopub.status.busy": "2024-06-25T19:40:34.746041Z", - "iopub.status.idle": "2024-06-25T19:40:49.559228Z", - "shell.execute_reply": "2024-06-25T19:40:49.558651Z" + "iopub.execute_input": "2024-06-25T23:22:00.358322Z", + "iopub.status.busy": "2024-06-25T23:22:00.357991Z", + "iopub.status.idle": "2024-06-25T23:22:14.992649Z", + "shell.execute_reply": "2024-06-25T23:22:14.992097Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:49.561671Z", - "iopub.status.busy": "2024-06-25T19:40:49.561368Z", - "iopub.status.idle": "2024-06-25T19:40:53.237064Z", - "shell.execute_reply": "2024-06-25T19:40:53.236459Z" + "iopub.execute_input": "2024-06-25T23:22:14.994920Z", + "iopub.status.busy": "2024-06-25T23:22:14.994721Z", + "iopub.status.idle": "2024-06-25T23:22:18.704345Z", + "shell.execute_reply": "2024-06-25T23:22:18.703738Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:53.239303Z", - "iopub.status.busy": "2024-06-25T19:40:53.238898Z", - "iopub.status.idle": "2024-06-25T19:40:54.630349Z", - "shell.execute_reply": "2024-06-25T19:40:54.629762Z" + "iopub.execute_input": "2024-06-25T23:22:18.706711Z", + "iopub.status.busy": "2024-06-25T23:22:18.706375Z", + "iopub.status.idle": "2024-06-25T23:22:20.102205Z", + "shell.execute_reply": "2024-06-25T23:22:20.101646Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c3ec5543994844ccbea4230fb9d7b4eb", + "model_id": "e9e7048a47b3430f853de8ee7bd0cedf", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:54.632662Z", - "iopub.status.busy": "2024-06-25T19:40:54.632244Z", - "iopub.status.idle": "2024-06-25T19:40:54.661012Z", - "shell.execute_reply": "2024-06-25T19:40:54.660347Z" + "iopub.execute_input": "2024-06-25T23:22:20.104725Z", + "iopub.status.busy": "2024-06-25T23:22:20.104340Z", + "iopub.status.idle": "2024-06-25T23:22:20.133203Z", + "shell.execute_reply": "2024-06-25T23:22:20.132635Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:54.663568Z", - "iopub.status.busy": "2024-06-25T19:40:54.663357Z", - "iopub.status.idle": "2024-06-25T19:41:00.730286Z", - "shell.execute_reply": "2024-06-25T19:41:00.729759Z" + "iopub.execute_input": "2024-06-25T23:22:20.135579Z", + "iopub.status.busy": "2024-06-25T23:22:20.135375Z", + "iopub.status.idle": "2024-06-25T23:22:26.135540Z", + "shell.execute_reply": "2024-06-25T23:22:26.134957Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:00.732454Z", - "iopub.status.busy": "2024-06-25T19:41:00.732273Z", - "iopub.status.idle": "2024-06-25T19:41:00.787546Z", - "shell.execute_reply": "2024-06-25T19:41:00.786977Z" + "iopub.execute_input": "2024-06-25T23:22:26.137624Z", + "iopub.status.busy": "2024-06-25T23:22:26.137444Z", + "iopub.status.idle": "2024-06-25T23:22:26.192958Z", + "shell.execute_reply": "2024-06-25T23:22:26.192454Z" }, "nbsphinx": "hidden" }, @@ -1038,60 +1038,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0413905ec4a1476eab10c5ca853497d3": { - "model_module": "@jupyter-widgets/base", + "0220fd3c35e849c1a03e5c8abca06016": { + "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 } }, - "04229ad3351b4f7aaf0a891a50bc135d": { + "0d5018ed1be44175807c78ce0ab57606": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1144,7 +1109,7 @@ "width": null } }, - "07201424d87b4671ac4b1cfa673d7986": { + "119e57b6fb1844baa93ee07fae718fb0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1159,33 +1124,125 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a832d5b427714b918a469d3e99a8e9ff", + "layout": "IPY_MODEL_d68c68f5e35245c4a160a0468811587d", "placeholder": "​", - "style": "IPY_MODEL_0965a0f7f5304df18ccd851b34fef9fb", + "style": "IPY_MODEL_bf890124360a4567a82d5d03676272de", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:21<00:00,  1.44it/s]" + "value": " 30/30 [00:01<00:00, 21.82it/s]" } }, - "0965a0f7f5304df18ccd851b34fef9fb": { + "198f978c68c04b42bb7f505400e75581": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a772478a0f704e3fa3325fbef037b4cd", + "IPY_MODEL_5777ae56d3c54e35b67a204b112139e6", + "IPY_MODEL_3e183c14ae544e71b619db5e0b367992" + ], + "layout": "IPY_MODEL_c28b42730873450e95180653991778ed", + "tabbable": null, + "tooltip": null + } + }, + "1b5e7f7edd7e41a48c9ee3f889c53756": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ca265e653b2b43779eb1e897adba0a8c", + "placeholder": "​", + "style": "IPY_MODEL_4b05b13a08364b5d9d060ee36c605fd5", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:21<00:00,  1.42it/s]" + } + }, + "28a8baec191044f3831c5b052b050cba": { + "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_da3ca18af3434b998fd38fd9dc359605", + "IPY_MODEL_8e863ef8f3304af0a1299abfe8ad02b8", + "IPY_MODEL_f9315252473d439f95f4fd58000719ed" + ], + "layout": "IPY_MODEL_cb251166fe62454094fce22ed9bd897a", + "tabbable": null, + "tooltip": null + } + }, + "367bfe277ffe4be9b8bae4bdaaa320ae": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "0b658cb143704dbbb7f0ac48f95429f2": { + "3e183c14ae544e71b619db5e0b367992": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e8e80dd483054ca091488456a8397301", + "placeholder": "​", + "style": "IPY_MODEL_0220fd3c35e849c1a03e5c8abca06016", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 758.41it/s]" + } + }, + "42d0aa720c6e4a2ca9f245568eca8de9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1201,7 +1258,7 @@ "description_width": "" } }, - "13d8b2f426d2467cbc47898751b6f6dd": { + "4b05b13a08364b5d9d060ee36c605fd5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1219,23 +1276,84 @@ "text_color": null } }, - "15f5d288daf5495ba283ee4fad4d58fd": { + "4b186141820047419c3ae004111754f6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8a43127ec4fc484eb12e55de443c3a15", + "IPY_MODEL_4ff8dfedbd2949a5bb4270735a77c539", + "IPY_MODEL_1b5e7f7edd7e41a48c9ee3f889c53756" + ], + "layout": "IPY_MODEL_b3e446b4b0bf43d0806173baf332511b", + "tabbable": null, + "tooltip": null + } + }, + "4bb96c22484d421c85c644ab03e8eb56": { + "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": "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 } }, - "215c528b84a347b1a955651a8792356a": { + "4ff8dfedbd2949a5bb4270735a77c539": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1251,40 +1369,43 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8d3f186b66e646faa88abf0438b28125", + "layout": "IPY_MODEL_c9d9ec1dbde84cafb318173cee06bece", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_476d964c770641a799b799e08d88ea6e", + "style": "IPY_MODEL_e3c898e2f5ff4cd480ab8c04bffe7dfb", "tabbable": null, "tooltip": null, "value": 30.0 } }, - "2184a8202604452c862d764c590163a7": { + "5777ae56d3c54e35b67a204b112139e6": { "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_e251eadf4c0d45e99ed477a752be3d71", - "placeholder": "​", - "style": "IPY_MODEL_9238a3aeb6694a54a16d5276afd69748", + "layout": "IPY_MODEL_d8fb8c2405ca482185ac5e7c6f667650", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_42d0aa720c6e4a2ca9f245568eca8de9", "tabbable": null, "tooltip": null, - "value": " 4997683/4997683 [00:32<00:00, 154374.13it/s]" + "value": 30.0 } }, - "2a9e65f5f4ec40d496f43ad1adfd040a": { + "5fe269722f6a4605a143155dac3bcd01": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1337,7 +1458,43 @@ "width": null } }, - "2f266a82a0cd46529aa761f6c67f7700": { + "680f5ec724904dd1a968ede02eacb19b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "69f5479f692f4d43b82f5df028e467d7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "85982aca8cc345109d878dbd7bbe9828": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1390,57 +1547,74 @@ "width": null } }, - "38cf33a9b7b9481cb9a58609d734b80f": { + "8a43127ec4fc484eb12e55de443c3a15": { "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_2f266a82a0cd46529aa761f6c67f7700", - "max": 4997683.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8978b1bc574944349ad8045c8d081583", + "layout": "IPY_MODEL_eb1f2e4f38da4c9190ae6c4d6714e087", + "placeholder": "​", + "style": "IPY_MODEL_f47a2b8a8ebb4314850e6e3193fcd946", "tabbable": null, "tooltip": null, - "value": 4997683.0 + "value": "number of examples processed for checking labels: 100%" } }, - "456e1a39f8a0484d84df60d119f7d9b3": { + "8b038c68d122401fbb4e96c1ad051c9f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8e863ef8f3304af0a1299abfe8ad02b8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_a858829ede1a452ba7407428df24b4fa", - "IPY_MODEL_8aaa8a35422241b2a265233527fbf1e7", - "IPY_MODEL_07201424d87b4671ac4b1cfa673d7986" - ], - "layout": "IPY_MODEL_cb515331da754c33a0c68f56fd6918b5", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0d5018ed1be44175807c78ce0ab57606", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_367bfe277ffe4be9b8bae4bdaaa320ae", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 4997683.0 } }, - "476d964c770641a799b799e08d88ea6e": { + "96ab128e8fca4ba6874dc9eef5cf4ff3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1456,43 +1630,79 @@ "description_width": "" } }, - "4e6a4c23dd054908be889d0f3d83ee3b": { + "9da954de4d5f49119869cd8370e5e154": { "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_85982aca8cc345109d878dbd7bbe9828", + "placeholder": "​", + "style": "IPY_MODEL_c5e54c3a78384206bc828910a703fec0", + "tabbable": null, + "tooltip": null, + "value": "images processed using softmin: 100%" } }, - "531002ec92784d8c8567de9c2f6b2001": { + "a772478a0f704e3fa3325fbef037b4cd": { "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_5fe269722f6a4605a143155dac3bcd01", + "placeholder": "​", + "style": "IPY_MODEL_680f5ec724904dd1a968ede02eacb19b", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" } }, - "5868ffb252c44ac4aa39f70440bc622f": { + "a9a87800c31f4b0c8cd52338e21b3cc8": { + "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_f9477ce7cdc74a22959c387b134e9c01", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_96ab128e8fca4ba6874dc9eef5cf4ff3", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "b3e446b4b0bf43d0806173baf332511b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1545,30 +1755,25 @@ "width": null } }, - "61c316c9d6e6417db8e8f67c88ca16b3": { + "bf890124360a4567a82d5d03676272de": { "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_ab2778b3faa74fc58eae618ac7ad06b7", - "placeholder": "​", - "style": "IPY_MODEL_d9e435b2b0064cb389b0eba2fdb3ae58", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 756.08it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "6a036f411aba4cdc9fa68388d47ac9c0": { + "c28b42730873450e95180653991778ed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1621,7 +1826,7 @@ "width": null } }, - "6c8bceb12179495a84b898f7d7fb9df2": { + "c5e54c3a78384206bc828910a703fec0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1639,7 +1844,7 @@ "text_color": null } }, - "6dabf219795b408dabf8afe1ed3b2ba9": { + "c9d9ec1dbde84cafb318173cee06bece": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1692,7 +1897,7 @@ "width": null } }, - "74b719a4753841d48ddef0f60728929c": { + "ca265e653b2b43779eb1e897adba0a8c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1745,49 +1950,7 @@ "width": null } }, - "8978b1bc574944349ad8045c8d081583": { - "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": "" - } - }, - "8aaa8a35422241b2a265233527fbf1e7": { - "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_5868ffb252c44ac4aa39f70440bc622f", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_15f5d288daf5495ba283ee4fad4d58fd", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "8d3f186b66e646faa88abf0438b28125": { + "cb251166fe62454094fce22ed9bd897a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1840,49 +2003,7 @@ "width": null } }, - "9238a3aeb6694a54a16d5276afd69748": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "944591b9a0384c6388bc6a076330ac62": { - "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_e3e1e81a9a074a9699b1c756208a42d1", - "IPY_MODEL_c2abb97f3c0f47e89640d65ea0507ceb", - "IPY_MODEL_61c316c9d6e6417db8e8f67c88ca16b3" - ], - "layout": "IPY_MODEL_f0c67deadadc41a681e33253811fe3c3", - "tabbable": null, - "tooltip": null - } - }, - "a10c8b997a924841b67b483401b9d8c3": { + "cf29f93a566f4e018c0e0d5c5ad96a05": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1935,25 +2056,7 @@ "width": null } }, - "a61e682d08cd4b079011fff3976213c6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a832d5b427714b918a469d3e99a8e9ff": { + "d68c68f5e35245c4a160a0468811587d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2006,30 +2109,7 @@ "width": null } }, - "a858829ede1a452ba7407428df24b4fa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a10c8b997a924841b67b483401b9d8c3", - "placeholder": "​", - "style": "IPY_MODEL_4e6a4c23dd054908be889d0f3d83ee3b", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" - } - }, - "ab2778b3faa74fc58eae618ac7ad06b7": { + "d8fb8c2405ca482185ac5e7c6f667650": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2082,7 +2162,7 @@ "width": null } }, - "b05c6a6bdf664a62a6cbd8f66104b29f": { + "da3ca18af3434b998fd38fd9dc359605": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2097,15 +2177,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_74b719a4753841d48ddef0f60728929c", + "layout": "IPY_MODEL_fcba50827939420b83ea40b9e3507089", "placeholder": "​", - "style": "IPY_MODEL_531002ec92784d8c8567de9c2f6b2001", + "style": "IPY_MODEL_69f5479f692f4d43b82f5df028e467d7", "tabbable": null, "tooltip": null, "value": "100%" } }, - "c1f546fd71c24d498d05b3908214bd7d": { + "e3c898e2f5ff4cd480ab8c04bffe7dfb": { + "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": "" + } + }, + "e8e80dd483054ca091488456a8397301": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2158,33 +2254,7 @@ "width": null } }, - "c2abb97f3c0f47e89640d65ea0507ceb": { - "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_c1f546fd71c24d498d05b3908214bd7d", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0b658cb143704dbbb7f0ac48f95429f2", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "c3ec5543994844ccbea4230fb9d7b4eb": { + "e9e7048a47b3430f853de8ee7bd0cedf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2199,16 +2269,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_f1b83d31de91416b8454f54a7486c222", - "IPY_MODEL_215c528b84a347b1a955651a8792356a", - "IPY_MODEL_cc71964c79fb4cc48ccbc1e58b722da3" + "IPY_MODEL_9da954de4d5f49119869cd8370e5e154", + "IPY_MODEL_a9a87800c31f4b0c8cd52338e21b3cc8", + "IPY_MODEL_119e57b6fb1844baa93ee07fae718fb0" ], - "layout": "IPY_MODEL_6a036f411aba4cdc9fa68388d47ac9c0", + "layout": "IPY_MODEL_4bb96c22484d421c85c644ab03e8eb56", "tabbable": null, "tooltip": null } }, - "cb515331da754c33a0c68f56fd6918b5": { + "eb1f2e4f38da4c9190ae6c4d6714e087": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2261,7 +2331,25 @@ "width": null } }, - "cc71964c79fb4cc48ccbc1e58b722da3": { + "f47a2b8a8ebb4314850e6e3193fcd946": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "f9315252473d439f95f4fd58000719ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2276,33 +2364,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0413905ec4a1476eab10c5ca853497d3", + "layout": "IPY_MODEL_cf29f93a566f4e018c0e0d5c5ad96a05", "placeholder": "​", - "style": "IPY_MODEL_6c8bceb12179495a84b898f7d7fb9df2", + "style": "IPY_MODEL_8b038c68d122401fbb4e96c1ad051c9f", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 21.71it/s]" - } - }, - "d9e435b2b0064cb389b0eba2fdb3ae58": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 4997683/4997683 [00:33<00:00, 150147.59it/s]" } }, - "e251eadf4c0d45e99ed477a752be3d71": { + "f9477ce7cdc74a22959c387b134e9c01": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2355,30 +2425,7 @@ "width": null } }, - "e3e1e81a9a074a9699b1c756208a42d1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6dabf219795b408dabf8afe1ed3b2ba9", - "placeholder": "​", - "style": "IPY_MODEL_a61e682d08cd4b079011fff3976213c6", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "f0c67deadadc41a681e33253811fe3c3": { + "fcba50827939420b83ea40b9e3507089": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2430,53 +2477,6 @@ "visibility": null, "width": null } - }, - "f1b83d31de91416b8454f54a7486c222": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2a9e65f5f4ec40d496f43ad1adfd040a", - "placeholder": "​", - "style": "IPY_MODEL_13d8b2f426d2467cbc47898751b6f6dd", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "f91d1545f3254e83bb88ef07ebe6e9fe": { - "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_b05c6a6bdf664a62a6cbd8f66104b29f", - "IPY_MODEL_38cf33a9b7b9481cb9a58609d734b80f", - "IPY_MODEL_2184a8202604452c862d764c590163a7" - ], - "layout": "IPY_MODEL_04229ad3351b4f7aaf0a891a50bc135d", - "tabbable": null, - "tooltip": null - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index c6bf67460..28feac438 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": "2024-06-25T19:41:02.971504Z", - "iopub.status.busy": "2024-06-25T19:41:02.971078Z", - "iopub.status.idle": "2024-06-25T19:41:04.919925Z", - "shell.execute_reply": "2024-06-25T19:41:04.919315Z" + "iopub.execute_input": "2024-06-25T23:22:28.297877Z", + "iopub.status.busy": "2024-06-25T23:22:28.297692Z", + "iopub.status.idle": "2024-06-25T23:22:29.566144Z", + "shell.execute_reply": "2024-06-25T23:22:29.565466Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-25 19:41:02-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-06-25 23:22:28-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,16 +94,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.249.162, 2400:52e0:1a01::984:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.249.162|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", - "Length: 982975 (960K) [application/zip]\r\n" + "185.93.1.250, 2400:52e0:1a00::1068:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "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", "\r", @@ -117,7 +125,7 @@ "\r", "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-06-25 19:41:03 (8.03 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-06-25 23:22:28 (6.31 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -137,22 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-25 19:41:03-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.196.49, 52.216.88.99, 3.5.9.136, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.196.49|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "--2024-06-25 23:22:29-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.25.196, 54.231.139.49, 52.216.48.57, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.25.196|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -173,15 +168,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 1%[ ] 296.53K 1.27MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 30%[=====> ] 4.94M 10.8MB/s " + "pred_probs.npz 58%[==========> ] 9.47M 47.3MB/s " ] }, { @@ -189,9 +176,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 25.4MB/s in 0.6s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 55.6MB/s in 0.3s \r\n", "\r\n", - "2024-06-25 19:41:04 (25.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-06-25 23:22:29 (55.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -208,10 +195,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:04.922457Z", - "iopub.status.busy": "2024-06-25T19:41:04.922075Z", - "iopub.status.idle": "2024-06-25T19:41:06.198016Z", - "shell.execute_reply": "2024-06-25T19:41:06.197533Z" + "iopub.execute_input": "2024-06-25T23:22:29.568875Z", + "iopub.status.busy": "2024-06-25T23:22:29.568431Z", + "iopub.status.idle": "2024-06-25T23:22:30.789853Z", + "shell.execute_reply": "2024-06-25T23:22:30.789338Z" }, "nbsphinx": "hidden" }, @@ -222,7 +209,7 @@ "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -248,10 +235,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:06.200733Z", - "iopub.status.busy": "2024-06-25T19:41:06.200196Z", - "iopub.status.idle": "2024-06-25T19:41:06.203668Z", - "shell.execute_reply": "2024-06-25T19:41:06.203192Z" + "iopub.execute_input": "2024-06-25T23:22:30.792349Z", + "iopub.status.busy": "2024-06-25T23:22:30.792077Z", + "iopub.status.idle": "2024-06-25T23:22:30.795305Z", + "shell.execute_reply": "2024-06-25T23:22:30.794873Z" } }, "outputs": [], @@ -301,10 +288,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:06.205901Z", - "iopub.status.busy": "2024-06-25T19:41:06.205502Z", - "iopub.status.idle": "2024-06-25T19:41:06.208636Z", - "shell.execute_reply": "2024-06-25T19:41:06.208180Z" + "iopub.execute_input": "2024-06-25T23:22:30.797547Z", + "iopub.status.busy": "2024-06-25T23:22:30.797222Z", + "iopub.status.idle": "2024-06-25T23:22:30.800066Z", + "shell.execute_reply": "2024-06-25T23:22:30.799649Z" }, "nbsphinx": "hidden" }, @@ -322,10 +309,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:06.210610Z", - "iopub.status.busy": "2024-06-25T19:41:06.210285Z", - "iopub.status.idle": "2024-06-25T19:41:15.082955Z", - "shell.execute_reply": "2024-06-25T19:41:15.082336Z" + "iopub.execute_input": "2024-06-25T23:22:30.801968Z", + "iopub.status.busy": "2024-06-25T23:22:30.801793Z", + "iopub.status.idle": "2024-06-25T23:22:39.539487Z", + "shell.execute_reply": "2024-06-25T23:22:39.538935Z" } }, "outputs": [], @@ -399,10 +386,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:15.085860Z", - "iopub.status.busy": "2024-06-25T19:41:15.085425Z", - "iopub.status.idle": "2024-06-25T19:41:15.091166Z", - "shell.execute_reply": "2024-06-25T19:41:15.090711Z" + "iopub.execute_input": "2024-06-25T23:22:39.542320Z", + "iopub.status.busy": "2024-06-25T23:22:39.541861Z", + "iopub.status.idle": "2024-06-25T23:22:39.547429Z", + "shell.execute_reply": "2024-06-25T23:22:39.546974Z" }, "nbsphinx": "hidden" }, @@ -442,10 +429,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:15.093228Z", - "iopub.status.busy": "2024-06-25T19:41:15.092906Z", - "iopub.status.idle": "2024-06-25T19:41:15.428454Z", - "shell.execute_reply": "2024-06-25T19:41:15.427900Z" + "iopub.execute_input": "2024-06-25T23:22:39.549434Z", + "iopub.status.busy": "2024-06-25T23:22:39.549088Z", + "iopub.status.idle": "2024-06-25T23:22:39.886323Z", + "shell.execute_reply": "2024-06-25T23:22:39.885773Z" } }, "outputs": [], @@ -482,10 +469,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:15.430886Z", - "iopub.status.busy": "2024-06-25T19:41:15.430536Z", - "iopub.status.idle": "2024-06-25T19:41:15.435028Z", - "shell.execute_reply": "2024-06-25T19:41:15.434547Z" + "iopub.execute_input": "2024-06-25T23:22:39.888760Z", + "iopub.status.busy": "2024-06-25T23:22:39.888567Z", + "iopub.status.idle": "2024-06-25T23:22:39.892822Z", + "shell.execute_reply": "2024-06-25T23:22:39.892289Z" } }, "outputs": [ @@ -557,10 +544,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:15.437005Z", - "iopub.status.busy": "2024-06-25T19:41:15.436676Z", - "iopub.status.idle": "2024-06-25T19:41:17.963765Z", - "shell.execute_reply": "2024-06-25T19:41:17.963047Z" + "iopub.execute_input": "2024-06-25T23:22:39.894754Z", + "iopub.status.busy": "2024-06-25T23:22:39.894582Z", + "iopub.status.idle": "2024-06-25T23:22:42.439150Z", + "shell.execute_reply": "2024-06-25T23:22:42.438377Z" } }, "outputs": [], @@ -582,10 +569,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:17.966718Z", - "iopub.status.busy": "2024-06-25T19:41:17.966151Z", - "iopub.status.idle": "2024-06-25T19:41:17.970271Z", - "shell.execute_reply": "2024-06-25T19:41:17.969727Z" + "iopub.execute_input": "2024-06-25T23:22:42.442203Z", + "iopub.status.busy": "2024-06-25T23:22:42.441641Z", + "iopub.status.idle": "2024-06-25T23:22:42.445478Z", + "shell.execute_reply": "2024-06-25T23:22:42.444915Z" } }, "outputs": [ @@ -621,10 +608,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:17.972401Z", - "iopub.status.busy": "2024-06-25T19:41:17.971969Z", - "iopub.status.idle": "2024-06-25T19:41:17.977900Z", - "shell.execute_reply": "2024-06-25T19:41:17.977348Z" + "iopub.execute_input": "2024-06-25T23:22:42.447472Z", + "iopub.status.busy": "2024-06-25T23:22:42.447297Z", + "iopub.status.idle": "2024-06-25T23:22:42.452716Z", + "shell.execute_reply": "2024-06-25T23:22:42.452215Z" } }, "outputs": [ @@ -802,10 +789,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:17.979833Z", - "iopub.status.busy": "2024-06-25T19:41:17.979657Z", - "iopub.status.idle": "2024-06-25T19:41:18.005794Z", - "shell.execute_reply": "2024-06-25T19:41:18.005228Z" + "iopub.execute_input": "2024-06-25T23:22:42.454685Z", + "iopub.status.busy": "2024-06-25T23:22:42.454421Z", + "iopub.status.idle": "2024-06-25T23:22:42.480225Z", + "shell.execute_reply": "2024-06-25T23:22:42.479796Z" } }, "outputs": [ @@ -907,10 +894,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:18.007758Z", - "iopub.status.busy": "2024-06-25T19:41:18.007580Z", - "iopub.status.idle": "2024-06-25T19:41:18.011709Z", - "shell.execute_reply": "2024-06-25T19:41:18.011185Z" + "iopub.execute_input": "2024-06-25T23:22:42.482279Z", + "iopub.status.busy": "2024-06-25T23:22:42.481978Z", + "iopub.status.idle": "2024-06-25T23:22:42.486286Z", + "shell.execute_reply": "2024-06-25T23:22:42.485735Z" } }, "outputs": [ @@ -984,10 +971,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:18.013608Z", - "iopub.status.busy": "2024-06-25T19:41:18.013435Z", - "iopub.status.idle": "2024-06-25T19:41:19.410422Z", - "shell.execute_reply": "2024-06-25T19:41:19.409926Z" + "iopub.execute_input": "2024-06-25T23:22:42.488404Z", + "iopub.status.busy": "2024-06-25T23:22:42.487905Z", + "iopub.status.idle": "2024-06-25T23:22:43.900411Z", + "shell.execute_reply": "2024-06-25T23:22:43.899904Z" } }, "outputs": [ @@ -1159,10 +1146,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:19.412440Z", - "iopub.status.busy": "2024-06-25T19:41:19.412255Z", - "iopub.status.idle": "2024-06-25T19:41:19.416447Z", - "shell.execute_reply": "2024-06-25T19:41:19.415988Z" + "iopub.execute_input": "2024-06-25T23:22:43.902625Z", + "iopub.status.busy": "2024-06-25T23:22:43.902291Z", + "iopub.status.idle": "2024-06-25T23:22:43.906202Z", + "shell.execute_reply": "2024-06-25T23:22:43.905768Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index 9bd33b173..a2c991a17 100644 Binary files a/master/.doctrees/tutorials/clean_learning/index.doctree and b/master/.doctrees/tutorials/clean_learning/index.doctree differ diff --git a/master/.doctrees/tutorials/clean_learning/tabular.doctree b/master/.doctrees/tutorials/clean_learning/tabular.doctree index 391f5d390..86cdf7b11 100644 Binary files a/master/.doctrees/tutorials/clean_learning/tabular.doctree and b/master/.doctrees/tutorials/clean_learning/tabular.doctree differ diff --git a/master/.doctrees/tutorials/clean_learning/text.doctree b/master/.doctrees/tutorials/clean_learning/text.doctree index 9cecc5941..0e303de35 100644 Binary files a/master/.doctrees/tutorials/clean_learning/text.doctree and b/master/.doctrees/tutorials/clean_learning/text.doctree differ diff --git a/master/.doctrees/tutorials/datalab/audio.doctree b/master/.doctrees/tutorials/datalab/audio.doctree index b43c79676..602ecbbe6 100644 Binary files a/master/.doctrees/tutorials/datalab/audio.doctree and b/master/.doctrees/tutorials/datalab/audio.doctree differ diff --git a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree index 778a47711..b0853cfee 100644 Binary files a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree and b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree differ diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree index 9e5b4b1d0..5ddac3a1f 100644 Binary files a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree and b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree differ diff --git a/master/.doctrees/tutorials/datalab/image.doctree b/master/.doctrees/tutorials/datalab/image.doctree index 8b1e6eba1..9dff83152 100644 Binary files a/master/.doctrees/tutorials/datalab/image.doctree and b/master/.doctrees/tutorials/datalab/image.doctree differ diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree index 049a3c95d..c98a3a970 100644 Binary files a/master/.doctrees/tutorials/datalab/index.doctree and b/master/.doctrees/tutorials/datalab/index.doctree differ diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree index 6a20fbf2c..ebaef2c8d 100644 Binary files a/master/.doctrees/tutorials/datalab/tabular.doctree and b/master/.doctrees/tutorials/datalab/tabular.doctree differ diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree index 0937a93f4..cbd01578d 100644 Binary files a/master/.doctrees/tutorials/datalab/text.doctree and b/master/.doctrees/tutorials/datalab/text.doctree differ diff --git a/master/.doctrees/tutorials/datalab/workflows.doctree b/master/.doctrees/tutorials/datalab/workflows.doctree index 14733790e..d1b065683 100644 Binary files a/master/.doctrees/tutorials/datalab/workflows.doctree and b/master/.doctrees/tutorials/datalab/workflows.doctree differ diff --git a/master/.doctrees/tutorials/dataset_health.doctree b/master/.doctrees/tutorials/dataset_health.doctree index 5a1aa5abe..934e2fe97 100644 Binary files a/master/.doctrees/tutorials/dataset_health.doctree and b/master/.doctrees/tutorials/dataset_health.doctree differ diff --git a/master/.doctrees/tutorials/faq.doctree b/master/.doctrees/tutorials/faq.doctree index 1075bcc60..8ec33956b 100644 Binary files a/master/.doctrees/tutorials/faq.doctree and b/master/.doctrees/tutorials/faq.doctree differ diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree index 574d6fd30..25c2482f9 100644 Binary files a/master/.doctrees/tutorials/indepth_overview.doctree and b/master/.doctrees/tutorials/indepth_overview.doctree differ diff --git a/master/.doctrees/tutorials/index.doctree b/master/.doctrees/tutorials/index.doctree index e409d84b4..03ce8436d 100644 Binary files a/master/.doctrees/tutorials/index.doctree and b/master/.doctrees/tutorials/index.doctree differ diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree index 7f161161c..55b5cb470 100644 Binary files a/master/.doctrees/tutorials/multiannotator.doctree and b/master/.doctrees/tutorials/multiannotator.doctree differ diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree index 7893d247c..56a491016 100644 Binary files a/master/.doctrees/tutorials/multilabel_classification.doctree and b/master/.doctrees/tutorials/multilabel_classification.doctree differ diff --git a/master/.doctrees/tutorials/object_detection.doctree b/master/.doctrees/tutorials/object_detection.doctree index 4df54c003..66fdc23a5 100644 Binary files a/master/.doctrees/tutorials/object_detection.doctree and b/master/.doctrees/tutorials/object_detection.doctree differ diff --git a/master/.doctrees/tutorials/outliers.doctree b/master/.doctrees/tutorials/outliers.doctree index f785496e8..07fdc75ce 100644 Binary files a/master/.doctrees/tutorials/outliers.doctree and b/master/.doctrees/tutorials/outliers.doctree differ diff --git a/master/.doctrees/tutorials/pred_probs_cross_val.doctree b/master/.doctrees/tutorials/pred_probs_cross_val.doctree index 24e263159..fab668fd2 100644 Binary files a/master/.doctrees/tutorials/pred_probs_cross_val.doctree and b/master/.doctrees/tutorials/pred_probs_cross_val.doctree differ diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree index 657dee698..897d75f4e 100644 Binary files a/master/.doctrees/tutorials/regression.doctree and b/master/.doctrees/tutorials/regression.doctree differ diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree index 963a5a4a3..71a8bf5e2 100644 Binary files a/master/.doctrees/tutorials/segmentation.doctree and b/master/.doctrees/tutorials/segmentation.doctree differ diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree index e7f4618df..a85278c82 100644 Binary files a/master/.doctrees/tutorials/token_classification.doctree and b/master/.doctrees/tutorials/token_classification.doctree differ diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb index 157e736d5..02da15562 100644 --- a/master/_sources/tutorials/clean_learning/tabular.ipynb +++ b/master/_sources/tutorials/clean_learning/tabular.ipynb @@ -120,7 +120,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb index 1c02419f2..1d4643a4c 100644 --- a/master/_sources/tutorials/clean_learning/text.ipynb +++ b/master/_sources/tutorials/clean_learning/text.ipynb @@ -129,7 +129,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb index cf4477477..e7aadf6ca 100644 --- a/master/_sources/tutorials/datalab/audio.ipynb +++ b/master/_sources/tutorials/datalab/audio.ipynb @@ -91,7 +91,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 4b526f0b4..7eaeed6b0 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 55fb7ca5d..c7ddd7477 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 258e19df6..16a8a36cf 100644 --- a/master/_sources/tutorials/datalab/tabular.ipynb +++ b/master/_sources/tutorials/datalab/tabular.ipynb @@ -80,7 +80,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 80446b3d9..b28ce2a19 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 cef9bfd87..e14cdce07 100644 --- a/master/_sources/tutorials/dataset_health.ipynb +++ b/master/_sources/tutorials/dataset_health.ipynb @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 24305a95c..aa12460c1 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 76838b1ed..2a6fe63a9 100644 --- a/master/_sources/tutorials/multiannotator.ipynb +++ b/master/_sources/tutorials/multiannotator.ipynb @@ -95,7 +95,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 73d78f419..1ea329f55 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 4e999fd56..b95a571a7 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 c175a3d2d..010ae4316 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 e418f0351..04c5f0872 100644 --- a/master/_sources/tutorials/regression.ipynb +++ b/master/_sources/tutorials/regression.ipynb @@ -110,7 +110,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 13d038510..1333c9749 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 9f5e5caa9..745c17b57 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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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 9ec7961a2..04d4a7903 100644 --- a/master/searchindex.js +++ b/master/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 85, 90, 91, 99, 101, 102], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 90, 91, 99, 101, 102], "generate_noise_matrix_from_trac": [0, 1, 90, 91, 99, 101, 102], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 17, 41, 46, 48, 49, 50, 51, 55, 56, 57, 69, 92, 96, 97, 108], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 27, 30, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 84, 85, 90, 97, 105], "benchmark": [1, 38, 84, 85, 90, 91, 99, 101, 102], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105], "": [1, 2, 3, 4, 10, 19, 33, 37, 38, 42, 46, 49, 52, 54, 55, 57, 62, 63, 67, 69, 70, 71, 72, 74, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "core": [1, 41, 44, 76, 78], "algorithm": [1, 2, 8, 10, 32, 39, 43, 54, 55, 57, 62, 71, 80, 82, 84, 96, 98, 99, 101, 108], "These": [1, 2, 3, 4, 5, 8, 10, 22, 38, 40, 42, 43, 44, 45, 52, 60, 62, 63, 66, 70, 71, 75, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "introduc": [1, 89, 98, 99], "synthet": [1, 101, 102, 107], "nois": [1, 2, 3, 37, 44, 47, 57, 63, 90, 91, 96, 97, 101, 106], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 16, 17, 21, 22, 23, 25, 30, 32, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 90, 96, 100, 104, 105], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 33, 35, 37, 41, 43, 44, 47, 49, 50, 57, 62, 63, 64, 65, 66, 71, 72, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 96, 100, 101, 104, 105, 106, 107], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 26, 27, 28, 29, 31, 32, 40, 41, 42, 43, 44, 47, 49, 53, 57, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 90, 94, 100, 101, 105], "specif": [1, 3, 5, 9, 15, 16, 17, 28, 34, 35, 40, 52, 53, 54, 60, 64, 67, 70, 79, 83, 92, 94, 95, 96, 99, 103, 108], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "modul": [1, 3, 14, 15, 16, 17, 22, 25, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 51, 52, 54, 55, 57, 60, 62, 67, 70, 71, 72, 84, 92, 98, 102], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 17, 19, 24, 31, 35, 37, 38, 39, 41, 42, 44, 47, 51, 52, 54, 55, 57, 61, 62, 63, 64, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108], "gener": [1, 2, 3, 7, 10, 19, 24, 26, 34, 37, 49, 52, 54, 57, 58, 71, 72, 74, 79, 88, 89, 90, 91, 92, 95, 97, 98, 99, 101, 102, 104, 105, 107, 108], "valid": [1, 2, 3, 5, 10, 13, 33, 35, 37, 44, 45, 47, 48, 49, 52, 54, 55, 57, 62, 64, 67, 70, 72, 74, 75, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "matric": [1, 3, 47, 98], "which": [1, 2, 3, 5, 7, 10, 13, 14, 15, 17, 19, 23, 27, 33, 34, 35, 37, 38, 42, 43, 44, 47, 49, 53, 54, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "learn": [1, 2, 3, 4, 5, 9, 10, 15, 17, 23, 31, 34, 39, 40, 41, 42, 44, 46, 48, 53, 54, 57, 60, 62, 64, 71, 73, 75, 78, 82, 84, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 106], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106, 107, 108], "possibl": [1, 2, 3, 7, 10, 37, 38, 42, 44, 46, 47, 49, 64, 65, 66, 67, 69, 70, 71, 72, 74, 80, 82, 83, 91, 96, 98, 99, 101, 102, 103, 106, 107, 108], "noisi": [1, 2, 3, 10, 37, 39, 42, 44, 47, 57, 63, 64, 66, 72, 74, 75, 76, 78, 79, 85, 90, 91, 94, 95, 96, 98, 100, 101], "given": [1, 2, 3, 5, 10, 15, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "matrix": [1, 2, 3, 5, 10, 17, 19, 32, 37, 44, 46, 47, 50, 52, 57, 58, 64, 67, 69, 70, 71, 72, 94, 96, 103, 104], "trace": [1, 90, 91, 99, 101, 102], "valu": [1, 2, 3, 4, 5, 10, 13, 14, 17, 19, 23, 27, 28, 33, 35, 37, 38, 39, 41, 42, 44, 46, 47, 49, 52, 53, 54, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 83, 88, 89, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "more": [1, 2, 3, 4, 5, 7, 9, 10, 14, 15, 17, 19, 27, 37, 38, 41, 42, 43, 46, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 107, 108], "function": [1, 2, 3, 4, 5, 7, 10, 14, 15, 17, 24, 27, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 107, 108], "noise_matrix": [1, 2, 3, 10, 47, 57, 90, 91, 99, 101, 102], "py": [1, 3, 34, 38, 39, 44, 47, 49, 84, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102], "verbos": [1, 2, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 41, 44, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 90, 99, 101], "fals": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 48, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 103, 104, 106, 107], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83], "prior": [1, 2, 3, 37, 44, 47, 49], "repres": [1, 2, 3, 7, 10, 13, 17, 19, 27, 33, 35, 37, 41, 44, 47, 50, 52, 53, 55, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 108], "p": [1, 2, 3, 5, 10, 37, 44, 46, 47, 55, 57, 62, 70, 71, 72, 76, 94, 95, 96, 99, 101, 108], "true_label": [1, 2, 3, 37, 47, 57, 99, 101], "k": [1, 2, 3, 4, 5, 8, 10, 13, 17, 19, 20, 24, 27, 29, 32, 37, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 65, 66, 67, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 87, 89, 90, 91, 96, 98, 99, 101, 102, 103, 104, 107, 108], "check": [1, 2, 5, 6, 9, 10, 13, 17, 28, 35, 38, 41, 42, 48, 58, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 98, 99, 101, 102, 106], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 14, 23, 27, 39, 42, 47, 49, 55, 69, 74, 88, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106], "achiev": [1, 2, 38, 39, 42, 74, 98, 101, 108], "better": [1, 5, 10, 44, 53, 62, 64, 72, 74, 75, 84, 88, 89, 91, 94, 95, 96, 98, 99, 102, 103, 104, 108], "than": [1, 2, 3, 4, 7, 9, 10, 27, 29, 32, 37, 44, 53, 57, 61, 62, 67, 69, 71, 72, 74, 78, 82, 87, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "random": [1, 2, 3, 7, 10, 19, 32, 41, 49, 52, 62, 72, 74, 87, 89, 90, 91, 92, 94, 96, 98, 99, 101, 102, 104], "perform": [1, 2, 4, 7, 10, 27, 29, 32, 38, 42, 49, 51, 52, 53, 70, 74, 84, 87, 88, 90, 98, 99, 101, 102, 105, 106], "averag": [1, 3, 5, 10, 23, 29, 37, 38, 42, 49, 55, 62, 63, 70, 71, 72, 98, 101, 104], "amount": [1, 3, 92], "paramet": [1, 2, 3, 4, 5, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 95, 96], "np": [1, 2, 3, 4, 5, 7, 17, 19, 32, 37, 39, 41, 43, 44, 46, 47, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "ndarrai": [1, 2, 3, 4, 5, 17, 24, 26, 27, 31, 32, 33, 37, 39, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 96, 108], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 54, 55, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 84, 87, 88, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 17, 19, 27, 33, 37, 39, 41, 42, 43, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "shape": [1, 2, 3, 4, 5, 17, 19, 37, 39, 41, 43, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 89, 96, 97, 98, 99, 102, 103, 104, 107, 108], "condit": [1, 2, 3, 47, 53, 56, 57, 72, 92, 99, 108], "probabl": [1, 2, 3, 5, 8, 10, 17, 24, 26, 29, 33, 37, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 107, 108], "k_": [1, 2, 3, 47, 57], "k_y": [1, 2, 3, 47, 57], "contain": [1, 2, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 46, 47, 51, 52, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107], "fraction": [1, 2, 3, 10, 21, 39, 47, 57, 62, 74, 94, 98], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 55, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 101, 102, 103, 105, 106, 107, 108], "everi": [1, 2, 3, 4, 5, 10, 17, 38, 42, 44, 47, 56, 57, 64, 72, 74, 75, 87, 89, 90, 91, 92, 94, 95, 98, 101, 103, 105, 107, 108], "class": [1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 54, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 105, 106, 108], "other": [1, 2, 3, 5, 10, 17, 23, 28, 37, 38, 40, 41, 42, 44, 47, 50, 52, 57, 58, 60, 62, 63, 66, 70, 71, 72, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 104, 107, 108], "assum": [1, 2, 3, 13, 44, 47, 52, 56, 57, 72, 76, 79, 98, 102, 104, 106, 107, 108], "column": [1, 2, 3, 5, 10, 11, 13, 14, 31, 37, 41, 44, 47, 49, 50, 53, 56, 57, 62, 63, 64, 66, 67, 70, 71, 72, 74, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "sum": [1, 2, 3, 27, 32, 33, 37, 47, 49, 57, 63, 64, 66, 69, 74, 90, 91, 92, 98, 99, 101, 102, 107, 108], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 97, 98, 105], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 21, 23, 24, 26, 27, 32, 33, 34, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "true": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 49, 52, 56, 57, 58, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "return": [1, 2, 3, 4, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 62, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 62, 63, 64, 66, 67, 83, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 62, 64, 66, 69, 70, 71, 72, 74, 75, 80, 82, 83, 84, 89, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 74, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 65, 71, 90, 91, 92, 94, 95, 99], "yield": [1, 38, 42], "between": [1, 5, 10, 16, 17, 22, 23, 25, 27, 30, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 52, 53, 54, 55, 60, 62, 63, 66, 69, 71, 72, 74, 75, 78, 82, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "below": [1, 3, 4, 5, 10, 37, 38, 41, 42, 44, 46, 49, 55, 62, 63, 64, 69, 70, 78, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "we": [1, 2, 3, 5, 7, 10, 14, 23, 38, 41, 42, 44, 49, 57, 58, 61, 62, 69, 70, 72, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "loop": [1, 3, 47, 57, 92, 103], "implement": [1, 2, 3, 4, 9, 15, 23, 38, 39, 41, 42, 47, 51, 53, 54, 57, 71, 74, 84, 87, 89, 90, 94, 104, 105], "what": [1, 5, 9, 10, 17, 34, 37, 39, 41, 44, 62, 63, 67, 69, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "doe": [1, 2, 3, 7, 10, 41, 42, 44, 49, 52, 55, 58, 69, 70, 74, 76, 78, 82, 88, 89, 90, 91, 92, 94, 95, 97, 102, 106, 107], "do": [1, 2, 5, 9, 10, 37, 41, 42, 57, 58, 71, 72, 76, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "fast": 1, "explain": [1, 10, 96], "python": [1, 2, 42, 61, 74, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 104], "pseudocod": [1, 105], "happen": [1, 10, 44, 64, 95, 101, 107], "n": [1, 2, 3, 5, 7, 37, 38, 41, 42, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 87, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "without": [1, 2, 5, 9, 10, 13, 15, 21, 38, 42, 54, 66, 74, 84, 88, 89, 95, 96, 98, 99, 103, 104], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 46, 48, 55, 56, 57, 61, 62, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107], "distinct": [1, 19, 57, 108], "natur": [1, 10, 101, 104], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 83, 85, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 107, 108], "0": [1, 2, 3, 4, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "count_joint": 1, "len": [1, 2, 3, 7, 37, 41, 47, 56, 57, 58, 71, 72, 74, 87, 88, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "y": [1, 2, 3, 5, 8, 19, 31, 32, 42, 47, 49, 57, 58, 61, 70, 74, 75, 88, 89, 90, 91, 94, 96, 98, 99, 101, 102, 104, 106], "round": [1, 41, 44, 57, 74, 96, 98, 106], "astyp": [1, 101], "int": [1, 2, 3, 4, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 39, 41, 42, 44, 49, 50, 52, 53, 54, 55, 56, 57, 58, 63, 64, 66, 70, 71, 72, 74, 76, 78, 79, 80, 83, 89, 90, 92, 96, 103, 104], "rang": [1, 3, 5, 7, 13, 47, 49, 55, 57, 70, 74, 75, 92, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 14, 17, 23, 37, 41, 44, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "pragma": 1, "cover": [1, 3, 85, 96, 97, 98], "choic": [1, 8, 44, 53, 55, 92, 98, 102, 104], "replac": [1, 56, 61, 72, 87, 88, 90, 91, 92, 95, 96, 97, 98, 101, 104], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 52, 72, 89, 90, 91], "05": [1, 10, 27, 31, 56, 70, 74, 80, 82, 94, 97, 98, 99, 103], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 90, 91, 99, 101, 102], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 90, 91, 92, 96, 98, 99, 101, 102, 107], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 27, 40, 42, 49, 74, 87, 89, 90, 91, 94, 96, 97, 99, 101, 102], "max_it": [1, 88, 89, 95, 104], "10000": [1, 41, 97, 98], "x": [1, 2, 3, 5, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 38, 39, 42, 44, 46, 47, 49, 52, 54, 56, 57, 58, 61, 62, 64, 70, 71, 72, 74, 76, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 104, 106], "diagon": [1, 3, 5, 44, 47, 57], "equal": [1, 3, 10, 13, 52, 64, 69, 79, 105], "creat": [1, 2, 9, 17, 19, 38, 41, 42, 44, 57, 74, 84, 88, 89, 92, 94, 95, 96, 98, 107, 108], "impli": [1, 10, 37, 63, 70], "float": [1, 2, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 40, 41, 42, 44, 46, 48, 49, 55, 56, 57, 62, 63, 64, 66, 69, 70, 74, 78, 82, 89, 90, 91, 99, 101, 102], "entri": [1, 3, 5, 10, 37, 38, 42, 44, 46, 50, 52, 55, 57, 62, 63, 64, 67, 87, 88, 94, 95, 99, 102, 103, 106], "maximum": [1, 10, 71, 79, 83, 107], "minimum": [1, 8, 10, 21, 44, 46, 64, 69, 82], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 17, 27, 38, 42, 44, 52, 69, 74, 90, 98, 99, 101, 103, 104], "default": [1, 2, 3, 4, 5, 7, 10, 11, 15, 17, 29, 31, 34, 37, 38, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 90, 92, 96, 98, 106, 107], "If": [1, 2, 3, 4, 5, 10, 13, 14, 17, 27, 29, 35, 37, 38, 41, 42, 44, 46, 47, 49, 52, 53, 56, 57, 61, 62, 63, 64, 67, 69, 70, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "have": [1, 2, 3, 4, 5, 7, 9, 10, 17, 22, 25, 27, 30, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "all": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 23, 34, 37, 38, 41, 42, 43, 44, 47, 49, 50, 52, 56, 57, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 90, 96], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 61, 62, 63, 65, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 62, 66, 70, 74, 79, 83, 84, 87, 88, 89, 91, 95, 98, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 60, 61, 64, 71, 72, 74, 76, 84, 85, 89, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 61, 62, 63, 64, 66, 69, 70, 71, 72, 74, 78, 79, 80, 82, 88, 91, 92, 95, 96, 97, 98, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 61, 64, 66, 67, 69, 71, 72, 74, 75, 87, 88, 90, 91, 92, 94, 95, 96, 97, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 89, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 61, 62, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 90, 91, 94, 95, 96, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 61, 62, 67, 69, 70, 71, 72, 74, 75, 79, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 60, 61, 62, 64, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 69, 72, 74, 87, 88, 90, 91, 92, 96, 97, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 62, 63, 67, 69, 70, 71, 72, 74, 76, 79, 83, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 61, 62, 71, 74, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 62, 84, 90, 91, 95, 96, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 90, 92, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 90, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 61, 62, 64, 66, 70, 71, 72, 74, 75, 78, 80, 82, 85, 87, 88, 89, 92, 94, 95, 96, 98, 99, 102, 103, 104, 106, 107, 108], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 17, 24, 27, 31, 37, 38, 41, 42, 43, 44, 46, 47, 52, 53, 55, 56, 57, 58, 61, 62, 71, 72, 74, 76, 78, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 101, 102, 103, 104, 105, 106, 107, 108], "guarante": [1, 3, 5, 16, 22, 25, 30, 38, 40, 42, 45, 47, 60, 85], "produc": [1, 2, 5, 9, 10, 17, 49, 62, 72, 74, 76, 78, 84, 87, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108], "higher": [1, 5, 10, 37, 44, 46, 47, 49, 55, 61, 62, 63, 74, 91, 95, 96, 98, 103], "opposit": [1, 108], "occur": [1, 3, 10, 37, 56, 69, 90, 91, 92, 98, 104], "small": [1, 3, 10, 37, 41, 49, 52, 55, 57, 63, 70, 88, 92, 95, 97, 102, 104], "numpi": [1, 3, 4, 5, 7, 10, 13, 19, 32, 33, 41, 42, 43, 49, 52, 55, 56, 58, 61, 66, 69, 74, 75, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "max": [1, 44, 71, 72, 91, 92, 96, 104], "tri": [1, 38, 42, 105], "befor": [1, 2, 3, 38, 42, 55, 57, 71, 74, 79, 87, 88, 95, 96, 98, 99, 101, 104, 106], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 17, 24, 29, 31, 37, 38, 41, 42, 44, 47, 49, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 89, 90, 91, 92, 94, 98, 99, 102, 106, 107], "left": [1, 2, 44, 46, 55, 57, 64, 67, 70, 90, 91, 102, 103, 104, 107], "stochast": 1, "exceed": 1, "m": [1, 5, 38, 42, 48, 49, 52, 53, 62, 67, 69, 70, 71, 90, 91, 97, 101, 102, 103, 108], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 38, 42, 61, 98, 99, 107], "length": [1, 5, 13, 27, 28, 37, 39, 44, 57, 64, 67, 71, 72, 74, 76, 79, 83, 87, 89, 102, 104, 107, 108], "must": [1, 2, 3, 4, 5, 7, 17, 37, 38, 39, 40, 42, 44, 47, 49, 50, 55, 57, 60, 61, 62, 63, 64, 71, 72, 74, 76, 78, 79, 80, 82, 83, 89, 92, 96, 101, 105, 107, 108], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 13, 37, 41, 44, 50, 57, 58, 62, 64, 70, 76, 78, 79, 80, 82, 83, 87, 88, 89, 98, 101, 102, 103, 107, 108], "ball": [1, 97], "bin": [1, 3, 64, 90, 91, 104], "ensur": [1, 2, 10, 38, 42, 52, 54, 55, 57, 58, 61, 69, 72, 74, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 104, 105, 106], "most": [1, 3, 5, 7, 10, 17, 37, 41, 44, 49, 61, 62, 63, 64, 67, 69, 70, 71, 72, 75, 78, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107], "least": [1, 4, 10, 19, 32, 37, 41, 62, 63, 69, 72, 82, 91, 92, 98, 101, 104, 107], "int_arrai": [1, 57], "can": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 102, 103, 104, 105, 106, 107, 108], "model": [2, 3, 4, 5, 9, 10, 11, 17, 19, 31, 33, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 54, 56, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105, 107, 108], "For": [2, 3, 5, 7, 9, 10, 12, 17, 23, 36, 37, 38, 41, 42, 44, 47, 49, 52, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 80, 82, 83, 84, 87, 88, 89, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108], "regular": [2, 3, 41, 61], "multi": [2, 3, 4, 10, 33, 37, 38, 41, 42, 44, 48, 49, 50, 57, 58, 63, 64, 65, 66, 71, 72, 84, 96, 98, 99, 100], "task": [2, 5, 7, 10, 11, 12, 13, 15, 16, 17, 26, 31, 34, 37, 41, 47, 49, 50, 55, 57, 62, 64, 72, 74, 84, 88, 89, 95, 96, 97, 98, 99, 102, 104, 106, 107, 108], "cleanlearn": [2, 3, 10, 24, 31, 38, 57, 61, 73, 74, 75, 84, 85, 87, 88, 106], "wrap": [2, 38, 42, 51, 61, 71, 74, 84, 87, 88, 90, 91, 94, 95, 99, 106], "instanc": [2, 3, 5, 6, 7, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 61, 70, 71, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 103], "sklearn": [2, 3, 4, 5, 8, 10, 19, 32, 37, 42, 49, 53, 54, 57, 61, 71, 74, 75, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106], "classifi": [2, 3, 42, 49, 57, 62, 65, 71, 72, 84, 85, 87, 88, 89, 94, 95, 98, 101, 102, 104, 105, 107, 108], "adher": [2, 42, 74], "estim": [2, 3, 4, 5, 9, 14, 23, 37, 41, 42, 44, 47, 57, 62, 63, 64, 69, 71, 74, 76, 78, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 98, 100, 103, 104, 105, 106, 107, 108], "api": [2, 3, 15, 61, 67, 70, 71, 74, 85, 96, 98, 106], "defin": [2, 3, 5, 7, 10, 15, 23, 37, 38, 39, 41, 42, 44, 72, 74, 76, 89, 90, 91, 94, 97, 98, 101, 104, 108], "four": [2, 10, 97, 99, 108], "clf": [2, 3, 5, 49, 74, 84, 87, 94, 96, 98, 99, 102], "fit": [2, 3, 5, 8, 10, 19, 40, 42, 52, 54, 60, 61, 71, 73, 74, 84, 87, 88, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 108], "sample_weight": [2, 42, 74, 99], "predict_proba": [2, 5, 37, 40, 42, 49, 60, 61, 87, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 104], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 17, 23, 24, 26, 29, 31, 33, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 88, 97, 98, 99, 100, 104, 106, 107, 108], "score": [2, 3, 4, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 43, 44, 46, 49, 55, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 104, 106], "data": [2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 49, 50, 52, 53, 54, 57, 60, 61, 62, 63, 64, 65, 69, 71, 72, 73, 74, 79, 80, 81, 82, 83, 85, 88, 92, 93, 100, 105], "e": [2, 3, 5, 10, 13, 23, 33, 37, 38, 41, 42, 44, 47, 49, 50, 52, 57, 58, 62, 63, 64, 65, 67, 70, 71, 72, 74, 76, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "featur": [2, 3, 4, 5, 8, 10, 11, 17, 19, 20, 24, 27, 28, 29, 31, 32, 49, 52, 53, 54, 57, 71, 74, 84, 87, 90, 91, 94, 95, 96, 98, 99, 101, 102, 106], "element": [2, 3, 5, 37, 43, 44, 46, 57, 62, 64, 72, 79, 80, 82, 88, 89, 95, 96, 98, 108], "first": [2, 5, 10, 18, 27, 28, 37, 41, 49, 52, 57, 62, 63, 67, 70, 72, 74, 87, 88, 89, 90, 92, 94, 96, 98, 101, 102, 103, 104, 106, 107, 108], "index": [2, 10, 27, 37, 44, 51, 52, 54, 56, 57, 58, 63, 72, 74, 79, 82, 83, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "should": [2, 3, 5, 7, 10, 15, 23, 27, 32, 33, 37, 38, 41, 42, 44, 46, 47, 49, 52, 54, 55, 56, 57, 61, 62, 63, 66, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 107, 108], "correspond": [2, 3, 5, 10, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 37, 38, 41, 42, 43, 44, 46, 47, 49, 52, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "differ": [2, 5, 7, 10, 14, 16, 22, 25, 27, 28, 30, 37, 38, 40, 41, 42, 44, 45, 49, 52, 55, 57, 58, 60, 62, 67, 69, 71, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 104, 105, 106], "sampl": [2, 3, 5, 8, 10, 17, 21, 44, 46, 49, 52, 53, 54, 64, 67, 70, 72, 74, 75, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "size": [2, 10, 32, 38, 41, 42, 44, 49, 52, 53, 64, 69, 70, 74, 76, 78, 88, 92, 94, 98, 99, 101, 102, 103, 105, 107], "here": [2, 5, 7, 10, 15, 41, 44, 47, 61, 62, 63, 64, 66, 67, 70, 71, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "re": [2, 5, 38, 42, 54, 56, 62, 74, 84, 87, 88, 89, 90, 94, 95, 98, 106, 107, 108], "weight": [2, 10, 38, 39, 42, 49, 52, 62, 69, 72, 74, 88, 89, 90, 91, 95], "loss": [2, 39, 61, 72, 74, 92], "while": [2, 3, 10, 38, 41, 42, 48, 49, 57, 74, 84, 92, 96, 98, 99, 101, 102, 106], "train": [2, 3, 4, 5, 9, 10, 17, 19, 33, 38, 39, 40, 42, 49, 57, 61, 62, 67, 70, 71, 74, 75, 85, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 105, 107, 108], "support": [2, 3, 4, 5, 13, 15, 34, 35, 41, 43, 49, 57, 58, 61, 71, 72, 82, 84, 85, 89, 90, 91, 92, 96, 98], "your": [2, 3, 5, 9, 10, 17, 37, 38, 40, 41, 42, 44, 49, 54, 57, 60, 61, 62, 63, 64, 66, 71, 72, 74, 75, 76, 78, 79, 85, 87, 88, 89, 92, 94, 97, 101, 102, 103, 104, 105, 106, 107, 108], "recommend": [2, 5, 7, 10, 14, 17, 41, 44, 62, 90, 91, 92, 96, 98, 105, 106], "furthermor": 2, "correctli": [2, 3, 10, 37, 38, 42, 44, 47, 52, 58, 63, 64, 69, 70, 74, 76, 88, 92, 95, 96, 98, 102, 103, 106, 107], "clonabl": [2, 74], "via": [2, 5, 7, 10, 11, 14, 17, 19, 23, 37, 39, 41, 42, 49, 53, 57, 62, 67, 70, 71, 72, 74, 75, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 102, 103, 104, 105, 106, 107, 108], "base": [2, 3, 4, 5, 7, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 47, 48, 49, 52, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 69, 71, 72, 74, 75, 78, 80, 82, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "clone": [2, 74, 102], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 66, 70, 74, 80, 85, 89, 90, 96, 98, 99, 101, 102, 103, 104, 106, 108], "multipl": [2, 3, 5, 10, 13, 14, 35, 37, 44, 55, 56, 62, 63, 64, 66, 69, 70, 74, 84, 90, 91, 92, 94, 98, 100, 102, 103, 106], "g": [2, 3, 5, 10, 13, 23, 33, 37, 38, 42, 44, 50, 52, 57, 64, 65, 67, 70, 71, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "manual": [2, 74, 87, 88, 89, 96, 98, 104, 105, 106, 108], "pytorch": [2, 38, 39, 42, 74, 84, 89, 92, 98, 100, 102, 107], "call": [2, 3, 5, 6, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 49, 57, 61, 71, 74, 88, 89, 90, 91, 95, 98, 99, 102, 104, 105, 106, 107, 108], "__init__": [2, 39, 74, 92], "independ": [2, 3, 10, 63, 74, 95, 96, 105, 106, 108], "compat": [2, 38, 41, 42, 54, 61, 74, 75, 78, 82, 84, 87, 88, 96, 98, 105, 106], "neural": [2, 39, 61, 71, 74, 89, 92, 98, 102, 104, 106], "network": [2, 38, 39, 42, 61, 71, 74, 88, 89, 92, 95, 98, 102, 104, 106], "typic": [2, 10, 38, 42, 54, 71, 74, 87, 88, 89, 91, 92, 94, 95, 104, 105], "initi": [2, 3, 14, 19, 38, 42, 52, 62, 74, 87, 95, 98], "insid": [2, 42, 74, 98, 99], "There": [2, 3, 7, 52, 84, 99, 101], "two": [2, 3, 10, 19, 27, 37, 38, 41, 42, 50, 52, 53, 54, 57, 67, 69, 70, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106, 107, 108], "new": [2, 7, 9, 10, 15, 23, 38, 41, 42, 48, 52, 56, 57, 62, 74, 88, 89, 90, 95, 97, 98, 104, 105, 108], "notion": 2, "confid": [2, 3, 10, 23, 37, 41, 44, 47, 49, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 78, 82, 84, 87, 92, 94, 95, 99, 101, 102, 103, 105, 107, 108], "packag": [2, 5, 7, 9, 10, 12, 16, 36, 40, 44, 45, 57, 60, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "prune": [2, 3, 44, 64, 74, 85, 103], "everyth": [2, 70, 99], "els": [2, 70, 90, 92, 96, 97, 98, 101, 102, 103], "mathemat": [2, 3, 10, 47, 102], "keep": [2, 14, 15, 57, 84, 90, 96, 97, 98, 107], "belong": [2, 3, 10, 37, 44, 46, 47, 52, 63, 64, 65, 66, 71, 72, 76, 80, 82, 83, 91, 92, 99, 102, 104, 107, 108], "2": [2, 3, 4, 5, 7, 10, 11, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 75, 79, 80, 82, 83, 97, 98, 105], "error": [2, 3, 5, 10, 38, 42, 43, 44, 46, 47, 57, 63, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 82, 85, 87, 89, 90, 91, 94, 95, 96, 97, 100], "erron": [2, 3, 37, 44, 47, 57, 63, 64, 72, 74, 75, 76, 104, 106], "import": [2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 41, 43, 49, 52, 55, 56, 62, 66, 69, 74, 75, 80, 82, 83, 84, 87, 88, 94, 95, 96, 98, 102, 103, 104, 106, 107, 108], "linear_model": [2, 5, 37, 57, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logisticregress": [2, 3, 5, 37, 57, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logreg": 2, "cl": [2, 15, 31, 74, 84, 87, 88, 98, 99, 106], "pass": [2, 3, 5, 8, 10, 11, 13, 14, 15, 17, 24, 31, 34, 38, 41, 42, 44, 48, 49, 52, 54, 57, 61, 62, 64, 70, 71, 72, 74, 79, 80, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 103, 104, 106], "x_train": [2, 87, 90, 91, 99, 101, 102, 106], "labels_maybe_with_error": 2, "had": [2, 3, 74, 103], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 52, 60, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 88, 93, 100, 101, 105, 106], "pred": [2, 44, 57, 87, 88, 105, 106], "x_test": [2, 87, 90, 91, 99, 102, 106], "might": [2, 5, 10, 52, 62, 74, 79, 87, 88, 90, 91, 92, 96, 98, 103], "case": [2, 3, 10, 14, 37, 49, 52, 62, 74, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 104, 106, 108], "standard": [2, 3, 5, 31, 37, 44, 61, 63, 64, 66, 72, 74, 84, 87, 90, 91, 94, 97, 99, 103], "adapt": [2, 38, 40, 57, 60, 74, 104], "skorch": [2, 74, 84, 98], "kera": [2, 60, 67, 70, 74, 84, 98, 103], "scikera": [2, 61, 74, 98], "open": [2, 41, 97, 103, 108], "doesn": [2, 10, 74, 84], "t": [2, 3, 4, 7, 10, 18, 28, 29, 38, 39, 41, 42, 43, 44, 49, 55, 56, 66, 71, 72, 74, 80, 82, 83, 84, 90, 91, 92, 94, 95, 96, 97, 99, 102, 103, 106, 108], "alreadi": [2, 5, 10, 17, 38, 41, 42, 47, 52, 61, 62, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 103, 104, 106], "exist": [2, 5, 10, 13, 19, 38, 41, 42, 54, 56, 61, 67, 69, 71, 74, 84, 85, 87, 88, 90, 91, 95, 101, 108], "made": [2, 5, 17, 38, 42, 53, 74, 87, 88, 92, 95, 96, 98, 101, 103, 105, 106], "easi": [2, 12, 47, 74, 90, 91, 97, 98, 99, 102], "inherit": [2, 7, 39, 74], "baseestim": [2, 42, 74], "yourmodel": [2, 74], "def": [2, 7, 15, 38, 42, 61, 74, 88, 89, 90, 91, 92, 96, 97, 98, 99, 101, 102, 104, 106, 108], "self": [2, 3, 5, 7, 10, 13, 14, 15, 17, 32, 38, 39, 41, 42, 44, 49, 71, 72, 74, 87, 88, 90, 92, 95, 96, 97, 102, 107, 108], "refer": [2, 10, 17, 38, 42, 43, 63, 64, 66, 67, 69, 70, 71, 74, 78, 79, 90, 91, 92, 94, 95, 96, 98, 99, 102, 105, 106], "origin": [2, 5, 10, 42, 43, 44, 56, 57, 61, 63, 64, 67, 70, 71, 74, 75, 78, 80, 82, 87, 88, 90, 92, 94, 95, 96, 98, 99, 103, 104, 106, 108], "total": [2, 3, 4, 37, 41, 57, 63, 83, 92, 98, 107], "state": [2, 3, 5, 38, 39, 42, 48, 74, 99, 102, 103, 108], "art": [2, 39, 99, 102], "northcutt": [2, 3, 37, 71, 72], "et": [2, 3, 37, 39, 71, 72], "al": [2, 3, 37, 39, 71, 72], "2021": [2, 3, 37, 71, 72], "weak": [2, 70], "supervis": [2, 10, 90, 91, 98, 101], "find": [2, 5, 9, 10, 14, 15, 17, 20, 21, 23, 24, 26, 27, 28, 29, 32, 33, 37, 38, 40, 41, 42, 43, 44, 48, 54, 56, 57, 60, 67, 70, 71, 72, 74, 76, 80, 82, 85, 90, 100, 105], "uncertainti": [2, 10, 46, 71, 74, 98, 104, 106], "It": [2, 3, 5, 7, 10, 13, 14, 17, 23, 28, 31, 33, 34, 35, 38, 42, 44, 47, 49, 52, 53, 55, 62, 69, 70, 74, 84, 88, 90, 91, 92, 95, 98, 99, 102, 105], "work": [2, 3, 7, 10, 13, 31, 37, 38, 41, 42, 44, 47, 56, 57, 58, 61, 62, 72, 74, 84, 85, 88, 90, 91, 96, 97, 104, 106], "includ": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 38, 40, 41, 42, 52, 56, 57, 60, 62, 63, 66, 67, 71, 72, 74, 78, 79, 80, 82, 84, 85, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 108], "deep": [2, 40, 42, 60, 61, 74, 95], "see": [2, 3, 5, 7, 10, 14, 15, 34, 37, 38, 41, 42, 43, 44, 49, 54, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "subfield": 2, "theori": [2, 99], "machin": [2, 4, 5, 9, 10, 15, 17, 34, 40, 55, 60, 74, 87, 88, 90, 91, 96, 97, 101], "across": [2, 3, 5, 7, 10, 14, 23, 37, 41, 49, 63, 70, 71, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 106], "varieti": [2, 87, 88, 98], "like": [2, 3, 5, 6, 7, 10, 15, 33, 37, 38, 41, 42, 44, 47, 57, 61, 62, 63, 66, 67, 69, 72, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "pu": [2, 57], "input": [2, 3, 5, 9, 17, 27, 37, 38, 41, 42, 47, 49, 52, 53, 56, 57, 58, 61, 70, 74, 84, 85, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "discret": [2, 35, 44, 47, 57, 71, 72, 76, 78, 79], "vector": [2, 3, 4, 5, 10, 17, 44, 47, 49, 50, 52, 57, 71, 72, 84, 88, 89, 90, 91, 92, 94, 95, 99, 102, 103, 104, 107, 108], "would": [2, 3, 5, 10, 38, 41, 42, 44, 53, 57, 64, 74, 84, 88, 90, 92, 98, 99, 104, 106, 108], "obtain": [2, 5, 8, 10, 17, 44, 62, 64, 67, 70, 72, 75, 89, 91, 95, 98, 101, 103, 105, 107, 108], "been": [2, 4, 37, 44, 47, 52, 56, 57, 62, 63, 67, 69, 71, 72, 74, 89, 90, 94, 98, 99, 101, 102, 103, 104, 107, 108], "dure": [2, 10, 17, 52, 54, 71, 74, 87, 88, 89, 94, 95, 96, 98, 99, 102, 105, 106, 108], "denot": [2, 3, 47, 49, 57, 64, 71, 72, 82], "tild": 2, "paper": [2, 4, 10, 62, 71, 80, 82, 97, 99, 101, 104, 106, 108], "cv_n_fold": [2, 3, 74, 88], "5": [2, 3, 4, 5, 8, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 42, 44, 46, 48, 49, 57, 62, 63, 66, 67, 70, 74, 75, 82, 88, 90, 95, 97, 98, 102, 103, 104, 105, 107, 108], "converge_latent_estim": [2, 3], "pulearn": [2, 57], "find_label_issues_kwarg": [2, 10, 74, 85, 98, 99], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 64, 80, 98], "clean": [2, 69, 72, 74, 75, 84, 87, 88, 90, 91, 97, 106], "even": [2, 3, 7, 9, 10, 37, 41, 46, 47, 57, 74, 89, 96, 98, 99, 101, 102, 103], "messi": [2, 74, 99], "ridden": [2, 74], "autom": [2, 9, 10, 74, 84, 91, 97, 98], "robust": [2, 47, 52, 74, 91, 98], "prone": [2, 74], "out": [2, 3, 5, 10, 17, 29, 38, 42, 44, 49, 52, 61, 64, 65, 67, 70, 71, 72, 74, 75, 83, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108], "current": [2, 3, 5, 7, 10, 11, 14, 15, 23, 38, 42, 43, 44, 49, 62, 69, 74, 90, 91, 98, 101, 103], "intend": [2, 14, 15, 16, 17, 33, 34, 35, 45, 52, 62, 78, 82, 89, 90, 91, 95, 99], "A": [2, 3, 4, 5, 7, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 66, 69, 70, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 103, 105, 108], "follow": [2, 3, 10, 15, 31, 35, 37, 38, 41, 42, 49, 51, 55, 62, 63, 67, 69, 70, 71, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "experiment": [2, 38, 39, 41, 42, 43, 64, 85, 98], "wrapper": [2, 61, 87, 88, 89, 106], "around": [2, 69, 90, 91, 103, 104, 108], "fasttext": [2, 60], "store": [2, 4, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 71, 74, 87, 88, 94, 95, 96, 97, 98, 107, 108], "along": [2, 49, 64, 82, 90, 91, 92, 96, 98, 104], "dimens": [2, 57, 76, 79, 92, 98, 104, 107], "select": [2, 9, 10, 27, 51, 62, 72, 92, 98, 101, 104], "split": [2, 3, 5, 10, 13, 41, 49, 56, 57, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 102, 105, 108], "cross": [2, 3, 10, 37, 44, 47, 48, 49, 64, 67, 70, 72, 74, 75, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "fold": [2, 3, 37, 44, 47, 74, 87, 89, 94, 97, 98, 103, 107], "By": [2, 37, 63, 64, 74, 90, 96, 107], "need": [2, 3, 10, 11, 37, 38, 41, 42, 44, 52, 54, 63, 64, 66, 71, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 107], "holdout": [2, 3, 74], "comput": [2, 3, 4, 5, 7, 8, 10, 20, 21, 23, 24, 27, 28, 29, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 52, 53, 54, 57, 62, 63, 64, 66, 69, 70, 71, 72, 74, 75, 76, 78, 84, 85, 88, 90, 91, 97, 99, 100, 103, 104, 106, 107], "them": [2, 3, 5, 7, 9, 10, 12, 13, 28, 33, 36, 38, 40, 41, 42, 44, 54, 60, 62, 71, 74, 85, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 107, 108], "numer": [2, 3, 4, 5, 10, 14, 23, 31, 35, 49, 52, 53, 69, 71, 74, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 99, 101, 102, 104, 106], "consist": [2, 3, 38, 42, 51, 57, 62, 96, 107, 108], "latent": [2, 3, 47], "thei": [2, 3, 5, 16, 22, 25, 27, 30, 38, 39, 40, 42, 44, 45, 52, 55, 57, 61, 64, 69, 72, 74, 75, 78, 82, 84, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106, 108], "relat": [2, 3, 10, 14, 20, 21, 27, 28, 29, 32, 47, 57, 63, 74, 91, 95, 96], "close": [2, 3, 10, 41, 47, 71, 89, 90, 91, 92, 94, 95, 96, 98, 99, 103], "form": [2, 3, 10, 38, 39, 42, 47, 56, 57, 72, 74, 98], "equival": [2, 3, 38, 42, 47, 71, 104, 106], "iter": [2, 3, 37, 38, 42, 44, 57, 63, 64, 74, 98, 101, 107], "enforc": [2, 38, 42, 57], "perfectli": [2, 37, 63, 99], "certain": [2, 3, 5, 38, 42, 61, 70, 74, 90, 91, 96, 97, 103, 104], "dict": [2, 3, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 48, 49, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 82, 90, 91, 92, 98, 108], "keyword": [2, 3, 5, 10, 11, 17, 24, 28, 31, 38, 41, 42, 44, 46, 49, 52, 54, 56, 61, 62, 64, 70, 71, 72, 74, 79, 80, 82, 90], "filter": [2, 3, 10, 41, 43, 56, 63, 65, 66, 68, 70, 77, 78, 79, 81, 82, 83, 84, 85, 87, 88, 89, 91, 92, 95, 97, 98, 102, 103, 106, 107, 108], "find_label_issu": [2, 3, 10, 31, 40, 41, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 98, 102, 103, 106, 107, 108], "particularli": [2, 84, 101, 104], "filter_bi": [2, 3, 41, 44, 64, 85, 98], "frac_nois": [2, 44, 64, 80, 98], "min_examples_per_class": [2, 44, 64, 91, 98, 99], "impact": [2, 4, 10, 90, 91, 92, 96], "ml": [2, 4, 5, 9, 10, 16, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 101, 102, 106], "accuraci": [2, 39, 72, 87, 88, 89, 92, 98, 99, 101, 104, 106, 107], "n_job": [2, 41, 44, 64, 76, 78, 80, 98, 104, 107], "disabl": [2, 38, 42, 44, 104], "process": [2, 3, 7, 14, 17, 33, 38, 41, 42, 44, 52, 56, 62, 64, 70, 76, 78, 80, 88, 89, 90, 96, 98, 101, 105], "caus": [2, 44, 49, 90, 91, 96, 98], "rank": [2, 3, 10, 37, 41, 43, 44, 49, 63, 64, 65, 67, 68, 70, 71, 73, 77, 79, 80, 81, 83, 84, 85, 87, 88, 90, 91, 97, 98, 102, 103, 104, 107, 108], "get_label_quality_scor": [2, 40, 41, 43, 44, 45, 49, 62, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77, 78, 80, 81, 82, 85, 98, 99, 102, 103, 107, 108], "adjust_pred_prob": [2, 10, 66, 71, 72, 99], "control": [2, 5, 9, 10, 17, 41, 44, 62, 70, 71, 74, 80, 82, 90, 91, 96, 97, 98], "how": [2, 3, 5, 10, 13, 14, 15, 17, 23, 37, 38, 39, 41, 42, 47, 57, 62, 63, 66, 67, 69, 71, 72, 74, 78, 82, 84, 87, 88, 90, 91, 92, 94, 95, 96, 97, 103, 104, 105, 106, 107], "much": [2, 10, 37, 41, 44, 74, 96, 97, 98, 99, 101, 104], "output": [2, 3, 5, 10, 17, 33, 38, 39, 42, 47, 57, 61, 62, 63, 67, 69, 70, 71, 74, 78, 79, 82, 83, 84, 85, 88, 89, 90, 92, 95, 97, 98, 103, 104, 105, 106], "print": [2, 5, 7, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 57, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "suppress": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79, 107, 108], "statement": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79], "big": [2, 41, 64, 70, 74, 99], "limit": [2, 5, 17, 41, 52, 64, 96, 103, 107, 108], "memori": [2, 38, 41, 42, 64, 70, 76, 78, 90, 107], "label_issues_batch": [2, 40, 64, 98], "find_label_issues_batch": [2, 40, 41, 64, 98], "pred_prob": [2, 3, 5, 8, 10, 11, 17, 24, 26, 27, 29, 32, 33, 37, 41, 43, 44, 46, 47, 48, 49, 50, 57, 58, 62, 63, 64, 66, 67, 70, 71, 72, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106], "threshold": [2, 3, 4, 7, 10, 19, 20, 21, 23, 29, 31, 32, 41, 55, 69, 70, 71, 72, 78, 82, 90, 96, 103, 104, 107, 108], "inverse_noise_matrix": [2, 3, 10, 47, 57, 85, 99], "label_issu": [2, 41, 44, 64, 67, 74, 76, 85, 87, 88, 89, 92, 95, 98, 99, 102, 106], "clf_kwarg": [2, 3, 10, 74], "clf_final_kwarg": [2, 74], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 37, 41, 44, 46, 52, 62, 63, 64, 66, 67, 69, 70, 72, 74, 75, 78, 82, 84, 89, 92, 94, 95, 99, 101, 103, 105, 106], "result": [2, 3, 9, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 41, 42, 44, 46, 55, 57, 64, 66, 67, 70, 72, 74, 75, 76, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 106, 107, 108], "identifi": [2, 3, 5, 7, 9, 10, 13, 17, 28, 34, 37, 41, 43, 44, 52, 64, 67, 70, 72, 74, 75, 76, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 102, 104, 106, 107, 108], "final": [2, 10, 74, 87, 94, 96, 103, 105, 106], "remain": [2, 74, 85, 87, 88, 92, 102, 106, 108], "datasetlik": [2, 57, 74], "beyond": [2, 5, 7, 9, 10, 12, 36, 84, 87, 88, 106, 107], "pd": [2, 3, 5, 7, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 48, 61, 62, 63, 74, 82, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 106, 108], "datafram": [2, 3, 5, 7, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 48, 57, 58, 61, 62, 63, 74, 79, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 106, 107, 108], "scipi": [2, 4, 5, 14, 53, 57, 71, 96], "spars": [2, 4, 5, 10, 14, 17, 19, 32, 52, 57, 58, 91, 92, 94, 95, 96, 99], "csr_matrix": [2, 4, 5, 14, 17, 19, 32, 52, 96], "torch": [2, 38, 39, 42, 88, 89, 92, 95, 97, 104], "util": [2, 5, 10, 17, 34, 38, 39, 42, 45, 52, 61, 62, 67, 70, 74, 84, 85, 89, 90, 91, 92, 98, 99, 104], "tensorflow": [2, 57, 61, 84, 89, 98], "object": [2, 5, 10, 13, 14, 17, 33, 34, 38, 39, 41, 42, 49, 52, 54, 57, 58, 61, 64, 67, 68, 69, 70, 71, 74, 82, 84, 88, 89, 91, 92, 94, 98, 99, 100, 102, 106], "list": [2, 3, 5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 43, 44, 50, 52, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 85, 88, 89, 90, 91, 92, 97, 98, 99, 102, 103, 106, 108], "index_list": 2, "subset": [2, 3, 5, 17, 37, 41, 44, 57, 72, 79, 83, 87, 88, 89, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 108], "wa": [2, 3, 13, 15, 41, 55, 57, 62, 63, 69, 71, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 105, 107, 108], "abl": [2, 3, 10, 74, 89, 98, 99, 101, 102], "format": [2, 3, 5, 10, 13, 33, 38, 41, 42, 44, 47, 48, 49, 50, 52, 57, 58, 61, 62, 63, 64, 67, 70, 71, 72, 74, 76, 78, 79, 82, 83, 87, 89, 90, 91, 92, 94, 96, 97, 101, 106, 107, 108], "make": [2, 3, 5, 19, 38, 41, 42, 49, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "sure": [2, 5, 41, 44, 49, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106], "shuffl": [2, 10, 57, 89, 92, 95, 96, 102, 104], "ha": [2, 3, 5, 6, 10, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 43, 47, 49, 52, 56, 57, 62, 67, 69, 74, 80, 82, 83, 84, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 108], "batch": [2, 41, 57, 61, 62, 76, 78, 92, 98, 104], "order": [2, 5, 10, 35, 37, 38, 42, 43, 44, 47, 48, 49, 55, 57, 62, 63, 64, 67, 70, 71, 72, 76, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 106, 107, 108], "destroi": [2, 57], "oper": [2, 38, 41, 42, 52, 57, 61, 72, 84, 87, 88, 95, 98, 104], "eg": [2, 5, 10, 57, 67, 70, 90, 91, 98], "repeat": [2, 57, 62, 101, 104], "appli": [2, 35, 38, 40, 42, 44, 49, 50, 52, 56, 57, 66, 71, 80, 87, 88, 89, 90, 91, 92, 94, 96, 98, 101, 102, 104, 105, 106, 107], "array_lik": [2, 3, 37, 44, 57, 64, 71, 75], "some": [2, 3, 5, 10, 15, 23, 37, 38, 40, 42, 44, 47, 52, 56, 57, 60, 62, 63, 64, 66, 67, 70, 71, 72, 74, 76, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "seri": [2, 3, 41, 57, 58, 74, 82, 98], "row": [2, 3, 5, 10, 14, 28, 33, 37, 41, 44, 46, 47, 52, 53, 57, 62, 63, 64, 66, 71, 72, 74, 79, 80, 82, 83, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 104, 108], "rather": [2, 3, 5, 10, 27, 37, 57, 61, 62, 69, 78, 82, 88, 97, 101, 105, 106, 107, 108], "leav": [2, 44], "per": [2, 3, 5, 7, 10, 14, 37, 41, 44, 49, 56, 62, 63, 64, 66, 69, 70, 72, 75, 76, 78, 82, 91, 98, 103, 108], "determin": [2, 3, 10, 13, 17, 23, 27, 31, 37, 41, 44, 49, 52, 57, 62, 64, 67, 69, 72, 78, 82, 90, 96, 98, 101, 103, 104, 106], "cutoff": [2, 3, 53, 104], "consid": [2, 3, 4, 5, 10, 14, 17, 24, 27, 29, 32, 37, 38, 42, 44, 52, 54, 57, 62, 69, 71, 72, 75, 78, 82, 87, 88, 89, 92, 94, 95, 96, 98, 99, 103, 104, 105, 106, 107], "section": [2, 3, 7, 10, 85, 92, 94, 96, 98, 103], "3": [2, 3, 4, 5, 7, 10, 11, 35, 37, 38, 42, 44, 47, 48, 49, 50, 53, 55, 56, 57, 61, 64, 71, 72, 74, 75, 80, 82, 97, 98, 105], "equat": [2, 3, 47], "advanc": [2, 3, 5, 9, 10, 17, 69, 71, 82, 85, 91, 93, 96, 98, 99], "user": [2, 3, 5, 9, 10, 15, 17, 28, 33, 34, 35, 38, 42, 44, 52, 61, 69, 71, 72, 74, 78, 82, 99], "specifi": [2, 3, 4, 5, 8, 10, 14, 15, 17, 19, 32, 34, 38, 41, 42, 44, 49, 52, 54, 56, 61, 62, 63, 64, 67, 69, 71, 72, 74, 75, 83, 85, 88, 89, 91, 92, 95, 101, 103, 106], "automat": [2, 3, 5, 27, 37, 84, 87, 88, 92, 94, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "greater": [2, 3, 4, 5, 7, 9, 10, 29, 41, 53, 57, 69, 91, 97, 98, 108], "count": [2, 23, 27, 37, 41, 44, 47, 57, 63, 64, 70, 85, 92, 96, 98, 103], "observ": [2, 3, 47, 54, 89, 90, 91, 101, 104, 106], "mislabel": [2, 10, 37, 41, 43, 44, 47, 62, 63, 64, 67, 69, 72, 78, 80, 82, 83, 84, 87, 88, 89, 92, 94, 95, 98, 99, 103, 106], "one": [2, 3, 5, 7, 10, 27, 37, 38, 41, 42, 43, 44, 49, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 104, 105, 106, 108], "get_label_issu": [2, 40, 41, 73, 74, 87, 88, 99, 106], "either": [2, 3, 4, 7, 10, 38, 41, 42, 44, 53, 62, 64, 69, 71, 72, 76, 78, 91, 96, 98, 102, 103], "boolean": [2, 7, 10, 23, 41, 44, 54, 56, 62, 64, 67, 72, 74, 76, 78, 79, 84, 88, 89, 91, 92, 95, 98, 103, 106, 107], "label_issues_mask": [2, 44, 72, 74, 85], "indic": [2, 3, 4, 5, 7, 10, 14, 23, 37, 41, 42, 43, 44, 46, 49, 52, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "its": [2, 5, 7, 9, 10, 17, 38, 41, 42, 44, 52, 54, 55, 56, 64, 67, 70, 71, 72, 74, 76, 80, 82, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108], "return_indices_ranked_bi": [2, 41, 44, 64, 80, 85, 87, 88, 98, 99], "significantli": [2, 10, 92, 96, 99, 101, 105], "reduc": [2, 41, 44, 57, 89, 98], "time": [2, 10, 38, 41, 42, 57, 62, 83, 85, 87, 88, 90, 92, 94, 97, 98, 99, 103, 104, 106, 107, 108], "take": [2, 5, 10, 37, 38, 42, 48, 49, 52, 54, 57, 61, 72, 87, 92, 94, 101, 102, 103, 108], "run": [2, 5, 6, 7, 9, 10, 11, 12, 15, 17, 27, 28, 33, 36, 38, 41, 42, 54, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 108], "skip": [2, 10, 38, 42, 74, 89, 96, 98, 102, 108], "slow": [2, 3], "step": [2, 7, 27, 49, 70, 92, 96, 99, 101, 105], "caution": [2, 5, 98], "previous": [2, 5, 14, 57, 71, 74, 85, 87, 89, 90, 94, 95, 101, 105], "assign": [2, 7, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 38, 42, 48, 49, 57, 74, 87, 90, 92, 94, 96, 98, 106, 107, 108], "individu": [2, 4, 7, 10, 14, 27, 38, 42, 43, 62, 66, 69, 72, 74, 80, 82, 85, 87, 91, 94, 96, 97, 98, 101, 102, 103, 108], "still": [2, 41, 42, 57, 71, 87, 89, 92, 98, 104], "extra": [2, 38, 42, 57, 61, 62, 63, 74, 92, 95, 98, 101, 104], "receiv": [2, 10, 38, 42, 43, 63, 66, 67, 74, 76, 80, 91, 103], "overwritten": [2, 74], "callabl": [2, 3, 4, 10, 27, 38, 42, 49, 52, 53, 54, 56, 61, 66, 98], "x_val": 2, "y_val": 2, "map": [2, 3, 13, 41, 42, 45, 48, 56, 57, 70, 72, 74, 79, 89, 90, 91, 92, 96, 98, 99, 102, 108], "appropri": [2, 10, 17, 35, 53, 64, 72, 90, 94, 102, 103], "earli": [2, 92], "stop": [2, 92], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 23, 37, 57, 71, 87, 90, 92, 94, 102, 106, 108], "f": [2, 7, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106], "ignor": [2, 38, 42, 56, 61, 74, 79, 83, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "allow": [2, 37, 38, 41, 42, 46, 54, 57, 62, 70, 71, 74, 76, 78, 88, 89, 92, 96, 98, 105, 107], "access": [2, 10, 14, 38, 42, 74, 88, 91, 92, 95, 97, 102], "hyperparamet": [2, 66, 71, 92], "purpos": [2, 52, 90, 91, 96, 98, 102, 106], "want": [2, 5, 10, 37, 41, 52, 58, 62, 64, 74, 88, 90, 92, 95, 97, 101, 103, 104, 105, 107, 108], "explicitli": [2, 8, 10, 42, 52, 74, 98], "yourself": [2, 5, 41, 91, 96], "altern": [2, 7, 10, 49, 54, 57, 61, 62, 72, 85, 88, 89, 92, 94, 95, 97, 98, 99, 101, 102, 104, 106], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 27, 31, 38, 41, 42, 44, 52, 57, 61, 62, 64, 71, 72, 74, 78, 79, 82, 83, 84, 87, 88, 90, 91, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 107], "effect": [2, 10, 28, 38, 42, 62, 71, 74, 92, 94, 95, 96, 98, 104], "offer": [2, 5, 9, 10, 88, 89, 90, 91, 95, 98, 99, 102], "after": [2, 3, 5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 62, 74, 88, 90, 92, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107], "attribut": [2, 5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 49, 54, 71, 74, 87, 90, 96], "label_issues_df": [2, 74, 92], "similar": [2, 10, 37, 38, 42, 54, 57, 62, 66, 67, 69, 71, 74, 78, 82, 90, 91, 92, 94, 95, 96, 98, 99, 103, 104, 107], "document": [2, 3, 5, 15, 17, 37, 38, 41, 42, 43, 44, 49, 56, 61, 63, 64, 66, 69, 70, 71, 74, 78, 79, 80, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "descript": [2, 5, 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 37, 43, 57, 67, 74, 90, 91], "were": [2, 3, 5, 10, 37, 42, 52, 63, 69, 82, 87, 89, 94, 96, 98, 99, 101, 103, 105, 107], "present": [2, 3, 5, 10, 13, 14, 21, 37, 57, 71, 79, 84, 92, 96, 98, 104], "actual": [2, 3, 5, 10, 37, 52, 62, 63, 72, 91, 98, 99, 108], "num_class": [2, 37, 41, 57, 61, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 104], "uniqu": [2, 32, 57, 79, 90, 96, 98, 102, 104], "given_label": [2, 5, 11, 26, 31, 37, 47, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107, 108], "normal": [2, 3, 19, 27, 32, 44, 46, 49, 55, 56, 57, 72, 96, 98, 99, 104], "trick": [2, 98], "distribut": [2, 3, 5, 10, 27, 29, 37, 42, 44, 48, 55, 62, 70, 71, 72, 84, 90, 91, 92, 94, 95, 96, 103, 104], "account": [2, 37, 62, 66, 71, 72, 88, 95, 98, 99, 101, 102, 104, 106], "word": [2, 3, 56, 82, 83, 98], "remov": [2, 10, 32, 37, 38, 42, 44, 74, 84, 87, 88, 91, 92, 94, 95, 96, 97, 98, 99, 102, 104, 106], "so": [2, 3, 5, 6, 7, 10, 15, 27, 35, 37, 38, 41, 42, 44, 52, 57, 62, 63, 69, 72, 74, 78, 82, 89, 90, 91, 92, 95, 96, 99, 102, 104, 107], "proportion": [2, 10, 44], "just": [2, 3, 5, 10, 14, 33, 37, 39, 41, 57, 61, 72, 74, 76, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106, 107], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 14, 32, 38, 39, 42, 44, 49, 55, 56, 57, 62, 64, 66, 71, 72, 74, 75, 76, 84, 87, 88, 89, 92, 95, 96, 97, 98, 99, 104, 105, 106], "detect": [2, 5, 7, 9, 14, 15, 17, 19, 23, 29, 43, 52, 55, 65, 67, 68, 69, 70, 71, 72, 73, 74, 77, 81, 84, 87, 88, 90, 93, 97, 100, 102, 106, 107, 108], "arg": [2, 13, 23, 28, 32, 38, 39, 42, 49, 57, 72, 74], "kwarg": [2, 7, 10, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 43, 49, 52, 61, 70, 74, 76, 78, 79, 80, 98], "test": [2, 5, 10, 27, 42, 49, 52, 61, 74, 84, 87, 88, 90, 91, 92, 94, 95, 105, 106, 108], "expect": [2, 3, 10, 38, 42, 44, 49, 52, 62, 71, 72, 74, 87, 88, 98, 99, 101, 102, 103, 106, 108], "class_predict": 2, "evalu": [2, 10, 38, 39, 40, 41, 42, 70, 74, 87, 88, 89, 90, 91, 92, 98, 99, 101, 105, 106, 107], "simpli": [2, 10, 37, 72, 88, 90, 91, 94, 95, 98, 99, 102, 106, 107, 108], "quantifi": [2, 4, 5, 7, 10, 14, 44, 66, 71, 74, 84, 91, 92, 94, 95, 96, 99, 103], "save_spac": [2, 10, 73, 74], "potenti": [2, 10, 37, 44, 56, 64, 67, 70, 72, 74, 76, 78, 83, 85, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "cach": [2, 88, 95], "panda": [2, 5, 7, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 57, 58, 61, 62, 63, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 106, 107], "unlik": [2, 10, 44, 46, 49, 61, 63, 64, 66, 82, 90, 101, 102, 104, 106], "both": [2, 5, 10, 17, 27, 37, 38, 42, 44, 52, 57, 62, 64, 72, 76, 78, 83, 84, 90, 92, 96, 98, 99, 101, 108], "mask": [2, 41, 44, 56, 57, 64, 67, 72, 74, 76, 78, 79, 84, 97, 98, 101, 103, 107, 108], "prefer": [2, 72, 80, 96, 102], "plan": 2, "subsequ": [2, 3, 38, 42, 54, 88, 95, 98, 99, 103], "invok": [2, 38, 42, 99, 105], "scratch": [2, 52, 74], "To": [2, 5, 7, 9, 10, 12, 14, 17, 27, 36, 38, 41, 42, 43, 44, 61, 62, 64, 66, 70, 71, 72, 74, 75, 76, 78, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "share": [2, 10, 72, 74], "mostli": [2, 57, 69, 74, 102, 106], "longer": [2, 35, 48, 49, 56, 74, 85, 88, 95, 98, 103], "info": [2, 5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 74, 82, 90, 91, 96, 97, 108], "about": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 39, 41, 46, 62, 63, 66, 70, 74, 79, 82, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 104], "docstr": [2, 37, 38, 42, 57, 74, 97, 99], "unless": [2, 38, 42, 52, 74, 98], "our": [2, 3, 10, 61, 62, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "is_label_issu": [2, 11, 31, 74, 88, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "entir": [2, 10, 27, 41, 44, 47, 63, 64, 69, 72, 74, 76, 78, 79, 84, 90, 91, 96, 98, 103, 104, 105, 107, 108], "accur": [2, 3, 5, 9, 10, 17, 37, 41, 44, 53, 62, 63, 64, 67, 70, 72, 74, 75, 76, 78, 79, 85, 91, 92, 94, 95, 96, 98, 101, 106], "label_qu": [2, 62, 74, 88, 99, 101, 106], "measur": [2, 5, 37, 62, 63, 74, 84, 87, 97, 98, 99, 101, 102, 106, 107, 108], "qualiti": [2, 3, 5, 7, 9, 10, 14, 31, 32, 37, 41, 43, 44, 46, 49, 62, 63, 64, 66, 67, 69, 72, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 106], "lower": [2, 4, 5, 7, 10, 14, 29, 41, 49, 55, 62, 63, 66, 69, 70, 72, 74, 75, 78, 82, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "eas": 2, "comparison": [2, 38, 42, 70, 99, 101], "against": [2, 38, 42, 90, 94, 96, 98, 101, 102], "predicted_label": [2, 5, 11, 26, 31, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107], "ad": [2, 38, 42, 91, 101, 106], "precis": [2, 53, 55, 64, 67, 70, 96, 97, 98, 99, 107, 108], "definit": [2, 7, 35, 49, 74, 87, 94], "accessor": [2, 74], "describ": [2, 10, 19, 62, 71, 72, 74, 80, 82, 99, 101, 102, 103, 105, 108], "precomput": [2, 4, 5, 47, 52, 74, 91, 92, 94, 95, 96, 97, 99], "clear": [2, 38, 42, 54, 74, 88, 95, 106], "save": [2, 5, 17, 38, 41, 42, 70, 74, 98, 103, 107, 108], "space": [2, 5, 10, 71, 74, 92, 94, 96, 97], "place": [2, 38, 42, 52, 57, 74, 87, 101], "larg": [2, 9, 10, 41, 52, 74, 92, 94, 95, 98, 103, 104, 107, 108], "deploi": [2, 9, 10, 74, 92, 94, 95, 98], "care": [2, 10, 38, 42, 52, 74, 95, 96, 98, 99], "avail": [2, 4, 5, 7, 10, 13, 15, 34, 42, 54, 74, 98, 99, 101, 103, 106], "cannot": [2, 5, 13, 15, 57, 105, 108], "anymor": 2, "classmethod": [2, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 42, 49, 74], "__init_subclass__": [2, 40, 42, 73, 74], "set_": [2, 42, 74], "_request": [2, 42, 74], "pep": [2, 42, 74], "487": [2, 42, 74], "look": [2, 5, 7, 10, 17, 38, 42, 57, 74, 79, 87, 90, 91, 94, 95, 98, 99, 101, 102, 103, 104, 107, 108], "inform": [2, 5, 7, 10, 14, 17, 34, 38, 42, 54, 57, 62, 63, 67, 70, 74, 79, 82, 83, 84, 89, 90, 94, 95, 96, 97, 99, 101, 104, 107, 108], "__metadata_request__": [2, 42, 74], "infer": [2, 42, 57, 74, 79, 83, 87, 88, 92, 101, 102], "signatur": [2, 38, 42, 74], "accept": [2, 38, 42, 54, 55, 72, 74, 90, 91, 98], "metadata": [2, 10, 42, 74, 92, 94, 95, 108], "through": [2, 5, 7, 42, 74, 88, 89, 91, 95, 96, 97, 98, 101, 103, 104], "develop": [2, 9, 42, 54, 74, 98, 99, 108], "request": [2, 42, 74, 87, 88, 91, 95, 96, 97, 102, 108], "those": [2, 3, 4, 10, 41, 42, 44, 51, 61, 62, 64, 70, 74, 78, 82, 83, 84, 89, 92, 96, 98, 103, 107], "http": [2, 4, 5, 7, 9, 10, 12, 19, 36, 38, 39, 41, 42, 46, 54, 57, 67, 70, 71, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "www": [2, 42, 74, 104], "org": [2, 4, 19, 38, 39, 42, 54, 57, 71, 74, 98, 99, 108], "dev": [2, 42, 74], "0487": [2, 42, 74], "get_metadata_rout": [2, 40, 42, 73, 74], "rout": [2, 42, 74], "pleas": [2, 38, 42, 61, 74, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 104, 106, 108], "guid": [2, 7, 10, 42, 74, 85, 89, 90, 91, 92, 93, 94, 95, 99], "mechan": [2, 38, 42, 74], "metadatarequest": [2, 42, 74], "encapsul": [2, 17, 42, 69, 74], "get_param": [2, 40, 42, 60, 61, 73, 74], "subobject": [2, 42, 74], "param": [2, 10, 38, 42, 61, 71, 74, 98], "name": [2, 5, 6, 7, 10, 11, 13, 14, 33, 35, 37, 38, 42, 48, 49, 53, 57, 61, 62, 63, 70, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 102, 106, 107, 108], "set_fit_request": [2, 40, 42, 73, 74], "str": [2, 3, 4, 5, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 47, 49, 52, 53, 54, 55, 56, 57, 61, 62, 63, 67, 69, 70, 72, 74, 79, 83, 89, 90, 98, 101, 102, 103, 108], "unchang": [2, 38, 42, 74, 108], "relev": [2, 17, 27, 42, 74, 92, 94, 96], "enable_metadata_rout": [2, 42, 74], "set_config": [2, 42, 74], "meta": [2, 42, 74], "rais": [2, 4, 5, 13, 14, 35, 38, 42, 46, 49, 52, 55, 74, 89, 98], "alia": [2, 38, 42, 74], "metadata_rout": [2, 42, 74], "retain": [2, 42, 57, 74], "chang": [2, 33, 35, 38, 41, 42, 46, 74, 82, 87, 88, 89, 90, 95, 98, 103, 104, 108], "version": [2, 4, 5, 7, 9, 10, 12, 16, 22, 25, 30, 36, 38, 40, 42, 45, 46, 57, 60, 61, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 108], "sub": [2, 42, 69, 74], "pipelin": [2, 42, 74, 106], "otherwis": [2, 4, 7, 10, 35, 37, 38, 41, 42, 44, 50, 53, 55, 56, 57, 64, 74, 76, 78, 79, 83, 88, 95, 98], "updat": [2, 14, 38, 41, 42, 52, 61, 74, 85, 90, 92], "set_param": [2, 40, 42, 60, 61, 73, 74], "simpl": [2, 38, 42, 44, 62, 72, 74, 87, 88, 90, 91, 92, 94, 95, 101, 104, 106], "well": [2, 3, 9, 10, 38, 42, 46, 47, 62, 64, 70, 72, 74, 79, 82, 83, 85, 90, 91, 92, 94, 95, 98, 99, 101, 103, 104], "nest": [2, 38, 42, 43, 58, 74, 80, 82, 83, 108], "latter": [2, 38, 42, 74, 104], "compon": [2, 42, 74], "__": [2, 42, 74], "set_score_request": [2, 73, 74], "structur": [3, 71, 94, 96, 98], "unobserv": 3, "less": [3, 4, 5, 10, 32, 41, 49, 62, 71, 72, 76, 78, 82, 91, 92, 94, 96, 97, 98, 99, 103, 108], "channel": [3, 89, 99], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 37, 47, 57, 63, 88, 91, 97], "inv": 3, "confident_joint": [3, 23, 37, 44, 57, 63, 64, 85, 98, 99], "un": 3, "under": [3, 10, 38, 42, 63, 70, 71, 91, 96, 104], "joint": [3, 37, 44, 47, 57, 63, 64, 97], "num_label_issu": [3, 41, 44, 64, 79, 83, 85], "estimation_method": [3, 41], "off_diagon": 3, "multi_label": [3, 37, 44, 57, 58, 64, 102], "don": [3, 84, 91, 92, 94, 95, 99, 103, 106], "statis": 3, "compute_confident_joint": [3, 37, 44, 57, 64, 99], "off": [3, 44, 57, 69, 92, 99, 103, 104], "j": [3, 5, 37, 38, 42, 43, 44, 64, 67, 70, 71, 80, 82, 83, 90, 91, 99, 107, 108], "confident_learn": [3, 44, 64, 99], "off_diagonal_calibr": 3, "calibr": [3, 4, 44, 57, 62, 101], "cj": [3, 47, 57], "axi": [3, 32, 47, 49, 55, 76, 79, 89, 90, 91, 92, 96, 98, 99, 101, 102, 104, 106, 107], "bincount": [3, 90, 91, 99, 101, 102], "alwai": [3, 10, 38, 42, 57, 87, 88, 89, 99, 106], "estimate_issu": 3, "over": [3, 5, 10, 38, 41, 42, 69, 70, 76, 78, 87, 91, 92, 94, 96, 97, 98, 99, 104, 106], "As": [3, 7, 84, 90, 91, 95, 99, 106, 108], "add": [3, 5, 7, 13, 14, 38, 42, 61, 70, 88, 89, 90, 91, 92, 95, 96, 98, 99, 102], "approach": [3, 37, 41, 44, 61, 87, 94, 96, 99, 102, 104, 106], "custom": [3, 7, 10, 12, 31, 38, 41, 42, 49, 56, 72, 88, 91, 92, 95, 96, 99, 106], "know": [3, 10, 90, 91, 92, 94, 95, 98, 99, 101, 106], "cut": [3, 69, 84, 99], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 33, 103, 104, 108], "underestim": 3, "few": [3, 9, 10, 70, 84, 91, 96, 98, 101, 102, 103, 104, 108], "4": [3, 4, 5, 10, 11, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 48, 49, 56, 66, 67, 69, 70, 72, 75, 82, 97, 98, 102, 107, 108], "detail": [3, 4, 5, 10, 15, 17, 34, 37, 38, 42, 43, 49, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 78, 79, 80, 84, 85, 89, 98, 102, 104, 108], "num_issu": [3, 7, 41, 89, 90, 91, 92, 94, 95, 96, 99], "calibrate_confident_joint": 3, "up": [3, 7, 10, 18, 27, 28, 31, 44, 49, 51, 61, 62, 88, 97, 98, 103, 106, 108], "p_": [3, 37, 44], "pair": [3, 5, 10, 37, 44, 99], "v": [3, 10, 41, 63, 64, 66, 72, 90, 91, 102, 103, 104, 105], "rest": [3, 5, 7, 9, 10, 12, 36, 63, 64, 66, 74, 87, 88, 90, 91, 92, 94, 95, 98, 99, 101, 106], "fashion": [3, 5, 76, 87], "2x2": 3, "incorrectli": [3, 37, 63, 64, 67, 94, 108], "calibrated_cj": 3, "c": [3, 10, 55, 56, 64, 72, 84, 87, 89, 90, 91, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106], "whose": [3, 4, 5, 10, 29, 38, 42, 47, 52, 56, 62, 66, 69, 75, 78, 82, 83, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 104, 107, 108], "truli": [3, 104, 107], "estimate_joint": [3, 37, 99], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 64, 70, 99, 103, 105, 107, 108], "return_indices_of_off_diagon": 3, "frequenc": [3, 27, 62, 63, 70, 79, 103, 104], "done": [3, 10, 61, 74, 90, 98, 99, 102, 104, 105], "overfit": [3, 10, 67, 70, 87, 89, 90, 91, 92, 94, 95, 105], "classifict": 3, "singl": [3, 5, 9, 10, 13, 27, 37, 38, 42, 43, 49, 50, 57, 62, 63, 69, 70, 71, 72, 82, 87, 89, 90, 96, 98, 99, 102, 103], "baselin": [3, 38, 44, 88, 104, 106], "proxi": 3, "union": [3, 5, 13, 27, 49, 52, 53, 54, 57, 58, 64, 70, 74, 82, 98], "tupl": [3, 32, 38, 42, 43, 47, 48, 50, 52, 56, 57, 62, 64, 70, 78, 80, 82, 83, 89, 108], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 41, 47, 52, 53, 62, 71, 76, 78, 84, 88, 92, 96, 98, 107], "practic": [3, 87, 88, 91, 92, 99, 104, 106], "complet": [3, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "gist": 3, "cj_ish": 3, "guess": [3, 47, 99, 101], "8": [3, 5, 7, 8, 48, 49, 50, 56, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "parallel": [3, 44, 70, 80, 97], "again": [3, 61, 87, 98, 104], "simplifi": [3, 15, 98], "understand": [3, 9, 10, 37, 63, 70, 91, 96, 99, 100, 106, 107, 108], "100": [3, 4, 38, 42, 52, 53, 55, 71, 72, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 102, 103, 104, 108], "optim": [3, 38, 39, 42, 61, 92, 96, 101], "speed": [3, 44, 88, 97, 98, 106], "dtype": [3, 24, 26, 27, 32, 38, 42, 56, 57, 66, 82, 89, 96, 103], "enumer": [3, 38, 42, 89, 90, 91, 92, 96, 108], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 42, 49, 57, 82, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "num_confident_bin": 3, "argmax": [3, 44, 72, 76, 79, 89, 96, 98, 99, 103, 104, 107], "elif": 3, "estimate_lat": 3, "py_method": [3, 47], "cnt": [3, 47], "1d": [3, 5, 13, 17, 33, 41, 44, 49, 50, 52, 57, 58, 66, 75, 87, 89, 96], "eqn": [3, 47], "margin": [3, 44, 47, 49, 72], "marginal_p": [3, 47], "shorthand": [3, 14], "proport": [3, 10, 37, 63, 99, 105], "poorli": [3, 47, 87, 96], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 99], "variabl": [3, 7, 15, 28, 57, 74, 75, 89, 90, 94, 99, 102, 106], "exact": [3, 10, 47, 52, 87, 90, 91, 92, 94, 96], "within": [3, 4, 5, 10, 16, 33, 38, 39, 42, 43, 45, 64, 69, 78, 80, 82, 90, 91, 92, 98, 103, 107], "percent": 3, "often": [3, 37, 47, 63, 98, 99, 105, 107], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 57, 58, 70, 87, 88, 89, 90, 92, 94, 95, 98, 102, 103, 104, 106], "wai": [3, 5, 10, 52, 61, 84, 85, 87, 88, 89, 90, 91, 94, 95, 98, 99, 101, 102, 103, 105], "pro": 3, "con": 3, "pred_proba": [3, 105], "combin": [3, 37, 90, 92, 96, 97, 98, 99, 105, 106], "becaus": [3, 47, 53, 57, 69, 95, 96, 98, 99, 101, 103], "littl": [3, 41, 97, 103, 108], "uniform": [3, 72, 97, 98, 99], "20": [3, 7, 43, 83, 89, 92, 95, 96, 97, 98, 99, 103, 106, 107, 108], "Such": [3, 92, 104], "bound": [3, 24, 26, 38, 42, 56, 66, 67, 69, 70, 103], "reason": [3, 23, 38, 42, 53, 71], "comment": [3, 56, 96, 108], "end": [3, 5, 38, 42, 54, 70], "file": [3, 5, 13, 40, 41, 60, 70, 87, 89, 90, 94, 95, 97, 98, 103, 104, 107, 108], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 99], "handl": [3, 5, 7, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 52, 53, 54, 85, 87, 88, 90, 91, 92, 94, 95, 96, 99, 107, 108], "five": [3, 67, 70, 99, 103], "estimate_cv_predicted_prob": [3, 99], "estimate_noise_matric": 3, "get_confident_threshold": [3, 40, 41], "amongst": [3, 10, 103], "confident_threshold": [3, 10, 23, 24, 41, 71], "point": [4, 5, 7, 9, 10, 19, 27, 38, 42, 52, 54, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101], "valuat": [4, 9, 19], "help": [4, 37, 38, 42, 70, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 108], "u": [4, 87, 88, 89, 90, 92, 94, 96, 98, 99, 101, 102, 105, 106, 107, 108], "assess": [4, 10, 96, 103], "contribut": [4, 10, 19, 96, 103], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 17, 19, 20, 27, 29, 32, 45, 51, 94, 96], "metric": [4, 5, 10, 19, 20, 22, 27, 29, 32, 45, 51, 52, 54, 55, 57, 61, 70, 71, 87, 88, 89, 92, 94, 95, 96, 99, 106], "10": [4, 10, 19, 20, 24, 27, 29, 32, 38, 39, 52, 70, 71, 72, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "shaplei": [4, 10, 19], "nearest": [4, 5, 10, 17, 24, 27, 29, 51, 52, 53, 54, 55, 71, 91, 95, 96, 104], "neighbor": [4, 5, 10, 17, 19, 24, 27, 29, 45, 52, 53, 54, 55, 71, 90, 91, 92, 94, 95, 96, 98, 99, 104], "knn": [4, 10, 14, 19, 27, 29, 32, 51, 52, 53, 54, 55, 71, 94, 104], "graph": [4, 5, 10, 14, 17, 19, 27, 32, 51, 52], "calcul": [4, 10, 19, 27, 41, 49, 51, 52, 55, 62, 66, 67, 69, 70, 71, 74, 78, 92, 97], "directli": [4, 5, 10, 15, 17, 34, 35, 41, 54, 61, 62, 88, 91, 95, 96, 98, 102, 103, 106], "lowest": [4, 10, 62, 70, 91, 92, 94, 96, 98, 101, 102, 103, 107], "fall": [4, 10, 69, 78, 82, 99, 104], "flag": [4, 10, 23, 27, 44, 49, 63, 64, 67, 74, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 104, 106, 107], "approxim": [4, 10, 19, 41, 54, 71, 96, 101], "top": [4, 5, 10, 37, 41, 43, 44, 57, 64, 67, 70, 72, 79, 83, 84, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 106, 108], "found": [4, 5, 7, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 102, 104, 106, 108], "arxiv": [4, 19, 99], "ab": [4, 19, 99, 103], "1908": 4, "08619": 4, "1911": [4, 19], "07128": [4, 19], "embed": [4, 5, 10, 17, 71, 84, 88, 89, 90, 91, 94, 95, 96, 99, 102, 106], "represent": [4, 5, 10, 17, 35, 38, 42, 50, 52, 64, 84, 88, 89, 90, 91, 92, 95, 98, 99, 104], "suppli": [4, 102, 103, 106], "2d": [4, 5, 17, 33, 41, 49, 50, 52, 56, 57, 62, 87, 89, 96, 102], "num_exampl": [4, 5, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 63, 89, 90, 91, 92, 94, 95, 99], "num_featur": [4, 5, 17, 38, 42, 61], "distanc": [4, 5, 10, 17, 19, 27, 29, 32, 51, 52, 53, 54, 55, 69, 71, 94, 96, 104], "construct": [4, 5, 7, 10, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 51, 52, 54, 61, 96], "nearestneighbor": [4, 5, 10, 19, 52, 54, 71, 94, 104], "cosin": [4, 10, 52, 53, 55, 71, 96, 104], "dim": [4, 71, 92, 107], "euclidean": [4, 5, 10, 52, 53, 55, 69, 71, 94], "dimension": [4, 27, 53, 57, 89, 99, 104], "scikit": [4, 42, 53, 54, 57, 71, 84, 87, 88, 89, 90, 91, 94, 95, 96, 98, 106], "fewer": [4, 10, 44, 57, 71, 96, 103], "stabl": [4, 16, 22, 25, 30, 40, 45, 54, 57, 60, 71, 85, 89, 90, 91, 92, 94, 95, 99], "exce": [4, 52, 92, 96], "transform": [4, 10, 33, 49, 52, 55, 57, 71, 72, 87, 88, 91, 92, 95, 96, 104, 108], "rel": [4, 10, 37, 52, 62, 63, 71, 90, 91, 92, 94, 95, 99, 104], "adjust": [4, 39, 44, 52, 66, 71, 72, 84, 96, 99], "closer": [4, 10, 69, 103], "highli": [4, 91, 92], "influenti": 4, "posit": [4, 5, 10, 38, 42, 55, 57, 70, 96, 97, 104], "convers": 4, "neg": [4, 10, 69, 70, 90, 91, 96, 97], "valueerror": [4, 5, 13, 14, 35, 46, 49, 52, 55, 98], "neither": [4, 5, 10, 15, 53, 103], "nor": [4, 5, 10, 15], "larger": [4, 19, 53, 74, 76, 78, 92, 95, 97, 98], "55": [4, 56, 96, 97, 103, 106], "525": [4, 92], "unifi": 5, "audit": [5, 9, 13, 14, 17, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 106], "kind": [5, 6, 7, 10, 96, 97], "addit": [5, 7, 9, 12, 14, 34, 36, 38, 42, 49, 52, 54, 58, 62, 70, 79, 80, 87, 88, 89, 90, 94, 95, 96, 99, 101, 104, 105], "depend": [5, 7, 9, 12, 13, 14, 36, 40, 44, 46, 57, 60, 64, 71, 74, 75, 84, 96], "instal": [5, 7, 9, 12, 36, 38, 40, 41, 42, 44, 60, 61, 76, 78], "pip": [5, 7, 9, 12, 36, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "development": [5, 7, 9, 12, 36], "git": [5, 7, 9, 12, 36, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "github": [5, 7, 9, 12, 36, 38, 39, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "com": [5, 7, 9, 12, 36, 38, 39, 41, 46, 57, 71, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "egg": [5, 7, 9, 12, 36, 84, 97], "label_nam": [5, 7, 8, 10, 11, 13, 19, 32, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "image_kei": [5, 10, 92], "interfac": [5, 9, 10, 54, 84, 98, 99], "librari": [5, 10, 42, 54, 67, 70, 71, 84, 88, 90, 95, 96, 97, 98], "goal": [5, 106], "track": [5, 7, 14, 15, 84, 90, 97, 98, 99], "intermedi": [5, 9, 91], "statist": [5, 10, 14, 23, 27, 37, 62, 63, 70, 91, 94, 95, 96, 99], "convert": [5, 10, 13, 35, 38, 42, 50, 55, 58, 62, 69, 78, 82, 85, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103], "hug": [5, 10, 13, 92], "face": [5, 10, 13, 17, 92, 97, 102], "kei": [5, 7, 10, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 49, 62, 63, 69, 71, 90, 91, 92, 95, 98, 99, 101, 103], "string": [5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 42, 53, 57, 62, 63, 75, 79, 82, 83, 88, 94, 95, 96, 98, 101, 102, 108], "dictionari": [5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 48, 57, 62, 63, 66, 67, 69, 70, 90, 91, 94, 95, 99, 101, 102, 103], "path": [5, 13, 38, 41, 42, 70, 89, 90, 98, 103], "local": [5, 7, 10, 13, 38, 39, 42, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "text": [5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 43, 49, 71, 80, 82, 83, 84, 86, 90, 91, 93, 97, 98, 99, 100, 101, 104], "txt": [5, 13, 108], "csv": [5, 13, 87, 88, 94, 95, 106], "json": [5, 13], "hub": [5, 13], "multiclass": [5, 13, 16, 49, 57, 62, 102], "regress": [5, 7, 10, 11, 13, 15, 17, 22, 31, 33, 35, 88, 90, 91, 95, 100, 101, 104], "multilabel": [5, 10, 11, 13, 15, 16, 22, 26, 33, 35, 50, 102], "imag": [5, 9, 37, 42, 67, 69, 70, 71, 76, 78, 79, 84, 90, 91, 93, 97, 98, 100, 101, 102, 103, 105, 107], "field": [5, 10, 38, 42], "themselv": [5, 87, 88, 96, 106], "pil": [5, 92], "cleanvis": [5, 10], "level": [5, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 52, 56, 80, 82, 91, 92, 98, 100, 102, 107], "load_dataset": [5, 13, 92], "glue": 5, "sst2": 5, "properti": [5, 13, 14, 35, 38, 42], "has_label": [5, 13], "class_nam": [5, 13, 21, 37, 43, 63, 70, 79, 83, 84, 97, 99, 103, 107, 108], "empti": [5, 13, 47, 62, 91, 96, 98, 102], "find_issu": [5, 6, 7, 8, 10, 11, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_typ": [5, 6, 7, 8, 10, 11, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "sort": [5, 17, 41, 44, 49, 62, 64, 67, 69, 70, 72, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 103, 106, 107, 108], "common": [5, 10, 14, 17, 91, 93, 96, 97, 98, 99, 102, 103, 107], "real": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "world": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "interact": [5, 17, 95, 98], "thereof": [5, 17], "insight": [5, 17, 70, 101], "best": [5, 9, 10, 17, 48, 62, 72, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 108], "properli": [5, 10, 41, 48, 52, 57, 58, 76, 89, 90, 91, 92, 94, 95, 98, 99, 102, 104, 106, 107], "respect": [5, 38, 42, 67, 70, 89, 90, 91, 92, 94, 95, 99, 102, 103], "lexicograph": [5, 48, 57, 89, 90, 91, 92, 94, 95, 99, 102], "squar": [5, 57, 74, 97, 106], "csr": [5, 52, 96], "evenli": 5, "omit": [5, 69, 70, 92, 96, 103], "itself": [5, 33, 38, 42, 52, 96, 103], "three": [5, 10, 37, 62, 63, 74, 79, 87, 89, 90, 91, 94, 97, 99, 101, 105, 106, 107, 108], "indptr": [5, 96], "wise": 5, "start": [5, 7, 10, 35, 38, 39, 42, 49, 84, 102, 108], "th": [5, 10, 43, 48, 56, 57, 62, 64, 67, 69, 70, 71, 80, 82, 83, 95, 102, 103, 108], "ascend": [5, 37, 63, 92, 99], "segment": [5, 76, 78, 79, 100], "reflect": [5, 10, 52, 87, 88, 94, 95, 101, 103, 104, 106], "maintain": [5, 61], "kneighbors_graph": [5, 19, 54, 94], "illustr": [5, 96], "todens": 5, "second": [5, 49, 57, 70, 72, 90, 94, 98, 99, 108], "duplic": [5, 9, 22, 23, 38, 42, 52, 84, 90, 96, 99, 106], "explicit": 5, "precend": 5, "collect": [5, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 62, 96, 98, 101, 108], "unspecifi": [5, 17, 44, 64], "interest": [5, 17, 23, 79, 83, 87, 88, 95, 96, 99, 106, 107, 108], "constructor": [5, 10, 11, 17, 24, 31, 52, 54], "issuemanag": [5, 9, 14, 15, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 34], "respons": [5, 17, 23, 54, 74, 75, 97, 106, 108], "random_st": [5, 87, 89, 90, 91, 92, 96, 99, 102, 104], "lab": [5, 6, 8, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 41, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106], "comprehens": [5, 84, 92, 102, 106], "nbr": 5, "n_neighbor": [5, 10, 19, 52, 54, 71, 96], "mode": [5, 12, 19, 38, 41, 42, 104], "4x4": 5, "float64": [5, 27, 38, 42, 82], "compress": [5, 10, 52, 57, 76, 78, 96], "toarrai": [5, 52, 96], "NOT": [5, 41, 95], "23606798": 5, "41421356": [5, 52], "configur": [5, 17, 49, 91], "suppos": [5, 10, 67, 87, 88, 104, 106], "who": [5, 69, 87, 94, 96, 99, 108], "manag": [5, 8, 9, 10, 14, 15, 16, 17, 18, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 61, 90, 96, 98], "clean_learning_kwarg": [5, 10, 11, 24, 31, 98, 106], "labelissuemanag": [5, 10, 15, 22, 24], "prune_method": [5, 85], "prune_by_noise_r": [5, 44, 64, 99], "report": [5, 7, 12, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 83, 84, 89, 90, 91, 94, 95, 98, 99, 102, 106, 108], "include_descript": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34], "show_summary_scor": [5, 34], "show_all_issu": [5, 34], "summari": [5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 43, 60, 61, 63, 68, 77, 78, 80, 81, 82, 85, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 106, 107, 108], "show": [5, 7, 27, 38, 42, 48, 57, 70, 79, 83, 87, 91, 92, 94, 95, 96, 97, 98, 99, 101, 104, 106, 107, 108], "suffer": [5, 10, 14, 23, 64, 72, 83, 96, 108], "onc": [5, 23, 37, 38, 42, 87, 90, 98, 99, 102, 103], "familiar": [5, 96], "overal": [5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 43, 49, 62, 63, 66, 69, 70, 74, 78, 79, 80, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 103, 108], "sever": [5, 7, 10, 13, 14, 23, 38, 41, 42, 44, 66, 69, 71, 72, 78, 82, 84, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 108], "compar": [5, 62, 71, 82, 90, 91, 94, 96, 99, 103], "issue_summari": [5, 7, 10, 14, 90, 96], "With": [5, 9, 10, 41, 88, 95, 98, 99, 101, 106, 107, 108], "usag": [5, 41, 61], "usual": [5, 13, 33, 34, 92, 101, 106], "ti": [5, 62], "exhibit": [5, 7, 10, 14, 79, 89, 90, 91, 92, 94, 95, 99, 103], "ie": [5, 74], "likelihood": [5, 10, 41, 43, 44, 64, 69, 71, 72, 76, 80, 96], "wherea": [5, 10, 57, 64, 87, 88, 105], "outlier": [5, 9, 11, 15, 22, 23, 32, 45, 52, 72, 84, 90, 91, 96, 99, 100, 106], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 99, 106], "global": [5, 7, 10, 23, 38, 42, 97], "non_iid": [5, 10, 11, 15, 27, 91, 92, 94, 95, 96, 99], "hypothesi": [5, 96], "iid": [5, 7, 9, 27, 94, 99], "never": [5, 89, 99, 102, 104, 105], "someth": [5, 7, 10, 38, 42, 72, 103], "123": [5, 90, 91], "456": [5, 87, 88, 89], "nearest_neighbor": 5, "7": [5, 10, 49, 50, 61, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "9": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 43, 49, 50, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "distance_to_nearest_neighbor": [5, 11, 90, 91, 92, 94, 95, 99], "789": 5, "get_issu": [5, 10, 14, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_nam": [5, 6, 7, 10, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91, 92, 94, 95, 99], "focu": [5, 10, 14, 95, 96, 107, 108], "full": [5, 10, 14, 41, 61, 70, 92, 108], "summar": [5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 63, 79, 83, 84, 107], "specific_issu": [5, 14], "lie": [5, 10, 71, 72, 88, 89, 90, 91, 92, 94, 95, 96, 99], "get_issue_summari": [5, 10, 14, 91, 96], "get_info": [5, 14, 91, 95, 96, 97], "yet": [5, 18, 28, 61, 97, 101], "list_possible_issue_typ": [5, 15, 16], "regist": [5, 7, 15, 16, 18, 28, 38, 42, 90], "rtype": [5, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42], "registri": [5, 15, 16], "list_default_issue_typ": [5, 15, 16], "folder": [5, 89, 90, 92], "load": [5, 13, 41, 70, 92, 97, 98, 99, 103, 104, 107, 108], "futur": [5, 10, 23, 38, 42, 62, 84, 88, 89, 90, 92, 95, 98], "overwrit": [5, 90], "separ": [5, 37, 49, 66, 90, 91, 92, 96, 98, 103, 105], "static": 5, "rememb": [5, 95, 98, 99], "part": [5, 10, 38, 42, 44, 67, 69, 70, 89, 90, 96, 97, 107, 108], "ident": [5, 10, 23, 57, 95, 96], "datalab": [6, 8, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 84, 87, 88, 97, 101, 106], "walk": 7, "alongsid": [7, 38, 42, 90, 98], "pre": [7, 8, 10, 38, 42, 90, 91, 106], "runtim": [7, 38, 41, 42, 74, 76, 78, 89, 92, 98], "issue_manager_factori": [7, 15, 90], "myissuemanag": [7, 15], "myissuemanagerforregress": 7, "decor": [7, 15], "ll": [7, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "thing": [7, 42, 88, 96, 99, 106], "next": [7, 62, 84, 87, 88, 89, 92, 94, 95, 96, 98, 101, 103, 106, 108], "dummi": 7, "randint": [7, 32, 49, 90, 91, 96], "mark": [7, 10, 85, 103, 104, 106], "regard": [7, 91, 99], "rand": [7, 49, 52, 90, 91, 96], "is_": [7, 10, 90], "_issu": [7, 10, 90], "issue_score_kei": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "whole": [7, 10, 27, 38, 42, 91, 96], "make_summari": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "popul": [7, 91, 95], "verbosity_level": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "std": [7, 103], "raw_scor": 7, "bit": 7, "involv": [7, 41, 79, 83, 96, 98, 102], "intermediate_arg": 7, "min": [7, 49, 69, 82, 90, 98, 104], "sin_filt": 7, "sin": 7, "arang": [7, 96], "kernel": [7, 96], "affect": [7, 10, 38, 42, 53, 76, 82, 95, 96, 98], "easili": [7, 47, 85, 87, 88, 89, 91, 94, 95, 99, 101, 102, 104, 105, 106, 107], "hard": [7, 42, 97, 104], "sai": [7, 10, 38, 42, 96, 102, 107], "anoth": [7, 10, 23, 37, 41, 53, 56, 69, 72, 88, 94, 95, 96, 98, 99, 101, 104], "try": [7, 9, 10, 41, 44, 61, 62, 76, 78, 84, 91, 92, 94, 95, 98, 99, 107], "won": [7, 38, 42, 90, 91, 98, 102], "issue_manag": [7, 10, 12, 14, 16, 19, 20, 21, 24, 26, 27, 28, 29, 31, 32, 90], "instanti": [7, 17, 41, 61, 71, 88, 89, 91, 94], "477762": 7, "286455": 7, "term": [7, 10, 47, 57, 70, 89, 90, 91, 92, 94, 95, 99], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 20, 29, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 103, 104, 106, 107, 108], "003042": 7, "058117": 7, "11": [7, 10, 61, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "121908": 7, "15": [7, 55, 61, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "169312": 7, "17": [7, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 90, 91, 96, 97, 99], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 32], "group": [8, 9, 27, 32, 97, 103, 108], "dbscan": [8, 10, 32], "hdbscan": 8, "etc": [8, 10, 23, 33, 38, 42, 47, 61, 62, 80, 84, 90, 91, 94, 95, 98, 99, 102, 106], "sensit": [8, 10, 55, 96], "ep": [8, 32, 70], "radiu": 8, "min_sampl": [8, 32], "kmean": [8, 96], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 32, 96], "cluster_id": [8, 10, 11, 32, 96], "labels_": 8, "underperforming_group": [8, 10, 11, 15, 22, 91, 92, 94, 95, 96, 99], "search": [9, 10, 21, 27, 28, 45, 51, 52, 53, 56, 74, 96, 98, 105], "nondefault": 9, "Near": [9, 98], "imbal": [9, 22, 66, 71, 72, 91], "null": [9, 11, 15, 22, 91, 92, 95, 99], "togeth": [9, 10, 47, 88, 90, 91, 92, 94, 95, 99, 106, 108], "built": [9, 49], "own": [9, 38, 40, 42, 54, 60, 66, 67, 70, 76, 80, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 106, 107, 108], "prerequisit": 9, "basic": [9, 42, 61, 94, 95, 96, 104], "fulli": [9, 10, 38, 42, 61, 98], "platform": [9, 10, 84, 92, 94, 95, 98], "write": [9, 10], "code": [9, 10, 38, 42, 47, 57, 61, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "being": [9, 10, 14, 37, 38, 42, 44, 49, 56, 57, 72, 87, 94, 98, 99, 106, 107], "100x": [9, 10], "faster": [9, 10, 41, 71, 74, 76, 78, 98, 99], "intellig": [9, 10], "quickli": [9, 10, 39, 87, 89, 92, 94, 95, 98, 102, 104, 107, 108], "fix": [9, 10, 62, 88, 95, 96, 99, 106], "scientist": [9, 10], "million": [9, 10, 108], "thank": [9, 10], "ai": [9, 10, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 101, 102, 104, 106, 108], "suggest": [9, 10, 37, 62, 63, 69, 88, 92, 95, 98, 106], "power": [9, 10, 92, 94, 95, 97, 99, 108], "automl": [9, 10, 84, 98], "system": [9, 10, 89, 92, 94, 95, 107], "foundat": [9, 10, 84, 96], "improv": [9, 10, 62, 87, 88, 91, 92, 97, 98, 99, 106, 107], "click": [9, 10, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "tune": [9, 10, 88, 89, 95, 97, 104], "serv": [9, 10, 14, 17, 101], "auto": [9, 10, 87, 88, 91, 97, 98, 106], "free": [9, 10, 84, 89, 91, 92, 94, 95, 98, 99], "page": [10, 91, 98, 99], "variou": [10, 14, 31, 40, 58, 60, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "why": [10, 95], "matter": [10, 37, 63, 88, 95], "didn": [10, 96], "plu": [10, 106], "ye": [10, 11], "near_dupl": [10, 11, 15, 20, 90, 91, 92, 94, 95, 96, 98, 99], "class_imbal": [10, 11, 15, 21, 91, 92, 94, 95, 96, 99], "data_valu": [10, 11, 15, 22, 96], "No": [10, 11, 87, 88, 95, 96, 98], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 69, 87, 88, 108], "issue_scor": 10, "atyp": [10, 71, 90, 91, 92, 94, 95, 99, 104], "datapoint": [10, 32, 44, 49, 57, 72, 75, 84, 87, 88, 89, 90, 91, 94, 95, 98, 105, 106], "is_issu": [10, 23], "primarili": 10, "former": [10, 38, 42], "investig": [10, 89], "expertis": 10, "interpret": [10, 97, 98, 99, 102, 106], "annot": [10, 37, 48, 62, 63, 64, 66, 67, 69, 70, 79, 82, 83, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 103, 107], "dissimilar": [10, 94, 95], "preced": 10, "incorrect": [10, 69, 72, 75, 87, 89, 90, 91, 92, 94, 95, 96, 99, 103, 106], "due": [10, 41, 44, 72, 76, 78, 89, 90, 91, 92, 94, 95, 99, 106], "appear": [10, 37, 48, 63, 64, 67, 75, 91, 92, 94, 95, 96, 106, 107], "now": [10, 41, 85, 87, 88, 89, 91, 96, 98, 101, 103, 104, 106, 108], "token": [10, 43, 56, 78, 79, 80, 81, 82, 83, 98, 99, 100], "hamper": [10, 92, 97], "analyt": [10, 84, 96, 98, 101], "lead": [10, 69, 72, 92, 96, 103], "draw": [10, 90, 91], "conclus": [10, 95], "let": [10, 38, 42, 71, 72, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "sort_valu": [10, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 106], "head": [10, 87, 88, 89, 91, 92, 94, 95, 96, 97, 99, 101, 106], "97": [10, 87, 97, 98, 99, 103, 106, 108], "064045": 10, "58": [10, 87, 91, 96, 97, 99, 103], "680894": 10, "41": [10, 96, 97, 103, 106, 108], "746043": 10, "794894": 10, "98": [10, 97, 98, 106], "802911": 10, "give": [10, 49, 72, 99, 101, 107], "li": [10, 71], "especi": [10, 87, 88, 92, 96, 98, 106], "veri": [10, 37, 63, 67, 69, 88, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106], "rare": [10, 44, 70, 90, 91, 92, 94, 95, 98, 99], "anomal": [10, 72, 90, 91, 92, 94, 95, 99], "articl": [10, 41, 98], "blog": 10, "unexpect": [10, 38, 42, 95], "consequ": 10, "inspect": [10, 88, 89, 91, 92, 99, 103, 106], "011562": 10, "62": [10, 96, 99, 103, 106], "019657": 10, "22": [10, 89, 90, 92, 96, 97, 99, 102, 103, 108], "035243": 10, "040907": 10, "42": [10, 49, 95, 96, 97, 103, 108], "056865": 10, "smaller": [10, 71, 102, 103], "extrem": [10, 90, 91, 92, 94, 95, 96, 98, 99], "record": [10, 38, 42, 89, 94, 106], "abbrevi": 10, "misspel": 10, "typo": [10, 83], "resolut": 10, "video": [10, 97], "audio": [10, 90, 91, 93, 98], "minor": [10, 56], "variat": 10, "translat": 10, "d": [10, 55, 87, 94, 95, 96, 98, 99, 102, 106, 108], "constant": [10, 32, 74], "median": [10, 31, 55], "question": [10, 23, 84, 99], "nearli": [10, 23, 91, 92, 94, 95], "awar": [10, 85, 99], "presenc": [10, 52, 54, 99], "36": [10, 96, 97, 108], "066009": 10, "80": [10, 39, 87, 94, 102, 106], "003906": 10, "093245": 10, "005599": 10, "27": [10, 94, 96, 97, 99, 103, 108], "156720": 10, "009751": 10, "72": [10, 96, 97, 99, 102, 106], "signific": [10, 94, 95, 99], "violat": [10, 94, 95, 96, 99], "assumpt": [10, 94, 95, 96, 99], "changepoint": [10, 94, 95, 99], "shift": [10, 52, 54, 94, 95, 99], "drift": [10, 91, 94, 96, 99], "autocorrel": [10, 94, 95, 99], "almost": [10, 94, 95, 99], "adjac": [10, 52, 94, 95, 99], "tend": [10, 37, 47, 94, 95, 99, 107, 108], "sequenti": [10, 38, 42, 61, 92], "pai": [10, 95], "attent": [10, 96], "realli": [10, 88, 95, 101, 107], "mere": 10, "highlight": [10, 79, 83, 90, 91, 94, 96, 107], "necessarili": [10, 62, 70, 95, 99], "wrong": [10, 62, 67, 69, 85, 88, 90, 91, 95, 98, 99, 103], "gap": 10, "b": [10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 56, 57, 82, 87, 94, 95, 96, 97, 98, 99, 105, 108], "x1": [10, 67, 70, 103], "x2": [10, 67, 70, 103], "10th": 10, "100th": 10, "90": [10, 82, 87, 94, 99, 105, 106], "similarli": [10, 38, 42, 90, 92, 94, 98, 103], "associ": [10, 13, 17, 33, 35, 38, 42, 70, 101], "blogpost": 10, "proper": [10, 57, 62, 67, 70, 87, 92, 95, 98, 101, 103], "scenario": [10, 52, 54, 72, 90, 91], "underli": [10, 43, 54, 71, 80, 82, 108], "stem": [10, 71, 104], "evolv": 10, "influenc": 10, "act": [10, 69, 90], "accordingli": [10, 33, 52], "emploi": [10, 102, 104], "partit": [10, 105], "ahead": 10, "good": [10, 38, 42, 55, 61, 63, 69, 72, 76, 78, 79, 84, 92, 94, 95], "problem": [10, 33, 41, 49, 79, 84, 90, 91, 92, 95, 98], "deploy": [10, 87, 88, 99, 106], "overlook": [10, 69, 103], "fact": 10, "thu": [10, 37, 42, 63, 87, 89, 94, 95, 99, 105, 108], "diagnos": [10, 91, 98], "24": [10, 89, 96, 97, 99, 101, 103, 106], "681458": 10, "37": [10, 90, 96, 97], "804582": 10, "64": [10, 42, 87, 92, 94, 96, 99, 103], "810646": 10, "815691": 10, "78": [10, 87, 94, 97, 99, 103, 106], "834293": 10, "Be": [10, 42], "cautiou": 10, "behavior": [10, 17, 37, 38, 42, 70, 98], "rarest": [10, 91], "q": [10, 103], "subpar": 10, "special": [10, 52, 56], "techniqu": [10, 103], "smote": 10, "asymmetr": [10, 37], "28": [10, 92, 95, 96, 97, 99, 101, 108], "75": [10, 49, 90, 91, 96, 97, 101, 102, 103, 106, 108], "33": [10, 38, 42, 96, 97, 103], "68": [10, 87, 97, 99, 103], "excess": [10, 92], "dark": [10, 107], "bright": [10, 108], "blurri": [10, 92], "lack": [10, 61, 96], "unusu": [10, 103, 104], "cluster": [10, 19, 32], "slice": 10, "poor": 10, "subpopul": 10, "faq": [10, 84, 91, 92, 94, 95, 100], "get_self_confidence_for_each_label": [10, 49, 72], "r": [10, 41, 74, 90, 91, 96, 106, 107], "tabular": [10, 84, 86, 90, 91, 93, 96, 98, 101], "categor": [10, 71, 86, 87, 90, 91, 93, 98, 106], "encod": [10, 50, 70, 76, 79, 87, 88, 94, 95, 98, 106, 107], "71": [10, 96, 97, 99, 103, 106], "70": [10, 82, 94, 96], "69": [10, 99, 106], "subgroup": [10, 96], "wors": [10, 96, 101], "ratio": 10, "miss": [10, 28, 38, 42, 57, 67, 69, 98, 103, 106], "pattern": [10, 96], "isn": [10, 18, 28], "scalabl": 10, "sacrific": 10, "One": [10, 57, 71, 98], "quantif": 10, "39": [10, 88, 89, 90, 92, 95, 96, 97, 98, 103, 106, 107, 108], "32": [10, 89, 90, 96, 97, 101, 103], "valuabl": [10, 19, 96], "exert": [10, 91], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 22, 24, 31], "health_summari": [10, 24, 37, 84, 97], "health_summary_kwarg": 10, "tandem": [10, 97], "view": [10, 38, 42, 43, 44, 78, 80, 82, 84, 87, 88, 89, 90, 91, 94, 95, 97, 99, 101, 102, 103, 104, 105, 106, 108], "ood_kwarg": 10, "outofdistribut": [10, 29, 71, 104], "outsid": [10, 98, 102], "outlierissuemanag": [10, 15, 22, 29, 90], "nearduplicateissuemanag": [10, 15, 20, 22], "noniidissuemanag": [10, 15, 22, 27], "num_permut": [10, 27], "permut": [10, 27], "significance_threshold": [10, 27], "signic": 10, "noniid": [10, 22], "classimbalanceissuemanag": [10, 15, 21, 22], "underperforminggroupissuemanag": [10, 15, 22, 32], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 32], "filter_cluster_id": [10, 22, 32], "clustering_kwarg": [10, 32], "nullissuemanag": [10, 15, 22, 28], "datavaluationissuemanag": [10, 15, 19, 22], "codeblock": 10, "demonstr": [10, 41, 52, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107], "howev": [10, 38, 42, 52, 57, 87, 88, 89, 92, 94, 95, 96, 101, 105, 107], "mandatori": [10, 92], "image_issue_types_kwarg": 10, "vice": [10, 63], "versa": [10, 63], "light": [10, 92, 97, 103, 107], "29": [10, 92, 96, 97, 101, 102, 103, 107, 108], "low_inform": [10, 92], "odd_aspect_ratio": [10, 92], "35": [10, 90, 96, 97, 101, 102, 103], "odd_siz": [10, 92], "doc": [10, 38, 42, 71, 84, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 104, 106, 108], "label_scor": [11, 24, 26, 31, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "is_outlier_issu": [11, 90, 91, 92, 94, 95, 96, 99], "outlier_scor": [11, 29, 90, 91, 92, 94, 95, 96, 99, 104], "is_near_duplicate_issu": [11, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_scor": [11, 20, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_set": [11, 20, 22, 90, 91, 92, 94, 95, 98, 99], "is_non_iid_issu": [11, 91, 94, 95, 96, 99], "non_iid_scor": [11, 27, 91, 94, 95, 96, 99], "is_class_imbalance_issu": [11, 91, 96], "class_imbalance_scor": [11, 21, 91, 96], "is_underperforming_group_issu": [11, 91, 96], "underperforming_group_scor": [11, 32, 91, 96], "is_null_issu": [11, 91, 96], "null_scor": [11, 28, 91, 96], "is_data_valuation_issu": [11, 96], "data_valuation_scor": [11, 19, 96], "studio": [12, 84, 91, 92, 94, 95, 98], "data_issu": [12, 16, 17, 34, 90], "issue_find": [12, 16, 96], "factori": [12, 16, 17], "model_output": [12, 16], "except": [13, 38, 42, 61, 72, 90, 91, 92, 101], "dataformaterror": [13, 16], "add_not": 13, "with_traceback": 13, "tb": 13, "__traceback__": 13, "datasetdicterror": [13, 16], "datasetdict": 13, "datasetloaderror": [13, 16], "dataset_typ": 13, "fail": 13, "hold": 13, "sublist": 13, "map_to_int": 13, "abc": [13, 23, 33], "is_avail": [13, 92], "dataissu": [14, 16, 17, 34], "central": [14, 108], "repositori": [14, 92], "strategi": [14, 49, 96, 98], "_infostrategi": 14, "basi": 14, "collect_statist": 14, "reus": [14, 23], "avoid": [14, 38, 41, 42, 44, 52, 57, 64, 67, 70, 74, 76, 78, 90, 91, 92, 98], "recomput": [14, 88], "weighted_knn_graph": 14, "issue_manager_that_computes_knn_graph": 14, "collect_issues_from_issue_manag": 14, "collect_issues_from_imagelab": 14, "imagelab": 14, "set_health_scor": 14, "health": [14, 24, 37, 63, 84], "get_data_statist": [14, 16], "concret": 15, "subclass": [15, 38, 42, 71, 90], "regressionlabelissuemanag": [15, 22, 30, 31], "multilabelissuemanag": [15, 22, 25, 26], "from_str": [15, 35, 45, 49], "my_issu": 15, "logic": [15, 35, 41, 44, 76, 78], "issuefind": [16, 17, 34], "modeloutput": [16, 33], "multiclasspredprob": [16, 33], "regressionpredict": [16, 33], "multilabelpredprob": [16, 33], "instati": 17, "public": [17, 96, 99, 103, 107, 108], "creation": [17, 42, 96], "execut": [17, 38, 42, 90, 92, 98, 103], "coordin": [17, 67, 69, 70, 103, 108], "At": [17, 70, 98], "get_available_issue_typ": 17, "direct": [18, 28, 38, 42, 54, 61], "vstack": [19, 57, 92, 97, 98, 99, 101, 102], "25": [19, 27, 38, 49, 55, 91, 92, 96, 97, 99, 101, 102, 103, 108], "classvar": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "short": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 56, 57], "item": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 90, 91, 92, 98, 99, 101, 102], "some_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "additional_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "default_threshold": [19, 22, 29], "collect_info": [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "info_to_omit": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "compos": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 38, 42, 88, 95, 104], "is_x_issu": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "x_score": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_a": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b1": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b2": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "report_str": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34], "_": [20, 21, 23, 24, 26, 27, 28, 31, 32, 49, 56, 57, 84, 87, 89, 90, 92, 96, 97, 99, 102], "occurr": [20, 21, 23, 27, 28, 29, 32, 56], "median_nn_dist": 20, "bleed": [22, 25, 30, 40], "edg": [22, 25, 30, 40, 69, 84, 99, 108], "sharp": [22, 25, 30, 40], "get_health_summari": [22, 24], "ood": [22, 29, 71, 72, 104], "simplified_kolmogorov_smirnov_test": [22, 27], "outlier_cluster_label": [22, 32], "no_underperforming_cluster_id": [22, 32], "perform_clust": [22, 32], "get_worst_clust": [22, 32], "find_issues_with_predict": [22, 30, 31], "find_issues_with_featur": [22, 30, 31], "believ": [23, 107], "priori": [23, 99], "abstract": [23, 33], "applic": [24, 62, 98, 99, 101, 108], "typevar": [24, 26, 38, 42, 56, 66, 69, 70], "scalartyp": [24, 26], "covari": [24, 26, 74, 106], "summary_dict": 24, "neighbor_histogram": 27, "non_neighbor_histogram": 27, "kolmogorov": 27, "smirnov": 27, "largest": [27, 41, 49, 52, 72, 76, 78, 103, 107], "empir": [27, 48, 62], "cumul": 27, "ecdf": 27, "histogram": [27, 94, 96, 106], "absolut": [27, 31], "trial": 27, "null_track": 28, "extend": [28, 50, 61, 92, 96, 103, 104, 108], "superclass": 28, "arbitrari": [28, 37, 78, 82, 90, 104, 106], "prompt": 28, "address": [28, 88, 90, 91, 95, 98], "enabl": [28, 42, 54], "scaling_factor": [29, 55], "37037": 29, "q3_avg_dist": 29, "iqr_avg_dist": 29, "median_outlier_scor": 29, "issue_threshold": 29, "multipli": [31, 55], "deleg": 31, "confus": [32, 33, 37, 38, 42, 44, 57, 70, 88, 108], "50": [32, 42, 96, 98, 99, 101, 103, 104, 106], "keepdim": [32, 98], "signifi": 32, "absenc": 32, "int64": [32, 89, 101], "npt": 32, "int_": 32, "id": [32, 62, 90, 92, 96, 98, 101], "unique_cluster_id": 32, "_description_": 32, "performed_clust": 32, "worst_cluster_id": 32, "convent": [33, 35], "subject": [33, 35], "meant": [33, 35], "Not": [33, 54], "mainli": [33, 104, 108], "content": [33, 71, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "fetch": [33, 41, 89, 91, 98], "datset": 34, "exclud": [34, 43, 79, 83, 90, 98, 108], "get_report": 34, "enum": [35, 49], "qualnam": [35, 49], "boundari": [35, 49, 90, 91], "continu": [35, 61, 87, 88, 92, 95, 98, 101, 103, 106, 108], "binari": [35, 49, 57, 64, 66, 99, 108], "simultan": [35, 106], "task_str": 35, "is_classif": 35, "__contains__": [35, 45, 49], "member": [35, 38, 42, 49, 90, 91], "typeerror": [35, 49], "12": [35, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "__getitem__": [35, 45, 49], "match": [35, 37, 38, 42, 44, 49, 62, 63, 72, 90, 91, 92, 97, 103, 105, 107], "__iter__": [35, 45, 49], "__len__": [35, 45, 49], "alias": [35, 49], "is_regress": 35, "is_multilabel": 35, "overview": [37, 52, 87, 88, 89, 91, 92, 94, 95, 101, 103, 104, 106, 108], "modifi": [37, 38, 41, 42, 52, 54, 57, 98, 99], "rank_classes_by_label_qu": [37, 91], "merg": [37, 52, 56, 84, 97, 98, 108], "find_overlapping_class": [37, 98, 99], "problemat": [37, 63, 79, 83, 89, 103, 108], "unnorm": [37, 63, 99], "abov": [37, 38, 41, 42, 54, 57, 62, 69, 70, 72, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 105, 106, 107, 108], "model_select": [37, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 106], "cross_val_predict": [37, 42, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 105, 106], "get_data_labels_from_dataset": 37, "yourfavoritemodel": [37, 99], "cv": [37, 49, 87, 89, 90, 91, 94, 96, 99, 101], "df": [37, 57, 83, 89, 96, 98], "overall_label_qu": [37, 63], "col": 37, "prob": [37, 56, 99, 105], "divid": [37, 63, 72], "label_nois": [37, 63], "human": [37, 97, 107, 108], "clearli": [37, 72, 92, 103, 107], "num": [37, 63, 97, 99], "overlap": [37, 84, 97, 98, 99], "ontolog": 37, "publish": [37, 108], "therefor": [37, 72, 96], "vehicl": [37, 97], "truck": [37, 97, 104, 107], "intuit": [37, 63], "car": [37, 97, 103, 107], "frequent": [37, 62, 96, 98, 106], "characterist": 37, "l": [37, 38, 42, 67, 69, 70], "class1": 37, "class2": 37, "relationship": 37, "dog": [37, 57, 63, 65, 79, 97, 98, 104, 105, 108], "cat": [37, 57, 63, 65, 97, 98, 104, 105], "captur": [37, 89, 103, 104, 107], "co": [37, 38, 39, 92], "noisy_label": [37, 90, 91, 102], "overlapping_class": 37, "descend": [37, 38, 42, 49, 63, 70], "overall_label_health_scor": [37, 63, 99], "half": [37, 38, 40, 42, 63, 97, 108], "health_scor": [37, 63], "classes_by_label_qu": [37, 91], "cnn": [38, 40, 42, 92], "cifar": [38, 39, 97, 104], "teach": [38, 39], "bhanml": 38, "blob": [38, 96], "master": [38, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106], "call_bn": [38, 40], "bn": 38, "input_channel": 38, "n_output": 38, "dropout_r": 38, "top_bn": 38, "architectur": [38, 42], "shown": [38, 70, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 105, 107, 108], "forward": [38, 39, 40, 42, 92, 101], "overridden": [38, 42], "although": [38, 42, 71, 87, 94], "recip": [38, 42], "afterward": [38, 42], "sinc": [38, 42, 46, 58, 63, 70, 78, 82, 98, 101, 102, 103, 105, 108], "hook": [38, 42, 97], "silent": [38, 41, 42], "t_destin": [38, 40, 42], "__call__": [38, 40, 42, 45, 49], "add_modul": [38, 40, 42], "child": [38, 42], "fn": [38, 42, 70], "recurs": [38, 42, 49], "submodul": [38, 42, 51], "children": [38, 40, 42, 108], "nn": [38, 39, 42, 52, 92], "init": [38, 42, 99], "no_grad": [38, 42, 92, 104], "init_weight": [38, 42], "linear": [38, 42, 88, 92, 95], "fill_": [38, 42], "net": [38, 42, 89, 92, 97], "in_featur": [38, 42], "out_featur": [38, 42], "bia": [38, 42, 92], "tensor": [38, 39, 42, 88, 89, 92, 95, 104], "requires_grad": [38, 42], "bfloat16": [38, 40, 42], "cast": [38, 42, 89], "buffer": [38, 40, 42], "datatyp": [38, 42], "xdoctest": [38, 42], "undefin": [38, 42], "var": [38, 42], "buf": [38, 42], "20l": [38, 42], "1l": [38, 42], "5l": [38, 42], "call_super_init": [38, 40, 42], "immedi": [38, 42, 104], "compil": [38, 40, 42, 61], "cpu": [38, 40, 42, 44, 89, 92], "move": [38, 42, 49, 85, 97], "cuda": [38, 40, 42, 89, 92], "devic": [38, 42, 89, 92], "gpu": [38, 42, 88, 89, 95], "live": [38, 42], "copi": [38, 42, 74, 87, 89, 90, 91, 94, 96, 98, 102, 105, 106], "doubl": [38, 40, 42], "dump_patch": [38, 40, 42], "eval": [38, 40, 42, 92, 102, 104], "dropout": [38, 42], "batchnorm": [38, 42], "grad": [38, 42], "extra_repr": [38, 40, 42], "line": [38, 42, 84, 90, 96, 97, 101, 104, 108], "get_buff": [38, 40, 42], "target": [38, 39, 42, 74, 75, 96, 104, 106], "throw": [38, 42], "get_submodul": [38, 40, 42], "explan": [38, 42], "qualifi": [38, 42], "referenc": [38, 42], "attributeerror": [38, 42], "invalid": [38, 42, 95], "resolv": [38, 42, 108], "get_extra_st": [38, 40, 42], "state_dict": [38, 40, 42], "set_extra_st": [38, 40, 42], "build": [38, 42, 52, 92, 96, 107], "picklabl": [38, 42], "serial": [38, 42], "backward": [38, 42, 92], "break": [38, 42, 92, 103], "pickl": [38, 42, 103], "get_paramet": [38, 40, 42], "net_b": [38, 42], "net_c": [38, 42], "conv": [38, 42], "conv2d": [38, 42, 92], "16": [38, 42, 49, 52, 61, 78, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 107, 108], "kernel_s": [38, 42], "stride": [38, 42], "200": [38, 42, 72, 97, 103, 108], "diagram": [38, 42, 105], "degre": [38, 42], "queri": [38, 42, 52, 54, 91, 92, 96, 98, 102], "named_modul": [38, 40, 42], "o": [38, 42, 55, 56, 89, 90, 91, 97, 98, 99, 102, 103, 108], "transit": [38, 42], "ipu": [38, 40, 42], "load_state_dict": [38, 40, 42], "strict": [38, 42, 49], "persist": [38, 42], "strictli": [38, 42], "inplac": [38, 42, 96, 101], "preserv": [38, 42, 57], "namedtupl": [38, 42], "missing_kei": [38, 42], "unexpected_kei": [38, 42], "runtimeerror": [38, 42], "idx": [38, 42, 57, 58, 70, 90, 92, 96, 98, 99, 101, 103, 104], "named_buff": [38, 40, 42], "prefix": [38, 42, 89, 108], "remove_dupl": [38, 42], "prepend": [38, 42], "running_var": [38, 42], "named_children": [38, 40, 42], "conv4": [38, 42], "conv5": [38, 42], "memo": [38, 42], "named_paramet": [38, 40, 42], "register_backward_hook": [38, 40, 42], "deprec": [38, 42, 46, 88, 89, 95, 98], "favor": [38, 42], "register_full_backward_hook": [38, 40, 42], "removablehandl": [38, 42], "register_buff": [38, 40, 42], "running_mean": [38, 42], "register_forward_hook": [38, 40, 42], "with_kwarg": [38, 42], "always_cal": [38, 42], "possibli": [38, 42, 87, 94], "fire": [38, 42, 97], "register_module_forward_hook": [38, 42], "regardless": [38, 42, 90, 91], "register_forward_pre_hook": [38, 40, 42], "And": [38, 42], "forward_pr": [38, 42], "register_module_forward_pre_hook": [38, 42], "gradient": [38, 42, 92, 94, 106], "grad_input": [38, 42], "grad_output": [38, 42], "technic": [38, 42], "caller": [38, 42], "register_module_full_backward_hook": [38, 42], "register_full_backward_pre_hook": [38, 40, 42], "backward_pr": [38, 42], "register_module_full_backward_pre_hook": [38, 42], "register_load_state_dict_post_hook": [38, 40, 42], "post": [38, 42, 52], "incompatible_kei": [38, 42], "modif": [38, 42, 52], "thrown": [38, 42], "register_modul": [38, 40, 42], "register_paramet": [38, 40, 42], "register_state_dict_pre_hook": [38, 40, 42], "keep_var": [38, 42], "requires_grad_": [38, 40, 42], "autograd": [38, 42], "freez": [38, 42, 88, 89, 95], "finetun": [38, 42], "gan": [38, 42], "share_memori": [38, 40, 42], "share_memory_": [38, 42], "destin": [38, 42], "shallow": [38, 42], "releas": [38, 42, 61, 85, 89, 92, 98], "design": [38, 42, 52], "ordereddict": [38, 42], "detach": [38, 42, 92], "non_block": [38, 42], "memory_format": [38, 42], "channels_last": [38, 42], "Its": [38, 42, 49, 63, 69], "complex": [38, 42, 89], "integr": [38, 42, 54, 84, 98], "asynchron": [38, 42], "host": [38, 42], "pin": [38, 42, 88, 95, 97], "desir": [38, 42, 52, 56, 70], "4d": [38, 42], "ignore_w": [38, 42], "determinist": [38, 42, 89], "1913": [38, 42], "3420": [38, 42], "5113": [38, 42], "2325": [38, 42], "env": [38, 42], "torch_doctest_cuda1": [38, 42], "gpu1": [38, 42], "1914": [38, 42], "5112": [38, 42], "2324": [38, 42], "float16": [38, 42], "cdoubl": [38, 42], "3741": [38, 42], "2382": [38, 42], "5593": [38, 42], "4443": [38, 42], "complex128": [38, 42], "6122": [38, 42], "1150": [38, 42], "to_empti": [38, 40, 42], "storag": [38, 42, 88, 95], "dst_type": [38, 42], "xpu": [38, 40, 42], "zero_grad": [38, 40, 42, 92], "set_to_non": [38, 42], "reset": [38, 42], "context": [38, 42, 103], "noisili": [39, 99], "han": 39, "2018": 39, "cifar_cnn": [39, 40], "loss_coteach": [39, 40], "y_1": 39, "y_2": 39, "forget_r": 39, "class_weight": 39, "logit": [39, 61, 92], "decim": [39, 57], "forget": [39, 49, 108], "rate_schedul": 39, "epoch": [39, 40, 42, 92, 98], "initialize_lr_schedul": [39, 40], "lr": [39, 40, 42], "001": [39, 72, 96, 98], "250": [39, 90, 91, 99, 103], "epoch_decay_start": 39, "schedul": 39, "beta": 39, "adam": 39, "adjust_learning_r": [39, 40], "alpha_plan": 39, "beta1_plan": 39, "forget_rate_schedul": [39, 40], "num_gradu": 39, "expon": 39, "tell": [39, 88, 92, 95, 99], "train_load": [39, 42], "model1": [39, 99], "optimizer1": 39, "model2": [39, 99], "optimizer2": 39, "dataload": [39, 92, 104], "parser": 39, "parse_arg": 39, "num_iter_per_epoch": 39, "print_freq": 39, "topk": 39, "top1": 39, "top5": 39, "test_load": 39, "offici": [40, 60, 96, 108], "wish": [40, 60, 104, 107, 108], "adj_confident_thresholds_shar": [40, 41], "labels_shar": [40, 41], "pred_probs_shar": [40, 41], "labelinspector": [40, 41, 98], "get_num_issu": [40, 41], "get_quality_scor": [40, 41], "update_confident_threshold": [40, 41], "score_label_qu": [40, 41], "split_arr": [40, 41], "span_classif": 40, "display_issu": [40, 43, 77, 78, 79, 80, 81, 82, 83, 107, 108], "mnist_pytorch": 40, "get_mnist_dataset": [40, 42], "get_sklearn_digits_dataset": [40, 42], "simplenet": [40, 42], "batch_siz": [40, 41, 42, 76, 78, 92, 98, 104, 107], "log_interv": [40, 42], "momentum": [40, 42], "no_cuda": [40, 42], "test_batch_s": [40, 42, 92], "loader": [40, 42, 92], "set_predict_proba_request": [40, 42], "set_predict_request": [40, 42], "coteach": [40, 85], "mini": [41, 76, 78, 98], "low_self_confid": [41, 44, 64], "self_confid": [41, 44, 45, 49, 64, 66, 72, 80, 82, 87, 88, 98, 99], "conveni": [41, 54, 87, 88, 89, 95], "script": 41, "labels_fil": [41, 98], "pred_probs_fil": [41, 98], "quality_score_kwarg": 41, "num_issue_kwarg": 41, "return_mask": 41, "variant": [41, 62, 107], "read": [41, 46, 91, 98, 99, 104, 108], "zarr": [41, 98], "memmap": [41, 107], "pythonspe": 41, "mmap": [41, 98], "hdf5": 41, "further": [41, 43, 63, 64, 66, 69, 70, 78, 79, 89, 98], "yourfil": 41, "npy": [41, 97, 98, 107], "mmap_mod": [41, 107], "tip": [41, 44, 61, 98], "save_arrai": 41, "your_arrai": 41, "disk": [41, 97, 98], "npz": [41, 108], "maxim": [41, 62, 76, 78, 107], "multiprocess": [41, 44, 64, 76, 78, 92, 98], "linux": [41, 76, 78], "physic": [41, 44, 76, 78, 103], "psutil": [41, 44, 76, 78], "labels_arrai": [41, 58], "predprob": 41, "pred_probs_arrai": 41, "back": [41, 52, 70, 90, 98, 103, 104], "store_result": 41, "becom": [41, 96, 104], "verifi": [41, 54, 98, 101, 104], "long": [41, 62, 71, 101], "enough": [41, 57, 96, 98], "chunk": [41, 105], "ram": [41, 97], "end_index": 41, "labels_batch": 41, "pred_probs_batch": 41, "batch_result": 41, "indices_of_examples_with_issu": [41, 98], "shortcut": 41, "encount": [41, 44, 76], "1000": [41, 89, 95, 98, 104], "aggreg": [41, 45, 49, 62, 66, 69, 72, 82, 98, 99, 101], "seen": [41, 98, 104, 108], "far": [41, 62], "label_quality_scor": [41, 66, 69, 72, 75, 99, 103], "method1": 41, "method2": 41, "normalized_margin": [41, 44, 45, 49, 64, 66, 72, 80, 82], "low_normalized_margin": [41, 44, 64], "issue_indic": [41, 69, 92], "update_num_issu": 41, "arr": [41, 98], "chunksiz": 41, "convnet": 42, "bespok": [42, 61], "download": [42, 89, 98, 104], "mnist": [42, 84, 89, 97], "handwritten": 42, "digit": [42, 89, 97], "last": [42, 49, 67, 70, 90, 91, 98, 101, 103, 108], "sklearn_digits_test_s": 42, "01": [42, 72, 74, 89, 96, 99, 102, 103], "templat": 42, "flexibli": 42, "among": [42, 62, 99], "test_set": 42, "overrid": 42, "train_idx": [42, 57, 104], "train_label": [42, 88, 104], "span": 43, "sentenc": [43, 56, 80, 82, 83, 88, 95], "token_classif": [43, 56, 80, 82, 83, 98], "encourag": [44, 64, 72, 75], "multilabel_classif": [44, 63, 64, 66, 72, 98, 102], "pred_probs_by_class": 44, "prune_count_matrix_col": 44, "rank_by_kwarg": [44, 64, 72, 99], "num_to_remove_per_class": [44, 64], "bad": [44, 52, 64, 69, 72, 95, 98], "seem": [44, 99, 102], "aren": 44, "confidence_weighted_entropi": [44, 45, 49, 64, 66, 72, 80, 82], "label_issues_idx": [44, 72], "entropi": [44, 46, 48, 49, 71, 72], "prune_by_class": [44, 64, 99], "predicted_neq_given": [44, 64, 99], "prune_counts_matrix": 44, "smallest": [44, 72], "unus": 44, "number_of_mislabeled_examples_in_class_k": 44, "delet": [44, 84, 88, 98], "too": [44, 49, 52, 71, 91, 92, 98, 103], "thread": [44, 64], "window": [44, 89, 97], "shorter": [44, 67], "find_predicted_neq_given": 44, "find_label_issues_using_argmax_confusion_matrix": 44, "remove_noise_from_class": [45, 57], "clip_noise_r": [45, 57], "clip_valu": [45, 57], "value_count": [45, 57, 98], "value_counts_fill_missing_class": [45, 57], "get_missing_class": [45, 57], "round_preserving_sum": [45, 57], "round_preserving_row_tot": [45, 57], "estimate_pu_f1": [45, 57], "confusion_matrix": [45, 57], "print_square_matrix": [45, 57], "print_noise_matrix": [45, 57, 99], "print_inverse_noise_matrix": [45, 57], "print_joint_matrix": [45, 57, 99], "compress_int_arrai": [45, 57], "train_val_split": [45, 57], "subset_x_i": [45, 57], "subset_label": [45, 57], "subset_data": [45, 57], "extract_indices_tf": [45, 57], "unshuffle_tensorflow_dataset": [45, 57], "is_torch_dataset": [45, 57], "is_tensorflow_dataset": [45, 57], "csr_vstack": [45, 57], "append_extra_datapoint": [45, 57], "get_num_class": [45, 57], "num_unique_class": [45, 57], "get_unique_class": [45, 57], "format_label": [45, 57], "smart_display_datafram": [45, 57], "force_two_dimens": [45, 57], "latent_algebra": [45, 85], "compute_ps_py_inv_noise_matrix": [45, 47], "compute_py_inv_noise_matrix": [45, 47], "compute_inv_noise_matrix": [45, 47], "compute_noise_matrix_from_invers": [45, 47], "compute_pi": [45, 47], "compute_pyx": [45, 47], "label_quality_util": 45, "get_normalized_entropi": [45, 46], "multilabel_util": [45, 102], "stack_compl": [45, 50], "get_onehot_num_class": [45, 50], "int2onehot": [45, 50, 102], "onehot2int": [45, 50, 102], "multilabel_scor": [45, 66], "classlabelscor": [45, 49], "exponential_moving_averag": [45, 49, 66], "softmin": [45, 49, 66, 69, 78, 82], "possible_method": [45, 49], "multilabelscor": [45, 49], "get_class_label_quality_scor": [45, 49], "multilabel_pi": [45, 49], "get_cross_validated_multilabel_pred_prob": [45, 49], "default_k": [45, 51, 52], "features_to_knn": [45, 51, 52], "construct_knn_graph_from_index": [45, 51, 52, 54], "create_knn_graph_and_index": [45, 51, 52], "correct_knn_graph": [45, 51, 52, 96], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [45, 51, 52], "correct_knn_distances_and_indic": [45, 51, 52], "high_dimension_cutoff": [45, 51, 53], "row_count_cutoff": [45, 51, 53], "decide_euclidean_metr": [45, 51, 53], "decide_default_metr": [45, 51, 53], "construct_knn": [45, 51, 54], "transform_distances_to_scor": [45, 55], "correct_precision_error": [45, 55], "token_classification_util": [45, 108], "get_sent": [45, 56, 108], "filter_sent": [45, 56, 108], "process_token": [45, 56], "merge_prob": [45, 56], "color_sent": [45, 56], "assert_valid_input": [45, 58], "assert_valid_class_label": [45, 58], "assert_nonempty_input": [45, 58], "assert_indexing_work": [45, 58], "labels_to_arrai": [45, 58], "labels_to_list_multilabel": [45, 58], "min_allowed_prob": 46, "wikipedia": 46, "activ": [46, 48, 61, 62, 84, 101], "towardsdatasci": 46, "cheatsheet": 46, "ec57bc067c0b": 46, "clip": [46, 57, 89, 96], "behav": 46, "unnecessari": [46, 98], "slightli": [46, 87, 88], "interv": [46, 49, 104], "herein": 47, "inexact": 47, "cours": 47, "propag": 47, "throughout": [47, 57, 74, 83, 89, 101, 107, 108], "increas": [47, 55, 69, 71, 72, 89, 90, 96, 98, 101, 102, 108], "dot": [47, 82, 98], "true_labels_class_count": 47, "pyx": 47, "multiannot": 48, "assert_valid_inputs_multiannot": 48, "labels_multiannot": [48, 62], "ensembl": [48, 49, 62, 72, 87, 94, 98, 102, 104, 106], "allow_single_label": 48, "annotator_id": 48, "assert_valid_pred_prob": 48, "pred_probs_unlabel": [48, 62], "format_multiannotator_label": [48, 62, 101], "formatted_label": [48, 57], "old": [48, 57, 85, 89, 97], "check_consensus_label_class": 48, "consensus_label": [48, 62, 101], "consensus_method": [48, 62], "consensu": [48, 62, 84, 100, 108], "establish": [48, 61, 88, 106], "compute_soft_cross_entropi": 48, "soft": [48, 97], "find_best_temp_scal": 48, "coarse_search_rang": [48, 74, 98], "fine_search_s": [48, 74, 98], "temperatur": [48, 49, 69, 78, 82], "scale": [48, 55, 87, 96, 97, 98, 104, 107], "factor": [48, 49, 55, 76, 78], "minim": [48, 69, 104], "temp_scale_pred_prob": 48, "temp": 48, "sharpen": [48, 97], "smoothen": 48, "get_normalized_margin_for_each_label": [49, 72], "get_confidence_weighted_entropy_for_each_label": [49, 72], "scorer": 49, "alpha": [49, 66, 69, 90, 91, 96, 99, 102, 106], "exponenti": 49, "ema": 49, "s_1": 49, "s_k": 49, "ema_k": 49, "accord": [49, 64, 94, 95, 99, 108], "formula": [49, 55], "_t": 49, "cdot": 49, "s_t": 49, "qquad": 49, "leq": 49, "_1": 49, "recent": [49, 108], "success": 49, "previou": [49, 52, 92, 94, 98, 103], "discount": 49, "s_ema": 49, "175": [49, 92, 99, 103], "underflow": 49, "nan": [49, 62, 87, 94, 96, 101, 106], "aggregated_scor": 49, "base_scor": 49, "base_scorer_kwarg": 49, "aggregator_kwarg": [49, 66], "n_sampl": [49, 96], "n_label": 49, "worst": [49, 101], "class_label_quality_scor": 49, "452": 49, "new_scor": 49, "575": 49, "get_label_quality_scores_per_class": [49, 65, 66], "ml_scorer": 49, "binar": [49, 50], "reformat": [49, 89], "wider": 49, "splitter": 49, "kfold": [49, 92], "onevsrestclassifi": [49, 102], "randomforestclassifi": [49, 99, 102], "n_split": [49, 91, 92, 102], "pred_prob_slic": 50, "onehot": 50, "hot": [50, 64, 70, 76, 79, 87, 94, 97, 98, 106, 107], "onehot_matrix": 50, "pairwis": [51, 53, 71], "reli": [52, 71, 88, 89, 90, 91, 95, 103, 104, 106], "sklearn_knn_kwarg": 52, "correction_featur": 52, "discourag": 52, "flexibl": [52, 98], "manner": [52, 66, 87, 88, 96, 101, 106], "701": 52, "900": [52, 87, 94, 106], "436": 52, "000": [52, 88, 92, 95, 97, 108], "idea": [52, 72, 103], "dens": [52, 61, 96], "33140006": 52, "76210367": 52, "correct_exact_dupl": 52, "mutual": [52, 63, 102], "vari": [52, 69, 91], "exact_duplicate_set": 52, "main": [52, 62], "front": [52, 97], "consider": 52, "capabl": [52, 84], "come": [52, 57, 90, 91, 98, 107], "misidentif": 52, "corrected_dist": 52, "corrected_indic": 52, "sqrt": 52, "distant": 52, "suitabl": [53, 62, 87, 94, 96], "slower": 53, "decid": [53, 62, 88, 95, 97, 101, 106, 108], "predefin": 53, "met": [53, 108], "euclidean_dist": [53, 71], "spatial": [53, 71], "decis": [53, 87, 90, 91], "That": [53, 99, 102], "cosine_dist": 53, "knn_kwarg": 54, "html": [54, 57, 67, 70, 71, 89, 90, 91, 92, 94, 95, 98, 99], "kneighbor": 54, "metric_param": 54, "n_features_in_": 54, "effective_metric_params_": 54, "effective_metric_": 54, "n_samples_fit_": 54, "__sklearn_is_fitted__": 54, "conduct": 54, "is_fit": 54, "trail": 54, "underscor": 54, "avg_dist": 55, "exp": [55, 71, 72, 90], "dt": 55, "right": [55, 67, 70, 88, 95, 102, 103, 104], "strength": [55, 70, 96], "pronounc": 55, "differenti": 55, "ly": 55, "rule": [55, 56, 97], "thumb": 55, "ood_features_scor": [55, 71, 104], "88988177": 55, "80519832": 55, "toler": 55, "minkowski": 55, "noth": 55, "epsilon": 55, "sensibl": 55, "fixed_scor": 55, "readabl": 56, "lambda": [56, 89, 90, 98, 101], "long_sent": 56, "headlin": 56, "charact": [56, 57], "s1": 56, "s2": 56, "processed_token": 56, "alecnlcb": 56, "entiti": [56, 84, 98, 108], "mapped_ent": 56, "unique_ident": 56, "loc": [56, 90, 91, 92, 94, 96, 108], "nbitbas": [56, 66], "probs_merg": 56, "0125": [56, 82], "0375": 56, "075": 56, "025": 56, "color": [56, 79, 90, 91, 94, 96, 99, 102, 104, 106, 107], "red": [56, 70, 90, 91, 96, 97, 99, 102, 103, 104, 107], "colored_sent": 56, "termcolor": 56, "31msentenc": 56, "0m": 56, "ancillari": 57, "class_without_nois": 57, "any_other_class": 57, "choos": [57, 72, 87, 94, 98, 99, 106], "tradition": 57, "new_sum": 57, "fill": 57, "major": [57, 62, 85, 92, 104], "versu": [57, 99], "obviou": 57, "cgdeboer": 57, "iteround": 57, "reach": 57, "prob_s_eq_1": 57, "claesen": 57, "f1": [57, 70, 95, 99], "BE": 57, "left_nam": 57, "top_nam": 57, "titl": [57, 90, 91, 96, 99, 102, 104], "short_titl": 57, "round_plac": 57, "pretti": [57, 99], "joint_matrix": 57, "num_possible_valu": 57, "holdout_idx": 57, "extract": [57, 71, 88, 89, 94, 95, 101, 104, 107], "allow_shuffl": 57, "turn": [57, 84, 103], "shuffledataset": 57, "histori": 57, "pre_x": 57, "buffer_s": 57, "csr_matric": 57, "append": [57, 89, 92, 97, 98, 99, 101, 102, 103, 104, 108], "bottom": [57, 67, 70, 96, 103], "to_data": 57, "from_data": 57, "taken": 57, "label_matrix": 57, "canon": 57, "displai": [57, 70, 79, 83, 88, 89, 94, 95, 96, 99, 108], "jupyt": [57, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "notebook": [57, 62, 89, 91, 97, 98, 99, 101, 102, 103, 107, 108], "consol": 57, "allow_missing_class": 58, "allow_one_class": 58, "length_x": 58, "labellik": 58, "labels_list": [58, 64], "keraswrappermodel": [60, 61, 84], "keraswrappersequenti": [60, 61], "tf": [61, 89], "legaci": 61, "newer": 61, "interim": 61, "advis": [61, 102], "stabil": [61, 71], "until": 61, "accommod": 61, "keraswrapp": 61, "huggingface_keras_imdb": 61, "unit": [61, 108], "model_kwarg": [61, 74], "compile_kwarg": 61, "sparsecategoricalcrossentropi": 61, "layer": [61, 88, 89, 95, 104], "my_keras_model": 61, "from_logit": 61, "declar": 61, "apply_softmax": 61, "analysi": 62, "analyz": [62, 84, 99, 101, 102], "get_label_quality_multiannot": [62, 101], "vote": 62, "crowdsourc": [62, 84, 101], "dawid": [62, 101], "skene": [62, 101], "analog": [62, 97, 101], "chosen": [62, 72, 98, 101], "crowdlab": [62, 101], "unlabel": [62, 92, 94, 95, 101, 104, 107], "get_active_learning_scor": [62, 101], "activelab": [62, 101], "priorit": [62, 69, 103, 107, 108], "showcas": 62, "best_qual": 62, "quality_method": 62, "calibrate_prob": 62, "return_detailed_qu": 62, "return_annotator_stat": 62, "return_weight": 62, "label_quality_score_kwarg": 62, "did": [62, 63, 87, 88, 89, 94, 99, 101, 106], "majority_vot": 62, "broken": [62, 70, 97, 106], "highest": [62, 70, 90, 92, 105], "0th": 62, "consensus_quality_scor": [62, 101], "annotator_agr": [62, 101], "reman": 62, "1st": 62, "2nd": [62, 76], "3rd": 62, "consensus_label_suffix": 62, "consensus_quality_score_suffix": 62, "suffix": 62, "emsembl": 62, "weigh": [62, 97], "agreement": [62, 101], "agre": 62, "prevent": [62, 98], "overconfid": [62, 105], "detailed_label_qu": [62, 101], "annotator_stat": [62, 101], "model_weight": 62, "annotator_weight": 62, "warn": [62, 90, 91, 92, 94, 95, 96, 98, 99], "labels_info": 62, "num_annot": [62, 101], "deriv": [62, 101], "quality_annotator_1": 62, "quality_annotator_2": 62, "quality_annotator_m": 62, "annotator_qu": [62, 101], "num_examples_label": [62, 101], "agreement_with_consensu": [62, 101], "worst_class": [62, 101], "trustworthi": [62, 101, 106], "get_label_quality_multiannotator_ensembl": 62, "weigtht": 62, "budget": 62, "retrain": [62, 88, 106], "active_learning_scor": 62, "active_learning_scores_unlabel": 62, "get_active_learning_scores_ensembl": 62, "henc": [62, 89, 90, 101], "get_majority_vote_label": [62, 101], "event": 62, "lastli": [62, 94], "convert_long_to_wide_dataset": 62, "labels_multiannotator_long": 62, "wide": [62, 87, 88, 89], "labels_multiannotator_wid": 62, "common_multilabel_issu": [63, 65], "exclus": [63, 102], "rank_classes_by_multilabel_qu": [63, 65], "overall_multilabel_health_scor": [63, 65], "multilabel_health_summari": [63, 65], "classes_by_multilabel_qu": 63, "inner": [64, 78, 96], "find_multilabel_issues_per_class": [64, 65], "per_class_label_issu": 64, "label_issues_list": 64, "pred_probs_list": [64, 72, 92, 99], "anim": [65, 104], "rat": 65, "predat": 65, "pet": 65, "reptil": 65, "box": [67, 69, 70, 97, 103], "object_detect": [67, 69, 70, 103], "return_indices_ranked_by_scor": [67, 103], "overlapping_label_check": [67, 69], "suboptim": [67, 69], "locat": [67, 69, 96, 103, 107, 108], "bbox": [67, 70, 103], "image_nam": [67, 70], "y1": [67, 70, 103], "y2": [67, 70, 103], "later": [67, 70, 71, 88, 108], "corner": [67, 70, 103], "xyxi": [67, 70, 103], "io": [67, 70, 89, 96, 97], "keras_cv": [67, 70], "bounding_box": [67, 70, 103], "detectron": [67, 70, 103], "detectron2": [67, 70, 103], "readthedoc": [67, 70], "en": [67, 70], "latest": [67, 70], "visual": [67, 68, 70, 87, 90, 91, 92, 106, 108], "draw_box": [67, 70], "mmdetect": [67, 70, 103], "swap": [67, 69, 79, 83], "penal": [67, 69], "concern": [67, 69, 84, 91], "issues_from_scor": [68, 69, 77, 78, 79, 81, 82, 83, 103, 107, 108], "compute_overlooked_box_scor": [68, 69], "compute_badloc_box_scor": [68, 69], "compute_swap_box_scor": [68, 69], "pool_box_scores_per_imag": [68, 69], "object_counts_per_imag": [68, 70, 103], "bounding_box_size_distribut": [68, 70, 103], "class_label_distribut": [68, 70, 103], "get_sorted_bbox_count_idx": [68, 70], "plot_class_size_distribut": [68, 70], "plot_class_distribut": [68, 70], "get_average_per_class_confusion_matrix": [68, 70], "calculate_per_class_metr": [68, 70], "aggregation_weight": 69, "imperfect": [69, 98], "chose": [69, 101, 103], "imperfectli": [69, 103], "dirti": [69, 72, 75, 106], "subtyp": 69, "badloc": 69, "nonneg": 69, "high_probability_threshold": 69, "auxiliary_input": [69, 70], "iou": [69, 70], "heavili": 69, "auxiliarytypesdict": 69, "pred_label": [69, 88], "pred_label_prob": 69, "pred_bbox": 69, "lab_label": 69, "lab_bbox": 69, "similarity_matrix": 69, "min_possible_similar": 69, "scores_overlook": 69, "low_probability_threshold": 69, "scores_badloc": 69, "accident": [69, 88, 94, 95, 98], "scores_swap": 69, "box_scor": 69, "image_scor": [69, 78, 107], "discov": [70, 91, 96, 108], "abnorm": [70, 92, 103], "auxiliari": [70, 104, 107], "_get_valid_inputs_for_compute_scor": 70, "object_count": 70, "down": 70, "bbox_siz": 70, "class_distribut": 70, "plot": [70, 90, 91, 96, 99, 102, 104, 106, 107], "sorted_idx": [70, 104], "class_to_show": 70, "hidden": [70, 104], "max_class_to_show": 70, "plt": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "matplotlib": [70, 79, 90, 91, 92, 96, 99, 102, 103, 104, 106], "pyplot": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "prediction_threshold": 70, "overlai": [70, 103], "figsiz": [70, 90, 91, 92, 96, 99, 102, 104], "save_path": [70, 103], "blue": [70, 97, 99, 103], "overlaid": 70, "side": [70, 97, 103], "figur": [70, 96, 99, 102, 104, 106], "extens": [70, 99, 101], "png": [70, 103], "pdf": [70, 71], "svg": 70, "num_proc": [70, 92], "intersect": [70, 98], "tp": 70, "fp": 70, "ground": [70, 97, 99, 101, 106], "truth": [70, 99, 101, 106], "bias": [70, 96], "avg_metr": 70, "distionari": 70, "95": [70, 80, 82, 94, 97, 99, 106], "per_class_metr": 70, "Of": 71, "find_top_issu": [71, 72, 104], "behind": [71, 99], "dist_metr": 71, "subtract": [71, 72], "renorm": [71, 72, 98], "least_confid": 71, "sum_": 71, "log": [71, 72, 85], "softmax": [71, 78, 82, 92], "literatur": 71, "gen": 71, "liu": 71, "lochman": 71, "zach": 71, "openaccess": 71, "thecvf": 71, "cvpr2023": 71, "liu_gen_pushing_the_limits_of_softmax": 71, "based_out": 71, "distribution_detection_cvpr_2023_pap": 71, "fit_scor": [71, 104], "ood_predictions_scor": 71, "pretrain": [71, 88, 89, 95, 104], "adjust_confident_threshold": 71, "probabilist": [71, 87, 89, 90, 91, 94, 95, 104, 105], "order_label_issu": [72, 85], "whichev": [72, 105], "argsort": [72, 88, 92, 95, 99, 103, 104, 106], "max_": 72, "get_label_quality_ensemble_scor": [72, 98, 99], "weight_ensemble_members_bi": 72, "custom_weight": 72, "log_loss_search_t_valu": 72, "0001": [72, 97], "scheme": 72, "log_loss_search": 72, "log_loss": [72, 95], "1e0": 72, "1e1": 72, "1e2": 72, "2e2": 72, "quality_scor": [72, 104], "forth": 72, "top_issue_indic": 72, "rank_bi": [72, 85], "weird": [72, 83], "minu": 72, "prob_label": 72, "max_prob_not_label": 72, "AND": [72, 95], "get_epistemic_uncertainti": [73, 74], "get_aleatoric_uncertainti": [73, 74], "corrupt": [74, 106], "linearregress": [74, 98, 106], "y_with_nois": 74, "n_boot": [74, 98], "include_aleatoric_uncertainti": [74, 98], "sole": [74, 87, 90, 101, 104], "bootstrap": [74, 98, 106], "resampl": [74, 89, 98], "epistem": [74, 98, 104, 106], "aleator": [74, 98, 106], "model_final_kwarg": 74, "coars": 74, "thorough": [74, 98], "fine": [74, 88, 89, 95, 104], "grain": 74, "grid": 74, "varianc": [74, 99], "epistemic_uncertainti": 74, "residu": [74, 75, 98], "deviat": [74, 103, 106], "aleatoric_uncertainti": 74, "outr": 75, "contin": 75, "raw": [75, 84, 85, 91, 92, 97, 98, 101, 103, 104, 106], "aka": [75, 89, 99, 103, 106, 108], "00323821": 75, "33692597": 75, "00191686": 75, "semant": [76, 78, 79, 100], "pixel": [76, 78, 79, 92, 104, 107], "h": [76, 78, 79, 107], "height": [76, 78, 79, 107], "w": [76, 78, 79, 107], "width": [76, 78, 79, 107], "labels_one_hot": [76, 79, 107], "stream": [76, 104, 108], "downsampl": [76, 78, 107], "shrink": [76, 78], "divis": [76, 78, 90], "common_label_issu": [77, 79, 81, 83, 107, 108], "filter_by_class": [77, 79, 107], "segmant": [78, 79], "num_pixel_issu": [78, 107], "product": [78, 92, 96, 98], "pixel_scor": [78, 107], "enter": 79, "legend": [79, 90, 91, 96, 102, 103, 106, 107], "colormap": 79, "background": [79, 96], "person": [79, 98, 103, 107, 108], "ambigu": [79, 83, 88, 89, 95, 97, 99, 108], "systemat": [79, 83, 101], "misunderstood": [79, 83], "issues_df": [79, 92], "class_index": 79, "issues_subset": [79, 83], "filter_by_token": [81, 83, 108], "token_score_method": 82, "sentence_score_method": 82, "sentence_score_kwarg": 82, "compris": [82, 83], "token_scor": [82, 108], "converg": 82, "toward": [82, 96], "_softmin_sentence_scor": 82, "sentence_scor": [82, 108], "token_info": 82, "02": [82, 90, 91, 96, 99, 103, 108], "03": [82, 94, 96, 97, 99, 103, 104, 108], "04": [82, 94, 96, 103, 108], "08": [82, 96, 99, 103, 106, 108], "commonli": [83, 85, 90, 91, 102, 108], "But": [83, 95, 99, 106, 108], "restrict": [83, 98], "reliabl": [84, 87, 89, 96, 98, 101, 107], "thousand": 84, "imagenet": [84, 97], "popular": [84, 101, 103], "centric": [84, 92, 94, 95, 100], "minut": [84, 87, 88, 89, 94, 95, 97, 101, 102, 103, 106, 107, 108], "conda": 84, "feature_embed": [84, 104], "Then": [84, 87, 88, 92, 98], "your_dataset": [84, 89, 90, 91, 92, 94, 95, 98], "column_name_of_label": [84, 89, 90, 91, 92, 94, 95], "plagu": [84, 91], "untrain": 84, "\u30c4": 84, "label_issues_info": [84, 91], "sklearn_compatible_model": 84, "framework": [84, 102, 103], "complianc": 84, "tag": [84, 102, 108], "sequenc": 84, "recognit": [84, 89, 98, 108], "train_data": [84, 87, 88, 104, 106], "gotten": 84, "test_data": [84, 87, 88, 99, 102, 104, 106], "deal": [84, 91, 96], "tutori": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "feel": [84, 89, 91, 98], "ask": [84, 98], "slack": [84, 98], "project": [84, 106], "welcom": 84, "commun": [84, 98], "guidelin": [84, 103], "piec": 84, "smart": [84, 92, 94, 95, 98], "edit": [84, 98], "easier": [84, 96, 99], "unreli": [84, 87, 89, 94, 95], "link": [84, 89, 97, 103], "older": 85, "outlin": 85, "substitut": 85, "v2": [85, 87, 94], "get_noise_indic": 85, "psx": 85, "sorted_index_method": 85, "order_label_error": 85, "label_errors_bool": 85, "latent_estim": 85, "num_label_error": 85, "learningwithnoisylabel": 85, "neatli": 85, "organ": [85, 87, 94, 97, 108], "reorgan": 85, "baseline_method": 85, "incorpor": [85, 99], "research": [85, 99], "polyplex": 85, "terminologi": 85, "label_error": 85, "quickstart": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 101, 102, 103, 104, 106, 107, 108], "sql": [87, 94], "databas": [87, 94], "excel": [87, 94], "parquet": [87, 94], "student": [87, 94, 106, 108], "grade": [87, 94, 106], "exam": [87, 94, 106], "letter": [87, 94, 108], "hundr": [87, 94], "mistak": [87, 88, 92, 94, 95], "extratreesclassifi": 87, "extratre": 87, "ranked_label_issu": [87, 88], "branch": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "preprocess": [87, 88, 91, 94, 96, 104, 106], "standardscal": [87, 94, 104], "labelencod": [87, 88], "train_test_split": [87, 88, 90, 91, 104], "accuracy_scor": [87, 88, 89, 95, 99], "grades_data": [87, 94], "read_csv": [87, 88, 94, 95, 96, 106], "demo": [87, 91, 94, 102], "stud_id": [87, 94], "exam_1": [87, 94, 106], "exam_2": [87, 94, 106], "exam_3": [87, 94, 106], "letter_grad": [87, 94], "f48f73": [87, 94], "53": [87, 90, 91, 94, 96, 97, 102, 103], "00": [87, 90, 91, 94, 96, 97, 104], "77": [87, 90, 91, 94, 103], "0bd4e7": [87, 94], "81": [87, 94, 95, 103, 106, 108], "great": [87, 94, 97], "particip": [87, 94], "cb9d7a": [87, 94], "61": [87, 94, 96, 99, 103, 106], "94": [87, 94, 97, 99, 103, 106], "9acca4": [87, 94], "48": [87, 94, 96, 97, 99, 103], "x_raw": [87, 94], "labels_raw": 87, "interg": [87, 88], "categorical_featur": [87, 106], "x_encod": [87, 94], "get_dummi": [87, 94, 106], "drop_first": [87, 94], "numeric_featur": [87, 94], "scaler": [87, 94, 104], "x_process": [87, 94], "fit_transform": [87, 94, 96], "bring": [87, 88, 92, 94, 95, 101, 106], "byod": [87, 88, 92, 94, 95, 101, 106], "tress": 87, "held": [87, 89, 94, 95, 97, 103, 104, 105], "straightforward": [87, 89, 94], "benefit": [87, 89, 105, 107], "num_crossval_fold": [87, 89, 94, 101], "tabl": [87, 94, 97, 101], "212": [87, 99], "review": [87, 88, 91, 94, 95, 97, 98, 99, 103, 106, 107, 108], "iloc": [87, 88, 89, 94, 95, 106], "92": [87, 90, 99, 103], "93": [87, 97, 103, 106], "827": 87, "99": [87, 96, 97, 99, 108], "86": [87, 91, 92, 94, 99, 103, 106], "74": [87, 96, 103, 106], "637": [87, 94], "79": [87, 97, 103], "65": [87, 90, 96, 103], "cheat": 87, "0pt": 87, "120": [87, 90, 91], "233": 87, "83": [87, 99, 103, 106, 108], "76": [87, 99, 102, 103, 106], "suspici": [87, 94], "carefulli": [87, 92, 94, 95], "examin": [87, 90, 91, 94, 96, 103], "labels_train": 87, "labels_test": 87, "test_siz": [87, 88, 90, 91], "acc_og": [87, 88], "783068783068783": 87, "robustli": [87, 88, 106], "14": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "acc_cl": [87, 88], "8095238095238095": 87, "blindli": [87, 88, 89, 98, 106], "trust": [87, 88, 89, 98, 99, 101, 105, 106], "effort": [87, 88, 106], "intent": [88, 95], "servic": [88, 95, 98], "onlin": [88, 95], "bank": [88, 95, 97], "banking77": [88, 95], "oo": [88, 95], "categori": [88, 92, 95, 96], "shortlist": [88, 95, 106], "scope": [88, 95], "logist": [88, 90, 91, 95, 101, 104], "probabilit": [88, 89], "drop": [88, 94, 96, 98, 101, 106], "earlier": [88, 108], "sentence_transform": [88, 95], "sentencetransform": [88, 95], "payment": [88, 95], "cancel_transf": [88, 95], "transfer": [88, 95], "fund": [88, 95], "cancel": [88, 95], "transact": [88, 95], "my": [88, 95], "revert": [88, 95], "morn": [88, 95], "realis": [88, 95], "yesterdai": [88, 95], "rent": [88, 95], "tomorrow": [88, 95], "raw_text": [88, 95], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "beneficiary_not_allow": [88, 95], "card_about_to_expir": [88, 95], "getting_spare_card": [88, 95], "supported_cards_and_curr": [88, 95], "card_payment_fee_charg": [88, 95], "change_pin": [88, 95], "apple_pay_or_google_pai": [88, 95], "lost_or_stolen_phon": [88, 95], "visa_or_mastercard": [88, 95], "card": [88, 95, 97], "utter": [88, 95], "encond": 88, "test_label": [88, 99, 102, 104], "suit": [88, 95, 96, 97, 98], "electra": [88, 95], "discrimin": [88, 95], "googl": [88, 95], "train_text": 88, "test_text": 88, "home": [88, 90, 91, 95, 96, 97], "runner": [88, 90, 91, 95, 96], "google_electra": [88, 95], "pool": [88, 95, 98, 104], "opt": [88, 89, 91, 92, 94, 95, 96, 99], "hostedtoolcach": [88, 89, 91, 92, 94, 95, 96, 99], "x64": [88, 89, 91, 92, 94, 95, 96, 99], "lib": [88, 89, 91, 92, 94, 95, 96, 99], "python3": [88, 89, 91, 92, 94, 95, 96, 99], "site": [88, 89, 91, 92, 94, 95, 96, 99], "_util": [88, 95], "831": [88, 95], "userwarn": [88, 89, 90, 91, 95, 96], "typedstorag": [88, 95], "untypedstorag": [88, 95], "untyped_storag": [88, 95], "fget": [88, 95], "__get__": [88, 95], "owner": [88, 95], "leverag": [88, 89, 95, 98, 99, 101], "computation": [88, 89, 95], "intens": [88, 89, 95], "400": [88, 95], "858371": 88, "547274": 88, "826228": 88, "966008": 88, "792449": 88, "identified_issu": [88, 106], "lowest_quality_label": [88, 89, 95, 99, 106], "to_numpi": [88, 95, 96, 106], "44": [88, 96, 97, 102, 103], "646": 88, "390": 88, "628": 88, "121": [88, 99], "702": 88, "863": [88, 89], "135": 88, "337": [88, 103], "735": 88, "print_as_df": 88, "inverse_transform": 88, "charg": [88, 95], "cash": [88, 95], "holidai": [88, 95], "sent": [88, 95, 108], "mine": [88, 95], "expir": [88, 95], "fight": 88, "hors": [88, 97, 104], "duck": [88, 97], "me": [88, 95, 96], "whoever": [88, 95], "consum": [88, 106], "18": [88, 89, 95, 96, 97, 98, 99, 103, 104, 106, 107], "baseline_model": [88, 106], "87": [88, 91, 92, 103, 106], "acceler": [88, 106], "19": [88, 89, 92, 95, 96, 97, 98, 99, 103, 104, 106, 107, 108], "89": [88, 90, 94, 103, 106], "spoken": 89, "500": [89, 104, 108], "english": [89, 97], "pronunci": 89, "wav": 89, "huggingfac": [89, 90, 91, 92, 98], "voxceleb": 89, "speech": [89, 108], "your_pred_prob": [89, 90, 91, 94, 95], "tensorflow_io": 89, "huggingface_hub": 89, "reproduc": [89, 94, 96, 99, 101], "command": 89, "wget": [89, 103, 107, 108], "navig": 89, "browser": 89, "jakobovski": 89, "archiv": [89, 108], "v1": 89, "tar": [89, 104], "gz": [89, 104], "mkdir": [89, 108], "spoken_digit": 89, "xf": 89, "6_nicolas_32": 89, "data_path": 89, "listdir": 89, "nondeterminist": 89, "file_nam": 89, "endswith": 89, "file_path": 89, "join": [89, 92, 96, 98], "7_george_26": 89, "0_nicolas_24": 89, "0_nicolas_6": 89, "listen": 89, "display_exampl": 89, "expand": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "pulldown": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "colab": [89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "tfio": 89, "pathlib": 89, "ipython": 89, "load_wav_16k_mono": 89, "filenam": 89, "khz": 89, "file_cont": 89, "read_fil": 89, "sample_r": 89, "decode_wav": 89, "desired_channel": 89, "squeez": 89, "rate_in": 89, "rate_out": 89, "16000": 89, "wav_file_nam": 89, "audio_r": 89, "wav_file_exampl": 89, "plai": [89, 97, 98], "button": 89, "wav_file_name_exampl": 89, "7_jackson_43": 89, "hear": 89, "extractor": 89, "encoderclassifi": 89, "spkrec": 89, "xvect": 89, "feature_extractor": 89, "from_hparam": 89, "run_opt": 89, "uncom": [89, 96], "ffmpeg": 89, "backend": 89, "wav_audio_file_path": 89, "torchaudio": 89, "extract_audio_embed": 89, "emb": [89, 92], "signal": 89, "encode_batch": 89, "embeddings_list": [89, 92], "embeddings_arrai": 89, "650": 89, "stft": 89, "return_complex": 89, "view_as_r": 89, "recov": 89, "trigger": 89, "aten": 89, "src": 89, "nativ": 89, "spectralop": 89, "cpp": 89, "_vf": 89, "n_fft": 89, "hop_length": 89, "win_length": 89, "attr": 89, "512": [89, 92], "196311": 89, "319459": 89, "478975": 89, "2890875": 89, "8170238": 89, "89265": 89, "898056": 89, "256195": 89, "559641": 89, "559721": 89, "62067": 89, "285245": 89, "21": [89, 90, 96, 97, 99, 103, 106, 108], "709627": 89, "5033693": 89, "913803": 89, "819831": 89, "1831515": 89, "208763": 89, "084257": 89, "3210397": 89, "005453": 89, "216152": 89, "478235": 89, "6821785": 89, "053807": 89, "242471": 89, "091424": 89, "78334856": 89, "03954": 89, "23": [89, 92, 96, 97, 99, 103, 106], "569176": 89, "761097": 89, "1258295": 89, "753237": 89, "3508866": 89, "598274": 89, "23712": 89, "2500": 89, "tol": 89, "decreas": [89, 98], "cv_accuraci": 89, "9708": 89, "issue_type_descript": [89, 90, 91, 92, 94, 95, 99], "lt": [89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 104], "gt": [89, 90, 91, 92, 94, 95, 96, 99, 101, 108], "9976": 89, "986": 89, "002161": 89, "176": [89, 97, 99, 102], "002483": 89, "2318": 89, "004411": 89, "1005": 89, "004857": 89, "1871": 89, "007494": 89, "040587": 89, "999207": 89, "999377": 89, "975220": 89, "999367": 89, "identified_label_issu": [89, 95], "516": 89, "1946": 89, "469": 89, "2132": 89, "worth": [89, 99], "6_yweweler_25": 89, "7_nicolas_43": 89, "6_theo_27": 89, "6_yweweler_36": 89, "6_yweweler_14": 89, "6_yweweler_35": 89, "6_nicolas_8": 89, "sound": 89, "quit": [89, 104], "underneath": 90, "hood": [90, 96, 98], "alert": 90, "introduct": 90, "mayb": [90, 91, 95], "your_feature_matrix": [90, 91], "toi": [90, 91, 92, 96, 97, 99, 101], "inf": [90, 91], "mid": [90, 91], "bins_map": [90, 91], "create_data": [90, 91], "y_bin": [90, 91], "y_i": [90, 91], "y_bin_idx": [90, 91], "y_train": [90, 91, 99, 106], "y_test": [90, 91, 99, 106], "y_train_idx": [90, 91], "y_test_idx": [90, 91], "slide": [90, 91, 97], "frame": [90, 91], "x_out": [90, 91], "tini": [90, 91], "concaten": [90, 91, 105], "y_out": [90, 91], "y_out_bin": [90, 91], "y_out_bin_idx": [90, 91], "exact_duplicate_idx": [90, 91], "x_duplic": [90, 91], "y_duplic": [90, 91], "y_duplicate_idx": [90, 91], "noisy_labels_idx": [90, 91, 102], "scatter": [90, 91, 96, 99, 102, 106], "black": [90, 91, 97, 106], "cyan": [90, 91], "plot_data": [90, 91, 96, 99, 102, 106], "fig": [90, 91, 92, 97, 104, 106], "ax": [90, 91, 92, 96, 104, 106], "subplot": [90, 91, 92, 104], "set_titl": [90, 91, 92, 104], "set_xlabel": [90, 91], "x_1": [90, 91], "fontsiz": [90, 91, 92, 96, 99, 102], "set_ylabel": [90, 91], "x_2": [90, 91], "set_xlim": [90, 91], "set_ylim": [90, 91], "linestyl": [90, 91, 96], "circl": [90, 91, 99, 102], "misclassifi": [90, 91], "zip": [90, 91, 92, 103, 108], "label_err": [90, 91], "180": [90, 91, 103], "marker": [90, 91], "facecolor": [90, 91, 96], "edgecolor": [90, 91, 96], "linewidth": [90, 91, 96, 104], "dup": [90, 91], "first_legend": [90, 91], "align": [90, 91], "title_fontproperti": [90, 91], "semibold": [90, 91], "second_legend": [90, 91], "45": [90, 91, 96, 97, 99, 103], "gca": [90, 91], "add_artist": [90, 91], "tight_layout": [90, 91, 96], "ideal": [90, 91], "remaind": 90, "modal": [90, 91, 98, 101], "132": [90, 91, 99, 103], "9318": 90, "006940": 90, "007830": 90, "40": [90, 91, 95, 96, 97], "014828": 90, "107": [90, 91, 99, 102], "021241": 90, "026407": 90, "notic": [90, 99, 101, 103], "3558": [90, 91], "126": [90, 91, 99, 103], "006636": [90, 91], "130": [90, 91], "012571": [90, 91], "129": [90, 91], "127": [90, 91], "014909": [90, 91], "128": [90, 91, 92], "017443": [90, 91], "6160": [90, 91], "131": [90, 91, 107], "000000e": [90, 91], "000002": [90, 91], "463180e": [90, 91], "07": [90, 91, 92, 94, 96, 99, 103, 106], "51": [90, 91, 94, 96, 97, 99, 103], "161148": [90, 91], "859087e": [90, 91], "30": [90, 91, 92, 96, 97, 98, 102, 107, 108], "3453": 90, "029542": 90, "031182": 90, "057961": 90, "058244": 90, "348": 90, "378": 90, "357": 90, "34": [90, 96, 97, 99, 101, 103, 108], "54": [90, 96, 97, 99, 103], "039122": 90, "044598": 90, "105": [90, 103], "105196": 90, "133654": 90, "43": [90, 96, 97, 99, 103], "168033": 90, "125": 90, "101107": 90, "183382": 90, "109": [90, 97, 103], "209259": 90, "211042": 90, "221316": 90, "average_ood_scor": 90, "34530442089193386": 90, "52": [90, 96, 97, 103, 108], "169820": 90, "087324e": 90, "259024": 90, "583757e": 90, "91": [90, 103], "346458": 90, "341292e": 90, "specfi": 90, "new_lab": 90, "scoring_funct": 90, "div": 90, "rem": 90, "inv_scal": 90, "49": [90, 96, 97, 99, 103, 108], "superstitionissuemanag": 90, "unlucki": 90, "superstit": 90, "to_seri": 90, "issues_mask": 90, "summary_scor": 90, "9242": 90, "is_superstition_issu": 90, "superstition_scor": 90, "26": [90, 92, 96, 97, 99, 101, 103], "047581": 90, "090635": 90, "129591": 90, "164840": 90, "lurk": [91, 92, 99], "_split": 91, "776": 91, "thoroughli": 91, "904": 91, "_base": [91, 92, 94, 95, 96, 99], "246": [91, 92, 94, 95, 96, 99, 103], "efficiencywarn": [91, 92, 94, 95, 96, 99], "sort_graph_by_row_valu": [91, 92, 94, 95, 96, 99], "warn_when_not_sort": [91, 92, 94, 95, 96, 99], "8561": 91, "001908": 91, "003564": 91, "007331": 91, "008963": 91, "009664": 91, "0227": 91, "022727": 91, "conceptu": 91, "856061": 91, "355772": 91, "616034": 91, "821750": 91, "901562": 91, "betweeen": 91, "859131": 91, "417707": 91, "664083": 91, "970324": 91, "816953": 91, "375317": 91, "641516": 91, "890575": 91, "531021": 91, "460593": 91, "601188": 91, "826147": 91, "752808": 91, "321635": 91, "562539": 91, "948362": 91, "090243": 91, "472909": 91, "746763": 91, "878267": 91, "examples_w_issu": [91, 98], "013445": 91, "025184": 91, "026376": 91, "inde": [91, 95], "miscellan": [91, 93, 108], "428571": 91, "111111": 91, "571429": 91, "407407": 91, "592593": 91, "337838": 91, "092593": 91, "662162": 91, "333333": [91, 97], "952381": 91, "666667": [91, 96], "portion": 91, "huge": [91, 99], "worri": [91, 95], "critic": 91, "60": [92, 96, 99, 106], "torchvis": [92, 104], "tensordataset": 92, "stratifiedkfold": [92, 102], "tqdm": 92, "autonotebook": 92, "math": 92, "fashion_mnist": 92, "1486": 92, "futurewarn": 92, "hf": 92, "messag": 92, "trust_remote_cod": 92, "num_row": 92, "60000": 92, "transformed_dataset": 92, "with_format": 92, "255": [92, 97], "cpu_count": 92, "torch_dataset": 92, "quick": [92, 102, 104], "super": [92, 94, 95], "relu": 92, "batchnorm2d": 92, "maxpool2d": 92, "lazylinear": 92, "flatten": 92, "get_test_accuraci": 92, "testload": [92, 104], "energi": 92, "trainload": [92, 104], "n_epoch": 92, "patienc": 92, "criterion": 92, "crossentropyloss": 92, "adamw": 92, "best_test_accuraci": 92, "start_epoch": 92, "running_loss": 92, "best_epoch": 92, "end_epoch": 92, "3f": [92, 106], "acc": [92, 99], "time_taken": 92, "compute_embed": 92, "compute_pred_prob": 92, "train_batch_s": 92, "num_work": 92, "worker": [92, 108], "train_id_list": 92, "test_id_list": 92, "train_id": 92, "test_id": 92, "embeddings_model": 92, "ntrain": 92, "trainset": 92, "testset": 92, "pin_memori": 92, "fold_embed": 92, "fold_pred_prob": 92, "finish": 92, "482": 92, "720": 92, "704": 92, "329": [92, 94, 103], "88": [92, 97, 98, 99, 102, 103, 106, 108], "195": 92, "493": 92, "060": 92, "714": 92, "330": [92, 103], "505": 92, "460": 92, "476": 92, "340": 92, "742": 92, "328": [92, 103], "310": 92, "468": 92, "reorder": 92, "hstack": [92, 98, 99, 101], "vision": 92, "grayscal": 92, "max_preval": 92, "7714": 92, "3772": 92, "3585": 92, "166": 92, "3651": 92, "27080": 92, "873833e": 92, "40378": 92, "915575e": 92, "25316": 92, "390277e": 92, "06": [92, 99, 103, 108], "2090": 92, "751164e": 92, "14999": 92, "881301e": 92, "9569": 92, "11262": 92, "000003": 92, "coat": [92, 97], "shirt": [92, 97], "19228": 92, "000010": 92, "dress": 92, "32657": 92, "000013": 92, "bag": [92, 97, 104, 105], "21282": 92, "000016": 92, "53564": 92, "000018": 92, "pullov": 92, "6321": 92, "30968": 92, "001267": 92, "30659": 92, "000022": [92, 108], "47824": 92, "001454": 92, "3370": 92, "000026": 92, "54565": 92, "001854": 92, "9762": 92, "258": 92, "47139": 92, "000033": 92, "166980": 92, "986195": 92, "997205": 92, "sandal": [92, 97], "948781": 92, "999358": 92, "54078": 92, "17371": 92, "000025": 92, "plot_label_issue_exampl": 92, "ncol": [92, 104], "nrow": [92, 104], "ceil": 92, "axes_list": 92, "label_issue_indic": 92, "gl": 92, "sl": 92, "fontdict": 92, "imshow": [92, 104], "cmap": [92, 96, 106], "grai": 92, "subplots_adjust": 92, "hspace": 92, "outsiz": 92, "outlier_issu": [92, 95], "outlier_issues_df": 92, "depict": [92, 102, 103, 104, 105, 107], "plot_outlier_issues_exampl": 92, "n_comparison_imag": 92, "sample_from_class": 92, "number_of_sampl": 92, "non_outlier_indic": 92, "isnul": [92, 96], "non_outlier_indices_excluding_curr": 92, "sampled_indic": 92, "label_scores_of_sampl": 92, "top_score_indic": 92, "top_label_indic": 92, "sampled_imag": 92, "get_image_given_label_and_sampl": 92, "image_from_dataset": 92, "corresponding_label": 92, "comparison_imag": 92, "images_to_plot": 92, "idlist": 92, "iterrow": 92, "near_duplicate_issu": [92, 98], "closest": 92, "counterpart": 92, "near_duplicate_issues_df": 92, "plot_near_duplicate_issue_exampl": 92, "seen_id_pair": 92, "get_image_and_given_label_and_predicted_label": 92, "duplicate_imag": 92, "nd_set": 92, "challeng": 92, "dark_issu": 92, "reveal": [92, 103, 107], "dark_scor": 92, "dark_issues_df": 92, "is_dark_issu": 92, "34848": 92, "203922": 92, "50270": 92, "204588": 92, "3936": 92, "213098": 92, "733": 92, "217686": 92, "8094": 92, "230118": 92, "plot_image_issue_exampl": 92, "difficult": 92, "disproportion": [92, 96], "lowinfo_issu": 92, "low_information_scor": 92, "lowinfo_issues_df": 92, "is_low_information_issu": 92, "53050": 92, "067975": 92, "40875": 92, "089929": 92, "9594": 92, "092601": 92, "34825": 92, "107744": 92, "37530": 92, "108516": 92, "lot": 92, "workflow": [93, 98, 100, 106], "histgradientboostingclassifi": 94, "cat_featur": 94, "boost": [94, 98, 101, 106], "xgboost": [94, 98, 106], "think": [94, 95, 98, 102, 107, 108], "nonzero": 94, "358": 94, "941": 94, "294": [94, 103], "46": [94, 96, 97, 99, 103], "7109": 94, "000005": [94, 95], "886": 94, "000059": 94, "709": 94, "000104": 94, "723": 94, "000169": 94, "689": 94, "000181": 94, "3590": 94, "051882e": 94, "683133e": 94, "536582e": 94, "406589e": 94, "324246e": 94, "6165": 94, "582": 94, "185": [94, 97, 103], "187": [94, 97], "898": 94, "0000": [94, 95, 97, 99], "865": 94, "515002": 94, "837": 94, "556480": 94, "622": 94, "593068": 94, "593207": 94, "920": 94, "618041": 94, "4386345844794593e": 94, "issue_result": 94, "000842": 94, "555944": 94, "004374": 94, "sorted_issu": 94, "73": [94, 96, 97, 102, 103, 106], "deserv": 94, "outlier_result": 94, "sorted_outli": 94, "56": [94, 96, 97, 106], "96": [94, 96, 97, 99, 102, 103, 106], "style": [94, 96, 107], "font": 94, "18px": 94, "ff00ff": 94, "bac": 94, "unintend": [94, 95], "duplicate_result": 94, "lowest_scoring_dupl": 94, "idxmin": [94, 98], "indices_to_displai": 94, "tolist": [94, 98, 102], "perhap": [94, 99, 101], "second_lowest_scoring_dupl": 94, "next_indices_to_displai": 94, "wari": [94, 95, 98], "dive": [95, 96], "your_featur": 95, "text_embed": 95, "data_dict": [95, 99, 101], "85": [95, 103], "38": [95, 96, 97, 103], "9710": 95, "981": 95, "974": 95, "000146": 95, "982": [95, 97], "000224": 95, "971": 95, "000507": 95, "980": [95, 97], "000960": 95, "3584": 95, "994": 95, "009642": 95, "999": 95, "013067": 95, "013841": 95, "433": 95, "014722": 95, "989": 95, "018224": 95, "6070": 95, "160": [95, 106], "095724": 95, "148": 95, "006237": 95, "546": 95, "099341": 95, "514": 95, "006485": 95, "481": 95, "123418": 95, "008165": 95, "313": [95, 103], "564102": 95, "572258": 95, "574915": 95, "31": [95, 96, 97, 99, 101, 103], "575507": 95, "575874": 95, "792090": 95, "257611": 95, "698710": 95, "182121": 95, "771619": 95, "data_with_suggested_label": 95, "suggested_label": 95, "withdraw": 95, "monei": 95, "lowest_quality_outli": 95, "OR": 95, "636c65616e6c616220697320617765736f6d6521": 95, "phone": [95, 97], "gone": 95, "samp": 95, "br": 95, "press": [95, 108], "nonsens": 95, "sens": 95, "detriment": 95, "duplicate_issu": 95, "fee": 95, "go": [95, 96, 97, 99], "strongli": 95, "p_valu": 95, "benign": 95, "curat": 95, "bigger": 96, "make_classif": 96, "5000": [96, 104], "n_featur": 96, "n_inform": 96, "n_redund": 96, "n_repeat": 96, "n_class": 96, "n_clusters_per_class": 96, "flip_i": 96, "class_sep": 96, "faiss": 96, "x_faiss": 96, "float32": [96, 103], "normalize_l2": 96, "index_factori": 96, "hnsw32": 96, "flat": [96, 97], "metric_inner_product": 96, "a_min": 96, "a_max": 96, "create_knn_graph": 96, "assert": 96, "indices_1d": 96, "ravel": 96, "distances_1d": 96, "50000": 96, "116": 96, "523": 96, "991400": 96, "356958": 96, "362": 96, "619565": 96, "108": [96, 103], "500000": 96, "651929": 96, "999827": 96, "031217": 96, "933716": 96, "627345": 96, "998540": 96, "530909": 96, "296974": 96, "646765": 96, "942721": 96, "332824": 96, "803246": 96, "625202": 96, "999816": 96, "474031": 96, "706253": 96, "655108": 96, "997703": 96, "131466": 96, "912389": 96, "639200": 96, "4995": 96, "998646": 96, "504755": 96, "746777": 96, "680033": 96, "4996": 96, "894230": 96, "340986": 96, "816472": 96, "640711": 96, "4997": 96, "999100": 96, "428545": 96, "592421": 96, "658949": 96, "4998": 96, "986792": 96, "273710": 96, "618033": 96, "4999": 96, "986776": 96, "273524": 96, "618084": 96, "instabl": 96, "proxim": 96, "analys": 96, "comfort": 96, "explor": [96, 103, 104], "third": 96, "parti": [96, 108], "newsgroup": 96, "alt": [96, 97], "atheism": [96, 97], "sci": [96, 97], "fetch_20newsgroup": 96, "newsgroups_train": 96, "header": 96, "footer": 96, "quot": 96, "df_text": 96, "target_nam": 96, "enlighten": 96, "omnipot": 96, "19apr199320262420": 96, "kelvin": 96, "jpl": 96, "nasa": 96, "gov": 96, "baa": 96, "nhenri": 96, "he": 96, "nno": 96, "ge": 96, "nlucki": 96, "babi": [96, 97], "tfidfvector": 96, "feature_extract": 96, "x_vector": 96, "data_valuation_issu": 96, "147": [96, 99, 103], "500047": 96, "500093": 96, "499953": 96, "1068": 96, "1069": 96, "1070": 96, "1071": 96, "1072": 96, "1073": 96, "concentr": 96, "seaborn": 96, "sn": 96, "distinguish": 96, "strip": 96, "stripplot": 96, "hue": [96, 106], "dodg": 96, "jitter": 96, "axvlin": [96, 104], "xlabel": 96, "ourselv": 96, "make_blob": 96, "center": [96, 97], "cluster_std": 96, "n_noisy_label": 96, "meaning": [96, 98, 104], "silhouette_scor": 96, "gridsearchcv": 96, "silhouett": 96, "cluster_label": 96, "fit_predict": 96, "param_grid": 96, "grid_search": 96, "best_kmean": 96, "best_estimator_": 96, "underperforming_group_issu": 96, "328308": 96, "tab10": 96, "domain": 96, "knowledg": [96, 99], "dataset_tsv": 96, "ag": [96, 106], "gender": 96, "educ": 96, "experi": 96, "highsalari": 96, "indiana": 96, "phd": 96, "male": 96, "bachelor": 96, "femal": 96, "kansa": 96, "school": [96, 97], "ohio": 96, "57": [96, 97, 99], "california": 96, "59": [96, 97, 103], "63": [96, 99, 103, 106], "47": [96, 97, 103], "stringio": 96, "sep": [96, 108], "simplic": [96, 102], "ordinalencod": 96, "columns_to_encod": 96, "encoded_df": 96, "salari": 96, "573681": 96, "underpin": 96, "caught": 96, "whenev": 96, "generate_data_depend": 96, "num_sampl": 96, "a1": 96, "a2": 96, "a3": 96, "375": 96, "975": 96, "non_iid_issu": 96, "796474": 96, "842432": 96, "922562": 96, "820759": 96, "873136": 96, "887373": 96, "825101": 96, "855875": 96, "751795": 96, "835796": 96, "ylabel": [96, 104], "coolwarm": 96, "colorbar": [96, 106], "strong": 96, "evid": 96, "inter": 96, "mitig": 96, "risk": 96, "deeper": 96, "tsv": 96, "tab": 96, "pars": 96, "annual_spend": 96, "number_of_transact": 96, "last_purchase_d": 96, "rural": 96, "4099": 96, "2024": [96, 108], "6421": 96, "nat": 96, "suburban": 96, "5436": 96, "4046": 96, "66": [96, 97], "3467": 96, "67": [96, 97, 103, 106], "4757": 96, "4199": 96, "4991": 96, "4655": 96, "82": [96, 97, 99, 103, 106], "5584": 96, "urban": 96, "3102": 96, "6637": 96, "9167": 96, "6790": 96, "5327": 96, "parse_d": 96, "lose": 96, "intact": 96, "encode_categorical_column": 96, "placehold": 96, "dropna": [96, 101], "category_to_numb": 96, "_encod": 96, "gender_encod": 96, "location_encod": 96, "focus": [96, 99, 101, 102, 106], "null_issu": 96, "833333": 96, "sorted_indic": [96, 103], "sorted_df": 96, "nice": 96, "styler": 96, "combined_df": 96, "concat": [96, 106], "highlight_null_valu": 96, "val": [96, 99], "yellow": [96, 97], "highlight_datalab_column": 96, "lightblu": 96, "highlight_is_null_issu": 96, "orang": [96, 97], "styled_df": 96, "nbsp": [96, 98, 99], "160000": 96, "820000": 96, "460000": 96, "470000": 96, "960000": 96, "620000": 96, "550000": 96, "660000": 96, "670000": [96, 97], "370000": 96, "530000": 96, "710000": 96, "020000": 96, "320000": 96, "990000": 96, "rarer": 96, "fairer": 96, "randomli": [96, 99], "class_imbalance_issu": 96, "countplot": 96, "xtick": 96, "rotat": 96, "ytick": 96, "filtered_df": 96, "xy": 96, "va": 96, "textual": 96, "get_ytick": 96, "nbar": 96, "nimbal": 96, "get_legend_handles_label": 96, "title_fonts": 96, "refin": 97, "instruct": [97, 98], "studi": [97, 103], "mnist_test_set": 97, "imagenet_val_set": 97, "tench": 97, "goldfish": 97, "white": [97, 108], "shark": 97, "tiger": 97, "hammerhead": 97, "electr": 97, "rai": 97, "stingrai": 97, "cock": 97, "hen": 97, "ostrich": 97, "brambl": 97, "goldfinch": 97, "hous": 97, "finch": 97, "junco": 97, "indigo": 97, "bunt": 97, "american": [97, 108], "robin": 97, "bulbul": 97, "jai": 97, "magpi": 97, "chickade": 97, "dipper": 97, "kite": 97, "bald": 97, "eagl": 97, "vultur": 97, "grei": 97, "owl": 97, "salamand": 97, "smooth": 97, "newt": 97, "spot": [97, 98, 103], "axolotl": 97, "bullfrog": 97, "tree": 97, "frog": [97, 104], "tail": 97, "loggerhead": 97, "sea": 97, "turtl": 97, "leatherback": 97, "mud": 97, "terrapin": 97, "band": 97, "gecko": 97, "green": [97, 108], "iguana": 97, "carolina": 97, "anol": 97, "desert": 97, "grassland": 97, "whiptail": 97, "lizard": 97, "agama": 97, "frill": 97, "neck": 97, "allig": 97, "gila": 97, "monster": 97, "european": 97, "chameleon": 97, "komodo": 97, "dragon": 97, "nile": 97, "crocodil": 97, "triceratop": 97, "worm": 97, "snake": 97, "ring": 97, "eastern": 97, "hog": 97, "nose": 97, "kingsnak": 97, "garter": 97, "water": 97, "vine": 97, "night": 97, "boa": 97, "constrictor": 97, "african": 97, "rock": 97, "indian": 97, "cobra": 97, "mamba": 97, "saharan": 97, "horn": 97, "viper": 97, "diamondback": 97, "rattlesnak": 97, "sidewind": 97, "trilobit": 97, "harvestman": 97, "scorpion": 97, "garden": 97, "spider": 97, "barn": 97, "southern": 97, "widow": 97, "tarantula": 97, "wolf": 97, "tick": 97, "centiped": 97, "grous": 97, "ptarmigan": 97, "ruf": 97, "prairi": 97, "peacock": 97, "quail": 97, "partridg": 97, "parrot": 97, "macaw": 97, "sulphur": 97, "crest": 97, "cockatoo": 97, "lorikeet": 97, "coucal": 97, "bee": 97, "eater": 97, "hornbil": 97, "hummingbird": 97, "jacamar": 97, "toucan": 97, "breast": 97, "mergans": 97, "goos": 97, "swan": 97, "tusker": 97, "echidna": 97, "platypu": 97, "wallabi": 97, "koala": 97, "wombat": 97, "jellyfish": 97, "anemon": 97, "brain": 97, "coral": 97, "flatworm": 97, "nematod": 97, "conch": 97, "snail": 97, "slug": 97, "chiton": 97, "chamber": 97, "nautilu": 97, "dung": 97, "crab": 97, "fiddler": 97, "king": 97, "lobster": 97, "spini": 97, "crayfish": 97, "hermit": 97, "isopod": 97, "stork": 97, "spoonbil": 97, "flamingo": 97, "heron": 97, "egret": 97, "bittern": 97, "crane": 97, "bird": [97, 104], "limpkin": 97, "gallinul": 97, "coot": 97, "bustard": 97, "ruddi": 97, "turnston": 97, "dunlin": 97, "redshank": 97, "dowitch": 97, "oystercatch": 97, "pelican": 97, "penguin": 97, "albatross": 97, "whale": 97, "killer": 97, "dugong": 97, "lion": 97, "chihuahua": 97, "japanes": 97, "chin": 97, "maltes": 97, "pekinges": 97, "shih": 97, "tzu": 97, "charl": 97, "spaniel": 97, "papillon": 97, "terrier": 97, "rhodesian": 97, "ridgeback": 97, "afghan": [97, 108], "hound": 97, "basset": 97, "beagl": 97, "bloodhound": 97, "bluetick": 97, "coonhound": 97, "tan": 97, "walker": 97, "foxhound": 97, "redbon": 97, "borzoi": 97, "irish": 97, "wolfhound": 97, "italian": 97, "greyhound": 97, "whippet": 97, "ibizan": 97, "norwegian": 97, "elkhound": 97, "otterhound": 97, "saluki": 97, "scottish": 97, "deerhound": 97, "weimaran": 97, "staffordshir": 97, "bull": 97, "bedlington": 97, "border": 97, "kerri": 97, "norfolk": 97, "norwich": 97, "yorkshir": 97, "wire": 97, "fox": 97, "lakeland": 97, "sealyham": 97, "airedal": 97, "cairn": 97, "australian": 97, "dandi": 97, "dinmont": 97, "boston": 97, "miniatur": 97, "schnauzer": 97, "giant": 97, "tibetan": 97, "silki": 97, "wheaten": 97, "west": 97, "highland": 97, "lhasa": 97, "apso": 97, "retriev": 97, "curli": 97, "golden": 97, "labrador": 97, "chesapeak": 97, "bai": 97, "german": [97, 108], "shorthair": 97, "pointer": 97, "vizsla": 97, "setter": 97, "gordon": 97, "brittani": 97, "clumber": 97, "springer": 97, "welsh": 97, "cocker": 97, "sussex": 97, "kuvasz": 97, "schipperk": 97, "groenendael": 97, "malinoi": 97, "briard": 97, "kelpi": 97, "komondor": 97, "sheepdog": 97, "shetland": 97, "colli": 97, "bouvier": 97, "de": 97, "flandr": 97, "rottweil": 97, "shepherd": 97, "dobermann": 97, "pinscher": 97, "swiss": [97, 108], "mountain": 97, "bernes": 97, "appenzel": 97, "sennenhund": 97, "entlebuch": 97, "boxer": 97, "bullmastiff": 97, "mastiff": 97, "french": 97, "bulldog": 97, "dane": 97, "st": 97, "bernard": 97, "huski": 97, "alaskan": 97, "malamut": 97, "siberian": 97, "dalmatian": 97, "affenpinsch": 97, "basenji": 97, "pug": 97, "leonberg": 97, "newfoundland": 97, "pyrenean": 97, "samoi": 97, "pomeranian": 97, "chow": 97, "keeshond": 97, "griffon": 97, "bruxelloi": 97, "pembrok": 97, "corgi": 97, "cardigan": 97, "poodl": 97, "mexican": 97, "hairless": 97, "tundra": 97, "coyot": 97, "dingo": 97, "dhole": 97, "wild": 97, "hyena": 97, "kit": 97, "arctic": 97, "tabbi": 97, "persian": 97, "siames": 97, "egyptian": 97, "mau": 97, "cougar": 97, "lynx": 97, "leopard": 97, "snow": 97, "jaguar": 97, "cheetah": 97, "brown": [97, 107], "bear": 97, "polar": 97, "sloth": 97, "mongoos": 97, "meerkat": 97, "beetl": 97, "ladybug": 97, "longhorn": 97, "leaf": 97, "rhinocero": 97, "weevil": 97, "fly": 97, "ant": 97, "grasshopp": 97, "cricket": 97, "stick": 97, "insect": 97, "cockroach": 97, "manti": 97, "cicada": 97, "leafhopp": 97, "lacew": 97, "dragonfli": 97, "damselfli": 97, "admir": 97, "ringlet": 97, "monarch": 97, "butterfli": 97, "gossam": 97, "wing": 97, "starfish": 97, "urchin": 97, "cucumb": 97, "cottontail": 97, "rabbit": 97, "hare": 97, "angora": 97, "hamster": 97, "porcupin": 97, "squirrel": 97, "marmot": 97, "beaver": 97, "guinea": 97, "pig": 97, "sorrel": 97, "zebra": 97, "boar": 97, "warthog": 97, "hippopotamu": 97, "ox": 97, "buffalo": 97, "bison": 97, "bighorn": 97, "sheep": 97, "alpin": 97, "ibex": 97, "hartebeest": 97, "impala": 97, "gazel": 97, "dromedari": 97, "llama": 97, "weasel": 97, "mink": 97, "polecat": 97, "foot": 97, "ferret": 97, "otter": 97, "skunk": 97, "badger": 97, "armadillo": 97, "toed": 97, "orangutan": 97, "gorilla": 97, "chimpanze": 97, "gibbon": 97, "siamang": 97, "guenon": 97, "pata": 97, "monkei": 97, "baboon": 97, "macaqu": 97, "langur": 97, "colobu": 97, "probosci": 97, "marmoset": 97, "capuchin": 97, "howler": 97, "titi": 97, "geoffroi": 97, "lemur": 97, "indri": 97, "asian": 97, "eleph": 97, "bush": 97, "snoek": 97, "eel": 97, "coho": 97, "salmon": 97, "beauti": 97, "clownfish": 97, "sturgeon": 97, "garfish": 97, "lionfish": 97, "pufferfish": 97, "abacu": 97, "abaya": 97, "academ": 97, "gown": 97, "accordion": 97, "acoust": 97, "guitar": 97, "aircraft": 97, "carrier": 97, "airlin": 97, "airship": 97, "altar": 97, "ambul": 97, "amphibi": 97, "clock": [97, 108], "apiari": 97, "apron": 97, "wast": 97, "assault": 97, "rifl": 97, "backpack": 97, "bakeri": 97, "balanc": 97, "beam": 97, "balloon": 97, "ballpoint": 97, "pen": 97, "aid": 97, "banjo": 97, "balust": 97, "barbel": 97, "barber": 97, "chair": [97, 103], "barbershop": 97, "baromet": 97, "barrel": 97, "wheelbarrow": 97, "basebal": 97, "basketbal": 97, "bassinet": 97, "bassoon": 97, "swim": 97, "cap": 97, "bath": 97, "towel": 97, "bathtub": 97, "station": 97, "wagon": 97, "lighthous": 97, "beaker": 97, "militari": 97, "beer": 97, "bottl": 97, "glass": 97, "bell": 97, "cot": 97, "bib": 97, "bicycl": [97, 107], "bikini": 97, "binder": 97, "binocular": 97, "birdhous": 97, "boathous": 97, "bobsleigh": 97, "bolo": 97, "tie": 97, "poke": 97, "bonnet": 97, "bookcas": 97, "bookstor": 97, "bow": 97, "brass": 97, "bra": 97, "breakwat": 97, "breastplat": 97, "broom": 97, "bucket": 97, "buckl": 97, "bulletproof": 97, "vest": 97, "butcher": 97, "shop": 97, "taxicab": 97, "cauldron": 97, "candl": 97, "cannon": 97, "cano": 97, "mirror": [97, 103], "carousel": 97, "tool": [97, 99, 101], "carton": 97, "wheel": 97, "teller": 97, "cassett": 97, "player": 97, "castl": 97, "catamaran": 97, "cd": 97, "cello": 97, "mobil": [97, 108], "chain": 97, "fenc": [97, 107], "mail": 97, "chainsaw": 97, "chest": 97, "chiffoni": 97, "chime": 97, "china": 97, "cabinet": 97, "christma": 97, "stock": 97, "church": 97, "movi": 97, "theater": 97, "cleaver": 97, "cliff": 97, "dwell": 97, "cloak": 97, "clog": 97, "cocktail": 97, "shaker": 97, "coffe": 97, "mug": 97, "coffeemak": 97, "coil": 97, "lock": 97, "keyboard": 97, "confectioneri": 97, "ship": [97, 104], "corkscrew": 97, "cornet": 97, "cowboi": 97, "boot": 97, "hat": 97, "cradl": 97, "crash": 97, "helmet": 97, "crate": 97, "infant": 97, "bed": 97, "crock": 97, "pot": 97, "croquet": 97, "crutch": 97, "cuirass": 97, "dam": 97, "desk": 97, "desktop": 97, "rotari": 97, "dial": 97, "telephon": 97, "diaper": 97, "watch": 97, "dine": 97, "dishcloth": 97, "dishwash": 97, "disc": 97, "brake": 97, "dock": 97, "sled": 97, "dome": 97, "doormat": 97, "drill": 97, "rig": 97, "drum": 97, "drumstick": 97, "dumbbel": 97, "dutch": 97, "oven": 97, "fan": 97, "locomot": 97, "entertain": 97, "envelop": 97, "espresso": 97, "powder": 97, "feather": 97, "fireboat": 97, "engin": [97, 107], "screen": 97, "sheet": 97, "flagpol": 97, "flute": 97, "footbal": 97, "forklift": 97, "fountain": 97, "poster": 97, "freight": 97, "fry": 97, "pan": 97, "fur": 97, "garbag": 97, "ga": 97, "pump": 97, "goblet": 97, "kart": 97, "golf": 97, "cart": 97, "gondola": 97, "gong": 97, "grand": 97, "piano": 97, "greenhous": 97, "grill": 97, "groceri": 97, "guillotin": 97, "barrett": 97, "hair": 97, "sprai": 97, "hammer": 97, "dryer": 97, "hand": [97, 99], "handkerchief": 97, "drive": 97, "harmonica": 97, "harp": 97, "harvest": 97, "hatchet": 97, "holster": 97, "honeycomb": 97, "hoop": 97, "skirt": 97, "horizont": 97, "bar": 97, "drawn": 97, "hourglass": 97, "ipod": 97, "cloth": 97, "iron": 97, "jack": 97, "lantern": 97, "jean": 97, "jeep": 97, "jigsaw": 97, "puzzl": 97, "pull": 97, "rickshaw": 97, "joystick": 97, "kimono": 97, "knee": 97, "pad": 97, "knot": 97, "ladl": 97, "lampshad": 97, "laptop": 97, "lawn": 97, "mower": 97, "knife": 97, "lifeboat": 97, "lighter": 97, "limousin": 97, "ocean": 97, "liner": 97, "lipstick": 97, "slip": 97, "shoe": 97, "lotion": 97, "speaker": 97, "loup": 97, "sawmil": 97, "magnet": 97, "compass": 97, "mailbox": 97, "tight": 97, "tank": 97, "manhol": 97, "maraca": 97, "marimba": 97, "maypol": 97, "maze": 97, "cup": [97, 103], "medicin": 97, "megalith": 97, "microphon": 97, "microwav": 97, "milk": 97, "minibu": 97, "miniskirt": 97, "minivan": 97, "missil": 97, "mitten": [97, 98], "mix": 97, "bowl": 97, "modem": 97, "monasteri": 97, "monitor": 97, "mope": 97, "mortar": 97, "mosqu": 97, "mosquito": 97, "scooter": 97, "bike": 97, "tent": 97, "mous": [97, 98], "mousetrap": 97, "van": 97, "muzzl": 97, "nail": 97, "brace": 97, "necklac": 97, "nippl": 97, "obelisk": 97, "obo": 97, "ocarina": 97, "odomet": 97, "oil": 97, "oscilloscop": 97, "overskirt": 97, "bullock": 97, "oxygen": 97, "packet": 97, "paddl": 97, "padlock": 97, "paintbrush": 97, "pajama": 97, "palac": [97, 108], "parachut": 97, "park": 97, "bench": 97, "meter": 97, "passeng": 97, "patio": 97, "payphon": 97, "pedest": 97, "pencil": 97, "perfum": 97, "petri": 97, "dish": 97, "photocopi": 97, "plectrum": 97, "pickelhaub": 97, "picket": 97, "pickup": 97, "pier": 97, "piggi": 97, "pill": 97, "pillow": 97, "ping": 97, "pong": 97, "pinwheel": 97, "pirat": 97, "pitcher": 97, "plane": 97, "planetarium": 97, "plastic": 97, "plate": 97, "rack": 97, "plow": 97, "plunger": 97, "polaroid": 97, "camera": 97, "pole": [97, 107], "polic": 97, "poncho": 97, "billiard": 97, "soda": 97, "potter": 97, "prayer": 97, "rug": 97, "printer": 97, "prison": 97, "projectil": 97, "projector": 97, "hockei": 97, "puck": 97, "punch": 97, "purs": 97, "quill": 97, "quilt": 97, "race": 97, "racket": 97, "radiat": 97, "radio": 97, "telescop": 97, "rain": 97, "recreat": 97, "reel": 97, "reflex": 97, "refriger": 97, "remot": 97, "restaur": 97, "revolv": 97, "rotisseri": 97, "eras": 97, "rugbi": 97, "ruler": 97, "safe": 97, "safeti": 97, "salt": 97, "sarong": 97, "saxophon": 97, "scabbard": 97, "bu": [97, 107], "schooner": 97, "scoreboard": 97, "crt": 97, "screw": 97, "screwdriv": 97, "seat": 97, "belt": 97, "sew": 97, "shield": 97, "shoji": 97, "basket": 97, "shovel": 97, "shower": 97, "curtain": 97, "ski": 97, "sleep": 97, "door": 97, "slot": 97, "snorkel": 97, "snowmobil": 97, "snowplow": 97, "soap": 97, "dispens": 97, "soccer": [97, 108], "sock": [97, 98], "solar": 97, "thermal": 97, "collector": 97, "sombrero": 97, "soup": 97, "heater": 97, "shuttl": 97, "spatula": 97, "motorboat": 97, "web": 97, "spindl": 97, "sport": [97, 108], "spotlight": 97, "stage": 97, "steam": 97, "arch": 97, "bridg": 97, "steel": 97, "stethoscop": 97, "scarf": 97, "stone": 97, "wall": [97, 107], "stopwatch": 97, "stove": 97, "strainer": 97, "tram": 97, "stretcher": 97, "couch": 97, "stupa": 97, "submarin": 97, "sundial": 97, "sunglass": 97, "sunscreen": 97, "suspens": 97, "mop": 97, "sweatshirt": 97, "swimsuit": 97, "swing": 97, "switch": 97, "syring": 97, "lamp": 97, "tape": 97, "teapot": 97, "teddi": 97, "televis": [97, 108], "tenni": 97, "thatch": 97, "roof": 97, "thimbl": 97, "thresh": 97, "throne": 97, "tile": 97, "toaster": 97, "tobacco": 97, "toilet": 97, "totem": 97, "tow": 97, "tractor": 97, "semi": 97, "trailer": 97, "trai": 97, "trench": 97, "tricycl": 97, "trimaran": 97, "tripod": 97, "triumphal": 97, "trolleybu": 97, "trombon": 97, "tub": 97, "turnstil": 97, "typewrit": 97, "umbrella": 97, "unicycl": 97, "upright": 97, "vacuum": 97, "cleaner": 97, "vase": 97, "vault": 97, "velvet": 97, "vend": 97, "vestment": 97, "viaduct": 97, "violin": 97, "volleybal": 97, "waffl": 97, "wallet": 97, "wardrob": 97, "sink": 97, "wash": 97, "jug": 97, "tower": 97, "whiskei": 97, "whistl": 97, "wig": 97, "shade": [97, 107], "windsor": 97, "wine": 97, "wok": 97, "wooden": 97, "spoon": 97, "wool": 97, "rail": 97, "shipwreck": 97, "yawl": 97, "yurt": 97, "websit": 97, "comic": 97, "book": 97, "crossword": 97, "traffic": [97, 103, 107], "sign": [97, 107, 108], "dust": 97, "jacket": [97, 103], "menu": 97, "guacamol": 97, "consomm": 97, "trifl": 97, "ic": 97, "cream": 97, "pop": 97, "baguett": 97, "bagel": 97, "pretzel": 97, "cheeseburg": 97, "mash": 97, "potato": 97, "cabbag": 97, "broccoli": 97, "cauliflow": 97, "zucchini": 97, "spaghetti": 97, "squash": 97, "acorn": 97, "butternut": 97, "artichok": 97, "pepper": [97, 98], "cardoon": 97, "mushroom": 97, "granni": 97, "smith": 97, "strawberri": 97, "lemon": 97, "pineappl": 97, "banana": 97, "jackfruit": 97, "custard": 97, "appl": 97, "pomegran": 97, "hai": 97, "carbonara": 97, "chocol": 97, "syrup": 97, "dough": 97, "meatloaf": 97, "pizza": 97, "pie": 97, "burrito": 97, "eggnog": 97, "alp": 97, "bubbl": 97, "reef": 97, "geyser": 97, "lakeshor": 97, "promontori": 97, "shoal": 97, "seashor": 97, "vallei": 97, "volcano": 97, "bridegroom": 97, "scuba": 97, "diver": 97, "rapese": 97, "daisi": 97, "ladi": 97, "slipper": 97, "corn": 97, "rose": 97, "hip": 97, "chestnut": 97, "fungu": 97, "agar": 97, "gyromitra": 97, "stinkhorn": 97, "earth": 97, "star": 97, "wood": 97, "bolet": 97, "ear": 97, "cifar10_test_set": 97, "airplan": [97, 104], "automobil": [97, 104], "deer": [97, 104], "cifar100_test_set": 97, "aquarium_fish": 97, "boi": 97, "camel": 97, "caterpillar": 97, "cattl": [97, 108], "cloud": 97, "dinosaur": 97, "dolphin": 97, "flatfish": 97, "forest": 97, "girl": 97, "kangaroo": 97, "lawn_mow": 97, "man": 97, "maple_tre": 97, "motorcycl": [97, 107], "oak_tre": 97, "orchid": 97, "palm_tre": 97, "pear": 97, "pickup_truck": 97, "pine_tre": 97, "plain": 97, "poppi": 97, "possum": 97, "raccoon": 97, "road": [97, 107], "rocket": 97, "seal": 97, "shrew": 97, "skyscrap": 97, "streetcar": 97, "sunflow": 97, "sweet_pepp": 97, "trout": 97, "tulip": 97, "willow_tre": 97, "woman": [97, 103], "caltech256": 97, "ak47": 97, "bat": 97, "glove": 97, "birdbath": 97, "blimp": 97, "bonsai": 97, "boom": 97, "breadmak": 97, "buddha": 97, "bulldoz": 97, "cactu": 97, "cake": 97, "tire": 97, "cartman": 97, "cereal": 97, "chandeli": 97, "chess": 97, "board": 97, "chimp": 97, "chopstick": 97, "coffin": 97, "coin": 97, "comet": 97, "cormor": 97, "globe": 97, "diamond": 97, "dice": 97, "doorknob": 97, "drink": 97, "straw": 97, "dumb": 97, "eiffel": 97, "elk": 97, "ewer": 97, "eyeglass": 97, "fern": 97, "fighter": 97, "jet": [97, 106], "extinguish": 97, "hydrant": 97, "firework": 97, "flashlight": 97, "floppi": 97, "fri": 97, "frisbe": 97, "galaxi": 97, "giraff": 97, "goat": 97, "gate": 97, "grape": 97, "pick": [97, 98], "hamburg": 97, "hammock": 97, "harpsichord": 97, "hawksbil": 97, "helicopt": 97, "hibiscu": 97, "homer": 97, "simpson": 97, "horsesho": 97, "air": 97, "skeleton": 97, "ibi": 97, "cone": 97, "iri": 97, "jesu": 97, "christ": 97, "joi": 97, "kayak": 97, "ketch": 97, "ladder": 97, "lath": 97, "licens": 97, "lightbulb": 97, "lightn": 97, "mandolin": 97, "mar": 97, "mattress": 97, "megaphon": 97, "menorah": 97, "microscop": 97, "minaret": 97, "minotaur": 97, "motorbik": 97, "mussel": 97, "neckti": 97, "octopu": 97, "palm": 97, "pilot": 97, "paperclip": 97, "shredder": 97, "pci": 97, "peopl": [97, 103], "pez": 97, "picnic": 97, "pram": 97, "prai": 97, "pyramid": 97, "rainbow": 97, "roulett": 97, "saddl": 97, "saturn": 97, "segwai": 97, "propel": 97, "sextant": 97, "music": 97, "skateboard": 97, "smokestack": 97, "sneaker": 97, "boat": 97, "stain": 97, "steer": 97, "stirrup": 97, "superman": 97, "sushi": 97, "armi": [97, 108], "sword": 97, "tambourin": 97, "teepe": 97, "court": 97, "theodolit": 97, "tomato": 97, "tombston": 97, "tour": 97, "pisa": 97, "treadmil": 97, "fork": 97, "tweezer": 97, "unicorn": 97, "vcr": 97, "waterfal": 97, "watermelon": 97, "weld": 97, "windmil": 97, "xylophon": 97, "yarmulk": 97, "yo": 97, "toad": 97, "twenty_news_test_set": 97, "comp": 97, "graphic": [97, 107], "misc": [97, 108], "sy": 97, "ibm": 97, "pc": 97, "hardwar": 97, "mac": 97, "forsal": 97, "rec": 97, "crypt": 97, "electron": 97, "med": 97, "soc": 97, "religion": 97, "christian": [97, 108], "talk": [97, 108], "polit": 97, "gun": 97, "mideast": 97, "amazon": 97, "neutral": 97, "imdb_test_set": 97, "all_class": 97, "20news_test_set": 97, "_load_classes_predprobs_label": 97, "dataset_nam": 97, "labelerror": 97, "url_bas": 97, "5392f6c71473055060be3044becdde1cbc18284d": 97, "url_label": 97, "original_test_label": 97, "_original_label": 97, "url_prob": 97, "cross_validated_predicted_prob": 97, "_pyx": 97, "num_part": 97, "datatset": 97, "bytesio": 97, "allow_pickl": 97, "pred_probs_part": 97, "url": 97, "_of_": 97, "nload": 97, "imdb": 97, "ve": [97, 98, 99, 101, 103], "capit": 97, "29780": 97, "256": [97, 98, 103], "780": 97, "medic": [97, 108], "doctor": 97, "254": [97, 103], "359223": 97, "640777": 97, "184": [97, 99], "258427": 97, "341176": 97, "263158": 97, "658824": 97, "337349": 97, "246575": 97, "662651": 97, "248": 97, "330000": 97, "355769": 97, "251": [97, 103], "167": [97, 99, 103], "252": 97, "112": 97, "253": [97, 103], "022989": 97, "049505": 97, "190": [97, 99, 103], "002216": 97, "000974": 97, "000873": 97, "000739": 97, "32635": 97, "32636": 97, "32637": 97, "32638": 97, "32639": 97, "32640": 97, "051": 97, "002242": 97, "997758": 97, "002088": 97, "001045": 97, "997912": 97, "002053": 97, "997947": 97, "001980": 97, "000991": 97, "998020": 97, "001946": 97, "002915": 97, "998054": 97, "001938": 97, "002904": 97, "998062": 97, "001020": 97, "998980": 97, "001018": 97, "002035": 97, "998982": 97, "999009": 97, "0003": 97, "0002": 97, "071": 97, "067269": 97, "929": 97, "046": 97, "058243": 97, "954": 97, "035": 97, "032096": 97, "965": 97, "031": 97, "012232": 97, "969": 97, "022": 97, "025896": 97, "978": 97, "020": [97, 99], "013092": 97, "018": 97, "013065": 97, "016": 97, "030542": 97, "984": [97, 108], "013": 97, "020833": 97, "987": 97, "012": 97, "010020": 97, "988": 97, "0073": 97, "0020": 97, "0016": 97, "0015": 97, "0014": 97, "0013": 97, "0012": 97, "0010": 97, "0008": 97, "0007": 97, "0006": 97, "0005": 97, "0004": 97, "244": [97, 103], "452381": 97, "459770": 97, "523364": 97, "460784": 97, "446602": 97, "103774": 97, "030612": 97, "110092": 97, "049020": 97, "0034": 97, "0032": 97, "0026": 97, "0025": 97, "4945": 97, "4946": 97, "4947": 97, "4948": 97, "4949": 97, "4950": 97, "846": 97, "7532": 97, "532": 97, "034483": 97, "009646": 97, "965517": 97, "030457": 97, "020513": 97, "969543": 97, "028061": 97, "035443": 97, "971939": 97, "025316": 97, "005168": 97, "974684": 97, "049751": 97, "979487": 97, "019920": 97, "042802": 97, "980080": 97, "017677": 97, "005115": 97, "982323": 97, "012987": 97, "005236": 97, "987013": 97, "012723": 97, "025126": 97, "987277": 97, "010989": 97, "008264": 97, "989011": 97, "010283": 97, "027778": 97, "989717": 97, "009677": 97, "990323": 97, "007614": 97, "010127": 97, "992386": 97, "005051": 97, "994949": 97, "005025": 97, "994975": 97, "005013": 97, "994987": 97, "001859": 97, "001328": 97, "000929": 97, "000664": 97, "186": [97, 99], "188": [97, 99, 102], "189": [97, 99], "snippet": 98, "nlp": [98, 108], "mind": [98, 99], "alphanumer": 98, "facilit": 98, "seamless": 98, "classlabel": 98, "guidanc": 98, "labels_str": 98, "datalab_str": 98, "labels_int": 98, "remap": 98, "datalab_int": 98, "my_dict": 98, "pet_nam": 98, "rover": 98, "rocki": 98, "speci": 98, "from_dict": 98, "datalab_dataset": 98, "number_of_class": 98, "total_number_of_data_point": 98, "feed": 98, "alphabet": 98, "labels_proper_format": 98, "your_classifi": 98, "issues_datafram": 98, "class_predicted_for_flagged_exampl": 98, "class_predicted_for_all_exampl": 98, "grant": 98, "On": [98, 99, 103], "merged_dataset": 98, "label_column_nam": 98, "datataset": 98, "fair": [98, 99], "game": 98, "speedup": [98, 104], "tempfil": 98, "mkdtemp": 98, "sped": 98, "anywai": 98, "pred_probs_merg": 98, "merge_rare_class": 98, "count_threshold": 98, "class_mapping_orig2new": 98, "heath_summari": 98, "num_examples_per_class": 98, "rare_class": 98, "num_classes_merg": 98, "other_class": 98, "labels_merg": 98, "new_c": 98, "merged_prob": 98, "new_class": 98, "original_class": 98, "num_check": 98, "ones_array_ref": 98, "isclos": 98, "though": [98, 99, 108], "successfulli": 98, "virtuou": [98, 101], "cycl": [98, 101], "jointli": 98, "junk": 98, "clutter": 98, "unknown": 98, "caltech": 98, "combined_boolean_mask": 98, "mask1": 98, "mask2": 98, "gradientboostingclassifi": [98, 99], "true_error": [98, 99, 102], "101": [98, 103], "102": [98, 102, 103], "104": [98, 99, 103], "model_to_find_error": 98, "model_to_return": 98, "cl0": 98, "randomizedsearchcv": 98, "expens": 98, "param_distribut": 98, "learning_r": [98, 99], "max_depth": [98, 99], "magnitud": 98, "coeffici": [98, 106], "optin": 98, "environ": [98, 99], "rerun": [98, 99], "cell": [98, 99], "unabl": [98, 99], "render": [98, 99], "nbviewer": [98, 99], "cleanlearninginot": [98, 99], "fittedcleanlearn": [98, 99], "linearregressionlinearregress": 98, "unexpectedli": 98, "emphas": 98, "crucial": 98, "merge_duplicate_set": 98, "merge_kei": 98, "construct_group_kei": 98, "merged_set": 98, "consolidate_set": 98, "issubset": 98, "frozenset": 98, "sets_list": 98, "mutabl": 98, "new_set": 98, "current_set": 98, "intersecting_set": 98, "lowest_score_strategi": 98, "sub_df": 98, "filter_near_dupl": 98, "strategy_fn": 98, "strategy_kwarg": 98, "duplicate_row": 98, "group_kei": 98, "to_keep_indic": 98, "groupbi": 98, "explod": 98, "to_remov": 98, "isin": [98, 104], "kept": 98, "ids_to_remove_seri": 98, "tmp": 98, "ipykernel_7655": 98, "1995098996": 98, "deprecationwarn": 98, "dataframegroupbi": 98, "include_group": 98, "silenc": 98, "assist": 98, "streamlin": 98, "ux": 98, "agpl": 98, "compani": 98, "commerci": 98, "alter": 98, "email": 98, "team": 98, "discuss": 98, "anywher": 98, "profession": 98, "expert": 98, "depth": 99, "survei": [99, 108], "scienc": 99, "multivariate_norm": [99, 101, 102], "make_data": [99, 101], "cov": [99, 101, 102], "avg_trac": [99, 102], "py_tru": 99, "noise_matrix_tru": 99, "noise_marix": 99, "s_test": 99, "noisy_test_label": 99, "purpl": 99, "namespac": 99, "exec": 99, "markerfacecolor": [99, 102], "markeredgecolor": [99, 102, 106], "markers": [99, 102, 106], "markeredgewidth": [99, 102, 106], "realist": 99, "7560": 99, "637318e": 99, "896262e": 99, "548391e": 99, "923417e": 99, "375075e": 99, "3454": 99, "014051": 99, "020451": 99, "249": [99, 103, 108], "042594": 99, "043859": 99, "045954": 99, "6120": 99, "023714": 99, "007136": 99, "119": [99, 103], "107266": 99, "103": [99, 103], "033738": 99, "238": [99, 103], "119505": 99, "236": [99, 103], "037843": 99, "222": 99, "614915": 99, "122": [99, 103], "624422": 99, "625965": 99, "626079": 99, "118": 99, "627675": 99, "695223": 99, "323529": 99, "523015": 99, "013720": 99, "675727": 99, "646521": 99, "anyth": 99, "enhanc": [99, 101, 103], "magic": 99, "liter": 99, "identif": 99, "x27": 99, "logisticregressionlogisticregress": 99, "ever": 99, "092": 99, "040": 99, "024": 99, "004": 99, "surpris": 99, "1705": 99, "01936": 99, "ton": 99, "yourfavoritemodel1": 99, "merged_label": 99, "merged_test_label": 99, "newli": [99, 101], "yourfavoritemodel2": 99, "yourfavoritemodel3": 99, "cl3": 99, "takeawai": 99, "my_test_pred_prob": 99, "my_test_pr": 99, "issues_test": 99, "corrected_test_label": 99, "pretend": 99, "cl_test_pr": 99, "fairli": 99, "label_acc": 99, "percentag": 99, "offset": 99, "nquestion": 99, "overestim": 99, "answer": 99, "experienc": 99, "prioiri": 99, "known": 99, "versatil": 99, "label_issues_indic": 99, "213": [99, 103], "218": [99, 103], "152": 99, "197": [99, 103], "196": [99, 103, 108], "170": 99, "214": 99, "164": [99, 102], "198": [99, 103], "191": [99, 103], "117": [99, 106], "206": [99, 103], "115": [99, 103], "193": 99, "194": 99, "201": [99, 103], "174": 99, "163": 99, "150": [99, 101, 103, 108], "169": [99, 108], "151": [99, 103], "168": 99, "precision_scor": 99, "recall_scor": 99, "f1_score": 99, "true_label_issu": 99, "filter_by_list": 99, "718750": [99, 101], "807018": 99, "912": 99, "733333": 99, "800000": 99, "721311": 99, "792793": 99, "908": 99, "676923": 99, "765217": 99, "892": 99, "567901": 99, "702290": 99, "844": 99, "gaug": 99, "label_issues_count": 99, "155": [99, 103], "156": 99, "172": [99, 102], "157": 99, "easiest": 99, "modular": 99, "penalti": 99, "l2": 99, "model3": 99, "n_estim": 99, "cv_pred_probs_1": 99, "cv_pred_probs_2": 99, "cv_pred_probs_3": 99, "label_quality_scores_best": 99, "cv_pred_probs_ensembl": 99, "label_quality_scores_bett": 99, "superior": [99, 105], "timm": 100, "glad": 101, "multiannotator_label": 101, "300": [101, 108], "noisier": 101, "111": [101, 106], "local_data": [101, 102], "true_labels_train": [101, 102], "noise_matrix_bett": 101, "noise_matrix_wors": 101, "transpos": [101, 104], "zfill": 101, "row_na_check": 101, "notna": 101, "reset_index": 101, "a0001": 101, "a0002": 101, "a0003": 101, "a0004": 101, "a0005": 101, "a0006": 101, "a0007": 101, "a0008": 101, "a0009": 101, "a0010": 101, "a0041": 101, "a0042": 101, "a0043": 101, "a0044": 101, "a0045": 101, "a0046": 101, "a0047": 101, "a0048": 101, "a0049": 101, "a0050": 101, "na": 101, "60856743": 101, "41693214": 101, "40908785": 101, "87147629": 101, "64941785": 101, "10774851": 101, "0524466": 101, "71853246": 101, "37169848": 101, "66031048": 101, "multiannotator_util": 101, "crude": 101, "straight": 101, "majority_vote_label": 101, "736118": 101, "757751": 101, "782232": 101, "715565": 101, "824256": 101, "quality_annotator_a0001": 101, "quality_annotator_a0002": 101, "quality_annotator_a0003": 101, "quality_annotator_a0004": 101, "quality_annotator_a0005": 101, "quality_annotator_a0006": 101, "quality_annotator_a0007": 101, "quality_annotator_a0008": 101, "quality_annotator_a0009": 101, "quality_annotator_a0010": 101, "quality_annotator_a0041": 101, "quality_annotator_a0042": 101, "quality_annotator_a0043": 101, "quality_annotator_a0044": 101, "quality_annotator_a0045": 101, "quality_annotator_a0046": 101, "quality_annotator_a0047": 101, "quality_annotator_a0048": 101, "quality_annotator_a0049": 101, "quality_annotator_a0050": 101, "070564": 101, "216078": 101, "119188": 101, "alongisd": 101, "244981": 101, "208333": 101, "295979": 101, "294118": 101, "324197": 101, "310345": 101, "355316": 101, "346154": 101, "439732": 101, "480000": 101, "a0031": 101, "523205": 101, "580645": 101, "a0034": 101, "535313": 101, "607143": 101, "a0021": 101, "606999": 101, "a0015": 101, "609526": 101, "678571": 101, "a0011": 101, "621103": 101, "692308": 101, "improved_consensus_label": 101, "majority_vote_accuraci": 101, "cleanlab_label_accuraci": 101, "8581081081081081": 101, "9797297297297297": 101, "besid": 101, "sorted_consensus_quality_scor": 101, "worst_qual": 101, "better_qu": 101, "worst_quality_accuraci": 101, "better_quality_accuraci": 101, "9893238434163701": 101, "improved_pred_prob": 101, "treat": [101, 102, 106, 108], "analzi": 101, "copyright": 102, "advertis": 102, "violenc": 102, "nsfw": 102, "celeba": 102, "make_multilabel_data": 102, "boxes_coordin": 102, "box_multilabel": 102, "make_multi": 102, "bx1": 102, "by1": 102, "bx2": 102, "by2": 102, "label_list": 102, "ur": 102, "upper": 102, "inidx": 102, "logical_and": 102, "inv_d": 102, "labels_idx": 102, "true_labels_test": 102, "dict_unique_label": 102, "get_color_arrai": 102, "dcolor": 102, "aa4400": 102, "55227f": 102, "55a100": 102, "00ff00": 102, "007f7f": 102, "386b55": 102, "0000ff": 102, "y_onehot": 102, "single_class_label": 102, "stratifi": [102, 105], "kf": 102, "train_index": 102, "test_index": 102, "clf_cv": 102, "x_train_cv": 102, "x_test_cv": 102, "y_train_cv": 102, "y_test_cv": 102, "y_pred_cv": 102, "saw": 102, "num_to_displai": 102, "09": [102, 103, 106], "275": 102, "267": 102, "225": 102, "171": 102, "234": 102, "165": 102, "227": [102, 103], "262": [102, 103], "263": [102, 103], "266": [102, 103], "139": 102, "143": [102, 103], "216": [102, 103, 108], "265": 102, "159": [102, 103], "despit": [102, 108], "suspect": 102, "888": 102, "8224": 102, "9632": 102, "968": 102, "6512": 102, "0444": 102, "774": 102, "labels_binary_format": 102, "labels_list_format": 102, "surround": 103, "scene": 103, "coco": 103, "everydai": 103, "has_label_issu": 103, "nc": [103, 107, 108], "s3": [103, 107, 108], "amazonaw": [103, 107, 108], "objectdetectionbenchmark": 103, "tutorial_obj": 103, "pkl": 103, "example_imag": 103, "unzip": [103, 108], "_separate_label": 103, "_separate_predict": 103, "begin": 103, "image_path": 103, "rb": 103, "image_to_visu": 103, "seg_map": 103, "334": 103, "bboxes_ignor": 103, "290": 103, "286": 103, "285": 103, "224": 103, "231": 103, "293": 103, "235": 103, "289": 103, "282": 103, "281": 103, "271": 103, "280": 103, "277": 103, "279": 103, "287": 103, "299": 103, "276": 103, "307": 103, "321": 103, "326": 103, "333": 103, "261": 103, "319": 103, "257": 103, "295": 103, "283": 103, "243": 103, "303": 103, "316": 103, "247": 103, "323": 103, "327": 103, "226": 103, "228": 103, "232": 103, "219": 103, "239": 103, "240": 103, "209": 103, "242": 103, "202": 103, "230": 103, "215": 103, "220": 103, "229": 103, "217": [103, 108], "237": 103, "207": 103, "204": 103, "84": [103, 106], "205": 103, "223": 103, "153": 103, "149": 103, "140": 103, "124": 103, "268": 103, "273": 103, "284": 103, "110": 103, "136": [103, 108], "145": 103, "173": 103, "297": 103, "317": 103, "192": 103, "332": 103, "324": 103, "203": 103, "320": 103, "314": 103, "199": 103, "291": 103, "000000481413": 103, "jpg": 103, "42398": 103, "44503": 103, "29968": 103, "336": 103, "21005": 103, "9978472": 103, "forgot": 103, "drew": 103, "label_issue_idx": 103, "num_examples_to_show": 103, "138": 103, "candid": 103, "97489622": 103, "70610878": 103, "98764951": 103, "88899237": 103, "99085805": 103, "issue_idx": 103, "95569726e": 103, "03354841e": 103, "57510169e": 103, "58447666e": 103, "39755858e": 103, "issue_to_visu": 103, "000000009483": 103, "95569726168054e": 103, "addition": [103, 107], "visibl": 103, "missmatch": 103, "likelei": 103, "agnost": 103, "vaidat": 103, "inconsist": 103, "000000395701": 103, "033548411774308e": 103, "armchair": 103, "tv": 103, "000000154004": 103, "38300759625496356": 103, "foreground": 103, "000000448410": 103, "0008575101690203273": 103, "crowd": 103, "alon": 103, "resembl": [103, 104], "000000499768": 103, "9748962231208227": 103, "000000521141": 103, "8889923658893665": 103, "000000143931": 103, "9876495074395956": 103, "bonu": 103, "uncov": 103, "irregular": 103, "anomali": 103, "object_detection_util": 103, "calculate_bounding_box_area": 103, "num_imgs_to_show": 103, "lab_object_count": 103, "pred_object_count": 103, "000000430073": 103, "000000183709": 103, "000000189475": 103, "label_norm": 103, "pred_norm": 103, "area": [103, 107], "lab_area": 103, "pred_area": 103, "lab_area_mean": 103, "lab_area_std": 103, "max_deviation_valu": 103, "max_deviation_class": 103, "deviation_valu": 103, "deviation_class": 103, "mean_area": 103, "std_area": 103, "class_area": 103, "deviations_awai": 103, "max_deviation_index": 103, "num_imgs_to_show_per_class": 103, "class_num": 103, "000000422886": 103, "000000341828": 103, "000000461009": 103, "train_feature_embed": 104, "ood_train_feature_scor": 104, "test_feature_embed": 104, "ood_test_feature_scor": 104, "ood_train_predictions_scor": 104, "train_pred_prob": 104, "ood_test_predictions_scor": 104, "test_pred_prob": 104, "pylab": 104, "rcparam": 104, "baggingclassifi": 104, "therebi": 104, "rescal": 104, "transform_norm": 104, "totensor": 104, "root": 104, "animal_class": 104, "non_animal_class": 104, "animal_idx": 104, "test_idx": 104, "toronto": 104, "edu": 104, "kriz": 104, "170498071": 104, "43010872": 104, "29it": 104, "plot_imag": 104, "visualize_outli": 104, "txt_class": 104, "img": [104, 106], "npimg": 104, "show_label": 104, "data_subset": 104, "resnet50": 104, "corpu": 104, "2048": 104, "embed_imag": 104, "create_model": 104, "strang": 104, "odd": 104, "train_ood_features_scor": 104, "top_train_ood_features_idx": 104, "fun": 104, "negat": 104, "homogen": 104, "bottom_train_ood_features_idx": 104, "test_ood_features_scor": 104, "top_ood_features_idx": 104, "inevit": 104, "trade": 104, "5th": 104, "percentil": 104, "fifth_percentil": 104, "plt_rang": 104, "hist": 104, "train_outlier_scor": 104, "test_outlier_scor": 104, "ood_features_indic": 104, "revisit": 104, "return_invers": 104, "train_feature_embeddings_sc": 104, "test_feature_embeddings_sc": 104, "train_pred_label": 104, "9702": 104, "train_ood_predictions_scor": 104, "test_ood_predictions_scor": 104, "lost": 104, "unsuit": 105, "ok": [105, 108], "convention": 105, "aforement": 105, "hypothet": 105, "contrast": 105, "tradit": 105, "disjoint": 105, "out_of_sample_pred_probs_for_a": 105, "out_of_sample_pred_probs_for_b": 105, "out_of_sample_pred_probs_for_c": 105, "out_of_sample_pred_prob": 105, "price": 106, "incom": 106, "sensor": 106, "histgradientboostingregressor": 106, "r2_score": 106, "student_grades_r": 106, "final_scor": 106, "true_final_scor": 106, "homework": 106, "3d": 106, "mpl_toolkit": 106, "mplot3d": 106, "axes3d": 106, "errors_idx": 106, "add_subplot": 106, "z": 106, "errors_mask": 106, "feature_column": 106, "predicted_column": 106, "x_train_raw": 106, "x_test_raw": 106, "randomforestregressor": 106, "385101": 106, "499503": 106, "698255": 106, "776647": 106, "109373": 106, "170547": 106, "481096": 106, "984759": 106, "645270": 106, "795928": 106, "141": 106, "659": 106, "367": 106, "318": 106, "305": 106, "560": 106, "657": 106, "688": 106, "view_datapoint": 106, "preds_og": 106, "r2_og": 106, "838": 106, "found_label_issu": 106, "preds_cl": 106, "r2_cl": 106, "926": 106, "favorit": 106, "968627e": 106, "228799": 106, "646674e": 106, "402962": 106, "323818e": 106, "952758": 106, "422144e": 106, "456908": 106, "465815e": 106, "753968": 106, "791186e": 106, "110719": 106, "485156e": 106, "670640": 106, "225300e": 106, "749976": 106, "499679e": 106, "947007": 106, "067882e": 106, "648396": 106, "synthia": 107, "imagesegment": 107, "given_mask": 107, "predicted_mask": 107, "set_printopt": [107, 108], "sky": 107, "sidewalk": 107, "veget": 107, "terrain": 107, "rider": 107, "pred_probs_filepath": 107, "1088": 107, "1920": 107, "label_filepath": 107, "synthia_class": 107, "maunal": 107, "100000": 107, "244800": 107, "leftmost": 107, "middl": [107, 108], "infact": 107, "rightmost": 107, "discrep": 107, "3263230": 107, "783381": 107, "275110": 107, "255917": 107, "78225": 107, "55990": 107, "54315": 107, "33591": 107, "24645": 107, "21054": 107, "15045": 107, "14171": 107, "13832": 107, "13498": 107, "11490": 107, "9164": 107, "8769": 107, "6999": 107, "6031": 107, "5011": 107, "mistakenli": 107, "class_issu": 107, "aim": [107, 108], "domin": 107, "bunch": 108, "conll": 108, "2003": 108, "love": 108, "n_i": 108, "optional_list_of_ordered_class_nam": 108, "deepai": 108, "conll2003": 108, "rm": 108, "tokenclassif": 108, "162": 108, "2400": 108, "52e0": 108, "1a01": 108, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "kb": 108, "mb": 108, "directori": 108, "inflat": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "4mb": 108, "bert": 108, "read_npz": 108, "filepath": 108, "corrsespond": 108, "iob2": 108, "given_ent": 108, "entity_map": 108, "readfil": 108, "startswith": 108, "docstart": 108, "isalpha": 108, "isupp": 108, "indices_to_preview": 108, "nsentenc": 108, "eu": 108, "reject": 108, "boycott": 108, "british": 108, "lamb": 108, "00030412": 108, "00023826": 108, "99936208": 108, "00007009": 108, "00002545": 108, "99998795": 108, "00000401": 108, "00000218": 108, "00000455": 108, "00000131": 108, "00000749": 108, "99996115": 108, "00001371": 108, "0000087": 108, "00000895": 108, "99998936": 108, "00000382": 108, "00000178": 108, "00000366": 108, "00000137": 108, "99999101": 108, "00000266": 108, "00000174": 108, "0000035": 108, "00000109": 108, "99998768": 108, "00000482": 108, "00000202": 108, "00000438": 108, "0000011": 108, "00000465": 108, "99996392": 108, "00001105": 108, "0000116": 108, "00000878": 108, "99998671": 108, "00000364": 108, "00000213": 108, "00000472": 108, "00000281": 108, "99999073": 108, "00000211": 108, "00000159": 108, "00000442": 108, "00000115": 108, "peter": 108, "blackburn": 108, "00000358": 108, "00000529": 108, "99995623": 108, "0000129": 108, "0000024": 108, "00001812": 108, "99994141": 108, "00001645": 108, "00002162": 108, "brussel": 108, "1996": 108, "00001172": 108, "00000821": 108, "00004661": 108, "0000618": 108, "99987167": 108, "99999061": 108, "00000201": 108, "00000195": 108, "00000408": 108, "00000135": 108, "2254": 108, "2907": 108, "19392": 108, "9962": 108, "8904": 108, "19303": 108, "12918": 108, "9256": 108, "11855": 108, "18392": 108, "20426": 108, "19402": 108, "14744": 108, "19371": 108, "4645": 108, "10331": 108, "9430": 108, "6143": 108, "18367": 108, "12914": 108, "todai": 108, "weather": 108, "march": 108, "scalfaro": 108, "northern": 108, "himself": 108, "said": 108, "germani": 108, "nastja": 108, "rysich": 108, "north": 108, "spla": 108, "fought": 108, "khartoum": 108, "govern": 108, "south": 108, "1983": 108, "autonomi": 108, "animist": 108, "region": 108, "moslem": 108, "arabis": 108, "mayor": 108, "antonio": 108, "gonzalez": 108, "garcia": 108, "revolutionari": 108, "wednesdai": 108, "troop": 108, "raid": 108, "farm": 108, "stole": 108, "rape": 108, "women": 108, "spring": 108, "chg": 108, "hrw": 108, "12pct": 108, "princ": 108, "photo": 108, "moment": 108, "spokeswoman": 108, "rainier": 108, "told": 108, "reuter": 108, "danila": 108, "carib": 108, "w224": 108, "equip": 108, "radiomet": 108, "earn": 108, "19996": 108, "london": 108, "denom": 108, "sale": 108, "uk": 108, "jp": 108, "fr": 108, "maccabi": 108, "hapoel": 108, "haifa": 108, "tel": 108, "aviv": 108, "hospit": 108, "rever": 108, "roman": 108, "cathol": 108, "nun": 108, "admit": 108, "calcutta": 108, "week": 108, "ago": 108, "fever": 108, "vomit": 108, "allianc": 108, "embattl": 108, "kabul": 108, "salang": 108, "highwai": 108, "mondai": 108, "tuesdai": 108, "suprem": 108, "council": 108, "led": 108, "jumbish": 108, "milli": 108, "movement": 108, "warlord": 108, "abdul": 108, "rashid": 108, "dostum": 108, "dollar": 108, "exchang": 108, "3570": 108, "12049": 108, "born": 108, "1937": 108, "provinc": 108, "anhui": 108, "dai": 108, "came": 108, "shanghai": 108, "citi": 108, "prolif": 108, "author": 108, "teacher": 108, "chines": 108, "16764": 108, "1990": 108, "historian": 108, "alan": 108, "john": 108, "percival": 108, "taylor": 108, "di": 108, "20446": 108, "pace": 108, "bowler": 108, "ian": 108, "harvei": 108, "claim": 108, "victoria": 108, "15514": 108, "cotti": 108, "osc": 108, "foreign": 108, "minist": 108, "7525": 108, "sultan": 108, "specter": 108, "crown": 108, "abdullah": 108, "defenc": 108, "aviat": 108, "jeddah": 108, "saudi": 108, "agenc": 108, "2288": 108, "hi": 108, "customari": 108, "outfit": 108, "champion": 108, "damp": 108, "scalp": 108, "canada": 108, "reign": 108, "olymp": 108, "donovan": 108, "bailei": 108, "1992": 108, "linford": 108, "christi": 108, "britain": 108, "1984": 108, "1988": 108, "carl": 108, "lewi": 108, "ambigi": 108, "punctuat": 108, "chicago": 108, "digest": 108, "philadelphia": 108, "usda": 108, "york": 108, "token_issu": 108, "471": 108, "kean": 108, "year": 108, "contract": 108, "manchest": 108, "19072": 108, "societi": 108, "bite": 108, "deliv": 108, "19910": 108, "father": 108, "clarenc": 108, "woolmer": 108, "renam": 108, "uttar": 108, "pradesh": 108, "india": 108, "ranji": 108, "trophi": 108, "nation": 108, "championship": 108, "captain": 108, "1949": 108, "15658": 108, "19879": 108, "iii": 108, "brian": 108, "shimer": 108, "randi": 108, "jone": 108, "19104": 108}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [37, 0, 0, "-", "dataset"], [40, 0, 0, "-", "experimental"], [44, 0, 0, "-", "filter"], [45, 0, 0, "-", "internal"], [60, 0, 0, "-", "models"], [62, 0, 0, "-", "multiannotator"], [65, 0, 0, "-", "multilabel_classification"], [68, 0, 0, "-", "object_detection"], [71, 0, 0, "-", "outlier"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "regression"], [77, 0, 0, "-", "segmentation"], [81, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [16, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[13, 0, 0, "-", "data"], [14, 0, 0, "-", "data_issues"], [17, 0, 0, "-", "issue_finder"], [15, 0, 0, "-", "issue_manager_factory"], [33, 0, 0, "-", "model_outputs"], [34, 0, 0, "-", "report"], [35, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[13, 2, 1, "", "Data"], [13, 5, 1, "", "DataFormatError"], [13, 5, 1, "", "DatasetDictError"], [13, 5, 1, "", "DatasetLoadError"], [13, 2, 1, "", "Label"], [13, 2, 1, "", "MultiClass"], [13, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[14, 2, 1, "", "DataIssues"], [14, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[14, 3, 1, "", "collect_issues_from_imagelab"], [14, 3, 1, "", "collect_issues_from_issue_manager"], [14, 3, 1, "", "collect_statistics"], [14, 3, 1, "", "get_info"], [14, 3, 1, "", "get_issue_summary"], [14, 3, 1, "", "get_issues"], [14, 6, 1, "", "info"], [14, 6, 1, "", "issue_summary"], [14, 6, 1, "", "issues"], [14, 3, 1, "", "set_health_score"], [14, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[17, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[17, 3, 1, "", "find_issues"], [17, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[19, 0, 0, "-", "data_valuation"], [20, 0, 0, "-", "duplicate"], [21, 0, 0, "-", "imbalance"], [23, 0, 0, "-", "issue_manager"], [24, 0, 0, "-", "label"], [27, 0, 0, "-", "noniid"], [28, 0, 0, "-", "null"], [29, 0, 0, "-", "outlier"], [32, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[19, 6, 1, "", "DEFAULT_THRESHOLD"], [19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 6, 1, "", "near_duplicate_sets"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[24, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 3, 1, "", "get_health_summary"], [24, 6, 1, "", "health_summary_parameters"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, 2, 1, "", "NonIIDIssueManager"], [27, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "find_issues"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[28, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[29, 6, 1, "", "DEFAULT_THRESHOLDS"], [29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 6, 1, "", "metric"], [29, 6, 1, "", "ood"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[31, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, 2, 1, "", "RegressionLabelIssueManager"], [31, 1, 1, "", "find_issues_with_features"], [31, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[32, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [32, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [32, 3, 1, "", "collect_info"], [32, 6, 1, "", "description"], [32, 3, 1, "", "filter_cluster_ids"], [32, 3, 1, "", "find_issues"], [32, 3, 1, "", "get_worst_cluster"], [32, 6, 1, "", "info"], [32, 6, 1, "", "issue_name"], [32, 6, 1, "", "issue_score_key"], [32, 6, 1, "", "issues"], [32, 3, 1, "", "make_summary"], [32, 3, 1, "", "perform_clustering"], [32, 3, 1, "", "report"], [32, 6, 1, "", "summary"], [32, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, 7, 1, "", "REGISTRY"], [15, 1, 1, "", "list_default_issue_types"], [15, 1, 1, "", "list_possible_issue_types"], [15, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[33, 2, 1, "", "ModelOutput"], [33, 2, 1, "", "MultiClassPredProbs"], [33, 2, 1, "", "MultiLabelPredProbs"], [33, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[34, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[34, 3, 1, "", "get_report"], [34, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[35, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[35, 6, 1, "", "CLASSIFICATION"], [35, 6, 1, "", "MULTILABEL"], [35, 6, 1, "", "REGRESSION"], [35, 3, 1, "", "__contains__"], [35, 3, 1, "", "__getitem__"], [35, 3, 1, "", "__iter__"], [35, 3, 1, "", "__len__"], [35, 3, 1, "", "from_str"], [35, 4, 1, "", "is_classification"], [35, 4, 1, "", "is_multilabel"], [35, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[37, 1, 1, "", "find_overlapping_classes"], [37, 1, 1, "", "health_summary"], [37, 1, 1, "", "overall_label_health_score"], [37, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[38, 0, 0, "-", "cifar_cnn"], [39, 0, 0, "-", "coteaching"], [41, 0, 0, "-", "label_issues_batched"], [42, 0, 0, "-", "mnist_pytorch"], [43, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[38, 2, 1, "", "CNN"], [38, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[38, 6, 1, "", "T_destination"], [38, 3, 1, "", "__call__"], [38, 3, 1, "", "add_module"], [38, 3, 1, "", "apply"], [38, 3, 1, "", "bfloat16"], [38, 3, 1, "", "buffers"], [38, 6, 1, "", "call_super_init"], [38, 3, 1, "", "children"], [38, 3, 1, "", "compile"], [38, 3, 1, "", "cpu"], [38, 3, 1, "", "cuda"], [38, 3, 1, "", "double"], [38, 6, 1, "", "dump_patches"], [38, 3, 1, "", "eval"], [38, 3, 1, "", "extra_repr"], [38, 3, 1, "", "float"], [38, 3, 1, "id0", "forward"], [38, 3, 1, "", "get_buffer"], [38, 3, 1, "", "get_extra_state"], [38, 3, 1, "", "get_parameter"], [38, 3, 1, "", "get_submodule"], [38, 3, 1, "", "half"], [38, 3, 1, "", "ipu"], [38, 3, 1, "", "load_state_dict"], [38, 3, 1, "", "modules"], [38, 3, 1, "", "named_buffers"], [38, 3, 1, "", "named_children"], [38, 3, 1, "", "named_modules"], [38, 3, 1, "", "named_parameters"], [38, 3, 1, "", "parameters"], [38, 3, 1, "", "register_backward_hook"], [38, 3, 1, "", "register_buffer"], [38, 3, 1, "", "register_forward_hook"], [38, 3, 1, "", "register_forward_pre_hook"], [38, 3, 1, "", "register_full_backward_hook"], [38, 3, 1, "", "register_full_backward_pre_hook"], [38, 3, 1, "", "register_load_state_dict_post_hook"], [38, 3, 1, "", "register_module"], [38, 3, 1, "", "register_parameter"], [38, 3, 1, "", "register_state_dict_pre_hook"], [38, 3, 1, "", "requires_grad_"], [38, 3, 1, "", "set_extra_state"], [38, 3, 1, "", "share_memory"], [38, 3, 1, "", "state_dict"], [38, 3, 1, "", "to"], [38, 3, 1, "", "to_empty"], [38, 3, 1, "", "train"], [38, 6, 1, "", "training"], [38, 3, 1, "", "type"], [38, 3, 1, "", "xpu"], [38, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[39, 1, 1, "", "adjust_learning_rate"], [39, 1, 1, "", "evaluate"], [39, 1, 1, "", "forget_rate_scheduler"], [39, 1, 1, "", "initialize_lr_scheduler"], [39, 1, 1, "", "loss_coteaching"], [39, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[41, 2, 1, "", "LabelInspector"], [41, 7, 1, "", "adj_confident_thresholds_shared"], [41, 1, 1, "", "find_label_issues_batched"], [41, 7, 1, "", "labels_shared"], [41, 7, 1, "", "pred_probs_shared"], [41, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[41, 3, 1, "", "get_confident_thresholds"], [41, 3, 1, "", "get_label_issues"], [41, 3, 1, "", "get_num_issues"], [41, 3, 1, "", "get_quality_scores"], [41, 3, 1, "", "score_label_quality"], [41, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[42, 2, 1, "", "CNN"], [42, 2, 1, "", "SimpleNet"], [42, 1, 1, "", "get_mnist_dataset"], [42, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[42, 3, 1, "", "__init_subclass__"], [42, 6, 1, "", "batch_size"], [42, 6, 1, "", "dataset"], [42, 6, 1, "", "epochs"], [42, 3, 1, "id0", "fit"], [42, 3, 1, "", "get_metadata_routing"], [42, 3, 1, "", "get_params"], [42, 6, 1, "", "loader"], [42, 6, 1, "", "log_interval"], [42, 6, 1, "", "lr"], [42, 6, 1, "", "momentum"], [42, 6, 1, "", "no_cuda"], [42, 3, 1, "id1", "predict"], [42, 3, 1, "id4", "predict_proba"], [42, 6, 1, "", "seed"], [42, 3, 1, "", "set_fit_request"], [42, 3, 1, "", "set_params"], [42, 3, 1, "", "set_predict_proba_request"], [42, 3, 1, "", "set_predict_request"], [42, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[42, 6, 1, "", "T_destination"], [42, 3, 1, "", "__call__"], [42, 3, 1, "", "add_module"], [42, 3, 1, "", "apply"], [42, 3, 1, "", "bfloat16"], [42, 3, 1, "", "buffers"], [42, 6, 1, "", "call_super_init"], [42, 3, 1, "", "children"], [42, 3, 1, "", "compile"], [42, 3, 1, "", "cpu"], [42, 3, 1, "", "cuda"], [42, 3, 1, "", "double"], [42, 6, 1, "", "dump_patches"], [42, 3, 1, "", "eval"], [42, 3, 1, "", "extra_repr"], [42, 3, 1, "", "float"], [42, 3, 1, "", "forward"], [42, 3, 1, "", "get_buffer"], [42, 3, 1, "", "get_extra_state"], [42, 3, 1, "", "get_parameter"], [42, 3, 1, "", "get_submodule"], [42, 3, 1, "", "half"], [42, 3, 1, "", "ipu"], [42, 3, 1, "", "load_state_dict"], [42, 3, 1, "", "modules"], [42, 3, 1, "", "named_buffers"], [42, 3, 1, "", "named_children"], [42, 3, 1, "", "named_modules"], [42, 3, 1, "", "named_parameters"], [42, 3, 1, "", "parameters"], [42, 3, 1, "", "register_backward_hook"], [42, 3, 1, "", "register_buffer"], [42, 3, 1, "", "register_forward_hook"], [42, 3, 1, "", "register_forward_pre_hook"], [42, 3, 1, "", "register_full_backward_hook"], [42, 3, 1, "", "register_full_backward_pre_hook"], [42, 3, 1, "", "register_load_state_dict_post_hook"], [42, 3, 1, "", "register_module"], [42, 3, 1, "", "register_parameter"], [42, 3, 1, "", "register_state_dict_pre_hook"], [42, 3, 1, "", "requires_grad_"], [42, 3, 1, "", "set_extra_state"], [42, 3, 1, "", "share_memory"], [42, 3, 1, "", "state_dict"], [42, 3, 1, "", "to"], [42, 3, 1, "", "to_empty"], [42, 3, 1, "", "train"], [42, 6, 1, "", "training"], [42, 3, 1, "", "type"], [42, 3, 1, "", "xpu"], [42, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[43, 1, 1, "", "display_issues"], [43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[44, 1, 1, "", "find_label_issues"], [44, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [44, 1, 1, "", "find_predicted_neq_given"], [44, 7, 1, "", "pred_probs_by_class"], [44, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[46, 0, 0, "-", "label_quality_utils"], [47, 0, 0, "-", "latent_algebra"], [48, 0, 0, "-", "multiannotator_utils"], [49, 0, 0, "-", "multilabel_scorer"], [50, 0, 0, "-", "multilabel_utils"], [51, 0, 0, "-", "neighbor"], [55, 0, 0, "-", "outlier"], [56, 0, 0, "-", "token_classification_utils"], [57, 0, 0, "-", "util"], [58, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[46, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, 1, 1, "", "compute_inv_noise_matrix"], [47, 1, 1, "", "compute_noise_matrix_from_inverse"], [47, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [47, 1, 1, "", "compute_py"], [47, 1, 1, "", "compute_py_inv_noise_matrix"], [47, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[48, 1, 1, "", "assert_valid_inputs_multiannotator"], [48, 1, 1, "", "assert_valid_pred_probs"], [48, 1, 1, "", "check_consensus_label_classes"], [48, 1, 1, "", "compute_soft_cross_entropy"], [48, 1, 1, "", "find_best_temp_scaler"], [48, 1, 1, "", "format_multiannotator_labels"], [48, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[49, 2, 1, "", "Aggregator"], [49, 2, 1, "", "ClassLabelScorer"], [49, 2, 1, "", "MultilabelScorer"], [49, 1, 1, "", "exponential_moving_average"], [49, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "multilabel_py"], [49, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[49, 3, 1, "", "__call__"], [49, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[49, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [49, 6, 1, "", "NORMALIZED_MARGIN"], [49, 6, 1, "", "SELF_CONFIDENCE"], [49, 3, 1, "", "__call__"], [49, 3, 1, "", "__contains__"], [49, 3, 1, "", "__getitem__"], [49, 3, 1, "", "__iter__"], [49, 3, 1, "", "__len__"], [49, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[49, 3, 1, "", "__call__"], [49, 3, 1, "", "aggregate"], [49, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[50, 1, 1, "", "get_onehot_num_classes"], [50, 1, 1, "", "int2onehot"], [50, 1, 1, "", "onehot2int"], [50, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[52, 0, 0, "-", "knn_graph"], [53, 0, 0, "-", "metric"], [54, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[52, 7, 1, "", "DEFAULT_K"], [52, 1, 1, "", "construct_knn_graph_from_index"], [52, 1, 1, "", "correct_knn_distances_and_indices"], [52, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [52, 1, 1, "", "correct_knn_graph"], [52, 1, 1, "", "create_knn_graph_and_index"], [52, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[53, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [53, 7, 1, "", "ROW_COUNT_CUTOFF"], [53, 1, 1, "", "decide_default_metric"], [53, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[55, 1, 1, "", "correct_precision_errors"], [55, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, 1, 1, "", "color_sentence"], [56, 1, 1, "", "filter_sentence"], [56, 1, 1, "", "get_sentence"], [56, 1, 1, "", "mapping"], [56, 1, 1, "", "merge_probs"], [56, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[57, 1, 1, "", "append_extra_datapoint"], [57, 1, 1, "", "clip_noise_rates"], [57, 1, 1, "", "clip_values"], [57, 1, 1, "", "compress_int_array"], [57, 1, 1, "", "confusion_matrix"], [57, 1, 1, "", "csr_vstack"], [57, 1, 1, "", "estimate_pu_f1"], [57, 1, 1, "", "extract_indices_tf"], [57, 1, 1, "", "force_two_dimensions"], [57, 1, 1, "", "format_labels"], [57, 1, 1, "", "get_missing_classes"], [57, 1, 1, "", "get_num_classes"], [57, 1, 1, "", "get_unique_classes"], [57, 1, 1, "", "is_tensorflow_dataset"], [57, 1, 1, "", "is_torch_dataset"], [57, 1, 1, "", "num_unique_classes"], [57, 1, 1, "", "print_inverse_noise_matrix"], [57, 1, 1, "", "print_joint_matrix"], [57, 1, 1, "", "print_noise_matrix"], [57, 1, 1, "", "print_square_matrix"], [57, 1, 1, "", "remove_noise_from_class"], [57, 1, 1, "", "round_preserving_row_totals"], [57, 1, 1, "", "round_preserving_sum"], [57, 1, 1, "", "smart_display_dataframe"], [57, 1, 1, "", "subset_X_y"], [57, 1, 1, "", "subset_data"], [57, 1, 1, "", "subset_labels"], [57, 1, 1, "", "train_val_split"], [57, 1, 1, "", "unshuffle_tensorflow_dataset"], [57, 1, 1, "", "value_counts"], [57, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[58, 1, 1, "", "assert_indexing_works"], [58, 1, 1, "", "assert_nonempty_input"], [58, 1, 1, "", "assert_valid_class_labels"], [58, 1, 1, "", "assert_valid_inputs"], [58, 1, 1, "", "labels_to_array"], [58, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[61, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[61, 2, 1, "", "KerasWrapperModel"], [61, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[62, 1, 1, "", "convert_long_to_wide_dataset"], [62, 1, 1, "", "get_active_learning_scores"], [62, 1, 1, "", "get_active_learning_scores_ensemble"], [62, 1, 1, "", "get_label_quality_multiannotator"], [62, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [62, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[63, 0, 0, "-", "dataset"], [64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[63, 1, 1, "", "common_multilabel_issues"], [63, 1, 1, "", "multilabel_health_summary"], [63, 1, 1, "", "overall_multilabel_health_score"], [63, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, 1, 1, "", "find_label_issues"], [64, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[69, 1, 1, "", "compute_badloc_box_scores"], [69, 1, 1, "", "compute_overlooked_box_scores"], [69, 1, 1, "", "compute_swap_box_scores"], [69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"], [69, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[70, 1, 1, "", "bounding_box_size_distribution"], [70, 1, 1, "", "calculate_per_class_metrics"], [70, 1, 1, "", "class_label_distribution"], [70, 1, 1, "", "get_average_per_class_confusion_matrix"], [70, 1, 1, "", "get_sorted_bbox_count_idxs"], [70, 1, 1, "", "object_counts_per_image"], [70, 1, 1, "", "plot_class_distribution"], [70, 1, 1, "", "plot_class_size_distributions"], [70, 1, 1, "", "visualize"]], "cleanlab.outlier": [[71, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[71, 3, 1, "", "fit"], [71, 3, 1, "", "fit_score"], [71, 3, 1, "", "score"]], "cleanlab.rank": [[72, 1, 1, "", "find_top_issues"], [72, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [72, 1, 1, "", "get_label_quality_ensemble_scores"], [72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "get_normalized_margin_for_each_label"], [72, 1, 1, "", "get_self_confidence_for_each_label"], [72, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[74, 0, 0, "-", "learn"], [75, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[74, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[74, 3, 1, "", "__init_subclass__"], [74, 3, 1, "", "find_label_issues"], [74, 3, 1, "", "fit"], [74, 3, 1, "", "get_aleatoric_uncertainty"], [74, 3, 1, "", "get_epistemic_uncertainty"], [74, 3, 1, "", "get_label_issues"], [74, 3, 1, "", "get_metadata_routing"], [74, 3, 1, "", "get_params"], [74, 3, 1, "", "predict"], [74, 3, 1, "", "save_space"], [74, 3, 1, "", "score"], [74, 3, 1, "", "set_fit_request"], [74, 3, 1, "", "set_params"], [74, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[75, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[76, 0, 0, "-", "filter"], [78, 0, 0, "-", "rank"], [79, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[76, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[78, 1, 1, "", "get_label_quality_scores"], [78, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[79, 1, 1, "", "common_label_issues"], [79, 1, 1, "", "display_issues"], [79, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[80, 0, 0, "-", "filter"], [82, 0, 0, "-", "rank"], [83, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[80, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[82, 1, 1, "", "get_label_quality_scores"], [82, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[83, 1, 1, "", "common_label_issues"], [83, 1, 1, "", "display_issues"], [83, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 87, 88, 92, 94, 95, 98, 99, 102, 108], "count": [3, 99], "data_valu": [4, 19], "datalab": [5, 7, 9, 10, 12, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102], "creat": [7, 90, 91, 99, 101], "your": [7, 84, 90, 91, 95, 96, 98, 99], "own": 7, "issu": [7, 9, 10, 22, 31, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "manag": [7, 22], "prerequisit": 7, "implement": 7, "issuemanag": [7, 90], "basic": 7, "check": [7, 96], "intermedi": 7, "advanc": [7, 90], "us": [7, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "gener": [8, 96], "cluster": [8, 96, 98], "id": 8, "guid": [9, 12], "type": [9, 10, 99], "custom": [9, 90], "cleanlab": [9, 10, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "studio": [9, 10], "easi": [9, 10, 84, 92, 94, 95], "mode": [9, 10, 84, 92, 94, 95], "can": [10, 91, 97, 98, 99, 101], "detect": [10, 89, 91, 92, 94, 95, 96, 98, 99, 103, 104], "estim": [10, 99, 101, 102], "each": 10, "input": 10, "label": [10, 24, 26, 31, 84, 87, 88, 89, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 29, 55, 71, 92, 94, 95, 102, 104], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 91, 92, 94, 95], "duplic": [10, 20, 91, 92, 94, 95, 98, 102], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 95, 96], "iid": [10, 95, 96], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 85, 96, 99, 107], "imbal": [10, 21, 96], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 92, 104], "specif": [10, 22, 107], "underperform": [10, 96, 98], "group": [10, 96, 98], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 28, 96], "is_null_issu": 10, "null_scor": 10, "data": [10, 13, 84, 87, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "valuat": [10, 96], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 96], "paramet": [10, 99], "get": [12, 90, 91, 101, 102, 103, 107, 108], "start": [12, 97], "api": 12, "refer": 12, "data_issu": 14, "factori": 15, "intern": [16, 45], "issue_find": 17, "issue_manag": [22, 23], "regist": 22, "ml": [22, 98, 99], "task": [22, 35], "multilabel": 25, "noniid": 27, "regress": [30, 73, 74, 75, 98, 106], "prioriti": 31, "order": 31, "find": [31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "underperforming_group": 32, "model_output": 33, "report": [34, 92], "dataset": [37, 63, 84, 88, 89, 91, 92, 95, 96, 97, 98, 99, 102, 103, 104, 106, 107, 108], "cifar_cnn": 38, "coteach": 39, "experiment": 40, "label_issues_batch": 41, "mnist_pytorch": 42, "span_classif": 43, "filter": [44, 64, 67, 76, 80, 99], "label_quality_util": 46, "latent_algebra": 47, "multiannotator_util": 48, "multilabel_scor": 49, "multilabel_util": 50, "neighbor": 51, "knn_graph": 52, "metric": 53, "search": [54, 90], "token_classification_util": 56, "util": 57, "valid": [58, 92, 105], "fasttext": 59, "model": [60, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106], "kera": 61, "multiannot": [62, 101], "multilabel_classif": 65, "rank": [66, 69, 72, 75, 78, 82, 99], "object_detect": 68, "summari": [70, 79, 83], "learn": [74, 91, 98, 99], "segment": [77, 107], "token_classif": [81, 108], "open": [84, 98], "sourc": [84, 98], "document": 84, "quickstart": 84, "1": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "instal": [84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "2": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "common": [84, 85, 108], "3": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "handl": [84, 98], "error": [84, 88, 92, 98, 99, 101, 102, 103, 106, 107, 108], "train": [84, 87, 88, 89, 96, 98, 104, 106], "robust": [84, 87, 88, 99, 106], "noisi": [84, 87, 88, 99, 106], "4": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 103, 104, 106], "curat": 84, "fix": [84, 98], "level": [84, 97, 99, 108], "5": [84, 87, 89, 91, 92, 94, 96, 99, 101, 106], "improv": [84, 101], "via": [84, 99, 101], "mani": [84, 99], "other": [84, 101, 103, 106], "techniqu": 84, "contribut": 84, "how": [85, 98, 99, 101, 102, 108], "migrat": 85, "version": 85, "0": 85, "from": [85, 87, 88, 90, 91, 99, 106], "pre": [85, 89, 96, 98, 104], "function": [85, 90], "name": 85, "chang": 85, "modul": [85, 99], "new": 85, "remov": 85, "argument": [85, 90], "variabl": 85, "cleanlearn": [86, 98, 99], "tutori": [86, 93, 97, 100], "structur": 87, "tabular": [87, 94], "requir": [87, 88, 90, 91, 92, 94, 95, 101, 102, 103, 104, 106, 107, 108], "depend": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "load": [87, 88, 89, 90, 91, 94, 95, 96, 106], "process": [87, 94, 104, 106], "select": [87, 94], "comput": [87, 89, 92, 94, 95, 96, 98, 101, 105], "out": [87, 89, 90, 91, 92, 94, 95, 101, 105], "sampl": [87, 89, 90, 91, 92, 94, 95, 101, 105], "predict": [87, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 105], "probabl": [87, 89, 90, 91, 92, 94, 95, 96, 101, 105], "more": [87, 88, 91, 99, 106], "text": [88, 95, 96, 108], "format": [88, 95, 98, 102, 103], "defin": [88, 92, 95, 96, 106], "potenti": [88, 101, 106], "an": [89, 92, 98], "audio": 89, "import": [89, 90, 91, 92, 97, 99, 101], "them": [89, 97, 99], "speechbrain": 89, "featur": [89, 92, 104], "fit": 89, "linear": 89, "workflow": [90, 96, 99], "audit": [90, 91], "classifi": [90, 91, 96], "instanti": 90, "object": [90, 103], "increment": 90, "specifi": [90, 98], "nondefault": 90, "save": 90, "ad": 90, "A": 91, "unifi": 91, "all": [91, 99], "kind": [91, 103], "skip": [91, 97, 99, 101], "detail": [91, 97, 99, 101], "about": 91, "addit": 91, "inform": [91, 92], "fetch": [92, 97], "normal": 92, "fashion": 92, "mnist": 92, "prepar": [92, 96], "k": [92, 94, 105], "fold": [92, 105], "cross": [92, 105], "embed": [92, 104], "7": [92, 99], "view": 92, "most": [92, 108], "like": 92, "exampl": [92, 98, 99, 104], "sever": 92, "set": [92, 99], "dark": 92, "top": [92, 107], "low": 92, "numer": 94, "categor": [94, 96], "column": 94, "construct": 94, "nearest": 94, "neighbour": 94, "graph": [94, 96], "drift": [95, 102], "miscellan": 96, "acceler": 96, "knn": 96, "obtain": 96, "identifi": [96, 98, 103], "explan": 96, "vector": 96, "perform": 96, "visual": [96, 99, 103, 104, 107], "score": [96, 99, 101, 102, 103, 107, 108], "synthet": 96, "result": 96, "predefin": 96, "slice": [96, 98], "i": [96, 98, 99, 105], "catch": 96, "valu": 96, "encod": 96, "initi": [96, 101], "sort": 96, "6": [96, 99], "understand": 97, "evalu": 97, "health": [97, 99], "8": [97, 99], "popular": 97, "faq": 98, "what": [98, 99, 105], "do": [98, 99], "infer": 98, "correct": 98, "ha": 98, "flag": 98, "should": 98, "v": 98, "test": [98, 99, 104], "big": 98, "limit": 98, "memori": 98, "why": 98, "isn": 98, "t": 98, "work": [98, 99, 101, 108], "me": 98, "differ": [98, 103], "clean": [98, 99], "final": 98, "hyperparamet": 98, "tune": 98, "onli": 98, "one": [98, 99, 102, 107], "doe": [98, 101, 108], "take": 98, "so": 98, "long": 98, "when": [98, 99], "run": 98, "licens": 98, "under": 98, "answer": 98, "question": 98, "The": 99, "centric": 99, "ai": 99, "machin": 99, "find_label_issu": 99, "line": 99, "code": 99, "twenti": 99, "lowest": 99, "qualiti": [99, 101, 102, 103, 107, 108], "see": 99, "now": 99, "let": 99, "": 99, "happen": 99, "we": 99, "merg": 99, "seafoam": 99, "green": 99, "yellow": 99, "too": 99, "you": 99, "re": 99, "One": 99, "rule": 99, "overal": [99, 107], "accur": 99, "thi": 99, "directli": 99, "fulli": 99, "character": 99, "nois": 99, "matrix": [99, 102], "joint": 99, "prior": 99, "true": 99, "distribut": 99, "flip": 99, "rate": 99, "ani": 99, "again": 99, "support": 99, "lot": 99, "method": 99, "filter_bi": 99, "automat": 99, "everi": 99, "uniqu": 99, "num_label_issu": 99, "threshold": 99, "found": 99, "Not": 99, "sure": 99, "ensembl": 99, "multipl": [99, 101], "predictor": 99, "consensu": 101, "annot": 101, "major": 101, "vote": 101, "better": 101, "statist": 101, "compar": 101, "inspect": 101, "retrain": 101, "further": 101, "multi": 102, "beyond": 102, "mislabel": [102, 107, 108], "given": 102, "hot": 102, "binari": 102, "without": 102, "applic": 102, "real": 102, "download": [103, 107, 108], "objectlab": 103, "exploratori": 103, "analysi": 103, "pytorch": 104, "timm": 104, "cifar10": 104, "some": 104, "pred_prob": [104, 107, 108], "wai": 106, "semant": 107, "which": 107, "ar": 107, "commonli": 107, "focus": 107, "token": 108, "word": 108, "sentenc": 108, "contain": 108, "particular": 108}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [19, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.data"], [14, "module-cleanlab.datalab.internal.data_issues"], [15, "module-cleanlab.datalab.internal.issue_manager_factory"], [16, "module-cleanlab.datalab.internal"], [17, "module-cleanlab.datalab.internal.issue_finder"], [19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [20, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [21, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [27, "module-cleanlab.datalab.internal.issue_manager.noniid"], [28, "module-cleanlab.datalab.internal.issue_manager.null"], [29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [33, "module-cleanlab.datalab.internal.model_outputs"], [34, "module-cleanlab.datalab.internal.report"], [35, "module-cleanlab.datalab.internal.task"], [37, "module-cleanlab.dataset"], [38, "module-cleanlab.experimental.cifar_cnn"], [39, "module-cleanlab.experimental.coteaching"], [40, "module-cleanlab.experimental"], [41, "module-cleanlab.experimental.label_issues_batched"], [42, "module-cleanlab.experimental.mnist_pytorch"], [43, "module-cleanlab.experimental.span_classification"], [44, "module-cleanlab.filter"], [45, "module-cleanlab.internal"], [46, "module-cleanlab.internal.label_quality_utils"], [47, "module-cleanlab.internal.latent_algebra"], [48, "module-cleanlab.internal.multiannotator_utils"], [49, "module-cleanlab.internal.multilabel_scorer"], [50, "module-cleanlab.internal.multilabel_utils"], [51, "module-cleanlab.internal.neighbor"], [52, "module-cleanlab.internal.neighbor.knn_graph"], [53, "module-cleanlab.internal.neighbor.metric"], [54, "module-cleanlab.internal.neighbor.search"], [55, "module-cleanlab.internal.outlier"], [56, "module-cleanlab.internal.token_classification_utils"], [57, "module-cleanlab.internal.util"], [58, "module-cleanlab.internal.validation"], [60, "module-cleanlab.models"], [61, "module-cleanlab.models.keras"], [62, "module-cleanlab.multiannotator"], [63, "module-cleanlab.multilabel_classification.dataset"], [64, "module-cleanlab.multilabel_classification.filter"], [65, "module-cleanlab.multilabel_classification"], [66, "module-cleanlab.multilabel_classification.rank"], [67, "module-cleanlab.object_detection.filter"], [68, "module-cleanlab.object_detection"], [69, "module-cleanlab.object_detection.rank"], [70, "module-cleanlab.object_detection.summary"], [71, "module-cleanlab.outlier"], [72, "module-cleanlab.rank"], [73, "module-cleanlab.regression"], [74, "module-cleanlab.regression.learn"], [75, "module-cleanlab.regression.rank"], [76, "module-cleanlab.segmentation.filter"], [77, "module-cleanlab.segmentation"], [78, "module-cleanlab.segmentation.rank"], [79, "module-cleanlab.segmentation.summary"], [80, "module-cleanlab.token_classification.filter"], [81, "module-cleanlab.token_classification"], [82, "module-cleanlab.token_classification.rank"], [83, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[13, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[13, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[13, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[13, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[13, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[16, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[17, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[28, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[34, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[34, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[35, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[35, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[37, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.forward"], [38, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[40, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [42, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [42, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [42, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[44, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[44, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[44, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[45, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[46, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}}) \ No newline at end of file +Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 85, 90, 91, 99, 101, 102], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 90, 91, 99, 101, 102], "generate_noise_matrix_from_trac": [0, 1, 90, 91, 99, 101, 102], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 17, 41, 46, 48, 49, 50, 51, 55, 56, 57, 69, 92, 96, 97, 108], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 27, 30, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 84, 85, 90, 97, 105], "benchmark": [1, 38, 84, 85, 90, 91, 99, 101, 102], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105], "": [1, 2, 3, 4, 10, 19, 33, 37, 38, 42, 46, 49, 52, 54, 55, 57, 62, 63, 67, 69, 70, 71, 72, 74, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "core": [1, 41, 44, 76, 78], "algorithm": [1, 2, 8, 10, 32, 39, 43, 54, 55, 57, 62, 71, 80, 82, 84, 96, 98, 99, 101, 108], "These": [1, 2, 3, 4, 5, 8, 10, 22, 38, 40, 42, 43, 44, 45, 52, 60, 62, 63, 66, 70, 71, 75, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "introduc": [1, 89, 98, 99], "synthet": [1, 101, 102, 107], "nois": [1, 2, 3, 37, 44, 47, 57, 63, 90, 91, 96, 97, 101, 106], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 16, 17, 21, 22, 23, 25, 30, 32, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 90, 96, 100, 104, 105], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 33, 35, 37, 41, 43, 44, 47, 49, 50, 57, 62, 63, 64, 65, 66, 71, 72, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 96, 100, 101, 104, 105, 106, 107], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 26, 27, 28, 29, 31, 32, 40, 41, 42, 43, 44, 47, 49, 53, 57, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 90, 94, 100, 101, 105], "specif": [1, 3, 5, 9, 15, 16, 17, 28, 34, 35, 40, 52, 53, 54, 60, 64, 67, 70, 79, 83, 92, 94, 95, 96, 99, 103, 108], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "modul": [1, 3, 14, 15, 16, 17, 22, 25, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 51, 52, 54, 55, 57, 60, 62, 67, 70, 71, 72, 84, 92, 98, 102], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 17, 19, 24, 31, 35, 37, 38, 39, 41, 42, 44, 47, 51, 52, 54, 55, 57, 61, 62, 63, 64, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108], "gener": [1, 2, 3, 7, 10, 19, 24, 26, 34, 37, 49, 52, 54, 57, 58, 71, 72, 74, 79, 88, 89, 90, 91, 92, 95, 97, 98, 99, 101, 102, 104, 105, 107, 108], "valid": [1, 2, 3, 5, 10, 13, 33, 35, 37, 44, 45, 47, 48, 49, 52, 54, 55, 57, 62, 64, 67, 70, 72, 74, 75, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "matric": [1, 3, 47, 98], "which": [1, 2, 3, 5, 7, 10, 13, 14, 15, 17, 19, 23, 27, 33, 34, 35, 37, 38, 42, 43, 44, 47, 49, 53, 54, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "learn": [1, 2, 3, 4, 5, 9, 10, 15, 17, 23, 31, 34, 39, 40, 41, 42, 44, 46, 48, 53, 54, 57, 60, 62, 64, 71, 73, 75, 78, 82, 84, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 106], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106, 107, 108], "possibl": [1, 2, 3, 7, 10, 37, 38, 42, 44, 46, 47, 49, 64, 65, 66, 67, 69, 70, 71, 72, 74, 80, 82, 83, 91, 96, 98, 99, 101, 102, 103, 106, 107, 108], "noisi": [1, 2, 3, 10, 37, 39, 42, 44, 47, 57, 63, 64, 66, 72, 74, 75, 76, 78, 79, 85, 90, 91, 94, 95, 96, 98, 100, 101], "given": [1, 2, 3, 5, 10, 15, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "matrix": [1, 2, 3, 5, 10, 17, 19, 32, 37, 44, 46, 47, 50, 52, 57, 58, 64, 67, 69, 70, 71, 72, 94, 96, 103, 104], "trace": [1, 90, 91, 99, 101, 102], "valu": [1, 2, 3, 4, 5, 10, 13, 14, 17, 19, 23, 27, 28, 33, 35, 37, 38, 39, 41, 42, 44, 46, 47, 49, 52, 53, 54, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 83, 88, 89, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "more": [1, 2, 3, 4, 5, 7, 9, 10, 14, 15, 17, 19, 27, 37, 38, 41, 42, 43, 46, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 107, 108], "function": [1, 2, 3, 4, 5, 7, 10, 14, 15, 17, 24, 27, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 107, 108], "noise_matrix": [1, 2, 3, 10, 47, 57, 90, 91, 99, 101, 102], "py": [1, 3, 34, 38, 39, 44, 47, 49, 84, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102], "verbos": [1, 2, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 41, 44, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 90, 99, 101], "fals": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 48, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 103, 104, 106, 107], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83], "prior": [1, 2, 3, 37, 44, 47, 49], "repres": [1, 2, 3, 7, 10, 13, 17, 19, 27, 33, 35, 37, 41, 44, 47, 50, 52, 53, 55, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 108], "p": [1, 2, 3, 5, 10, 37, 44, 46, 47, 55, 57, 62, 70, 71, 72, 76, 94, 95, 96, 99, 101, 108], "true_label": [1, 2, 3, 37, 47, 57, 99, 101], "k": [1, 2, 3, 4, 5, 8, 10, 13, 17, 19, 20, 24, 27, 29, 32, 37, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 65, 66, 67, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 87, 89, 90, 91, 96, 98, 99, 101, 102, 103, 104, 107, 108], "check": [1, 2, 5, 6, 9, 10, 13, 17, 28, 35, 38, 41, 42, 48, 58, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 98, 99, 101, 102, 106], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 14, 23, 27, 39, 42, 47, 49, 55, 69, 74, 88, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106], "achiev": [1, 2, 38, 39, 42, 74, 98, 101, 108], "better": [1, 5, 10, 44, 53, 62, 64, 72, 74, 75, 84, 88, 89, 91, 94, 95, 96, 98, 99, 102, 103, 104, 108], "than": [1, 2, 3, 4, 7, 9, 10, 27, 29, 32, 37, 44, 53, 57, 61, 62, 67, 69, 71, 72, 74, 78, 82, 87, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "random": [1, 2, 3, 7, 10, 19, 32, 41, 49, 52, 62, 72, 74, 87, 89, 90, 91, 92, 94, 96, 98, 99, 101, 102, 104], "perform": [1, 2, 4, 7, 10, 27, 29, 32, 38, 42, 49, 51, 52, 53, 70, 74, 84, 87, 88, 90, 98, 99, 101, 102, 105, 106], "averag": [1, 3, 5, 10, 23, 29, 37, 38, 42, 49, 55, 62, 63, 70, 71, 72, 98, 101, 104], "amount": [1, 3, 92], "paramet": [1, 2, 3, 4, 5, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 95, 96], "np": [1, 2, 3, 4, 5, 7, 17, 19, 32, 37, 39, 41, 43, 44, 46, 47, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "ndarrai": [1, 2, 3, 4, 5, 17, 24, 26, 27, 31, 32, 33, 37, 39, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 96, 108], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 54, 55, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 84, 87, 88, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 17, 19, 27, 33, 37, 39, 41, 42, 43, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "shape": [1, 2, 3, 4, 5, 17, 19, 37, 39, 41, 43, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 89, 96, 97, 98, 99, 102, 103, 104, 107, 108], "condit": [1, 2, 3, 47, 53, 56, 57, 72, 92, 99, 108], "probabl": [1, 2, 3, 5, 8, 10, 17, 24, 26, 29, 33, 37, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 107, 108], "k_": [1, 2, 3, 47, 57], "k_y": [1, 2, 3, 47, 57], "contain": [1, 2, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 46, 47, 51, 52, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107], "fraction": [1, 2, 3, 10, 21, 39, 47, 57, 62, 74, 94, 98], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 55, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 101, 102, 103, 105, 106, 107, 108], "everi": [1, 2, 3, 4, 5, 10, 17, 38, 42, 44, 47, 56, 57, 64, 72, 74, 75, 87, 89, 90, 91, 92, 94, 95, 98, 101, 103, 105, 107, 108], "class": [1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 54, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 105, 106, 108], "other": [1, 2, 3, 5, 10, 17, 23, 28, 37, 38, 40, 41, 42, 44, 47, 50, 52, 57, 58, 60, 62, 63, 66, 70, 71, 72, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 104, 107, 108], "assum": [1, 2, 3, 13, 44, 47, 52, 56, 57, 72, 76, 79, 98, 102, 104, 106, 107, 108], "column": [1, 2, 3, 5, 10, 11, 13, 14, 31, 37, 41, 44, 47, 49, 50, 53, 56, 57, 62, 63, 64, 66, 67, 70, 71, 72, 74, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "sum": [1, 2, 3, 27, 32, 33, 37, 47, 49, 57, 63, 64, 66, 69, 74, 90, 91, 92, 98, 99, 101, 102, 107, 108], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 97, 98, 105], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 21, 23, 24, 26, 27, 32, 33, 34, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "true": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 49, 52, 56, 57, 58, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "return": [1, 2, 3, 4, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 62, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 62, 63, 64, 66, 67, 83, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 62, 64, 66, 69, 70, 71, 72, 74, 75, 80, 82, 83, 84, 89, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 74, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 65, 71, 90, 91, 92, 94, 95, 99], "yield": [1, 38, 42], "between": [1, 5, 10, 16, 17, 22, 23, 25, 27, 30, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 52, 53, 54, 55, 60, 62, 63, 66, 69, 71, 72, 74, 75, 78, 82, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "below": [1, 3, 4, 5, 10, 37, 38, 41, 42, 44, 46, 49, 55, 62, 63, 64, 69, 70, 78, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "we": [1, 2, 3, 5, 7, 10, 14, 23, 38, 41, 42, 44, 49, 57, 58, 61, 62, 69, 70, 72, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "loop": [1, 3, 47, 57, 92, 103], "implement": [1, 2, 3, 4, 9, 15, 23, 38, 39, 41, 42, 47, 51, 53, 54, 57, 71, 74, 84, 87, 89, 90, 94, 104, 105], "what": [1, 5, 9, 10, 17, 34, 37, 39, 41, 44, 62, 63, 67, 69, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "doe": [1, 2, 3, 7, 10, 41, 42, 44, 49, 52, 55, 58, 69, 70, 74, 76, 78, 82, 88, 89, 90, 91, 92, 94, 95, 97, 102, 106, 107], "do": [1, 2, 5, 9, 10, 37, 41, 42, 57, 58, 71, 72, 76, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "fast": 1, "explain": [1, 10, 96], "python": [1, 2, 42, 61, 74, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 104], "pseudocod": [1, 105], "happen": [1, 10, 44, 64, 95, 101, 107], "n": [1, 2, 3, 5, 7, 37, 38, 41, 42, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 87, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "without": [1, 2, 5, 9, 10, 13, 15, 21, 38, 42, 54, 66, 74, 84, 88, 89, 95, 96, 98, 99, 103, 104], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 46, 48, 55, 56, 57, 61, 62, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107], "distinct": [1, 19, 57, 108], "natur": [1, 10, 101, 104], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 83, 85, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 107, 108], "0": [1, 2, 3, 4, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "count_joint": 1, "len": [1, 2, 3, 7, 37, 41, 47, 56, 57, 58, 71, 72, 74, 87, 88, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "y": [1, 2, 3, 5, 8, 19, 31, 32, 42, 47, 49, 57, 58, 61, 70, 74, 75, 88, 89, 90, 91, 94, 96, 98, 99, 101, 102, 104, 106], "round": [1, 41, 44, 57, 74, 96, 98, 106], "astyp": [1, 101], "int": [1, 2, 3, 4, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 39, 41, 42, 44, 49, 50, 52, 53, 54, 55, 56, 57, 58, 63, 64, 66, 70, 71, 72, 74, 76, 78, 79, 80, 83, 89, 90, 92, 96, 103, 104], "rang": [1, 3, 5, 7, 13, 47, 49, 55, 57, 70, 74, 75, 92, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 14, 17, 23, 37, 41, 44, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "pragma": 1, "cover": [1, 3, 85, 96, 97, 98], "choic": [1, 8, 44, 53, 55, 92, 98, 102, 104], "replac": [1, 56, 61, 72, 87, 88, 90, 91, 92, 95, 96, 97, 98, 101, 104], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 52, 72, 89, 90, 91], "05": [1, 10, 27, 31, 56, 70, 74, 80, 82, 94, 97, 98, 99, 103], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 90, 91, 99, 101, 102], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 90, 91, 92, 96, 98, 99, 101, 102, 107], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 27, 40, 42, 49, 74, 87, 89, 90, 91, 94, 96, 97, 99, 101, 102], "max_it": [1, 88, 89, 95, 104], "10000": [1, 41, 97, 98], "x": [1, 2, 3, 5, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 38, 39, 42, 44, 46, 47, 49, 52, 54, 56, 57, 58, 61, 62, 64, 70, 71, 72, 74, 76, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 104, 106], "diagon": [1, 3, 5, 44, 47, 57], "equal": [1, 3, 10, 13, 52, 64, 69, 79, 105], "creat": [1, 2, 9, 17, 19, 38, 41, 42, 44, 57, 74, 84, 88, 89, 92, 94, 95, 96, 98, 107, 108], "impli": [1, 10, 37, 63, 70], "float": [1, 2, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 40, 41, 42, 44, 46, 48, 49, 55, 56, 57, 62, 63, 64, 66, 69, 70, 74, 78, 82, 89, 90, 91, 99, 101, 102], "entri": [1, 3, 5, 10, 37, 38, 42, 44, 46, 50, 52, 55, 57, 62, 63, 64, 67, 87, 88, 94, 95, 99, 102, 103, 106], "maximum": [1, 10, 71, 79, 83, 107], "minimum": [1, 8, 10, 21, 44, 46, 64, 69, 82], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 17, 27, 38, 42, 44, 52, 69, 74, 90, 98, 99, 101, 103, 104], "default": [1, 2, 3, 4, 5, 7, 10, 11, 15, 17, 29, 31, 34, 37, 38, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 90, 92, 96, 98, 106, 107], "If": [1, 2, 3, 4, 5, 10, 13, 14, 17, 27, 29, 35, 37, 38, 41, 42, 44, 46, 47, 49, 52, 53, 56, 57, 61, 62, 63, 64, 67, 69, 70, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "have": [1, 2, 3, 4, 5, 7, 9, 10, 17, 22, 25, 27, 30, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "all": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 23, 34, 37, 38, 41, 42, 43, 44, 47, 49, 50, 52, 56, 57, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 90, 96], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 61, 62, 63, 65, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 62, 66, 70, 74, 79, 83, 84, 87, 88, 89, 91, 95, 98, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 60, 61, 64, 71, 72, 74, 76, 84, 85, 89, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 61, 62, 63, 64, 66, 69, 70, 71, 72, 74, 78, 79, 80, 82, 88, 91, 92, 95, 96, 97, 98, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 61, 64, 66, 67, 69, 71, 72, 74, 75, 87, 88, 90, 91, 92, 94, 95, 96, 97, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 89, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 61, 62, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 90, 91, 94, 95, 96, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 61, 62, 67, 69, 70, 71, 72, 74, 75, 79, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 60, 61, 62, 64, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 69, 72, 74, 87, 88, 90, 91, 92, 96, 97, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 62, 63, 67, 69, 70, 71, 72, 74, 76, 79, 83, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 61, 62, 71, 74, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 62, 84, 90, 91, 95, 96, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 90, 92, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 90, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 61, 62, 64, 66, 70, 71, 72, 74, 75, 78, 80, 82, 85, 87, 88, 89, 92, 94, 95, 96, 98, 99, 102, 103, 104, 106, 107, 108], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 17, 24, 27, 31, 37, 38, 41, 42, 43, 44, 46, 47, 52, 53, 55, 56, 57, 58, 61, 62, 71, 72, 74, 76, 78, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 101, 102, 103, 104, 105, 106, 107, 108], "guarante": [1, 3, 5, 16, 22, 25, 30, 38, 40, 42, 45, 47, 60, 85], "produc": [1, 2, 5, 9, 10, 17, 49, 62, 72, 74, 76, 78, 84, 87, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108], "higher": [1, 5, 10, 37, 44, 46, 47, 49, 55, 61, 62, 63, 74, 91, 95, 96, 98, 103], "opposit": [1, 108], "occur": [1, 3, 10, 37, 56, 69, 90, 91, 92, 98, 104], "small": [1, 3, 10, 37, 41, 49, 52, 55, 57, 63, 70, 88, 92, 95, 97, 102, 104], "numpi": [1, 3, 4, 5, 7, 10, 13, 19, 32, 33, 41, 42, 43, 49, 52, 55, 56, 58, 61, 66, 69, 74, 75, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "max": [1, 44, 71, 72, 91, 92, 96, 104], "tri": [1, 38, 42, 105], "befor": [1, 2, 3, 38, 42, 55, 57, 71, 74, 79, 87, 88, 95, 96, 98, 99, 101, 104, 106], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 17, 24, 29, 31, 37, 38, 41, 42, 44, 47, 49, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 89, 90, 91, 92, 94, 98, 99, 102, 106, 107], "left": [1, 2, 44, 46, 55, 57, 64, 67, 70, 90, 91, 102, 103, 104, 107], "stochast": 1, "exceed": 1, "m": [1, 5, 38, 42, 48, 49, 52, 53, 62, 67, 69, 70, 71, 90, 91, 97, 101, 102, 103, 108], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 38, 42, 61, 98, 99, 107], "length": [1, 5, 13, 27, 28, 37, 39, 44, 57, 64, 67, 71, 72, 74, 76, 79, 83, 87, 89, 102, 104, 107, 108], "must": [1, 2, 3, 4, 5, 7, 17, 37, 38, 39, 40, 42, 44, 47, 49, 50, 55, 57, 60, 61, 62, 63, 64, 71, 72, 74, 76, 78, 79, 80, 82, 83, 89, 92, 96, 101, 105, 107, 108], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 13, 37, 41, 44, 50, 57, 58, 62, 64, 70, 76, 78, 79, 80, 82, 83, 87, 88, 89, 98, 101, 102, 103, 107, 108], "ball": [1, 97], "bin": [1, 3, 64, 90, 91, 104], "ensur": [1, 2, 10, 38, 42, 52, 54, 55, 57, 58, 61, 69, 72, 74, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 104, 105, 106], "most": [1, 3, 5, 7, 10, 17, 37, 41, 44, 49, 61, 62, 63, 64, 67, 69, 70, 71, 72, 75, 78, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107], "least": [1, 4, 10, 19, 32, 37, 41, 62, 63, 69, 72, 82, 91, 92, 98, 101, 104, 107], "int_arrai": [1, 57], "can": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 102, 103, 104, 105, 106, 107, 108], "model": [2, 3, 4, 5, 9, 10, 11, 17, 19, 31, 33, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 54, 56, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105, 107, 108], "For": [2, 3, 5, 7, 9, 10, 12, 17, 23, 36, 37, 38, 41, 42, 44, 47, 49, 52, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 80, 82, 83, 84, 87, 88, 89, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108], "regular": [2, 3, 41, 61], "multi": [2, 3, 4, 10, 33, 37, 38, 41, 42, 44, 48, 49, 50, 57, 58, 63, 64, 65, 66, 71, 72, 84, 96, 98, 99, 100], "task": [2, 5, 7, 10, 11, 12, 13, 15, 16, 17, 26, 31, 34, 37, 41, 47, 49, 50, 55, 57, 62, 64, 72, 74, 84, 88, 89, 95, 96, 97, 98, 99, 102, 104, 106, 107, 108], "cleanlearn": [2, 3, 10, 24, 31, 38, 57, 61, 73, 74, 75, 84, 85, 87, 88, 106], "wrap": [2, 38, 42, 51, 61, 71, 74, 84, 87, 88, 90, 91, 94, 95, 99, 106], "instanc": [2, 3, 5, 6, 7, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 61, 70, 71, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 103], "sklearn": [2, 3, 4, 5, 8, 10, 19, 32, 37, 42, 49, 53, 54, 57, 61, 71, 74, 75, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106], "classifi": [2, 3, 42, 49, 57, 62, 65, 71, 72, 84, 85, 87, 88, 89, 94, 95, 98, 101, 102, 104, 105, 107, 108], "adher": [2, 42, 74], "estim": [2, 3, 4, 5, 9, 14, 23, 37, 41, 42, 44, 47, 57, 62, 63, 64, 69, 71, 74, 76, 78, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 98, 100, 103, 104, 105, 106, 107, 108], "api": [2, 3, 15, 61, 67, 70, 71, 74, 85, 96, 98, 106], "defin": [2, 3, 5, 7, 10, 15, 23, 37, 38, 39, 41, 42, 44, 72, 74, 76, 89, 90, 91, 94, 97, 98, 101, 104, 108], "four": [2, 10, 97, 99, 108], "clf": [2, 3, 5, 49, 74, 84, 87, 94, 96, 98, 99, 102], "fit": [2, 3, 5, 8, 10, 19, 40, 42, 52, 54, 60, 61, 71, 73, 74, 84, 87, 88, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 108], "sample_weight": [2, 42, 74, 99], "predict_proba": [2, 5, 37, 40, 42, 49, 60, 61, 87, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 104], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 17, 23, 24, 26, 29, 31, 33, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 88, 97, 98, 99, 100, 104, 106, 107, 108], "score": [2, 3, 4, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 43, 44, 46, 49, 55, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 104, 106], "data": [2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 49, 50, 52, 53, 54, 57, 60, 61, 62, 63, 64, 65, 69, 71, 72, 73, 74, 79, 80, 81, 82, 83, 85, 88, 92, 93, 100, 105], "e": [2, 3, 5, 10, 13, 23, 33, 37, 38, 41, 42, 44, 47, 49, 50, 52, 57, 58, 62, 63, 64, 65, 67, 70, 71, 72, 74, 76, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "featur": [2, 3, 4, 5, 8, 10, 11, 17, 19, 20, 24, 27, 28, 29, 31, 32, 49, 52, 53, 54, 57, 71, 74, 84, 87, 90, 91, 94, 95, 96, 98, 99, 101, 102, 106], "element": [2, 3, 5, 37, 43, 44, 46, 57, 62, 64, 72, 79, 80, 82, 88, 89, 95, 96, 98, 108], "first": [2, 5, 10, 18, 27, 28, 37, 41, 49, 52, 57, 62, 63, 67, 70, 72, 74, 87, 88, 89, 90, 92, 94, 96, 98, 101, 102, 103, 104, 106, 107, 108], "index": [2, 10, 27, 37, 44, 51, 52, 54, 56, 57, 58, 63, 72, 74, 79, 82, 83, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "should": [2, 3, 5, 7, 10, 15, 23, 27, 32, 33, 37, 38, 41, 42, 44, 46, 47, 49, 52, 54, 55, 56, 57, 61, 62, 63, 66, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 107, 108], "correspond": [2, 3, 5, 10, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 37, 38, 41, 42, 43, 44, 46, 47, 49, 52, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "differ": [2, 5, 7, 10, 14, 16, 22, 25, 27, 28, 30, 37, 38, 40, 41, 42, 44, 45, 49, 52, 55, 57, 58, 60, 62, 67, 69, 71, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 104, 105, 106], "sampl": [2, 3, 5, 8, 10, 17, 21, 44, 46, 49, 52, 53, 54, 64, 67, 70, 72, 74, 75, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "size": [2, 10, 32, 38, 41, 42, 44, 49, 52, 53, 64, 69, 70, 74, 76, 78, 88, 92, 94, 98, 99, 101, 102, 103, 105, 107], "here": [2, 5, 7, 10, 15, 41, 44, 47, 61, 62, 63, 64, 66, 67, 70, 71, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "re": [2, 5, 38, 42, 54, 56, 62, 74, 84, 87, 88, 89, 90, 94, 95, 98, 106, 107, 108], "weight": [2, 10, 38, 39, 42, 49, 52, 62, 69, 72, 74, 88, 89, 90, 91, 95], "loss": [2, 39, 61, 72, 74, 92], "while": [2, 3, 10, 38, 41, 42, 48, 49, 57, 74, 84, 92, 96, 98, 99, 101, 102, 106], "train": [2, 3, 4, 5, 9, 10, 17, 19, 33, 38, 39, 40, 42, 49, 57, 61, 62, 67, 70, 71, 74, 75, 85, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 105, 107, 108], "support": [2, 3, 4, 5, 13, 15, 34, 35, 41, 43, 49, 57, 58, 61, 71, 72, 82, 84, 85, 89, 90, 91, 92, 96, 98], "your": [2, 3, 5, 9, 10, 17, 37, 38, 40, 41, 42, 44, 49, 54, 57, 60, 61, 62, 63, 64, 66, 71, 72, 74, 75, 76, 78, 79, 85, 87, 88, 89, 92, 94, 97, 101, 102, 103, 104, 105, 106, 107, 108], "recommend": [2, 5, 7, 10, 14, 17, 41, 44, 62, 90, 91, 92, 96, 98, 105, 106], "furthermor": 2, "correctli": [2, 3, 10, 37, 38, 42, 44, 47, 52, 58, 63, 64, 69, 70, 74, 76, 88, 92, 95, 96, 98, 102, 103, 106, 107], "clonabl": [2, 74], "via": [2, 5, 7, 10, 11, 14, 17, 19, 23, 37, 39, 41, 42, 49, 53, 57, 62, 67, 70, 71, 72, 74, 75, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 102, 103, 104, 105, 106, 107, 108], "base": [2, 3, 4, 5, 7, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 47, 48, 49, 52, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 69, 71, 72, 74, 75, 78, 80, 82, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "clone": [2, 74, 102], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 66, 70, 74, 80, 85, 89, 90, 96, 98, 99, 101, 102, 103, 104, 106, 108], "multipl": [2, 3, 5, 10, 13, 14, 35, 37, 44, 55, 56, 62, 63, 64, 66, 69, 70, 74, 84, 90, 91, 92, 94, 98, 100, 102, 103, 106], "g": [2, 3, 5, 10, 13, 23, 33, 37, 38, 42, 44, 50, 52, 57, 64, 65, 67, 70, 71, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "manual": [2, 74, 87, 88, 89, 96, 98, 104, 105, 106, 108], "pytorch": [2, 38, 39, 42, 74, 84, 89, 92, 98, 100, 102, 107], "call": [2, 3, 5, 6, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 49, 57, 61, 71, 74, 88, 89, 90, 91, 95, 98, 99, 102, 104, 105, 106, 107, 108], "__init__": [2, 39, 74, 92], "independ": [2, 3, 10, 63, 74, 95, 96, 105, 106, 108], "compat": [2, 38, 41, 42, 54, 61, 74, 75, 78, 82, 84, 87, 88, 96, 98, 105, 106], "neural": [2, 39, 61, 71, 74, 89, 92, 98, 102, 104, 106], "network": [2, 38, 39, 42, 61, 71, 74, 88, 89, 92, 95, 98, 102, 104, 106], "typic": [2, 10, 38, 42, 54, 71, 74, 87, 88, 89, 91, 92, 94, 95, 104, 105], "initi": [2, 3, 14, 19, 38, 42, 52, 62, 74, 87, 95, 98], "insid": [2, 42, 74, 98, 99], "There": [2, 3, 7, 52, 84, 99, 101], "two": [2, 3, 10, 19, 27, 37, 38, 41, 42, 50, 52, 53, 54, 57, 67, 69, 70, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106, 107, 108], "new": [2, 7, 9, 10, 15, 23, 38, 41, 42, 48, 52, 56, 57, 62, 74, 88, 89, 90, 95, 97, 98, 104, 105, 108], "notion": 2, "confid": [2, 3, 10, 23, 37, 41, 44, 47, 49, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 78, 82, 84, 87, 92, 94, 95, 99, 101, 102, 103, 105, 107, 108], "packag": [2, 5, 7, 9, 10, 12, 16, 36, 40, 44, 45, 57, 60, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "prune": [2, 3, 44, 64, 74, 85, 103], "everyth": [2, 70, 99], "els": [2, 70, 90, 92, 96, 97, 98, 101, 102, 103], "mathemat": [2, 3, 10, 47, 102], "keep": [2, 14, 15, 57, 84, 90, 96, 97, 98, 107], "belong": [2, 3, 10, 37, 44, 46, 47, 52, 63, 64, 65, 66, 71, 72, 76, 80, 82, 83, 91, 92, 99, 102, 104, 107, 108], "2": [2, 3, 4, 5, 7, 10, 11, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 75, 79, 80, 82, 83, 97, 98, 105], "error": [2, 3, 5, 10, 38, 42, 43, 44, 46, 47, 57, 63, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 82, 85, 87, 89, 90, 91, 94, 95, 96, 97, 100], "erron": [2, 3, 37, 44, 47, 57, 63, 64, 72, 74, 75, 76, 104, 106], "import": [2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 41, 43, 49, 52, 55, 56, 62, 66, 69, 74, 75, 80, 82, 83, 84, 87, 88, 94, 95, 96, 98, 102, 103, 104, 106, 107, 108], "linear_model": [2, 5, 37, 57, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logisticregress": [2, 3, 5, 37, 57, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logreg": 2, "cl": [2, 15, 31, 74, 84, 87, 88, 98, 99, 106], "pass": [2, 3, 5, 8, 10, 11, 13, 14, 15, 17, 24, 31, 34, 38, 41, 42, 44, 48, 49, 52, 54, 57, 61, 62, 64, 70, 71, 72, 74, 79, 80, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 103, 104, 106], "x_train": [2, 87, 90, 91, 99, 101, 102, 106], "labels_maybe_with_error": 2, "had": [2, 3, 74, 103], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 52, 60, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 88, 93, 100, 101, 105, 106], "pred": [2, 44, 57, 87, 88, 105, 106], "x_test": [2, 87, 90, 91, 99, 102, 106], "might": [2, 5, 10, 52, 62, 74, 79, 87, 88, 90, 91, 92, 96, 98, 103], "case": [2, 3, 10, 14, 37, 49, 52, 62, 74, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 104, 106, 108], "standard": [2, 3, 5, 31, 37, 44, 61, 63, 64, 66, 72, 74, 84, 87, 90, 91, 94, 97, 99, 103], "adapt": [2, 38, 40, 57, 60, 74, 104], "skorch": [2, 74, 84, 98], "kera": [2, 60, 67, 70, 74, 84, 98, 103], "scikera": [2, 61, 74, 98], "open": [2, 41, 97, 103, 108], "doesn": [2, 10, 74, 84], "t": [2, 3, 4, 7, 10, 18, 28, 29, 38, 39, 41, 42, 43, 44, 49, 55, 56, 66, 71, 72, 74, 80, 82, 83, 84, 90, 91, 92, 94, 95, 96, 97, 99, 102, 103, 106, 108], "alreadi": [2, 5, 10, 17, 38, 41, 42, 47, 52, 61, 62, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 103, 104, 106], "exist": [2, 5, 10, 13, 19, 38, 41, 42, 54, 56, 61, 67, 69, 71, 74, 84, 85, 87, 88, 90, 91, 95, 101, 108], "made": [2, 5, 17, 38, 42, 53, 74, 87, 88, 92, 95, 96, 98, 101, 103, 105, 106], "easi": [2, 12, 47, 74, 90, 91, 97, 98, 99, 102], "inherit": [2, 7, 39, 74], "baseestim": [2, 42, 74], "yourmodel": [2, 74], "def": [2, 7, 15, 38, 42, 61, 74, 88, 89, 90, 91, 92, 96, 97, 98, 99, 101, 102, 104, 106, 108], "self": [2, 3, 5, 7, 10, 13, 14, 15, 17, 32, 38, 39, 41, 42, 44, 49, 71, 72, 74, 87, 88, 90, 92, 95, 96, 97, 102, 107, 108], "refer": [2, 10, 17, 38, 42, 43, 63, 64, 66, 67, 69, 70, 71, 74, 78, 79, 90, 91, 92, 94, 95, 96, 98, 99, 102, 105, 106], "origin": [2, 5, 10, 42, 43, 44, 56, 57, 61, 63, 64, 67, 70, 71, 74, 75, 78, 80, 82, 87, 88, 90, 92, 94, 95, 96, 98, 99, 103, 104, 106, 108], "total": [2, 3, 4, 37, 41, 57, 63, 83, 92, 98, 107], "state": [2, 3, 5, 38, 39, 42, 48, 74, 99, 102, 103, 108], "art": [2, 39, 99, 102], "northcutt": [2, 3, 37, 71, 72], "et": [2, 3, 37, 39, 71, 72], "al": [2, 3, 37, 39, 71, 72], "2021": [2, 3, 37, 71, 72], "weak": [2, 70], "supervis": [2, 10, 90, 91, 98, 101], "find": [2, 5, 9, 10, 14, 15, 17, 20, 21, 23, 24, 26, 27, 28, 29, 32, 33, 37, 38, 40, 41, 42, 43, 44, 48, 54, 56, 57, 60, 67, 70, 71, 72, 74, 76, 80, 82, 85, 90, 100, 105], "uncertainti": [2, 10, 46, 71, 74, 98, 104, 106], "It": [2, 3, 5, 7, 10, 13, 14, 17, 23, 28, 31, 33, 34, 35, 38, 42, 44, 47, 49, 52, 53, 55, 62, 69, 70, 74, 84, 88, 90, 91, 92, 95, 98, 99, 102, 105], "work": [2, 3, 7, 10, 13, 31, 37, 38, 41, 42, 44, 47, 56, 57, 58, 61, 62, 72, 74, 84, 85, 88, 90, 91, 96, 97, 104, 106], "includ": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 38, 40, 41, 42, 52, 56, 57, 60, 62, 63, 66, 67, 71, 72, 74, 78, 79, 80, 82, 84, 85, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 108], "deep": [2, 40, 42, 60, 61, 74, 95], "see": [2, 3, 5, 7, 10, 14, 15, 34, 37, 38, 41, 42, 43, 44, 49, 54, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "subfield": 2, "theori": [2, 99], "machin": [2, 4, 5, 9, 10, 15, 17, 34, 40, 55, 60, 74, 87, 88, 90, 91, 96, 97, 101], "across": [2, 3, 5, 7, 10, 14, 23, 37, 41, 49, 63, 70, 71, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 106], "varieti": [2, 87, 88, 98], "like": [2, 3, 5, 6, 7, 10, 15, 33, 37, 38, 41, 42, 44, 47, 57, 61, 62, 63, 66, 67, 69, 72, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "pu": [2, 57], "input": [2, 3, 5, 9, 17, 27, 37, 38, 41, 42, 47, 49, 52, 53, 56, 57, 58, 61, 70, 74, 84, 85, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "discret": [2, 35, 44, 47, 57, 71, 72, 76, 78, 79], "vector": [2, 3, 4, 5, 10, 17, 44, 47, 49, 50, 52, 57, 71, 72, 84, 88, 89, 90, 91, 92, 94, 95, 99, 102, 103, 104, 107, 108], "would": [2, 3, 5, 10, 38, 41, 42, 44, 53, 57, 64, 74, 84, 88, 90, 92, 98, 99, 104, 106, 108], "obtain": [2, 5, 8, 10, 17, 44, 62, 64, 67, 70, 72, 75, 89, 91, 95, 98, 101, 103, 105, 107, 108], "been": [2, 4, 37, 44, 47, 52, 56, 57, 62, 63, 67, 69, 71, 72, 74, 89, 90, 94, 98, 99, 101, 102, 103, 104, 107, 108], "dure": [2, 10, 17, 52, 54, 71, 74, 87, 88, 89, 94, 95, 96, 98, 99, 102, 105, 106, 108], "denot": [2, 3, 47, 49, 57, 64, 71, 72, 82], "tild": 2, "paper": [2, 4, 10, 62, 71, 80, 82, 97, 99, 101, 104, 106, 108], "cv_n_fold": [2, 3, 74, 88], "5": [2, 3, 4, 5, 8, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 42, 44, 46, 48, 49, 57, 62, 63, 66, 67, 70, 74, 75, 82, 88, 90, 95, 97, 98, 102, 103, 104, 105, 107, 108], "converge_latent_estim": [2, 3], "pulearn": [2, 57], "find_label_issues_kwarg": [2, 10, 74, 85, 98, 99], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 64, 80, 98], "clean": [2, 69, 72, 74, 75, 84, 87, 88, 90, 91, 97, 106], "even": [2, 3, 7, 9, 10, 37, 41, 46, 47, 57, 74, 89, 96, 98, 99, 101, 102, 103], "messi": [2, 74, 99], "ridden": [2, 74], "autom": [2, 9, 10, 74, 84, 91, 97, 98], "robust": [2, 47, 52, 74, 91, 98], "prone": [2, 74], "out": [2, 3, 5, 10, 17, 29, 38, 42, 44, 49, 52, 61, 64, 65, 67, 70, 71, 72, 74, 75, 83, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108], "current": [2, 3, 5, 7, 10, 11, 14, 15, 23, 38, 42, 43, 44, 49, 62, 69, 74, 90, 91, 98, 101, 103], "intend": [2, 14, 15, 16, 17, 33, 34, 35, 45, 52, 62, 78, 82, 89, 90, 91, 95, 99], "A": [2, 3, 4, 5, 7, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 66, 69, 70, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 103, 105, 108], "follow": [2, 3, 10, 15, 31, 35, 37, 38, 41, 42, 49, 51, 55, 62, 63, 67, 69, 70, 71, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "experiment": [2, 38, 39, 41, 42, 43, 64, 85, 98], "wrapper": [2, 61, 87, 88, 89, 106], "around": [2, 69, 90, 91, 103, 104, 108], "fasttext": [2, 60], "store": [2, 4, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 71, 74, 87, 88, 94, 95, 96, 97, 98, 107, 108], "along": [2, 49, 64, 82, 90, 91, 92, 96, 98, 104], "dimens": [2, 57, 76, 79, 92, 98, 104, 107], "select": [2, 9, 10, 27, 51, 62, 72, 92, 98, 101, 104], "split": [2, 3, 5, 10, 13, 41, 49, 56, 57, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 102, 105, 108], "cross": [2, 3, 10, 37, 44, 47, 48, 49, 64, 67, 70, 72, 74, 75, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "fold": [2, 3, 37, 44, 47, 74, 87, 89, 94, 97, 98, 103, 107], "By": [2, 37, 63, 64, 74, 90, 96, 107], "need": [2, 3, 10, 11, 37, 38, 41, 42, 44, 52, 54, 63, 64, 66, 71, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 107], "holdout": [2, 3, 74], "comput": [2, 3, 4, 5, 7, 8, 10, 20, 21, 23, 24, 27, 28, 29, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 52, 53, 54, 57, 62, 63, 64, 66, 69, 70, 71, 72, 74, 75, 76, 78, 84, 85, 88, 90, 91, 97, 99, 100, 103, 104, 106, 107], "them": [2, 3, 5, 7, 9, 10, 12, 13, 28, 33, 36, 38, 40, 41, 42, 44, 54, 60, 62, 71, 74, 85, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 107, 108], "numer": [2, 3, 4, 5, 10, 14, 23, 31, 35, 49, 52, 53, 69, 71, 74, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 99, 101, 102, 104, 106], "consist": [2, 3, 38, 42, 51, 57, 62, 96, 107, 108], "latent": [2, 3, 47], "thei": [2, 3, 5, 16, 22, 25, 27, 30, 38, 39, 40, 42, 44, 45, 52, 55, 57, 61, 64, 69, 72, 74, 75, 78, 82, 84, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106, 108], "relat": [2, 3, 10, 14, 20, 21, 27, 28, 29, 32, 47, 57, 63, 74, 91, 95, 96], "close": [2, 3, 10, 41, 47, 71, 89, 90, 91, 92, 94, 95, 96, 98, 99, 103], "form": [2, 3, 10, 38, 39, 42, 47, 56, 57, 72, 74, 98], "equival": [2, 3, 38, 42, 47, 71, 104, 106], "iter": [2, 3, 37, 38, 42, 44, 57, 63, 64, 74, 98, 101, 107], "enforc": [2, 38, 42, 57], "perfectli": [2, 37, 63, 99], "certain": [2, 3, 5, 38, 42, 61, 70, 74, 90, 91, 96, 97, 103, 104], "dict": [2, 3, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 48, 49, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 82, 90, 91, 92, 98, 108], "keyword": [2, 3, 5, 10, 11, 17, 24, 28, 31, 38, 41, 42, 44, 46, 49, 52, 54, 56, 61, 62, 64, 70, 71, 72, 74, 79, 80, 82, 90], "filter": [2, 3, 10, 41, 43, 56, 63, 65, 66, 68, 70, 77, 78, 79, 81, 82, 83, 84, 85, 87, 88, 89, 91, 92, 95, 97, 98, 102, 103, 106, 107, 108], "find_label_issu": [2, 3, 10, 31, 40, 41, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 98, 102, 103, 106, 107, 108], "particularli": [2, 84, 101, 104], "filter_bi": [2, 3, 41, 44, 64, 85, 98], "frac_nois": [2, 44, 64, 80, 98], "min_examples_per_class": [2, 44, 64, 91, 98, 99], "impact": [2, 4, 10, 90, 91, 92, 96], "ml": [2, 4, 5, 9, 10, 16, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 101, 102, 106], "accuraci": [2, 39, 72, 87, 88, 89, 92, 98, 99, 101, 104, 106, 107], "n_job": [2, 41, 44, 64, 76, 78, 80, 98, 104, 107], "disabl": [2, 38, 42, 44, 104], "process": [2, 3, 7, 14, 17, 33, 38, 41, 42, 44, 52, 56, 62, 64, 70, 76, 78, 80, 88, 89, 90, 96, 98, 101, 105], "caus": [2, 44, 49, 90, 91, 96, 98], "rank": [2, 3, 10, 37, 41, 43, 44, 49, 63, 64, 65, 67, 68, 70, 71, 73, 77, 79, 80, 81, 83, 84, 85, 87, 88, 90, 91, 97, 98, 102, 103, 104, 107, 108], "get_label_quality_scor": [2, 40, 41, 43, 44, 45, 49, 62, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77, 78, 80, 81, 82, 85, 98, 99, 102, 103, 107, 108], "adjust_pred_prob": [2, 10, 66, 71, 72, 99], "control": [2, 5, 9, 10, 17, 41, 44, 62, 70, 71, 74, 80, 82, 90, 91, 96, 97, 98], "how": [2, 3, 5, 10, 13, 14, 15, 17, 23, 37, 38, 39, 41, 42, 47, 57, 62, 63, 66, 67, 69, 71, 72, 74, 78, 82, 84, 87, 88, 90, 91, 92, 94, 95, 96, 97, 103, 104, 105, 106, 107], "much": [2, 10, 37, 41, 44, 74, 96, 97, 98, 99, 101, 104], "output": [2, 3, 5, 10, 17, 33, 38, 39, 42, 47, 57, 61, 62, 63, 67, 69, 70, 71, 74, 78, 79, 82, 83, 84, 85, 88, 89, 90, 92, 95, 97, 98, 103, 104, 105, 106], "print": [2, 5, 7, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 57, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "suppress": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79, 107, 108], "statement": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79], "big": [2, 41, 64, 70, 74, 99], "limit": [2, 5, 17, 41, 52, 64, 96, 103, 107, 108], "memori": [2, 38, 41, 42, 64, 70, 76, 78, 90, 107], "label_issues_batch": [2, 40, 64, 98], "find_label_issues_batch": [2, 40, 41, 64, 98], "pred_prob": [2, 3, 5, 8, 10, 11, 17, 24, 26, 27, 29, 32, 33, 37, 41, 43, 44, 46, 47, 48, 49, 50, 57, 58, 62, 63, 64, 66, 67, 70, 71, 72, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106], "threshold": [2, 3, 4, 7, 10, 19, 20, 21, 23, 29, 31, 32, 41, 55, 69, 70, 71, 72, 78, 82, 90, 96, 103, 104, 107, 108], "inverse_noise_matrix": [2, 3, 10, 47, 57, 85, 99], "label_issu": [2, 41, 44, 64, 67, 74, 76, 85, 87, 88, 89, 92, 95, 98, 99, 102, 106], "clf_kwarg": [2, 3, 10, 74], "clf_final_kwarg": [2, 74], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 37, 41, 44, 46, 52, 62, 63, 64, 66, 67, 69, 70, 72, 74, 75, 78, 82, 84, 89, 92, 94, 95, 99, 101, 103, 105, 106], "result": [2, 3, 9, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 41, 42, 44, 46, 55, 57, 64, 66, 67, 70, 72, 74, 75, 76, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 106, 107, 108], "identifi": [2, 3, 5, 7, 9, 10, 13, 17, 28, 34, 37, 41, 43, 44, 52, 64, 67, 70, 72, 74, 75, 76, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 102, 104, 106, 107, 108], "final": [2, 10, 74, 87, 94, 96, 103, 105, 106], "remain": [2, 74, 85, 87, 88, 92, 102, 106, 108], "datasetlik": [2, 57, 74], "beyond": [2, 5, 7, 9, 10, 12, 36, 84, 87, 88, 106, 107], "pd": [2, 3, 5, 7, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 48, 61, 62, 63, 74, 82, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 106, 108], "datafram": [2, 3, 5, 7, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 48, 57, 58, 61, 62, 63, 74, 79, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 106, 107, 108], "scipi": [2, 4, 5, 14, 53, 57, 71, 96], "spars": [2, 4, 5, 10, 14, 17, 19, 32, 52, 57, 58, 91, 92, 94, 95, 96, 99], "csr_matrix": [2, 4, 5, 14, 17, 19, 32, 52, 96], "torch": [2, 38, 39, 42, 88, 89, 92, 95, 97, 104], "util": [2, 5, 10, 17, 34, 38, 39, 42, 45, 52, 61, 62, 67, 70, 74, 84, 85, 89, 90, 91, 92, 98, 99, 104], "tensorflow": [2, 57, 61, 84, 89, 98], "object": [2, 5, 10, 13, 14, 17, 33, 34, 38, 39, 41, 42, 49, 52, 54, 57, 58, 61, 64, 67, 68, 69, 70, 71, 74, 82, 84, 88, 89, 91, 92, 94, 98, 99, 100, 102, 106], "list": [2, 3, 5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 43, 44, 50, 52, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 85, 88, 89, 90, 91, 92, 97, 98, 99, 102, 103, 106, 108], "index_list": 2, "subset": [2, 3, 5, 17, 37, 41, 44, 57, 72, 79, 83, 87, 88, 89, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 108], "wa": [2, 3, 13, 15, 41, 55, 57, 62, 63, 69, 71, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 105, 107, 108], "abl": [2, 3, 10, 74, 89, 98, 99, 101, 102], "format": [2, 3, 5, 10, 13, 33, 38, 41, 42, 44, 47, 48, 49, 50, 52, 57, 58, 61, 62, 63, 64, 67, 70, 71, 72, 74, 76, 78, 79, 82, 83, 87, 89, 90, 91, 92, 94, 96, 97, 101, 106, 107, 108], "make": [2, 3, 5, 19, 38, 41, 42, 49, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "sure": [2, 5, 41, 44, 49, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106], "shuffl": [2, 10, 57, 89, 92, 95, 96, 102, 104], "ha": [2, 3, 5, 6, 10, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 43, 47, 49, 52, 56, 57, 62, 67, 69, 74, 80, 82, 83, 84, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 108], "batch": [2, 41, 57, 61, 62, 76, 78, 92, 98, 104], "order": [2, 5, 10, 35, 37, 38, 42, 43, 44, 47, 48, 49, 55, 57, 62, 63, 64, 67, 70, 71, 72, 76, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 106, 107, 108], "destroi": [2, 57], "oper": [2, 38, 41, 42, 52, 57, 61, 72, 84, 87, 88, 95, 98, 104], "eg": [2, 5, 10, 57, 67, 70, 90, 91, 98], "repeat": [2, 57, 62, 101, 104], "appli": [2, 35, 38, 40, 42, 44, 49, 50, 52, 56, 57, 66, 71, 80, 87, 88, 89, 90, 91, 92, 94, 96, 98, 101, 102, 104, 105, 106, 107], "array_lik": [2, 3, 37, 44, 57, 64, 71, 75], "some": [2, 3, 5, 10, 15, 23, 37, 38, 40, 42, 44, 47, 52, 56, 57, 60, 62, 63, 64, 66, 67, 70, 71, 72, 74, 76, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "seri": [2, 3, 41, 57, 58, 74, 82, 98], "row": [2, 3, 5, 10, 14, 28, 33, 37, 41, 44, 46, 47, 52, 53, 57, 62, 63, 64, 66, 71, 72, 74, 79, 80, 82, 83, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 104, 108], "rather": [2, 3, 5, 10, 27, 37, 57, 61, 62, 69, 78, 82, 88, 97, 101, 105, 106, 107, 108], "leav": [2, 44], "per": [2, 3, 5, 7, 10, 14, 37, 41, 44, 49, 56, 62, 63, 64, 66, 69, 70, 72, 75, 76, 78, 82, 91, 98, 103, 108], "determin": [2, 3, 10, 13, 17, 23, 27, 31, 37, 41, 44, 49, 52, 57, 62, 64, 67, 69, 72, 78, 82, 90, 96, 98, 101, 103, 104, 106], "cutoff": [2, 3, 53, 104], "consid": [2, 3, 4, 5, 10, 14, 17, 24, 27, 29, 32, 37, 38, 42, 44, 52, 54, 57, 62, 69, 71, 72, 75, 78, 82, 87, 88, 89, 92, 94, 95, 96, 98, 99, 103, 104, 105, 106, 107], "section": [2, 3, 7, 10, 85, 92, 94, 96, 98, 103], "3": [2, 3, 4, 5, 7, 10, 11, 35, 37, 38, 42, 44, 47, 48, 49, 50, 53, 55, 56, 57, 61, 64, 71, 72, 74, 75, 80, 82, 97, 98, 105], "equat": [2, 3, 47], "advanc": [2, 3, 5, 9, 10, 17, 69, 71, 82, 85, 91, 93, 96, 98, 99], "user": [2, 3, 5, 9, 10, 15, 17, 28, 33, 34, 35, 38, 42, 44, 52, 61, 69, 71, 72, 74, 78, 82, 99], "specifi": [2, 3, 4, 5, 8, 10, 14, 15, 17, 19, 32, 34, 38, 41, 42, 44, 49, 52, 54, 56, 61, 62, 63, 64, 67, 69, 71, 72, 74, 75, 83, 85, 88, 89, 91, 92, 95, 101, 103, 106], "automat": [2, 3, 5, 27, 37, 84, 87, 88, 92, 94, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "greater": [2, 3, 4, 5, 7, 9, 10, 29, 41, 53, 57, 69, 91, 97, 98, 108], "count": [2, 23, 27, 37, 41, 44, 47, 57, 63, 64, 70, 85, 92, 96, 98, 103], "observ": [2, 3, 47, 54, 89, 90, 91, 101, 104, 106], "mislabel": [2, 10, 37, 41, 43, 44, 47, 62, 63, 64, 67, 69, 72, 78, 80, 82, 83, 84, 87, 88, 89, 92, 94, 95, 98, 99, 103, 106], "one": [2, 3, 5, 7, 10, 27, 37, 38, 41, 42, 43, 44, 49, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 104, 105, 106, 108], "get_label_issu": [2, 40, 41, 73, 74, 87, 88, 99, 106], "either": [2, 3, 4, 7, 10, 38, 41, 42, 44, 53, 62, 64, 69, 71, 72, 76, 78, 91, 96, 98, 102, 103], "boolean": [2, 7, 10, 23, 41, 44, 54, 56, 62, 64, 67, 72, 74, 76, 78, 79, 84, 88, 89, 91, 92, 95, 98, 103, 106, 107], "label_issues_mask": [2, 44, 72, 74, 85], "indic": [2, 3, 4, 5, 7, 10, 14, 23, 37, 41, 42, 43, 44, 46, 49, 52, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "its": [2, 5, 7, 9, 10, 17, 38, 41, 42, 44, 52, 54, 55, 56, 64, 67, 70, 71, 72, 74, 76, 80, 82, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108], "return_indices_ranked_bi": [2, 41, 44, 64, 80, 85, 87, 88, 98, 99], "significantli": [2, 10, 92, 96, 99, 101, 105], "reduc": [2, 41, 44, 57, 89, 98], "time": [2, 10, 38, 41, 42, 57, 62, 83, 85, 87, 88, 90, 92, 94, 97, 98, 99, 103, 104, 106, 107, 108], "take": [2, 5, 10, 37, 38, 42, 48, 49, 52, 54, 57, 61, 72, 87, 92, 94, 101, 102, 103, 108], "run": [2, 5, 6, 7, 9, 10, 11, 12, 15, 17, 27, 28, 33, 36, 38, 41, 42, 54, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 108], "skip": [2, 10, 38, 42, 74, 89, 96, 98, 102, 108], "slow": [2, 3], "step": [2, 7, 27, 49, 70, 92, 96, 99, 101, 105], "caution": [2, 5, 98], "previous": [2, 5, 14, 57, 71, 74, 85, 87, 89, 90, 94, 95, 101, 105], "assign": [2, 7, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 38, 42, 48, 49, 57, 74, 87, 90, 92, 94, 96, 98, 106, 107, 108], "individu": [2, 4, 7, 10, 14, 27, 38, 42, 43, 62, 66, 69, 72, 74, 80, 82, 85, 87, 91, 94, 96, 97, 98, 101, 102, 103, 108], "still": [2, 41, 42, 57, 71, 87, 89, 92, 98, 104], "extra": [2, 38, 42, 57, 61, 62, 63, 74, 92, 95, 98, 101, 104], "receiv": [2, 10, 38, 42, 43, 63, 66, 67, 74, 76, 80, 91, 103], "overwritten": [2, 74], "callabl": [2, 3, 4, 10, 27, 38, 42, 49, 52, 53, 54, 56, 61, 66, 98], "x_val": 2, "y_val": 2, "map": [2, 3, 13, 41, 42, 45, 48, 56, 57, 70, 72, 74, 79, 89, 90, 91, 92, 96, 98, 99, 102, 108], "appropri": [2, 10, 17, 35, 53, 64, 72, 90, 94, 102, 103], "earli": [2, 92], "stop": [2, 92], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 23, 37, 57, 71, 87, 90, 92, 94, 102, 106, 108], "f": [2, 7, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106], "ignor": [2, 38, 42, 56, 61, 74, 79, 83, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "allow": [2, 37, 38, 41, 42, 46, 54, 57, 62, 70, 71, 74, 76, 78, 88, 89, 92, 96, 98, 105, 107], "access": [2, 10, 14, 38, 42, 74, 88, 91, 92, 95, 97, 102], "hyperparamet": [2, 66, 71, 92], "purpos": [2, 52, 90, 91, 96, 98, 102, 106], "want": [2, 5, 10, 37, 41, 52, 58, 62, 64, 74, 88, 90, 92, 95, 97, 101, 103, 104, 105, 107, 108], "explicitli": [2, 8, 10, 42, 52, 74, 98], "yourself": [2, 5, 41, 91, 96], "altern": [2, 7, 10, 49, 54, 57, 61, 62, 72, 85, 88, 89, 92, 94, 95, 97, 98, 99, 101, 102, 104, 106], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 27, 31, 38, 41, 42, 44, 52, 57, 61, 62, 64, 71, 72, 74, 78, 79, 82, 83, 84, 87, 88, 90, 91, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 107], "effect": [2, 10, 28, 38, 42, 62, 71, 74, 92, 94, 95, 96, 98, 104], "offer": [2, 5, 9, 10, 88, 89, 90, 91, 95, 98, 99, 102], "after": [2, 3, 5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 62, 74, 88, 90, 92, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107], "attribut": [2, 5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 49, 54, 71, 74, 87, 90, 96], "label_issues_df": [2, 74, 92], "similar": [2, 10, 37, 38, 42, 54, 57, 62, 66, 67, 69, 71, 74, 78, 82, 90, 91, 92, 94, 95, 96, 98, 99, 103, 104, 107], "document": [2, 3, 5, 15, 17, 37, 38, 41, 42, 43, 44, 49, 56, 61, 63, 64, 66, 69, 70, 71, 74, 78, 79, 80, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "descript": [2, 5, 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 37, 43, 57, 67, 74, 90, 91], "were": [2, 3, 5, 10, 37, 42, 52, 63, 69, 82, 87, 89, 94, 96, 98, 99, 101, 103, 105, 107], "present": [2, 3, 5, 10, 13, 14, 21, 37, 57, 71, 79, 84, 92, 96, 98, 104], "actual": [2, 3, 5, 10, 37, 52, 62, 63, 72, 91, 98, 99, 108], "num_class": [2, 37, 41, 57, 61, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 104], "uniqu": [2, 32, 57, 79, 90, 96, 98, 102, 104], "given_label": [2, 5, 11, 26, 31, 37, 47, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107, 108], "normal": [2, 3, 19, 27, 32, 44, 46, 49, 55, 56, 57, 72, 96, 98, 99, 104], "trick": [2, 98], "distribut": [2, 3, 5, 10, 27, 29, 37, 42, 44, 48, 55, 62, 70, 71, 72, 84, 90, 91, 92, 94, 95, 96, 103, 104], "account": [2, 37, 62, 66, 71, 72, 88, 95, 98, 99, 101, 102, 104, 106], "word": [2, 3, 56, 82, 83, 98], "remov": [2, 10, 32, 37, 38, 42, 44, 74, 84, 87, 88, 91, 92, 94, 95, 96, 97, 98, 99, 102, 104, 106], "so": [2, 3, 5, 6, 7, 10, 15, 27, 35, 37, 38, 41, 42, 44, 52, 57, 62, 63, 69, 72, 74, 78, 82, 89, 90, 91, 92, 95, 96, 99, 102, 104, 107], "proportion": [2, 10, 44], "just": [2, 3, 5, 10, 14, 33, 37, 39, 41, 57, 61, 72, 74, 76, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106, 107], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 14, 32, 38, 39, 42, 44, 49, 55, 56, 57, 62, 64, 66, 71, 72, 74, 75, 76, 84, 87, 88, 89, 92, 95, 96, 97, 98, 99, 104, 105, 106], "detect": [2, 5, 7, 9, 14, 15, 17, 19, 23, 29, 43, 52, 55, 65, 67, 68, 69, 70, 71, 72, 73, 74, 77, 81, 84, 87, 88, 90, 93, 97, 100, 102, 106, 107, 108], "arg": [2, 13, 23, 28, 32, 38, 39, 42, 49, 57, 72, 74], "kwarg": [2, 7, 10, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 43, 49, 52, 61, 70, 74, 76, 78, 79, 80, 98], "test": [2, 5, 10, 27, 42, 49, 52, 61, 74, 84, 87, 88, 90, 91, 92, 94, 95, 105, 106, 108], "expect": [2, 3, 10, 38, 42, 44, 49, 52, 62, 71, 72, 74, 87, 88, 98, 99, 101, 102, 103, 106, 108], "class_predict": 2, "evalu": [2, 10, 38, 39, 40, 41, 42, 70, 74, 87, 88, 89, 90, 91, 92, 98, 99, 101, 105, 106, 107], "simpli": [2, 10, 37, 72, 88, 90, 91, 94, 95, 98, 99, 102, 106, 107, 108], "quantifi": [2, 4, 5, 7, 10, 14, 44, 66, 71, 74, 84, 91, 92, 94, 95, 96, 99, 103], "save_spac": [2, 10, 73, 74], "potenti": [2, 10, 37, 44, 56, 64, 67, 70, 72, 74, 76, 78, 83, 85, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "cach": [2, 88, 95], "panda": [2, 5, 7, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 57, 58, 61, 62, 63, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 106, 107], "unlik": [2, 10, 44, 46, 49, 61, 63, 64, 66, 82, 90, 101, 102, 104, 106], "both": [2, 5, 10, 17, 27, 37, 38, 42, 44, 52, 57, 62, 64, 72, 76, 78, 83, 84, 90, 92, 96, 98, 99, 101, 108], "mask": [2, 41, 44, 56, 57, 64, 67, 72, 74, 76, 78, 79, 84, 97, 98, 101, 103, 107, 108], "prefer": [2, 72, 80, 96, 102], "plan": 2, "subsequ": [2, 3, 38, 42, 54, 88, 95, 98, 99, 103], "invok": [2, 38, 42, 99, 105], "scratch": [2, 52, 74], "To": [2, 5, 7, 9, 10, 12, 14, 17, 27, 36, 38, 41, 42, 43, 44, 61, 62, 64, 66, 70, 71, 72, 74, 75, 76, 78, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "share": [2, 10, 72, 74], "mostli": [2, 57, 69, 74, 102, 106], "longer": [2, 35, 48, 49, 56, 74, 85, 88, 95, 98, 103], "info": [2, 5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 74, 82, 90, 91, 96, 97, 108], "about": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 39, 41, 46, 62, 63, 66, 70, 74, 79, 82, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 104], "docstr": [2, 37, 38, 42, 57, 74, 97, 99], "unless": [2, 38, 42, 52, 74, 98], "our": [2, 3, 10, 61, 62, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "is_label_issu": [2, 11, 31, 74, 88, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "entir": [2, 10, 27, 41, 44, 47, 63, 64, 69, 72, 74, 76, 78, 79, 84, 90, 91, 96, 98, 103, 104, 105, 107, 108], "accur": [2, 3, 5, 9, 10, 17, 37, 41, 44, 53, 62, 63, 64, 67, 70, 72, 74, 75, 76, 78, 79, 85, 91, 92, 94, 95, 96, 98, 101, 106], "label_qu": [2, 62, 74, 88, 99, 101, 106], "measur": [2, 5, 37, 62, 63, 74, 84, 87, 97, 98, 99, 101, 102, 106, 107, 108], "qualiti": [2, 3, 5, 7, 9, 10, 14, 31, 32, 37, 41, 43, 44, 46, 49, 62, 63, 64, 66, 67, 69, 72, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 106], "lower": [2, 4, 5, 7, 10, 14, 29, 41, 49, 55, 62, 63, 66, 69, 70, 72, 74, 75, 78, 82, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "eas": 2, "comparison": [2, 38, 42, 70, 99, 101], "against": [2, 38, 42, 90, 94, 96, 98, 101, 102], "predicted_label": [2, 5, 11, 26, 31, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107], "ad": [2, 38, 42, 91, 101, 106], "precis": [2, 53, 55, 64, 67, 70, 96, 97, 98, 99, 107, 108], "definit": [2, 7, 35, 49, 74, 87, 94], "accessor": [2, 74], "describ": [2, 10, 19, 62, 71, 72, 74, 80, 82, 99, 101, 102, 103, 105, 108], "precomput": [2, 4, 5, 47, 52, 74, 91, 92, 94, 95, 96, 97, 99], "clear": [2, 38, 42, 54, 74, 88, 95, 106], "save": [2, 5, 17, 38, 41, 42, 70, 74, 98, 103, 107, 108], "space": [2, 5, 10, 71, 74, 92, 94, 96, 97], "place": [2, 38, 42, 52, 57, 74, 87, 101], "larg": [2, 9, 10, 41, 52, 74, 92, 94, 95, 98, 103, 104, 107, 108], "deploi": [2, 9, 10, 74, 92, 94, 95, 98], "care": [2, 10, 38, 42, 52, 74, 95, 96, 98, 99], "avail": [2, 4, 5, 7, 10, 13, 15, 34, 42, 54, 74, 98, 99, 101, 103, 106], "cannot": [2, 5, 13, 15, 57, 105, 108], "anymor": 2, "classmethod": [2, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 42, 49, 74], "__init_subclass__": [2, 40, 42, 73, 74], "set_": [2, 42, 74], "_request": [2, 42, 74], "pep": [2, 42, 74], "487": [2, 42, 74], "look": [2, 5, 7, 10, 17, 38, 42, 57, 74, 79, 87, 90, 91, 94, 95, 98, 99, 101, 102, 103, 104, 107, 108], "inform": [2, 5, 7, 10, 14, 17, 34, 38, 42, 54, 57, 62, 63, 67, 70, 74, 79, 82, 83, 84, 89, 90, 94, 95, 96, 97, 99, 101, 104, 107, 108], "__metadata_request__": [2, 42, 74], "infer": [2, 42, 57, 74, 79, 83, 87, 88, 92, 101, 102], "signatur": [2, 38, 42, 74], "accept": [2, 38, 42, 54, 55, 72, 74, 90, 91, 98], "metadata": [2, 10, 42, 74, 92, 94, 95, 108], "through": [2, 5, 7, 42, 74, 88, 89, 91, 95, 96, 97, 98, 101, 103, 104], "develop": [2, 9, 42, 54, 74, 98, 99, 108], "request": [2, 42, 74, 87, 88, 91, 95, 96, 97, 102, 108], "those": [2, 3, 4, 10, 41, 42, 44, 51, 61, 62, 64, 70, 74, 78, 82, 83, 84, 89, 92, 96, 98, 103, 107], "http": [2, 4, 5, 7, 9, 10, 12, 19, 36, 38, 39, 41, 42, 46, 54, 57, 67, 70, 71, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "www": [2, 42, 74, 104], "org": [2, 4, 19, 38, 39, 42, 54, 57, 71, 74, 98, 99, 108], "dev": [2, 42, 74], "0487": [2, 42, 74], "get_metadata_rout": [2, 40, 42, 73, 74], "rout": [2, 42, 74], "pleas": [2, 38, 42, 61, 74, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 104, 106, 108], "guid": [2, 7, 10, 42, 74, 85, 89, 90, 91, 92, 93, 94, 95, 99], "mechan": [2, 38, 42, 74], "metadatarequest": [2, 42, 74], "encapsul": [2, 17, 42, 69, 74], "get_param": [2, 40, 42, 60, 61, 73, 74], "subobject": [2, 42, 74], "param": [2, 10, 38, 42, 61, 71, 74, 98], "name": [2, 5, 6, 7, 10, 11, 13, 14, 33, 35, 37, 38, 42, 48, 49, 53, 57, 61, 62, 63, 70, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 102, 106, 107, 108], "set_fit_request": [2, 40, 42, 73, 74], "str": [2, 3, 4, 5, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 47, 49, 52, 53, 54, 55, 56, 57, 61, 62, 63, 67, 69, 70, 72, 74, 79, 83, 89, 90, 98, 101, 102, 103, 108], "unchang": [2, 38, 42, 74, 108], "relev": [2, 17, 27, 42, 74, 92, 94, 96], "enable_metadata_rout": [2, 42, 74], "set_config": [2, 42, 74], "meta": [2, 42, 74], "rais": [2, 4, 5, 13, 14, 35, 38, 42, 46, 49, 52, 55, 74, 89, 98], "alia": [2, 38, 42, 74], "metadata_rout": [2, 42, 74], "retain": [2, 42, 57, 74], "chang": [2, 33, 35, 38, 41, 42, 46, 74, 82, 87, 88, 89, 90, 95, 98, 103, 104, 108], "version": [2, 4, 5, 7, 9, 10, 12, 16, 22, 25, 30, 36, 38, 40, 42, 45, 46, 57, 60, 61, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 108], "sub": [2, 42, 69, 74], "pipelin": [2, 42, 74, 106], "otherwis": [2, 4, 7, 10, 35, 37, 38, 41, 42, 44, 50, 53, 55, 56, 57, 64, 74, 76, 78, 79, 83, 88, 95, 98], "updat": [2, 14, 38, 41, 42, 52, 61, 74, 85, 90, 92], "set_param": [2, 40, 42, 60, 61, 73, 74], "simpl": [2, 38, 42, 44, 62, 72, 74, 87, 88, 90, 91, 92, 94, 95, 101, 104, 106], "well": [2, 3, 9, 10, 38, 42, 46, 47, 62, 64, 70, 72, 74, 79, 82, 83, 85, 90, 91, 92, 94, 95, 98, 99, 101, 103, 104], "nest": [2, 38, 42, 43, 58, 74, 80, 82, 83, 108], "latter": [2, 38, 42, 74, 104], "compon": [2, 42, 74], "__": [2, 42, 74], "set_score_request": [2, 73, 74], "structur": [3, 71, 94, 96, 98], "unobserv": 3, "less": [3, 4, 5, 10, 32, 41, 49, 62, 71, 72, 76, 78, 82, 91, 92, 94, 96, 97, 98, 99, 103, 108], "channel": [3, 89, 99], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 37, 47, 57, 63, 88, 91, 97], "inv": 3, "confident_joint": [3, 23, 37, 44, 57, 63, 64, 85, 98, 99], "un": 3, "under": [3, 10, 38, 42, 63, 70, 71, 91, 96, 104], "joint": [3, 37, 44, 47, 57, 63, 64, 97], "num_label_issu": [3, 41, 44, 64, 79, 83, 85], "estimation_method": [3, 41], "off_diagon": 3, "multi_label": [3, 37, 44, 57, 58, 64, 102], "don": [3, 84, 91, 92, 94, 95, 99, 103, 106], "statis": 3, "compute_confident_joint": [3, 37, 44, 57, 64, 99], "off": [3, 44, 57, 69, 92, 99, 103, 104], "j": [3, 5, 37, 38, 42, 43, 44, 64, 67, 70, 71, 80, 82, 83, 90, 91, 99, 107, 108], "confident_learn": [3, 44, 64, 99], "off_diagonal_calibr": 3, "calibr": [3, 4, 44, 57, 62, 101], "cj": [3, 47, 57], "axi": [3, 32, 47, 49, 55, 76, 79, 89, 90, 91, 92, 96, 98, 99, 101, 102, 104, 106, 107], "bincount": [3, 90, 91, 99, 101, 102], "alwai": [3, 10, 38, 42, 57, 87, 88, 89, 99, 106], "estimate_issu": 3, "over": [3, 5, 10, 38, 41, 42, 69, 70, 76, 78, 87, 91, 92, 94, 96, 97, 98, 99, 104, 106], "As": [3, 7, 84, 90, 91, 95, 99, 106, 108], "add": [3, 5, 7, 13, 14, 38, 42, 61, 70, 88, 89, 90, 91, 92, 95, 96, 98, 99, 102], "approach": [3, 37, 41, 44, 61, 87, 94, 96, 99, 102, 104, 106], "custom": [3, 7, 10, 12, 31, 38, 41, 42, 49, 56, 72, 88, 91, 92, 95, 96, 99, 106], "know": [3, 10, 90, 91, 92, 94, 95, 98, 99, 101, 106], "cut": [3, 69, 84, 99], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 33, 103, 104, 108], "underestim": 3, "few": [3, 9, 10, 70, 84, 91, 96, 98, 101, 102, 103, 104, 108], "4": [3, 4, 5, 10, 11, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 48, 49, 56, 66, 67, 69, 70, 72, 75, 82, 97, 98, 102, 107, 108], "detail": [3, 4, 5, 10, 15, 17, 34, 37, 38, 42, 43, 49, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 78, 79, 80, 84, 85, 89, 98, 102, 104, 108], "num_issu": [3, 7, 41, 89, 90, 91, 92, 94, 95, 96, 99], "calibrate_confident_joint": 3, "up": [3, 7, 10, 18, 27, 28, 31, 44, 49, 51, 61, 62, 88, 97, 98, 103, 106, 108], "p_": [3, 37, 44], "pair": [3, 5, 10, 37, 44, 99], "v": [3, 10, 41, 63, 64, 66, 72, 90, 91, 102, 103, 104, 105], "rest": [3, 5, 7, 9, 10, 12, 36, 63, 64, 66, 74, 87, 88, 90, 91, 92, 94, 95, 98, 99, 101, 106], "fashion": [3, 5, 76, 87], "2x2": 3, "incorrectli": [3, 37, 63, 64, 67, 94, 108], "calibrated_cj": 3, "c": [3, 10, 55, 56, 64, 72, 84, 87, 89, 90, 91, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106], "whose": [3, 4, 5, 10, 29, 38, 42, 47, 52, 56, 62, 66, 69, 75, 78, 82, 83, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 104, 107, 108], "truli": [3, 104, 107], "estimate_joint": [3, 37, 99], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 64, 70, 99, 103, 105, 107, 108], "return_indices_of_off_diagon": 3, "frequenc": [3, 27, 62, 63, 70, 79, 103, 104], "done": [3, 10, 61, 74, 90, 98, 99, 102, 104, 105], "overfit": [3, 10, 67, 70, 87, 89, 90, 91, 92, 94, 95, 105], "classifict": 3, "singl": [3, 5, 9, 10, 13, 27, 37, 38, 42, 43, 49, 50, 57, 62, 63, 69, 70, 71, 72, 82, 87, 89, 90, 96, 98, 99, 102, 103], "baselin": [3, 38, 44, 88, 104, 106], "proxi": 3, "union": [3, 5, 13, 27, 49, 52, 53, 54, 57, 58, 64, 70, 74, 82, 98], "tupl": [3, 32, 38, 42, 43, 47, 48, 50, 52, 56, 57, 62, 64, 70, 78, 80, 82, 83, 89, 108], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 41, 47, 52, 53, 62, 71, 76, 78, 84, 88, 92, 96, 98, 107], "practic": [3, 87, 88, 91, 92, 99, 104, 106], "complet": [3, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "gist": 3, "cj_ish": 3, "guess": [3, 47, 99, 101], "8": [3, 5, 7, 8, 48, 49, 50, 56, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "parallel": [3, 44, 70, 80, 97], "again": [3, 61, 87, 98, 104], "simplifi": [3, 15, 98], "understand": [3, 9, 10, 37, 63, 70, 91, 96, 99, 100, 106, 107, 108], "100": [3, 4, 38, 42, 52, 53, 55, 71, 72, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 102, 103, 104, 108], "optim": [3, 38, 39, 42, 61, 92, 96, 101], "speed": [3, 44, 88, 97, 98, 106], "dtype": [3, 24, 26, 27, 32, 38, 42, 56, 57, 66, 82, 89, 96, 103], "enumer": [3, 38, 42, 89, 90, 91, 92, 96, 108], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 42, 49, 57, 82, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "num_confident_bin": 3, "argmax": [3, 44, 72, 76, 79, 89, 96, 98, 99, 103, 104, 107], "elif": 3, "estimate_lat": 3, "py_method": [3, 47], "cnt": [3, 47], "1d": [3, 5, 13, 17, 33, 41, 44, 49, 50, 52, 57, 58, 66, 75, 87, 89, 96], "eqn": [3, 47], "margin": [3, 44, 47, 49, 72], "marginal_p": [3, 47], "shorthand": [3, 14], "proport": [3, 10, 37, 63, 99, 105], "poorli": [3, 47, 87, 96], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 99], "variabl": [3, 7, 15, 28, 57, 74, 75, 89, 90, 94, 99, 102, 106], "exact": [3, 10, 47, 52, 87, 90, 91, 92, 94, 96], "within": [3, 4, 5, 10, 16, 33, 38, 39, 42, 43, 45, 64, 69, 78, 80, 82, 90, 91, 92, 98, 103, 107], "percent": 3, "often": [3, 37, 47, 63, 98, 99, 105, 107], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 57, 58, 70, 87, 88, 89, 90, 92, 94, 95, 98, 102, 103, 104, 106], "wai": [3, 5, 10, 52, 61, 84, 85, 87, 88, 89, 90, 91, 94, 95, 98, 99, 101, 102, 103, 105], "pro": 3, "con": 3, "pred_proba": [3, 105], "combin": [3, 37, 90, 92, 96, 97, 98, 99, 105, 106], "becaus": [3, 47, 53, 57, 69, 95, 96, 98, 99, 101, 103], "littl": [3, 41, 97, 103, 108], "uniform": [3, 72, 97, 98, 99], "20": [3, 7, 43, 83, 89, 92, 95, 96, 97, 98, 99, 103, 106, 107, 108], "Such": [3, 92, 104], "bound": [3, 24, 26, 38, 42, 56, 66, 67, 69, 70, 103], "reason": [3, 23, 38, 42, 53, 71], "comment": [3, 56, 96, 108], "end": [3, 5, 38, 42, 54, 70], "file": [3, 5, 13, 40, 41, 60, 70, 87, 89, 90, 94, 95, 97, 98, 103, 104, 107, 108], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 99], "handl": [3, 5, 7, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 52, 53, 54, 85, 87, 88, 90, 91, 92, 94, 95, 96, 99, 107, 108], "five": [3, 67, 70, 99, 103], "estimate_cv_predicted_prob": [3, 99], "estimate_noise_matric": 3, "get_confident_threshold": [3, 40, 41], "amongst": [3, 10, 103], "confident_threshold": [3, 10, 23, 24, 41, 71], "point": [4, 5, 7, 9, 10, 19, 27, 38, 42, 52, 54, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101], "valuat": [4, 9, 19], "help": [4, 37, 38, 42, 70, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 108], "u": [4, 87, 88, 89, 90, 92, 94, 96, 98, 99, 101, 102, 105, 106, 107, 108], "assess": [4, 10, 96, 103], "contribut": [4, 10, 19, 96, 103], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 17, 19, 20, 27, 29, 32, 45, 51, 94, 96], "metric": [4, 5, 10, 19, 20, 22, 27, 29, 32, 45, 51, 52, 54, 55, 57, 61, 70, 71, 87, 88, 89, 92, 94, 95, 96, 99, 106], "10": [4, 10, 19, 20, 24, 27, 29, 32, 38, 39, 52, 70, 71, 72, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "shaplei": [4, 10, 19], "nearest": [4, 5, 10, 17, 24, 27, 29, 51, 52, 53, 54, 55, 71, 91, 95, 96, 104], "neighbor": [4, 5, 10, 17, 19, 24, 27, 29, 45, 52, 53, 54, 55, 71, 90, 91, 92, 94, 95, 96, 98, 99, 104], "knn": [4, 10, 14, 19, 27, 29, 32, 51, 52, 53, 54, 55, 71, 94, 104], "graph": [4, 5, 10, 14, 17, 19, 27, 32, 51, 52], "calcul": [4, 10, 19, 27, 41, 49, 51, 52, 55, 62, 66, 67, 69, 70, 71, 74, 78, 92, 97], "directli": [4, 5, 10, 15, 17, 34, 35, 41, 54, 61, 62, 88, 91, 95, 96, 98, 102, 103, 106], "lowest": [4, 10, 62, 70, 91, 92, 94, 96, 98, 101, 102, 103, 107], "fall": [4, 10, 69, 78, 82, 99, 104], "flag": [4, 10, 23, 27, 44, 49, 63, 64, 67, 74, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 104, 106, 107], "approxim": [4, 10, 19, 41, 54, 71, 96, 101], "top": [4, 5, 10, 37, 41, 43, 44, 57, 64, 67, 70, 72, 79, 83, 84, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 106, 108], "found": [4, 5, 7, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 102, 104, 106, 108], "arxiv": [4, 19, 99], "ab": [4, 19, 99, 103], "1908": 4, "08619": 4, "1911": [4, 19], "07128": [4, 19], "embed": [4, 5, 10, 17, 71, 84, 88, 89, 90, 91, 94, 95, 96, 99, 102, 106], "represent": [4, 5, 10, 17, 35, 38, 42, 50, 52, 64, 84, 88, 89, 90, 91, 92, 95, 98, 99, 104], "suppli": [4, 102, 103, 106], "2d": [4, 5, 17, 33, 41, 49, 50, 52, 56, 57, 62, 87, 89, 96, 102], "num_exampl": [4, 5, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 63, 89, 90, 91, 92, 94, 95, 99], "num_featur": [4, 5, 17, 38, 42, 61], "distanc": [4, 5, 10, 17, 19, 27, 29, 32, 51, 52, 53, 54, 55, 69, 71, 94, 96, 104], "construct": [4, 5, 7, 10, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 51, 52, 54, 61, 96], "nearestneighbor": [4, 5, 10, 19, 52, 54, 71, 94, 104], "cosin": [4, 10, 52, 53, 55, 71, 96, 104], "dim": [4, 71, 92, 107], "euclidean": [4, 5, 10, 52, 53, 55, 69, 71, 94], "dimension": [4, 27, 53, 57, 89, 99, 104], "scikit": [4, 42, 53, 54, 57, 71, 84, 87, 88, 89, 90, 91, 94, 95, 96, 98, 106], "fewer": [4, 10, 44, 57, 71, 96, 103], "stabl": [4, 16, 22, 25, 30, 40, 45, 54, 57, 60, 71, 85, 89, 90, 91, 92, 94, 95, 99], "exce": [4, 52, 92, 96], "transform": [4, 10, 33, 49, 52, 55, 57, 71, 72, 87, 88, 91, 92, 95, 96, 104, 108], "rel": [4, 10, 37, 52, 62, 63, 71, 90, 91, 92, 94, 95, 99, 104], "adjust": [4, 39, 44, 52, 66, 71, 72, 84, 96, 99], "closer": [4, 10, 69, 103], "highli": [4, 91, 92], "influenti": 4, "posit": [4, 5, 10, 38, 42, 55, 57, 70, 96, 97, 104], "convers": 4, "neg": [4, 10, 69, 70, 90, 91, 96, 97], "valueerror": [4, 5, 13, 14, 35, 46, 49, 52, 55, 98], "neither": [4, 5, 10, 15, 53, 103], "nor": [4, 5, 10, 15], "larger": [4, 19, 53, 74, 76, 78, 92, 95, 97, 98], "55": [4, 56, 96, 97, 103, 106, 108], "525": 4, "unifi": 5, "audit": [5, 9, 13, 14, 17, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 106], "kind": [5, 6, 7, 10, 96, 97], "addit": [5, 7, 9, 12, 14, 34, 36, 38, 42, 49, 52, 54, 58, 62, 70, 79, 80, 87, 88, 89, 90, 94, 95, 96, 99, 101, 104, 105], "depend": [5, 7, 9, 12, 13, 14, 36, 40, 44, 46, 57, 60, 64, 71, 74, 75, 84, 96], "instal": [5, 7, 9, 12, 36, 38, 40, 41, 42, 44, 60, 61, 76, 78], "pip": [5, 7, 9, 12, 36, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "development": [5, 7, 9, 12, 36], "git": [5, 7, 9, 12, 36, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "github": [5, 7, 9, 12, 36, 38, 39, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "com": [5, 7, 9, 12, 36, 38, 39, 41, 46, 57, 71, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "egg": [5, 7, 9, 12, 36, 84, 97], "label_nam": [5, 7, 8, 10, 11, 13, 19, 32, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "image_kei": [5, 10, 92], "interfac": [5, 9, 10, 54, 84, 98, 99], "librari": [5, 10, 42, 54, 67, 70, 71, 84, 88, 90, 95, 96, 97, 98], "goal": [5, 106], "track": [5, 7, 14, 15, 84, 90, 97, 98, 99], "intermedi": [5, 9, 91], "statist": [5, 10, 14, 23, 27, 37, 62, 63, 70, 91, 94, 95, 96, 99], "convert": [5, 10, 13, 35, 38, 42, 50, 55, 58, 62, 69, 78, 82, 85, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103], "hug": [5, 10, 13, 92], "face": [5, 10, 13, 17, 92, 97, 102], "kei": [5, 7, 10, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 49, 62, 63, 69, 71, 90, 91, 92, 95, 98, 99, 101, 103], "string": [5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 42, 53, 57, 62, 63, 75, 79, 82, 83, 88, 94, 95, 96, 98, 101, 102, 108], "dictionari": [5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 48, 57, 62, 63, 66, 67, 69, 70, 90, 91, 94, 95, 99, 101, 102, 103], "path": [5, 13, 38, 41, 42, 70, 89, 90, 98, 103], "local": [5, 7, 10, 13, 38, 39, 42, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "text": [5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 43, 49, 71, 80, 82, 83, 84, 86, 90, 91, 93, 97, 98, 99, 100, 101, 104], "txt": [5, 13, 108], "csv": [5, 13, 87, 88, 94, 95, 106], "json": [5, 13], "hub": [5, 13], "multiclass": [5, 13, 16, 49, 57, 62, 102], "regress": [5, 7, 10, 11, 13, 15, 17, 22, 31, 33, 35, 88, 90, 91, 95, 100, 101, 104], "multilabel": [5, 10, 11, 13, 15, 16, 22, 26, 33, 35, 50, 102], "imag": [5, 9, 37, 42, 67, 69, 70, 71, 76, 78, 79, 84, 90, 91, 93, 97, 98, 100, 101, 102, 103, 105, 107], "field": [5, 10, 38, 42], "themselv": [5, 87, 88, 96, 106], "pil": [5, 92], "cleanvis": [5, 10], "level": [5, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 52, 56, 80, 82, 91, 92, 98, 100, 102, 107], "load_dataset": [5, 13, 92], "glue": 5, "sst2": 5, "properti": [5, 13, 14, 35, 38, 42], "has_label": [5, 13], "class_nam": [5, 13, 21, 37, 43, 63, 70, 79, 83, 84, 97, 99, 103, 107, 108], "empti": [5, 13, 47, 62, 91, 96, 98, 102], "find_issu": [5, 6, 7, 8, 10, 11, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_typ": [5, 6, 7, 8, 10, 11, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "sort": [5, 17, 41, 44, 49, 62, 64, 67, 69, 70, 72, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 103, 106, 107, 108], "common": [5, 10, 14, 17, 91, 93, 96, 97, 98, 99, 102, 103, 107], "real": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "world": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "interact": [5, 17, 95, 98], "thereof": [5, 17], "insight": [5, 17, 70, 101], "best": [5, 9, 10, 17, 48, 62, 72, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 108], "properli": [5, 10, 41, 48, 52, 57, 58, 76, 89, 90, 91, 92, 94, 95, 98, 99, 102, 104, 106, 107], "respect": [5, 38, 42, 67, 70, 89, 90, 91, 92, 94, 95, 99, 102, 103], "lexicograph": [5, 48, 57, 89, 90, 91, 92, 94, 95, 99, 102], "squar": [5, 57, 74, 97, 106], "csr": [5, 52, 96], "evenli": 5, "omit": [5, 69, 70, 92, 96, 103], "itself": [5, 33, 38, 42, 52, 96, 103], "three": [5, 10, 37, 62, 63, 74, 79, 87, 89, 90, 91, 94, 97, 99, 101, 105, 106, 107, 108], "indptr": [5, 96], "wise": 5, "start": [5, 7, 10, 35, 38, 39, 42, 49, 84, 102, 108], "th": [5, 10, 43, 48, 56, 57, 62, 64, 67, 69, 70, 71, 80, 82, 83, 95, 102, 103, 108], "ascend": [5, 37, 63, 92, 99], "segment": [5, 76, 78, 79, 100], "reflect": [5, 10, 52, 87, 88, 94, 95, 101, 103, 104, 106], "maintain": [5, 61], "kneighbors_graph": [5, 19, 54, 94], "illustr": [5, 96], "todens": 5, "second": [5, 49, 57, 70, 72, 90, 94, 98, 99, 108], "duplic": [5, 9, 22, 23, 38, 42, 52, 84, 90, 96, 99, 106], "explicit": 5, "precend": 5, "collect": [5, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 62, 96, 98, 101, 108], "unspecifi": [5, 17, 44, 64], "interest": [5, 17, 23, 79, 83, 87, 88, 95, 96, 99, 106, 107, 108], "constructor": [5, 10, 11, 17, 24, 31, 52, 54], "issuemanag": [5, 9, 14, 15, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 34], "respons": [5, 17, 23, 54, 74, 75, 97, 106, 108], "random_st": [5, 87, 89, 90, 91, 92, 96, 99, 102, 104], "lab": [5, 6, 8, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 41, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106], "comprehens": [5, 84, 92, 102, 106], "nbr": 5, "n_neighbor": [5, 10, 19, 52, 54, 71, 96], "mode": [5, 12, 19, 38, 41, 42, 104], "4x4": 5, "float64": [5, 27, 38, 42, 82], "compress": [5, 10, 52, 57, 76, 78, 96], "toarrai": [5, 52, 96], "NOT": [5, 41, 95], "23606798": 5, "41421356": [5, 52], "configur": [5, 17, 49, 91], "suppos": [5, 10, 67, 87, 88, 104, 106], "who": [5, 69, 87, 94, 96, 99, 108], "manag": [5, 8, 9, 10, 14, 15, 16, 17, 18, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 61, 90, 96, 98], "clean_learning_kwarg": [5, 10, 11, 24, 31, 98, 106], "labelissuemanag": [5, 10, 15, 22, 24], "prune_method": [5, 85], "prune_by_noise_r": [5, 44, 64, 99], "report": [5, 7, 12, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 83, 84, 89, 90, 91, 94, 95, 98, 99, 102, 106, 108], "include_descript": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34], "show_summary_scor": [5, 34], "show_all_issu": [5, 34], "summari": [5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 43, 60, 61, 63, 68, 77, 78, 80, 81, 82, 85, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 106, 107, 108], "show": [5, 7, 27, 38, 42, 48, 57, 70, 79, 83, 87, 91, 92, 94, 95, 96, 97, 98, 99, 101, 104, 106, 107, 108], "suffer": [5, 10, 14, 23, 64, 72, 83, 96, 108], "onc": [5, 23, 37, 38, 42, 87, 90, 98, 99, 102, 103], "familiar": [5, 96], "overal": [5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 43, 49, 62, 63, 66, 69, 70, 74, 78, 79, 80, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 103, 108], "sever": [5, 7, 10, 13, 14, 23, 38, 41, 42, 44, 66, 69, 71, 72, 78, 82, 84, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 108], "compar": [5, 62, 71, 82, 90, 91, 94, 96, 99, 103], "issue_summari": [5, 7, 10, 14, 90, 96], "With": [5, 9, 10, 41, 88, 95, 98, 99, 101, 106, 107, 108], "usag": [5, 41, 61], "usual": [5, 13, 33, 34, 92, 101, 106], "ti": [5, 62], "exhibit": [5, 7, 10, 14, 79, 89, 90, 91, 92, 94, 95, 99, 103], "ie": [5, 74], "likelihood": [5, 10, 41, 43, 44, 64, 69, 71, 72, 76, 80, 96], "wherea": [5, 10, 57, 64, 87, 88, 105], "outlier": [5, 9, 11, 15, 22, 23, 32, 45, 52, 72, 84, 90, 91, 96, 99, 100, 106], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 99, 106], "global": [5, 7, 10, 23, 38, 42, 97], "non_iid": [5, 10, 11, 15, 27, 91, 92, 94, 95, 96, 99], "hypothesi": [5, 96], "iid": [5, 7, 9, 27, 94, 99], "never": [5, 89, 99, 102, 104, 105], "someth": [5, 7, 10, 38, 42, 72, 103], "123": [5, 90, 91], "456": [5, 87, 88, 89], "nearest_neighbor": 5, "7": [5, 10, 49, 50, 61, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "9": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 43, 49, 50, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "distance_to_nearest_neighbor": [5, 11, 90, 91, 92, 94, 95, 99], "789": 5, "get_issu": [5, 10, 14, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_nam": [5, 6, 7, 10, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91, 92, 94, 95, 99], "focu": [5, 10, 14, 95, 96, 107, 108], "full": [5, 10, 14, 41, 61, 70, 92, 108], "summar": [5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 63, 79, 83, 84, 107], "specific_issu": [5, 14], "lie": [5, 10, 71, 72, 88, 89, 90, 91, 92, 94, 95, 96, 99], "get_issue_summari": [5, 10, 14, 91, 96], "get_info": [5, 14, 91, 95, 96, 97], "yet": [5, 18, 28, 61, 97, 101], "list_possible_issue_typ": [5, 15, 16], "regist": [5, 7, 15, 16, 18, 28, 38, 42, 90], "rtype": [5, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42], "registri": [5, 15, 16], "list_default_issue_typ": [5, 15, 16], "folder": [5, 89, 90, 92], "load": [5, 13, 41, 70, 92, 97, 98, 99, 103, 104, 107, 108], "futur": [5, 10, 23, 38, 42, 62, 84, 88, 89, 90, 92, 95, 98], "overwrit": [5, 90], "separ": [5, 37, 49, 66, 90, 91, 92, 96, 98, 103, 105], "static": 5, "rememb": [5, 95, 98, 99], "part": [5, 10, 38, 42, 44, 67, 69, 70, 89, 90, 96, 97, 107, 108], "ident": [5, 10, 23, 57, 95, 96], "datalab": [6, 8, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 84, 87, 88, 97, 101, 106], "walk": 7, "alongsid": [7, 38, 42, 90, 98], "pre": [7, 8, 10, 38, 42, 90, 91, 106], "runtim": [7, 38, 41, 42, 74, 76, 78, 89, 92, 98], "issue_manager_factori": [7, 15, 90], "myissuemanag": [7, 15], "myissuemanagerforregress": 7, "decor": [7, 15], "ll": [7, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "thing": [7, 42, 88, 96, 99, 106], "next": [7, 62, 84, 87, 88, 89, 92, 94, 95, 96, 98, 101, 103, 106, 108], "dummi": 7, "randint": [7, 32, 49, 90, 91, 96], "mark": [7, 10, 85, 103, 104, 106], "regard": [7, 91, 99], "rand": [7, 49, 52, 90, 91, 96], "is_": [7, 10, 90], "_issu": [7, 10, 90], "issue_score_kei": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "whole": [7, 10, 27, 38, 42, 91, 96], "make_summari": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "popul": [7, 91, 95], "verbosity_level": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "std": [7, 103], "raw_scor": 7, "bit": 7, "involv": [7, 41, 79, 83, 96, 98, 102], "intermediate_arg": 7, "min": [7, 49, 69, 82, 90, 98, 104], "sin_filt": 7, "sin": 7, "arang": [7, 96], "kernel": [7, 96], "affect": [7, 10, 38, 42, 53, 76, 82, 95, 96, 98], "easili": [7, 47, 85, 87, 88, 89, 91, 94, 95, 99, 101, 102, 104, 105, 106, 107], "hard": [7, 42, 97, 104], "sai": [7, 10, 38, 42, 96, 102, 107], "anoth": [7, 10, 23, 37, 41, 53, 56, 69, 72, 88, 94, 95, 96, 98, 99, 101, 104], "try": [7, 9, 10, 41, 44, 61, 62, 76, 78, 84, 91, 92, 94, 95, 98, 99, 107], "won": [7, 38, 42, 90, 91, 98, 102], "issue_manag": [7, 10, 12, 14, 16, 19, 20, 21, 24, 26, 27, 28, 29, 31, 32, 90], "instanti": [7, 17, 41, 61, 71, 88, 89, 91, 94], "477762": 7, "286455": 7, "term": [7, 10, 47, 57, 70, 89, 90, 91, 92, 94, 95, 99], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 20, 29, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 103, 104, 106, 107, 108], "003042": 7, "058117": 7, "11": [7, 10, 61, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "121908": 7, "15": [7, 55, 61, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "169312": 7, "17": [7, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 90, 91, 96, 97, 99], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 32], "group": [8, 9, 27, 32, 97, 103, 108], "dbscan": [8, 10, 32], "hdbscan": 8, "etc": [8, 10, 23, 33, 38, 42, 47, 61, 62, 80, 84, 90, 91, 94, 95, 98, 99, 102, 106], "sensit": [8, 10, 55, 96], "ep": [8, 32, 70], "radiu": 8, "min_sampl": [8, 32], "kmean": [8, 96], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 32, 96], "cluster_id": [8, 10, 11, 32, 96], "labels_": 8, "underperforming_group": [8, 10, 11, 15, 22, 91, 92, 94, 95, 96, 99], "search": [9, 10, 21, 27, 28, 45, 51, 52, 53, 56, 74, 96, 98, 105], "nondefault": 9, "Near": [9, 98], "imbal": [9, 22, 66, 71, 72, 91], "null": [9, 11, 15, 22, 91, 92, 95, 99], "togeth": [9, 10, 47, 88, 90, 91, 92, 94, 95, 99, 106, 108], "built": [9, 49], "own": [9, 38, 40, 42, 54, 60, 66, 67, 70, 76, 80, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 106, 107, 108], "prerequisit": 9, "basic": [9, 42, 61, 94, 95, 96, 104], "fulli": [9, 10, 38, 42, 61, 98], "platform": [9, 10, 84, 92, 94, 95, 98], "write": [9, 10], "code": [9, 10, 38, 42, 47, 57, 61, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "being": [9, 10, 14, 37, 38, 42, 44, 49, 56, 57, 72, 87, 94, 98, 99, 106, 107], "100x": [9, 10], "faster": [9, 10, 41, 71, 74, 76, 78, 98, 99], "intellig": [9, 10], "quickli": [9, 10, 39, 87, 89, 92, 94, 95, 98, 102, 104, 107, 108], "fix": [9, 10, 62, 88, 95, 96, 99, 106], "scientist": [9, 10], "million": [9, 10, 108], "thank": [9, 10], "ai": [9, 10, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 101, 102, 104, 106, 108], "suggest": [9, 10, 37, 62, 63, 69, 88, 92, 95, 98, 106], "power": [9, 10, 92, 94, 95, 97, 99, 108], "automl": [9, 10, 84, 98], "system": [9, 10, 89, 92, 94, 95, 107], "foundat": [9, 10, 84, 96], "improv": [9, 10, 62, 87, 88, 91, 92, 97, 98, 99, 106, 107], "click": [9, 10, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "tune": [9, 10, 88, 89, 95, 97, 104], "serv": [9, 10, 14, 17, 101], "auto": [9, 10, 87, 88, 91, 97, 98, 106], "free": [9, 10, 84, 89, 91, 92, 94, 95, 98, 99], "page": [10, 91, 98, 99], "variou": [10, 14, 31, 40, 58, 60, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "why": [10, 95], "matter": [10, 37, 63, 88, 95], "didn": [10, 96], "plu": [10, 106], "ye": [10, 11], "near_dupl": [10, 11, 15, 20, 90, 91, 92, 94, 95, 96, 98, 99], "class_imbal": [10, 11, 15, 21, 91, 92, 94, 95, 96, 99], "data_valu": [10, 11, 15, 22, 96], "No": [10, 11, 87, 88, 95, 96, 98], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 69, 87, 88, 108], "issue_scor": 10, "atyp": [10, 71, 90, 91, 92, 94, 95, 99, 104], "datapoint": [10, 32, 44, 49, 57, 72, 75, 84, 87, 88, 89, 90, 91, 94, 95, 98, 105, 106], "is_issu": [10, 23], "primarili": 10, "former": [10, 38, 42], "investig": [10, 89], "expertis": 10, "interpret": [10, 97, 98, 99, 102, 106], "annot": [10, 37, 48, 62, 63, 64, 66, 67, 69, 70, 79, 82, 83, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 103, 107], "dissimilar": [10, 94, 95], "preced": 10, "incorrect": [10, 69, 72, 75, 87, 89, 90, 91, 92, 94, 95, 96, 99, 103, 106], "due": [10, 41, 44, 72, 76, 78, 89, 90, 91, 92, 94, 95, 99, 106], "appear": [10, 37, 48, 63, 64, 67, 75, 91, 92, 94, 95, 96, 106, 107], "now": [10, 41, 85, 87, 88, 89, 91, 96, 98, 101, 103, 104, 106, 108], "token": [10, 43, 56, 78, 79, 80, 81, 82, 83, 98, 99, 100], "hamper": [10, 92, 97], "analyt": [10, 84, 96, 98, 101], "lead": [10, 69, 72, 92, 96, 103], "draw": [10, 90, 91], "conclus": [10, 95], "let": [10, 38, 42, 71, 72, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "sort_valu": [10, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 106], "head": [10, 87, 88, 89, 91, 92, 94, 95, 96, 97, 99, 101, 106], "97": [10, 87, 97, 98, 99, 103, 106, 108], "064045": 10, "58": [10, 87, 91, 96, 97, 99, 103], "680894": 10, "41": [10, 96, 97, 103, 106], "746043": 10, "794894": 10, "98": [10, 97, 98, 106], "802911": 10, "give": [10, 49, 72, 99, 101, 107], "li": [10, 71], "especi": [10, 87, 88, 92, 96, 98, 106], "veri": [10, 37, 63, 67, 69, 88, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106], "rare": [10, 44, 70, 90, 91, 92, 94, 95, 98, 99], "anomal": [10, 72, 90, 91, 92, 94, 95, 99], "articl": [10, 41, 98], "blog": 10, "unexpect": [10, 38, 42, 95], "consequ": 10, "inspect": [10, 88, 89, 91, 92, 99, 103, 106], "011562": 10, "62": [10, 96, 99, 103, 106], "019657": 10, "22": [10, 89, 90, 92, 96, 97, 99, 102, 103, 108], "035243": 10, "040907": 10, "42": [10, 49, 95, 96, 97, 103, 108], "056865": 10, "smaller": [10, 71, 102, 103], "extrem": [10, 90, 91, 92, 94, 95, 96, 98, 99], "record": [10, 38, 42, 89, 94, 106], "abbrevi": 10, "misspel": 10, "typo": [10, 83], "resolut": 10, "video": [10, 97], "audio": [10, 90, 91, 93, 98], "minor": [10, 56], "variat": 10, "translat": 10, "d": [10, 55, 87, 94, 95, 96, 98, 99, 102, 106, 108], "constant": [10, 32, 74], "median": [10, 31, 55], "question": [10, 23, 84, 99], "nearli": [10, 23, 91, 92, 94, 95], "awar": [10, 85, 99], "presenc": [10, 52, 54, 99], "36": [10, 96, 97, 108], "066009": 10, "80": [10, 39, 87, 94, 102, 106], "003906": 10, "093245": 10, "005599": 10, "27": [10, 94, 96, 97, 99, 103, 108], "156720": 10, "009751": 10, "72": [10, 96, 97, 99, 102, 106], "signific": [10, 94, 95, 99], "violat": [10, 94, 95, 96, 99], "assumpt": [10, 94, 95, 96, 99], "changepoint": [10, 94, 95, 99], "shift": [10, 52, 54, 94, 95, 99], "drift": [10, 91, 94, 96, 99], "autocorrel": [10, 94, 95, 99], "almost": [10, 94, 95, 99], "adjac": [10, 52, 94, 95, 99], "tend": [10, 37, 47, 94, 95, 99, 107, 108], "sequenti": [10, 38, 42, 61, 92], "pai": [10, 95], "attent": [10, 96], "realli": [10, 88, 95, 101, 107], "mere": 10, "highlight": [10, 79, 83, 90, 91, 94, 96, 107], "necessarili": [10, 62, 70, 95, 99], "wrong": [10, 62, 67, 69, 85, 88, 90, 91, 95, 98, 99, 103], "gap": 10, "b": [10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 56, 57, 82, 87, 94, 95, 96, 97, 98, 99, 105, 108], "x1": [10, 67, 70, 103], "x2": [10, 67, 70, 103], "10th": 10, "100th": 10, "90": [10, 82, 87, 94, 99, 105, 106], "similarli": [10, 38, 42, 90, 92, 94, 98, 103], "associ": [10, 13, 17, 33, 35, 38, 42, 70, 101], "blogpost": 10, "proper": [10, 57, 62, 67, 70, 87, 92, 95, 98, 101, 103], "scenario": [10, 52, 54, 72, 90, 91], "underli": [10, 43, 54, 71, 80, 82, 108], "stem": [10, 71, 104], "evolv": 10, "influenc": 10, "act": [10, 69, 90], "accordingli": [10, 33, 52], "emploi": [10, 102, 104], "partit": [10, 105], "ahead": 10, "good": [10, 38, 42, 55, 61, 63, 69, 72, 76, 78, 79, 84, 92, 94, 95], "problem": [10, 33, 41, 49, 79, 84, 90, 91, 92, 95, 98], "deploy": [10, 87, 88, 99, 106], "overlook": [10, 69, 103], "fact": 10, "thu": [10, 37, 42, 63, 87, 89, 94, 95, 99, 105, 108], "diagnos": [10, 91, 98], "24": [10, 89, 96, 97, 99, 101, 103, 106], "681458": 10, "37": [10, 90, 96, 97], "804582": 10, "64": [10, 42, 87, 92, 94, 96, 99, 103], "810646": 10, "815691": 10, "78": [10, 87, 94, 97, 99, 103, 106], "834293": 10, "Be": [10, 42], "cautiou": 10, "behavior": [10, 17, 37, 38, 42, 70, 98], "rarest": [10, 91], "q": [10, 103], "subpar": 10, "special": [10, 52, 56], "techniqu": [10, 103], "smote": 10, "asymmetr": [10, 37], "28": [10, 92, 95, 96, 97, 99, 101, 108], "75": [10, 49, 90, 91, 96, 97, 101, 102, 103, 106, 108], "33": [10, 38, 42, 96, 97, 103], "68": [10, 87, 97, 99, 103], "excess": [10, 92], "dark": [10, 107], "bright": [10, 108], "blurri": [10, 92], "lack": [10, 61, 96], "unusu": [10, 103, 104], "cluster": [10, 19, 32], "slice": 10, "poor": 10, "subpopul": 10, "faq": [10, 84, 91, 92, 94, 95, 100], "get_self_confidence_for_each_label": [10, 49, 72], "r": [10, 41, 74, 90, 91, 96, 106, 107], "tabular": [10, 84, 86, 90, 91, 93, 96, 98, 101], "categor": [10, 71, 86, 87, 90, 91, 93, 98, 106], "encod": [10, 50, 70, 76, 79, 87, 88, 94, 95, 98, 106, 107], "71": [10, 96, 97, 99, 103, 106], "70": [10, 82, 94, 96], "69": [10, 99, 106], "subgroup": [10, 96], "wors": [10, 96, 101], "ratio": 10, "miss": [10, 28, 38, 42, 57, 67, 69, 98, 103, 106], "pattern": [10, 96], "isn": [10, 18, 28], "scalabl": 10, "sacrific": 10, "One": [10, 57, 71, 98], "quantif": 10, "39": [10, 88, 89, 90, 92, 95, 96, 97, 98, 103, 106, 107, 108], "32": [10, 89, 90, 96, 97, 101, 103], "valuabl": [10, 19, 96], "exert": [10, 91], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 22, 24, 31], "health_summari": [10, 24, 37, 84, 97], "health_summary_kwarg": 10, "tandem": [10, 97], "view": [10, 38, 42, 43, 44, 78, 80, 82, 84, 87, 88, 89, 90, 91, 94, 95, 97, 99, 101, 102, 103, 104, 105, 106, 108], "ood_kwarg": 10, "outofdistribut": [10, 29, 71, 104], "outsid": [10, 98, 102], "outlierissuemanag": [10, 15, 22, 29, 90], "nearduplicateissuemanag": [10, 15, 20, 22], "noniidissuemanag": [10, 15, 22, 27], "num_permut": [10, 27], "permut": [10, 27], "significance_threshold": [10, 27], "signic": 10, "noniid": [10, 22], "classimbalanceissuemanag": [10, 15, 21, 22], "underperforminggroupissuemanag": [10, 15, 22, 32], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 32], "filter_cluster_id": [10, 22, 32], "clustering_kwarg": [10, 32], "nullissuemanag": [10, 15, 22, 28], "datavaluationissuemanag": [10, 15, 19, 22], "codeblock": 10, "demonstr": [10, 41, 52, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107], "howev": [10, 38, 42, 52, 57, 87, 88, 89, 92, 94, 95, 96, 101, 105, 107], "mandatori": [10, 92], "image_issue_types_kwarg": 10, "vice": [10, 63], "versa": [10, 63], "light": [10, 92, 97, 103, 107], "29": [10, 92, 96, 97, 101, 102, 103, 107, 108], "low_inform": [10, 92], "odd_aspect_ratio": [10, 92], "35": [10, 90, 96, 97, 101, 102, 103], "odd_siz": [10, 92], "doc": [10, 38, 42, 71, 84, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 104, 106, 108], "label_scor": [11, 24, 26, 31, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "is_outlier_issu": [11, 90, 91, 92, 94, 95, 96, 99], "outlier_scor": [11, 29, 90, 91, 92, 94, 95, 96, 99, 104], "is_near_duplicate_issu": [11, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_scor": [11, 20, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_set": [11, 20, 22, 90, 91, 92, 94, 95, 98, 99], "is_non_iid_issu": [11, 91, 94, 95, 96, 99], "non_iid_scor": [11, 27, 91, 94, 95, 96, 99], "is_class_imbalance_issu": [11, 91, 96], "class_imbalance_scor": [11, 21, 91, 96], "is_underperforming_group_issu": [11, 91, 96], "underperforming_group_scor": [11, 32, 91, 96], "is_null_issu": [11, 91, 96], "null_scor": [11, 28, 91, 96], "is_data_valuation_issu": [11, 96], "data_valuation_scor": [11, 19, 96], "studio": [12, 84, 91, 92, 94, 95, 98], "data_issu": [12, 16, 17, 34, 90], "issue_find": [12, 16, 96], "factori": [12, 16, 17], "model_output": [12, 16], "except": [13, 38, 42, 61, 72, 90, 91, 92, 101], "dataformaterror": [13, 16], "add_not": 13, "with_traceback": 13, "tb": 13, "__traceback__": 13, "datasetdicterror": [13, 16], "datasetdict": 13, "datasetloaderror": [13, 16], "dataset_typ": 13, "fail": 13, "hold": 13, "sublist": 13, "map_to_int": 13, "abc": [13, 23, 33], "is_avail": [13, 92], "dataissu": [14, 16, 17, 34], "central": [14, 108], "repositori": [14, 92], "strategi": [14, 49, 96, 98], "_infostrategi": 14, "basi": 14, "collect_statist": 14, "reus": [14, 23], "avoid": [14, 38, 41, 42, 44, 52, 57, 64, 67, 70, 74, 76, 78, 90, 91, 92, 98], "recomput": [14, 88], "weighted_knn_graph": 14, "issue_manager_that_computes_knn_graph": 14, "collect_issues_from_issue_manag": 14, "collect_issues_from_imagelab": 14, "imagelab": 14, "set_health_scor": 14, "health": [14, 24, 37, 63, 84], "get_data_statist": [14, 16], "concret": 15, "subclass": [15, 38, 42, 71, 90], "regressionlabelissuemanag": [15, 22, 30, 31], "multilabelissuemanag": [15, 22, 25, 26], "from_str": [15, 35, 45, 49], "my_issu": 15, "logic": [15, 35, 41, 44, 76, 78], "issuefind": [16, 17, 34], "modeloutput": [16, 33], "multiclasspredprob": [16, 33], "regressionpredict": [16, 33], "multilabelpredprob": [16, 33], "instati": 17, "public": [17, 96, 99, 103, 107, 108], "creation": [17, 42, 96], "execut": [17, 38, 42, 90, 92, 98, 103], "coordin": [17, 67, 69, 70, 103, 108], "At": [17, 70, 98], "get_available_issue_typ": 17, "direct": [18, 28, 38, 42, 54, 61], "vstack": [19, 57, 92, 97, 98, 99, 101, 102], "25": [19, 27, 38, 49, 55, 91, 92, 96, 97, 99, 101, 102, 103, 108], "classvar": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "short": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 56, 57], "item": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 90, 91, 92, 98, 99, 101, 102], "some_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "additional_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "default_threshold": [19, 22, 29], "collect_info": [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "info_to_omit": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "compos": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 38, 42, 88, 95, 104], "is_x_issu": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "x_score": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_a": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b1": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b2": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "report_str": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34], "_": [20, 21, 23, 24, 26, 27, 28, 31, 32, 49, 56, 57, 84, 87, 89, 90, 92, 96, 97, 99, 102], "occurr": [20, 21, 23, 27, 28, 29, 32, 56], "median_nn_dist": 20, "bleed": [22, 25, 30, 40], "edg": [22, 25, 30, 40, 69, 84, 99, 108], "sharp": [22, 25, 30, 40], "get_health_summari": [22, 24], "ood": [22, 29, 71, 72, 104], "simplified_kolmogorov_smirnov_test": [22, 27], "outlier_cluster_label": [22, 32], "no_underperforming_cluster_id": [22, 32], "perform_clust": [22, 32], "get_worst_clust": [22, 32], "find_issues_with_predict": [22, 30, 31], "find_issues_with_featur": [22, 30, 31], "believ": [23, 107], "priori": [23, 99], "abstract": [23, 33], "applic": [24, 62, 98, 99, 101, 108], "typevar": [24, 26, 38, 42, 56, 66, 69, 70], "scalartyp": [24, 26], "covari": [24, 26, 74, 106], "summary_dict": 24, "neighbor_histogram": 27, "non_neighbor_histogram": 27, "kolmogorov": 27, "smirnov": 27, "largest": [27, 41, 49, 52, 72, 76, 78, 103, 107], "empir": [27, 48, 62], "cumul": 27, "ecdf": 27, "histogram": [27, 94, 96, 106], "absolut": [27, 31], "trial": 27, "null_track": 28, "extend": [28, 50, 61, 92, 96, 103, 104, 108], "superclass": 28, "arbitrari": [28, 37, 78, 82, 90, 104, 106], "prompt": 28, "address": [28, 88, 90, 91, 95, 98], "enabl": [28, 42, 54], "scaling_factor": [29, 55], "37037": 29, "q3_avg_dist": 29, "iqr_avg_dist": 29, "median_outlier_scor": 29, "issue_threshold": 29, "multipli": [31, 55], "deleg": 31, "confus": [32, 33, 37, 38, 42, 44, 57, 70, 88, 108], "50": [32, 42, 96, 98, 99, 101, 103, 104, 106], "keepdim": [32, 98], "signifi": 32, "absenc": 32, "int64": [32, 89, 101], "npt": 32, "int_": 32, "id": [32, 62, 90, 92, 96, 98, 101], "unique_cluster_id": 32, "_description_": 32, "performed_clust": 32, "worst_cluster_id": 32, "convent": [33, 35], "subject": [33, 35], "meant": [33, 35], "Not": [33, 54], "mainli": [33, 104, 108], "content": [33, 71, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "fetch": [33, 41, 89, 91, 98], "datset": 34, "exclud": [34, 43, 79, 83, 90, 98, 108], "get_report": 34, "enum": [35, 49], "qualnam": [35, 49], "boundari": [35, 49, 90, 91], "continu": [35, 61, 87, 88, 92, 95, 98, 101, 103, 106, 108], "binari": [35, 49, 57, 64, 66, 99, 108], "simultan": [35, 106], "task_str": 35, "is_classif": 35, "__contains__": [35, 45, 49], "member": [35, 38, 42, 49, 90, 91], "typeerror": [35, 49], "12": [35, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "__getitem__": [35, 45, 49], "match": [35, 37, 38, 42, 44, 49, 62, 63, 72, 90, 91, 92, 97, 103, 105, 107], "__iter__": [35, 45, 49], "__len__": [35, 45, 49], "alias": [35, 49], "is_regress": 35, "is_multilabel": 35, "overview": [37, 52, 87, 88, 89, 91, 92, 94, 95, 101, 103, 104, 106, 108], "modifi": [37, 38, 41, 42, 52, 54, 57, 98, 99], "rank_classes_by_label_qu": [37, 91], "merg": [37, 52, 56, 84, 97, 98, 108], "find_overlapping_class": [37, 98, 99], "problemat": [37, 63, 79, 83, 89, 103, 108], "unnorm": [37, 63, 99], "abov": [37, 38, 41, 42, 54, 57, 62, 69, 70, 72, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 105, 106, 107, 108], "model_select": [37, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 106], "cross_val_predict": [37, 42, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 105, 106], "get_data_labels_from_dataset": 37, "yourfavoritemodel": [37, 99], "cv": [37, 49, 87, 89, 90, 91, 94, 96, 99, 101], "df": [37, 57, 83, 89, 96, 98], "overall_label_qu": [37, 63], "col": 37, "prob": [37, 56, 99, 105], "divid": [37, 63, 72], "label_nois": [37, 63], "human": [37, 97, 107, 108], "clearli": [37, 72, 92, 103, 107], "num": [37, 63, 97, 99], "overlap": [37, 84, 97, 98, 99], "ontolog": 37, "publish": [37, 108], "therefor": [37, 72, 96], "vehicl": [37, 97], "truck": [37, 97, 104, 107], "intuit": [37, 63], "car": [37, 97, 103, 107], "frequent": [37, 62, 96, 98, 106], "characterist": 37, "l": [37, 38, 42, 67, 69, 70], "class1": 37, "class2": 37, "relationship": 37, "dog": [37, 57, 63, 65, 79, 97, 98, 104, 105, 108], "cat": [37, 57, 63, 65, 97, 98, 104, 105], "captur": [37, 89, 103, 104, 107], "co": [37, 38, 39, 92], "noisy_label": [37, 90, 91, 102], "overlapping_class": 37, "descend": [37, 38, 42, 49, 63, 70], "overall_label_health_scor": [37, 63, 99], "half": [37, 38, 40, 42, 63, 97, 108], "health_scor": [37, 63], "classes_by_label_qu": [37, 91], "cnn": [38, 40, 42, 92], "cifar": [38, 39, 97, 104], "teach": [38, 39], "bhanml": 38, "blob": [38, 96], "master": [38, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106], "call_bn": [38, 40], "bn": 38, "input_channel": 38, "n_output": 38, "dropout_r": 38, "top_bn": 38, "architectur": [38, 42], "shown": [38, 70, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 105, 107, 108], "forward": [38, 39, 40, 42, 92, 101], "overridden": [38, 42], "although": [38, 42, 71, 87, 94], "recip": [38, 42], "afterward": [38, 42], "sinc": [38, 42, 46, 58, 63, 70, 78, 82, 98, 101, 102, 103, 105, 108], "hook": [38, 42, 97], "silent": [38, 41, 42], "t_destin": [38, 40, 42], "__call__": [38, 40, 42, 45, 49], "add_modul": [38, 40, 42], "child": [38, 42], "fn": [38, 42, 70], "recurs": [38, 42, 49], "submodul": [38, 42, 51], "children": [38, 40, 42, 108], "nn": [38, 39, 42, 52, 92], "init": [38, 42, 99], "no_grad": [38, 42, 92, 104], "init_weight": [38, 42], "linear": [38, 42, 88, 92, 95], "fill_": [38, 42], "net": [38, 42, 89, 92, 97], "in_featur": [38, 42], "out_featur": [38, 42], "bia": [38, 42, 92], "tensor": [38, 39, 42, 88, 89, 92, 95, 104], "requires_grad": [38, 42], "bfloat16": [38, 40, 42], "cast": [38, 42, 89], "buffer": [38, 40, 42], "datatyp": [38, 42], "xdoctest": [38, 42], "undefin": [38, 42], "var": [38, 42], "buf": [38, 42], "20l": [38, 42], "1l": [38, 42], "5l": [38, 42], "call_super_init": [38, 40, 42], "immedi": [38, 42, 104], "compil": [38, 40, 42, 61], "cpu": [38, 40, 42, 44, 89, 92], "move": [38, 42, 49, 85, 97], "cuda": [38, 40, 42, 89, 92], "devic": [38, 42, 89, 92], "gpu": [38, 42, 88, 89, 95], "live": [38, 42], "copi": [38, 42, 74, 87, 89, 90, 91, 94, 96, 98, 102, 105, 106], "doubl": [38, 40, 42], "dump_patch": [38, 40, 42], "eval": [38, 40, 42, 92, 102, 104], "dropout": [38, 42], "batchnorm": [38, 42], "grad": [38, 42], "extra_repr": [38, 40, 42], "line": [38, 42, 84, 90, 96, 97, 101, 104, 108], "get_buff": [38, 40, 42], "target": [38, 39, 42, 74, 75, 96, 104, 106], "throw": [38, 42], "get_submodul": [38, 40, 42], "explan": [38, 42], "qualifi": [38, 42], "referenc": [38, 42], "attributeerror": [38, 42], "invalid": [38, 42, 95], "resolv": [38, 42, 108], "get_extra_st": [38, 40, 42], "state_dict": [38, 40, 42], "set_extra_st": [38, 40, 42], "build": [38, 42, 52, 92, 96, 107], "picklabl": [38, 42], "serial": [38, 42], "backward": [38, 42, 92], "break": [38, 42, 92, 103], "pickl": [38, 42, 103], "get_paramet": [38, 40, 42], "net_b": [38, 42], "net_c": [38, 42], "conv": [38, 42], "conv2d": [38, 42, 92], "16": [38, 42, 49, 52, 61, 78, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 107, 108], "kernel_s": [38, 42], "stride": [38, 42], "200": [38, 42, 72, 97, 103, 108], "diagram": [38, 42, 105], "degre": [38, 42], "queri": [38, 42, 52, 54, 91, 92, 96, 98, 102], "named_modul": [38, 40, 42], "o": [38, 42, 55, 56, 89, 90, 91, 97, 98, 99, 102, 103, 108], "transit": [38, 42], "ipu": [38, 40, 42], "load_state_dict": [38, 40, 42], "strict": [38, 42, 49], "persist": [38, 42], "strictli": [38, 42], "inplac": [38, 42, 96, 101], "preserv": [38, 42, 57], "namedtupl": [38, 42], "missing_kei": [38, 42], "unexpected_kei": [38, 42], "runtimeerror": [38, 42], "idx": [38, 42, 57, 58, 70, 90, 92, 96, 98, 99, 101, 103, 104], "named_buff": [38, 40, 42], "prefix": [38, 42, 89, 108], "remove_dupl": [38, 42], "prepend": [38, 42], "running_var": [38, 42], "named_children": [38, 40, 42], "conv4": [38, 42], "conv5": [38, 42], "memo": [38, 42], "named_paramet": [38, 40, 42], "register_backward_hook": [38, 40, 42], "deprec": [38, 42, 46, 88, 89, 95, 98], "favor": [38, 42], "register_full_backward_hook": [38, 40, 42], "removablehandl": [38, 42], "register_buff": [38, 40, 42], "running_mean": [38, 42], "register_forward_hook": [38, 40, 42], "with_kwarg": [38, 42], "always_cal": [38, 42], "possibli": [38, 42, 87, 94], "fire": [38, 42, 97], "register_module_forward_hook": [38, 42], "regardless": [38, 42, 90, 91], "register_forward_pre_hook": [38, 40, 42], "And": [38, 42], "forward_pr": [38, 42], "register_module_forward_pre_hook": [38, 42], "gradient": [38, 42, 92, 94, 106], "grad_input": [38, 42], "grad_output": [38, 42], "technic": [38, 42], "caller": [38, 42], "register_module_full_backward_hook": [38, 42], "register_full_backward_pre_hook": [38, 40, 42], "backward_pr": [38, 42], "register_module_full_backward_pre_hook": [38, 42], "register_load_state_dict_post_hook": [38, 40, 42], "post": [38, 42, 52], "incompatible_kei": [38, 42], "modif": [38, 42, 52], "thrown": [38, 42], "register_modul": [38, 40, 42], "register_paramet": [38, 40, 42], "register_state_dict_pre_hook": [38, 40, 42], "keep_var": [38, 42], "requires_grad_": [38, 40, 42], "autograd": [38, 42], "freez": [38, 42, 88, 89, 95], "finetun": [38, 42], "gan": [38, 42], "share_memori": [38, 40, 42], "share_memory_": [38, 42], "destin": [38, 42], "shallow": [38, 42], "releas": [38, 42, 61, 85, 89, 92, 98], "design": [38, 42, 52], "ordereddict": [38, 42], "detach": [38, 42, 92], "non_block": [38, 42], "memory_format": [38, 42], "channels_last": [38, 42], "Its": [38, 42, 49, 63, 69], "complex": [38, 42, 89], "integr": [38, 42, 54, 84, 98], "asynchron": [38, 42], "host": [38, 42], "pin": [38, 42, 88, 95, 97], "desir": [38, 42, 52, 56, 70], "4d": [38, 42], "ignore_w": [38, 42], "determinist": [38, 42, 89], "1913": [38, 42], "3420": [38, 42], "5113": [38, 42], "2325": [38, 42], "env": [38, 42], "torch_doctest_cuda1": [38, 42], "gpu1": [38, 42], "1914": [38, 42], "5112": [38, 42], "2324": [38, 42], "float16": [38, 42], "cdoubl": [38, 42], "3741": [38, 42], "2382": [38, 42], "5593": [38, 42], "4443": [38, 42], "complex128": [38, 42], "6122": [38, 42], "1150": [38, 42], "to_empti": [38, 40, 42], "storag": [38, 42, 88, 95], "dst_type": [38, 42], "xpu": [38, 40, 42], "zero_grad": [38, 40, 42, 92], "set_to_non": [38, 42], "reset": [38, 42], "context": [38, 42, 103], "noisili": [39, 99], "han": 39, "2018": 39, "cifar_cnn": [39, 40], "loss_coteach": [39, 40], "y_1": 39, "y_2": 39, "forget_r": 39, "class_weight": 39, "logit": [39, 61, 92], "decim": [39, 57], "forget": [39, 49, 108], "rate_schedul": 39, "epoch": [39, 40, 42, 92, 98], "initialize_lr_schedul": [39, 40], "lr": [39, 40, 42], "001": [39, 72, 96, 98], "250": [39, 90, 91, 99, 103, 108], "epoch_decay_start": 39, "schedul": 39, "beta": 39, "adam": 39, "adjust_learning_r": [39, 40], "alpha_plan": 39, "beta1_plan": 39, "forget_rate_schedul": [39, 40], "num_gradu": 39, "expon": 39, "tell": [39, 88, 92, 95, 99], "train_load": [39, 42], "model1": [39, 99], "optimizer1": 39, "model2": [39, 99], "optimizer2": 39, "dataload": [39, 92, 104], "parser": 39, "parse_arg": 39, "num_iter_per_epoch": 39, "print_freq": 39, "topk": 39, "top1": 39, "top5": 39, "test_load": 39, "offici": [40, 60, 96, 108], "wish": [40, 60, 104, 107, 108], "adj_confident_thresholds_shar": [40, 41], "labels_shar": [40, 41], "pred_probs_shar": [40, 41], "labelinspector": [40, 41, 98], "get_num_issu": [40, 41], "get_quality_scor": [40, 41], "update_confident_threshold": [40, 41], "score_label_qu": [40, 41], "split_arr": [40, 41], "span_classif": 40, "display_issu": [40, 43, 77, 78, 79, 80, 81, 82, 83, 107, 108], "mnist_pytorch": 40, "get_mnist_dataset": [40, 42], "get_sklearn_digits_dataset": [40, 42], "simplenet": [40, 42], "batch_siz": [40, 41, 42, 76, 78, 92, 98, 104, 107], "log_interv": [40, 42], "momentum": [40, 42], "no_cuda": [40, 42], "test_batch_s": [40, 42, 92], "loader": [40, 42, 92], "set_predict_proba_request": [40, 42], "set_predict_request": [40, 42], "coteach": [40, 85], "mini": [41, 76, 78, 98], "low_self_confid": [41, 44, 64], "self_confid": [41, 44, 45, 49, 64, 66, 72, 80, 82, 87, 88, 98, 99], "conveni": [41, 54, 87, 88, 89, 95], "script": 41, "labels_fil": [41, 98], "pred_probs_fil": [41, 98], "quality_score_kwarg": 41, "num_issue_kwarg": 41, "return_mask": 41, "variant": [41, 62, 107], "read": [41, 46, 91, 98, 99, 104, 108], "zarr": [41, 98], "memmap": [41, 107], "pythonspe": 41, "mmap": [41, 98], "hdf5": 41, "further": [41, 43, 63, 64, 66, 69, 70, 78, 79, 89, 98], "yourfil": 41, "npy": [41, 97, 98, 107], "mmap_mod": [41, 107], "tip": [41, 44, 61, 98], "save_arrai": 41, "your_arrai": 41, "disk": [41, 97, 98], "npz": [41, 108], "maxim": [41, 62, 76, 78, 107], "multiprocess": [41, 44, 64, 76, 78, 92, 98], "linux": [41, 76, 78], "physic": [41, 44, 76, 78, 103], "psutil": [41, 44, 76, 78], "labels_arrai": [41, 58], "predprob": 41, "pred_probs_arrai": 41, "back": [41, 52, 70, 90, 98, 103, 104], "store_result": 41, "becom": [41, 96, 104], "verifi": [41, 54, 98, 101, 104], "long": [41, 62, 71, 101], "enough": [41, 57, 96, 98], "chunk": [41, 105], "ram": [41, 97], "end_index": 41, "labels_batch": 41, "pred_probs_batch": 41, "batch_result": 41, "indices_of_examples_with_issu": [41, 98], "shortcut": 41, "encount": [41, 44, 76], "1000": [41, 89, 95, 98, 104], "aggreg": [41, 45, 49, 62, 66, 69, 72, 82, 98, 99, 101], "seen": [41, 98, 104, 108], "far": [41, 62], "label_quality_scor": [41, 66, 69, 72, 75, 99, 103], "method1": 41, "method2": 41, "normalized_margin": [41, 44, 45, 49, 64, 66, 72, 80, 82], "low_normalized_margin": [41, 44, 64], "issue_indic": [41, 69, 92], "update_num_issu": 41, "arr": [41, 98], "chunksiz": 41, "convnet": 42, "bespok": [42, 61], "download": [42, 89, 98, 104], "mnist": [42, 84, 89, 97], "handwritten": 42, "digit": [42, 89, 97], "last": [42, 49, 67, 70, 90, 91, 98, 101, 103, 108], "sklearn_digits_test_s": 42, "01": [42, 72, 74, 89, 96, 99, 102, 103, 104], "templat": 42, "flexibli": 42, "among": [42, 62, 99], "test_set": 42, "overrid": 42, "train_idx": [42, 57, 104], "train_label": [42, 88, 104], "span": 43, "sentenc": [43, 56, 80, 82, 83, 88, 95], "token_classif": [43, 56, 80, 82, 83, 98], "encourag": [44, 64, 72, 75], "multilabel_classif": [44, 63, 64, 66, 72, 98, 102], "pred_probs_by_class": 44, "prune_count_matrix_col": 44, "rank_by_kwarg": [44, 64, 72, 99], "num_to_remove_per_class": [44, 64], "bad": [44, 52, 64, 69, 72, 95, 98], "seem": [44, 99, 102], "aren": 44, "confidence_weighted_entropi": [44, 45, 49, 64, 66, 72, 80, 82], "label_issues_idx": [44, 72], "entropi": [44, 46, 48, 49, 71, 72], "prune_by_class": [44, 64, 99], "predicted_neq_given": [44, 64, 99], "prune_counts_matrix": 44, "smallest": [44, 72], "unus": 44, "number_of_mislabeled_examples_in_class_k": 44, "delet": [44, 84, 88, 98], "too": [44, 49, 52, 71, 91, 92, 98, 103], "thread": [44, 64], "window": [44, 89, 97], "shorter": [44, 67], "find_predicted_neq_given": 44, "find_label_issues_using_argmax_confusion_matrix": 44, "remove_noise_from_class": [45, 57], "clip_noise_r": [45, 57], "clip_valu": [45, 57], "value_count": [45, 57, 98], "value_counts_fill_missing_class": [45, 57], "get_missing_class": [45, 57], "round_preserving_sum": [45, 57], "round_preserving_row_tot": [45, 57], "estimate_pu_f1": [45, 57], "confusion_matrix": [45, 57], "print_square_matrix": [45, 57], "print_noise_matrix": [45, 57, 99], "print_inverse_noise_matrix": [45, 57], "print_joint_matrix": [45, 57, 99], "compress_int_arrai": [45, 57], "train_val_split": [45, 57], "subset_x_i": [45, 57], "subset_label": [45, 57], "subset_data": [45, 57], "extract_indices_tf": [45, 57], "unshuffle_tensorflow_dataset": [45, 57], "is_torch_dataset": [45, 57], "is_tensorflow_dataset": [45, 57], "csr_vstack": [45, 57], "append_extra_datapoint": [45, 57], "get_num_class": [45, 57], "num_unique_class": [45, 57], "get_unique_class": [45, 57], "format_label": [45, 57], "smart_display_datafram": [45, 57], "force_two_dimens": [45, 57], "latent_algebra": [45, 85], "compute_ps_py_inv_noise_matrix": [45, 47], "compute_py_inv_noise_matrix": [45, 47], "compute_inv_noise_matrix": [45, 47], "compute_noise_matrix_from_invers": [45, 47], "compute_pi": [45, 47], "compute_pyx": [45, 47], "label_quality_util": 45, "get_normalized_entropi": [45, 46], "multilabel_util": [45, 102], "stack_compl": [45, 50], "get_onehot_num_class": [45, 50], "int2onehot": [45, 50, 102], "onehot2int": [45, 50, 102], "multilabel_scor": [45, 66], "classlabelscor": [45, 49], "exponential_moving_averag": [45, 49, 66], "softmin": [45, 49, 66, 69, 78, 82], "possible_method": [45, 49], "multilabelscor": [45, 49], "get_class_label_quality_scor": [45, 49], "multilabel_pi": [45, 49], "get_cross_validated_multilabel_pred_prob": [45, 49], "default_k": [45, 51, 52], "features_to_knn": [45, 51, 52], "construct_knn_graph_from_index": [45, 51, 52, 54], "create_knn_graph_and_index": [45, 51, 52], "correct_knn_graph": [45, 51, 52, 96], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [45, 51, 52], "correct_knn_distances_and_indic": [45, 51, 52], "high_dimension_cutoff": [45, 51, 53], "row_count_cutoff": [45, 51, 53], "decide_euclidean_metr": [45, 51, 53], "decide_default_metr": [45, 51, 53], "construct_knn": [45, 51, 54], "transform_distances_to_scor": [45, 55], "correct_precision_error": [45, 55], "token_classification_util": [45, 108], "get_sent": [45, 56, 108], "filter_sent": [45, 56, 108], "process_token": [45, 56], "merge_prob": [45, 56], "color_sent": [45, 56], "assert_valid_input": [45, 58], "assert_valid_class_label": [45, 58], "assert_nonempty_input": [45, 58], "assert_indexing_work": [45, 58], "labels_to_arrai": [45, 58], "labels_to_list_multilabel": [45, 58], "min_allowed_prob": 46, "wikipedia": 46, "activ": [46, 48, 61, 62, 84, 101], "towardsdatasci": 46, "cheatsheet": 46, "ec57bc067c0b": 46, "clip": [46, 57, 89, 96], "behav": 46, "unnecessari": [46, 98], "slightli": [46, 87, 88], "interv": [46, 49, 104], "herein": 47, "inexact": 47, "cours": 47, "propag": 47, "throughout": [47, 57, 74, 83, 89, 101, 107, 108], "increas": [47, 55, 69, 71, 72, 89, 90, 96, 98, 101, 102, 108], "dot": [47, 82, 98], "true_labels_class_count": 47, "pyx": 47, "multiannot": 48, "assert_valid_inputs_multiannot": 48, "labels_multiannot": [48, 62], "ensembl": [48, 49, 62, 72, 87, 94, 98, 102, 104, 106], "allow_single_label": 48, "annotator_id": 48, "assert_valid_pred_prob": 48, "pred_probs_unlabel": [48, 62], "format_multiannotator_label": [48, 62, 101], "formatted_label": [48, 57], "old": [48, 57, 85, 89, 97], "check_consensus_label_class": 48, "consensus_label": [48, 62, 101], "consensus_method": [48, 62], "consensu": [48, 62, 84, 100, 108], "establish": [48, 61, 88, 106], "compute_soft_cross_entropi": 48, "soft": [48, 97], "find_best_temp_scal": 48, "coarse_search_rang": [48, 74, 98], "fine_search_s": [48, 74, 98], "temperatur": [48, 49, 69, 78, 82], "scale": [48, 55, 87, 96, 97, 98, 104, 107], "factor": [48, 49, 55, 76, 78], "minim": [48, 69, 104], "temp_scale_pred_prob": 48, "temp": 48, "sharpen": [48, 97], "smoothen": 48, "get_normalized_margin_for_each_label": [49, 72], "get_confidence_weighted_entropy_for_each_label": [49, 72], "scorer": 49, "alpha": [49, 66, 69, 90, 91, 96, 99, 102, 106], "exponenti": 49, "ema": 49, "s_1": 49, "s_k": 49, "ema_k": 49, "accord": [49, 64, 94, 95, 99, 108], "formula": [49, 55], "_t": 49, "cdot": 49, "s_t": 49, "qquad": 49, "leq": 49, "_1": 49, "recent": [49, 108], "success": 49, "previou": [49, 52, 92, 94, 98, 103], "discount": 49, "s_ema": 49, "175": [49, 92, 99, 103], "underflow": 49, "nan": [49, 62, 87, 94, 96, 101, 106], "aggregated_scor": 49, "base_scor": 49, "base_scorer_kwarg": 49, "aggregator_kwarg": [49, 66], "n_sampl": [49, 96], "n_label": 49, "worst": [49, 101], "class_label_quality_scor": 49, "452": 49, "new_scor": 49, "575": 49, "get_label_quality_scores_per_class": [49, 65, 66], "ml_scorer": 49, "binar": [49, 50], "reformat": [49, 89], "wider": 49, "splitter": 49, "kfold": [49, 92], "onevsrestclassifi": [49, 102], "randomforestclassifi": [49, 99, 102], "n_split": [49, 91, 92, 102], "pred_prob_slic": 50, "onehot": 50, "hot": [50, 64, 70, 76, 79, 87, 94, 97, 98, 106, 107], "onehot_matrix": 50, "pairwis": [51, 53, 71], "reli": [52, 71, 88, 89, 90, 91, 95, 103, 104, 106], "sklearn_knn_kwarg": 52, "correction_featur": 52, "discourag": 52, "flexibl": [52, 98], "manner": [52, 66, 87, 88, 96, 101, 106], "701": 52, "900": [52, 87, 94, 106], "436": 52, "000": [52, 88, 92, 95, 97, 108], "idea": [52, 72, 103], "dens": [52, 61, 96], "33140006": 52, "76210367": 52, "correct_exact_dupl": 52, "mutual": [52, 63, 102], "vari": [52, 69, 91], "exact_duplicate_set": 52, "main": [52, 62], "front": [52, 97], "consider": 52, "capabl": [52, 84], "come": [52, 57, 90, 91, 98, 107], "misidentif": 52, "corrected_dist": 52, "corrected_indic": 52, "sqrt": 52, "distant": 52, "suitabl": [53, 62, 87, 94, 96], "slower": 53, "decid": [53, 62, 88, 95, 97, 101, 106, 108], "predefin": 53, "met": [53, 108], "euclidean_dist": [53, 71], "spatial": [53, 71], "decis": [53, 87, 90, 91], "That": [53, 99, 102], "cosine_dist": 53, "knn_kwarg": 54, "html": [54, 57, 67, 70, 71, 89, 90, 91, 92, 94, 95, 98, 99], "kneighbor": 54, "metric_param": 54, "n_features_in_": 54, "effective_metric_params_": 54, "effective_metric_": 54, "n_samples_fit_": 54, "__sklearn_is_fitted__": 54, "conduct": 54, "is_fit": 54, "trail": 54, "underscor": 54, "avg_dist": 55, "exp": [55, 71, 72, 90], "dt": 55, "right": [55, 67, 70, 88, 95, 102, 103, 104], "strength": [55, 70, 96], "pronounc": 55, "differenti": 55, "ly": 55, "rule": [55, 56, 97], "thumb": 55, "ood_features_scor": [55, 71, 104], "88988177": 55, "80519832": 55, "toler": 55, "minkowski": 55, "noth": 55, "epsilon": 55, "sensibl": 55, "fixed_scor": 55, "readabl": 56, "lambda": [56, 89, 90, 98, 101], "long_sent": 56, "headlin": 56, "charact": [56, 57], "s1": 56, "s2": 56, "processed_token": 56, "alecnlcb": 56, "entiti": [56, 84, 98, 108], "mapped_ent": 56, "unique_ident": 56, "loc": [56, 90, 91, 92, 94, 96, 108], "nbitbas": [56, 66], "probs_merg": 56, "0125": [56, 82], "0375": 56, "075": 56, "025": 56, "color": [56, 79, 90, 91, 94, 96, 99, 102, 104, 106, 107], "red": [56, 70, 90, 91, 96, 97, 99, 102, 103, 104, 107], "colored_sent": 56, "termcolor": 56, "31msentenc": 56, "0m": 56, "ancillari": 57, "class_without_nois": 57, "any_other_class": 57, "choos": [57, 72, 87, 94, 98, 99, 106], "tradition": 57, "new_sum": 57, "fill": 57, "major": [57, 62, 85, 92, 104], "versu": [57, 99], "obviou": 57, "cgdeboer": 57, "iteround": 57, "reach": 57, "prob_s_eq_1": 57, "claesen": 57, "f1": [57, 70, 95, 99], "BE": 57, "left_nam": 57, "top_nam": 57, "titl": [57, 90, 91, 96, 99, 102, 104], "short_titl": 57, "round_plac": 57, "pretti": [57, 99], "joint_matrix": 57, "num_possible_valu": 57, "holdout_idx": 57, "extract": [57, 71, 88, 89, 94, 95, 101, 104, 107], "allow_shuffl": 57, "turn": [57, 84, 103], "shuffledataset": 57, "histori": 57, "pre_x": 57, "buffer_s": 57, "csr_matric": 57, "append": [57, 89, 92, 97, 98, 99, 101, 102, 103, 104, 108], "bottom": [57, 67, 70, 96, 103], "to_data": 57, "from_data": 57, "taken": 57, "label_matrix": 57, "canon": 57, "displai": [57, 70, 79, 83, 88, 89, 94, 95, 96, 99, 108], "jupyt": [57, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "notebook": [57, 62, 89, 91, 97, 98, 99, 101, 102, 103, 107, 108], "consol": 57, "allow_missing_class": 58, "allow_one_class": 58, "length_x": 58, "labellik": 58, "labels_list": [58, 64], "keraswrappermodel": [60, 61, 84], "keraswrappersequenti": [60, 61], "tf": [61, 89], "legaci": 61, "newer": 61, "interim": 61, "advis": [61, 102], "stabil": [61, 71], "until": 61, "accommod": 61, "keraswrapp": 61, "huggingface_keras_imdb": 61, "unit": [61, 108], "model_kwarg": [61, 74], "compile_kwarg": 61, "sparsecategoricalcrossentropi": 61, "layer": [61, 88, 89, 95, 104], "my_keras_model": 61, "from_logit": 61, "declar": 61, "apply_softmax": 61, "analysi": 62, "analyz": [62, 84, 99, 101, 102], "get_label_quality_multiannot": [62, 101], "vote": 62, "crowdsourc": [62, 84, 101], "dawid": [62, 101], "skene": [62, 101], "analog": [62, 97, 101], "chosen": [62, 72, 98, 101], "crowdlab": [62, 101], "unlabel": [62, 92, 94, 95, 101, 104, 107], "get_active_learning_scor": [62, 101], "activelab": [62, 101], "priorit": [62, 69, 103, 107, 108], "showcas": 62, "best_qual": 62, "quality_method": 62, "calibrate_prob": 62, "return_detailed_qu": 62, "return_annotator_stat": 62, "return_weight": 62, "label_quality_score_kwarg": 62, "did": [62, 63, 87, 88, 89, 94, 99, 101, 106], "majority_vot": 62, "broken": [62, 70, 97, 106], "highest": [62, 70, 90, 92, 105], "0th": 62, "consensus_quality_scor": [62, 101], "annotator_agr": [62, 101], "reman": 62, "1st": 62, "2nd": [62, 76], "3rd": 62, "consensus_label_suffix": 62, "consensus_quality_score_suffix": 62, "suffix": 62, "emsembl": 62, "weigh": [62, 97], "agreement": [62, 101], "agre": 62, "prevent": [62, 98], "overconfid": [62, 105], "detailed_label_qu": [62, 101], "annotator_stat": [62, 101], "model_weight": 62, "annotator_weight": 62, "warn": [62, 90, 91, 92, 94, 95, 96, 98, 99], "labels_info": 62, "num_annot": [62, 101], "deriv": [62, 101], "quality_annotator_1": 62, "quality_annotator_2": 62, "quality_annotator_m": 62, "annotator_qu": [62, 101], "num_examples_label": [62, 101], "agreement_with_consensu": [62, 101], "worst_class": [62, 101], "trustworthi": [62, 101, 106], "get_label_quality_multiannotator_ensembl": 62, "weigtht": 62, "budget": 62, "retrain": [62, 88, 106], "active_learning_scor": 62, "active_learning_scores_unlabel": 62, "get_active_learning_scores_ensembl": 62, "henc": [62, 89, 90, 101], "get_majority_vote_label": [62, 101], "event": 62, "lastli": [62, 94], "convert_long_to_wide_dataset": 62, "labels_multiannotator_long": 62, "wide": [62, 87, 88, 89], "labels_multiannotator_wid": 62, "common_multilabel_issu": [63, 65], "exclus": [63, 102], "rank_classes_by_multilabel_qu": [63, 65], "overall_multilabel_health_scor": [63, 65], "multilabel_health_summari": [63, 65], "classes_by_multilabel_qu": 63, "inner": [64, 78, 96], "find_multilabel_issues_per_class": [64, 65], "per_class_label_issu": 64, "label_issues_list": 64, "pred_probs_list": [64, 72, 92, 99], "anim": [65, 104], "rat": 65, "predat": 65, "pet": 65, "reptil": 65, "box": [67, 69, 70, 97, 103], "object_detect": [67, 69, 70, 103], "return_indices_ranked_by_scor": [67, 103], "overlapping_label_check": [67, 69], "suboptim": [67, 69], "locat": [67, 69, 96, 103, 107, 108], "bbox": [67, 70, 103], "image_nam": [67, 70], "y1": [67, 70, 103], "y2": [67, 70, 103], "later": [67, 70, 71, 88, 108], "corner": [67, 70, 103], "xyxi": [67, 70, 103], "io": [67, 70, 89, 96, 97], "keras_cv": [67, 70], "bounding_box": [67, 70, 103], "detectron": [67, 70, 103], "detectron2": [67, 70, 103], "readthedoc": [67, 70], "en": [67, 70], "latest": [67, 70], "visual": [67, 68, 70, 87, 90, 91, 92, 106, 108], "draw_box": [67, 70], "mmdetect": [67, 70, 103], "swap": [67, 69, 79, 83], "penal": [67, 69], "concern": [67, 69, 84, 91], "issues_from_scor": [68, 69, 77, 78, 79, 81, 82, 83, 103, 107, 108], "compute_overlooked_box_scor": [68, 69], "compute_badloc_box_scor": [68, 69], "compute_swap_box_scor": [68, 69], "pool_box_scores_per_imag": [68, 69], "object_counts_per_imag": [68, 70, 103], "bounding_box_size_distribut": [68, 70, 103], "class_label_distribut": [68, 70, 103], "get_sorted_bbox_count_idx": [68, 70], "plot_class_size_distribut": [68, 70], "plot_class_distribut": [68, 70], "get_average_per_class_confusion_matrix": [68, 70], "calculate_per_class_metr": [68, 70], "aggregation_weight": 69, "imperfect": [69, 98], "chose": [69, 101, 103], "imperfectli": [69, 103], "dirti": [69, 72, 75, 106], "subtyp": 69, "badloc": 69, "nonneg": 69, "high_probability_threshold": 69, "auxiliary_input": [69, 70], "iou": [69, 70], "heavili": 69, "auxiliarytypesdict": 69, "pred_label": [69, 88], "pred_label_prob": 69, "pred_bbox": 69, "lab_label": 69, "lab_bbox": 69, "similarity_matrix": 69, "min_possible_similar": 69, "scores_overlook": 69, "low_probability_threshold": 69, "scores_badloc": 69, "accident": [69, 88, 94, 95, 98], "scores_swap": 69, "box_scor": 69, "image_scor": [69, 78, 107], "discov": [70, 91, 96, 108], "abnorm": [70, 92, 103], "auxiliari": [70, 104, 107], "_get_valid_inputs_for_compute_scor": 70, "object_count": 70, "down": 70, "bbox_siz": 70, "class_distribut": 70, "plot": [70, 90, 91, 96, 99, 102, 104, 106, 107], "sorted_idx": [70, 104], "class_to_show": 70, "hidden": [70, 104], "max_class_to_show": 70, "plt": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "matplotlib": [70, 79, 90, 91, 92, 96, 99, 102, 103, 104, 106], "pyplot": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "prediction_threshold": 70, "overlai": [70, 103], "figsiz": [70, 90, 91, 92, 96, 99, 102, 104], "save_path": [70, 103], "blue": [70, 97, 99, 103], "overlaid": 70, "side": [70, 97, 103], "figur": [70, 96, 99, 102, 104, 106], "extens": [70, 99, 101], "png": [70, 103], "pdf": [70, 71], "svg": 70, "num_proc": [70, 92], "intersect": [70, 98], "tp": 70, "fp": 70, "ground": [70, 97, 99, 101, 106], "truth": [70, 99, 101, 106], "bias": [70, 96], "avg_metr": 70, "distionari": 70, "95": [70, 80, 82, 94, 97, 99, 106], "per_class_metr": 70, "Of": 71, "find_top_issu": [71, 72, 104], "behind": [71, 99], "dist_metr": 71, "subtract": [71, 72], "renorm": [71, 72, 98], "least_confid": 71, "sum_": 71, "log": [71, 72, 85], "softmax": [71, 78, 82, 92], "literatur": 71, "gen": 71, "liu": 71, "lochman": 71, "zach": 71, "openaccess": 71, "thecvf": 71, "cvpr2023": 71, "liu_gen_pushing_the_limits_of_softmax": 71, "based_out": 71, "distribution_detection_cvpr_2023_pap": 71, "fit_scor": [71, 104], "ood_predictions_scor": 71, "pretrain": [71, 88, 89, 95, 104], "adjust_confident_threshold": 71, "probabilist": [71, 87, 89, 90, 91, 94, 95, 104, 105], "order_label_issu": [72, 85], "whichev": [72, 105], "argsort": [72, 88, 92, 95, 99, 103, 104, 106], "max_": 72, "get_label_quality_ensemble_scor": [72, 98, 99], "weight_ensemble_members_bi": 72, "custom_weight": 72, "log_loss_search_t_valu": 72, "0001": [72, 97], "scheme": 72, "log_loss_search": 72, "log_loss": [72, 95], "1e0": 72, "1e1": 72, "1e2": 72, "2e2": 72, "quality_scor": [72, 104], "forth": 72, "top_issue_indic": 72, "rank_bi": [72, 85], "weird": [72, 83], "minu": 72, "prob_label": 72, "max_prob_not_label": 72, "AND": [72, 95], "get_epistemic_uncertainti": [73, 74], "get_aleatoric_uncertainti": [73, 74], "corrupt": [74, 106], "linearregress": [74, 98, 106], "y_with_nois": 74, "n_boot": [74, 98], "include_aleatoric_uncertainti": [74, 98], "sole": [74, 87, 90, 101, 104], "bootstrap": [74, 98, 106], "resampl": [74, 89, 98], "epistem": [74, 98, 104, 106], "aleator": [74, 98, 106], "model_final_kwarg": 74, "coars": 74, "thorough": [74, 98], "fine": [74, 88, 89, 95, 104], "grain": 74, "grid": 74, "varianc": [74, 99], "epistemic_uncertainti": 74, "residu": [74, 75, 98], "deviat": [74, 103, 106], "aleatoric_uncertainti": 74, "outr": 75, "contin": 75, "raw": [75, 84, 85, 91, 92, 97, 98, 101, 103, 104, 106], "aka": [75, 89, 99, 103, 106, 108], "00323821": 75, "33692597": 75, "00191686": 75, "semant": [76, 78, 79, 100], "pixel": [76, 78, 79, 92, 104, 107], "h": [76, 78, 79, 107], "height": [76, 78, 79, 107], "w": [76, 78, 79, 107], "width": [76, 78, 79, 107], "labels_one_hot": [76, 79, 107], "stream": [76, 104, 108], "downsampl": [76, 78, 107], "shrink": [76, 78], "divis": [76, 78, 90], "common_label_issu": [77, 79, 81, 83, 107, 108], "filter_by_class": [77, 79, 107], "segmant": [78, 79], "num_pixel_issu": [78, 107], "product": [78, 92, 96, 98], "pixel_scor": [78, 107], "enter": 79, "legend": [79, 90, 91, 96, 102, 103, 106, 107], "colormap": 79, "background": [79, 96], "person": [79, 98, 103, 107, 108], "ambigu": [79, 83, 88, 89, 95, 97, 99, 108], "systemat": [79, 83, 101], "misunderstood": [79, 83], "issues_df": [79, 92], "class_index": 79, "issues_subset": [79, 83], "filter_by_token": [81, 83, 108], "token_score_method": 82, "sentence_score_method": 82, "sentence_score_kwarg": 82, "compris": [82, 83], "token_scor": [82, 108], "converg": 82, "toward": [82, 96], "_softmin_sentence_scor": 82, "sentence_scor": [82, 108], "token_info": 82, "02": [82, 90, 91, 96, 99, 103], "03": [82, 94, 96, 97, 99, 103, 108], "04": [82, 94, 96, 103], "08": [82, 96, 99, 103, 106, 108], "commonli": [83, 85, 90, 91, 102, 108], "But": [83, 95, 99, 106, 108], "restrict": [83, 98], "reliabl": [84, 87, 89, 96, 98, 101, 107], "thousand": 84, "imagenet": [84, 97], "popular": [84, 101, 103], "centric": [84, 92, 94, 95, 100], "minut": [84, 87, 88, 89, 94, 95, 97, 101, 102, 103, 106, 107, 108], "conda": 84, "feature_embed": [84, 104], "Then": [84, 87, 88, 92, 98], "your_dataset": [84, 89, 90, 91, 92, 94, 95, 98], "column_name_of_label": [84, 89, 90, 91, 92, 94, 95], "plagu": [84, 91], "untrain": 84, "\u30c4": 84, "label_issues_info": [84, 91], "sklearn_compatible_model": 84, "framework": [84, 102, 103], "complianc": 84, "tag": [84, 102, 108], "sequenc": 84, "recognit": [84, 89, 98, 108], "train_data": [84, 87, 88, 104, 106], "gotten": 84, "test_data": [84, 87, 88, 99, 102, 104, 106], "deal": [84, 91, 96], "tutori": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "feel": [84, 89, 91, 98], "ask": [84, 98], "slack": [84, 98], "project": [84, 106], "welcom": 84, "commun": [84, 98], "guidelin": [84, 103], "piec": 84, "smart": [84, 92, 94, 95, 98], "edit": [84, 98], "easier": [84, 96, 99], "unreli": [84, 87, 89, 94, 95], "link": [84, 89, 97, 103], "older": 85, "outlin": 85, "substitut": 85, "v2": [85, 87, 94], "get_noise_indic": 85, "psx": 85, "sorted_index_method": 85, "order_label_error": 85, "label_errors_bool": 85, "latent_estim": 85, "num_label_error": 85, "learningwithnoisylabel": 85, "neatli": 85, "organ": [85, 87, 94, 97, 108], "reorgan": 85, "baseline_method": 85, "incorpor": [85, 99], "research": [85, 99], "polyplex": 85, "terminologi": 85, "label_error": 85, "quickstart": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 101, 102, 103, 104, 106, 107, 108], "sql": [87, 94], "databas": [87, 94], "excel": [87, 94], "parquet": [87, 94], "student": [87, 94, 106, 108], "grade": [87, 94, 106], "exam": [87, 94, 106], "letter": [87, 94, 108], "hundr": [87, 94], "mistak": [87, 88, 92, 94, 95], "extratreesclassifi": 87, "extratre": 87, "ranked_label_issu": [87, 88], "branch": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "preprocess": [87, 88, 91, 94, 96, 104, 106], "standardscal": [87, 94, 104], "labelencod": [87, 88], "train_test_split": [87, 88, 90, 91, 104], "accuracy_scor": [87, 88, 89, 95, 99], "grades_data": [87, 94], "read_csv": [87, 88, 94, 95, 96, 106], "demo": [87, 91, 94, 102], "stud_id": [87, 94], "exam_1": [87, 94, 106], "exam_2": [87, 94, 106], "exam_3": [87, 94, 106], "letter_grad": [87, 94], "f48f73": [87, 94], "53": [87, 90, 91, 94, 96, 97, 102, 103], "00": [87, 90, 91, 94, 96, 97, 104], "77": [87, 90, 91, 94, 103], "0bd4e7": [87, 94], "81": [87, 94, 95, 103, 106, 108], "great": [87, 94, 97], "particip": [87, 94], "cb9d7a": [87, 94], "61": [87, 94, 96, 99, 103, 106], "94": [87, 94, 97, 99, 103, 106], "9acca4": [87, 94], "48": [87, 94, 96, 97, 99, 103, 108], "x_raw": [87, 94], "labels_raw": 87, "interg": [87, 88], "categorical_featur": [87, 106], "x_encod": [87, 94], "get_dummi": [87, 94, 106], "drop_first": [87, 94], "numeric_featur": [87, 94], "scaler": [87, 94, 104], "x_process": [87, 94], "fit_transform": [87, 94, 96], "bring": [87, 88, 92, 94, 95, 101, 106], "byod": [87, 88, 92, 94, 95, 101, 106], "tress": 87, "held": [87, 89, 94, 95, 97, 103, 104, 105], "straightforward": [87, 89, 94], "benefit": [87, 89, 105, 107], "num_crossval_fold": [87, 89, 94, 101], "tabl": [87, 94, 97, 101], "212": [87, 99], "review": [87, 88, 91, 94, 95, 97, 98, 99, 103, 106, 107, 108], "iloc": [87, 88, 89, 94, 95, 106], "92": [87, 90, 99, 103], "93": [87, 97, 103, 106, 108], "827": 87, "99": [87, 96, 97, 99], "86": [87, 91, 92, 94, 99, 103, 106], "74": [87, 96, 103, 106], "637": [87, 94], "79": [87, 97, 103], "65": [87, 90, 96, 103], "cheat": 87, "0pt": 87, "120": [87, 90, 91], "233": 87, "83": [87, 99, 103, 106, 108], "76": [87, 99, 102, 103, 106], "suspici": [87, 94], "carefulli": [87, 92, 94, 95], "examin": [87, 90, 91, 94, 96, 103], "labels_train": 87, "labels_test": 87, "test_siz": [87, 88, 90, 91], "acc_og": [87, 88], "783068783068783": 87, "robustli": [87, 88, 106], "14": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "acc_cl": [87, 88], "8095238095238095": 87, "blindli": [87, 88, 89, 98, 106], "trust": [87, 88, 89, 98, 99, 101, 105, 106], "effort": [87, 88, 106], "intent": [88, 95], "servic": [88, 95, 98], "onlin": [88, 95], "bank": [88, 95, 97], "banking77": [88, 95], "oo": [88, 95], "categori": [88, 92, 95, 96], "shortlist": [88, 95, 106], "scope": [88, 95], "logist": [88, 90, 91, 95, 101, 104], "probabilit": [88, 89], "drop": [88, 94, 96, 98, 101, 106], "earlier": [88, 108], "sentence_transform": [88, 95], "sentencetransform": [88, 95], "payment": [88, 95], "cancel_transf": [88, 95], "transfer": [88, 95], "fund": [88, 95], "cancel": [88, 95], "transact": [88, 95], "my": [88, 95], "revert": [88, 95], "morn": [88, 95], "realis": [88, 95], "yesterdai": [88, 95], "rent": [88, 95], "tomorrow": [88, 95], "raw_text": [88, 95], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "card_about_to_expir": [88, 95], "supported_cards_and_curr": [88, 95], "apple_pay_or_google_pai": [88, 95], "beneficiary_not_allow": [88, 95], "getting_spare_card": [88, 95], "visa_or_mastercard": [88, 95], "lost_or_stolen_phon": [88, 95], "change_pin": [88, 95], "card_payment_fee_charg": [88, 95], "card": [88, 95, 97], "utter": [88, 95], "encond": 88, "test_label": [88, 99, 102, 104], "suit": [88, 95, 96, 97, 98], "electra": [88, 95], "discrimin": [88, 95], "googl": [88, 95], "train_text": 88, "test_text": 88, "home": [88, 90, 91, 95, 96, 97], "runner": [88, 90, 91, 95, 96], "google_electra": [88, 95], "pool": [88, 95, 98, 104], "opt": [88, 89, 91, 92, 94, 95, 96, 99], "hostedtoolcach": [88, 89, 91, 92, 94, 95, 96, 99], "x64": [88, 89, 91, 92, 94, 95, 96, 99], "lib": [88, 89, 91, 92, 94, 95, 96, 99], "python3": [88, 89, 91, 92, 94, 95, 96, 99], "site": [88, 89, 91, 92, 94, 95, 96, 99], "_util": [88, 95], "831": [88, 95], "userwarn": [88, 89, 90, 91, 95, 96], "typedstorag": [88, 95], "untypedstorag": [88, 95], "untyped_storag": [88, 95], "fget": [88, 95], "__get__": [88, 95], "owner": [88, 95], "leverag": [88, 89, 95, 98, 99, 101], "computation": [88, 89, 95], "intens": [88, 89, 95], "400": [88, 95], "858371": 88, "547274": 88, "826228": 88, "966008": 88, "792449": 88, "identified_issu": [88, 106], "lowest_quality_label": [88, 89, 95, 99, 106], "to_numpi": [88, 95, 96, 106], "44": [88, 96, 97, 102, 103], "646": 88, "390": 88, "628": 88, "121": [88, 99], "702": 88, "863": [88, 89], "135": 88, "337": [88, 103], "735": 88, "print_as_df": 88, "inverse_transform": 88, "charg": [88, 95], "cash": [88, 95], "holidai": [88, 95], "sent": [88, 95, 108], "mine": [88, 95], "expir": [88, 95], "fight": 88, "hors": [88, 97, 104], "duck": [88, 97], "me": [88, 95, 96], "whoever": [88, 95], "consum": [88, 106], "18": [88, 89, 95, 96, 97, 98, 99, 103, 104, 106, 107], "baseline_model": [88, 106], "87": [88, 91, 92, 103, 106], "acceler": [88, 106], "19": [88, 89, 92, 95, 96, 97, 98, 99, 103, 104, 106, 107], "89": [88, 90, 94, 103, 106], "spoken": 89, "500": [89, 104, 108], "english": [89, 97], "pronunci": 89, "wav": 89, "huggingfac": [89, 90, 91, 92, 98], "voxceleb": 89, "speech": [89, 108], "your_pred_prob": [89, 90, 91, 94, 95], "tensorflow_io": 89, "huggingface_hub": 89, "reproduc": [89, 94, 96, 99, 101], "command": 89, "wget": [89, 103, 107, 108], "navig": 89, "browser": 89, "jakobovski": 89, "archiv": [89, 108], "v1": 89, "tar": [89, 104], "gz": [89, 104], "mkdir": [89, 108], "spoken_digit": 89, "xf": 89, "6_nicolas_32": 89, "data_path": 89, "listdir": 89, "nondeterminist": 89, "file_nam": 89, "endswith": 89, "file_path": 89, "join": [89, 92, 96, 98], "7_george_26": 89, "0_nicolas_24": 89, "0_nicolas_6": 89, "listen": 89, "display_exampl": 89, "expand": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "pulldown": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "colab": [89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "tfio": 89, "pathlib": 89, "ipython": 89, "load_wav_16k_mono": 89, "filenam": 89, "khz": 89, "file_cont": 89, "read_fil": 89, "sample_r": 89, "decode_wav": 89, "desired_channel": 89, "squeez": 89, "rate_in": 89, "rate_out": 89, "16000": 89, "wav_file_nam": 89, "audio_r": 89, "wav_file_exampl": 89, "plai": [89, 97, 98], "button": 89, "wav_file_name_exampl": 89, "7_jackson_43": 89, "hear": 89, "extractor": 89, "encoderclassifi": 89, "spkrec": 89, "xvect": 89, "feature_extractor": 89, "from_hparam": 89, "run_opt": 89, "uncom": [89, 96], "ffmpeg": 89, "backend": 89, "wav_audio_file_path": 89, "torchaudio": 89, "extract_audio_embed": 89, "emb": [89, 92], "signal": 89, "encode_batch": 89, "embeddings_list": [89, 92], "embeddings_arrai": 89, "650": 89, "stft": 89, "return_complex": 89, "view_as_r": 89, "recov": 89, "trigger": 89, "aten": 89, "src": 89, "nativ": 89, "spectralop": 89, "cpp": 89, "_vf": 89, "n_fft": 89, "hop_length": 89, "win_length": 89, "attr": 89, "512": [89, 92], "196311": 89, "319459": 89, "478975": 89, "2890875": 89, "8170238": 89, "89265": 89, "898056": 89, "256195": 89, "559641": 89, "559721": 89, "62067": 89, "285245": 89, "21": [89, 90, 96, 97, 99, 103, 106, 108], "709627": 89, "5033693": 89, "913803": 89, "819831": 89, "1831515": 89, "208763": 89, "084257": 89, "3210397": 89, "005453": 89, "216152": 89, "478235": 89, "6821785": 89, "053807": 89, "242471": 89, "091424": 89, "78334856": 89, "03954": 89, "23": [89, 92, 96, 97, 99, 103, 106, 108], "569176": 89, "761097": 89, "1258295": 89, "753237": 89, "3508866": 89, "598274": 89, "23712": 89, "2500": 89, "tol": 89, "decreas": [89, 98], "cv_accuraci": 89, "9708": 89, "issue_type_descript": [89, 90, 91, 92, 94, 95, 99], "lt": [89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 104], "gt": [89, 90, 91, 92, 94, 95, 96, 99, 101, 108], "9976": 89, "986": 89, "002161": 89, "176": [89, 97, 99, 102], "002483": 89, "2318": 89, "004411": 89, "1005": 89, "004857": 89, "1871": 89, "007494": 89, "040587": 89, "999207": 89, "999377": 89, "975220": 89, "999367": 89, "identified_label_issu": [89, 95], "516": 89, "1946": 89, "469": 89, "2132": 89, "worth": [89, 99], "6_yweweler_25": 89, "7_nicolas_43": 89, "6_theo_27": 89, "6_yweweler_36": 89, "6_yweweler_14": 89, "6_yweweler_35": 89, "6_nicolas_8": 89, "sound": 89, "quit": [89, 104], "underneath": 90, "hood": [90, 96, 98], "alert": 90, "introduct": 90, "mayb": [90, 91, 95], "your_feature_matrix": [90, 91], "toi": [90, 91, 92, 96, 97, 99, 101], "inf": [90, 91], "mid": [90, 91], "bins_map": [90, 91], "create_data": [90, 91], "y_bin": [90, 91], "y_i": [90, 91], "y_bin_idx": [90, 91], "y_train": [90, 91, 99, 106], "y_test": [90, 91, 99, 106], "y_train_idx": [90, 91], "y_test_idx": [90, 91], "slide": [90, 91, 97], "frame": [90, 91], "x_out": [90, 91], "tini": [90, 91], "concaten": [90, 91, 105], "y_out": [90, 91], "y_out_bin": [90, 91], "y_out_bin_idx": [90, 91], "exact_duplicate_idx": [90, 91], "x_duplic": [90, 91], "y_duplic": [90, 91], "y_duplicate_idx": [90, 91], "noisy_labels_idx": [90, 91, 102], "scatter": [90, 91, 96, 99, 102, 106], "black": [90, 91, 97, 106], "cyan": [90, 91], "plot_data": [90, 91, 96, 99, 102, 106], "fig": [90, 91, 92, 97, 104, 106], "ax": [90, 91, 92, 96, 104, 106], "subplot": [90, 91, 92, 104], "set_titl": [90, 91, 92, 104], "set_xlabel": [90, 91], "x_1": [90, 91], "fontsiz": [90, 91, 92, 96, 99, 102], "set_ylabel": [90, 91], "x_2": [90, 91], "set_xlim": [90, 91], "set_ylim": [90, 91], "linestyl": [90, 91, 96], "circl": [90, 91, 99, 102], "misclassifi": [90, 91], "zip": [90, 91, 92, 103, 108], "label_err": [90, 91], "180": [90, 91, 103], "marker": [90, 91], "facecolor": [90, 91, 96], "edgecolor": [90, 91, 96], "linewidth": [90, 91, 96, 104], "dup": [90, 91], "first_legend": [90, 91], "align": [90, 91], "title_fontproperti": [90, 91], "semibold": [90, 91], "second_legend": [90, 91], "45": [90, 91, 96, 97, 99, 103], "gca": [90, 91], "add_artist": [90, 91], "tight_layout": [90, 91, 96], "ideal": [90, 91], "remaind": 90, "modal": [90, 91, 98, 101], "132": [90, 91, 99, 103], "9318": 90, "006940": 90, "007830": 90, "40": [90, 91, 95, 96, 97], "014828": 90, "107": [90, 91, 99, 102], "021241": 90, "026407": 90, "notic": [90, 99, 101, 103], "3558": [90, 91], "126": [90, 91, 99, 103], "006636": [90, 91], "130": [90, 91], "012571": [90, 91], "129": [90, 91], "127": [90, 91], "014909": [90, 91], "128": [90, 91, 92], "017443": [90, 91], "6160": [90, 91], "131": [90, 91, 107], "000000e": [90, 91], "000002": [90, 91], "463180e": [90, 91], "07": [90, 91, 92, 94, 96, 99, 103, 106], "51": [90, 91, 94, 96, 97, 99, 103], "161148": [90, 91], "859087e": [90, 91], "30": [90, 91, 92, 96, 97, 98, 102, 107, 108], "3453": 90, "029542": 90, "031182": 90, "057961": 90, "058244": 90, "348": 90, "378": 90, "357": 90, "34": [90, 96, 97, 99, 101, 103, 108], "54": [90, 96, 97, 99, 103, 108], "039122": 90, "044598": 90, "105": [90, 103], "105196": 90, "133654": 90, "43": [90, 96, 97, 99, 103], "168033": 90, "125": 90, "101107": 90, "183382": 90, "109": [90, 97, 103], "209259": 90, "211042": 90, "221316": 90, "average_ood_scor": 90, "34530442089193386": 90, "52": [90, 96, 97, 103, 108], "169820": 90, "087324e": 90, "259024": 90, "583757e": 90, "91": [90, 103], "346458": 90, "341292e": 90, "specfi": 90, "new_lab": 90, "scoring_funct": 90, "div": 90, "rem": 90, "inv_scal": 90, "49": [90, 96, 97, 99, 103, 108], "superstitionissuemanag": 90, "unlucki": 90, "superstit": 90, "to_seri": 90, "issues_mask": 90, "summary_scor": 90, "9242": 90, "is_superstition_issu": 90, "superstition_scor": 90, "26": [90, 92, 96, 97, 99, 101, 103], "047581": 90, "090635": 90, "129591": 90, "164840": 90, "lurk": [91, 92, 99], "_split": 91, "776": 91, "thoroughli": 91, "904": 91, "_base": [91, 92, 94, 95, 96, 99], "246": [91, 92, 94, 95, 96, 99, 103], "efficiencywarn": [91, 92, 94, 95, 96, 99], "sort_graph_by_row_valu": [91, 92, 94, 95, 96, 99], "warn_when_not_sort": [91, 92, 94, 95, 96, 99], "8561": 91, "001908": 91, "003564": 91, "007331": 91, "008963": 91, "009664": 91, "0227": 91, "022727": 91, "conceptu": 91, "856061": 91, "355772": 91, "616034": 91, "821750": 91, "901562": 91, "betweeen": 91, "859131": 91, "417707": 91, "664083": 91, "970324": 91, "816953": 91, "375317": 91, "641516": 91, "890575": 91, "531021": 91, "460593": 91, "601188": 91, "826147": 91, "752808": 91, "321635": 91, "562539": 91, "948362": 91, "090243": 91, "472909": 91, "746763": 91, "878267": 91, "examples_w_issu": [91, 98], "013445": 91, "025184": 91, "026376": 91, "inde": [91, 95], "miscellan": [91, 93, 108], "428571": 91, "111111": 91, "571429": 91, "407407": 91, "592593": 91, "337838": 91, "092593": 91, "662162": 91, "333333": [91, 97], "952381": 91, "666667": [91, 96], "portion": 91, "huge": [91, 99], "worri": [91, 95], "critic": 91, "60": [92, 96, 99, 106], "torchvis": [92, 104], "tensordataset": 92, "stratifiedkfold": [92, 102], "tqdm": 92, "autonotebook": 92, "math": 92, "fashion_mnist": 92, "1486": 92, "futurewarn": 92, "hf": 92, "messag": 92, "trust_remote_cod": 92, "num_row": 92, "60000": 92, "transformed_dataset": 92, "with_format": 92, "255": [92, 97], "cpu_count": 92, "torch_dataset": 92, "quick": [92, 102, 104], "super": [92, 94, 95], "relu": 92, "batchnorm2d": 92, "maxpool2d": 92, "lazylinear": 92, "flatten": 92, "get_test_accuraci": 92, "testload": [92, 104], "energi": 92, "trainload": [92, 104], "n_epoch": 92, "patienc": 92, "criterion": 92, "crossentropyloss": 92, "adamw": 92, "best_test_accuraci": 92, "start_epoch": 92, "running_loss": 92, "best_epoch": 92, "end_epoch": 92, "3f": [92, 106], "acc": [92, 99], "time_taken": 92, "compute_embed": 92, "compute_pred_prob": 92, "train_batch_s": 92, "num_work": 92, "worker": [92, 108], "train_id_list": 92, "test_id_list": 92, "train_id": 92, "test_id": 92, "embeddings_model": 92, "ntrain": 92, "trainset": 92, "testset": 92, "pin_memori": 92, "fold_embed": 92, "fold_pred_prob": 92, "finish": 92, "482": 92, "720": 92, "649": 92, "329": [92, 94, 103], "88": [92, 97, 98, 99, 102, 103, 106], "195": 92, "481": [92, 95], "493": 92, "060": 92, "663": 92, "330": [92, 103], "505": 92, "476": 92, "340": 92, "680": 92, "328": [92, 103], "310": 92, "450": 92, "reorder": 92, "hstack": [92, 98, 99, 101], "vision": 92, "grayscal": 92, "max_preval": 92, "7714": 92, "3772": 92, "3585": 92, "166": 92, "3651": 92, "27080": 92, "873833e": 92, "40378": 92, "915575e": 92, "25316": 92, "390277e": 92, "06": [92, 99, 103, 108], "2090": 92, "751164e": 92, "14999": 92, "881301e": 92, "9569": 92, "11262": 92, "000003": 92, "coat": [92, 97], "shirt": [92, 97], "19228": 92, "000010": 92, "dress": 92, "32657": 92, "000013": 92, "bag": [92, 97, 104, 105], "21282": 92, "000016": 92, "53564": 92, "000018": 92, "pullov": 92, "6321": 92, "30968": 92, "001267": 92, "30659": 92, "000022": [92, 108], "47824": 92, "001454": 92, "3370": 92, "000026": 92, "54565": 92, "001854": 92, "9762": 92, "258": 92, "47139": 92, "000033": 92, "166980": 92, "986195": 92, "997205": 92, "sandal": [92, 97], "948781": 92, "999358": 92, "54078": 92, "17371": 92, "000025": 92, "plot_label_issue_exampl": 92, "ncol": [92, 104], "nrow": [92, 104], "ceil": 92, "axes_list": 92, "label_issue_indic": 92, "gl": 92, "sl": 92, "fontdict": 92, "imshow": [92, 104], "cmap": [92, 96, 106], "grai": 92, "subplots_adjust": 92, "hspace": 92, "outsiz": 92, "outlier_issu": [92, 95], "outlier_issues_df": 92, "depict": [92, 102, 103, 104, 105, 107], "plot_outlier_issues_exampl": 92, "n_comparison_imag": 92, "sample_from_class": 92, "number_of_sampl": 92, "non_outlier_indic": 92, "isnul": [92, 96], "non_outlier_indices_excluding_curr": 92, "sampled_indic": 92, "label_scores_of_sampl": 92, "top_score_indic": 92, "top_label_indic": 92, "sampled_imag": 92, "get_image_given_label_and_sampl": 92, "image_from_dataset": 92, "corresponding_label": 92, "comparison_imag": 92, "images_to_plot": 92, "idlist": 92, "iterrow": 92, "near_duplicate_issu": [92, 98], "closest": 92, "counterpart": 92, "near_duplicate_issues_df": 92, "plot_near_duplicate_issue_exampl": 92, "seen_id_pair": 92, "get_image_and_given_label_and_predicted_label": 92, "duplicate_imag": 92, "nd_set": 92, "challeng": 92, "dark_issu": 92, "reveal": [92, 103, 107], "dark_scor": 92, "dark_issues_df": 92, "is_dark_issu": 92, "34848": 92, "203922": 92, "50270": 92, "204588": 92, "3936": 92, "213098": 92, "733": 92, "217686": 92, "8094": 92, "230118": 92, "plot_image_issue_exampl": 92, "difficult": 92, "disproportion": [92, 96], "lowinfo_issu": 92, "low_information_scor": 92, "lowinfo_issues_df": 92, "is_low_information_issu": 92, "53050": 92, "067975": 92, "40875": 92, "089929": 92, "9594": 92, "092601": 92, "34825": 92, "107744": 92, "37530": 92, "108516": 92, "lot": 92, "workflow": [93, 98, 100, 106], "histgradientboostingclassifi": 94, "cat_featur": 94, "boost": [94, 98, 101, 106], "xgboost": [94, 98, 106], "think": [94, 95, 98, 102, 107, 108], "nonzero": 94, "358": 94, "941": 94, "294": [94, 103], "46": [94, 96, 97, 99, 103], "7109": 94, "000005": [94, 95], "886": 94, "000059": 94, "709": 94, "000104": 94, "723": 94, "000169": 94, "689": 94, "000181": 94, "3590": 94, "051882e": 94, "683133e": 94, "536582e": 94, "406589e": 94, "324246e": 94, "6165": 94, "582": 94, "185": [94, 97, 103, 108], "187": [94, 97], "898": 94, "0000": [94, 95, 97, 99], "865": 94, "515002": 94, "837": 94, "556480": 94, "622": 94, "593068": 94, "593207": 94, "920": 94, "618041": 94, "4386345844794593e": 94, "issue_result": 94, "000842": 94, "555944": 94, "004374": 94, "sorted_issu": 94, "73": [94, 96, 97, 102, 103, 106], "deserv": 94, "outlier_result": 94, "sorted_outli": 94, "56": [94, 96, 97, 106], "96": [94, 96, 97, 99, 102, 103, 106], "style": [94, 96, 107], "font": 94, "18px": 94, "ff00ff": 94, "bac": 94, "unintend": [94, 95], "duplicate_result": 94, "lowest_scoring_dupl": 94, "idxmin": [94, 98], "indices_to_displai": 94, "tolist": [94, 98, 102], "perhap": [94, 99, 101], "second_lowest_scoring_dupl": 94, "next_indices_to_displai": 94, "wari": [94, 95, 98], "dive": [95, 96], "your_featur": 95, "text_embed": 95, "data_dict": [95, 99, 101], "85": [95, 103], "38": [95, 96, 97, 103], "9710": 95, "981": 95, "974": 95, "000146": 95, "982": [95, 97], "000224": 95, "971": 95, "000507": 95, "980": [95, 97], "000960": 95, "3584": 95, "994": 95, "009642": 95, "999": 95, "013067": 95, "013841": 95, "433": 95, "014722": 95, "989": 95, "018224": 95, "6070": 95, "160": [95, 106], "095724": 95, "148": 95, "006237": 95, "546": 95, "099341": 95, "514": 95, "006485": 95, "123418": 95, "008165": 95, "313": [95, 103], "564102": 95, "572258": 95, "574915": 95, "31": [95, 96, 97, 99, 101, 103, 108], "575507": 95, "575874": 95, "792090": 95, "257611": 95, "698710": 95, "182121": 95, "771619": 95, "data_with_suggested_label": 95, "suggested_label": 95, "withdraw": 95, "monei": 95, "lowest_quality_outli": 95, "OR": 95, "636c65616e6c616220697320617765736f6d6521": 95, "phone": [95, 97], "gone": 95, "samp": 95, "br": 95, "press": [95, 108], "nonsens": 95, "sens": 95, "detriment": 95, "duplicate_issu": 95, "fee": 95, "go": [95, 96, 97, 99], "strongli": 95, "p_valu": 95, "benign": 95, "curat": 95, "bigger": 96, "make_classif": 96, "5000": [96, 104], "n_featur": 96, "n_inform": 96, "n_redund": 96, "n_repeat": 96, "n_class": 96, "n_clusters_per_class": 96, "flip_i": 96, "class_sep": 96, "faiss": 96, "x_faiss": 96, "float32": [96, 103], "normalize_l2": 96, "index_factori": 96, "hnsw32": 96, "flat": [96, 97], "metric_inner_product": 96, "a_min": 96, "a_max": 96, "create_knn_graph": 96, "assert": 96, "indices_1d": 96, "ravel": 96, "distances_1d": 96, "50000": 96, "116": 96, "523": 96, "991400": 96, "356958": 96, "362": 96, "619565": 96, "108": [96, 103], "500000": 96, "651929": 96, "999827": 96, "031217": 96, "933716": 96, "627345": 96, "998540": 96, "530909": 96, "296974": 96, "646765": 96, "942721": 96, "332824": 96, "803246": 96, "625202": 96, "999816": 96, "474031": 96, "706253": 96, "655108": 96, "997703": 96, "131466": 96, "912389": 96, "639200": 96, "4995": 96, "998646": 96, "504755": 96, "746777": 96, "680033": 96, "4996": 96, "894230": 96, "340986": 96, "816472": 96, "640711": 96, "4997": 96, "999100": 96, "428545": 96, "592421": 96, "658949": 96, "4998": 96, "986792": 96, "273710": 96, "618033": 96, "4999": 96, "986776": 96, "273524": 96, "618084": 96, "instabl": 96, "proxim": 96, "analys": 96, "comfort": 96, "explor": [96, 103, 104], "third": 96, "parti": [96, 108], "newsgroup": 96, "alt": [96, 97], "atheism": [96, 97], "sci": [96, 97], "fetch_20newsgroup": 96, "newsgroups_train": 96, "header": 96, "footer": 96, "quot": 96, "df_text": 96, "target_nam": 96, "enlighten": 96, "omnipot": 96, "19apr199320262420": 96, "kelvin": 96, "jpl": 96, "nasa": 96, "gov": 96, "baa": 96, "nhenri": 96, "he": 96, "nno": 96, "ge": 96, "nlucki": 96, "babi": [96, 97], "tfidfvector": 96, "feature_extract": 96, "x_vector": 96, "data_valuation_issu": 96, "147": [96, 99, 103], "500047": 96, "500093": 96, "499953": 96, "1068": [96, 108], "1069": 96, "1070": 96, "1071": 96, "1072": 96, "1073": 96, "concentr": 96, "seaborn": 96, "sn": 96, "distinguish": 96, "strip": 96, "stripplot": 96, "hue": [96, 106], "dodg": 96, "jitter": 96, "axvlin": [96, 104], "xlabel": 96, "ourselv": 96, "make_blob": 96, "center": [96, 97], "cluster_std": 96, "n_noisy_label": 96, "meaning": [96, 98, 104], "silhouette_scor": 96, "gridsearchcv": 96, "silhouett": 96, "cluster_label": 96, "fit_predict": 96, "param_grid": 96, "grid_search": 96, "best_kmean": 96, "best_estimator_": 96, "underperforming_group_issu": 96, "328308": 96, "tab10": 96, "domain": 96, "knowledg": [96, 99], "dataset_tsv": 96, "ag": [96, 106], "gender": 96, "educ": 96, "experi": 96, "highsalari": 96, "indiana": 96, "phd": 96, "male": 96, "bachelor": 96, "femal": 96, "kansa": 96, "school": [96, 97], "ohio": 96, "57": [96, 97, 99, 108], "california": 96, "59": [96, 97, 103], "63": [96, 99, 103, 106], "47": [96, 97, 103], "stringio": 96, "sep": [96, 108], "simplic": [96, 102], "ordinalencod": 96, "columns_to_encod": 96, "encoded_df": 96, "salari": 96, "573681": 96, "underpin": 96, "caught": 96, "whenev": 96, "generate_data_depend": 96, "num_sampl": 96, "a1": 96, "a2": 96, "a3": 96, "375": 96, "975": 96, "non_iid_issu": 96, "796474": 96, "842432": 96, "922562": 96, "820759": 96, "873136": 96, "887373": 96, "825101": 96, "855875": 96, "751795": 96, "835796": 96, "ylabel": [96, 104], "coolwarm": 96, "colorbar": [96, 106], "strong": 96, "evid": 96, "inter": 96, "mitig": 96, "risk": 96, "deeper": 96, "tsv": 96, "tab": 96, "pars": 96, "annual_spend": 96, "number_of_transact": 96, "last_purchase_d": 96, "rural": 96, "4099": 96, "2024": [96, 108], "6421": 96, "nat": 96, "suburban": 96, "5436": 96, "4046": 96, "66": [96, 97], "3467": 96, "67": [96, 97, 103, 106], "4757": 96, "4199": 96, "4991": 96, "4655": 96, "82": [96, 97, 99, 103, 106], "5584": 96, "urban": 96, "3102": 96, "6637": 96, "9167": 96, "6790": 96, "5327": 96, "parse_d": 96, "lose": 96, "intact": 96, "encode_categorical_column": 96, "placehold": 96, "dropna": [96, 101], "category_to_numb": 96, "_encod": 96, "gender_encod": 96, "location_encod": 96, "focus": [96, 99, 101, 102, 106], "null_issu": 96, "833333": 96, "sorted_indic": [96, 103], "sorted_df": 96, "nice": 96, "styler": 96, "combined_df": 96, "concat": [96, 106], "highlight_null_valu": 96, "val": [96, 99], "yellow": [96, 97], "highlight_datalab_column": 96, "lightblu": 96, "highlight_is_null_issu": 96, "orang": [96, 97], "styled_df": 96, "nbsp": [96, 98, 99], "160000": 96, "820000": 96, "460000": 96, "470000": 96, "960000": 96, "620000": 96, "550000": 96, "660000": 96, "670000": [96, 97], "370000": 96, "530000": 96, "710000": 96, "020000": 96, "320000": 96, "990000": 96, "rarer": 96, "fairer": 96, "randomli": [96, 99], "class_imbalance_issu": 96, "countplot": 96, "xtick": 96, "rotat": 96, "ytick": 96, "filtered_df": 96, "xy": 96, "va": 96, "textual": 96, "get_ytick": 96, "nbar": 96, "nimbal": 96, "get_legend_handles_label": 96, "title_fonts": 96, "refin": 97, "instruct": [97, 98], "studi": [97, 103], "mnist_test_set": 97, "imagenet_val_set": 97, "tench": 97, "goldfish": 97, "white": [97, 108], "shark": 97, "tiger": 97, "hammerhead": 97, "electr": 97, "rai": 97, "stingrai": 97, "cock": 97, "hen": 97, "ostrich": 97, "brambl": 97, "goldfinch": 97, "hous": 97, "finch": 97, "junco": 97, "indigo": 97, "bunt": 97, "american": [97, 108], "robin": 97, "bulbul": 97, "jai": 97, "magpi": 97, "chickade": 97, "dipper": 97, "kite": 97, "bald": 97, "eagl": 97, "vultur": 97, "grei": 97, "owl": 97, "salamand": 97, "smooth": 97, "newt": 97, "spot": [97, 98, 103], "axolotl": 97, "bullfrog": 97, "tree": 97, "frog": [97, 104], "tail": 97, "loggerhead": 97, "sea": 97, "turtl": 97, "leatherback": 97, "mud": 97, "terrapin": 97, "band": 97, "gecko": 97, "green": [97, 108], "iguana": 97, "carolina": 97, "anol": 97, "desert": 97, "grassland": 97, "whiptail": 97, "lizard": 97, "agama": 97, "frill": 97, "neck": 97, "allig": 97, "gila": 97, "monster": 97, "european": 97, "chameleon": 97, "komodo": 97, "dragon": 97, "nile": 97, "crocodil": 97, "triceratop": 97, "worm": 97, "snake": 97, "ring": 97, "eastern": 97, "hog": 97, "nose": 97, "kingsnak": 97, "garter": 97, "water": 97, "vine": 97, "night": 97, "boa": 97, "constrictor": 97, "african": 97, "rock": 97, "indian": 97, "cobra": 97, "mamba": 97, "saharan": 97, "horn": 97, "viper": 97, "diamondback": 97, "rattlesnak": 97, "sidewind": 97, "trilobit": 97, "harvestman": 97, "scorpion": 97, "garden": 97, "spider": 97, "barn": 97, "southern": 97, "widow": 97, "tarantula": 97, "wolf": 97, "tick": 97, "centiped": 97, "grous": 97, "ptarmigan": 97, "ruf": 97, "prairi": 97, "peacock": 97, "quail": 97, "partridg": 97, "parrot": 97, "macaw": 97, "sulphur": 97, "crest": 97, "cockatoo": 97, "lorikeet": 97, "coucal": 97, "bee": 97, "eater": 97, "hornbil": 97, "hummingbird": 97, "jacamar": 97, "toucan": 97, "breast": 97, "mergans": 97, "goos": 97, "swan": 97, "tusker": 97, "echidna": 97, "platypu": 97, "wallabi": 97, "koala": 97, "wombat": 97, "jellyfish": 97, "anemon": 97, "brain": 97, "coral": 97, "flatworm": 97, "nematod": 97, "conch": 97, "snail": 97, "slug": 97, "chiton": 97, "chamber": 97, "nautilu": 97, "dung": 97, "crab": 97, "fiddler": 97, "king": 97, "lobster": 97, "spini": 97, "crayfish": 97, "hermit": 97, "isopod": 97, "stork": 97, "spoonbil": 97, "flamingo": 97, "heron": 97, "egret": 97, "bittern": 97, "crane": 97, "bird": [97, 104], "limpkin": 97, "gallinul": 97, "coot": 97, "bustard": 97, "ruddi": 97, "turnston": 97, "dunlin": 97, "redshank": 97, "dowitch": 97, "oystercatch": 97, "pelican": 97, "penguin": 97, "albatross": 97, "whale": 97, "killer": 97, "dugong": 97, "lion": 97, "chihuahua": 97, "japanes": 97, "chin": 97, "maltes": 97, "pekinges": 97, "shih": 97, "tzu": 97, "charl": 97, "spaniel": 97, "papillon": 97, "terrier": 97, "rhodesian": 97, "ridgeback": 97, "afghan": [97, 108], "hound": 97, "basset": 97, "beagl": 97, "bloodhound": 97, "bluetick": 97, "coonhound": 97, "tan": 97, "walker": 97, "foxhound": 97, "redbon": 97, "borzoi": 97, "irish": 97, "wolfhound": 97, "italian": 97, "greyhound": 97, "whippet": 97, "ibizan": 97, "norwegian": 97, "elkhound": 97, "otterhound": 97, "saluki": 97, "scottish": 97, "deerhound": 97, "weimaran": 97, "staffordshir": 97, "bull": 97, "bedlington": 97, "border": 97, "kerri": 97, "norfolk": 97, "norwich": 97, "yorkshir": 97, "wire": 97, "fox": 97, "lakeland": 97, "sealyham": 97, "airedal": 97, "cairn": 97, "australian": 97, "dandi": 97, "dinmont": 97, "boston": 97, "miniatur": 97, "schnauzer": 97, "giant": 97, "tibetan": 97, "silki": 97, "wheaten": 97, "west": 97, "highland": 97, "lhasa": 97, "apso": 97, "retriev": 97, "curli": 97, "golden": 97, "labrador": 97, "chesapeak": 97, "bai": 97, "german": [97, 108], "shorthair": 97, "pointer": 97, "vizsla": 97, "setter": 97, "gordon": 97, "brittani": 97, "clumber": 97, "springer": 97, "welsh": 97, "cocker": 97, "sussex": 97, "kuvasz": 97, "schipperk": 97, "groenendael": 97, "malinoi": 97, "briard": 97, "kelpi": 97, "komondor": 97, "sheepdog": 97, "shetland": 97, "colli": 97, "bouvier": 97, "de": 97, "flandr": 97, "rottweil": 97, "shepherd": 97, "dobermann": 97, "pinscher": 97, "swiss": [97, 108], "mountain": 97, "bernes": 97, "appenzel": 97, "sennenhund": 97, "entlebuch": 97, "boxer": 97, "bullmastiff": 97, "mastiff": 97, "french": 97, "bulldog": 97, "dane": 97, "st": 97, "bernard": 97, "huski": 97, "alaskan": 97, "malamut": 97, "siberian": 97, "dalmatian": 97, "affenpinsch": 97, "basenji": 97, "pug": 97, "leonberg": 97, "newfoundland": 97, "pyrenean": 97, "samoi": 97, "pomeranian": 97, "chow": 97, "keeshond": 97, "griffon": 97, "bruxelloi": 97, "pembrok": 97, "corgi": 97, "cardigan": 97, "poodl": 97, "mexican": 97, "hairless": 97, "tundra": 97, "coyot": 97, "dingo": 97, "dhole": 97, "wild": 97, "hyena": 97, "kit": 97, "arctic": 97, "tabbi": 97, "persian": 97, "siames": 97, "egyptian": 97, "mau": 97, "cougar": 97, "lynx": 97, "leopard": 97, "snow": 97, "jaguar": 97, "cheetah": 97, "brown": [97, 107], "bear": 97, "polar": 97, "sloth": 97, "mongoos": 97, "meerkat": 97, "beetl": 97, "ladybug": 97, "longhorn": 97, "leaf": 97, "rhinocero": 97, "weevil": 97, "fly": 97, "ant": 97, "grasshopp": 97, "cricket": 97, "stick": 97, "insect": 97, "cockroach": 97, "manti": 97, "cicada": 97, "leafhopp": 97, "lacew": 97, "dragonfli": 97, "damselfli": 97, "admir": 97, "ringlet": 97, "monarch": 97, "butterfli": 97, "gossam": 97, "wing": 97, "starfish": 97, "urchin": 97, "cucumb": 97, "cottontail": 97, "rabbit": 97, "hare": 97, "angora": 97, "hamster": 97, "porcupin": 97, "squirrel": 97, "marmot": 97, "beaver": 97, "guinea": 97, "pig": 97, "sorrel": 97, "zebra": 97, "boar": 97, "warthog": 97, "hippopotamu": 97, "ox": 97, "buffalo": 97, "bison": 97, "bighorn": 97, "sheep": 97, "alpin": 97, "ibex": 97, "hartebeest": 97, "impala": 97, "gazel": 97, "dromedari": 97, "llama": 97, "weasel": 97, "mink": 97, "polecat": 97, "foot": 97, "ferret": 97, "otter": 97, "skunk": 97, "badger": 97, "armadillo": 97, "toed": 97, "orangutan": 97, "gorilla": 97, "chimpanze": 97, "gibbon": 97, "siamang": 97, "guenon": 97, "pata": 97, "monkei": 97, "baboon": 97, "macaqu": 97, "langur": 97, "colobu": 97, "probosci": 97, "marmoset": 97, "capuchin": 97, "howler": 97, "titi": 97, "geoffroi": 97, "lemur": 97, "indri": 97, "asian": 97, "eleph": 97, "bush": 97, "snoek": 97, "eel": 97, "coho": 97, "salmon": 97, "beauti": 97, "clownfish": 97, "sturgeon": 97, "garfish": 97, "lionfish": 97, "pufferfish": 97, "abacu": 97, "abaya": 97, "academ": 97, "gown": 97, "accordion": 97, "acoust": 97, "guitar": 97, "aircraft": 97, "carrier": 97, "airlin": 97, "airship": 97, "altar": 97, "ambul": 97, "amphibi": 97, "clock": [97, 108], "apiari": 97, "apron": 97, "wast": 97, "assault": 97, "rifl": 97, "backpack": 97, "bakeri": 97, "balanc": 97, "beam": 97, "balloon": 97, "ballpoint": 97, "pen": 97, "aid": 97, "banjo": 97, "balust": 97, "barbel": 97, "barber": 97, "chair": [97, 103], "barbershop": 97, "baromet": 97, "barrel": 97, "wheelbarrow": 97, "basebal": 97, "basketbal": 97, "bassinet": 97, "bassoon": 97, "swim": 97, "cap": 97, "bath": 97, "towel": 97, "bathtub": 97, "station": 97, "wagon": 97, "lighthous": 97, "beaker": 97, "militari": 97, "beer": 97, "bottl": 97, "glass": 97, "bell": 97, "cot": 97, "bib": 97, "bicycl": [97, 107], "bikini": 97, "binder": 97, "binocular": 97, "birdhous": 97, "boathous": 97, "bobsleigh": 97, "bolo": 97, "tie": 97, "poke": 97, "bonnet": 97, "bookcas": 97, "bookstor": 97, "bow": 97, "brass": 97, "bra": 97, "breakwat": 97, "breastplat": 97, "broom": 97, "bucket": 97, "buckl": 97, "bulletproof": 97, "vest": 97, "butcher": 97, "shop": 97, "taxicab": 97, "cauldron": 97, "candl": 97, "cannon": 97, "cano": 97, "mirror": [97, 103], "carousel": 97, "tool": [97, 99, 101], "carton": 97, "wheel": 97, "teller": 97, "cassett": 97, "player": 97, "castl": 97, "catamaran": 97, "cd": 97, "cello": 97, "mobil": [97, 108], "chain": 97, "fenc": [97, 107], "mail": 97, "chainsaw": 97, "chest": 97, "chiffoni": 97, "chime": 97, "china": 97, "cabinet": 97, "christma": 97, "stock": 97, "church": 97, "movi": 97, "theater": 97, "cleaver": 97, "cliff": 97, "dwell": 97, "cloak": 97, "clog": 97, "cocktail": 97, "shaker": 97, "coffe": 97, "mug": 97, "coffeemak": 97, "coil": 97, "lock": 97, "keyboard": 97, "confectioneri": 97, "ship": [97, 104], "corkscrew": 97, "cornet": 97, "cowboi": 97, "boot": 97, "hat": 97, "cradl": 97, "crash": 97, "helmet": 97, "crate": 97, "infant": 97, "bed": 97, "crock": 97, "pot": 97, "croquet": 97, "crutch": 97, "cuirass": 97, "dam": 97, "desk": 97, "desktop": 97, "rotari": 97, "dial": 97, "telephon": 97, "diaper": 97, "watch": 97, "dine": 97, "dishcloth": 97, "dishwash": 97, "disc": 97, "brake": 97, "dock": 97, "sled": 97, "dome": 97, "doormat": 97, "drill": 97, "rig": 97, "drum": 97, "drumstick": 97, "dumbbel": 97, "dutch": 97, "oven": 97, "fan": 97, "locomot": 97, "entertain": 97, "envelop": 97, "espresso": 97, "powder": 97, "feather": 97, "fireboat": 97, "engin": [97, 107], "screen": 97, "sheet": 97, "flagpol": 97, "flute": 97, "footbal": 97, "forklift": 97, "fountain": 97, "poster": 97, "freight": 97, "fry": 97, "pan": 97, "fur": 97, "garbag": 97, "ga": 97, "pump": 97, "goblet": 97, "kart": 97, "golf": 97, "cart": 97, "gondola": 97, "gong": 97, "grand": 97, "piano": 97, "greenhous": 97, "grill": 97, "groceri": 97, "guillotin": 97, "barrett": 97, "hair": 97, "sprai": 97, "hammer": 97, "dryer": 97, "hand": [97, 99], "handkerchief": 97, "drive": 97, "harmonica": 97, "harp": 97, "harvest": 97, "hatchet": 97, "holster": 97, "honeycomb": 97, "hoop": 97, "skirt": 97, "horizont": 97, "bar": 97, "drawn": 97, "hourglass": 97, "ipod": 97, "cloth": 97, "iron": 97, "jack": 97, "lantern": 97, "jean": 97, "jeep": 97, "jigsaw": 97, "puzzl": 97, "pull": 97, "rickshaw": 97, "joystick": 97, "kimono": 97, "knee": 97, "pad": 97, "knot": 97, "ladl": 97, "lampshad": 97, "laptop": 97, "lawn": 97, "mower": 97, "knife": 97, "lifeboat": 97, "lighter": 97, "limousin": 97, "ocean": 97, "liner": 97, "lipstick": 97, "slip": 97, "shoe": 97, "lotion": 97, "speaker": 97, "loup": 97, "sawmil": 97, "magnet": 97, "compass": 97, "mailbox": 97, "tight": 97, "tank": 97, "manhol": 97, "maraca": 97, "marimba": 97, "maypol": 97, "maze": 97, "cup": [97, 103], "medicin": 97, "megalith": 97, "microphon": 97, "microwav": 97, "milk": 97, "minibu": 97, "miniskirt": 97, "minivan": 97, "missil": 97, "mitten": [97, 98], "mix": 97, "bowl": 97, "modem": 97, "monasteri": 97, "monitor": 97, "mope": 97, "mortar": 97, "mosqu": 97, "mosquito": 97, "scooter": 97, "bike": 97, "tent": 97, "mous": [97, 98], "mousetrap": 97, "van": 97, "muzzl": 97, "nail": 97, "brace": 97, "necklac": 97, "nippl": 97, "obelisk": 97, "obo": 97, "ocarina": 97, "odomet": 97, "oil": 97, "oscilloscop": 97, "overskirt": 97, "bullock": 97, "oxygen": 97, "packet": 97, "paddl": 97, "padlock": 97, "paintbrush": 97, "pajama": 97, "palac": [97, 108], "parachut": 97, "park": 97, "bench": 97, "meter": 97, "passeng": 97, "patio": 97, "payphon": 97, "pedest": 97, "pencil": 97, "perfum": 97, "petri": 97, "dish": 97, "photocopi": 97, "plectrum": 97, "pickelhaub": 97, "picket": 97, "pickup": 97, "pier": 97, "piggi": 97, "pill": 97, "pillow": 97, "ping": 97, "pong": 97, "pinwheel": 97, "pirat": 97, "pitcher": 97, "plane": 97, "planetarium": 97, "plastic": 97, "plate": 97, "rack": 97, "plow": 97, "plunger": 97, "polaroid": 97, "camera": 97, "pole": [97, 107], "polic": 97, "poncho": 97, "billiard": 97, "soda": 97, "potter": 97, "prayer": 97, "rug": 97, "printer": 97, "prison": 97, "projectil": 97, "projector": 97, "hockei": 97, "puck": 97, "punch": 97, "purs": 97, "quill": 97, "quilt": 97, "race": 97, "racket": 97, "radiat": 97, "radio": 97, "telescop": 97, "rain": 97, "recreat": 97, "reel": 97, "reflex": 97, "refriger": 97, "remot": 97, "restaur": 97, "revolv": 97, "rotisseri": 97, "eras": 97, "rugbi": 97, "ruler": 97, "safe": 97, "safeti": 97, "salt": 97, "sarong": 97, "saxophon": 97, "scabbard": 97, "bu": [97, 107], "schooner": 97, "scoreboard": 97, "crt": 97, "screw": 97, "screwdriv": 97, "seat": 97, "belt": 97, "sew": 97, "shield": 97, "shoji": 97, "basket": 97, "shovel": 97, "shower": 97, "curtain": 97, "ski": 97, "sleep": 97, "door": 97, "slot": 97, "snorkel": 97, "snowmobil": 97, "snowplow": 97, "soap": 97, "dispens": 97, "soccer": [97, 108], "sock": [97, 98], "solar": 97, "thermal": 97, "collector": 97, "sombrero": 97, "soup": 97, "heater": 97, "shuttl": 97, "spatula": 97, "motorboat": 97, "web": 97, "spindl": 97, "sport": [97, 108], "spotlight": 97, "stage": 97, "steam": 97, "arch": 97, "bridg": 97, "steel": 97, "stethoscop": 97, "scarf": 97, "stone": 97, "wall": [97, 107], "stopwatch": 97, "stove": 97, "strainer": 97, "tram": 97, "stretcher": 97, "couch": 97, "stupa": 97, "submarin": 97, "sundial": 97, "sunglass": 97, "sunscreen": 97, "suspens": 97, "mop": 97, "sweatshirt": 97, "swimsuit": 97, "swing": 97, "switch": 97, "syring": 97, "lamp": 97, "tape": 97, "teapot": 97, "teddi": 97, "televis": [97, 108], "tenni": 97, "thatch": 97, "roof": 97, "thimbl": 97, "thresh": 97, "throne": 97, "tile": 97, "toaster": 97, "tobacco": 97, "toilet": 97, "totem": 97, "tow": 97, "tractor": 97, "semi": 97, "trailer": 97, "trai": 97, "trench": 97, "tricycl": 97, "trimaran": 97, "tripod": 97, "triumphal": 97, "trolleybu": 97, "trombon": 97, "tub": 97, "turnstil": 97, "typewrit": 97, "umbrella": 97, "unicycl": 97, "upright": 97, "vacuum": 97, "cleaner": 97, "vase": 97, "vault": 97, "velvet": 97, "vend": 97, "vestment": 97, "viaduct": 97, "violin": 97, "volleybal": 97, "waffl": 97, "wallet": 97, "wardrob": 97, "sink": 97, "wash": 97, "jug": 97, "tower": 97, "whiskei": 97, "whistl": 97, "wig": 97, "shade": [97, 107], "windsor": 97, "wine": 97, "wok": 97, "wooden": 97, "spoon": 97, "wool": 97, "rail": 97, "shipwreck": 97, "yawl": 97, "yurt": 97, "websit": 97, "comic": 97, "book": 97, "crossword": 97, "traffic": [97, 103, 107], "sign": [97, 107, 108], "dust": 97, "jacket": [97, 103], "menu": 97, "guacamol": 97, "consomm": 97, "trifl": 97, "ic": 97, "cream": 97, "pop": 97, "baguett": 97, "bagel": 97, "pretzel": 97, "cheeseburg": 97, "mash": 97, "potato": 97, "cabbag": 97, "broccoli": 97, "cauliflow": 97, "zucchini": 97, "spaghetti": 97, "squash": 97, "acorn": 97, "butternut": 97, "artichok": 97, "pepper": [97, 98], "cardoon": 97, "mushroom": 97, "granni": 97, "smith": 97, "strawberri": 97, "lemon": 97, "pineappl": 97, "banana": 97, "jackfruit": 97, "custard": 97, "appl": 97, "pomegran": 97, "hai": 97, "carbonara": 97, "chocol": 97, "syrup": 97, "dough": 97, "meatloaf": 97, "pizza": 97, "pie": 97, "burrito": 97, "eggnog": 97, "alp": 97, "bubbl": 97, "reef": 97, "geyser": 97, "lakeshor": 97, "promontori": 97, "shoal": 97, "seashor": 97, "vallei": 97, "volcano": 97, "bridegroom": 97, "scuba": 97, "diver": 97, "rapese": 97, "daisi": 97, "ladi": 97, "slipper": 97, "corn": 97, "rose": 97, "hip": 97, "chestnut": 97, "fungu": 97, "agar": 97, "gyromitra": 97, "stinkhorn": 97, "earth": 97, "star": 97, "wood": 97, "bolet": 97, "ear": 97, "cifar10_test_set": 97, "airplan": [97, 104], "automobil": [97, 104], "deer": [97, 104], "cifar100_test_set": 97, "aquarium_fish": 97, "boi": 97, "camel": 97, "caterpillar": 97, "cattl": [97, 108], "cloud": 97, "dinosaur": 97, "dolphin": 97, "flatfish": 97, "forest": 97, "girl": 97, "kangaroo": 97, "lawn_mow": 97, "man": 97, "maple_tre": 97, "motorcycl": [97, 107], "oak_tre": 97, "orchid": 97, "palm_tre": 97, "pear": 97, "pickup_truck": 97, "pine_tre": 97, "plain": 97, "poppi": 97, "possum": 97, "raccoon": 97, "road": [97, 107], "rocket": 97, "seal": 97, "shrew": 97, "skyscrap": 97, "streetcar": 97, "sunflow": 97, "sweet_pepp": 97, "trout": 97, "tulip": 97, "willow_tre": 97, "woman": [97, 103], "caltech256": 97, "ak47": 97, "bat": 97, "glove": 97, "birdbath": 97, "blimp": 97, "bonsai": 97, "boom": 97, "breadmak": 97, "buddha": 97, "bulldoz": 97, "cactu": 97, "cake": 97, "tire": 97, "cartman": 97, "cereal": 97, "chandeli": 97, "chess": 97, "board": 97, "chimp": 97, "chopstick": 97, "coffin": 97, "coin": 97, "comet": 97, "cormor": 97, "globe": 97, "diamond": 97, "dice": 97, "doorknob": 97, "drink": 97, "straw": 97, "dumb": 97, "eiffel": 97, "elk": 97, "ewer": 97, "eyeglass": 97, "fern": 97, "fighter": 97, "jet": [97, 106], "extinguish": 97, "hydrant": 97, "firework": 97, "flashlight": 97, "floppi": 97, "fri": 97, "frisbe": 97, "galaxi": 97, "giraff": 97, "goat": 97, "gate": 97, "grape": 97, "pick": [97, 98], "hamburg": 97, "hammock": 97, "harpsichord": 97, "hawksbil": 97, "helicopt": 97, "hibiscu": 97, "homer": 97, "simpson": 97, "horsesho": 97, "air": 97, "skeleton": 97, "ibi": 97, "cone": 97, "iri": 97, "jesu": 97, "christ": 97, "joi": 97, "kayak": 97, "ketch": 97, "ladder": 97, "lath": 97, "licens": 97, "lightbulb": 97, "lightn": 97, "mandolin": 97, "mar": 97, "mattress": 97, "megaphon": 97, "menorah": 97, "microscop": 97, "minaret": 97, "minotaur": 97, "motorbik": 97, "mussel": 97, "neckti": 97, "octopu": 97, "palm": 97, "pilot": 97, "paperclip": 97, "shredder": 97, "pci": 97, "peopl": [97, 103], "pez": 97, "picnic": 97, "pram": 97, "prai": 97, "pyramid": 97, "rainbow": 97, "roulett": 97, "saddl": 97, "saturn": 97, "segwai": 97, "propel": 97, "sextant": 97, "music": 97, "skateboard": 97, "smokestack": 97, "sneaker": 97, "boat": 97, "stain": 97, "steer": 97, "stirrup": 97, "superman": 97, "sushi": 97, "armi": [97, 108], "sword": 97, "tambourin": 97, "teepe": 97, "court": 97, "theodolit": 97, "tomato": 97, "tombston": 97, "tour": 97, "pisa": 97, "treadmil": 97, "fork": 97, "tweezer": 97, "unicorn": 97, "vcr": 97, "waterfal": 97, "watermelon": 97, "weld": 97, "windmil": 97, "xylophon": 97, "yarmulk": 97, "yo": 97, "toad": 97, "twenty_news_test_set": 97, "comp": 97, "graphic": [97, 107], "misc": [97, 108], "sy": 97, "ibm": 97, "pc": 97, "hardwar": 97, "mac": 97, "forsal": 97, "rec": 97, "crypt": 97, "electron": 97, "med": 97, "soc": 97, "religion": 97, "christian": [97, 108], "talk": [97, 108], "polit": 97, "gun": 97, "mideast": 97, "amazon": 97, "neutral": 97, "imdb_test_set": 97, "all_class": 97, "20news_test_set": 97, "_load_classes_predprobs_label": 97, "dataset_nam": 97, "labelerror": 97, "url_bas": 97, "5392f6c71473055060be3044becdde1cbc18284d": 97, "url_label": 97, "original_test_label": 97, "_original_label": 97, "url_prob": 97, "cross_validated_predicted_prob": 97, "_pyx": 97, "num_part": 97, "datatset": 97, "bytesio": 97, "allow_pickl": 97, "pred_probs_part": 97, "url": 97, "_of_": 97, "nload": 97, "imdb": 97, "ve": [97, 98, 99, 101, 103], "capit": 97, "29780": 97, "256": [97, 98, 103], "780": 97, "medic": [97, 108], "doctor": 97, "254": [97, 103], "359223": 97, "640777": 97, "184": [97, 99], "258427": 97, "341176": 97, "263158": 97, "658824": 97, "337349": 97, "246575": 97, "662651": 97, "248": 97, "330000": 97, "355769": 97, "251": [97, 103], "167": [97, 99, 103], "252": 97, "112": 97, "253": [97, 103], "022989": 97, "049505": 97, "190": [97, 99, 103], "002216": 97, "000974": 97, "000873": 97, "000739": 97, "32635": 97, "32636": 97, "32637": 97, "32638": 97, "32639": 97, "32640": 97, "051": 97, "002242": 97, "997758": 97, "002088": 97, "001045": 97, "997912": 97, "002053": 97, "997947": 97, "001980": 97, "000991": 97, "998020": 97, "001946": 97, "002915": 97, "998054": 97, "001938": 97, "002904": 97, "998062": 97, "001020": 97, "998980": 97, "001018": 97, "002035": 97, "998982": 97, "999009": 97, "0003": 97, "0002": 97, "071": 97, "067269": 97, "929": 97, "046": 97, "058243": 97, "954": 97, "035": 97, "032096": 97, "965": 97, "031": 97, "012232": 97, "969": 97, "022": 97, "025896": 97, "978": 97, "020": [97, 99], "013092": 97, "018": 97, "013065": 97, "016": 97, "030542": 97, "984": 97, "013": 97, "020833": 97, "987": 97, "012": 97, "010020": 97, "988": 97, "0073": 97, "0020": 97, "0016": 97, "0015": 97, "0014": 97, "0013": 97, "0012": 97, "0010": 97, "0008": 97, "0007": 97, "0006": 97, "0005": 97, "0004": 97, "244": [97, 103], "452381": 97, "459770": 97, "523364": 97, "460784": 97, "446602": 97, "103774": 97, "030612": 97, "110092": 97, "049020": 97, "0034": 97, "0032": 97, "0026": 97, "0025": 97, "4945": 97, "4946": 97, "4947": 97, "4948": 97, "4949": 97, "4950": 97, "846": 97, "7532": 97, "532": 97, "034483": 97, "009646": 97, "965517": 97, "030457": 97, "020513": 97, "969543": 97, "028061": 97, "035443": 97, "971939": 97, "025316": 97, "005168": 97, "974684": 97, "049751": 97, "979487": 97, "019920": 97, "042802": 97, "980080": 97, "017677": 97, "005115": 97, "982323": 97, "012987": 97, "005236": 97, "987013": 97, "012723": 97, "025126": 97, "987277": 97, "010989": 97, "008264": 97, "989011": 97, "010283": 97, "027778": 97, "989717": 97, "009677": 97, "990323": 97, "007614": 97, "010127": 97, "992386": 97, "005051": 97, "994949": 97, "005025": 97, "994975": 97, "005013": 97, "994987": 97, "001859": 97, "001328": 97, "000929": 97, "000664": 97, "186": [97, 99], "188": [97, 99, 102], "189": [97, 99], "snippet": 98, "nlp": [98, 108], "mind": [98, 99], "alphanumer": 98, "facilit": 98, "seamless": 98, "classlabel": 98, "guidanc": 98, "labels_str": 98, "datalab_str": 98, "labels_int": 98, "remap": 98, "datalab_int": 98, "my_dict": 98, "pet_nam": 98, "rover": 98, "rocki": 98, "speci": 98, "from_dict": 98, "datalab_dataset": 98, "number_of_class": 98, "total_number_of_data_point": 98, "feed": 98, "alphabet": 98, "labels_proper_format": 98, "your_classifi": 98, "issues_datafram": 98, "class_predicted_for_flagged_exampl": 98, "class_predicted_for_all_exampl": 98, "grant": 98, "On": [98, 99, 103], "merged_dataset": 98, "label_column_nam": 98, "datataset": 98, "fair": [98, 99], "game": 98, "speedup": [98, 104], "tempfil": 98, "mkdtemp": 98, "sped": 98, "anywai": 98, "pred_probs_merg": 98, "merge_rare_class": 98, "count_threshold": 98, "class_mapping_orig2new": 98, "heath_summari": 98, "num_examples_per_class": 98, "rare_class": 98, "num_classes_merg": 98, "other_class": 98, "labels_merg": 98, "new_c": 98, "merged_prob": 98, "new_class": 98, "original_class": 98, "num_check": 98, "ones_array_ref": 98, "isclos": 98, "though": [98, 99, 108], "successfulli": 98, "virtuou": [98, 101], "cycl": [98, 101], "jointli": 98, "junk": 98, "clutter": 98, "unknown": 98, "caltech": 98, "combined_boolean_mask": 98, "mask1": 98, "mask2": 98, "gradientboostingclassifi": [98, 99], "true_error": [98, 99, 102], "101": [98, 103], "102": [98, 102, 103], "104": [98, 99, 103], "model_to_find_error": 98, "model_to_return": 98, "cl0": 98, "randomizedsearchcv": 98, "expens": 98, "param_distribut": 98, "learning_r": [98, 99], "max_depth": [98, 99], "magnitud": 98, "coeffici": [98, 106], "optin": 98, "environ": [98, 99], "rerun": [98, 99], "cell": [98, 99], "unabl": [98, 99], "render": [98, 99], "nbviewer": [98, 99], "cleanlearninginot": [98, 99], "fittedcleanlearn": [98, 99], "linearregressionlinearregress": 98, "unexpectedli": 98, "emphas": 98, "crucial": 98, "merge_duplicate_set": 98, "merge_kei": 98, "construct_group_kei": 98, "merged_set": 98, "consolidate_set": 98, "issubset": 98, "frozenset": 98, "sets_list": 98, "mutabl": 98, "new_set": 98, "current_set": 98, "intersecting_set": 98, "lowest_score_strategi": 98, "sub_df": 98, "filter_near_dupl": 98, "strategy_fn": 98, "strategy_kwarg": 98, "duplicate_row": 98, "group_kei": 98, "to_keep_indic": 98, "groupbi": 98, "explod": 98, "to_remov": 98, "isin": [98, 104], "kept": 98, "ids_to_remove_seri": 98, "tmp": 98, "ipykernel_7878": 98, "1995098996": 98, "deprecationwarn": 98, "dataframegroupbi": 98, "include_group": 98, "silenc": 98, "assist": 98, "streamlin": 98, "ux": 98, "agpl": 98, "compani": 98, "commerci": 98, "alter": 98, "email": 98, "team": 98, "discuss": 98, "anywher": 98, "profession": 98, "expert": 98, "depth": 99, "survei": [99, 108], "scienc": 99, "multivariate_norm": [99, 101, 102], "make_data": [99, 101], "cov": [99, 101, 102], "avg_trac": [99, 102], "py_tru": 99, "noise_matrix_tru": 99, "noise_marix": 99, "s_test": 99, "noisy_test_label": 99, "purpl": 99, "namespac": 99, "exec": 99, "markerfacecolor": [99, 102], "markeredgecolor": [99, 102, 106], "markers": [99, 102, 106], "markeredgewidth": [99, 102, 106], "realist": 99, "7560": 99, "637318e": 99, "896262e": 99, "548391e": 99, "923417e": 99, "375075e": 99, "3454": 99, "014051": 99, "020451": 99, "249": [99, 103], "042594": 99, "043859": 99, "045954": 99, "6120": 99, "023714": 99, "007136": 99, "119": [99, 103], "107266": 99, "103": [99, 103], "033738": 99, "238": [99, 103], "119505": 99, "236": [99, 103], "037843": 99, "222": 99, "614915": 99, "122": [99, 103], "624422": 99, "625965": 99, "626079": 99, "118": 99, "627675": 99, "695223": 99, "323529": 99, "523015": 99, "013720": 99, "675727": 99, "646521": 99, "anyth": 99, "enhanc": [99, 101, 103], "magic": 99, "liter": 99, "identif": 99, "x27": 99, "logisticregressionlogisticregress": 99, "ever": 99, "092": 99, "040": 99, "024": 99, "004": 99, "surpris": 99, "1705": 99, "01936": 99, "ton": 99, "yourfavoritemodel1": 99, "merged_label": 99, "merged_test_label": 99, "newli": [99, 101], "yourfavoritemodel2": 99, "yourfavoritemodel3": 99, "cl3": 99, "takeawai": 99, "my_test_pred_prob": 99, "my_test_pr": 99, "issues_test": 99, "corrected_test_label": 99, "pretend": 99, "cl_test_pr": 99, "fairli": 99, "label_acc": 99, "percentag": 99, "offset": 99, "nquestion": 99, "overestim": 99, "answer": 99, "experienc": 99, "prioiri": 99, "known": 99, "versatil": 99, "label_issues_indic": 99, "213": [99, 103], "218": [99, 103], "152": 99, "197": [99, 103], "196": [99, 103, 108], "170": 99, "214": 99, "164": [99, 102], "198": [99, 103], "191": [99, 103], "117": [99, 106], "206": [99, 103], "115": [99, 103], "193": 99, "194": 99, "201": [99, 103], "174": 99, "163": 99, "150": [99, 101, 103], "169": 99, "151": [99, 103], "168": 99, "precision_scor": 99, "recall_scor": 99, "f1_score": 99, "true_label_issu": 99, "filter_by_list": 99, "718750": [99, 101], "807018": 99, "912": 99, "733333": 99, "800000": 99, "721311": 99, "792793": 99, "908": 99, "676923": 99, "765217": 99, "892": 99, "567901": 99, "702290": 99, "844": 99, "gaug": 99, "label_issues_count": 99, "155": [99, 103], "156": 99, "172": [99, 102], "157": 99, "easiest": 99, "modular": 99, "penalti": 99, "l2": 99, "model3": 99, "n_estim": 99, "cv_pred_probs_1": 99, "cv_pred_probs_2": 99, "cv_pred_probs_3": 99, "label_quality_scores_best": 99, "cv_pred_probs_ensembl": 99, "label_quality_scores_bett": 99, "superior": [99, 105], "timm": 100, "glad": 101, "multiannotator_label": 101, "300": [101, 108], "noisier": 101, "111": [101, 106], "local_data": [101, 102], "true_labels_train": [101, 102], "noise_matrix_bett": 101, "noise_matrix_wors": 101, "transpos": [101, 104], "zfill": 101, "row_na_check": 101, "notna": 101, "reset_index": 101, "a0001": 101, "a0002": 101, "a0003": 101, "a0004": 101, "a0005": 101, "a0006": 101, "a0007": 101, "a0008": 101, "a0009": 101, "a0010": 101, "a0041": 101, "a0042": 101, "a0043": 101, "a0044": 101, "a0045": 101, "a0046": 101, "a0047": 101, "a0048": 101, "a0049": 101, "a0050": 101, "na": 101, "60856743": 101, "41693214": 101, "40908785": 101, "87147629": 101, "64941785": 101, "10774851": 101, "0524466": 101, "71853246": 101, "37169848": 101, "66031048": 101, "multiannotator_util": 101, "crude": 101, "straight": 101, "majority_vote_label": 101, "736118": 101, "757751": 101, "782232": 101, "715565": 101, "824256": 101, "quality_annotator_a0001": 101, "quality_annotator_a0002": 101, "quality_annotator_a0003": 101, "quality_annotator_a0004": 101, "quality_annotator_a0005": 101, "quality_annotator_a0006": 101, "quality_annotator_a0007": 101, "quality_annotator_a0008": 101, "quality_annotator_a0009": 101, "quality_annotator_a0010": 101, "quality_annotator_a0041": 101, "quality_annotator_a0042": 101, "quality_annotator_a0043": 101, "quality_annotator_a0044": 101, "quality_annotator_a0045": 101, "quality_annotator_a0046": 101, "quality_annotator_a0047": 101, "quality_annotator_a0048": 101, "quality_annotator_a0049": 101, "quality_annotator_a0050": 101, "070564": 101, "216078": 101, "119188": 101, "alongisd": 101, "244981": 101, "208333": 101, "295979": 101, "294118": 101, "324197": 101, "310345": 101, "355316": 101, "346154": 101, "439732": 101, "480000": 101, "a0031": 101, "523205": 101, "580645": 101, "a0034": 101, "535313": 101, "607143": 101, "a0021": 101, "606999": 101, "a0015": 101, "609526": 101, "678571": 101, "a0011": 101, "621103": 101, "692308": 101, "improved_consensus_label": 101, "majority_vote_accuraci": 101, "cleanlab_label_accuraci": 101, "8581081081081081": 101, "9797297297297297": 101, "besid": 101, "sorted_consensus_quality_scor": 101, "worst_qual": 101, "better_qu": 101, "worst_quality_accuraci": 101, "better_quality_accuraci": 101, "9893238434163701": 101, "improved_pred_prob": 101, "treat": [101, 102, 106, 108], "analzi": 101, "copyright": 102, "advertis": 102, "violenc": 102, "nsfw": 102, "celeba": 102, "make_multilabel_data": 102, "boxes_coordin": 102, "box_multilabel": 102, "make_multi": 102, "bx1": 102, "by1": 102, "bx2": 102, "by2": 102, "label_list": 102, "ur": 102, "upper": 102, "inidx": 102, "logical_and": 102, "inv_d": 102, "labels_idx": 102, "true_labels_test": 102, "dict_unique_label": 102, "get_color_arrai": 102, "dcolor": 102, "aa4400": 102, "55227f": 102, "55a100": 102, "00ff00": 102, "007f7f": 102, "386b55": 102, "0000ff": 102, "y_onehot": 102, "single_class_label": 102, "stratifi": [102, 105], "kf": 102, "train_index": 102, "test_index": 102, "clf_cv": 102, "x_train_cv": 102, "x_test_cv": 102, "y_train_cv": 102, "y_test_cv": 102, "y_pred_cv": 102, "saw": 102, "num_to_displai": 102, "09": [102, 103, 106], "275": 102, "267": 102, "225": 102, "171": 102, "234": 102, "165": 102, "227": [102, 103], "262": [102, 103], "263": [102, 103], "266": [102, 103], "139": [102, 108], "143": [102, 103], "216": [102, 103, 108], "265": 102, "159": [102, 103], "despit": [102, 108], "suspect": 102, "888": 102, "8224": 102, "9632": 102, "968": 102, "6512": 102, "0444": 102, "774": 102, "labels_binary_format": 102, "labels_list_format": 102, "surround": 103, "scene": 103, "coco": 103, "everydai": 103, "has_label_issu": 103, "nc": [103, 107, 108], "s3": [103, 107, 108], "amazonaw": [103, 107, 108], "objectdetectionbenchmark": 103, "tutorial_obj": 103, "pkl": 103, "example_imag": 103, "unzip": [103, 108], "_separate_label": 103, "_separate_predict": 103, "begin": 103, "image_path": 103, "rb": 103, "image_to_visu": 103, "seg_map": 103, "334": 103, "bboxes_ignor": 103, "290": 103, "286": 103, "285": 103, "224": 103, "231": [103, 108], "293": 103, "235": 103, "289": 103, "282": 103, "281": 103, "271": 103, "280": 103, "277": 103, "279": 103, "287": 103, "299": 103, "276": 103, "307": 103, "321": 103, "326": 103, "333": 103, "261": 103, "319": 103, "257": 103, "295": 103, "283": 103, "243": 103, "303": 103, "316": 103, "247": 103, "323": 103, "327": 103, "226": 103, "228": 103, "232": 103, "219": 103, "239": 103, "240": 103, "209": 103, "242": 103, "202": 103, "230": 103, "215": 103, "220": 103, "229": 103, "217": 103, "237": 103, "207": 103, "204": 103, "84": [103, 106], "205": 103, "223": 103, "153": 103, "149": 103, "140": 103, "124": 103, "268": 103, "273": 103, "284": 103, "110": 103, "136": 103, "145": 103, "173": 103, "297": 103, "317": 103, "192": 103, "332": 103, "324": 103, "203": 103, "320": 103, "314": 103, "199": 103, "291": 103, "000000481413": 103, "jpg": 103, "42398": 103, "44503": 103, "29968": 103, "336": 103, "21005": 103, "9978472": 103, "forgot": 103, "drew": 103, "label_issue_idx": 103, "num_examples_to_show": 103, "138": 103, "candid": 103, "97489622": 103, "70610878": 103, "98764951": 103, "88899237": 103, "99085805": 103, "issue_idx": 103, "95569726e": 103, "03354841e": 103, "57510169e": 103, "58447666e": 103, "39755858e": 103, "issue_to_visu": 103, "000000009483": 103, "95569726168054e": 103, "addition": [103, 107], "visibl": 103, "missmatch": 103, "likelei": 103, "agnost": 103, "vaidat": 103, "inconsist": 103, "000000395701": 103, "033548411774308e": 103, "armchair": 103, "tv": 103, "000000154004": 103, "38300759625496356": 103, "foreground": 103, "000000448410": 103, "0008575101690203273": 103, "crowd": 103, "alon": 103, "resembl": [103, 104], "000000499768": 103, "9748962231208227": 103, "000000521141": 103, "8889923658893665": 103, "000000143931": 103, "9876495074395956": 103, "bonu": 103, "uncov": 103, "irregular": 103, "anomali": 103, "object_detection_util": 103, "calculate_bounding_box_area": 103, "num_imgs_to_show": 103, "lab_object_count": 103, "pred_object_count": 103, "000000430073": 103, "000000183709": 103, "000000189475": 103, "label_norm": 103, "pred_norm": 103, "area": [103, 107], "lab_area": 103, "pred_area": 103, "lab_area_mean": 103, "lab_area_std": 103, "max_deviation_valu": 103, "max_deviation_class": 103, "deviation_valu": 103, "deviation_class": 103, "mean_area": 103, "std_area": 103, "class_area": 103, "deviations_awai": 103, "max_deviation_index": 103, "num_imgs_to_show_per_class": 103, "class_num": 103, "000000422886": 103, "000000341828": 103, "000000461009": 103, "train_feature_embed": 104, "ood_train_feature_scor": 104, "test_feature_embed": 104, "ood_test_feature_scor": 104, "ood_train_predictions_scor": 104, "train_pred_prob": 104, "ood_test_predictions_scor": 104, "test_pred_prob": 104, "pylab": 104, "rcparam": 104, "baggingclassifi": 104, "therebi": 104, "rescal": 104, "transform_norm": 104, "totensor": 104, "root": 104, "animal_class": 104, "non_animal_class": 104, "animal_idx": 104, "test_idx": 104, "toronto": 104, "edu": 104, "kriz": 104, "170498071": 104, "107997102": 104, "42it": 104, "plot_imag": 104, "visualize_outli": 104, "txt_class": 104, "img": [104, 106], "npimg": 104, "show_label": 104, "data_subset": 104, "resnet50": 104, "corpu": 104, "2048": 104, "embed_imag": 104, "create_model": 104, "strang": 104, "odd": 104, "train_ood_features_scor": 104, "top_train_ood_features_idx": 104, "fun": 104, "negat": 104, "homogen": 104, "bottom_train_ood_features_idx": 104, "test_ood_features_scor": 104, "top_ood_features_idx": 104, "inevit": 104, "trade": 104, "5th": 104, "percentil": 104, "fifth_percentil": 104, "plt_rang": 104, "hist": 104, "train_outlier_scor": 104, "test_outlier_scor": 104, "ood_features_indic": 104, "revisit": 104, "return_invers": 104, "train_feature_embeddings_sc": 104, "test_feature_embeddings_sc": 104, "train_pred_label": 104, "9702": 104, "train_ood_predictions_scor": 104, "test_ood_predictions_scor": 104, "lost": 104, "unsuit": 105, "ok": [105, 108], "convention": 105, "aforement": 105, "hypothet": 105, "contrast": 105, "tradit": 105, "disjoint": 105, "out_of_sample_pred_probs_for_a": 105, "out_of_sample_pred_probs_for_b": 105, "out_of_sample_pred_probs_for_c": 105, "out_of_sample_pred_prob": 105, "price": 106, "incom": 106, "sensor": 106, "histgradientboostingregressor": 106, "r2_score": 106, "student_grades_r": 106, "final_scor": 106, "true_final_scor": 106, "homework": 106, "3d": 106, "mpl_toolkit": 106, "mplot3d": 106, "axes3d": 106, "errors_idx": 106, "add_subplot": 106, "z": 106, "errors_mask": 106, "feature_column": 106, "predicted_column": 106, "x_train_raw": 106, "x_test_raw": 106, "randomforestregressor": 106, "385101": 106, "499503": 106, "698255": 106, "776647": 106, "109373": 106, "170547": 106, "481096": 106, "984759": 106, "645270": 106, "795928": 106, "141": 106, "659": 106, "367": 106, "318": 106, "305": 106, "560": 106, "657": 106, "688": 106, "view_datapoint": 106, "preds_og": 106, "r2_og": 106, "838": 106, "found_label_issu": 106, "preds_cl": 106, "r2_cl": 106, "926": 106, "favorit": 106, "968627e": 106, "228799": 106, "646674e": 106, "402962": 106, "323818e": 106, "952758": 106, "422144e": 106, "456908": 106, "465815e": 106, "753968": 106, "791186e": 106, "110719": 106, "485156e": 106, "670640": 106, "225300e": 106, "749976": 106, "499679e": 106, "947007": 106, "067882e": 106, "648396": 106, "synthia": 107, "imagesegment": 107, "given_mask": 107, "predicted_mask": 107, "set_printopt": [107, 108], "sky": 107, "sidewalk": 107, "veget": 107, "terrain": 107, "rider": 107, "pred_probs_filepath": 107, "1088": 107, "1920": 107, "label_filepath": 107, "synthia_class": 107, "maunal": 107, "100000": 107, "244800": 107, "leftmost": 107, "middl": [107, 108], "infact": 107, "rightmost": 107, "discrep": 107, "3263230": 107, "783381": 107, "275110": 107, "255917": 107, "78225": 107, "55990": 107, "54315": 107, "33591": 107, "24645": 107, "21054": 107, "15045": 107, "14171": 107, "13832": 107, "13498": 107, "11490": 107, "9164": 107, "8769": 107, "6999": 107, "6031": 107, "5011": 107, "mistakenli": 107, "class_issu": 107, "aim": [107, 108], "domin": 107, "bunch": 108, "conll": 108, "2003": 108, "love": 108, "n_i": 108, "optional_list_of_ordered_class_nam": 108, "deepai": 108, "conll2003": 108, "rm": 108, "tokenclassif": 108, "2400": 108, "52e0": 108, "1a00": 108, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "kb": 108, "mb": 108, "directori": 108, "inflat": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "6mb": 108, "bert": 108, "read_npz": 108, "filepath": 108, "corrsespond": 108, "iob2": 108, "given_ent": 108, "entity_map": 108, "readfil": 108, "startswith": 108, "docstart": 108, "isalpha": 108, "isupp": 108, "indices_to_preview": 108, "nsentenc": 108, "eu": 108, "reject": 108, "boycott": 108, "british": 108, "lamb": 108, "00030412": 108, "00023826": 108, "99936208": 108, "00007009": 108, "00002545": 108, "99998795": 108, "00000401": 108, "00000218": 108, "00000455": 108, "00000131": 108, "00000749": 108, "99996115": 108, "00001371": 108, "0000087": 108, "00000895": 108, "99998936": 108, "00000382": 108, "00000178": 108, "00000366": 108, "00000137": 108, "99999101": 108, "00000266": 108, "00000174": 108, "0000035": 108, "00000109": 108, "99998768": 108, "00000482": 108, "00000202": 108, "00000438": 108, "0000011": 108, "00000465": 108, "99996392": 108, "00001105": 108, "0000116": 108, "00000878": 108, "99998671": 108, "00000364": 108, "00000213": 108, "00000472": 108, "00000281": 108, "99999073": 108, "00000211": 108, "00000159": 108, "00000442": 108, "00000115": 108, "peter": 108, "blackburn": 108, "00000358": 108, "00000529": 108, "99995623": 108, "0000129": 108, "0000024": 108, "00001812": 108, "99994141": 108, "00001645": 108, "00002162": 108, "brussel": 108, "1996": 108, "00001172": 108, "00000821": 108, "00004661": 108, "0000618": 108, "99987167": 108, "99999061": 108, "00000201": 108, "00000195": 108, "00000408": 108, "00000135": 108, "2254": 108, "2907": 108, "19392": 108, "9962": 108, "8904": 108, "19303": 108, "12918": 108, "9256": 108, "11855": 108, "18392": 108, "20426": 108, "19402": 108, "14744": 108, "19371": 108, "4645": 108, "10331": 108, "9430": 108, "6143": 108, "18367": 108, "12914": 108, "todai": 108, "weather": 108, "march": 108, "scalfaro": 108, "northern": 108, "himself": 108, "said": 108, "germani": 108, "nastja": 108, "rysich": 108, "north": 108, "spla": 108, "fought": 108, "khartoum": 108, "govern": 108, "south": 108, "1983": 108, "autonomi": 108, "animist": 108, "region": 108, "moslem": 108, "arabis": 108, "mayor": 108, "antonio": 108, "gonzalez": 108, "garcia": 108, "revolutionari": 108, "wednesdai": 108, "troop": 108, "raid": 108, "farm": 108, "stole": 108, "rape": 108, "women": 108, "spring": 108, "chg": 108, "hrw": 108, "12pct": 108, "princ": 108, "photo": 108, "moment": 108, "spokeswoman": 108, "rainier": 108, "told": 108, "reuter": 108, "danila": 108, "carib": 108, "w224": 108, "equip": 108, "radiomet": 108, "earn": 108, "19996": 108, "london": 108, "denom": 108, "sale": 108, "uk": 108, "jp": 108, "fr": 108, "maccabi": 108, "hapoel": 108, "haifa": 108, "tel": 108, "aviv": 108, "hospit": 108, "rever": 108, "roman": 108, "cathol": 108, "nun": 108, "admit": 108, "calcutta": 108, "week": 108, "ago": 108, "fever": 108, "vomit": 108, "allianc": 108, "embattl": 108, "kabul": 108, "salang": 108, "highwai": 108, "mondai": 108, "tuesdai": 108, "suprem": 108, "council": 108, "led": 108, "jumbish": 108, "milli": 108, "movement": 108, "warlord": 108, "abdul": 108, "rashid": 108, "dostum": 108, "dollar": 108, "exchang": 108, "3570": 108, "12049": 108, "born": 108, "1937": 108, "provinc": 108, "anhui": 108, "dai": 108, "came": 108, "shanghai": 108, "citi": 108, "prolif": 108, "author": 108, "teacher": 108, "chines": 108, "16764": 108, "1990": 108, "historian": 108, "alan": 108, "john": 108, "percival": 108, "taylor": 108, "di": 108, "20446": 108, "pace": 108, "bowler": 108, "ian": 108, "harvei": 108, "claim": 108, "victoria": 108, "15514": 108, "cotti": 108, "osc": 108, "foreign": 108, "minist": 108, "7525": 108, "sultan": 108, "specter": 108, "crown": 108, "abdullah": 108, "defenc": 108, "aviat": 108, "jeddah": 108, "saudi": 108, "agenc": 108, "2288": 108, "hi": 108, "customari": 108, "outfit": 108, "champion": 108, "damp": 108, "scalp": 108, "canada": 108, "reign": 108, "olymp": 108, "donovan": 108, "bailei": 108, "1992": 108, "linford": 108, "christi": 108, "britain": 108, "1984": 108, "1988": 108, "carl": 108, "lewi": 108, "ambigi": 108, "punctuat": 108, "chicago": 108, "digest": 108, "philadelphia": 108, "usda": 108, "york": 108, "token_issu": 108, "471": 108, "kean": 108, "year": 108, "contract": 108, "manchest": 108, "19072": 108, "societi": 108, "bite": 108, "deliv": 108, "19910": 108, "father": 108, "clarenc": 108, "woolmer": 108, "renam": 108, "uttar": 108, "pradesh": 108, "india": 108, "ranji": 108, "trophi": 108, "nation": 108, "championship": 108, "captain": 108, "1949": 108, "15658": 108, "19879": 108, "iii": 108, "brian": 108, "shimer": 108, "randi": 108, "jone": 108, "19104": 108}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [37, 0, 0, "-", "dataset"], [40, 0, 0, "-", "experimental"], [44, 0, 0, "-", "filter"], [45, 0, 0, "-", "internal"], [60, 0, 0, "-", "models"], [62, 0, 0, "-", "multiannotator"], [65, 0, 0, "-", "multilabel_classification"], [68, 0, 0, "-", "object_detection"], [71, 0, 0, "-", "outlier"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "regression"], [77, 0, 0, "-", "segmentation"], [81, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [16, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[13, 0, 0, "-", "data"], [14, 0, 0, "-", "data_issues"], [17, 0, 0, "-", "issue_finder"], [15, 0, 0, "-", "issue_manager_factory"], [33, 0, 0, "-", "model_outputs"], [34, 0, 0, "-", "report"], [35, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[13, 2, 1, "", "Data"], [13, 5, 1, "", "DataFormatError"], [13, 5, 1, "", "DatasetDictError"], [13, 5, 1, "", "DatasetLoadError"], [13, 2, 1, "", "Label"], [13, 2, 1, "", "MultiClass"], [13, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[14, 2, 1, "", "DataIssues"], [14, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[14, 3, 1, "", "collect_issues_from_imagelab"], [14, 3, 1, "", "collect_issues_from_issue_manager"], [14, 3, 1, "", "collect_statistics"], [14, 3, 1, "", "get_info"], [14, 3, 1, "", "get_issue_summary"], [14, 3, 1, "", "get_issues"], [14, 6, 1, "", "info"], [14, 6, 1, "", "issue_summary"], [14, 6, 1, "", "issues"], [14, 3, 1, "", "set_health_score"], [14, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[17, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[17, 3, 1, "", "find_issues"], [17, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[19, 0, 0, "-", "data_valuation"], [20, 0, 0, "-", "duplicate"], [21, 0, 0, "-", "imbalance"], [23, 0, 0, "-", "issue_manager"], [24, 0, 0, "-", "label"], [27, 0, 0, "-", "noniid"], [28, 0, 0, "-", "null"], [29, 0, 0, "-", "outlier"], [32, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[19, 6, 1, "", "DEFAULT_THRESHOLD"], [19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 6, 1, "", "near_duplicate_sets"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[24, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 3, 1, "", "get_health_summary"], [24, 6, 1, "", "health_summary_parameters"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, 2, 1, "", "NonIIDIssueManager"], [27, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "find_issues"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[28, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[29, 6, 1, "", "DEFAULT_THRESHOLDS"], [29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 6, 1, "", "metric"], [29, 6, 1, "", "ood"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[31, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, 2, 1, "", "RegressionLabelIssueManager"], [31, 1, 1, "", "find_issues_with_features"], [31, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[32, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [32, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [32, 3, 1, "", "collect_info"], [32, 6, 1, "", "description"], [32, 3, 1, "", "filter_cluster_ids"], [32, 3, 1, "", "find_issues"], [32, 3, 1, "", "get_worst_cluster"], [32, 6, 1, "", "info"], [32, 6, 1, "", "issue_name"], [32, 6, 1, "", "issue_score_key"], [32, 6, 1, "", "issues"], [32, 3, 1, "", "make_summary"], [32, 3, 1, "", "perform_clustering"], [32, 3, 1, "", "report"], [32, 6, 1, "", "summary"], [32, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, 7, 1, "", "REGISTRY"], [15, 1, 1, "", "list_default_issue_types"], [15, 1, 1, "", "list_possible_issue_types"], [15, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[33, 2, 1, "", "ModelOutput"], [33, 2, 1, "", "MultiClassPredProbs"], [33, 2, 1, "", "MultiLabelPredProbs"], [33, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[34, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[34, 3, 1, "", "get_report"], [34, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[35, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[35, 6, 1, "", "CLASSIFICATION"], [35, 6, 1, "", "MULTILABEL"], [35, 6, 1, "", "REGRESSION"], [35, 3, 1, "", "__contains__"], [35, 3, 1, "", "__getitem__"], [35, 3, 1, "", "__iter__"], [35, 3, 1, "", "__len__"], [35, 3, 1, "", "from_str"], [35, 4, 1, "", "is_classification"], [35, 4, 1, "", "is_multilabel"], [35, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[37, 1, 1, "", "find_overlapping_classes"], [37, 1, 1, "", "health_summary"], [37, 1, 1, "", "overall_label_health_score"], [37, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[38, 0, 0, "-", "cifar_cnn"], [39, 0, 0, "-", "coteaching"], [41, 0, 0, "-", "label_issues_batched"], [42, 0, 0, "-", "mnist_pytorch"], [43, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[38, 2, 1, "", "CNN"], [38, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[38, 6, 1, "", "T_destination"], [38, 3, 1, "", "__call__"], [38, 3, 1, "", "add_module"], [38, 3, 1, "", "apply"], [38, 3, 1, "", "bfloat16"], [38, 3, 1, "", "buffers"], [38, 6, 1, "", "call_super_init"], [38, 3, 1, "", "children"], [38, 3, 1, "", "compile"], [38, 3, 1, "", "cpu"], [38, 3, 1, "", "cuda"], [38, 3, 1, "", "double"], [38, 6, 1, "", "dump_patches"], [38, 3, 1, "", "eval"], [38, 3, 1, "", "extra_repr"], [38, 3, 1, "", "float"], [38, 3, 1, "id0", "forward"], [38, 3, 1, "", "get_buffer"], [38, 3, 1, "", "get_extra_state"], [38, 3, 1, "", "get_parameter"], [38, 3, 1, "", "get_submodule"], [38, 3, 1, "", "half"], [38, 3, 1, "", "ipu"], [38, 3, 1, "", "load_state_dict"], [38, 3, 1, "", "modules"], [38, 3, 1, "", "named_buffers"], [38, 3, 1, "", "named_children"], [38, 3, 1, "", "named_modules"], [38, 3, 1, "", "named_parameters"], [38, 3, 1, "", "parameters"], [38, 3, 1, "", "register_backward_hook"], [38, 3, 1, "", "register_buffer"], [38, 3, 1, "", "register_forward_hook"], [38, 3, 1, "", "register_forward_pre_hook"], [38, 3, 1, "", "register_full_backward_hook"], [38, 3, 1, "", "register_full_backward_pre_hook"], [38, 3, 1, "", "register_load_state_dict_post_hook"], [38, 3, 1, "", "register_module"], [38, 3, 1, "", "register_parameter"], [38, 3, 1, "", "register_state_dict_pre_hook"], [38, 3, 1, "", "requires_grad_"], [38, 3, 1, "", "set_extra_state"], [38, 3, 1, "", "share_memory"], [38, 3, 1, "", "state_dict"], [38, 3, 1, "", "to"], [38, 3, 1, "", "to_empty"], [38, 3, 1, "", "train"], [38, 6, 1, "", "training"], [38, 3, 1, "", "type"], [38, 3, 1, "", "xpu"], [38, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[39, 1, 1, "", "adjust_learning_rate"], [39, 1, 1, "", "evaluate"], [39, 1, 1, "", "forget_rate_scheduler"], [39, 1, 1, "", "initialize_lr_scheduler"], [39, 1, 1, "", "loss_coteaching"], [39, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[41, 2, 1, "", "LabelInspector"], [41, 7, 1, "", "adj_confident_thresholds_shared"], [41, 1, 1, "", "find_label_issues_batched"], [41, 7, 1, "", "labels_shared"], [41, 7, 1, "", "pred_probs_shared"], [41, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[41, 3, 1, "", "get_confident_thresholds"], [41, 3, 1, "", "get_label_issues"], [41, 3, 1, "", "get_num_issues"], [41, 3, 1, "", "get_quality_scores"], [41, 3, 1, "", "score_label_quality"], [41, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[42, 2, 1, "", "CNN"], [42, 2, 1, "", "SimpleNet"], [42, 1, 1, "", "get_mnist_dataset"], [42, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[42, 3, 1, "", "__init_subclass__"], [42, 6, 1, "", "batch_size"], [42, 6, 1, "", "dataset"], [42, 6, 1, "", "epochs"], [42, 3, 1, "id0", "fit"], [42, 3, 1, "", "get_metadata_routing"], [42, 3, 1, "", "get_params"], [42, 6, 1, "", "loader"], [42, 6, 1, "", "log_interval"], [42, 6, 1, "", "lr"], [42, 6, 1, "", "momentum"], [42, 6, 1, "", "no_cuda"], [42, 3, 1, "id1", "predict"], [42, 3, 1, "id4", "predict_proba"], [42, 6, 1, "", "seed"], [42, 3, 1, "", "set_fit_request"], [42, 3, 1, "", "set_params"], [42, 3, 1, "", "set_predict_proba_request"], [42, 3, 1, "", "set_predict_request"], [42, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[42, 6, 1, "", "T_destination"], [42, 3, 1, "", "__call__"], [42, 3, 1, "", "add_module"], [42, 3, 1, "", "apply"], [42, 3, 1, "", "bfloat16"], [42, 3, 1, "", "buffers"], [42, 6, 1, "", "call_super_init"], [42, 3, 1, "", "children"], [42, 3, 1, "", "compile"], [42, 3, 1, "", "cpu"], [42, 3, 1, "", "cuda"], [42, 3, 1, "", "double"], [42, 6, 1, "", "dump_patches"], [42, 3, 1, "", "eval"], [42, 3, 1, "", "extra_repr"], [42, 3, 1, "", "float"], [42, 3, 1, "", "forward"], [42, 3, 1, "", "get_buffer"], [42, 3, 1, "", "get_extra_state"], [42, 3, 1, "", "get_parameter"], [42, 3, 1, "", "get_submodule"], [42, 3, 1, "", "half"], [42, 3, 1, "", "ipu"], [42, 3, 1, "", "load_state_dict"], [42, 3, 1, "", "modules"], [42, 3, 1, "", "named_buffers"], [42, 3, 1, "", "named_children"], [42, 3, 1, "", "named_modules"], [42, 3, 1, "", "named_parameters"], [42, 3, 1, "", "parameters"], [42, 3, 1, "", "register_backward_hook"], [42, 3, 1, "", "register_buffer"], [42, 3, 1, "", "register_forward_hook"], [42, 3, 1, "", "register_forward_pre_hook"], [42, 3, 1, "", "register_full_backward_hook"], [42, 3, 1, "", "register_full_backward_pre_hook"], [42, 3, 1, "", "register_load_state_dict_post_hook"], [42, 3, 1, "", "register_module"], [42, 3, 1, "", "register_parameter"], [42, 3, 1, "", "register_state_dict_pre_hook"], [42, 3, 1, "", "requires_grad_"], [42, 3, 1, "", "set_extra_state"], [42, 3, 1, "", "share_memory"], [42, 3, 1, "", "state_dict"], [42, 3, 1, "", "to"], [42, 3, 1, "", "to_empty"], [42, 3, 1, "", "train"], [42, 6, 1, "", "training"], [42, 3, 1, "", "type"], [42, 3, 1, "", "xpu"], [42, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[43, 1, 1, "", "display_issues"], [43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[44, 1, 1, "", "find_label_issues"], [44, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [44, 1, 1, "", "find_predicted_neq_given"], [44, 7, 1, "", "pred_probs_by_class"], [44, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[46, 0, 0, "-", "label_quality_utils"], [47, 0, 0, "-", "latent_algebra"], [48, 0, 0, "-", "multiannotator_utils"], [49, 0, 0, "-", "multilabel_scorer"], [50, 0, 0, "-", "multilabel_utils"], [51, 0, 0, "-", "neighbor"], [55, 0, 0, "-", "outlier"], [56, 0, 0, "-", "token_classification_utils"], [57, 0, 0, "-", "util"], [58, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[46, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, 1, 1, "", "compute_inv_noise_matrix"], [47, 1, 1, "", "compute_noise_matrix_from_inverse"], [47, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [47, 1, 1, "", "compute_py"], [47, 1, 1, "", "compute_py_inv_noise_matrix"], [47, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[48, 1, 1, "", "assert_valid_inputs_multiannotator"], [48, 1, 1, "", "assert_valid_pred_probs"], [48, 1, 1, "", "check_consensus_label_classes"], [48, 1, 1, "", "compute_soft_cross_entropy"], [48, 1, 1, "", "find_best_temp_scaler"], [48, 1, 1, "", "format_multiannotator_labels"], [48, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[49, 2, 1, "", "Aggregator"], [49, 2, 1, "", "ClassLabelScorer"], [49, 2, 1, "", "MultilabelScorer"], [49, 1, 1, "", "exponential_moving_average"], [49, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "multilabel_py"], [49, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[49, 3, 1, "", "__call__"], [49, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[49, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [49, 6, 1, "", "NORMALIZED_MARGIN"], [49, 6, 1, "", "SELF_CONFIDENCE"], [49, 3, 1, "", "__call__"], [49, 3, 1, "", "__contains__"], [49, 3, 1, "", "__getitem__"], [49, 3, 1, "", "__iter__"], [49, 3, 1, "", "__len__"], [49, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[49, 3, 1, "", "__call__"], [49, 3, 1, "", "aggregate"], [49, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[50, 1, 1, "", "get_onehot_num_classes"], [50, 1, 1, "", "int2onehot"], [50, 1, 1, "", "onehot2int"], [50, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[52, 0, 0, "-", "knn_graph"], [53, 0, 0, "-", "metric"], [54, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[52, 7, 1, "", "DEFAULT_K"], [52, 1, 1, "", "construct_knn_graph_from_index"], [52, 1, 1, "", "correct_knn_distances_and_indices"], [52, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [52, 1, 1, "", "correct_knn_graph"], [52, 1, 1, "", "create_knn_graph_and_index"], [52, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[53, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [53, 7, 1, "", "ROW_COUNT_CUTOFF"], [53, 1, 1, "", "decide_default_metric"], [53, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[55, 1, 1, "", "correct_precision_errors"], [55, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, 1, 1, "", "color_sentence"], [56, 1, 1, "", "filter_sentence"], [56, 1, 1, "", "get_sentence"], [56, 1, 1, "", "mapping"], [56, 1, 1, "", "merge_probs"], [56, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[57, 1, 1, "", "append_extra_datapoint"], [57, 1, 1, "", "clip_noise_rates"], [57, 1, 1, "", "clip_values"], [57, 1, 1, "", "compress_int_array"], [57, 1, 1, "", "confusion_matrix"], [57, 1, 1, "", "csr_vstack"], [57, 1, 1, "", "estimate_pu_f1"], [57, 1, 1, "", "extract_indices_tf"], [57, 1, 1, "", "force_two_dimensions"], [57, 1, 1, "", "format_labels"], [57, 1, 1, "", "get_missing_classes"], [57, 1, 1, "", "get_num_classes"], [57, 1, 1, "", "get_unique_classes"], [57, 1, 1, "", "is_tensorflow_dataset"], [57, 1, 1, "", "is_torch_dataset"], [57, 1, 1, "", "num_unique_classes"], [57, 1, 1, "", "print_inverse_noise_matrix"], [57, 1, 1, "", "print_joint_matrix"], [57, 1, 1, "", "print_noise_matrix"], [57, 1, 1, "", "print_square_matrix"], [57, 1, 1, "", "remove_noise_from_class"], [57, 1, 1, "", "round_preserving_row_totals"], [57, 1, 1, "", "round_preserving_sum"], [57, 1, 1, "", "smart_display_dataframe"], [57, 1, 1, "", "subset_X_y"], [57, 1, 1, "", "subset_data"], [57, 1, 1, "", "subset_labels"], [57, 1, 1, "", "train_val_split"], [57, 1, 1, "", "unshuffle_tensorflow_dataset"], [57, 1, 1, "", "value_counts"], [57, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[58, 1, 1, "", "assert_indexing_works"], [58, 1, 1, "", "assert_nonempty_input"], [58, 1, 1, "", "assert_valid_class_labels"], [58, 1, 1, "", "assert_valid_inputs"], [58, 1, 1, "", "labels_to_array"], [58, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[61, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[61, 2, 1, "", "KerasWrapperModel"], [61, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[62, 1, 1, "", "convert_long_to_wide_dataset"], [62, 1, 1, "", "get_active_learning_scores"], [62, 1, 1, "", "get_active_learning_scores_ensemble"], [62, 1, 1, "", "get_label_quality_multiannotator"], [62, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [62, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[63, 0, 0, "-", "dataset"], [64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[63, 1, 1, "", "common_multilabel_issues"], [63, 1, 1, "", "multilabel_health_summary"], [63, 1, 1, "", "overall_multilabel_health_score"], [63, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, 1, 1, "", "find_label_issues"], [64, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[69, 1, 1, "", "compute_badloc_box_scores"], [69, 1, 1, "", "compute_overlooked_box_scores"], [69, 1, 1, "", "compute_swap_box_scores"], [69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"], [69, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[70, 1, 1, "", "bounding_box_size_distribution"], [70, 1, 1, "", "calculate_per_class_metrics"], [70, 1, 1, "", "class_label_distribution"], [70, 1, 1, "", "get_average_per_class_confusion_matrix"], [70, 1, 1, "", "get_sorted_bbox_count_idxs"], [70, 1, 1, "", "object_counts_per_image"], [70, 1, 1, "", "plot_class_distribution"], [70, 1, 1, "", "plot_class_size_distributions"], [70, 1, 1, "", "visualize"]], "cleanlab.outlier": [[71, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[71, 3, 1, "", "fit"], [71, 3, 1, "", "fit_score"], [71, 3, 1, "", "score"]], "cleanlab.rank": [[72, 1, 1, "", "find_top_issues"], [72, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [72, 1, 1, "", "get_label_quality_ensemble_scores"], [72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "get_normalized_margin_for_each_label"], [72, 1, 1, "", "get_self_confidence_for_each_label"], [72, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[74, 0, 0, "-", "learn"], [75, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[74, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[74, 3, 1, "", "__init_subclass__"], [74, 3, 1, "", "find_label_issues"], [74, 3, 1, "", "fit"], [74, 3, 1, "", "get_aleatoric_uncertainty"], [74, 3, 1, "", "get_epistemic_uncertainty"], [74, 3, 1, "", "get_label_issues"], [74, 3, 1, "", "get_metadata_routing"], [74, 3, 1, "", "get_params"], [74, 3, 1, "", "predict"], [74, 3, 1, "", "save_space"], [74, 3, 1, "", "score"], [74, 3, 1, "", "set_fit_request"], [74, 3, 1, "", "set_params"], [74, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[75, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[76, 0, 0, "-", "filter"], [78, 0, 0, "-", "rank"], [79, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[76, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[78, 1, 1, "", "get_label_quality_scores"], [78, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[79, 1, 1, "", "common_label_issues"], [79, 1, 1, "", "display_issues"], [79, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[80, 0, 0, "-", "filter"], [82, 0, 0, "-", "rank"], [83, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[80, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[82, 1, 1, "", "get_label_quality_scores"], [82, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[83, 1, 1, "", "common_label_issues"], [83, 1, 1, "", "display_issues"], [83, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 87, 88, 92, 94, 95, 98, 99, 102, 108], "count": [3, 99], "data_valu": [4, 19], "datalab": [5, 7, 9, 10, 12, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102], "creat": [7, 90, 91, 99, 101], "your": [7, 84, 90, 91, 95, 96, 98, 99], "own": 7, "issu": [7, 9, 10, 22, 31, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "manag": [7, 22], "prerequisit": 7, "implement": 7, "issuemanag": [7, 90], "basic": 7, "check": [7, 96], "intermedi": 7, "advanc": [7, 90], "us": [7, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "gener": [8, 96], "cluster": [8, 96, 98], "id": 8, "guid": [9, 12], "type": [9, 10, 99], "custom": [9, 90], "cleanlab": [9, 10, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "studio": [9, 10], "easi": [9, 10, 84, 92, 94, 95], "mode": [9, 10, 84, 92, 94, 95], "can": [10, 91, 97, 98, 99, 101], "detect": [10, 89, 91, 92, 94, 95, 96, 98, 99, 103, 104], "estim": [10, 99, 101, 102], "each": 10, "input": 10, "label": [10, 24, 26, 31, 84, 87, 88, 89, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 29, 55, 71, 92, 94, 95, 102, 104], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 91, 92, 94, 95], "duplic": [10, 20, 91, 92, 94, 95, 98, 102], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 95, 96], "iid": [10, 95, 96], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 85, 96, 99, 107], "imbal": [10, 21, 96], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 92, 104], "specif": [10, 22, 107], "underperform": [10, 96, 98], "group": [10, 96, 98], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 28, 96], "is_null_issu": 10, "null_scor": 10, "data": [10, 13, 84, 87, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "valuat": [10, 96], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 96], "paramet": [10, 99], "get": [12, 90, 91, 101, 102, 103, 107, 108], "start": [12, 97], "api": 12, "refer": 12, "data_issu": 14, "factori": 15, "intern": [16, 45], "issue_find": 17, "issue_manag": [22, 23], "regist": 22, "ml": [22, 98, 99], "task": [22, 35], "multilabel": 25, "noniid": 27, "regress": [30, 73, 74, 75, 98, 106], "prioriti": 31, "order": 31, "find": [31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "underperforming_group": 32, "model_output": 33, "report": [34, 92], "dataset": [37, 63, 84, 88, 89, 91, 92, 95, 96, 97, 98, 99, 102, 103, 104, 106, 107, 108], "cifar_cnn": 38, "coteach": 39, "experiment": 40, "label_issues_batch": 41, "mnist_pytorch": 42, "span_classif": 43, "filter": [44, 64, 67, 76, 80, 99], "label_quality_util": 46, "latent_algebra": 47, "multiannotator_util": 48, "multilabel_scor": 49, "multilabel_util": 50, "neighbor": 51, "knn_graph": 52, "metric": 53, "search": [54, 90], "token_classification_util": 56, "util": 57, "valid": [58, 92, 105], "fasttext": 59, "model": [60, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106], "kera": 61, "multiannot": [62, 101], "multilabel_classif": 65, "rank": [66, 69, 72, 75, 78, 82, 99], "object_detect": 68, "summari": [70, 79, 83], "learn": [74, 91, 98, 99], "segment": [77, 107], "token_classif": [81, 108], "open": [84, 98], "sourc": [84, 98], "document": 84, "quickstart": 84, "1": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "instal": [84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "2": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "common": [84, 85, 108], "3": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "handl": [84, 98], "error": [84, 88, 92, 98, 99, 101, 102, 103, 106, 107, 108], "train": [84, 87, 88, 89, 96, 98, 104, 106], "robust": [84, 87, 88, 99, 106], "noisi": [84, 87, 88, 99, 106], "4": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 103, 104, 106], "curat": 84, "fix": [84, 98], "level": [84, 97, 99, 108], "5": [84, 87, 89, 91, 92, 94, 96, 99, 101, 106], "improv": [84, 101], "via": [84, 99, 101], "mani": [84, 99], "other": [84, 101, 103, 106], "techniqu": 84, "contribut": 84, "how": [85, 98, 99, 101, 102, 108], "migrat": 85, "version": 85, "0": 85, "from": [85, 87, 88, 90, 91, 99, 106], "pre": [85, 89, 96, 98, 104], "function": [85, 90], "name": 85, "chang": 85, "modul": [85, 99], "new": 85, "remov": 85, "argument": [85, 90], "variabl": 85, "cleanlearn": [86, 98, 99], "tutori": [86, 93, 97, 100], "structur": 87, "tabular": [87, 94], "requir": [87, 88, 90, 91, 92, 94, 95, 101, 102, 103, 104, 106, 107, 108], "depend": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "load": [87, 88, 89, 90, 91, 94, 95, 96, 106], "process": [87, 94, 104, 106], "select": [87, 94], "comput": [87, 89, 92, 94, 95, 96, 98, 101, 105], "out": [87, 89, 90, 91, 92, 94, 95, 101, 105], "sampl": [87, 89, 90, 91, 92, 94, 95, 101, 105], "predict": [87, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 105], "probabl": [87, 89, 90, 91, 92, 94, 95, 96, 101, 105], "more": [87, 88, 91, 99, 106], "text": [88, 95, 96, 108], "format": [88, 95, 98, 102, 103], "defin": [88, 92, 95, 96, 106], "potenti": [88, 101, 106], "an": [89, 92, 98], "audio": 89, "import": [89, 90, 91, 92, 97, 99, 101], "them": [89, 97, 99], "speechbrain": 89, "featur": [89, 92, 104], "fit": 89, "linear": 89, "workflow": [90, 96, 99], "audit": [90, 91], "classifi": [90, 91, 96], "instanti": 90, "object": [90, 103], "increment": 90, "specifi": [90, 98], "nondefault": 90, "save": 90, "ad": 90, "A": 91, "unifi": 91, "all": [91, 99], "kind": [91, 103], "skip": [91, 97, 99, 101], "detail": [91, 97, 99, 101], "about": 91, "addit": 91, "inform": [91, 92], "fetch": [92, 97], "normal": 92, "fashion": 92, "mnist": 92, "prepar": [92, 96], "k": [92, 94, 105], "fold": [92, 105], "cross": [92, 105], "embed": [92, 104], "7": [92, 99], "view": 92, "most": [92, 108], "like": 92, "exampl": [92, 98, 99, 104], "sever": 92, "set": [92, 99], "dark": 92, "top": [92, 107], "low": 92, "numer": 94, "categor": [94, 96], "column": 94, "construct": 94, "nearest": 94, "neighbour": 94, "graph": [94, 96], "drift": [95, 102], "miscellan": 96, "acceler": 96, "knn": 96, "obtain": 96, "identifi": [96, 98, 103], "explan": 96, "vector": 96, "perform": 96, "visual": [96, 99, 103, 104, 107], "score": [96, 99, 101, 102, 103, 107, 108], "synthet": 96, "result": 96, "predefin": 96, "slice": [96, 98], "i": [96, 98, 99, 105], "catch": 96, "valu": 96, "encod": 96, "initi": [96, 101], "sort": 96, "6": [96, 99], "understand": 97, "evalu": 97, "health": [97, 99], "8": [97, 99], "popular": 97, "faq": 98, "what": [98, 99, 105], "do": [98, 99], "infer": 98, "correct": 98, "ha": 98, "flag": 98, "should": 98, "v": 98, "test": [98, 99, 104], "big": 98, "limit": 98, "memori": 98, "why": 98, "isn": 98, "t": 98, "work": [98, 99, 101, 108], "me": 98, "differ": [98, 103], "clean": [98, 99], "final": 98, "hyperparamet": 98, "tune": 98, "onli": 98, "one": [98, 99, 102, 107], "doe": [98, 101, 108], "take": 98, "so": 98, "long": 98, "when": [98, 99], "run": 98, "licens": 98, "under": 98, "answer": 98, "question": 98, "The": 99, "centric": 99, "ai": 99, "machin": 99, "find_label_issu": 99, "line": 99, "code": 99, "twenti": 99, "lowest": 99, "qualiti": [99, 101, 102, 103, 107, 108], "see": 99, "now": 99, "let": 99, "": 99, "happen": 99, "we": 99, "merg": 99, "seafoam": 99, "green": 99, "yellow": 99, "too": 99, "you": 99, "re": 99, "One": 99, "rule": 99, "overal": [99, 107], "accur": 99, "thi": 99, "directli": 99, "fulli": 99, "character": 99, "nois": 99, "matrix": [99, 102], "joint": 99, "prior": 99, "true": 99, "distribut": 99, "flip": 99, "rate": 99, "ani": 99, "again": 99, "support": 99, "lot": 99, "method": 99, "filter_bi": 99, "automat": 99, "everi": 99, "uniqu": 99, "num_label_issu": 99, "threshold": 99, "found": 99, "Not": 99, "sure": 99, "ensembl": 99, "multipl": [99, 101], "predictor": 99, "consensu": 101, "annot": 101, "major": 101, "vote": 101, "better": 101, "statist": 101, "compar": 101, "inspect": 101, "retrain": 101, "further": 101, "multi": 102, "beyond": 102, "mislabel": [102, 107, 108], "given": 102, "hot": 102, "binari": 102, "without": 102, "applic": 102, "real": 102, "download": [103, 107, 108], "objectlab": 103, "exploratori": 103, "analysi": 103, "pytorch": 104, "timm": 104, "cifar10": 104, "some": 104, "pred_prob": [104, 107, 108], "wai": 106, "semant": 107, "which": 107, "ar": 107, "commonli": 107, "focus": 107, "token": 108, "word": 108, "sentenc": 108, "contain": 108, "particular": 108}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [19, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.data"], [14, "module-cleanlab.datalab.internal.data_issues"], [15, "module-cleanlab.datalab.internal.issue_manager_factory"], [16, "module-cleanlab.datalab.internal"], [17, "module-cleanlab.datalab.internal.issue_finder"], [19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [20, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [21, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [27, "module-cleanlab.datalab.internal.issue_manager.noniid"], [28, "module-cleanlab.datalab.internal.issue_manager.null"], [29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [33, "module-cleanlab.datalab.internal.model_outputs"], [34, "module-cleanlab.datalab.internal.report"], [35, "module-cleanlab.datalab.internal.task"], [37, "module-cleanlab.dataset"], [38, "module-cleanlab.experimental.cifar_cnn"], [39, "module-cleanlab.experimental.coteaching"], [40, "module-cleanlab.experimental"], [41, "module-cleanlab.experimental.label_issues_batched"], [42, "module-cleanlab.experimental.mnist_pytorch"], [43, "module-cleanlab.experimental.span_classification"], [44, "module-cleanlab.filter"], [45, "module-cleanlab.internal"], [46, "module-cleanlab.internal.label_quality_utils"], [47, "module-cleanlab.internal.latent_algebra"], [48, "module-cleanlab.internal.multiannotator_utils"], [49, "module-cleanlab.internal.multilabel_scorer"], [50, "module-cleanlab.internal.multilabel_utils"], [51, "module-cleanlab.internal.neighbor"], [52, "module-cleanlab.internal.neighbor.knn_graph"], [53, "module-cleanlab.internal.neighbor.metric"], [54, "module-cleanlab.internal.neighbor.search"], [55, "module-cleanlab.internal.outlier"], [56, "module-cleanlab.internal.token_classification_utils"], [57, "module-cleanlab.internal.util"], [58, "module-cleanlab.internal.validation"], [60, "module-cleanlab.models"], [61, "module-cleanlab.models.keras"], [62, "module-cleanlab.multiannotator"], [63, "module-cleanlab.multilabel_classification.dataset"], [64, "module-cleanlab.multilabel_classification.filter"], [65, "module-cleanlab.multilabel_classification"], [66, "module-cleanlab.multilabel_classification.rank"], [67, "module-cleanlab.object_detection.filter"], [68, "module-cleanlab.object_detection"], [69, "module-cleanlab.object_detection.rank"], [70, "module-cleanlab.object_detection.summary"], [71, "module-cleanlab.outlier"], [72, "module-cleanlab.rank"], [73, "module-cleanlab.regression"], [74, "module-cleanlab.regression.learn"], [75, "module-cleanlab.regression.rank"], [76, "module-cleanlab.segmentation.filter"], [77, "module-cleanlab.segmentation"], [78, "module-cleanlab.segmentation.rank"], [79, "module-cleanlab.segmentation.summary"], [80, "module-cleanlab.token_classification.filter"], [81, "module-cleanlab.token_classification"], [82, "module-cleanlab.token_classification.rank"], [83, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[13, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[13, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[13, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[13, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[13, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[16, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[17, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[28, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[34, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[34, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[35, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[35, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[37, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.forward"], [38, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[40, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [42, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [42, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [42, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[44, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[44, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[44, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[45, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[46, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}}) \ No newline at end of file diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb index cc8956d28..f3d888536 100644 --- a/master/tutorials/clean_learning/tabular.ipynb +++ b/master/tutorials/clean_learning/tabular.ipynb @@ -113,10 +113,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:27.766466Z", - "iopub.status.busy": "2024-06-25T19:31:27.766073Z", - "iopub.status.idle": "2024-06-25T19:31:28.950995Z", - "shell.execute_reply": "2024-06-25T19:31:28.950453Z" + "iopub.execute_input": "2024-06-25T23:13:19.683650Z", + "iopub.status.busy": "2024-06-25T23:13:19.683483Z", + "iopub.status.idle": "2024-06-25T23:13:20.876411Z", + "shell.execute_reply": "2024-06-25T23:13:20.875863Z" }, "nbsphinx": "hidden" }, @@ -126,7 +126,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -151,10 +151,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:28.953618Z", - "iopub.status.busy": "2024-06-25T19:31:28.953345Z", - "iopub.status.idle": "2024-06-25T19:31:28.970797Z", - "shell.execute_reply": "2024-06-25T19:31:28.970252Z" + "iopub.execute_input": "2024-06-25T23:13:20.879016Z", + "iopub.status.busy": "2024-06-25T23:13:20.878582Z", + "iopub.status.idle": "2024-06-25T23:13:20.895831Z", + "shell.execute_reply": "2024-06-25T23:13:20.895402Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:28.973223Z", - "iopub.status.busy": "2024-06-25T19:31:28.972835Z", - "iopub.status.idle": "2024-06-25T19:31:29.167625Z", - "shell.execute_reply": "2024-06-25T19:31:29.167053Z" + "iopub.execute_input": "2024-06-25T23:13:20.897855Z", + "iopub.status.busy": "2024-06-25T23:13:20.897628Z", + "iopub.status.idle": "2024-06-25T23:13:21.010572Z", + "shell.execute_reply": "2024-06-25T23:13:21.009996Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.197486Z", - "iopub.status.busy": "2024-06-25T19:31:29.197079Z", - "iopub.status.idle": "2024-06-25T19:31:29.200622Z", - "shell.execute_reply": "2024-06-25T19:31:29.200145Z" + "iopub.execute_input": "2024-06-25T23:13:21.037181Z", + "iopub.status.busy": "2024-06-25T23:13:21.036568Z", + "iopub.status.idle": "2024-06-25T23:13:21.040405Z", + "shell.execute_reply": "2024-06-25T23:13:21.039967Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.202620Z", - "iopub.status.busy": "2024-06-25T19:31:29.202441Z", - "iopub.status.idle": "2024-06-25T19:31:29.210646Z", - "shell.execute_reply": "2024-06-25T19:31:29.210233Z" + "iopub.execute_input": "2024-06-25T23:13:21.042333Z", + "iopub.status.busy": "2024-06-25T23:13:21.042161Z", + "iopub.status.idle": "2024-06-25T23:13:21.050408Z", + "shell.execute_reply": "2024-06-25T23:13:21.049993Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.212637Z", - "iopub.status.busy": "2024-06-25T19:31:29.212443Z", - "iopub.status.idle": "2024-06-25T19:31:29.214911Z", - "shell.execute_reply": "2024-06-25T19:31:29.214495Z" + "iopub.execute_input": "2024-06-25T23:13:21.052411Z", + "iopub.status.busy": "2024-06-25T23:13:21.052111Z", + "iopub.status.idle": "2024-06-25T23:13:21.054810Z", + "shell.execute_reply": "2024-06-25T23:13:21.054263Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.216761Z", - "iopub.status.busy": "2024-06-25T19:31:29.216593Z", - "iopub.status.idle": "2024-06-25T19:31:29.731597Z", - "shell.execute_reply": "2024-06-25T19:31:29.730952Z" + "iopub.execute_input": "2024-06-25T23:13:21.056799Z", + "iopub.status.busy": "2024-06-25T23:13:21.056479Z", + "iopub.status.idle": "2024-06-25T23:13:21.584928Z", + "shell.execute_reply": "2024-06-25T23:13:21.584385Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:29.733935Z", - "iopub.status.busy": "2024-06-25T19:31:29.733740Z", - "iopub.status.idle": "2024-06-25T19:31:31.552423Z", - "shell.execute_reply": "2024-06-25T19:31:31.551801Z" + "iopub.execute_input": "2024-06-25T23:13:21.587427Z", + "iopub.status.busy": "2024-06-25T23:13:21.587080Z", + "iopub.status.idle": "2024-06-25T23:13:23.402116Z", + "shell.execute_reply": "2024-06-25T23:13:23.401472Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.554814Z", - "iopub.status.busy": "2024-06-25T19:31:31.554296Z", - "iopub.status.idle": "2024-06-25T19:31:31.564323Z", - "shell.execute_reply": "2024-06-25T19:31:31.563854Z" + "iopub.execute_input": "2024-06-25T23:13:23.404837Z", + "iopub.status.busy": "2024-06-25T23:13:23.404191Z", + "iopub.status.idle": "2024-06-25T23:13:23.414068Z", + "shell.execute_reply": "2024-06-25T23:13:23.413559Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.566389Z", - "iopub.status.busy": "2024-06-25T19:31:31.566065Z", - "iopub.status.idle": "2024-06-25T19:31:31.570002Z", - "shell.execute_reply": "2024-06-25T19:31:31.569569Z" + "iopub.execute_input": "2024-06-25T23:13:23.416257Z", + "iopub.status.busy": "2024-06-25T23:13:23.415941Z", + "iopub.status.idle": "2024-06-25T23:13:23.420056Z", + "shell.execute_reply": "2024-06-25T23:13:23.419521Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.572029Z", - "iopub.status.busy": "2024-06-25T19:31:31.571709Z", - "iopub.status.idle": "2024-06-25T19:31:31.579030Z", - "shell.execute_reply": "2024-06-25T19:31:31.578475Z" + "iopub.execute_input": "2024-06-25T23:13:23.422287Z", + "iopub.status.busy": "2024-06-25T23:13:23.421904Z", + "iopub.status.idle": "2024-06-25T23:13:23.429186Z", + "shell.execute_reply": "2024-06-25T23:13:23.428630Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.581187Z", - "iopub.status.busy": "2024-06-25T19:31:31.580887Z", - "iopub.status.idle": "2024-06-25T19:31:31.691824Z", - "shell.execute_reply": "2024-06-25T19:31:31.691204Z" + "iopub.execute_input": "2024-06-25T23:13:23.431342Z", + "iopub.status.busy": "2024-06-25T23:13:23.431023Z", + "iopub.status.idle": "2024-06-25T23:13:23.542534Z", + "shell.execute_reply": "2024-06-25T23:13:23.542044Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.694170Z", - "iopub.status.busy": "2024-06-25T19:31:31.693686Z", - "iopub.status.idle": "2024-06-25T19:31:31.696628Z", - "shell.execute_reply": "2024-06-25T19:31:31.696102Z" + "iopub.execute_input": "2024-06-25T23:13:23.544624Z", + "iopub.status.busy": "2024-06-25T23:13:23.544286Z", + "iopub.status.idle": "2024-06-25T23:13:23.546943Z", + "shell.execute_reply": "2024-06-25T23:13:23.546515Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:31.698847Z", - "iopub.status.busy": "2024-06-25T19:31:31.698415Z", - "iopub.status.idle": "2024-06-25T19:31:33.679358Z", - "shell.execute_reply": "2024-06-25T19:31:33.678623Z" + "iopub.execute_input": "2024-06-25T23:13:23.548943Z", + "iopub.status.busy": "2024-06-25T23:13:23.548635Z", + "iopub.status.idle": "2024-06-25T23:13:25.510005Z", + "shell.execute_reply": "2024-06-25T23:13:25.509395Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:33.682516Z", - "iopub.status.busy": "2024-06-25T19:31:33.681890Z", - "iopub.status.idle": "2024-06-25T19:31:33.693245Z", - "shell.execute_reply": "2024-06-25T19:31:33.692694Z" + "iopub.execute_input": "2024-06-25T23:13:25.513097Z", + "iopub.status.busy": "2024-06-25T23:13:25.512371Z", + "iopub.status.idle": "2024-06-25T23:13:25.523496Z", + "shell.execute_reply": "2024-06-25T23:13:25.522944Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:33.695397Z", - "iopub.status.busy": "2024-06-25T19:31:33.695096Z", - "iopub.status.idle": "2024-06-25T19:31:33.841440Z", - "shell.execute_reply": "2024-06-25T19:31:33.840949Z" + "iopub.execute_input": "2024-06-25T23:13:25.525641Z", + "iopub.status.busy": "2024-06-25T23:13:25.525323Z", + "iopub.status.idle": "2024-06-25T23:13:25.545176Z", + "shell.execute_reply": "2024-06-25T23:13:25.544739Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index 22a2112a2..bab4e39b3 100644 --- a/master/tutorials/clean_learning/text.html +++ b/master/tutorials/clean_learning/text.html @@ -817,7 +817,7 @@

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

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

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

2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1215,7 +1215,7 @@

4. Train a more robust model from noisy labels -{"state": {"ff59fb88d4aa46d481bfabbfb12c3c08": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8f663dc8059d42e4a1c16507affd40e3": {"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": ""}}, "ca2793614f594cf4a384748bfd36b073": {"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_ff59fb88d4aa46d481bfabbfb12c3c08", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_8f663dc8059d42e4a1c16507affd40e3", "tabbable": null, "tooltip": null, "value": 391.0}}, "77399e1417a148bbaaf8c99c6e4b2bb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3c288e9049ba4814a9020bbda1512944": {"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}}, "baa0ee8552c34f9f808dc9985cb43d88": {"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_77399e1417a148bbaaf8c99c6e4b2bb5", "placeholder": "\u200b", "style": "IPY_MODEL_3c288e9049ba4814a9020bbda1512944", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "4c673f7ac4ec44149b7f219298381ca5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e3282f6972624465bd15f84c183446ed": {"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}}, "3e906d417956421a978812281c844e7e": {"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_4c673f7ac4ec44149b7f219298381ca5", "placeholder": "\u200b", "style": "IPY_MODEL_e3282f6972624465bd15f84c183446ed", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200764.5kB/s]"}}, "68d5eef2d9154625b81ca5a98ad95302": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e9ebd3cab6ee4b38af6e19b1c2a2b7a0": {"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_baa0ee8552c34f9f808dc9985cb43d88", "IPY_MODEL_ca2793614f594cf4a384748bfd36b073", "IPY_MODEL_3e906d417956421a978812281c844e7e"], "layout": "IPY_MODEL_68d5eef2d9154625b81ca5a98ad95302", "tabbable": null, "tooltip": null}}, "3b3726b7d15f4242bbe2a0e0323f5edd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "054e6f2fd5d14973972b7db9b5e4ed03": {"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": ""}}, "c768456d220342ec8cc60cc2528c3732": {"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_3b3726b7d15f4242bbe2a0e0323f5edd", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_054e6f2fd5d14973972b7db9b5e4ed03", "tabbable": null, "tooltip": null, "value": 2211.0}}, "e95f58118fca41f79754cf64dc768e55": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5d0e81a247724335969fb0549b56194b": {"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}}, "2db6e61311574d2799f341e0f2b280db": {"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_e95f58118fca41f79754cf64dc768e55", "placeholder": "\u200b", "style": "IPY_MODEL_5d0e81a247724335969fb0549b56194b", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "d98e8d3d73134c40b8256b723de4c9ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "55cc185655a64110894ebe751347ee97": {"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}}, "4c280f6c890c468db24cfa085162f8eb": {"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_d98e8d3d73134c40b8256b723de4c9ba", "placeholder": "\u200b", "style": "IPY_MODEL_55cc185655a64110894ebe751347ee97", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007422kB/s]"}}, "dd1d6b6bcb61451a9984bb2bddd1826e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a8fe72969fe348a99c98be80dccd6c53": {"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_2db6e61311574d2799f341e0f2b280db", "IPY_MODEL_c768456d220342ec8cc60cc2528c3732", "IPY_MODEL_4c280f6c890c468db24cfa085162f8eb"], "layout": "IPY_MODEL_dd1d6b6bcb61451a9984bb2bddd1826e", "tabbable": null, "tooltip": null}}, "a7fc4b58838f4350b7cce239e6cf7c4d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a7755505c8604626b66bd90ac49165ab": {"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": ""}}, "4105d4a519b74c618417f38830a92826": {"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_a7fc4b58838f4350b7cce239e6cf7c4d", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a7755505c8604626b66bd90ac49165ab", "tabbable": null, "tooltip": null, "value": 665.0}}, "c693822d0e9f45a5a0c3d5598c8464f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6c14e14271b4450f89db48de72dffebe": {"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}}, "5ccb993dd72c4674bf4c5129e4e19f7b": {"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_c693822d0e9f45a5a0c3d5598c8464f4", "placeholder": "\u200b", "style": "IPY_MODEL_6c14e14271b4450f89db48de72dffebe", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "8b998a37935c4d30856e95a2cd3f1439": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "200586c52a4549678a949ca57213c920": {"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}}, "007989e5e6cd44a5ada30dda1923d065": {"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_8b998a37935c4d30856e95a2cd3f1439", "placeholder": "\u200b", "style": "IPY_MODEL_200586c52a4549678a949ca57213c920", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u2007121kB/s]"}}, "811eb3464f8b4658af7b92cb22fd5db1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2e5e14c62e1a4cf09b6fb8b0bb5ca451": {"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_5ccb993dd72c4674bf4c5129e4e19f7b", "IPY_MODEL_4105d4a519b74c618417f38830a92826", "IPY_MODEL_007989e5e6cd44a5ada30dda1923d065"], "layout": "IPY_MODEL_811eb3464f8b4658af7b92cb22fd5db1", "tabbable": null, "tooltip": null}}, "68caf9f94f8b49e29f2522245cfc1967": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0bd860a7540947f98823d4a40bdafb86": {"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": ""}}, "acd50c68e1bb474ca7e642fdbecd2231": {"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_68caf9f94f8b49e29f2522245cfc1967", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_0bd860a7540947f98823d4a40bdafb86", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "6d917afdd1bf47ea943ba43a44498f21": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "47c94c5344ac480795afc89535d19275": {"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}}, "f9e7d310eb764ed9abf54e3695e98058": {"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_6d917afdd1bf47ea943ba43a44498f21", "placeholder": "\u200b", "style": "IPY_MODEL_47c94c5344ac480795afc89535d19275", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "371271d754f04722b9ba2102ec6453b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "72118120122f4623822582e300065947": {"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}}, "a4ea888fe4004256885e50c60385b700": {"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_371271d754f04722b9ba2102ec6453b3", "placeholder": "\u200b", "style": "IPY_MODEL_72118120122f4623822582e300065947", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:00<00:00,\u2007137MB/s]"}}, "91bc4e78a8934597a32b965150f632af": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "293b01a69e094447aeebb1e7e866fd51": {"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_f9e7d310eb764ed9abf54e3695e98058", "IPY_MODEL_acd50c68e1bb474ca7e642fdbecd2231", "IPY_MODEL_a4ea888fe4004256885e50c60385b700"], "layout": "IPY_MODEL_91bc4e78a8934597a32b965150f632af", "tabbable": null, "tooltip": null}}, "6da0de8fbedc40cca53799f8e45cf74b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b0f309cd65ee48e4902fba4c141424a9": {"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": ""}}, "3156c56a2d1e46779554f6e680768b77": {"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_6da0de8fbedc40cca53799f8e45cf74b", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b0f309cd65ee48e4902fba4c141424a9", "tabbable": null, "tooltip": null, "value": 466062.0}}, "a2f3a938880745e29369216068f395dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "79bb28d92fc14400815dc3c2c98b31e6": {"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}}, "65fb96fdd89c4a7aa6863a61cd8c8281": {"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_a2f3a938880745e29369216068f395dc", "placeholder": "\u200b", "style": "IPY_MODEL_79bb28d92fc14400815dc3c2c98b31e6", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "2ed7ba0d1cb94817b121646f00c5a089": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e45f1523c8d6445cacf72f4752609a80": {"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}}, "5e1fb144070e4665b06631fd83e8a769": {"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_2ed7ba0d1cb94817b121646f00c5a089", "placeholder": "\u200b", "style": "IPY_MODEL_e45f1523c8d6445cacf72f4752609a80", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u20073.42MB/s]"}}, "5c2455143c7b4c9d8179735a17395106": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9bbf8e629233461d84330aac6c38bc36": {"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_65fb96fdd89c4a7aa6863a61cd8c8281", "IPY_MODEL_3156c56a2d1e46779554f6e680768b77", "IPY_MODEL_5e1fb144070e4665b06631fd83e8a769"], "layout": "IPY_MODEL_5c2455143c7b4c9d8179735a17395106", "tabbable": null, "tooltip": null}}, "c467bba142d440348c677b9b88920801": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9e515a523986480cbbe1094ca437f513": {"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": ""}}, "703a1805a6e14459bff5e7bc196e2924": {"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_c467bba142d440348c677b9b88920801", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_9e515a523986480cbbe1094ca437f513", "tabbable": null, "tooltip": null, "value": 48.0}}, "69f6ccf772ad48fa86cea5e7b57f6c92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2606990b2b654e949895c6ea13bace1b": {"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}}, "684d44672b7e4f3e87afb7ff19a14a7d": {"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_69f6ccf772ad48fa86cea5e7b57f6c92", "placeholder": "\u200b", "style": "IPY_MODEL_2606990b2b654e949895c6ea13bace1b", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "7930ae36275240238ba62db1afd25ce2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "226f358553304154b872be466bcda2cc": {"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}}, "9678a77b61324383a27e704a34123184": {"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_7930ae36275240238ba62db1afd25ce2", "placeholder": "\u200b", "style": "IPY_MODEL_226f358553304154b872be466bcda2cc", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20079.10kB/s]"}}, "7f83efe5fa43477c83b7dd0eab30b115": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "38046751c5324a119490bbe8a5ec326c": {"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_684d44672b7e4f3e87afb7ff19a14a7d", "IPY_MODEL_703a1805a6e14459bff5e7bc196e2924", "IPY_MODEL_9678a77b61324383a27e704a34123184"], "layout": "IPY_MODEL_7f83efe5fa43477c83b7dd0eab30b115", "tabbable": null, "tooltip": null}}, "d8c1ad41f4fe4cf0a3f44d363086f32b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "43e54781cf104522a0a7479b4684f176": {"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": ""}}, "f674728a058e4ebc9cda63400ca9a97a": {"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_d8c1ad41f4fe4cf0a3f44d363086f32b", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_43e54781cf104522a0a7479b4684f176", "tabbable": null, "tooltip": null, "value": 231508.0}}, "1047a464f8ad4f08a9c39e32ef88f77b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d22816db94ed4b3aa623fb9f9eb42646": {"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}}, "6854a7c0f0b846a99e2cc07332f5e2ad": {"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_1047a464f8ad4f08a9c39e32ef88f77b", "placeholder": "\u200b", "style": "IPY_MODEL_d22816db94ed4b3aa623fb9f9eb42646", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "d76c41fd805e466081dfeb1d93477c92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "76977b7a679849439d4159579b51c480": {"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}}, "b780cdac15fd4ff5bf0b64d9369ef63b": {"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_d76c41fd805e466081dfeb1d93477c92", "placeholder": "\u200b", "style": "IPY_MODEL_76977b7a679849439d4159579b51c480", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u20073.55MB/s]"}}, "52f1b4fab3d74ad180e90f63881f6b32": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7f84e049288f438cbb050b771815ee1a": {"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_6854a7c0f0b846a99e2cc07332f5e2ad", "IPY_MODEL_f674728a058e4ebc9cda63400ca9a97a", "IPY_MODEL_b780cdac15fd4ff5bf0b64d9369ef63b"], "layout": "IPY_MODEL_52f1b4fab3d74ad180e90f63881f6b32", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"de1cc30d88f74d2299519f8846818183": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6e166f17ea384c918f9777404649330c": {"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": ""}}, "42243b174d064f20a73f6c60315575eb": {"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_de1cc30d88f74d2299519f8846818183", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6e166f17ea384c918f9777404649330c", "tabbable": null, "tooltip": null, "value": 391.0}}, "65b932c28ff3495098c0db8b56dd18de": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e098c9499e5142759ba844e4486a6da6": {"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}}, "075ca50b902b46989673d1147902560d": {"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_65b932c28ff3495098c0db8b56dd18de", "placeholder": "\u200b", "style": "IPY_MODEL_e098c9499e5142759ba844e4486a6da6", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "f933a441f363402d9fc2e796940cf4d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "328a64a36aa14cc4a13e8c232d198977": {"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}}, "bbe4f79369114ed9839dd57535856697": {"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_f933a441f363402d9fc2e796940cf4d3", "placeholder": "\u200b", "style": "IPY_MODEL_328a64a36aa14cc4a13e8c232d198977", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200768.8kB/s]"}}, "1b04ac435f764f288a38e492f152ec20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6477ae421e3e43aa814150445e014ac0": {"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_075ca50b902b46989673d1147902560d", "IPY_MODEL_42243b174d064f20a73f6c60315575eb", "IPY_MODEL_bbe4f79369114ed9839dd57535856697"], "layout": "IPY_MODEL_1b04ac435f764f288a38e492f152ec20", "tabbable": null, "tooltip": null}}, "4a7d8e7b7ba3464fa41165f58db05e12": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c7cdf638b52549a3923f4d9f4dacf40d": {"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": ""}}, "a859632225cf4c338d075b33f4abd2e0": {"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_4a7d8e7b7ba3464fa41165f58db05e12", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c7cdf638b52549a3923f4d9f4dacf40d", "tabbable": null, "tooltip": null, "value": 2211.0}}, "dfd5800b7e4c41fb90cf34888c17c960": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9bdcb01ac1254e4ca46f786f1f48635e": {"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}}, "8674027fdc534d3791107a43a5e491ab": {"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_dfd5800b7e4c41fb90cf34888c17c960", "placeholder": "\u200b", "style": "IPY_MODEL_9bdcb01ac1254e4ca46f786f1f48635e", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "c363fe3444424a62b964efb6ec305f7f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d622adb2d6724d848794f6e7c40de0b3": {"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}}, "1d787eed2c504168b62c71e3e8b823c8": {"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_c363fe3444424a62b964efb6ec305f7f", "placeholder": "\u200b", "style": "IPY_MODEL_d622adb2d6724d848794f6e7c40de0b3", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007422kB/s]"}}, "5da1ee68b4f848c98bea7614ebf85f59": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e62034a9e6c043cc997861592486168a": {"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_8674027fdc534d3791107a43a5e491ab", "IPY_MODEL_a859632225cf4c338d075b33f4abd2e0", "IPY_MODEL_1d787eed2c504168b62c71e3e8b823c8"], "layout": "IPY_MODEL_5da1ee68b4f848c98bea7614ebf85f59", "tabbable": null, "tooltip": null}}, "e98f8f44d5174617bc22e566a83f2e14": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9e9b474bbbd44555ac3344b5d173ecd4": {"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": ""}}, "8784e3b2891144468a5d989adaf62ae7": {"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_e98f8f44d5174617bc22e566a83f2e14", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_9e9b474bbbd44555ac3344b5d173ecd4", "tabbable": null, "tooltip": null, "value": 665.0}}, "8c2a54a50b174504b67275f6fc1758ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fd9acfb5facf4aeaa71d2088d344d085": {"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}}, "e7466595e3ae479d9ee9e989b0fa9efb": {"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_8c2a54a50b174504b67275f6fc1758ea", "placeholder": "\u200b", "style": "IPY_MODEL_fd9acfb5facf4aeaa71d2088d344d085", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "2b78fa97669e4067a4bd3de6ccaaa88a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7dbd67a2cb264612b0d638e5e63a9a07": {"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}}, "a6280db66f3e434bbdb20ebab4989dec": {"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_2b78fa97669e4067a4bd3de6ccaaa88a", "placeholder": "\u200b", "style": "IPY_MODEL_7dbd67a2cb264612b0d638e5e63a9a07", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u2007129kB/s]"}}, "32522284f75645f69b58819ffe34d2a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1e2c610098e54fea94839bb48d055f22": {"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_e7466595e3ae479d9ee9e989b0fa9efb", "IPY_MODEL_8784e3b2891144468a5d989adaf62ae7", "IPY_MODEL_a6280db66f3e434bbdb20ebab4989dec"], "layout": "IPY_MODEL_32522284f75645f69b58819ffe34d2a7", "tabbable": null, "tooltip": null}}, "ab64f8b595ef4648a47b1b2da8eac241": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "03d9becbdd8046bf81525afd016ed573": {"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": ""}}, "2e901b505de34984a7c06e4ff7be8ebe": {"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_ab64f8b595ef4648a47b1b2da8eac241", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_03d9becbdd8046bf81525afd016ed573", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "2fe0de05b5e847e89ba80b8e4d398c4c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6f576e9c9374443588b496e63f781624": {"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}}, "fdddfa973dd841b39245eda900cf0339": {"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_2fe0de05b5e847e89ba80b8e4d398c4c", "placeholder": "\u200b", "style": "IPY_MODEL_6f576e9c9374443588b496e63f781624", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "5dc7272ce64644afa609af3b2046a38e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c220349ac77649e3bffdb0998b40b102": {"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}}, "20471efe14204b3fa973ce7c173612f3": {"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_5dc7272ce64644afa609af3b2046a38e", "placeholder": "\u200b", "style": "IPY_MODEL_c220349ac77649e3bffdb0998b40b102", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:00<00:00,\u2007298MB/s]"}}, "b0d211d5585d414d93c249182a668daf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5b6e029c7f61484d880737df09cb3291": {"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_fdddfa973dd841b39245eda900cf0339", "IPY_MODEL_2e901b505de34984a7c06e4ff7be8ebe", "IPY_MODEL_20471efe14204b3fa973ce7c173612f3"], "layout": "IPY_MODEL_b0d211d5585d414d93c249182a668daf", "tabbable": null, "tooltip": null}}, "8f55a6cbd0184173b7c3ecca3b2be6a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "670785469ef5401e82177c280f877928": {"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": ""}}, "2808a2dd85f840dc8384e96b6e31d382": {"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_8f55a6cbd0184173b7c3ecca3b2be6a1", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_670785469ef5401e82177c280f877928", "tabbable": null, "tooltip": null, "value": 466062.0}}, "93beb5b9f7914314ac973bada268bcc2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "71736b2d04e142cea126cbddad9801e2": {"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}}, "35b5e0b171c7498d8e1a3633c745e073": {"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_93beb5b9f7914314ac973bada268bcc2", "placeholder": "\u200b", "style": "IPY_MODEL_71736b2d04e142cea126cbddad9801e2", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "1a996c0fb30c4181aef76b9d0b39bd20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8d98dfc145534a779ad6c609609abd31": {"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}}, "7261cd19786a4a0b95b770c51f033b0c": {"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_1a996c0fb30c4181aef76b9d0b39bd20", "placeholder": "\u200b", "style": "IPY_MODEL_8d98dfc145534a779ad6c609609abd31", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u200714.0MB/s]"}}, "485477b3f18244cb8e82240fdfc321c6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e750160df873483c9096b064baeab112": {"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_35b5e0b171c7498d8e1a3633c745e073", "IPY_MODEL_2808a2dd85f840dc8384e96b6e31d382", "IPY_MODEL_7261cd19786a4a0b95b770c51f033b0c"], "layout": "IPY_MODEL_485477b3f18244cb8e82240fdfc321c6", "tabbable": null, "tooltip": null}}, "4d312e2d3f524fb1bf8dba710216c761": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b11b3d0461c94c138ecf254a642eb544": {"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": ""}}, "937b241a390b4aa1b76cb3c07905b257": {"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_4d312e2d3f524fb1bf8dba710216c761", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b11b3d0461c94c138ecf254a642eb544", "tabbable": null, "tooltip": null, "value": 48.0}}, "537d23f9aefe4d35aa82e5454ab78815": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3c700601fd7d4148894afa4961283a88": {"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}}, "aa703043d8574c99856909333746a558": {"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_537d23f9aefe4d35aa82e5454ab78815", "placeholder": "\u200b", "style": "IPY_MODEL_3c700601fd7d4148894afa4961283a88", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "197c89430b124236a2a7ca6374f1edf0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "634f7b4ddf0e46f3b98d4b3c9ce87c86": {"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}}, "c9a979755e9a403eb0ca342120826ee6": {"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_197c89430b124236a2a7ca6374f1edf0", "placeholder": "\u200b", "style": "IPY_MODEL_634f7b4ddf0e46f3b98d4b3c9ce87c86", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20079.85kB/s]"}}, "5f117a6515e1436cad7028680580b6a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ddf577727f0e42a1b074bcb455a4258a": {"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_aa703043d8574c99856909333746a558", "IPY_MODEL_937b241a390b4aa1b76cb3c07905b257", "IPY_MODEL_c9a979755e9a403eb0ca342120826ee6"], "layout": "IPY_MODEL_5f117a6515e1436cad7028680580b6a5", "tabbable": null, "tooltip": null}}, "1aab5456eec04063a5df26734e017b79": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d7c835da9be44280b48466c92cb851d6": {"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": ""}}, "02a072ed32924507837162ad70da9612": {"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_1aab5456eec04063a5df26734e017b79", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_d7c835da9be44280b48466c92cb851d6", "tabbable": null, "tooltip": null, "value": 231508.0}}, "68a06976e74d4773829c8f1f5d4cc2ab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7bc89cc511714eb3b9db0329b0ad4f47": {"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}}, "0ee27b719c824572adc4d14d07e72cf6": {"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_68a06976e74d4773829c8f1f5d4cc2ab", "placeholder": "\u200b", "style": "IPY_MODEL_7bc89cc511714eb3b9db0329b0ad4f47", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "66d43ed9bf3f4ef6aedf76e3d08d36c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3c2efa0519364cd7ad5d4149de79bec8": {"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}}, "c73c253ce5ce4c0c9744f5c0cb9d0530": {"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_66d43ed9bf3f4ef6aedf76e3d08d36c7", "placeholder": "\u200b", "style": "IPY_MODEL_3c2efa0519364cd7ad5d4149de79bec8", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u200716.1MB/s]"}}, "bd8af8fcf5f34f848183718d176ce55f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "97155a33cf37454f8282b21b3806031d": {"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_0ee27b719c824572adc4d14d07e72cf6", "IPY_MODEL_02a072ed32924507837162ad70da9612", "IPY_MODEL_c73c253ce5ce4c0c9744f5c0cb9d0530"], "layout": "IPY_MODEL_bd8af8fcf5f34f848183718d176ce55f", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb index a83013185..9af680b6f 100644 --- a/master/tutorials/clean_learning/text.ipynb +++ b/master/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:37.218802Z", - "iopub.status.busy": "2024-06-25T19:31:37.218626Z", - "iopub.status.idle": "2024-06-25T19:31:40.132819Z", - "shell.execute_reply": "2024-06-25T19:31:40.132198Z" + "iopub.execute_input": "2024-06-25T23:13:28.905676Z", + "iopub.status.busy": "2024-06-25T23:13:28.905503Z", + "iopub.status.idle": "2024-06-25T23:13:31.555296Z", + "shell.execute_reply": "2024-06-25T23:13:31.554730Z" }, "nbsphinx": "hidden" }, @@ -135,7 +135,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -160,10 +160,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.135382Z", - "iopub.status.busy": "2024-06-25T19:31:40.135098Z", - "iopub.status.idle": "2024-06-25T19:31:40.138344Z", - "shell.execute_reply": "2024-06-25T19:31:40.137917Z" + "iopub.execute_input": "2024-06-25T23:13:31.557860Z", + "iopub.status.busy": "2024-06-25T23:13:31.557469Z", + "iopub.status.idle": "2024-06-25T23:13:31.560897Z", + "shell.execute_reply": "2024-06-25T23:13:31.560352Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.140291Z", - "iopub.status.busy": "2024-06-25T19:31:40.139985Z", - "iopub.status.idle": "2024-06-25T19:31:40.143618Z", - "shell.execute_reply": "2024-06-25T19:31:40.143162Z" + "iopub.execute_input": "2024-06-25T23:13:31.562942Z", + "iopub.status.busy": "2024-06-25T23:13:31.562629Z", + "iopub.status.idle": "2024-06-25T23:13:31.565542Z", + "shell.execute_reply": "2024-06-25T23:13:31.565096Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.145468Z", - "iopub.status.busy": "2024-06-25T19:31:40.145298Z", - "iopub.status.idle": "2024-06-25T19:31:40.303499Z", - "shell.execute_reply": "2024-06-25T19:31:40.302894Z" + "iopub.execute_input": "2024-06-25T23:13:31.567524Z", + "iopub.status.busy": "2024-06-25T23:13:31.567195Z", + "iopub.status.idle": "2024-06-25T23:13:31.589244Z", + "shell.execute_reply": "2024-06-25T23:13:31.588737Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.305557Z", - "iopub.status.busy": "2024-06-25T19:31:40.305379Z", - "iopub.status.idle": "2024-06-25T19:31:40.309091Z", - "shell.execute_reply": "2024-06-25T19:31:40.308646Z" + "iopub.execute_input": "2024-06-25T23:13:31.591105Z", + "iopub.status.busy": "2024-06-25T23:13:31.590840Z", + "iopub.status.idle": "2024-06-25T23:13:31.594215Z", + "shell.execute_reply": "2024-06-25T23:13:31.593789Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.311111Z", - "iopub.status.busy": "2024-06-25T19:31:40.310718Z", - "iopub.status.idle": "2024-06-25T19:31:40.314252Z", - "shell.execute_reply": "2024-06-25T19:31:40.313796Z" + "iopub.execute_input": "2024-06-25T23:13:31.596064Z", + "iopub.status.busy": "2024-06-25T23:13:31.595883Z", + "iopub.status.idle": "2024-06-25T23:13:31.599153Z", + "shell.execute_reply": "2024-06-25T23:13:31.598670Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'change_pin', 'apple_pay_or_google_pay', 'cancel_transfer', 'lost_or_stolen_phone', 'visa_or_mastercard'}\n" + "Classes: {'card_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_payment_fee_charged'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.316289Z", - "iopub.status.busy": "2024-06-25T19:31:40.315953Z", - "iopub.status.idle": "2024-06-25T19:31:40.318817Z", - "shell.execute_reply": "2024-06-25T19:31:40.318324Z" + "iopub.execute_input": "2024-06-25T23:13:31.601175Z", + "iopub.status.busy": "2024-06-25T23:13:31.600751Z", + "iopub.status.idle": "2024-06-25T23:13:31.603901Z", + "shell.execute_reply": "2024-06-25T23:13:31.603365Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.320894Z", - "iopub.status.busy": "2024-06-25T19:31:40.320580Z", - "iopub.status.idle": "2024-06-25T19:31:40.323708Z", - "shell.execute_reply": "2024-06-25T19:31:40.323263Z" + "iopub.execute_input": "2024-06-25T23:13:31.606046Z", + "iopub.status.busy": "2024-06-25T23:13:31.605618Z", + "iopub.status.idle": "2024-06-25T23:13:31.608973Z", + "shell.execute_reply": "2024-06-25T23:13:31.608424Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:40.325657Z", - "iopub.status.busy": "2024-06-25T19:31:40.325357Z", - "iopub.status.idle": "2024-06-25T19:31:46.067731Z", - "shell.execute_reply": "2024-06-25T19:31:46.067125Z" + "iopub.execute_input": "2024-06-25T23:13:31.610942Z", + "iopub.status.busy": "2024-06-25T23:13:31.610641Z", + "iopub.status.idle": "2024-06-25T23:13:35.909329Z", + "shell.execute_reply": "2024-06-25T23:13:35.908695Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e9ebd3cab6ee4b38af6e19b1c2a2b7a0", + "model_id": "6477ae421e3e43aa814150445e014ac0", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8fe72969fe348a99c98be80dccd6c53", + "model_id": "e62034a9e6c043cc997861592486168a", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e5e14c62e1a4cf09b6fb8b0bb5ca451", + "model_id": "1e2c610098e54fea94839bb48d055f22", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "293b01a69e094447aeebb1e7e866fd51", + "model_id": "5b6e029c7f61484d880737df09cb3291", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9bbf8e629233461d84330aac6c38bc36", + "model_id": "e750160df873483c9096b064baeab112", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38046751c5324a119490bbe8a5ec326c", + "model_id": "ddf577727f0e42a1b074bcb455a4258a", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f84e049288f438cbb050b771815ee1a", + "model_id": "97155a33cf37454f8282b21b3806031d", "version_major": 2, "version_minor": 0 }, @@ -609,10 +609,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:46.070234Z", - "iopub.status.busy": "2024-06-25T19:31:46.070036Z", - "iopub.status.idle": "2024-06-25T19:31:46.072782Z", - "shell.execute_reply": "2024-06-25T19:31:46.072301Z" + "iopub.execute_input": "2024-06-25T23:13:35.912144Z", + "iopub.status.busy": "2024-06-25T23:13:35.911799Z", + "iopub.status.idle": "2024-06-25T23:13:35.914630Z", + "shell.execute_reply": "2024-06-25T23:13:35.914095Z" } }, "outputs": [], @@ -634,10 +634,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:46.074901Z", - "iopub.status.busy": "2024-06-25T19:31:46.074488Z", - "iopub.status.idle": "2024-06-25T19:31:46.077148Z", - "shell.execute_reply": "2024-06-25T19:31:46.076714Z" + "iopub.execute_input": "2024-06-25T23:13:35.916621Z", + "iopub.status.busy": "2024-06-25T23:13:35.916300Z", + "iopub.status.idle": "2024-06-25T23:13:35.918968Z", + "shell.execute_reply": "2024-06-25T23:13:35.918524Z" } }, "outputs": [], @@ -652,10 +652,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:46.078970Z", - "iopub.status.busy": "2024-06-25T19:31:46.078798Z", - "iopub.status.idle": "2024-06-25T19:31:48.698188Z", - "shell.execute_reply": "2024-06-25T19:31:48.697474Z" + "iopub.execute_input": "2024-06-25T23:13:35.920827Z", + "iopub.status.busy": "2024-06-25T23:13:35.920512Z", + "iopub.status.idle": "2024-06-25T23:13:38.614446Z", + "shell.execute_reply": "2024-06-25T23:13:38.613776Z" }, "scrolled": true }, @@ -678,10 +678,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.701194Z", - "iopub.status.busy": "2024-06-25T19:31:48.700490Z", - "iopub.status.idle": "2024-06-25T19:31:48.707774Z", - "shell.execute_reply": "2024-06-25T19:31:48.707224Z" + "iopub.execute_input": "2024-06-25T23:13:38.617773Z", + "iopub.status.busy": "2024-06-25T23:13:38.616881Z", + "iopub.status.idle": "2024-06-25T23:13:38.624576Z", + "shell.execute_reply": "2024-06-25T23:13:38.624128Z" } }, "outputs": [ @@ -782,10 +782,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.709878Z", - "iopub.status.busy": "2024-06-25T19:31:48.709556Z", - "iopub.status.idle": "2024-06-25T19:31:48.713210Z", - "shell.execute_reply": "2024-06-25T19:31:48.712781Z" + "iopub.execute_input": "2024-06-25T23:13:38.626671Z", + "iopub.status.busy": "2024-06-25T23:13:38.626274Z", + "iopub.status.idle": "2024-06-25T23:13:38.630173Z", + "shell.execute_reply": "2024-06-25T23:13:38.629644Z" } }, "outputs": [], @@ -799,10 +799,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.715198Z", - "iopub.status.busy": "2024-06-25T19:31:48.714884Z", - "iopub.status.idle": "2024-06-25T19:31:48.717917Z", - "shell.execute_reply": "2024-06-25T19:31:48.717397Z" + "iopub.execute_input": "2024-06-25T23:13:38.632131Z", + "iopub.status.busy": "2024-06-25T23:13:38.631753Z", + "iopub.status.idle": "2024-06-25T23:13:38.634921Z", + "shell.execute_reply": "2024-06-25T23:13:38.634406Z" } }, "outputs": [ @@ -837,10 +837,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.719920Z", - "iopub.status.busy": "2024-06-25T19:31:48.719615Z", - "iopub.status.idle": "2024-06-25T19:31:48.722447Z", - "shell.execute_reply": "2024-06-25T19:31:48.722011Z" + "iopub.execute_input": "2024-06-25T23:13:38.636934Z", + "iopub.status.busy": "2024-06-25T23:13:38.636530Z", + "iopub.status.idle": "2024-06-25T23:13:38.639393Z", + "shell.execute_reply": "2024-06-25T23:13:38.638964Z" } }, "outputs": [], @@ -860,10 +860,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.724470Z", - "iopub.status.busy": "2024-06-25T19:31:48.724080Z", - "iopub.status.idle": "2024-06-25T19:31:48.730838Z", - "shell.execute_reply": "2024-06-25T19:31:48.730303Z" + "iopub.execute_input": "2024-06-25T23:13:38.641436Z", + "iopub.status.busy": "2024-06-25T23:13:38.641098Z", + "iopub.status.idle": "2024-06-25T23:13:38.647659Z", + "shell.execute_reply": "2024-06-25T23:13:38.647204Z" } }, "outputs": [ @@ -988,10 +988,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.733056Z", - "iopub.status.busy": "2024-06-25T19:31:48.732753Z", - "iopub.status.idle": "2024-06-25T19:31:48.956488Z", - "shell.execute_reply": "2024-06-25T19:31:48.955930Z" + "iopub.execute_input": "2024-06-25T23:13:38.649778Z", + "iopub.status.busy": "2024-06-25T23:13:38.649479Z", + "iopub.status.idle": "2024-06-25T23:13:38.874003Z", + "shell.execute_reply": "2024-06-25T23:13:38.873478Z" }, "scrolled": true }, @@ -1030,10 +1030,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:48.958874Z", - "iopub.status.busy": "2024-06-25T19:31:48.958385Z", - "iopub.status.idle": "2024-06-25T19:31:49.133338Z", - "shell.execute_reply": "2024-06-25T19:31:49.132807Z" + "iopub.execute_input": "2024-06-25T23:13:38.876436Z", + "iopub.status.busy": "2024-06-25T23:13:38.876051Z", + "iopub.status.idle": "2024-06-25T23:13:39.048915Z", + "shell.execute_reply": "2024-06-25T23:13:39.048432Z" }, "scrolled": true }, @@ -1066,10 +1066,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:49.135767Z", - "iopub.status.busy": "2024-06-25T19:31:49.135370Z", - "iopub.status.idle": "2024-06-25T19:31:49.139272Z", - "shell.execute_reply": "2024-06-25T19:31:49.138786Z" + "iopub.execute_input": "2024-06-25T23:13:39.051340Z", + "iopub.status.busy": "2024-06-25T23:13:39.050964Z", + "iopub.status.idle": "2024-06-25T23:13:39.054620Z", + "shell.execute_reply": "2024-06-25T23:13:39.054132Z" }, "nbsphinx": "hidden" }, @@ -1113,30 +1113,33 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "007989e5e6cd44a5ada30dda1923d065": { + "02a072ed32924507837162ad70da9612": { "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_8b998a37935c4d30856e95a2cd3f1439", - "placeholder": "​", - "style": "IPY_MODEL_200586c52a4549678a949ca57213c920", + "layout": "IPY_MODEL_1aab5456eec04063a5df26734e017b79", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d7c835da9be44280b48466c92cb851d6", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 121kB/s]" + "value": 231508.0 } }, - "054e6f2fd5d14973972b7db9b5e4ed03": { + "03d9becbdd8046bf81525afd016ed573": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1152,23 +1155,53 @@ "description_width": "" } }, - "0bd860a7540947f98823d4a40bdafb86": { + "075ca50b902b46989673d1147902560d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_65b932c28ff3495098c0db8b56dd18de", + "placeholder": "​", + "style": "IPY_MODEL_e098c9499e5142759ba844e4486a6da6", + "tabbable": null, + "tooltip": null, + "value": ".gitattributes: 100%" + } + }, + "0ee27b719c824572adc4d14d07e72cf6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_68a06976e74d4773829c8f1f5d4cc2ab", + "placeholder": "​", + "style": "IPY_MODEL_7bc89cc511714eb3b9db0329b0ad4f47", + "tabbable": null, + "tooltip": null, + "value": "vocab.txt: 100%" } }, - "1047a464f8ad4f08a9c39e32ef88f77b": { + "197c89430b124236a2a7ca6374f1edf0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1221,132 +1254,7 @@ "width": null } }, - "200586c52a4549678a949ca57213c920": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "226f358553304154b872be466bcda2cc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "2606990b2b654e949895c6ea13bace1b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "293b01a69e094447aeebb1e7e866fd51": { - "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_f9e7d310eb764ed9abf54e3695e98058", - "IPY_MODEL_acd50c68e1bb474ca7e642fdbecd2231", - "IPY_MODEL_a4ea888fe4004256885e50c60385b700" - ], - "layout": "IPY_MODEL_91bc4e78a8934597a32b965150f632af", - "tabbable": null, - "tooltip": null - } - }, - "2db6e61311574d2799f341e0f2b280db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e95f58118fca41f79754cf64dc768e55", - "placeholder": "​", - "style": "IPY_MODEL_5d0e81a247724335969fb0549b56194b", - "tabbable": null, - "tooltip": null, - "value": "README.md: 100%" - } - }, - "2e5e14c62e1a4cf09b6fb8b0bb5ca451": { - "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_5ccb993dd72c4674bf4c5129e4e19f7b", - "IPY_MODEL_4105d4a519b74c618417f38830a92826", - "IPY_MODEL_007989e5e6cd44a5ada30dda1923d065" - ], - "layout": "IPY_MODEL_811eb3464f8b4658af7b92cb22fd5db1", - "tabbable": null, - "tooltip": null - } - }, - "2ed7ba0d1cb94817b121646f00c5a089": { + "1a996c0fb30c4181aef76b9d0b39bd20": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1399,33 +1307,7 @@ "width": null } }, - "3156c56a2d1e46779554f6e680768b77": { - "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_6da0de8fbedc40cca53799f8e45cf74b", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b0f309cd65ee48e4902fba4c141424a9", - "tabbable": null, - "tooltip": null, - "value": 466062.0 - } - }, - "371271d754f04722b9ba2102ec6453b3": { + "1aab5456eec04063a5df26734e017b79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1478,31 +1360,7 @@ "width": null } }, - "38046751c5324a119490bbe8a5ec326c": { - "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_684d44672b7e4f3e87afb7ff19a14a7d", - "IPY_MODEL_703a1805a6e14459bff5e7bc196e2924", - "IPY_MODEL_9678a77b61324383a27e704a34123184" - ], - "layout": "IPY_MODEL_7f83efe5fa43477c83b7dd0eab30b115", - "tabbable": null, - "tooltip": null - } - }, - "3b3726b7d15f4242bbe2a0e0323f5edd": { + "1b04ac435f764f288a38e492f152ec20": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1555,25 +1413,54 @@ "width": null } }, - "3c288e9049ba4814a9020bbda1512944": { + "1d787eed2c504168b62c71e3e8b823c8": { "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_c363fe3444424a62b964efb6ec305f7f", + "placeholder": "​", + "style": "IPY_MODEL_d622adb2d6724d848794f6e7c40de0b3", + "tabbable": null, + "tooltip": null, + "value": " 2.21k/2.21k [00:00<00:00, 422kB/s]" + } + }, + "1e2c610098e54fea94839bb48d055f22": { + "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_e7466595e3ae479d9ee9e989b0fa9efb", + "IPY_MODEL_8784e3b2891144468a5d989adaf62ae7", + "IPY_MODEL_a6280db66f3e434bbdb20ebab4989dec" + ], + "layout": "IPY_MODEL_32522284f75645f69b58819ffe34d2a7", + "tabbable": null, + "tooltip": null } }, - "3e906d417956421a978812281c844e7e": { + "20471efe14204b3fa973ce7c173612f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1588,15 +1475,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4c673f7ac4ec44149b7f219298381ca5", + "layout": "IPY_MODEL_5dc7272ce64644afa609af3b2046a38e", "placeholder": "​", - "style": "IPY_MODEL_e3282f6972624465bd15f84c183446ed", + "style": "IPY_MODEL_c220349ac77649e3bffdb0998b40b102", "tabbable": null, "tooltip": null, - "value": " 391/391 [00:00<00:00, 64.5kB/s]" + "value": " 54.2M/54.2M [00:00<00:00, 298MB/s]" } }, - "4105d4a519b74c618417f38830a92826": { + "2808a2dd85f840dc8384e96b6e31d382": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1612,81 +1499,24 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a7fc4b58838f4350b7cce239e6cf7c4d", - "max": 665.0, + "layout": "IPY_MODEL_8f55a6cbd0184173b7c3ecca3b2be6a1", + "max": 466062.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a7755505c8604626b66bd90ac49165ab", + "style": "IPY_MODEL_670785469ef5401e82177c280f877928", "tabbable": null, "tooltip": null, - "value": 665.0 + "value": 466062.0 } }, - "43e54781cf104522a0a7479b4684f176": { - "model_module": "@jupyter-widgets/controls", + "2b78fa97669e4067a4bd3de6ccaaa88a": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "47c94c5344ac480795afc89535d19275": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4c280f6c890c468db24cfa085162f8eb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d98e8d3d73134c40b8256b723de4c9ba", - "placeholder": "​", - "style": "IPY_MODEL_55cc185655a64110894ebe751347ee97", - "tabbable": null, - "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 422kB/s]" - } - }, - "4c673f7ac4ec44149b7f219298381ca5": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -1732,7 +1562,33 @@ "width": null } }, - "52f1b4fab3d74ad180e90f63881f6b32": { + "2e901b505de34984a7c06e4ff7be8ebe": { + "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_ab64f8b595ef4648a47b1b2da8eac241", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_03d9becbdd8046bf81525afd016ed573", + "tabbable": null, + "tooltip": null, + "value": 54245363.0 + } + }, + "2fe0de05b5e847e89ba80b8e4d398c4c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1785,25 +1641,7 @@ "width": null } }, - "55cc185655a64110894ebe751347ee97": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "5c2455143c7b4c9d8179735a17395106": { + "32522284f75645f69b58819ffe34d2a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1856,30 +1694,7 @@ "width": null } }, - "5ccb993dd72c4674bf4c5129e4e19f7b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c693822d0e9f45a5a0c3d5598c8464f4", - "placeholder": "​", - "style": "IPY_MODEL_6c14e14271b4450f89db48de72dffebe", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" - } - }, - "5d0e81a247724335969fb0549b56194b": { + "328a64a36aa14cc4a13e8c232d198977": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1897,7 +1712,7 @@ "text_color": null } }, - "5e1fb144070e4665b06631fd83e8a769": { + "35b5e0b171c7498d8e1a3633c745e073": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1912,84 +1727,77 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2ed7ba0d1cb94817b121646f00c5a089", + "layout": "IPY_MODEL_93beb5b9f7914314ac973bada268bcc2", "placeholder": "​", - "style": "IPY_MODEL_e45f1523c8d6445cacf72f4752609a80", + "style": "IPY_MODEL_71736b2d04e142cea126cbddad9801e2", "tabbable": null, "tooltip": null, - "value": " 466k/466k [00:00<00:00, 3.42MB/s]" + "value": "tokenizer.json: 100%" } }, - "65fb96fdd89c4a7aa6863a61cd8c8281": { + "3c2efa0519364cd7ad5d4149de79bec8": { "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_a2f3a938880745e29369216068f395dc", - "placeholder": "​", - "style": "IPY_MODEL_79bb28d92fc14400815dc3c2c98b31e6", - "tabbable": null, - "tooltip": null, - "value": "tokenizer.json: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "684d44672b7e4f3e87afb7ff19a14a7d": { + "3c700601fd7d4148894afa4961283a88": { "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_69f6ccf772ad48fa86cea5e7b57f6c92", - "placeholder": "​", - "style": "IPY_MODEL_2606990b2b654e949895c6ea13bace1b", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "6854a7c0f0b846a99e2cc07332f5e2ad": { + "42243b174d064f20a73f6c60315575eb": { "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_1047a464f8ad4f08a9c39e32ef88f77b", - "placeholder": "​", - "style": "IPY_MODEL_d22816db94ed4b3aa623fb9f9eb42646", + "layout": "IPY_MODEL_de1cc30d88f74d2299519f8846818183", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6e166f17ea384c918f9777404649330c", "tabbable": null, "tooltip": null, - "value": "vocab.txt: 100%" + "value": 391.0 } }, - "68caf9f94f8b49e29f2522245cfc1967": { + "485477b3f18244cb8e82240fdfc321c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2042,7 +1850,7 @@ "width": null } }, - "68d5eef2d9154625b81ca5a98ad95302": { + "4a7d8e7b7ba3464fa41165f58db05e12": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2095,7 +1903,7 @@ "width": null } }, - "69f6ccf772ad48fa86cea5e7b57f6c92": { + "4d312e2d3f524fb1bf8dba710216c761": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2148,25 +1956,7 @@ "width": null } }, - "6c14e14271b4450f89db48de72dffebe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6d917afdd1bf47ea943ba43a44498f21": { + "537d23f9aefe4d35aa82e5454ab78815": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2219,7 +2009,31 @@ "width": null } }, - "6da0de8fbedc40cca53799f8e45cf74b": { + "5b6e029c7f61484d880737df09cb3291": { + "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_fdddfa973dd841b39245eda900cf0339", + "IPY_MODEL_2e901b505de34984a7c06e4ff7be8ebe", + "IPY_MODEL_20471efe14204b3fa973ce7c173612f3" + ], + "layout": "IPY_MODEL_b0d211d5585d414d93c249182a668daf", + "tabbable": null, + "tooltip": null + } + }, + "5da1ee68b4f848c98bea7614ebf85f59": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2272,69 +2086,7 @@ "width": null } }, - "703a1805a6e14459bff5e7bc196e2924": { - "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_c467bba142d440348c677b9b88920801", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9e515a523986480cbbe1094ca437f513", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } - }, - "72118120122f4623822582e300065947": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "76977b7a679849439d4159579b51c480": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "77399e1417a148bbaaf8c99c6e4b2bb5": { + "5dc7272ce64644afa609af3b2046a38e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2387,7 +2139,7 @@ "width": null } }, - "7930ae36275240238ba62db1afd25ce2": { + "5f117a6515e1436cad7028680580b6a5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2440,7 +2192,7 @@ "width": null } }, - "79bb28d92fc14400815dc3c2c98b31e6": { + "634f7b4ddf0e46f3b98d4b3c9ce87c86": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2458,63 +2210,10 @@ "text_color": null } }, - "7f83efe5fa43477c83b7dd0eab30b115": { - "model_module": "@jupyter-widgets/base", + "6477ae421e3e43aa814150445e014ac0": { + "model_module": "@jupyter-widgets/controls", "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 - } - }, - "7f84e049288f438cbb050b771815ee1a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", @@ -2526,16 +2225,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_6854a7c0f0b846a99e2cc07332f5e2ad", - "IPY_MODEL_f674728a058e4ebc9cda63400ca9a97a", - "IPY_MODEL_b780cdac15fd4ff5bf0b64d9369ef63b" + "IPY_MODEL_075ca50b902b46989673d1147902560d", + "IPY_MODEL_42243b174d064f20a73f6c60315575eb", + "IPY_MODEL_bbe4f79369114ed9839dd57535856697" ], - "layout": "IPY_MODEL_52f1b4fab3d74ad180e90f63881f6b32", + "layout": "IPY_MODEL_1b04ac435f764f288a38e492f152ec20", "tabbable": null, "tooltip": null } }, - "811eb3464f8b4658af7b92cb22fd5db1": { + "65b932c28ff3495098c0db8b56dd18de": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2588,7 +2287,7 @@ "width": null } }, - "8b998a37935c4d30856e95a2cd3f1439": { + "66d43ed9bf3f4ef6aedf76e3d08d36c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2641,7 +2340,7 @@ "width": null } }, - "8f663dc8059d42e4a1c16507affd40e3": { + "670785469ef5401e82177c280f877928": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2657,7 +2356,7 @@ "description_width": "" } }, - "91bc4e78a8934597a32b965150f632af": { + "68a06976e74d4773829c8f1f5d4cc2ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2710,7 +2409,59 @@ "width": null } }, - "9678a77b61324383a27e704a34123184": { + "6e166f17ea384c918f9777404649330c": { + "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": "" + } + }, + "6f576e9c9374443588b496e63f781624": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "71736b2d04e142cea126cbddad9801e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7261cd19786a4a0b95b770c51f033b0c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2725,55 +2476,100 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7930ae36275240238ba62db1afd25ce2", + "layout": "IPY_MODEL_1a996c0fb30c4181aef76b9d0b39bd20", "placeholder": "​", - "style": "IPY_MODEL_226f358553304154b872be466bcda2cc", + "style": "IPY_MODEL_8d98dfc145534a779ad6c609609abd31", "tabbable": null, "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 9.10kB/s]" + "value": " 466k/466k [00:00<00:00, 14.0MB/s]" } }, - "9bbf8e629233461d84330aac6c38bc36": { + "7bc89cc511714eb3b9db0329b0ad4f47": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7dbd67a2cb264612b0d638e5e63a9a07": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8674027fdc534d3791107a43a5e491ab": { + "model_module": "@jupyter-widgets/controls", + "model_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_65fb96fdd89c4a7aa6863a61cd8c8281", - "IPY_MODEL_3156c56a2d1e46779554f6e680768b77", - "IPY_MODEL_5e1fb144070e4665b06631fd83e8a769" - ], - "layout": "IPY_MODEL_5c2455143c7b4c9d8179735a17395106", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dfd5800b7e4c41fb90cf34888c17c960", + "placeholder": "​", + "style": "IPY_MODEL_9bdcb01ac1254e4ca46f786f1f48635e", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "README.md: 100%" } }, - "9e515a523986480cbbe1094ca437f513": { + "8784e3b2891144468a5d989adaf62ae7": { "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_e98f8f44d5174617bc22e566a83f2e14", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9e9b474bbbd44555ac3344b5d173ecd4", + "tabbable": null, + "tooltip": null, + "value": 665.0 } }, - "a2f3a938880745e29369216068f395dc": { + "8c2a54a50b174504b67275f6fc1758ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2826,46 +2622,25 @@ "width": null } }, - "a4ea888fe4004256885e50c60385b700": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_371271d754f04722b9ba2102ec6453b3", - "placeholder": "​", - "style": "IPY_MODEL_72118120122f4623822582e300065947", - "tabbable": null, - "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 137MB/s]" - } - }, - "a7755505c8604626b66bd90ac49165ab": { + "8d98dfc145534a779ad6c609609abd31": { "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 } }, - "a7fc4b58838f4350b7cce239e6cf7c4d": { + "8f55a6cbd0184173b7c3ecca3b2be6a1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2918,31 +2693,7 @@ "width": null } }, - "a8fe72969fe348a99c98be80dccd6c53": { - "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_2db6e61311574d2799f341e0f2b280db", - "IPY_MODEL_c768456d220342ec8cc60cc2528c3732", - "IPY_MODEL_4c280f6c890c468db24cfa085162f8eb" - ], - "layout": "IPY_MODEL_dd1d6b6bcb61451a9984bb2bddd1826e", - "tabbable": null, - "tooltip": null - } - }, - "acd50c68e1bb474ca7e642fdbecd2231": { + "937b241a390b4aa1b76cb3c07905b257": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2958,59 +2709,131 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_68caf9f94f8b49e29f2522245cfc1967", - "max": 54245363.0, + "layout": "IPY_MODEL_4d312e2d3f524fb1bf8dba710216c761", + "max": 48.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_0bd860a7540947f98823d4a40bdafb86", + "style": "IPY_MODEL_b11b3d0461c94c138ecf254a642eb544", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": 48.0 } }, - "b0f309cd65ee48e4902fba4c141424a9": { - "model_module": "@jupyter-widgets/controls", + "93beb5b9f7914314ac973bada268bcc2": { + "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": "" - } - }, - "b780cdac15fd4ff5bf0b64d9369ef63b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d76c41fd805e466081dfeb1d93477c92", - "placeholder": "​", - "style": "IPY_MODEL_76977b7a679849439d4159579b51c480", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 3.55MB/s]" - } - }, - "baa0ee8552c34f9f808dc9985cb43d88": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "97155a33cf37454f8282b21b3806031d": { + "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_0ee27b719c824572adc4d14d07e72cf6", + "IPY_MODEL_02a072ed32924507837162ad70da9612", + "IPY_MODEL_c73c253ce5ce4c0c9744f5c0cb9d0530" + ], + "layout": "IPY_MODEL_bd8af8fcf5f34f848183718d176ce55f", + "tabbable": null, + "tooltip": null + } + }, + "9bdcb01ac1254e4ca46f786f1f48635e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "9e9b474bbbd44555ac3344b5d173ecd4": { + "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": "" + } + }, + "a6280db66f3e434bbdb20ebab4989dec": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", @@ -3022,15 +2845,64 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_77399e1417a148bbaaf8c99c6e4b2bb5", + "layout": "IPY_MODEL_2b78fa97669e4067a4bd3de6ccaaa88a", "placeholder": "​", - "style": "IPY_MODEL_3c288e9049ba4814a9020bbda1512944", + "style": "IPY_MODEL_7dbd67a2cb264612b0d638e5e63a9a07", "tabbable": null, "tooltip": null, - "value": ".gitattributes: 100%" + "value": " 665/665 [00:00<00:00, 129kB/s]" } }, - "c467bba142d440348c677b9b88920801": { + "a859632225cf4c338d075b33f4abd2e0": { + "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_4a7d8e7b7ba3464fa41165f58db05e12", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c7cdf638b52549a3923f4d9f4dacf40d", + "tabbable": null, + "tooltip": null, + "value": 2211.0 + } + }, + "aa703043d8574c99856909333746a558": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_537d23f9aefe4d35aa82e5454ab78815", + "placeholder": "​", + "style": "IPY_MODEL_3c700601fd7d4148894afa4961283a88", + "tabbable": null, + "tooltip": null, + "value": "tokenizer_config.json: 100%" + } + }, + "ab64f8b595ef4648a47b1b2da8eac241": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3083,7 +2955,7 @@ "width": null } }, - "c693822d0e9f45a5a0c3d5598c8464f4": { + "b0d211d5585d414d93c249182a668daf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3136,77 +3008,46 @@ "width": null } }, - "c768456d220342ec8cc60cc2528c3732": { + "b11b3d0461c94c138ecf254a642eb544": { "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_3b3726b7d15f4242bbe2a0e0323f5edd", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_054e6f2fd5d14973972b7db9b5e4ed03", - "tabbable": null, - "tooltip": null, - "value": 2211.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "ca2793614f594cf4a384748bfd36b073": { + "bbe4f79369114ed9839dd57535856697": { "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_ff59fb88d4aa46d481bfabbfb12c3c08", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8f663dc8059d42e4a1c16507affd40e3", + "layout": "IPY_MODEL_f933a441f363402d9fc2e796940cf4d3", + "placeholder": "​", + "style": "IPY_MODEL_328a64a36aa14cc4a13e8c232d198977", "tabbable": null, "tooltip": null, - "value": 391.0 - } - }, - "d22816db94ed4b3aa623fb9f9eb42646": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": " 391/391 [00:00<00:00, 68.8kB/s]" } }, - "d76c41fd805e466081dfeb1d93477c92": { + "bd8af8fcf5f34f848183718d176ce55f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3259,7 +3100,25 @@ "width": null } }, - "d8c1ad41f4fe4cf0a3f44d363086f32b": { + "c220349ac77649e3bffdb0998b40b102": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c363fe3444424a62b964efb6ec305f7f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3312,7 +3171,127 @@ "width": null } }, - "d98e8d3d73134c40b8256b723de4c9ba": { + "c73c253ce5ce4c0c9744f5c0cb9d0530": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_66d43ed9bf3f4ef6aedf76e3d08d36c7", + "placeholder": "​", + "style": "IPY_MODEL_3c2efa0519364cd7ad5d4149de79bec8", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 16.1MB/s]" + } + }, + "c7cdf638b52549a3923f4d9f4dacf40d": { + "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": "" + } + }, + "c9a979755e9a403eb0ca342120826ee6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_197c89430b124236a2a7ca6374f1edf0", + "placeholder": "​", + "style": "IPY_MODEL_634f7b4ddf0e46f3b98d4b3c9ce87c86", + "tabbable": null, + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 9.85kB/s]" + } + }, + "d622adb2d6724d848794f6e7c40de0b3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d7c835da9be44280b48466c92cb851d6": { + "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": "" + } + }, + "ddf577727f0e42a1b074bcb455a4258a": { + "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_aa703043d8574c99856909333746a558", + "IPY_MODEL_937b241a390b4aa1b76cb3c07905b257", + "IPY_MODEL_c9a979755e9a403eb0ca342120826ee6" + ], + "layout": "IPY_MODEL_5f117a6515e1436cad7028680580b6a5", + "tabbable": null, + "tooltip": null + } + }, + "de1cc30d88f74d2299519f8846818183": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3365,7 +3344,7 @@ "width": null } }, - "dd1d6b6bcb61451a9984bb2bddd1826e": { + "dfd5800b7e4c41fb90cf34888c17c960": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3418,7 +3397,7 @@ "width": null } }, - "e3282f6972624465bd15f84c183446ed": { + "e098c9499e5142759ba844e4486a6da6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3436,25 +3415,78 @@ "text_color": null } }, - "e45f1523c8d6445cacf72f4752609a80": { + "e62034a9e6c043cc997861592486168a": { "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_8674027fdc534d3791107a43a5e491ab", + "IPY_MODEL_a859632225cf4c338d075b33f4abd2e0", + "IPY_MODEL_1d787eed2c504168b62c71e3e8b823c8" + ], + "layout": "IPY_MODEL_5da1ee68b4f848c98bea7614ebf85f59", + "tabbable": null, + "tooltip": null } }, - "e95f58118fca41f79754cf64dc768e55": { + "e7466595e3ae479d9ee9e989b0fa9efb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8c2a54a50b174504b67275f6fc1758ea", + "placeholder": "​", + "style": "IPY_MODEL_fd9acfb5facf4aeaa71d2088d344d085", + "tabbable": null, + "tooltip": null, + "value": "config.json: 100%" + } + }, + "e750160df873483c9096b064baeab112": { + "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_35b5e0b171c7498d8e1a3633c745e073", + "IPY_MODEL_2808a2dd85f840dc8384e96b6e31d382", + "IPY_MODEL_7261cd19786a4a0b95b770c51f033b0c" + ], + "layout": "IPY_MODEL_485477b3f18244cb8e82240fdfc321c6", + "tabbable": null, + "tooltip": null + } + }, + "e98f8f44d5174617bc22e566a83f2e14": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3507,80 +3539,7 @@ "width": null } }, - "e9ebd3cab6ee4b38af6e19b1c2a2b7a0": { - "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_baa0ee8552c34f9f808dc9985cb43d88", - "IPY_MODEL_ca2793614f594cf4a384748bfd36b073", - "IPY_MODEL_3e906d417956421a978812281c844e7e" - ], - "layout": "IPY_MODEL_68d5eef2d9154625b81ca5a98ad95302", - "tabbable": null, - "tooltip": null - } - }, - "f674728a058e4ebc9cda63400ca9a97a": { - "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_d8c1ad41f4fe4cf0a3f44d363086f32b", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_43e54781cf104522a0a7479b4684f176", - "tabbable": null, - "tooltip": null, - "value": 231508.0 - } - }, - "f9e7d310eb764ed9abf54e3695e98058": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6d917afdd1bf47ea943ba43a44498f21", - "placeholder": "​", - "style": "IPY_MODEL_47c94c5344ac480795afc89535d19275", - "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" - } - }, - "ff59fb88d4aa46d481bfabbfb12c3c08": { + "f933a441f363402d9fc2e796940cf4d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3632,6 +3591,47 @@ "visibility": null, "width": null } + }, + "fd9acfb5facf4aeaa71d2088d344d085": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fdddfa973dd841b39245eda900cf0339": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2fe0de05b5e847e89ba80b8e4d398c4c", + "placeholder": "​", + "style": "IPY_MODEL_6f576e9c9374443588b496e63f781624", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/audio.html b/master/tutorials/datalab/audio.html index b4b5b5038..fd7c7c4e4 100644 --- a/master/tutorials/datalab/audio.html +++ b/master/tutorials/datalab/audio.html @@ -1357,7 +1357,7 @@

5. Use cleanlab to find label issues -{"state": {"1aea1d112c054eec950bd686a0ca1d14": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e83b02ee750444739a17f78b4d3f184f": {"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": ""}}, "dcd54291f5a341bb9379d41671c00d95": {"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_1aea1d112c054eec950bd686a0ca1d14", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e83b02ee750444739a17f78b4d3f184f", "tabbable": null, "tooltip": null, "value": 2041.0}}, "0f81bdbdfded4d61a103fbc884fdb8b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d7ac88820da9447ebde38eee5408d525": {"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}}, "bfc1a00c4e8f4270af1f947f378411f7": {"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_0f81bdbdfded4d61a103fbc884fdb8b7", "placeholder": "\u200b", "style": "IPY_MODEL_d7ac88820da9447ebde38eee5408d525", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "cec08439b11541149be6a96ed609992c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "95a7d374743e47b785481b53802557f9": {"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}}, "bec49e231d374b619cca6a3dbb6c9b4f": {"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_cec08439b11541149be6a96ed609992c", "placeholder": "\u200b", "style": "IPY_MODEL_95a7d374743e47b785481b53802557f9", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007514kB/s]"}}, "5375b29786b24fc182c5b06dc46c21f5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "295a0df817014d6fbb6377dcc33be2b3": {"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_bfc1a00c4e8f4270af1f947f378411f7", "IPY_MODEL_dcd54291f5a341bb9379d41671c00d95", "IPY_MODEL_bec49e231d374b619cca6a3dbb6c9b4f"], "layout": "IPY_MODEL_5375b29786b24fc182c5b06dc46c21f5", "tabbable": null, "tooltip": null}}, "09e511f313fe4deaa3db651dc2ad1f38": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ff0fbdda3a2745cab371f835eea2071a": {"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": ""}}, "e4e0fafef42746d1a3f83998a48e68fb": {"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_09e511f313fe4deaa3db651dc2ad1f38", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_ff0fbdda3a2745cab371f835eea2071a", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "8a3fcca569d54278b1f243ca468eb2e8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e8bef6ec94b841649328908a1e03f796": {"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}}, "7b1eae32f9cd4561b35edcb6a450623c": {"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_8a3fcca569d54278b1f243ca468eb2e8", "placeholder": "\u200b", "style": "IPY_MODEL_e8bef6ec94b841649328908a1e03f796", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "f8e6a2a4f4ac44968d7ab7e8b41cd9e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fd7987d5664246479c5c48a19962bcff": {"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}}, "b61aaac35e2a498c8712ccc1a79e72b4": {"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_f8e6a2a4f4ac44968d7ab7e8b41cd9e5", "placeholder": "\u200b", "style": "IPY_MODEL_fd7987d5664246479c5c48a19962bcff", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u200733.4MB/s]"}}, "13b1cd45cfea4f85bfb6caa46af5a63a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5b63774c214c467f836bfdcb44bf9b9f": {"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_7b1eae32f9cd4561b35edcb6a450623c", "IPY_MODEL_e4e0fafef42746d1a3f83998a48e68fb", "IPY_MODEL_b61aaac35e2a498c8712ccc1a79e72b4"], "layout": "IPY_MODEL_13b1cd45cfea4f85bfb6caa46af5a63a", "tabbable": null, "tooltip": null}}, "feef464c625b4ce98e733ec61ac7047a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "67c2bbd993f544be9aaa55f1059ee0c5": {"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": ""}}, "9926def0468e4ef78a40becf734e408b": {"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_feef464c625b4ce98e733ec61ac7047a", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_67c2bbd993f544be9aaa55f1059ee0c5", "tabbable": null, "tooltip": null, "value": 3201.0}}, "efde3254ff184e58b1e14968ab98b2eb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e51bddc0c26e45c28f6280a03fa6c570": {"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}}, "d3aabef313ae48b184adb3612e11f7c0": {"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_efde3254ff184e58b1e14968ab98b2eb", "placeholder": "\u200b", "style": "IPY_MODEL_e51bddc0c26e45c28f6280a03fa6c570", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "d05aa5c2f7ed4f049aeb55cefb06f57f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "df36694caa324dc08d075a690e55fed5": {"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}}, "5de7ce2185e149b199420cef3937b004": {"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_d05aa5c2f7ed4f049aeb55cefb06f57f", "placeholder": "\u200b", "style": "IPY_MODEL_df36694caa324dc08d075a690e55fed5", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007860kB/s]"}}, "cad71209689044bf95c6a56cd4254c5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8d0cc257082a482b8a9c20f2623d1dd0": {"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_d3aabef313ae48b184adb3612e11f7c0", "IPY_MODEL_9926def0468e4ef78a40becf734e408b", "IPY_MODEL_5de7ce2185e149b199420cef3937b004"], "layout": "IPY_MODEL_cad71209689044bf95c6a56cd4254c5d", "tabbable": null, "tooltip": null}}, "1c18c2753518415ea3cf39b985a054ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bc925d23337a4990bcbb6ad1d3ba0714": {"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": ""}}, "cfb6db0858744f68a8074c3433dec014": {"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_1c18c2753518415ea3cf39b985a054ad", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_bc925d23337a4990bcbb6ad1d3ba0714", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "6c2f4eab88df4fcfbf60ea1461b407fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "47f71961c8254da1a0cb8fe2f90ef9f5": {"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}}, "0160f38ecac34b8a9a6e60b79941cb42": {"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_6c2f4eab88df4fcfbf60ea1461b407fb", "placeholder": "\u200b", "style": "IPY_MODEL_47f71961c8254da1a0cb8fe2f90ef9f5", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "7b142eabfac84901b7edb8e5cf1db87e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "751e8bbb861243c0b292b81647f5a7c8": {"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}}, "1b43dce850cb49019716fe7f052cd3f9": {"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_7b142eabfac84901b7edb8e5cf1db87e", "placeholder": "\u200b", "style": "IPY_MODEL_751e8bbb861243c0b292b81647f5a7c8", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u200732.9MB/s]"}}, "5f9ac971b36144c99f166c6afca8b98c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "48203e358e5e4c599815faec66f84717": {"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_0160f38ecac34b8a9a6e60b79941cb42", "IPY_MODEL_cfb6db0858744f68a8074c3433dec014", "IPY_MODEL_1b43dce850cb49019716fe7f052cd3f9"], "layout": "IPY_MODEL_5f9ac971b36144c99f166c6afca8b98c", "tabbable": null, "tooltip": null}}, "edb975a882cd4029ab886291208a5a5c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "386b0038b80f44249027e46144d33c35": {"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": ""}}, "ec7119530ac242a8bf5cd0417e5460c5": {"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_edb975a882cd4029ab886291208a5a5c", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_386b0038b80f44249027e46144d33c35", "tabbable": null, "tooltip": null, "value": 128619.0}}, "9be1debd539947d495510d3e71e91a33": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9593182600264e2aa5c5035fced2937b": {"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}}, "1965785719184160b13d07134d9c01cd": {"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_9be1debd539947d495510d3e71e91a33", "placeholder": "\u200b", "style": "IPY_MODEL_9593182600264e2aa5c5035fced2937b", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "7c6fa0df6a7a4993ae1942f5e66eb942": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fe2e22def9dd4fb9b35244ac0e211f6d": {"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}}, "dc42c71988f64ceba56aa56cc9c662d3": {"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_7c6fa0df6a7a4993ae1942f5e66eb942", "placeholder": "\u200b", "style": "IPY_MODEL_fe2e22def9dd4fb9b35244ac0e211f6d", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20077.29MB/s]"}}, "5a7fcc571ae3417ca7f32e5ac2e0f7ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f26aa1a5503c462e89477a33079f160a": {"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_1965785719184160b13d07134d9c01cd", "IPY_MODEL_ec7119530ac242a8bf5cd0417e5460c5", "IPY_MODEL_dc42c71988f64ceba56aa56cc9c662d3"], "layout": "IPY_MODEL_5a7fcc571ae3417ca7f32e5ac2e0f7ed", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"78b9bcb39b554c0290d9800dc940ff71": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5869d031c1004d448f247c32b8c702e1": {"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": ""}}, "227af90915cb483c9537459db20c2dcc": {"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_78b9bcb39b554c0290d9800dc940ff71", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5869d031c1004d448f247c32b8c702e1", "tabbable": null, "tooltip": null, "value": 2041.0}}, "4e926992584a4181a8551035087cce4c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "065d70114b74423d9f2fa424de1b7a1e": {"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}}, "7906a23ebb7146d593158f78421f5ed4": {"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_4e926992584a4181a8551035087cce4c", "placeholder": "\u200b", "style": "IPY_MODEL_065d70114b74423d9f2fa424de1b7a1e", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "ecd21cd3f9be4e8587ead71ef6eddacf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a873413b3495435bb8797c848d57b59a": {"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}}, "40d6d738f9e542e98ecc6d953d704d68": {"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_ecd21cd3f9be4e8587ead71ef6eddacf", "placeholder": "\u200b", "style": "IPY_MODEL_a873413b3495435bb8797c848d57b59a", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007508kB/s]"}}, "4bdd74d6b6f54d1fb924effdb6c35e73": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e4e22d23b27f43a5843b7ba91eb92b25": {"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_7906a23ebb7146d593158f78421f5ed4", "IPY_MODEL_227af90915cb483c9537459db20c2dcc", "IPY_MODEL_40d6d738f9e542e98ecc6d953d704d68"], "layout": "IPY_MODEL_4bdd74d6b6f54d1fb924effdb6c35e73", "tabbable": null, "tooltip": null}}, "acd3ca854a9b41b0a7e7c7c976d08dce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a7376840cbcd4df89be2e35d1d83b7a7": {"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": ""}}, "b83e50a6f4c94c46b81bc9ccac82d8ce": {"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_acd3ca854a9b41b0a7e7c7c976d08dce", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a7376840cbcd4df89be2e35d1d83b7a7", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "3d0bc58be6a64dbb9402e4f43247bbd9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a172e9a2a9464f0597dc89df0bc665f5": {"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}}, "bd56af9d1df24f959c2e27458bf28564": {"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_3d0bc58be6a64dbb9402e4f43247bbd9", "placeholder": "\u200b", "style": "IPY_MODEL_a172e9a2a9464f0597dc89df0bc665f5", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "4673aa7828d449fdb54b14aa87a8046b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f63a5476d3574388b856445769573334": {"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}}, "d74890bb53b24653a178d14fa9fb1027": {"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_4673aa7828d449fdb54b14aa87a8046b", "placeholder": "\u200b", "style": "IPY_MODEL_f63a5476d3574388b856445769573334", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u2007194MB/s]"}}, "12f57656dd504d218c054ef511da9ab4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1510d16cc79a49c88e3542daf3152481": {"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_bd56af9d1df24f959c2e27458bf28564", "IPY_MODEL_b83e50a6f4c94c46b81bc9ccac82d8ce", "IPY_MODEL_d74890bb53b24653a178d14fa9fb1027"], "layout": "IPY_MODEL_12f57656dd504d218c054ef511da9ab4", "tabbable": null, "tooltip": null}}, "f1d6704797ff4e7da238df4899d6eb75": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "54403dba12ff4f26beb5b09e37a94253": {"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": ""}}, "c7c838e69efb43a2b0ac48deb78c922d": {"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_f1d6704797ff4e7da238df4899d6eb75", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_54403dba12ff4f26beb5b09e37a94253", "tabbable": null, "tooltip": null, "value": 3201.0}}, "c25723012a2d443ba16eb7f6c52bc3a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0c2f7ca60ab147c99ae03f759db46297": {"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}}, "fe276545fde34f708d296c6f254d81e8": {"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_c25723012a2d443ba16eb7f6c52bc3a5", "placeholder": "\u200b", "style": "IPY_MODEL_0c2f7ca60ab147c99ae03f759db46297", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "f3b0e8a956184e818936ebd0416f606e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "67850b52aee848a899a4c1cc7561c6dd": {"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}}, "d8e5131818de460ba01fb92aed10a852": {"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_f3b0e8a956184e818936ebd0416f606e", "placeholder": "\u200b", "style": "IPY_MODEL_67850b52aee848a899a4c1cc7561c6dd", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007795kB/s]"}}, "5f72fda67a4f475ebe8560f39a8c3d76": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fb4875c647a74da78ce0b565e117d890": {"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_fe276545fde34f708d296c6f254d81e8", "IPY_MODEL_c7c838e69efb43a2b0ac48deb78c922d", "IPY_MODEL_d8e5131818de460ba01fb92aed10a852"], "layout": "IPY_MODEL_5f72fda67a4f475ebe8560f39a8c3d76", "tabbable": null, "tooltip": null}}, "8ca4da4898bb4451994f9434ae2ffbed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "98428c59f17b40cdb1c0a7543994cae1": {"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": ""}}, "186fee51c3124d608de6a5c71a289764": {"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_8ca4da4898bb4451994f9434ae2ffbed", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_98428c59f17b40cdb1c0a7543994cae1", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "59bb523ac7f646b8b418eac0ba2650ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "91f8c32305604aa6ad4753866f6428a8": {"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}}, "ae1c35581029419bac126538cc5ba8f3": {"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_59bb523ac7f646b8b418eac0ba2650ec", "placeholder": "\u200b", "style": "IPY_MODEL_91f8c32305604aa6ad4753866f6428a8", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "50e8d183b2e146bfa57d74e4574d6bf6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7936b993c44f45fc8f53ff71690b204e": {"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}}, "1e7ea41fcad145359a3915227cd3a572": {"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_50e8d183b2e146bfa57d74e4574d6bf6", "placeholder": "\u200b", "style": "IPY_MODEL_7936b993c44f45fc8f53ff71690b204e", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u2007328MB/s]"}}, "fe755d796bba4ebfa0a4fd20368887fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3740245a44544188bb5216cfbbe7c096": {"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_ae1c35581029419bac126538cc5ba8f3", "IPY_MODEL_186fee51c3124d608de6a5c71a289764", "IPY_MODEL_1e7ea41fcad145359a3915227cd3a572"], "layout": "IPY_MODEL_fe755d796bba4ebfa0a4fd20368887fa", "tabbable": null, "tooltip": null}}, "048952d61d4143eeb7a3064dac9fe4ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cd596207fea4447b96658e0f41eec607": {"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": ""}}, "11546fd8d68648da9c138662014585e5": {"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_048952d61d4143eeb7a3064dac9fe4ac", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_cd596207fea4447b96658e0f41eec607", "tabbable": null, "tooltip": null, "value": 128619.0}}, "01d773c5fc084c74bf3898f08469f8f7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1dc5d0b009d545d1922ea03e17fa99b3": {"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}}, "ab9f826f70f04ba3b63e2e5db1d325f2": {"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_01d773c5fc084c74bf3898f08469f8f7", "placeholder": "\u200b", "style": "IPY_MODEL_1dc5d0b009d545d1922ea03e17fa99b3", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "14c9df2de7cc4e1bbaf4f1a58d5e3037": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c805d0141fdb44038dfd6b1b6edf7abe": {"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}}, "b5a2a7d91c7b447f8108b8896269f607": {"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_14c9df2de7cc4e1bbaf4f1a58d5e3037", "placeholder": "\u200b", "style": "IPY_MODEL_c805d0141fdb44038dfd6b1b6edf7abe", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20077.47MB/s]"}}, "8954d8479e4b48759b6e0dbf92041c0c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4c68ee845d584f78b9ec889e427c9ab8": {"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_ab9f826f70f04ba3b63e2e5db1d325f2", "IPY_MODEL_11546fd8d68648da9c138662014585e5", "IPY_MODEL_b5a2a7d91c7b447f8108b8896269f607"], "layout": "IPY_MODEL_8954d8479e4b48759b6e0dbf92041c0c", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/audio.ipynb b/master/tutorials/datalab/audio.ipynb index 411158583..d40b1db54 100644 --- a/master/tutorials/datalab/audio.ipynb +++ b/master/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:53.133508Z", - "iopub.status.busy": "2024-06-25T19:31:53.133336Z", - "iopub.status.idle": "2024-06-25T19:31:58.248746Z", - "shell.execute_reply": "2024-06-25T19:31:58.248110Z" + "iopub.execute_input": "2024-06-25T23:13:42.048585Z", + "iopub.status.busy": "2024-06-25T23:13:42.048169Z", + "iopub.status.idle": "2024-06-25T23:13:47.015851Z", + "shell.execute_reply": "2024-06-25T23:13:47.015219Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:31:58.251604Z", - "iopub.status.busy": "2024-06-25T19:31:58.251034Z", - "iopub.status.idle": "2024-06-25T19:31:58.254389Z", - "shell.execute_reply": "2024-06-25T19:31:58.253843Z" + "iopub.execute_input": "2024-06-25T23:13:47.018616Z", + "iopub.status.busy": "2024-06-25T23:13:47.018295Z", + "iopub.status.idle": "2024-06-25T23:13:47.021447Z", + "shell.execute_reply": "2024-06-25T23:13:47.020989Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:31:58.256549Z", - "iopub.status.busy": "2024-06-25T19:31:58.256239Z", - "iopub.status.idle": "2024-06-25T19:31:58.260899Z", - "shell.execute_reply": "2024-06-25T19:31:58.260338Z" + "iopub.execute_input": "2024-06-25T23:13:47.023397Z", + "iopub.status.busy": "2024-06-25T23:13:47.023066Z", + "iopub.status.idle": "2024-06-25T23:13:47.027579Z", + "shell.execute_reply": "2024-06-25T23:13:47.027038Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T19:31:58.263210Z", - "iopub.status.busy": "2024-06-25T19:31:58.262770Z", - "iopub.status.idle": "2024-06-25T19:32:00.256796Z", - "shell.execute_reply": "2024-06-25T19:32:00.256144Z" + "iopub.execute_input": "2024-06-25T23:13:47.029706Z", + "iopub.status.busy": "2024-06-25T23:13:47.029408Z", + "iopub.status.idle": "2024-06-25T23:13:48.557949Z", + "shell.execute_reply": "2024-06-25T23:13:48.557324Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:00.259356Z", - "iopub.status.busy": "2024-06-25T19:32:00.259045Z", - "iopub.status.idle": "2024-06-25T19:32:00.269498Z", - "shell.execute_reply": "2024-06-25T19:32:00.269022Z" + "iopub.execute_input": "2024-06-25T23:13:48.560586Z", + "iopub.status.busy": "2024-06-25T23:13:48.560204Z", + "iopub.status.idle": "2024-06-25T23:13:48.570753Z", + "shell.execute_reply": "2024-06-25T23:13:48.570316Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:00.271550Z", - "iopub.status.busy": "2024-06-25T19:32:00.271221Z", - "iopub.status.idle": "2024-06-25T19:32:00.276417Z", - "shell.execute_reply": "2024-06-25T19:32:00.275932Z" + "iopub.execute_input": "2024-06-25T23:13:48.572948Z", + "iopub.status.busy": "2024-06-25T23:13:48.572614Z", + "iopub.status.idle": "2024-06-25T23:13:48.578335Z", + "shell.execute_reply": "2024-06-25T23:13:48.577906Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:00.278484Z", - "iopub.status.busy": "2024-06-25T19:32:00.278163Z", - "iopub.status.idle": "2024-06-25T19:32:00.762955Z", - "shell.execute_reply": "2024-06-25T19:32:00.762362Z" + "iopub.execute_input": "2024-06-25T23:13:48.580333Z", + "iopub.status.busy": "2024-06-25T23:13:48.580014Z", + "iopub.status.idle": "2024-06-25T23:13:49.044116Z", + "shell.execute_reply": "2024-06-25T23:13:49.043554Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:00.765136Z", - "iopub.status.busy": "2024-06-25T19:32:00.764811Z", - "iopub.status.idle": "2024-06-25T19:32:03.050183Z", - "shell.execute_reply": "2024-06-25T19:32:03.049698Z" + "iopub.execute_input": "2024-06-25T23:13:49.046459Z", + "iopub.status.busy": "2024-06-25T23:13:49.046048Z", + "iopub.status.idle": "2024-06-25T23:13:49.682286Z", + "shell.execute_reply": "2024-06-25T23:13:49.681791Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:03.052760Z", - "iopub.status.busy": "2024-06-25T19:32:03.052414Z", - "iopub.status.idle": "2024-06-25T19:32:03.070136Z", - "shell.execute_reply": "2024-06-25T19:32:03.069620Z" + "iopub.execute_input": "2024-06-25T23:13:49.685227Z", + "iopub.status.busy": "2024-06-25T23:13:49.684826Z", + "iopub.status.idle": "2024-06-25T23:13:49.703315Z", + "shell.execute_reply": "2024-06-25T23:13:49.702790Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:03.072148Z", - "iopub.status.busy": "2024-06-25T19:32:03.071949Z", - "iopub.status.idle": "2024-06-25T19:32:03.075039Z", - "shell.execute_reply": "2024-06-25T19:32:03.074605Z" + "iopub.execute_input": "2024-06-25T23:13:49.705384Z", + "iopub.status.busy": "2024-06-25T23:13:49.705205Z", + "iopub.status.idle": "2024-06-25T23:13:49.708482Z", + "shell.execute_reply": "2024-06-25T23:13:49.708013Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:03.077054Z", - "iopub.status.busy": "2024-06-25T19:32:03.076730Z", - "iopub.status.idle": "2024-06-25T19:32:17.091941Z", - "shell.execute_reply": "2024-06-25T19:32:17.091336Z" + "iopub.execute_input": "2024-06-25T23:13:49.710490Z", + "iopub.status.busy": "2024-06-25T23:13:49.710159Z", + "iopub.status.idle": "2024-06-25T23:14:03.836426Z", + "shell.execute_reply": "2024-06-25T23:14:03.835865Z" }, "id": "2FSQ2GR9R_YA" }, @@ -627,10 +627,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.094601Z", - "iopub.status.busy": "2024-06-25T19:32:17.094212Z", - "iopub.status.idle": "2024-06-25T19:32:17.098282Z", - "shell.execute_reply": "2024-06-25T19:32:17.097813Z" + "iopub.execute_input": "2024-06-25T23:14:03.839037Z", + "iopub.status.busy": "2024-06-25T23:14:03.838661Z", + "iopub.status.idle": "2024-06-25T23:14:03.842744Z", + "shell.execute_reply": "2024-06-25T23:14:03.842282Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.100415Z", - "iopub.status.busy": "2024-06-25T19:32:17.100030Z", - "iopub.status.idle": "2024-06-25T19:32:17.781950Z", - "shell.execute_reply": "2024-06-25T19:32:17.781387Z" + "iopub.execute_input": "2024-06-25T23:14:03.844717Z", + "iopub.status.busy": "2024-06-25T23:14:03.844392Z", + "iopub.status.idle": "2024-06-25T23:14:04.554198Z", + "shell.execute_reply": "2024-06-25T23:14:04.553609Z" }, "id": "i_drkY9YOcw4" }, @@ -727,10 +727,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.785604Z", - "iopub.status.busy": "2024-06-25T19:32:17.784675Z", - "iopub.status.idle": "2024-06-25T19:32:17.791417Z", - "shell.execute_reply": "2024-06-25T19:32:17.790891Z" + "iopub.execute_input": "2024-06-25T23:14:04.557144Z", + "iopub.status.busy": "2024-06-25T23:14:04.556722Z", + "iopub.status.idle": "2024-06-25T23:14:04.561566Z", + "shell.execute_reply": "2024-06-25T23:14:04.561058Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -777,10 +777,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.794921Z", - "iopub.status.busy": "2024-06-25T19:32:17.794005Z", - "iopub.status.idle": "2024-06-25T19:32:17.890859Z", - "shell.execute_reply": "2024-06-25T19:32:17.890238Z" + "iopub.execute_input": "2024-06-25T23:14:04.563987Z", + "iopub.status.busy": "2024-06-25T23:14:04.563613Z", + "iopub.status.idle": "2024-06-25T23:14:04.661144Z", + "shell.execute_reply": "2024-06-25T23:14:04.660555Z" } }, "outputs": [ @@ -817,10 +817,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.893215Z", - "iopub.status.busy": "2024-06-25T19:32:17.892852Z", - "iopub.status.idle": "2024-06-25T19:32:17.904591Z", - "shell.execute_reply": "2024-06-25T19:32:17.904119Z" + "iopub.execute_input": "2024-06-25T23:14:04.663549Z", + "iopub.status.busy": "2024-06-25T23:14:04.663180Z", + "iopub.status.idle": "2024-06-25T23:14:04.675655Z", + "shell.execute_reply": "2024-06-25T23:14:04.675200Z" }, "scrolled": true }, @@ -880,10 +880,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.906706Z", - "iopub.status.busy": "2024-06-25T19:32:17.906385Z", - "iopub.status.idle": "2024-06-25T19:32:17.914138Z", - "shell.execute_reply": "2024-06-25T19:32:17.913686Z" + "iopub.execute_input": "2024-06-25T23:14:04.677803Z", + "iopub.status.busy": "2024-06-25T23:14:04.677480Z", + "iopub.status.idle": "2024-06-25T23:14:04.685163Z", + "shell.execute_reply": "2024-06-25T23:14:04.684654Z" } }, "outputs": [ @@ -987,10 +987,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.916135Z", - "iopub.status.busy": "2024-06-25T19:32:17.915799Z", - "iopub.status.idle": "2024-06-25T19:32:17.919765Z", - "shell.execute_reply": "2024-06-25T19:32:17.919222Z" + "iopub.execute_input": "2024-06-25T23:14:04.687288Z", + "iopub.status.busy": "2024-06-25T23:14:04.686965Z", + "iopub.status.idle": "2024-06-25T23:14:04.691175Z", + "shell.execute_reply": "2024-06-25T23:14:04.690734Z" } }, "outputs": [ @@ -1028,10 +1028,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.921837Z", - "iopub.status.busy": "2024-06-25T19:32:17.921500Z", - "iopub.status.idle": "2024-06-25T19:32:17.926898Z", - "shell.execute_reply": "2024-06-25T19:32:17.926399Z" + "iopub.execute_input": "2024-06-25T23:14:04.693198Z", + "iopub.status.busy": "2024-06-25T23:14:04.692832Z", + "iopub.status.idle": "2024-06-25T23:14:04.698386Z", + "shell.execute_reply": "2024-06-25T23:14:04.697923Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1158,10 +1158,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:17.929118Z", - "iopub.status.busy": "2024-06-25T19:32:17.928697Z", - "iopub.status.idle": "2024-06-25T19:32:18.039116Z", - "shell.execute_reply": "2024-06-25T19:32:18.038547Z" + "iopub.execute_input": "2024-06-25T23:14:04.700375Z", + "iopub.status.busy": "2024-06-25T23:14:04.700060Z", + "iopub.status.idle": "2024-06-25T23:14:04.810591Z", + "shell.execute_reply": "2024-06-25T23:14:04.810025Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1215,10 +1215,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:18.041334Z", - "iopub.status.busy": "2024-06-25T19:32:18.040985Z", - "iopub.status.idle": "2024-06-25T19:32:18.143028Z", - "shell.execute_reply": "2024-06-25T19:32:18.142549Z" + "iopub.execute_input": "2024-06-25T23:14:04.812699Z", + "iopub.status.busy": "2024-06-25T23:14:04.812492Z", + "iopub.status.idle": "2024-06-25T23:14:04.914526Z", + "shell.execute_reply": "2024-06-25T23:14:04.913959Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1263,10 +1263,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T19:32:18.145023Z", - "iopub.status.busy": "2024-06-25T19:32:18.144735Z", - "iopub.status.idle": "2024-06-25T19:32:18.245208Z", - "shell.execute_reply": "2024-06-25T19:32:18.244749Z" + "iopub.execute_input": "2024-06-25T23:14:04.916775Z", + "iopub.status.busy": "2024-06-25T23:14:04.916438Z", + "iopub.status.idle": "2024-06-25T23:14:05.017283Z", + "shell.execute_reply": "2024-06-25T23:14:05.016746Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1307,10 +1307,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:18.247314Z", - "iopub.status.busy": "2024-06-25T19:32:18.246984Z", - "iopub.status.idle": "2024-06-25T19:32:18.345698Z", - "shell.execute_reply": "2024-06-25T19:32:18.345236Z" + "iopub.execute_input": "2024-06-25T23:14:05.019268Z", + "iopub.status.busy": "2024-06-25T23:14:05.019087Z", + "iopub.status.idle": "2024-06-25T23:14:05.125584Z", + "shell.execute_reply": "2024-06-25T23:14:05.124990Z" } }, "outputs": [ @@ -1358,10 +1358,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:18.347691Z", - "iopub.status.busy": "2024-06-25T19:32:18.347361Z", - "iopub.status.idle": "2024-06-25T19:32:18.350505Z", - "shell.execute_reply": "2024-06-25T19:32:18.349977Z" + "iopub.execute_input": "2024-06-25T23:14:05.127830Z", + "iopub.status.busy": "2024-06-25T23:14:05.127392Z", + "iopub.status.idle": "2024-06-25T23:14:05.130680Z", + "shell.execute_reply": "2024-06-25T23:14:05.130142Z" }, "nbsphinx": "hidden" }, @@ -1402,30 +1402,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0160f38ecac34b8a9a6e60b79941cb42": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6c2f4eab88df4fcfbf60ea1461b407fb", - "placeholder": "​", - "style": "IPY_MODEL_47f71961c8254da1a0cb8fe2f90ef9f5", - "tabbable": null, - "tooltip": null, - "value": "classifier.ckpt: 100%" - } - }, - "09e511f313fe4deaa3db651dc2ad1f38": { + "01d773c5fc084c74bf3898f08469f8f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1478,7 +1455,7 @@ "width": null } }, - "0f81bdbdfded4d61a103fbc884fdb8b7": { + "048952d61d4143eeb7a3064dac9fe4ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1531,83 +1508,69 @@ "width": null } }, - "13b1cd45cfea4f85bfb6caa46af5a63a": { - "model_module": "@jupyter-widgets/base", + "065d70114b74423d9f2fa424de1b7a1e": { + "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 } }, - "1965785719184160b13d07134d9c01cd": { + "0c2f7ca60ab147c99ae03f759db46297": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "11546fd8d68648da9c138662014585e5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9be1debd539947d495510d3e71e91a33", - "placeholder": "​", - "style": "IPY_MODEL_9593182600264e2aa5c5035fced2937b", + "layout": "IPY_MODEL_048952d61d4143eeb7a3064dac9fe4ac", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cd596207fea4447b96658e0f41eec607", "tabbable": null, "tooltip": null, - "value": "label_encoder.txt: 100%" + "value": 128619.0 } }, - "1aea1d112c054eec950bd686a0ca1d14": { + "12f57656dd504d218c054ef511da9ab4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1660,30 +1623,7 @@ "width": null } }, - "1b43dce850cb49019716fe7f052cd3f9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7b142eabfac84901b7edb8e5cf1db87e", - "placeholder": "​", - "style": "IPY_MODEL_751e8bbb861243c0b292b81647f5a7c8", - "tabbable": null, - "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 32.9MB/s]" - } - }, - "1c18c2753518415ea3cf39b985a054ad": { + "14c9df2de7cc4e1bbaf4f1a58d5e3037": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1736,7 +1676,7 @@ "width": null } }, - "295a0df817014d6fbb6377dcc33be2b3": { + "1510d16cc79a49c88e3542daf3152481": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1751,32 +1691,42 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_bfc1a00c4e8f4270af1f947f378411f7", - "IPY_MODEL_dcd54291f5a341bb9379d41671c00d95", - "IPY_MODEL_bec49e231d374b619cca6a3dbb6c9b4f" + "IPY_MODEL_bd56af9d1df24f959c2e27458bf28564", + "IPY_MODEL_b83e50a6f4c94c46b81bc9ccac82d8ce", + "IPY_MODEL_d74890bb53b24653a178d14fa9fb1027" ], - "layout": "IPY_MODEL_5375b29786b24fc182c5b06dc46c21f5", + "layout": "IPY_MODEL_12f57656dd504d218c054ef511da9ab4", "tabbable": null, "tooltip": null } }, - "386b0038b80f44249027e46144d33c35": { + "186fee51c3124d608de6a5c71a289764": { "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_8ca4da4898bb4451994f9434ae2ffbed", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_98428c59f17b40cdb1c0a7543994cae1", + "tabbable": null, + "tooltip": null, + "value": 15856877.0 } }, - "47f71961c8254da1a0cb8fe2f90ef9f5": { + "1dc5d0b009d545d1922ea03e17fa99b3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1794,7 +1744,56 @@ "text_color": null } }, - "48203e358e5e4c599815faec66f84717": { + "1e7ea41fcad145359a3915227cd3a572": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_50e8d183b2e146bfa57d74e4574d6bf6", + "placeholder": "​", + "style": "IPY_MODEL_7936b993c44f45fc8f53ff71690b204e", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 328MB/s]" + } + }, + "227af90915cb483c9537459db20c2dcc": { + "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_78b9bcb39b554c0290d9800dc940ff71", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5869d031c1004d448f247c32b8c702e1", + "tabbable": null, + "tooltip": null, + "value": 2041.0 + } + }, + "3740245a44544188bb5216cfbbe7c096": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1809,16 +1808,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0160f38ecac34b8a9a6e60b79941cb42", - "IPY_MODEL_cfb6db0858744f68a8074c3433dec014", - "IPY_MODEL_1b43dce850cb49019716fe7f052cd3f9" + "IPY_MODEL_ae1c35581029419bac126538cc5ba8f3", + "IPY_MODEL_186fee51c3124d608de6a5c71a289764", + "IPY_MODEL_1e7ea41fcad145359a3915227cd3a572" ], - "layout": "IPY_MODEL_5f9ac971b36144c99f166c6afca8b98c", + "layout": "IPY_MODEL_fe755d796bba4ebfa0a4fd20368887fa", "tabbable": null, "tooltip": null } }, - "5375b29786b24fc182c5b06dc46c21f5": { + "3d0bc58be6a64dbb9402e4f43247bbd9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1871,7 +1870,30 @@ "width": null } }, - "5a7fcc571ae3417ca7f32e5ac2e0f7ed": { + "40d6d738f9e542e98ecc6d953d704d68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ecd21cd3f9be4e8587ead71ef6eddacf", + "placeholder": "​", + "style": "IPY_MODEL_a873413b3495435bb8797c848d57b59a", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 508kB/s]" + } + }, + "4673aa7828d449fdb54b14aa87a8046b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1924,54 +1946,7 @@ "width": null } }, - "5b63774c214c467f836bfdcb44bf9b9f": { - "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_7b1eae32f9cd4561b35edcb6a450623c", - "IPY_MODEL_e4e0fafef42746d1a3f83998a48e68fb", - "IPY_MODEL_b61aaac35e2a498c8712ccc1a79e72b4" - ], - "layout": "IPY_MODEL_13b1cd45cfea4f85bfb6caa46af5a63a", - "tabbable": null, - "tooltip": null - } - }, - "5de7ce2185e149b199420cef3937b004": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d05aa5c2f7ed4f049aeb55cefb06f57f", - "placeholder": "​", - "style": "IPY_MODEL_df36694caa324dc08d075a690e55fed5", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 860kB/s]" - } - }, - "5f9ac971b36144c99f166c6afca8b98c": { + "4bdd74d6b6f54d1fb924effdb6c35e73": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2024,26 +1999,34 @@ "width": null } }, - "67c2bbd993f544be9aaa55f1059ee0c5": { + "4c68ee845d584f78b9ec889e427c9ab8": { "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": "" - } - }, - "6c2f4eab88df4fcfbf60ea1461b407fb": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ab9f826f70f04ba3b63e2e5db1d325f2", + "IPY_MODEL_11546fd8d68648da9c138662014585e5", + "IPY_MODEL_b5a2a7d91c7b447f8108b8896269f607" + ], + "layout": "IPY_MODEL_8954d8479e4b48759b6e0dbf92041c0c", + "tabbable": null, + "tooltip": null + } + }, + "4e926992584a4181a8551035087cce4c": { + "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", @@ -2093,25 +2076,7 @@ "width": null } }, - "751e8bbb861243c0b292b81647f5a7c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "7b142eabfac84901b7edb8e5cf1db87e": { + "50e8d183b2e146bfa57d74e4574d6bf6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2164,30 +2129,39 @@ "width": null } }, - "7b1eae32f9cd4561b35edcb6a450623c": { + "54403dba12ff4f26beb5b09e37a94253": { "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_8a3fcca569d54278b1f243ca468eb2e8", - "placeholder": "​", - "style": "IPY_MODEL_e8bef6ec94b841649328908a1e03f796", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "5869d031c1004d448f247c32b8c702e1": { + "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": "" } }, - "7c6fa0df6a7a4993ae1942f5e66eb942": { + "59bb523ac7f646b8b418eac0ba2650ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2240,7 +2214,7 @@ "width": null } }, - "8a3fcca569d54278b1f243ca468eb2e8": { + "5f72fda67a4f475ebe8560f39a8c3d76": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2293,49 +2267,7 @@ "width": null } }, - "8d0cc257082a482b8a9c20f2623d1dd0": { - "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_d3aabef313ae48b184adb3612e11f7c0", - "IPY_MODEL_9926def0468e4ef78a40becf734e408b", - "IPY_MODEL_5de7ce2185e149b199420cef3937b004" - ], - "layout": "IPY_MODEL_cad71209689044bf95c6a56cd4254c5d", - "tabbable": null, - "tooltip": null - } - }, - "9593182600264e2aa5c5035fced2937b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "95a7d374743e47b785481b53802557f9": { + "67850b52aee848a899a4c1cc7561c6dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2353,33 +2285,7 @@ "text_color": null } }, - "9926def0468e4ef78a40becf734e408b": { - "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_feef464c625b4ce98e733ec61ac7047a", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_67c2bbd993f544be9aaa55f1059ee0c5", - "tabbable": null, - "tooltip": null, - "value": 3201.0 - } - }, - "9be1debd539947d495510d3e71e91a33": { + "78b9bcb39b554c0290d9800dc940ff71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2432,7 +2338,7 @@ "width": null } }, - "b61aaac35e2a498c8712ccc1a79e72b4": { + "7906a23ebb7146d593158f78421f5ed4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2447,77 +2353,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f8e6a2a4f4ac44968d7ab7e8b41cd9e5", + "layout": "IPY_MODEL_4e926992584a4181a8551035087cce4c", "placeholder": "​", - "style": "IPY_MODEL_fd7987d5664246479c5c48a19962bcff", + "style": "IPY_MODEL_065d70114b74423d9f2fa424de1b7a1e", "tabbable": null, "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 33.4MB/s]" + "value": "hyperparams.yaml: 100%" } }, - "bc925d23337a4990bcbb6ad1d3ba0714": { + "7936b993c44f45fc8f53ff71690b204e": { "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": "" - } - }, - "bec49e231d374b619cca6a3dbb6c9b4f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_cec08439b11541149be6a96ed609992c", - "placeholder": "​", - "style": "IPY_MODEL_95a7d374743e47b785481b53802557f9", - "tabbable": null, - "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 514kB/s]" - } - }, - "bfc1a00c4e8f4270af1f947f378411f7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0f81bdbdfded4d61a103fbc884fdb8b7", - "placeholder": "​", - "style": "IPY_MODEL_d7ac88820da9447ebde38eee5408d525", - "tabbable": null, - "tooltip": null, - "value": "hyperparams.yaml: 100%" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "cad71209689044bf95c6a56cd4254c5d": { + "8954d8479e4b48759b6e0dbf92041c0c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2570,7 +2432,7 @@ "width": null } }, - "cec08439b11541149be6a96ed609992c": { + "8ca4da4898bb4451994f9434ae2ffbed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2623,33 +2485,116 @@ "width": null } }, - "cfb6db0858744f68a8074c3433dec014": { + "91f8c32305604aa6ad4753866f6428a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "98428c59f17b40cdb1c0a7543994cae1": { + "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": "" + } + }, + "a172e9a2a9464f0597dc89df0bc665f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a7376840cbcd4df89be2e35d1d83b7a7": { + "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": "" + } + }, + "a873413b3495435bb8797c848d57b59a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ab9f826f70f04ba3b63e2e5db1d325f2": { + "model_module": "@jupyter-widgets/controls", + "model_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_1c18c2753518415ea3cf39b985a054ad", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_bc925d23337a4990bcbb6ad1d3ba0714", + "layout": "IPY_MODEL_01d773c5fc084c74bf3898f08469f8f7", + "placeholder": "​", + "style": "IPY_MODEL_1dc5d0b009d545d1922ea03e17fa99b3", "tabbable": null, "tooltip": null, - "value": 15856877.0 + "value": "label_encoder.txt: 100%" } }, - "d05aa5c2f7ed4f049aeb55cefb06f57f": { + "acd3ca854a9b41b0a7e7c7c976d08dce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2702,7 +2647,7 @@ "width": null } }, - "d3aabef313ae48b184adb3612e11f7c0": { + "ae1c35581029419bac126538cc5ba8f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2717,33 +2662,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_efde3254ff184e58b1e14968ab98b2eb", + "layout": "IPY_MODEL_59bb523ac7f646b8b418eac0ba2650ec", "placeholder": "​", - "style": "IPY_MODEL_e51bddc0c26e45c28f6280a03fa6c570", + "style": "IPY_MODEL_91f8c32305604aa6ad4753866f6428a8", "tabbable": null, "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "d7ac88820da9447ebde38eee5408d525": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": "classifier.ckpt: 100%" } }, - "dc42c71988f64ceba56aa56cc9c662d3": { + "b5a2a7d91c7b447f8108b8896269f607": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2758,15 +2685,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7c6fa0df6a7a4993ae1942f5e66eb942", + "layout": "IPY_MODEL_14c9df2de7cc4e1bbaf4f1a58d5e3037", "placeholder": "​", - "style": "IPY_MODEL_fe2e22def9dd4fb9b35244ac0e211f6d", + "style": "IPY_MODEL_c805d0141fdb44038dfd6b1b6edf7abe", "tabbable": null, "tooltip": null, - "value": " 129k/129k [00:00<00:00, 7.29MB/s]" + "value": " 129k/129k [00:00<00:00, 7.47MB/s]" } }, - "dcd54291f5a341bb9379d41671c00d95": { + "b83e50a6f4c94c46b81bc9ccac82d8ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2782,35 +2709,93 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1aea1d112c054eec950bd686a0ca1d14", - "max": 2041.0, + "layout": "IPY_MODEL_acd3ca854a9b41b0a7e7c7c976d08dce", + "max": 16887676.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e83b02ee750444739a17f78b4d3f184f", + "style": "IPY_MODEL_a7376840cbcd4df89be2e35d1d83b7a7", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": 16887676.0 } }, - "df36694caa324dc08d075a690e55fed5": { + "bd56af9d1df24f959c2e27458bf28564": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3d0bc58be6a64dbb9402e4f43247bbd9", + "placeholder": "​", + "style": "IPY_MODEL_a172e9a2a9464f0597dc89df0bc665f5", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "c25723012a2d443ba16eb7f6c52bc3a5": { + "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": "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 } }, - "e4e0fafef42746d1a3f83998a48e68fb": { + "c7c838e69efb43a2b0ac48deb78c922d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2826,17 +2811,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_09e511f313fe4deaa3db651dc2ad1f38", - "max": 16887676.0, + "layout": "IPY_MODEL_f1d6704797ff4e7da238df4899d6eb75", + "max": 3201.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_ff0fbdda3a2745cab371f835eea2071a", + "style": "IPY_MODEL_54403dba12ff4f26beb5b09e37a94253", "tabbable": null, "tooltip": null, - "value": 16887676.0 + "value": 3201.0 } }, - "e51bddc0c26e45c28f6280a03fa6c570": { + "c805d0141fdb44038dfd6b1b6edf7abe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2854,7 +2839,7 @@ "text_color": null } }, - "e83b02ee750444739a17f78b4d3f184f": { + "cd596207fea4447b96658e0f41eec607": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2870,51 +2855,77 @@ "description_width": "" } }, - "e8bef6ec94b841649328908a1e03f796": { + "d74890bb53b24653a178d14fa9fb1027": { "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_4673aa7828d449fdb54b14aa87a8046b", + "placeholder": "​", + "style": "IPY_MODEL_f63a5476d3574388b856445769573334", + "tabbable": null, + "tooltip": null, + "value": " 16.9M/16.9M [00:00<00:00, 194MB/s]" } }, - "ec7119530ac242a8bf5cd0417e5460c5": { + "d8e5131818de460ba01fb92aed10a852": { "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_edb975a882cd4029ab886291208a5a5c", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_386b0038b80f44249027e46144d33c35", + "layout": "IPY_MODEL_f3b0e8a956184e818936ebd0416f606e", + "placeholder": "​", + "style": "IPY_MODEL_67850b52aee848a899a4c1cc7561c6dd", "tabbable": null, "tooltip": null, - "value": 128619.0 + "value": " 3.20k/3.20k [00:00<00:00, 795kB/s]" + } + }, + "e4e22d23b27f43a5843b7ba91eb92b25": { + "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_7906a23ebb7146d593158f78421f5ed4", + "IPY_MODEL_227af90915cb483c9537459db20c2dcc", + "IPY_MODEL_40d6d738f9e542e98ecc6d953d704d68" + ], + "layout": "IPY_MODEL_4bdd74d6b6f54d1fb924effdb6c35e73", + "tabbable": null, + "tooltip": null } }, - "edb975a882cd4029ab886291208a5a5c": { + "ecd21cd3f9be4e8587ead71ef6eddacf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2967,7 +2978,7 @@ "width": null } }, - "efde3254ff184e58b1e14968ab98b2eb": { + "f1d6704797ff4e7da238df4899d6eb75": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3020,31 +3031,7 @@ "width": null } }, - "f26aa1a5503c462e89477a33079f160a": { - "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_1965785719184160b13d07134d9c01cd", - "IPY_MODEL_ec7119530ac242a8bf5cd0417e5460c5", - "IPY_MODEL_dc42c71988f64ceba56aa56cc9c662d3" - ], - "layout": "IPY_MODEL_5a7fcc571ae3417ca7f32e5ac2e0f7ed", - "tabbable": null, - "tooltip": null - } - }, - "f8e6a2a4f4ac44968d7ab7e8b41cd9e5": { + "f3b0e8a956184e818936ebd0416f606e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3097,7 +3084,7 @@ "width": null } }, - "fd7987d5664246479c5c48a19962bcff": { + "f63a5476d3574388b856445769573334": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3115,25 +3102,54 @@ "text_color": null } }, - "fe2e22def9dd4fb9b35244ac0e211f6d": { + "fb4875c647a74da78ce0b565e117d890": { "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_fe276545fde34f708d296c6f254d81e8", + "IPY_MODEL_c7c838e69efb43a2b0ac48deb78c922d", + "IPY_MODEL_d8e5131818de460ba01fb92aed10a852" + ], + "layout": "IPY_MODEL_5f72fda67a4f475ebe8560f39a8c3d76", + "tabbable": null, + "tooltip": null + } + }, + "fe276545fde34f708d296c6f254d81e8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c25723012a2d443ba16eb7f6c52bc3a5", + "placeholder": "​", + "style": "IPY_MODEL_0c2f7ca60ab147c99ae03f759db46297", + "tabbable": null, + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" } }, - "feef464c625b4ce98e733ec61ac7047a": { + "fe755d796bba4ebfa0a4fd20368887fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3185,22 +3201,6 @@ "visibility": null, "width": null } - }, - "ff0fbdda3a2745cab371f835eea2071a": { - "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": "" - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html index e73adf499..54ecba124 100644 --- a/master/tutorials/datalab/datalab_advanced.html +++ b/master/tutorials/datalab/datalab_advanced.html @@ -1304,7 +1304,7 @@

Functionality 3: Save and load Datalab objects

-
+
@@ -1579,7 +1579,7 @@

Functionality 4: Adding a custom IssueManager -{"state": {"31beebcbb1ed40e98e72f51b7111d288": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "495eb3b3071f43e1bf30631ee18ec38f": {"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": ""}}, "e905864ed9704e29b5c76d93504b2f9f": {"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_31beebcbb1ed40e98e72f51b7111d288", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_495eb3b3071f43e1bf30631ee18ec38f", "tabbable": null, "tooltip": null, "value": 132.0}}, "066581bfe5e048229e24f4456c525a9f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e9790c7defa446cc93cb40beb3946950": {"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}}, "514a0c7d656e41c7a0ef7156f7db70a4": {"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_066581bfe5e048229e24f4456c525a9f", "placeholder": "\u200b", "style": "IPY_MODEL_e9790c7defa446cc93cb40beb3946950", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "74ffd90dc17c436bb5ca21cab1e64b31": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1c1065c6e3be428aa1ed6d3ef05adb52": {"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}}, "5b75a7f5ee634ecbb513ceba0cf27697": {"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_74ffd90dc17c436bb5ca21cab1e64b31", "placeholder": "\u200b", "style": "IPY_MODEL_1c1065c6e3be428aa1ed6d3ef05adb52", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200712588.35\u2007examples/s]"}}, "e0f1e9f09fb84534aee2fa9a795b3c91": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9160678898ef4935b4c5e9badf645887": {"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_514a0c7d656e41c7a0ef7156f7db70a4", "IPY_MODEL_e905864ed9704e29b5c76d93504b2f9f", "IPY_MODEL_5b75a7f5ee634ecbb513ceba0cf27697"], "layout": "IPY_MODEL_e0f1e9f09fb84534aee2fa9a795b3c91", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"daa1004e74fa4197b88715988591c621": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "781b877c2bbc46b7969db8529c1eb5c3": {"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": ""}}, "f70f2f9233844bc59865eab3649c0e10": {"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_daa1004e74fa4197b88715988591c621", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_781b877c2bbc46b7969db8529c1eb5c3", "tabbable": null, "tooltip": null, "value": 132.0}}, "2b2ed3b3a7ae4284a9bdf276850e3b28": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ce12a71d028f4bb7883a05d9bc842498": {"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}}, "261be93bad834528939faa8054552fd1": {"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_2b2ed3b3a7ae4284a9bdf276850e3b28", "placeholder": "\u200b", "style": "IPY_MODEL_ce12a71d028f4bb7883a05d9bc842498", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "1300012c40154e1da99babb9fe20b16e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a579e009a0944dd19902993fdacfd504": {"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}}, "6489f0184cc74d83bb9ddc2889d4a3e0": {"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_1300012c40154e1da99babb9fe20b16e", "placeholder": "\u200b", "style": "IPY_MODEL_a579e009a0944dd19902993fdacfd504", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200713503.61\u2007examples/s]"}}, "c6f6f871cbae4bd6b342d6cfded8728e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a7755a803c3a40ef93dda582347c1c91": {"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_261be93bad834528939faa8054552fd1", "IPY_MODEL_f70f2f9233844bc59865eab3649c0e10", "IPY_MODEL_6489f0184cc74d83bb9ddc2889d4a3e0"], "layout": "IPY_MODEL_c6f6f871cbae4bd6b342d6cfded8728e", "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 33af481ea..17bb19429 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": "2024-06-25T19:32:21.525415Z", - "iopub.status.busy": "2024-06-25T19:32:21.525221Z", - "iopub.status.idle": "2024-06-25T19:32:22.681975Z", - "shell.execute_reply": "2024-06-25T19:32:22.681418Z" + "iopub.execute_input": "2024-06-25T23:14:09.178246Z", + "iopub.status.busy": "2024-06-25T23:14:09.177763Z", + "iopub.status.idle": "2024-06-25T23:14:10.319594Z", + "shell.execute_reply": "2024-06-25T23:14:10.319043Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:32:22.684626Z", - "iopub.status.busy": "2024-06-25T19:32:22.684217Z", - "iopub.status.idle": "2024-06-25T19:32:22.687052Z", - "shell.execute_reply": "2024-06-25T19:32:22.686634Z" + "iopub.execute_input": "2024-06-25T23:14:10.322187Z", + "iopub.status.busy": "2024-06-25T23:14:10.321743Z", + "iopub.status.idle": "2024-06-25T23:14:10.324748Z", + "shell.execute_reply": "2024-06-25T23:14:10.324303Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:22.689175Z", - "iopub.status.busy": "2024-06-25T19:32:22.688918Z", - "iopub.status.idle": "2024-06-25T19:32:22.697425Z", - "shell.execute_reply": "2024-06-25T19:32:22.696900Z" + "iopub.execute_input": "2024-06-25T23:14:10.326836Z", + "iopub.status.busy": "2024-06-25T23:14:10.326547Z", + "iopub.status.idle": "2024-06-25T23:14:10.335582Z", + "shell.execute_reply": "2024-06-25T23:14:10.335001Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:22.699485Z", - "iopub.status.busy": "2024-06-25T19:32:22.699153Z", - "iopub.status.idle": "2024-06-25T19:32:22.703881Z", - "shell.execute_reply": "2024-06-25T19:32:22.703445Z" + "iopub.execute_input": "2024-06-25T23:14:10.337605Z", + "iopub.status.busy": "2024-06-25T23:14:10.337298Z", + "iopub.status.idle": "2024-06-25T23:14:10.342294Z", + "shell.execute_reply": "2024-06-25T23:14:10.341736Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:22.706034Z", - "iopub.status.busy": "2024-06-25T19:32:22.705704Z", - "iopub.status.idle": "2024-06-25T19:32:22.888024Z", - "shell.execute_reply": "2024-06-25T19:32:22.887415Z" + "iopub.execute_input": "2024-06-25T23:14:10.344309Z", + "iopub.status.busy": "2024-06-25T23:14:10.344016Z", + "iopub.status.idle": "2024-06-25T23:14:10.523981Z", + "shell.execute_reply": "2024-06-25T23:14:10.523494Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:22.890902Z", - "iopub.status.busy": "2024-06-25T19:32:22.890533Z", - "iopub.status.idle": "2024-06-25T19:32:23.256766Z", - "shell.execute_reply": "2024-06-25T19:32:23.256201Z" + "iopub.execute_input": "2024-06-25T23:14:10.526345Z", + "iopub.status.busy": "2024-06-25T23:14:10.525993Z", + "iopub.status.idle": "2024-06-25T23:14:10.892857Z", + "shell.execute_reply": "2024-06-25T23:14:10.892276Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:23.259088Z", - "iopub.status.busy": "2024-06-25T19:32:23.258748Z", - "iopub.status.idle": "2024-06-25T19:32:23.281774Z", - "shell.execute_reply": "2024-06-25T19:32:23.281210Z" + "iopub.execute_input": "2024-06-25T23:14:10.895029Z", + "iopub.status.busy": "2024-06-25T23:14:10.894803Z", + "iopub.status.idle": "2024-06-25T23:14:10.917811Z", + "shell.execute_reply": "2024-06-25T23:14:10.917259Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:23.284159Z", - "iopub.status.busy": "2024-06-25T19:32:23.283828Z", - "iopub.status.idle": "2024-06-25T19:32:23.294542Z", - "shell.execute_reply": "2024-06-25T19:32:23.294124Z" + "iopub.execute_input": "2024-06-25T23:14:10.920262Z", + "iopub.status.busy": "2024-06-25T23:14:10.919694Z", + "iopub.status.idle": "2024-06-25T23:14:10.930815Z", + "shell.execute_reply": "2024-06-25T23:14:10.930406Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:23.296628Z", - "iopub.status.busy": "2024-06-25T19:32:23.296301Z", - "iopub.status.idle": "2024-06-25T19:32:25.256890Z", - "shell.execute_reply": "2024-06-25T19:32:25.256295Z" + "iopub.execute_input": "2024-06-25T23:14:10.932988Z", + "iopub.status.busy": "2024-06-25T23:14:10.932566Z", + "iopub.status.idle": "2024-06-25T23:14:12.886828Z", + "shell.execute_reply": "2024-06-25T23:14:12.886199Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.259406Z", - "iopub.status.busy": "2024-06-25T19:32:25.258951Z", - "iopub.status.idle": "2024-06-25T19:32:25.279987Z", - "shell.execute_reply": "2024-06-25T19:32:25.279554Z" + "iopub.execute_input": "2024-06-25T23:14:12.889582Z", + "iopub.status.busy": "2024-06-25T23:14:12.888947Z", + "iopub.status.idle": "2024-06-25T23:14:12.909728Z", + "shell.execute_reply": "2024-06-25T23:14:12.909257Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.281917Z", - "iopub.status.busy": "2024-06-25T19:32:25.281742Z", - "iopub.status.idle": "2024-06-25T19:32:25.300001Z", - "shell.execute_reply": "2024-06-25T19:32:25.299441Z" + "iopub.execute_input": "2024-06-25T23:14:12.911863Z", + "iopub.status.busy": "2024-06-25T23:14:12.911538Z", + "iopub.status.idle": "2024-06-25T23:14:12.929776Z", + "shell.execute_reply": "2024-06-25T23:14:12.929180Z" } }, "outputs": [ @@ -949,10 +949,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.302199Z", - "iopub.status.busy": "2024-06-25T19:32:25.301790Z", - "iopub.status.idle": "2024-06-25T19:32:25.315854Z", - "shell.execute_reply": "2024-06-25T19:32:25.315290Z" + "iopub.execute_input": "2024-06-25T23:14:12.931746Z", + "iopub.status.busy": "2024-06-25T23:14:12.931414Z", + "iopub.status.idle": "2024-06-25T23:14:12.945573Z", + "shell.execute_reply": "2024-06-25T23:14:12.945130Z" } }, "outputs": [ @@ -1087,17 +1087,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.317886Z", - "iopub.status.busy": "2024-06-25T19:32:25.317705Z", - "iopub.status.idle": "2024-06-25T19:32:25.337431Z", - "shell.execute_reply": "2024-06-25T19:32:25.336840Z" + "iopub.execute_input": "2024-06-25T23:14:12.947591Z", + "iopub.status.busy": "2024-06-25T23:14:12.947261Z", + "iopub.status.idle": "2024-06-25T23:14:12.966119Z", + "shell.execute_reply": "2024-06-25T23:14:12.965554Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9160678898ef4935b4c5e9badf645887", + "model_id": "a7755a803c3a40ef93dda582347c1c91", "version_major": 2, "version_minor": 0 }, @@ -1133,10 +1133,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.339545Z", - "iopub.status.busy": "2024-06-25T19:32:25.339254Z", - "iopub.status.idle": "2024-06-25T19:32:25.353979Z", - "shell.execute_reply": "2024-06-25T19:32:25.353546Z" + "iopub.execute_input": "2024-06-25T23:14:12.967941Z", + "iopub.status.busy": "2024-06-25T23:14:12.967765Z", + "iopub.status.idle": "2024-06-25T23:14:12.982515Z", + "shell.execute_reply": "2024-06-25T23:14:12.981981Z" } }, "outputs": [ @@ -1259,10 +1259,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.355891Z", - "iopub.status.busy": "2024-06-25T19:32:25.355714Z", - "iopub.status.idle": "2024-06-25T19:32:25.361530Z", - "shell.execute_reply": "2024-06-25T19:32:25.360999Z" + "iopub.execute_input": "2024-06-25T23:14:12.984568Z", + "iopub.status.busy": "2024-06-25T23:14:12.984190Z", + "iopub.status.idle": "2024-06-25T23:14:12.989904Z", + "shell.execute_reply": "2024-06-25T23:14:12.989416Z" } }, "outputs": [], @@ -1319,10 +1319,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:25.363640Z", - "iopub.status.busy": "2024-06-25T19:32:25.363214Z", - "iopub.status.idle": "2024-06-25T19:32:25.380614Z", - "shell.execute_reply": "2024-06-25T19:32:25.380174Z" + "iopub.execute_input": "2024-06-25T23:14:12.991867Z", + "iopub.status.busy": "2024-06-25T23:14:12.991560Z", + "iopub.status.idle": "2024-06-25T23:14:13.010114Z", + "shell.execute_reply": "2024-06-25T23:14:13.009557Z" } }, "outputs": [ @@ -1459,7 +1459,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "066581bfe5e048229e24f4456c525a9f": { + "1300012c40154e1da99babb9fe20b16e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1512,25 +1512,30 @@ "width": null } }, - "1c1065c6e3be428aa1ed6d3ef05adb52": { + "261be93bad834528939faa8054552fd1": { "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_2b2ed3b3a7ae4284a9bdf276850e3b28", + "placeholder": "​", + "style": "IPY_MODEL_ce12a71d028f4bb7883a05d9bc842498", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" } }, - "31beebcbb1ed40e98e72f51b7111d288": { + "2b2ed3b3a7ae4284a9bdf276850e3b28": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1583,7 +1588,30 @@ "width": null } }, - "495eb3b3071f43e1bf30631ee18ec38f": { + "6489f0184cc74d83bb9ddc2889d4a3e0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1300012c40154e1da99babb9fe20b16e", + "placeholder": "​", + "style": "IPY_MODEL_a579e009a0944dd19902993fdacfd504", + "tabbable": null, + "tooltip": null, + "value": " 132/132 [00:00<00:00, 13503.61 examples/s]" + } + }, + "781b877c2bbc46b7969db8529c1eb5c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1599,53 +1627,49 @@ "description_width": "" } }, - "514a0c7d656e41c7a0ef7156f7db70a4": { + "a579e009a0944dd19902993fdacfd504": { "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_066581bfe5e048229e24f4456c525a9f", - "placeholder": "​", - "style": "IPY_MODEL_e9790c7defa446cc93cb40beb3946950", - "tabbable": null, - "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5b75a7f5ee634ecbb513ceba0cf27697": { + "a7755a803c3a40ef93dda582347c1c91": { "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_74ffd90dc17c436bb5ca21cab1e64b31", - "placeholder": "​", - "style": "IPY_MODEL_1c1065c6e3be428aa1ed6d3ef05adb52", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_261be93bad834528939faa8054552fd1", + "IPY_MODEL_f70f2f9233844bc59865eab3649c0e10", + "IPY_MODEL_6489f0184cc74d83bb9ddc2889d4a3e0" + ], + "layout": "IPY_MODEL_c6f6f871cbae4bd6b342d6cfded8728e", "tabbable": null, - "tooltip": null, - "value": " 132/132 [00:00<00:00, 12588.35 examples/s]" + "tooltip": null } }, - "74ffd90dc17c436bb5ca21cab1e64b31": { + "c6f6f871cbae4bd6b342d6cfded8728e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1698,31 +1722,25 @@ "width": null } }, - "9160678898ef4935b4c5e9badf645887": { + "ce12a71d028f4bb7883a05d9bc842498": { "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_514a0c7d656e41c7a0ef7156f7db70a4", - "IPY_MODEL_e905864ed9704e29b5c76d93504b2f9f", - "IPY_MODEL_5b75a7f5ee634ecbb513ceba0cf27697" - ], - "layout": "IPY_MODEL_e0f1e9f09fb84534aee2fa9a795b3c91", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e0f1e9f09fb84534aee2fa9a795b3c91": { + "daa1004e74fa4197b88715988591c621": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1775,7 +1793,7 @@ "width": null } }, - "e905864ed9704e29b5c76d93504b2f9f": { + "f70f2f9233844bc59865eab3649c0e10": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1791,33 +1809,15 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_31beebcbb1ed40e98e72f51b7111d288", + "layout": "IPY_MODEL_daa1004e74fa4197b88715988591c621", "max": 132.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_495eb3b3071f43e1bf30631ee18ec38f", + "style": "IPY_MODEL_781b877c2bbc46b7969db8529c1eb5c3", "tabbable": null, "tooltip": null, "value": 132.0 } - }, - "e9790c7defa446cc93cb40beb3946950": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 701b2fb18..4fb163767 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": "2024-06-25T19:32:28.076768Z", - "iopub.status.busy": "2024-06-25T19:32:28.076417Z", - "iopub.status.idle": "2024-06-25T19:32:29.230065Z", - "shell.execute_reply": "2024-06-25T19:32:29.229522Z" + "iopub.execute_input": "2024-06-25T23:14:15.711188Z", + "iopub.status.busy": "2024-06-25T23:14:15.711012Z", + "iopub.status.idle": "2024-06-25T23:14:16.870873Z", + "shell.execute_reply": "2024-06-25T23:14:16.870268Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:32:29.232655Z", - "iopub.status.busy": "2024-06-25T19:32:29.232386Z", - "iopub.status.idle": "2024-06-25T19:32:29.235452Z", - "shell.execute_reply": "2024-06-25T19:32:29.234935Z" + "iopub.execute_input": "2024-06-25T23:14:16.873481Z", + "iopub.status.busy": "2024-06-25T23:14:16.873232Z", + "iopub.status.idle": "2024-06-25T23:14:16.876287Z", + "shell.execute_reply": "2024-06-25T23:14:16.875762Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.237717Z", - "iopub.status.busy": "2024-06-25T19:32:29.237325Z", - "iopub.status.idle": "2024-06-25T19:32:29.246331Z", - "shell.execute_reply": "2024-06-25T19:32:29.245844Z" + "iopub.execute_input": "2024-06-25T23:14:16.878379Z", + "iopub.status.busy": "2024-06-25T23:14:16.878075Z", + "iopub.status.idle": "2024-06-25T23:14:16.887427Z", + "shell.execute_reply": "2024-06-25T23:14:16.886901Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.248332Z", - "iopub.status.busy": "2024-06-25T19:32:29.248005Z", - "iopub.status.idle": "2024-06-25T19:32:29.252728Z", - "shell.execute_reply": "2024-06-25T19:32:29.252172Z" + "iopub.execute_input": "2024-06-25T23:14:16.889377Z", + "iopub.status.busy": "2024-06-25T23:14:16.889034Z", + "iopub.status.idle": "2024-06-25T23:14:16.893463Z", + "shell.execute_reply": "2024-06-25T23:14:16.893025Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.254880Z", - "iopub.status.busy": "2024-06-25T19:32:29.254709Z", - "iopub.status.idle": "2024-06-25T19:32:29.437885Z", - "shell.execute_reply": "2024-06-25T19:32:29.437386Z" + "iopub.execute_input": "2024-06-25T23:14:16.895454Z", + "iopub.status.busy": "2024-06-25T23:14:16.895124Z", + "iopub.status.idle": "2024-06-25T23:14:17.076668Z", + "shell.execute_reply": "2024-06-25T23:14:17.076135Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.440194Z", - "iopub.status.busy": "2024-06-25T19:32:29.440000Z", - "iopub.status.idle": "2024-06-25T19:32:29.811011Z", - "shell.execute_reply": "2024-06-25T19:32:29.810430Z" + "iopub.execute_input": "2024-06-25T23:14:17.079016Z", + "iopub.status.busy": "2024-06-25T23:14:17.078687Z", + "iopub.status.idle": "2024-06-25T23:14:17.444945Z", + "shell.execute_reply": "2024-06-25T23:14:17.444376Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.813249Z", - "iopub.status.busy": "2024-06-25T19:32:29.812907Z", - "iopub.status.idle": "2024-06-25T19:32:29.815709Z", - "shell.execute_reply": "2024-06-25T19:32:29.815239Z" + "iopub.execute_input": "2024-06-25T23:14:17.447239Z", + "iopub.status.busy": "2024-06-25T23:14:17.446903Z", + "iopub.status.idle": "2024-06-25T23:14:17.449525Z", + "shell.execute_reply": "2024-06-25T23:14:17.449111Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.817838Z", - "iopub.status.busy": "2024-06-25T19:32:29.817412Z", - "iopub.status.idle": "2024-06-25T19:32:29.852590Z", - "shell.execute_reply": "2024-06-25T19:32:29.852033Z" + "iopub.execute_input": "2024-06-25T23:14:17.451586Z", + "iopub.status.busy": "2024-06-25T23:14:17.451263Z", + "iopub.status.idle": "2024-06-25T23:14:17.486312Z", + "shell.execute_reply": "2024-06-25T23:14:17.485793Z" } }, "outputs": [ @@ -647,10 +647,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:29.854728Z", - "iopub.status.busy": "2024-06-25T19:32:29.854340Z", - "iopub.status.idle": "2024-06-25T19:32:31.848165Z", - "shell.execute_reply": "2024-06-25T19:32:31.847549Z" + "iopub.execute_input": "2024-06-25T23:14:17.488380Z", + "iopub.status.busy": "2024-06-25T23:14:17.488048Z", + "iopub.status.idle": "2024-06-25T23:14:19.486184Z", + "shell.execute_reply": "2024-06-25T23:14:19.485493Z" } }, "outputs": [ @@ -710,10 +710,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.850423Z", - "iopub.status.busy": "2024-06-25T19:32:31.850093Z", - "iopub.status.idle": "2024-06-25T19:32:31.868614Z", - "shell.execute_reply": "2024-06-25T19:32:31.868087Z" + "iopub.execute_input": "2024-06-25T23:14:19.488814Z", + "iopub.status.busy": "2024-06-25T23:14:19.488306Z", + "iopub.status.idle": "2024-06-25T23:14:19.506666Z", + "shell.execute_reply": "2024-06-25T23:14:19.506238Z" } }, "outputs": [ @@ -846,10 +846,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.870858Z", - "iopub.status.busy": "2024-06-25T19:32:31.870547Z", - "iopub.status.idle": "2024-06-25T19:32:31.877139Z", - "shell.execute_reply": "2024-06-25T19:32:31.876698Z" + "iopub.execute_input": "2024-06-25T23:14:19.508882Z", + "iopub.status.busy": "2024-06-25T23:14:19.508469Z", + "iopub.status.idle": "2024-06-25T23:14:19.514832Z", + "shell.execute_reply": "2024-06-25T23:14:19.514311Z" } }, "outputs": [ @@ -960,10 +960,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.879087Z", - "iopub.status.busy": "2024-06-25T19:32:31.878912Z", - "iopub.status.idle": "2024-06-25T19:32:31.884810Z", - "shell.execute_reply": "2024-06-25T19:32:31.884314Z" + "iopub.execute_input": "2024-06-25T23:14:19.516791Z", + "iopub.status.busy": "2024-06-25T23:14:19.516482Z", + "iopub.status.idle": "2024-06-25T23:14:19.522090Z", + "shell.execute_reply": "2024-06-25T23:14:19.521611Z" } }, "outputs": [ @@ -1030,10 +1030,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.886777Z", - "iopub.status.busy": "2024-06-25T19:32:31.886603Z", - "iopub.status.idle": "2024-06-25T19:32:31.897515Z", - "shell.execute_reply": "2024-06-25T19:32:31.897079Z" + "iopub.execute_input": "2024-06-25T23:14:19.524150Z", + "iopub.status.busy": "2024-06-25T23:14:19.523753Z", + "iopub.status.idle": "2024-06-25T23:14:19.533902Z", + "shell.execute_reply": "2024-06-25T23:14:19.533440Z" } }, "outputs": [ @@ -1225,10 +1225,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.899532Z", - "iopub.status.busy": "2024-06-25T19:32:31.899178Z", - "iopub.status.idle": "2024-06-25T19:32:31.908000Z", - "shell.execute_reply": "2024-06-25T19:32:31.907553Z" + "iopub.execute_input": "2024-06-25T23:14:19.535870Z", + "iopub.status.busy": "2024-06-25T23:14:19.535545Z", + "iopub.status.idle": "2024-06-25T23:14:19.544125Z", + "shell.execute_reply": "2024-06-25T23:14:19.543654Z" } }, "outputs": [ @@ -1344,10 +1344,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.910005Z", - "iopub.status.busy": "2024-06-25T19:32:31.909678Z", - "iopub.status.idle": "2024-06-25T19:32:31.916574Z", - "shell.execute_reply": "2024-06-25T19:32:31.916117Z" + "iopub.execute_input": "2024-06-25T23:14:19.546177Z", + "iopub.status.busy": "2024-06-25T23:14:19.545853Z", + "iopub.status.idle": "2024-06-25T23:14:19.552700Z", + "shell.execute_reply": "2024-06-25T23:14:19.552255Z" }, "scrolled": true }, @@ -1472,10 +1472,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.918557Z", - "iopub.status.busy": "2024-06-25T19:32:31.918225Z", - "iopub.status.idle": "2024-06-25T19:32:31.927581Z", - "shell.execute_reply": "2024-06-25T19:32:31.927120Z" + "iopub.execute_input": "2024-06-25T23:14:19.554580Z", + "iopub.status.busy": "2024-06-25T23:14:19.554407Z", + "iopub.status.idle": "2024-06-25T23:14:19.563718Z", + "shell.execute_reply": "2024-06-25T23:14:19.563190Z" } }, "outputs": [ @@ -1578,10 +1578,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:31.929562Z", - "iopub.status.busy": "2024-06-25T19:32:31.929235Z", - "iopub.status.idle": "2024-06-25T19:32:31.940775Z", - "shell.execute_reply": "2024-06-25T19:32:31.940218Z" + "iopub.execute_input": "2024-06-25T23:14:19.565768Z", + "iopub.status.busy": "2024-06-25T23:14:19.565442Z", + "iopub.status.idle": "2024-06-25T23:14:19.576977Z", + "shell.execute_reply": "2024-06-25T23:14:19.576554Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index d02e8abdb..1c428fccc 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -738,49 +738,49 @@

2. Fetch and normalize the Fashion-MNIST dataset

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

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

@@ -1093,7 +1093,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1125,7 +1125,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1157,7 +1157,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1957,35 +1957,35 @@

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 @@ -2079,35 +2079,35 @@

Low information images - is_low_information_issue low_information_score + is_low_information_issue 53050 - True 0.067975 + True 40875 - True 0.089929 + True 9594 - True 0.092601 + True 34825 - True 0.107744 + True 37530 - True 0.108516 + True @@ -2135,7 +2135,7 @@

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

diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb index a549a7040..da3ecdeb8 100644 --- a/master/tutorials/datalab/image.ipynb +++ b/master/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:34.714453Z", - "iopub.status.busy": "2024-06-25T19:32:34.714061Z", - "iopub.status.idle": "2024-06-25T19:32:37.483269Z", - "shell.execute_reply": "2024-06-25T19:32:37.482729Z" + "iopub.execute_input": "2024-06-25T23:14:22.349033Z", + "iopub.status.busy": "2024-06-25T23:14:22.348862Z", + "iopub.status.idle": "2024-06-25T23:14:25.155777Z", + "shell.execute_reply": "2024-06-25T23:14:25.155231Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:37.485856Z", - "iopub.status.busy": "2024-06-25T19:32:37.485436Z", - "iopub.status.idle": "2024-06-25T19:32:37.489039Z", - "shell.execute_reply": "2024-06-25T19:32:37.488503Z" + "iopub.execute_input": "2024-06-25T23:14:25.158288Z", + "iopub.status.busy": "2024-06-25T23:14:25.158017Z", + "iopub.status.idle": "2024-06-25T23:14:25.161499Z", + "shell.execute_reply": "2024-06-25T23:14:25.161043Z" } }, "outputs": [], @@ -152,10 +152,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:37.491139Z", - "iopub.status.busy": "2024-06-25T19:32:37.490837Z", - "iopub.status.idle": "2024-06-25T19:32:52.986261Z", - "shell.execute_reply": "2024-06-25T19:32:52.985738Z" + "iopub.execute_input": "2024-06-25T23:14:25.163549Z", + "iopub.status.busy": "2024-06-25T23:14:25.163223Z", + "iopub.status.idle": "2024-06-25T23:14:35.757240Z", + "shell.execute_reply": "2024-06-25T23:14:35.756685Z" } }, "outputs": [ @@ -172,7 +172,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e85af83531bc4182b052d4cfe7f1020e", + "model_id": "99fb59566db2452bab382261d05e2879", "version_major": 2, "version_minor": 0 }, @@ -186,7 +186,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9abe31b01bc04cc89ff967d26e368fdf", + "model_id": "cacaca4358c34e93a46a3e2019d188d4", "version_major": 2, "version_minor": 0 }, @@ -200,7 +200,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1585cf2dc2e448068ac19676773a2a4b", + "model_id": "46c5f1e4a9ca403d83a2aa33da63b600", "version_major": 2, "version_minor": 0 }, @@ -214,7 +214,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9c34fb99987402ba4f521a988475574", + "model_id": "cc7010cd50844e48a3db713a6ea5f850", "version_major": 2, "version_minor": 0 }, @@ -228,7 +228,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "815effa183cf4ca4a7160696d4e9eb83", + "model_id": "1e806f052f23419ba6ec80aa76644ed5", "version_major": 2, "version_minor": 0 }, @@ -242,7 +242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79a15df271d14bfa8e4ed6dbe1c37a8a", + "model_id": "3590fcc9756749e0b9130b8809114216", "version_major": 2, "version_minor": 0 }, @@ -256,7 +256,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4d2025fc902f41b2b7c3474d4e9cd2fb", + "model_id": "489746a2a7db4406b7ebfd5f2a155361", "version_major": 2, "version_minor": 0 }, @@ -270,7 +270,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdbb1b6b96824b1ba8715b85852886fe", + "model_id": "b9c41de7ac0442aabfb15bbf3b5308c8", "version_major": 2, "version_minor": 0 }, @@ -312,10 +312,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:52.988486Z", - "iopub.status.busy": "2024-06-25T19:32:52.988150Z", - "iopub.status.idle": "2024-06-25T19:32:52.992110Z", - "shell.execute_reply": "2024-06-25T19:32:52.991660Z" + "iopub.execute_input": "2024-06-25T23:14:35.759372Z", + "iopub.status.busy": "2024-06-25T23:14:35.759148Z", + "iopub.status.idle": "2024-06-25T23:14:35.763037Z", + "shell.execute_reply": "2024-06-25T23:14:35.762503Z" } }, "outputs": [ @@ -340,17 +340,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:32:52.994131Z", - "iopub.status.busy": "2024-06-25T19:32:52.993820Z", - "iopub.status.idle": "2024-06-25T19:33:03.871847Z", - "shell.execute_reply": "2024-06-25T19:33:03.871235Z" + "iopub.execute_input": "2024-06-25T23:14:35.765199Z", + "iopub.status.busy": "2024-06-25T23:14:35.764868Z", + "iopub.status.idle": "2024-06-25T23:14:46.667044Z", + "shell.execute_reply": "2024-06-25T23:14:46.666518Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f2d29dc28e7140b792fc1ee3fcb857cb", + "model_id": "e075f5bd416a447eb67433e0d225370f", "version_major": 2, "version_minor": 0 }, @@ -388,10 +388,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:03.874363Z", - "iopub.status.busy": "2024-06-25T19:33:03.874071Z", - "iopub.status.idle": "2024-06-25T19:33:22.807154Z", - "shell.execute_reply": "2024-06-25T19:33:22.806523Z" + "iopub.execute_input": "2024-06-25T23:14:46.669519Z", + "iopub.status.busy": "2024-06-25T23:14:46.669228Z", + "iopub.status.idle": "2024-06-25T23:15:05.072765Z", + "shell.execute_reply": "2024-06-25T23:15:05.072224Z" } }, "outputs": [], @@ -424,10 +424,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.809827Z", - "iopub.status.busy": "2024-06-25T19:33:22.809607Z", - "iopub.status.idle": "2024-06-25T19:33:22.814504Z", - "shell.execute_reply": "2024-06-25T19:33:22.813958Z" + "iopub.execute_input": "2024-06-25T23:15:05.075378Z", + "iopub.status.busy": "2024-06-25T23:15:05.075000Z", + "iopub.status.idle": "2024-06-25T23:15:05.080668Z", + "shell.execute_reply": "2024-06-25T23:15:05.080229Z" } }, "outputs": [], @@ -465,10 +465,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.816419Z", - "iopub.status.busy": "2024-06-25T19:33:22.816239Z", - "iopub.status.idle": "2024-06-25T19:33:22.820245Z", - "shell.execute_reply": "2024-06-25T19:33:22.819819Z" + "iopub.execute_input": "2024-06-25T23:15:05.082696Z", + "iopub.status.busy": "2024-06-25T23:15:05.082377Z", + "iopub.status.idle": "2024-06-25T23:15:05.086277Z", + "shell.execute_reply": "2024-06-25T23:15:05.085865Z" }, "nbsphinx": "hidden" }, @@ -605,10 +605,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.822196Z", - "iopub.status.busy": "2024-06-25T19:33:22.822022Z", - "iopub.status.idle": "2024-06-25T19:33:22.831142Z", - "shell.execute_reply": "2024-06-25T19:33:22.830697Z" + "iopub.execute_input": "2024-06-25T23:15:05.088252Z", + "iopub.status.busy": "2024-06-25T23:15:05.087933Z", + "iopub.status.idle": "2024-06-25T23:15:05.096769Z", + "shell.execute_reply": "2024-06-25T23:15:05.096319Z" }, "nbsphinx": "hidden" }, @@ -733,10 +733,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.833164Z", - "iopub.status.busy": "2024-06-25T19:33:22.832846Z", - "iopub.status.idle": "2024-06-25T19:33:22.860883Z", - "shell.execute_reply": "2024-06-25T19:33:22.860460Z" + "iopub.execute_input": "2024-06-25T23:15:05.098773Z", + "iopub.status.busy": "2024-06-25T23:15:05.098471Z", + "iopub.status.idle": "2024-06-25T23:15:05.125306Z", + "shell.execute_reply": "2024-06-25T23:15:05.124855Z" } }, "outputs": [], @@ -773,10 +773,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:22.862805Z", - "iopub.status.busy": "2024-06-25T19:33:22.862633Z", - "iopub.status.idle": "2024-06-25T19:33:54.927685Z", - "shell.execute_reply": "2024-06-25T19:33:54.927065Z" + "iopub.execute_input": "2024-06-25T23:15:05.127482Z", + "iopub.status.busy": "2024-06-25T23:15:05.127151Z", + "iopub.status.idle": "2024-06-25T23:15:37.033092Z", + "shell.execute_reply": "2024-06-25T23:15:37.032511Z" } }, "outputs": [ @@ -792,21 +792,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.704\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.649\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.525\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.481\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b56125fc059b47e3b228dc3ed3b629c0", + "model_id": "8835da69dbeb4826a96baa0561232a18", "version_major": 2, "version_minor": 0 }, @@ -827,7 +827,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c565e132b5a46d398435caf4df461d4", + "model_id": "1141e88c1cd549c1ad36f5867b926978", "version_major": 2, "version_minor": 0 }, @@ -850,21 +850,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.714\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.663\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.460\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.663\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "63e4117109d44d79bcece5146781039a", + "model_id": "2a3f5349b34148209445198c9ae64559", "version_major": 2, "version_minor": 0 }, @@ -885,7 +885,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "09c8fb8f5f2945a4948b758b41efb311", + "model_id": "2c1834764c78450699f4a69ba292fe8e", "version_major": 2, "version_minor": 0 }, @@ -908,21 +908,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.742\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.680\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.468\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.450\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85c627e125a94180abe254acf928a1fc", + "model_id": "02ef28fe5e5647e49f15e9889ac88c8f", "version_major": 2, "version_minor": 0 }, @@ -943,7 +943,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85af3d53abef4aa8a6046017943dc826", + "model_id": "ebc081ac7cef42f58f0c46bdca672b27", "version_major": 2, "version_minor": 0 }, @@ -1022,10 +1022,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:54.930258Z", - "iopub.status.busy": "2024-06-25T19:33:54.929870Z", - "iopub.status.idle": "2024-06-25T19:33:54.943872Z", - "shell.execute_reply": "2024-06-25T19:33:54.943339Z" + "iopub.execute_input": "2024-06-25T23:15:37.035751Z", + "iopub.status.busy": "2024-06-25T23:15:37.035236Z", + "iopub.status.idle": "2024-06-25T23:15:37.049525Z", + "shell.execute_reply": "2024-06-25T23:15:37.049035Z" } }, "outputs": [], @@ -1050,10 +1050,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:54.946038Z", - "iopub.status.busy": "2024-06-25T19:33:54.945618Z", - "iopub.status.idle": "2024-06-25T19:33:55.403627Z", - "shell.execute_reply": "2024-06-25T19:33:55.402981Z" + "iopub.execute_input": "2024-06-25T23:15:37.051460Z", + "iopub.status.busy": "2024-06-25T23:15:37.051284Z", + "iopub.status.idle": "2024-06-25T23:15:37.533678Z", + "shell.execute_reply": "2024-06-25T23:15:37.533181Z" } }, "outputs": [], @@ -1073,10 +1073,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:33:55.406220Z", - "iopub.status.busy": "2024-06-25T19:33:55.406041Z", - "iopub.status.idle": "2024-06-25T19:35:30.535430Z", - "shell.execute_reply": "2024-06-25T19:35:30.534808Z" + "iopub.execute_input": "2024-06-25T23:15:37.536010Z", + "iopub.status.busy": "2024-06-25T23:15:37.535826Z", + "iopub.status.idle": "2024-06-25T23:17:13.081610Z", + "shell.execute_reply": "2024-06-25T23:17:13.080989Z" } }, "outputs": [ @@ -1123,7 +1123,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d65cb8246aa14189b49a0eeae6f3bad0", + "model_id": "55c0a386d760485f92009bb75259396b", "version_major": 2, "version_minor": 0 }, @@ -1162,10 +1162,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:30.537781Z", - "iopub.status.busy": "2024-06-25T19:35:30.537412Z", - "iopub.status.idle": "2024-06-25T19:35:30.983712Z", - "shell.execute_reply": "2024-06-25T19:35:30.983121Z" + "iopub.execute_input": "2024-06-25T23:17:13.084039Z", + "iopub.status.busy": "2024-06-25T23:17:13.083667Z", + "iopub.status.idle": "2024-06-25T23:17:13.530568Z", + "shell.execute_reply": "2024-06-25T23:17:13.530038Z" } }, "outputs": [ @@ -1311,10 +1311,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:30.986665Z", - "iopub.status.busy": "2024-06-25T19:35:30.986208Z", - "iopub.status.idle": "2024-06-25T19:35:31.048426Z", - "shell.execute_reply": "2024-06-25T19:35:31.047866Z" + "iopub.execute_input": "2024-06-25T23:17:13.532958Z", + "iopub.status.busy": "2024-06-25T23:17:13.532616Z", + "iopub.status.idle": "2024-06-25T23:17:13.595525Z", + "shell.execute_reply": "2024-06-25T23:17:13.594969Z" } }, "outputs": [ @@ -1418,10 +1418,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.050749Z", - "iopub.status.busy": "2024-06-25T19:35:31.050363Z", - "iopub.status.idle": "2024-06-25T19:35:31.060546Z", - "shell.execute_reply": "2024-06-25T19:35:31.060026Z" + "iopub.execute_input": "2024-06-25T23:17:13.597922Z", + "iopub.status.busy": "2024-06-25T23:17:13.597476Z", + "iopub.status.idle": "2024-06-25T23:17:13.606785Z", + "shell.execute_reply": "2024-06-25T23:17:13.606218Z" } }, "outputs": [ @@ -1551,10 +1551,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.062742Z", - "iopub.status.busy": "2024-06-25T19:35:31.062469Z", - "iopub.status.idle": "2024-06-25T19:35:31.068251Z", - "shell.execute_reply": "2024-06-25T19:35:31.067801Z" + "iopub.execute_input": "2024-06-25T23:17:13.609099Z", + "iopub.status.busy": "2024-06-25T23:17:13.608903Z", + "iopub.status.idle": "2024-06-25T23:17:13.613602Z", + "shell.execute_reply": "2024-06-25T23:17:13.613147Z" }, "nbsphinx": "hidden" }, @@ -1600,10 +1600,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.070241Z", - "iopub.status.busy": "2024-06-25T19:35:31.069928Z", - "iopub.status.idle": "2024-06-25T19:35:31.828844Z", - "shell.execute_reply": "2024-06-25T19:35:31.828271Z" + "iopub.execute_input": "2024-06-25T23:17:13.615408Z", + "iopub.status.busy": "2024-06-25T23:17:13.615233Z", + "iopub.status.idle": "2024-06-25T23:17:14.118370Z", + "shell.execute_reply": "2024-06-25T23:17:14.117787Z" } }, "outputs": [ @@ -1638,10 +1638,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.831222Z", - "iopub.status.busy": "2024-06-25T19:35:31.830895Z", - "iopub.status.idle": "2024-06-25T19:35:31.839311Z", - "shell.execute_reply": "2024-06-25T19:35:31.838857Z" + "iopub.execute_input": "2024-06-25T23:17:14.120491Z", + "iopub.status.busy": "2024-06-25T23:17:14.120304Z", + "iopub.status.idle": "2024-06-25T23:17:14.128885Z", + "shell.execute_reply": "2024-06-25T23:17:14.128442Z" } }, "outputs": [ @@ -1808,10 +1808,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.841482Z", - "iopub.status.busy": "2024-06-25T19:35:31.841163Z", - "iopub.status.idle": "2024-06-25T19:35:31.848176Z", - "shell.execute_reply": "2024-06-25T19:35:31.847749Z" + "iopub.execute_input": "2024-06-25T23:17:14.131053Z", + "iopub.status.busy": "2024-06-25T23:17:14.130639Z", + "iopub.status.idle": "2024-06-25T23:17:14.433754Z", + "shell.execute_reply": "2024-06-25T23:17:14.433138Z" }, "nbsphinx": "hidden" }, @@ -1887,10 +1887,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:31.850195Z", - "iopub.status.busy": "2024-06-25T19:35:31.849881Z", - "iopub.status.idle": "2024-06-25T19:35:32.292692Z", - "shell.execute_reply": "2024-06-25T19:35:32.292043Z" + "iopub.execute_input": "2024-06-25T23:17:14.437185Z", + "iopub.status.busy": "2024-06-25T23:17:14.436581Z", + "iopub.status.idle": "2024-06-25T23:17:14.910179Z", + "shell.execute_reply": "2024-06-25T23:17:14.909590Z" } }, "outputs": [ @@ -1927,10 +1927,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.295116Z", - "iopub.status.busy": "2024-06-25T19:35:32.294759Z", - "iopub.status.idle": "2024-06-25T19:35:32.310913Z", - "shell.execute_reply": "2024-06-25T19:35:32.310451Z" + "iopub.execute_input": "2024-06-25T23:17:14.912393Z", + "iopub.status.busy": "2024-06-25T23:17:14.912024Z", + "iopub.status.idle": "2024-06-25T23:17:14.927515Z", + "shell.execute_reply": "2024-06-25T23:17:14.926933Z" } }, "outputs": [ @@ -2087,10 +2087,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.313089Z", - "iopub.status.busy": "2024-06-25T19:35:32.312752Z", - "iopub.status.idle": "2024-06-25T19:35:32.318396Z", - "shell.execute_reply": "2024-06-25T19:35:32.317860Z" + "iopub.execute_input": "2024-06-25T23:17:14.929547Z", + "iopub.status.busy": "2024-06-25T23:17:14.929372Z", + "iopub.status.idle": "2024-06-25T23:17:14.935923Z", + "shell.execute_reply": "2024-06-25T23:17:14.935427Z" }, "nbsphinx": "hidden" }, @@ -2135,10 +2135,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.320370Z", - "iopub.status.busy": "2024-06-25T19:35:32.320196Z", - "iopub.status.idle": "2024-06-25T19:35:32.779377Z", - "shell.execute_reply": "2024-06-25T19:35:32.778856Z" + "iopub.execute_input": "2024-06-25T23:17:14.937944Z", + "iopub.status.busy": "2024-06-25T23:17:14.937612Z", + "iopub.status.idle": "2024-06-25T23:17:15.400691Z", + "shell.execute_reply": "2024-06-25T23:17:15.399712Z" } }, "outputs": [ @@ -2220,10 +2220,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.782553Z", - "iopub.status.busy": "2024-06-25T19:35:32.782090Z", - "iopub.status.idle": "2024-06-25T19:35:32.791666Z", - "shell.execute_reply": "2024-06-25T19:35:32.790923Z" + "iopub.execute_input": "2024-06-25T23:17:15.403380Z", + "iopub.status.busy": "2024-06-25T23:17:15.403170Z", + "iopub.status.idle": "2024-06-25T23:17:15.412375Z", + "shell.execute_reply": "2024-06-25T23:17:15.411801Z" } }, "outputs": [ @@ -2248,47 +2248,47 @@ " \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, @@ -2351,10 +2351,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.794003Z", - "iopub.status.busy": "2024-06-25T19:35:32.793805Z", - "iopub.status.idle": "2024-06-25T19:35:32.799849Z", - "shell.execute_reply": "2024-06-25T19:35:32.799106Z" + "iopub.execute_input": "2024-06-25T23:17:15.414938Z", + "iopub.status.busy": "2024-06-25T23:17:15.414744Z", + "iopub.status.idle": "2024-06-25T23:17:15.420451Z", + "shell.execute_reply": "2024-06-25T23:17:15.419881Z" }, "nbsphinx": "hidden" }, @@ -2391,10 +2391,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:32.802393Z", - "iopub.status.busy": "2024-06-25T19:35:32.802198Z", - "iopub.status.idle": "2024-06-25T19:35:33.003653Z", - "shell.execute_reply": "2024-06-25T19:35:33.003206Z" + "iopub.execute_input": "2024-06-25T23:17:15.422902Z", + "iopub.status.busy": "2024-06-25T23:17:15.422710Z", + "iopub.status.idle": "2024-06-25T23:17:15.624779Z", + "shell.execute_reply": "2024-06-25T23:17:15.624291Z" } }, "outputs": [ @@ -2436,10 +2436,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:33.005778Z", - "iopub.status.busy": "2024-06-25T19:35:33.005613Z", - "iopub.status.idle": "2024-06-25T19:35:33.013113Z", - "shell.execute_reply": "2024-06-25T19:35:33.012647Z" + "iopub.execute_input": "2024-06-25T23:17:15.627227Z", + "iopub.status.busy": "2024-06-25T23:17:15.626865Z", + "iopub.status.idle": "2024-06-25T23:17:15.634605Z", + "shell.execute_reply": "2024-06-25T23:17:15.634166Z" } }, "outputs": [ @@ -2464,47 +2464,47 @@ " \n", " \n", " \n", - " is_low_information_issue\n", " low_information_score\n", + " is_low_information_issue\n", " \n", " \n", " \n", " \n", " 53050\n", - " True\n", " 0.067975\n", + " True\n", " \n", " \n", " 40875\n", - " True\n", " 0.089929\n", + " True\n", " \n", " \n", " 9594\n", - " True\n", " 0.092601\n", + " True\n", " \n", " \n", " 34825\n", - " True\n", " 0.107744\n", + " True\n", " \n", " \n", " 37530\n", - " True\n", " 0.108516\n", + " True\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " 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" + " 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" ] }, "execution_count": 29, @@ -2525,10 +2525,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:33.015062Z", - "iopub.status.busy": "2024-06-25T19:35:33.014721Z", - "iopub.status.idle": "2024-06-25T19:35:33.209360Z", - "shell.execute_reply": "2024-06-25T19:35:33.208767Z" + "iopub.execute_input": "2024-06-25T23:17:15.636681Z", + "iopub.status.busy": "2024-06-25T23:17:15.636363Z", + "iopub.status.idle": "2024-06-25T23:17:15.834587Z", + "shell.execute_reply": "2024-06-25T23:17:15.834003Z" } }, "outputs": [ @@ -2568,10 +2568,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:33.211913Z", - "iopub.status.busy": "2024-06-25T19:35:33.211538Z", - "iopub.status.idle": "2024-06-25T19:35:33.216052Z", - "shell.execute_reply": "2024-06-25T19:35:33.215616Z" + "iopub.execute_input": "2024-06-25T23:17:15.836769Z", + "iopub.status.busy": "2024-06-25T23:17:15.836460Z", + "iopub.status.idle": "2024-06-25T23:17:15.840847Z", + "shell.execute_reply": "2024-06-25T23:17:15.840296Z" }, "nbsphinx": "hidden" }, @@ -2608,7 +2608,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0011fb26fc5d4baa896547da4133122f": { + "003b69c44f834dd6bd767bd85d0282c1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "01e9581b8f904ce9bd99cf2093d8e0b6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2661,48 +2679,31 @@ "width": null } }, - "00644b1a57b2451e9cdebb4eb7250f78": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "007876448c144ee39084540ed6eb06b9": { + "02ef28fe5e5647e49f15e9889ac88c8f": { "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_5745328a69db48878efba6c4bdb99305", - "placeholder": "​", - "style": "IPY_MODEL_b54eac280dff4e5dbe0c1d2b48743535", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_17898a71a5eb4556a9c02c78df55aeb3", + "IPY_MODEL_aae89dad290345f5acc2526904d6d4e5", + "IPY_MODEL_18560e45d46d48bf99bbf1a153e2502c" + ], + "layout": "IPY_MODEL_22708ad181354289ac5fa640bd1c4121", "tabbable": null, - "tooltip": null, - "value": " 8.85k/8.85k [00:00<00:00, 1.45MB/s]" + "tooltip": null } }, - "0120148eaefd42fd9a38edc77bc35ac7": { + "0302bede64084627a211292664570913": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2720,7 +2721,7 @@ "text_color": null } }, - "0261398aaa894092b6eca7f630c39440": { + "040097e76c3b414292ef486207e132ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2773,113 +2774,76 @@ "width": null } }, - "031211ba610a4802b4a54cdf1bba56a7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b9a030c7a18e43e0a9ade7e2489ba818", - "placeholder": "​", - "style": "IPY_MODEL_7824f895073b4989aabcc1118fe1e570", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "03ab3894bf4242d8999349575ac3ab13": { + "04bfeccaeea6416e98641eb5e663ae0b": { "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 - } - }, - "03b60960744f469c8562f78a5948c4a9": { - "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_46f3bc1415b94874ba19257d22e4bfed", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_44210013b27e41639717a483f6daeb8c", - "tabbable": null, - "tooltip": null, - "value": 10000.0 - } - }, - "0419dce41e82427eba5f201dcd3224ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fe37a821e1f04c6f9903e3722f9ab4c2", - "placeholder": "​", - "style": "IPY_MODEL_7f8b4b75f0c545ac966463807f510230", - "tabbable": null, - "tooltip": null, - "value": "100%" + "bar_color": null, + "description_width": "" } }, - "05b056eafe984430926b52278f208393": { - "model_module": "@jupyter-widgets/controls", + "08971fe179644eda8bcd42e21d601e92": { + "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 } }, - "0637c05b667541e782a074923de85b45": { + "08fa3b07fef4433e8a950677af858234": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2894,39 +2858,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_74f4d829ffbb4508957bfb49f70fe734", + "layout": "IPY_MODEL_72b19493a1ca42c793bcda945b230961", "placeholder": "​", - "style": "IPY_MODEL_13a871b41cce4be6bd9b0c8fb1116aec", + "style": "IPY_MODEL_afdd5bafd5ba49909abaaef0bb7f6038", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.59it/s]" - } - }, - "09c8fb8f5f2945a4948b758b41efb311": { - "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_031211ba610a4802b4a54cdf1bba56a7", - "IPY_MODEL_42fc637a41cf41029345fe4e9b68d6c9", - "IPY_MODEL_c1899c2c83a146b4b5eccb8d3c0892ae" - ], - "layout": "IPY_MODEL_44661251f61148bab2d1f3993f625718", - "tabbable": null, - "tooltip": null + "value": " 26.4M/26.4M [00:00<00:00, 114MB/s]" } }, - "0b157a2c7cf7434fbc4448eaf5ad92d7": { + "0ada4c3d2a88412599516fae13401849": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2942,48 +2882,7 @@ "description_width": "" } }, - "0d79223cae9246eaaa4f673e81a780a8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0f7c857000414450bdc92196b4466489": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_88408a65c5eb4eb48911a14aadbc74cf", - "placeholder": "​", - "style": "IPY_MODEL_26f5985a24fd4b3cb9fb40bc0c1b22a2", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "112a5fd388db49e4b8a0f9dffab06426": { + "0fc5778c044d4fcd9e8f745663d100c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3036,94 +2935,10 @@ "width": null } }, - "12693e79b18144d2be2b4b4399f4789e": { - "model_module": "@jupyter-widgets/controls", + "103b52da5fd24c24a14b4605ba5c3120": { + "model_module": "@jupyter-widgets/base", "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_bf14a805f3944138b263cb978e9fcf8b", - "max": 29515.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_51c208e957a24da184c9f20ebc42f546", - "tabbable": null, - "tooltip": null, - "value": 29515.0 - } - }, - "12da0c3d2bd5449dbb811de7fd8b2093": { - "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": "" - } - }, - "13a871b41cce4be6bd9b0c8fb1116aec": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "1585cf2dc2e448068ac19676773a2a4b": { - "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_744802ca32e849ab9fea4e8e3861b154", - "IPY_MODEL_2cca5f207862410483ac41718382a443", - "IPY_MODEL_5b650c7c7114407ca633a6f913edbef2" - ], - "layout": "IPY_MODEL_90c7589ac13b443eb422dd2f647490a3", - "tabbable": null, - "tooltip": null - } - }, - "175a6c09ad6c4cbfbdd9543952ef9ffc": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", @@ -3173,30 +2988,7 @@ "width": null } }, - "1a2d0d43f24f4a179401f7bde3fcbf11": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ec23491fb0024cb8a1b7dfd205775551", - "placeholder": "​", - "style": "IPY_MODEL_8946f270b2954b22801e92ce10bf7c63", - "tabbable": null, - "tooltip": null, - "value": "Downloading readme: 100%" - } - }, - "1b72621c613b4da3999f4f434c596d23": { + "10833dd9b558436e8141086cd6457744": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3249,82 +3041,49 @@ "width": null } }, - "1bb8a6dd435048b28340d4eac6730019": { - "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_c1f07c0e6cb7453cb234322863c6974b", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_494b87319f8d45ab8596d3cc2028f7f6", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "1c96211615924c808f262fae38d4ab01": { + "1141e88c1cd549c1ad36f5867b926978": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1b72621c613b4da3999f4f434c596d23", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b461446aa899462d865a83e0c727b5c5", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7a70b6d736914713b75013fd280cd1fb", + "IPY_MODEL_233edc846cd2411699f747246a5c017b", + "IPY_MODEL_9b8b5db0d4ec47ae8f14fbae0720d3e0" + ], + "layout": "IPY_MODEL_32715003f9374a25a98d8e94a71e934d", "tabbable": null, - "tooltip": null, - "value": 40.0 + "tooltip": null } }, - "1d5a9e2ed91b479f9781063cdce66b7e": { + "11828cd7a97146ac81748612b0601834": { "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_4266637c8f4c47fbb4c9e7bd9cc2cabf", - "placeholder": "​", - "style": "IPY_MODEL_2047417b76614aad92d50e9d57fa28a2", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:06<00:00, 8928.67 examples/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1f1d67e48a1c45258572b6fbf8ebe4e2": { + "11e93782fa6f464e9f6900e0c646f5cf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3377,7 +3136,7 @@ "width": null } }, - "1fbec845f4cf42bb8efb9b83163e0b9b": { + "1232a69e4cd748539b2d4cdde35b417c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3430,25 +3189,60 @@ "width": null } }, - "2047417b76614aad92d50e9d57fa28a2": { - "model_module": "@jupyter-widgets/controls", + "12fb080c89ea44b788b8036e7c269e2e": { + "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 } }, - "2426b36b4bcd4538a7a1a6c6f443b609": { + "176f4fe537ab44709eed5f4771e5a748": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3501,67 +3295,48 @@ "width": null } }, - "26f5985a24fd4b3cb9fb40bc0c1b22a2": { + "17898a71a5eb4556a9c02c78df55aeb3": { "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_a805af9f6458409cbfcf1ef1829794dc", + "placeholder": "​", + "style": "IPY_MODEL_7f159f23ebfd44699351ad51261161f4", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "2854eeec578a4c3e9a9828ec08c1652c": { + "17af51078a0c4c0f92db9850c8c773d7": { "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": "" - } - }, - "2a37f3b6e2e64eda9d5aad4e57bb6475": { - "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_1f1d67e48a1c45258572b6fbf8ebe4e2", - "max": 5148.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9b58b8eff8e745eb9e937354fedb1807", - "tabbable": null, - "tooltip": null, - "value": 5148.0 + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2c916b2e66ff458bae959988d3edfc7f": { + "18560e45d46d48bf99bbf1a153e2502c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3576,15 +3351,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0011fb26fc5d4baa896547da4133122f", + "layout": "IPY_MODEL_040097e76c3b414292ef486207e132ee", "placeholder": "​", - "style": "IPY_MODEL_9d3b7c0890814f2c9985eb79f97a04ac", + "style": "IPY_MODEL_0302bede64084627a211292664570913", "tabbable": null, "tooltip": null, - "value": "Generating train split: 100%" + "value": " 40/40 [00:00<00:00, 60.88it/s]" } }, - "2cca5f207862410483ac41718382a443": { + "18e5b3625d2a4b3abbfea651a20eef56": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3600,74 +3375,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ecde01d38be144db9d996498b953e5b4", - "max": 26421880.0, + "layout": "IPY_MODEL_01e9581b8f904ce9bd99cf2093d8e0b6", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_555d30c920d344059abb5d85084268de", - "tabbable": null, - "tooltip": null, - "value": 26421880.0 - } - }, - "2f9e9f6f7ebe42afa59802bdf7306ccb": { - "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": "" - } - }, - "30a335f232c04fe7b668ed6a418d27b2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "3253c6b3e3464da88809a4ce236555f1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_495d2ed12e8446bc80ad465a9f89a896", - "placeholder": "​", - "style": "IPY_MODEL_00644b1a57b2451e9cdebb4eb7250f78", + "style": "IPY_MODEL_293b9b52ffaa471e9c10ec006eefc31e", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.41it/s]" + "value": 40.0 } }, - "32b6d99a486e461eacd6e063c5420eff": { + "1a2ffe0f3d68433d9fc120a9b894e762": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3720,7 +3438,7 @@ "width": null } }, - "38eb030b83c64212b25df93cfe516570": { + "1cdb544e05fe49e5b616044f82615b00": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3735,33 +3453,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_77b7d71484644232938d588a6fbcc4ed", + "layout": "IPY_MODEL_12fb080c89ea44b788b8036e7c269e2e", "placeholder": "​", - "style": "IPY_MODEL_cd436773aeeb4e5f811c805cd47f7071", + "style": "IPY_MODEL_2b67454e45604716a3d08831b42d274a", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "39314c631f444d01903b4f50ccfc66df": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": "Downloading data: 100%" } }, - "420adb2a809a4b7693bda83cb040e60c": { + "1e4148a5a5464b22b94684bad558cbec": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3814,86 +3514,49 @@ "width": null } }, - "4266637c8f4c47fbb4c9e7bd9cc2cabf": { - "model_module": "@jupyter-widgets/base", + "1e806f052f23419ba6ec80aa76644ed5": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1cdb544e05fe49e5b616044f82615b00", + "IPY_MODEL_b602e59393ab410ba774d7c2419c556d", + "IPY_MODEL_d6337601ed54490eb0d452da36303706" + ], + "layout": "IPY_MODEL_1a2ffe0f3d68433d9fc120a9b894e762", + "tabbable": null, + "tooltip": null } }, - "4268b194fef1470096c0d165c23da82f": { + "1f7f1fac2dd34d07b7fc07f799729df6": { "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_b218eb25d1544b2198406c8d0daa0439", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_53d0e7a8e50247a88566f4b59b14d553", - "tabbable": null, - "tooltip": null, - "value": 60000.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "42c998dc50734d8c93d723bb78939244": { + "20376214a3ff45e29c894539ac53a963": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3946,109 +3609,30 @@ "width": null } }, - "42e73c1ad6b74b238d722d1c7ee516b1": { - "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": "" - } - }, - "42fc637a41cf41029345fe4e9b68d6c9": { + "20aa2cb116fa42218107305be2c9121a": { "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_175a6c09ad6c4cbfbdd9543952ef9ffc", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0b157a2c7cf7434fbc4448eaf5ad92d7", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "434f1175a8614296b63392ddd951670e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "44210013b27e41639717a483f6daeb8c": { - "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": "" - } - }, - "4439d6c4067d42b2bc7b5b49e651de05": { - "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_70c9b3de67694dc4a8c38a542842ae3c", - "max": 4.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2854eeec578a4c3e9a9828ec08c1652c", + "layout": "IPY_MODEL_dd549dafe96d457a9f3e32fa6dda0ce5", + "placeholder": "​", + "style": "IPY_MODEL_6f6e9c74347e4652b414406bd4c67238", "tabbable": null, "tooltip": null, - "value": 4.0 + "value": " 29.5k/29.5k [00:00<00:00, 4.38MB/s]" } }, - "44661251f61148bab2d1f3993f625718": { + "20e8c6a9ab644e3da69655bfbc794894": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4101,7 +3685,33 @@ "width": null } }, - "46692e2429834f6b8ad6d2e3bd669ac5": { + "2253900d1539484ea6c5fb5f87e34fae": { + "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_8b39eaec8f5843a3942f7b65e5320363", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e675357d5efc42b3968d61c273ca5f7f", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "22708ad181354289ac5fa640bd1c4121": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4154,7 +3764,7 @@ "width": null } }, - "46f3bc1415b94874ba19257d22e4bfed": { + "22e4e1da97fe49e290e9dac292880148": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4207,7 +3817,127 @@ "width": null } }, - "470cbbcbb3d44bb493031e8387fe6adc": { + "22ece7076054425f8784c9f44cd9c512": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "231c040b6a13479f944b5bef41c9994a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "233edc846cd2411699f747246a5c017b": { + "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_816719582fe14c11a648b648bb9e2cbb", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_474d936989bb4423af34fd74a8db3b23", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "293b9b52ffaa471e9c10ec006eefc31e": { + "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": "" + } + }, + "2a3f5349b34148209445198c9ae64559": { + "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_bf0ad0d606f04b7aa5013f43b6f32049", + "IPY_MODEL_5788b965507d455ca17eabfc155de530", + "IPY_MODEL_453bcb1f0a7e4e41aa72dab72cbeed75" + ], + "layout": "IPY_MODEL_10833dd9b558436e8141086cd6457744", + "tabbable": null, + "tooltip": null + } + }, + "2b67454e45604716a3d08831b42d274a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "2bf71c3624ce4413bc347a20e07c26d5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4260,23 +3990,31 @@ "width": null } }, - "494b87319f8d45ab8596d3cc2028f7f6": { + "2c1834764c78450699f4a69ba292fe8e": { "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_c6eb4afdd2a54d899795e4359d8c989d", + "IPY_MODEL_caabab78e31d438eb521e9c3f2e1c0a6", + "IPY_MODEL_e9b9c9bbe0374dd9b98a3a4c1553822f" + ], + "layout": "IPY_MODEL_20376214a3ff45e29c894539ac53a963", + "tabbable": null, + "tooltip": null } }, - "495d2ed12e8446bc80ad465a9f89a896": { + "2d69655ca2a545e9a4b05f6865cdf2a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4329,7 +4067,53 @@ "width": null } }, - "4b1c1c0a8eb2477b91cae9a52c47e58c": { + "307355aaf24e41138019598af025ac1b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_715dab8821f5465eade13b7f8765ed47", + "placeholder": "​", + "style": "IPY_MODEL_71217b3eed354f73861bde4bfaf66ffd", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:36<00:00, 1535.58it/s]" + } + }, + "311422b25c3d435280a7ae2e6c1b5cbc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_485dd48b720d4e1dbfce8f76953b154e", + "placeholder": "​", + "style": "IPY_MODEL_8c6b20c613cf48bd9f826dbeb5124369", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "32715003f9374a25a98d8e94a71e934d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4382,7 +4166,7 @@ "width": null } }, - "4b9e51775e974654a075cfdb1d1b699c": { + "32d10bf33ee54d2bac909d0748596dac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4435,25 +4219,7 @@ "width": null } }, - "4cfef00083ee41c18334522b0aaafbf6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4d2025fc902f41b2b7c3474d4e9cd2fb": { + "3590fcc9756749e0b9130b8809114216": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4468,16 +4234,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2c916b2e66ff458bae959988d3edfc7f", - "IPY_MODEL_f3a3a4eba3674c53882dfe2a1c081653", - "IPY_MODEL_1d5a9e2ed91b479f9781063cdce66b7e" + "IPY_MODEL_9c8e988dcc76462ea4a6543f9f1dd759", + "IPY_MODEL_61530f93052c4966a3ed9c90ec90c547", + "IPY_MODEL_3c58f7da1e954e5f85811b8c931dddf3" ], - "layout": "IPY_MODEL_e7dae7ac77284ca7a938a5140aea14ba", + "layout": "IPY_MODEL_839cc7aeb6cf41cba2328b772dda1354", "tabbable": null, "tooltip": null } }, - "4f814c4fdc474d7e8305f032c2caaf1d": { + "37059badcffc423fbad95494b9745859": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4492,81 +4258,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7437bf57da394059a3d162f5d7a1a77a", + "layout": "IPY_MODEL_50aef71653f04eb49e220d1870cbf7ff", "placeholder": "​", - "style": "IPY_MODEL_434f1175a8614296b63392ddd951670e", + "style": "IPY_MODEL_eb33301487694b0f9f4a664fce743134", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "51c208e957a24da184c9f20ebc42f546": { - "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": "" - } - }, - "53d0e7a8e50247a88566f4b59b14d553": { - "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": "" - } - }, - "555d30c920d344059abb5d85084268de": { - "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": "" - } - }, - "569d3453747c444a9c2661732c8b1fb5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 60000/60000 [00:06<00:00, 8813.66 examples/s]" } }, - "5745328a69db48878efba6c4bdb99305": { + "373768b5fd4c42118d61459d55c65ad2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4619,7 +4319,46 @@ "width": null } }, - "594f571cc61a464d8934a2a6ebaff1ed": { + "3764b604f5ba43288217fe3f4372b542": { + "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": "" + } + }, + "3779e0b1881642b8b80007dfa03126bb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1e4148a5a5464b22b94684bad558cbec", + "placeholder": "​", + "style": "IPY_MODEL_885a1c18bad74f47ae49ef86de2c3a67", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" + } + }, + "38bd0231f7c34338a8ccd2536adb5809": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4672,7 +4411,48 @@ "width": null } }, - "5a36ed53de5c4a9c8637a7b4013dfb54": { + "3c58f7da1e954e5f85811b8c931dddf3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d8441cb494324dad83d4a88dd3c805e6", + "placeholder": "​", + "style": "IPY_MODEL_3e99f4f8639445f0810c65d14dccca89", + "tabbable": null, + "tooltip": null, + "value": " 5.15k/5.15k [00:00<00:00, 906kB/s]" + } + }, + "3c76ee6a2244430cbb3e8e8416d1a23c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3e86a20ac6b044d4adccfeaba4245d25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4725,86 +4505,77 @@ "width": null } }, - "5b650c7c7114407ca633a6f913edbef2": { + "3e99f4f8639445f0810c65d14dccca89": { "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_d6b409257aab47b888b2954d7b04073e", - "placeholder": "​", - "style": "IPY_MODEL_d067169b2a1745bd893cbaa56705df57", - "tabbable": null, - "tooltip": null, - "value": " 26.4M/26.4M [00:00<00:00, 124MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5c565e132b5a46d398435caf4df461d4": { + "40e314c496664e698c489a3f5c279e1c": { "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_4f814c4fdc474d7e8305f032c2caaf1d", - "IPY_MODEL_bb91a65b9f7c4bcbae0545c6e0c0c603", - "IPY_MODEL_e39cdb7fe88f4310bfd8b1dcd8ac5ae2" - ], - "layout": "IPY_MODEL_a7668c39f7324f3abf6dee2fb270336f", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_08971fe179644eda8bcd42e21d601e92", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0ada4c3d2a88412599516fae13401849", "tabbable": null, - "tooltip": null - } - }, - "5c5b477c793d4588a25705aece5a2a2e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "tooltip": null, + "value": 10000.0 } }, - "5d69e819f44b493a8d07e58cb39a6a69": { + "41aa359adc6640ab9d5ea2369493623a": { "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_4ae6fd916d6d470190a53d061fb0b02f", + "max": 8845.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_63f97e8188cb4370abab083114492da0", + "tabbable": null, + "tooltip": null, + "value": 8845.0 } }, - "5efe1df058ed48ae9674a8448d855c64": { + "4251756ae8d344e8923025afa5b3be9a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4857,31 +4628,23 @@ "width": null } }, - "63e4117109d44d79bcece5146781039a": { + "42c5eeb19b2a4542b4159e63110273e4": { "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_eb56ed8bd1a74a87a2a7cf25c0992594", - "IPY_MODEL_ee448c322caa4ddbae258419893e01e8", - "IPY_MODEL_3253c6b3e3464da88809a4ce236555f1" - ], - "layout": "IPY_MODEL_659e46eb96e64415a9a947aea4256de6", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "6566ace6c0aa4a21abbf6d708cb457b0": { + "42f688e5fa0a4f698ca681a4433e115d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4934,7 +4697,7 @@ "width": null } }, - "659e46eb96e64415a9a947aea4256de6": { + "431d46eafa8c450b8c8f1bf1f7883cf3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4987,7 +4750,7 @@ "width": null } }, - "670532c752594999bbc5db2f86da0bfa": { + "4536c7080de841acaaec729cdf955c0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5040,51 +4803,70 @@ "width": null } }, - "67bb2dfe0bed499ea09cbd03e6cf7fd1": { + "453bcb1f0a7e4e41aa72dab72cbeed75": { "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_470cbbcbb3d44bb493031e8387fe6adc", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_42e73c1ad6b74b238d722d1c7ee516b1", + "layout": "IPY_MODEL_103b52da5fd24c24a14b4605ba5c3120", + "placeholder": "​", + "style": "IPY_MODEL_9d8907e922884b4c98182bd90146f805", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": " 40/40 [00:00<00:00, 60.08it/s]" } }, - "6b4971a3c52247978439a6e52e61bca9": { + "46c5f1e4a9ca403d83a2aa33da63b600": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_667ac6e91c614fc09ad352122a696161", + "IPY_MODEL_59358b3c018e40c1b73065b80810af12", + "IPY_MODEL_08fa3b07fef4433e8a950677af858234" + ], + "layout": "IPY_MODEL_4251756ae8d344e8923025afa5b3be9a", + "tabbable": null, + "tooltip": null + } + }, + "474d936989bb4423af34fd74a8db3b23": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "6bf38f634a0d459bbfabdc8331eb6e3b": { + "485dd48b720d4e1dbfce8f76953b154e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5137,60 +4919,49 @@ "width": null } }, - "70c9b3de67694dc4a8c38a542842ae3c": { - "model_module": "@jupyter-widgets/base", + "489746a2a7db4406b7ebfd5f2a155361": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ae4bdbf2d6494345acc4f7003fcd9cd9", + "IPY_MODEL_dd4b960c1fc54808ac7ec7abfbe1ccbc", + "IPY_MODEL_37059badcffc423fbad95494b9745859" + ], + "layout": "IPY_MODEL_73593795a4ac4b21a0677dccf1a525cb", + "tabbable": null, + "tooltip": null + } + }, + "4a74983359df462095cf64e3c2706a4b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "71c3b5aba9c94572b460be6639753985": { + "4ae6fd916d6d470190a53d061fb0b02f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5243,7 +5014,23 @@ "width": null } }, - "720932772aa64aba973045693c189137": { + "4d2f1756b094437bb3ff9f30ea93cbb2": { + "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": "" + } + }, + "4de2163b87c545ed89dfc257d6a7e304": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5296,7 +5083,7 @@ "width": null } }, - "7437bf57da394059a3d162f5d7a1a77a": { + "50aef71653f04eb49e220d1870cbf7ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5349,7 +5136,7 @@ "width": null } }, - "744802ca32e849ab9fea4e8e3861b154": { + "52638684784d4b5fb05e436a7f614243": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5364,38 +5151,107 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f19d1922e1974c3fa19988e4d4701f69", + "layout": "IPY_MODEL_ed7068ef4b08440eaae3bd1ba5cca147", "placeholder": "​", - "style": "IPY_MODEL_f924af4748c740ce948540ad741071d9", + "style": "IPY_MODEL_91c032ac4bfb4eceba18d1021672143e", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": " 4.83k/4.83k [00:00<00:00, 586kB/s]" } }, - "74a2396570a8494a948c33421fd20593": { + "55c0a386d760485f92009bb75259396b": { "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", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ed6f311b0f784e268d2c2f765d55a94a", + "IPY_MODEL_9e8ad22d82bc455da31ccd997a15763c", + "IPY_MODEL_307355aaf24e41138019598af025ac1b" + ], + "layout": "IPY_MODEL_1232a69e4cd748539b2d4cdde35b417c", + "tabbable": null, + "tooltip": null + } + }, + "5788b965507d455ca17eabfc155de530": { + "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_32b6d99a486e461eacd6e063c5420eff", - "placeholder": "​", - "style": "IPY_MODEL_8ec14b76495d43b599a2c06778d3e9c7", + "layout": "IPY_MODEL_b37bca5ee3994510a0c5bf88495f1a10", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6c1210fd37c047b69d6bb77d7a585576", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "59358b3c018e40c1b73065b80810af12": { + "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_6b3ea7da7caf4dc294c20842ed8cac54", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3764b604f5ba43288217fe3f4372b542", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:35<00:00, 1632.10it/s]" + "value": 26421880.0 + } + }, + "5a5ab3c1640f48e7953b2212cf787f29": { + "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": "" } }, - "74f4d829ffbb4508957bfb49f70fe734": { + "5aed3d8999204da4abd99966f9859da8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5448,7 +5304,7 @@ "width": null } }, - "757e89f53d174fc6b18a1a8380eb5700": { + "5c2cc530fc7746919e9df959510c6dc7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5501,23 +5357,51 @@ "width": null } }, - "778338997ef84774b5fc1b3e363f63c9": { + "5cbd075f38af4a35bd49255f7c6854c3": { "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 + } + }, + "61530f93052c4966a3ed9c90ec90c547": { + "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_0fc5778c044d4fcd9e8f745663d100c5", + "max": 5148.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bbcf2512f0a048e99a7d2ad46cb7b594", + "tabbable": null, + "tooltip": null, + "value": 5148.0 } }, - "77b7d71484644232938d588a6fbcc4ed": { + "629ccac58fe74198b172012097674576": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5570,102 +5454,69 @@ "width": null } }, - "7824f895073b4989aabcc1118fe1e570": { + "63e3c00666534cf78aef58362ab798ef": { "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_8abe4aff024b461ea93d3d85dd84f1f7", + "placeholder": "​", + "style": "IPY_MODEL_5cbd075f38af4a35bd49255f7c6854c3", + "tabbable": null, + "tooltip": null, + "value": "Downloading builder script: 100%" } }, - "789ca2ff29a44242935c400b234b1969": { - "model_module": "@jupyter-widgets/base", + "63f97e8188cb4370abab083114492da0": { + "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": "" } }, - "79a15df271d14bfa8e4ed6dbe1c37a8a": { + "667ac6e91c614fc09ad352122a696161": { "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_0f7c857000414450bdc92196b4466489", - "IPY_MODEL_2a37f3b6e2e64eda9d5aad4e57bb6475", - "IPY_MODEL_beba078ac8d64eb893f533808fb9cfdd" - ], - "layout": "IPY_MODEL_1fbec845f4cf42bb8efb9b83163e0b9b", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f8852f488ece4717923d24f0f949869a", + "placeholder": "​", + "style": "IPY_MODEL_11828cd7a97146ac81748612b0601834", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Downloading data: 100%" } }, - "7a1637c606824542971bdd53178cb547": { + "6a96595df722450495b69e3d9407c566": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5683,7 +5534,7 @@ "text_color": null } }, - "7ea218ed3bac4e3fb0038d64392dab79": { + "6b3ea7da7caf4dc294c20842ed8cac54": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5736,166 +5587,93 @@ "width": null } }, - "7f8b4b75f0c545ac966463807f510230": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "815effa183cf4ca4a7160696d4e9eb83": { - "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_a36a0824e2bc4b84adf3d6f24bae08e7", - "IPY_MODEL_f05de778ce8641daad6aaad095cd0a12", - "IPY_MODEL_fa8ee62999bf48be85571066708c3a70" - ], - "layout": "IPY_MODEL_c22ca0c1ac314ae3b7526a4ab19f5d59", - "tabbable": null, - "tooltip": null - } - }, - "826306b901dc424696b95e114b1441ca": { + "6c1210fd37c047b69d6bb77d7a585576": { "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_5efe1df058ed48ae9674a8448d855c64", - "placeholder": "​", - "style": "IPY_MODEL_569d3453747c444a9c2661732c8b1fb5", - "tabbable": null, - "tooltip": null, - "value": " 29.5k/29.5k [00:00<00:00, 4.61MB/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "8310ed48648242c0a8109e15f13f0f84": { + "6c53714694714ec184a3175a51eca22d": { "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_bf1140bdc30747b7875150c89a9b56d2", - "placeholder": "​", - "style": "IPY_MODEL_c9d9f8c0348541bc93ce2df33d9b1138", - "tabbable": null, - "tooltip": null, - "value": " 4/4 [00:00<00:00, 1391.72it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "85af3d53abef4aa8a6046017943dc826": { + "6f6e9c74347e4652b414406bd4c67238": { "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_f293407e061d47a9b2a576f458cfc91a", - "IPY_MODEL_67bb2dfe0bed499ea09cbd03e6cf7fd1", - "IPY_MODEL_0637c05b667541e782a074923de85b45" - ], - "layout": "IPY_MODEL_db5a222bfbda444ca14ba447f3de1c6b", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "85c627e125a94180abe254acf928a1fc": { + "6fa798d1f46540b9bd3e051f98c21430": { "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_0419dce41e82427eba5f201dcd3224ce", - "IPY_MODEL_fe9dd2d447a247b29fd9568449e4c772", - "IPY_MODEL_f1b29538d40a44f88d457938817aa9ca" - ], - "layout": "IPY_MODEL_ddf398ca235a4371928fa707d2edfab3", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "877f435c18c84cdb9acff7996168eee1": { + "71217b3eed354f73861bde4bfaf66ffd": { "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_ff64e02006ba4b3b90e1f252f3c58e25", - "placeholder": "​", - "style": "IPY_MODEL_8dcbfb9f172b4a8296952470fd844435", - "tabbable": null, - "tooltip": null, - "value": "Generating test split: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "88408a65c5eb4eb48911a14aadbc74cf": { + "715dab8821f5465eade13b7f8765ed47": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5948,25 +5726,7 @@ "width": null } }, - "8946f270b2954b22801e92ce10bf7c63": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "895c5faa034242eeb651e5c6bf3b1a51": { + "72b19493a1ca42c793bcda945b230961": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6019,7 +5779,7 @@ "width": null } }, - "8a47898db9234aa796ec7fb93a91d6e9": { + "73593795a4ac4b21a0677dccf1a525cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6072,25 +5832,7 @@ "width": null } }, - "8dcbfb9f172b4a8296952470fd844435": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "8ec14b76495d43b599a2c06778d3e9c7": { + "7439b162e309483580266f93e7ac4add": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6108,7 +5850,7 @@ "text_color": null } }, - "90c7589ac13b443eb422dd2f647490a3": { + "7461d590e55e4b9389cb1fa4b8b01858": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6161,7 +5903,30 @@ "width": null } }, - "965ba01b14474336b39a9ed4b2b5dcb5": { + "7a70b6d736914713b75013fd280cd1fb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5aed3d8999204da4abd99966f9859da8", + "placeholder": "​", + "style": "IPY_MODEL_003b69c44f834dd6bd767bd85d0282c1", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "7b9f6a000ece4e85bd677afa0c44a483": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6176,15 +5941,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f984f1911a3a4d3d97fb914d1b6abbce", + "layout": "IPY_MODEL_a261db39bdb34321bc03d5b7cdb18bd6", "placeholder": "​", - "style": "IPY_MODEL_03ab3894bf4242d8999349575ac3ab13", + "style": "IPY_MODEL_4a74983359df462095cf64e3c2706a4b", "tabbable": null, "tooltip": null, - "value": " 10000/10000 [00:01<00:00, 8813.96 examples/s]" + "value": " 40/40 [00:00<00:00, 61.37it/s]" } }, - "96d31dd7865d4b63adc5a9c556d6d472": { + "7ba456d195e144f7ba05947f8e466206": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6237,7 +6002,30 @@ "width": null } }, - "9a2bf66dd6c64060b9a09eaba40a26a8": { + "7c3c537f030046e892febf09fbb3953f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_42f688e5fa0a4f698ca681a4433e115d", + "placeholder": "​", + "style": "IPY_MODEL_a034a2ade0914168baff132569eeeb46", + "tabbable": null, + "tooltip": null, + "value": "Downloading readme: 100%" + } + }, + "7defd4db8e0e4d9d806b06b0ad18bad9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6290,70 +6078,7 @@ "width": null } }, - "9abe31b01bc04cc89ff967d26e368fdf": { - "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_1a2d0d43f24f4a179401f7bde3fcbf11", - "IPY_MODEL_e87b060be03d4626b55c248e0abe4a42", - "IPY_MODEL_007876448c144ee39084540ed6eb06b9" - ], - "layout": "IPY_MODEL_9a2bf66dd6c64060b9a09eaba40a26a8", - "tabbable": null, - "tooltip": null - } - }, - "9b58b8eff8e745eb9e937354fedb1807": { - "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": "" - } - }, - "9bb5412853cd41ffb7c03f1615108a7c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d69fd7a89a8640babdbb4265c990aff2", - "placeholder": "​", - "style": "IPY_MODEL_a40425de9d3b4923a12d5dfc419b6cf3", - "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" - } - }, - "9c72cef284e84cdaa2856f28c3593985": { + "7f159f23ebfd44699351ad51261161f4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6371,25 +6096,60 @@ "text_color": null } }, - "9d3b7c0890814f2c9985eb79f97a04ac": { - "model_module": "@jupyter-widgets/controls", + "816719582fe14c11a648b648bb9e2cbb": { + "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 } }, - "9dd47964eda34050b58a0ab2b3415ef0": { + "839cc7aeb6cf41cba2328b772dda1354": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6442,7 +6202,7 @@ "width": null } }, - "9fb3ff4fed8c462793870fe1a08d713c": { + "83fe2423c4494c2b9394412c402eca60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6460,7 +6220,7 @@ "text_color": null } }, - "a36a0824e2bc4b84adf3d6f24bae08e7": { + "865a8adbaefe44eb930f2df86dcd3a25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6475,15 +6235,39 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_46692e2429834f6b8ad6d2e3bd669ac5", + "layout": "IPY_MODEL_f35f77b3dc2d4f87b2f6435eb17b5f51", "placeholder": "​", - "style": "IPY_MODEL_0120148eaefd42fd9a38edc77bc35ac7", + "style": "IPY_MODEL_231c040b6a13479f944b5bef41c9994a", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": "Map (num_proc=4): 100%" + } + }, + "8835da69dbeb4826a96baa0561232a18": { + "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_f99e5e0dbb254dd0a57ef096794a0277", + "IPY_MODEL_9444514d4589415d940c94d2a40f0c7a", + "IPY_MODEL_7b9f6a000ece4e85bd677afa0c44a483" + ], + "layout": "IPY_MODEL_8a730989a49141659124fe8758fe6b9f", + "tabbable": null, + "tooltip": null } }, - "a40425de9d3b4923a12d5dfc419b6cf3": { + "885a1c18bad74f47ae49ef86de2c3a67": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6501,7 +6285,7 @@ "text_color": null } }, - "a67a308439454f8eb0f02917f9c68472": { + "8a730989a49141659124fe8758fe6b9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6554,25 +6338,7 @@ "width": null } }, - "a75ebf7ef9174d5e9cc59c872badcf44": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a7668c39f7324f3abf6dee2fb270336f": { + "8abe4aff024b461ea93d3d85dd84f1f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6625,55 +6391,7 @@ "width": null } }, - "a9c34fb99987402ba4f521a988475574": { - "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_d76da02ff0a84d74ad2c21a60fdbf6a8", - "IPY_MODEL_12693e79b18144d2be2b4b4399f4789e", - "IPY_MODEL_826306b901dc424696b95e114b1441ca" - ], - "layout": "IPY_MODEL_594f571cc61a464d8934a2a6ebaff1ed", - "tabbable": null, - "tooltip": null - } - }, - "abcdd0c8b869449f849ce66d0123d168": { - "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_c336f45d284843d786cfce257d33793c", - "IPY_MODEL_4439d6c4067d42b2bc7b5b49e651de05", - "IPY_MODEL_8310ed48648242c0a8109e15f13f0f84" - ], - "layout": "IPY_MODEL_cb33ab7c5bf847199febe8a7955b0a6b", - "tabbable": null, - "tooltip": null - } - }, - "af23bba915a74358b250ef2bb1a7d577": { + "8b39eaec8f5843a3942f7b65e5320363": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6726,7 +6444,25 @@ "width": null } }, - "b218eb25d1544b2198406c8d0daa0439": { + "8c6b20c613cf48bd9f826dbeb5124369": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8ce5df9efdba4fd1bad996ee729dec94": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6779,23 +6515,25 @@ "width": null } }, - "b461446aa899462d865a83e0c727b5c5": { + "91c032ac4bfb4eceba18d1021672143e": { "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 } }, - "b496abba866a4243bc5d3e643a27bfaa": { + "91e935457d624042a2d02c107985c146": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6810,57 +6548,41 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5a36ed53de5c4a9c8637a7b4013dfb54", + "layout": "IPY_MODEL_3e86a20ac6b044d4adccfeaba4245d25", "placeholder": "​", - "style": "IPY_MODEL_c5dd4307fb0e435f9e6eecf7eba58ea3", + "style": "IPY_MODEL_e522f4fa76294f30ae3dc6a49425837b", "tabbable": null, "tooltip": null, - "value": " 4.83k/4.83k [00:00<00:00, 604kB/s]" - } - }, - "b54eac280dff4e5dbe0c1d2b48743535": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 40/40 [00:00<00:00, 65.93it/s]" } }, - "b56125fc059b47e3b228dc3ed3b629c0": { + "92f49f3012f849ceaece9962b9690310": { "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_38eb030b83c64212b25df93cfe516570", - "IPY_MODEL_1c96211615924c808f262fae38d4ab01", - "IPY_MODEL_bed8037a62494179a7e38b4603feef04" - ], - "layout": "IPY_MODEL_8a47898db9234aa796ec7fb93a91d6e9", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_373768b5fd4c42118d61459d55c65ad2", + "max": 29515.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d3230f794b7e47c9beebfa7400ee3522", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 29515.0 } }, - "b9a030c7a18e43e0a9ade7e2489ba818": { + "92fcc9156b2a48328c1fb11582bdbb81": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6913,23 +6635,7 @@ "width": null } }, - "bb0480efe0bc4bd3801a7f7315323cf5": { - "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": "" - } - }, - "bb91a65b9f7c4bcbae0545c6e0c0c603": { + "9444514d4589415d940c94d2a40f0c7a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -6945,17 +6651,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_757e89f53d174fc6b18a1a8380eb5700", + "layout": "IPY_MODEL_7ba456d195e144f7ba05947f8e466206", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_05b056eafe984430926b52278f208393", + "style": "IPY_MODEL_d837ff833a594ea2b02ce7f7523c9dcd", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "bdbb1b6b96824b1ba8715b85852886fe": { + "99fb59566db2452bab382261d05e2879": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6970,16 +6676,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_877f435c18c84cdb9acff7996168eee1", - "IPY_MODEL_03b60960744f469c8562f78a5948c4a9", - "IPY_MODEL_965ba01b14474336b39a9ed4b2b5dcb5" + "IPY_MODEL_63e3c00666534cf78aef58362ab798ef", + "IPY_MODEL_bee58d3e475246fba7aa8b971564b1a7", + "IPY_MODEL_52638684784d4b5fb05e436a7f614243" ], - "layout": "IPY_MODEL_4b1c1c0a8eb2477b91cae9a52c47e58c", + "layout": "IPY_MODEL_dbc28ec812a84d3f80f20f8fcab2939a", "tabbable": null, "tooltip": null } }, - "beba078ac8d64eb893f533808fb9cfdd": { + "9b8b5db0d4ec47ae8f14fbae0720d3e0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6994,38 +6700,142 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_789ca2ff29a44242935c400b234b1969", + "layout": "IPY_MODEL_d1333e62f1304c11beca8dd72e8aa9f9", "placeholder": "​", - "style": "IPY_MODEL_0d79223cae9246eaaa4f673e81a780a8", + "style": "IPY_MODEL_c2680fc6a8544c869d7830d31ed37cea", "tabbable": null, "tooltip": null, - "value": " 5.15k/5.15k [00:00<00:00, 823kB/s]" + "value": " 40/40 [00:00<00:00, 59.41it/s]" } }, - "bed8037a62494179a7e38b4603feef04": { + "9c8e988dcc76462ea4a6543f9f1dd759": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { - "_dom_classes": [], + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f08b9948f05248b3b3ef10bd15ffc7d8", + "placeholder": "​", + "style": "IPY_MODEL_bcf73aedcb8442be80cd3903dca5729d", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "9d8907e922884b4c98182bd90146f805": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "9e8ad22d82bc455da31ccd997a15763c": { + "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_b9a6301405d54fc48fe5e9c1fcb3ff2f", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4d2f1756b094437bb3ff9f30ea93cbb2", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "9f427bf1023e48e693afcd4ee468c279": { + "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": "" + } + }, + "9f7de506f89c48b29c2cbb938a4e7210": { + "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_7461d590e55e4b9389cb1fa4b8b01858", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_42c5eeb19b2a4542b4159e63110273e4", + "tabbable": null, + "tooltip": null, + "value": 4.0 + } + }, + "a034a2ade0914168baff132569eeeb46": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { "_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_af23bba915a74358b250ef2bb1a7d577", - "placeholder": "​", - "style": "IPY_MODEL_4cfef00083ee41c18334522b0aaafbf6", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.66it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bf1140bdc30747b7875150c89a9b56d2": { + "a261db39bdb34321bc03d5b7cdb18bd6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7078,7 +6888,48 @@ "width": null } }, - "bf14a805f3944138b263cb978e9fcf8b": { + "a3d6f09afbca4e6482b56fd6d71d9b25": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a518055f2d994914bf1de3d11ea03a82": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c32f5e002a7a4c4ea804958d4deec82a", + "placeholder": "​", + "style": "IPY_MODEL_f332684f2b70406ab76c95d24f2f40ed", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "a805af9f6458409cbfcf1ef1829794dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7131,7 +6982,33 @@ "width": null } }, - "c1899c2c83a146b4b5eccb8d3c0892ae": { + "aae89dad290345f5acc2526904d6d4e5": { + "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_38bd0231f7c34338a8ccd2536adb5809", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c78a7456a9d940dd9a6d4e769ce24dc3", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "ae4bdbf2d6494345acc4f7003fcd9cd9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7146,15 +7023,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9dd47964eda34050b58a0ab2b3415ef0", + "layout": "IPY_MODEL_32d10bf33ee54d2bac909d0748596dac", "placeholder": "​", - "style": "IPY_MODEL_da7255e4a0a1407392cd379cdb23eb3c", + "style": "IPY_MODEL_bfc04f78f5024cc4b45fce30bcc4e7b2", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.06it/s]" + "value": "Generating train split: 100%" + } + }, + "afdd5bafd5ba49909abaaef0bb7f6038": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c1f07c0e6cb7453cb234322863c6974b": { + "b37bca5ee3994510a0c5bf88495f1a10": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7207,7 +7102,25 @@ "width": null } }, - "c22ca0c1ac314ae3b7526a4ab19f5d59": { + "b45281dbfb444428b286e76808d4a658": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "b5852aa7675d4ad7ab4bb8a846b7eead": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7260,48 +7173,33 @@ "width": null } }, - "c336f45d284843d786cfce257d33793c": { + "b602e59393ab410ba774d7c2419c556d": { "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_670532c752594999bbc5db2f86da0bfa", - "placeholder": "​", - "style": "IPY_MODEL_9fb3ff4fed8c462793870fe1a08d713c", + "layout": "IPY_MODEL_2d69655ca2a545e9a4b05f6865cdf2a8", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9f427bf1023e48e693afcd4ee468c279", "tabbable": null, "tooltip": null, - "value": "Computing checksums: 100%" - } - }, - "c5dd4307fb0e435f9e6eecf7eba58ea3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": 4422102.0 } }, - "c63bcc18843a48cd9b4c1b91951a299e": { + "b6bcf233e0084809823abddff92adea2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7316,15 +7214,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_71c3b5aba9c94572b460be6639753985", + "layout": "IPY_MODEL_4de2163b87c545ed89dfc257d6a7e304", "placeholder": "​", - "style": "IPY_MODEL_7a1637c606824542971bdd53178cb547", + "style": "IPY_MODEL_7439b162e309483580266f93e7ac4add", "tabbable": null, "tooltip": null, - "value": "Downloading builder script: 100%" + "value": " 8.85k/8.85k [00:00<00:00, 1.46MB/s]" } }, - "c6b17708d8404f668a926cfeef0a478f": { + "b9a6301405d54fc48fe5e9c1fcb3ff2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7377,25 +7275,47 @@ "width": null } }, - "c8922b8978bf4fc58458e70640f1b5c5": { + "b9c41de7ac0442aabfb15bbf3b5308c8": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3779e0b1881642b8b80007dfa03126bb", + "IPY_MODEL_40e314c496664e698c489a3f5c279e1c", + "IPY_MODEL_c6f60e04093c46f981b6f7b80f94e2d4" + ], + "layout": "IPY_MODEL_d6092c5dc4c74213a968d6db282bed10", + "tabbable": null, + "tooltip": null + } + }, + "bbcf2512f0a048e99a7d2ad46cb7b594": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "c9d9f8c0348541bc93ce2df33d9b1138": { + "bcf73aedcb8442be80cd3903dca5729d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7413,78 +7333,56 @@ "text_color": null } }, - "cb33ab7c5bf847199febe8a7955b0a6b": { - "model_module": "@jupyter-widgets/base", + "bee58d3e475246fba7aa8b971564b1a7": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b5852aa7675d4ad7ab4bb8a846b7eead", + "max": 4833.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5a5ab3c1640f48e7953b2212cf787f29", + "tabbable": null, + "tooltip": null, + "value": 4833.0 } }, - "cd436773aeeb4e5f811c805cd47f7071": { + "bf0ad0d606f04b7aa5013f43b6f32049": { "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_4536c7080de841acaaec729cdf955c0a", + "placeholder": "​", + "style": "IPY_MODEL_6a96595df722450495b69e3d9407c566", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "d067169b2a1745bd893cbaa56705df57": { + "bfc04f78f5024cc4b45fce30bcc4e7b2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7502,7 +7400,7 @@ "text_color": null } }, - "d10a94f6f1994a14b0ce8071ec84d170": { + "c1f41731ec4247e3ab4c57371cd20bf3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7555,7 +7453,25 @@ "width": null } }, - "d50d3c5c4ecd49cfbbcb44e787a993ba": { + "c2680fc6a8544c869d7830d31ed37cea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c32f5e002a7a4c4ea804958d4deec82a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7608,7 +7524,53 @@ "width": null } }, - "d65cb8246aa14189b49a0eeae6f3bad0": { + "c6eb4afdd2a54d899795e4359d8c989d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5c2cc530fc7746919e9df959510c6dc7", + "placeholder": "​", + "style": "IPY_MODEL_22ece7076054425f8784c9f44cd9c512", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "c6f60e04093c46f981b6f7b80f94e2d4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_92fcc9156b2a48328c1fb11582bdbb81", + "placeholder": "​", + "style": "IPY_MODEL_e06347bb350642c28c5755357c9f6b4f", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:01<00:00, 8738.82 examples/s]" + } + }, + "c75b1e8b04fc4bbe8b789d1cfa5fe576": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -7623,16 +7585,32 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_ee7c6e7d3d1b43138e41e0146c75cd98", - "IPY_MODEL_4268b194fef1470096c0d165c23da82f", - "IPY_MODEL_74a2396570a8494a948c33421fd20593" + "IPY_MODEL_d8736cab840549168545a3a77aa8e714", + "IPY_MODEL_9f7de506f89c48b29c2cbb938a4e7210", + "IPY_MODEL_dc708bb2c5314b17862c251eeb9db099" ], - "layout": "IPY_MODEL_d10a94f6f1994a14b0ce8071ec84d170", + "layout": "IPY_MODEL_22e4e1da97fe49e290e9dac292880148", "tabbable": null, "tooltip": null } }, - "d69fd7a89a8640babdbb4265c990aff2": { + "c78a7456a9d940dd9a6d4e769ce24dc3": { + "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": "" + } + }, + "c82a1f05b0ec418a9670fcf0e44708f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7685,7 +7663,7 @@ "width": null } }, - "d6b409257aab47b888b2954d7b04073e": { + "c8e6913e68754e7a9e614de447103505": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7738,101 +7716,81 @@ "width": null } }, - "d76da02ff0a84d74ad2c21a60fdbf6a8": { + "caabab78e31d438eb521e9c3f2e1c0a6": { "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_2426b36b4bcd4538a7a1a6c6f443b609", - "placeholder": "​", - "style": "IPY_MODEL_c8922b8978bf4fc58458e70640f1b5c5", + "layout": "IPY_MODEL_c1f41731ec4247e3ab4c57371cd20bf3", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6fa798d1f46540b9bd3e051f98c21430", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": 40.0 } }, - "da7255e4a0a1407392cd379cdb23eb3c": { + "cacaca4358c34e93a46a3e2019d188d4": { "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_7c3c537f030046e892febf09fbb3953f", + "IPY_MODEL_41aa359adc6640ab9d5ea2369493623a", + "IPY_MODEL_b6bcf233e0084809823abddff92adea2" + ], + "layout": "IPY_MODEL_fb9b135b97bd45978b3759750aac7be4", + "tabbable": null, + "tooltip": null } }, - "db5a222bfbda444ca14ba447f3de1c6b": { - "model_module": "@jupyter-widgets/base", + "cc7010cd50844e48a3db713a6ea5f850": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_311422b25c3d435280a7ae2e6c1b5cbc", + "IPY_MODEL_92f49f3012f849ceaece9962b9690310", + "IPY_MODEL_20aa2cb116fa42218107305be2c9121a" + ], + "layout": "IPY_MODEL_20e8c6a9ab644e3da69655bfbc794894", + "tabbable": null, + "tooltip": null } }, - "ddf398ca235a4371928fa707d2edfab3": { + "d1333e62f1304c11beca8dd72e8aa9f9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7885,30 +7843,23 @@ "width": null } }, - "e0619bd42a814114974327c4c92cbc96": { + "d3230f794b7e47c9beebfa7400ee3522": { "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_6566ace6c0aa4a21abbf6d708cb457b0", - "placeholder": "​", - "style": "IPY_MODEL_e45644a5a73d43e8a12aaa8ea0e71934", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:10<00:00, 7472.24 examples/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "e2caa9efa27847b7b3dd441f4a803cb2": { + "d6092c5dc4c74213a968d6db282bed10": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7961,7 +7912,7 @@ "width": null } }, - "e39cdb7fe88f4310bfd8b1dcd8ac5ae2": { + "d6337601ed54490eb0d452da36303706": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7976,33 +7927,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_720932772aa64aba973045693c189137", + "layout": "IPY_MODEL_c8e6913e68754e7a9e614de447103505", "placeholder": "​", - "style": "IPY_MODEL_39314c631f444d01903b4f50ccfc66df", + "style": "IPY_MODEL_6c53714694714ec184a3175a51eca22d", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.47it/s]" + "value": " 4.42M/4.42M [00:00<00:00, 95.7MB/s]" } }, - "e45644a5a73d43e8a12aaa8ea0e71934": { + "d837ff833a594ea2b02ce7f7523c9dcd": { "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": "" } }, - "e7dae7ac77284ca7a938a5140aea14ba": { + "d8441cb494324dad83d4a88dd3c805e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8055,57 +8004,7 @@ "width": null } }, - "e85af83531bc4182b052d4cfe7f1020e": { - "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_c63bcc18843a48cd9b4c1b91951a299e", - "IPY_MODEL_f0a66657e7724cf4a18612d673b0df8e", - "IPY_MODEL_b496abba866a4243bc5d3e643a27bfaa" - ], - "layout": "IPY_MODEL_420adb2a809a4b7693bda83cb040e60c", - "tabbable": null, - "tooltip": null - } - }, - "e87b060be03d4626b55c248e0abe4a42": { - "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_0261398aaa894092b6eca7f630c39440", - "max": 8845.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5d69e819f44b493a8d07e58cb39a6a69", - "tabbable": null, - "tooltip": null, - "value": 8845.0 - } - }, - "eb56ed8bd1a74a87a2a7cf25c0992594": { + "d8736cab840549168545a3a77aa8e714": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8120,15 +8019,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4b9e51775e974654a075cfdb1d1b699c", + "layout": "IPY_MODEL_11e93782fa6f464e9f6900e0c646f5cf", "placeholder": "​", - "style": "IPY_MODEL_a75ebf7ef9174d5e9cc59c872badcf44", + "style": "IPY_MODEL_83fe2423c4494c2b9394412c402eca60", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Computing checksums: 100%" } }, - "ec23491fb0024cb8a1b7dfd205775551": { + "dbc28ec812a84d3f80f20f8fcab2939a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8181,7 +8080,56 @@ "width": null } }, - "ecde01d38be144db9d996498b953e5b4": { + "dc708bb2c5314b17862c251eeb9db099": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c82a1f05b0ec418a9670fcf0e44708f1", + "placeholder": "​", + "style": "IPY_MODEL_3c76ee6a2244430cbb3e8e8416d1a23c", + "tabbable": null, + "tooltip": null, + "value": " 4/4 [00:00<00:00, 1332.58it/s]" + } + }, + "dd4b960c1fc54808ac7ec7abfbe1ccbc": { + "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_7defd4db8e0e4d9d806b06b0ad18bad9", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_04bfeccaeea6416e98641eb5e663ae0b", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "dd549dafe96d457a9f3e32fa6dda0ce5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8234,108 +8182,83 @@ "width": null } }, - "ee448c322caa4ddbae258419893e01e8": { + "e06347bb350642c28c5755357c9f6b4f": { "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_c6b17708d8404f668a926cfeef0a478f", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_778338997ef84774b5fc1b3e363f63c9", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ee7c6e7d3d1b43138e41e0146c75cd98": { + "e075f5bd416a447eb67433e0d225370f": { "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_96d31dd7865d4b63adc5a9c556d6d472", - "placeholder": "​", - "style": "IPY_MODEL_6b4971a3c52247978439a6e52e61bca9", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_865a8adbaefe44eb930f2df86dcd3a25", + "IPY_MODEL_2253900d1539484ea6c5fb5f87e34fae", + "IPY_MODEL_f669341590db4e77b2110a49a3fa808c" + ], + "layout": "IPY_MODEL_431d46eafa8c450b8c8f1bf1f7883cf3", "tabbable": null, - "tooltip": null, - "value": "100%" + "tooltip": null } }, - "f05de778ce8641daad6aaad095cd0a12": { + "e522f4fa76294f30ae3dc6a49425837b": { "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_e2caa9efa27847b7b3dd441f4a803cb2", - "max": 4422102.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_12da0c3d2bd5449dbb811de7fd8b2093", - "tabbable": null, - "tooltip": null, - "value": 4422102.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f0a66657e7724cf4a18612d673b0df8e": { + "e675357d5efc42b3968d61c273ca5f7f": { "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_6bf38f634a0d459bbfabdc8331eb6e3b", - "max": 4833.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5c5b477c793d4588a25705aece5a2a2e", - "tabbable": null, - "tooltip": null, - "value": 4833.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f19d1922e1974c3fa19988e4d4701f69": { + "e7e60d48deb84f8681aa2b4aaa1ad387": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8388,7 +8311,7 @@ "width": null } }, - "f1b29538d40a44f88d457938817aa9ca": { + "e9b9c9bbe0374dd9b98a3a4c1553822f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8403,38 +8326,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d50d3c5c4ecd49cfbbcb44e787a993ba", + "layout": "IPY_MODEL_629ccac58fe74198b172012097674576", "placeholder": "​", - "style": "IPY_MODEL_fe0a6190f0794548839615a5f024d71f", + "style": "IPY_MODEL_1f7f1fac2dd34d07b7fc07f799729df6", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 60.94it/s]" + "value": " 40/40 [00:00<00:00, 59.81it/s]" } }, - "f293407e061d47a9b2a576f458cfc91a": { + "eb33301487694b0f9f4a664fce743134": { "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_42c998dc50734d8c93d723bb78939244", - "placeholder": "​", - "style": "IPY_MODEL_30a335f232c04fe7b668ed6a418d27b2", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f2d29dc28e7140b792fc1ee3fcb857cb": { + "ebc081ac7cef42f58f0c46bdca672b27": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -8449,42 +8367,145 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_9bb5412853cd41ffb7c03f1615108a7c", - "IPY_MODEL_1bb8a6dd435048b28340d4eac6730019", - "IPY_MODEL_e0619bd42a814114974327c4c92cbc96" + "IPY_MODEL_a518055f2d994914bf1de3d11ea03a82", + "IPY_MODEL_18e5b3625d2a4b3abbfea651a20eef56", + "IPY_MODEL_91e935457d624042a2d02c107985c146" ], - "layout": "IPY_MODEL_7ea218ed3bac4e3fb0038d64392dab79", + "layout": "IPY_MODEL_2bf71c3624ce4413bc347a20e07c26d5", "tabbable": null, "tooltip": null } }, - "f3a3a4eba3674c53882dfe2a1c081653": { + "ed6f311b0f784e268d2c2f765d55a94a": { "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_a67a308439454f8eb0f02917f9c68472", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2f9e9f6f7ebe42afa59802bdf7306ccb", + "layout": "IPY_MODEL_e7e60d48deb84f8681aa2b4aaa1ad387", + "placeholder": "​", + "style": "IPY_MODEL_17af51078a0c4c0f92db9850c8c773d7", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": "100%" + } + }, + "ed7068ef4b08440eaae3bd1ba5cca147": { + "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 + } + }, + "f08b9948f05248b3b3ef10bd15ffc7d8": { + "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 } }, - "f924af4748c740ce948540ad741071d9": { + "f332684f2b70406ab76c95d24f2f40ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -8502,7 +8523,7 @@ "text_color": null } }, - "f984f1911a3a4d3d97fb914d1b6abbce": { + "f35f77b3dc2d4f87b2f6435eb17b5f51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8555,7 +8576,7 @@ "width": null } }, - "fa8ee62999bf48be85571066708c3a70": { + "f669341590db4e77b2110a49a3fa808c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8570,33 +8591,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_895c5faa034242eeb651e5c6bf3b1a51", + "layout": "IPY_MODEL_8ce5df9efdba4fd1bad996ee729dec94", "placeholder": "​", - "style": "IPY_MODEL_9c72cef284e84cdaa2856f28c3593985", + "style": "IPY_MODEL_a3d6f09afbca4e6482b56fd6d71d9b25", "tabbable": null, "tooltip": null, - "value": " 4.42M/4.42M [00:00<00:00, 61.8MB/s]" - } - }, - "fe0a6190f0794548839615a5f024d71f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 60000/60000 [00:10<00:00, 5815.53 examples/s]" } }, - "fe37a821e1f04c6f9903e3722f9ab4c2": { + "f8852f488ece4717923d24f0f949869a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8649,33 +8652,30 @@ "width": null } }, - "fe9dd2d447a247b29fd9568449e4c772": { + "f99e5e0dbb254dd0a57ef096794a0277": { "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_112a5fd388db49e4b8a0f9dffab06426", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_bb0480efe0bc4bd3801a7f7315323cf5", + "layout": "IPY_MODEL_176f4fe537ab44709eed5f4771e5a748", + "placeholder": "​", + "style": "IPY_MODEL_b45281dbfb444428b286e76808d4a658", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "100%" } }, - "ff64e02006ba4b3b90e1f252f3c58e25": { + "fb9b135b97bd45978b3759750aac7be4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index d470496b0..7f5df08d9 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:36.731110Z", - "iopub.status.busy": "2024-06-25T19:35:36.730936Z", - "iopub.status.idle": "2024-06-25T19:35:37.834580Z", - "shell.execute_reply": "2024-06-25T19:35:37.833954Z" + "iopub.execute_input": "2024-06-25T23:17:19.488251Z", + "iopub.status.busy": "2024-06-25T23:17:19.488091Z", + "iopub.status.idle": "2024-06-25T23:17:20.586301Z", + "shell.execute_reply": "2024-06-25T23:17:20.585756Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.837363Z", - "iopub.status.busy": "2024-06-25T19:35:37.837076Z", - "iopub.status.idle": "2024-06-25T19:35:37.855298Z", - "shell.execute_reply": "2024-06-25T19:35:37.854810Z" + "iopub.execute_input": "2024-06-25T23:17:20.589007Z", + "iopub.status.busy": "2024-06-25T23:17:20.588566Z", + "iopub.status.idle": "2024-06-25T23:17:20.607142Z", + "shell.execute_reply": "2024-06-25T23:17:20.606704Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.857546Z", - "iopub.status.busy": "2024-06-25T19:35:37.857301Z", - "iopub.status.idle": "2024-06-25T19:35:37.902804Z", - "shell.execute_reply": "2024-06-25T19:35:37.902282Z" + "iopub.execute_input": "2024-06-25T23:17:20.609262Z", + "iopub.status.busy": "2024-06-25T23:17:20.608896Z", + "iopub.status.idle": "2024-06-25T23:17:20.630509Z", + "shell.execute_reply": "2024-06-25T23:17:20.630057Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.904835Z", - "iopub.status.busy": "2024-06-25T19:35:37.904541Z", - "iopub.status.idle": "2024-06-25T19:35:37.907889Z", - "shell.execute_reply": "2024-06-25T19:35:37.907366Z" + "iopub.execute_input": "2024-06-25T23:17:20.632342Z", + "iopub.status.busy": "2024-06-25T23:17:20.632168Z", + "iopub.status.idle": "2024-06-25T23:17:20.635695Z", + "shell.execute_reply": "2024-06-25T23:17:20.635234Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.909808Z", - "iopub.status.busy": "2024-06-25T19:35:37.909561Z", - "iopub.status.idle": "2024-06-25T19:35:37.917137Z", - "shell.execute_reply": "2024-06-25T19:35:37.916719Z" + "iopub.execute_input": "2024-06-25T23:17:20.637844Z", + "iopub.status.busy": "2024-06-25T23:17:20.637544Z", + "iopub.status.idle": "2024-06-25T23:17:20.644982Z", + "shell.execute_reply": "2024-06-25T23:17:20.644551Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.919119Z", - "iopub.status.busy": "2024-06-25T19:35:37.918944Z", - "iopub.status.idle": "2024-06-25T19:35:37.921447Z", - "shell.execute_reply": "2024-06-25T19:35:37.921009Z" + "iopub.execute_input": "2024-06-25T23:17:20.646840Z", + "iopub.status.busy": "2024-06-25T23:17:20.646673Z", + "iopub.status.idle": "2024-06-25T23:17:20.649384Z", + "shell.execute_reply": "2024-06-25T23:17:20.648911Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:37.923229Z", - "iopub.status.busy": "2024-06-25T19:35:37.923058Z", - "iopub.status.idle": "2024-06-25T19:35:40.863311Z", - "shell.execute_reply": "2024-06-25T19:35:40.862782Z" + "iopub.execute_input": "2024-06-25T23:17:20.651376Z", + "iopub.status.busy": "2024-06-25T23:17:20.651062Z", + "iopub.status.idle": "2024-06-25T23:17:23.603750Z", + "shell.execute_reply": "2024-06-25T23:17:23.603132Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:40.866333Z", - "iopub.status.busy": "2024-06-25T19:35:40.865865Z", - "iopub.status.idle": "2024-06-25T19:35:40.875269Z", - "shell.execute_reply": "2024-06-25T19:35:40.874719Z" + "iopub.execute_input": "2024-06-25T23:17:23.606640Z", + "iopub.status.busy": "2024-06-25T23:17:23.606173Z", + "iopub.status.idle": "2024-06-25T23:17:23.615532Z", + "shell.execute_reply": "2024-06-25T23:17:23.614991Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:40.877815Z", - "iopub.status.busy": "2024-06-25T19:35:40.877412Z", - "iopub.status.idle": "2024-06-25T19:35:42.759452Z", - "shell.execute_reply": "2024-06-25T19:35:42.758773Z" + "iopub.execute_input": "2024-06-25T23:17:23.617787Z", + "iopub.status.busy": "2024-06-25T23:17:23.617408Z", + "iopub.status.idle": "2024-06-25T23:17:25.503397Z", + "shell.execute_reply": "2024-06-25T23:17:25.502726Z" } }, "outputs": [ @@ -484,10 +484,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.761931Z", - "iopub.status.busy": "2024-06-25T19:35:42.761498Z", - "iopub.status.idle": "2024-06-25T19:35:42.780225Z", - "shell.execute_reply": "2024-06-25T19:35:42.779777Z" + "iopub.execute_input": "2024-06-25T23:17:25.506132Z", + "iopub.status.busy": "2024-06-25T23:17:25.505476Z", + "iopub.status.idle": "2024-06-25T23:17:25.524117Z", + "shell.execute_reply": "2024-06-25T23:17:25.523676Z" }, "scrolled": true }, @@ -617,10 +617,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.782418Z", - "iopub.status.busy": "2024-06-25T19:35:42.782011Z", - "iopub.status.idle": "2024-06-25T19:35:42.789930Z", - "shell.execute_reply": "2024-06-25T19:35:42.789485Z" + "iopub.execute_input": "2024-06-25T23:17:25.526096Z", + "iopub.status.busy": "2024-06-25T23:17:25.525830Z", + "iopub.status.idle": "2024-06-25T23:17:25.533770Z", + "shell.execute_reply": "2024-06-25T23:17:25.533230Z" } }, "outputs": [ @@ -724,10 +724,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.791897Z", - "iopub.status.busy": "2024-06-25T19:35:42.791568Z", - "iopub.status.idle": "2024-06-25T19:35:42.800098Z", - "shell.execute_reply": "2024-06-25T19:35:42.799646Z" + "iopub.execute_input": "2024-06-25T23:17:25.535755Z", + "iopub.status.busy": "2024-06-25T23:17:25.535435Z", + "iopub.status.idle": "2024-06-25T23:17:25.544816Z", + "shell.execute_reply": "2024-06-25T23:17:25.544397Z" } }, "outputs": [ @@ -856,10 +856,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.802085Z", - "iopub.status.busy": "2024-06-25T19:35:42.801908Z", - "iopub.status.idle": "2024-06-25T19:35:42.809958Z", - "shell.execute_reply": "2024-06-25T19:35:42.809510Z" + "iopub.execute_input": "2024-06-25T23:17:25.546828Z", + "iopub.status.busy": "2024-06-25T23:17:25.546524Z", + "iopub.status.idle": "2024-06-25T23:17:25.554523Z", + "shell.execute_reply": "2024-06-25T23:17:25.554077Z" } }, "outputs": [ @@ -973,10 +973,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.811794Z", - "iopub.status.busy": "2024-06-25T19:35:42.811623Z", - "iopub.status.idle": "2024-06-25T19:35:42.820374Z", - "shell.execute_reply": "2024-06-25T19:35:42.819927Z" + "iopub.execute_input": "2024-06-25T23:17:25.556497Z", + "iopub.status.busy": "2024-06-25T23:17:25.556176Z", + "iopub.status.idle": "2024-06-25T23:17:25.564618Z", + "shell.execute_reply": "2024-06-25T23:17:25.564170Z" } }, "outputs": [ @@ -1087,10 +1087,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.822187Z", - "iopub.status.busy": "2024-06-25T19:35:42.822017Z", - "iopub.status.idle": "2024-06-25T19:35:42.829544Z", - "shell.execute_reply": "2024-06-25T19:35:42.829102Z" + "iopub.execute_input": "2024-06-25T23:17:25.566583Z", + "iopub.status.busy": "2024-06-25T23:17:25.566262Z", + "iopub.status.idle": "2024-06-25T23:17:25.573703Z", + "shell.execute_reply": "2024-06-25T23:17:25.573162Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.831790Z", - "iopub.status.busy": "2024-06-25T19:35:42.831383Z", - "iopub.status.idle": "2024-06-25T19:35:42.838578Z", - "shell.execute_reply": "2024-06-25T19:35:42.838124Z" + "iopub.execute_input": "2024-06-25T23:17:25.575840Z", + "iopub.status.busy": "2024-06-25T23:17:25.575524Z", + "iopub.status.idle": "2024-06-25T23:17:25.582660Z", + "shell.execute_reply": "2024-06-25T23:17:25.582224Z" } }, "outputs": [ @@ -1308,10 +1308,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:42.840523Z", - "iopub.status.busy": "2024-06-25T19:35:42.840354Z", - "iopub.status.idle": "2024-06-25T19:35:42.848877Z", - "shell.execute_reply": "2024-06-25T19:35:42.848311Z" + "iopub.execute_input": "2024-06-25T23:17:25.584694Z", + "iopub.status.busy": "2024-06-25T23:17:25.584373Z", + "iopub.status.idle": "2024-06-25T23:17:25.592350Z", + "shell.execute_reply": "2024-06-25T23:17:25.591901Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 82cfdab78..1b14ea34d 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -791,7 +791,7 @@

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

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

diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 5e2df2074..47d0847e3 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": "2024-06-25T19:35:45.390789Z", - "iopub.status.busy": "2024-06-25T19:35:45.390619Z", - "iopub.status.idle": "2024-06-25T19:35:48.008658Z", - "shell.execute_reply": "2024-06-25T19:35:48.008097Z" + "iopub.execute_input": "2024-06-25T23:17:28.279893Z", + "iopub.status.busy": "2024-06-25T23:17:28.279723Z", + "iopub.status.idle": "2024-06-25T23:17:30.902204Z", + "shell.execute_reply": "2024-06-25T23:17:30.901649Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:35:48.011088Z", - "iopub.status.busy": "2024-06-25T19:35:48.010783Z", - "iopub.status.idle": "2024-06-25T19:35:48.014230Z", - "shell.execute_reply": "2024-06-25T19:35:48.013782Z" + "iopub.execute_input": "2024-06-25T23:17:30.904858Z", + "iopub.status.busy": "2024-06-25T23:17:30.904404Z", + "iopub.status.idle": "2024-06-25T23:17:30.907555Z", + "shell.execute_reply": "2024-06-25T23:17:30.907124Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.016267Z", - "iopub.status.busy": "2024-06-25T19:35:48.015916Z", - "iopub.status.idle": "2024-06-25T19:35:48.019094Z", - "shell.execute_reply": "2024-06-25T19:35:48.018529Z" + "iopub.execute_input": "2024-06-25T23:17:30.909531Z", + "iopub.status.busy": "2024-06-25T23:17:30.909235Z", + "iopub.status.idle": "2024-06-25T23:17:30.912305Z", + "shell.execute_reply": "2024-06-25T23:17:30.911777Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.021232Z", - "iopub.status.busy": "2024-06-25T19:35:48.020813Z", - "iopub.status.idle": "2024-06-25T19:35:48.073023Z", - "shell.execute_reply": "2024-06-25T19:35:48.072456Z" + "iopub.execute_input": "2024-06-25T23:17:30.914377Z", + "iopub.status.busy": "2024-06-25T23:17:30.913988Z", + "iopub.status.idle": "2024-06-25T23:17:30.934290Z", + "shell.execute_reply": "2024-06-25T23:17:30.933773Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.075330Z", - "iopub.status.busy": "2024-06-25T19:35:48.074995Z", - "iopub.status.idle": "2024-06-25T19:35:48.078963Z", - "shell.execute_reply": "2024-06-25T19:35:48.078513Z" + "iopub.execute_input": "2024-06-25T23:17:30.936266Z", + "iopub.status.busy": "2024-06-25T23:17:30.935961Z", + "iopub.status.idle": "2024-06-25T23:17:30.939627Z", + "shell.execute_reply": "2024-06-25T23:17:30.939095Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies'}\n" + "Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_about_to_expire', 'getting_spare_card', 'change_pin', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'cancel_transfer'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.080913Z", - "iopub.status.busy": "2024-06-25T19:35:48.080733Z", - "iopub.status.idle": "2024-06-25T19:35:48.083997Z", - "shell.execute_reply": "2024-06-25T19:35:48.083535Z" + "iopub.execute_input": "2024-06-25T23:17:30.941560Z", + "iopub.status.busy": "2024-06-25T23:17:30.941250Z", + "iopub.status.idle": "2024-06-25T23:17:30.944331Z", + "shell.execute_reply": "2024-06-25T23:17:30.943818Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:48.086043Z", - "iopub.status.busy": "2024-06-25T19:35:48.085869Z", - "iopub.status.idle": "2024-06-25T19:35:52.539336Z", - "shell.execute_reply": "2024-06-25T19:35:52.538772Z" + "iopub.execute_input": "2024-06-25T23:17:30.946381Z", + "iopub.status.busy": "2024-06-25T23:17:30.946063Z", + "iopub.status.idle": "2024-06-25T23:17:34.606408Z", + "shell.execute_reply": "2024-06-25T23:17:34.605752Z" } }, "outputs": [ @@ -424,10 +424,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:52.541851Z", - "iopub.status.busy": "2024-06-25T19:35:52.541641Z", - "iopub.status.idle": "2024-06-25T19:35:53.417381Z", - "shell.execute_reply": "2024-06-25T19:35:53.416793Z" + "iopub.execute_input": "2024-06-25T23:17:34.609229Z", + "iopub.status.busy": "2024-06-25T23:17:34.608851Z", + "iopub.status.idle": "2024-06-25T23:17:35.466411Z", + "shell.execute_reply": "2024-06-25T23:17:35.465834Z" }, "scrolled": true }, @@ -459,10 +459,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:53.420304Z", - "iopub.status.busy": "2024-06-25T19:35:53.419913Z", - "iopub.status.idle": "2024-06-25T19:35:53.422789Z", - "shell.execute_reply": "2024-06-25T19:35:53.422303Z" + "iopub.execute_input": "2024-06-25T23:17:35.469450Z", + "iopub.status.busy": "2024-06-25T23:17:35.469026Z", + "iopub.status.idle": "2024-06-25T23:17:35.471951Z", + "shell.execute_reply": "2024-06-25T23:17:35.471467Z" } }, "outputs": [], @@ -482,10 +482,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:53.425167Z", - "iopub.status.busy": "2024-06-25T19:35:53.424776Z", - "iopub.status.idle": "2024-06-25T19:35:55.333188Z", - "shell.execute_reply": "2024-06-25T19:35:55.332528Z" + "iopub.execute_input": "2024-06-25T23:17:35.474346Z", + "iopub.status.busy": "2024-06-25T23:17:35.473954Z", + "iopub.status.idle": "2024-06-25T23:17:37.379211Z", + "shell.execute_reply": "2024-06-25T23:17:37.378561Z" }, "scrolled": true }, @@ -537,10 +537,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.336733Z", - "iopub.status.busy": "2024-06-25T19:35:55.336306Z", - "iopub.status.idle": "2024-06-25T19:35:55.363099Z", - "shell.execute_reply": "2024-06-25T19:35:55.362613Z" + "iopub.execute_input": "2024-06-25T23:17:37.383383Z", + "iopub.status.busy": "2024-06-25T23:17:37.382233Z", + "iopub.status.idle": "2024-06-25T23:17:37.408704Z", + "shell.execute_reply": "2024-06-25T23:17:37.408212Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.366640Z", - "iopub.status.busy": "2024-06-25T19:35:55.365705Z", - "iopub.status.idle": "2024-06-25T19:35:55.376030Z", - "shell.execute_reply": "2024-06-25T19:35:55.375622Z" + "iopub.execute_input": "2024-06-25T23:17:37.412193Z", + "iopub.status.busy": "2024-06-25T23:17:37.411277Z", + "iopub.status.idle": "2024-06-25T23:17:37.421651Z", + "shell.execute_reply": "2024-06-25T23:17:37.421256Z" }, "scrolled": true }, @@ -783,10 +783,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.378837Z", - "iopub.status.busy": "2024-06-25T19:35:55.378517Z", - "iopub.status.idle": "2024-06-25T19:35:55.382599Z", - "shell.execute_reply": "2024-06-25T19:35:55.382206Z" + "iopub.execute_input": "2024-06-25T23:17:37.424437Z", + "iopub.status.busy": "2024-06-25T23:17:37.423704Z", + "iopub.status.idle": "2024-06-25T23:17:37.428917Z", + "shell.execute_reply": "2024-06-25T23:17:37.428520Z" } }, "outputs": [ @@ -824,10 +824,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.384693Z", - "iopub.status.busy": "2024-06-25T19:35:55.384439Z", - "iopub.status.idle": "2024-06-25T19:35:55.390208Z", - "shell.execute_reply": "2024-06-25T19:35:55.389819Z" + "iopub.execute_input": "2024-06-25T23:17:37.430883Z", + "iopub.status.busy": "2024-06-25T23:17:37.430707Z", + "iopub.status.idle": "2024-06-25T23:17:37.438445Z", + "shell.execute_reply": "2024-06-25T23:17:37.437883Z" } }, "outputs": [ @@ -944,10 +944,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.392385Z", - "iopub.status.busy": "2024-06-25T19:35:55.392130Z", - "iopub.status.idle": "2024-06-25T19:35:55.398230Z", - "shell.execute_reply": "2024-06-25T19:35:55.397669Z" + "iopub.execute_input": "2024-06-25T23:17:37.440387Z", + "iopub.status.busy": "2024-06-25T23:17:37.440214Z", + "iopub.status.idle": "2024-06-25T23:17:37.446599Z", + "shell.execute_reply": "2024-06-25T23:17:37.446157Z" } }, "outputs": [ @@ -1030,10 +1030,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.400097Z", - "iopub.status.busy": "2024-06-25T19:35:55.399777Z", - "iopub.status.idle": "2024-06-25T19:35:55.405709Z", - "shell.execute_reply": "2024-06-25T19:35:55.405249Z" + "iopub.execute_input": "2024-06-25T23:17:37.448520Z", + "iopub.status.busy": "2024-06-25T23:17:37.448196Z", + "iopub.status.idle": "2024-06-25T23:17:37.454046Z", + "shell.execute_reply": "2024-06-25T23:17:37.453485Z" } }, "outputs": [ @@ -1141,10 +1141,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.407786Z", - "iopub.status.busy": "2024-06-25T19:35:55.407389Z", - "iopub.status.idle": "2024-06-25T19:35:55.415929Z", - "shell.execute_reply": "2024-06-25T19:35:55.415484Z" + "iopub.execute_input": "2024-06-25T23:17:37.456157Z", + "iopub.status.busy": "2024-06-25T23:17:37.455839Z", + "iopub.status.idle": "2024-06-25T23:17:37.464219Z", + "shell.execute_reply": "2024-06-25T23:17:37.463796Z" } }, "outputs": [ @@ -1255,10 +1255,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.417871Z", - "iopub.status.busy": "2024-06-25T19:35:55.417696Z", - "iopub.status.idle": "2024-06-25T19:35:55.422924Z", - "shell.execute_reply": "2024-06-25T19:35:55.422488Z" + "iopub.execute_input": "2024-06-25T23:17:37.466195Z", + "iopub.status.busy": "2024-06-25T23:17:37.465883Z", + "iopub.status.idle": "2024-06-25T23:17:37.471233Z", + "shell.execute_reply": "2024-06-25T23:17:37.470679Z" } }, "outputs": [ @@ -1326,10 +1326,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.424972Z", - "iopub.status.busy": "2024-06-25T19:35:55.424657Z", - "iopub.status.idle": "2024-06-25T19:35:55.429929Z", - "shell.execute_reply": "2024-06-25T19:35:55.429503Z" + "iopub.execute_input": "2024-06-25T23:17:37.473304Z", + "iopub.status.busy": "2024-06-25T23:17:37.472970Z", + "iopub.status.idle": "2024-06-25T23:17:37.478474Z", + "shell.execute_reply": "2024-06-25T23:17:37.478028Z" } }, "outputs": [ @@ -1408,10 +1408,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.431977Z", - "iopub.status.busy": "2024-06-25T19:35:55.431649Z", - "iopub.status.idle": "2024-06-25T19:35:55.435259Z", - "shell.execute_reply": "2024-06-25T19:35:55.434820Z" + "iopub.execute_input": "2024-06-25T23:17:37.480531Z", + "iopub.status.busy": "2024-06-25T23:17:37.480222Z", + "iopub.status.idle": "2024-06-25T23:17:37.483860Z", + "shell.execute_reply": "2024-06-25T23:17:37.483411Z" } }, "outputs": [ @@ -1459,10 +1459,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:55.437276Z", - "iopub.status.busy": "2024-06-25T19:35:55.436956Z", - "iopub.status.idle": "2024-06-25T19:35:55.441824Z", - "shell.execute_reply": "2024-06-25T19:35:55.441386Z" + "iopub.execute_input": "2024-06-25T23:17:37.485748Z", + "iopub.status.busy": "2024-06-25T23:17:37.485580Z", + "iopub.status.idle": "2024-06-25T23:17:37.490849Z", + "shell.execute_reply": "2024-06-25T23:17:37.490382Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index 370aa25d4..965f5ea9c 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -3158,224 +3158,224 @@

6. (Optional) Visualize the Results - +
- - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index 073e233c2..05570c79a 100644 --- a/master/tutorials/datalab/workflows.ipynb +++ b/master/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:59.467250Z", - "iopub.status.busy": "2024-06-25T19:35:59.467073Z", - "iopub.status.idle": "2024-06-25T19:35:59.885710Z", - "shell.execute_reply": "2024-06-25T19:35:59.885107Z" + "iopub.execute_input": "2024-06-25T23:17:40.853361Z", + "iopub.status.busy": "2024-06-25T23:17:40.852930Z", + "iopub.status.idle": "2024-06-25T23:17:41.272322Z", + "shell.execute_reply": "2024-06-25T23:17:41.271713Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:35:59.888637Z", - "iopub.status.busy": "2024-06-25T19:35:59.888151Z", - "iopub.status.idle": "2024-06-25T19:36:00.014649Z", - "shell.execute_reply": "2024-06-25T19:36:00.014148Z" + "iopub.execute_input": "2024-06-25T23:17:41.275299Z", + "iopub.status.busy": "2024-06-25T23:17:41.274749Z", + "iopub.status.idle": "2024-06-25T23:17:41.403175Z", + "shell.execute_reply": "2024-06-25T23:17:41.402663Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:00.016873Z", - "iopub.status.busy": "2024-06-25T19:36:00.016623Z", - "iopub.status.idle": "2024-06-25T19:36:00.039876Z", - "shell.execute_reply": "2024-06-25T19:36:00.039305Z" + "iopub.execute_input": "2024-06-25T23:17:41.405438Z", + "iopub.status.busy": "2024-06-25T23:17:41.405028Z", + "iopub.status.idle": "2024-06-25T23:17:41.427834Z", + "shell.execute_reply": "2024-06-25T23:17:41.427281Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:00.042285Z", - "iopub.status.busy": "2024-06-25T19:36:00.041898Z", - "iopub.status.idle": "2024-06-25T19:36:02.696869Z", - "shell.execute_reply": "2024-06-25T19:36:02.696318Z" + "iopub.execute_input": "2024-06-25T23:17:41.430652Z", + "iopub.status.busy": "2024-06-25T23:17:41.430206Z", + "iopub.status.idle": "2024-06-25T23:17:44.079438Z", + "shell.execute_reply": "2024-06-25T23:17:44.078785Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:02.699546Z", - "iopub.status.busy": "2024-06-25T19:36:02.698988Z", - "iopub.status.idle": "2024-06-25T19:36:11.210546Z", - "shell.execute_reply": "2024-06-25T19:36:11.209947Z" + "iopub.execute_input": "2024-06-25T23:17:44.082102Z", + "iopub.status.busy": "2024-06-25T23:17:44.081500Z", + "iopub.status.idle": "2024-06-25T23:17:51.711133Z", + "shell.execute_reply": "2024-06-25T23:17:51.710550Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:11.212912Z", - "iopub.status.busy": "2024-06-25T19:36:11.212489Z", - "iopub.status.idle": "2024-06-25T19:36:11.354224Z", - "shell.execute_reply": "2024-06-25T19:36:11.353605Z" + "iopub.execute_input": "2024-06-25T23:17:51.713313Z", + "iopub.status.busy": "2024-06-25T23:17:51.713127Z", + "iopub.status.idle": "2024-06-25T23:17:51.857400Z", + "shell.execute_reply": "2024-06-25T23:17:51.856753Z" } }, "outputs": [], @@ -854,10 +854,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:11.356684Z", - "iopub.status.busy": "2024-06-25T19:36:11.356497Z", - "iopub.status.idle": "2024-06-25T19:36:12.692416Z", - "shell.execute_reply": "2024-06-25T19:36:12.691867Z" + "iopub.execute_input": "2024-06-25T23:17:51.860009Z", + "iopub.status.busy": "2024-06-25T23:17:51.859627Z", + "iopub.status.idle": "2024-06-25T23:17:53.181642Z", + "shell.execute_reply": "2024-06-25T23:17:53.181004Z" } }, "outputs": [ @@ -1016,10 +1016,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:12.694507Z", - "iopub.status.busy": "2024-06-25T19:36:12.694321Z", - "iopub.status.idle": "2024-06-25T19:36:13.110943Z", - "shell.execute_reply": "2024-06-25T19:36:13.110403Z" + "iopub.execute_input": "2024-06-25T23:17:53.183695Z", + "iopub.status.busy": "2024-06-25T23:17:53.183507Z", + "iopub.status.idle": "2024-06-25T23:17:53.614506Z", + "shell.execute_reply": "2024-06-25T23:17:53.613154Z" } }, "outputs": [ @@ -1098,10 +1098,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.113354Z", - "iopub.status.busy": "2024-06-25T19:36:13.112876Z", - "iopub.status.idle": "2024-06-25T19:36:13.121876Z", - "shell.execute_reply": "2024-06-25T19:36:13.121426Z" + "iopub.execute_input": "2024-06-25T23:17:53.617165Z", + "iopub.status.busy": "2024-06-25T23:17:53.616488Z", + "iopub.status.idle": "2024-06-25T23:17:53.625569Z", + "shell.execute_reply": "2024-06-25T23:17:53.625088Z" } }, "outputs": [], @@ -1131,10 +1131,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.123927Z", - "iopub.status.busy": "2024-06-25T19:36:13.123749Z", - "iopub.status.idle": "2024-06-25T19:36:13.143234Z", - "shell.execute_reply": "2024-06-25T19:36:13.142805Z" + "iopub.execute_input": "2024-06-25T23:17:53.627646Z", + "iopub.status.busy": "2024-06-25T23:17:53.627333Z", + "iopub.status.idle": "2024-06-25T23:17:53.647292Z", + "shell.execute_reply": "2024-06-25T23:17:53.646870Z" } }, "outputs": [], @@ -1162,10 +1162,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.145167Z", - "iopub.status.busy": "2024-06-25T19:36:13.144993Z", - "iopub.status.idle": "2024-06-25T19:36:13.369942Z", - "shell.execute_reply": "2024-06-25T19:36:13.369417Z" + "iopub.execute_input": "2024-06-25T23:17:53.649278Z", + "iopub.status.busy": "2024-06-25T23:17:53.648956Z", + "iopub.status.idle": "2024-06-25T23:17:53.876935Z", + "shell.execute_reply": "2024-06-25T23:17:53.876376Z" } }, "outputs": [], @@ -1205,10 +1205,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.372709Z", - "iopub.status.busy": "2024-06-25T19:36:13.372266Z", - "iopub.status.idle": "2024-06-25T19:36:13.391271Z", - "shell.execute_reply": "2024-06-25T19:36:13.390786Z" + "iopub.execute_input": "2024-06-25T23:17:53.879777Z", + "iopub.status.busy": "2024-06-25T23:17:53.879575Z", + "iopub.status.idle": "2024-06-25T23:17:53.898417Z", + "shell.execute_reply": "2024-06-25T23:17:53.897956Z" } }, "outputs": [ @@ -1406,10 +1406,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.393275Z", - "iopub.status.busy": "2024-06-25T19:36:13.392955Z", - "iopub.status.idle": "2024-06-25T19:36:13.562067Z", - "shell.execute_reply": "2024-06-25T19:36:13.561518Z" + "iopub.execute_input": "2024-06-25T23:17:53.900637Z", + "iopub.status.busy": "2024-06-25T23:17:53.900291Z", + "iopub.status.idle": "2024-06-25T23:17:54.067010Z", + "shell.execute_reply": "2024-06-25T23:17:54.066325Z" } }, "outputs": [ @@ -1476,10 +1476,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.564551Z", - "iopub.status.busy": "2024-06-25T19:36:13.564210Z", - "iopub.status.idle": "2024-06-25T19:36:13.574249Z", - "shell.execute_reply": "2024-06-25T19:36:13.573705Z" + "iopub.execute_input": "2024-06-25T23:17:54.069491Z", + "iopub.status.busy": "2024-06-25T23:17:54.069138Z", + "iopub.status.idle": "2024-06-25T23:17:54.080042Z", + "shell.execute_reply": "2024-06-25T23:17:54.079594Z" } }, "outputs": [ @@ -1745,10 +1745,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.576275Z", - "iopub.status.busy": "2024-06-25T19:36:13.575975Z", - "iopub.status.idle": "2024-06-25T19:36:13.585430Z", - "shell.execute_reply": "2024-06-25T19:36:13.584885Z" + "iopub.execute_input": "2024-06-25T23:17:54.083209Z", + "iopub.status.busy": "2024-06-25T23:17:54.082726Z", + "iopub.status.idle": "2024-06-25T23:17:54.092500Z", + "shell.execute_reply": "2024-06-25T23:17:54.092040Z" } }, "outputs": [ @@ -1935,10 +1935,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.587370Z", - "iopub.status.busy": "2024-06-25T19:36:13.587068Z", - "iopub.status.idle": "2024-06-25T19:36:13.629038Z", - "shell.execute_reply": "2024-06-25T19:36:13.628478Z" + "iopub.execute_input": "2024-06-25T23:17:54.094651Z", + "iopub.status.busy": "2024-06-25T23:17:54.094321Z", + "iopub.status.idle": "2024-06-25T23:17:54.125818Z", + "shell.execute_reply": "2024-06-25T23:17:54.122177Z" } }, "outputs": [], @@ -1972,10 +1972,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.631050Z", - "iopub.status.busy": "2024-06-25T19:36:13.630746Z", - "iopub.status.idle": "2024-06-25T19:36:13.633461Z", - "shell.execute_reply": "2024-06-25T19:36:13.632931Z" + "iopub.execute_input": "2024-06-25T23:17:54.128194Z", + "iopub.status.busy": "2024-06-25T23:17:54.127850Z", + "iopub.status.idle": "2024-06-25T23:17:54.130729Z", + "shell.execute_reply": "2024-06-25T23:17:54.130269Z" } }, "outputs": [], @@ -1997,10 +1997,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.635387Z", - "iopub.status.busy": "2024-06-25T19:36:13.635196Z", - "iopub.status.idle": "2024-06-25T19:36:13.655022Z", - "shell.execute_reply": "2024-06-25T19:36:13.654546Z" + "iopub.execute_input": "2024-06-25T23:17:54.132753Z", + "iopub.status.busy": "2024-06-25T23:17:54.132426Z", + "iopub.status.idle": "2024-06-25T23:17:54.151669Z", + "shell.execute_reply": "2024-06-25T23:17:54.151107Z" } }, "outputs": [ @@ -2158,10 +2158,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.657280Z", - "iopub.status.busy": "2024-06-25T19:36:13.656950Z", - "iopub.status.idle": "2024-06-25T19:36:13.661121Z", - "shell.execute_reply": "2024-06-25T19:36:13.660700Z" + "iopub.execute_input": "2024-06-25T23:17:54.153875Z", + "iopub.status.busy": "2024-06-25T23:17:54.153542Z", + "iopub.status.idle": "2024-06-25T23:17:54.157885Z", + "shell.execute_reply": "2024-06-25T23:17:54.157427Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.663070Z", - "iopub.status.busy": "2024-06-25T19:36:13.662753Z", - "iopub.status.idle": "2024-06-25T19:36:13.690582Z", - "shell.execute_reply": "2024-06-25T19:36:13.690034Z" + "iopub.execute_input": "2024-06-25T23:17:54.159942Z", + "iopub.status.busy": "2024-06-25T23:17:54.159537Z", + "iopub.status.idle": "2024-06-25T23:17:54.187254Z", + "shell.execute_reply": "2024-06-25T23:17:54.186748Z" } }, "outputs": [ @@ -2343,10 +2343,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:13.692558Z", - "iopub.status.busy": "2024-06-25T19:36:13.692385Z", - "iopub.status.idle": "2024-06-25T19:36:14.062207Z", - "shell.execute_reply": "2024-06-25T19:36:14.061647Z" + "iopub.execute_input": "2024-06-25T23:17:54.189370Z", + "iopub.status.busy": "2024-06-25T23:17:54.189020Z", + "iopub.status.idle": "2024-06-25T23:17:54.563581Z", + "shell.execute_reply": "2024-06-25T23:17:54.563004Z" } }, "outputs": [ @@ -2413,10 +2413,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.064536Z", - "iopub.status.busy": "2024-06-25T19:36:14.064346Z", - "iopub.status.idle": "2024-06-25T19:36:14.067724Z", - "shell.execute_reply": "2024-06-25T19:36:14.067250Z" + "iopub.execute_input": "2024-06-25T23:17:54.566043Z", + "iopub.status.busy": "2024-06-25T23:17:54.565580Z", + "iopub.status.idle": "2024-06-25T23:17:54.568905Z", + "shell.execute_reply": "2024-06-25T23:17:54.568452Z" } }, "outputs": [ @@ -2467,10 +2467,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.069713Z", - "iopub.status.busy": "2024-06-25T19:36:14.069543Z", - "iopub.status.idle": "2024-06-25T19:36:14.082545Z", - "shell.execute_reply": "2024-06-25T19:36:14.082110Z" + "iopub.execute_input": "2024-06-25T23:17:54.570928Z", + "iopub.status.busy": "2024-06-25T23:17:54.570747Z", + "iopub.status.idle": "2024-06-25T23:17:54.584558Z", + "shell.execute_reply": "2024-06-25T23:17:54.584061Z" } }, "outputs": [ @@ -2749,10 +2749,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.084372Z", - "iopub.status.busy": "2024-06-25T19:36:14.084199Z", - "iopub.status.idle": "2024-06-25T19:36:14.097558Z", - "shell.execute_reply": "2024-06-25T19:36:14.097135Z" + "iopub.execute_input": "2024-06-25T23:17:54.586622Z", + "iopub.status.busy": "2024-06-25T23:17:54.586423Z", + "iopub.status.idle": "2024-06-25T23:17:54.600724Z", + "shell.execute_reply": "2024-06-25T23:17:54.600241Z" } }, "outputs": [ @@ -3019,10 +3019,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.099340Z", - "iopub.status.busy": "2024-06-25T19:36:14.099173Z", - "iopub.status.idle": "2024-06-25T19:36:14.108741Z", - "shell.execute_reply": "2024-06-25T19:36:14.108314Z" + "iopub.execute_input": "2024-06-25T23:17:54.602957Z", + "iopub.status.busy": "2024-06-25T23:17:54.602518Z", + "iopub.status.idle": "2024-06-25T23:17:54.612377Z", + "shell.execute_reply": "2024-06-25T23:17:54.611952Z" } }, "outputs": [], @@ -3047,10 +3047,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.110562Z", - "iopub.status.busy": "2024-06-25T19:36:14.110394Z", - "iopub.status.idle": "2024-06-25T19:36:14.119786Z", - "shell.execute_reply": "2024-06-25T19:36:14.119280Z" + "iopub.execute_input": "2024-06-25T23:17:54.614486Z", + "iopub.status.busy": "2024-06-25T23:17:54.614156Z", + "iopub.status.idle": "2024-06-25T23:17:54.623497Z", + "shell.execute_reply": "2024-06-25T23:17:54.622945Z" } }, "outputs": [ @@ -3222,10 +3222,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.121705Z", - "iopub.status.busy": "2024-06-25T19:36:14.121535Z", - "iopub.status.idle": "2024-06-25T19:36:14.125253Z", - "shell.execute_reply": "2024-06-25T19:36:14.124849Z" + "iopub.execute_input": "2024-06-25T23:17:54.625637Z", + "iopub.status.busy": "2024-06-25T23:17:54.625295Z", + "iopub.status.idle": "2024-06-25T23:17:54.630830Z", + "shell.execute_reply": "2024-06-25T23:17:54.629019Z" } }, "outputs": [], @@ -3257,10 +3257,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.127233Z", - "iopub.status.busy": "2024-06-25T19:36:14.126914Z", - "iopub.status.idle": "2024-06-25T19:36:14.177262Z", - "shell.execute_reply": "2024-06-25T19:36:14.176812Z" + "iopub.execute_input": "2024-06-25T23:17:54.633124Z", + "iopub.status.busy": "2024-06-25T23:17:54.632790Z", + "iopub.status.idle": "2024-06-25T23:17:54.684363Z", + "shell.execute_reply": "2024-06-25T23:17:54.683802Z" } }, "outputs": [ @@ -3268,230 +3268,230 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3567,10 +3567,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.179445Z", - "iopub.status.busy": "2024-06-25T19:36:14.179018Z", - "iopub.status.idle": "2024-06-25T19:36:14.184786Z", - "shell.execute_reply": "2024-06-25T19:36:14.184224Z" + "iopub.execute_input": "2024-06-25T23:17:54.686913Z", + "iopub.status.busy": "2024-06-25T23:17:54.686475Z", + "iopub.status.idle": "2024-06-25T23:17:54.692178Z", + "shell.execute_reply": "2024-06-25T23:17:54.691645Z" } }, "outputs": [], @@ -3609,10 +3609,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.186887Z", - "iopub.status.busy": "2024-06-25T19:36:14.186471Z", - "iopub.status.idle": "2024-06-25T19:36:14.196806Z", - "shell.execute_reply": "2024-06-25T19:36:14.196244Z" + "iopub.execute_input": "2024-06-25T23:17:54.694316Z", + "iopub.status.busy": "2024-06-25T23:17:54.693981Z", + "iopub.status.idle": "2024-06-25T23:17:54.705261Z", + "shell.execute_reply": "2024-06-25T23:17:54.704802Z" } }, "outputs": [ @@ -3648,10 +3648,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.198752Z", - "iopub.status.busy": "2024-06-25T19:36:14.198440Z", - "iopub.status.idle": "2024-06-25T19:36:14.412825Z", - "shell.execute_reply": "2024-06-25T19:36:14.412259Z" + "iopub.execute_input": "2024-06-25T23:17:54.707234Z", + "iopub.status.busy": "2024-06-25T23:17:54.707059Z", + "iopub.status.idle": "2024-06-25T23:17:54.923905Z", + "shell.execute_reply": "2024-06-25T23:17:54.923350Z" } }, "outputs": [ @@ -3703,10 +3703,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:14.414958Z", - "iopub.status.busy": "2024-06-25T19:36:14.414688Z", - "iopub.status.idle": "2024-06-25T19:36:14.422114Z", - "shell.execute_reply": "2024-06-25T19:36:14.421663Z" + "iopub.execute_input": "2024-06-25T23:17:54.926218Z", + "iopub.status.busy": "2024-06-25T23:17:54.925878Z", + "iopub.status.idle": "2024-06-25T23:17:54.933331Z", + "shell.execute_reply": "2024-06-25T23:17:54.932869Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 6a954c6b0..d462fdaea 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:17.536909Z", - "iopub.status.busy": "2024-06-25T19:36:17.536739Z", - "iopub.status.idle": "2024-06-25T19:36:18.659278Z", - "shell.execute_reply": "2024-06-25T19:36:18.658730Z" + "iopub.execute_input": "2024-06-25T23:17:58.501344Z", + "iopub.status.busy": "2024-06-25T23:17:58.500004Z", + "iopub.status.idle": "2024-06-25T23:17:59.801482Z", + "shell.execute_reply": "2024-06-25T23:17:59.800950Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:18.661733Z", - "iopub.status.busy": "2024-06-25T19:36:18.661422Z", - "iopub.status.idle": "2024-06-25T19:36:18.664275Z", - "shell.execute_reply": "2024-06-25T19:36:18.663748Z" + "iopub.execute_input": "2024-06-25T23:17:59.804004Z", + "iopub.status.busy": "2024-06-25T23:17:59.803708Z", + "iopub.status.idle": "2024-06-25T23:17:59.806736Z", + "shell.execute_reply": "2024-06-25T23:17:59.806281Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:18.666336Z", - "iopub.status.busy": "2024-06-25T19:36:18.666027Z", - "iopub.status.idle": "2024-06-25T19:36:18.678092Z", - "shell.execute_reply": "2024-06-25T19:36:18.677567Z" + "iopub.execute_input": "2024-06-25T23:17:59.808933Z", + "iopub.status.busy": "2024-06-25T23:17:59.808705Z", + "iopub.status.idle": "2024-06-25T23:17:59.821999Z", + "shell.execute_reply": "2024-06-25T23:17:59.821381Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:18.680164Z", - "iopub.status.busy": "2024-06-25T19:36:18.679860Z", - "iopub.status.idle": "2024-06-25T19:36:28.874863Z", - "shell.execute_reply": "2024-06-25T19:36:28.874371Z" + "iopub.execute_input": "2024-06-25T23:17:59.824481Z", + "iopub.status.busy": "2024-06-25T23:17:59.824047Z", + "iopub.status.idle": "2024-06-25T23:18:03.535596Z", + "shell.execute_reply": "2024-06-25T23:18:03.535061Z" }, "id": "dhTHOg8Pyv5G" }, @@ -694,13 +694,7 @@ "\n", "\n", "🎯 Mnist_test_set 🎯\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "\n", "Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n", "\n", @@ -2565,7 +2559,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " * Overall, about 18% (1,846 of the 10,000) labels in your dataset have potential issues.\n", " ** The overall label health score for this dataset is: 0.82.\n", "\n", diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index 5a286f7d2..acbd3933c 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -831,13 +831,13 @@

How can I find label issues in big datasets with limited memory?
-
+
-
+
@@ -1711,7 +1711,7 @@

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

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

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 649612439..713861397 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:31.054579Z", - "iopub.status.busy": "2024-06-25T19:36:31.054404Z", - "iopub.status.idle": "2024-06-25T19:36:32.183683Z", - "shell.execute_reply": "2024-06-25T19:36:32.183056Z" + "iopub.execute_input": "2024-06-25T23:18:05.926443Z", + "iopub.status.busy": "2024-06-25T23:18:05.926263Z", + "iopub.status.idle": "2024-06-25T23:18:07.103304Z", + "shell.execute_reply": "2024-06-25T23:18:07.102799Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:32.186495Z", - "iopub.status.busy": "2024-06-25T19:36:32.186073Z", - "iopub.status.idle": "2024-06-25T19:36:32.189610Z", - "shell.execute_reply": "2024-06-25T19:36:32.189148Z" + "iopub.execute_input": "2024-06-25T23:18:07.106148Z", + "iopub.status.busy": "2024-06-25T23:18:07.105603Z", + "iopub.status.idle": "2024-06-25T23:18:07.109155Z", + "shell.execute_reply": "2024-06-25T23:18:07.108679Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:32.191776Z", - "iopub.status.busy": "2024-06-25T19:36:32.191309Z", - "iopub.status.idle": "2024-06-25T19:36:35.412500Z", - "shell.execute_reply": "2024-06-25T19:36:35.411739Z" + "iopub.execute_input": "2024-06-25T23:18:07.111219Z", + "iopub.status.busy": "2024-06-25T23:18:07.110877Z", + "iopub.status.idle": "2024-06-25T23:18:10.366450Z", + "shell.execute_reply": "2024-06-25T23:18:10.365818Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.415868Z", - "iopub.status.busy": "2024-06-25T19:36:35.414996Z", - "iopub.status.idle": "2024-06-25T19:36:35.452492Z", - "shell.execute_reply": "2024-06-25T19:36:35.451863Z" + "iopub.execute_input": "2024-06-25T23:18:10.369846Z", + "iopub.status.busy": "2024-06-25T23:18:10.369009Z", + "iopub.status.idle": "2024-06-25T23:18:10.408435Z", + "shell.execute_reply": "2024-06-25T23:18:10.407723Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.455265Z", - "iopub.status.busy": "2024-06-25T19:36:35.454795Z", - "iopub.status.idle": "2024-06-25T19:36:35.489174Z", - "shell.execute_reply": "2024-06-25T19:36:35.488560Z" + "iopub.execute_input": "2024-06-25T23:18:10.411187Z", + "iopub.status.busy": "2024-06-25T23:18:10.410945Z", + "iopub.status.idle": "2024-06-25T23:18:10.447524Z", + "shell.execute_reply": "2024-06-25T23:18:10.446786Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.491931Z", - "iopub.status.busy": "2024-06-25T19:36:35.491449Z", - "iopub.status.idle": "2024-06-25T19:36:35.494631Z", - "shell.execute_reply": "2024-06-25T19:36:35.494157Z" + "iopub.execute_input": "2024-06-25T23:18:10.450344Z", + "iopub.status.busy": "2024-06-25T23:18:10.450101Z", + "iopub.status.idle": "2024-06-25T23:18:10.453289Z", + "shell.execute_reply": "2024-06-25T23:18:10.452762Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.496822Z", - "iopub.status.busy": "2024-06-25T19:36:35.496395Z", - "iopub.status.idle": "2024-06-25T19:36:35.499017Z", - "shell.execute_reply": "2024-06-25T19:36:35.498537Z" + "iopub.execute_input": "2024-06-25T23:18:10.455428Z", + "iopub.status.busy": "2024-06-25T23:18:10.455099Z", + "iopub.status.idle": "2024-06-25T23:18:10.457834Z", + "shell.execute_reply": "2024-06-25T23:18:10.457357Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.501249Z", - "iopub.status.busy": "2024-06-25T19:36:35.500816Z", - "iopub.status.idle": "2024-06-25T19:36:35.525422Z", - "shell.execute_reply": "2024-06-25T19:36:35.524821Z" + "iopub.execute_input": "2024-06-25T23:18:10.459894Z", + "iopub.status.busy": "2024-06-25T23:18:10.459627Z", + "iopub.status.idle": "2024-06-25T23:18:10.483748Z", + "shell.execute_reply": "2024-06-25T23:18:10.483202Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d8af54b634f1457680edc574c7fcb110", + "model_id": "558d7887a3b248ccbc78e41ae8f6a2ad", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84b64175499142ae9cf770d1e88b80ac", + "model_id": "633ecf7c235f443883ad78f8a1d748cd", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.532028Z", - "iopub.status.busy": "2024-06-25T19:36:35.531847Z", - "iopub.status.idle": "2024-06-25T19:36:35.538645Z", - "shell.execute_reply": "2024-06-25T19:36:35.538198Z" + "iopub.execute_input": "2024-06-25T23:18:10.488896Z", + "iopub.status.busy": "2024-06-25T23:18:10.488605Z", + "iopub.status.idle": "2024-06-25T23:18:10.495342Z", + "shell.execute_reply": "2024-06-25T23:18:10.494804Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.540612Z", - "iopub.status.busy": "2024-06-25T19:36:35.540437Z", - "iopub.status.idle": "2024-06-25T19:36:35.543848Z", - "shell.execute_reply": "2024-06-25T19:36:35.543410Z" + "iopub.execute_input": "2024-06-25T23:18:10.497491Z", + "iopub.status.busy": "2024-06-25T23:18:10.497223Z", + "iopub.status.idle": "2024-06-25T23:18:10.500578Z", + "shell.execute_reply": "2024-06-25T23:18:10.500143Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.545806Z", - "iopub.status.busy": "2024-06-25T19:36:35.545508Z", - "iopub.status.idle": "2024-06-25T19:36:35.551703Z", - "shell.execute_reply": "2024-06-25T19:36:35.551260Z" + "iopub.execute_input": "2024-06-25T23:18:10.502533Z", + "iopub.status.busy": "2024-06-25T23:18:10.502242Z", + "iopub.status.idle": "2024-06-25T23:18:10.508483Z", + "shell.execute_reply": "2024-06-25T23:18:10.507959Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.553602Z", - "iopub.status.busy": "2024-06-25T19:36:35.553415Z", - "iopub.status.idle": "2024-06-25T19:36:35.589414Z", - "shell.execute_reply": "2024-06-25T19:36:35.588805Z" + "iopub.execute_input": "2024-06-25T23:18:10.510615Z", + "iopub.status.busy": "2024-06-25T23:18:10.510302Z", + "iopub.status.idle": "2024-06-25T23:18:10.546530Z", + "shell.execute_reply": "2024-06-25T23:18:10.545827Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.592001Z", - "iopub.status.busy": "2024-06-25T19:36:35.591752Z", - "iopub.status.idle": "2024-06-25T19:36:35.628128Z", - "shell.execute_reply": "2024-06-25T19:36:35.627508Z" + "iopub.execute_input": "2024-06-25T23:18:10.548998Z", + "iopub.status.busy": "2024-06-25T23:18:10.548767Z", + "iopub.status.idle": "2024-06-25T23:18:10.582483Z", + "shell.execute_reply": "2024-06-25T23:18:10.581909Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.630864Z", - "iopub.status.busy": "2024-06-25T19:36:35.630509Z", - "iopub.status.idle": "2024-06-25T19:36:35.751028Z", - "shell.execute_reply": "2024-06-25T19:36:35.750367Z" + "iopub.execute_input": "2024-06-25T23:18:10.585385Z", + "iopub.status.busy": "2024-06-25T23:18:10.584868Z", + "iopub.status.idle": "2024-06-25T23:18:10.710386Z", + "shell.execute_reply": "2024-06-25T23:18:10.709794Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:35.753981Z", - "iopub.status.busy": "2024-06-25T19:36:35.753115Z", - "iopub.status.idle": "2024-06-25T19:36:38.820276Z", - "shell.execute_reply": "2024-06-25T19:36:38.819614Z" + "iopub.execute_input": "2024-06-25T23:18:10.713077Z", + "iopub.status.busy": "2024-06-25T23:18:10.712538Z", + "iopub.status.idle": "2024-06-25T23:18:13.846109Z", + "shell.execute_reply": "2024-06-25T23:18:13.845478Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.822817Z", - "iopub.status.busy": "2024-06-25T19:36:38.822359Z", - "iopub.status.idle": "2024-06-25T19:36:38.881135Z", - "shell.execute_reply": "2024-06-25T19:36:38.880677Z" + "iopub.execute_input": "2024-06-25T23:18:13.848642Z", + "iopub.status.busy": "2024-06-25T23:18:13.848179Z", + "iopub.status.idle": "2024-06-25T23:18:13.910214Z", + "shell.execute_reply": "2024-06-25T23:18:13.909621Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.883155Z", - "iopub.status.busy": "2024-06-25T19:36:38.882856Z", - "iopub.status.idle": "2024-06-25T19:36:38.922999Z", - "shell.execute_reply": "2024-06-25T19:36:38.922558Z" + "iopub.execute_input": "2024-06-25T23:18:13.912512Z", + "iopub.status.busy": "2024-06-25T23:18:13.912056Z", + "iopub.status.idle": "2024-06-25T23:18:13.955394Z", + "shell.execute_reply": "2024-06-25T23:18:13.954784Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "91d13c0b", + "id": "411cb3b4", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "838b0e29", + "id": "c0fc51ac", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "72c82160", + "id": "31d0af7b", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "c8ef0e49", + "id": "ddefd054", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.925175Z", - "iopub.status.busy": "2024-06-25T19:36:38.924869Z", - "iopub.status.idle": "2024-06-25T19:36:38.933100Z", - "shell.execute_reply": "2024-06-25T19:36:38.932519Z" + "iopub.execute_input": "2024-06-25T23:18:13.957642Z", + "iopub.status.busy": "2024-06-25T23:18:13.957445Z", + "iopub.status.idle": "2024-06-25T23:18:13.965853Z", + "shell.execute_reply": "2024-06-25T23:18:13.965258Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "bfd8eea7", + "id": "96a1ec22", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "7515c699", + "id": "d478ad17", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.935170Z", - "iopub.status.busy": "2024-06-25T19:36:38.934961Z", - "iopub.status.idle": "2024-06-25T19:36:38.958819Z", - "shell.execute_reply": "2024-06-25T19:36:38.958261Z" + "iopub.execute_input": "2024-06-25T23:18:13.968394Z", + "iopub.status.busy": "2024-06-25T23:18:13.968108Z", + "iopub.status.idle": "2024-06-25T23:18:13.989832Z", + "shell.execute_reply": "2024-06-25T23:18:13.989245Z" } }, "outputs": [ @@ -1495,7 +1495,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_7655/1995098996.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_7878/1995098996.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" ] } @@ -1529,13 +1529,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "0be681e4", + "id": "ff936017", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:38.960846Z", - "iopub.status.busy": "2024-06-25T19:36:38.960529Z", - "iopub.status.idle": "2024-06-25T19:36:38.963912Z", - "shell.execute_reply": "2024-06-25T19:36:38.963342Z" + "iopub.execute_input": "2024-06-25T23:18:13.992065Z", + "iopub.status.busy": "2024-06-25T23:18:13.991705Z", + "iopub.status.idle": "2024-06-25T23:18:13.994946Z", + "shell.execute_reply": "2024-06-25T23:18:13.994403Z" } }, "outputs": [ @@ -1630,7 +1630,75 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1ac8a486230942529a1f92b9b04d7e25": { + "01de1302b1ac41a68c4d605171741bc4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "05b22c53719c4c21a23fad4a52106f28": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0e5d155060264c219bd191119ba7e533": { + "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": "" + } + }, + "0f44c68a58214c4e8e72391024cc96e8": { + "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": "" + } + }, + "20b4f1fb000f40e69908d463dce3c07d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1683,7 +1751,7 @@ "width": null } }, - "1e692354f23845bf94b6ee7d9d7b2637": { + "21bd4c1e909b435f96eccf76ffa92cec": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1701,7 +1769,7 @@ "text_color": null } }, - "27e85dda33254222a2c4e9d42bf88ff4": { + "2414d070f8b94c0488b54d3ad24457fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1719,7 +1787,31 @@ "text_color": null } }, - "2f598ca1115244399eac8ba3ae5fcba3": { + "558d7887a3b248ccbc78e41ae8f6a2ad": { + "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_95142643f01f48508a522fc8ef8c8553", + "IPY_MODEL_e962ea686d414b5ca4d9f4f904cd468f", + "IPY_MODEL_db994fd696e94ac29224215d7f867ee2" + ], + "layout": "IPY_MODEL_5eb199074cca45eebcd2fae654f3d219", + "tabbable": null, + "tooltip": null + } + }, + "5baa096c30154543accc624858291e39": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1772,7 +1864,7 @@ "width": null } }, - "3e18aac5a76f4d2f8ce8f5acb53421d0": { + "5eb199074cca45eebcd2fae654f3d219": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1825,49 +1917,31 @@ "width": null } }, - "45a1fa5ae2f84e1b9e7c58b238c02698": { - "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": "" - } - }, - "58d31513ffc848d98369cbd1a5fadbf4": { + "633ecf7c235f443883ad78f8a1d748cd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2f598ca1115244399eac8ba3ae5fcba3", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_45a1fa5ae2f84e1b9e7c58b238c02698", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a6fc608ec49049b99b50be3158f92470", + "IPY_MODEL_b5ef166a707247e98779b17c09955c28", + "IPY_MODEL_9218df49202c42f186276c2b0c86c0ef" + ], + "layout": "IPY_MODEL_81ac2032ca0b474aba6329ab92b63efd", "tabbable": null, - "tooltip": null, - "value": 50.0 + "tooltip": null } }, - "83e8730e8f4b4919bc838506ae45e9f1": { + "80ffedccfbbc46d0bd395580acfaf87f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1920,31 +1994,60 @@ "width": null } }, - "84b64175499142ae9cf770d1e88b80ac": { - "model_module": "@jupyter-widgets/controls", + "81ac2032ca0b474aba6329ab92b63efd": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_89f81d1899184862ad07ccb4185e5559", - "IPY_MODEL_58d31513ffc848d98369cbd1a5fadbf4", - "IPY_MODEL_f724bd9057e2403697996412c3965090" - ], - "layout": "IPY_MODEL_1ac8a486230942529a1f92b9b04d7e25", - "tabbable": null, - "tooltip": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "89f81d1899184862ad07ccb4185e5559": { + "9218df49202c42f186276c2b0c86c0ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1959,41 +2062,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bd49361ee585455e9e0ea110e22cd11e", + "layout": "IPY_MODEL_5baa096c30154543accc624858291e39", "placeholder": "​", - "style": "IPY_MODEL_1e692354f23845bf94b6ee7d9d7b2637", + "style": "IPY_MODEL_05b22c53719c4c21a23fad4a52106f28", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: " + "value": " 10000/? [00:00<00:00, 1516982.17it/s]" } }, - "9e433498da854beaad7447e349a6c08e": { + "95142643f01f48508a522fc8ef8c8553": { "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_3e18aac5a76f4d2f8ce8f5acb53421d0", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_dcebb06a360d4208903b16c601b8b9f0", + "layout": "IPY_MODEL_c84021d7d3264151a5fb14b29eaf1cee", + "placeholder": "​", + "style": "IPY_MODEL_21bd4c1e909b435f96eccf76ffa92cec", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": "number of examples processed for estimating thresholds: " } }, - "a4ce410049214dbe89d19e4dbe853b25": { + "a6fc608ec49049b99b50be3158f92470": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2008,91 +2108,41 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bbe8dc417f9d43f1ae6a2da9fe46bcfd", + "layout": "IPY_MODEL_efd5cc9a580b4925b2b71b81f5bd69d9", "placeholder": "​", - "style": "IPY_MODEL_c0572681372e40379a108207eae59244", + "style": "IPY_MODEL_01de1302b1ac41a68c4d605171741bc4", "tabbable": null, "tooltip": null, - "value": "number of examples processed for estimating thresholds: " - } - }, - "b2b8153149be48568be2b6c09e02fa2f": { - "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": "number of examples processed for checking labels: " } }, - "ba90ec735ab140eda8d3905b17d24bf0": { + "b5ef166a707247e98779b17c09955c28": { "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_b2b8153149be48568be2b6c09e02fa2f", - "placeholder": "​", - "style": "IPY_MODEL_27e85dda33254222a2c4e9d42bf88ff4", + "layout": "IPY_MODEL_c4f061a7d9044c3cbc2d5dc4162c65ef", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0f44c68a58214c4e8e72391024cc96e8", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1015594.57it/s]" + "value": 50.0 } }, - "bbe8dc417f9d43f1ae6a2da9fe46bcfd": { + "c4f061a7d9044c3cbc2d5dc4162c65ef": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2145,7 +2195,7 @@ "width": null } }, - "bd49361ee585455e9e0ea110e22cd11e": { + "c84021d7d3264151a5fb14b29eaf1cee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2198,65 +2248,56 @@ "width": null } }, - "c0572681372e40379a108207eae59244": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d8af54b634f1457680edc574c7fcb110": { + "db994fd696e94ac29224215d7f867ee2": { "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_a4ce410049214dbe89d19e4dbe853b25", - "IPY_MODEL_9e433498da854beaad7447e349a6c08e", - "IPY_MODEL_ba90ec735ab140eda8d3905b17d24bf0" - ], - "layout": "IPY_MODEL_83e8730e8f4b4919bc838506ae45e9f1", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_20b4f1fb000f40e69908d463dce3c07d", + "placeholder": "​", + "style": "IPY_MODEL_2414d070f8b94c0488b54d3ad24457fb", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1059381.69it/s]" } }, - "dcebb06a360d4208903b16c601b8b9f0": { + "e962ea686d414b5ca4d9f4f904cd468f": { "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_80ffedccfbbc46d0bd395580acfaf87f", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0e5d155060264c219bd191119ba7e533", + "tabbable": null, + "tooltip": null, + "value": 50.0 } }, - "ef7c6e423a9041328b6cffec28d2d266": { + "efd5cc9a580b4925b2b71b81f5bd69d9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2308,47 +2349,6 @@ "visibility": null, "width": null } - }, - "f724bd9057e2403697996412c3965090": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ef7c6e423a9041328b6cffec28d2d266", - "placeholder": "​", - "style": "IPY_MODEL_f9185cf0646b4409bb846af3b144c5a1", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1581801.18it/s]" - } - }, - "f9185cf0646b4409bb846af3b144c5a1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index 3a6310e80..902b836cf 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": "2024-06-25T19:36:41.976010Z", - "iopub.status.busy": "2024-06-25T19:36:41.975837Z", - "iopub.status.idle": "2024-06-25T19:36:43.122752Z", - "shell.execute_reply": "2024-06-25T19:36:43.122216Z" + "iopub.execute_input": "2024-06-25T23:18:17.256748Z", + "iopub.status.busy": "2024-06-25T23:18:17.256569Z", + "iopub.status.idle": "2024-06-25T23:18:18.418999Z", + "shell.execute_reply": "2024-06-25T23:18:18.418397Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:36:43.125429Z", - "iopub.status.busy": "2024-06-25T19:36:43.124947Z", - "iopub.status.idle": "2024-06-25T19:36:43.300656Z", - "shell.execute_reply": "2024-06-25T19:36:43.300064Z" + "iopub.execute_input": "2024-06-25T23:18:18.421550Z", + "iopub.status.busy": "2024-06-25T23:18:18.421304Z", + "iopub.status.idle": "2024-06-25T23:18:18.599266Z", + "shell.execute_reply": "2024-06-25T23:18:18.598641Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:43.303142Z", - "iopub.status.busy": "2024-06-25T19:36:43.302695Z", - "iopub.status.idle": "2024-06-25T19:36:43.314281Z", - "shell.execute_reply": "2024-06-25T19:36:43.313721Z" + "iopub.execute_input": "2024-06-25T23:18:18.601825Z", + "iopub.status.busy": "2024-06-25T23:18:18.601625Z", + "iopub.status.idle": "2024-06-25T23:18:18.613136Z", + "shell.execute_reply": "2024-06-25T23:18:18.612703Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:43.316604Z", - "iopub.status.busy": "2024-06-25T19:36:43.316167Z", - "iopub.status.idle": "2024-06-25T19:36:43.522010Z", - "shell.execute_reply": "2024-06-25T19:36:43.521428Z" + "iopub.execute_input": "2024-06-25T23:18:18.615067Z", + "iopub.status.busy": "2024-06-25T23:18:18.614888Z", + "iopub.status.idle": "2024-06-25T23:18:18.849624Z", + "shell.execute_reply": "2024-06-25T23:18:18.849023Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:43.524390Z", - "iopub.status.busy": "2024-06-25T19:36:43.524031Z", - "iopub.status.idle": "2024-06-25T19:36:43.550098Z", - "shell.execute_reply": "2024-06-25T19:36:43.549668Z" + "iopub.execute_input": "2024-06-25T23:18:18.851953Z", + "iopub.status.busy": "2024-06-25T23:18:18.851541Z", + "iopub.status.idle": "2024-06-25T23:18:18.877468Z", + "shell.execute_reply": "2024-06-25T23:18:18.877017Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:43.552184Z", - "iopub.status.busy": "2024-06-25T19:36:43.551843Z", - "iopub.status.idle": "2024-06-25T19:36:45.543682Z", - "shell.execute_reply": "2024-06-25T19:36:45.542976Z" + "iopub.execute_input": "2024-06-25T23:18:18.879561Z", + "iopub.status.busy": "2024-06-25T23:18:18.879211Z", + "iopub.status.idle": "2024-06-25T23:18:20.899666Z", + "shell.execute_reply": "2024-06-25T23:18:20.898976Z" } }, "outputs": [ @@ -482,10 +482,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:45.546502Z", - "iopub.status.busy": "2024-06-25T19:36:45.545811Z", - "iopub.status.idle": "2024-06-25T19:36:45.563579Z", - "shell.execute_reply": "2024-06-25T19:36:45.563096Z" + "iopub.execute_input": "2024-06-25T23:18:20.901960Z", + "iopub.status.busy": "2024-06-25T23:18:20.901648Z", + "iopub.status.idle": "2024-06-25T23:18:20.919398Z", + "shell.execute_reply": "2024-06-25T23:18:20.918920Z" }, "scrolled": true }, @@ -615,10 +615,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:45.565656Z", - "iopub.status.busy": "2024-06-25T19:36:45.565330Z", - "iopub.status.idle": "2024-06-25T19:36:46.995317Z", - "shell.execute_reply": "2024-06-25T19:36:46.994691Z" + "iopub.execute_input": "2024-06-25T23:18:20.921440Z", + "iopub.status.busy": "2024-06-25T23:18:20.921079Z", + "iopub.status.idle": "2024-06-25T23:18:22.361092Z", + "shell.execute_reply": "2024-06-25T23:18:22.360462Z" }, "id": "AaHC5MRKjruT" }, @@ -737,10 +737,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:46.998304Z", - "iopub.status.busy": "2024-06-25T19:36:46.997491Z", - "iopub.status.idle": "2024-06-25T19:36:47.010764Z", - "shell.execute_reply": "2024-06-25T19:36:47.010231Z" + "iopub.execute_input": "2024-06-25T23:18:22.363669Z", + "iopub.status.busy": "2024-06-25T23:18:22.363060Z", + "iopub.status.idle": "2024-06-25T23:18:22.376718Z", + "shell.execute_reply": "2024-06-25T23:18:22.376170Z" }, "id": "Wy27rvyhjruU" }, @@ -789,10 +789,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.012994Z", - "iopub.status.busy": "2024-06-25T19:36:47.012685Z", - "iopub.status.idle": "2024-06-25T19:36:47.092028Z", - "shell.execute_reply": "2024-06-25T19:36:47.091384Z" + "iopub.execute_input": "2024-06-25T23:18:22.378792Z", + "iopub.status.busy": "2024-06-25T23:18:22.378469Z", + "iopub.status.idle": "2024-06-25T23:18:22.452119Z", + "shell.execute_reply": "2024-06-25T23:18:22.451527Z" }, "id": "Db8YHnyVjruU" }, @@ -899,10 +899,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.094203Z", - "iopub.status.busy": "2024-06-25T19:36:47.093979Z", - "iopub.status.idle": "2024-06-25T19:36:47.306398Z", - "shell.execute_reply": "2024-06-25T19:36:47.305822Z" + "iopub.execute_input": "2024-06-25T23:18:22.454608Z", + "iopub.status.busy": "2024-06-25T23:18:22.454246Z", + "iopub.status.idle": "2024-06-25T23:18:22.662363Z", + "shell.execute_reply": "2024-06-25T23:18:22.661824Z" }, "id": "iJqAHuS2jruV" }, @@ -939,10 +939,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.308648Z", - "iopub.status.busy": "2024-06-25T19:36:47.308280Z", - "iopub.status.idle": "2024-06-25T19:36:47.324852Z", - "shell.execute_reply": "2024-06-25T19:36:47.324401Z" + "iopub.execute_input": "2024-06-25T23:18:22.664593Z", + "iopub.status.busy": "2024-06-25T23:18:22.664244Z", + "iopub.status.idle": "2024-06-25T23:18:22.681198Z", + "shell.execute_reply": "2024-06-25T23:18:22.680722Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1408,10 +1408,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.326839Z", - "iopub.status.busy": "2024-06-25T19:36:47.326575Z", - "iopub.status.idle": "2024-06-25T19:36:47.335814Z", - "shell.execute_reply": "2024-06-25T19:36:47.335352Z" + "iopub.execute_input": "2024-06-25T23:18:22.683372Z", + "iopub.status.busy": "2024-06-25T23:18:22.682962Z", + "iopub.status.idle": "2024-06-25T23:18:22.692419Z", + "shell.execute_reply": "2024-06-25T23:18:22.691897Z" }, "id": "0lonvOYvjruV" }, @@ -1558,10 +1558,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.337943Z", - "iopub.status.busy": "2024-06-25T19:36:47.337629Z", - "iopub.status.idle": "2024-06-25T19:36:47.419127Z", - "shell.execute_reply": "2024-06-25T19:36:47.418522Z" + "iopub.execute_input": "2024-06-25T23:18:22.694507Z", + "iopub.status.busy": "2024-06-25T23:18:22.694073Z", + "iopub.status.idle": "2024-06-25T23:18:22.776192Z", + "shell.execute_reply": "2024-06-25T23:18:22.775639Z" }, "id": "MfqTCa3kjruV" }, @@ -1642,10 +1642,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.421368Z", - "iopub.status.busy": "2024-06-25T19:36:47.421141Z", - "iopub.status.idle": "2024-06-25T19:36:47.538207Z", - "shell.execute_reply": "2024-06-25T19:36:47.537601Z" + "iopub.execute_input": "2024-06-25T23:18:22.778586Z", + "iopub.status.busy": "2024-06-25T23:18:22.778226Z", + "iopub.status.idle": "2024-06-25T23:18:22.894081Z", + "shell.execute_reply": "2024-06-25T23:18:22.893472Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1705,10 +1705,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.540745Z", - "iopub.status.busy": "2024-06-25T19:36:47.540377Z", - "iopub.status.idle": "2024-06-25T19:36:47.544346Z", - "shell.execute_reply": "2024-06-25T19:36:47.543812Z" + "iopub.execute_input": "2024-06-25T23:18:22.896294Z", + "iopub.status.busy": "2024-06-25T23:18:22.896067Z", + "iopub.status.idle": "2024-06-25T23:18:22.899817Z", + "shell.execute_reply": "2024-06-25T23:18:22.899290Z" }, "id": "0rXP3ZPWjruW" }, @@ -1746,10 +1746,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.546251Z", - "iopub.status.busy": "2024-06-25T19:36:47.546076Z", - "iopub.status.idle": "2024-06-25T19:36:47.549903Z", - "shell.execute_reply": "2024-06-25T19:36:47.549356Z" + "iopub.execute_input": "2024-06-25T23:18:22.901918Z", + "iopub.status.busy": "2024-06-25T23:18:22.901602Z", + "iopub.status.idle": "2024-06-25T23:18:22.905366Z", + "shell.execute_reply": "2024-06-25T23:18:22.904793Z" }, "id": "-iRPe8KXjruW" }, @@ -1804,10 +1804,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.552020Z", - "iopub.status.busy": "2024-06-25T19:36:47.551608Z", - "iopub.status.idle": "2024-06-25T19:36:47.587995Z", - "shell.execute_reply": "2024-06-25T19:36:47.587566Z" + "iopub.execute_input": "2024-06-25T23:18:22.907395Z", + "iopub.status.busy": "2024-06-25T23:18:22.907096Z", + "iopub.status.idle": "2024-06-25T23:18:22.943768Z", + "shell.execute_reply": "2024-06-25T23:18:22.943295Z" }, "id": "ZpipUliyjruW" }, @@ -1858,10 +1858,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.589852Z", - "iopub.status.busy": "2024-06-25T19:36:47.589680Z", - "iopub.status.idle": "2024-06-25T19:36:47.630699Z", - "shell.execute_reply": "2024-06-25T19:36:47.630137Z" + "iopub.execute_input": "2024-06-25T23:18:22.945705Z", + "iopub.status.busy": "2024-06-25T23:18:22.945390Z", + "iopub.status.idle": "2024-06-25T23:18:22.987000Z", + "shell.execute_reply": "2024-06-25T23:18:22.986556Z" }, "id": "SLq-3q4xjruX" }, @@ -1930,10 +1930,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.632515Z", - "iopub.status.busy": "2024-06-25T19:36:47.632349Z", - "iopub.status.idle": "2024-06-25T19:36:47.720647Z", - "shell.execute_reply": "2024-06-25T19:36:47.719956Z" + "iopub.execute_input": "2024-06-25T23:18:22.989099Z", + "iopub.status.busy": "2024-06-25T23:18:22.988778Z", + "iopub.status.idle": "2024-06-25T23:18:23.079367Z", + "shell.execute_reply": "2024-06-25T23:18:23.078808Z" }, "id": "g5LHhhuqFbXK" }, @@ -1965,10 +1965,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.723084Z", - "iopub.status.busy": "2024-06-25T19:36:47.722899Z", - "iopub.status.idle": "2024-06-25T19:36:47.802159Z", - "shell.execute_reply": "2024-06-25T19:36:47.801549Z" + "iopub.execute_input": "2024-06-25T23:18:23.081992Z", + "iopub.status.busy": "2024-06-25T23:18:23.081632Z", + "iopub.status.idle": "2024-06-25T23:18:23.163660Z", + "shell.execute_reply": "2024-06-25T23:18:23.163108Z" }, "id": "p7w8F8ezBcet" }, @@ -2025,10 +2025,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:47.804647Z", - "iopub.status.busy": "2024-06-25T19:36:47.804175Z", - "iopub.status.idle": "2024-06-25T19:36:48.012610Z", - "shell.execute_reply": "2024-06-25T19:36:48.012009Z" + "iopub.execute_input": "2024-06-25T23:18:23.166170Z", + "iopub.status.busy": "2024-06-25T23:18:23.165696Z", + "iopub.status.idle": "2024-06-25T23:18:23.373652Z", + "shell.execute_reply": "2024-06-25T23:18:23.373076Z" }, "id": "WETRL74tE_sU" }, @@ -2063,10 +2063,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:48.014974Z", - "iopub.status.busy": "2024-06-25T19:36:48.014733Z", - "iopub.status.idle": "2024-06-25T19:36:48.197734Z", - "shell.execute_reply": "2024-06-25T19:36:48.197109Z" + "iopub.execute_input": "2024-06-25T23:18:23.375920Z", + "iopub.status.busy": "2024-06-25T23:18:23.375563Z", + "iopub.status.idle": "2024-06-25T23:18:23.542133Z", + "shell.execute_reply": "2024-06-25T23:18:23.541601Z" }, "id": "kCfdx2gOLmXS" }, @@ -2228,10 +2228,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:48.200133Z", - "iopub.status.busy": "2024-06-25T19:36:48.199890Z", - "iopub.status.idle": "2024-06-25T19:36:48.206211Z", - "shell.execute_reply": "2024-06-25T19:36:48.205745Z" + "iopub.execute_input": "2024-06-25T23:18:23.544310Z", + "iopub.status.busy": "2024-06-25T23:18:23.544080Z", + "iopub.status.idle": "2024-06-25T23:18:23.550244Z", + "shell.execute_reply": "2024-06-25T23:18:23.549696Z" }, "id": "-uogYRWFYnuu" }, @@ -2285,10 +2285,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:48.208373Z", - "iopub.status.busy": "2024-06-25T19:36:48.207949Z", - "iopub.status.idle": "2024-06-25T19:36:48.423251Z", - "shell.execute_reply": "2024-06-25T19:36:48.422679Z" + "iopub.execute_input": "2024-06-25T23:18:23.552552Z", + "iopub.status.busy": "2024-06-25T23:18:23.552102Z", + "iopub.status.idle": "2024-06-25T23:18:23.765551Z", + "shell.execute_reply": "2024-06-25T23:18:23.764971Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2335,10 +2335,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:48.425569Z", - "iopub.status.busy": "2024-06-25T19:36:48.425133Z", - "iopub.status.idle": "2024-06-25T19:36:49.482076Z", - "shell.execute_reply": "2024-06-25T19:36:49.481529Z" + "iopub.execute_input": "2024-06-25T23:18:23.767794Z", + "iopub.status.busy": "2024-06-25T23:18:23.767426Z", + "iopub.status.idle": "2024-06-25T23:18:24.838654Z", + "shell.execute_reply": "2024-06-25T23:18:24.838036Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index 906c55fbe..b4c4a33f9 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:52.983005Z", - "iopub.status.busy": "2024-06-25T19:36:52.982831Z", - "iopub.status.idle": "2024-06-25T19:36:54.092198Z", - "shell.execute_reply": "2024-06-25T19:36:54.091645Z" + "iopub.execute_input": "2024-06-25T23:18:28.410867Z", + "iopub.status.busy": "2024-06-25T23:18:28.410704Z", + "iopub.status.idle": "2024-06-25T23:18:29.523341Z", + "shell.execute_reply": "2024-06-25T23:18:29.522804Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.094787Z", - "iopub.status.busy": "2024-06-25T19:36:54.094431Z", - "iopub.status.idle": "2024-06-25T19:36:54.097617Z", - "shell.execute_reply": "2024-06-25T19:36:54.097173Z" + "iopub.execute_input": "2024-06-25T23:18:29.525967Z", + "iopub.status.busy": "2024-06-25T23:18:29.525510Z", + "iopub.status.idle": "2024-06-25T23:18:29.528645Z", + "shell.execute_reply": "2024-06-25T23:18:29.528187Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.099720Z", - "iopub.status.busy": "2024-06-25T19:36:54.099372Z", - "iopub.status.idle": "2024-06-25T19:36:54.107610Z", - "shell.execute_reply": "2024-06-25T19:36:54.107140Z" + "iopub.execute_input": "2024-06-25T23:18:29.530912Z", + "iopub.status.busy": "2024-06-25T23:18:29.530502Z", + "iopub.status.idle": "2024-06-25T23:18:29.538778Z", + "shell.execute_reply": "2024-06-25T23:18:29.538338Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.109674Z", - "iopub.status.busy": "2024-06-25T19:36:54.109247Z", - "iopub.status.idle": "2024-06-25T19:36:54.157412Z", - "shell.execute_reply": "2024-06-25T19:36:54.156840Z" + "iopub.execute_input": "2024-06-25T23:18:29.540895Z", + "iopub.status.busy": "2024-06-25T23:18:29.540489Z", + "iopub.status.idle": "2024-06-25T23:18:29.587259Z", + "shell.execute_reply": "2024-06-25T23:18:29.586733Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.159654Z", - "iopub.status.busy": "2024-06-25T19:36:54.159472Z", - "iopub.status.idle": "2024-06-25T19:36:54.177229Z", - "shell.execute_reply": "2024-06-25T19:36:54.176762Z" + "iopub.execute_input": "2024-06-25T23:18:29.589466Z", + "iopub.status.busy": "2024-06-25T23:18:29.589277Z", + "iopub.status.idle": "2024-06-25T23:18:29.606524Z", + "shell.execute_reply": "2024-06-25T23:18:29.606095Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.179344Z", - "iopub.status.busy": "2024-06-25T19:36:54.179010Z", - "iopub.status.idle": "2024-06-25T19:36:54.182993Z", - "shell.execute_reply": "2024-06-25T19:36:54.182561Z" + "iopub.execute_input": "2024-06-25T23:18:29.608443Z", + "iopub.status.busy": "2024-06-25T23:18:29.608267Z", + "iopub.status.idle": "2024-06-25T23:18:29.612218Z", + "shell.execute_reply": "2024-06-25T23:18:29.611771Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.185097Z", - "iopub.status.busy": "2024-06-25T19:36:54.184777Z", - "iopub.status.idle": "2024-06-25T19:36:54.198824Z", - "shell.execute_reply": "2024-06-25T19:36:54.198358Z" + "iopub.execute_input": "2024-06-25T23:18:29.614226Z", + "iopub.status.busy": "2024-06-25T23:18:29.614054Z", + "iopub.status.idle": "2024-06-25T23:18:29.631367Z", + "shell.execute_reply": "2024-06-25T23:18:29.630956Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.200845Z", - "iopub.status.busy": "2024-06-25T19:36:54.200664Z", - "iopub.status.idle": "2024-06-25T19:36:54.227151Z", - "shell.execute_reply": "2024-06-25T19:36:54.226585Z" + "iopub.execute_input": "2024-06-25T23:18:29.633306Z", + "iopub.status.busy": "2024-06-25T23:18:29.632964Z", + "iopub.status.idle": "2024-06-25T23:18:29.658440Z", + "shell.execute_reply": "2024-06-25T23:18:29.658012Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:54.229370Z", - "iopub.status.busy": "2024-06-25T19:36:54.228984Z", - "iopub.status.idle": "2024-06-25T19:36:56.088954Z", - "shell.execute_reply": "2024-06-25T19:36:56.088321Z" + "iopub.execute_input": "2024-06-25T23:18:29.660435Z", + "iopub.status.busy": "2024-06-25T23:18:29.660092Z", + "iopub.status.idle": "2024-06-25T23:18:31.561212Z", + "shell.execute_reply": "2024-06-25T23:18:31.560640Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.091797Z", - "iopub.status.busy": "2024-06-25T19:36:56.091365Z", - "iopub.status.idle": "2024-06-25T19:36:56.098121Z", - "shell.execute_reply": "2024-06-25T19:36:56.097667Z" + "iopub.execute_input": "2024-06-25T23:18:31.563955Z", + "iopub.status.busy": "2024-06-25T23:18:31.563327Z", + "iopub.status.idle": "2024-06-25T23:18:31.570324Z", + "shell.execute_reply": "2024-06-25T23:18:31.569880Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.100176Z", - "iopub.status.busy": "2024-06-25T19:36:56.099747Z", - "iopub.status.idle": "2024-06-25T19:36:56.112314Z", - "shell.execute_reply": "2024-06-25T19:36:56.111779Z" + "iopub.execute_input": "2024-06-25T23:18:31.572276Z", + "iopub.status.busy": "2024-06-25T23:18:31.571950Z", + "iopub.status.idle": "2024-06-25T23:18:31.584255Z", + "shell.execute_reply": "2024-06-25T23:18:31.583817Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.114307Z", - "iopub.status.busy": "2024-06-25T19:36:56.113989Z", - "iopub.status.idle": "2024-06-25T19:36:56.120308Z", - "shell.execute_reply": "2024-06-25T19:36:56.119759Z" + "iopub.execute_input": "2024-06-25T23:18:31.586203Z", + "iopub.status.busy": "2024-06-25T23:18:31.585878Z", + "iopub.status.idle": "2024-06-25T23:18:31.591999Z", + "shell.execute_reply": "2024-06-25T23:18:31.591576Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.122321Z", - "iopub.status.busy": "2024-06-25T19:36:56.122009Z", - "iopub.status.idle": "2024-06-25T19:36:56.124766Z", - "shell.execute_reply": "2024-06-25T19:36:56.124216Z" + "iopub.execute_input": "2024-06-25T23:18:31.594128Z", + "iopub.status.busy": "2024-06-25T23:18:31.593809Z", + "iopub.status.idle": "2024-06-25T23:18:31.596328Z", + "shell.execute_reply": "2024-06-25T23:18:31.595895Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.126666Z", - "iopub.status.busy": "2024-06-25T19:36:56.126364Z", - "iopub.status.idle": "2024-06-25T19:36:56.129930Z", - "shell.execute_reply": "2024-06-25T19:36:56.129387Z" + "iopub.execute_input": "2024-06-25T23:18:31.598281Z", + "iopub.status.busy": "2024-06-25T23:18:31.597974Z", + "iopub.status.idle": "2024-06-25T23:18:31.601541Z", + "shell.execute_reply": "2024-06-25T23:18:31.600983Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.132039Z", - "iopub.status.busy": "2024-06-25T19:36:56.131738Z", - "iopub.status.idle": "2024-06-25T19:36:56.134411Z", - "shell.execute_reply": "2024-06-25T19:36:56.133864Z" + "iopub.execute_input": "2024-06-25T23:18:31.603595Z", + "iopub.status.busy": "2024-06-25T23:18:31.603264Z", + "iopub.status.idle": "2024-06-25T23:18:31.605889Z", + "shell.execute_reply": "2024-06-25T23:18:31.605456Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.136459Z", - "iopub.status.busy": "2024-06-25T19:36:56.136150Z", - "iopub.status.idle": "2024-06-25T19:36:56.140438Z", - "shell.execute_reply": "2024-06-25T19:36:56.139976Z" + "iopub.execute_input": "2024-06-25T23:18:31.607856Z", + "iopub.status.busy": "2024-06-25T23:18:31.607558Z", + "iopub.status.idle": "2024-06-25T23:18:31.611501Z", + "shell.execute_reply": "2024-06-25T23:18:31.611048Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.142440Z", - "iopub.status.busy": "2024-06-25T19:36:56.142121Z", - "iopub.status.idle": "2024-06-25T19:36:56.170976Z", - "shell.execute_reply": "2024-06-25T19:36:56.170425Z" + "iopub.execute_input": "2024-06-25T23:18:31.613408Z", + "iopub.status.busy": "2024-06-25T23:18:31.613238Z", + "iopub.status.idle": "2024-06-25T23:18:31.641822Z", + "shell.execute_reply": "2024-06-25T23:18:31.641266Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:36:56.173162Z", - "iopub.status.busy": "2024-06-25T19:36:56.172858Z", - "iopub.status.idle": "2024-06-25T19:36:56.177426Z", - "shell.execute_reply": "2024-06-25T19:36:56.176864Z" + "iopub.execute_input": "2024-06-25T23:18:31.644000Z", + "iopub.status.busy": "2024-06-25T23:18:31.643674Z", + "iopub.status.idle": "2024-06-25T23:18:31.648272Z", + "shell.execute_reply": "2024-06-25T23:18:31.647708Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 9e634f2f3..9593cdb90 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": "2024-06-25T19:36:58.919980Z", - "iopub.status.busy": "2024-06-25T19:36:58.919807Z", - "iopub.status.idle": "2024-06-25T19:37:00.071287Z", - "shell.execute_reply": "2024-06-25T19:37:00.070749Z" + "iopub.execute_input": "2024-06-25T23:18:34.388005Z", + "iopub.status.busy": "2024-06-25T23:18:34.387509Z", + "iopub.status.idle": "2024-06-25T23:18:35.555688Z", + "shell.execute_reply": "2024-06-25T23:18:35.555141Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:00.073825Z", - "iopub.status.busy": "2024-06-25T19:37:00.073418Z", - "iopub.status.idle": "2024-06-25T19:37:00.265456Z", - "shell.execute_reply": "2024-06-25T19:37:00.264849Z" + "iopub.execute_input": "2024-06-25T23:18:35.558285Z", + "iopub.status.busy": "2024-06-25T23:18:35.557842Z", + "iopub.status.idle": "2024-06-25T23:18:35.751397Z", + "shell.execute_reply": "2024-06-25T23:18:35.750860Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:00.268256Z", - "iopub.status.busy": "2024-06-25T19:37:00.267860Z", - "iopub.status.idle": "2024-06-25T19:37:00.281177Z", - "shell.execute_reply": "2024-06-25T19:37:00.280743Z" + "iopub.execute_input": "2024-06-25T23:18:35.754209Z", + "iopub.status.busy": "2024-06-25T23:18:35.753733Z", + "iopub.status.idle": "2024-06-25T23:18:35.767096Z", + "shell.execute_reply": "2024-06-25T23:18:35.766635Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:00.283272Z", - "iopub.status.busy": "2024-06-25T19:37:00.282948Z", - "iopub.status.idle": "2024-06-25T19:37:02.915319Z", - "shell.execute_reply": "2024-06-25T19:37:02.914720Z" + "iopub.execute_input": "2024-06-25T23:18:35.769292Z", + "iopub.status.busy": "2024-06-25T23:18:35.768939Z", + "iopub.status.idle": "2024-06-25T23:18:38.460798Z", + "shell.execute_reply": "2024-06-25T23:18:38.460293Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:02.917655Z", - "iopub.status.busy": "2024-06-25T19:37:02.917303Z", - "iopub.status.idle": "2024-06-25T19:37:04.262113Z", - "shell.execute_reply": "2024-06-25T19:37:04.261389Z" + "iopub.execute_input": "2024-06-25T23:18:38.463138Z", + "iopub.status.busy": "2024-06-25T23:18:38.462688Z", + "iopub.status.idle": "2024-06-25T23:18:39.817391Z", + "shell.execute_reply": "2024-06-25T23:18:39.816843Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:04.264665Z", - "iopub.status.busy": "2024-06-25T19:37:04.264273Z", - "iopub.status.idle": "2024-06-25T19:37:04.268776Z", - "shell.execute_reply": "2024-06-25T19:37:04.268171Z" + "iopub.execute_input": "2024-06-25T23:18:39.819916Z", + "iopub.status.busy": "2024-06-25T23:18:39.819475Z", + "iopub.status.idle": "2024-06-25T23:18:39.823477Z", + "shell.execute_reply": "2024-06-25T23:18:39.822931Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:04.271017Z", - "iopub.status.busy": "2024-06-25T19:37:04.270694Z", - "iopub.status.idle": "2024-06-25T19:37:06.209152Z", - "shell.execute_reply": "2024-06-25T19:37:06.208542Z" + "iopub.execute_input": "2024-06-25T23:18:39.825523Z", + "iopub.status.busy": "2024-06-25T23:18:39.825189Z", + "iopub.status.idle": "2024-06-25T23:18:41.816360Z", + "shell.execute_reply": "2024-06-25T23:18:41.815747Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:06.211688Z", - "iopub.status.busy": "2024-06-25T19:37:06.211198Z", - "iopub.status.idle": "2024-06-25T19:37:06.218564Z", - "shell.execute_reply": "2024-06-25T19:37:06.218036Z" + "iopub.execute_input": "2024-06-25T23:18:41.818930Z", + "iopub.status.busy": "2024-06-25T23:18:41.818429Z", + "iopub.status.idle": "2024-06-25T23:18:41.826321Z", + "shell.execute_reply": "2024-06-25T23:18:41.825851Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:06.220591Z", - "iopub.status.busy": "2024-06-25T19:37:06.220264Z", - "iopub.status.idle": "2024-06-25T19:37:08.793564Z", - "shell.execute_reply": "2024-06-25T19:37:08.792970Z" + "iopub.execute_input": "2024-06-25T23:18:41.828406Z", + "iopub.status.busy": "2024-06-25T23:18:41.828097Z", + "iopub.status.idle": "2024-06-25T23:18:44.431218Z", + "shell.execute_reply": "2024-06-25T23:18:44.430687Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:08.795901Z", - "iopub.status.busy": "2024-06-25T19:37:08.795549Z", - "iopub.status.idle": "2024-06-25T19:37:08.798884Z", - "shell.execute_reply": "2024-06-25T19:37:08.798350Z" + "iopub.execute_input": "2024-06-25T23:18:44.433395Z", + "iopub.status.busy": "2024-06-25T23:18:44.433032Z", + "iopub.status.idle": "2024-06-25T23:18:44.436462Z", + "shell.execute_reply": "2024-06-25T23:18:44.435934Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:08.800984Z", - "iopub.status.busy": "2024-06-25T19:37:08.800677Z", - "iopub.status.idle": "2024-06-25T19:37:08.804151Z", - "shell.execute_reply": "2024-06-25T19:37:08.803635Z" + "iopub.execute_input": "2024-06-25T23:18:44.438430Z", + "iopub.status.busy": "2024-06-25T23:18:44.438123Z", + "iopub.status.idle": "2024-06-25T23:18:44.441587Z", + "shell.execute_reply": "2024-06-25T23:18:44.441125Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:08.806163Z", - "iopub.status.busy": "2024-06-25T19:37:08.805988Z", - "iopub.status.idle": "2024-06-25T19:37:08.809167Z", - "shell.execute_reply": "2024-06-25T19:37:08.808609Z" + "iopub.execute_input": "2024-06-25T23:18:44.443586Z", + "iopub.status.busy": "2024-06-25T23:18:44.443248Z", + "iopub.status.idle": "2024-06-25T23:18:44.446272Z", + "shell.execute_reply": "2024-06-25T23:18:44.445845Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index aebe787bb..ceb7220d6 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:11.308794Z", - "iopub.status.busy": "2024-06-25T19:37:11.308627Z", - "iopub.status.idle": "2024-06-25T19:37:12.452711Z", - "shell.execute_reply": "2024-06-25T19:37:12.452159Z" + "iopub.execute_input": "2024-06-25T23:18:46.821534Z", + "iopub.status.busy": "2024-06-25T23:18:46.821356Z", + "iopub.status.idle": "2024-06-25T23:18:47.991566Z", + "shell.execute_reply": "2024-06-25T23:18:47.991020Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:12.455258Z", - "iopub.status.busy": "2024-06-25T19:37:12.454827Z", - "iopub.status.idle": "2024-06-25T19:37:14.890620Z", - "shell.execute_reply": "2024-06-25T19:37:14.889969Z" + "iopub.execute_input": "2024-06-25T23:18:47.994044Z", + "iopub.status.busy": "2024-06-25T23:18:47.993746Z", + "iopub.status.idle": "2024-06-25T23:18:49.077383Z", + "shell.execute_reply": "2024-06-25T23:18:49.076740Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:14.893309Z", - "iopub.status.busy": "2024-06-25T19:37:14.892942Z", - "iopub.status.idle": "2024-06-25T19:37:14.896049Z", - "shell.execute_reply": "2024-06-25T19:37:14.895624Z" + "iopub.execute_input": "2024-06-25T23:18:49.079931Z", + "iopub.status.busy": "2024-06-25T23:18:49.079715Z", + "iopub.status.idle": "2024-06-25T23:18:49.083128Z", + "shell.execute_reply": "2024-06-25T23:18:49.082576Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:14.898040Z", - "iopub.status.busy": "2024-06-25T19:37:14.897713Z", - "iopub.status.idle": "2024-06-25T19:37:14.903653Z", - "shell.execute_reply": "2024-06-25T19:37:14.903185Z" + "iopub.execute_input": "2024-06-25T23:18:49.085315Z", + "iopub.status.busy": "2024-06-25T23:18:49.084875Z", + "iopub.status.idle": "2024-06-25T23:18:49.090995Z", + "shell.execute_reply": "2024-06-25T23:18:49.090565Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:14.905688Z", - "iopub.status.busy": "2024-06-25T19:37:14.905360Z", - "iopub.status.idle": "2024-06-25T19:37:15.391751Z", - "shell.execute_reply": "2024-06-25T19:37:15.391128Z" + "iopub.execute_input": "2024-06-25T23:18:49.092987Z", + "iopub.status.busy": "2024-06-25T23:18:49.092664Z", + "iopub.status.idle": "2024-06-25T23:18:49.578049Z", + "shell.execute_reply": "2024-06-25T23:18:49.577480Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:15.394507Z", - "iopub.status.busy": "2024-06-25T19:37:15.394142Z", - "iopub.status.idle": "2024-06-25T19:37:15.399398Z", - "shell.execute_reply": "2024-06-25T19:37:15.398860Z" + "iopub.execute_input": "2024-06-25T23:18:49.581141Z", + "iopub.status.busy": "2024-06-25T23:18:49.580804Z", + "iopub.status.idle": "2024-06-25T23:18:49.586187Z", + "shell.execute_reply": "2024-06-25T23:18:49.585728Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:15.401545Z", - "iopub.status.busy": "2024-06-25T19:37:15.401225Z", - "iopub.status.idle": "2024-06-25T19:37:15.404995Z", - "shell.execute_reply": "2024-06-25T19:37:15.404569Z" + "iopub.execute_input": "2024-06-25T23:18:49.588207Z", + "iopub.status.busy": "2024-06-25T23:18:49.587912Z", + "iopub.status.idle": "2024-06-25T23:18:49.592364Z", + "shell.execute_reply": "2024-06-25T23:18:49.591919Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:15.407039Z", - "iopub.status.busy": "2024-06-25T19:37:15.406711Z", - "iopub.status.idle": "2024-06-25T19:37:16.295944Z", - "shell.execute_reply": "2024-06-25T19:37:16.295383Z" + "iopub.execute_input": "2024-06-25T23:18:49.594287Z", + "iopub.status.busy": "2024-06-25T23:18:49.594112Z", + "iopub.status.idle": "2024-06-25T23:18:50.586165Z", + "shell.execute_reply": "2024-06-25T23:18:50.585507Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:16.298196Z", - "iopub.status.busy": "2024-06-25T19:37:16.297999Z", - "iopub.status.idle": "2024-06-25T19:37:16.525061Z", - "shell.execute_reply": "2024-06-25T19:37:16.524590Z" + "iopub.execute_input": "2024-06-25T23:18:50.588520Z", + "iopub.status.busy": "2024-06-25T23:18:50.588324Z", + "iopub.status.idle": "2024-06-25T23:18:50.808698Z", + "shell.execute_reply": "2024-06-25T23:18:50.808228Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:16.527288Z", - "iopub.status.busy": "2024-06-25T19:37:16.526859Z", - "iopub.status.idle": "2024-06-25T19:37:16.531244Z", - "shell.execute_reply": "2024-06-25T19:37:16.530747Z" + "iopub.execute_input": "2024-06-25T23:18:50.810921Z", + "iopub.status.busy": "2024-06-25T23:18:50.810585Z", + "iopub.status.idle": "2024-06-25T23:18:50.815013Z", + "shell.execute_reply": "2024-06-25T23:18:50.814577Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:16.533265Z", - "iopub.status.busy": "2024-06-25T19:37:16.533088Z", - "iopub.status.idle": "2024-06-25T19:37:16.979069Z", - "shell.execute_reply": "2024-06-25T19:37:16.978477Z" + "iopub.execute_input": "2024-06-25T23:18:50.816841Z", + "iopub.status.busy": "2024-06-25T23:18:50.816666Z", + "iopub.status.idle": "2024-06-25T23:18:51.264514Z", + "shell.execute_reply": "2024-06-25T23:18:51.263937Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:16.981738Z", - "iopub.status.busy": "2024-06-25T19:37:16.981547Z", - "iopub.status.idle": "2024-06-25T19:37:17.310927Z", - "shell.execute_reply": "2024-06-25T19:37:17.310336Z" + "iopub.execute_input": "2024-06-25T23:18:51.267205Z", + "iopub.status.busy": "2024-06-25T23:18:51.266984Z", + "iopub.status.idle": "2024-06-25T23:18:51.597569Z", + "shell.execute_reply": "2024-06-25T23:18:51.596965Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:17.313292Z", - "iopub.status.busy": "2024-06-25T19:37:17.312887Z", - "iopub.status.idle": "2024-06-25T19:37:17.645849Z", - "shell.execute_reply": "2024-06-25T19:37:17.645269Z" + "iopub.execute_input": "2024-06-25T23:18:51.599806Z", + "iopub.status.busy": "2024-06-25T23:18:51.599595Z", + "iopub.status.idle": "2024-06-25T23:18:51.933374Z", + "shell.execute_reply": "2024-06-25T23:18:51.932766Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:17.649071Z", - "iopub.status.busy": "2024-06-25T19:37:17.648711Z", - "iopub.status.idle": "2024-06-25T19:37:18.056258Z", - "shell.execute_reply": "2024-06-25T19:37:18.055723Z" + "iopub.execute_input": "2024-06-25T23:18:51.936579Z", + "iopub.status.busy": "2024-06-25T23:18:51.936094Z", + "iopub.status.idle": "2024-06-25T23:18:52.348181Z", + "shell.execute_reply": "2024-06-25T23:18:52.347588Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.060462Z", - "iopub.status.busy": "2024-06-25T19:37:18.060093Z", - "iopub.status.idle": "2024-06-25T19:37:18.505775Z", - "shell.execute_reply": "2024-06-25T19:37:18.505169Z" + "iopub.execute_input": "2024-06-25T23:18:52.352428Z", + "iopub.status.busy": "2024-06-25T23:18:52.351994Z", + "iopub.status.idle": "2024-06-25T23:18:52.773521Z", + "shell.execute_reply": "2024-06-25T23:18:52.772929Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.508548Z", - "iopub.status.busy": "2024-06-25T19:37:18.508203Z", - "iopub.status.idle": "2024-06-25T19:37:18.698418Z", - "shell.execute_reply": "2024-06-25T19:37:18.697831Z" + "iopub.execute_input": "2024-06-25T23:18:52.776870Z", + "iopub.status.busy": "2024-06-25T23:18:52.776447Z", + "iopub.status.idle": "2024-06-25T23:18:52.965633Z", + "shell.execute_reply": "2024-06-25T23:18:52.965014Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.700790Z", - "iopub.status.busy": "2024-06-25T19:37:18.700610Z", - "iopub.status.idle": "2024-06-25T19:37:18.880703Z", - "shell.execute_reply": "2024-06-25T19:37:18.880186Z" + "iopub.execute_input": "2024-06-25T23:18:52.968518Z", + "iopub.status.busy": "2024-06-25T23:18:52.968035Z", + "iopub.status.idle": "2024-06-25T23:18:53.169696Z", + "shell.execute_reply": "2024-06-25T23:18:53.169139Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.882941Z", - "iopub.status.busy": "2024-06-25T19:37:18.882765Z", - "iopub.status.idle": "2024-06-25T19:37:18.885792Z", - "shell.execute_reply": "2024-06-25T19:37:18.885246Z" + "iopub.execute_input": "2024-06-25T23:18:53.171908Z", + "iopub.status.busy": "2024-06-25T23:18:53.171701Z", + "iopub.status.idle": "2024-06-25T23:18:53.174679Z", + "shell.execute_reply": "2024-06-25T23:18:53.174135Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:18.887722Z", - "iopub.status.busy": "2024-06-25T19:37:18.887391Z", - "iopub.status.idle": "2024-06-25T19:37:19.791276Z", - "shell.execute_reply": "2024-06-25T19:37:19.790730Z" + "iopub.execute_input": "2024-06-25T23:18:53.176658Z", + "iopub.status.busy": "2024-06-25T23:18:53.176332Z", + "iopub.status.idle": "2024-06-25T23:18:54.151841Z", + "shell.execute_reply": "2024-06-25T23:18:54.151257Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:19.793943Z", - "iopub.status.busy": "2024-06-25T19:37:19.793573Z", - "iopub.status.idle": "2024-06-25T19:37:19.935555Z", - "shell.execute_reply": "2024-06-25T19:37:19.935101Z" + "iopub.execute_input": "2024-06-25T23:18:54.153970Z", + "iopub.status.busy": "2024-06-25T23:18:54.153788Z", + "iopub.status.idle": "2024-06-25T23:18:54.367334Z", + "shell.execute_reply": "2024-06-25T23:18:54.366782Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:19.937552Z", - "iopub.status.busy": "2024-06-25T19:37:19.937378Z", - "iopub.status.idle": "2024-06-25T19:37:20.088397Z", - "shell.execute_reply": "2024-06-25T19:37:20.087796Z" + "iopub.execute_input": "2024-06-25T23:18:54.369532Z", + "iopub.status.busy": "2024-06-25T23:18:54.369222Z", + "iopub.status.idle": "2024-06-25T23:18:54.583472Z", + "shell.execute_reply": "2024-06-25T23:18:54.582875Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:20.090556Z", - "iopub.status.busy": "2024-06-25T19:37:20.090235Z", - "iopub.status.idle": "2024-06-25T19:37:20.751985Z", - "shell.execute_reply": "2024-06-25T19:37:20.751385Z" + "iopub.execute_input": "2024-06-25T23:18:54.585760Z", + "iopub.status.busy": "2024-06-25T23:18:54.585359Z", + "iopub.status.idle": "2024-06-25T23:18:55.323353Z", + "shell.execute_reply": "2024-06-25T23:18:55.322814Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:20.754413Z", - "iopub.status.busy": "2024-06-25T19:37:20.753942Z", - "iopub.status.idle": "2024-06-25T19:37:20.757882Z", - "shell.execute_reply": "2024-06-25T19:37:20.757342Z" + "iopub.execute_input": "2024-06-25T23:18:55.325548Z", + "iopub.status.busy": "2024-06-25T23:18:55.325207Z", + "iopub.status.idle": "2024-06-25T23:18:55.329284Z", + "shell.execute_reply": "2024-06-25T23:18:55.328852Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index afc8ab86a..c663c5ea2 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -780,7 +780,7 @@

2. Pre-process the Cifar10 dataset
-100%|██████████| 170498071/170498071 [00:03<00:00, 43010872.29it/s]
+100%|██████████| 170498071/170498071 [00:01<00:00, 107997102.42it/s]
 
-
+
@@ -1124,7 +1124,7 @@

4. Use cleanlab and here.

diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index 3359ecfd0..4aeee095a 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:22.937714Z", - "iopub.status.busy": "2024-06-25T19:37:22.937546Z", - "iopub.status.idle": "2024-06-25T19:37:25.620183Z", - "shell.execute_reply": "2024-06-25T19:37:25.619593Z" + "iopub.execute_input": "2024-06-25T23:18:57.455185Z", + "iopub.status.busy": "2024-06-25T23:18:57.455007Z", + "iopub.status.idle": "2024-06-25T23:19:00.140522Z", + "shell.execute_reply": "2024-06-25T23:19:00.139964Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:37:25.622737Z", - "iopub.status.busy": "2024-06-25T19:37:25.622414Z", - "iopub.status.idle": "2024-06-25T19:37:25.936079Z", - "shell.execute_reply": "2024-06-25T19:37:25.935452Z" + "iopub.execute_input": "2024-06-25T23:19:00.143299Z", + "iopub.status.busy": "2024-06-25T23:19:00.142777Z", + "iopub.status.idle": "2024-06-25T23:19:00.459330Z", + "shell.execute_reply": "2024-06-25T23:19:00.458710Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:25.938723Z", - "iopub.status.busy": "2024-06-25T19:37:25.938422Z", - "iopub.status.idle": "2024-06-25T19:37:25.942622Z", - "shell.execute_reply": "2024-06-25T19:37:25.942185Z" + "iopub.execute_input": "2024-06-25T23:19:00.461903Z", + "iopub.status.busy": "2024-06-25T23:19:00.461603Z", + "iopub.status.idle": "2024-06-25T23:19:00.465997Z", + "shell.execute_reply": "2024-06-25T23:19:00.465462Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:25.944514Z", - "iopub.status.busy": "2024-06-25T19:37:25.944341Z", - "iopub.status.idle": "2024-06-25T19:37:33.410224Z", - "shell.execute_reply": "2024-06-25T19:37:33.409701Z" + "iopub.execute_input": "2024-06-25T23:19:00.468073Z", + "iopub.status.busy": "2024-06-25T23:19:00.467652Z", + "iopub.status.idle": "2024-06-25T23:19:04.713802Z", + "shell.execute_reply": "2024-06-25T23:19:04.713212Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 32768/170498071 [00:00<10:33, 269061.34it/s]" + " 1%| | 1867776/170498071 [00:00<00:09, 18674661.14it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 229376/170498071 [00:00<02:43, 1044330.69it/s]" + " 8%|▊ | 13533184/170498071 [00:00<00:02, 76238255.65it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 884736/170498071 [00:00<00:56, 2986468.56it/s]" + " 15%|█▍ | 25133056/170498071 [00:00<00:01, 94330786.80it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 3506176/170498071 [00:00<00:15, 10508236.75it/s]" + " 22%|██▏ | 36732928/170498071 [00:00<00:01, 102749472.78it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 8552448/170498071 [00:00<00:06, 23273913.94it/s]" + " 28%|██▊ | 48431104/170498071 [00:00<00:01, 107856210.55it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 12877824/170498071 [00:00<00:05, 29531831.70it/s]" + " 35%|███▍ | 59244544/170498071 [00:00<00:01, 104969542.54it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 17661952/170498071 [00:00<00:04, 34683944.45it/s]" + " 41%|████▏ | 70385664/170498071 [00:00<00:00, 106967167.13it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22478848/170498071 [00:00<00:03, 38419021.56it/s]" + " 48%|████▊ | 82051072/170498071 [00:00<00:00, 109967427.14it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 27590656/170498071 [00:00<00:03, 42218756.72it/s]" + " 55%|█████▍ | 93618176/170498071 [00:00<00:00, 111587704.91it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32145408/170498071 [00:01<00:03, 42979378.57it/s]" + " 62%|██████▏ | 105381888/170498071 [00:01<00:00, 113341455.44it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 37552128/170498071 [00:01<00:02, 46248329.44it/s]" + " 69%|██████▊ | 116850688/170498071 [00:01<00:00, 113640126.52it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 42237952/170498071 [00:01<00:02, 45894868.35it/s]" + " 75%|███████▌ | 128221184/170498071 [00:01<00:00, 112577089.48it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 47546368/170498071 [00:01<00:02, 47987784.85it/s]" + " 82%|████████▏ | 139886592/170498071 [00:01<00:00, 113733636.99it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 52396032/170498071 [00:01<00:02, 47660760.18it/s]" + " 89%|████████▉ | 151355392/170498071 [00:01<00:00, 113996738.39it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 57212928/170498071 [00:01<00:02, 46221672.73it/s]" + " 96%|█████████▌| 163020800/170498071 [00:01<00:00, 114693076.37it/s]" ] }, { @@ -372,183 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 62259200/170498071 [00:01<00:02, 47420663.64it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 39%|███▉ | 67043328/170498071 [00:01<00:02, 46870615.91it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 42%|████▏ | 72187904/170498071 [00:01<00:02, 48207181.06it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 45%|████▌ | 77037568/170498071 [00:02<00:01, 47426096.98it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 48%|████▊ | 82477056/170498071 [00:02<00:01, 48903231.41it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 51%|█████▏ | 87392256/170498071 [00:02<00:01, 48876811.20it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 54%|█████▍ | 92307456/170498071 [00:02<00:01, 46456343.45it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 57%|█████▋ | 97714176/170498071 [00:02<00:01, 48622672.55it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 60%|██████ | 102629376/170498071 [00:02<00:01, 47303583.33it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 63%|██████▎ | 107839488/170498071 [00:02<00:01, 48660290.70it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 66%|██████▌ | 112754688/170498071 [00:02<00:01, 48170138.42it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 69%|██████▉ | 117604352/170498071 [00:02<00:01, 48147632.32it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 72%|███████▏ | 122552320/170498071 [00:02<00:01, 47622185.83it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 75%|███████▍ | 127336448/170498071 [00:03<00:00, 46393005.90it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 78%|███████▊ | 133300224/170498071 [00:03<00:00, 50178503.28it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 81%|████████ | 138346496/170498071 [00:03<00:00, 48020765.51it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 84%|████████▍ | 143196160/170498071 [00:03<00:00, 46334061.22it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 87%|████████▋ | 149061632/170498071 [00:03<00:00, 49353288.69it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 90%|█████████ | 154042368/170498071 [00:03<00:00, 48340337.16it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 93%|█████████▎| 158924800/170498071 [00:03<00:00, 46140400.23it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 97%|█████████▋| 164921344/170498071 [00:03<00:00, 49140811.61it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|█████████▉| 169869312/170498071 [00:03<00:00, 48444737.50it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:03<00:00, 43010872.29it/s]" + "100%|██████████| 170498071/170498071 [00:01<00:00, 107997102.42it/s]" ] }, { @@ -666,10 +490,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:33.412458Z", - "iopub.status.busy": "2024-06-25T19:37:33.412129Z", - "iopub.status.idle": "2024-06-25T19:37:33.416824Z", - "shell.execute_reply": "2024-06-25T19:37:33.416305Z" + "iopub.execute_input": "2024-06-25T23:19:04.715884Z", + "iopub.status.busy": "2024-06-25T23:19:04.715703Z", + "iopub.status.idle": "2024-06-25T23:19:04.720331Z", + "shell.execute_reply": "2024-06-25T23:19:04.719901Z" }, "nbsphinx": "hidden" }, @@ -720,10 +544,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:33.418809Z", - "iopub.status.busy": "2024-06-25T19:37:33.418495Z", - "iopub.status.idle": "2024-06-25T19:37:33.960026Z", - "shell.execute_reply": "2024-06-25T19:37:33.959496Z" + "iopub.execute_input": "2024-06-25T23:19:04.722383Z", + "iopub.status.busy": "2024-06-25T23:19:04.722067Z", + "iopub.status.idle": "2024-06-25T23:19:05.264665Z", + "shell.execute_reply": "2024-06-25T23:19:05.264156Z" } }, "outputs": [ @@ -756,10 +580,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:33.962172Z", - "iopub.status.busy": "2024-06-25T19:37:33.961834Z", - "iopub.status.idle": "2024-06-25T19:37:34.466908Z", - "shell.execute_reply": "2024-06-25T19:37:34.466308Z" + "iopub.execute_input": "2024-06-25T23:19:05.266862Z", + "iopub.status.busy": "2024-06-25T23:19:05.266533Z", + "iopub.status.idle": "2024-06-25T23:19:05.782683Z", + "shell.execute_reply": "2024-06-25T23:19:05.782187Z" } }, "outputs": [ @@ -797,10 +621,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:34.469197Z", - "iopub.status.busy": "2024-06-25T19:37:34.468825Z", - "iopub.status.idle": "2024-06-25T19:37:34.472416Z", - "shell.execute_reply": "2024-06-25T19:37:34.471962Z" + "iopub.execute_input": "2024-06-25T23:19:05.784979Z", + "iopub.status.busy": "2024-06-25T23:19:05.784496Z", + "iopub.status.idle": "2024-06-25T23:19:05.788141Z", + "shell.execute_reply": "2024-06-25T23:19:05.787582Z" } }, "outputs": [], @@ -823,17 +647,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:34.474255Z", - "iopub.status.busy": "2024-06-25T19:37:34.474084Z", - "iopub.status.idle": "2024-06-25T19:37:46.991980Z", - "shell.execute_reply": "2024-06-25T19:37:46.991418Z" + "iopub.execute_input": "2024-06-25T23:19:05.790197Z", + "iopub.status.busy": "2024-06-25T23:19:05.789889Z", + "iopub.status.idle": "2024-06-25T23:19:18.262418Z", + "shell.execute_reply": "2024-06-25T23:19:18.261753Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3a6eebd9a9694b07864d194c78cdb317", + "model_id": "c487c0c381e74a26a4f49ff121b50dc9", "version_major": 2, "version_minor": 0 }, @@ -892,10 +716,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:46.994340Z", - "iopub.status.busy": "2024-06-25T19:37:46.994149Z", - "iopub.status.idle": "2024-06-25T19:37:49.075191Z", - "shell.execute_reply": "2024-06-25T19:37:49.074609Z" + "iopub.execute_input": "2024-06-25T23:19:18.264580Z", + "iopub.status.busy": "2024-06-25T23:19:18.264398Z", + "iopub.status.idle": "2024-06-25T23:19:20.351765Z", + "shell.execute_reply": "2024-06-25T23:19:20.351216Z" } }, "outputs": [ @@ -939,10 +763,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:49.077233Z", - "iopub.status.busy": "2024-06-25T19:37:49.077055Z", - "iopub.status.idle": "2024-06-25T19:37:49.302913Z", - "shell.execute_reply": "2024-06-25T19:37:49.302334Z" + "iopub.execute_input": "2024-06-25T23:19:20.354001Z", + "iopub.status.busy": "2024-06-25T23:19:20.353823Z", + "iopub.status.idle": "2024-06-25T23:19:20.594457Z", + "shell.execute_reply": "2024-06-25T23:19:20.593862Z" } }, "outputs": [ @@ -978,10 +802,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:49.305112Z", - "iopub.status.busy": "2024-06-25T19:37:49.304932Z", - "iopub.status.idle": "2024-06-25T19:37:49.943529Z", - "shell.execute_reply": "2024-06-25T19:37:49.942935Z" + "iopub.execute_input": "2024-06-25T23:19:20.597502Z", + "iopub.status.busy": "2024-06-25T23:19:20.596997Z", + "iopub.status.idle": "2024-06-25T23:19:21.266452Z", + "shell.execute_reply": "2024-06-25T23:19:21.265865Z" } }, "outputs": [ @@ -1031,10 +855,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:49.945968Z", - "iopub.status.busy": "2024-06-25T19:37:49.945641Z", - "iopub.status.idle": "2024-06-25T19:37:50.264955Z", - "shell.execute_reply": "2024-06-25T19:37:50.264439Z" + "iopub.execute_input": "2024-06-25T23:19:21.269364Z", + "iopub.status.busy": "2024-06-25T23:19:21.269161Z", + "iopub.status.idle": "2024-06-25T23:19:21.610132Z", + "shell.execute_reply": "2024-06-25T23:19:21.609580Z" } }, "outputs": [ @@ -1082,10 +906,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:50.267076Z", - "iopub.status.busy": "2024-06-25T19:37:50.266888Z", - "iopub.status.idle": "2024-06-25T19:37:50.495188Z", - "shell.execute_reply": "2024-06-25T19:37:50.494600Z" + "iopub.execute_input": "2024-06-25T23:19:21.612438Z", + "iopub.status.busy": "2024-06-25T23:19:21.612030Z", + "iopub.status.idle": "2024-06-25T23:19:21.854392Z", + "shell.execute_reply": "2024-06-25T23:19:21.853886Z" } }, "outputs": [ @@ -1141,10 +965,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:50.497736Z", - "iopub.status.busy": "2024-06-25T19:37:50.497230Z", - "iopub.status.idle": "2024-06-25T19:37:50.573192Z", - "shell.execute_reply": "2024-06-25T19:37:50.572575Z" + "iopub.execute_input": "2024-06-25T23:19:21.857100Z", + "iopub.status.busy": "2024-06-25T23:19:21.856718Z", + "iopub.status.idle": "2024-06-25T23:19:21.949451Z", + "shell.execute_reply": "2024-06-25T23:19:21.948923Z" } }, "outputs": [], @@ -1165,10 +989,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:37:50.575685Z", - "iopub.status.busy": "2024-06-25T19:37:50.575502Z", - "iopub.status.idle": "2024-06-25T19:38:00.831598Z", - "shell.execute_reply": "2024-06-25T19:38:00.830941Z" + "iopub.execute_input": "2024-06-25T23:19:21.951971Z", + "iopub.status.busy": "2024-06-25T23:19:21.951792Z", + "iopub.status.idle": "2024-06-25T23:19:32.299281Z", + "shell.execute_reply": "2024-06-25T23:19:32.298639Z" } }, "outputs": [ @@ -1205,10 +1029,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:00.834016Z", - "iopub.status.busy": "2024-06-25T19:38:00.833627Z", - "iopub.status.idle": "2024-06-25T19:38:02.973271Z", - "shell.execute_reply": "2024-06-25T19:38:02.972713Z" + "iopub.execute_input": "2024-06-25T23:19:32.301554Z", + "iopub.status.busy": "2024-06-25T23:19:32.301299Z", + "iopub.status.idle": "2024-06-25T23:19:34.462906Z", + "shell.execute_reply": "2024-06-25T23:19:34.462279Z" } }, "outputs": [ @@ -1239,10 +1063,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:02.975964Z", - "iopub.status.busy": "2024-06-25T19:38:02.975476Z", - "iopub.status.idle": "2024-06-25T19:38:03.180118Z", - "shell.execute_reply": "2024-06-25T19:38:03.179626Z" + "iopub.execute_input": "2024-06-25T23:19:34.465814Z", + "iopub.status.busy": "2024-06-25T23:19:34.465269Z", + "iopub.status.idle": "2024-06-25T23:19:34.668011Z", + "shell.execute_reply": "2024-06-25T23:19:34.667512Z" } }, "outputs": [], @@ -1256,10 +1080,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:03.182510Z", - "iopub.status.busy": "2024-06-25T19:38:03.182166Z", - "iopub.status.idle": "2024-06-25T19:38:03.185175Z", - "shell.execute_reply": "2024-06-25T19:38:03.184750Z" + "iopub.execute_input": "2024-06-25T23:19:34.670373Z", + "iopub.status.busy": "2024-06-25T23:19:34.670183Z", + "iopub.status.idle": "2024-06-25T23:19:34.673433Z", + "shell.execute_reply": "2024-06-25T23:19:34.672977Z" } }, "outputs": [], @@ -1281,10 +1105,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:03.187201Z", - "iopub.status.busy": "2024-06-25T19:38:03.186922Z", - "iopub.status.idle": "2024-06-25T19:38:03.194967Z", - "shell.execute_reply": "2024-06-25T19:38:03.194449Z" + "iopub.execute_input": "2024-06-25T23:19:34.675236Z", + "iopub.status.busy": "2024-06-25T23:19:34.675068Z", + "iopub.status.idle": "2024-06-25T23:19:34.683440Z", + "shell.execute_reply": "2024-06-25T23:19:34.682879Z" }, "nbsphinx": "hidden" }, @@ -1329,31 +1153,67 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "3a6eebd9a9694b07864d194c78cdb317": { + "2a6c0688ba264a97aee90eb6e37e9dc2": { "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": "" + } + }, + "2ed8f22a488e446d9dd09c9e760c1fb2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3a433760fb114684b1ce550e725410f3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d314e5a0eb894d12bc8321b569f74cd0", - "IPY_MODEL_932ee016f6a74520b9da6cc833f044b5", - "IPY_MODEL_de1dfc7949d842468505de0b2a43b4f7" - ], - "layout": "IPY_MODEL_8876ddc2f4f248ff81bbe8f98f0e2826", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cac77136c4f843fea7898f1735deb973", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2a6c0688ba264a97aee90eb6e37e9dc2", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 102469840.0 } }, - "8876ddc2f4f248ff81bbe8f98f0e2826": { + "55e4c9a3e7c7475b9b79a0916ea74432": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1406,49 +1266,48 @@ "width": null } }, - "9117500d199d44088b40d306a6001f86": { + "7b2c005a31c74359a9e6040ef88246ae": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_55e4c9a3e7c7475b9b79a0916ea74432", + "placeholder": "​", + "style": "IPY_MODEL_2ed8f22a488e446d9dd09c9e760c1fb2", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" } }, - "932ee016f6a74520b9da6cc833f044b5": { + "acf4b18dbb8e42a391b0dfa7d26e3612": { "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_be482c428a914ad9bc5accbfcda59810", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9117500d199d44088b40d306a6001f86", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "9345c22a7d26444e94e7dc281c6f7083": { + "b8d3a0d6f70d45e596f645995e60a137": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1501,7 +1360,31 @@ "width": null } }, - "a0c0e9532ae04b61941f4f996fe124d2": { + "c487c0c381e74a26a4f49ff121b50dc9": { + "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_7b2c005a31c74359a9e6040ef88246ae", + "IPY_MODEL_3a433760fb114684b1ce550e725410f3", + "IPY_MODEL_d732fe2e90af4c978675ca6771e1718c" + ], + "layout": "IPY_MODEL_d9309721bf15440c9369f140e0453a0f", + "tabbable": null, + "tooltip": null + } + }, + "cac77136c4f843fea7898f1735deb973": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1554,7 +1437,30 @@ "width": null } }, - "be482c428a914ad9bc5accbfcda59810": { + "d732fe2e90af4c978675ca6771e1718c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b8d3a0d6f70d45e596f645995e60a137", + "placeholder": "​", + "style": "IPY_MODEL_acf4b18dbb8e42a391b0dfa7d26e3612", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 218MB/s]" + } + }, + "d9309721bf15440c9369f140e0453a0f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1606,88 +1512,6 @@ "visibility": null, "width": null } - }, - "d314e5a0eb894d12bc8321b569f74cd0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9345c22a7d26444e94e7dc281c6f7083", - "placeholder": "​", - "style": "IPY_MODEL_ed999755dbfa4b068fb1bc1851f1d2ea", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" - } - }, - "d8c964fe861d44eeb4663c74d1331470": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "de1dfc7949d842468505de0b2a43b4f7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a0c0e9532ae04b61941f4f996fe124d2", - "placeholder": "​", - "style": "IPY_MODEL_d8c964fe861d44eeb4663c74d1331470", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 265MB/s]" - } - }, - "ed999755dbfa4b068fb1bc1851f1d2ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 1e51dfcdc..4dccd9a0a 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:07.555838Z", - "iopub.status.busy": "2024-06-25T19:38:07.555668Z", - "iopub.status.idle": "2024-06-25T19:38:08.722369Z", - "shell.execute_reply": "2024-06-25T19:38:08.721811Z" + "iopub.execute_input": "2024-06-25T23:19:38.796252Z", + "iopub.status.busy": "2024-06-25T23:19:38.796082Z", + "iopub.status.idle": "2024-06-25T23:19:39.953258Z", + "shell.execute_reply": "2024-06-25T23:19:39.952691Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:08.724901Z", - "iopub.status.busy": "2024-06-25T19:38:08.724626Z", - "iopub.status.idle": "2024-06-25T19:38:08.741782Z", - "shell.execute_reply": "2024-06-25T19:38:08.741233Z" + "iopub.execute_input": "2024-06-25T23:19:39.955862Z", + "iopub.status.busy": "2024-06-25T23:19:39.955512Z", + "iopub.status.idle": "2024-06-25T23:19:39.972881Z", + "shell.execute_reply": "2024-06-25T23:19:39.972463Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:08.744094Z", - "iopub.status.busy": "2024-06-25T19:38:08.743687Z", - "iopub.status.idle": "2024-06-25T19:38:08.746763Z", - "shell.execute_reply": "2024-06-25T19:38:08.746228Z" + "iopub.execute_input": "2024-06-25T23:19:39.975108Z", + "iopub.status.busy": "2024-06-25T23:19:39.974726Z", + "iopub.status.idle": "2024-06-25T23:19:39.977547Z", + "shell.execute_reply": "2024-06-25T23:19:39.977124Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:08.748783Z", - "iopub.status.busy": "2024-06-25T19:38:08.748471Z", - "iopub.status.idle": "2024-06-25T19:38:09.023742Z", - "shell.execute_reply": "2024-06-25T19:38:09.023127Z" + "iopub.execute_input": "2024-06-25T23:19:39.979571Z", + "iopub.status.busy": "2024-06-25T23:19:39.979249Z", + "iopub.status.idle": "2024-06-25T23:19:40.010006Z", + "shell.execute_reply": "2024-06-25T23:19:40.009548Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.025867Z", - "iopub.status.busy": "2024-06-25T19:38:09.025685Z", - "iopub.status.idle": "2024-06-25T19:38:09.204489Z", - "shell.execute_reply": "2024-06-25T19:38:09.203970Z" + "iopub.execute_input": "2024-06-25T23:19:40.012066Z", + "iopub.status.busy": "2024-06-25T23:19:40.011740Z", + "iopub.status.idle": "2024-06-25T23:19:40.191233Z", + "shell.execute_reply": "2024-06-25T23:19:40.190672Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.206625Z", - "iopub.status.busy": "2024-06-25T19:38:09.206444Z", - "iopub.status.idle": "2024-06-25T19:38:09.445281Z", - "shell.execute_reply": "2024-06-25T19:38:09.444670Z" + "iopub.execute_input": "2024-06-25T23:19:40.193662Z", + "iopub.status.busy": "2024-06-25T23:19:40.193313Z", + "iopub.status.idle": "2024-06-25T23:19:40.401417Z", + "shell.execute_reply": "2024-06-25T23:19:40.400809Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.447540Z", - "iopub.status.busy": "2024-06-25T19:38:09.447186Z", - "iopub.status.idle": "2024-06-25T19:38:09.451599Z", - "shell.execute_reply": "2024-06-25T19:38:09.451044Z" + "iopub.execute_input": "2024-06-25T23:19:40.403764Z", + "iopub.status.busy": "2024-06-25T23:19:40.403425Z", + "iopub.status.idle": "2024-06-25T23:19:40.407638Z", + "shell.execute_reply": "2024-06-25T23:19:40.407217Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.453555Z", - "iopub.status.busy": "2024-06-25T19:38:09.453375Z", - "iopub.status.idle": "2024-06-25T19:38:09.460592Z", - "shell.execute_reply": "2024-06-25T19:38:09.460157Z" + "iopub.execute_input": "2024-06-25T23:19:40.409677Z", + "iopub.status.busy": "2024-06-25T23:19:40.409360Z", + "iopub.status.idle": "2024-06-25T23:19:40.415770Z", + "shell.execute_reply": "2024-06-25T23:19:40.415356Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.462899Z", - "iopub.status.busy": "2024-06-25T19:38:09.462366Z", - "iopub.status.idle": "2024-06-25T19:38:09.465304Z", - "shell.execute_reply": "2024-06-25T19:38:09.464836Z" + "iopub.execute_input": "2024-06-25T23:19:40.417772Z", + "iopub.status.busy": "2024-06-25T23:19:40.417455Z", + "iopub.status.idle": "2024-06-25T23:19:40.420042Z", + "shell.execute_reply": "2024-06-25T23:19:40.419591Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:09.467150Z", - "iopub.status.busy": "2024-06-25T19:38:09.466976Z", - "iopub.status.idle": "2024-06-25T19:38:18.068771Z", - "shell.execute_reply": "2024-06-25T19:38:18.068131Z" + "iopub.execute_input": "2024-06-25T23:19:40.421960Z", + "iopub.status.busy": "2024-06-25T23:19:40.421649Z", + "iopub.status.idle": "2024-06-25T23:19:48.997759Z", + "shell.execute_reply": "2024-06-25T23:19:48.997063Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.071591Z", - "iopub.status.busy": "2024-06-25T19:38:18.071196Z", - "iopub.status.idle": "2024-06-25T19:38:18.078371Z", - "shell.execute_reply": "2024-06-25T19:38:18.077824Z" + "iopub.execute_input": "2024-06-25T23:19:49.000433Z", + "iopub.status.busy": "2024-06-25T23:19:49.000048Z", + "iopub.status.idle": "2024-06-25T23:19:49.007281Z", + "shell.execute_reply": "2024-06-25T23:19:49.006704Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.080333Z", - "iopub.status.busy": "2024-06-25T19:38:18.080152Z", - "iopub.status.idle": "2024-06-25T19:38:18.083810Z", - "shell.execute_reply": "2024-06-25T19:38:18.083366Z" + "iopub.execute_input": "2024-06-25T23:19:49.009612Z", + "iopub.status.busy": "2024-06-25T23:19:49.009171Z", + "iopub.status.idle": "2024-06-25T23:19:49.013898Z", + "shell.execute_reply": "2024-06-25T23:19:49.013343Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.085821Z", - "iopub.status.busy": "2024-06-25T19:38:18.085497Z", - "iopub.status.idle": "2024-06-25T19:38:18.088621Z", - "shell.execute_reply": "2024-06-25T19:38:18.088109Z" + "iopub.execute_input": "2024-06-25T23:19:49.016095Z", + "iopub.status.busy": "2024-06-25T23:19:49.015919Z", + "iopub.status.idle": "2024-06-25T23:19:49.019068Z", + "shell.execute_reply": "2024-06-25T23:19:49.018547Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.090576Z", - "iopub.status.busy": "2024-06-25T19:38:18.090262Z", - "iopub.status.idle": "2024-06-25T19:38:18.093389Z", - "shell.execute_reply": "2024-06-25T19:38:18.092821Z" + "iopub.execute_input": "2024-06-25T23:19:49.020914Z", + "iopub.status.busy": "2024-06-25T23:19:49.020745Z", + "iopub.status.idle": "2024-06-25T23:19:49.023808Z", + "shell.execute_reply": "2024-06-25T23:19:49.023350Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.095470Z", - "iopub.status.busy": "2024-06-25T19:38:18.095154Z", - "iopub.status.idle": "2024-06-25T19:38:18.103228Z", - "shell.execute_reply": "2024-06-25T19:38:18.102775Z" + "iopub.execute_input": "2024-06-25T23:19:49.025803Z", + "iopub.status.busy": "2024-06-25T23:19:49.025488Z", + "iopub.status.idle": "2024-06-25T23:19:49.033564Z", + "shell.execute_reply": "2024-06-25T23:19:49.033138Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.105003Z", - "iopub.status.busy": "2024-06-25T19:38:18.104832Z", - "iopub.status.idle": "2024-06-25T19:38:18.107625Z", - "shell.execute_reply": "2024-06-25T19:38:18.107128Z" + "iopub.execute_input": "2024-06-25T23:19:49.035573Z", + "iopub.status.busy": "2024-06-25T23:19:49.035256Z", + "iopub.status.idle": "2024-06-25T23:19:49.037707Z", + "shell.execute_reply": "2024-06-25T23:19:49.037270Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.109671Z", - "iopub.status.busy": "2024-06-25T19:38:18.109367Z", - "iopub.status.idle": "2024-06-25T19:38:18.236233Z", - "shell.execute_reply": "2024-06-25T19:38:18.235732Z" + "iopub.execute_input": "2024-06-25T23:19:49.039639Z", + "iopub.status.busy": "2024-06-25T23:19:49.039383Z", + "iopub.status.idle": "2024-06-25T23:19:49.162747Z", + "shell.execute_reply": "2024-06-25T23:19:49.162268Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.238299Z", - "iopub.status.busy": "2024-06-25T19:38:18.237942Z", - "iopub.status.idle": "2024-06-25T19:38:18.347132Z", - "shell.execute_reply": "2024-06-25T19:38:18.346641Z" + "iopub.execute_input": "2024-06-25T23:19:49.164799Z", + "iopub.status.busy": "2024-06-25T23:19:49.164444Z", + "iopub.status.idle": "2024-06-25T23:19:49.269361Z", + "shell.execute_reply": "2024-06-25T23:19:49.268836Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.349402Z", - "iopub.status.busy": "2024-06-25T19:38:18.349044Z", - "iopub.status.idle": "2024-06-25T19:38:18.839672Z", - "shell.execute_reply": "2024-06-25T19:38:18.839073Z" + "iopub.execute_input": "2024-06-25T23:19:49.271927Z", + "iopub.status.busy": "2024-06-25T23:19:49.271573Z", + "iopub.status.idle": "2024-06-25T23:19:49.761626Z", + "shell.execute_reply": "2024-06-25T23:19:49.760979Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.841931Z", - "iopub.status.busy": "2024-06-25T19:38:18.841755Z", - "iopub.status.idle": "2024-06-25T19:38:18.912662Z", - "shell.execute_reply": "2024-06-25T19:38:18.912091Z" + "iopub.execute_input": "2024-06-25T23:19:49.764216Z", + "iopub.status.busy": "2024-06-25T23:19:49.763834Z", + "iopub.status.idle": "2024-06-25T23:19:49.843367Z", + "shell.execute_reply": "2024-06-25T23:19:49.842743Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.915065Z", - "iopub.status.busy": "2024-06-25T19:38:18.914579Z", - "iopub.status.idle": "2024-06-25T19:38:18.923159Z", - "shell.execute_reply": "2024-06-25T19:38:18.922730Z" + "iopub.execute_input": "2024-06-25T23:19:49.845464Z", + "iopub.status.busy": "2024-06-25T23:19:49.845235Z", + "iopub.status.idle": "2024-06-25T23:19:49.853788Z", + "shell.execute_reply": "2024-06-25T23:19:49.853340Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.925120Z", - "iopub.status.busy": "2024-06-25T19:38:18.924947Z", - "iopub.status.idle": "2024-06-25T19:38:18.927502Z", - "shell.execute_reply": "2024-06-25T19:38:18.927067Z" + "iopub.execute_input": "2024-06-25T23:19:49.855684Z", + "iopub.status.busy": "2024-06-25T23:19:49.855513Z", + "iopub.status.idle": "2024-06-25T23:19:49.858498Z", + "shell.execute_reply": "2024-06-25T23:19:49.857932Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:18.929453Z", - "iopub.status.busy": "2024-06-25T19:38:18.929127Z", - "iopub.status.idle": "2024-06-25T19:38:24.397527Z", - "shell.execute_reply": "2024-06-25T19:38:24.396937Z" + "iopub.execute_input": "2024-06-25T23:19:49.860435Z", + "iopub.status.busy": "2024-06-25T23:19:49.860125Z", + "iopub.status.idle": "2024-06-25T23:19:55.315583Z", + "shell.execute_reply": "2024-06-25T23:19:55.315005Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:24.400077Z", - "iopub.status.busy": "2024-06-25T19:38:24.399563Z", - "iopub.status.idle": "2024-06-25T19:38:24.408142Z", - "shell.execute_reply": "2024-06-25T19:38:24.407603Z" + "iopub.execute_input": "2024-06-25T23:19:55.317789Z", + "iopub.status.busy": "2024-06-25T23:19:55.317611Z", + "iopub.status.idle": "2024-06-25T23:19:55.326379Z", + "shell.execute_reply": "2024-06-25T23:19:55.325834Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:24.410281Z", - "iopub.status.busy": "2024-06-25T19:38:24.409820Z", - "iopub.status.idle": "2024-06-25T19:38:24.473861Z", - "shell.execute_reply": "2024-06-25T19:38:24.473281Z" + "iopub.execute_input": "2024-06-25T23:19:55.328528Z", + "iopub.status.busy": "2024-06-25T23:19:55.328125Z", + "iopub.status.idle": "2024-06-25T23:19:55.396039Z", + "shell.execute_reply": "2024-06-25T23:19:55.395563Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index ea4121942..bfe1a0e8a 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -800,13 +800,13 @@

3. Use cleanlab to find label issues

-
+
-
+

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

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

Get label quality scores -{"state": {"c1f546fd71c24d498d05b3908214bd7d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0b658cb143704dbbb7f0ac48f95429f2": {"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": ""}}, "c2abb97f3c0f47e89640d65ea0507ceb": {"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_c1f546fd71c24d498d05b3908214bd7d", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_0b658cb143704dbbb7f0ac48f95429f2", "tabbable": null, "tooltip": null, "value": 30.0}}, "6dabf219795b408dabf8afe1ed3b2ba9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a61e682d08cd4b079011fff3976213c6": {"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}}, "e3e1e81a9a074a9699b1c756208a42d1": {"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_6dabf219795b408dabf8afe1ed3b2ba9", "placeholder": "\u200b", "style": "IPY_MODEL_a61e682d08cd4b079011fff3976213c6", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "ab2778b3faa74fc58eae618ac7ad06b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d9e435b2b0064cb389b0eba2fdb3ae58": {"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}}, "61c316c9d6e6417db8e8f67c88ca16b3": {"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_ab2778b3faa74fc58eae618ac7ad06b7", "placeholder": "\u200b", "style": "IPY_MODEL_d9e435b2b0064cb389b0eba2fdb3ae58", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007756.08it/s]"}}, "f0c67deadadc41a681e33253811fe3c3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "944591b9a0384c6388bc6a076330ac62": {"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_e3e1e81a9a074a9699b1c756208a42d1", "IPY_MODEL_c2abb97f3c0f47e89640d65ea0507ceb", "IPY_MODEL_61c316c9d6e6417db8e8f67c88ca16b3"], "layout": "IPY_MODEL_f0c67deadadc41a681e33253811fe3c3", "tabbable": null, "tooltip": null}}, "5868ffb252c44ac4aa39f70440bc622f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "15f5d288daf5495ba283ee4fad4d58fd": {"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": ""}}, "8aaa8a35422241b2a265233527fbf1e7": {"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_5868ffb252c44ac4aa39f70440bc622f", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_15f5d288daf5495ba283ee4fad4d58fd", "tabbable": null, "tooltip": null, "value": 30.0}}, "a10c8b997a924841b67b483401b9d8c3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4e6a4c23dd054908be889d0f3d83ee3b": {"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}}, "a858829ede1a452ba7407428df24b4fa": {"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_a10c8b997a924841b67b483401b9d8c3", "placeholder": "\u200b", "style": "IPY_MODEL_4e6a4c23dd054908be889d0f3d83ee3b", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "a832d5b427714b918a469d3e99a8e9ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0965a0f7f5304df18ccd851b34fef9fb": {"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}}, "07201424d87b4671ac4b1cfa673d7986": {"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_a832d5b427714b918a469d3e99a8e9ff", "placeholder": "\u200b", "style": "IPY_MODEL_0965a0f7f5304df18ccd851b34fef9fb", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:21<00:00,\u2007\u20071.44it/s]"}}, "cb515331da754c33a0c68f56fd6918b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "456e1a39f8a0484d84df60d119f7d9b3": {"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_a858829ede1a452ba7407428df24b4fa", "IPY_MODEL_8aaa8a35422241b2a265233527fbf1e7", "IPY_MODEL_07201424d87b4671ac4b1cfa673d7986"], "layout": "IPY_MODEL_cb515331da754c33a0c68f56fd6918b5", "tabbable": null, "tooltip": null}}, "2f266a82a0cd46529aa761f6c67f7700": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8978b1bc574944349ad8045c8d081583": {"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": ""}}, "38cf33a9b7b9481cb9a58609d734b80f": {"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_2f266a82a0cd46529aa761f6c67f7700", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_8978b1bc574944349ad8045c8d081583", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "74b719a4753841d48ddef0f60728929c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "531002ec92784d8c8567de9c2f6b2001": {"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}}, "b05c6a6bdf664a62a6cbd8f66104b29f": {"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_74b719a4753841d48ddef0f60728929c", "placeholder": "\u200b", "style": "IPY_MODEL_531002ec92784d8c8567de9c2f6b2001", "tabbable": null, "tooltip": null, "value": "100%"}}, "e251eadf4c0d45e99ed477a752be3d71": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9238a3aeb6694a54a16d5276afd69748": {"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}}, "2184a8202604452c862d764c590163a7": {"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_e251eadf4c0d45e99ed477a752be3d71", "placeholder": "\u200b", "style": "IPY_MODEL_9238a3aeb6694a54a16d5276afd69748", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:32<00:00,\u2007154374.13it/s]"}}, "04229ad3351b4f7aaf0a891a50bc135d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f91d1545f3254e83bb88ef07ebe6e9fe": {"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_b05c6a6bdf664a62a6cbd8f66104b29f", "IPY_MODEL_38cf33a9b7b9481cb9a58609d734b80f", "IPY_MODEL_2184a8202604452c862d764c590163a7"], "layout": "IPY_MODEL_04229ad3351b4f7aaf0a891a50bc135d", "tabbable": null, "tooltip": null}}, "8d3f186b66e646faa88abf0438b28125": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "476d964c770641a799b799e08d88ea6e": {"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": ""}}, "215c528b84a347b1a955651a8792356a": {"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_8d3f186b66e646faa88abf0438b28125", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_476d964c770641a799b799e08d88ea6e", "tabbable": null, "tooltip": null, "value": 30.0}}, "2a9e65f5f4ec40d496f43ad1adfd040a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "13d8b2f426d2467cbc47898751b6f6dd": {"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}}, "f1b83d31de91416b8454f54a7486c222": {"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_2a9e65f5f4ec40d496f43ad1adfd040a", "placeholder": "\u200b", "style": "IPY_MODEL_13d8b2f426d2467cbc47898751b6f6dd", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "0413905ec4a1476eab10c5ca853497d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6c8bceb12179495a84b898f7d7fb9df2": {"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}}, "cc71964c79fb4cc48ccbc1e58b722da3": {"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_0413905ec4a1476eab10c5ca853497d3", "placeholder": "\u200b", "style": "IPY_MODEL_6c8bceb12179495a84b898f7d7fb9df2", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200721.71it/s]"}}, "6a036f411aba4cdc9fa68388d47ac9c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c3ec5543994844ccbea4230fb9d7b4eb": {"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_f1b83d31de91416b8454f54a7486c222", "IPY_MODEL_215c528b84a347b1a955651a8792356a", "IPY_MODEL_cc71964c79fb4cc48ccbc1e58b722da3"], "layout": "IPY_MODEL_6a036f411aba4cdc9fa68388d47ac9c0", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"d8fb8c2405ca482185ac5e7c6f667650": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "42d0aa720c6e4a2ca9f245568eca8de9": {"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": ""}}, "5777ae56d3c54e35b67a204b112139e6": {"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_d8fb8c2405ca482185ac5e7c6f667650", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_42d0aa720c6e4a2ca9f245568eca8de9", "tabbable": null, "tooltip": null, "value": 30.0}}, "5fe269722f6a4605a143155dac3bcd01": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "680f5ec724904dd1a968ede02eacb19b": {"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}}, "a772478a0f704e3fa3325fbef037b4cd": {"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_5fe269722f6a4605a143155dac3bcd01", "placeholder": "\u200b", "style": "IPY_MODEL_680f5ec724904dd1a968ede02eacb19b", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "e8e80dd483054ca091488456a8397301": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0220fd3c35e849c1a03e5c8abca06016": {"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}}, "3e183c14ae544e71b619db5e0b367992": {"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_e8e80dd483054ca091488456a8397301", "placeholder": "\u200b", "style": "IPY_MODEL_0220fd3c35e849c1a03e5c8abca06016", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007758.41it/s]"}}, "c28b42730873450e95180653991778ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "198f978c68c04b42bb7f505400e75581": {"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_a772478a0f704e3fa3325fbef037b4cd", "IPY_MODEL_5777ae56d3c54e35b67a204b112139e6", "IPY_MODEL_3e183c14ae544e71b619db5e0b367992"], "layout": "IPY_MODEL_c28b42730873450e95180653991778ed", "tabbable": null, "tooltip": null}}, "c9d9ec1dbde84cafb318173cee06bece": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e3c898e2f5ff4cd480ab8c04bffe7dfb": {"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": ""}}, "4ff8dfedbd2949a5bb4270735a77c539": {"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_c9d9ec1dbde84cafb318173cee06bece", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e3c898e2f5ff4cd480ab8c04bffe7dfb", "tabbable": null, "tooltip": null, "value": 30.0}}, "eb1f2e4f38da4c9190ae6c4d6714e087": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f47a2b8a8ebb4314850e6e3193fcd946": {"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}}, "8a43127ec4fc484eb12e55de443c3a15": {"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_eb1f2e4f38da4c9190ae6c4d6714e087", "placeholder": "\u200b", "style": "IPY_MODEL_f47a2b8a8ebb4314850e6e3193fcd946", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "ca265e653b2b43779eb1e897adba0a8c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4b05b13a08364b5d9d060ee36c605fd5": {"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}}, "1b5e7f7edd7e41a48c9ee3f889c53756": {"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_ca265e653b2b43779eb1e897adba0a8c", "placeholder": "\u200b", "style": "IPY_MODEL_4b05b13a08364b5d9d060ee36c605fd5", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:21<00:00,\u2007\u20071.42it/s]"}}, "b3e446b4b0bf43d0806173baf332511b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4b186141820047419c3ae004111754f6": {"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_8a43127ec4fc484eb12e55de443c3a15", "IPY_MODEL_4ff8dfedbd2949a5bb4270735a77c539", "IPY_MODEL_1b5e7f7edd7e41a48c9ee3f889c53756"], "layout": "IPY_MODEL_b3e446b4b0bf43d0806173baf332511b", "tabbable": null, "tooltip": null}}, "0d5018ed1be44175807c78ce0ab57606": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "367bfe277ffe4be9b8bae4bdaaa320ae": {"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": ""}}, "8e863ef8f3304af0a1299abfe8ad02b8": {"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_0d5018ed1be44175807c78ce0ab57606", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_367bfe277ffe4be9b8bae4bdaaa320ae", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "fcba50827939420b83ea40b9e3507089": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "69f5479f692f4d43b82f5df028e467d7": {"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}}, "da3ca18af3434b998fd38fd9dc359605": {"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_fcba50827939420b83ea40b9e3507089", "placeholder": "\u200b", "style": "IPY_MODEL_69f5479f692f4d43b82f5df028e467d7", "tabbable": null, "tooltip": null, "value": "100%"}}, "cf29f93a566f4e018c0e0d5c5ad96a05": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8b038c68d122401fbb4e96c1ad051c9f": {"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}}, "f9315252473d439f95f4fd58000719ed": {"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_cf29f93a566f4e018c0e0d5c5ad96a05", "placeholder": "\u200b", "style": "IPY_MODEL_8b038c68d122401fbb4e96c1ad051c9f", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:33<00:00,\u2007150147.59it/s]"}}, "cb251166fe62454094fce22ed9bd897a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "28a8baec191044f3831c5b052b050cba": {"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_da3ca18af3434b998fd38fd9dc359605", "IPY_MODEL_8e863ef8f3304af0a1299abfe8ad02b8", "IPY_MODEL_f9315252473d439f95f4fd58000719ed"], "layout": "IPY_MODEL_cb251166fe62454094fce22ed9bd897a", "tabbable": null, "tooltip": null}}, "f9477ce7cdc74a22959c387b134e9c01": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "96ab128e8fca4ba6874dc9eef5cf4ff3": {"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": ""}}, "a9a87800c31f4b0c8cd52338e21b3cc8": {"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_f9477ce7cdc74a22959c387b134e9c01", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_96ab128e8fca4ba6874dc9eef5cf4ff3", "tabbable": null, "tooltip": null, "value": 30.0}}, "85982aca8cc345109d878dbd7bbe9828": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c5e54c3a78384206bc828910a703fec0": {"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}}, "9da954de4d5f49119869cd8370e5e154": {"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_85982aca8cc345109d878dbd7bbe9828", "placeholder": "\u200b", "style": "IPY_MODEL_c5e54c3a78384206bc828910a703fec0", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "d68c68f5e35245c4a160a0468811587d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bf890124360a4567a82d5d03676272de": {"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}}, "119e57b6fb1844baa93ee07fae718fb0": {"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_d68c68f5e35245c4a160a0468811587d", "placeholder": "\u200b", "style": "IPY_MODEL_bf890124360a4567a82d5d03676272de", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200721.82it/s]"}}, "4bb96c22484d421c85c644ab03e8eb56": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e9e7048a47b3430f853de8ee7bd0cedf": {"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_9da954de4d5f49119869cd8370e5e154", "IPY_MODEL_a9a87800c31f4b0c8cd52338e21b3cc8", "IPY_MODEL_119e57b6fb1844baa93ee07fae718fb0"], "layout": "IPY_MODEL_4bb96c22484d421c85c644ab03e8eb56", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index 253b92cf5..d70cfaf4e 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:27.445776Z", - "iopub.status.busy": "2024-06-25T19:38:27.445616Z", - "iopub.status.idle": "2024-06-25T19:38:29.357688Z", - "shell.execute_reply": "2024-06-25T19:38:29.356961Z" + "iopub.execute_input": "2024-06-25T23:19:58.399516Z", + "iopub.status.busy": "2024-06-25T23:19:58.399339Z", + "iopub.status.idle": "2024-06-25T23:19:59.729255Z", + "shell.execute_reply": "2024-06-25T23:19:59.728521Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:38:29.360485Z", - "iopub.status.busy": "2024-06-25T19:38:29.360106Z", - "iopub.status.idle": "2024-06-25T19:39:24.167594Z", - "shell.execute_reply": "2024-06-25T19:39:24.166933Z" + "iopub.execute_input": "2024-06-25T23:19:59.731986Z", + "iopub.status.busy": "2024-06-25T23:19:59.731605Z", + "iopub.status.idle": "2024-06-25T23:20:48.710370Z", + "shell.execute_reply": "2024-06-25T23:20:48.709721Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:24.170328Z", - "iopub.status.busy": "2024-06-25T19:39:24.169968Z", - "iopub.status.idle": "2024-06-25T19:39:25.274825Z", - "shell.execute_reply": "2024-06-25T19:39:25.274283Z" + "iopub.execute_input": "2024-06-25T23:20:48.712844Z", + "iopub.status.busy": "2024-06-25T23:20:48.712648Z", + "iopub.status.idle": "2024-06-25T23:20:49.819754Z", + "shell.execute_reply": "2024-06-25T23:20:49.819207Z" }, "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\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": "2024-06-25T19:39:25.277334Z", - "iopub.status.busy": "2024-06-25T19:39:25.276961Z", - "iopub.status.idle": "2024-06-25T19:39:25.280274Z", - "shell.execute_reply": "2024-06-25T19:39:25.279814Z" + "iopub.execute_input": "2024-06-25T23:20:49.822551Z", + "iopub.status.busy": "2024-06-25T23:20:49.821983Z", + "iopub.status.idle": "2024-06-25T23:20:49.825360Z", + "shell.execute_reply": "2024-06-25T23:20:49.824898Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:25.282318Z", - "iopub.status.busy": "2024-06-25T19:39:25.282060Z", - "iopub.status.idle": "2024-06-25T19:39:25.285902Z", - "shell.execute_reply": "2024-06-25T19:39:25.285458Z" + "iopub.execute_input": "2024-06-25T23:20:49.827409Z", + "iopub.status.busy": "2024-06-25T23:20:49.827081Z", + "iopub.status.idle": "2024-06-25T23:20:49.830739Z", + "shell.execute_reply": "2024-06-25T23:20:49.830321Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:25.287764Z", - "iopub.status.busy": "2024-06-25T19:39:25.287595Z", - "iopub.status.idle": "2024-06-25T19:39:25.291186Z", - "shell.execute_reply": "2024-06-25T19:39:25.290735Z" + "iopub.execute_input": "2024-06-25T23:20:49.832814Z", + "iopub.status.busy": "2024-06-25T23:20:49.832481Z", + "iopub.status.idle": "2024-06-25T23:20:49.835986Z", + "shell.execute_reply": "2024-06-25T23:20:49.835546Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:25.293004Z", - "iopub.status.busy": "2024-06-25T19:39:25.292834Z", - "iopub.status.idle": "2024-06-25T19:39:25.296491Z", - "shell.execute_reply": "2024-06-25T19:39:25.296049Z" + "iopub.execute_input": "2024-06-25T23:20:49.837816Z", + "iopub.status.busy": "2024-06-25T23:20:49.837650Z", + "iopub.status.idle": "2024-06-25T23:20:49.841360Z", + "shell.execute_reply": "2024-06-25T23:20:49.840870Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:25.298372Z", - "iopub.status.busy": "2024-06-25T19:39:25.298196Z", - "iopub.status.idle": "2024-06-25T19:39:58.536591Z", - "shell.execute_reply": "2024-06-25T19:39:58.535983Z" + "iopub.execute_input": "2024-06-25T23:20:49.843348Z", + "iopub.status.busy": "2024-06-25T23:20:49.843045Z", + "iopub.status.idle": "2024-06-25T23:21:23.312097Z", + "shell.execute_reply": "2024-06-25T23:21:23.311395Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "944591b9a0384c6388bc6a076330ac62", + "model_id": "198f978c68c04b42bb7f505400e75581", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "456e1a39f8a0484d84df60d119f7d9b3", + "model_id": "4b186141820047419c3ae004111754f6", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:58.539357Z", - "iopub.status.busy": "2024-06-25T19:39:58.538990Z", - "iopub.status.idle": "2024-06-25T19:39:59.206448Z", - "shell.execute_reply": "2024-06-25T19:39:59.205970Z" + "iopub.execute_input": "2024-06-25T23:21:23.314740Z", + "iopub.status.busy": "2024-06-25T23:21:23.314519Z", + "iopub.status.idle": "2024-06-25T23:21:23.985247Z", + "shell.execute_reply": "2024-06-25T23:21:23.984646Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:39:59.208781Z", - "iopub.status.busy": "2024-06-25T19:39:59.208330Z", - "iopub.status.idle": "2024-06-25T19:40:01.948266Z", - "shell.execute_reply": "2024-06-25T19:40:01.947672Z" + "iopub.execute_input": "2024-06-25T23:21:23.987564Z", + "iopub.status.busy": "2024-06-25T23:21:23.987136Z", + "iopub.status.idle": "2024-06-25T23:21:26.705173Z", + "shell.execute_reply": "2024-06-25T23:21:26.704585Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:01.950510Z", - "iopub.status.busy": "2024-06-25T19:40:01.950173Z", - "iopub.status.idle": "2024-06-25T19:40:34.744210Z", - "shell.execute_reply": "2024-06-25T19:40:34.743718Z" + "iopub.execute_input": "2024-06-25T23:21:26.707473Z", + "iopub.status.busy": "2024-06-25T23:21:26.707134Z", + "iopub.status.idle": "2024-06-25T23:22:00.356055Z", + "shell.execute_reply": "2024-06-25T23:22:00.355524Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f91d1545f3254e83bb88ef07ebe6e9fe", + "model_id": "28a8baec191044f3831c5b052b050cba", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:34.746367Z", - "iopub.status.busy": "2024-06-25T19:40:34.746041Z", - "iopub.status.idle": "2024-06-25T19:40:49.559228Z", - "shell.execute_reply": "2024-06-25T19:40:49.558651Z" + "iopub.execute_input": "2024-06-25T23:22:00.358322Z", + "iopub.status.busy": "2024-06-25T23:22:00.357991Z", + "iopub.status.idle": "2024-06-25T23:22:14.992649Z", + "shell.execute_reply": "2024-06-25T23:22:14.992097Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:49.561671Z", - "iopub.status.busy": "2024-06-25T19:40:49.561368Z", - "iopub.status.idle": "2024-06-25T19:40:53.237064Z", - "shell.execute_reply": "2024-06-25T19:40:53.236459Z" + "iopub.execute_input": "2024-06-25T23:22:14.994920Z", + "iopub.status.busy": "2024-06-25T23:22:14.994721Z", + "iopub.status.idle": "2024-06-25T23:22:18.704345Z", + "shell.execute_reply": "2024-06-25T23:22:18.703738Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:53.239303Z", - "iopub.status.busy": "2024-06-25T19:40:53.238898Z", - "iopub.status.idle": "2024-06-25T19:40:54.630349Z", - "shell.execute_reply": "2024-06-25T19:40:54.629762Z" + "iopub.execute_input": "2024-06-25T23:22:18.706711Z", + "iopub.status.busy": "2024-06-25T23:22:18.706375Z", + "iopub.status.idle": "2024-06-25T23:22:20.102205Z", + "shell.execute_reply": "2024-06-25T23:22:20.101646Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c3ec5543994844ccbea4230fb9d7b4eb", + "model_id": "e9e7048a47b3430f853de8ee7bd0cedf", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:54.632662Z", - "iopub.status.busy": "2024-06-25T19:40:54.632244Z", - "iopub.status.idle": "2024-06-25T19:40:54.661012Z", - "shell.execute_reply": "2024-06-25T19:40:54.660347Z" + "iopub.execute_input": "2024-06-25T23:22:20.104725Z", + "iopub.status.busy": "2024-06-25T23:22:20.104340Z", + "iopub.status.idle": "2024-06-25T23:22:20.133203Z", + "shell.execute_reply": "2024-06-25T23:22:20.132635Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:40:54.663568Z", - "iopub.status.busy": "2024-06-25T19:40:54.663357Z", - "iopub.status.idle": "2024-06-25T19:41:00.730286Z", - "shell.execute_reply": "2024-06-25T19:41:00.729759Z" + "iopub.execute_input": "2024-06-25T23:22:20.135579Z", + "iopub.status.busy": "2024-06-25T23:22:20.135375Z", + "iopub.status.idle": "2024-06-25T23:22:26.135540Z", + "shell.execute_reply": "2024-06-25T23:22:26.134957Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:00.732454Z", - "iopub.status.busy": "2024-06-25T19:41:00.732273Z", - "iopub.status.idle": "2024-06-25T19:41:00.787546Z", - "shell.execute_reply": "2024-06-25T19:41:00.786977Z" + "iopub.execute_input": "2024-06-25T23:22:26.137624Z", + "iopub.status.busy": "2024-06-25T23:22:26.137444Z", + "iopub.status.idle": "2024-06-25T23:22:26.192958Z", + "shell.execute_reply": "2024-06-25T23:22:26.192454Z" }, "nbsphinx": "hidden" }, @@ -1038,60 +1038,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0413905ec4a1476eab10c5ca853497d3": { - "model_module": "@jupyter-widgets/base", + "0220fd3c35e849c1a03e5c8abca06016": { + "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 } }, - "04229ad3351b4f7aaf0a891a50bc135d": { + "0d5018ed1be44175807c78ce0ab57606": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1144,7 +1109,7 @@ "width": null } }, - "07201424d87b4671ac4b1cfa673d7986": { + "119e57b6fb1844baa93ee07fae718fb0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1159,33 +1124,125 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a832d5b427714b918a469d3e99a8e9ff", + "layout": "IPY_MODEL_d68c68f5e35245c4a160a0468811587d", "placeholder": "​", - "style": "IPY_MODEL_0965a0f7f5304df18ccd851b34fef9fb", + "style": "IPY_MODEL_bf890124360a4567a82d5d03676272de", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:21<00:00,  1.44it/s]" + "value": " 30/30 [00:01<00:00, 21.82it/s]" } }, - "0965a0f7f5304df18ccd851b34fef9fb": { + "198f978c68c04b42bb7f505400e75581": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a772478a0f704e3fa3325fbef037b4cd", + "IPY_MODEL_5777ae56d3c54e35b67a204b112139e6", + "IPY_MODEL_3e183c14ae544e71b619db5e0b367992" + ], + "layout": "IPY_MODEL_c28b42730873450e95180653991778ed", + "tabbable": null, + "tooltip": null + } + }, + "1b5e7f7edd7e41a48c9ee3f889c53756": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ca265e653b2b43779eb1e897adba0a8c", + "placeholder": "​", + "style": "IPY_MODEL_4b05b13a08364b5d9d060ee36c605fd5", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:21<00:00,  1.42it/s]" + } + }, + "28a8baec191044f3831c5b052b050cba": { + "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_da3ca18af3434b998fd38fd9dc359605", + "IPY_MODEL_8e863ef8f3304af0a1299abfe8ad02b8", + "IPY_MODEL_f9315252473d439f95f4fd58000719ed" + ], + "layout": "IPY_MODEL_cb251166fe62454094fce22ed9bd897a", + "tabbable": null, + "tooltip": null + } + }, + "367bfe277ffe4be9b8bae4bdaaa320ae": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "0b658cb143704dbbb7f0ac48f95429f2": { + "3e183c14ae544e71b619db5e0b367992": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e8e80dd483054ca091488456a8397301", + "placeholder": "​", + "style": "IPY_MODEL_0220fd3c35e849c1a03e5c8abca06016", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 758.41it/s]" + } + }, + "42d0aa720c6e4a2ca9f245568eca8de9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1201,7 +1258,7 @@ "description_width": "" } }, - "13d8b2f426d2467cbc47898751b6f6dd": { + "4b05b13a08364b5d9d060ee36c605fd5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1219,23 +1276,84 @@ "text_color": null } }, - "15f5d288daf5495ba283ee4fad4d58fd": { + "4b186141820047419c3ae004111754f6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8a43127ec4fc484eb12e55de443c3a15", + "IPY_MODEL_4ff8dfedbd2949a5bb4270735a77c539", + "IPY_MODEL_1b5e7f7edd7e41a48c9ee3f889c53756" + ], + "layout": "IPY_MODEL_b3e446b4b0bf43d0806173baf332511b", + "tabbable": null, + "tooltip": null + } + }, + "4bb96c22484d421c85c644ab03e8eb56": { + "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": "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 } }, - "215c528b84a347b1a955651a8792356a": { + "4ff8dfedbd2949a5bb4270735a77c539": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1251,40 +1369,43 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8d3f186b66e646faa88abf0438b28125", + "layout": "IPY_MODEL_c9d9ec1dbde84cafb318173cee06bece", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_476d964c770641a799b799e08d88ea6e", + "style": "IPY_MODEL_e3c898e2f5ff4cd480ab8c04bffe7dfb", "tabbable": null, "tooltip": null, "value": 30.0 } }, - "2184a8202604452c862d764c590163a7": { + "5777ae56d3c54e35b67a204b112139e6": { "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_e251eadf4c0d45e99ed477a752be3d71", - "placeholder": "​", - "style": "IPY_MODEL_9238a3aeb6694a54a16d5276afd69748", + "layout": "IPY_MODEL_d8fb8c2405ca482185ac5e7c6f667650", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_42d0aa720c6e4a2ca9f245568eca8de9", "tabbable": null, "tooltip": null, - "value": " 4997683/4997683 [00:32<00:00, 154374.13it/s]" + "value": 30.0 } }, - "2a9e65f5f4ec40d496f43ad1adfd040a": { + "5fe269722f6a4605a143155dac3bcd01": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1337,7 +1458,43 @@ "width": null } }, - "2f266a82a0cd46529aa761f6c67f7700": { + "680f5ec724904dd1a968ede02eacb19b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "69f5479f692f4d43b82f5df028e467d7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "85982aca8cc345109d878dbd7bbe9828": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1390,57 +1547,74 @@ "width": null } }, - "38cf33a9b7b9481cb9a58609d734b80f": { + "8a43127ec4fc484eb12e55de443c3a15": { "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_2f266a82a0cd46529aa761f6c67f7700", - "max": 4997683.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8978b1bc574944349ad8045c8d081583", + "layout": "IPY_MODEL_eb1f2e4f38da4c9190ae6c4d6714e087", + "placeholder": "​", + "style": "IPY_MODEL_f47a2b8a8ebb4314850e6e3193fcd946", "tabbable": null, "tooltip": null, - "value": 4997683.0 + "value": "number of examples processed for checking labels: 100%" } }, - "456e1a39f8a0484d84df60d119f7d9b3": { + "8b038c68d122401fbb4e96c1ad051c9f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8e863ef8f3304af0a1299abfe8ad02b8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_a858829ede1a452ba7407428df24b4fa", - "IPY_MODEL_8aaa8a35422241b2a265233527fbf1e7", - "IPY_MODEL_07201424d87b4671ac4b1cfa673d7986" - ], - "layout": "IPY_MODEL_cb515331da754c33a0c68f56fd6918b5", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0d5018ed1be44175807c78ce0ab57606", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_367bfe277ffe4be9b8bae4bdaaa320ae", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 4997683.0 } }, - "476d964c770641a799b799e08d88ea6e": { + "96ab128e8fca4ba6874dc9eef5cf4ff3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1456,43 +1630,79 @@ "description_width": "" } }, - "4e6a4c23dd054908be889d0f3d83ee3b": { + "9da954de4d5f49119869cd8370e5e154": { "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_85982aca8cc345109d878dbd7bbe9828", + "placeholder": "​", + "style": "IPY_MODEL_c5e54c3a78384206bc828910a703fec0", + "tabbable": null, + "tooltip": null, + "value": "images processed using softmin: 100%" } }, - "531002ec92784d8c8567de9c2f6b2001": { + "a772478a0f704e3fa3325fbef037b4cd": { "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_5fe269722f6a4605a143155dac3bcd01", + "placeholder": "​", + "style": "IPY_MODEL_680f5ec724904dd1a968ede02eacb19b", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" } }, - "5868ffb252c44ac4aa39f70440bc622f": { + "a9a87800c31f4b0c8cd52338e21b3cc8": { + "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_f9477ce7cdc74a22959c387b134e9c01", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_96ab128e8fca4ba6874dc9eef5cf4ff3", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "b3e446b4b0bf43d0806173baf332511b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1545,30 +1755,25 @@ "width": null } }, - "61c316c9d6e6417db8e8f67c88ca16b3": { + "bf890124360a4567a82d5d03676272de": { "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_ab2778b3faa74fc58eae618ac7ad06b7", - "placeholder": "​", - "style": "IPY_MODEL_d9e435b2b0064cb389b0eba2fdb3ae58", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 756.08it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "6a036f411aba4cdc9fa68388d47ac9c0": { + "c28b42730873450e95180653991778ed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1621,7 +1826,7 @@ "width": null } }, - "6c8bceb12179495a84b898f7d7fb9df2": { + "c5e54c3a78384206bc828910a703fec0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1639,7 +1844,7 @@ "text_color": null } }, - "6dabf219795b408dabf8afe1ed3b2ba9": { + "c9d9ec1dbde84cafb318173cee06bece": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1692,7 +1897,7 @@ "width": null } }, - "74b719a4753841d48ddef0f60728929c": { + "ca265e653b2b43779eb1e897adba0a8c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1745,49 +1950,7 @@ "width": null } }, - "8978b1bc574944349ad8045c8d081583": { - "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": "" - } - }, - "8aaa8a35422241b2a265233527fbf1e7": { - "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_5868ffb252c44ac4aa39f70440bc622f", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_15f5d288daf5495ba283ee4fad4d58fd", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "8d3f186b66e646faa88abf0438b28125": { + "cb251166fe62454094fce22ed9bd897a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1840,49 +2003,7 @@ "width": null } }, - "9238a3aeb6694a54a16d5276afd69748": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "944591b9a0384c6388bc6a076330ac62": { - "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_e3e1e81a9a074a9699b1c756208a42d1", - "IPY_MODEL_c2abb97f3c0f47e89640d65ea0507ceb", - "IPY_MODEL_61c316c9d6e6417db8e8f67c88ca16b3" - ], - "layout": "IPY_MODEL_f0c67deadadc41a681e33253811fe3c3", - "tabbable": null, - "tooltip": null - } - }, - "a10c8b997a924841b67b483401b9d8c3": { + "cf29f93a566f4e018c0e0d5c5ad96a05": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1935,25 +2056,7 @@ "width": null } }, - "a61e682d08cd4b079011fff3976213c6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a832d5b427714b918a469d3e99a8e9ff": { + "d68c68f5e35245c4a160a0468811587d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2006,30 +2109,7 @@ "width": null } }, - "a858829ede1a452ba7407428df24b4fa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a10c8b997a924841b67b483401b9d8c3", - "placeholder": "​", - "style": "IPY_MODEL_4e6a4c23dd054908be889d0f3d83ee3b", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" - } - }, - "ab2778b3faa74fc58eae618ac7ad06b7": { + "d8fb8c2405ca482185ac5e7c6f667650": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2082,7 +2162,7 @@ "width": null } }, - "b05c6a6bdf664a62a6cbd8f66104b29f": { + "da3ca18af3434b998fd38fd9dc359605": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2097,15 +2177,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_74b719a4753841d48ddef0f60728929c", + "layout": "IPY_MODEL_fcba50827939420b83ea40b9e3507089", "placeholder": "​", - "style": "IPY_MODEL_531002ec92784d8c8567de9c2f6b2001", + "style": "IPY_MODEL_69f5479f692f4d43b82f5df028e467d7", "tabbable": null, "tooltip": null, "value": "100%" } }, - "c1f546fd71c24d498d05b3908214bd7d": { + "e3c898e2f5ff4cd480ab8c04bffe7dfb": { + "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": "" + } + }, + "e8e80dd483054ca091488456a8397301": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2158,33 +2254,7 @@ "width": null } }, - "c2abb97f3c0f47e89640d65ea0507ceb": { - "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_c1f546fd71c24d498d05b3908214bd7d", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0b658cb143704dbbb7f0ac48f95429f2", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "c3ec5543994844ccbea4230fb9d7b4eb": { + "e9e7048a47b3430f853de8ee7bd0cedf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2199,16 +2269,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_f1b83d31de91416b8454f54a7486c222", - "IPY_MODEL_215c528b84a347b1a955651a8792356a", - "IPY_MODEL_cc71964c79fb4cc48ccbc1e58b722da3" + "IPY_MODEL_9da954de4d5f49119869cd8370e5e154", + "IPY_MODEL_a9a87800c31f4b0c8cd52338e21b3cc8", + "IPY_MODEL_119e57b6fb1844baa93ee07fae718fb0" ], - "layout": "IPY_MODEL_6a036f411aba4cdc9fa68388d47ac9c0", + "layout": "IPY_MODEL_4bb96c22484d421c85c644ab03e8eb56", "tabbable": null, "tooltip": null } }, - "cb515331da754c33a0c68f56fd6918b5": { + "eb1f2e4f38da4c9190ae6c4d6714e087": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2261,7 +2331,25 @@ "width": null } }, - "cc71964c79fb4cc48ccbc1e58b722da3": { + "f47a2b8a8ebb4314850e6e3193fcd946": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "f9315252473d439f95f4fd58000719ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2276,33 +2364,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0413905ec4a1476eab10c5ca853497d3", + "layout": "IPY_MODEL_cf29f93a566f4e018c0e0d5c5ad96a05", "placeholder": "​", - "style": "IPY_MODEL_6c8bceb12179495a84b898f7d7fb9df2", + "style": "IPY_MODEL_8b038c68d122401fbb4e96c1ad051c9f", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 21.71it/s]" - } - }, - "d9e435b2b0064cb389b0eba2fdb3ae58": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 4997683/4997683 [00:33<00:00, 150147.59it/s]" } }, - "e251eadf4c0d45e99ed477a752be3d71": { + "f9477ce7cdc74a22959c387b134e9c01": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2355,30 +2425,7 @@ "width": null } }, - "e3e1e81a9a074a9699b1c756208a42d1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6dabf219795b408dabf8afe1ed3b2ba9", - "placeholder": "​", - "style": "IPY_MODEL_a61e682d08cd4b079011fff3976213c6", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "f0c67deadadc41a681e33253811fe3c3": { + "fcba50827939420b83ea40b9e3507089": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2430,53 +2477,6 @@ "visibility": null, "width": null } - }, - "f1b83d31de91416b8454f54a7486c222": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2a9e65f5f4ec40d496f43ad1adfd040a", - "placeholder": "​", - "style": "IPY_MODEL_13d8b2f426d2467cbc47898751b6f6dd", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "f91d1545f3254e83bb88ef07ebe6e9fe": { - "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_b05c6a6bdf664a62a6cbd8f66104b29f", - "IPY_MODEL_38cf33a9b7b9481cb9a58609d734b80f", - "IPY_MODEL_2184a8202604452c862d764c590163a7" - ], - "layout": "IPY_MODEL_04229ad3351b4f7aaf0a891a50bc135d", - "tabbable": null, - "tooltip": null - } } }, "version_major": 2, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 525f1b13d..643260310 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -710,16 +710,16 @@

1. Install required dependencies and download data

diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index c6bf67460..28feac438 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:02.971504Z", - "iopub.status.busy": "2024-06-25T19:41:02.971078Z", - "iopub.status.idle": "2024-06-25T19:41:04.919925Z", - "shell.execute_reply": "2024-06-25T19:41:04.919315Z" + "iopub.execute_input": "2024-06-25T23:22:28.297877Z", + "iopub.status.busy": "2024-06-25T23:22:28.297692Z", + "iopub.status.idle": "2024-06-25T23:22:29.566144Z", + "shell.execute_reply": "2024-06-25T23:22:29.565466Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-25 19:41:02-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-06-25 23:22:28-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,16 +94,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.249.162, 2400:52e0:1a01::984:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.249.162|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", - "Length: 982975 (960K) [application/zip]\r\n" + "185.93.1.250, 2400:52e0:1a00::1068:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "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", "\r", @@ -117,7 +125,7 @@ "\r", "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-06-25 19:41:03 (8.03 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-06-25 23:22:28 (6.31 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -137,22 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-25 19:41:03-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.196.49, 52.216.88.99, 3.5.9.136, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.196.49|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "--2024-06-25 23:22:29-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.25.196, 54.231.139.49, 52.216.48.57, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.25.196|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -173,15 +168,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 1%[ ] 296.53K 1.27MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 30%[=====> ] 4.94M 10.8MB/s " + "pred_probs.npz 58%[==========> ] 9.47M 47.3MB/s " ] }, { @@ -189,9 +176,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 25.4MB/s in 0.6s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 55.6MB/s in 0.3s \r\n", "\r\n", - "2024-06-25 19:41:04 (25.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-06-25 23:22:29 (55.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -208,10 +195,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:04.922457Z", - "iopub.status.busy": "2024-06-25T19:41:04.922075Z", - "iopub.status.idle": "2024-06-25T19:41:06.198016Z", - "shell.execute_reply": "2024-06-25T19:41:06.197533Z" + "iopub.execute_input": "2024-06-25T23:22:29.568875Z", + "iopub.status.busy": "2024-06-25T23:22:29.568431Z", + "iopub.status.idle": "2024-06-25T23:22:30.789853Z", + "shell.execute_reply": "2024-06-25T23:22:30.789338Z" }, "nbsphinx": "hidden" }, @@ -222,7 +209,7 @@ "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@e604611b9bbdc89f91103c8112289faf56854619\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -248,10 +235,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:06.200733Z", - "iopub.status.busy": "2024-06-25T19:41:06.200196Z", - "iopub.status.idle": "2024-06-25T19:41:06.203668Z", - "shell.execute_reply": "2024-06-25T19:41:06.203192Z" + "iopub.execute_input": "2024-06-25T23:22:30.792349Z", + "iopub.status.busy": "2024-06-25T23:22:30.792077Z", + "iopub.status.idle": "2024-06-25T23:22:30.795305Z", + "shell.execute_reply": "2024-06-25T23:22:30.794873Z" } }, "outputs": [], @@ -301,10 +288,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:06.205901Z", - "iopub.status.busy": "2024-06-25T19:41:06.205502Z", - "iopub.status.idle": "2024-06-25T19:41:06.208636Z", - "shell.execute_reply": "2024-06-25T19:41:06.208180Z" + "iopub.execute_input": "2024-06-25T23:22:30.797547Z", + "iopub.status.busy": "2024-06-25T23:22:30.797222Z", + "iopub.status.idle": "2024-06-25T23:22:30.800066Z", + "shell.execute_reply": "2024-06-25T23:22:30.799649Z" }, "nbsphinx": "hidden" }, @@ -322,10 +309,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:06.210610Z", - "iopub.status.busy": "2024-06-25T19:41:06.210285Z", - "iopub.status.idle": "2024-06-25T19:41:15.082955Z", - "shell.execute_reply": "2024-06-25T19:41:15.082336Z" + "iopub.execute_input": "2024-06-25T23:22:30.801968Z", + "iopub.status.busy": "2024-06-25T23:22:30.801793Z", + "iopub.status.idle": "2024-06-25T23:22:39.539487Z", + "shell.execute_reply": "2024-06-25T23:22:39.538935Z" } }, "outputs": [], @@ -399,10 +386,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:15.085860Z", - "iopub.status.busy": "2024-06-25T19:41:15.085425Z", - "iopub.status.idle": "2024-06-25T19:41:15.091166Z", - "shell.execute_reply": "2024-06-25T19:41:15.090711Z" + "iopub.execute_input": "2024-06-25T23:22:39.542320Z", + "iopub.status.busy": "2024-06-25T23:22:39.541861Z", + "iopub.status.idle": "2024-06-25T23:22:39.547429Z", + "shell.execute_reply": "2024-06-25T23:22:39.546974Z" }, "nbsphinx": "hidden" }, @@ -442,10 +429,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:15.093228Z", - "iopub.status.busy": "2024-06-25T19:41:15.092906Z", - "iopub.status.idle": "2024-06-25T19:41:15.428454Z", - "shell.execute_reply": "2024-06-25T19:41:15.427900Z" + "iopub.execute_input": "2024-06-25T23:22:39.549434Z", + "iopub.status.busy": "2024-06-25T23:22:39.549088Z", + "iopub.status.idle": "2024-06-25T23:22:39.886323Z", + "shell.execute_reply": "2024-06-25T23:22:39.885773Z" } }, "outputs": [], @@ -482,10 +469,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:15.430886Z", - "iopub.status.busy": "2024-06-25T19:41:15.430536Z", - "iopub.status.idle": "2024-06-25T19:41:15.435028Z", - "shell.execute_reply": "2024-06-25T19:41:15.434547Z" + "iopub.execute_input": "2024-06-25T23:22:39.888760Z", + "iopub.status.busy": "2024-06-25T23:22:39.888567Z", + "iopub.status.idle": "2024-06-25T23:22:39.892822Z", + "shell.execute_reply": "2024-06-25T23:22:39.892289Z" } }, "outputs": [ @@ -557,10 +544,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:15.437005Z", - "iopub.status.busy": "2024-06-25T19:41:15.436676Z", - "iopub.status.idle": "2024-06-25T19:41:17.963765Z", - "shell.execute_reply": "2024-06-25T19:41:17.963047Z" + "iopub.execute_input": "2024-06-25T23:22:39.894754Z", + "iopub.status.busy": "2024-06-25T23:22:39.894582Z", + "iopub.status.idle": "2024-06-25T23:22:42.439150Z", + "shell.execute_reply": "2024-06-25T23:22:42.438377Z" } }, "outputs": [], @@ -582,10 +569,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:17.966718Z", - "iopub.status.busy": "2024-06-25T19:41:17.966151Z", - "iopub.status.idle": "2024-06-25T19:41:17.970271Z", - "shell.execute_reply": "2024-06-25T19:41:17.969727Z" + "iopub.execute_input": "2024-06-25T23:22:42.442203Z", + "iopub.status.busy": "2024-06-25T23:22:42.441641Z", + "iopub.status.idle": "2024-06-25T23:22:42.445478Z", + "shell.execute_reply": "2024-06-25T23:22:42.444915Z" } }, "outputs": [ @@ -621,10 +608,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:17.972401Z", - "iopub.status.busy": "2024-06-25T19:41:17.971969Z", - "iopub.status.idle": "2024-06-25T19:41:17.977900Z", - "shell.execute_reply": "2024-06-25T19:41:17.977348Z" + "iopub.execute_input": "2024-06-25T23:22:42.447472Z", + "iopub.status.busy": "2024-06-25T23:22:42.447297Z", + "iopub.status.idle": "2024-06-25T23:22:42.452716Z", + "shell.execute_reply": "2024-06-25T23:22:42.452215Z" } }, "outputs": [ @@ -802,10 +789,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:17.979833Z", - "iopub.status.busy": "2024-06-25T19:41:17.979657Z", - "iopub.status.idle": "2024-06-25T19:41:18.005794Z", - "shell.execute_reply": "2024-06-25T19:41:18.005228Z" + "iopub.execute_input": "2024-06-25T23:22:42.454685Z", + "iopub.status.busy": "2024-06-25T23:22:42.454421Z", + "iopub.status.idle": "2024-06-25T23:22:42.480225Z", + "shell.execute_reply": "2024-06-25T23:22:42.479796Z" } }, "outputs": [ @@ -907,10 +894,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:18.007758Z", - "iopub.status.busy": "2024-06-25T19:41:18.007580Z", - "iopub.status.idle": "2024-06-25T19:41:18.011709Z", - "shell.execute_reply": "2024-06-25T19:41:18.011185Z" + "iopub.execute_input": "2024-06-25T23:22:42.482279Z", + "iopub.status.busy": "2024-06-25T23:22:42.481978Z", + "iopub.status.idle": "2024-06-25T23:22:42.486286Z", + "shell.execute_reply": "2024-06-25T23:22:42.485735Z" } }, "outputs": [ @@ -984,10 +971,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:18.013608Z", - "iopub.status.busy": "2024-06-25T19:41:18.013435Z", - "iopub.status.idle": "2024-06-25T19:41:19.410422Z", - "shell.execute_reply": "2024-06-25T19:41:19.409926Z" + "iopub.execute_input": "2024-06-25T23:22:42.488404Z", + "iopub.status.busy": "2024-06-25T23:22:42.487905Z", + "iopub.status.idle": "2024-06-25T23:22:43.900411Z", + "shell.execute_reply": "2024-06-25T23:22:43.899904Z" } }, "outputs": [ @@ -1159,10 +1146,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T19:41:19.412440Z", - "iopub.status.busy": "2024-06-25T19:41:19.412255Z", - "iopub.status.idle": "2024-06-25T19:41:19.416447Z", - "shell.execute_reply": "2024-06-25T19:41:19.415988Z" + "iopub.execute_input": "2024-06-25T23:22:43.902625Z", + "iopub.status.busy": "2024-06-25T23:22:43.902291Z", + "iopub.status.idle": "2024-06-25T23:22:43.906202Z", + "shell.execute_reply": "2024-06-25T23:22:43.905768Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index f34225120..f2c68c3f2 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "e604611b9bbdc89f91103c8112289faf56854619", + commit_hash: "bd550980fa8b7af85d37f375e0cc0e3ff9ced23e", }; \ No newline at end of file